Measurements of Cellular Intrinsic Magnetism with Cell Tracking Velocimetry and Separation with Magnetic Deposition Microscopy

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of the Ohio State University

Wei Xue

Graduate Program in Chemical and Biomolecular Engineering

The Ohio State University

2016

Dissertation Committee:

Dr. Jeffrey Chalmers, Advisor

Dr. David Wood

Dr. Andre Palmer

Wei Xue

2016

Abstract

Magnetic cell separation has been widely used in biotechnology because of its advantages in relatively to us, high sensitivity and selectivity. Though most magnetic separation is based on the immuno-labeling with magnetic particles, the recent remarkable increase on the strength of permanent magnets has enables label-less magnetic separation, which has its unique advantages when applicable. To better design and understand such magnetic separation process dependent on the intrinsic magnetism of cells, the technology with high sensitivity of characterizing such weakly paramagnetic cells is critical, yet rarely reported. The detection of paramagnetic levels of magnetic moment of cells enables the analysis of iron containing proteins such as ferritin, which is also of great interest because of their abnormal expression is closely related to cancer.

In this dissertation, a magnetophoretic analysis instrument, cell tracking velocimetry

(CTV) was reviewed, upgraded, and applied to different intrinsic magnetic cells, and its sensitivity and accuracy was discussed in details. The recent innovations of CTV, which were used through the whole dissertation including internal control method, elevated magnetic energy density gradient and fluorescent options were elaborated. Then CTV was compared with a more commercially available magnetometer superconducting quantum interference-magnetic properties measurement system (SQUID-MPMS) by measuring 3 different forms of red blood cells (RBCs). The accuracy of the CTV is on par with the SQUID-MPMS and CTV is advantageous for its remarkable sensitivity, low ii

sample size required, large quantity of results, and the ease of operation for biological cell samples. CTV were applied on different types of cells to analyze their weak magnetism.

With the established superiority of CTV, the magnetic properties of HeLa cells with various iron supplementations were evaluated as a cancer iron metabolism model. The elevated magnetic susceptibility and successful deposition of HeLa cells on the magnetic deposition microscopy (MDM) demonstrated the potential of analysis and separation of circulating tumor cells in the peripheral blood. Also the HeLa proliferation was evaluated to discuss the role of iron in terms of HeLa growth. Also, with CTV we were able to select 3 strains of genetic modified green algae Auxenochlorella protothecoides (A.p.) for potential magnetic separation of bio-fuel producing algae based on their superior magnetic susceptibility. The iron content, the expression of iron related gene and the feasibility of magnetic separation were also confirmed.

In terms of magnetic separation, an open fringing field magnetic separation device, magnetic deposition microscopy (MDM), was elaborated. The strong fringing field gradient allows the deposition of weakly magnetic cells onto a Mylar slide for the further analysis. The deposition could be predicted with simulation software, which closely corresponded with the experiment result of Bacillus spores and methemoglobin red blood cells.

The magnetic field flow fractionation (FFF) introduced as an analytical tool for magnetic particles. By manipulating the electro-magnetic field, a profile of the capture rate against the magnetic field and flow rate and of a magnetic nano-particle was generated, which could provide guidance in the magnetic separation process development.

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Dedication

This document is dedicated to my family.

Acknowledgments

First and foremost I would like to thank my advisor, Dr. Jeffrey Chalmers, for his strong support, guidance and encouragement during all these years. His dedication to research and optimism has always inspired me, not only for my dissertation research but also through my years of maturation as a professional researcher. It would have not been possible to finish my doctoral research and dissertation without his continuous support.

Around half of my work is completed in Dr. Zborowski’s lab, in Cleveland Clinic

Foundation, Lerner Research Institute. It has been an incredible experience, during which his vision and attention to details had a great impact on me. His support for my work is deeply appreciated.

I am grateful to Dr. David Wood, Dr. Andre Palmer, Dr. Jessica Winter, Dr. Umit

Ozkan, who have served in my qualifying exam, candidacy exam, and/or dissertation committee. It is their comments and inputs that makes this work more comprehensive and complete.

I would like to thank my colleagues, Lee Moore, Pow Joshi, Dr. Brandon Miller, Dr.

Jie Xu, Alejandra Garcia Villa, Dr. Yongqi Wu, Clayton Deighan, Jenny Park, Peter

Amaya, Eric Plencner, Boris Kligman, Daniel Candrea, Amy Buck, for their generous help and stimulating discussions. It has been a privilege to work with all of you.

Last but not least, I would like to thank my parents and my fiancé Yuanxin Chen for their unconditional love, support and understanding throughout my Ph.D. studies.

Vita

2003-2006 ...... Harbin No.3 High School, Harbin, China

2006-2010 ...... B.S. Biochemical Engineering, Zhejiang

University, Hangzhou, China

2010-present ...... Graduate Research Associate, Department

of Chemical and Biomolecular Engineering,

The Ohio State University, Columbus, OH

2012-2014 ...... Visiting Graduate Student, Cleveland Clinic

Foundation, Lerner Research Institute,

Cleveland, OH

2015-2016 ...... Scientific Student Worker, GlaxoSmithKline,

King of Prussia, PA

Publications

1. Xu, J., Mahajan, K., Xue, W., Winter, J. O., Zborowski, M., & Chalmers, J. J. (2012).

Simultaneous, single particle, magnetization and size measurements of micron sized, magnetic particles. Journal of magnetism and magnetic materials, 324(24), 4189-4199.

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2. Buck, A., Moore, L. R., Lane, C. D., Kumar, A., Stroff, C., White, N., Xue, W,

Chalmers, J.J & Zborowski, M. (2015). Magnetic separation of algae genetically modified for increased intracellular iron uptake. Journal of Magnetism and Magnetic

Materials, 380, 201-204.

Fields of Study

Major Field: Chemical and Biomolecular Engineering

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Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments...... v

Vita ...... vii

List of Tables ...... xv

List of Figures ...... xvii

Chapter 1. Introduction ...... 1

1.1. Basics of magnetism ...... 1

1.2. Cell tracking velocimetry, magnetophoresis and magnetic mobility ...... 5

1.3. Intrinsic cellular magnetism ...... 8

1.4. Magnetic deposition microscopy ...... 10

1.5. Magnetic field flow fractionation ...... 12

1.6. Organization of the dissertation ...... 13

Chapter 2. Innovations of cell tracking velocimetry ...... 17

2.1. Introduction ...... 17

2.2. Methods and Materials ...... 20

2.2.1. Reference particle internal control method ...... 20

2.2.2. Cell tracking velocimetry Mk V magnet assembly designs...... 22

2.2.3. Experimental Measurement of the Sm ...... 24

2.2.4. Fluorescent option for CTV ...... 25

2.3. Results and discussion ...... 26

2.3.1. Reference particle internal control method ...... 26

2.3.2. The measurement of the Sm value of the CTV Mk V ...... 27

2.3.3. Fluorescent CTV result of auto-fluorescent Auxenochlorella

protothecoides (A.p.) ...... 28

2.3.4. Fluorescent CTV result of fluorescent quantum dot container ...... 29

2.4. Conclusion ...... 30

Chapter 3. Cell magnetic susceptibility determination by cell tracking velocimetry and by SQUID-MPMS in physiological state ...... 44

3.1. Introduction ...... 44

3.2. Experimental Section ...... 46

3.2.1. RBC magnetic susceptibility by SQUID-MPMS ...... 46

3.2.2. Limit of detection (LOD) and error analysis of RBC susceptibility by

SQUID-MPMS ...... 49

3.2.3. RBC magnetic susceptibility by magnetophoretic analysis ...... 51

3.2.4. Limit of detection (LOD) and error analysis of the magnetophoretic CTV

...... 54 x

3.3. Results and analysis ...... 60

3.4. Discussion ...... 61

3.5. Conclusion ...... 64

Chapter 4. Intrinsic magnetism and iron uptake of HeLa cell ...... 79

4.1. Introduction ...... 79

4.2. Review of iron metabolism, its regulation and its implication with cancer .... 82

4.3. Method and Material ...... 85

4.3.1. HeLa Cell culture ...... 85

4.3.2. Magnetic mobility analysis with Cell tracking velocimetry ...... 86

4.3.3. Salicylaldehyde isonicotinoyl hydrazone (SIH) synthesis ...... 87

4.3.4. Analysis of transferrin receptor (TfR1) expression level with

Flowcytometry ...... 88

4.3.5. Magnetic deposition microscopy (MDM) of iron treated HeLa Cells ..... 89

4.3.6. Pico-green assay for measuring HeLa proliferation affected by iron

fortification ...... 90

4.4. Results and discussion ...... 92

4.4.1. Magnetic mobility result with Fe(NO3)3 fortification ...... 92

4.4.2. Effect of iron fortification on transferrin receptor (TfR1) expression ..... 94

4.4.3. The magnetic deposition microscopy result ...... 97

4.4.4. Iron fortification effect on HeLa proliferation ...... 97

4.5. Conclusion ...... 99

Chapter 5. Intrinsic magnetism of genetic engineered green algae Auxenochlorella protothecoides ...... 113

5.1. Introduction ...... 113

5.2. Method and Materials ...... 116

5.2.1. Genetic engineering of A.p. strain...... 116

5.2.2. Algae culture and growth measurements ...... 117

5.2.3. Cell tracking velocimetry ...... 117

5.2.4. ICP-AA measurement ...... 118

5.2.5. Magnetic Deposition Microscopy ...... 119

5.3. Results and discussion ...... 120

5.3.1. Preliminary magnetic mobility over time ...... 120

5.3.2. Comparison of wild type and genetic engineered algae ...... 120

5.4. Conclusion ...... 122

Chapter 6. Magnetic deposition microscopy ...... 130

6.1. Introduction ...... 130

6.2. Method and materials ...... 133

6.2.1. MDM Mk I ...... 133

6.2.2. MDM Mk IV ...... 134

6.2.3. Tested cells...... 135

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6.3. MDM simulation program ...... 136

6.4. Results and discussion ...... 139

6.4.1. Comparison between the experiment result and simulation result ...... 139

6.4.2. Performance prediction with the simulation program ...... 141

6.5. Conclusion ...... 144

Chapter 7. Characterization of magnetic micron-particles with magnetic field flow fractionation…………………………………………………………………….………………………152

7.1. Introduction ...... 152

7.2. Method and materials ...... 153

7.2.1. Materials ...... 153

7.2.2. Magnetic field flow fractionation apparatus ...... 153

7.2.3. Procedures ...... 154

7.3. Results and discussion ...... 155

7.4. Conclusion ...... 156

Chapter 8. Conclusion and recommendations ...... 162

8.1. Summary ...... 162

8.1.1. Cell tracking velocimetry ...... 163

8.1.2. Intrinsic magnetism of cells ...... 163

8.1.3. Magnetic separation devices...... 164

8.2. Recommendations ...... 165

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8.2.1. Multiple color fluorescent CTV ...... 165

8.2.2. Large scale magnetic separation ...... 166

8.2.3. Effect of other iron compound on HeLa ...... 167

Reference ...... 172

Appendix A: Detailed deduction of Brownian motion limitation of magnetophoresis method...... 188

Appendix B: Recipe for the algae media ...... 192

Appendix C: Matlab code for the MDM simulation ...... 195

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List of Tables

Table 1.1. Unit conversions between CGS-Gaussian system and SI system...... 15

Table 2.1. Summary of the latest Sm values of versions of CTV. From Mk I to Mk V are

CTV with permanent magnets and EM is CTV with electromagnet so the Sm could vary.

...... 32

Table 2.2. Summary of the result of magnetic velocity (Umag) with internal control method (Adj.: adjusted). The level of the mean is based on the Tukey HDS method, and the same letter indicates no significant difference between the mean results of the two tests...... 33

Table 3.1. Source of terms in equation 3.3 ...... 66

Table 3.2. Volumetric magnetic susceptibility of RBCs from SQUID-MPMS (SI, dimensionless, value ± error), magnetophoresis (SI, dimensionless, mean ± standard deviation), and theoretical model (SI, dimensionless)...... 67

Table 3.3. Detailed data for SQUID-MPMS. The Error shows the absolute error from the measurement instrument (electronic balance, pipette), or standard error for the slope, or calculated error for the calculated magnetic susceptibility of the cell/quartz/epoxy. The propagated error was calculated based on the error propagation rules. CV stands for the coefficient of variation, or relative error...... 68

Table 4.1. Magnetic mobility of Iron treated HeLa cells. NF stands for non-filtered media, and F stands for filtered media. All magnetic mobilities are in the unit of mm3/T-

A-s ...... 101

Table 4.2. The concentration of free iron ion, SIH, transferrin and their complex in the media ...... 102

Table 5.1. Number of ferritin and iron atom per cell ...... 124

Table 7.1. Relation between supply electrical current, B-field and field gradient of magnetic FFF ...... 157

Table 8.1. Candidate tunable filter for multiple color CTV ...... 168

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List of Figures

Figure 1.1. The diagram of cell tracking velocimetry (Jin 2010) ...... 16

Figure 2.1. Example scatter plot of met-RBC result with the internal control method. The top cluster is considered as polystyrene particles and the bottom cluster is considered as met-RBC. The cut-off line is at 3.5 μm/s...... 34

Figure 2.2. A picture of the CTVMk V. From the left to right are the computer, the CCD camera, the microscope, and the magnet assembly with the glass channels and the syringes...... 35

Figure 2.3. The design of the core of magnetic assembly of the CTV Mk V. The different materials were color-labeled, and the intended, leak and side board magnetic flux is highlighted with arrows...... 36

Figure 2.4. The design of magnet assembly of CTV Mk V. from the side...... 37

Figure 2.5. 3D simulation result of the magnetic field in proxy of the region of the interest...... 38

Figure 2.6. The magnetic velocity (Umag) of 3 tests of polystyrene control beads (PSM) and 3 tests of met-RBC. According to Tukey HSD analysis, there were 3 different levels of Umag of met-RBC and 2 different levels of Umag of polystyrene beads were ...... 39

Figure 2.7. Measurement of the Sm in the CTV Mk V system with Gd(III) solution...... 40

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Figure 2.8. The auto-fluorescence spectrum of the Auxenochlorella protothecoides (A.p.).

The excitation a spectrum was measured at fixed emission at 690 nm, and the emission spectrum was measured at fixed excitation of 439 nm (one of the peak). The intensity was normalized against the peak value. The theoretical pass for the excitation and emission filter were also shown...... 41

Figure 2.9. Comparison of the image of Auxenochlorella protothecoides (A.p.) acquired from the fluorescent CTV (left) and dark field CTV (right). A clearer image with larger apparent size of cell is shown in fluorescent CTV, which is easier to analyze...... 42

Figure 2.10. The histogram of magnetic velocity of nano-size micelles containing quantum dots and SPIONs...... 43

Figure 3.1. Operating principle of SQUID–MPMS. The interaction of the RBC sample and the signal pick-up coil with reciprocating sample option (RSO) and the signal generated are shown. The RBC magnetic moment, m at particular magnetic field, H, is proportional to the amplitude of the voltage signal, V. (Adapted from (Bland 2002))..... 72

Figure 3.2. The diagram of sample mounting in SQUID-MPMS. Though detectable signal is generated for less than 1μL cell volume, 10-15μL was used for the in the experiment to reduce error...... 73

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The objective of the microscope goes through the cutout in the front. C) A blow-up view of the core building block b, magnetic field between pole pieces and the region of interest

(ROI, shown as a blue rectangle). The magnetic field was denoted in solid lines in the lower zoom-in image. D) a picture of the magnet assembly setup with parallel-piped glass channel which holds sample...... 74

Figure 3.4. The magnetic field map within the region of interest. A) 3D magnetostatic field modeling with the software Amperes (Integrated Engineering Software, Winnipeg,

Canada) to predict the magnetic field in the interpolar gap, with inputs of geometry and material properties. d(B2) /dx and db/dy were calculated based on the 4-th order polynomial fitting. The dashed line at y = 4.46mm indicates the max dB2/dy, which is the center of ROI. B) A plot of mean, standard deviation and coefficient of variance (CV) in the gradient as a function of viewing width (i.e. width of ROI). Apparently grad B2 is more constant for a smaller viewing width. For example, at a typical setting, in which the viewing width is 1.84mm, the CV in grad B2 is as low as 0.0196...... 75

Figure 3.5. Magnetophoresis experiment schematic for deoxygenated red blood cells. The

RBCs were pretreated with wet N2 into deoxygenated RBCs before measurement. During the measurement the glass channel, which holds deoxygenated RBCs are protected by wet N2 sheath ...... 76

Figure 3.6. Magnetic mobility and magnetic susceptibility histograms of oxygenated red blood cells, de-oxygenated red blood cells, and methemoglobin red blood cells by magnetophoretic CTV...... 77

Figure 3.7. Operational scope magnetophoresis method. Solid line represents for physical limitation for the method and the dashed line showed the limitation for current setup. The xix

result for oxygenated RBCs (oxyRBC), methemoglobin RBCs (metRBC) and deoxygenated RBCs (deoxy) are also shown (error bar as standard deviation). The metRBC and deoxyRBC results, which are essentially different, overlap in the figure due to the scale vertical axis. The oxyRBC magnetic susceptibility difference only shows upper error, due to the limitation of logarithmic scale of vertical axis...... 78

Figure 4.1. The summary of iron metabolism and its regulation of cells. Note that all the pathways are not shown in a single type of cell...... 103

Figure 4.2. The diagram of the cell tracking velocimetry, Mk V...... 104

Figure 4.3. Salicylaldehyde Isonicotinoyl Hydrazone as trident iron chelator (2:1) and the

Schiff base synthesis reaction ...... 105

Magnetic field strength measured along y axis. C) Blow-up view of MDM assembles of magnet assembly (1), Mylar slide (2), rubber spacer (3), plastic manifold (4) steel platen

(5). (Buck et al. 2015) ...... 106

Figure 4.5. Box plots of the magnetic mobility of HeLa cells with different iron supplementation conditions. The box indicates the first, second and the third quartile and the whisker indicates the 5% and 95%...... 107

Figure 4.6. An example result (Fe 500 24h NF) to show the long tail and wide distribution of the magnetic mobility result...... 108

Figure 4.7. Relative Expression level of transferrin receptor with flow cytometry method.

Test 1 result is in blue and test 2 result is in red. The levels of expression of all conditions were normalized based on the level of the HeLa in complete media (CM). The number indicate the Fe(NO3)3 concentration in μg/mL ...... 109

Figure 4.8. The magnetic deposition microscopy of Fe(NO3)3 treated HeLa cells for 72 hours and methemoglobin red blood cell (Met RBC) as an positive control...... 110

Figure 4.9. The relative DNA content of HeLa cells treated with Fe(NO3)3 (A) and FAC

(B) ...... 111

Figure 4.10. The doubling time from Day 1 to Day 4 (i.e. after the adaption phase) of

HeLa cells treated with Fe(NO3)3 (A) and FAC (B) ...... 112

Fre1. C: no template control, WT: wild type A.p. strain, +ve: plasmid control. The selected strains with both expression (113, 119 and 166) were highlighted in red color.

...... 125

Figure 5.2. Magnetic mobility of wild type in media MAM and MHS over time by the internal control method...... 126

Figure 5.3. Growth of the wild type in algae with varied iron concentration. 1x iron equals to 37 μmol [Fe(III)EDTA]-. The growth was measured with spectrometer at

750nm. The experiments were run with triplicate and the error bar shows the standard deviation. No significant impact was seen in different iron concentration except 0x iron

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addition, which indicates the growth inhabitation on iron-deficiency. Hence the highest concentration 8x iron addition was used in the following iron loading culturing...... 127

Figure 5.4. Magnetic mobility of wild type (WT) and genetic modified strains (113, 119, and 166). Genetic modified strains apparently showing a higher mean magnetic mobility.

A) The histograms of magnetic mobility of the 4 strains. B) Box plot of the magnetic mobility of the 4 strains. Not all outliers are showed, 1% and 99% percentile is shown as dots...... 128

Figure 5.5. Capture rate with magnetic deposition microscopy of wild type and 113 in 1x iron (light grey) and wild type, 113, and 119 in 8 x iron (dark grey). The error bar shows the standard deviation and the sample size were indicated by the N number on top. The capture rate was significantly increased with high iron supplement.(Buck et al. 2015) 129

Figure 6.1. Schematic diagram of magnetic deposition microscopy (MDM) system, and an enlarged d an enlargement in the magnetic deposition zone...... 145

Figure 6.2. Actual and predicted fraction of metRBC deposited using the MDM instrument. A is an enlargement of the deposition region of the slide. B is a picture of the metRBC depositing on MDM slide. C is a 2-D plot of fraction of cells deposited at a function of x location with the predicted deposition frequency pattern (blue) cumulative frequency of the deposition (red), and the experimental deposition frequency obtained by image analysis (green)...... 146

Figure 6.3. Experimental and predicted fraction of Bacillus spores deposited using the

MDM instrument. A is an enlargement of the deposition region of the slide. B is a picture of the Bacillus spores depositing on MDM slide. C is a 2-D plot of fraction of cells deposited at a function of x location with the predicted deposition frequency pattern xxii

of spores before sterilization (blue), after sterilization (red), and cumulative frequency for spores before sterilization (green)...... 147

Figure 6.4. Actual and predicted fraction of genetically modified alage deposited using the MDM instrument. A is a picture of the metRBC depositing on MDM slide and heat map of the local B-field. B is the light absorption scan of the slide. C is a 2-D plot of fraction of cells deposited at a function of x location with the predicted deposition frequency pattern (bar) and cumulative frequency (line)(Buck et al. 2015) ...... 148

Figure 6.5. Simulated trajectories of metRBC in the MDM Mk I. Two regions with the most trajectories touching the slide surface are around ±0.6mm, where the fringing field is strongest...... 149

Figure 6.6. Predicted capture rate of metRBC as a function of flow rate, A; and predicted deposited position vs. capture rate as opposed to different flow rates, B...... 150

Figure 6.7. The simulated capture rate of cells vs. magnetophoretic mobility, A; and magnetophoretic mobility cut-off vs. flow rate, B...... 151

Figure 7.1. Flow diagram of the Magnetic FFF system...... 158

Figure 7.2. A picture of quadrupole electromagnets of the magnetic FFF system. The center stainless steel tube holding the capture channel is lowered into the magnetic poles during the experiments (Orita et al. 2013) ...... 159

Figure 7.3. An example raw result for the capture/release of magnetic particles. The two black lines are the simulated baseline for the peak and the equation was listed in the figure...... 160

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Figure 7.4. The summary of capture rate with different magnetic field and flow rate. Each experiment was done in triplicates and the standard deviation does not exceed 3.5%. .. 161

Figure 8.1. Diagram for multiple color CTV ...... 169

Figure 8.2. Timeline for “quasi-simultaneous” measurement with multiple color CTV.170

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Chapter 1. Introduction

1.1. Basics of magnetism

The first scientific discussion of a magnetic phenomenon is attributed to Aristotle around

585BC (Fowler 1997), and by 12th century the compass was invented by Chinese for navigation. The relationship between magnetism and electricity was first found by Hans

Christian Ørsted in 1819, which eventually lead to the modern understanding of the origin of magnetism: from either electricity current, or spin of magnetic moments of elementary particles such as electrons. The latter source is more important in this dissertation as many materials are magnetic because of the spins of unpaired electrons in the molecule. It is the focus of this dissertation on the weakly intrinsically magnetic cells, in order to explore the potential of magnetic separation and iron related metabolism of these cells.

The magnetic field can be defined in several ways, based on its effect on the environment.

Two common vectors are magnetic induction (B) and magnetic field (H). In this dissertation those two vectors are simply referred as B-field (T) and H-field (A/m) due to the variations of nomenclatures and confusion of “magnetic field” term being possible referred to as either B or H in different sources. In the SI unit system, the relation between B-field and H-field is given by:

B = μ0(H + M) (1.1) where M is the magnetization of the object in the magnetic field (A/m). In the scenario of vacuum M = 0 and B-field and H-field are proportional. This conclusion is generally applicable for the earth atmosphere also, due to the low magnetization of air. μ0 is the magnetic permeability of the vacuum with a value of 4π x 10-7 T-m/A. The physical meaning of the equation is essentially the total magnetic field is the sum of the applied field and induced magnetization.

The magnetization M is the macroscopic phenomenon of electron spins within the object under the effect of the magnetic field, which is essentially the magnetic property of the object. The dependence between M and H is defined by volumetric magnetic susceptibility, 흌v, given by:

M χ = (1.2) v H

흌v is dimensionless. Magnetic susceptibility based on other quantities than volume such as mass and molar could also be defined simply by a conversion of density (ρ, kg/m3) and molar mass (M, kg/mol):

χmass = χV/ρ (1.3)

χmolar = MχV/ρ (1.4)

The most common experimental method of measuring 흌v is to measure the change of force exerted on an object in a varying magnetic field, such as Gouy balance (Cullity

1972). Modern alternatives include device measuring the magnetic field change of the object inserted into a magnetic field, such as superconducting quantum interference

device (SQUID), and device measuring the magnetic resonance (Wang, Li, and

Haselgrove 1999).

