Mass Spectrometry-Based Investigations to Characterize Specific Cell Types Within the Brain

MASS SPECTROMETRY-BASED INVESTIGATIONS TO CHARACTERIZE SPECIFIC CELL TYPES WITHIN THE BRAIN

ANN M. KNOLHOFF

DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Chemistry in the Graduate College of the University of Illinois at Urbana-Champaign, 2011

Urbana, Illinois

Doctoral Committee:

Professor Jonathan V. Sweedler, Chair Assistant Professor Ryan C. Bailey Professor Martha U. Gillette Professor Alexander Scheeline

ABSTRACT

Investigations of the chemical content of the brain and its many constitutive cell types yield information regarding normal and abnormal brain function. Frequently, proteomics, peptidomics, and metabolomics experiments survey brain regions and attribute detected species to neurons. However, many other cell types are present, including astrocytes, oligodendrocytes, and microglia. It is also known that different cell types can vary greatly in their analyte content and concentration; therefore, to obtain an adequate representation of brain function, specific cell types require further characterization. Cell types of particular interest are astrocytes, which are involved in neuronal communication, and mast cells, which are involved in allergic response. Moreover, morphologically similar cells can exhibit chemical heterogeneity; to characterize these differences, single-cell analyses are necessary.

In this case, single cells from the model organism, Aplysia californica, are isolated for

analysis. Mass spectrometry (MS) is well-suited for such applications because it has limits

of detection in the attomole range for both small molecule metabolites and peptides, has a

wide dynamic concentration range, and can detect and identify molecules of interest without

a priori knowledge of sample content.

To characterize metabolite profiles in single cells and tissue homogenates, a

laboratory-built capillary electrophoresis (CE) system coupled to electrospray ionization

(ESI) MS is implemented. This is a useful technique for such measurements because it only

requires a small amount of sample (injection of nanoliter volumes), it can efficiently separate

and detect hundreds of analytes within a single CE separation, and it has detection limits in

the low nanomolar (attomole) range for several neurotransmitters. This technique has been

ii successfully applied to characterize and chemically distinguish single cells of various neuron types from Aplysia californica. Here, hundreds of ions are detected within single-cell samples and 36 ions are identified. Furthermore, neuron-specific analytes are revealed, chemical classification of neurons is achieved via principal component analysis, and cellular concentrations of serine and glutamic acid are determined for the different neuron types. CE-

ESI-MS has also been effective in determining relative chemical differences between different sample types. To determine the chemical contribution that mast cells make to the brain, tissue homogenates from mast cell-deficient mice are compared to their heterozygous littermates. In this comparison, a number of metabolites, including amino acids and choline, are statistically different between the two sample types; furthermore, this data agrees with differences observed in gene expression data. The combined metabolite and transcriptomics data reveal global chemical differences that affect a number of metabolic pathways, which may be related to behavioral and developmental traits observed in mast cell-deficient mice.

The characterization of peptide content in astrocytes is also achieved by employing several instrumental platforms, such as CE-ESI-MS and matrix-assisted laser desorption/ionization

MS. By carefully selecting defined samples, the identification of a large number of peptides from purified astrocyte samples is accomplished.

iii

ACKNOWLEDGMENTS

There are a number of people that I would like to thank who contributed to the success of this work. First of all, I would like to thank my advisor, Prof. Jonathan Sweedler, for his continual guidance and encouragement throughout my graduate career. His mentoring style, approach to problem solving, and ability to see the big picture have helped me to become an independent and better scientist. I would also like to thank the members of my committee,

Profs. Ryan Bailey, Martha Gillette, and Alexander Scheeline, for their encouragement and support. I would also like to thank Prof. Bill Greenough, who originally served on my preliminary exam committee, for his initial input regarding this work.

I have had the wonderful opportunity to work with a number of collaborators on interesting projects. I would like to thank Larry Millet from Prof. Martha Gillette’s lab for discussing research ideas, providing samples, and initial training with respect to cell culture.

Harry Rosenberg, also from Prof. Martha Gillette’s lab, provided me with many samples to test and was always willing to try new research avenues. I also had the opportunity to collaborate with Prof. Rae Silver and Kate Nautiyal from Columbia University and Sergey

Kalachikov and Irina Morozova from the Columbia Genome Center. I very much appreciate the exchange of ideas and effort put forth by this group of people.

It has been a privilege to work in the Sweedler research group. Both past and present group members have been willing to lend a hand when needed and to discuss different ways of approaching challenges that arise. In particular, I would like to thank Michael Heien who provided my initial training when I joined the group, Ping Yin for collaborative efforts and discussions, Ted Lapainis for original training on the CE-ESI-MS instrument platform, Peter

Nemes for collaborative efforts in CE-ESI-MS investigations, and Stanislav Rubakhin for cell isolations and helpful discussions. Additionally, multiple people have helped on various aspects of the performed research and provided general advice; this list includes Christine

Cecala, Chris Dailey, Xiaowen Hou, Jamie Iannacone, Zhen Li, Eric Monroe, Nobutoshi Ota,

Elena Romanova, Ting Shi, and Fang Xie.

I would also like to thank Stephanie Baker for her efforts in finalizing manuscripts prior to publication. I also gratefully acknowledge Julie Sides, who is always willing to help graduate students with anything they need. Finally, I would like to thank my friends and family. Their continual friendship and support is very much appreciated.

TABLE OF CONTENTS

CHAPTER 1. INTRODUCTION ...... 1

1.1 Research Motivation ...... 1

1.2 Thesis and Research Overview ...... 2

1.3 Cell Types and Model Systems ...... 3

1.3.1 Aplysia californica...... 3

1.3.1.1 Aplysia Neurons ...... 4

1.3.2 Mus musculus and Rattus norvegicus ...... 5

1.3.2.1 Mast Cells ...... 6

1.3.2.2 Astrocytes ...... 7

1.4 Conclusions ...... 9

1.5 Tables ...... 10

1.6 References ...... 11

CHAPTER 2. SINGLE-CELL MASS SPECTROMETRY ...... 16

2.1 Introduction ...... 16

2.2 Mass Spectrometry ...... 18

2.2.1 Matrix-Assisted Laser Desorption/Ionization ...... 19

2.2.1.1 Sample Preparation for Single-Cell MALDI ...... 21

2.2.1.2 Recent Applications of Single-Cell MALDI ...... 23

2.2.2 Secondary Ion Mass Spectrometry ...... 27

2.2.2.1 Sample Preparation for Single-Cell SIMS ...... 28

2.2.2.2 Recent Applications of Single-Cell SIMS ...... 29

vi 2.2.3 Electrospray Ionization ...... 33

2.2.3.1 Recent Applications of Single-Cell ESI ...... 34

2.2.4 Other MS Approaches ...... 35

2.3 Overall Outlook for Single-Cell MS ...... 36

2.4 Figures ...... 37

2.5 References ...... 43

CHAPTER 3. MS-BASED METHODOLOGIES FOR SINGLE-CELL METABOLITE DETECTION AND IDENTIFICATION ...... 51

3.1 Introduction ...... 51

3.2 Methodologies: Overview and Timing ...... 55

3.3 Materials ...... 57

3.3.1 Sample Preparation ...... 57

3.3.1.1 Animals ...... 57

3.3.1.2 Reagents and Solutions ...... 58

3.3.1.3 Disposables ...... 58

3.3.1.4 Equipment ...... 59

3.3.2 CE-ESI-MS System ...... 59

3.3.2.1 Reagents and Solutions ...... 59

3.3.2.2 Equipment ...... 59

3.3.2.3 Software ...... 61

3.3.3 MALDI MS system ...... 61

3.3.3.1 Reagents and Solutions ...... 61

3.3.3.2 Equipment ...... 61

3.3.3.3 Software ...... 61

vii 3.4 Procedures and Troubleshooting ...... 62

3.4.1 Sample Preparation ...... 62

3.4.1.1 Single Cell Isolation ...... 62

3.4.1.2 Sample Preparation for CE-ESI-MS ...... 62

3.4.1.3 Sample Preparation for MALDI MS...... 63

3.4.2 Instrument System Setup ...... 64

3.4.2.1 CE-ESI-MS ...... 64

3.4.2.2 MALDI MS ...... 65

3.4.3 Single-Cell CE-ESI-MS Analysis ...... 66

3.4.4 Single-Cell MALDI-MS Analysis ...... 67

3.4.5 Data Interpretation ...... 69

3.4.5.1 CE-ESI-MS Results ...... 69

3.4.5.2 MALDI MS Results ...... 70

3.5 Exemplary Applications ...... 71

3.5.1 Metabolomics with Single-Cell CE-ESI-MS ...... 71

3.5.2 Metabolomics with Single-Cell MALDI MS ...... 72

3.6 Conclusions ...... 74

3.7 Figures and Tables ...... 77

3.8 References ...... 83

CHAPTER 4. METABOLIC DIFFERENTIATION OF NEURONAL CELL TYPES BY SINGLE-CELL CE-ESI-MS ...... 88

4.1 Introduction ...... 88

4.2 Experimental Procedure ...... 91

4.3 Results and Discussion ...... 94

viii 4.4 Conclusions and Future Work ...... 99

4.5 Figures and Tables ...... 102

4.6 References ...... 110

CHAPTER 5. A LACK OF MAST CELLS CAUSES STRIKING METABOLITE DEFECTS IN THE HIPPOCAMPUS ...... 114

5.1 Introduction ...... 115

5.2 Experimental Procedure ...... 116

5.2.1 Chemicals ...... 116

5.2.2 Animals ...... 117

5.2.3 Sample Preparation for CE-ESI-MS ...... 118

5.2.4 Allergy Induction ...... 118

5.2.5 CE-ESI-MS...... 119

5.2.6 CE-ESI-MS Data Analysis ...... 119

5.2.7 Gene Array ...... 120

5.3 Results ...... 121

5.3.1 Metabolomics ...... 122

5.3.2 Transcriptomics ...... 122

5.4 Discussion ...... 123

5.4.1 Molecular Differences Observed Between Mast Cell-Deficient and Control Mice ...... 124

5.4.2 The Combination of Molecular Approaches Yield Insight into Mast Cell Involvement in the Hippocampus ...... 126

5.5 Conclusions and Future Work ...... 128

5.6 Figures and Tables ...... 130

5.7 References ...... 136

ix CHAPTER 6. PEPTIDE CHARACTERIZATION IN ASTROCYTES: SINGLE CELL AND CELL HOMOGENATE STRATEGIES ...... 142

6.1 Introduction ...... 143

6.2 Experimental Procedure ...... 146

6.2.1 Sample Types ...... 146

6.2.2 Single-Cell MALDI Sample Preparation ...... 147

6.2.3 Glia Homogenate Extraction Protocols ...... 148

6.2.4 Instrumentation ...... 149

6.3 Results and Discussion ...... 149

6.3.1 Single-Cell MALDI ...... 149

6.3.2 Assaying Astrocyte Homogenates ...... 150

6.4 Conclusions and Future Work ...... 152

6.5 Figures and Tables ...... 155

6.6 References ...... 160

CHAPTER 7. METABOLIC DIFFERENCES OF SPECIALIZED SAMPLES: NEURONAL CULTURES, CELL CLUSTERS, AND SINGLE CELLS FROM APLYSIA CALIFORNICA...... 164

7.1 Introduction ...... 164

7.2 Experimental Procedure ...... 168

7.2.1 Sample Preparation ...... 168

7.2.2 CE-ESI-MS...... 168

7.2.3 Data Analysis...... 169

7.3 Results and Discussion ...... 170

7.3.1 Cultured Neurons ...... 170

7.3.2 Investigation of R3-13 Cluster ...... 172

x 7.3.3 L7 Analysis ...... 173

7.4 Conclusions and Future Work ...... 175

7.5 Figures and Tables ...... 177

7.6 References ...... 182

CURRICULUM VITAE ...... 185

xi CHAPTER 1

INTRODUCTION

1.1 Research Motivation

Characterizing how the brain functions is a challenging and ongoing task. While different brain regions are associated with certain tasks, such as the hippocampus with learning and memory, the specific mechanisms underlying complex behaviors and responses, such as how memory is retained, remain somewhat elusive. There is also a large diversity of cell types present in the central nervous system (CNS), such as neurons, astrocytes, microglia, and mast cells. Even cells of the same type exhibit chemical heterogeneity.1-5 However, many bulk

chemical analyses of tissue homogenates from the brain attribute major findings to be

neuron-specific, despite this cellular diversity. To determine the chemical influences that

non-neuronal cell types contribute to the brain, defined samples are obtained either via

isolation, cell culture, or using genetic knockouts. Also, single neurons are also isolated to

gain specific chemical information that is lost in homogenate studies. Metabolites to

peptides are investigated from these specialized samples to better characterize the chemistry

of these biological systems.

Mass spectrometry (MS) is an incredibly useful analytical technique that has been

applied to a variety of biological sample matrices. A particularly attractive feature is that a

priori knowledge of chemical content is not necessary to detect and identify molecular

species that are present. Furthermore, the limits of detection, sensitivity, and dynamic

concentration range are sufficient for single-cell analyses. Both electrospray ionization (ESI)

and matrix-assisted laser desorption/ionization (MALDI) are heavily utilized in bioanalytical applications due to their ability to analyze a large variety of analytes of interest, such as

metabolites, peptides, and proteins.6-9 Due to the chemical complexity that is often

associated with biological sample extracts, a separation step prior to ionization can provide

additional confirmation regarding analyte identity and reduce interfering species. Capillary

electrophoresis (CE) is implemented due to its compatibility with small sample volumes and

ability to separate analytes by size and charge rather than relying on polarity differences.10-12

Liquid chromatography is also coupled to MS for the separation, detection, and identification of peptides in cellular sample matrices. The combination of these analytical techniques enables measurements from multiple biological samples of interest, which yields a better understanding of the chemistry of these specific cell types.

1.2 Thesis and Research Overview

The remaining sections of Chapter 1 will discuss the different cell types and model systems/organisms investigated in the current work and why they were chosen. Chapter 2 presents an overview of different ionization approaches that are hyphenated to mass spectrometry for the detection of diverse chemical species from single cells. Chapter 3 is a protocol chapter which gives explicit instructions for isolating single cells from Aplysia californica and characterizing them for metabolite content using MALDI MS and ESI MS.

Chapter 4 describes the metabolic profiles of single cells isolated from Aplysia californica.

Cell types are chemically distinguished via principle component analysis (PCA), cellular heterogeneity within the same neuron type is characterized, unique molecular features corresponding to individual neurons are discovered, and quantitation of two amino acids

within single neurons is achieved. Chapter 5 discusses the differences in chemical content of

mouse hippocampi that contain or lack mast cells with the use of a genetic knockout. Global

molecular changes are determined by combining metabolite and transcriptomic data. In

particular, choline and multiple amino acids differ, which may influence behavior, learning,

and neurogenesis. Chapter 6 provides information regarding peptide characterization in a

pure population of astrocytes that is achieved through single-cell analyses, cell culture, and a

transgenic mouse with fluorescently identifiable cells; this research yields further insight into

the complexities of chemical communication of astrocytes. Chapter 7 is an expansion upon

experiments described in Chapter 4 where metabolic content is characterized for neuronal

cultures, cell clusters, and isolated neurons via CE-ESI-MS; metabolite profiling using three

different approaches results in a better understanding of investigated Aplysia neurons and

neuron clusters and how they respond to different environments.

1.3 Cell Types and Model Systems

1.3.1 Aplysia californica

The model organism, Aplysia californica, provides an excellent opportunity for investigating

individual neurons that assemble into networks that are responsible for complex actions and

behaviors. It was the characterization of this model that led to Eric Kandel receiving the

Nobel Prize for Physiology or Medicine in 2000. For example, distinct, identifiable neurons

are responsible for inducing the gill-withdrawal reflex, where studies of learning and

memory are performed.13 Furthermore, the neurons are some of the largest in the animal

kingdom, with some neurons up to 1 mm in diameter.14 Aplysia have approximately 10,000 neurons in their CNS,15 while the mammalian brain has on the order of 1011 neurons.16 The

3 mapping of the Aplysia genome is also underway, which will enable comparisons of this model organism to more complex systems, such as mice, rats, and humans. Aplysia neurons are also relatively easy to isolate and the location of respective identifiable neurons has been characterized and mapped.17 With this defined system, a variety of experiments can be performed on a single-cell level. In this work, the metabolite content of individual, identified neurons is characterized to better understand how the chemical milieu relates to function.

1.3.1.1 Aplysia Neurons

The Aplysia neurons of interest are listed in Table 1.1. While these neurons have been researched extensively, CE-ESI-MS has the capability to measure over one hundred analytes from a single cell.11 Thus, experiments were performed to determine if neurons could be distinguished chemically based on their biochemical profile. Neurons that were chosen to be investigated are diverse in function and location, while others have similar properties. For example, while the R2 and LPl1 are located in different ganglia, they are similar both in their electrophysiological properties and levels of acetylcholinesterase.18-19 Similarly, the B1 and

B2 neurons have been shown to be similar, but their chemical characterization has been mostly for neuropeptide content.20-21 However, the full metabolic similarities of these neurons are unknown. While the R3-13 cluster is in close proximity to the R15 neuron in the abdominal ganglion, they are involved in very different functional activities, which are influenced by neuropeptides.22-27 In contrast, the metacerebral cell (MCC) is involved in feeding28 and is known to be serotoninergic29. However, these neurons may have additional small molecule transmitters and in some cases the neurotransmitter is unknown. By using principal component analysis (PCA), the extent of these cellular similarities and differences

4 with respect to chemical content are visualized. Unique chemical features that are neuron- specific are also obtained.

Another specific neuron of interest is the L7 motor neuron; it is involved in the gill- withdrawal reflex, which is a model for investigating learning and memory, but the chemical mediator for this action is unknown.16, 30-32 Furthermore, this neuron is difficult to unambiguously identify without lengthy electrophysiological testing; however, identification of the L7 neuron can be visually limited to two candidate neurons based on location and size.

By chemically characterizing multiple biological replicates of these two candidate L7 neurons, it appears that these two neurons can be chemically distinguished. CE-ESI-MS is also used to examine changes in metabolite profiles that result from culturing. This is necessary because cell culture is common in bioanalyses and is an attractive option compared to in vivo work due to it being a less complicated system to study under defined conditions; furthermore, the results of culturing studies are often extended to the in vivo system.

Culturing can be a somewhat harsh process, due to potentially necessary components being absent. This analytical technique enables single cell responses to culturing to be investigated. It is determined that the extent of neuronal response under these conditions is dependent upon cell type.

1.3.2 Mus musculus and Rattus norvegicus

Mice and rats are typically used as model organisms to characterize biological and chemical properties of mammalian systems, which can be a precursor or substitute for research of human subjects. Both share physiological and anatomical similarities, which make them ideal for biomedical investigations as well as other research avenues regarding the

fundamental understanding of underlying biochemical processes. Furthermore, with the

characterization of the human, mouse, and rat genomes, direct comparisons can be made on a

molecular level between these organisms. Mice are relatively easy to genetically modify,

which enables a number of functional studies to be accomplished, which are typically based

on a knock-out of a definable trait to determine what effect it locally and globally inflicts on

the animal. Rats are relatively larger animals with larger anatomical features than mice,

resulting in easier working volumes for tissue sampling. Both mice and rats offer

complementary advantages; therefore, both are utilized to study the following cell types in

the mammalian brain.

1.3.2.1 Mast Cells

Mast cells are well known for their involvement in allergic response. Upon binding of an

antigen to surface-bound immunoglobulin E, these cells generate and release chemical

mediators, including histamine.33 These meditators are localized in large vesicles that can be

quickly released upon activation. These cells have also been implicated in immune

response;34-36 mast cells release cytokines, such as tumor necrosis factor and interleukin-4, in response to pathogens.35 These cells are also heterogeneous;37 mast cells tend to be

characterized under two categories: mucosal and connective tissue mast cell types. They can

even change phenotype if isolated and placed from one environment to another.38-39

However, characterization of mast cells in the brain requires further study.

Mast cells have been confirmed to be present in the brain relatively recently.40 The presence of mast cells in the brain was surprising because the brain was considered an immune-privileged site that excludes circulating cells.41 Mast cells have to cross the blood-

brain barrier to gain access to the brain.42 Their location in the brain is limited to distinct

regions, which is also species dependent.43 The number of mast cells present has been

demonstrated to be a dynamic process, where certain stimuli will induce mast cell numbers to

change in the brain.41-42, 44-47 The function of mast cells within the brain has started to be

investigated.

As mentioned previously, mice are often genetically modified to determine functional

properties of a particular characteristic. Likewise, a mutant is available that lacks mast cells,

allowing mast cell effects to be investigated in vivo.48-49 For example, mast cell-deficient

mice were compared against controls to determine if any behavioral differences existed, and

therefore define mast cell-dependent behavior;50 it was shown that mast cell-deficient mice exhibit anxiety. In the current work, mast cell-deficient mice are compared to controls to ascertain global chemical differences in the hippocampus, which can then be related to this observed behavioral difference.

1.3.2.2 Astrocytes

Glia outnumber neurons in the mammalian brain and the size of the glia population is

dependent upon the area of the brain and the organism. Glia are a heterogeneous cell

population, which includes astrocytes, oligodendrocytes, and Schwann cells, among others.

Even within the individual glia subtypes, there is heterogeneity.5 Schwann cells are

responsible for the myelination of the peripheral nervous system, while oligodendrocytes

myelinate axons in the central nervous system. Astrocytes, however, seem to have diverse functional capabilities.

Astrocytes are well positioned to chemically communicate with neurons; their

processes are intricately connected to pre- and post-synaptic neuronal connections. Chemical

signaling was demonstrated after the observation that increases in intracellular Ca2+ concentration in cultured astrocytes induced glutamate release followed by neuronal activation.51 “Gliotransmitters” have been shown to be released by astrocytes and include glutamate, adenosine triphosphate, adenosine, and D-serine.52 To be characterized as a

gliotransmitter, it has been proposed that it should fulfill the following parameters: (i)

synthesis by and/or storage in glia, (ii) regulated release triggered by physiological stimuli,

(iii) activation of rapid (milliseconds to seconds) responses in neighboring cells, and (iv) a

role in physiological processes.52

While the mechanisms of the release of these small molecule transmitters are still

being researched, peptide signaling in astrocytes also requires further investigation. There is

mRNA expression data that indicates that peptides could be produced, such as

angiotensinogen and proenkephalin.53-54 The process for the synthesis of neuropeptides is as

follows: (i) translation of mRNA to generate the preprohormone precursor, (ii) proteolytic

removal of the NH2-terminal signal peptide at the rough endoplasmic reticulum, (iii) the prohormone is packaged into vesicles with processing proteases in the Golgi apparatus, and

(iv) proteolytic processing of the peptides are achieved by the proteases in the vesicle,

typically at dibasic or monobasic sites.55 Neuropeptides may also undergo posttranslational

modifications, which can modify the biological activity of the peptide. MS has proven to be

an indispensible tool in identifying neuropeptides in tissue homogenates and single cells and

also yields information about they are processed. In this work, MS is implemented to

characterize peptide content from pure populations of astrocytes by using a cell line, primary

cultures, and isolated cells directly from the brain using a transgenic mouse model and

fluorescence-activated cell sorting. The identification of a large number of peptides from

astrocytes was achieved; however, no classical neuropeptides were detected, which could be

a result of a lack of context-specific input or lack of processing enzymes.

1.4 Conclusions

A variety of cell types are analyzed for chemical content, from small molecules to peptides, using MS. Single-cell analyses of Aplysia neurons enable investigations of cell-to-cell heterogeneity, metabolically distinguishing characteristics of different cell types, and effects induced by cell culture. These analyses complement transcriptomic, proteomic, and peptidomic investigations, which results in a more complete understanding of the chemical content of these individual neurons and how this relates to function. Characterization of non- neuronal cell types in the brain is also necessary to fully understand how the brain works. It is determined that mast cells alter a surprising range of metabolites and gene expression in the hippocampus, which are implicated in a number of critical pathways. Astrocytes are studied using several approaches, which include cell culturing techniques and genetically- modified mice to better understand how astrocytes communicate chemically. The combined information gained from these investigations stress the importance of non-neuronal and single-cell investigations with regard to function.

1.5 Tables

Table 1.1 Aplysia neurons that are isolated and investigated for metabolic composition. While one transmitter is listed, others are/may be present. The location refers to the ganglion in which this cell is located. Key: Left pleural 1 (LPl1), Metacerebral cell (MCC)

Neuron Location Transmitter Action/Involved in B1 Buccal neuropeptides Gut motility B2 Buccal neuropeptides Gut motility LPl1 Pleural acetylcholine Regulation of mucus release, Control of the feeding process MCC Cerebral serotonin Control of the feeding process L7 Abdominal unknown Gill-withdrawl reflex R2 Abdominal acetylcholine Regulation of mucus release R3-13 Abdominal neuropeptides Cardiac function R15 Abdominal neuropeptides Egg laying, Regulation of water balance

1.6 References

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25. Weiss, K. R.; Bayley, H.; Lloyd, P. E.; Tenenbaum, R.; Kolks, M. A.; Buck, L.; Cropper, E. C.; Rosen, S. C.; Kupfermann, I., Purification and sequencing of neuropeptides

contained in neuron R15 of Aplysia californica. Proc. Natl. Acad. Sci. U. S. A. 1989, 86 (8), 2913-7.

26. Campanelli, J. T.; Scheller, R. H., Histidine-rich basic peptide: a cardioactive neuropeptide from Aplysia neurons R3-14. J. Neurophysiol. 1987, 57 (4), 1201-9.

27. Newcomb, R.; Scheller, R. H., Proteolytic processing of the Aplysia egg-laying hormone and R3-14 neuropeptide precursors. J. Neurosci. 1987, 7 (3), 854-63.

28. Weiss, K. R.; Cohen, J. L.; Kupfermann, I., Modulatory control of buccal musculature by a serotonergic neuron (metacerebral cell) in Aplysia. J. Neurophysiol. 1978, 41 (1), 181-203.

29. Eisenstadt, M.; Goldman, J. E.; Kandel, E. R.; Koike, H.; Koester, J.; Schwartz, J. H., Intrasomatic injection of radioactive precursors for studying transmitter synthesis in identified neurons of Aplysia californica. Proc. Natl. Acad. Sci. U. S. A. 1973, 70 (12), 3371- 5.

30. Castellucci, V. F.; Carew, T. J.; Kandel, E. R., Cellular analysis of long-term habituation of the gill-withdrawal reflex of Aplysia californica. Science 1978, 202 (4374), 1306-8.

31. Kupfermann, I.; Carew, T. J.; Kandel, E. R., Local, reflex, and central commands controlling gill and siphon movements in Aplysia. J. Neurophysiol. 1974, 37 (5), 996-1019.

32. Kupfermann, I.; Pinsker, H.; Castellucci, V.; Kandel, E. R., Central and peripheral control of gill movements in Aplysia. Science 1971, 174 (15), 1252-6.

33. Metcalfe, D. D.; Baram, D.; Mekori, Y. A., Mast cells. Physiol. Rev. 1997, 77 (4), 1033-79.

34. Galli, S. J.; Tsai, M., Mast cells in allergy and infection: versatile effector and regulatory cells in innate and adaptive immunity. Eur. J. Immunol. 2010, 40 (7), 1843-51.

35. Abraham, S. N.; St John, A. L., Mast cell-orchestrated immunity to pathogens. Nat. Rev. Immunol. 2010, 10 (6), 440-52.

36. Kumar, V.; Sharma, A., Mast cells: emerging sentinel innate immune cells with diverse role in immunity. Mol. Immunol. 2010, 48 (1-3), 14-25.

37. Abe, T.; Swieter, M.; Imai, T.; Hollander, N. D.; Befus, A. D., Mast cell heterogeneity: two-dimensional gel electrophoretic analyses of rat peritoneal and intestinal mucosal mast cells. Eur. J. Immunol. 1990, 20 (9), 1941-7.

38. Kitamura, Y.; Kanakura, Y.; Fujita, J.; Nakano, T., Differentiation and transdifferentiation of mast cells; a unique member of the hematopoietic cell family. Int. J. Cell Cloning 1987, 5 (2), 108-21.

39. Kitamura, Y.; Kanakura, Y.; Sonoda, S.; Asai, H.; Nakano, T., Mutual phenotypic changes between connective tissue type and mucosal mast cells. Int. Arch. Allergy Appl. Immunol. 1987, 82 (3-4), 244-8.

40. Silver, R.; Silverman, A. J.; Vitkovic, L.; Lederhendler, I. I., Mast cells in the brain: evidence and functional significance. Trends Neurosci. 1996, 19 (1), 25-31.

41. Cirulli, F.; Pistillo, L.; de Acetis, L.; Alleva, E.; Aloe, L., Increased number of mast cells in the central nervous system of adult male mice following chronic subordination stress. Brain Behav. Immun. 1998, 12 (2), 123-33.

42. Silverman, A. J.; Sutherland, A. K.; Wilhelm, M.; Silver, R., Mast cells migrate from blood to brain. J. Neurosci. 2000, 20 (1), 401-8.

43. Silverman, A. J.; Asarian, L.; Khalil, M.; Silver, R., GnRH, brain mast cells and behavior. Prog. Brain Res. 2002, 141, 315-25.

44. Asarian, L.; Yousefzadeh, E.; Silverman, A. J.; Silver, R., Stimuli from conspecifics influence brain mast cell population in male rats. Horm. Behav. 2002, 42 (1), 1-12.

45. Kovacs, K. J.; Larson, A. A., Mast cells accumulate in the anogenital region of somatosensory thalamic nuclei during estrus in female mice. Brain Res. 2006, 1114 (1), 85- 97.

46. Persinger, M. A., Handling factors not body marking influence thalamic mast cell numbers in the preweaned albino rat. Behav. Neural Biol. 1980, 30 (4), 448-59.

47. Yang, M.; Chien, C.; Lu, K., Morphological, immunohistochemical and quantitative studies of murine brain mast cells after mating. Brain Res. 1999, 846 (1), 30-9.

48. Duttlinger, R.; Manova, K.; Chu, T. Y.; Gyssler, C.; Zelenetz, A. D.; Bachvarova, R. F.; Besmer, P., W-sash affects positive and negative elements controlling c-kit expression: ectopic c-kit expression at sites of kit-ligand expression affects melanogenesis. Development 1993, 118 (3), 705-17.