Based on the different value of 흌v the most common class of magnetic materials are 1) diamagnetic materials with 흌<0; 2) paramagnetic materials with 흌>0 but very small value (10-5 to 10-3); 3) ferromagnetic materials with 흌>>1 and M saturate quickly when H increases. The different 흌 values are attributed to the electron spins within the materials.

In the first group, the diamagnetic materials, there is no net magnetic moment when an applied magnetic field is not present. Under the influence of a magnetic field, the orbital spins of the electrons generate a moment that is oriented against the applied field. Hence, diamagnetism is universal in all materials, yet masked by other types of stronger magnetism in other classes of magnetic materials. The magnetic susceptibility of diamagnetic material is usually independent of temperature and applied field. The first theory of diamagnetism was worked out by Langevin in 1905, with the accuracy of prediction within an order of magnitude. Typical examples of diamagnetic materials include water, silicon, gold and most organic molecules and biomolecules such as protein, nucleic acid and carbohydrates. Hence, most cells are diamagnetic.

In the second group, paramagnetic materials, the magnetic moments of the atoms generated by the unpaired electrons are randomly oriented due to the thermal agitation, and the applied magnetic field will create a weak alignment of those moments and result in a positive magnetization. The magnetic susceptibility of paramagnetic materials is usually independent of applied field (i.e. no saturation), but decrease with the rise of temperature, described by Curie-Weiss Law:

C χ = (1.5) T − TC where C is the material Curie constant (dimensionless), T is the temperature (K) and Tc is the Curie temperature. Typical examples of this class include aluminum, oxygen molecule, and even some iron rich proteins such as hemoglobin (without oxygen binding) and ferritin. Iron rich molecules are the key driving factor of the magnetophoresis of cells based on their intrinsic magnetic properties, given that water has very similar magnetic susceptibility to cell building blocks such as protein and carbohydrates.

In the third group, ferromagnetic materials, the magnetic moments of the atoms are aligned parallel within the magnetic domain, which results in a strong magnetization once an external magnetic field is introduced. On the other hand due to the presence of the rigid magnetic domains, ferromagnetic materials have present the properties of hysteresis; the magnetization does not fully disappear after removal of the applied magnetic field. Further, ferromagnetic material exhibits a saturation of the magnetization; beyond the imposition of a saturation H-field value an increase of H-field will no longer increase the magnetization. Typical examples include the solid iron and nickel.

It is worth mentioning some other types of magnetic materials. Superparamagnetic material, such as superparamagnetic iron oxide nanoparticles (SPIONs), is essentially nano-sized ferromagnetic material, so that only one magnetic domain

(on the order of 1000 nm) is presented in the bulk of materials and demonstrates many unique and intriguing properties such as zero hysteresis. Antiferromagnetic material, such as Mn and Cr, is very similar to ferromagnetic material except that

magnetic moments presented in the molecule are oriented anti-parallel in pairs, and hence under the applied magnetic field the material behaves like paramagnetic materials. Ferrimagnetic material, such as Fe3O4, is only presented in compounds. It is also very similar to ferromagnetic materials, except that the moment orientation is mixed with parallel orientation and anti-parallel orientation and hence under the influence of the applied magnetic field the materials behave like ferromagnetic material but with a lower saturated magnetization.

In parallel with the SI unit system, there is also a CGS-Gaussian system which defines the relations between the B-field and H-field differently, shown in equation

1.6:

퐵 = 퐻 + 4휋푀 (1.6)

This relation essentially drops the μ0 in the SI system, and results in very intricate conversion of magnetism related physical terms, which is necessary in the scope of the dissertation. Cell tracking velocimetry (CTV) and superconducting quantum interference device magnetic property measurement system (SQUID-MPMS) used in this thesis present data in SI system and CGS system, respectively . The key conversion is summarized in table 1.1.

1.2. Cell tracking velocimetry, magnetophoresis and magnetic mobility

Cell tracking velocimetry is a magnetophoresis device dedicated to cell/particle magnetic properties analysis developed in Dr. Chalmers lab and Dr. Zborowski lab since 1999

(Chalmers, Zhao, et al. 1999; Moore et al. 2000; Nakamura et al. 2001). The term magnetophoresis refers to the movement of particle/cells suspended in a liquid carrier 5

under the influence of the magnetic field, in analogy to electrophoresis (a movement induced by electric field) (Hartig et al. 1992; Winoto‐Morbach, Tchikov, and Mueller‐

Ruchholtz 1994), and the principle is applied in both magnetic separation and magnetic property analysis.

Consider a spherical paramagnetic or diamagnetic particle with volumetric magnetic susceptibility of 흌c in a magnetic field, the force exerted on it is given by:

2 B0 Fm = (χc − χf)V ∙ ∇ ( ) (1.7) 2μ0

where Fm is the magnetic force, 흌f is the magnetic susceptibility of the suspending carrier

-6 (usually as water, -9.035x10 , dimensionless), V is the volume of the particle, B0 is the

B-field of applied magnetic field, μ0 is the magnetic permeability of the vacuum with a value of 4π x 10-7 T-m/A.

Under low Reynolds number (lower than 0.1) (Rhodes 1998)the drag of the particle follows Stokes’ Law, given by:

Fd = 3πηdum (1.8)

0 where Fd is the Stokes’ drag, η is the viscosity of the carrier (usually water, at 20 C,

-3 1x10 Pa∙s), D is the diameter of the spherical particle (m), and um is the velocity of the particle (m/s).

In a steady state, where the drag is balanced with the magnetic driving force, or Fd = Fm, and plugging in the equation of the sphere volume, one obtained:

2 2 (χc − χf)D B0 um = ∇ ( ) (1.9) 18η 2μ0

Here we can separate the terms in equation 1.9 into magnetic field related part and non- magnetic field related part:

(χ − χ )D2 m = c f (1.10) 18η

2 B0 Sm = ∇ ( ) (1.11) 2μ0 where m is the magnetophoretic mobility, or magnetic mobility of the particle (m3/T-A-s),

2 and Sm is the magnetic energy gradient (T-A/m ).

In the design of the cell tracking velocimetry (Figure 1.1) (Jin 2010), we intend to keep

Sm constant and well-characterized within the region of interest (ROI) by magnetizing a hyperbolical steel pole pieces with permanent magnet or electro magnet. The velocity induced by magnetic field, um, is designed to be horizontal to eliminate the effect of gravity. The motion of hundreds of particles suspended in the buffer carrier within the

ROI are recorded with a microscopic method and the images are analyzed by in-house computer software “ImageView”. The different versions of CTV are elaborated in

Chapter 2.

The magnetophoretic mobility, m, is a collective term for the intrinsic properties of the particles. It is the recommended form of the magnetic properties of particle when referred to magnetophoresis since the particle velocity could be easily predicted in a different magnetic field, by simply multiplying by Sm.

Note that the equation 1.7 – 1.10 is under the assumption that the particle is diamagnetic or paramagnetic (i.e. 흌c is independent of applied field). For ferromagnetic or superparamagnetic particles 흌c could vary with the field and hence it is more reliable

approach to use the magnetization of the particles instead of the magnetic susceptibility.

Also, if the shape of the cells is not close enough to spheres (e.g. donut-shape red blood cells), the diameter in the equation 1.7-1.10 could be replaced with the hydrodynamic diameters, which an equivalent diameter for Stokes’ drag.

1.3. Intrinsic cellular magnetism

As most biological matters, including DNA, protein or intact cell, have very similar diamagnetic magnetic susceptibility with water (Zborowski et al. 1995), most magnetic separation approaches depend on the label of paramagnetic or super-paramagnetic particles on the biological matter. By proper surface chemistry, the surface of the particles is directly conjugated or indirectly linked by strepavidin-biotin interaction with antibodies, which binds to the target cells. Essentially the magnetic particles are the main contributor of the driving force for the cell motion in the static magnetic field, and the selectivity of the target cells for labeled magnetic separation.

As stated earlier, though most cells have magnetic susceptibility similar to water, there are quite a few iron-rich or manganese-rich cell types as notable exceptions. Either naturally or with proper treatment, these cells have high iron or manganese contents, and hence has significantly higher magnetic susceptibility than water to enable label-less magnetic separation. It has several unique advantages. First, without labeling reagent, label-less magnetic separation is easier to operate and far more cost effective than labeled magnetic separation. These traits are important for large scale separation. Second, label- less approach avoids any improper selection of target cells caused by the non-specific binding and/or agglomeration of the antibody-particle complex. Third, label-less

magnetic separation avoids the blocking of any receptor on the cell surface, which may be essential for the subsequent treatment. Last, label-less approach is based on the magnetic susceptibility of the cells, or essentially the expression of iron/manganese containing protein in the cell. This approach could serve as a selection and characterization for iron containing cells.

Methemoglobin and deoxygenated hemoglobin has been identified as paramagnetic due to the unpaired electrons while oxygenated hemoglobin is diamagnetic because of the covalent nature of the Fe-N bond since 1930s (Pauling and Coryell 1936b, 1936a).

Zborowski et. al (Zborowski et al. 2003) used cell tracking velocimetry to show that deoxygenated red blood and met-hemoglobin red blood cells have magnetic mobility of

3.86 × 10−6 and 3.66 × 10−6 mm3/T-A-s, respectively. While low, these levels of mobility present the potential of magnetic separation of red blood cells. Also, malaria- infected red blood cells also showed a higher magnetic mobility, which could be enriched by magnetic deposition microscopy (MDM) and further analyzed(Zimmerman et al.

2006).

Jin et.al (Jin, Chalmers, and Zborowski 2012) tested 8 cancer cell lines and found HeLa, with proper iron treatment, has a magnetic mobility as high as 4.0 × 10−5mm3/T-A-s. In addition to the potential magnetic separation of circulating tumor cells, the high iron uptake of HeLa also provides evidence of many cellular dysfunctions related to iron metabolism.

Melnik et al. (Melnik et al. 2007) found several strains of Bacillus spores showed magnetophoretic properties, and later Sun et. al. (Sun, Zborowski, and Chalmers 2011) showed the Bacillus atrophaeusspores, especially, has a mean volumetric magnetic 9

susceptibility of 1.86 × 10−4 (SI) because of its high manganese content up to 26% by mass, which was further characterized by X-ray diffraction.

In conclusion, the capability of enriching or holding magnetic metal such as iron and manganese enables the magnetic separation based on the intrinsic magnetic properties of the cells. Due to the weak magnetism, a stronger magnetic field is also required. Such magnetic field is now readily available due to the increasing accessibility of strong rare- earth magnets (e.g. neodymium magnets) and improved design of the magnetic separator with simulation software such as Amperes. The magnetic deposition microscopy introduced in the next section is one such example.

1.4. Magnetic deposition microscopy

Magnetic deposition microscopy (MDM) is an open gradient magnetic separator and cell analysis tool. The cell suspension is fed at a low flow rate (on the order of mL/h) by a syringe pump through a thin rectangle channel, which locates on top of the fringing field creating by the narrow gap between two steel pole pieces magnetized by permanent magnets. The fringing magnetic field drives the magnetic cells moving towards the Mylar slide (and eventually to deposit on it) creating a specific pattern of deposition consistent with the locations of the magnet assembly. The motion of cells induced by the magnetic field is designed to be perpendicular to the gravity so that the deposition is solely due to the magnetic force. After the cell suspension is chased out by air the Mylar slide is ready for microscopy inspection, with staining if needed. Another advantage of the MDM is that with the CTV result on the magnetic mobility result of the targeted cell population,

the deposition could be fully predicted with a Matlab simulation program, as the flow profile in the MDM channel and the magnetic field is readily available.

Many versions of MDM have been designed in Dr. Zborowski’s lab and other labs and different organisms were tested with it. The magnetic deposition system was firstly developed for depositing Er3+ treated Escherichia coli (E. coli) and observed in the dark field microscopy (Zborowski et al. 1993) and a linear correlation was established between the average scatter light intensity on the slide and the E.coli CFU, which potentially provide a method of detecting E.coli in environmental water. Then it was used in the analysis of ferritin-labeled lymphocytes (Zborowski et al. 1995) and linear regression method to determine its magnetic susceptibility (2.92±0.24) x 10-6 higher than water, and one lymphocyte containing (1.75±0.44) x 107 ferritin molecules. In 1997,

Fang et.al. (Fang, Zborowski, and Moore 1999) used MDM to enrich the immuno-labeled breast carcinoma cell MFC-7 from the peripheral lymphocyte mixture and a result similar to cytospin smear is observed for MFC-7 frequency in the cell as low as 10-6. Recently

MDM has been established as a potentially economically viable and superior method in diagnosing malaria infected erythrocytes in less-developed region (Zimmerman et al.

2006; Karl et al. 2008). The magnetic deposition concentrates up to 250 fold of sickle cells than the than traditional blood smear method by exploiting the magnetic property of hemozoin, which is a crystalline by-product during the hemoglobin digestion by

Plasmodium parasite species. A new model of multiple stage MDM was also developed in other research group (Nath et al. 2008). With this model the deposition slide is immediately inspected by the fluorescent microscopy and the deposited cells could be enumerated. A reasonable capture rate and images were achieved with Jurkat cells

immuno-labeled with a sandwich-antibody protocol, with which the cells were labeled both with magnetic nano-particles and fluorescent dye.

Overall, MDM has been demonstrated with many labeled and unlabeled magnetic cell deposition for the purpose of separation and analysis, including the ones introduced above and ones that is elaborated in the following chapters. It is showing potential in small-magnetic separation in the applications such as disease diagnosis and rare cell characterization.

1.5. Magnetic field flow fractionation

Magnetic field flow fractionation (magnetic FFF) is a relatively new technique in the large family of flow fractionation approaches, which was invented in 1966(Giddings

1966). Field flow fractionation is an analytical/separation technology, in which the separation happens when the particle flows through a thin channel with a uniform thickness, where also a constant field and field gradient are applied. Later, different component will eluate at different time, just like chromatography, based on its interaction with the field.

Magnetic FFF is of unique value due because of the importance of characterization of magnetic particles. A quadrupole magnetic FFF was designed and built in Dr. Zborowski lab, which presents a constant magnetic field gradient within the apertures of the 4 pole pieces. Several magnetic particles has been studied in terms of iron content and magnetic properties with programmed field and flow rate conditions (Carpino et al. 2005; Williams,

Carpino, and Zborowski 2010).

1.6. Organization of the dissertation

The organization of Chapter 2 to Chapter 8 is stated as followed.

In Chapter 2, the principle and operation of cell tracking velocimetry (CTV) was elaborated, and innovations with CTV including the internal control method with polystyrene beads to avoid the background flow, new version with high magnetic energy gradient (Sm = 365 T-A/mm2), and fluorescent option of CTV. All innovations were employed in the following chapters.

In Chapter 3, CTV was compared with state-of-art magnetometer superconducting quantum interference device magnetic properties analysis system (SQUID-MPMS) by measuring 3 different forms of red blood cells. The CTV operation scope was also discussed. We found for measuring cell samples in physiological states, CTV has an accuracy on par with SQUID-MPMS, and have many other advantages such as high sensitivity, low sample requirement and single cell base.

In Chapter 4, the magnetic properties of human cervical cell line HeLa were analyzed with CTV as a model of studying the iron metabolism in cancer. Many iron fortification conditions were applied to the HeLa cells to evaluate the effect of its magnetic properties and the proliferation. Also the magnetic separation with MDM of iron treated HeLa cells were also performed to demonstrate the potential of depositing circulating tumor cells based on their intrinsic magnetic mobility.

In Chapter 5, three genetic engineered Auxenochlorella protothecoides strains carrying iron metabolism related genes Fre1, Fer1 and Fea1 were selected with PCR screening, iron elemental mass evaluation and magnetic mobility analysis with CTV as functionality

assay for potential strains that are susceptible to magnetic separation based on their intrinsic magnetism. MDM was used to demonstrate the proof of concept of magnetic separation.

In Chapter 6, 2 versions of magnetic deposition microscopy were elaborated, and the simulation program was developed. The close match of experimental result and simulation results of methemoglobin red blood cells, Bacillus spores and genetic engineered algae were demonstrated. Also with the simulation program a simplified correlation between the magnetic capture rate, cell magnetic mobility and flow rate.

In Chapter 7, the magnetic field flow fractionation was introduced. The capture rate of an anti-PE in the on-and-off operation mode magnetic nanoparticle was evaluated with different flow rate and B-field strength.

In Chapter 8, the study was concluded and future recommendation was given.

Table 1.1. Unit conversions between CGS-Gaussian system and SI system.

Physical term Symbol Gaussian & CGS Conversion factor SI Length L cm 0.01 M Mass M g 0.001 kg Time T s 1 s Energy E erg 7-Oct J B-field B Gauss(G) 4-Oct T Magnetic flux ϕ G∙cm2 8-Oct T∙m2 H-field H Oersted (Oe) 103/4π A/m Magnetization M Oersted (Oe) 103/4π A/m Magnetic moment m erg/G 10-3 J/T

Volumetric 흌v dimensionless 4π dimensionless susceptibility

Figure 1.1. The diagram of cell tracking velocimetry (Jin 2010)

Chapter 2. Innovations of cell tracking velocimetry

Part of the content of this chapter was prepared for publication: Xu, J., K. Mahajan, W.

Xue, J. O. Winter, M. Zborowski, and J. J. Chalmers. 2012. 'Simultaneous, single particle, magnetization and size measurements of micron sized, magnetic particles',

Journal of Magnetism and Magnetic Materials, 324: 4189-99.

2.1. Introduction

Over the past three decades, magnetic separation has become an important technique in life science, applied in the separation of biological matters including cells (Zborowski et al. 1999), and DNA(Mahmoudi, Simchi, and Imani 2010). Among these applications, magnetic cell separation is the most developed commercially (e.g. MACS system). Given this significant commercial application, the focus in the Chalmers and Zborowski labs have turned to the sensitivity (lowest applicable concentration of target cells), selectivity over non-target cells, and target cell purity has been extensively improved to meet the need of subsequent analysis/process of a particular cell collection (Zborowski and

Chalmers 2011). Such level of separation is hard to be achieved with other techniques. To better design the magnetic cell separation processes, it is of great interest of measuring the magnetic properties of cells or particles involved in the separation, especially those of intrinsic magnetic cells due to the separation based on the intrinsic magnetism has unique 17

advantages. Further, the analysis of the intricate magnetic properties of certain cancer cells is also important because it provides insight of ill-regulated iron metabolism of such cells. Yet such analysis is challenging due to the magnetic properties of cells, even for iron-rich ones, which are very weak since most of the building blocks of cells such as protein and carbohydrates are diamagnetic.

Cell tracking velocimetry is a magnetophoresis device dedicated to cell/particle magnetic properties analysis developed in Dr. Chalmers lab and Dr. Zborowski lab since 1999

(Chalmers, Zhao, et al. 1999; Moore et al. 2000; Nakamura et al. 2001). Figure 1.1 is a diagram of the CTV setup. In the region of interest (ROI), a highly constant, horizontal and well-defined magnetic field gradient is generated by magnetized hyperbolic-shape pole pieces. A glass channel, holding the suspension of the cell to be analyzed, is located in the ROI. When the fluid is static and the particles are in a steady state motion, the vertical motion of the cell is induced only by the gravity, and the horizontal motion is induced only by the magnetic field. The magnetic susceptibility of the cell can be calculated with the horizontal velocity with the following equation:

(χ − χ )D2 u = c f S (2.1) m 18πη m

where 푢푚 is the horizontal velocity (or magnetic velocity) (m/s), or magnetic velocity, 흌c and 흌f are the magnetic susceptibility of cell and water (dimensionless, SI unit), D is the diameter of the cell, 휂 is the viscosity of water (Pa∙s), 푆푚 is the magnetic energy gradient

(T-A/m2). The motions of the cells are recorded by a CCD camera through a microscope, and the acquired images are analyzed by custom made software “ImageView”. This software recovers the trajectory of the cell motion from the image taken consecutively

and gives the velocity by a linear regression. Typically a few hundred cells can be analyzed in one set of images.

As shown in equation 2.1, Sm is a key parameter of the CTV system. Over the years, the

Sm has been improved in both the CCF and OSU labs, thanks to easier accessibility of rare-earth element based permanent magnets with strong magnetic field and honed design of magnet assemblies. The versions of CTV are summarized in Table 2.1. Most versions of CTV (Mk I- Mk V) are based on a strong permanent magnet, and in the recent version

Mk V the Sm value is as high as 365(T-A/mm2), to facilitate the need of analyzing weakly magnetic cells. An electromagnetic version (Xu 2012) was also built to address the issue of analyzing magnetic particles: within the strong magnetic field generated by the permanent magnets the particles move too fast to be properly tracked, and the magnetic particles always reach saturated magnetization.

In this Chapter, several innovations involved with cell tracking velocity are discussed.

First, the issue of unstable horizontal background carrier flow is observed in the Mk I system. Since the final result is averaged of 10-20 sets of images, the set to set variance could lead to inaccuracy in measuring the magnetic velocity such as shifted mean and larger standard deviation. This issue was addressed by an internal control method with polystyrene standard particles. Second, the new magnet assembly design (Mk V) is elaborated and its magnetic gradient energy, Sm is experimentally calibrated with standard polystyrene particles suspended in gadolinium solutions. Third, the fluorescent options for CTV is introduced and compared with the normal dark field option of CTV in terms of quality of the images, and also the capability of measuring sub-micron fluorescent particle was shown.

2.2. Methods and Materials

2.2.1. Reference particle internal control method

To demonstrate the issue of background flow and the effect of internal control method, we select methemoglobin red blood cells (metRBC) as a model cell for several reasons: 1) metRBCs are uniform in terms of content and size (Persons 1929), 2) metRBCs show little tendency of forming aggregates; 3) metRBCs shows small positive magnetic velocity (Umag), which simulates many intrinsic magnetic cells.

The preparation met-RBC is summarized below: 5 mM oxidant solution was prepared by dissolving sodium nitrite (NaNO2, Cat. No.524379, Sigma-Aldrich Co., Milwaukee,

WI) in PBS at room temperature. Red blood cells (RBCs) from the RBC stock suspension prepared was centrifuged and resuspended in 10 mL of 5 mM sodium nitrite solution, which was then incubated for about 1.5 hours to achieve a 100% met-hemoglobin oxidation. The final concentration of the metRBC suspension is 1x108 cell/mL, and washed with phosphate buffer saline (PBS) before use.

Essentially an internal control method refers to adding control particles, which are uniform in terms of size and magnetic susceptibility, and hence based on equation 2.1 their magnetic velocity should be uniform. Any measurement result deviate from a “true” value of the um should be generated by the background carrier flow. If we assume the background carrier flow within the ROI is independent of locations, but varies in different sets of images, the adjusted magnetic velocity of red blood cells is given by:

Um,cell,adj = Um, cell − U̅̅m̅̅̅,ctrl̅̅̅̅ + U̅̅̅m̅̅,ctrl̅̅̅̅,true̅̅̅̅ (2.2)

where 푈푚,푐푒푙푙,푎푑푗 is the adjusted magnetic velocity of target cells, 푈푚, 푐푒푙푙 is the measured magnetic velocity of cells, U̅̅m̅̅̅,ctrl̅̅̅̅ is the average of measured magnetic velocity of control particles within the set of images, and U̅̅̅m̅̅,ctrl̅̅̅̅,true̅̅̅̅ is the magnetic velocity of theoretical of the control particles.

We selected the polystyrene microspheres (PSM, Duke scientific corporation, Cat. No.

4215A) as our internal control. As reported by the manufacture, the diameter of this PSM is 15.02±0.08um, with the standard deviation of 0.15um (1% coefficient of variation), validated by national institute of standard and technology (NIST). Based on equation 2.1, the variance of diameter should not contribute more than 1.5% the variance of final um.

The extreme uniformity and chemical inertness of PSMs make it a good choice as the internal control. U̅̅̅m̅̅,ctrl̅̅̅̅,true̅̅̅̅ for this internal control particle was experimentally determined with 12 independent CTV experiments as -0.315 μm/s, and this result was also reported by Sun et al(Sun, Zborowski, and Chalmers 2011).

The differentiation between polystyrene particles and met-RBC is based on the settling velocity: the difference is significant and the cutoff is set as 3.5 x μm/s as figure 2.1 shows. 0.6 x 106 cell/mL met-RBC of and 0.6 x 106 particle/mL polystyrene beads were tested individually to see the potential effect of the background flow. Then the mixture of

3 different concentrations of polystyrene beads (0.06, 0.10, and 0.14 x 106 particles/mL) and constant concentration of met-RBC (0.6 x 106 cells/mL) were tested to see the effect of the internal control method. All cells and particles were suspended in PBS.