49. Grimbaldeston, M. A.; Chen, C. C.; Piliponsky, A. M.; Tsai, M.; Tam, S. Y.; Galli, S. J., Mast cell-deficient W-sash c-kit mutant Kit W-sh/W-sh mice as a model for investigating mast cell biology in vivo. Am. J. Pathol. 2005, 167 (3), 835-48.

50. Nautiyal, K. M.; Ribeiro, A. C.; Pfaff, D. W.; Silver, R., Brain mast cells link the immune system to anxiety-like behavior. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (46), 18053-7.

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52. Volterra, A.; Meldolesi, J., Astrocytes, from brain glue to communication elements: the revolution continues. Nat. Rev. Neurosci. 2005, 6 (8), 626-40.

53. Stornetta, R. L.; Hawelu-Johnson, C. L.; Guyenet, P. G.; Lynch, K. R., Astrocytes synthesize angiotensinogen in brain. Science 1988, 242 (4884), 1444-6.

54. Vilijn, M. H.; Vaysse, P. J.; Zukin, R. S.; Kessler, J. A., Expression of preproenkephalin mRNA by cultured astrocytes and neurons. Proc. Natl. Acad. Sci. U. S. A. 1988, 85 (17), 6551-5.

55. Hook, V.; Funkelstein, L.; Lu, D.; Bark, S.; Wegrzyn, J.; Hwang, S. R., Proteases for processing proneuropeptides into peptide neurotransmitters and hormones. Annu. Rev. Pharmacol. Toxicol. 2008, 48, 393-423.

CHAPTER 2

SINGLE-CELL MASS SPECTROMETRY

Notes and Acknowledgments

This chapter was adapted from the following review: Single-cell mass spectrometry (Chapter

12) by Ann M. Knolhoff, Stanislav S. Rubakhin, and Jonathan V. Sweedler that appeared in

Chemical cytometry: ultrasensitive analysis of single cells, Lu, C., Ed. Wiley-VCH Verlag

GmbH & Co. KGaA: 2010; pp 197-218.1 It is reprinted here with the permission of John

Wiley & Sons, Inc. The authors would like to thank Zhen Li for helpful discussions during

the initial writing process. This material is based upon work supported by the National

Institute on Drug Abuse under Award No. DA 017940 and Award No. DA 018310 to the

UIUC Neuroproteomics Center of Cell-Cell Signaling.

2.1 Introduction

Mass spectrometry (MS) has been widely used to investigate a variety of biological systems,

ranging from subcellular structures to entire organisms. This powerful analytical technique

has enabled the characterization and identification of a plethora of analytes including

elements, metabolites, peptides, and proteins. Furthermore, spatial, temporal, and chemical

information can be obtained from bioanalytical MS measurements. One promising

application of this information-rich approach has been in single-cell investigations—cellular

measurements that have advanced the understanding of tissues and organisms at a

fundamental level. These experiments have yielded detailed insights regarding many

biological functions. Although there are multiple techniques capable of detecting analytes of

interest within a cell, what sets MS apart is its ability to simultaneously detect and identify

many analytes without preselection or tagging. Post-translational modifications and

prohormone processing can also be determined. Moreover, cell to cell differences in

chemical heterogeneity can be characterized via MS.

Different cell types often vary greatly in their analyte amounts and concentrations,

and even morphologically similar cells show differences in analyte profiles. Accordingly, single-cell studies demand methods that meet three key requirements: high sensitivity, chemically-rich information, and a wide dynamic range. Because MS is a highly sensitive technique, with detection limits for many MS-based approaches in the attomole range, it is well-suited for probing single-cell biochemistry. In fact, efforts to improve detectors in MS instrumentation have resulted in detection efficiencies approaching 100%.2 Finally, the wide

dynamic range of MS facilitates the characterization of analytes over large variations in their

concentration.

Single-cell MS has been used to examine many cell types from a variety of

organisms, ranging from unicellular organisms such as bacteria and yeast, to complex

animals such as mammals. The invertebrates Aplysia californica and Lymnaea stagnalis

have been popular model systems for the development of single-cell MS because of their

well-defined neuronal systems in which cells are relatively easy to identify, isolate, and

physiologically test.3-8 These single-cell MS studies have resulted in the discovery of many

cell-to-cell signaling molecules, including information on their location, release and function.

Smaller cells from a variety of insect models—the moth,9 fruit fly,10-11 and cockroach12— have also been successfully investigated with single-cell MS. The success of these

invertebrate studies has led to the application of this technology to more structurally complex

mammalian tissues, with most cells being no larger than 10–20 µm in size. It follows that

increasing the sensitivity of MS detection, as well as improving sample quality and

preparation, must be emphasized in continued efforts to advance and optimize this

methodology.

Given the small sample volumes and relatively low levels of analyte inherent to single-cell experiments, it is not surprising that their success depends heavily on sample quality. Exceptional attention must be given to cell isolation, handling, and analyte

extraction. Choosing the right sample preparation technique, which is highly dependent upon

the analytical method being used and the analyte of interest, is also important. Here, three

major ionization approaches used in single-cell MS studies are discussed: matrix-assisted laser/desorption ionization (MALDI) MS, secondary ion mass spectrometry (SIMS), and electrospray ionization (ESI), as well as the appropriate sample preparation process for each.

2.2 Mass Spectrometry

In MS, analytes are ionized in some fashion and then introduced into the mass analyzer, where the ions are focused and separated based on analyte properties, such as kinetic energy or momentum. From this, the mass-to-charge ratio (m/z) of the ion is calculated. One can determine the charge and experimental mass of the detected species by considering the isotopic distribution and position of the ions in the resulting mass spectrum. Modern mass spectrometers can characterize analytes with high mass accuracy, often in the low parts per million range. Maintaining high mass accuracy aids in analyte identification, in part by matching the measured and theoretical masses for specific analytes; however, one often

18 cannot unequivocally identify an analyte based on its detected mass alone. Additional techniques can serve to confirm MS identifications; for example, using standards, implementing in situ and immunohistochemical approaches, and/or incorporating additional

MS data. In an approach known as tandem MS (MS/MS), the molecular ion of the analyte of interest can be fragmented and the identity determined by the resulting fragmentation pattern.

If the concentration of the analyte is not high enough for tandem-assisted studies, which is often the case for single-cell analyses, samples can be pooled to increase the amount of analyte available for investigation.

The most common ionization methods used for complex biological samples are highlighted here. Note that a single ionization method is not always sufficient; rather, they can provide complementary information. A more in-depth coverage of other aspects of mass spectrometers, such as mass analyzers and detectors, can be found elsewhere.13-14

2.2.1 Matrix-Assisted Laser Desorption/Ionization

A great advantage of using MALDI MS is that it allows the direct analysis of individual cells and can ionize a wide range of analyte classes, including lipids and proteins. As mentioned previously, this technique has a high sensitivity with a dynamic range amenable to single-cell analysis. In MALDI, analytes are incorporated into a matrix, such as α-cyano-4- hydroxycinnamic acid (CHCA) or 2,5-dihydroxybenzoic acid (DHB). These specific matrixes are commonly used to analyze peptides and proteins, but there are many other

MALDI matrixes available, often ideal for specific analyte classes. During sample preparation, the matrix is dissolved in solvent and applied to the sample—the solvent evaporates and the analytes are incorporated into the matrix as it crystallizes. To ionize

analytes, a laser, with a wavelength strongly absorbed by the matrix, irradiates the sample

spot (Figure 2.1A). This causes rapid vaporization and ionization of the matrix molecules;

the analyte is also vaporized and ionized during this process. MALDI is considered a "soft"

ionization technique because intact molecules are ionized with little to no fragmented species

observed. For a more detailed explanation of the ionization process, there are several

reviews that thoroughly describe the specific mechanisms involved.15-18

A number of factors must be considered when planning a MALDI MS experiment.

For example, the MALDI matrix should be selected according to the class of analyte being

examined. The matrix should also be soluble in the solvent chosen and used at concentrations known to lead to efficient MS detection. The solvent plays an important role in the sample preparation process; slower solvent evaporation results in more time for analyte extraction and incorporation into the MALDI matrix. Some substances may hinder MALDI matrix crystal formation, including organic buffers and inorganic salts. However, in comparison with other ionization methods, MALDI is fairly tolerant to these factors. Using a

higher matrix concentration can aid in appropriate crystal formation in the presence of physiological salts.19 The matrix concentration and volume applied to a sample should also

be optimized for that sample. In addition, mixed matrixes can aid in obtaining better results

with MS profiling.20 Recrystallizing the MALDI matrix while on the sample can create

better crystals and aid in the removal of salt;21 however, with single-cell sampling, if the

matrix spot is too large, this results in dilution of the analyte, making its characterization

problematic. Efforts to promote increased ionization with the addition of ion pairing agents

to the matrix, or coating the sample with gold,22 have been reported. As mentioned, due to

the small size of most cells and thus, the typically low amounts of available analytes, the

quality of cell isolation, handling, and analyte extraction is often essential for experimental

success. The sample preparation strategies for single-cell MALDI must be selected with

regard to the specific organism and cell type under investigation.

2.2.1.1 Sample Preparation for Single-Cell MALDI

Several strategies have been implemented to obtain chemical information from single-cell

samples with MALDI MS. Typically, individual cells are isolated from tissues or organs

using enzymatic or nonenzymatic approaches and transferred onto the MALDI target where

matrix is applied onto the cell. Early studies using single-cell MALDI MS were performed

with neurons from the mollusks Lymnaea stagnalis and Aplysia californica.3-8 Aplysia, a

marine sea slug, presents an additional challenge because its extracellular environment

includes >450 mM NaCl. To address this issue, a procedure of washing Aplysia cells with an

aqueous solution of DHB was introduced to remove excess salts, while maintaining the

integrity of the cell membrane.23 The concentration of inorganic salts also can be

significantly decreased by microextraction procedures; for example, analytes released from

stimulated Aplysia bag cell neurons were captured by solid-phase extraction beads placed at the site of release.24-25 The beads are then washed to reduce the amount of salts in samples

without significant loss of analyte, resulting in an increase in signal intensity. In situations where extracellular inorganic salts are present in lower amounts, sample conditioning steps can be omitted. For example, smaller insect cells have been isolated without enzyme treatment, exposed directly to the matrix, and analyzed with MALDI MS, with an

outstanding profile of signaling peptides demonstrated.9, 11-12 Additional optimization of

sample preparation and signal acquisition protocols is required when smaller cells with lower

concentrations of analytes need to be investigated using MALDI MS.

Direct analyses of cells with MALDI MS benefit from the confinement of cellular

analytes to a small surface area, especially with smaller cell types. This is necessary to

reduce analyte spreading during sample preparation. One successful approach has used a

small volume capillary for cell lysis with spatially defined analyte deposition.26 In this case,

red blood cells were lysed inside a capillary and spotted onto a MALDI target; the detected analytes were from hemoglobin, which is obviously highly abundant in red blood cells.

Laser capture microdissection can also be employed for precise isolations of single mammalian cells from tissue sections for protein detection.27 Another isolation protocol for

single mammalian cell detection involves the addition of a glycerol-containing solution to stabilize the cell membranes and prevent cell lysis during isolation, without a detectable

change in the biochemical profile of the cell.28-30 After cells are incubated in a glycerol

solution for approximately 15 min, a glass micropipette is used to isolate single cells (Figure

2.2). Next, the individual cells are transferred to the surface of a clean, indium-tin-oxide-

coated microscope slide or glass coverslip. One must be careful at this point to make sure

that the isolated cell does not include any excess glycerol because it may interfere with

MALDI matrix crystal formation. Using another glass micropipette filled with MALDI

matrix, nanoliter volumes of matrix can be applied to single cells by gently touching the tip

to the surface, where spot sizes less than 50 µm are achievable. It is especially important to

conduct multiple controls when such small samples are analyzed. For example, the

extracellular solution should be analyzed as a control to make sure that the detected signals

are truly coming from the studied cells and not the extracellular media, which may be contaminated by compounds from damaged cells.

2.2.1.2 Recent Applications of Single-Cell MALDI

To date, most single-cell MALDI reports relate to the investigation of the nervous system, in

part because of the interest in understanding the cell-to-cell differences in key compounds in

neurons. There have been multiple reports of single-cell MS of the molluskan nervous

system, demonstrating peptide identification in these samples via MS/MS.6, 31 Recently,

similar work has been applied to insect neurons. For example, specific neurons from

Drosophila melanogaster, with 10–20 µm diameter somas, were identified by cell-specific

expression of fluorescent protein, isolated, profiled with MALDI MS, and several peptides

identified from these single cells.10 Moreover, previously unknown peptides can be

discovered by detection and identification by MS/MS in various organisms. This approach

also helps to elucidate peptide processing and post-translational modifications that may be present. In terms of peptide discovery in other organisms, the first MS-based identification

of a prohormone from the arthropods Ixodes ricinus and Boophilus microplus was

accomplished recently using single-cell analysis; these ticks have neurons smaller than 30

µm.32

In the Sweedler group, single-cell MALDI research has ranged from detecting

peptides from Aplysia californica in single isolated organelles33 to spatial profiling of a

single neuron34 to quantifying peptides in individual cells35. These experiments have resulted in the discovery of a number of new neuropeptide prohormones,36-39 as well as information

on unique peptide post-translational modifications.5, 40-41 Moving toward smaller cell types,

single mammalian cells from the rat pituitary have also been analyzed, where approximately

ten peptides were detected per cell, with more than 15 observed overall.29 As seen in Figure

2.2, there is relatively high signal intensity from a single cell. While the identities of many peaks in the mass spectra have been determined, there are other detected analytes that await identification. This methodology can be applied to other cell types in other tissues and organs.

A limitation of direct single-cell analyses via MS has been quantitation, although this is beginning to be addressed. Quantitative measurements directly from tissues with MALDI are inherently difficult, in part because the incorporation of the analyte into the matrix needs to be reproducible among samples and the matrix spot should be homogeneous to ensure even analyte distribution and reproducible ionization. Despite the inherent challenges of analyte quantification in cells with MALDI, a relative quantitation protocol has been developed using single Aplysia neurons.35 As shown in Figure 2.3, iTRAQ reagents were

implemented to relatively quantitate components present within the cell. The differences in

analyte concentrations among different cells are revealed in the MS/MS data. This protocol

could certainly be implemented in other cell types to aid in functional studies.

Another exciting technological advance that has been applied to single cell studies is mass spectrometric imaging (MSI). MSI allows one to visualize the spatial distribution of many analytes within a particular sample and is a growing field in bioanalytical MS.42-44 In

MALDI MSI, the sample stage is moved in small increments so that the laser ionizes each

spot, thereby acquiring mass spectra at an array of locations across the sample surface.

Images of specific analyte distributions in the sample are created by extracting the signal at a

particular mass as a function of the spatial positions of the sample stage. The spatial

distribution of many analytes, in some cases hundreds, can be determined in a single experiment; however, MALDI imaging at the single-cell level is problematic because of

limitations in the spatial resolution of MALDI MSI. Spatial resolution is determined by the

laser spot size, the movement of the sample stage, or spreading of analytes. For optimal

sensitivity, it is important to extract the analytes from the cell and fully incorporate them into

the matrix. To achieve optimal spatial resolution, one needs to reduce analyte spreading

because it reduces analyte extraction. Accordingly, spatial resolution and sensitivity are

linked. Addressing this issue, Monroe, et al.45 developed a protocol whereby tissue sections

are placed onto the surface of a layer of 40 µm glass beads embedded in Parafilm M. When

the substrate is stretched, each bead has on the order of a single cell attached to it. After

matrix is applied to the sample, recrystallization of the matrix enhances the quality of the

matrix crystals. Furthermore, due to the hydrophobic nature of Parafilm M, analyte

redistribution is minimized. Instead of trying to profile each individual bead, the entire

sample can be profiled and the data stitched together to reconstruct an image of analyte

localization from the original tissue sample.46

Another approach using MALDI MSI on the cellular scale has been introduced by the

Heeren group.47-48 Rather than decreasing the size of the laser spot to increase spatial

resolution, a mass-microscope uses a large laser spot, but the spatial orientation of the ions is

maintained during separation in the mass analyzer. This technique is capable of a spatial

resolution of 4 µm with 500 nm pixel sizes. Although single-cell detection was not

demonstrated, the scale for single-cell ionization and detection certainly has been achieved.

MSI has also been employed in measuring time-resolved release of peptides from

neurons using a microfluidic-based collection approach.49 In this work, a single Aplysia

25 neuron is confined inside a channel where three connected channels are used to collect releasates from a neuron before stimulation, during chemical stimulation, and post- stimulation. Each channel is also functionalized with a C18 layer to collect the released analytes. This approach for temporal isolation of released peptides could be further improved by including additional channels for better temporal resolution.

As mentioned previously, MALDI MS is more commonly used for detection and identification of larger molecules, but metabolites can also be studied with this approach. As an example, metabolites in yeast were detected with single cell sensitivity by the Zenobi group.50 As noted in the supplementary material of their report, several sample protocols and optimizations were employed to increase sensitivity. The optimized protocol included measuring the number of cells within a certain volume with flow cytometry and performing a cell extract on that sample. Matrix was sprayed onto a target and the extract of various volumes was spotted onto it with a piezo printer. It was determined that 390 pL was sufficient for obtaining metabolite signal from the extract, while the volume of a single yeast cell contains less than 100 fL. Although not specifically single-cell MALDI, these results demonstrate that small cells and their small molecule contents can be assayed using this approach.

Without a doubt, the figures of merit for single-cell MALDI MS are currently impressive, and they continue to be improved. As enhanced sample preparation protocols and more sensitive instruments are developed, the efficacy of such measurements will progress. This will allow a greater range of analytes to be measured from more cell types at higher spatial resolutions. High throughput analyses of individual cells using MALDI MS

will expand applications of the technique to include fundamental research, clinical studies, and industrial investigations.

2.2.2 Secondary Ion Mass Spectrometry

Like MALDI, SIMS can directly profile samples and has been applied to single-cell studies.

As one major difference, SIMS typically does not require the addition of a specific matrix to

the sample. The ionization process for SIMS utilizes an energetic, focused ion beam to

sputter secondary ions from the sample surface (Figure 2.1B). This technique is considered a

"hard" ionization technique, which produces internal fragmentation of analyte molecules.

SIMS is implemented for smaller molecular weight compounds, with the majority of detected analytes having molecular masses less than 500 Da. The upper mass threshold is limited by the sputtering and desorption processes of SIMS because the ejection of intact molecules is a lower probability event for larger molecular weight compounds. Thus, the high mass limit depends on the amount of material present, as well as the sampling and sputtering processes.

There are two modes of operation with SIMS: static and dynamic. Static SIMS can generally be considered as a non-destructive method because less than 1% of the surface is probed by the primary ion beam.51 In contrast, dynamic SIMS uses a large number of primary ions where the surface layer is generally damaged and removed during analysis.52 Experiments

using this method are also known as depth-profiling studies.

A variety of ion beams can be implemented for sputtering with the analytical figures of merit dependent on the details of the ion beam. Atomic and diatomic sources available

+ + + + + + - x+ include Au , Ar , Cs , Ga , In , N2 , and O2 , but the use of cluster ion sources, such as C60 ,

+ x+ + x+ x+ SF5 , Arn , CsnIy , Bin and Aun , are becoming more popular. Cluster ion beams have

27 gained increasing attention because they generate higher secondary ion yields with greater efficiencies than atomic sources, improving the limit of detection for larger molecules while also decreasing sample damage. One of the more impressive advantages of using SIMS is that analyte distribution can be imaged within a single cell, with spatial resolutions less than

50 nm possible.53 SIMS has also been implemented in three-dimensional (3D) molecular analysis, where the ion beam initially rasters across the surface, generating secondary ions.

A sputter beam removes this analyzed layer, thus exposing a new layer for further analysis.

A detailed description of cluster sources, and their application to depth imaging, can be found elsewhere.54

Another difference between MALDI and SIMS is that MALDI detects cellular content, and SIMS, as a surface technique, characterizes analytes on the cell membrane.

Because of the fragmentation that occurs with the SIMS process, the detected analytes are typically identified by characteristic mass fragments and their comparison to standards.

MS/MS analyte identification is not common, but has been developed.55-56

2.2.2.1 Sample Preparation for Single-Cell SIMS

Most studies that employ SIMS for single-cell analyses are interested in its imaging capability; thus, the overall goal of sample preparation in SIMS is preserving the original localization of analytes to obtain a representative view of processes occurring in functioning biological systems. Freeze-fracture procedures for sample preparation are used because the process is rapid and preserves cellular morphology.57 However, this protocol tends to yield few cells for analysis and requires a specially designed cryochamber for sample handling.

Although inorganic salts can be imaged with SIMS, these salts can interfere with the

detection of other analytes. If salt is present, which is common for cellular samples, it can

dominate the spectrum. Ammonium acetate can be employed to remove salt and other

interferants from the cell while still maintaining the integrity of the cell membrane in a

reproducible fashion.58 Furthermore, multiple fixation techniques can be used, such as

rinsing the sample in water,59 70% ethanol,60 trehalose and glycerol,61 or sucrose and water.22

Other sample preparation steps have also proven useful for improving signal with

SIMS. Charging can commonly occur, which results in poorly resolved peaks. To alleviate

this, a cell can be deposited or cultured on silicon, rather than on glass, or the entire sample

can be coated with a thin layer of gold.22 This sample coating provides the additional benefit

of increasing secondary ion yields with less fragmentation. It has also resulted in the detectability of larger analytes; however, the resulting spectrum may include metal adducts and clusters, which can hamper data analysis. Adding a MALDI matrix for SIMS application has also been demonstrated to increase the detectable mass range, but has not yet been

implemented for single cell applications.

2.2.2.2 Recent Applications of Single-Cell SIMS

Measuring analyte distribution in cells can yield information not only on the state of the tissue, but also on the role of these molecules in various processes. With the demonstration that SIMS was capable of imaging diffusible ions on a subcellular level, it was appreciated

that a variety of biological processes could be monitored.62 For example, imaging of boron

has been useful in determining cellular uptake in normal and tumor tissue for applications in

cancer therapy.63 Furthermore, with the progression of SIMS instrumentation, molecular

species could be detected and imaged simultaneously with atomic species on a subcellular

level, which led to its application in biological samples.64

More recently, research has focused on membrane lipids and their changes during dynamic events. As one example, Tetrahymena thermophila is a single-cell organism that mates by adjoining two cells to allow the migration of micronuclei between them. This process requires lipid synthesis and rearrangement. Winograd and Ewing’s65 groups

demonstrated that the micron-sized junction between the two cell bodies has a decrease in

phosphatidylcholine relative to the rest of the cell body. Furthermore, the appearance of an

unidentified peak increased in this junction, which was not present in other cell types and

may be significant in forming this connection. In another application of determining analyte

localization, Monroe, et al.66 demonstrated that vitamin E is not homogeneously distributed

in an Aplysia neuron but is found at higher levels at the soma-neurite junction.

Similar to MALDI MS, quantitation of analytes in a tissue requires attention to detail in SIMS. The resulting signal intensity depends on the chemical composition and

topography of the cell, primary ion interactions, instrumental transmission, and detector

response.52 Ostrowski and coworkers67 developed a method to relatively quantify cholesterol

between control and cholesterol-treated cells. Cholesterol has a variety of roles, including

functions in metabolism, controlling phase behavior of membranes, and being a precursor to

hormones and vitamins.68-69 Also, abnormalities in cholesterol content have been observed in

several diseases.70-72 The use of an internal standard that remains constant in treated and

+ non-treated cells is needed for normalization of the spectra; in this case, C5H9 was used.

The control and cholesterol-treated cells were separately incubated with two different

fluorophores known to adhere only to the outer membrane of the cell. To make a direct

comparison, results for two cells under different treatments were analyzed in the same SIMS

image to control for potential instrumental differences. Different fragments of cholesterol

were produced with varying degrees of cholesterol elevations and standard deviations in

signal, results which stress that fragment ions must be analyzed and chosen carefully for

accurate quantitation. This methodology was able to detect a significant increase in

cholesterol after incubation and could be applied for relative differences in concentration for

other treatment conditions.

An enhancement to single-cell SIMS is 3D imaging. An early demonstration is the

imaging of Xenopis laevis oocytes,59 large cells (0.8–1.3 mm in diameter) that also contain

larger cellular components. These cells are also resistant to osmotic changes, which limits

potential analyte diffusion.73 Different component localizations were detected in the x, y, and z planes of this model system, as shown in Figure 2.4, top panel, where some analytes have greater intensities below the cell plasma membrane. Even though these cells are resistant to osmotic changes, they did exhibit a morphological change after rinsing with water and after freeze-fracture. Sample preparation will need to be optimized to maintain the original morphology and distributions of analyte.

In another example of 3D SIMS imaging, a new SIMS instrument design, the

+ 55 Ionoptika J105 3D Chemical Imager, incorporates a continuous C60 ion source. This results in continuous secondary ion production, which greatly enhances the duty cycle and

SIMS signal. A pre-cooled sample stage is also included for flash-frozen samples to minimize analyte redistribution. The resulting chemical image is shown in Figure 2.4, bottom panel, where adenine is localized in the center of the cell and the lipid signal is on the outer portion of the cell, as would be expected. These demonstrations of 3D-imaging yield a

more complete view of cellular analyte distribution and will certainly lead to future

applications in functional studies.

Further enhancements to SIMS instrumentation continue. Mass resolution

improvements to the previously mentioned J105 3D chemical imager provided MS/MS

capability,55 resulting in the ability to distinguish two different species with similar molecular

weights. For example, analytes with m/z values of 102.8 and 103.0 had different spatial

distributions in cheek cells. In another advance, an Applied Biosystems QStar mass

spectrometer with MS/MS capability has been modified and converted into a SIMS

56, 74 instrument by adding a C60 source. With the development of new systems for analyte

identification, SIMS implementation in bioanalytical investigations will become more frequent.

Developments in SIMS have been primarily focused on sample preparation improvements, ion source development, and demonstrations of different capabilities of instrumentation, while actual functional bioanalytical studies have been somewhat limited.

Given the high spatial resolution that SIMS affords, future studies should progress the field with more functional applications. In one report, bacterial metabolism rates of carbon and ammonium were detected and quantified with SIMS on a single-cell basis via incubation of the cells with 13C and 15N.75 It was demonstrated that a high variability in metabolism

existed between three different strains from a common environment, each having their own purpose. Variability in metabolism was also detected in cells from the same strain. Studies such as these can further our knowledge of bacteria and how they interact with the environment. Other applications, such as imaging RNA localization with respect to

translation76 or tracing copper intake of algae for environmental monitoring77, offer data

important for both fundamental and applied sciences.

2.2.3 Electrospray Ionization

ESI MS has been successful in characterizing a range of samples and is the preeminent

approach for proteomics. Interestingly, it is not routinely used in single-cell analysis. Unlike

MALDI and SIMS, ESI involves the use of liquids and therefore requires analyte extraction

from the sample prior to introduction into the mass analyzer. Therefore, sampling, extraction,

and analyte dilution are important in this application. Once the sample is prepared, the analyte solution is electrosprayed (Figure 2.1C). ESI yields multiply-charged species for larger molecules, with the same species being detected at different m/z values, often reducing detection limits. Separation techniques, such as liquid chromatography or capillary electrophoresis (CE) are commonly hyphenated to ESI to reduce sample complexity and to concentrate analyte bands. Because ESI is not as tolerant to many inorganic salts and organic additives as are SIMS and MALDI, the separations also serve to desalt and condition the samples prior to measurement.

Nevertheless, ESI has the capability to detect a broad range of analytes from a single cell, from metabolites to large proteins, especially when optimized sample preparation approaches are implemented. There are multiple commercial nanospray ESI sources available, which are important as these minimize sample consumption and therefore fit well with single-cell ESI MS. With such approaches, the sensitivity and limits of detection for

ESI MS instruments are sufficient for the detection of analytes from single cells.78

2.2.3.1 Recent Applications of Single-Cell ESI

Most reports of single-cell ESI include hyphenation to CE, which separates analytes based on

their size and charge with high efficiency. Using this combined approach, hemoglobin was

detected in human erythrocytes, where it is found at high levels.79 The cell was directly

injected into the capillary with no prior stabilization and then lysed due to the osmotic

pressure difference of the running buffer. Similar results were obtained on single

erythrocytes using a different mass analyzer, thus demonstrating capability on multiple

instrument platforms.80 Other analytes, besides proteins, can be studied using single-cell ESI

MS as well. Preliminary results reported by Lapainis, et al.81 demonstrate that characterizing

a single neuron from A. californica for metabolite content is possible. To achieve these

results, a home-built CE system was interfaced with ESI MS and an experimental protocol

was developed. Several substances were detected, including acetylcholine.

Recently, there was a report of single-cell ESI MS with mammalian cells without the

need for prior separation.82 The nanospray tip was implemented as a micropipette under a

video microscope, where the cytoplasm or granules were removed from the cell (Figure 2.5).

Ionization solvent was added to the tip, and the sample directly introduced into the mass

spectrometer. The cytoplasm, cell medium, and solvent had similar, but distinct profiles.

Principal component analysis was applied to the data from each sampling, where data points

from each sample set were all clearly separated (Figure 2.6). Several peaks were also identified by MS/MS, such as histamine and serotonin.

Single-cell ESI MS certainly has unrealized potential. Separations prior to ionization allow more complex samples to be measured and so protocols need to be optimized for each sample and analyte of interest. Direct infusion with ESI is advantageous because sample

dilution is minimized and analysis times are much shorter. With the large range of analytes

that can be detected and identified with ESI MS, efforts to improve sample introduction

approaches will continue.

2.2.4 Other MS Approaches

MALDI, SIMS, and ESI are the most widely used ionization techniques for MS

investigations of biological samples and single-cell studies. However, it is worth mentioning

several of the other MS techniques that are becoming more prominent and have potential in

this area. Some are related to laser desorption/ionization (LDI), which does not require a

matrix for ionization. For example, with the pioneering work of Hillenkamp83 in the

development of laser microprobe mass analysis (LAMMA), spatial resolution can be on the

micron scale and a variety of small analytes can be detected.84 Desorption/Ionization on

porous silicon (DIOS) can ionize peptides from single neurons and has the capability to

image small molecules from single cells.60, 85 A recent development is nanostructure-initiator

mass spectrometry (NIMS), which utilizes a laser or an ion beam to ionize samples on a

substrate embedded with clathrate structures to promote ionization.86 This technique

produces little fragmentation, has a lateral resolution of 150 nm, and has already achieved

single-cell detection. Laser ablation electrospray ionization (LAESI) combines an ablation

plume with ESI-like ionization, where plant tissue can be directly ionized and imaged.87-88

This technique currently has a 200–300 µm spatial resolution with fmol level detection limits.

Like LAESI, there are other ambient ionization methods that have been developed as well.89 Desorption electrospray ionization (DESI) utilizes a charged spray to extract and

ionize analytes directly from the sample surface, and has already been implemented in

imaging tissue sections for metabolites and lipids.90-91 Another example is direct analysis in real time (DART), which uses reactive ionized species to interact with the sample of interest and is beginning to be used on biological samples.92-93 While this is not a comprehensive list of techniques that have the potential for single-cell MS detection, it indicates the power of these emerging methodologies for direct tissue measurements.

2.3 Overall Outlook for Single-Cell MS

The implementation of single-cell MS has been useful in studying neuronal systems, metabolic profiles, cell membrane components, and many other systems and analytes.

MALDI, SIMS, and ESI are complementary techniques that yield a wealth of information about the presence of identified and novel analytes. With the currently available sample preparation and signal acquisition protocols, as well as instrumentation, many biological questions can be answered. Continuing to develop quantitative approaches is key to understanding the relationships between analytes. Incorporating MSI in these studies aids in determining what roles are being played by individual cells within a cellular network. The rapid growth of a plethora of sampling and ionization approaches that offer a new range of capabilities is also exciting. The rapid evolution of technology with the increasing role of proteomic and metabolomic investigations certainly proves that single-cell MS is an area of growth. It is expected that further applications and discoveries will expand over during the coming decades.

2.4 Figures

Figure 2.1 Mass spectrometric ionization processes. (A) MALDI utilizes a pulsed laser to irradiate a mixture of analyte and matrix. (B) SIMS implements an ion gun to sputter secondary ions from the sample surface. (C) ESI requires the sample to be in solution where a voltage difference creates a Taylor cone of ions. Reproduced with permission from John Wiley & Sons, Inc.

Figure 2.2 Single-cell sample preparation and subsequent mass spectrometric profiling. (A) Attaching the cell to the glass micropipette. (B) Transfer and (C) deposition of the cell onto a clean glass surface. (D) Application of MALDI matrix to the spot containing the cell. (E) Representative mass spectrum of a pituitary blot and (F) a single pituitary cell. (G) Portions of the mass spectra obtained from six different, isolated pituitary cells. Reproduced with permission from the American Chemical Society.29

Figure 2.3 iTRAQ labeling allows the determination of relative qquantities of peptides present in single neurons. (A) F-cluster with individual cell bodies visible (left). Individual CFT neuron deposited on a MALDI sample plate (right). (B) Protocol for relative peptide quantitation in single neurons using iTRAQ reagents. (C) Top mass spectrum represents the peptide profile of an individual CFT neuron before labeling. Two bottom mass spectra are mixtures of two different pairs of CFT cells. (D) Tandem mass spectra of a peptide acquired from the mixed contents of two pairs of individual CFT neurons after separate labeling with iTRAQ 114 and iTRAQ 117. The insets show the mass range containing isotopic labels cleaved from labeled molecular ions during the fragmentation process. IS, internal standard. Reproduced with permission from the American Chemical Society.35

Figure 2.4 Three-dimensional SIMS imaging. Top panel: Three-dimensional biochemical images of an oocyte demonstrating different disttributions of the following masses: (a) phosphocholine peaks m/z 58, 86, 166, and 184, (b) signal summed over the m/z range 540– 650, (c) signal summed over the m/z range 815–960, and (d) cholesterol peak at m/z 369. Color scale normalized for total counts per pixel for each variable (m/z range). Reproduced with permission from the American Chemical Societty 59. Bottom panel: Three-dimensional image of cells. (Left) The protonated molecular ion from adenine, which is localized to the center of the cells. (Right) Phosphocholine-containing lipids, which are observed as rings around the edge of the cell. A larger view of the orthogonal slice through a single cell is also shown for clarity. Reproduced with permission from the American Chemical Society.55 40

Figure 2.5 Live single-cell video mass spectrometry. Step 1: Scheme and visualizatioon of injecting single-cell contents with resulting data. Step 2: Scheme for data analysis. Reproduced with permission from John Wiley & Sons, Ltd.82

Figure 2.6 Principal component analysis on the mass spectra of live single cell molecular detection. Key: cytoplasm (filled triangle), granulle (open triangle), cell culture medium (circle), solvent (diamond). Adapted with permission from John Wiley & Sons, Ltd.82

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CHAPTER 3

MS-BASED METHODOLOGIES FOR SINGLE-CELL METABOLITE DETECTION

AND IDENTIFICATION

Notes and Acknowledgments

This chapter outlines protocols used for single-cell metabolite detection and is based on a chapter submitted as “MS-based methodologies for single-cell metabolite detection and identification” in Methodologies for metabolomics: experimental strategies and techniques,

Sweedler, J. V., Ed. Cambridge University Press: 2011 with coauthors Peter Nemes,

Stanislav S. Rubakhin, and Jonathan V. Sweedler.1 It is reprinted here with the permission of

Cambridge University Press. Peter and I focused on sections specific to CE-ESI-MS; I wrote sections including the introduction, materials, data analysis methods, and conclusion, while

Peter wrote the methods and procedures section. Stanislav wrote sections specific to MALDI

MS. The entire protocol is reproduced here for completeness. The authors would like to thank Xiying Wang for assistance in the single-cell sample preparation. This work was supported by the National Science Foundation (NSF) under Award Number CHE-0526692 and the National Institutes of Health (NIH) under Award Numbers DK070285, NS031609, and U54GM093342.

3.1 Introduction

This chapter describes detailed protocols required for single-cell metabolomics.

Metabolomic studies strive to determine the small molecule composition (e.g., sugars, lipids,

amino acids, but not peptides, proteins, DNA, and RNA) of a biological sample in a given

state. Information generated from the sample, which is often complementary to gene

expression and proteomic data, serves to link chemical content to the observed phenotype.

For example, metabolomics is often used to examine metabolic differences between a control

and an altered state, such as normal and diseased.2-4 Metabolites are typically measured in

complex, multicellular biological sample matrices—blood, serum, urine, and tissue biopsies.

Moreover, there can be a diversity of cell types present within these specimens; for instance,

brain tissue consists of neurons, glia, and other cell types. Furthermore, cell-to-cell heterogeneity is known to exist, even within the same cell type,5-9 especially in the nervous

system10-12. To improve the fundamental understanding of the underlying biochemistry of

biological systems, chemical analysis is beneficial when performed at single-cell levels.

Significant technology advances for single-cell analysis, and understanding their

crucial role in answering biological and medical questions, have resulted in their widespread

applications in the metabolomics field.13-15 For example, electrochemical detection is a

highly sensitive technique that has been applied to monitoring cellular exocytosis and

secretion.16-20 Fluorescence detection is also suitable for single-cell investigations and has been implemented to study the metabolism of particular pathways by fluorescently tagging

an analyte of interest within a cell and observing the resulting enzymatic products.21 Single- cell separations have also been accomplished using capillary-scale separations with electrochemical and fluorescence detection, enabling the characterization of multiple analytes.22-23 Mass spectrometry (MS) is a particularly attractive method of single-cell

microanalysis because a priori knowledge of analyte content is not necessary and many

analytes can be simultaneously detected. In addition, the combination of accurate mass

measurements and tandem mass spectrometry (MS/MS) provides a straightforward system to

confidently identify metabolites. MS also has the required sensitivity, limits of detection,

and dynamic concentration range for single-cell metabolomics applications. While the depth

of metabolome coverage will decrease when scaling down from tissue homogenates to

single-cell samples, the advantage is the capability to analyze heterogeneity on a single-cell

level. For recent reviews regarding single-cell MS, the reader is referred elsewhere.24-25

Traditionally, two ionization sources have been particularly successful in single-cell

MS: matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI).

In the former method, the sample is mixed with an infrared or ultraviolet-absorbing compound (matrix) to help generate analyte ions in vacuum. MALDI has been incredibly useful in detecting analytes, such as peptides, in single-cell applications and has been utilized for this purpose for approximately the last 15 years.26-27 More recently, MALDI has been

used in metabolite detection; for example, it has been implemented to profile metabolite

differences in unicellular organisms, with applications in determining how organisms

respond to different environments and stimuli.28 In an effort to achieve high-throughput

analysis in single cells, a protocol has been developed to create patterns of single yeast cells for subsequent MALDI analysis.29 In ESI MS, analytes are first extracted into the liquid phase and then converted into gas-phase ions by an electrospray source. When coupled to capillary electrophoresis (CE), ESI MS enables the detection and identification of a number of metabolites in single cells and subcellular features.30 Both ionization sources are

beneficial for the analysis of biological samples because they can ionize a wide range of

intact analytes, from metabolites to large proteins.

Several other ionization techniques have been coupled with MS for metabolite

detection in single cells. Laser ablation electrospray ionization (LAESI)31 is a relatively new

ionization technique that has been implemented to directly analyze metabolites from single

plant cells with no sample preparation32. Single-cell video-MS can be used to investigate

metabolites from cells and their subcellular features; cellular contents such as the granules

from mast cells were collected intracellularly with an electrospray emitter and directly

electrosprayed into the mass spectrometer.33 Subcellular analysis and spatial imaging can be achieved with ionization techniques such as secondary ion mass spectrometry (SIMS)34 or

nanostructure-initiator mass spectrometry (NIMS)35. While gas chromatography (GC)-MS

has been routinely used for metabolomics approaches with multicellular samples, it may have

been the first MS approach used for single-cell analysis, specifically, of neurons from

Aplysia californica.36 More recently, GC-MS has demonstrated its applicability in single-cell

analysis with the detection of over 400 low mass signals from single oocytes.37 For more

extensive reviews of applications in single-cell metabolomics, the reader is referred

elsewhere.1, 38-39

Here, MS strategies are presented using MALDI and CE-ESI to investigate the

chemical composition of individual neurons in the nervous system of A. californica, a

common neurological model. The MALDI MS protocol enables the direct analysis of

nanoliter-volume samples and provides immediate feedback about the sample content. The

CE-ESI-MS method described provides complementary information on metabolite coverage

and enhances the confidence of chemical identifications by incorporating chemical

separation. With hundreds of metabolites detected, these protocols allow the routine analysis

of individual neurons, and with some modification, these approaches can be applied to other

cell types.

3.2 Methodologies: Overview and Timing

The general workflow for MS-based metabolite measurements in individual cells is shown in

Figure 3.1. Initially, the organism of interest is anesthetized (if needed), dissected, and the tissue optionally treated with an enzyme, in part to reduce connective tissue that surrounds

the cell(s) of interest to loosen or remove its contact between the cells. Individual cells are

then isolated, a task for which micromanipulators can provide excellent reproducibility.

Depending on the goal of the study and the applied detection method, cells can be exposed to

different degrees of chemical modification during and after the isolation step. For example,

delicate cells can be isolated in a glycerol-containing environment, which enters the cells and

mechanically stabilizes them, thereby preventing unintentional cell lysis.40-42 Following

isolation, cells may be rinsed with a small amount of water to remove extracellular organic

compounds and inorganic salts. Lowering the concentration of nonvolatile salts in the

sample extract can improve analyte ionization in both MALDI and ESI experiments, which

can in turn improve detection limits for metabolite measurement.

A variety of MALDI matrixes can be used for metabolite detection. The compound

2,5-dihydroxybenzoic acid (DHB) is particularly useful because it allows the ionization of

small metabolites and larger molecules (e.g., peptides) from the same prepared sample.

Furthermore, it can also enable detection of analytes under salty conditions and act as a

stabilizing agent for cell samples.26 Other MALDI matrixes that can be employed in single-

cell MS metabolomics investigations include 9-aminoacridine43-44 and alpha-cyano-4-

hydroxycinnamic acid45. In some cases, the MALDI matrix can be eliminated and a focused

laser beam can be used to desorb and ionize molecules directly from samples in vacuum, an

approach called laser desorption ionization.46 For MALDI MS experiments, the sample may

be directly spotted on a substrate, co-crystallized with a suitable matrix, and after air- or

vacuum-drying, analyzed or chemically imaged.

In contrast to single-cell MALDI MS, analytes are typically extracted and diluted in a

suitable solution prior to ESI analysis. This minimizes or eliminates potential interferences

with the ESI process, such as cell debris and salt, and provides a well-controlled chemical

environment for the study of selected analytes. The extraction solution of choice should also

hinder, or ideally prevent, degradation or formation of metabolites in enzymatic reactions.

To obtain optimal extraction efficiency, the composition of the solution must be tailored to

the physicochemical properties of the compound(s) of interest, including the size, polarity,

and net charge of the molecule. Permanently charged compounds (e.g., potassium+, choline+, and acetylcholine+) and zwitterionic structures (e.g., amino acids) are readily incorporated

into water, whereas apolar molecules can be extracted with higher yields in the presence of

organic modifiers (e.g., methanol and acetonitrile), which should be tested for compatibility

with ESI or CE-ESI processes. The efficiency of extraction can be further adjusted by

altering the solution pH. For small metabolites, aqueous organic solvents containing acidic or basic pH-modifiers have been successfully applied.30

The addition of a chemical separation step prior to direct MS detection can often be

advantageous. Not only can separation aid in the detection of isobaric compounds, but the

analyte elution time also provides additional information to confirm identification. There are

several fundamental reasons why CE is a method of choice for the separation of various

compounds in single-cell investigations: high-efficiency, compatibility with relatively salty

samples, small injection volumes (nL), the availability of a number of CE-based analyte

concentration techniques, and applicability for a wide range of molecules (molecules do not

necessarily need to be charged).22-23, 47-48 One critical element of CE and MS hyphenation is

that the composition of the background electrolyte must be compatible with the operating

conditions of ESI. To further facilitate hyphenation, a coaxial sheath-flow CE-MS interface

achieves the analytical performance required to detect neurotransmitters from a single

neuron.30, 48

The following sections provide guidance in the selection of materials and procedures to enable metabolic analysis of single cells. The analytical steps presented here can be

further optimized for a particular cell type, tissue, or animal of interest. Here, the following protocols apply to the investigation of isolated cells from the central nervous system (CNS) of A. californica and the detection of metabolites below m/z 1,000.

3.3 Materials

3.3.1 Sample Preparation

3.3.1.1 Animals

A. californica weighing 175-250 g can be obtained from The National Resource for Aplysia

(Rosenstiel School of Marine and Atmospheric Science, University of Miami/NIH, FL; http://aplysia.miami.edu/) and should be maintained in constantly circulated, aerated seawater chilled to 14 °C. Artificial seawater for use in the aquarium can be made in the laboratory by dissolving sea salts (Instant Ocean; Aquarium Systems, Mentor, OH) in water according to the manufacturer’s instructions. While well-suited for animal maintenance, this

57 type of artificial seawater is not recommended for analytical measurements because the exact ion composition is not defined.

3.3.1.2 Reagents and Solutions

The chemicals and reagents used in these protocols were from Sigma-Aldrich (St. Louis,

MO) unless otherwise noted.

 Chemicals: Water (LC-MS grade Chromosolv®), methanol (Optima®, Fisher

Scientific, Fair Lawn, NJ), formic acid (FA) (99+%, Thermo Scientific, Rockford,

IL), acetic acid (99+%), 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

(HEPES) (99.5%), DHB (98%), ammonium citrate (99.7%, Fisher Scientific), ethanol

(99.5%, Acros Organics, Morris Plains, NJ), phosphoric acid (85%, EM Science,

Gibbstown, NJ), and water with 0.1% FA and 0.01% trifluoroacetic acid (TFA) (v/v)

(Fisher Scientific).

 Artificial seawater (ASW) supplemented with antibiotics: (mM) 460 NaCl, 10 KCl,

10 CaCl2, 22 MgCl2, 26 MgSO4, and 10 HEPES (pH 7.7), 100 units/mL penicillin G,

100 μg/mL streptomycin, and 100 μg/mL gentamicin.

 Enzyme solution: 1% protease type IX in ASW supplemented with antibiotics.

 Solution for cell stabilization: 33% glycerol with 67% ASW (v/v).

 Extraction solution for analyte sampling: 50% methanol containing 0.5% acetic acid.

 MALDI standards: serotonin, aspartate, glutamate, alanine, and serine.

3.3.1.3 Disposables

 PCR vials (MidSci, St. Louis, MO).

 Sharp metal needles (World Precision Instruments (WPI), Inc., Sarasota, FL).

 Glass Petri dishes for cell isolation that are half-filled with Sylgard (Dow Corning,

Midland, MI).

3.3.1.4 Equipment

Visualization system: high-performance stereoscope with 7.9:1 zoom (e.g., similar to the MZ

7.5, Leica Microsystems Inc., Bannockburn, IL) and optional digital camera for recording.

3.3.2 CE-ESI-MS System

3.3.2.1 Reagents and Solutions

 Background electrolyte for CE separation: 1% FA.

 Electrospray solution: 50% methanol prepared with 0.1% FA.

3.3.2.2 Equipment

 Regulated high-voltage power supply (HVPS) capable of at least 20 kV output with

optional remote-programmed operation, similar to the Bertan 2320 series (Spellman

High Voltage Electronics Corporation, Valhalla, NY) or Glassman PS/MJ series

(Glassman High Voltage, Inc., High Bridge, NJ).

 Injection stage capable of elevating a platform 15 cm within ~3 s and housing

stainless steel vials for sample loading and the background electrolyte.

 Enclosure equipped with an interlock system to provide protection against exposure

to components kept at an elevated potential (e.g., stainless steel vials and fused silica

capillary).

 Separation capillary: fused silica capillary with ~100 cm length and optimized

dimensions (e.g., 40 µm inner diameter (i.d.), 105 µm outer diameter (o.d.),

Polymicro Technologies, Phoenix, AZ).

 Solvent-delivery PEEK tubing or capillary: ~50 cm length and optimized dimensions

(e.g., 75 µm i.d., 105 µm o.d., Polymicro Technologies, Phoenix, AZ).

 T-union and corresponding components (e.g., fittings, ferrules, and sleeves) to adapt

the separation and solvent-delivery capillaries, e.g., stainless steel hypodermic tubing

(211 µm o.d., 165 µm i.d., Small Parts, Inc., Miramar, FL), PEEK tee or MicroTee

(Idex Health & Science LLC, Oak Harbor, WA).30

 Three-axis translation stage (e.g. from Newport Corp., Irvine, CA).

 Solvent-delivery system such as a syringe pump (similar to a PHD 22/2000, Harvard

Apparatus, Holliston, MA) capable of supplying solutions at ~300-1,000 nL/min

rates.

 Gas-tight syringes for supplying the electrospray solvent and the background

electrolyte, e.g., 1 mL gastight syringe (Hamilton Company, Reno, NV).

 ESI mass spectrometer: The CE-ESI interface can be adapted to a number of mass

spectrometers that feature an atmospheric pressure interface. The present protocol

utilizes single-stage and tandem mass spectrometers for metabolic profiling and

collision-induced fragmentation (micrOTOF ESI-TOF-MS and maXis ESI-Qq-TOF-

MS/MS systems, Bruker Daltonics, Billerica, MA).

3.3.2.3 Software

 Software to process and analyze the recorded mass spectra (e.g., Compass, Bruker

Daltonics).

 (Optional) Software and equipment to remotely control the HVPS (e.g., LabVIEW-

or MATLAB-based custom-written software).

3.3.3 MALDI MS System

3.3.3.1 Reagents and Solutions

 MALDI matrix solution: 10 mg/mL DHB in a 79:20:1% 15 mM ammonium

citrate:ethanol:concentrated phosphoric acid mixture.

 Standards: 100 mM or saturated solutions of serotonin, aspartate, glutamate, alanine,

and serine in 0.1% FA, 0.01% TFA in water.

3.3.3.2 Equipment

MALDI mass spectrometer: The protocols listed here use the ultrafleXtreme (MALDI

TOF/TOF mass spectrometer) from Bruker Daltonics, which is optionally operated in single-

stage or MS/MS (LIFT) modes.49

3.3.3.3 Software

 Software to process and analyze recorded mass spectra (e.g., Compass, Bruker

Daltonics).

 (Optional) Software for multivariate analysis of MS data (e.g., ClinProTools, Bruker

Daltonics).

3.4 Procedures and Troubleshooting

3.4.1 Sample Preparation

3.4.1.1 Single Cell Isolation

1. Anesthetize the animal using vascular cavity injection with isotonic MgCl2 solution

equal to one-third to one-half of the animal’s body weight. Dissect the ganglia of

interest from the CNS of A. californica.

2. Incubate the ganglia in 1% protease IX solution at 34 °C for 20-120 min depending

on the cells that need to be isolated, ganglia type, animal weight, and season. It is

practical to optimize the duration of enzymatic treatment for the ganglia of interest.

Wash the ganglia several times with fresh ASW to remove the enzyme after

treatment.

3. (▲Critical step) Pin down the ganglia on the Sylgard surface of a petri dish filled

with ASW and mechanically isolate target neurons with sharp metal or glass

needles.50 During this step, the solution for cell stabilization may be used to improve

cell stability during mechanical stress. Control measurements have demonstrated that

this solution does not appreciably interfere with ion generation during MALDI or

ESI.

4. (Optional) Rinse the isolated cell with ~1 µL water using a pipette.

3.4.1.2 Sample Preparation for CE-ESI-MS

1. Place the neuron into 5 µL of extraction solution.

2. (Optional) Chemical standards may be added to the extraction solution for analyte

quantitation and/or internal mass-calibration of the mass spectrometer. The use of

internal standards is particularly helpful when metabolic quantitation is desired.

3. (■Pause point) (Optional) Store cells in the extraction solution at –20 °C for several

days. For long-term storage, utilize a freezer providing –80 °C to minimize metabolic

degradation. In both cases prolonged storage may result in sample drying and

therefore liquid nitrogen storage can be considered.

3.4.1.3 Sample Preparation for MALDI MS

1. Place individual cells directly onto a stainless steel MALDI sample plate and apply

0.3 µL of matrix solution on top of the cell; allow the matrix solution to dry at room

temperature. Alternatively, incubate the isolated cell in 3 µL of matrix solution

overnight at 4 °C and deposit aliquots of controlled volume (e.g., 0.3 µL) on the

sample plate in duplicate.

2. Create two control spots by applying 0.3 µL of the matrix solution on the sample

plate.

3. Apply 0.3 µL of a mixture of standards, such as serotonin, aspartate, glutamate,

alanine, and serine, on two spots and allow the solution to dry at room temperature.

Add 1 µL of the matrix solution on top of the dried spot.

3.4.2 Instrument System Setup

3.4.2.1 CE-ESI-MS

1. (▲Critical step) Prepare a CE-ESI interface using a T-union that incorporates an

electrospray emitter on one end. In the other end of the union, feed a fused silica

capillary coaxially through the emitter. Allow for this separation capillary to protrude

~200-1,000 µm beyond the tip of the ES emitter and finger-tighten connections for a

stable arrangement. Use the third end of the T-union to supply the electrospray

solvent through a fused silica capillary. Further technical details on assembling a

stable CE-ESI ion source are detailed elsewhere.30

2. Mount the CE-ESI interface on a three-axis translation stage and position it ~10 cm

from the skimmer of the mass spectrometer. Ground the electrospray emitter directly

or through the T-union. Inspect the setup for both electrical and mechanical stability.

3. Using a syringe pump, supply the electrospray solution through the metal emitter at

350-1,000 nL/min rate and simultaneously flush the background electrolyte through

the separation capillary for 5-30 min upon first use (following assembly) and about 5-

10 min thereafter.

4. (■Pause point) Stop the solution supply through the separation capillary and transfer

its injection end into a vial containing the background electrolyte.

5. (▲Critical step) With respect to ground, apply ~1700 V on the skimmer of the mass

spectrometer (labeled “Source: Capillary” for the micrOTOF and maXis systems) and

position the CE-ESI interface within 5 mm from the skimmer or until electrospray is

initiated. Using a stereomicroscope or by following the ion signal, finely adjust this

distance to ensure operation in the cone-jet spraying mode to achieve efficient ion

generation.51 (? See Table 3.6, Troubleshooting Hint #1.)

6. Leave ample time for the solvent-delivery and CE-ESI-MS system to reach physical

and chemical stability, and inspect the CE-ESI interface for mass spectrometric

stability over at least 10 min. (? See Table 3.6, Troubleshooting Hint #2.)

7. (▲Critical step) Optimize the applied voltage applied to the capillary that provides

the desired chemical separation and separation times for the metabolites of interest.

Successful results can be obtained by increasing the potential difference from 0 kV to

20 kV over ~15 s and keeping it constant thereafter.30 Controlling the HVPS via

computer provides more reproducible voltage programs.

8. Test the CE-ESI-MS system for analytical stability for at least 20 min. (? See Table

3.6, Troubleshooting Hint #3)

9. Lower the potential difference between the separation capillary ends from 20 kV (or

that applied) to 0 V over ~15 s.

10. Increase the MS skimmer-ES emitter tip distance to 5-10 cm or until electrospray

ceases, and adjust the MS skimmer potential to 0 V with respect to ground.

11. (■Pause point) Flush the background electrolyte through the separation capillary for

at least 5 min.

3.4.2.2 MALDI MS

1. (Optional) Set a mass range for metabolites between m/z 20 and 1500. The low

boundary is set to include ion signal from sodium because it can be used as an

internal calibration point and is present in the majority of biological samples. When

DHB is used, the larger mass range enables simultaneous detection of peptides.

2. Optimize instrument settings for sufficient metabolite detection at low concentrations

for the mass range of interest. Critical settings are laser beam intensity, laser focal

point size, and pulsed ion extraction delay time.

3. Save the optimized instrument method in the same directory where data from

comparative analyses are going to be saved. This organized arrangement ensures that

the optimized instrumental settings are consistent with comparative analyses, which is

critical.

4. Optimize instrument settings similarly for MS/MS experiments using a variety of

standards at different m/z values and save this method as in step 3 above.

3.4.3 Single-Cell CE-ESI-MS Analysis

1. (Optional) If the sample was kept frozen, allow ample time for the solution to defrost,

and if needed, homogenize the sample extract mechanically (vortex). Afterward,

centrifuge the sample extract for 1 min at ~6,600 rpm to reduce cellular debris in the

supernatant. Cellular debris can have dimensions comparable with the inner diameter

of the separation capillary and can clog it.

2. Repeat step 7 from section 3.4.2.1, maintaining the separation potential at 20kV (or

whatever voltage was used) for 2 min. Afterward, proceed with step 9 from section

3.4.2.1.

3. Transfer a 200-500 nL aliquot of the sample extract into the sample-loading vial of

the injection stage.

4. Position the injection end of the separation capillary so that it is in contact with the

deposited sample extract in the sample-loading vial.

5. (▲Critical step) While keeping the injection end of the separation capillary in the

sample-loading vial, lift the stage 15 cm and keep it elevated for 60 s, gravimetrically

injecting ~6 nL of the extract solution into the capillary. (? See Table 3.6,

Troubleshooting Hint #4.)

6. (▲Critical step) Lower the injection end of the capillary back to level with the outlet

within 1 s, and within ~3 s, move the injection end of the capillary into a vial filled

with the background electrolyte.

7. (Optional) Elevate or lower the injection end of the capillary above or below the

outlet end to apply positive or negative hydrostatic pressure during CE separation,

respectively.

8. Repeat step 7 from section 3.4.2.1 and simultaneously enable mass spectrometric data

acquisition at a minimum 2 Hz spectral rate (preferably higher) and acquire mass

spectra for the entire duration of the separation experiment. If the mass spectrometer

features a time limit, set it past the expected duration of the separation experiment.

9. When the compounds of interest have eluted from the separation capillary, repeat

steps 9-11 from section 3.4.2.1 to stop the experiment. Small metabolites typically

elute within the first 10-30 min of the separation, yet data are collected up to 50 min

to enhance the metabolome coverage.

3.4.4 Single-Cell MALDI-MS Analysis

1. (▲Critical step) Ensure the sample and standard spots are dry prior to loading the

MALDI sample plate into the mass spectrometer. This is important because the

evaporation of liquid will prevent the instrument from achieving high vacuum

quickly. If glycerol is applied for cell stabilization, place the samples under vacuum

for at least 15 min prior to loading the sample plate.

2. Load the prepared sample plate into the mass spectrometer.

3. Calibrate the MALDI MS instrument using ion signals from the DHB matrix and

sodium. This can be performed directly on the single-cell sample. However, external

calibration is useful, especially with MS/MS data; if this is the case, the MALDI

standard mix can be used.

4. Save the calibration information as an individual file.

5. (Optional) It is recommended to find the best type of sample surface for the analyte(s)

of interest and conduct comparative analyses using this particular type of sample

region. MALDI matrix sample spots can be heterogeneous; depending on the

MALDI matrix implemented, analyte, and location of the cell on the sample plate,

different locations for the MALDI matrix spot can be explored in order to optimize

signal intensity. For example, the center of the sample spot containing a more

amorphous and uniform DHB coating may produce better metabolite signals.

6. Acquire mass spectra from single-cell samples. This can be done in a manual or

automatic fashion. (? See Table 3.6, Troubleshooting Hint #5.)

7. (▲Critical step) File compatibility with the data analysis software must be confirmed.

This is imperative when working with software packages that originate from sources

other than the instrument manufacturer.

8. Choose or create a directory and save the results. The structure of data directories for

each set of experiments should be considered in advance for straightforward data

retrieval and processing, especially when multivariate analysis (e.g., using

ClinProTools) is planned.

3.4.5 Data Interpretation

3.4.5.1 CE-ESI-MS Results

1. Mass-calibrate the acquired data. Internal mass calibration can be enabled by sodium

formate clusters that form in situ in the electrospray source as sodium elutes from the

separation capillary.