The tests were done in the CTV Mk I system, and the procedure is summarized below:

Before using the CTV, the glass channel was flushed with 20mL 70% ethanol, followed

by 20mL PBS. The cell/particle suspension, as stated above, was then infused it into the glass channel with a Luer-tip syringe. To reduce as much unwanted movement as possible, the valves on the both sides of glass channel are then closed, and the operator waits until the cell motion is uniform (eliminating effect of the initial inertia of the fluid and cells). Then the motions of the cells are recorded into a set of images (20-40 images) using the camera system software. The process is then repeated until the motion of around 1000 cells are recorded (usually 10-20 sets of images), care being taken to make sure a new batch of cells is investigated each time by pushing the syringe plunger on the end. Finally, all of the suspension is removed and the glass channel cleaned with 70% ethanol and PBS. The images are analyzed by the software “ImageView” to give the cell moving path and the velocity: horizontal induced by the magnetic field, and vertical induced by gravity.

2.2.2. Cell tracking velocimetry Mk V magnet assembly designs.

The cell tracking velocimetry Mk V is the latest version of the permanent magnet CTV, which almost double the Sm value of the previous version. Figure 2.2 is a picture of CTV

Mk V system, showing from the left to right, the computer connects to the system, the

CCD camera recording the images, the dark field microscope, and the magnet assembly with the glass channel through it. The innovation of this version of CTV is using an opposite flux from the side board magnet assembly to quench the magnetic flux leakage from the core magnets, so that the magnetic field gradient is strengthened on the pole pieces, as figure 2.3 shows. The constant magnetic field between the two pole pieces

(pink, low carbon steel, grade 1018) along the x direction is driven by the 1.5” core

magnet cubes (grey, Neodymium-Iron-Boron, energy product = 42 MGOe, CMS

Magnetics), and two steel yoke pieces are on the sides to conduct the magnetic flux.

Ideally, all the magnet flux travels from the blocks into the left yoke, into the left pole piece, across the air gap, into the right pole piece and yoke, and returns to the right side of the magnet blocks (yellow arrows). However, the flux leakage happens inevitably

(blue arrows). To quench the leakage, two steel sideboards with magnets (not shown in this figure) providing opposite flux (orange arrows) are fixed on the top and bottom.

The whole magnet assembly is shown in Figure 2.4, with the sideboard magnets. A cut- off on the steel plates allows the 5X microscope objective into the viewing area. Custom- drilled holes in each 1.5”core magnet blocks, allow insertion of a glass channel whose axis is along y. The hole centers are 11.4 mm (along z) from the front magnet surface.

This is less than the 15 mm working distance of the objective, but sufficiently deep to avoid magnetic field edge effects that would cause a z-force component on the cells inside the channel.

Figure 2.5 makes use of the 3D magnetostatic field modeling software Amperes

(Integrated Engineering Software, Winnipeg, Canada) to predict the magnetic field in the interpolar gap, with inputs of geometry and material properties. B values are reported from Amperes along the mid-line at x=0, while a 4th order polynomial is fitted given by:

B(T) = 3.9368 + 0.5994y – 0.3308 y2 + 0.0307 y3 – 6.857e-04 y4. Good agreement is shown by the regression plot overlaying the software output. dB/dy and dB2/dy is calculated based on this equation. From the plot, we see that dB2/dy reaches a maximum at 4.46 mm, and this is where the microscope objective is centered. Calibrating this position requires locating the air/magnet boundary, corresponding to y=0 so that it is

centered on the display. Then a linear translation stage and micrometer (model 462,

Newport, Irvine, CA) supported on a precision lab jack (model 271, Newport) is used to move the magnet and channel 4.46 mm relative to the fixed microscope. dB2/dy around the peak is fairly constant according to Figure 2.5: for a common width of ROI such as

1200 pixels or 1.84mm, the CV of dB2/dy is as low as 0.0196.

2.2.3. Experimental Measurement of the Sm

A compact design of the magnet assembly was adopted in the Mk V magnetophoresis to maximize its Sm. However, such design with little space between the magnetic pole pieces has prohibited the direct measurement of the Sm with hall probe. Here we propose to use mono-disperse particle and carrier with varying magnet susceptibility to calibrate the Sm of the system.

Again, the particle velocity induced by the magnetic field is

um = m ∙ Sm (2.3)

(χ − χ )D2 m = c f (1.10) 18πη

where um is the horizontal velocity induced by the magnetic field (m/s), m is the magnetic

3 2 mobility of the particle (m /T-A-s), Sm is the magnetic energy gradient (T-A/m ), 흌c is the volumetric magnetic susceptibility of the particle/cell, 흌f is the magnetic susceptibility of the suspending carrier, η is the viscosity of the carrier (usually water, at

200C, 1x10-3 Pa∙s), D is the diameter of the spherical particle (m),

If we vary the magnetophoresis mobility (m) by suspending the same mono-disperse particles in carriers with different 흌f, different um in the CTV Mk V system could be 24

obtained. By performing a linear regression on a plot of um against m, corresponding slope will be Sm, The 흌f of the carrier is varied by adding various concentration of diethylenetriaminepentaacetic acid, Gadolinium(III) dihytrogen salt (Gd-DTPA, Sigma-

Aldrich, U.S. CAS # 80529-93-7), and the 흌f of the carrier is given by:

χf = χwater + cGdχGd,molar (2.4)

-6 where 흌water is the volumetric magnetic susceptibility of water (SI, -9.05 x 10 , dimensionless, from CRC Phy. & Chem. Handbook), cGd is the molar concentration of

3 -7 Gadolinium (mol/m ), χGd,molar is the Gadolinium magnetic susceptibility (SI, 3.39 x 10 m3/mol, from CRC Phy. & Chem. Handbook).

The mono-disperse standard particle used in this study is polystyrene particles with

9.0±0.3μm diameter and 10.0% CV (Duke Scientific, U.S., Cat. # 137). The size and distribution of this particle has been verified with Coulter counter (data not shown). Its magnetic susceptibility was assumed as -7.5 x 10-6 (volumetric, SI, dimensionless, from

CRC Phy. & Chem. Handbook).

2.2.4. Fluorescent option for CTV

Instead of taking image with the dark field as introduced earlier, the CTV analysis can also be performed with fluorescent microcopy. In this study, filter cube with 470-490 nm band-pass excitation, 505 nm dichroic mirror, and 515nm long pass emission. A mercury burner serves as the light source as it provides similar intensity across the visible light spectrum. The magnet assembly was kept the same as section 2.2.3 described.

Auxenochlorella protothecoides (A.p.) is used as an example of fluorescent CTV due to its auto-fluorescence. The algae cells were cultured in modified high salt media (Appendix

A). Cell cultures were maintained in 125 ml Erlenmeyer flasks at room temperature and agitated at 150rpm in an Environ shaker (Lab-line Instruments, Melrose Park, IL). The lighting conditions used were about 500 μM photons/m2-s.

The auto-fluorescence excitation and emission spectrum of algae was tested with a fluorometer (PTI QuantaMasterTM 300, NJ, U.S.). The excitation spectrum was tested at emission at 690 nm, and then the emission spectrum was measured at the peak excitation wavelength.

As a proof of concept that sub-micron sized magnetic particles can be tracked in the CTV system, magnetic, quantum dot nanocontainers, of a nominal diameter of 35 nanometers, the synthesis and initial characteristics described previously (Ruan et al. 2010) were also

2 tested. . The permanent magnetic assembly Mk II (Sm equal to 141 T-A/mm , B0 ≈ 1.4 T, dB0/dx = 500 T/m) was used in this experiment.

2.3. Results and discussion

2.3.1. Reference particle internal control method

Figure 2.6 shows the magnetic velocity for the red blood cells and polystyrene beads without the internal control method. Considering the highly uniform cells/particle content and size, and the constant Sm value within the ROI, the high standard deviation within each test and very different means among the triplicates suggested background flow issue introduced a non-negligible variance to the result and the necessity of the internal control method. 26

The result of the mixture of polystyrene internal control particles and met-RBC is summarized in Table 2.2. The adjustment method has lowered the standard deviation of the Umag in all cases, and mean of the triplicate tests are more repeatable: before the adjustment method, the means in the first 2 groups of triplicate were statistically significantly different (Tukey HSD method), but after the adjustment the levels of means were reduced. These two results show that the internal control method could reduce the error induced from the background flow.

It is interesting that the magnetic susceptibility of polystyrene was reported higher than water such as -7.5 x 10-6 to 8.2 x 10-6 (Watarai and Namba 2001), -7.7 x 10-6, (Jin et al.

2008), and -8.0 x 10-6(Zhang et al. 2005), yet in our test it is smaller than water. It is clear that the polystyrene beads moves against the magnetic field gradient and hence the magnetic susceptibility of polystyrene is smaller than water, yet it may not be conflict with other value reported as they were not obtained by comparing it directly to water but paramagnetic metal ion solutions such as gadolinium solutions. The susceptibility of polystyrene obtained by the extrapolation method may be subjected to error, led to this counterintuitive result.

2.3.2. The measurement of the Sm value of the CTV Mk V

2 The um against Sm plot is shown in Figure 2.7. The slope was 365 T-A/mm . A stead relative error of magnetic velocity around 30% was observed in different Gd-DTPA concentration, suggesting a steady Sm spread over the region of interest. The relative error for the particle is 10% and the magnetic velocity is proportional to its square. According

to the error propagation rule, the other factors roughly accounts for 20% relative error, including Sm variance and wall effect.

Interestingly, we found the slope did not pass the origin. Such outcome was probably caused by the inaccurate magnetic susceptibility of polystyrene particle, which essentially change the magnetophoresis mobility, or horizontally translate the regression curve. The potential cause has been discussed in the previous section. Nonetheless, the intercept does not affect the calibration of Sm as it is essentially proportional to the accuracy of concentration and susceptibility of Gadolinium ion.

2.3.3. Fluorescent CTV result of auto-fluorescent Auxenochlorella protothecoides

(A.p.)

The auto-fluorescence spectrum of A.p. is shown in figure 2.8. The excitation peaks were found at 438nm and 470nm, and one emission peak was found at 681 nm, which perfectly falls in the wavelength interval of the filter cube.

Figure 2.9 shows the image comparison between the dark field CTV and fluorescent CTV for the same concentration of A.p. cells. The contrast of the images generated by fluorescent CTV is clearly stronger than the once generated with dark field CTV, which is beneficial for the image analysis. Also because of the strong fluorescence, lower cell density (0.5 x106 cell/ml compared to 1x106 cell/mL with dark field CTV) is required to ensure around 100 cells tracked in one set of images. This also reduced the error introduced by the cell-to-cell interaction, as the particle is assumed to be in an infinite volume of carrier with Stokes drag. Due to the higher image quality, this method will be used in Chapter 5.

2.3.4. Fluorescent CTV result of fluorescent quantum dot container

To demonstrate the potential to use the CTV instrument on particles that are significantly below typical bright-field and dark-field microscopic limits (on the order of 0.5 microns), we tested the nominally 35 nanometer, magnetic, quantum dot nanocontainers. Again the fluorescent filter cube is compatible with the excitation and emission of the QDOTs in the nanocontainers. The visual (microscopic) observation of the appropriate color of the

QDOTs was observed and CTV code was able to track the particle movements. The visual (microscopic) observation of the appropriate color of the QDOTs was observed and CTV code was able to track the particle movements. Figure 2.10 (Xu et al. 2012) presents a histogram of the magnetically induced velocity. Since the settling velocity data is not presented/used given that the particles are too small to settle over the time scales of the experiment.

With respect to the magnetic, QDOT nanocontainers, a theoretical analysis can be made to predict the maximum, magnetically induced velocity. If one assumes that these nanocontainers are 35 nanometers in diameter, and are solid, saturated (with respect to B fields which is the case in the CTV measurements presented) maghemite (γ-Fe2O3), using the previously discussed value of magnetization of 385 kA/m, the theoretical, maximum, magnetically induced velocity is given by:

μ0Ms 2 ( ⁄ −χf)D u = B0 S = 0.005mm/s (2.5) m 18 m where Ms is the magnetization of the particle

To be tracked by the CTV code, an entity must move in a “coherent path” for at least five frames, which significantly precludes the reported velocities in Figure 2.10 to be the 29

result of random motion induced by Brownian motion. Clearly, velocities significantly higher than the theoretical maximum was recorded; in addition, based on SEM photos previous reported on these types of particles, it is more likely that on the order of 10, 5 nanometers maghemite particles are contained in each nano-container. This corresponds to approximately 3 percent of the nano-container volume, or a 97% reduction in the average magnetization, which corresponds to a decrease in the magnetically induced velocity to 1.5 x 10-5 mm/s. Clearly, a large number of entities moved faster than these theoretical calculations indicate. It is highly likely that these entities are clusters of nanocontainers. It has been previously shown, that as particles cluster, while the effective diameter increases, the volume increases more rapidly. Since magnetic susceptibility is a volumetric quantity, the actual clusters of particles can result in an increase in magnetically induced velocity that is greater than initial intuitive thinking would indicate. Future studies, involving other independent measurement techniques will be used to better characterize these nanocontainers in the CTV system.

2.4. Conclusion

Cell tracking velocimetry as an instrument of measuring the magnetic properties of cells and particles have been developed improved for the past two decades in terms of the magnetic field power and other aspects. Several innovations have been introduced in this chapter. The internal control method significantly reduced the background flow in the

CTV Mk I system. To apply minimum interaction to the target cells, a minimum 0.06 x

106 cells/mL is recommended.

By quenching the magnetic flux leakage with an opposite flux, the CTV Mk V achieved

2 Sm value as high as 365 T-A/mm , which was validated by magnetophoresis method with

Gd(III) salt solution. Such high Sm value will enable the delicate magnetic properties of metal containing cells such as iron treated cancer cells or algae cells.

Finally the fluorescent option of CTV is introduced and proved to provide better quality images with less sample concentration to minimize the particle interactions for auto- fluorescent cells such as A.p. More importantly, the fluorescent option enables the measurement of nano-size fluorescent magnetic particle, which is otherwise impossible because of the diffraction limitation of dark-field microscope. In general this option is recommend whenever applicable, and extensively used in Chapter 5.

Table 2.1. Summary of the latest Sm values of versions of CTV. From Mk I to Mk V are

CTV with permanent magnets and EM is CTV with electromagnet so the Sm could vary.

2 2 2 Magnet Last updated gradB ( T /mm) Sm(T-A/mm ) Magnet materials

Mk. I 2007 0.367 146 Ne36

Mk. II 2006 0.358 142.4 Ne40

Mk. III 2001 0.380 151.2 Ne42

Mk. IV 2007 0.00189 0.752 ceramic 5

Mk. V 2007 0.755 365.0 Ne42

EM 2001 0-0.28 0-114 Steel

Mean Umag -4 Std Dev Umag (10 mm/s) levels Component Test (10-4 mm/s)

Raw Adj. Raw Adj. Reduction % Raw Adj.

0.6 x 106 1 7.15 4.94 3.68 3.48 5.43% A A

metRBC 2 3.62 4.72 3.29 2.47 24.92% C A

0.06 x 106 3 6.21 4.91 3.11 2.67 14.15% B A PSM

0.6 x 106 1 5.08 4.11 3.14 3.06 2.55% B B

metRBC 2 5.86 4.10 3.46 3.23 6.65% A B

0.1 x 106 3 3.94 4.51 3.62 3.34 7.73% C A PSM

0.6 x 106 1 3.25 4.11 4.83 4.43 8.28% A A

metRBC 2 3.06 4.56 8.48 8.19 3.42% A A

0.15 x 106 3 3.07 4.23 4.49 4.34 3.34% A A PSM

Figure 2.2. A picture of the CTVMk V. From the left to right are the computer, the CCD camera, the microscope, and the magnet assembly with the glass channels and the syringes.

Figure 2.3. The design of the core of magnetic assembly of the CTV Mk V. The different materials were color-labeled, and the intended, leak and side board magnetic flux is highlighted with arrows.

Figure 2.4. The design of magnet assembly of CTV Mk V. from the side.

Figure 2.5. 3D simulation result of the magnetic field in proxy of the region of the interest.

Figure 2.7. Measurement of the Sm in the CTV Mk V system with Gd(III) solution.

Figure 2.8. The auto-fluorescence spectrum of the Auxenochlorella protothecoides (A.p.). The excitation a spectrum was measured at fixed emission at 690 nm, and the emission spectrum was measured at fixed excitation of 439 nm (one of the peak). The intensity was normalized against the peak value. The theoretical pass for the excitation and emission filter were also shown.

Figure 2.10. The histogram of magnetic velocity of nano-size micelles containing quantum dots and SPIONs.

Chapter 3. Cell magnetic susceptibility determination by cell

tracking velocimetry and by SQUID-MPMS in physiological state

3.1. Introduction

Magnetic separation has become an indispensable technique in life science because of its unparalleled sensitivity, selectivity and purity for separation of cells, DNA and RNA

(Zborowski et al. 1999; Zborowski and Chalmers 2011; Mahmoudi, Simchi, and Imani

2010; Berensmeier 2006). The magnetic properties of the biological matter to be separated, with or without magnetic label, are vital for the process design. Recently, with the increased accessibility of strong rare-earth magnets (e.g. neodymium magnets) and improved design of the magnetic separator, it is possible to magnetically separate label- less, weakly paramagnetic cells. The feasibility of label-less magnetic cell separation has been demonstrated by others (Hackett and St Pierre 2005) and by us, such as in application to intraerythrocytic malaria detection (Zimmerman et al. 2006) and in field studies rivaling the sensitivity of the RT PCR(Karl et al. 2008), to separation of bacterial spores rich in paramagnetic element manganese (Mn)(Melnik et al. 2007; Sun,

Zborowski, and Chalmers 2011), to separation of algae genetically modified for elevated expression of ferritin(Buck et al. 2015), to enrichment of mature erythrocytes from hematopoietic cell cultures (Jin et al. 2012), to their depletion from whole blood

preparations as a type of a “magnetic centrifuge” (Moore et al. 2013), and to studies on hemoglobin interconversion between low-spin and high-spin electronic (Zborowski et al.

2003) states based on pioneering work by Pauling et al.(Pauling and Coryell 1936b,

1936a). We have developed a theory of magnetophoresis that links the field-induced cell motion to the underlying molecular mechanisms (Jin et al. 2008; Zborowski et al. 2003).

Others have developed a practical system using permanent magnets for field operation for detecting paramagnetic contaminants in food or environmental water (Mirica et al. 2010) and demonstrated an atomic-level sensitivity of the magnetic ponderomotive forces to the paramagnetic contribution in the predominantly diamagnetic materials (Mirica et al.

2008).

However, the low magnetic susceptibility of such cells challenges the sensitivity of characterization devices. Many measurements (e.g. Mössbauer spectroscopy (Greenwood and Greatrex 2007), nuclear magnetic resonance (NMR) spectroscopy(Sanders and

Hunter 1989), electron paramagnetic resonance (EPR)(Atherton, Davies, and Gilbert

1996), etc. ) and imaging techniques (magnetic resonance imaging (Haacke et al. 1999)) are based on the magnetic properties of matter, but only some of them provides results in absolute terms (emu, Bohr magnetons, etc.), such as Gouy method (Oconnor 1982), vibrating sample magnetometer (VSM)(Foner 1985), and superconducting quantum interference device magnetic properties measurement system (SQUID-MPMS). Though

SQUID-MPMS is extensively used in magnetic properties analysis because of its extraordinary sensitivity and wide applicable range of applied magnetic field and temperature, not many biological samples were examined with it. Among these few examples, most samples (Hackett et al. 2009; Karl et al. 2013; Mejias et al. 2013) were

measured in lyophilized but not physiological form. Hence, we developed an approach to evaluate the magnetic properties of whole cells in suspension with SQUID-MPMS.

Cell tracking velocimetry (CTV), or single cell magnetophoresis, is an alternative method to measure the magnetic susceptibility of cell, magnetic particle or cell-particle complex.

Magnetophoresis, or migration of suspended particle in magnetic field, was applied for the purposes of separation(Furlani 2007) and analysis (Hackett and St Pierre 2005;

Watarai and Namba 2002; Watarai et al. 2014; Mair and Superfine 2014). In our system, we integrated the techniques of magnetic field construction & characterization, microscopy, and image analysis, so that we are capable of analyzing the magnetic properties of several hundred weakly paramagnetic cells/particles simultaneously.

To evaluate the effectiveness of magnetophoresis method, we compare the magnetic susceptibility measurements of various forms of red blood cells (RBCs, including oxygenated RBCs, met-hemoglobin RBCs, and deoxygenated RBCs) with SQUID-

MPMS and magnetophoresis. RBCs were chosen for its uniformity in size and magnetic properties (Persons 1929). The results were also compared with theoretical models. At last, the sensitivity and applicable scope of magnetophoresis and SQUID-MPMS are also discussed.

3.2. Experimental Section

3.2.1. RBC magnetic susceptibility by SQUID-MPMS

The SQUID MPMS-5 (Quantum Design, San Diego, USA) was used in this study. The center piece of SQUID-MPMS is rf-SQUID, which is a very sensitive magnetometer

(flux-to-voltage converter) based on the Josephson’s tunneling effect (Josephson 1962) 46

and magnetic flux quantization (Deaver and Fairbank 1961). However, instead of directly interacting with the rf-SQUID, the sample directly interacts with the signal pick-up loop, which is a superconducting second-order gradiometer, as shown in Figure 3.1. The uniform field through all four loops (such as the applied field) generates net zero signal, and hence its drifting does not interfere the result. At given applied field, as the sample, which is considered as mass point, goes through the pick-up loop, it generates a voltage- position pattern as shown in Figure 3.1. The amplitude of the voltage curve indicates the magnetic moment of the sample, and eventually a magnetic moment vs. applied field curve is generated. Reciprocating sample option (RSO) was available during the measurement. In this option the sample is reciprocated at each position, and hence the signal generated could be averaged to improve accuracy.

The main challenges of measuring cell suspension include: 1) biological hazard of the sample; 2) liquid sample containing and leakage; 3) weak signal. We encountered these issues by holding cell suspension in test-tube shape quartz tube (O.D. 4mm, length 6mm,

Glassblowing laboratory, OSU) and seal with epoxy resin glue, as shown in Figure 3.2.

All red blood cells (American Red Cross, OH) were washed with phosphate buffer saline

(PBS), and then treated into different forms: a) met-hemoglobin RBCs: the pre-counted cell suspension was treated with 5μM NaNO2 for 1h; b) deoxygenated RBCs: 2mL cell

2 suspension was spread in 57cm Petri dish and fully exposed in the glove box (90%N2 and 10%H2) for 1h, and the subsequent sealing measures were also taken in the glove box; c) oxygenated RBCs: the cells concentration was counted. 20μL each sample with pre-counted cell concentration was added into the quartz tube, and then the tube was sealed with epoxy. The epoxy curing time was 1h. Then the sealed tubes were tested in

vacuum for 1h to ensure they were airtight. The sample is then fixed in to the plastic straw and ready for the SQUID-MPMS measurements. All measurements were taken at

300K (pre-equilibrated), and the applied field ranges from -70000Oe to 70000Oe (500Oe step-size, 29 points in total).

As all the parts in the sample have similar magnetic susceptibility (diamagnetic), the contribution of each part should be considered. We assume that the magnetization of sample is the linear combination of that from all parts, including the cells, the suspending media (PBS), the quartz tube, the gas in the tube and the epoxy seal. Each part is considered as homogeneous in terms of magnetic properties, and its magnetic moment could be calculated from either volume or weight basis. So we have:

n mmmRBCi  (3.1) i1

mHVRBCRBCRBC  mHVik ,1... iii (3.2) mHWjljjj ,1... nkl

m kl  sVVW RBC RBCi ij j (3.3) H ij11

kl  si V i j W j ij11 RBC  (3.4) VRBC where H is the applied field strength (Oe), m is the total magnetic moment of the sample at applied field H (emu),mi is the magnetic moment of an individual part i at applied field 48

H (emu), i is the volume magnetic susceptibility of part i (CGS unit, dimensionless), Vi

3 is the volume of part i (cm ), j is the mass magnetic susceptibility of part j (CGS unit,

3 cm /g), Wj is the mass of part j (g), s is the slope of the experimentally determined regression line of m on H (emu/Oe). By knowing the value of all terms except the

magnetic susceptibility of RBC, RBC in equation 3.3, we can calculate the magnetic susceptibility of the RBC. The numerical value of the magnetic susceptibility in CGS system is converted to that in SI units system by multiplying by 4π. The source of all these values is summarized in Table 3.1.

3.2.2. Limit of detection (LOD) and error analysis of RBC susceptibility by SQUID-

MPMS

The lowest detection limit for the SQUID MPMS-7 system is claimed as 1x10-8 emu at 0-

2.5 T applied field, and 6x10-7 emu at 2.5-7 T applied filed by the manufacturer. Yet such high sensitivities may be difficult to reach for dilute magnetic materials such as biological samples (Hautot, Pankhurst, and Dobson 2005). Nonetheless an optimistic estimation of minimum RBC cells could be calculated based on the theoretical magnetic susceptibility of oxygenated RBC (-9.22x10-6, SI, dimensionless), RBC volume (88.6fL) and minimum signal of 6x10-7emu at 7T. This form of RBC is chosen because its susceptibility farthest from 0 (detail calculation shown below). The minimum cell number needed is 1.3x105 cells in total.

In the proposed method of SQUID-MPMS measurement, the error is potentially generated from the sources below: 1) Error of mass/volume of parts from the electronic balance/pipette, especially the measurement of the small cell volume. Currently used 49

hemacytometer gives around 10% relative error, which is the major contributor to the final error. Also, with the current sample holder, the cells only contribute to about 10% of total magnetic moment, which amplified such error. A thinner yet well reliable tube in the future work will alleviate such problem. 2) Deviation from mass point assumption of the sample: large size, disorientation, and heterogeneity of the sample will introduce error to the measurement (Stamenov and Coey 2006; Sawicki, Stefanowicz, and Ney 2011).

Less than 3% error is introduced if the volume of the sample is not larger than 5% of the gradiometer volume, in symmetrical geometry and properly aligns through the centerline of the gradiometer and uniform. Our sample is small enough and symmetric, but not uniform. However, the extent of non-uniformity is relatively small, since all parts of the sample have similar magnetic susceptibility, and this non-uniformity is further mitigated by RSO. 3) The extent of de-oxygenation of red blood cells may not be complete, which partially explain the larger deviation of deoxygenated RBC from the predicted value. In the proposed method of SQUID-MPMS measurement, the error is potentially generated from the sources below: 1) Error of mass/volume of parts from the electronic balance/pipette, especially the measurement of the small cell volume. Currently used hemacytometer gives around 10% relative error, which is the major contributor to the final error. Also, with the current sample holder, the cells only contribute to about 10% of total magnetic moment, which amplified such error. A thinner yet well reliable tube in the future work will alleviate such problem. 2) Deviation from mass point assumption of the sample: large size, disorientation, and heterogeneity of the sample will introduce error to the measurement (Stamenov and Coey 2006; Sawicki, Stefanowicz, and Ney 2011).

Less than 3% error is introduced if the volume of the sample is not larger than 5% of the

gradiometer volume, in symmetrical geometry and properly aligns through the centerline of the gradiometer and uniform. Our sample is small enough and symmetric, but not uniform. However, the extent of non-uniformity is relatively small, since all parts of the sample have similar magnetic susceptibility, and this non-uniformity is further mitigated by RSO. 3) The extent of de-oxygenation of red blood cells may not be complete, which partially explain the larger deviation of deoxygenated RBC from the predicted value.

3.2.3. RBC magnetic susceptibility by magnetophoretic analysis

Magnetophoresis method was described previously (Chalmers, Haam, et al. 1999) and the current setup (Mk V) is introduced here (Figure 3.3). The permanent magnet blocks

(Neodymium-Iron-Boron, energy product = 42 MGOe, CMS Magnetics) are in blue, high permeability low carbon steel (grade 1018) pole pieces are in light green, and aluminum supports are in yellow. The hyperbolic pole pieces create a magnetic field with horizontal, nearly constant, well-characterized gradient within the region of interest

(ROI), where the glass channel containing the cell suspension goes through horizontally.

At steady state motion, the horizontal component of the cell migration is solely driven by the magnetic field. With the aid of image analyzer, the horizontal velocity of several hundred cells could be acquired simultaneously. Assuming the drag follows the Stokes’ law, the magnetic susceptibility of cells can be calculated by the following equations:

RBCH O22 RBCRBCHV O 2 uSDmmRBC S m (3.5) 318 DRBC

dB2 Sm   (3.6) dx 20 51

18 RBCmHO2 u 2 (3.7) DSRBCm

where um is the experimentally determined mean horizontal velocity of the RBC induced by the predominantly horizontal gradient of the applied magnetic field (m/s),

RBCHO, 2 are the volume magnetic susceptibility of cell and water, respectively (SI

3 unit, dimensionless). VRBC is the cell volume (m ), η is the viscosity of water (Pa∙s), DRBC

2 is the hydrodynamic diameter of the cell, Sm is the magnetic energy gradient (T-A/m ), B is the local magnetic field (T), µ0 is the magnetic permeability of free space

(4π × 10−7 N A−2). The second formula in Equation 3.5 is valid for an RBC spherical volume equivalent.

The current magnetic assembly with the new design to quench magnetic flux leakage

8 2 improved Sm to as high as 3.65x10 T-A/m within the region of interest (as shown in

Chapter 2). The magnetic field map is shown in Figure 3.4. Such high Sm was achieved by adding the building block a and c (shown in figure 3.3-A) to quench the magnetic flux leak. Cell suspension were held in the glass channel of 1mm I.D. and 1.6mm O.D., which is placed horizontally and attached to a Hamiltonian valve on each end to eliminate interference flow during the measurement. The motion of migrating cells were monitored through 5x objective lens (LMPlanFl, Olympus, Japan), photo-eyepiece (U-

PMTVC, Olympus, Japan), and CCD camera (Retiga 200R, QImaging, Canada). The acquired images are recorded with Video Savant software and then analyzed by the in- house image analyzing software ImageView. The image analyzer identifies a particle by threshold the pixel grayscale, and then record the location of the particle. This particular particle is then searched in the vicinity of the next frame. By connecting particle location 52

frame by frame, the velocity could be calculated. The unavoidable field and gradient heterogeneity in the ROI of the magnetophoretic analyzer contributes to a relative error of the field induced velocity, um measurement of 3% (Figure 3.4).

To accommodate the RBC deoxygenation, one end of the glass channel was connected with 35 mL plastic cylindrical deoxygenator (Figure 3.5). During the preparation of the deoxygenated RBC, the blood sample in the deoxygenator was constantly bubbled with humidified nitrogen, agitated by magnetic stirring bar and monitored by the dissolved oxygen probe. Also two N2 environment sheaths were set up around the channel-tubing connections to avoid re-oxygenation of the cells. Before the measurement, the red blood cells were washed with PBS then treated into different forms: 1) oxygenated blood cells: the cells were diluted with PBS to the final concentration of 5x105 cell/mL before infused to the channel by syringe; 2) met-hemoglobin RBC: the cells were treated with 5μM

6 NaNO2/PBS for 1h at cell concentration of 5x10 cells/mL, and then they were washed with PBS and diluted to the final concentration of cell/mL 5x105 cell/mL with PBS. Then the sample was infused into the channel. 3) deoxygenated RBCs: whole blood were suspended with PBS to the final concentration of 5x105 cell/mL and then infused in to the channel-deoxygenator system. Valves were closed to prevent the exposure of air and start the nitrogen flow in the deoxygenator. The sample was deoxygenated for 1 hour to ensure the dissolved oxygen plateau before the measurement started.

Before recording the cell migration, the sample was left undisturbed with the valves closed for 100-200 seconds. Then the motion of cells was recorded within 50 frames and frame interval of 2 seconds. The valves were then opened and new cell collection was pushed into the region of interest by syringe. Then the valves were closed again and the

recording was repeated until 10 sets of images (~1,000 cells) were recorded. The images were subsequently analyzed to acquire their velocity, um and calculate the magnetic mobility, χRBC (Equation 3.7). The distribution of the measured quantities in the sample was displayed as histograms (an example is shown in Figure 3.6).

3.2.4. Limit of detection (LOD) and error analysis of the magnetophoretic CTV

The magnetophoretic measurement limits are related to physical constraints, such as maximum field value and gradient homogeneity throughout ROI available for the CTV apparatus, the dark field microscope diffraction limit, thermal noise floor due to

Brownian motion, the creeping flow ceiling imposed by low particle Stokes number assumption, and wall effect of the channel. The additional constraints are imposed by the cell tracking software algorithm, including the image pixel resolution. The physical constraints imposed by the available magnetic field and gradient were already discussed in the preceding section. The additional details are discussed below and the results and detection limitation are summarized in Figure 3.7.

3.2.4.1. Light diffraction limit and image resolution

In dark-field microscopy, the minimum trackable particle size is around 200nm according to Rayleigh criterion. Smaller particles will be not clearly tracked because of the light diffraction. In our current magnetophoresis imaging system, the image resolution is

1.53μm/pixel. Particles smaller than that are considered not trackable, however if the particle is fluorescent, the minimum trackable size could be lower (Xu et al. 2012). The

RBC shape and dimensions (a discoid of average dimension of 7 µm by 2 µm) make it

trackable in the current version of the CTV apparatus, even if they present themselves edge-on to the microscope viewing objective.

3.2.4.2. Thermal noise limit

Thermal noise causes Brownian motion of microscopic particles and introduces uncertainty as to the magnitude of particle displacement associated with weak body forces, such as magnetic force acting on the RBC. If the magnetic induced velocity of a number of cells/particles is too low to be identified from such randomness, we consider such magnetic velocity is not detectable. We address this issue with the following assumptions: 1) identical diameter and magnetic susceptibility for all particles tested; 2) uniform magnetic field gradient within the region of interest; 3) negligible inter-particle interaction. Then the particles horizontal velocity follows the Gaussian distribution, with the mean of magnetic induced velocity um, and standard deviation solely generated from

Brownian motion. The magnitude of Brownian motion was described by Einstein as equation 3.8 (Einstein 1956):

2 2kTB xtRBC  (3.8) 3DRBC

2 where x is the mean square displacement of the RBC, kB is the Boltzmann constant

(1.38×10-23 m2kg s-2 K-1), T is the absolute temperature (293K), t is the observation time

-3 (here 100s), η is the viscosity of water (1.00×10 Pa ∙s), and DRBC is the RBC hydrodynamic diameter (5.56 µm assuming mean RBC volume of 90 fL). The resulting root-mean-square displacement due to Brownian motion over time of RBC tracking is

x2 = 3.9 µm, the resulting uncertainty of the magnetic field-induced RBC velocity is

2 um =  xt/ = 0.039 µm/s.

The foregoing analysis allows one to estimate thermal noise contribution to the error of magnetic susceptibility measured by the RBC magnetophoresis, χRBC by inserting um

-7 into Equation 3.7. The resulting error is χRBC = 1.96×10 , whose magnitude is approximately 20% that of water magnetic susceptibility (-0.905×10-6). The error contribution from other parameters entering Equation 3.7 is discussed in the Error

Analysis section, below.

The detectable difference from 0 of the measured RBC magnetophoretic velocity magnitude is calculated from one-sample case formula(van Belle 2008):

1.96δu u  m (3.9) m min N

For a representative number of tracks of N = 1,000 during a typical magnetophoretic

-9 analysis and um = 0.039 µm/s quoted above, one obtains u = 2.42×10 m/s. This m min exceedingly small value indicates that with the current CTV technique, the thermal noise and the resulting Brownian motion do not interfere in a measurable way with the RBC magnetophoresis. More detailed theory deduction is elaborated in Appendix A.

3.2.4.3. Creeping flow limit

Equations 3.5 and 3.7 hold for Stokes flow regime (creeping flow). Allowing for less than 1% relative error in the terminal RBC magnetophoretic velocity when calculated

from Stokes formula for the viscous drag force imposes a maximum allowable particle

Reynolds number value of not greater than 0.1 (Rhodes, 1998). Hence we have:

Du Re0.1RBCm max max   (3. 10)  u  0.1 m max DRBC where ρ is the density of water at room temperature (1×103 kg/m3). The resulting u m m a x

=1.8 cm/s is orders of magnitude higher than the expected RBC magnetophoretic velocity in the current apparatus. The corresponding maximum expected magnetic susceptibility of such fast moving microparticle is calculated from Equation 3.7,  =9.05×10-2. max

Again, this is four orders of magnitude higher than the absolute susceptibility of water and comparable to high susceptibility of magnetic microparticles used in our previous work (Zborowski et al. 2003; Xu et al. 2012). Consequently, the RBC magnetophoresis is well within the limit of the creeping flow model.

3.2.4.4. Wall effect

The foregoing discussion of the Stokes drag is applicable to particles moving in an infinite volume of the viscous fluid. Yet in the RBC magnetophoresis experiment the cells were suspended in a long, parallel-piped glass channel of 1mm ×1 mm cross section, which introduces deviation from Stokes drag that depends on the cell diameter, here

DRBC(5.56 µm) and the distance between the walls of the square cross section channel, l

(1 mm). We approximate the resulting fractional deviation of the actual cell velocity from

δu the Stokes velocity, by using Happel and Bart’s work (Happel and Bart 1974)for the u sphere settling along the axis of a square duct at low Reynolds’s number:

δu D 1.9032660.011RBC (3.11) ul

The deviation is just above 1% and therefore the wall effects can be neglected in the magnetophoretic RBC tracks analysis.

3.2.4.5. Particle-particle interactions

According to Famularo’s work on correction of settling velocity of dilute random suspension (Happel and Brenner 1983), the fractional deviation, of the actual cell velocity from the Stokes velocity is:

1 u 1.30 3  (3.12) u 1 1 1.30 3 where ϕ is the volume fraction of suspended cells. For an RBC suspension of concentration of 5×10-5 cells/mL, the volume fraction ϕ=4.4x10-5 and the effect of particle-particle interaction in suspension lowers the RBC velocity by 4.5% relative to that of a single cell.

3.2.4.6. Image analyzer algorithm

The minimum tractable velocity by the image analyzer is determined by the minimum particle displacement between the first and last frame of the image acquisition run (here

set at 50 frames) and the maximum frame time interval between two consecutive frames, as long as within such time the particle migrates for 1 pixel distance (corresponding to

1.5 µm physical displacement). But with consideration of analysis throughput, here set at

300s per 100 tracks (200 s of rest phase plus 100 s of image acquisition time) repeated 10

× for a total of approximately 1,000 tracks per sample, amounting to a total data acquisition time of 5 min. per sample, the frame time interval upper limit is set at 2 seconds. This sets a lower limit of the field-induced, single cell velocity detection for the current CTV system at 1.5 µm/(50×2) s = 0.015 µm/s, which is below the Brownian motion noise limit of 0.039 µm/s discussed above.

The maximum tractable velocity by the image analyzer is determined by the maximum particle displacement between two consecutive frames and the minimum frame time interval. As described earlier, the particle velocity was calculated by searching particle location through consecutive frames. To ensure the accuracy of finding one particular particle, the maximum displacement of one particle between 2 consecutive frames is set as 1/10 of the frame width. With current resolution of 1,200 ×500 pixels (set with consideration of uniformity of Sm, and analysis throughput), and 1.53μm/pixel pixel size, the maximum displacement is 184 um. The minimum frame time interval is limited by data transition rate from the camera to the PC, which is 5 frames/s at current settings.

Hence we have the upper limit velocity as 918μm/s. Combined with Equation 3.7, we conclude that the maximum susceptibility accessible to the current image analysis technique is 4.6×10-3, which is well within the Stokes regime limit of 9.05×10-2, discussed above.

It is worth mentioning a few ways of increasing the maximum susceptibility measurement capability, including switching to a magnet assembly with weaker Sm, increasing the viscosity of liquid (e.g. by adding glycerol), increasing liquid magnetic susceptibility (e.g. by adding Gd3+ ions), and increasing the binning of the image, which essentially lower the quality of the image and increase the number of frames transferred in given time.

3.3. Results and analysis

The results of experimental measurements of RBC magnetic susceptibility containing three different forms of hemoglobin, by two different methods, SQUID-MPMS and magnetophoretic CTV are summarized in Table 3.2. Typical examples of raw data from

SQUID-MPMS and magnetophoretic CTV analyses are shown in Figure 3.6. The standard deviation of the SQUID result was calculated based on the propagation of uncertainty (Table 3.3). There is no difference in results between the two experimental methods, within the limits of experimental error.

We apply the model of magnetic susceptibility of RBCs by work of Cerdonio(Cerdonio et al. 1978), which was also used in other works (Fabry and San George 1983; Spees et al. 2001; Schenck 2005; Zborowski et al. 2003). Essentially in this model the magnetic susceptibility of single cell is the contribution from hemoglobin (various types) and water:

χRBC = xH2O휒H2O + xHb휒Hb (3.13)

where χRBC , 휒H2O , 휒Hb is the volumetric magnetic susceptibility of RBC, water and

hemoglobin, respectively. xH2O and xHb is the volumetric fraction of water and 60

hemoglobin. The predicted magnetic susceptibility is adapted from (Zborowski et al.

2003).

In the magnetophoresis method, first thing to note is that the standard deviation is not only contributed from the measurement error, but also from the natural size distribution of RBCs. The relative error of the RBC volume used in the experiment is around 28%

(validated by Coulter Counter, data not shown). As for the measurement relative error, which is roughly 20%, it was sourced from the aspects discussed earlier; generates from the deviation of the assumptions: 1) Steady state of cell migration: before measurement, the suspension is not disturbed for 100~200s to approximate to steady state. Both horizontal and vertical velocity measured after 100/200/300 seconds after closing the valve showed very similar result (data not shown). 2) The Stokes drag: very small error is introduced because of the wall effect and the high Reynolds number as discussed in the operation scope section. 3) Variation of magnetic driving force in ROI: The CV of gradient of B2 within the ROI is less than 3% as shown in Figure 3.4. 4) Error induced from Brownian motion, as discussed in the operation scope section. 5) Particle-particle interaction: according to Famularo’s work on correction of settling velocity of dilute random suspension (Happel and Brenner 1983), for a suspension of concentration of

5x10-5 cells/mL RBC, the effect of particle-particle interaction hinders the velocity by

4.5%.

3.4. Discussion

Previously, SQUID-MPMS was introduced in biological research in a few examples.

Hackett et. al. (Hackett et al. 2009) examined the iron susceptibility in various form of

hemoglobin including oxyhemoglobin, methehmoglobin and hemoglobin from malaria- infected RBCs by measuring the susceptibility of frozen RBC lysate in SQUID-MPMS at

10K and 265K. They also managed to establish a positive correlation between the iron susecptibility from malaria-infected RBCs and the maturation of malaria parasite. Karl et. al. (Karl et al. 2013) examined the magnetic properties of lympholyzed Schistosome eggshells (S. mansoni and S. japonicum) from 10 to 300K. The results suggested a mixture of high spin and low spin of iron presented in the sample. Mejias et.al. (Mejias et al. 2013) examined the magnetic properties of nanoparticles uptaken by tissue samples.

The lympholized sample was measured within 10-100K and shown decreasing susceptibility over time and shift of peak suseptibility over time, which suggests the disaggregation and degradation of the particles. Haishimoto (Hashimoto et al. 2009) developed a paper strip adhesion method to measure the concentration of magnetic particles attached to cell surface. All the measurements used a frozen/lympholized/dry sample to avoid hazard from liquid, and hence the cells were not examined as in physilogical states.

Magnetophoresis has been also adopted for the purpose of analysis and separation by other groups. Watarai(Watarai and Namba 2002) used a similar magnetophoresis system to track the magnetophoresis of blood cells and polystyrene beads in paramagnetic carrier. Later (Watarai et al. 2014), an alternative approach was developed within the same system by varying the magnetic susceptibility of carrier and hence to determine the susceptibility of “Zero-velocity carrier”. This method reduced the error introduced by velocity measurement, despite that the viability of cells may be compromised by the non- physiological carrier. Hackett et.al.(Hackett and St Pierre 2005) developed the

magnetophoresis system “Thales”, which provides a lower magnetic field and gradient to specialize 1-10um particle characterization. Mair and Superfine(Mair and Superfine

2014) used magnetophoresis to reveal the interaction between magnetic nanorod and extracellular matrix. The non-Newtonian nature of extracellular matrix and specific size range of nanorod lead to unique biphasic pattern of nanorod migration. However, none of the systems provides a near homogeneous magnetic field in ROI and effective image analysis method, which essentially limits the throughput of measurements and excludes the possibility of statistical analysis with large sample size. Also the high Sm of our Mk V system ensures the migration of diamagnetic cell in physiological carrier (e.g. PBS), which eliminates potential cell damage using paramagnetic carrier with transitional metals.

In this work, we took red blood cells as an example to compare the demonstrate the feasibility of magnetic analysis of cells in physilogical states with SQUID-MPMS and cell tracking velocimetry. We have demonstrated that the magnetophoresis method could measure the cell suscepitbility with similar accuracy than the SQUID-MPMS system.

Furthermore, magnetophoresis method has shown some advantages for measurement of cells: 1) significantly smaller sample size: in terms of detection limit, the number of

RBCs needed in SQUID-MPMS is 1.3x10-5 yet in magnetophoresis is a single-cell based method, which only needs a few cells (<100) with specilized capiliary channel. Such difference will be more pronounced for cells with magnet susceptibility closer to 0. In a more practical perspective, such as this work, the cell number used per test in magnetophoresis method and SQUID-MPMS method were ~2x108 and 2x105, respectively, which is a 1000 times sample size difference. In either case, we see the

sensitivity difference of 1000 times in favor of magentophoresis method. 2) easier sample preparation: cell suspension with proper concentration could be measured directly. 3) capbility of analyzing heterogenous sample: our magentophoresis system measures the magnetic susceptibility on single-cell basis and provide a distribution for the cell collection. Such capability may lead to finding of rare species with biological implication. For example, in Figure 3.6. the deoxygenated red blood cells showed a wider spread (tail) on the left side. This pattern was not shown in the methemoglobin red blood cells. This spread suggests that failure of deoxygenation occurred for a small subset of the red blood cells. 4) Applicability of the data in cell separation: as the magnetophoresis method directly measures the cell migrating in the magnetic field, the dirrect result magnetophoresis mobility could be readily applied in the design of other separation system. This is especially valueble for non spherical cells like red blood cells because of the irregular shape modification of motion comes from experiment rather than estimation.

In summary, despite the lack of varibility of temperature, which is not of interest for living cells,, magnetophoresis is a superior method to measure the magnetic suceptibility of cells.

3.5. Conclusion

Paramagnetic cell constituents have strong effect on cell’s volume magnetic susceptibility even at low volume fraction because of their high susceptibility relative to diamagnetic cell constituents. The effect can be measured at a single cell level by measuring cell terminal velocity in viscous media with a microscope equipped with a well-defined field and gradient magnet configuration (a method dubbed cell tracking velocimetry, CTV).

The sensitivity of such a microscopic-scale magnetometry was compared to that of a reference method of superconducting quantum interference-magnetic properties measurement system (SQUID-MPMS) using a red blood cell (RBC) suspension model.

The RBC hemoglobin oxygen saturation determines the hemoglobin molecular magnetic susceptibility (diamagnetic when fully oxygenated, paramagnetic when fully deoxygenated or converted to methemoglobin). The SQUID-MPMS measurements were performed on an average of 5×103 RBCs in 20 µL physiological phosphate buffer at room temperature, those by CTV on an average of 1,000 individual cell tracks per sample in a mean magnetic field of 1 T and gradient of 300 T/m. The mean RBC magnetic susceptibilities were statistically the same between the two methods, for all three forms of the hemoglobin, but only the magnetophoretic analysis provided information about the

RBC susceptibility distribution in the sample. A minimum of 5,000 RBCs were necessary to perform a single SQUID-MPMS measurement where only 1 RBC was sufficient to perform the same by CTV, therefore we conclude that the magnetophoretic method is

5,000× more sensitive than SQUID-MPMS providing means to study emergence of paramagnetic reaction products in the cell.