2. Plot the extracted ion electropherograms (XIEs) for the ions of interest.

3. (Optional) To identify the eluting compounds, generate XIEs across the entire

acquired m/z range by a selected m/z width (e.g., m/z 50-500 range screened, + 500

mDa). Use automated data processing (e.g., a script) to decrease data processing

time.

4. (Optional) Use mathematic algorithms to filter noise and instabilities in the generated

XIEs (e.g., 3-point Gaussian smoothing).

5. Identify chromatographic peaks in each XIE and compile a list of eluting analytes.

Data processing can be aided by implementing a peak-picking algorithm within the

data analysis software. Tabulate the accurate mass of each compound detected.

6. (Optional) Plot XIEs for each detected accurate mass from step 5 above with a narrow

mass-selection window (e.g., 5 mDa). For quantitative analysis, determine the area or

intensity of each detected compound using these generated XIEs.

7. (Optional) To enhance the confidence of metabolite identifications, in an independent

CE-ESI separation experiment, perform MS/MS experiments for the ions tabulated in

step 5 above.

8. Identify detected compounds by comparing the accumulated molecular information

(accurate mass, isotope distribution, migration time, and fragmentation behavior) with

chemical standards and data available from metabolite databases, such as Metlin52 or

MassBank53.

3.4.5.2 MALDI MS Results

1. Load acquired MALDI MS and MS/MS data into the data analysis software (e.g.,

FlexAnalysis).

2. (Optional) Use mathematical algorithms to filter the data and correct for shifts in the

baseline. However, the low mass range of the mass spectra typically is dominated by

the most concentrated analytes and MALDI matrix signals, and the spectra typically

do not require smoothing and baseline correction.

3. (Optional) Recalibration can be done for MS and MS/MS data sets, which is an

especially useful option when an uneven sample plate surface is employed.

4. (Optional) Obtain a list of observed masses in the single-stage MS spectra to develop

a list for future MS/MS experiments.

5. Obtain mass lists for fragment ions present in the MS/MS data. This can often be

accomplished manually.

6. (Optional) Load MALDI MS data into a statistical software package (e.g.,

ClinProTools) for principal component analysis (PCA) if a comparison of complex

data sets is necessary. PCA can highlight data points which have the largest variance,

and therefore uncover analytes that contribute to differences between sample types.

7. (Optional) To reduce the complexity of the data, implementing corrective factors,

such as baseline subtraction, smoothing, data reduction, and automatic exclusion of

noisy or empty mass spectra, can be useful.

8. (Optional) Run PCA analysis.

9. Compare obtained m/z values with data available in databases such as Metlin or

MassBank to putatively assign identities to investigated compounds. Fragmentation

data can also be compared in these databases; however, most of the MS/MS data for

metabolites stored in databases are from ESI rather than MALDI, so there may be

some fragmentation differences.

3.5 Exemplary Applications

3.5.1 Metabolomics with Single-Cell CE-ESI-MS

The protocols presented here facilitate metabolic investigations of larger individual cells,

providing information on the cell-to-cell differences in metabolomes. Various A. californica

neurons have been isolated and chemically interrogated in the procedures described for CE-

ESI and ultraviolet MALDI MS experiments. As these two methods apply slightly different

sample manipulation steps and convert metabolites to ions via fundamentally different

mechanisms, the metabolome coverage is expected to vary accordingly. Figures 3.2 and 3.4

showcase representative results for the metacerebral cell (MCC), R2, and LP1 neurons by the

two respective methodologies, providing complementary sets of information.

With the single-cell CE-ESI-MS approach, a large number of small metabolites can

be efficiently sampled and identified. As an example, over 100 small metabolites have been

measured in a single MCC neuron30 and the number of distinctive ions can further extend to

~500 as other cells are being analyzed. Most recently, the ion signal for each of the recorded ions has been monitored among various cell types, which in turn revealed characteristic metabolic features for different neuron types of the A. californica CNS, including B1, B2,

MCC, PL1, R15, and R2 cells. For example, Figure 3.2 shows that arginine and betaine are detected in comparable ion counts in both R2 and MCC cells. In contrast, acetylcholine and serotonin are measured with a signal-to-noise ratio of >3 exclusively in the R2 and the MCC neuron, respectively. Relative signal intensity ratios can also aid in differentiating among neuron types; the arginine/glutamic acid ratio is significantly higher for the MCC cell.

Sub-cellular features can also be studied on a metabolic level with CE-ESI-MS. As an example, the sampling step can be adjusted to isolate the soma and neurite regions of a giant R2 neuron. The respective structures were placed in the extraction solution and the generated extract solution was analyzed by CE-ESI-MS according to the presented protocol.30 Figure 3.3 shows an electropherogram of the ion m/z 146.0 ± 0.5. It is apparent

from the traces that the two structures exhibited differing metabolite content for this

particular m/z value.

3.5.2 Metabolomics with Single-Cell MALDI MS

Two sample preparation approaches were examined in this part of the work: on-plate cell deposition and in-vial analyte extraction. Both approaches enabled the detection of a variety of metabolites (Figure 3.4). More than 80 metabolite-like signals can be observed in

individual A. californica LP1 and R2 cells between 20 and 1000 Da. Lower molecular weight species are better detected in the on-plate cell deposition strategy (Figure 3.4A, B). In contrast, the in-vial analyte extraction procedure yielded higher molecular weight compounds, some of which could be peptides. These differences are most likely due to the longer exposure time of MALDI matrix solution to the cell. While favorable for the extraction of larger intracellular molecules, this would result in ion suppression of low molecular weight metabolite signals by the higher molecular weight compounds present in samples, including the peptides and proteins with an m/z above 1-1.5 kDa that were detected

(not shown).

PCA analysis demonstrated clear separation of data acquired by these two sample preparation procedures applied to these cells (Figure 3.4C). LP1 and R2 are homologous cholinergic neurons; therefore, the resulting co-clustering of data points obtained from these neurons is not surprising. Similar results were obtained for serotoninergic A. californica

MCC cells (Figure 3.4D). Extracted MCC samples (two cells, four aliquots, four technical replicates) are different from the on-plate treated MCC #3 cell and control DHB samples.

MCC #1 was damaged during isolation and therefore mass spectra acquired from this cell were dominated by MALDI matrix signals. As a result, these mass spectra are grouped in the same cluster with control DHB mass spectra. Ion suppression due to DHB signal is lessened in the analyses of samples prepared on-plate; this is why the resulting data groups cluster along the same PC1 scale as the control DHB cluster (Figure 3.4C, D).

Identification of detected metabolites in MALDI requires MS/MS sequencing because accurate m/z values are not sufficient to distinguish between isobaric compounds that can be present in individual cell samples. For example, m/z 177.10 corresponds to at least

two compounds, such as serotonin and cotinine. Furthermore, the sodium adduct of DHB

has a measured m/z value of 177.12 (Figure 3.5A). Fragmentation of m/z 177 analytes,

performed in MS/MS mode, revealed the presence of several compounds with similar m/z

values in the single-cell samples, DHB control sample, and serotonin standard (Figure 3.5).

As expected, resulting mass spectra from the serotoninergic MCC cell demonstrated an intense fragment ion with m/z 160, which is a characteristic serotonin fragment. This

fragment has been detected with the CE-ESI-MS/MS approach and is listed in metabolite

databases (see Metlin and MassBank). This signal is also present in the MALDI mass

spectrum of the serotonin standard (Figure 3.5B).

3.6 Conclusions

Single-cell MS is a powerful approach for the detection and identification of metabolites in a

variety of cell types. Assisted by a new generation of highly sensitive mass spectrometers

and supported by the development of new methods to investigate cellular heterogeneity,

metabolomics is becoming a mainstream discipline for bioanalytical microanalysis.

Comprehensive experimental protocols are playing an important role in the success of

complex, interdisciplinary projects, such as investigations of single-cell metabolomes.

Sample preparation is a key element of single-cell analyses. Whether investigations

are targeted or exploratory, sample preparation steps require in-depth optimization. Manual

sample preparation can obviously be a bottleneck point in an analytical experiment; thus,

advancements in automated cell isolation strategies or utilizing the aforementioned direct

ionization techniques will enable more high-throughput sampling on the single-cell level.

Although a relatively recent application in the field of metabolomics, MALDI MS has

proven advantageous for high-throughput metabolomic investigations. The study of cell-to-

cell heterogeneity inherently requires the rapid analysis of large numbers of cells, one feature

that is already realized by any MALDI mass spectrometer capable of adapting multi-array

sample holders (e.g., 96-well-plates) and performing analysis in an automated fashion. These

instruments also offer imaging capabilities with spatial resolutions comparable to the size of

an average single cell. Sub-cellular resolution imaging is one direction in metabolomics that

will likely benefit from recent developments in sample preparation approaches, including the

stretched sample method.54 Because MALDI mass spectra are typically populated by a

significant number of signals related to the matrix, its clusters, and impurities, automated ion

filtering is an important requirement in advancing high-throughput investigations.

The coupling of CE to ESI MS enables unambiguous identification of metabolites at the single-cell level through the combination of accurate mass, retention time, and MS/MS data. While each separation can take up to an hour, the wealth of chemical information collected on the sample composition is impressive. Data analysis is currently a limiting step in CE-ESI-MS experiments. Software that enables automated data interpretation is highly desirable in metabolomics applications, especially because low detection limits allow measurements of hundreds of metabolites in a single cell. This efficiency in sampling the metabolome opens new avenues in metabolomics. Chemical differentiation of cell types, and the analysis of metabolic responses to stimuli or environmental differences, are among the most exciting developments enabled by CE-ESI-MS.

While strategies for metabolite detection in single cells continue to be developed and refined, there are vast possibilities for their application in fundamental research and in the

biotechnology and medical industries. Further investigations with established detection

strategies, and the development of new methodologies to probe different biological systems on a cellular level, will help in a better understanding of the underlying biochemistry of phenotypic differences.

3.7 Figures and Tables

Figure 3.1 General workflow of MS-based single-cell investigations. Addditional steps may involve tissue/cell treatments, such as stabilizing a cell with glycerol. The dotted path indicates the abbreviated sample manipulation offered by direct MS methods.

Figure 3.2 Single-neuronal metabolic profiling in the CNS of Aplysia californica. The selected-ion electropherographs are labeled for identified metabolites in two identified neurons: the (top) R2 and (bottom) MCC. The ion signal of the m/z values for acetylcholine and serotonin are multiplied by a factor of 3 and 5, rrespectively. The MCC and R2 neurons exhibited characteristic profiles, allowing them to be differentiated on a chemical level.

Figure 3.3 Subcellular metabolic analysis by CE-ESI-MS. (Inset) The soma and neurite regions of an R2 giant neuron of the A. californica weere isolated and anallyzed. Electropherograms for the ion m/z 146 ± 0.5 obtained for (top) the soma and (bottom) the neurite revealed different metabolic aspects. Adapted with permission from the American Chemical Society.30

Figure 3.4 Single-cell MALDI MS detects multiple metabolites in individual Aplysia californica neurons. A. Mass spectra illustrating different single cell sample preparation approaches using the left pleural #1 neuron (LP1) as a model and a control measurement of blank sample containing only MALDI matrix (DHB). B. The gel view representation summarizes multiple measurements similar to A; many m/z values can be visualized between 100 and 1,000 Da for each cell that are distinct from the control measurements. At least four technical replicates/measurements were made for each sample. C. PCA analyses of the MS profiles (m/z 20-300) obtained from LP1 and R2 neurons. The DHB sample was used as a reference/control. D. PCA analysis of MCC data. Inn (C) and (D), ellipses are used to outline areas corresponding to the labeled data points.

Figure 3.5 Metabolites in single-cell samples can be identified using MALDI MS/MS. A. MS/MS spectra illustrating fragmentation patterns of m/z 177 in five different samples, including three single-cell samples (MCC, LP1, R2) and two controls (DHB matrix and serotonin standard). The fragmentation patterns of the parent ion demonstrate the presence of serotonin in the MCC cells and at least two other compounds with m/z 177 in other cell samples. B. and C. Expansions of different mass regions of these mass spectra are shown. Grey areas mark peaks with similar m/z values.

Table 3.1 Troubleshooting hints

Hint # Problem Possible cause(s) Solution(s)

1 Sparse electric sparks Electric breakdown Increase skimmer-ES emitter distance; lower ambient humidity; water- then methanol-rinse and dry ES emitter and MS skimmer

2 Total ion chromatograms Discontinuous solvent Inspect electrospray solution and/or spray currents are supply at emitter tip in syringe for bubbles; revisit not stable during ESI caused by connections of the interface bubbles in the electrospray solvent; loose connections

3 Discontinuous solvent Electrochemical- Sonicate and replace solutions; supply at emitter tip upon induced bubble test for Joule-heating via CE voltage application formation; solvent current-voltage plots heating

4 Irreproducible analytical Injection end of Inspect the injection step results for quantitation separation capillary visually; increase the lost contact with deposited sample volume if sample extract needed

5 Analyte signal is not MALDI Investigate the detection of detected or is too weak matrix/detection analytes with different polarity is not optimal MALDI matrixes/detection for analyte detection polarities

Cell isolation Practice procedure for procedure resulted in isolation of cells with known possible cell lysis, metabolites to optimize cell metabolites were transfer manipulation; use released from the cell, pharmacological agents to or the analytes were prevent these processes prior metabolized to CNS dissection or protease treatment

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CHAPTER 4

METABOLIC DIFFERENTIATION OF NEURONAL CELL TYPES BY SINGLE-

CELL CE-ESI-MS

Notes and Acknowledgments

This chapter is adapted from an article submitted under “Metabolic differentiation of

Neuronal Phenotypes by Single-Cell CE-ESI-MS with coauthors Peter Nemes, Stanislav S.

Rubakhin, and Jonathan V. Sweedler. Peter contributed to the modification of the CE-ESI-

MS platform, ran the samples, performed principal component analysis, calculated concentrations of analytes within single neurons, and completed calculations of variability

(biological and analytical). Peter and I shared the responsibility of designing the data analysis workflow, acquiring intensities for the eluting analytes for input into PCA, minimizing the mass list for PCA, and identifying the analytes. I also ran the standards for the quantitation work. Stanislav was responsible for sample preparation, including cell isolation. The authors would like to thank Xiying Wang for the single neuron preparations.

This work was supported by the National Science Foundation (NSF) and the National

Institutes of Health (NIH) through grant numbers NIH DK070285, NS031609, and NSF

CHE-0400768.

4.1 Introduction

In the field of biomarker research and discovery, methods to investigate metabolic heterogeneity among selected cells within a cell population provide unique information on

cell function and fate.1-2 Cell-to-cell heterogeneity can be surprisingly broad, even for cells of the same type.3-4 Cell heterogeneity is also prevalent within the nervous system, with

many different cell types present; furthermore, chemical diversity even exists between cells of the same type. Metabolomic approaches are beginning to be performed on the brain to

gain a better understanding of disease, disorders, responses to drug treatments, and functional

roles.5-7 These findings can also complement genomic and proteomic data for complete

characterization of these systems. However, the majority of metabolomics assays provide

information about average metabolite levels rather than levels in individual cells within a

population. While the metabolome coverage is better for tissue homogenates, if the cell

population is heterogeneous, the chemical complexity increases when distinct cells are

pooled before measurement. There is currently a great, unmet demand for methodologies

capable of investigating and quantifying the metabolome of single cells.

Aplysia californica is a common physiological model organism that is useful for

understanding how neurons and neural circuits control behavior. They have relatively well-

defined neural networks within their central nervous system (CNS), with identifiable neurons

that are responsible for physiological and functional changes. For example, many studies

have been accomplished with regard to learning, behavior, and memory.8-9 Furthermore,

their CNS is relatively simple, and composed of approximately 10,000 central nerve cells10, while the mammalian brain consists of about 1011 neurons11, which makes it a much less

complicated system to study. Also, the neurons are relatively large (range of sizes), which

enable cells, and their compartments, to be studied on multiple levels (e.g. physiological,

biochemical, and genomic). While studies of this system have included select metabolites

and genomic, proteomic, and peptidomic information, metabolomic studies have lagged and

require further investigation.

Mass spectrometry (MS) is an incredibly useful tool for metabolomic investigations,

due in part to its high sensitivity and selectivity. The number of MS-based approaches

capable of single-cell and subcellular investigations have steadily increased,2, 12 and include

electrospray ionization (ESI),13 secondary ion mass spectrometry (SIMS),14-16 matrix-assisted

laser desorption/ionization (MALDI),17 nanostructure initiator mass spectrometry (NIMS),18 laser ablation electrospray ionization (LAESI),19-20 and live video MS21. The chemical

complexity observed in most living systems poses challenges for broad-range molecular

identification when applying MS alone, prompting the addition of orthogonal approaches for

molecular characterization.

Here, a bioanalytical approach for the metabolic analysis and classification of

individual cells in the CNS of Aplysia californica is reported. Label-free separation via

capillary electrophoresis (CE) is combined with ESI MS for the detection and quantitation of

metabolites, while integrating multivariate data analysis to identify the unique chemical

components and patterns that distinguish cellular individuality and heterogeneity. A number

of different neuronal networks (and corresponding neurons) in the A. californica CNS that

are known to play specific physiological roles are investigated: the control of the feeding

process (metacerebral cell (MCC) and left pleural 1, (LPl1)), regulation of mucus release

(LPl1 and R2) and gut motility (B1 and/or B2), and the mechanism of egg laying (R15).22-26

The combined analytical approach facilitates investigations of small-volume samples, including the differentiation of individual neurons based on their cellular chemistry.

Specifically, using a number of technological and methodological advancements to single-

90 cell CE-ESI-MS, qualitative profiling, comprehensive identification, and quantitation for a number of metabolites in 50 identified and isolated A. californica neurons is achieved. These advances build upon an MS-based platform recently described for volume-limited samples.13

4.2 Experimental Procedure

The sample preparation procedure (Figure 4.1) entailed the sacrifice of adult A. californica animals, dissection of their CNS, and isolation of select neurons. These animals were 175–

250 g in weight, purchased from the National Resource for Aplysia (Rosenstiel School of

Marine & Atmospheric Science, University of Miami, FL, USA), and maintained in continuously circulated, aerated artificial seawater cooled to 14 °C. Eight animals were anesthetized by injecting 390 mM of aqueous magnesium chloride into the vascular cavity, equal by mass to approximately one-third of each animal’s body weight. Ganglia and adjacent nerves were surgically dissected, enzymatically treated to remove connective tissues, and identified neurons were manually isolated in artificial seawater that consisted of

460 mM NaCl, 10 mM KCl, 10 mM CaCl2, 22 mM MgCl2, 26 mM MgSO4, and 10 mM 4-

(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), pH 7.8, mixed with glycerol

(v/v, 66.7%/33.3%) to stabilize the cell membrane. The isolated cells included the MCC,

LPl1, R2, B1, B2, and R15 neurons. Cells were quickly rinsed with deionized water to minimize inorganic salt and extracellular metabolites on their surfaces. Finally, isolated neurons were placed into 50% (v/v) methanol solution prepared with 0.5% (v/v) acetic acid to facilitate analyte extraction and quenching of enzymatic processes ex vivo. The samples were stored in the solution at –20 °C until analysis. Control experiments confirmed that this

environment minimized metabolite decomposition for periods as long as two weeks and also

facilitated analyte extraction into the solution (data not shown).

A schematic of the instrument platform is shown in Figure 4.2. A multi-channel data

acquisition and control card (model USB-6008, National Instruments, Austin, TX, USA) was

operated by a personal computer that executed a custom-written program (LabVIEW 8.2

interface, National Instruments); the card was programmed to generate a time-dependent

voltage function. The voltage was ramped over a period of 10 s from 0 kV to 20 kV and

maintained at this voltage during the separation. A 10 kΩ resistor was connected in series

into the electrophoretic circuit to monitor the CE capillary current.

The instrument platform also featured a co-axial sheath-flow arrangement similar to

that described in earlier work.13 In the present study, a microtee assembly (part P-875, IDEX

Health & Science, Corp., Oak Harbor, WA, USA) housed a stainless steel emitter (130 µm

ID, 260 µm OD, part 21031A, Hamilton Company, Reno, NV, USA). The distal end of the metal emitter was cleaved and finely polished to provide a well-defined anchoring rim for the electrified liquid meniscus. For separation, a fused silica capillary (FSC in Figure 4.2) with

an internal diameter (ID) of 40 µm and an outer diameter (OD) of 110 µm (Polymicro

Technologies, Phoenix, AZ, USA) was used. Both ends of the capillary were cleaned and the

polyimide coating thermally removed. The fused silica capillary was coaxially fed through

the emitter and protruded ~50–80 µm into the Taylor-cone.

The sheath liquid (50% methanol with 0.1% formic acid) was supplied through the

emitter at a 750 nL/min flow rate using a syringe pump (model 70-2000 or 55-2222, Harvard

Apparatus, Holliston, MA, USA) and a fused silica capillary for the delivery of the sheath

flow (S in Figure 4.2) (75 µm ID, 363 µm OD, Polymicro Technologies). The emitter was

grounded and initially positioned ~20 mm in front of the skimmer plate of the mass

spectrometer. The potential on this plate was adjusted to –1700 V using the software of the

mass spectrometer, and the emitter-plate distance was cautiously decreased until cone-jet

spraying mode was achieved.

The cell extract solutions were centrifuged for 1 min at 2000 × g, and 500 nL aliquots

were deposited into a stainless steel sample loading vial (SV in Figure 4.2). The injection

end of the fused silica capillary was positioned into the sample loading vial and elevated 15

cm with respect to the outlet end for 60 s. The hydrostatic pressure generated across the

capillary allowed the reproducible introduction of 6 nL of the deposited sample solution into

the separation capillary (equivalent to ~0.1% of the total cell content). Following this step,

the sample loading vial was lowered to 3 cm above level with the outlet end, and the inlet end of the capillary was positioned into a stainless steel vial containing 1% formic acid as the background electrolyte (BV in Figure 4.2). A 20 kV separation potential was applied.

Generated ions were analyzed by a micrOTOF ESI-time-of-flight (TOF) mass spectrometer or a maXis ESI-Qq-TOF-MS/MS mass spectrometer (Bruker Daltonics,

Billerica, MA, USA) with ~5 ppm mass accuracy and ~8000 FHWM (full width at half maximum) resolution. These instruments were run in positive ion mode. MS/MS experiments with 20–35 eV collision energies facilitated the molecular identifications. The analytical reproducibility of the CE-ESI-MS setup was also tested with a chemical standard solution containing acetylcholine, dopamine, and histamine, each at a 50 nM concentration.

The acquired electropherograms were initially surveyed by generating m/z-selected ion chromatograms in the 50–500 m/z-range with a step size of 500 mDa. After determining the eluting species from the identified neurons, the total analyte list was minimized to 140

analytes that would be monitored for the identified neurons (Table 4.1). This list was generated by removing interfering chemical species, adducts, and ions with intensities less than 5 × 105 counts. Selected ion-electropherograms were generated for each of these ions

with a ± 50 mDa mass window and the corresponding electrophoretic peak maxima was determined for subsequent principal component analysis (PCA). Chemometric data interpretation and quantitation were performed on signal intensities of eluting species with

unsupervised PCA (Markerview 1.1; Applied Biosystems, Carlsbad, CA, USA). Data preprocessing weighting and scaling was logarithmic and mean center, respectively. All data

were plotted by a scientific visualization software package (Origin 8.0; Northampton, MA,

USA).

4.3 Results and Discussion

The CE separation step reduced the chemical complexity of the neuron samples prior to MS

detection, enabling the simultaneous measurement of a vast number of intracellular

compounds. A survey of the detected ions indicated the presence of at least 300 different

molecular species within a single neuron in some cases. Many of them were identified using

accurate mass measurements combined with online metabolite database searches, as well as

collision-induced dissociation tandem MS (MS/MS) data and migration-time comparisons

against chemical standards. Thirty-six ions were ascribed with high analytical confidence to

small metabolites, osmolytes, and classic neurotransmitters (Table 4.2).

A total of 140 ionic species were then selected from the remaining ion list for further

analysis and are tabulated in Table 4.1. This evaluation revealed signals for over ~450

different accurate mass-to-charge ratios, each corresponding to a different ionic species.

Careful control experiments were conducted to analytically evaluate the cellular and analytical environments. A number of interfering chemical species were found and ascribed to polymer and plasticizer molecules, likely originating from solvent bottles, impurities present in the solutions used (methanol, water, and formic acid), as well as noncovalent adducts that formed in situ during electrospray ionization.

Measurements performed on the standard solution on four consecutive days revealed an analytical reproducibility of 12 ± 2 relative %. Likewise, the biological reproducibility was assessed by monitoring 144 different ion signal intensities for different neuron extracts, as follows: B1 (10), B2 (8), MCC (9), LPl1 (6), R15 (8), and R2 (8). Relative standard deviations were calculated for each ion signal as a function of neuron type and are summarized in Figure 4.3. The histograms show that the distribution of the relative error was normal for most cell types and had a median of ~25–50 relative % for the majority of the cell types, except for the B2 neurons that occasionally reached 100%. Because the analytical reproducibility of the CE-ESI-MS setup was below 20 relative %, these higher error levels observed for the neuron types were ascribed to the biological variability among the animals and their phsyiological states.

Analysis of selected-ion electropherograms revealed the neuron-specific presence of a number of ions. Representative examples are shown for the MCC, LPl1, R15, and R2 neuron extracts in Figure 4.4. Acetylcholine (compound 9, see also Table 4.2) registered in high ion counts in both the R2 and LPl1 neurons. In contrast, serotonin (13) was only measured in the

MCC cells, an observation confirmed by independent studies reporting that MCC neurons use serotonin as a neurotransmitter.27-28 Similarly, the ion m/z 267.1333 (unknown identity,

UN) was exclusively found in the R15 neurons. This unknown mass matches the formula

C13H18N2O4 within 1 ppm accuracy, and may correspond to the dipeptide ThrPhe (or

PheThr); future work will validate the assignment of this cell-specific metabolite. In Figure

4.4, the registered mass-to-charge ratio (m/z)-selected electropherograms were temporally

aligned by using retention times for the HEPES buffering agent and endogenous lysine,

tryptophan, and glutathione for nonlinear multi-parameter regression.

Other compounds populated the cell extracts in fairly uniform ion counts. Glycine

betaine (32) was generally detected in high abundance in most of the interrogated cells

(Figure 4.4). In agreement with this observation, glycine betaine is a well-known

intracellular osmolyte and is present at 100–300 mM in the intracellular space.29 These results impart confidence that the CE-ESI-MS platform not only measured endogenous metabolites from neuron extracts, but also yielded data that represents the biological composition of the neurons.

The results provided an opportunity to investigate the cellular heterogeneity and classify individual neurons at a chemical level. PCA was employed to further distinguish the cell types. Seven to nine individual neurons (biological replicates) were isolated from

different A. californica animals and were analyzed in duplicate (technical replicate) for each

of the following neuron types: B1, B2, MCC, LPl1, R2, and R15. Using PCA on 144

different selected ions (Table 4.1), the vast datasets yielded in these experiments (over

~14,000 ion intensity values) were mathematically transformed into orthogonal components

to reduce data dimensionality, helping to reveal underlying chemical correlations among the

studied neurons.

PCA of the CE-ESI-MS data demonstrated that the neurons exhibited individual as

well as cell type differences in their chemistry. The first two principal components (Figure

4.5) explained ~70% of the total variance among the measured ion intensities. The biological

variability (~25–50%., Figure 4.3), exceeded the analytical reproducibility (below 20%);

thus, only single measurements are included in Figure 4.5 to aid in visual data interpretation.

Notably, data points from the LPl1 and R2 neuron extracts form overlapping clusters in the

first two orthogonal dimensions. Likewise, most B1 and B2 single cells are virtually

indistinguishable in Figure 4.5. These PCA results demonstrate that these neuron pairs

possess similar chemical compositions. In contrast, the MCC and R15 data clusters populated in distant quadrants of the scores plot, indicating significantly different metabolic

profiles for these neurons as compared to the other four cell types.

The chemical observations add an important piece of evidence to the existing physiological differences of neuronal cell type. Previously, the evidence that these cells were

homologous included morphological observations on the almost symmetrical axonal trees

over the body walls of the animal, and that both neurons are cholinergic.24, 30 The PCA

results confirmed that, even though located in nonsymmetrical ganglia, the LPl1 and R2

neurons were biochemically homologous.

Chemical heterogeneity was observed for single neurons within the same neuronal

type. As shown in Figure 4.3, the R2 and LPl1 neurons arranged into tight data clusters

(small cluster radius) whereas the variance appeared to increase in the following order: R15,

LPl1 < MCC < B2 << B1. Moreover, three of the B1 and one of the B2 neurons had

significantly different chemistry than the others. Because cell isolations and chemical

analyses were performed with sufficient confidence, the observed cellular heterogeneity may

be explained by biological variation among animals and their specific chemical and

biological state.

Metabolites correlating with the observed chemical relationships among cells and cell types were screened using PCA. The loading plot in Figure 4.5 helps to distinguish the ions that made the highest contribution in differentiating the studied neurons. For example, the histamine (1) and serotonin (13) ions occupy the left lower quadrant of the plot. These ions are specific to MCC neurons. In contrast, acetylcholine (9) has the highest PC2 value in the top right region in Figure 4.5. This piece of information allows for the screening of cholinergic cells. Notably, glycine betaine (32) and proline betaine (33) are in close vicinity to the origin, which indicates low specificity for any of the studied neurons. It is in accordance that both these compounds are intracellular osmolytes and are found in usually high abundance in some marine animals, including sea slugs.29, 31

This bioanalytical platform also enables the quantitation of metabolites detected in the neurons. Following external calibration, the serine and glutamic acid levels were measured in the following six different neurons: B1, LPl1, R2, and R15 (Figure 4.6). In excellent agreement with independent results,32-33 the intracellular glutamic acid concentration was measured to be ~11.4 mM in the R2. The glutamic acid levels were found to be lowest in the B1 and highest in the LPl1 extracts on average (Figure 4.6), with the intracellular concentration difference reaching one order of magnitude (Table 4.3). In contrast, the serine amounts did not appear to significantly change with cell type but rather, with neuron individuality. For example, the concentration difference was almost a magnitude greater between the B1 neurons a and c (Table 4.3). These results further stress the importance of quantitatively assessing cell chemistry with single-cell resolution.