Table 3.1. Source of terms in equation 3.3

Term Source s (emu/Oe) SQUID-MPMS

Quartz tube mass (g) Electronic balance

Quartz magnetic susceptibility(cgs, Previous SQUID measurements mass, cm3/g)

Epoxy mass(g): Electronic balance

Epoxy magnetic susceptibility(cgs, Previous SQUID measurements mass, cm3/g)

Assumed the gas volume in the tube Estimation

(cm3)

Mean magnetic susceptibility of gas CRC Handbook of Chemistry and Physics, 94th

(cgs, volume, dimensionless) edition

Total cell number Hemacytometer measurements

Total cell volume (cm3) Single cell volume from Reference (McLaren,

Brittenham, and Hasselblad 1987)

PBS volume (cm3) 20μL pipette. Suspension volume subtract cell

volume

PBS(water) magnetic susceptibility CRC Handbook of Chemistry and Physics, 94th

(cgs, volume, dimensionless) edition

Samples SQUID result Magnetophoresis result Theoretical prediction

metRBC (-6.02±1. 10)x10-6 (-5.45±1.22) x10-6 -5.25 x10-6

dxyRBC (-7.34±1.17)x10-6 (-5.58±1.48) x10-6 -5.71 x10-6

oxyRBC (-9.73±1.34) x10-6 (-9.18±0.59) x10-6 -9.22 x10-6

Value Error CV

Quartz tube mass(g): 8.60E-02 1.00E-04 0.12%

Slope(emu/Oe) -3.26E-08 4.50E-12 0.01%

Mass magnetic susceptibility (CGS) from slope -3.79E-07 -4.44E-10 0.12%

Epoxy:

Value Error CV

Quartz tube mass(g): 9.84E-02 1.00E-04 0.10%

Quartz tube magnetic susceptibility -3.79E-07 -4.44E-10 0.12%

Epoxy mass(g): 2.17E-02 1.00E-04 0.46%

Estimated the volume in the tube (cm3) 3.00E-02 3.00E-03 10.00%

Mean magnetic susceptibility of gas (cgs, 5.92E-09 0.00E+00 0.00% volume)

Measured slope (emu/Oe) -4.67E-08 -1.09E-10 0.23%

Calculated epoxy magnetic -4.41E-07 6.09E-09 1.38% susceptibility(mass,cgs) continued 68

Table 3.2 continued: Oxygenated RBC:

Value Error CV

Quartz tube mass(g): 8.24E-02 1.00E-04 0.12%

Quartz magnetic susceptibility(cgs, mass) -3.79E-07 -4.44E-10 0.12%

Epoxy mass(g): 3.08E-02 1.00E-04 0.32%

Epoxy magnetic susceptibility(cgs, mass) -4.41E-07 6.09E-09 1.38%

Liquid volume(cm3) 4.87E-03 1.52E-03 31.17%

Water magnetic susceptibility(cgs, volume) -7.19E-07 0.00E+00 0.00%

Estimated the gas volume in the tube (cm3) 1.00E-02 1.00E-03 10.00%

Mean magnetic susceptibility of gas inside 5.92E-09 0.00E+00 0.00% (cgs, volume)

Total cell number 1.71E+08 1.71E+07 10.00%

Total cell volume(cm3) 1.51E-02 1.51E-03 10.00%

Measured slope (emu/Oe) -6.00E-08 -9.88E-12 0.02%

Calculated oxyRBC magnetic susceptibility -7.75E-07 -1.07E-07 13.76% (cgs, volume)

Calculated oxyRBC magnetic susceptibility -9.74E-06 -1.34E-06 13.76% (SI, volume)

Continued

Table 3.2 continued: Methemoglobin RBCs:

Value Error CV

Quartz tube mass(g): 9.37E-02 1.00E-04 0.11%

Quartz magnetic susceptibility(cgs, mass) -3.79E-07 -4.44E-10 0.12%

Epoxy mass(g): 1.63E-02 1.00E-04 0.61%

Epoxy magnetic susceptibility(cgs, mass) -4.42E-07 6.09E-09 1.38%

Liquid volume(cm3) 7.34E-03 1.27E-03 17.30%

Water magnetic susceptibility(cgs, volume) -7.19E-07 0.00E+00 0.00%

Estimated the gas volume in the tube (cm3) 1.00E-02 1.00E-03 10.00%

Mean magnetic susceptibility of gas inside 5.92E-09 0.00E+00 0.00% (cgs, volume)

Total cell number 1.43E+08 1.43E+07 10.00%

Total cell volume(cm3) 1.27E-02 1.27E-03 10.00%

Measured slope (emu/Oe) -6.00E-08 -9.88E-12 0.02%

Calculated metRBC magnetic susceptibility -7.75E-07 -1.07E-07 13.76% (cgs, volume)

Calculated metRBC magnetic susceptibility -9.74E-06 -1.34E-06 13.76% (SI, volume) continued

Table 3.2 continued: Deoxygenated RBCs:

Value Error CV

Quartz tube mass(g): 9.34E-02 1.00E-04 0.11%

Quartz magnetic susceptibility(cgs, mass) -3.79E-07 -4.44E-10 0.12%

Epoxy mass(g): 2.43E-02 1.00E-04 0.41%

Epoxy magnetic susceptibility(cgs, mass) -4.42E-07 6.09E-09 1.38%

Liquid volume(cm3) 4.87E-03 1.52E-03 31.17%

Water magnetic susceptibility(cgs, volume) -7.19E-07 0.00E+00 0.00%

Estimated the gas volume in the tube (cm3) 1.00E-02 1.00E-03 10.00%

Mean magnetic susceptibility of gas inside -1.70E-09 0.00E+00 0.00% (cgs, volume)

Total cell number 1.71E+08 1.71E+07 10.00%

Total cell volume(cm3) 1.51E-02 1.51E-03 10.00%

Measured slope (emu/Oe) -5.85E-08 -3.68E-11 0.06%

Calculated metRBC magnetic susceptibility -5.84E-07 -9.34E-08 15.99% (cgs, volume)

Calculated metRBC magnetic susceptibility -7.34E-06 -1.17E-06 15.99% (SI, volume)

Figure 3.2. The diagram of sample mounting in SQUID-MPMS. Though detectable signal is generated for less than 1μL cell volume, 10-15μL was used for the in the experiment to reduce error.

Figure 3.3. Operating principle of cell tracking velocimetry (CTV) based on cell magnetophoresis in a nearly-isodynamic magnetic field. A) an exploded view of the magnet assembly. The magnetic flux circuit is shown with arrows. The core building block b generates the field gradient between the two hyperbolic pole pieces, and building block a and c suppress the magnetic flux leakage from b. B) Figure of magnet assembly. The objective of the microscope goes through the cutout in the front. C) A blow-up view of the core building block b, magnetic field between pole pieces and the region of interest (ROI, shown as a blue rectangle). The magnetic field was denoted in solid lines in the lower zoom-in image. D) a picture of the magnet assembly setup with parallel-piped glass channel which holds sample.

Figure 3.4. The magnetic field map within the region of interest. A) 3D magnetostatic field modeling with the software Amperes (Integrated Engineering Software, Winnipeg, Canada) to predict the magnetic field in the interpolar gap, with inputs of geometry and material properties. d(B2) /dx and db/dy were calculated based on the 4-th order polynomial fitting. The dashed line at y = 4.46mm indicates the max dB2/dy, which is the center of ROI. B) A plot of mean, standard deviation and coefficient of variance (CV) in the gradient as a function of viewing width (i.e. width of ROI). Apparently grad B2 is more constant for a smaller viewing width. For example, at a typical setting, in which the viewing width is 1.84mm, the CV in grad B2 is as low as 0.0196.

N2 outlet

Dissolved RBC Oxygen sample Detector Mixer Humidifier

N2 Flow meter Tank RBC sample

Magnet Assembly Syringe Glass N Protection Valve Connection 2 Channel Sheath Figure 3.5. Magnetophoresis experiment schematic for deoxygenated red blood cells. The RBCs were pretreated with wet N2 into deoxygenated RBCs before measurement. During the measurement the glass channel, which holds deoxygenated RBCs are protected by wet N2 sheath

Figure 3.6. Magnetic mobility and magnetic susceptibility histograms of oxygenated red blood cells, de-oxygenated red blood cells, and methemoglobin red blood cells by magnetophoretic CTV.

1e-1 Max magnetic suceptibility Non-Stokesian dynamics 1e-2

1e-3 Max trackable velocity

1e-4 Diffraction

1e-5 metRBC/deoxyRBC

1e-6 Image resolution

1e-7 , SI unit, dimensionless) oxyRBC



Wall effects ( 1e-8

Magnetic susceptibility difference susceptibility Magnetic Brownian motion / Thermal noise 1e-9

1e-10 1e-6 1e-5 Particle hydrodynamic diameter (D, m)

Figure 3.7. Operational scope magnetophoresis method. Solid line represents for physical limitation for the method and the dashed line showed the limitation for current setup. The result for oxygenated RBCs (oxyRBC), methemoglobin RBCs (metRBC) and deoxygenated RBCs (deoxy) are also shown (error bar as standard deviation). The metRBC and deoxyRBC results, which are essentially different, overlap in the figure due to the scale vertical axis. The oxyRBC magnetic susceptibility difference only shows upper error, due to the limitation of logarithmic scale of vertical axis.

Chapter 4. Intrinsic magnetism and iron uptake of HeLa cell

4.1. Introduction

Cancer contributes to 50% of deaths worldwide and new anti-tumor therapeutics with novel mechanisms of actions are essential to develop. A growing body of literature indicates the irregular iron metabolism is closely related to cancer development. The implication is complex because iron is involved in many processes related to cell proliferation including oxygen transport, oxygen transport, oxidative phosphorylation

(Cammack, Wrigglesworth, and Baum 1990), DNA synthesis (as a cofactor of ribonuceotide reductase) (Thelander, Gräslund, and Thelander 1983; Thelander,

Gräslund, and Thelander 1985). On the other hand the excess iron, especially free iron

2+ 3+ ions, will generate reactive oxygen species with Fenton reaction (Fe + H2O2 → Fe +

∙OH + OH-),. ROS may cause mutation or cellular damage by oxidation of membrane lipids, and degradation, cross-linking and agglomeration of DNA (Karihtala and Soini

2007; Rice-Evans and Burdon 1993; Wiseman and Halliwell 1996), and hence related to the development of cancer. Up-regulation of protein related to iron uptake and accumulation is widely seen in many cancer cells including breast cancer, colorectal cancer and etc(Daniels, Delgado, Rodriguez, et al. 2006; Daniels, Delgado, Helguera, et al. 2006), which makes the identification with iron essential in cancer research.

Recently, the enumeration and characterization of circulating tumor cells (CTC) has become an emerging prognosis method for tumors (Alix-Panabières and Pantel 2013), though better biomarker analysis and cell separation has to be employed to ensure this clinical available, because looking for CTC in whole blood is often a “needle in a haystack” situation: in 1mL whole blood typically there is around 5 billion red blood cells, 6 million nucleated cells, while CTC are usually reported as less than 100

(McKenzie 1996). Methods based on the biomarker selection such as epithelial cell adherent molecules (EpCAM) has been established, such as CellSearchTM(Ozkumur et al.

2013), yet much criticism of potential false negative has been raised due to the EpCAM down regulation of the metastatic cancers(Thurm et al. 2003).

In this chapter we are seeking the potential employing the magnetic properties of the tumor cells. The typical magnetic separation method is via immuno-binding the target cells with magnetic particles, yet such method still has issues such as expensive reagent, intense labor and potential false negative result. Though the major component of the cells such as protein, carbohydrates, and water are diamagnetic, as the high iron demand and load of tumor cells, it is possible to turn tumor cells to a more paramagnetic state compared to water. Such difference will enable the intrinsic magnetic separations.

As the most of the current method of magnetic moment detection such as Mössbauer spectroscopy (Greenwood and Greatrex 2007), nuclear magnetic resonance (NMR) spectroscopy (Sanders and Hunter 1989), electron paramagnetic resonance requires bulk of materials instead of single cells, we are relying our in house, highly sensitive magnetophoretic system cell tracking velocimetry as introduced in Chapter 2. The magnetic separation of iron treated HeLa cells was demonstrated with the magnetic

deposition microscopy (MDM) due to its strong magnetic field and simplicity of result display. The method will be summarized below and elaborated in Chapter 6.

HeLa cells were chosen as the cell model because it has been shown to have elevated magnetic mobility in previous study (Jin, Chalmers, and Zborowski 2012) if treated with

Fe(NO3)3. In this chapter, we present the determination of the magnetic susceptibility or mobility of HeLa cells with our CTV system. The HeLa cells were treated with different condition of iron supplement to see the change of their magnetic properties. With this information the separation procedure could be determined and its performance could be expected.

Another reagent used here is a strong iron chelating agent, salicylaldehyde isonicotinoyl hydrazone (SIH), The constant of stability of this strong trident iron chelator has been reported from 1027.3 (Buss and Ponka 2003) by a method of EDTA competition to 1050

(Vitolo et al. 1990) by a combination of spectrophotometry and potentiometry.

Nonetheless it is confirmed to bind iron stronger than transferrin, which has the stability

21.2 22.3 constant of 10 and 10 (Martin et al. 1987). Its ability of free traveling through the cell membranes without using transferrin receptor (Laskey et al. 1988) makes it an interesting candidate for delivering iron to the cells, but its role interacting with cell metabolism of iron is still not clear: it has been shown to provide iron for reticulocytes(Poňka et al. 1979), but on the other hand deprive iron from erythoid precursors(Leimberg et al. 2008). Here we test its effect on HeLa cell iron metabolism.

Finally, the effect of iron supplementation on the proliferation rate of HeLa cells was evaluated with the pico-green DNA assay to analyze the potential damage caused by the non-chelated iron supplementation on cell growth.

4.2. Review of iron metabolism, its regulation and its implication with cancer

The major iron metabolism pathways and regulations are summarized in Figure 4.1. Note that those pathways do not occur in the same types of cells.

Iron is transported into cells mainly by transferrin (Tf) and its receptor (TfR1), as pathway I showed in Figure 4.1. The Fe (III) in the surroundings of cells binds to the Tf to form di-ferric Tf. Then it interacts with TfR1 on the cell membrane to form the TfR1-

Tf complex. The binding mechanism on the molecular level was first described by Cheng and Zak(Cheng et al. 2004). The complex then enters the cell by endocytosis. Then the pH within the endosome is lowered to 5.5 by proton pump, which causes the disassociation of the Fe(III) from the Tf-TfR1 complex. The Fe(III) is then reduced by a ferrireductase STEAP3 to Fe(II) and then transported to the cytosol or labile iron pool

(LIP) by divalent metal transporter-1 (DMT1), which is presented on the endosome. Then the TfR1 is recycled to the cell membrane and Tf to the circulation system (Klausner et al. 1983; Richardson and Ponka 1997). A different transferrin receptor TfR2 was also identified and is potential additional iron transporter. Chinese hamster ovary cells with

TfR2 expression showed resistance to iron chelators and aggressiveness (Kawabata et al.

2000).

Uptake pathway II is showed the intake of heme, which is an important iron source for human up to 20% of total iron intake, and it is easier to intake comparing to inorganic iron. (Anderson et al. 2005; Collins 2008; Morrison 1965). Heme carrier protein 1

(HCP1) is identified as the transporter in enterocyte in small intestine, despite its main function of transporting folate (Inoue et al. 2008; Shayeghi et al. 2005; Nakai et al. 2007).

The main catabolism pathway of heme is through heme oxygenase 1 (HO-1). Heme is 82

oxidized to biliverdin, CO and Fe(II) is released (Maines 1988). Fe(II) end up in cytosol or LIB, and biliverdin is rapidly reduced to bilirubin by biliverdin reductase and

NAD(P)H (Tenhunen, Marver, and Schmid 1969). Other HO (HO-2 and HO-3) were also reported (Maines 1997).

Heme’s role in physiological system depends on its concentration: at low concentration, it works by itself or as functional group of heme proteins (e.g. hemoglobin, myoglobin, cytochromes, uanylate cyclase, and nitric oxide synthase) to provide important cellular functions, including oxygen transport, respiration, drug detoxification and signal transduction (PONKA 1999); while at excessive concentration, free heme can cause severe cell or tissue damage by generating ROS, which is similar to free iron. Its toxicity is indicated by the correlation of red meat and colorectal cancer (Cross, Pollock, and

Bingham 2003).

Uptake pathway III shown in Figure 4.1 mainly occurs in the tissues with major iron uptake such as duodenal enterocytes (Wang, Elliott, and Head 1998). Iron in the gut is first reduced to Fe(II) by ferriductase Dcytb, and then transferred in to the cells through the divalent iron transporter 1, or DMT 1 (Mims and Prchal 2005).

Once the iron is transported into the cell, it reached the labile iron pool (LIP) and chelated with complex like citrates (Kruszewski 2003), from where iron could be future stored, exported or utilized. Iron is stored in a ubiquitous protein ferritin. Usually one ferritin molecule can store up to 4500 iron atoms (Theil 1987). The main iron exporter is ferroportin (FPN1), which is mostly found on the basolateral membrane of enterocytes and liver macrophage (Zoller et al. 2002) where iron recycle and transport to be expected.

The intracellular ferroxidase, hephaestin, also appears to play a role in this Fe export

pathway (Han and Kim 2007). Mitochondrion is a focal point for iron metabolism with various intricate trafficking, communication and function such as synthesizing FeS clusters and heme. Though the detailed mechanism is yet know, minor deregulation may lead to mitochondrial dysfunction in iron metabolism, which plays a role in tumorgensis: a decrease in oxidative phosphorylation and a consequent increase of glycolysis in tumor cells , or referred as Warburg effect(Warburg 1956; Carew and Huang 2002)

The iron metabolism is regulated on both systemic and cellular level (Hentze et al. 2010).

On the systemic level, iron metabolism is regulated by hormone hepcidin, which decrease the expression and endocytosis of ferroportin and stimulate the synthesis of ferritin

(Nemeth et al. 2004). On the cellular level, iron metabolism is regulated by the interaction of iron-regulation proteins (IRP-1 and IRP-2) and the iron-regulation element

(IRE) on the mRNA of ferritin, ferroportin, and TfR1. As Figure 4.1 shows, at low iron concentration, IRP-1 and IRP-2 can bind to the IRE on the 3’ end of TfR1 mRNA, to prevent it from being degraded by RNase, so the TfR1 could be up-regulated. Also IRP-1 can bind to the IRE on the 5’ end of ferritin and FPN1, to prevent its contact with ribosome and hence down-regulate these 2 proteins. At a high iron concentration, excess of FeS clusters will be generated by mitochondria and competitively bind to IRP-1 and

IRP-2, to inhibit the processes described above.

Up-regulation of Tf and TfR1 expression level is closely related to the malignancy, invasiveness and growth of breast carcinoma(Lazarus and Baines 1985; Shindelman,

Ortmeyer, and Sussman 1981). A comparison between MCF7 (breast cancer cell) and

MCF12(normal epithelial cells) shows the up-regulation of all iron transporter(TfR1,

TfR2, DMT1) and down-regulation of ferroportin (Jiang, Elliott, and Head 2010).

Brookes (Brookes et al. 2006) also suggest that colorectal cancer is associated with increased expression of Fe importers such as Dcytb, DMT1 and TfR1 and decreased expression of Fe exporter such as FPN1 and hephaestin. The up-regulation of TfR1 is also well-known in prostate cancer (Ellwood-Yen et al. 2003). In addition, TfR1 is also indicated as the downstream target of the c-myc proto-oncogene, which makes it a good target for anti-tumor drugs. They could target TfR1 protein as an inhibitor, as platinum transferrin complex MPTC-63 (Elliott, Stjernholm, and Elliott 1987) and TfR1 antibodies

(Brekelmans et al. 1994), or target TfR1 mRNA as an inhibitor (Yang et al. 2000) or utilizing the pathway to deliver other drug (Qian et al. 2002). Yet such methods may not be selective enough, due to the high expression of TfR1 on some fast growing normal cells such as erythropoietic progenitors (Iacopetta, Morgan, and Yeoh 1982). Ferritin was found increased in many neoplastic cells (Richardson and Ponka 1997). High serum ferritin was related to a poor prognosis(Hann, Levy, and Evans 1980), and it seems this protein is involved with cell proliferation (Kikyo et al. 1994). In summary, the body of data demonstrates that accumulation of iron is commonly seen in cancer cells. Such cells could be potentially highly invasive, due to their resistance to oxidative stress-induced apoptosis and increased growth.

4.3. Method and Material

4.3.1. HeLa Cell culture

HeLa (ATCC-No. CCL-2) cells was acquired from ATCC (The American Type Culture

Collection, Manassas, VA) and cultured in 25cm2 or 75cm2 T-flask (BD Bioscience,

Bedford, MA) in the complete media before treatment. The complete media for HeLa

culture was RPMI-1640(Cleveland Clinic Foundation Cell service, Cleveland, OH) +

10%FBS (Gibco, Grand Island, NY)+ Penicillin/Streptomycin (Pen: 100 unit/mL, Strep:

100ug/mL, Cleveland Clinic Foundation Cell service, Cleveland, OH). The cell cultures were maintained at 37°C and 5% CO2 and split every 3 or 4 days using sterile technique to sustain viability. Up to 20 cell passages were used. Adherent cells were detached by trypsin-EDTA solution (0.05% trypsin, 0.53 mM EDTA, Cleveland Clinic Foundation

Cell service, Cleveland, OH). Fe(NO3) 3 (Sigma-Aldrich F8508-100G, St Louis, MO) was used as the main iron treatment reagent. The amount added was based on the final iron concentration. In the course of study we found precipitation emerge after adding

Fe(NO3)3, and hence later in some conditions the media was filtered through 0.22um filter before added to the cell culture to see if any difference is shown in cell magnetic mobility.

4.3.2. Magnetic mobility analysis with Cell tracking velocimetry

The principle and procedure of Cell tracking velocimetry was elaborated in Chapter 2 and previous publications (Chalmers, Zhao, et al. 1999; Xu et al. 2012), and will be only summarized here, as shown in figure 4-2. The cells suspension is pumped into the glass channel, which is located in a well-characterized magnetic field. Allow for sufficient time, the horizontal motions of the cells are solely driven by the magnetic field. The zoom-in figure shows the hyperbolic shape pole piece generates a uniform, horizontal magnetic field gradient (365 T-A/mm2) in the region of interest (ROI, 1.8mm x 0.75mm), where the cell motion is monitored through the microscope and tracked by the charge- coupled device (CCD) camera. The captured motion is sent to the computer and analyzed

by the software “ImageView”, and the horizontal velocity of individual cell is given.

Before the analysis, the cell sample was counted with Z2 Coulter Counter (Beckman

Coulter Inc., Fullerton, CA, U.S.A) and washed with phosphate buffer saline (PBS) +

0.1% Pluronic F-68. Each test takes in 2-3mL samples with the cell concentration 2-4 x105 cells/mL, and takes around 30min. Assuming the drag obeys Stokes Law, the magnetic (horizontal) velocity um is:

(χ − χ )D2 u = cell water S = m ∙ S (4.1) m 18η m m

2 where, Sm is the magnetic field gradient energy (T-A/mm ), D is the diameter of the cells

(m), η is the viscosity of water (Pa· s), χcell, χwater are the volumetric magnetic susceptibility of cell and water, respectively (dimensionless), m is the magnetic mobility of the cell (mm3/T-A-s), which is the final form of the cell magnetic property result.

4.3.3. Salicylaldehyde isonicotinoyl hydrazone (SIH) synthesis

Salicylaldehyde isonicotinoyl hydrazone is readily available with a simple Schiff base reaction as shown in Figure 4.3(Edward et al. 1988). Dissolve salicylaldehyde in 70% ethanol with the volume proportion of 1:1. Then dissolve isoniazide in DI-water to saturation. Then combine equal-molar of the two reactants and heat to 1000C with a water bath. The final SIH product will precipitate in 5 minutes. After the mixture is cooled, filter the mixture and keep the precipitation. Wash the solid with DI water once, and then dry in open air.

Before preparing the media, SIH is firstly dissolved in 0.1M NaOH to concentration of

1mg/mL. Same volume of 0.1M HCl is added in the media to neutralize the NaOH when

preparing the media. The 1mM of NaCl is generated in such process, but the amount is not substantial because the NaCl concentration in physiological buffer (e.g. phosphate buffer saline) is as high as 137mM.

4.3.4. Analysis of transferrin receptor (TfR1) expression level with Flowcytometry

To test the cell response to the high soluble iron concentration supplemented by

Fe(NO3)3, the expression level of transferrin receptor was tested with flow cytometry.

The assay is based on the PE-conjugated monoclonal anti-human transferrin receptor

(anti-TfR1) antibody. After collected from the T-flask, HeLa cells were counted with Z2

Coulter Counter (Beckman Coulter Inc., Fullerton, CA, U.S.A) and then washed with a degassed labeling buffer (PBS with 2mM EDTA and 0.5% BSA). Set aside 1x 106 HeLa cells, and centrifuge to get the cell pellet. Add 30 μL/106 cells of primary antibody, anti- human TfR1-PE (BD Pharmingen) to the cell pallet. The mixture was incubated on ice and kept from light for 30 minutes, and then washed with labeling buffer again and then suspended in 0.4mL PBS and ready for the flow cytometry analysis. One negative control sample which is not stained with the antibody was also submitted to monitor the auto- fluorescence of the cell. If the cells were not immediately analyzed, a fixing step with a

1% V/V formaldehyde (Polysciences, Inc., Warrington, PA) in PBS solution is required.

The cells is then stored in 40C before analysis. The fluorescence analyses were performed in the LRI Flow Cytometry Core (FACS, BD LSR I, BD Biosciences, San Jose, CA, using a FACSDiva software V5.2.0). The number of PE molecule (i.e. the number of anti-TfR1 antibody bound per cell) is estimated with QuantiBRITE™ PE fluorescent beads (BD Pharmingen). The Beads are resuspended in 500μL PBS before analysis. At

least 10,000 cells were evaluated for each sample and the analysis of the flow cytometry data. HeLa cells incubated for 5 hours in complete media (RPMI1640 with 10%BSA and

Penicillin/Streptomycin), complete media supplemented 5, 10, 50, 100, and 500 ug/mL

Fe(NO3)3, and complete media plus 10 ug/mL salicylaldehyde isonicotinoyl hydrazone

(as iron scrubber) were tested in this experiment.