4.4 Conclusions and Future Work

Label-free analysis, metabolite quantitation, and chemical screening are demonstrated for

isolated identified single cells from the CNS of one of the most investigated neurobiological

models, A. californica. A total of 50 individual identified neurons, including B1, B2, MCC,

LPl1, R2, and R15, from eight animals were classified on a biochemical level. Furthermore,

this analysis strategy could also be implemented to determine the effects of chemical or

electrical stimuli for different cell types to gain further information about the chemical

cellular response.

It is worthwhile noting that the signal filtering criterion did not bias among chemical classes (e.g., osmolyte versus neurotransmitter molecules), but exhibited preference for signal intensity. Data analysis is commonly the most time consuming part of metabolomic analyses; developments in automation and computer processing can help simultaneously monitor the ~300 metabolites detected in future experiments. Nevertheless, 144 of these ions were targeted here, and the obtained results demonstrate the broad applicability of the single- cell analytical platform and methodology. Future work will involve identifying additional analytes that contribute to cell differentiation, such as those shown in the loadings plot in

Figure 4.5.

Although single cell isolation presents a bottleneck in the current experiment

workflow, enhancements in cell sampling have the potential to significantly increase

analytical throughput. The combination of continuous cell delivery and lysis with microfluidic devices poses an elegant solution, and this approach has recently demonstrated protein analysis, feasible with sampling rates as high as 12 cells a minute.34 Furthermore,

microfabrication and micropatterning opens an opportunity to mimic the cell environment,

allow the separation of spatial and temporal factors in cell regulation, and apply rapid

nutrient gradients. These innovations can also help to unravel the parameter space of

experimental conditions to facilitate the design of extended cell studies. The analysis of

large numbers of single cells in turn will yield statistically sound data to represent the

biochemistry of a given cell population.

Advances in software frameworks for mass spectral data exploration are expected to

greatly complement research efforts in metabolomics, including the present single-cell

analysis approach. CE-ESI-MS of the neurons studied in this work generated ~200 GB

electropherogram data in single-stage MS mode. The interpretation of this large amount of

information demands physical computing power, as well as the availability of sophisticated

software platforms. In addition, manual data analysis revealed that the CE-ESI-MS platform

registered molecular features over a broad range of peak widths (ranging from 5 s to ~60 s);

this in combination with occasional low migration time reproducibility among analyses

challenged molecular feature detection using commercial software packages.

Sophisticated mathematical algorithms such as the open-source XCMS35, MZmine36,

MathDAMP37, and most recently metaXCMS38 data-analysis tool-boxes are promising means

to aid the analytical workflow. Reduction in the data set size, exploration of spectral features

with high temporal resolution, and the feasibility of direct statistical analysis on non-targeted

datasets are apparent advantages of similar software venues for metabolomics. Once spectral

features are identified, ideally these software platforms would allow the direct screening of

on-line high mass-resolution MS(-MS) metabolite depositories such as the Metlin39 database for automated metabolite identification.

100

The interrogation of cell individuality and cell type by the herein described approach adds chemical insight to cellular bioanalyses. It is expected that single-cell studies on the mammalian CNS will show similar and striking cell to cell heterogeneity. Enhancements in cell sampling, perhaps via microfluidic devices, will further increase analytical throughput.

These protocols and techniques are readily adaptable from cells to other volume-limited samples requiring the detection and quantitation of nanoliter volumes of metabolites.

101

4.5 Figures and Tables

Figure 4.1 Single-neuron sampling workflow. Optical images show (left) the CNS removed from A. californica, (middle) manual isolation of an identified R2 neuron, and (right) the solution-extraction of the intracellular analytes. White scale bars = 1 mm.

Figure 4.2 CE-ESI-MS platform for single-cell metabolic analyses. Major compoonents included a regulated high voltage power supply (HVVPS) remotely controlled by a personal computer (PC), stainless steel sample and buffer viaals (SV and BF, respectively) for sample- loading and separation into the fused silica capilllary (FSC), a custom-designed coaxial sheath-flow CE-ESI interface (CE-ESI) and a 6.3× stereomicroscope (CAM) to operate the electrospray in the cone-jet spraying mode. White bars denote the scale.

102

Figure 4.3 Biological variability for neuron phennotypes. Relative standard deviations calculated for 144 different ion signals (see Table 4.1) among the neuron ttypes indicated a variability of ~25–50% for most of the cells, exceeding the analytical reproducibility of the CE-ESI-MS setup (below 20 relative%). Red curves show normal distributions fitted on the data.

103

Figure 4.4 Metabolic differences observed among neuronal types. Representative selected- ion electropherograms are shown for the ions (signal multiplied by indicated factor) m/z 146.116 (navy, 1×), 177.103 (black, 2×), 267.133 (blue, 2×), 147.081 (orange, 5×), and 118.087 (green, 0.2×) with a ± 5 mDa m/z window. Key: acetylcholine (9), serotonin (13), an unidentified ion with m/z 267.1333 (UN), glutamine (26), and glycine betaine (32). Migration times were corrected by using elution timmes for HEPES, Lys, Trp, and glutathione to create a second-order working curve.

104

Figure 4.5 Chemometric evaluation of neuronal heterogeneityy and phenotype. (Top) The PCA scores plot revealed chemical differences between neurons and neuron types, and (bottom) the loading plot helped find characteristic metabolites among the cells. Numbers in burgundy identifyy analytes listed in Table 4.2.

105

Figure 4.6 Label-free quantitation of glutamic acid (■) and serine (▲) in single B1, LPl1, R2, and R15 neurons. Letters (underlined and bold) identify single neurons and are shown in Table 4.3, along with the linear curve equations. The relative standard deviation was below 20% for the calibration data points.

106

Table 4.1 Ions monitored for principle component analyses. These 144 ions were selected from over 300 distinctive ionic species detected from among single neurons analyzed by CE- ESI-MS. Ions highlighted in grey and bold were ascribed to specific metabolite molecules and are listed in Table 4.2. 76.04 90.05 90.06 90.06 94.05 104.07 104.07 104.07 104.07 104.11 106.05 112.09 116.07 118.09 118.09 118.12 120.07 120.07 122.08 122.08 123.06 126.02 128.01 130.09 132.10 132.10 132.10 132.10 133.10 134.05 134.08 144.10 144.10 146.12 146.12 146.12 147.08 147.11 148.06 150.11 150.11 151.61 156.08 160.10 161.09 161.12 162.08 162.08 162.08 162.11 164.07 165.10 166.09 170.09 175.11 175.11 175.12 177.10 179.05 180.10 187.05 188.07 189.12 189.12 189.12 189.13 189.15 191.07 193.07 193.09 196.10 203.14 182.08 203.14 203.14 204.12 204.13 205.10 205.12 209.09 217.16 218.14 218.15 218.15 218.15 221.10 221.12 223.08 231.17 232.13 232.14 232.16 233.11 233.15 233.15 234.15 235.11 237.12 237.12 239.11 242.56 242.63 244.09 245.18 245.19 246.14 246.16 246.16 248.09 248.16 248.16 248.16 249.12 253.12 254.07 255.10 260.16 260.16 260.16 260.20 260.20 260.20 261.14 263.12 263.14 265.11 265.12 267.13 267.13 267.14 268.10 273.12 274.18 274.19 274.21 275.16 284.10 288.20 291.13 301.11 308.09 322.11

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Table 4.2 Metabolites identified from single neurons of the A. californica CNS. All accurate masses were calculated with Molecular Weight Calculator, version 6.46 (available from http://ncrr.pnl.gov/software/). Key: migration time, tm; absolute and relative mass accuracy, ; MS/MS confirmed, *.

Compound m/z m/z

Identifier Name Formula t m (min) meas ured theoretical Δ (mDa) Δ (ppm)

1 histamine C5H9N3 8.6 112.0875 112.0875 -0.1 -0.89

2 thiamine C12H17N4OS 12.2 265.1115 265.1123 0.4 3.84

3 choline C5H14NO 13.1 104.1078 104.1075 0.8 3.02

4 ornithine* C5H12N2O2 14.1 133.0983 133.0977 -0.6 4.51

5 lysine* C6H14N2O2 14.2 147.1136 147.1133 -0.6 -4.51

6 β-alanine C3H7NO2 14.3 90.0558 90.0555 -0.3 -2.04

7 nicotinamide C6H6N2O 14.6 123.0588 123.0558 -0.3 -3.33

8 arginine* C6H14N4O2 14.8 175.1191 175.1195 0.4 2.28

9 acetylcholine* C7H16NO2 14.8 146.118 146.1181 0.1 0.68

10 -aminobutyric acid C4H9NO2 15.0 104.071 104.0711 0.1 0.96

11 histidine* C6H9N3O2 15.1 156.0775 156.0773 -0.2 -1.28

12 carnitine* C7H15NO3 17.2 162.1129 162.113 0.1 0.62

13 serotonin C10H12N2O 17.5 177.102 177.1028 0.8 4.52

14 acetylcarnitine* C9H17NO4 18.7 204.1233 204.1236 0.3 1.47

15 glycine C2H5NO2 19.4 76.04 76.0399 -0.1 -1.32

16 cytidine C9H13N3O5 20.1 244.093 244.0933 0.3 1.23

17 adenosine* C10H13N5O4 20.7 268.1045 268.1046 0.1 0.37

18 alanine C3H7NO2 21.5 90.0553 90.0555 0.2 2.22

19 valine* C5H11NO2 24.9 118.0864 118.0868 0.4 3.39

20 isoleucine* C6H13NO2 25.3 132.1026 132.1024 -0.2 -1.51

21 serine C3H7NO3 25.5 106.0506 106.0504 -0.2 -1.89

22 leucine* C6H13NO2 25.6 132.1025 132.1024 -0.1 -0.76

23 threonine C4H9NO3 27.3 120.0657 120.0661 0.4 3.33

24 indoleacrylic acid* C11H9NO2 27.8 188.071 188.0711 0.1 0.53

25 tryptophan C11H12N2O2 27.8 205.0974 205.0977 0.3 1.46

26 glutamine* C5H10N2O3 28.1 147.0768 147.077 -0.2 -1.36

27 glutamic acid* C5H9NO4 28.7 148.0611 148.061 -0.1 -0.68

28 phenylalanine* C9H11NO2 29.1 166.0871 166.0868 -0.3 -1.81

29 tyrosine* C9H11NO3 29.6 182.0814 182.0817 0.3 1.65

30 proline* C5H9NO2 30.1 116.0714 116.0711 -0.3 -2.58

31 aspartic acid* C4H7NO4 32.7 134.0454 134.0453 -0.1 -0.75

32 glycine betaine C5H11NO2 32.8 118.0872 118.0868 -0.4 -3.39

33 proline betaine* C7H13NO2 33.6 144.1021 144.1024 0.3 2.08

34 β-alanine betaine C6H13NO2 37.0 132.1026 132.1024 -0.2 -1.51

35 glutathione C10H17N3O6S 37.9 308.0913 308.0916 0.3 0.97 36 taurine C2H7NO3S 50.2 126.0226 126.0225 -0.1 -0.79

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Table 4.3 Representative examples for glutamic acid and serine concentrations measured in identified isolated neurons from the A. californica CNS. Concentrations are given for [a] the extract solution and [b] cellular concentration assuming a homogeneous distribution and an average cell diameter of 100 µm for the B1 neurons, 200 µm for the R15 neurons, and 300 µm for the LPl1 and R2 neurons. Calibration curves for ion intensity, I (counts), and concentration, c (µM), from linear fittings in Figure 4.6 were log(I) = 11.75 + 0.88 log(c) for glutamic acid (R2 = 0.998) and log(I) = 12.49 + 1.07 log(c) for serine (R2 = 0.997). The relative standard deviation for quantitation was below 20%.

Neuron Glutamic acid Serine [a] [b] [a] [b] Identifier Type (µM) / (mM) (µM) / (mM) a B1 1.7 / 0.6 1.5 / 0.5 b B1 2.0 / 0.7 0.5 / 0.2 c B1 3.5 / 1.2 5.1 / 1.8 d B1 4.6 / 1.6 12.6 / 4.5 e LPl1 34.0 / 12.0 6.9 / 2.5 f LPl1 36.1 / 12.8 7.8 / 2.7 g LPl1 36.9 / 13.1 9.6 / 3.4 h LPl1 39.4 / 13.9 8.7 / 3.1 i R2 23.7 / 8.4 6.5 / 2.3 j R2 33.5 / 11.8 6.8 / 2.4 k R2 30.6 / 10.8 4.1 / 1.4 l R2 40.9 / 14.5 6.9 / 2.4 m R15 8.5 / 10.1 1.4 / 1.7 n R15 9.9 / 11.9 2.2 / 2.6 o R15 18.0 / 21.5 3.5 / 4.2 p R15 22.9 / 27.3 5.3 / 6.4

109

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11. Moroz, L. L.; Edwards, J. R.; Puthanveettil, S. V.; Kohn, A. B.; Ha, T.; Heyland, A.; Knudsen, B.; Sahni, A.; Yu, F.; Liu, L.; Jezzini, S.; Lovell, P.; Iannucculli, W.; Chen, M.; Nguyen, T.; Sheng, H.; Shaw, R.; Kalachikov, S.; Panchin, Y. V.; Farmerie, W.; Russo, J. J.; Ju, J.; Kandel, E. R., Neuronal transcriptome of Aplysia: neuronal compartments and circuitry. Cell 2006, 127 (7), 1453-1467.

12. Amantonico, A.; Urban, P. L.; Zenobi, R., Analytical techniques for single-cell metabolomics: state of the art and trends. Anal. Bioanal. Chem. 2010, 398 (6), 2493-2504.

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13. Lapainis, T.; Rubakhin, S. S.; Sweedler, J. V., Capillary electrophoresis with electrospray ionization mass spectrometric detection for single-cell metabolomics. Anal. Chem. 2009, 81 (14), 5858-5864.

14. Fletcher, J. S.; Lockyer, N. P.; Vaidyanathan, S.; Vickerman, J. C., TOF-SIMS 3D biomolecular imaging of Xenopus laevis oocytes using buckminsterfullerene (C-60) primary ions. Anal. Chem. 2007, 79 (6), 2199-2206.

15. Kurczy, M. E.; Piehowski, P. D.; Van Bell, C. T.; Heien, M. L.; Winograd, N.; Ewing, A. G., Mass spectrometry imaging of mating Tetrahymena show that changes in cell morphology regulate lipid domain formation. Proc. Natl. Acad. Sci. U.S.A. 2010, 107 (7), 2751-2756.

16. Monroe, E. B.; Jurchen, J. C.; Lee, J.; Rubakhin, S. S.; Sweedler, J. V., Vitamin E imaging and localization in the neuronal membrane. J. Am. Chem. Soc. 2005, 127 (35), 12152-12153.

17. Rubakhin, S. S.; Garden, R. W.; Fuller, R. R.; Sweedler, J. V., Measuring the peptides in individual organelles with mass spectrometry. Nat. Biotech. 2000, 18 (2), 172- 175.

18. Northen, T. R.; Yanes, O.; Northen, M. T.; Marrinucci, D.; Uritboonthai, W.; Apon, J.; Golledge, S. L.; Nordstrom, A.; Siuzdak, G., Clathrate nanostructures for mass spectrometry. Nature 2007, 449 (7165), 1033-U3.

19. Nemes, P.; Vertes, A., Laser ablation electrospray ionization for atmospheric pressure, in vivo, and imaging mass spectrometry. Anal. Chem. 2007, 79 (21), 8098-8106.

20. Shrestha, B.; Vertes, A., In situ metabolic profiling of single cells by laser ablation electrospray ionization mass spectrometry. Anal. Chem. 2009, 81 (20), 8265-8271.

21. Mizuno, H.; Tsuyama, N.; Harada, T.; Masujima, T., Live single-cell video-mass spectrometry for cellular and subcellular molecular detection and cell classification. J. Mass Spectrom. 2008, 43 (12), 1692-1700.

22. Bablanian, G. M.; Weiss, K. R.; Kupfermann, I., Motor control of the appetitive phase of feeding beharior in Aplysia. Behav. Neural Biol. 1987, 48 (3), 394-407.

23. Lloyd, P. E.; Kupfermann, I.; Weiss, K. R., Central peptidergic neurons regulate gut motility in Aplysia. J. Neurophys. 1988, 59 (5), 1613-1626.

24. Rayport, S. G.; Ambron, R. T.; Babiarz, J., Identified cholinergic neuron R2 and neuron LPl1 control mucus release in Aplysia. J. Neurophysiol. 1983, 49 (4), 864-876.

25. Rosen, S. C.; Weiss, K. R.; Goldstein, R. S.; Kupfermann, I., The role of a modulatory neuron in feeding and satiation in Aplysia - effects of lesioning of the serotonergic metacerebral cells. J. Neurosci. 1989, 9 (5), 1562-1578.

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26. Skelton, M. E.; Koester, J., The morphology, innervation and neural control of the anterior arterial system of Aplysia californica. J. Comp. Physiol. A 1992, 171 (2), 141-155.

27. Hatcher, N. G.; Zhang, X.; Stuart, J. N.; Moroz, L. L.; Sweedler, J. V.; Gillette, R., 5- HT and 5-HT-SO4, but not tryptophan or 5-HIAA levels in single feeding neurons track animal hunger state. J. Neurochem. 2008, 104 (5), 1358-1363.

28. Weinreich, D.; McCaman, M. W.; McCaman, R. E.; Vaughn, J. E., Chemical, enzymatic and ultrastructural characterization of 5-hydroxytryptamine containing neurons from ganglia of Aplysia californica and Tritonia diomedia. J. Neurochem. 1973, 20 (4), 969- &.

29. Grant, S. C.; Aiken, N. R.; Plant, H. D.; Gibbs, S.; Mareci, T. H.; Webb, A. G.; Blackband, S. J., NMR spectroscopy of single neurons. Magn. Reson. in Med. 2000, 44 (1), 19-22.

30. Eisenstadt, M.; Goldman, J. E.; Kandel, E. R.; Koike, H.; Koester, J.; Schwartz, J. H., Intrasomatic injection of radioactive precursors for studying transmitter synthesis in identified neurons of Aplysia californica. Proc. Natl. Acad. Sci. U.S.A. 1973, 70 (12), 3371- 3375.

31. Pierce, S. K.; Rowland, L. M., Proline betaine and amino acid accumulation in sea slugs (Elysia elysia chlorotica) exposed to extreme hyperosmotic conditions. Physiol. Zool. 1988, 61 (3), 205-212.

32. Brownstein, M. J.; Saavedra, J. M.; Axelrod, J.; Zeman, G. H.; Carpenter, D. O., Coexistence of several putative neurotransmitters in single identified neurons of Aplysia. Proc. Natl. Acad. Sci. U.S.A. 1974, 71 (12), 4662-4665.

33. Iliffe, T. M.; McAdoo, D. J.; Beyer, C. B.; Haber, B., Amino acid concentrations in Aplysia nervous system: neurons with high glycine concentrations. J. Neurochem. 1977, 28 (5), 1037-1042.

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39. Smith, C. A.; O'Maille, G.; Want, E. J.; Qin, C.; Trauger, S. A.; Brandon, T. R.; Custodio, D. E.; Abagyan, R.; Siuzdak, G., METLIN: a metabolite mass spectral database. Ther. Drug Monit. 2005, 27 (6), 747-51.

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CHAPTER 5

A LACK OF MAST CELLS CAUSES STRIKING METABOLITE DEFECTS IN THE

HIPPOCAMPUS

Notes and Acknowledgments

This work was a collaboration with Prof. Rae Silver’s laboratory at Columbia University and scientists at the Columbia Genome Center; Katherine Nautiyal (Silver lab) performed the sample preparation, while Sergey Kalachikov (Columbia Genome Center) and Irina

Morozova (Columbia Genome Center) performed the gene expression analysis. My contribution to this work was the analysis of metabolites; I ran the samples, designed the data analysis workflow, and performed the data analysis. I would also like to acknowledge Peter

Nemes for modifications to the CE-ESI-MS instrument platform and Christine Cecala for helpful discussions regarding data analysis. This work was supported by the National

Institutes of Health (NIH) through P30-081310 and 5R01-018866. The freely available molecular weight calculator that was used was supported by the NIH National Center for

Research Resources (Grant RR018522), and the W.R. Wiley Environmental Molecular

Science Laboratory (a national scientific user facility sponsored by the U.S. Department of

Energy's Office of Biological and Environmental Research and located at PNNL). PNNL is operated by Battelle Memorial Institute for the U.S. Department of Energy under contract

DE-AC05-76RL0 1830.

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5.1 Introduction

Mast cells are hematopoietic cells that are involved in immune and allergic responses. These

cells have large granules containing a number of chemical mediators, such as histamine,

serotonin, and cytokines, which can be generated and released upon activation.1-2 Mast cells

are located both in the periphery and the brain, but their involvement and role in the brain is

less clear. For example, it has been demonstrated that mast cells can migrate from blood to

the brain3 and that mast cells numbers increase in the central nervous system (CNS) following stress4. Furthermore, a lack of mast cells has been shown to cause anxiety-like

behavior.5 It has also been demonstrated that mast cells influence memory, spatial learning,

and neurogenesis, despite their relatively small numbers in the hippocampus.6

Given the interesting, emerging roles that mast cells contribute to behavior and

development, chemical influences of this cell type to the brain would provide additional

insight into mast cell involvement. In this study, mast cell-deficient W-sash (Wsh/Wsh) mice

are used to determine metabolites that mast cells contribute to the hippocampus and their

involvement in metabolic pathways. These mice carry a naturally occurring mutation in the

white spotting locus (W), which causes reduced c-kit receptor expression.7 Mast cell

proliferation, differentiation, survival, and regulation are dependent upon this expression.8

Therefore, this mutation results in abnormalities in an inability for mast cells to differentiate from their myeloid progenitors, which provides an excellent model for studying mast cell biology.9-10 Wsh/Wsh mice have been shown to have no abnormalities in any other

hematopoietic cell lineage, although they do exhibit abnormalities in pigmentation.11-12

Metabolomics investigations characterize the small molecule content of a given sample and are often useful in revealing differences between sample types or physiological

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states, especially with regard to disease.13-15 These approaches have been useful in a variety

of applications, such as determining molecular differences in brain tumors16 to characterizing

the molecular composition of single neurons.17-18 Transcriptomic analyses have also been

indispensible in brain research; for example, gene expression was characterized in different

cell types in the brain, including neurons, astrocytes, and oligodendrocytes, where oligodendrocytes and astrocytes were found to be as dissimilar as they are to neurons, despite both being considered glia.19 Given that prior work has shown that a mast cell deficit has

profound effects on animal behavior and neurogenesis within the hippocampus,5-6 the chemistry of this system is investigated to understand how relatively few cells, located within and surrounding the hippocampus, yields such a result. Taking a systems biology approach, metabolism and cell-to-cell signaling information is combined with transcriptomic data to determine the chemical contributions that mast cells make to the hippocampus. Metabolite profiling is achieved with capillary electrophoresis (CE) coupled to electrospray mass spectrometry (ESI MS) for metabolite detection and identification and transcriptomic data is collected using microarrays to determine differentially expressed genes. The combination of this data reveals that mast cells alter a surprising range of metabolites and are implicated in a number of critical pathways.

5.2 Experimental Procedure

5.2.1 Chemicals

The chemicals and reagents used were from Sigma-Aldrich (St. Louis, MO) unless indicated

otherwise: 1xHBSS without phenol red (Invitrogen, Carlsbad, CA), Water (LC-MS grade

Chromosolv®), methanol (Optima®, Fisher Scientific, Fair Lawn, NJ), formic acid (FA)

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(99+%, Thermo Scientific, Rockford, IL), acetic acid (99+%), 4-(2-hydroxyethyl)-1-

piperazineethanesulfonic acid (HEPES) (99.5%), RNAlater-ICE (Applied Biosystems,

Austin, TX)

5.2.2 Animals

All animal care and testing were approved by the Columbia University Institutional Animal

Care and Use Committee. Mast cell-deficient mice (Wsh/Wsh) were originally obtained from

Jackson Laboratories and a colony was established at the Columbia University animal

facility. The Wsh/Wsh mice were crossed with wild-type C57BL/6 mice, to generate heterozygous mice (Wsh/+), which have brain mast cells. Male mice from Wsh/+ x Wsh/+ crosses were used in these experiments and were also used to obtain homozygous mice with mast cells (+/+). Additionally, wild-type mice, WT, (C57BL/6, Charles River) were used for the gene expression analysis. Litters were weaned at 28 days and mice were housed 2-5/cage in transparent plastic bins (36×20×20 cm) in a 12:12 light-dark cycle. Cages had corn cob bedding (Bed-o’cobs, Maumee, OH) and food and water were provided ad libitum. For the

CE-ESI-MS analysis, animals were sacrificed by rapid decapitation and the brains were removed from the crania and placed in ice cold HBSS. Mice used in the gene array studies were sacrificed by decapitation and brains were rapidly removed from crania, flash frozen in liquid nitrogen, and then transferred to a cryostat kept at -20 °C. Coronal brain sections were cut at 50 μm and the right hippocampus was dissected out of three adjacent sections and pooled together in 0.5 mL of RNAlater-ICE pre-cooled to -20 °C. Following 24 h at -20 °C,

RNAlater-ICE was removed and the tissue sample was frozen at -80 °C until processing.

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5.2.3 Sample Preparation for CE-ESI-MS

Brain slices were cut on a vibratome in ice cold HBSS, where the left caudal hippocampus

was dissected. Extraction solution (50/50 methanol/water with 0.5% (v/v) acetic acid) was

applied for a concentration of 20 µL/mg of tissue. The tissue was grossly homogenized and

was extracted for 90 min at 4 °C. The sample was centrifuged at 15,000 g for 15 min and the

supernatants were transferred to polymerase chain reaction (PCR) tubes to better

accommodate the sample volume; the samples were frozen at -80 °C until analysis. An

internal standard, HEPES, was added to the sample extracts for a final concentration of 1

µM. The number of biological replicates is as follows: Wsh/Wsh(10), Wsh/+(9), +/+(5).

5.2.4 Allergy Induction

For the gene microarray study, Wsh/+ and Wsh/Wsh littermates were subjected to allergy or

saline control treatment. Induction of mast cell-dependent allergy was performed as previously described.20 Briefly, mice were injected with ovalbumin (10 μg; Sigma Aldrich,

St. Louis, MO) in Imject Alum (2 mg/mL; Pierce, Rockford, IL) or an equivalent volume of

saline (0.5 mL) via intraperitoneal injection. Fourteen days later, mice were challenged 4 times, every 48h, with an intranasal injection of ovalbumin (10 μL in 8 μL of saline) or saline

(8 μL). Twenty-four hours following the last intranasal injection, mice were sacrificed.

Trunk blood was also collected at this time for measure of serum IgE to confirm allergy

induction, which was not different between genotypes in saline or allergy conditions (data

not shown).

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5.2.5 CE-ESI-MS

The instrument platform is similar to what was used previously18 and has been described in

detail elsewhere.21-22 Briefly, a coaxial sheath flow interface was used to hyphenate the CE

and MS. The CE conditions used were 20 kV for the separation voltage, 6 nL injection

volumes via hydrodynamic injection, 1% formic acid for the background electrolyte, and

50/50 methanol/water with 0.1% (v/v) formic acid for the sheath flow introduced at a rate of

750 nL/min. The CE capillary (Polymicro Technologies, Phoenix, AZ) was 90 cm in length

with a 40 µm inner diameter and 105 µm outer diameter. Single-stage experiments were

performed using the micrOTOF ESI-TOF-MS (Bruker Daltonics) and tandem MS (MS/MS) experiments used the maXis ESI-Qq-TOF-MS/MS (Bruker Daltonics). Three technical

replicates were run for each sample.

5.2.6 CE-ESI-MS Data Analysis

Data processing was performed using the instrument software from Bruker Daltonics

(DataAnalysis). Initial data mining was done to determine the molecular content of the

mouse hippocampus. Extracted ion chromatograms, from m/z 50 to 500 were plotted at a

+0.5 Da window. When an eluting peak was observed, the accurate mass of the ion was

obtained to create an analyte list (Table 5.1). Initial analyte assignments were performed

using Metlin, the Scripps metabolite database.23 Accurate mass calculations were performed

using a molecular weight calculator, which is free, downloadable software through Pacific

Northwest National Laboratory. The tandem MS (MS/MS) data obtained from the sample

was compared against fragmentation data listed in Metlin when available. Standards were

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also analyzed for additional analyte identification using MS/MS data and retention time

order.

After analytes were identified, ion electropherograms were generated at a 0.01 Da

window and the peaks were integrated to obtain the peak intensity. The list of monitored

ions is listed in Table 5.1. In some cases, the concentration of analyte was large enough to

cause potential saturation of the detector; several analytes were also monitored for the second

peak in the isotopic series, since the isotopic ratios remain constant. Intensities were

normalized with respect to the peak intensity of the internal standard, HEPES. Respective

intensities from the technical replicates were averaged to obtain representative peak

intensities for each individual analyte. The p-values were determined using a two-tailed

distribution with two-sample equal variance (homoscedastic) in Microsoft Excel.

5.2.7 Gene Array

Total RNA was isolated using RNAqueous Micro RNA (Ambion) isolation kits. RNA was

quantified with Quant-IT RNA fluorometric assay (Invitrogen) and checked for integrity

using an Agilent Bioanalyzer. The resulting RNA samples were amplified using a

conventional IVT linear amplification procedure (MessageAmp, Ambion), reciprocally

labeled with AlexaFluor 546 or AlexaFluor 647 for induced allergy and control samples, and

hybridized to whole-genome gene expression arrays (SurePrint G3 Mouse GE 8x60K Kit,

Agilent). The resulting raw signal intensity data was filtered for background and technical

outliers, normalized for dye and array effects, and compared for differential gene expression

using hypothesis testing statistics similar to those previously described24-25 using appropriate statistical packages and subroutines implemented in R and Bioconductor26. Differentially

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expressed genes between the groups were selected at < 0.5% FDR (typically 1 to 3 expected false-positives per set) and more than two-fold change in expression. PCA analysis of all the profiles shows separation between allergic and control groups (data not shown). Differences

in genes of interest related to the CE-MS data were pulled out of the dataset, with specific

focus on genes in choline and autophagy pathways.