4.3.5. Magnetic deposition microscopy (MDM) of iron treated HeLa Cells

The Magnetic deposition microscopy will be elaborated in Chapter 6, and only be summarized here. MDM is based on open-gradient magnetic field separator and a thin- film magnetophoresis process developed for cell analysis (Zborowski et al. 1993;

Zimmerman et al. 2006). A high magnetic is gradient generated by a magnet assembly in order to pull weakly magnetic cells from a flowing cell suspension and deposit the cells on an optically transparent, thin sheet of a Mylar for microscopic analysis (Buck et al.

2015). Before assembled to the MDM system, the Mylar slide was rinsed with 70% ethanol, air dried, and then soaked with 0.01% poly-L-Lysine (Sigma-Aldrich, St. Louis,

MO, U.S.) overnight at 40C in a properly sealed Petri dish. This step is to enhance the adherence of the cell once it deposits on the Mylar slide. The direction of the resulting magnetic force acting on algae cells was essentially in the plane perpendicular to the magnet surface, reducing the problem of cell trajectories to two dimensions. Five flow channels were created with a polycarbonate manifold to connect each flow channel to sample inlet and outlet tubing (Figure 4.4C). 1 mL tuberculin syringes provided means for cell suspensions pumping through the flow channels at precisely controlled volumetric flow rate, resulting in two passes of the majority of the cell suspension

volume over the magnet’s two interpolar gaps) (Buck et al. 2015). Before the sample was loaded onto the MDM the concentration of the HeLa sample was washed with MDM

Carrier (PBS with 2mM EDTA and 0.1% FL-68), and its concentration was determined using the Z2 particle counter/size analyzer (Beckman-Coulter). 1 mL of 1 x 106 cells/mL cell sample was set aside for each Eppendoff tube. The refill flow rate (cell suspension going upwards was 0.026mL/min and the infuse flow rate was 0.013mL/min. The difference was to compensate the settling effect of the cells. After the cell suspension pass through both ways the fixative from HEMA-3 staining kit (Fisher Healthcare,

Pittsburg, PA) were chased through the depositing chamber at rate of 0.1mL/min for 5 minutes. Finally the Mylar slides were released from the MDM to observe the deposition.

The HeLa cells were treated with 500 ug/mL, 100 ug/mL Fe(NO3)3 for 72 hours and the media was filtered with 0.45 um filter, similar to previous method in this chapter. The positive control of methemoglobin red blood (met-RBC) cells were prepared in the following method: 100μL of whole blood was added to 10mL PBS and then votexed and centrifuged at 200g for 5minutes; then add 5mL 5μM NaNO2 solution to the cell pallet, then vortex and wait for 30 minutes. Then the cells were washed with MDM carrier and dilute to 1x107 cells/mL

4.3.6. Pico-green assay for measuring HeLa proliferation affected by iron fortification

HeLa cells cultured in complete media were harvested and counted with Coulter counter

(Beckman Coulter Inc., Fullerton, CA, U.S.). Set aside 8 of 1.5 mL Eppendoff tubes containing 1 x 105 cells each, and add complete media to the total volume of 1mL. Add

the 1000 cells (100μL cell suspension) to the cell culture 96 plate well. Triplicates were run for each condition of iron compound, concentration and culture time. Then the HeLa

0 cells were culture at 37 C and 5% CO2 for 3 hours to allow the cell adhere to the bottom.

Then the media is removed and replaced with 200μL of filtered media fortified by different iron compound including Fe(NO3)3, Fe nitrilotriacetate (Fe-NTA) and ferric ammonium citrate (FAC), and 8 different concentration that varies from 0 to 2000μM, and cultured for 24, 48, 72, 96 hours. Once the culture time is up, the spent media from each well was removed and each well was washed with 100μL PBS for 3 times. Then

135μL of 1x Proteinase K solution was added to each well. The 1x Proteinase K solution contains 0.1mg/mL Proteinase K (Invitrogen, Grand Island, NY), 10mM ammonium acetate and 0.01mg/mL sodium dodecyl sulfate. The plate was then sealed with Parafilm and incubated at 600C for 4 hours. Then all samples were moved to a black 96 well plate for fluorescent plate reader and ready for the Pico-green assay.

The Pico-green working solution needs to be prepared in half an hour before the assay due to it is not stable with light. The Pico-green working solution is prepared by diluting

Pico-green reagent (Life technologies, Carlsbad, CA) with Tris-EDTA buffer (10mM

Tris-HCl, 1mM EDTA, pH 7.5) with dilution of 1:200 in a plastic container and kept away from light. 135μL of Pico-green working solution was added to each well (1:1 ratio) and the plate was quickly read with excitation/emission of 485/538 nm. A standard curve with from 0-1000ng/mL double strand DNA with triplicate test was performed for each plate read, and the final result of the DNA concentration will be normalized based on this curve.

4.4. Results and discussion

4.4.1. Magnetic mobility result with Fe(NO3)3 fortification

The magnetic mobility result is summarized in Table 4.1 and the box plot is shown in

Figure 4.5. In some cases, the mean magnetic mobility is much higher than the median result. It is due to the wide-spread, long-tailed distribution of the magnetic mobility profile of the population, especially in conditions with large standard deviation, some tracked events/cells with order of magnitude higher magnetic mobility compared to other events with close-to-zero magnetic mobility, could greatly increase the mean. Figure 4.6 showed an example for wide distribution and long tail of magnetophoresis mobility of one culture sample. Whether those events are cells is still debatable and hence we will value the median magnetic mobility in the comparisons.

As expected, the HeLa cells cultured in the complete media (without iron fortification) have the lowest mean and median magnetic mobility. The iron fortification condition of

500ug/mL Fe(NO3)3 and precipitate not filtered for 24 hours and (denoted as 500 Fe 24h

NF) showed a great increase in magnetic mobility compared to the complete media.

Extending the time to 72 hours (denoted as 500 Fe 24h NF) does not change the magnetic mobility much, which indicates a saturation of iron uptake over 24 hours. A greatly lowered N was due to the lowered cell number due to the harsh iron treatment condition over a longer period of time. The standard deviation was increased, too. Interestingly, although the elevated magnetic mobility with iron supplemented is shown as expected in this work and previous work (Jin, Chalmers, and Zborowski 2012), the result for both

HeLa cells in complete media and HeLa cells in 500ug/mL Fe(NO3)3 are not the same:

Jin et.al showed 3.5 x 10-6 and 5x 10-5 mm3/T-A-s while we showed -1.42 x 10-6 and 1.14

-5 x 10 for the cells in complete media and 500ug/mL Fe(NO3)3 media, respectively. It may indicate a systematic error between the previous version and the new version of the

Cell tracking velocimetry. As shown in Chapter 2, the internal drifting was a potential error source for the previous version of device.

As we seen precipitate shows when adding high concentration of Fe(NO3)3 to the media there is concern that the precipitates which potentially contains iron may be adsorbed to the cells and cause false positive magnetic mobility of the cells. Also as the image analyzer could not differentiate between the cells and larger particle, it could be false positive even without being adsorbed to the cells. To address these issues we simply tried to filter the media with 0.22um filter. For the filtered media results (500 Fe 72h F and

500 Fe 24h F), compared to the corresponded non-filtered media results, we see a major drop in terms of the main and median magnetic mobility. The cells treated for 24 hours even showed no change against the cells treated in complete media, and its standard deviation was small too. Interestingly, extending the treatment time to 72 hours, the magnetic mobility starts to increase and the standard deviation too. It indicates that removing the precipitate slows down the magnetic mobility increasing, and potentially the magnetic mobility we get from non-filtered media may be caused from adsorption.

HeLa cells growth rate was inhibited with high concentration iron supplement

(500ug/mL) for a prolonged time (72 hours), though the magnetic mobility could increase. Hence we are looking into the effect of a lower concentration that would not cause precipitation. 10ug/mL was chosen because that is the highest concentration that we found not to induce major precipitation and the media was filtered to be consistent.

According to Kakuta et al.(Kakuta et al. 1997), iron in the complete media, which is solely from fetal bovine serum, is around 0.24ug/mL. 10ug/mL Fe(NO3)3 corresponds to

2.3ug/mL Fe, which is still a 10 fold increase of iron supply. The cells were cultured for

72 hours With this iron treatment condition (10 Fe 72h F) we see median magnetic mobility increased to about half of the that of HeLa cells in non-filtered media, which is still a significant increase compared to the complete media. This result shows that the increase of the magnetic mobility of HeLa cells could be achieved with a much less aggressive condition and the concern of adsorption could be eliminated.

The effect of Salicylaldehyde isonicotinoyl hydrazone (SIH) was also tested here. The condition was chosen (10ug/mL SIH, 10 ug/mL Fe(NO3)3, 72 hours) to compare with the previous condition (10 ug/mL Fe(NO3)3, 72 hours). With SIH the magnetic mobility of

HeLa cells dropped, indicating SIH in this situation does not play a role of delivering iron to the cells but more of an iron scrubber.

4.4.2. Effect of iron fortification on transferrin receptor (TfR1) expression

The result of transferrin receptor expression is shown in Figure 4.7. The relative expression level was normalized based on the expression level of HeLa in complete media (CM). At lower concentration of Fe(NO3)3 (5 and 10ug/mL) the TfR1 expression level was slightly increasing. At a medium concentration (50 and 100ug/mL), the TfR1 expression level was slightly decreasing. What was surprising is at high concentration

(500ug/mL) TfR1 expression level was greatly increased to around 1.5 times of the control level. The reason of the elevated expression remains unknown. Considering the

low growth rate at this high iron concentration, the regulation the TfR1 expression might no longer function to present this counterintuitive result.

Based on the flow cytometry data, the number of transferrin receptor TfR1 expressed on the cell surface with no iron treatment was around 2.2 x 104 molecules/cell (the number of TfR1 is in 1;1 ratio with the number of PE molecules). The actual number during the cell culture may be even lower because of the adherence nature of the HeLa cells. In a culture with more than 90% confluency in a 75cm2 cell, the number of TfR1 would reach

2.2 x 10-13 mol, while the transferrin molecules in the media would be 2.5 x 108 mol

(Kakuta et al. 1997). A 105 difference was seen between the two molecules. Also compared to the common culture condition of many other cell type such as A431 epidermoid carcinoma(Barnes 1982), and WIL2-NS lymphocyte (Hashizume, Kuroda, and Murakami 1987), the transferrin concentration in the complete media has been much higher (Kakuta et al. 1997). Hence transferrin was not added to the media as we assume the saturation was reached.

We also tested the SIH effect as an iron scrubber. The concentration was 10ug/mL, and no significant change was seen. At this SIH concentration over 97% of iron was bound to

SIH and the calculation is elaborated below: in this media mixture SIH and transferrin will competitively bind to Fe3+. Transferrin iron binding system is fairly complex in

- terms of equilibrium constants. The process is aided by HCO3 and greatly inhibited by

H+. In our system pH is 7.4 as physiological pH. Assuming the HCO3 concentration equals to concentration in RPMI, 1.5g/L (or 17.8mM). The conditional stability constant

Ks of the 2 binding sites, defined below and calculated from Martin et al. (Martin et al.

1987), are 1022.3 and 1021.2.

SIH is a trident chelator of iron, with the molar ratio of SIH: Fe = 2: 1. The stability of

SIH defined below is reported differently among literatures. Buss et al (Buss and Ponka

2003) used EDTA competition method and calculated the stability as 1027.3. While Vitolo et al. (Vitolo et al. 1990) reported Ka as high as 1050 by a combination of spectrophotometry and potentiometry. In this calculation we choose 1027.3 to be conservative.

Hence we have three equations for equilibrium:

[Fe(SIH) ] K = 1027.3 = 2 (4.2) s,SIH [Fe3+][SIH]2

[Tf − Fe] K = 1022.3 = (4.3) 1,tf [Tf][Fe3+]

[Tf − Fe ] K = 1021.2 = 2 (4.4) 2,tf [Tf − Fe][Fe3+]

According to Kakuta et.al (Kakuta et al. 1997), the concentration of iron and transferrin are 4.28 x 10-6 M and 2.5 x 10-6 M, respectively. The concentration of SIH is 10ug/mL in this condition, which is corresponding to 41.4 x 10-6M. So we have 3 equations for mass balance

−6 Total SIH: 41.4 ∗ 10 = [SIH] + 2[Fe(SIH)2] (4.5)

3+ −6 3+ Total Fe : 4.28 ∗ 10 = [Fe ] + [Fe(SIH)2] + [Tf − Fe] + 2[Tf − Fe2] (4.6)

−6 Total Tf: 2.5 ∗ 10 = [Tf] + [Tf − Fe] + [Tf − Fe2] (4.7)

And with 6 equations we could solve for the 6 unknowns, and the result is summarized in

3+ the Table 4.2. The ratio between Fe(SIH)2 and total Fe is 97.8%, meaning SIH has

scavenged most iron in this situation. Also in the case of adding SIH or not adding SIH, the free iron ion (Fe3+) was kept very low, as expected.

4.4.3. The magnetic deposition microscopy result

The MDM slides are shown in Figure 4.8. The methemoglobin red blood cells were included as the positive control of MDM device performance. For the positive control, 4 clear bands of met-RBC deposit were shown at the fringing magnetic field. The cell slides were slightly smudged because the surface tension disturbed the cell deposit when the liquid were removed from the Mylar slide. A strong deposit was observed for HeLa treated with 500 ug/mL Fe(NO3)3 at the fringing magnetic field area too. The bands were less clear than the positive control due to the larger and more spread size of HeLa cells.

Also the cell concentration 1x106 cells/mL might also contribute to a less clear band.

Such deposit were not observed with HeLa treated with 100ug/mL Fe(NO3)3 and HeLa cells cultured in complete media, indicating the deposit were due to the elevated magnetic mobility of the HeLa cells with high concentration iron treatment.

4.4.4. Iron fortification effect on HeLa proliferation

The cell proliferation rate of HeLa cells is represented by the relative DNA content of cell cultured in different iron supplemental condition. The relative DNA condition for day 1 to 4 is defined as:

Relative DNA content

DNA content of HeLa cultured in x Fe concentration at day n = DNA content of HeLa cultured in no Fe fortification at day n

which essentially defines the DNA contents for treated cells compared to the control.

Relative DNA content decrease from Day n to Day n+1 does not necessarily mean the cell number decrease, but the cell number increase slower than control.

The relative DNA content of Fe(NO3)3 and FAC treated HeLa cells are shown in Figure

4.9A and Figure 4.9B, respectively. For FAC and Fe(NO3)3 we see a similar pattern: the relative DNA content plummeted at Day 1 for all concentration, which suggest an inhibition/adaption of HeLa cell growth in the new media from day 0 to day 1. Then from

Day 2 to Day 4 the relative DNA content recovered, where the pattern differs: for

Fe(NO3)3 fortified HeLa the recovery depends on the Fe(NO3)3 concentrations. At day 4 the relative DNA content decreases as the Fe(NO3)3 concentration increases. For FAC, the recovery seems independent with the FAC concentration. In both cases, the growth rate of the iron fortified cells is faster than control in the time interval of day 1 to day 4

(with a few exceptions), which is more readily shown in Figure 4.10A and Figure 4.10B: all doubling time except 2000μM Fe(NO3)3 is lower than the control. These results suggest that with the effect of Fe fortification is biphasic: a selection/adaption for HeLa occurred in the first 24 hours that eliminate a significant portion of all cells, then the cells survived start to recover, or growing faster than the ones in complete media. The different recovery pattern for Fe(NO3)3 and FAC (i.e. the dependence and independence of concentration, respectively) could be explained by the chelating effect of citrate in FAC.

With FAC fortification the free iron in the media was maintained at a lower level by citrate. That is potential why high concentration FAC (e.g. 2000μM) shows similar effect as lower concentration (e.g. 500μM). However, in the case of Fe(NO3)3, the free iron in the media increase as the Fe(NO3)3 concentration increases. The damaging effect of free

iron such as generating reactive oxide species becomes more prominent, which explained the decreasing relative DNA content recovery with the increasing Fe(NO3)3 concentration.

4.5. Conclusion

With the growing body showing the close relation of iron irregular regulation in cancer cells, the iron metabolism of cancer cells become particularly interesting due to both the potential of new diagnostic method and the therapies. In this chapter, the iron treatment condition of cell line HeLa, which was selected by previous work (Jin, Chalmers, and

Zborowski 2012), was optimized and re-evaluated. The potential ambiguity caused by the emerging precipitation was cleared by removing such precipitation, and the increased magnetic mobility of cells was shown with a much lower concentration iron fortification, which was much less stressful to the HeLa growth. Though magnetic mobility was not agreeing with the previous work, the general trend was the same. Such difference may occur due to the potential background flow from the previous version of the cell tracking velocimetry. All in all this result further proves the feasibility of separation of circulating tumor cells by iron supplement, as the iron treatment was more amenable.

The magnetic separation of iron treated HeLa cells was also demonstrated by the magnetic deposition microscopy device. With the positive control of methemoglobin red blood cells and negative control of non-iron treated HeLa cells, it was ensured that the deposit of HeLa cells was caused by the iron treatment.

The expression of TfR1 was suppressed with mid level iron (50-100 mg/mL Fe(NO3)3) supplement as expected according to the regulation mechanism of iron uptake. What was

interesting is the elevated transferrin receptor level at high concentration fortification

(500mg/mL). One potential explanation was the harsh oxidative stress has already caused the HeLa iron regulation system dysfunction to show such counterintuitive result.

The iron fortification effect on the proliferation of HeLa cells were also measured with a fluorescent pico-green assay. An interesting biphasic growth pattern with initial adaption/elimination followed by a faster growth was observed. Also the higher growth rate with chelated iron source (FAC) than non-chelated iron (Fe(NO3)3) was seen and

This is likely to explain the HeLa cells with harsh iron treatment condition could still partially survive and elevate in terms of magnetic mobility.

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Table 4.1. Magnetic mobility of Iron treated HeLa cells. NF stands for non-filtered media, and F stands for filtered media. All magnetic mobilities are in the unit of mm3/T- A-s

Number of Treatment (ug/mL for Standard events Mean median reagent) deviation tracked

10 Fe 10 SIH 72h F 3178 1.36E-06 1.20E-06 3.85E-06

10 Fe 72h F 3204 2.18E-06 2.02E-06 4.05E-06

500 Fe 24h F 2151 -1.30E-06 -1.53E-06 5.69E-06

500 Fe 24h NF 1557 8.72E-06 3.86E-06 1.83E-05

500 Fe 72h F 726 6.32E-06 4.78E-07 3.18E-05

500 Fe 72h NF 3124 1.06E-05 4.15E-06 2.75E-05

Complete media 8319 -1.42E-06 -1.47E-06 3.98E-06

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Table 4.2. The concentration of free iron ion, SIH, transferrin and their complex in the media 3+ [Fe ] [SIH] [Fe(SIH)2] [Tf] [Tf-Fe] [Tf-Fe2] Without 1.67e-21 N/A N/A 2.04e-8 6.79e-7 1.80e-6 SIH With 1.92e-24 3.30e-5 4.19e-6 2.41e-6 9.24e-8 2.82e-10 SIH

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Figure 4.1. The summary of iron metabolism and its regulation of cells. Note that all the pathways are not shown in a single type of cell.

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Figure 4.2. The diagram of the cell tracking velocimetry, Mk V.

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Figure 4.3. Salicylaldehyde Isonicotinoyl Hydrazone as trident iron chelator (2:1) and the Schiff base synthesis reaction

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Figure 4.4. A) MDM Magnetic field strength heat map of z-x plane at x = 0. The deposition effect is strongest at the fringing field area (white area). The 250um space is where the cell culture flow through, corresponding to the rubber space area in C). B) Magnetic field strength measured along y axis. C) Blow-up view of MDM assembles of magnet assembly (1), Mylar slide (2), rubber spacer (3), plastic manifold (4) steel platen (5). (Buck et al. 2015) 106

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0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

-2e-5 0 2e-5 4e-5 6e-5 8e-5 1e-4

Figure 4.6. An example result (Fe 500 24h NF) to show the long tail and wide distribution of the magnetic mobility result.

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Transferrin recptor expression on HeLa 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 Relative Relative Expression 0.2 0 HeLa CM HeLa SIH HeLa 5 HeLa 10 HeLa 50 HeLa 100 HeLa 500 test 1 1 1.12265918 1.13319288 1.17944757 0.71652622 0.91924157 1.6502809 test 2 1 0.92282847 0.97885535 0 0.8758882 0 1.25812842

Figure 4.7. Relative Expression level of transferrin receptor with flow cytometry method. Test 1 result is in blue and test 2 result is in red. The levels of expression of all conditions were normalized based on the level of the HeLa in complete media (CM). The number indicate the Fe(NO3)3 concentration in μg/mL

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Figure 4.8. The magnetic deposition microscopy of Fe(NO3)3 treated HeLa cells for 72 hours and methemoglobin red blood cell (Met RBC) as an positive control

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Relative DNA content of with Fe(NO3)3 1.2

1 Day 1 0.8 Day 2 0.6 Day 3 Day 4 0.4

0.2 Relative Relative DNA content 0 0 500 1000 1500 2000 2500

Fe(NO ) concentration (uM) 3 3

1.4

1.2

1 Day 1 0.8 Day 2 0.6 Day 3 Day 4 0.4

Relative Relative DNA content 0.2

0 0 500 1000 1500 2000 2500 FAC concentration (uM)

Figure 4.9. The relative DNA content of HeLa cells treated with Fe(NO3)3 (A) and FAC (B)

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Doubling time with Fe(NO3)3 (Day 1 to Day 4, h) 50

Doubling Doubling time(h) 10

0 0 40 200 400 600 800 1200 2000

Fe(NO3)3 concentration (uM)

Doubling time with FAC (Day 1 to Day 4, h) 40 35 30 25 20 15

10 Doubling Doubling time(h) 5 0 0 40 200 400 600 800 1200 2000 FAC concentration (uM)

Figure 4.10. The doubling time from Day 1 to Day 4 (i.e. after the adaption phase) of HeLa cells treated with Fe(NO3)3 (A) and FAC (B)

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Chapter 5. Intrinsic magnetism of genetic engineered green algae

Auxenochlorella protothecoides

Part of content of this chapter was prepared for the publication: Buck, A., Moore, L. R.,

Lane, C. D., Kumar, A., Stroff, C., White, N., Xue, W, Chalmers, J.J & Zborowski, M.

(2015). Magnetic separation of algae genetically modified for increased intracellular iron uptake. Journal of Magnetism and Magnetic Materials, 380, 201-204.

5.1. Introduction

Biofuel derived from microalgaes are essential to meet the challenge facing the U.S and global population to provide sufficient and sustainable level of energy (Hannon et al.

2010), due to microalgae’s advantages including 4-5 times of times more energy density per acre of land as compared to conventional crops and biomass (Sayre 2010), adaptability in either fresh or brackish water (Anelia and Eric 2010), fast growth rate, and high content of oil and lipid, especially triacyl glycerol (TAG) up to 50% of its total biomass (Demirbas and Demirbas 2011). However, the harvest, or dewatering step of the dilute culture remains the major hurdle which prevents the bioprocess being economically viable (Coward, Lee, and Caldwell 2014; Sharma et al. 2013), as most currently available technologies, including sedimentation, centrifugation, air flotation,

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filtration and flocculation, have drawbacks such as high energy and consumable cost, flocculant toxicity and infeasibility of scale-up (Shelef, Sukenik, and Green 1984).

Another separation technique considered for microalgal cells is magnetic separation.

Taking advantage of the magnetic particles significantly improved the efficiency of separation with removal efficiencies as high as 94% when combined with a metal flocculant and magnetic filter (Liu, Zhao, and Jiang 2008; Takafuji et al. 2004; Bitton,

Fox, and Strickland 1975). Though the removal and recycle of flocculant remains a challenge, this method serves as a proof of concept for microalgae magnetic separation.

Hence, we propose to develop intrinsic magnetic algae cultures, which enables the transformative magnetic harvesting that employs advantage of low energy cost of magnetic separation and avoids the disadvantage of chemical/nanoparticles addition

(Yavuz et al. 2009).

Though most cell constituents are non-magnetic (i.e. diamagnetic), intracellular metal, especially iron does display magnetic properties. Iron is an essential element in vital cell processes such as respiration and photosynthesis, and also constituent of protein complex such as ferritin and heme group of cytochrome c. Some algae strains have been shown to accumulate amounts of iron as high as 360mg/100g, which is far exceeding their intracellular needs, due to their capacity of accumulating iron with efficiently(Becker

1994; Park, Craggs, and Shilton 2011). Yet such capacity is subjected to the strain and the culture condition(García-Casal et al. 2007) and up to optimization. Also it indicates the different mechanisms of iron uptake and subsequent responses to iron starvation and excess(Sutak et al. 2012).

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Two iron uptake mechanisms are currently known in microorganisms. In one mechanism, the absorbed iron starts as water soluble ferric iron (Fe3+) chelated by iron-chelating ligands such as EDTA or citric. However, to cross the microalgae cell membrane, the ferric iron must be reduced by membrane localized ferri-reductase protein (Fre1) in order to release free ferrous iron ferrous iron (Fe2+). This step appears to be a universal requirement for Eukarya (Allen et al. 2007). Then Fe2+ is transported into the cells with the aid Fe2+ specific iron assimilation protein (Fea1), which selectively transports ferrous iron but no other cytotoxic heavy metals if non-selectively assimilated (Robinson et al.

1999; Long et al. 2008; Allen et al. 2007). The ferrous iron are transported into the membrane through the iron transporter Irt1 and Irt2 as ferrous iron, or a multi-copper ferroxidase/ferric iron transporter system FOX1/Ftr1(Blaby-Haas and Merchant 2012).

The latter system is induced during iron-deficiency (Herbik, Bölling, and Buckhout

2002). Finally, a third gene Fer1 mediates the iron homeostasis of the algae(Harrison and

Arosio 1996). Ferritin stores the iron in the form of ferrihydrite and only releases the free iron under iron-limited conditions, which effectively limits the free iron inside the cell, also it plays a protective role under photo-oxidative stress condition(Busch et al. 2008). It is also reported as paramagnetic (Zborowski et al. 1995) and contribute most of the driving force in magnetic separation. Another means to increase iron content in algae is through the action of siderophores (Sandy and Butler 2009), which are high-affinity iron chelating small molecules secreted by plant and other species and are directly taken up by species-specific transporters.

In this Chapter, the preliminary study of the magnetic mobility with of wild type was first presented and its change over time was tested with cell tracking velocimetry (CTV). The

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internal control particle method was used in the CTV Mk II system(Sm equal to 141 T-

2 A/mm , B0 ≈ 1.4 T, dB0/dx = 500 T/m). The media component was tweaked with addition of MnSO4 to see its effect on the magnetic mobility. Later, as gene Fea1, Fre1 and Fer1 are all known in algae species such as Chlamydomonas reinhardtii (Herbik,

Bölling, and Buckhout 2002),we will over-express these genes in the model algae,

Auxenochlorella protothecoides (A.p.) (that are transformable) with high endogenous iron content in order to increase the intracellular iron content further. In addition to verification of successful transformation by polymerase chain reaction (PCR) and iron content with inductively couple plasma atomic absorption (ICP-AA), the physical properties, i.e. the magnetic properties of the strains were also analyzed by cell tracking velocimetry (CTV), and the magnetic separation was demonstrated with magnetic deposition microscopy (MDM).

5.2. Method and Materials

5.2.1. Genetic engineering of A.p. strain

Nuclear transformation of A. protothecoides strain KRT1006 (referred as wild type, or

WT) was undertaken with simultaneous bombardment by vector pP0176 carrying the gene Fer1 and Fea1 vector pP0175 carrying gene Fre1 (Long et al. 2008; Rupprecht

2009; Purton 2007). Two transformants exhibiting resistance to both hygromycin (from vector pP0176) and paromomycin (from vector pP0175) were then screened by PCR and electrophoresis to test for presence of Fer1 and Fre1 (Figure 5. 1). As Fea1 is on the same vector as Fer1, transgenic clones confirmed with Fer1 presence is also believed to be Fea1 positive(Long et al. 2008; Purton 2007).

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5.2.2. Algae culture and growth measurements

The algae cells were cultured in 3 different media: modified high salt media (MHS), manganese algae media (MAM), and Ferric: EDTA Molar Equivalent Media (FEME) with various levels of iron. The components of the media are elaborated in Appendix B. Algae cultures were inoculated at a 0.1 OD (750nm) in 50mL media at room temperature. The culture was agitated at 150rpm in an Environ shaker with an orbit of 2.5 cm (Lab-line

Instruments, Melrose Park, IL). A constant light condition (about 100μM photons/m2-s) was kept.

The algae cell concentration was measured with UV-Vis spectrometer at 750nm (A750).

The cell concentration is proportion to absorption. Before measurement, the algae sample was centrifuged, then washed with PBS, and then diluted properly to fit the linear range of the spectrometer. The dilution ratio was constant and determined from the initial Day 0

A750 reading: after diluting the Day 0 adsorption should be 0.1.

5.2.3. Cell tracking velocimetry

The cell tracking velocimetry (CTV), were elaborated in Chapter 2 and will be summarized here. The cells suspension is pumped into the glass channel, which is located in a well-characterized magnetic field. Allow for sufficient time, the horizontal motions of the cells are solely driven by the magnetic field. The hyperbolic shape pole piece generates a uniform, horizontal magnetic field gradient in the region of interest (ROI), where the cell motion is monitored through the microscope and tracked by the charge- coupled device (CCD) camera. The captured motion is sent to the computer and analyzed

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by the software “ImageView”, and the horizontal velocity of individual cell is given.

Assuming the drag obeys Stoke’s Law, the magnetic (horizontal) velocity um is:

(휒 − 휒 )퐷2 푢 = 푐푒푙푙 푤푎푡푒푟 ∙ 푆 (2.1) 푚 18휂 푚

푢푚 = 푚 ∙ 푆푚 (2.3) where m is the magnet mobility of cells, Sm is the magnetic field gradient energy (365 T-

A/mm2 for current Mk V system, and 142 T-A/mm2 for previous Mk II system), D is the diameter of the cells (m), η is the viscosity of water (Pa· s), χcell, χwater are the volumetric magnetic susceptibility of cell and water, respectively (dimensionless). The magnetic mobility m essentially characterize the reponse of a particular cell to a given magnetic field such as magnetic sorter, or how ‘magnetic’ one cell is.

In the preliminary study of wild type, the algae culture was tested daily from Day 2 to Day 9 for magnetic mobility with Mk II CTV and internal control particle method. In the comparison between the wild type and the genetic modified strains, the algae culture was kept for 7 days and then tested with CTV MkV and the fluorescent option was used.

5.2.4. ICP-AA measurement

The algae culture were harvested at their late log phase, and then centrifuged to a pallet.

The pallet was then resuspended in an EDTA buffer, then centrifuged again and lyophilized. The lyophilized powder was then sent for elemental analysis with ICP-AA

(National Testing Laboratories, Ltd., Cleveland, OH).

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5.2.5. Magnetic Deposition Microscopy

MDM is based on open-gradient magnetic field separator and a thin-film magnetophoresis process developed for cell analysis (Zborowski et al. 1993; Zborowski et al. 1995; Zimmerman et al. 2006) The magnet generates a high magnetic gradient generated by a neodymium permanent magnet assembly in order to pull weakly magnetic cells from a flowing cell suspension and deposit the cells on an optically transparent, thin sheet of a Mylar for microscopic analysis (Buck et al. 2015). The direction of the resulting magnetic force acting on algae cells was essentially in the plane perpendicular to the magnet surface, reducing the problem of cell trajectories to two dimensions. Five flow channels were created with a polycarbonate manifold to connect each flow channel to sample inlet and outlet tubing (Figure 4.4C). 1 mL tuberculin syringes provided means for cell suspensions pumping through the flow channels at precisely controlled volumetric flow rate, resulting in two passes of the majority of the cell suspension volume over the magnet’s two interpolar gaps) (Buck et al. 2015). Before the sample was loaded onto the MDM the concentration of the algae sample was determined using the Z2 particle counter/size analyzer (Beckman-Coulter). The pump was set to infuse the cell suspension, and the eluate algae fraction was also counted by the Coulter Counter. The concentration difference between before and after flow through MDM was considered capture on the Mylar slides. More detailed protocol and principle of MDM will be elaborated in Chapter 6.

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5.3. Results and discussion

5.3.1. Preliminary magnetic mobility over time

The results of the wild type (KRT 1006) cultured in MAM media MHS media is shown in Figure 5.2 over the culture time from Day 2 to Day 9. Though the standard deviation is Compared to the MHS culture, the MAM culture increases the magnetic mobility slightly faster, especially at Day 8 and Day 9. The difference was essentially due to the higher iron concentration and manganese concentration. This result proved that the capability of wild type enriching magnetic metal such as iron and manganese.

5.3.2. Comparison of wild type and genetic engineered algae

The media was optimized for the strain growth and iron loading. In this set of experiment

EDTA was chosen over citrates as the iron chelator because citrate is a poor chelator in the neutral pH, and it serves as a carbon source which increases the chance of contamination. Figure 5.3 shows the wild type growth under different iron concentration

(0, 1, 3, 5, 8x iron). 1x iron equals to 37μM. No significant growth difference was seen except the growth inhibition of the iron deprived condition.

The magnetic properties of the strains were measured with cell tracking velocimetry system(Jin et al. 2008; Xu 2012). The results are shown in Figure 5.4. T test shows that the genetic modified strains are all have significantly higher magnetic mobility than the wild type (p < 0.001). All genetically modified strains have a larger spread than the wild type, indicating disperse iron uptake rate within the population. Also, though the cells were washed with PBS before any measurements, potential crystallization or adsorption

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on the cell membrane rather than intake is still possible. The non-specific iron absorption to the cell membrane surface is not uncommon for marine algae(Hutchins et al. 1999), which could be driven by electrostatic attraction, precipitation of iron hydroxide due to the higher pH around cell surface, or other mechanism(Milligan, Mioni, and Morel 2009).

Nonetheless in terms of magnetic separation it is equivalently effective.

As the ferritin is the main iron storage molecule, it is reasonable to assume the magnetic cell response to the magnetic field is predominately driven by the iron content in ferritin molecules. Hence, the number of ferritin molecule per cell could be estimated. Consider one cell is constituted of diamagnetic non-ferritin biomass and paramagnetic ferritin, we have:

(VC − nVf)χB + nVfχf χc = (5.1) Vc where 흌c is the volumetric magnetic susceptibility measured from the CTV, Vc is the cell volume (mean volume 4.017x10-17m3, measured with coulter counter), n is number of

-24 3 ferritin molecules per cell, Vf is the volume of ferritin molecule (1.77 x 10 m , assuming 15nm ferritin molecule diameter), 흌f is the volumetric magnetic susceptibility

- ferritin, 흌B is the volumetric magnetic susceptibility non-ferritin biomass (-1.4x10

5 )(Chalmers et al. 2010). 흌c is given by:

18ηm χ = + χ (5.2) c d2 f

The diameter of the cell was previously measured by Xu et. al. (Xu 2012) as 4.25μm.

Then with equation 5.1 n could be calculated. Considering each ferritin molecule contains

4500 Fe atoms the number of Fe atom(Zborowski et al. 1995), the number of iron atoms

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per cell could also be estimated. The result is summarized in the Table 1. We could see that the transformants hold significantly higher elemental Fe.

It is again interesting to see the magnetic mobility result from CTV Mk II is higher than

CTV Mk V, which is similar to the situation we had in Chapter 4. Again this could indicate the systematic error between the two systems.

The ICP-AA result was demonstrated in Table 5.1. The genetic modified strains showed a higher iron content than wild type, which corresponds to the trend we observed with

CTV. Assuming 5 x 106 cells/mL in the stationary phase of the culture, the atoms per cell is in the same order of magnitude with the estimation from CTV.

The magnetic separation is demonstrated with magnetic deposition microscopy (MDM).

The cell concentration was counted with Coulter counter before and after loading into the

MDM system(Buck et al. 2015). The capture result is shown in Figure 5.5. We found with 1x Fe supplement media, the capture rate of transgenic strain 113 is similar to the wild type (~5%), which indicates that in 1x Fe concentration media the wild type has effectively uptake most of the iron, or the transgenic strain does not shown any advantage in terms of iron uptake. With 8 x Fe concentration, all strains including wild type have shown a significantly higher capture rate and the wild type shows a lower capture rate

(15%) than the transgenic strains (25%).

5.4. Conclusion

The magnetic separation of the bio-fuel producing algae could potentially reduce the energy cost of the dewatering process to make the process viable. In this Chapter we tested the metal enriching capability of Auxenochlorella protothecoides strain KRT1006 122

(wild type) and proved over time this strain could slowly pick up the iron. 3 different genetically engineered strains 113, 119 and 166, which incorporates Fer 1, Fea 1 and Fre

1 were selected and verified by PCR screening. The functionality of those genes were further tested by the magnetic properties of iron containing cells: a significantly higher magnetic mobility was shown in strain 113, 119 and 166 and the proof of concept of algae deposition was demonstrated with magnetic deposition microscopy. Around 30% of the strain 113 and 119 cultured in 8x iron media could be captured with MDM. Though the scaling up of the magnetic separation remains a challenge, this chapter serves as a proof of concept for the capability of A.p. enriching Fe in the media and be magnetic enough to enable dewatering process.

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Table 5.1. Number of ferritin and iron atom per cell

CTV result ICP-AA result Strains m (mm3/T- Fe atom/ Iron mass in Fe atom/ 흌c Ferritin/cell A-s) cell culture(mg/L) cell WT -6.73E-7 -9.7E-06 2.08E+05 9.36E+08 1.5 3.23E+09 113 2.56E-6 -6.5E-06 5.00E+05 2.25E+09 2 4.30E+09 119 9.95E-7 -8.0E-06 3.59E+05 1.61E+09 3 6.45E+09 166 9.06E-7 -8.1E-06 3.51E+05 1.58E+09 -

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Figure 5.1. Results of PCR screening to test presence of Fer1, Fea1 and Fre1 among triple A.p. transformants. The upper panel of electrophoresis gel shows results of PCR to test for presence of Fer1 and the lower panel shows results of PCR to test for presence of Fre1. C: no template control, WT: wild type A.p. strain, +ve: plasmid control. The selected strains with both expression (113, 119 and 166) were highlighted in red color.

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Figure 5.2. Magnetic mobility of wild type in media MAM and MHS over time by the internal control method.

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0.900

0.800

0.700

0.600 0x iron 0.500 3x iron 0.400 5x iron

0.300 8x iron Absorption at 750nm 1x iron 0.200

0.100

0.000 0 1 2 3 4 Culture time (Days)

Figure 5.3. Growth of the wild type in algae with varied iron concentration. 1x iron equals to 37 μmol [Fe(III)EDTA]-. The growth was measured with spectrometer at 750nm. The experiments were run with triplicate and the error bar shows the standard deviation. No significant impact was seen in different iron concentration except 0x iron addition, which indicates the growth inhabitation on iron-deficiency. Hence the highest concentration 8x iron addition was used in the following iron loading culturing.

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Figure 5.4. Magnetic mobility of wild type (WT) and genetic modified strains (113, 119, and 166). Genetic modified strains apparently showing a higher mean magnetic mobility. A) The histograms of magnetic mobility of the 4 strains. B) Box plot of the magnetic mobility of the 4 strains. Not all outliers are showed, 1% and 99% percentile is shown as dots.

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Chapter 6. Magnetic deposition microscopy

6.1. Introduction

Within the past three decades, magnetic separation has evolved into an indispensible technique in life science. Although the separation of cell is mostly developed and commercialized (e.g. MACS systems), separation of DNA (Mahmoudi, Simchi, and

Imani 2010) are also reported. Nowadays the sensitivity (lowest applicable concentration of target cells), selectivity over non-target cells, and target cell purity has been extensively improved to meet the need of subsequent analysis and process of most cell collections(Zborowski and Chalmers 2011). As most biological matters, including DNA, protein or intact cell, have very similar magnetic susceptibility with water (diamagnetic), most magnetic separation approaches depend on the label of paramagnetic or superparamagnetic particles on the biological matter. By proper surface chemistry, the surface of the particles is directly linked (i.e. by conjugation) or indirectly linked (e.g. by strepavidin-biotin interaction) with antibodies, which binds to the target cells. Essentially the magnetic particles are the main contributor of the driving force for the cell motion in the static magnetic field, and the selectivity of the target cells for labeled magnetic separation.

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Though most cells have magnetic susceptibility similar to water, there are a few exceptions. Either naturally or with proper treatment, these cells has higher iron or manganese contents and hence has higher magnetic susceptibility than water. Zborowski et. al. (Zborowski et al. 2003) used cell tracking velocimetry to show that deoxygenated red blood and met-hemoglobin red blood cells have magnetic mobility of 3.86 × 10−6 and 3.66 × 10−6 mm3/TAs, respectively. As introduced early, magnetic mobility is a parameter characterizing the motion of a particle (or cell) driven by static magnetic field in viscous liquid (Zborowski et al. 2002). A positive magnetic mobility indicates the particle (or cell) has a magnetic susceptibility difference between itself and the suspending buffer that is positive (i.e. χparticle minus χbuffer). Sun et. al. (Sun, Zborowski, and Chalmers 2011) showed the Bacillus atrophaeusspores have a mean magnetic susceptibility of 1.86 × 10−4 (SI, volume) because of its high manganese content. With proper magnetic separator, these cells types enable the label-less magnetic separation, which has its unique advantages. First, without labeling reagent, label-less magnetic separation is easier to operate and far more cost effective than labeled magnetic separation. These traits are important for large scale separation. Second, label-less approach avoids any improper selection of target cells caused by the non-specific binding and/or agglomeration of the antibody-particle complex. Third, label-less magnetic separation avoids the blocking of any receptor on the cell surface, which may be essential for the subsequent treatment. Last, label-less approach is based on the magnetic susceptibility of the cells, or essentially the expression of iron/manganese containing protein in the cell. This approach could serve as a selection and characterization for iron containing cells.

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With all these advantages, we introduce the magnetic deposition microscopy (MDM) as the label-less magnetic separation device. The magnetic deposition system was firstly developed in Dr. Zborowski lab for the depositing Er3+ treated E.coli and observed in the dark field microscopy (Zborowski et al. 1993). Then it was used in the analysis of ferritin-labeled lymphocytes (Zborowski et al. 1995). Recently MDM has been established as an economical viable yet superior method in diagnosing malaria infected erythrocytes in less-developed region (Zimmerman et al. 2006; Karl et al. 2008). A multiple stage MDM was also developed in other research group (Nath et al. 2008) and a reasonable throughput of immuno-labeled Jurkat cells was achieved.

The MDM trap the cells which are more magnetic susceptible than water with a narrow fringing magnetic field, and deposit them on a polyester slides. The strong magnetic field and field gradient in the MDM makes it a perfect sorter for label-less cells, which are typically not as paramagnetic as cells labeled by magnetic particles. The deposition slide is readily treated with subsequent characterization steps like immunocytochemistry (ICC) staining. Also, a simulation program predicting the cell movement in the MDM is available. The magnetic mobility of the target cell population, which is needed by this simulation program, is acquired by cell tracking velocimetry (CTV). As shown in previous chapters, CTV gives a distribution of the magnetic mobility of the target cell population on single-cell basis, which could be a direct input to the simulation program.

In this chapter, two models of MDM (Mk I and Mk II) and the simulation program are introduced. Then the experimental result of methemoglobin red blood cells (metRBC) and Bacillus spores on the Mk I, genetically modified algae mentioned in Chapter 5, are

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compared with the simulation result. More simulation predictions with the MDM are also presented.

6.2. Method and materials

6.2.1. MDM Mk I

The MDM Mk I specifics has been described in previous publication(Zimmerman et al.

2006), and the key parameters will be summarized here and shown in Figure 6.1. The fringing magnetic field between the two magnetic poles magnetized by ferrite magnet assembly is designed to maximize the magnetic field gradient that drives the magnetic deposition. The direction of the resulting magnetic force acting on target cells was essentially in the plane perpendicular to the magnet surface, reducing the problem of cell trajectories to two dimensions. A thin flow channel, where the cell suspension is passed through and the magnetic cell of interest is deposited, is created with, from bottom to top, the Mylar deposition slide, a silicone rubber spacer with near-rectangular shape cut-out to serve as the channel side wall, and an acrylic top cover as an manifold of tubing to allow the cell suspension flowing in and out. The major parts of all 5 parallel flow channels are right on top of the interpolar gap where the magnetic pressure is at maximum. The interpolar gap was 1.27mm. The surface H-field and B-field at the midline of the interpolar gap are 1.35 x 106 A/m and 1.426 T, and the gradient is 804 T/m. The cell suspension was washed with PBS and then suspended in the MDM carrier (PBS + 2mM

EDTA + 0.1% Pluronic 68) with proper concentration (5 x 107 cells/mL for metRBC and

1 x 107 CFU/mL for Bacillus spores) and before the deposition experiment. Around

500µl cell suspension are delivered in a continuous manner into the flow channel (s) by a

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syringe pump (PHD 2000 Programmable Syringe Pump, Harvard Apparatus Inc.) connected to inlet tubing, and evacuated from flow channels by outlet tubing leading to waste containers. The flow channel cross-section was 6.4 mm × 0.25 mm, the volumetric flow rate was 0.7 ml/hr for the Bacillus spore suspension and 2mL/hr upward and 1.2 mL/hr downward for metRBC (i.e. the cell suspension pass through the deposition region twice). After pushing the cell suspension through, the flow channel was disassembled with caution to avoid air intrusion which could change the deposition lines. The Mylar slide could be then stored by fixed to a glass slide for the ease of microscopic observation.

The rubber spacer was washed and stored in 70% ethanol.

6.2.2. MDM Mk IV

Although sharing the general principle with MDM Mk I, the MDM Mk IV is featured with stronger neodymium permanent magnets, which makes the miniaturization possible for the MDM device and a new design for the magnet assembly (Figure 4.4C). The new designs allow incorporates two inter-polar gaps and fringing field to increase the capture of the cell of interest. The maximum magnetic field intensity measured at the midline between the two interpolar gaps in the 0y direction was By = 0.475 T (Figure 4.4B); when interpolated to the interpolar gap region (using Amperes 3D boundary element method field modeling software) the field was in excess of 1.4 T (Figure. 4.4A). The general procedure with the genetic engineered algae with MDM Mk IV is very similar to Mk I except for the following points: 1) Before assembled to the MDM system, the Mylar slide was rinsed with 70% ethanol, air dried, and then soaked with 0.01% poly-L-Lysine

(Sigma-Aldrich, St. Louis, MO, U.S.) overnight at 40C in a properly sealed Petri dish.

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This step is to enhance the adherence of the cell once it deposits on the Mylar slide. 2) 1 mL of 1 x 106 cells/mL cell sample was set aside for each Eppendoff tube. The refill flow rate (cell suspension going upwards was 0.026mL/min and the infuse flow rate was

0.013mL/min. 3) After the cell suspension pass through both ways the fixative from

HEMA-3 staining kit (Fisher Healthcare, Pittsburg, PA) were chased through the depositing chamber at rate of 0.1mL/min for 5 minutes. This step serves as both fixing the cell onto the Mylar slides and reduce the chance of rupture of deposition band. The main reason of the band rupture is ascribed to the high surface tension between air and the MDM carrier (essentially water). With fixative such surface tension is greatly reduced and hence the deposition band could be better preserved.

6.2.3. Tested cells

Methemoglobin red blood cells (metRBC) are prepared as described in previous chapters:

5 mM oxidant solution was prepared by dissolving sodium nitrite (NaNO2, Cat.

No.524379, Sigma-Aldrich Co., Milwaukee, WI) in PBS at room temperature. Red blood cells (RBCs) from the RBC stock suspension prepared was centrifuged and resuspended in 10 mL of 5 mM sodium nitrite solution, which was then incubated for about 1.5 hours to achieve a 100% met-hemoglobin oxidation. The final concentration of the metRBC suspension is 1x108 cell/mL, and washed with phosphate buffer saline (PBS) before use.

Bacillus atrophaeus was obtained from ATCC (#9732) were cultured, a processed into spores, as described previously (Melnik et al. 2007) and only summarized here. The

Bacillus atrophaeus culture was streaked on trypticase soy agar (TSA) in a three phase streak method and expanded to 200mL form a single colony. Then the culture was heat

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shocked at 600C for one hour to initiate sporulation. The percentage of sporulation was determined by TSA plate. The spores were eventually washed with DI water and collected for the deposition experiment.

Genetic algae cells were described in Chapter 5 and are only summarized here. The wild type and 2 genetic modified strains of Auxenochlorella protothecoides (113 and 119) with insertion of Fre1, Fer1 and Fea1 genes were cultured in FEME media with various till late-log phase before the MDM test.

6.3. MDM simulation program

A first-principle prediction of the magnetic sorter is valuable, yet not always available due to the complex flow pattern (e.g. high gradient magnetic sorter, HGMS) and poorly defined magnetic field (e.g. MACS system). For those systems, only empirical correlation is available, however, for a well-defined open gradient system with laminar flow, such as MDM, the first-hand prediction is complex yet available from numerical method. Hence, a simulation program was created with Matlab to predict the deposition pattern on the slide and the capture rate of the system. The full simulation program is shown in Appendix C. In summary, this program predicts the trajectory of individual cells with certain magnetic mobility in the magnetic field, and then sum up the result by weighing the frequency of this cell subpopulation in the magnetic mobility distribution, and eventually calculates the deposition percentage and pattern on the deposition slide with the consideration of the flow profile in the rectangular duct.