5.3 Results

The hippocampi from mast cell-deficient mice (Wsh/Wsh) were compared against hippocampi

from their heterozygous (Wsh/+) and homozygous littermates (+/+) to determine molecular

contributions from mast cells. Extracts from these tissues were monitored and characterized

for metabolite content using CE-ESI-MS. The relative levels of individual metabolites were

obtained to determine if any statistical molecular differences were present. Transcriptome

data was collected via total RNA isolation followed by hybridization to whole-genome gene

expression arrays to compare gene expression differences in the hippocampus between wild-

type mice (WT) and mast cell-deficient mice (Wsh/Wsh), which are saline- and allergy-treated.

The allergy treatment allows additional information regarding mast cells to be gained due to

their integral involvement in allergic response. The results of these combined molecular

investigations are detailed below. Additionally, mast cell numbers were counted in the right

hemisphere in every other section of the hippocampus; this is shown in Figure 5.1. The

intracranial locations of mast cells in mice have been documented previously.6

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5.3.1 Metabolomics

In this work, 42 analytes were monitored in hippocampi from Wsh/Wsh, Wsh/+, and +/+ mice, which are shown in Table 5.1. The majority of eluting ions were identified by a combination of accurate mass, retention time, and tandem MS (MS/MS) fragmentation; the ions are listed in order of increasing mass. Fragmentation data was confirmed either by running a standard for the putative compound or comparing it to published fragmentation data in metabolite databases, such as Metlin.23

Out of these 42 analytes, 11 analytes displayed statistically significant differences (p-

value < 0.05) between the Wsh/Wsh and Wsh/+ mice. Included in these differences are amino

acids, choline, nicotinamide, hypoxanthine, and carnosine, as shown in Figure 5.2. It is

curious that among these differences, Wsh/Wsh mice all have relatively lower analyte levels

when compared to their heterozygous littermates. The +/+ mice were also analyzed to

determine if similar trends were present when comparing against these different genotypes.

It was observed that statistical differences between the Wsh/Wsh and +/+ mice were also

present, but in varying degrees. These additional differences are shown in Table 5.2 and include creatinine, betaine, acetylcholine, and glutathione, and a few additional amino acids.

Furthermore, it was observed that there are graded effects for a number of analytes, despite these analytes not exhibiting statistical significance (Wsh/Wsh < Wsh/+ <+/+).

5.3.2 Transcriptomics

The hippocampi from Wsh/Wsh mice were compared to that of Wsh/+ and WT mice to

determine differences in gene expression. Because mast cells are intimately involved in

allergic response, gene expression data was also collected for mice exposed to either allergy

122 or saline. Several gene expression differences, that are related to the observed metabolite changes, were detected between both allergy-treated versus saline-treated mice and between

Wsh/Wsh and Wsh/+ mice; these differences are displayed in Table 5.3. Among these observed differences are acetylcholinesterase, pyruvate dehydrogenase (lipoamide) beta (Pdhb), and genes involved in autophagy.

Because the differentially expressed genes appeared to be related to the observed changes in metabolites, the data was further examined to determine relationships between the gene expression and metabolite data. First, a list of involved metabolic pathways was generated for metabolites. A similar list was generated for differentially expressed genes.

Here, differences were examined between WT mice that were allergy- and saline-treated; similarly, allergy- and saline-differences were determined for Wsh/Wsh mice. Then, attention was focused on genes where saline- and allergy-exposed differences were observed in the

Wsh/Wsh mice, but not in the WT mice to generate a list of genes that are specifically associated with mast cell response. Both the metabolite and differentially expressed gene lists appeared to share many of the same pathways. A list of shared pathways was then generated and put into a graphical format through hierarchal clustering analysis; this is shown in Figure 5.3. In the figure, a yellow block corresponds to a match between a metabolite and a corresponding affected pathway found through the differentially expressed gene analysis. It was observed that the majority of affected pathways and metabolites are connected, forming a single network, despite the use of different control mice (WT and +/+).

5.4 Discussion

Metabolite differences are observed between Wsh/Wsh and Wsh/+ mice, which can be explained by information in the literature and through combining metabolomic and

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transcriptomic approaches. Given the relatively few mast cells that are present in and

surrounding the hippocampus, it is surprising that the chemistry of the hippocampus would

be affected to this degree. The relationship between observed metabolite differences and

differential gene expression is described. Finally, specific molecular changes are addressed that may be involved in the observed mast cell-deficient genotype.

5.4.1 Molecular Differences Observed Between Mast Cell-Deficient and Control Mice

As mentioned previously, histamine is utilized by mast cells in allergic responses. It has been shown previously that histamine levels were lower in Wsh/Wsh mice when compared to

the Wsh/+ mice.5 Histidine is a precursor to histamine; therefore, it is reasonable that

histidine levels are lower in Wsh/Wsh mice compared to Wsh/+ mice. The levels of histamine

measured in the aforementioned paper were below the limits of detection for the instrument

platform; therefore, it is not surprising that histamine was not detected. Furthermore,

histidine and carnosine are directly involved in the same pathway as histamine. Similarly, hypoxanthine is in the same line of metabolism as histidine, even though it is in the purine metabolism pathway, which aids in explaining observed chemical differences between the

genotype.

In the microarray data, pyruvate dehydrogenase beta (Pdhb) was observed to be

different between Wsh/Wsh and Wsh/+ mice under the allergy treatment. Pdhb is involved in the citrate, or Krebs cycle, which is involved in multiple amino acid pathways, including leucine degradation, alanine metabolism, and tyrosine metabolism. Pdhb is also involved in

pyruvate metabolism, which is connected to the nicotinamide metabolism pathway, the biosynthesis of several amino acids including lysine biosynthesis, and is directly involved in

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leucine biosynthesis. The nicotinamide pathway is also listed under the affected pathway list

determined by the gene expression analysis. This disregulation of Pdhb expression could be

responsible for the decrease in the amino acids and nicotinamide between the Wsh/+ and

Wsh/Wsh mice.

Acetylcholinesterase is statistically different between saline-treated Wsh/Wsh and

Wsh/+ mice, as well as between allergy-treated Wsh/Wsh and Wsh/+ mice.

Acetylcholinesterase is responsible for the conversion of acetylcholine into choline, explaining the choline deficit in Wsh/Wsh mice. While there was not a statistical difference

for acetylcholine between Wsh/Wsh and Wsh/+ mice, there is a statistical difference between

Wsh/Wsh and +/+ mice, as well a relative decrease in acetylcholine concentrations (Wsh/Wsh <

Wsh/+ < +/+) as shown in Table 5.2. Prenatal choline administration has been shown to affect acetylcholinesterase levels,27 although it is difficult to know the causality in this case.

Furthermore, mast cells have also been found to express acetylcholinesterase,28 which supports the observed choline and acetylcholine differences being mast cell specific.

The microarray results also show a difference for choline dehydrogenase, which converts choline into betaine aldehyde in the glycine, serine, and threonine metabolism pathway. The down-regulation of choline dehydrogenase could be a result of the lower levels of choline. Furthermore, downstream metabolites in this pathway are also shown to be statistically different between the genotypes. For example, methionine is statistically lower in the Wsh/Wsh sample set in comparison with the Wsh/+ samples; betaine is significantly

different between Wsh/Wsh and +/+ mice. Furthermore, this pathway connects to the

synthesis of multiple amino acids.

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Given the apparent connectivity between gene expression and metabolite differences,

a list of metabolic pathways that may be affected was generated. Hierarchal clustering was

then performed to determine the connectivity of this generated information. As shown in

Figure 5.3, the majority of statistically different metabolites have a matching differentially

expressed pathway. Furthermore, the pathways and metabolites are interconnected, forming

a single network.

5.4.2 The Combination of Molecular Approaches Yield Insight into Mast Cell

Involvement in the Hippocampus

It was previously shown that mast cell-derived serotonin contributes to neurogenesis and that

a lack of mast cells results in spatial memory and learning deficits.6 It appears that there are

global chemical differences present when comparing Wsh/Wsh and Wsh/+ mice, outside of the

repertoire of chemical mediators that are localized within mast cell granules. Furthermore,

serotonin may not be solely responsible for neurogenesis impairment. Pdhb is differentially

expressed in this analysis and has been shown to increase in the rat hippocampus after

chronic antidepressant treatments (like serotonin), in addition to other proteins that are

associated with neurogenesis.29 Stress has also been shown to suppress hippocampal neurogenesis, which was found to be mediated by interleukin-1,30-31 a cytokine that mast

cells contain2. As previously mentioned, the Wsh/Wsh mice have been shown to exhibit

anxiety-like, or stressed, behavior.5

Similarly, stress has resulted in amino acid level changes in different biological

sample matrices. For example, amino acids have been found to change in blood plasma,

urine, and regions of the brain as a result of stress32-37; furthermore, one of these mouse

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models for stress also have been shown to have severe immunosuppression,38-39 which is

interesting given that mast cells are involved in immune response. Additional support for a

link between mast cells and stress is that chronic stress results in increased numbers of mast

cells in the CNS.4 Stress can also induce increased permeability of the blood-brain-barrier

through mast cell activation,40 which could be responsible for molecular differences.

The large number of amino acids that are affected by the presence of mast cells is

certainly interesting. A depletion of amino acids can result in autophagy, a catabolic process

in which cells are degraded for energy production.41 Increased autophagy in the brain is

observed after injury42 and complete impairment of autophagy in the brain results in

neurodegeneration43-44. This has not been observed; however, autophagy may be reduced

which would result in amino acid differences in addition to hampering maintenance of the

cellular environment and health. Furthermore, as shown in Table 5.3, two genes for proteins

involved in autophagy are differentially expressed.

Choline deficiency has been linked to several behaviors or disorders. For example, hippocampal neurogenesis is modulated by prenatal choline availability,45 which could be

participating in the observed neurogenesis of the Wsh/Wsh genotype. Likewise, investigations

have demonstrated that choline can also affect visuospatial memory as a function of choline

administration46-48 and affect hippocampal plasticity49, which are also affected in mast cell-

deficient mice. This research indicates that choline may be integral to these observed

differences.

Acetylcholine does statistically differ between Wsh/Wsh and +/+ mice. While

acetylcholine has not been reported in mast cells, the presence of acetylcholinesterase has been shown,28 although its purpose has not been documented. Acetylcholine is a known

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neurotransmitter involved in a variety of functions, including learning and memory in the

hippocampus.50 The acetylcholine deficit could be contributing to the observed defects in

spatial memory and learning in the mast cell-deficient mice. Furthermore, acetylcholine

release in the hippocampus in response to anxiety and stress has been documented, which

results in an anxiolytic effect.51-55 If acetylcholine levels are impaired, this may be

contributing to the observed anxiety-like behavior in these mice.

Furthermore, nicotinamide has been shown to inhibit histamine release from mast

cells,56-58 while mast cells release histamine in response to acetylcholine.59-60 Perhaps the

lack of mast cells results in lower expression levels of these analytes in other cell types in the

hippocampus because they are no longer needed to regulate mast cell release. It is possible

that mast cell communication is necessary with other hippocampal cells to regulate the

observed impairments in the mast cell-deficient genotype.

Cytokines are one class of molecules that are included in mast cell granules.

Cytokines have been related to disorders such as depression.61-62 They are also involved in

the mediation of stress.30-31 While cytokines were not specifically investigated in this work,

it does demonstrate another supportive link for mast cell involvement in behavior.

Additional evidence is that mast cells have also been implicated in neuroinflammatory

diseases, such as migraines and multiple sclerosis, which are worsened by stress.63

5.5 Conclusions and Future Work

Research has demonstrated mast cell involvement in behavior, neurogenesis, and memory.5-6

Prior studies on this link between the immune system and the brain have focused on known mast cell chemical mediators, such as serotonin. However, mast cells contribute global

128

chemical differences to the hippocampus, which may result in severe impacts on other cell

types, such as glia and neurons. This drastic chemical effect is observed both in brain metabolism and in a remarkable range of pathways through the combination of metabolomics and gene expression data. Furthermore, the effect is even more surprising given that relatively few mast cells are present in and surrounding the hippocampus in comparison with

other cell types.

These global chemical differences may be implicated in the observation of behavioral

and developmental differences observed in mice that lack mast cells. Of particular interest is

choline and its link to memory and neurogenesis, as well as acetylcholine and its relationship

to stress, learning and memory, and a potential impairment of signaling in the hippocampus.

Future work will determine if these mast cell inflicted deficiencies are contributing to these

impairments, perhaps through supplementing Wsh/Wsh animals with these chemical species.

Similarly, the effect of multiple amino acids differing between genotypes implies that brain

health may be compromised, perhaps through the inefficiency of autophagy. There are a

variety of autophagy detection kits available to test if this is the case. There may also be a

cumulative developmental link, which is evidenced by significantly less neurogenesis

observed in mast cell-deficient mice. The combination of these high-throughput data sets

help elucidate a variety of chemical changes, which is desirable for other biological systems

of interest. The global chemical differences that are observed provide a framework for future

experiments to directly obtain specific mechanistic information for how individual analytes

are affecting observed deficits that are inflicted by mast cells.

129

5.6 Figures and Tables

sh sh sh W /W W /+ +/+

Figure 5.1 Mast cell numbers in the hippocampus. The right hemisphere was sectioned and mast cells were counted in every other section in five biological replicates for each genotype. The black dots represent individual animals, while the grey bar is the group average + the standard error of the mean.

130

35 *

30 Wsh/+ Wsh/Wsh

wrt to I.S. wrt * y y 20 * 15

* 10

Normalized intensit ** * * * * 5 * *

Analyte

Figure 5.2 Analytes with statistical differences between Wsh/Wsh and Wsh/+ mice. The data represent the mean of the biological replicates taken from each genotype and the error bars represent standard error. The intensity for nicotinaammide is refllective of the second peak in the isotopic series due to its high concentration. Key: I.S. internal standard, p-value: * <0.05, ** <0.01

131

Figure 5.3 Graphical representation of affected metabolites and pathways. The differentially expressed pathways were clustered against metabolites monitored by CE-ESI-MS. The majority of the pathways form a single, interconnected network. A yellow block indicates that the metabolite is associated with the listed pathway. 132

Table 5.1 Ions monitored in samples. Key: mass-to-charge ratio (m/z), observed (obs), theoretical (theor), the difference between observed and theoretical m/z values (Δ), retention time (RT), *MSMS confirmed, grey: RT confirmed

metabolite ID For m ula m/z (obs) m/z (theor) ∆ (mDa) ∆ (ppm) RT (min)

Gly C2H5NO2 76.0397 76.0398 0.1 1.3 24.5

Beta-alanine C3H7NO2 90.0552 90.0555 0.3 3.3 17.9

Ala C3H7NO2 90.0553 90.0555 0.2 2.2 27.7

GABA* C4H9NO2 104.0710 104.0711 0.1 1.0 18.9

choline* C5H13NO 104.1076 104.1075 -0.1 -1.0 16.2

Ser* C3H7NO3 106.0507 106.0504 -0.3 -2.8 33.1

creatinine* C4H7N3O 114.0666 114.0667 0.1 0.9 17.6

Pr o* C5H9NO2 116.0713 116.0711 -0.2 -1.7 40.6

Val* C5H11NO2 118.0872 118.0868 -0.4 -3.4 32.8

betaine* C5H11NO2 118.0873 118.0868 -0.5 -4.2 45.0

Thr* C4H9NO3 120.0664 120.0661 -0.3 -2.5 35.9

Cys* C3H7NO2S 122.0282 122.0276 -0.6 -4.9 43.5

niacinamide* C6H6N2O 123.0558 123.0558 0.0 0.0 18.2

creatine* C4H9N3O2 132.0779 132.0774 -0.5 -3.8 25.2

Ile* C6H13NO2 132.1028 132.1024 -0.4 -3.0 33.4

Leu* C6H13NO2 132.1028 132.1024 -0.4 -3.0 33.8

ornithine* C5H12N2O2 133.0971 133.0977 0.6 4.5 17.5

Asp* C4H7NO4 134.0450 134.0453 0.3 2.2 44.0

hypoxanthine* C3H7NO2S 137.0468 137.0463 -0.5 -3.6 40.6

acetylcholine* C7H15NO2 146.1183 146.1181 -0.2 -1.4 18.6

Gln* C5H10N2O3 147.0766 147.0770 0.4 2.7 37.2

Lys* C6H14N2O2 147.1134 147.1133 -0.1 -0.7 17.7

Glu* C5H9NO4 148.0617 148.0610 -0.7 -4.7 38.2

Met C5H11NO2S 150.0596 150.0590 -0.6 -4.0 37.1

His * C6H9N3O2 156.0775 156.0772 -0.3 -1.9 18.8

3-amino-octanoic acid C8H17NO2 160.1342 160.1337 -0.5 -3.1 21.3

methyl-lysine* C7H16N2O2 161.1283 161.1290 0.7 4.3 13.8

carnitine* C7H15NO3 162.1138 162.1130 -0.8 -4.9 21.8

Phe* C9H11NO2 166.0870 166.0868 -0.2 -1.2 39.1

Arg* C6H14N4O2 175.1187 175.1195 0.8 4.6 18.5

citrulline* C6H13N3O3 176.1027 176.1035 0.8 4.5 38.0

Tyr* C9H11NO3 182.0822 182.0817 -0.5 -2.7 39.9

acetylcarnitine* C9H17NO4 204.1238 204.1236 -0.2 -1.0 23.9

Trp C11H12N2O2 205.0968 205.0977 0.9 4.4 37.0

propanoyl-carnitine* C10H19NO4 218.1402 218.1392 -1.0 -4.6 24.9

carnosine C9H14N4O3 227.1151 227.1144 -0.7 -3.1 16.6

homocarnosine* C10H16N4O3 241.1301 241.1300 -0.1 -0.4 17.0

cytidine C9H13N3O5 244.0933 244.0933 0.0 0.0 25.8

adenosine* C10H13N5O4 268.1044 268.1046 0.2 0.7 26.8

guanosine C10H13N5O5 284.0989 284.0995 0.6 2.1 47.5

glutathione* C10H17N3O6S 308.0906 308.0916 1.0 3.2 53.5 unknow n 322.1075 50.7 133

Table 5.2 Observed metabolite levels in Wsh/Wsh, Wsh/+, +/+ mice, their corresponding p- values, and affected associated gene pathways. Analytes marked with an asterisk indicate that this is the second peak in the isotopic series due to the high concentration in addition to the corresponding intensity that was also monitored for the monoisotopic peak.

Intensity+Standard Error p-value W sh /W sh & W sh /W sh & W sh /+ & Analyte W sh /W sh W sh /+ +/+ W sh /+ +/+ +/+ Gly 2.16±0.17 2.86±0.29 2.63±0.45 0.05 0.25 0.67 Beta-alanine 1.26±0.12 1.58±0.17 1.32±0.27 0.14 0.82 0.41 Ala 14.42±0.72 17.61±1.41 18.05±2.44 0.05 0.09 0.87 GABA 38.03±0.55 39.84±1.21 36.35±1.63 0.17 0.24 0.11 choline 24.40±2.00 31.08±1.36 27.93±2.25 0.02 0.30 0.23 *GABA 5.16±0.43 6.14±0.52 4.39±0.69 0.17 0.34 0.07 *choline 1.81±0.24 2.69±0.29 2.25±0.32 0.03 0.30 0.35 Ser 10.04±0.61 11.90±0.98 13.47±1.99 0.12 0.05 0.44 creatinine 2.82±0.32 3.33±0.45 1.88±0.22 0.36 0.08 0.04 Pro 5.50±0.33 6.57±0.54 6.97±1.05 0.10 0.11 0.72 Val 7.75±0.38 8.49±0.44 9.89±1.11 0.22 0.04 0.19 betaine 1.80±0.15 1.79±0.09 2.20±0.16 0.98 0.13 0.03 Thr 6.47±0.42 7.49±0.54 8.43±1.10 0.15 0.06 0.40 Cys 4.37±0.46 5.64±0.75 5.26±1.42 0.16 0.46 0.80 nicotinamide 15.26±0.76 18.39±1.37 17.55±2.79 0.06 0.31 0.77 *nicotinamide 1.58±0.08 1.95±0.15 1.97±0.38 0.04 0.19 0.95 creatine 34.44±0.54 35.77±1.03 33.10±1.14 0.26 0.24 0.13 Ile 4.87±0.25 5.47±0.27 6.15±0.92 0.12 0.10 0.38 Leu 7.57±0.37 8.71±0.40 9.00±0.95 0.05 0.11 0.75 *creatine 15.31±0.45 16.22±0.99 13.86±0.89 0.40 0.12 0.14 ornithine 2.66±0.44 2.69±0.20 3.05±0.45 0.95 0.59 0.41 Asp 25.83±2.13 28.26±1.83 30.59±2.08 0.40 0.18 0.44 *Asp 2.18±0.23 2.65±0.35 3.59±0.73 0.27 0.03 0.21 hypoxanthine 3.53±0.36 4.72±0.43 4.15±1.00 0.05 0.48 0.55 acetylcholine 4.52±0.46 5.41±0.65 6.55±0.63 0.27 0.02 0.27 Gln 34.31±0.63 35.89±0.88 34.34±1.23 0.16 0.98 0.32 Lys 11.66±0.91 14.53±1.05 12.39±1.60 0.05 0.67 0.27 Glu 35.48±0.49 37.33±1.07 34.59±1.17 0.12 0.41 0.13 *Gln 5.84±0.43 6.91±0.50 7.67±0.82 0.12 0.05 0.42 *Lys 1.32±0.10 1.65±0.11 1.47±0.24 0.04 0.48 0.45 *Glu 9.00±0.52 9.69±0.47 10.56±0.69 0.34 0.10 0.31 Met 3.06±0.23 3.88±0.27 3.87±0.47 0.04 0.11 0.98 His 4.99±0.24 6.28±0.33 5.36±0.43 0.01 0.43 0.12 3-amino-octanoic acid 2.24±0.29 2.56±0.31 2.84±0.37 0.45 0.24 0.60 methyl-lysine 2.82±0.22 3.48±0.30 2.95±0.43 0.08 0.77 0.31 carnitine 11.19±0.71 12.32±0.50 11.92±1.54 0.22 0.62 0.77 Phe 4.26±0.27 5.04±0.29 5.57±0.69 0.07 0.05 0.42 Arg 8.90±0.86 10.45±0.78 9.07±1.13 0.21 0.91 0.33 citrulline 1.06±0.12 1.37±0.17 0.86±0.11 0.15 0.28 0.06 Tyr 3.06±0.25 3.81±0.26 4.17±0.57 0.05 0.06 0.52 acetylcarnitine 8.57±0.55 9.30±0.74 9.56±0.95 0.43 0.35 0.84 Trp 1.20±0.07 1.43±0.11 1.50±0.26 0.09 0.18 0.78 propanoyl-carnitine 1.02±0.11 1.03±0.09 1.18±0.30 0.96 0.56 0.56 carnosine 2.70±0.20 3.45±0.29 3.30±0.52 0.04 0.21 0.78 homocarnosine 3.17±0.25 3.98±0.34 3.60±0.68 0.07 0.47 0.59 cytidine 0.71±0.06 0.86±0.11 0.79±0.15 0.24 0.56 0.72 adenosine 13.11±0.76 15.06±0.89 14.95±2.54 0.11 0.39 0.96 *adenosine 2.43±0.14 2.87±0.19 3.01±0.61 0.07 0.22 0.79 guanosine 0.34±0.02 0.37±0.04 0.40±0.07 0.40 0.30 0.72 glutathione 4.01±0.30 4.18±0.25 5.29±0.57 0.67 0.05 0.06 unknown 0.06±0.02 0.07±0.02 0.04±0.02 0.76 0.48 0.38

134

Table 5.3 Gene array analysis for Wsh/Wsh and Wsh/+ mice; differences are shown for allergy- treated versus saline-treated and for Wsh/Wsh versus Wsh/+ mice for the saline or allergy treatment; Key: allergy (a), saline (s), Wsh/Wsh (SH), Wsh/+ (HET), difference (diff)

Diff Diff Diff Diff Involved SHs SHa HETs HETa SH HET SHs/ SHa/ Gene Description in: a/s a/s HETs HETa NFKB inhibitor interacting Ras-like Yes No No Yes Autophagy protein 2 (Nkiras2)

Yes No No No Choline choline dehydrogenase (Chdh)

No No Yes Yes Choline acetylcholinesterase (Ache)

RAS-like, family 10, member A No No No Yes Autophagy (Rasl10a) Amino pyruvate dehydrogenase (lipoamide) No No No Yes Acids beta (Pdhb)

135

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57. Wyczolkowska, J.; Maslinski, C., Inhibition of nicotinamide of antigen-induced histamine release from mouse peritoneal mast cells. Int. Arch. Aller. A. Imm. 1976, 50 (6), 729-736.

58. Bekier, E., The inhibitory effect of nicotinamide on asthma like symptoms and eosinophilia in guinea pigs, anaphylactic mast cell degranulation in mice, and histamine release from rat isolated peritoneal mast cells by compound 48/80. Int. Arch. Aller. A. Imm. 1974, 47 (5), 737-748.

59. Blandina, P.; Fantozzi, R.; Mannaioni, P. F.; Masini, E., Characteristics of histamine release evoked by acetylcholine in isolated rat mast cells. J. Physiol. 1980, 301 (1), 281-293.

60. Fantozzi, R.; Masini, E.; Blandina, P.; Mannaioni, P. F.; Bani-Sacchi, T., Release of histamine from rat mast cells by acetylcholine. Nature 1978, 273 (5662), 473-474.

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61. Loftis, J. M.; Huckans, M.; Morasco, B. J., Neuroimmune mechanisms of cytokine- induced depression: current theories and novel treatment strategies. Neurobiol. Dis. 2010, 37 (3), 519-33.

62. Raison, C. L.; Capuron, L.; Miller, A. H., Cytokines sing the blues: inflammation and the pathogenesis of depression. Trends Immunol. 2006, 27 (1), 24-31.

63. Theoharides, T. C.; Cochrane, D. E., Critical role of mast cells in inflammatory diseases and the effect of acute stress. J. Neuroimmunol. 2004, 146 (1-2), 1-12.

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CHAPTER 6

PEPTIDE CHARACTERIZATION IN ASTROCYTES: SINGLE CELL AND CELL

HOMOGENATE STRATEGIES

Notes and Acknowledgments

This chapter describes several efforts at characterizing astrocyte peptide content. This was a

collaboration with Prof. Martha Gillette’s laboratory and the described work was started by

Ping Yin and myself. Michael Heien provided guidance and training as I started this project.

Ping and I established and maintained the astrocyte cell line. Larry Millet from the Gillette

lab taught Ping and I how to culture cells, provided primary cultures for us to test, and

characterized cultures for glial fibrillary acid protein content; Harry Rosenberg, another

student from the Gillette lab, prepared and provided primary cortical cultures of astrocytes

and prepared single cell astrocyte suspensions prior to fluorescence-activated cell sorting

(FACS). Jennifer Mitchell, also from Gillette’s lab, provided assistance in the preparation of the SCN samples. Additionally, the majority of the LC-MS work described in this chapter was performed by Ping; Xiaowen Hou analyzed the FACS sample with LC-MS. Stanislav

Rubakhin assisted in the sample preparation for the transgenic mice. Finally, I would also

like to thank Prof. Mathew Whim from Penn State University for sending additional details

regarding the acid extraction protocol. This work was supported by the National Science

Foundation (NSF) through CHE-0526692 and National Institutes of Health (NIH) through

P30-081310.

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6.1 Introduction

Cell-to-cell signaling among neurons is fundamental to information transfer in the brain.

However, neurons are not the most common cell type in the brain, with more glia present

than neurons. Glia are a heterogeneous cell type, which include, among others, astrocytes,

oligodendrocytes, and Schwann cells. While many differences exist between neurons and

glia, a large difference is that neurons utilize action potentials during intercellular signaling,

while glia do not. However, this view has recently been challenged with the finding that a

specific subset of glia do signal in this manner.1 Because of the traditional view that astrocytes do not fire action potentials, this cell type was previously thought to only play a

role in the maintenance of neuronal health and was not believed to be involved in modulating

neuronal signaling. However, glia do express voltage- and ligand-gated ion channels2 and G- protein-coupled receptors,3 protein machinery that is also used in neuronal communication.

Furthermore, studies suggest that glia also participate in chemical communication, between other glia and between glia and neurons. Several roles for glia have been established, including the regulation of synapse formation, control of synaptic strength, and the production of myelin to enhance conduction of action potentials.4-5 Furthermore, glia are

also involved or implicated in several diseases, although their roles in disorders, such as

multiple sclerosis and glioblastoma, are not thoroughly defined.6-7

Astrocytes, a subset of glia, are not fully characterized in terms of function and

physiology and are believed to have the most diverse functions compared to other subsets of glia. Astrocytes envelop pre- and postsynaptic terminals of neurons, which is strategic placement for involvement in synaptic transmission; this network is known as the tripartite synapse.8 Initially, glutamate was shown to induce Ca2+ changes in astrocytes,9-10 although

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other transmitters have also produced this effect.11-12 Calcium oscillations have been demonstrated for astrocyte-to-astrocyte communication13-15 as well as bidirectional

communication between neurons and astrocytes.16 Astrocytes also release “gliotransmitters,”

many of which appear to be small-molecule transmitters.17 For example, it has been

demonstrated that astrocytes can release glutamate through a SNARE-dependent, quantal

mechanism, which was previously thought to be specific to neuronal synapses.18 Several release mechanisms have been documented, and in general, release is governed by either a volume regulatory response,19 which releases analyte from the cytoplasm into the

extracellular space, or an intracellular calcium-dependent vesicle-mediated exocytotic

release.20-21 Additionally, a large range of potential functions for astrocytes have been

proposed. It has been suggested that astrocytes are involved in long-term memory

formation22 and the regulation of synaptic strength, plasticity, and transmission;4, 23-25 however, their role in synaptic plasticity has been challenged recently.26 Furthermore, astrocytes exhibit heterogeneity with respect to morphology and physiology,27 which stresses

the importance of single-cell investigations for this cell type.

There is also evidence that neuropeptides are expressed by astrocytes.28 For example, a number of studies have determined astrocytes express neuropeptide prohormone mRNA.29-

31 To become active, a neuropeptide prohormone must undergo extensive post-translational modifications (PTMs) via a number of enzymatic steps. However, there is currently little information about peptide processing or potential PTMs. Similarly, immunohistochemistry has also been used to label neuropeptide prohormones in astrocytes.32 While this technique

enables unparalleled visualization of spatial localization, it lacks information regarding peptide processing. Furthermore, antibodies are needed for the specific neuropeptide of

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interest. Additionally, it has been shown that regulated release of neuropeptides is possible

by observing Ca2+-dependent release in transfected astrocytes expressing pro-atrial natriuretic peptide fused with emerald green fluorescent protein.33 Furthermore, the presence

of secretory granules has recently been shown in astrocytes from human brain tissue.34

Mass spectrometry (MS) is well-suited to detect and identify processed neuropeptides and determine potential post-translational modifications. Because a prior knowledge of peptide content is not needed for this technique, novel peptide discovery is also possible.

Neuropeptide discovery and processing in a variety of samples have been successfully identified using MS.35-38 Both electrospray ionization (ESI) and matrix-assisted laser

desorption/ionization (MALDI) are utilized for more complete characterization given their

complementary nature. Furthermore, MS is also capable of detecting peptides within single

mammalian cells.39-40 Given the heterogeneous nature of astrocytes, single-cell analysis is

imperative.

What peptides have been detected in astrocytes? Neuropeptide Y (NPY), was shown to be colocalized with markers of the regulated secretory pathway, and a mass matching NPY

was detected via MS, although not confirmed with tandem MS (MS/MS).41 However, it was

not a pure astrocyte culture; >70% of the cells were glial fibrillary acid protein (GFAP)

immunoreactive, a commonly used marker for astrocytes, although, a lack of neurons was

confirmed. Another work served to identify proteins in astrocytes using MS, and fragments

from neuropeptide prohormones including ProSAAS and FMRFamide were detected and

identified.42 However, the cell cultures were not tested for GFAP expression, leaving

unanswered questions about the purity of the investigated astrocyte culture. Furthermore, trypsin was used in the sample preparation, so it is unlikely that these would be the native

145 peptide forms. In the current work, a careful selection of samples is chosen to ensure a pure astrocyte population to determine peptide content. The progress on measuring these peptides is highlighted in this chapter.

6.2 Experimental Procedure

6.2.1 Sample Types

A variety of different sample types were used to isolate peptides for identification in astrocytes. The first samples that were analyzed were from an astrocyte cell line (C8-D1A,

Astrocyte type I clone, ATCC).43 Using a cell line allows for optimization of sample preparation and instrumentation protocols with a homogeneous sample with unlimited sample quantities. This cell line was cultured in Dulbecco’s modified Eagle media without phenol red until confluency under 37 °C, 5% CO2. Trypsin was applied to the cell flasks and the resulting solution was diluted with media. The cell solution was applied to culture flasks for passaging or to prepared coverslips for sampling; the cells were allowed to grow to desired confluency. Coverslips were prepared for culturing by soaking them in dilute sulfuric acid overnight. The coverslips were rinsed continuously for several minutes and then air dried. Then, the coverslips were coated with poly-l-lysine (PLL) for one hour. Both single cell and cell homogenates were investigated for the cell line. Primary rat cortical cultures were also used for cell homogenate investigations; primary cultures are established directly from the brain.

Transgenic mice (FVB/N-Tg(GFAPGFP)14Mes/J, Jackson Labs) that overexpress green fluorescent protein (GFP) under the control of a GFAP promoter were obtained for the direct isolation of astrocytes without the need for culturing. The brain was minced and

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digested to obtain a single cell suspension. This suspension was sorted using fluorescence-

activated cell sorting (FACS).

6.2.2 Single-Cell MALDI Sample Preparation

The single-cell sample preparation workflow is shown in Figure 6.1. Two stabilization

methods were used to preserve and prepare the sample, the first of which involved incubating

the sample in a 5% glycerol solution to stabilize the cell membrane.39-40 After removing

excess media from the coverslips, a 5% glycerol in media solution was added and the sample

was incubated for 15 minutes for cell stabilization. The solution was removed from the

coverslip by wicking it off of the surface with a Kimwipe® or by gently removing the

solution by applying vacuum to the edges of the coverslip. The sample is left overnight in a

desiccator. Matrix deposition methods used include spraying a 50 mg/mL solution of 2,5-

dihydroxybenzoic acid (DHB) matrix on the cellular surface of the glass slide via a nebulizer

or manually depositing matrix on the surface through a capillary using gravity flow.

Recrystallization of the matrix was performed three times with sprayed samples for improved

ionization. The second preparation protocol involved quickly rinsing the sample in a

concentrated solution of DHB.44-45 This acts as a cell preservative and serves to remove salt from the sample, aiding in ionization. Optionally, additional DHB can be spotted on the surface via gravity flow if ionization directly off of the surface is insufficient. The coverslips were used as MALDI targets for direct cellular analysis.

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6.2.3 Glia Homogenate Extraction Protocols

Two different procedures were used for the extraction of peptides. The first is a DHB

extraction, based on the work of Romanova, et al.45 This involved applying a concentrated

aqueous DHB solution to the culture flask to extract peptides from the sample. Then, excess

salt was removed from the sample (ZipTip, Millipore) and a combination of DHB and alpha-

cyano-4-hydroxycinnamic acid matrixes was applied to the sample to obtain a homogenous

matrix surface for MS/MS identification.46 This procedure was applied to the astrocyte cell

line.

The second procedure is an acid extraction based on the work of Whim, et al.41

Briefly, the culture medium was removed from the purified rat cortical astrocyte culture and the cells were rinsed with HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) buffered salt solution. The cells were extracted with 10 mM hydrochloric acid with 0.1% formic acid, sonicated for 2-3 minutes, and then incubated at 70 °C for 20 minutes to deactivate enzymes. Next the sample was centrifuged at 3300 rcf for 10 min and the supernatant was applied to a 10 kDa molecular weight cutoff filter (Microsep Centrifugal

Devices, Pall Corporation). The supernatant was lyophilized, resuspended in 0.1% formic acid, desalted (ZipTip, Millipore), and reconstituted in 0.1% formic acid. This procedure was used on both the primary glia cultures and astrocytes isolated from the transgenic mice.

This extraction protocol was also applied to the suprachiasmatic nucleus (SCN) to confirm that it is suitable for peptide extraction; this area has a well-known set of well-characterized neuropeptides.35 More specifically, the SCN is a neuroendocrine structure that contains

approximately 10,000 highly peptidergic neurons within a small structure that is used to

validate the implemented protocols.

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6.2.4 Instrumentation

A variety of instrumentation was utilized for both the single-cell and homogenate analyses with each offering differing performance specifications and capabilities. Single-cell detection was achieved using a MALDI TOF/TOF (time-of-flight) MS instrument (Ultraflex

II, Bruker Daltonics). The DHB extract identifications were achieved using a MALDI Q-

TOF (quadrupole time-of-flight) MS instrument (QSTAR XL, Applied Biosystems).

Primary cultures and transgenic mouse samples were analyzed using two additional instrument platforms: capillary electrophoresis (CE) coupled to an ESI-Qq-TOF-MS/MS instrument (maXis, Bruker Daltonics)47 and liquid chromatography (CapLC, Micromass) hyphenated to an ESI-IT (ion trap) MS instrument (HCTultra, Bruker Daltonics). The LC-

MS system was also used to characterize the astrocyte cell line.

6.3 Results and Discussion

6.3.1 Single-Cell MALDI

Initially, single-cell preparations with MALDI MS and cell homogenate preparations for LC-

MS were concurrently analyzed. As shown in Figure 6.2, the two single-cell sample preparation strategies detect overlapping, but distinct peptide signals. Complete mass lists from these analyses were compared against the LC-MS detected mass list. This comparison is shown in Table 6.1. The combination of matched analytes from LC-MS and distinct profiles from each preparation implies that both single-cell sampling strategies are needed for a more complete characterization. While the LC-MS analysis identified a number of analytes

(data not shown), none were classical neuropeptides and the analytes detected in single cells were not able to be identified using this technique. However, the combined peak matches

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from the two analytical techniques do provide confirmation that the sample preparations are

sufficient to obtain molecular content information.

6.3.2 Assaying Astrocyte Homogenates

The DHB extraction protocol on astrocyte homogenates was done in an effort to identify

some of the molecular species detected in the single cell preparations that were unable to be

identified by LC-MS. The identifications from this work are listed in Table 6.2. While the

identities of analytes detected in the single-cell experiments were revealed, unfortunately

these are not classical neuropeptides. Interestingly, most of the identifications were histone

fragments. Based on the article that describes this sampling protocol, there is increased

porosity of the tissue surface.45 It is therefore feasible, that the DHB created pores in the cell nucleus to extract the histones. Additionally, while the source of the cell line stated that these cells express GFAP, it was determined that the expression level was rare to non- existent (data not shown). At this point, efforts were shifted to the characterization of astrocytes in primary cultures to gain a more representative view of this cell type.

The promising results from Whim’s work regarding NPY41 prompted the replication

of this experiment, both to reproduce NPY detection and to detect other classical

neuropeptides. Because Whim, et al. used MALDI MS for NPY confirmation, the sample

was initially analyzed by MALDI; NPY was not detected. CE-ESI-MS/MS was then utilized

for identifying the detected molecular species in the sample; the identifications are listed in

Table 6.3. The identification of 39 peptides from 23 proteins and enzymes was accomplished

within a single separation. A similarly prepared sample was also run with LC-ESI-MS/MS

to identify additional analytes from the primary astrocyte culture. The combined

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identifications from LC-MS/MS and CE-MS/MS analysis are shown in Table 6.4. There

were 12 common identifications between the two techniques, while there were 67 total

identifications from 25 proteins and enzymes. However, neither of these techniques were

able to detect or identify NPY or other neuropeptides. To confirm that this acid extraction sample protocol was sufficient for neuropeptide detection, the SCN was removed and prepared for peptide content. With this procedure, 12 analytes matched the identifications made elsewhere, including peptides from provasopressin and ProSAAS.35 This confirms that

this procedure can be used for peptide identification and detection.

In Tables 6.2, 6.3, and 6.4, some of the peptides identified were from a different

animal; more specifically, they were identified from the bovine genome. As is typical, the

culture media contains bovine serum and this is added for routine cell culture as it aids in cell

growth. Cultures were also analyzed after remaining in serum-free media overnight, but

peaks originating from serum were still detected, albeit the peak intensities were minimized.

Furthermore, while culturing cells is advantageous in obtaining larger amounts of purified

cell content, it is possible that these cells behave in a context dependent manner. For

example, the presence of neurons may be needed for expression of neuropeptides. The

culturing process may also lead to extraneous results which could arise from a variety of factors such as the the age of the culture, the number of passages, how the culture was established, or the types of cells present. An ideal sample would enable the collection of purified glia directly from the brain without cell culture. To obtain this type of sample,

transgenic mice were obtained that exhibit GFP/GFAP positive astrocytes.

A suspension of single cells was prepared to be separated via FACS to obtain purified astrocytes directly from the mouse brain. The pooling of two brains from these mice strains

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enabled approximately 1 million cells to be sorted and a cell pellet was observed following

gentle centrifugation. This was sufficient to detect molecular species; however, the intensity was not sufficient for adequate fragmentation data. One analyte was able to be detected, which was a fragment of ATP synthase-coupling factor 6. Current work continues to identify more peptides from these samples.

6.4 Conclusions and Future Work

The identification of a large number of peptides from astrocytes was achieved, both in a cell line and primary cultures. By utilizing both LC-ESI-IT-MS/MS and CE-ESI-TOF-MS/MS, a more complete picture of molecular content was achieved. However, classical neuropeptides

were not detected. There are a variety of reasons why this may be the case. It is possible that

astrocytic release of peptides is constitutive rather than regulated, which would indicate that

the peptide vesicles would be released as they are made, and so do not accumulate. If this is

the case, peptide concentrations within the cell could be difficult to detect. However,

regulated release was reported for NPY.41 Further characterization of release pathways in

astrocytes would provide additional insight into astrocyte signaling. Developing a sample preparation procedure for the isolation of peptidergic vesicles would also be beneficial; this

would aid in minimizing the protein and enzyme content and maximizing the concentration of neuropeptides. Furthermore, it is unknown if and what cues are necessary for increased peptide expression in astrocytes; the selected samples may lack context-specific input.

As mentioned previously, there is sufficient evidence that astrocytes contain neuropeptide prohormones, both with mRNA expression and immunochemistry data. This question is still unaddressed on whether the prohormones are processed into the expected

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peptides. One interesting point is that most peptidomics experiments have worked with brain

tissue samples that contain both neurons and glia and thus would contain peptides processed from both. Of course, such neuropeptidomics experiments would not characterize the intact prohormone.

As far as the small body of work reporting proteomics results from glia, the results are less conclusive. The ProSAAS and FMRFa fragments detected the work from Li, et al. does not support or reject the presence of processed peptides because they used trypsin to digest the proteins for identification. Furthermore, there are several known enzymatic pathways through which a neuropeptide can be processed.48 The subtilisin-like protease

pathway utilizes several prohormone convertases (PC1/3, PC2, PC5) followed by

carboxypeptidase E/H. Transcriptomics data is now available for astrocytes, neurons, and

oligodendrocytes.49 According to this data, PC2 and PC5 are absent in astrocytes, but are

present in neurons. Furthermore, astrocytes have significantly less PC1 when compared to

neurons. This work really makes one question whether astrocytes would process expressed

prohormones into neuropeptides. Additionally, two reports50-51 have shown that astrocytes

secrete carboxypeptidase E, which may indicate this enzyme would not be available for peptide processing, or possibly suggest that if the prohormones are released, perhaps processing occurs extracellularly from the astrocyte. This combined data suggests that the commonly accepted prohormone processing pathway is unlikely in astrocytes, which is certainly interesting given the evidence supporting neuropeptide prohormone expression and vesicle release. Defining the functional purpose for carboxypeptidase E release in astrocytes would help to indicate if this is the case. Furthermore, characterizing enzyme content and

153 release from astrocytes will yield information regarding potential processing pathways, which may facilitate novel peptide predictions.

A wide range of diverse and interesting functions have been established for glia (and astrocytes). There is still much that remains to be discovered. Further characterization of this cell type will provide a more complete view of how the brain functions, and will certainly yield new insights into cell-to-cell communication within the brain.

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6.5 Figures and Tables

Figure 6.1 Sample preparation strategies toward single-cell analysis. Key: poly-l-lysine (PLL), 2,5-dihydroxybenzoic acid (DHB).

155

Figure 6.2 Different sample preparations allow the detection of overlapping, but different, peptide subsets in astrocyte cell line samples. Top spectrum: Glycerol with DHB spot, Middle spectrum: Glycerol with DHB spray, Bottom spectrum: DHB rinse, no glycerol.

156

Table 6.1 Comparison between the single-cell preparation strategies and ions detected by LC-MS. An X indicates that the m/z value was detected both with LC-MS and the indicated single-cell preparation.

m/z glycerol rinse 1304.6 x 1307.9 x 1346.6 x 1384.5 x 1518.4 x 1542.0 x x 1582.5 x 1593.2 x 1607.6 x 1763.3 x 1821.1 x 2031.9 x 2035.0 x 2348.0 x 2935.3 x 2984.4 x 3003.4 x 4936.4 x 4963.7 x x

Table 6.2 Ions identified with MS/MS from the astrocyte cell line with the DHB rinse extract protocol. Key: observed (obs), theoretical (theor).

Precursor Sequence m/z (obs) m/z (theor) Histone H4 (Osteogenic growth peptide) Y.ALKRQGRTLYGFGG.- 1523.8 1523.8 Histone H2A type 3 A.VLLPKKTESHHKAKGK.- 1800.9 1801.1 Histone H4 D.VVYALKRQGRTLYGFGG.- 1885.0 1885.0 Histone H4 M.DVVYALKRQGRTLYGFGG.- 2000.0 2000.1 Bovine albumin R.DTHKSEIAHRFKDLGEEHFKGLVL.I 2806.5 2806.5 Histone H2B type1-B A.VRLLLPGELAKHAVSEGTKAVTKYTSSK.- 2983.7 2983.7 Histone H2B type1-B T.AVRLLLPGELAKHAVSEGTKAVTKYTSSK.- 3054.7 3054.7

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Table 6.3 CE-ESI-MS/MS identification of peptides/proteins in an astrocyte culture from the rat cortex. Key: charge (z), mass difference (Δ).

m/z Mascot, z Precursor Matched Sequence observed ∆ (mDa) 822.9302 2+ -6 Ac2-067 M.GFGDLKTPAGLQVLND.Y 626.3260 3+ -12.8 Acyl-CoA-binding protein M.(Ac)SQADFDKAAEEVKRLK.T 680.3455 2+ 0 Beta actin D.ESGPSIVHRKCF.- 987.0067 2+ -2.5 Beta actin W.ISKQEYDESGPSIVHRK.C 1146.6249 1+ -1.6 Beta actin L.RVAPEEHPVL.L 694.8775 2+ -5.5 Beta actin S.ELRVAPEEHPVL.L 751.8970 2+ -9.4 Beta actin Y.NELRVAPEEHPVL.L 1119.5181 1+ 4.2 Elongation factor 1-alpha 1 D.PPMEAAGFTAQ.V 955.5333 1+ -2.6 Enolase L.YTAKGLFR.A 487.2950 2+ -3.6 Eukaryotic translation initiation factor 5A-1 E.AAVAIKAMAK.- 1072.5994 1+ -4.3 Fetuin precursor - bovine S.IPLDPVAGYK.E 1232.5793 1+ -0.3 Fibrinogen alpha chain - bovine D.PPSGDFLTEGGGV.R 975.4285 2+ -1.4 Fibrinogen beta chain - bovine -.pQFPTDYDEGQDDRPKVG.L 836.4440 2+ -25.7 Glucose-6-phosphate isomerase M.(Ac)AALTRNPEFQKLLE.W 777.8566 2+ -1.1 Glutamine synthetase 1 L.LNETGDEPFQYKN.- 930.4077 1+ 1.8 Glyceraldehyde-3-phosphate dehydrogenase L.MAYMASKE.- 965.5020 1+ 3.0 Glyceraldehyde-3-phosphate dehydrogenase G.YSNRVVDL.M 786.4319 2+ -11.4 Glyceraldehyde-3-phosphate dehydrogenase G.KVDIVAINDPFIDL.N 1046.9815 2+ -5.3 Glyceraldehyde-3-phosphate dehydrogenase L.ISWYDNEYGYSNRVVDL.M 857.9189 2+ -4.9 Heat shock 27 protein F.EARAQIGGPESEQSGAK.- 822.4454 2+ -3.6 Myosin regulatory light chain RLC-A E.FTRILKHGAKDKDD. 1143.5617 1+ -2.3 Peptidylprolyl isomerase A F.DITADGEPLGR.V 1290.6312 1+ -1.2 Peptidylprolyl isomerase A F.FDITADGEPLGR.V 761.9013 2+ -5.9 Peptidylprolyl isomerase A L.FADKVPKTAENFR.A 755.9240 2+ -6.7 Phosphoglycerate kinase 1 L.LEGKVLPGVDALSNV.- 981.0332 2+ -11 Prolifin-1 F.VSITPAEVGVLVGKDRSSF.F 487.2922 2+ -1.8 Protein S100-A11 F.LQTSQKRI.- 614.8816 2+ -6.4 Protein transport protein Sec31A L.KVVLSQASKLGV.- 833.9548 2+ -9.9 Ptbp1 protein L.LVSNLNPERVTPQSL.F 1012.5445 1+ -16.3 Pyruvate kinase isozyme M1 T.NTMRVVPVP.- 792.3981 2+ -3.5 Serum albumin precursor - bovine R.DTHKSEIAHRFKD.L 877.4514 2+ -2.4 Serum albumin precursor - bovine R.DTHKSEIAHRFKDLG.E 783.8550 2+ 3.6 Thymosin beta-4 precursor M.(Ac)SDKPDMAEIEKFD.K 717.7021 3+ -8.4 Thymosin beta-4 precursor -.(Ac)SDKPDMAEIEKFDKSKLK.K 693.3671 2+ -0.1 Vimentin L.RETNLESLPLVD.T 1091.4948 2+ -7.1 Vimentin K.TVETRDGQVINETSQHHDD.L 1212.0552 2+ -3.8 Vimentin K.TVETRDGQVINETSQHHDDLE.- 861.3898 2+ -0.2 Vimentin D.GQVINETSQHHDDLE.- 689.8805 2+ -15.1 Voltage-dependent anion channel-like protein N.AGGHKLGLALELEA.-

158

Table 6.4 Combined identifications from CE-MS/MS and LC-MS/MS.

# of fragments Precusor identified Ac2-067 1 Acyl-CoA-binding protein 1 Beta actin 5 Elongation factor 1-alpha 1 1 Enolase 1 Eukaryotic translation initiation factor 5A-1 1 Fetuin precursor - bovine 1 Fibrinogen beta chain - bovine 2 Glucose-6-phosphate isomerase 1 Glutamine synthetase 1 1 Glyceraldehyde-3-phosphate dehydrogenase 4 Heat shock 27 protein 2 Myosin regulatory light chain RLC-A 1 Peptidylprolyl isomerase A 5 Phosphoglycerate kinase 1 1 Profilin-1 1 Protein S100-A11 1 pyruvate kinase isozyme M1 1 Pyrimidine-binding protein 1 Sec31l1 protein (Fragment) 1 Serum albumin precursor - bovine 2 Thymosin beta-4 3 Vimentin 4 Voltage-dependent anion channel-like protein 1 Voltage-dependent anion-selective channel protein 1 1 Total 44 # of proteins/enzymes ID'd 25

159

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12. Wang, Z.; Haydon, P. G.; Yeung, E. S., Direct observation of calcium-independent intercellular ATP signaling in astrocytes. Anal. Chem. 2000, 72 (9), 2001-2007.

13. Guthrie, P. B.; Knappenberger, J.; Segal, M.; Bennett, M. V. L.; Charles, A. C.; Kater, S. B., ATP released from astrocytes mediates glial calcium waves. J. Neurosci. 1999, 19 (2), 520-528.

14. Hassinger, T. D.; Guthrie, P. B.; Atkinson, P. B.; Bennett, M. V. L.; Kater, S. B., An extracellular signaling component in propagation of astrocytic calcium waves. Proc. Natl. Acad. Sci. U.S.A. 1996, 93 (23), 13268-13273.

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15. Charles, A. C.; Merrill, J. E.; Dirksen, E. R.; Sandersont, M. J., Intercellular signaling in glial cells: calcium waves and oscillations in response to mechanical stimulation and glutamate. Neuron 1991, 6 (6), 983-992.

16. Pasti, L.; Volterra, A.; Pozzan, T.; Carmignoto, G., Intracellular calcium oscillations in astrocytes: a highly plastic, bidirectional form of communication between neurons and astrocytes in situ. J. Neurosci. 1997, 17 (20), 7817-7830.

17. Volterra, A.; Meldolesi, J., Astrocytes, from brain glue to communication elements: the revolution continues. Nat. Rev. Neurosci. 2005, 6 (8), 626-640.

18. Bezzi, P.; Gundersen, V.; Galbete, J. L.; Seifert, G.; Steinhäuser, C.; Pilati, E.; Volterra, A., Astrocytes contain a vesicular compartment that is competent for regulated exocytosis of glutamate. Nat. Neurosci. 2004, 7 (6), 613-620.

19. Kimelberg, H.; Goderie, S.; Higman, S.; Pang, S.; Waniewski, R., Swelling-induced release of glutamate, aspartate, and taurine from astrocyte cultures. J. Neurosci. 1990, 10 (5), 1583-1591.

20. Evanko, D. S.; Zhang, Q.; Zorec, R.; Haydon, P. G., Defining pathways of loss and secretion of chemical messengers from astrocytes. Glia 2004, 47 (3), 233-240.

21. Ni, Y.; Malarkey, E. B.; Parpura, V., Vesicular release of glutamate mediates bidirectional signaling between astrocytes and neurons. J. Neurochem. 2007, 103 (4), 1273- 1284.

22. Suzuki, A.; Stern, Sarah A.; Bozdagi, O.; Huntley, George W.; Walker, Ruth H.; Magistretti, Pierre J.; Alberini, Cristina M., Astrocyte-neuron lactate transport is required for long-term memory formation. Cell 2011, 144 (5), 810-823.

23. Pascual, O.; Casper, K. B.; Kubera, C.; Zhang, J.; Revilla-Sanchez, R.; Sul, J.-Y.; Takano, H.; Moss, S. J.; McCarthy, K.; Haydon, P. G., Astrocytic purinergic signaling coordinates synaptic networks. Science 2005, 310 (5745), 113-116.

24. Volterra, A.; Steinhäuser, C., Glial modulation of synaptic transmission in the hippocampus. Glia 2004, 47 (3), 249-257.

25. Perea, G.; Araque, A., Astrocytes potentiate transmitter release at single hippocampal synapses. Science 2007, 317 (5841), 1083-1086.

26. Agulhon, C.; Fiacco, T. A.; McCarthy, K. D., Hippocampal short- and long-term plasticity are not modulated by astrocyte Ca2+ signaling. Science 2010, 327 (5970), 1250- 1254.

27. Matyash, V.; Kettenmann, H., Heterogeneity in astrocyte morphology and physiology. Brain Res. Rev. 2010, 63 (1-2), 2-10.

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28. Ubink, R.; Calza, L.; Hökfelt, T., `Neuro'-peptides in glia: focus on NPY and galanin. Trends Neurosci. 2003, 26 (11), 604-609.

29. Stornetta, R. L., Astrocytes synthesize angiotensinogen in brain. Science 1988, 242 (4884), 1444-1446.

30. Vilijn, M. H.; Vaysse, P. J. J.; Zukin, R. S.; Kessler, J. A., Expression of preproenkephalin mRNA by cultured astrocytes and neurons. Proc. Natl. Acad. Sci. U.S.A. 1988, 85 (17), 6551-6555.

31. Shinoda, H.; Marini, A. M.; Cosi, C.; Schwartz, J. P., Brain region and gene specificity of neuropeptide gene expression in cultured astrocytes. Science 1989, 245 (4916), 415-417.

32. Hauser, K. F.; Osborne, J. G.; Stiene-Martin, A.; Melner, M. H., Cellular localization of proenkephalin mRNA and enkephalin peptide products in cultured astrocytes. Brain Res. 1990, 522 (2), 347-353.

33. Krz̄ an, M.; Stenovec, M.; Kreft, M.; Pangršič, T.; Grilc, S.; Haydon, P. G.; Zorec, R., Calcium-dependent exocytosis of atrial natriuretic peptide from astrocytes. J. Neurosci. 2003, 23 (5), 1580-1583.

34. Hur, Y. S.; Kim, K. D.; Paek, S. H.; Yoo, S. H., Evidence for the existence of secretory granule (dense-core vesicle)-based inositol 1,4,5-trisphosphate-dependent Ca2+ signaling system in astrocytes. PLoS ONE 2010, 5 (8), e11973.

35. Lee, J. E.; Atkins, N.; Hatcher, N. G.; Zamdborg, L.; Gillette, M. U.; Sweedler, J. V.; Kelleher, N. L., Endogenous peptide discovery of the rat circadian clock. Mol. Cell. Proteomics 2010, 9 (2), 285-297.

36. Xie, F.; London, S.; Southey, B.; Annangudi, S.; Amare, A.; Rodriguez-Zas, S.; Clayton, D.; Sweedler, J., The zebra finch neuropeptidome: prediction, detection and expression. BMC Biol. 2010, 8 (1), 28.

37. Boonen, K.; Landuyt, B.; Baggerman, G.; Husson, S. J.; Huybrechts, J.; Schoofs, L., Peptidomics: the integrated approach of MS, hyphenated techniques and bioinformatics for neuropeptide analysis. J. Sep. Sci. 2008, 31 (3), 427-445.

38. Collins, J. J., III; Hou, X.; Romanova, E. V.; Lambrus, B. G.; Miller, C. M.; Saberi, A.; Sweedler, J. V.; Newmark, P. A., Genome-wide analyses reveal a role for peptide hormones in planarian germline development. PLoS Biol. 2010, 8 (10), e1000509.

39. Rubakhin, S. S.; Churchill, J. D.; Greenough, W. T.; Sweedler, J. V., Profiling signaling peptides in single mammalian cells using mass spectrometry. Anal. Chem. 2006, 78 (20), 7267-7272.

40. Rubakhin, S. S.; Sweedler, J. V., Characterizing peptides in individual mammalian cells using mass spectrometry. Nat. Protoc. 2007, 2 (8), 1987-1997.

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41. Ramamoorthy, P., Trafficking and fusion of neuropeptide Y-containing dense-core granules in astrocytes. J. Neurosci. 2008, 28 (51), 13815-13827.

42. Dowell, J. A.; Johnson, J. A.; Li, L., Identification of astrocyte secreted proteins with a combination of shotgun proteomics and bioinformatics. J. Prot. Res. 2009, 8 (8), 4135- 4143.

43. Alliot, F.; Pessac, B., Astrocytic cell clones derived from established cultures of 8- day postnatal mouse cerebella. Brain Res. 1984, 306 (1-2), 283-291.

44. Garden, R. W.; Moroz, L. L.; Moroz, T. P.; Shippy, S. A.; Sweedler, J. V., Excess salt removal with matrix rinsing: direct peptide profiling of neurons from marine invertebrates using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J. Mass Spectrom. 1996, 31 (10), 1126-1130.

45. Romanova, E. V.; Rubakhin, S. S.; Sweedler, J. V., One-step sampling, extraction, and storage protocol for peptidomics using dihydroxybenzoic acid. Anal. Chem. 2008, 80 (9), 3379-3386.

46. Laugesen, S.; Roepstorff, P., Combination of two matrices results in improved performance of MALDI MS for peptide mass mapping and protein analysis. J. Am. Soc. Mass. Spectrom. 2003, 14 (9), 992-1002.

47. Lapainis, T.; Rubakhin, S. S.; Sweedler, J. V., Capillary electrophoresis with electrospray ionization mass spectrometric detection for single-cell metabolomics. Anal. Chem. 2009, 81 (14), 5858-5864.

48. Hook, V.; Funkelstein, L.; Lu, D.; Bark, S.; Wegrzyn, J.; Hwang, S. R., Proteases for processing proneuropeptides into peptide neurotransmitters and hormones. Annu. Rev. Pharmacol. Toxicol. 2008, 48, 393-423.

49. Cahoy, J. D.; Emery, B.; Kaushal, A.; Foo, L. C.; Zamanian, J. L.; Christopherson, K. S.; Xing, Y.; Lubischer, J. L.; Krieg, P. A.; Krupenko, S. A.; Thompson, W. J.; Barres, B. A., A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 2008, 28 (1), 264-278.

50. Klein, R. S.; Das, B.; Fricker, L. D., Secretion of carboxypeptidase E from cultured astrocytes and from AtT-20 cells, a neuroendocrine cell line: implications for neuropeptide biosynthesis. J. Neurochem. 1992, 58 (6), 2011-2018.

51. Greco, T. M.; Seeholzer, S. H.; Mak, A.; Spruce, L.; Ischiropoulos, H., Quantitative mass spectrometry-based proteomics reveals the dynamic range of primary mouse astrocyte protein secretion. J. Prot. Res. 2010, 9 (5), 2764-2774.

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CHAPTER 7

METABOLIC DIFFERENCES OF SPECIALIZED SAMPLES: NEURONAL

CULTURES, CELL CLUSTERS, AND SINGLE CELLS FROM APLYSIA

CALIFORNICA

Notes and Acknowledgments

Content in this chapter was contributed by Ann M. Knolhoff, Peter Nemes, Stanislav S.

Rubakhin, Leonid Moroz (University of Florida), and Jonathan V. Sweedler. Stanislav isolated the neurons, while both Leonid and Stanislav isolated neurons for the L7 motor neuron experiments. Peter ran the samples, and Peter and I shared the responsibility for data analysis, with further details of the CE-MS and analyses approaches described in Chapters 3 and 4. Xiying Wang’s assistance during the sample preparation is also gratefully acknowledged. This material is based upon work supported by the National Science

Foundation (NSF) and the National Institutes of Health (NIH) through grant numbers NIH

DK070285, NS031609, and NSF CHE-0400768.

7.1 Introduction

Microscale analyses of single cells or cell clusters can provide useful information that is often lost with tissue homogenates. Especially in the central nervous system (CNS), diverse cell types are present, and even cells of the same types exhibit chemical heterogeneity.1-3 By moving to single-cell samples, complexity is also minimized. There are several analytical technologies that are capable of making measurements of cellular content on a single-cell

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level.4-6 Mass spectrometry (MS) is particularly useful due to its low limits of detection, wide dynamic concentration range, and its ability to detect and identify analytes via fragmentation. Like the metabolomics investigations in Chapters 4 and 5, capillary electrophoresis (CE) coupled to electrospray ionization mass spectrometry (ESI MS) is used for the following metabolic analyses. This work is expands upon prior work listed in Chapter

4; here, preliminary data is presented for isolated single Aplysia californica neurons and

neuronal clusters. The cells investigated represent three separate applications of

metabolomics to highlight changes induced by cell culture, to alleviate difficulties in the

selection of neurons, and to examine the molecular content of a seemingly homogeneous cell

cluster.

The first investigation involves cell culture. Cell culture is often implemented with

Aplysia neurons in order to perform experiments under a predefined set of conditions to gain

functional or biological information in a less complicated environment. Furthermore,

culturing can yield valuable information that is difficult to obtain in vivo, such as axon and

synapse formation and strength.7-10 Culturing also enables the intrinsic properties of the

neuron to be monitored without synaptic input or the function of a small neuronal circuit to

be determined, which would be difficult to obtain in the intact system.11 How do cultured

versus freshly isolated neurons differ? This is important to know if one is to make biological or functional conclusions from experiments with cultured cells, which are then extended to what occurs in vivo. For this work, two similar neurons are compared against their cultured

counterparts, the B1 and B2 neurons from the buccal ganglion. These neurons have been

shown to be similar both in neuropeptide12 and metabolite content (Chapter 4) and are

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involved in gut motility.13 They have also been included in cell culture experiments

regarding neurite outgrowth and synapse formation.8, 14

The second investigation includes metabolically characterizing the R3-13 ganglion.

The R3-13 cluster is involved in cardiac function,15 which is induced by a neuropeptide16.

This neuronal cluster is easily identifiable and is well characterized in terms of physiology and function as a whole. However, the individual neurons within this cluster are difficult to identify and isolate without damaging the other neurons in the cluster. Many of the investigations regarding this ganglion characterize it as a whole, in part due to the difficulty in isolation and because the individual neurons within the cluster were thought to be homogeneous. However, the neurons of this ganglion innervate different areas, such as the heart and kidneys, which indicates that each neuron may be responsible for different actions.17 Furthermore, little information is available regarding the metabolites that are

present, with the exception of glycine, which has also been suggested to be a

neurotransmitter.18-19 Because of the difficulty in identifying the individual neurons within

the cluster, the entire cell cluster was extracted; therefore, cell-to-cell differences were not

investigated. To quickly determine molecular content, the same ion list used to characterize

Aplysia neurons in Chapter 4 was used to observe eluting analytes. In an effort to determine if unique molecular entities were present, the cluster was compared against the other cell types that were described in Chapter 4 because these isolated neurons are very different from the cluster with respect to appearance and functionality. Unique signals to the cluster have the potential to be localized to individual neurons, which could yield information regarding their function.

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The last example of a single neuron study represents initial efforts to characterize the

L7 motor neuron, which is involved in the gill-withdrawal reflex.20-22 This reflex has been

thoroughly investigated by Kandel and colleagues because of its unique role in specific types of learning and memory.23 However, the chemical signal responsible for this response is not

well characterized.24 Transcriptome data has been collected to characterize the CNS and

specific neurons from Aplysia, including the L7 neuron.24 Three proteins that were L7-

specific were determined to be candidate signaling molecules, while 362 transcripts were

found to be enriched specifically in L7 neurons. To complement the transcriptomics data, the

small molecule content of this cell type is examined here. However, as is typical for neurons,

the identification of this neuron involves functional tests and cannot be solely identified

based on location. The number of candidate neurons is able to be limited to two; here, these

two neurons are isolated, one of which is the L7 motor neuron, and the chemical differences

between these two neurons are highlighted as well as their molecular content. Multiple

animals are used to distinguish chemical differences between these neurons. Once these two

neurons are well characterized, the hope is that future L7 experiments can use the specific

small molecular signature for identification.

Metabolite profiling enables three different approaches to better understand Aplysia

neurons. First, it is determined that the extent of metabolic change induced by cell culture is

dependent on the neuron type. Second, initial chemical characterization of the R3-13 cluster

results in the observation of unidentified analytes that appear to be specific to the cluster.

Finally, preliminary investigations into probing the chemical content of the L7 neuron allow

it to be determined chemically, which is a necessary precursor to future experiments of this

cell type.

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7.2 Experimental Procedure

7.2.1 Sample Preparation

The sample preparation strategy is similar to that stated in Chapter 4, so only differences are highlighted here. Three different experiments with isolated neurons are described: comparison of freshly isolated and cultured B1 and B2 neurons, putative L7 neuron analysis, and investigation of the R3-13 cluster. Cell culturing experiments were performed as shown previously.25 Briefly, individual B1 and B2 neurons were isolated and placed in pre-

manufactured cell culture plastic Petri dishes. The cell culture was allowed to develop for 24

h in artificial seawater (ASW) supplemented with antibiotics (100 units/mL penicillin G, 100

μg/mL streptomycin, and 100 μg/mL gentamicin) at room temperature. The cultured neurons

were immersed in the ASW/glycerol (v/v 66.7%/33.3%) solution prior to isolation. In a different experiment, two neurons were isolated from the same animal, one of which is the

L7 neuron, with neuron pairs being isolated from four total animals. These neurons were chosen by their location and size; however, the absence of electrophysiological identification and presence of several similar neurons in the same area of the abdominal ganglion resulted in uncertainty in L7 identification. Three R3-13 clusters were also isolated for metabolite characterization. Isolated neurons were placed and preserved in an extraction solution (50%

(v/v) methanol with 0.5% (v/v) acetic acid) prior to CE-ESI-MS analysis.

7.2.2 CE-ESI-MS

Detailed explanations of the instrument platform have been previously described in Chapters

3 and 4. Briefly, the CE separation is achieved with 1% formic acid as the background electrolyte and by applying 20 kV at the inlet, while the outlet is at ground. Hydrodynamic

168

injections of 6 nL were achieved via a 15 cm height difference with respect to the CE capillary outlet. A MicroTee (Idex Health & Science LLC, Oak Harbor, WA) is utilized for the CE-ESI sheath flow interface, equipped with a grounded stainless steel emitter (130 µm

ID, 260 µm OD, part 21031A, Hamilton Company, Reno, NV, USA). The sheath flow (50%

(v/v) methanol with 0.1% (v/v) formic acid) is supplied through the emitter at 750 nL/min.

The ions were monitored with a micrOTOF ESI-time-of-flight (TOF) mass spectrometer

(Bruker Daltonics, Billerica, MA).

7.2.3 Data Analysis

For the R3-13 cluster and culturing comparison experiments, data processing was performed as mentioned in Chapter 4. Briefly, initial data mining was completed by determining eluting ions in m/z-selected ion electropherograms from m/z 50 to 500 with a step size of 0.5 Da.

After determining the eluting species from these isolated, identified neurons, the total analyte list was minimized to 140 analytes that would be monitored. Similarly, for the L7 experiment, a total of 241 combined analytes from the two putative L7 neurons from 4 animals (8 total neurons) were detected and monitored. This analyte list was minimized to

169 compounds after the removal of peaks due to contamination, HEPES-related ions, ion fragments, adducts, and ions that were specific to only one animal. Unsupervised principal component analysis (PCA) was used for all of the described experiments using Markerview

1.1 software (Applied Biosystems, Carlsbad, CA, USA). It was determined that the molecular profile of the neurons from one of the animals differed significantly from the other three animals and was excluded from the analysis.

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7.3 Results and Discussion

Three separate investigations were performed to gain insight into different aspects of Aplysia

neurons. The first set of experiments served to determine how two different neurons respond

to cell culture conditions, where the variability between freshly isolated and cultured neurons

was determined via PCA. The second investigation involved initial efforts to characterize the

metabolic content of the R3-13 cell cluster to determine any uniquely identifiable markers in

comparison with other cell types from Aplysia. Finally, two candidates for the L7 motor

neuron are characterized for metabolite content and can be separated by PCA, indicating that

the L7 neuron can be identified chemically.

7.3.1 Cultured Neurons

In investigations where culturing is utilized, one should assume that differences may be

present with respect to the cell in its intact system. PCA was implemented to determine the

extent of the chemical differences between cultured and isolated neurons; the PCA results are

shown in Figure 7.1. For the B1 neuron, the majority of the variance is observed along the

PC1 score axis (70.4%), which is also the axis along which clustering is occurring.

Conversely, the B2 neurons separate along the PC2 score axis, where only an 8.2% variance is observed between cultured and isolated cells. These results suggest that the variability between cultured and non-cultured cells is dependent upon cell type. Furthermore, it is interesting that these two neurons respond differently to culturing given that they are metabolically similar compared to other isolated neurons from Aplysia, as shown in Chapter

4 (Figure 4.5); they are also similar with regard to peptide expression and distribution.12, 26

B1 and B2 neurons have also been shown to be similar in culture, exhibiting analogous

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axonal formation.8 B1 neurons did show relatively higher metabolite variability (Chapter 4); perhaps this metabolic variability results in differences in response to culturing.

While these two different neurons appear to respond differently in culture from a metabolomics standpoint, the cultured versus freshly isolated neurons do both cluster separately from one another. This is not surprising given that culturing does expose the cell to an environment that is drastically different than what is encountered in vivo. A direct chemical comparison between cultured and freshly isolated neurons was unable to be found in the literature, likely because many investigations utilizing cell culture with Aplysia focus on synapse formation and electrophysiological responses. However, there have been reports of neuron-neuron connections that can be established in culture that do not occur in vivo.27-28

When observing specific metabolite differences between cultured and freshly isolated

neurons, cultured neurons appeared to have relatively lower intensities of amino acids. This

is not too surprising given that the neurons are cultured in artificial sea water, but it is

surprising that these two different neurons, which are similar in many respects, respond

differently to this treatment. Perhaps, media with essential amino acids, similar to what is

used in mammalian cell culture, would prevent the cell from being metabolically depleted.

What other chemical changes may be present? Because these cells were monitored only for

small molecule content, it is unknown how culturing affects neuropeptide or gene expression.

While analytes may be present in large enough quantities to elicit a cellular response in a

neuronal network, the response may be more pronounced in vivo. Since these cells are

peptidergic, a similar experiment could be performed on neurons with a known small

molecule neurotransmitter to determine if there is a concentration difference. These results

demonstrate that caution must be taken when performing experiments in culture and to

171 consider what differences, chemical or otherwise, may be present between isolated and cultured neurons.

7.3.2 Investigation of R3-13 Cluster

As mentioned previously, the R3-13 ganglion has not been fully characterized with respect to metabolite content. Previous investigations regarding this ganglion have traditionally been done on the entire ganglion rather than its individual cellular components. As a first approach, the cluster was initially characterized using the predefined analyte list from

Chapter 4 and was used as a comparison with the single neurons analyzed in Chapter 4 utilizing PCA. The ganglion does cluster separately from the other cells (data not shown), so the individual ion profiles were compared between the cluster and the other isolated neurons to determine how they are metabolically distinct. For the most part, many of the ions from the cluster were similar to those detected in the single neurons from Chapter 4. However, a few analytes appeared to be somewhat specific to the ganglion. In Figure 7.2, three ions are displayed that appear to be either highly expressed in the cluster in comparison with the other neurons. The top panel of Figure 7.2 is a representation of glycine content for the investigated neurons. The R3-13 neurons appear to have relatively high glycine content, as do the R2 and LPl1 neurons. Previous work has demonstrated that the R3-13 ganglion does contain increased levels of glycine and has been suggested to serve as a potential neurotransmitter in this cluster.18-19 The other two ions that appear to distinguish the ganglion from other neurons are unidentified. The m/z 242 ion (middle panel, Figure 7.2) is doubly charged and could be an unidentified neuropeptide (M+H 484.25); however, this mass does not match any of the previously determined peptides from this cluster.15 A

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metabolite search to determine the possible identity of the m/z 265 yields two potential

matches: alpha-N-phenylacetyl-L-glutamine and N-γ-acetyl-N-2-formyl-5-

methoxykynurenamine. These metabolites are involved in phenylalanine and tryptophan

metabolism, respectively. Future work will characterize the identity of these ions and to determine if they have any biological significance. Single cell metabolomics investigations could also be done to the individual neurons within this cluster to determine if they are really homogeneous cells that happen to innervate different areas or if they are heterogeneous in their chemical composition.

7.3.3 L7 Analysis

A combined total of 241 analytes were detected in the two L7 candidate neurons. This list of analytes was minimized to 169 analytes after removing contamination peaks, adducts,

HEPES-related ions, ion fragments, or ions that were specific to the neurons of one animal; these ions were monitored in the two L7 candidate neurons. Rather than identifying all of the combined 169 analytes that were detected in the two isolated neurons, PCA was first applied to the sample set to determine if the two neurons could be chemically differentiated. In the isolation of L7 neurons, limiting the possibilities to two neurons is achievable, but it is difficult to unambiguously identify the true L7 neuron out of these two candidates without lengthy electrophysiology experiments. Therefore, the potential for distinguishing these two neurons from each other using their metabolic profiles was investigated. It appears that the two can be chemically distinguished, as shown in Figure 7.3, where the neurons from three animals were included (6 total neurons, 2 technical replicates). Also, during the isolation, the first neuron to be isolated, labeled L7-1, was visually determined based on location to be the

173

most likely candidate out of the two to be the L7 neuron. Therefore, it is promising that the

L7-1 labeled neurons cluster separately from the L7-2 labeled neurons. This also implies that the identity of the L7 neuron can be distinguished based on its metabolite profile.

Next, individual ions were investigated to determine the cause of this observed clustering; several of these m/z values are listed in Figure 7.4. Two of these ions are acetylcarnitine and propanoyl-carnitine; however, the functional reasoning for this difference in abundance is unknown. The m/z 203 ion matches to the calculated mass of several dipeptides, while searching metabolite databases for the potential identity of the m/z 255 ion yielded no results. Fragmentation of these ions may provide additional insight into the identities of these ions. While it is unknown if these molecules serve some functional purpose for the L7 neuron, these analytes can be used as a chemical marker to identify these neurons.

The transcriptome and in situ hybridization data collected for the L7 motor neuron suggested that this neuron may use peptides from the Mytilus inhibitory peptide (MIP)

prohormone for cell-to-cell signaling.24 Previous work has shown how this prohormone is

processed,29 so the data was searched for matches to these known peptides. As shown in

Table 7.1, two detected masses correspond with MIP-related peptides. Upon further

inspection, two additional masses were found that match FMRF-amide-related peptides.

These two eluting ions also have similar retention times (15.6 and 15.7 min), which is not

surprising because their respective sequences differ by only one amino acid. These detected

ions do not appear to be specific to either L7-1 or L7-2 neurons; however, this preliminary

data is promising that neuropeptides may be present in these samples. Future work will

involve identifying these signals with MS/MS, analyzing standards for additional

174 confirmation using retention time, or using complementary ionization techniques to confirm their presence.

7.4 Conclusions and Future Work

This work expanded upon the characterization of Aplysia neurons in Chapter 4 with three different applications. The data from the culturing experiments suggest that the extent of neuronal response under these conditions is cell type dependent, even if the cells are similar biochemically. Other Aplysia neurons that are commonly cultured can be investigated to establish the degree of biochemical differences that may be present. Complementary chemical analyses, including peptide and protein characterization, would yield a more complete view of chemical response as a result of cell culture.

Several ions appear to be metabolically unique to R3-13 ganglion; future investigations will serve to identify these ions with MS/MS. Additional work can be done to determine if these analytes are localized to any of the individual neurons of this cluster and/or if they serve any functional purpose. Likewise, it appears that the L7 neuron can be chemically identified from the other candidate neuron. Electrophysiological confirmation of the L7 neuron followed by metabolite extraction can confirm which neuron corresponds to which molecular profile. This analytical approach can also detect metabolites and peptides from the same sample. Future work will implement MS/MS for confirmation of the presence of putative neuropeptides in these neurons; if they are indeed neuropeptides, follow-up experiments will determine if they are involved in the gill-withdrawal reflex.

In all of these experiments, data was obtained that reveals molecular differences that are unique to a cell type, cluster, or cellular environment. The characterizations of these

175 different biological models provide a strong foundation for future experiments regarding these cell types and the influences that cellular environments can induce on the single-cell level.

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7.5 Figures and Tables

Figure 7.1 The impact of culturing on B1 and B2 neurons. Top: Cultured B1 (cB1-purple) and freshly isolated B1 (B1-orange) neurons; Bottom: Cultured B2 (cB2-pink) and freshly isolated B2 (B2-green) neurons.

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Figure 7.2 Histogram plot of isolated neurons and intensity for ions that appear to distinguish the R3-13 cluster from other cells. Top: glycine, Middle: 242.6271 (doubly charged), Bottom: 265.1212.

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PC2 Score

Loadings for PC1 (44.7 %) versus PC2 (19.2 %), Pareto 132.108

0.45

0.40 144.1071

0.35

0.30

0.25 203.1415 0.20 191.0708

0.15 162.0789 118.0902 287.2005 106.0529 146.119 118.093 0.10 258.1119 PC2 Loading 139.0546 150.0604 182.0834 148.0663 0.05 136.101 238.0152 166.0895 132.1066 132.1041 0.00 156.0789 90.0556 274.0903 134.0492 140.069 -0.05 176.1035 122.0828

-0.10 155.0294 126.0211 235.1675 219.0823 143.0495 255.0865 -0.15 187.0582 143.0592 175.1239 -0.20 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 PC1 Loading

Figure 7.3 PCA for putative, isolated L7 neurons. Top: PCA plot of L7-1 (blue) and L7-2 (red) neurons. The first letter of the sample name refers to the animal and the last digit refers to a technical replicate (1 or 2). Bottom: Scores Plot with ions detected from the two neurons.

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Figure 7.4 Selected m/z values that may be contributing to the clustering of L7 neurons in PCA. 204.1263 (acetylcarnitine), 218.1416 (propanoyl-carnitine), 203.1415 (dipeptide matches), 255.1023 (unknown).

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Table 7.1 Putative matches for peptides present in the L7 motor neuron. Key: ࢤ (theoretical- observed mass).

Peptide theoretical observed Precursor Sequence mass mass ࢤ (mDa) MIP-related peptides GAPRFVa 644.375 644.375 0 MIP-related peptides GSPHFIa 655.344 655.330 14 FMRFa-related peptides FMRFa 598.304 598.304 0 FMRFa-related peptides FLRFa 580.348 580.357 -9

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7.6 References

2. Kamme, F.; Salunga, R.; Yu, J.; Tran, D. T.; Zhu, J.; Luo, L.; Bittner, A.; Guo, H. Q.; Miller, N.; Wan, J.; Erlander, M., Single-cell microarray analysis in hippocampus CA1: demonstration and validation of cellular heterogeneity. J. Neurosci. 2003, 23 (9), 3607-15.

3. Zhang, Y.; Barres, B. A., Astrocyte heterogeneity: an underappreciated topic in neurobiology. Curr. Opin. in Neurobiol. 2010, 20 (5), 588-94.

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CURRICULUM VITAE

ANN M. KNOLHOFF

EDUCATION

Ph.D., Chemistry, University of Illinois, Urbana, IL, June 2011

Certificate in Business Administration, University of Illinois, Urbana, IL, May 2008

B.S., Chemistry (ACS-certified), Truman State University, Kirksville, MO, May 2005

RESEARCH EXPERIENCE

. Research assistant, University of Illinois, Advisor: Prof. Jonathan Sweedler, Fall 2005 – present . Determined the chemical impact of mast cells on mouse hippocampi using CE-ESI- MS; these global chemical differences may be responsible for observed learning, behavioral, and developmental deficits . Chemically distinguished single cells and quantified analytes within the nervous system of the model organism Aplysia californica by implementing CE-ESI-MS and principal component analysis (PCA) . Characterized peptide content in glia to better understand chemical communication in the brain

. Research assistant, Truman State University, Advisor: Prof. Barbara Kramer, Fall 2004 – Spring 2005 . Repaired a Gas Chromatography-Mass Spectrometry (GC-MS) instrument for facilitating organophosphate pesticide detection.

. Research assistant, Truman State University, Advisor: Prof. Maria Nagan, Fall 2002 – Spring 2004 . Examined the interactions between the HIV Rev-Responsive Element and Rev protein with molecular dynamics simulations to better understand a specific stage in the HIV life cycle

DIAGNOSTIC EXPERIENCE

. Student employee/Chemist I, Illinois Department of Agriculture, Animal Disease Laboratory, Summers 2002-2004 . Prepared and analyzed a variety of samples for different analytes based on customer request; examples of samples analyzed were soil, water, animal feed, and organs for toxicological screening, while instrumentation included atomic absorption (AA), ultraviolet-visible spectroscopy (UV-Vis), GC, and GC-MS

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TEACHING EXPERIENCE

. Teaching assistant, University of Illinois, Instrumental Chemistry Systems Lab, Spring 2006, Fall 2006 . Instructed students in the laboratory, constructed quizzes, maintained instruments, and graded laboratory reports with the intent of developing and improving the students’ scientific writing skills

. Teaching assistant, University of Illinois, Introductory Chemistry, Fall 2005 . Directed laboratory sessions and prepared and delivered lectures to approximately 50 students with little to no chemistry background

. Teaching assistant, Truman State University, Instrumental Analysis, Spring 2005 . Assisted students in performing laboratory experiments with multiple instrumentation platforms

PUBLICATIONS

. Knolhoff, A. M., Nautiyal, K. M., Nemes, P., Kalachikov, S., Morozova, I., Silver, R., Sweedler, J. V. “A Lack of Mast Cells Causes Striking Metabolite Defects in the Hippocampus” In preparation.

. Yin, P., Knolhoff, A. M., Rosenberg, H. J., Gillette, M. L., Sweedler, J. V. “Peptidomics Analysis of Astrocyte Secretion.” In preparation.

. Nemes, P., Knolhoff, A. M., Rubakhin, S. S., Sweedler, J. V. “Metabolic Differentiation among Neuronal Phenotypes by Single-cell CE-ESI-MS.” Submitted.

. Knolhoff, A. M., Nemes, P., Rubakhin, S. S., Sweedler, J. V. “MS-based Methodologies for Single-cell Metabolite Detection and Identification” in Methodologies for Metabolomics: Experimental Strategies and Techniques, 2011, Accepted.

. Knolhoff, A. M., Rubakhin, S. S., Sweedler, J. V. “Single-cell Mass Spectrometry” in Chemical Cytometry: Ultrasensitive Analysis of Single Cells, John Wiley & Sons, Inc., 2010, 197-218.

. Michael, L.A., Chenault, J.A., Miller III, B.R., Knolhoff, A.M., Nagan, M.C. “Water, Shape Recognition, Salt Bridges, and Cation-Pi Interactions Differentiate Peptide Recognition of the HIV Rev-Responsive Element.” J. Mol. Biol. 2009, 392 (3), 774- 786.

PRESENTATIONS

. Knolhoff, A. M., Sweedler, J. V. “Capillary electrophoresis coupled to electrospray ionization mass spectrometry: metabolite detection from single cells to tissue homogenates.” Invited lecture at Truman State University, November 2010.

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. Knolhoff, A. M., Nemes, P., and Sweedler, J. V. “Capillary electrophoresis (CE) coupled to electrospray ionization mass spectrometry (ESI MS) for comparative metabolomics of mast cell-deficient mice.” Poster presentation at the Turkey Run Analytical Chemistry Conference, November 2010.

. Knolhoff, A. M. Nemes, P., Sweedler, J. V. “Investigation of metabolites, signaling molecules, peptides, and proteins in mammalian cells using Capillary Electrophoresis- Electrospray Ionization-Mass Spectrometry.” Poster presentation at the American Society for Mass Spectrometry Conference on Mass Spectrometry and Allied Topics, May 2010.

. Knolhoff, A. M., Rubakhin, S. S., Sweedler, J. V., “Capillary Electrophoresis- Electrospray Ionization Mass Spectrometry for Brain Metabolomics.” Poster presentation at the PittCon Conference and Expo, March 2010.

. Knolhoff, A. M, Nemes, P., Rubakhin, S. S., Sweedler, J. V. “Capillary electrophoresis electrospray ionization mass spectrometry for brain metabolomics.” Poster presentation at the Turkey Run Analytical Chemistry Conference, October 2009.

. Knolhoff, A. M., Yin, P., Millet, L., Gillette, M. U., Sweedler, J. V. “Characterization of Glia Using Direct Single-Cell MALDI-TOF MS Analysis” Poster presentation at the American Society for Mass Spectrometry Conference on Mass Spectrometry and Allied Topics, June 2008.

. Knolhoff, A. M., Yin, P., Millet, L., Gillette, M. U., Sweedler, J. V., “Characterization of Individual Astrocytes Using Direct MALDI-TOF MS Analysis”, Poster presentation at the Turkey Run Analytical Chemistry Conference, September 2007.

. Knolhoff, A. M., Kennett, K. J., Turner, A. S., Robinson, C. L., Nagan, M. C. “Molecular Dynamics Simulations of the HIV Rev-RRE Complex” Poster Presentation at the National American Chemical Society Meeting, March 2005.

. Knolhoff, A. M., Kennett, K. J., Nagan, M. C. “Understanding the Rev-RRE Complex using Computational Chemistry.” Poster presentation at the National Conference on Undergraduate Research, April 2004.

. Knolhoff, A. M., Nagan, M. C. “Understanding the Rev-RRE Complex using Computational Chemistry.” Poster presentation at the Truman State University Student Research Conference, April 2004.

PROFESSIONAL ACTIVITIES

. Web administrator, Women Chemists Committee at the University of Illinois, 2009 . Maintained the website and Facebook page for the organization, including posting meeting notes and updating events.

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. Co-organizer of the Turkey Run Analytical Chemistry Conference, 2008 . Representative responsibilities included obtaining donations for the conference proceedings, making a budget, reserving accommodations for posters and oral presentations, arranging the conference schedule, and coordinating speakers for the round table discussion; the conference is held yearly and is attended by greater than 100 faculty members and graduate students of the analytical chemistry programs from the University of Illinois, Purdue University, Indiana University, and the University of Notre Dame.

. Co-president, American Chemical Society-Student Affiliates, Fall 2004 – Spring 2005 . Recruited and scheduled outside speakers for weekly chemistry seminars, helped organize group fundraisers, pursued public service activities, and coordinated National Chemistry Week demonstrations.

. Secretary, American Chemical Society-Student Affiliates, Fall 2003 – Spring 2004 . Drafted notes of regularly scheduled meetings, participated in organizational endeavors, and generated ideas to be implemented by the chapter; the combined effort of the group resulted in obtaining a Commendable Chapter Award from ACS.

AWARDS/AFFILIATIONS

. American Society for Mass Spectrometry, 2008 – present

. American Chemical Society, 2008 – present

. Women Chemists Committee at the University of Illinois, 2006 – present

. Who’s Who Among Students in American Colleges and Universities, 2004 – 2005

. Women in Chemistry at Truman State University, Fall 2004 – Spring 2005

. Vice President for Academic Affairs List, Fall 2003, Spring 2004

. Junior Test for Academic Proficiency, High Distinction, 2003

. Alpha Chi Sigma (professional chemistry fraternity), Spring 2002 – Spring 2005

. National Society for Collegiate Scholars, Fall 2002 – Spring 2005

. American Chemical Society-Student Affiliates, Fall 2001 – Spring 2005

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