Several assumptions are included in the simulation: 1) the flow is fully developed; 2) cells are uniform distributed in the suspension before entering the channel; 3) cells are

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considered point masses; 4) inter-particle effect is neglected; 5) the motion stops when the cells reach the slide, 6) the z-component (along the inter-polar gap) of the magnetic field is negligible;7) the flow profile solely depends on y, or the distance between the cell and the deposition surface. So the simulation is simplified to a 2-D analysis. The motion of a particular cell at position (x, y) in the fringing magnetic field is governed by the following equations:

dx m d(B2) = (x, y) + used + uf(y) (6.1 ) dt 2μ0 dx

dy m d(B2) = (x, y) (6.2) dt 2μ0 dy

In these coordinates, the gravity direction is in the x-direction, and the direction perpendicular to the deposition surface is y. m is the magnetic mobility of the cell (m3/T-

A-s), B(x,y) is the magnetic field at position (x,y), 푢푠푒푑 is the settling velocity of the cell

(m/s), and 푢푓is the velocity of the flow (m/s). If all information on the right-hand side of the equation 6.1 and 6.2 is given, one can simulate the trajectory of this cell with a numerical differential equation solver. If the trajectory lands on the deposition slide (y =

0.1mm, which is the thickness of the deposition slide), this cell is considered captured.

The magnetic field map (resolution of 500 x 40) in the region of analysis (5mm x

0.25mm), or B(x,y), is generated by the field modeling software Magneto(IES, Winnipeg,

Manitoba, Canada), with boundary element method. To ensure Magneto generates the correct field map, it is “calibrated” with the measurement result of MDM with a F.W. bell

Gaussmeter and Hall probe (Orlando, FL). The x component of B is measured along y direction, in the center of the inter-polar gap. The predicted field B is adjusted until it

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closely matches the measurement results. The gradient of B2 is calculated by 5-point

Lagrange method in the simulation program.

푢푠푒푑 is generated by CTV measurements. The average sedimentation velocity of the total population is used, as the sedimentation of the individual cell is hard to relate to the

“simulated” cells. The flow velocity uf(y) is averaged from the local velocity at different z positions on this y position, or

n 1 u (y) = ∑ u (y, z ) (6.3) f n f i i=1

The local flow velocity within the rectangular channel calculated by the equation:

2 cosh(√3 AR θ) − 1 uf(y, z) = umax(1 − ϕ ) (1 − ) (6.4) cosh(√3 AR) − 1

Where AR is the channel aspect ratio, or AR = channel width/channel height, and θ and φ are dimensionless variables along y and z directions, normalized to the half-width and half-depth with the origin at the duct center. The maximum velocity umax is calculated from the Purday flow profile. m is provided by the CTV measurements, which generates a distribution of magnetic mobility of the cell population. With the assumption of even distributed cells in the suspension, the cell with particular magnetic mobility may enter the channel at any given y position at the entrance, and the frequency is proportional to the local flow velocity. For each bin in the mobility distribution, 50 initial position of trajectory is simulated in the program, and their end-point and capture rate are averaged by weighing the local flow velocity, as the throughput of the cell number is proportion to the flow velocity. Then the

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results from each bin are averaged weighing their frequency. In this way the deposition pattern and the capture rate of the whole population is predicted.

6.4. Results and discussion

6.4.1. Comparison between the experiment result and simulation result

The magnetic deposition of metRBC with MDM Mk I is demonstrated in Figure 6.2.

Figure 6.2B showed a picture of stained metRBC deposited MDM slide. Figure 6.2A is the blow up of the deposition area in Figure 6.2B. Figure 6.2C showed is a 2-D plot of fraction of cells deposited at a function of x location with the predicted deposition frequency pattern in blue, cumulative frequency of the deposition in red, and experimental deposition pattern of metRBC in green. The experimental cell deposition distribution was obtained by first high-resolution cell deposition image acquisition using flat-bed scanner (Epson Perfection 164OSU Scanner, Epson Corp.) followed by image segmentation into 8383 = 3,064 pixel rectangular segments covering the cell deposition image and a portion of the adjacent areas devoid of cells, and recording mean 8-bit pixel luminosity for each segment (Adobe Photoshop, Adobe Inc.) as a function of distance across the cell deposition area. The experimental result demonstrated two peaks that corresponds the two fringing field area as predicted, but the two peaks were not resolved as the prediction. This may be because some effect such as adhesion force was not taken into consideration in the simulation, or when chasing the liquid in the channel with air, the deposition pattern was smudged by the surface tension between water and air. The capture rate of the metRBC by the MDM device is calculated by the difference between the initial metRBC number and the collected metRBC number. Considering the loss of 139

cells in the process, the experimentally measured fractional cell recovery in the liquid collected downstream from the magnet (30%) was in close agreement with that predicted by the model (23%).

The magnetic deposition of Bacillus spores is shown in Figure 6.3. Figure 6.3A is an enlargement of the deposition region of the slide. Figure 6.3B is a picture of the Bacillus spores depositing on MDM slide. Figure 6.3C is a 2-D plot of fraction of cells deposited at a function of x location with the predicted deposition frequency pattern of spores before sterilization (blue), after sterilization (red), and cumulative frequency for spores before sterilization (green). The deposition pattern of the Bacillus spores is notably different from metRBC: there is only one peak. The difference is essentially because of the high magnetic mobility of Bacillus spores and with the current flow rate almost all spores before sterilization and the magnetic portion of spores after sterilization were captured, leaving no spores (or magnetic spores) to capture in the second fringing field area. The predicted value of capture rate of spore before separation rate (95%) is very close to the experimental value (98%).

The magnetic deposition of genetic modified algae strains is shown in Figure 6.4 (Buck et al. 2015). Figure 6.4A is a picture of the metRBC depositing on MDM slide with the heat map of the local B-field. Figure 6.4B is the light absorption scan of the slide (from software Image J). C is a 2-D plot of fraction of cells deposited at a function of x location with the predicted deposition frequency pattern (bar) and cumulative frequency (line).

Again the predicted deposition “4-band” pattern well matched the experimental deposition pattern.

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6.4.2. Performance prediction with the simulation program

As the previous section showed, the predicted deposition pattern and capture rate is in well agreement with the experimental result. On this basis, we use the Matlab simulation program to predict the behavior of the metRBC in the MDM Mk I in several aspects.

Figure 6.5 is a fraction of trajectories of metRBC in the MDM system with a flow rate of

0.5ml/hr. The figure shows the region of the analysis, which is ± 2.5 mm on the x direction (the magnetic field is symmetrical) and from 0.05 to 0.35 mm on the y direction. For clarity, 15 rather than the actual 50 seed positions used in the simulation are shown. 10 trajectories are shown for each seeding position, which is a more rough division of the magnetic mobility than actual program. As described earlier, the deposition frequency of cell with different magnetic mobility but from the same seeding position is summed by weighing the frequency of the magnetic mobility in the distribution. This data is best generated with CTV. Then these deposition frequency patterns from different seeding locations are summed with weighing the flow rate of different seeding location, based on the evaluation of the finite rectangular flow profile.

Hence in Figure 6.5, the trajectories merely sever as the possible trajectories of cells yet the number is not accurate for calculating the capture rate. Nonetheless, visual inspection shows that many trajectories in Figure 6.5 land on the slide surface at ±0.6 mm, corresponding the predicted peak position and position with the strongest fringing field.

The actual ratio of trajectories landing this region is even higher than what is shown in

Figure 6.5 as the many of the trajectories landing this region comes from the middle seeding position and middle magnetic mobility, which have a higher weight overall.

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The relation between the magnetic deposition capture rate, deposition pattern and flow rate of metRBC is shown in Figure 6.6 with only one pass. At 0.1mL flow/h, a total recovery is observed (98.66%). As capture rate decreases sharply with the increasing of flow rate, at 1ml/h flow the capture rate decreased to less than 25%, however, the capture rate dropped slower when flow rate increased above 1ml/h. The decrease of capture rate could result from comparative low magnetism, and the metRBC escaped from the channel due to a faster flow before deposited on the slide. Figure 6.6B shows the simulation results of met RBC local capture rate versus deposition location under different flow rate. The minus location -2.45mm is the entrance of the flow. It was indicated that the flow rate does not change the 2-peak deposition patter, except for

0.1ml/h: the second peak diminished in this situation simply because most cells have already deposited on the slide before getting to the second peak region.

Two cells examples were demonstrated by both experiment and simulation about the relationship between capture rate and flow rate, with underlining magnetophoretic mobilities. For spores and metRBC which both have constant paramagnetism, capture rate is only correlated with flow rate at a specific magnetic mobility. The relationship can be generalized for all particles or cells that have constant paramagnetism (Figure 6.7).

Figure 6.7A shows the theoretical capture rate at different flow rates of hypothetical cells with negligible sedimentation velocity and certain magnetic mobility, hence this plot can be considered as an estimate for most behavior of cells. A linear increase of capture rate with the magnetophoretic mobility, from 0 to almost 100% capture can be observed for all flow rates. The noise is due to the numerical nature of the simulation. In Figure 6.7B, the theoretical magnetophoretic mobility cut-off was shown, which is defined as the

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minimum magnetic mobility allowing 100% capture rate, or the turning points in Figure

6.7A, versus the flow rate. Again a linear increase is observed.

According to the linear relationship shown in Figure 7, the capture rate (cr) for a particular cell population with uniform and positive magnetic mobility m (mm3s/kg) and no sedimentation, under the flow rate of Q (ml/h) is:

1 if m > 2 × 10−5Q or cr = { m if m < 2 × 10−5Q 2 × 10−5Q with this equation, for any cell we can estimate the maximum flow rate for the required capture rate. For example, for the spores before sterilization, if we need to achieve a 90% capture rate, the maximum flow rate is about 1.9mL/min.

As for metRBC and Bacillus spores with magnetophoretic mobility of around 4.15 x 10-6 mm3/TAs and 2 x 10-4 mm3/TAs, using claimed flow rate during the experiment, the capture rate should be close to 1 for spores and 25% for metRBC. Not exact 100% recovery of cells was observed due to the distribution of the magnetism of spores. In other words, even spores have constant magnetic susceptibility and the average is well above the cutoff shown in Figure 6.7A, the magnetic susceptibility of each spore cluster could vary from -1.9 x 10-5 to 2.8 x 10-3 mm3/T•A•s according to past report(Sun,

Zborowski, and Chalmers 2011).

The generalized relationship between capture rate, flow rate and magnetophoretic mobility fits well for the experimental cases for RBC and spores, which could be useful information for developing and characterizing magnetic separation.

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6.5. Conclusion

In this chapter the magnetic deposition microscopy (MDM) was introduced as a separation/analysis tool. The strong magnetic fringing field in both Mk I and Mk IV makes the deposition of weakly magnetic cells such as metRBC possible. The well- characterized open magnetic field and the laminar flow enable a simulation program based on the first-principle. With the magnetic mobility result generated from CTV, the simulated magnetic deposition pattern and capture rate well matched the experimental result with 3 different cell populations including metRBC, Bacillus spores and genetic modified algae. The coupling of the two technologies is especially exciting as the whole process of the magnetic separation thoroughly understood and well controlled. Then with the simulation program the approximate relation between the capture rate, magnetic mobility and flow rate were generated. It is of great value in determining the flow rate based on the goal capture rate of MDM before experiment to generate ideal deposition slide. Though the MDM is not easily scaled up, its small-scale application in field such as cell separation in clinical sample is very promising.

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Figure 6.1. Schematic diagram of magnetic deposition microscopy (MDM) system, and an enlarged d an enlargement in the magnetic deposition zone.

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A B

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B A

0.08 1 C 0.07 0.9 0.8 0.06 0.7 0.05 0.6

0.04 0.5 Before Sterilization Fraction 0.4 0.03 After Sterilization 0.3 0.02 Cumulative fraction (before 0.2 sterilization) 0.01 0.1 Capture rate : Before: 94.1% 0 0 After: 76.0% -0.003 -0.002 -0.001 0 0.001 0.002 0.003 x position (mm/s)

Figure 6.3. Experimental and predicted fraction of Bacillus spores deposited using the MDM instrument. A is an enlargement of the deposition region of the slide. B is a picture of the Bacillus spores depositing on MDM slide. C is a 2-D plot of fraction of cells deposited at a function of x location with the predicted deposition frequency pattern of spores before sterilization (blue), after sterilization (red), and cumulative frequency for spores before sterilization (green).

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Figure 6.5. Simulated trajectories of metRBC in the MDM Mk I. Two regions with the most trajectories touching the slide surface are around ±0.6mm, where the fringing field is strongest.

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1 0.9 0.8 0.7 0.6 0.5

0.4 Capturerate 0.3 0.2 0.1 0 0 2 4 6 8 10 12 Flow rate (ml/h) B

0.18 0.16 0.14 0.12 Q = 10 ml/h 0.1 Q = 1 ml/h 0.08 Q = 0.5 ml/h

Capturerate 0.06 0.04 Q = 0.2 ml/h 0.02 Q = 0.1 ml/h 0 -3 -2 -1 0 1 2 3 location (m)

Figure 6.6. Predicted capture rate of metRBC as a function of flow rate, A; and predicted deposited position vs. capture rate as opposed to different flow rates, B.

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1 0.9 0.8 0.7 Q = 0.1ml/hr 0.6 0.5 Q = 0.5ml/h

0.4 Q = 0.7ml/h Capturerate 0.3 Q = 1 ml/h 0.2 Q = 3ml /h 0.1 0 0.00E+00 2.00E-05 4.00E-05 6.00E-05 8.00E-05 Magnetophoretic mobility(mm3 s/kg)

2.50E-04

2.00E-04 y = 2.00E-05x + 1.02E-08 R² = 1.00E+00

1.50E-04

1.00E-04

5.00E-05 mobilty cutoff(mm^3s/kg)

0.00E+00 0 2 4 6 8 10 12 flow rate(ml/h)

Figure 6.7. The simulated capture rate of cells vs. magnetophoretic mobility, A; and magnetophoretic mobility cut-off vs. flow rate, B.

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Chapter 7. Characterization of magnetic micron-particles with

magnetic field flow fractionation

7.1. Introduction

Magnetic nanoparticles have received extensive attention in the applications such as biomedical imaging (Haacke et al. 1999), drug delivery (Sun, Lee, and Zhang 2008), information storage (Hyeon 2003), medical diagnosis and therapy (Mornet et al. 2004) and biomedicine (Pankhurst et al. 2003), as they exhibit unique magnetic properties from bulk material or micro-particles, due to the single-grain size of those particles. As it is well understood that the magnetic properties of nanoparticles strongly depend on the chemical composition and size of the particles, the characterization is very important for particles.

Magnetic field flow fractionation (magnetic FFF) is a relatively new technique in the large family of field flow fractionation, which was invented in 1966(Giddings 1966).

Previous work in Zborowski’s group has been dedicated to construction of quadrupole magnetic FFF system, and the performance of magnetic nano-particles and microparticles in a number of programmed field decay and flow rate conditions (Williams,

Carpino, and Zborowski 2010; Carpino et al. 2005). The catch and release ratio for the

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particles essentially depends on their magnetic properties and the competition between the magnetic field force and the flow shear force.

In this chapter, the quadrupole magnetic FFF was operated in the on-and-off mode to evaluate the property of an Anti-R-Phycoerythrin (PE) magnetic particle. The result could potentially guide the magnetic separation processes with such particles and serve as an analytical separation method for sub-populations or weakly magnetic contaminants within the particle.

7.2. Method and materials

7.2.1. Materials

The Anti-R-Phycoerythrin (PE) magnetic nano-particles (Cat. No. 557899, BD

Bioscience, U.S.) were analyzed with the magnetic FFF. This particle was optimized for magnetic separation of leukocytes subpopulations. The magnetic FFF carrier was prepared with bio-degradable FL-70 (Cat. No. SF105-1, Fisher scientific, Massachusetts,

U.S.) and Milli-Q water (0.1% v/v). This carrier serves as the carrier fluid and sample diluent.

7.2.2. Magnetic field flow fractionation apparatus

The apparatus of magnetic FFF was described in previous publication (Orita et al. 2013) and only summarized here. A flow diagram of the system is shown in Figure 7.1. The 0.1%

FL-70 carrier serves as a carrier fluid through the system maintaining a constant flow rate, driven by a mobile phase pump (515 HPLC pump, Waters Corp., Milford, MA). The particle suspension sample was injected through an injection valve (7725i, Rheodyne, 153

Cotati, CA) into a shallow rectangular duct (23.5cm x 1.6cm x 0.025cm), which was surrounded by a stainless steel tube and then quadrupole electromagnets. With the electromagnet turned on then turned off, two subpopulation of particle will be observed in the eluate with an optical detector (fixed wavelength 254 nm, HyperQuan, Inc.,

Colorado Springs, CO).

The electromagnets ((aperture 1.6 cm, length 15.24 cm) of magnetic FFF apparatus

(shown in Figure 7.2 )was powered by a Xantrex HPD60-5 regulated DC power supply

(Xantrex Technology Inc., British Columbia, Canada). The maximum magnetic field in this study was 0.52T. The control of the magnetic field was well defined in previous publication and 3 values used in this study were summarized in Table 7.1.

7.2.3. Procedures

Before use, the carrier (0.1 % FL-70 in MilliQ water) was degassed in the vacuum for 30 min. The magnetic particle sample was then diluted with it in ratio of around 1:5, aiming for the majority of the signal from the optical detector is within a linear range (no more than 1). Connect the HPLC pump with the carrier, and remove any bubble in the eluate side with a 10mL syringe. Switch on the HPLC pump, power supplies for the magnetic coil and the detector. Run the carrier at 1ml/min for 20min to clean the system, then switch to target flow rate for the experiment. The voltage of the coil was set as 60V and the current was set according to the desired magnetic field, according to Table 7.1. The

UV detector scale was set as 0.5, and the baseline is auto-zeroed until flat before every experiment. Denote this time point as t0. Use 20μL syringe to wash the channel 3 times with 0.1% FL-70 carrier through the loading port, and then load 20uL sample. This time

154

point is denoted as t1. Caution should be used to prevent any bubble into the system. Wait until the first peak fully develops. When the signal from UV detector is flat again, set the current as 0.0854A (magnetic field = 0T) to release the particles. This time point is denoted as t2. Again wait until the second peak fully develops and the UV detector reads the baseline level again, and then end the run. Denote this time point as t3. Essentially the signal between t1 and t2 was generated by magnetic particles that flow through the magnetic field and signal between t2 and t3was generated by magnetic particle that was trapped in the field. After the run, flow the 0.1% FL-70 carrier through the channel for another 20min to clean up. For triplicate experiment, the same timeline for procedures

(time point t0, t1, t2, t3) followed to ensure the same peak was captured. The B field used in the set of experiment is 0.1T, 0.21T and 0.52T, and flow rate used were 0.05mL/min,

0.1mL/min, 0.5mL/min and 1mL/min.

7.3. Results and discussion

An example of the analysis result is shown in Figure 7.3. The magnetic particle concentration in the eluate is essentially proportional to the absorbance measured by the optical detector. Hence peak area is proportional to the total amount of the magnetic particle subpopulation. As the magnetic FFF was operated in a on and off mode, so the first peak corresponds to the particles that were not captured by the electromagnetic field with desired B-field, and the second peak corresponds to the particles that were captured.

Hence the yield of the electromagnetic capture (Y) is given by:

Peak #2 Area Y = (7.1) Peak#1 Area + Peak #2 Area

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The peak #1 and peak #2 corresponds to the eluate signal when the field is turned on and off, and it was calculated numerically. Another issue in the measurement is the baseline of the magnetic field is drifting over the course of the measurement. It is compensated by assuming the drifting is linear increasing and deducted from the peak area.

Figure 4.2 summarized the capture rate of the anti-PE particle with different flow rate and magnetic field. The capture of the magnetic particles is essentially the competition between the magnetic force and the flow shear force. As expected, the capture rate is a monotonic function of flow rate and the magnetic field: the higher the magnetic field and lower flow rate, the higher the capture rate.

7.4. Conclusion

In this Chapter, we were able to manipulate the magnetic capture rate of magnetic particles in the on and off operation of the magnetic field flow fractionation device by altering the magnetic field and the carrier flow rate. It could be potentially applied in the analytical separation of particle sub-populations with low and high magnetic susceptibility with a pre-defined and easily changed cut-off. Also the capture rate could serve as guidance for development of magnetic separation process with proper magnetic sorter and flow rate.

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Table 7.1. Relation between supply electrical current, B-field and field gradient of magnetic FFF

Electric current supply (A) B-field (T) Magnetic field gradient (T/m) 0.5 0.1 12.5 1 0.21 26.25 3 0.52 65

157

Figure 7.1. Flow diagram of the Magnetic FFF system.

158

159

0.1 0.08 0.06 0.04 0.02 0 y = 1.097E-03x - 2.828E-02 Absorbance -0.02 -0.04 y = 8.746E-03x - 3.134E-02 -0.06 0 1 2 3 4 5 Volume (mL)

Figure 7.3. An example raw result for the capture/release of magnetic particles. The two black lines are the simulated baseline for the peak and the equation was listed in the figure.

160

Capture Yield %

80.00%

60.00%

40.00% Yield %

20.00%

0.00% 0.05 0.1 0.52T 0.5 0.21T 1 0.1T Flowrate(ml/min) magnetic field(T) 1 0.5 0.1 0.05 0.52T 66.38% 74.19% 70.51% 77.19% 0.21T 41.31% 50.06% 68.31% 72.48% 0.1T 26.46% 40.27% 61.94% 66.24%

Figure 7.4. The summary of capture rate with different magnetic field and flow rate. Each experiment was done in triplicates and the standard deviation does not exceed 3.5%.

161

Chapter 8. Conclusion and recommendations

8.1. Summary

In this dissertation, the analysis of intrinsic magnetic properties of cells with cell tracking velocimetry (CTV) and the separation with magnetic deposition microscopy (MDM) were discussed. CTV was developed and improved over the last 2 decades in Dr.

Chalmer’s lab and Dr. Zborowski’s lab. With the recent innovations of CTV, its extraordinary capability of analyzing the weak magnetic properties of cells in physiological state was demonstrated by comparing with commercially available magnetometer and analysis of different weakly magnetic cells. The separation device,

MDM was elaborated in terms of structure and performance. Thanks to its simple laminar flow pattern, and the magnetic mobility date available from CTV, theoretical calculations of cell deposition were carried out with a Matlab simulation program, which closely agrees with the experimental result. A magnetic separation/analytical device magnetic

FFF was introduced as a parallel method for magnetic nanoparticles. With CTV and

MDM, the intrinsic magnetism of iron treated HeLa and genetic modified

Auxenochlorella protothecoides was analyzed and the separation was demonstrated.

Overall, the potential of analyzing and utilizing the magnetic properties of weakly magnetic cells was demonstrated.

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8.1.1. Cell tracking velocimetry

In Chapter 2, CTV developed for the past 2 decades were summarized and several innovations were elaborated. The internal control method with polystyrene particles were established and verified with methemoglobin red blood cells to mitigate the potential background flow of CTV. The new version of CTV MkV with high magnetic field

2 energy gradient (Sm =365 T-A/mm ) was introduced and the Sm was verified with particles suspending in paramagnetic Gd3+ carrier. The fluorescent option of CTV was introduced and provides with better image quality with lower sample concentration, which could reduce the effect of particle interactions.

In Chapter 3, the current version CTV MkV was compared with a commercial system, superconducting quantum interference device magnetic properties measurement systems

(SQUID-MPMS), by measuring three different types of red blood cells. A protocol for measuring the magnetic properties in physiological state with SQUID-MPMS was also developed. As an in house device, the limitation of detection for CTV was discussed theoretically. Though the two devices presents similar accuracy, CTV is advantageous for cell sample in physiological states in terms of sensitivity, throughput, low sample requirement and single cell base.

8.1.2. Intrinsic magnetism of cells

In Chapter 4, the magnetic properties of HeLa with Fe(NO3)3 fortification were analyzed with CTV. The fortification condition was optimized so the magnetic mobility of HeLa could increase in less oxidation stressful conditions. The separation of iron fortified HeLa cells was also demonstrated with MDM. Then the iron metabolism of HeLa in the 163

conditions shown earlier was explored by measuring the transferrin receptor surface marker and a DNA assay. An adaption pattern for the growth and iron intake observed.

The magnetic properties could serve as an indicator of the iron metabolism of cancer cells and a potential way of analytical separation.

In Chapter 5, CTV was also used as a function assay for genetic engineered algae cells

(Auxenochlorella protothecoides) which incorporates iron metabolism related genes including Fre1, Fea1 and Fer1. 3 strains were selected and with CTV a higher magnetic mobility than the wild type was demonstrated. The magnetic separation was demonstrated with MDM to show the potential of the magnetic dewatering of the bio-fuel producing strains.

8.1.3. Magnetic separation devices.

In Chapter 6, in addition to the application in HeLa and algae stated earlier, MDM was elaborated and a simulation program was introduced. The simulation results employ the magnetic mobility data generated from the CTV, and output results closely agreed with the experimental result in terms of deposition patterns and capture rate for methemoglobin red blood cells, genetic engineered algae and Bacillus spores. Hence a simplified model was proposed with the simulation result for general cell species. MDM has been demonstrated as a magnetic sorter perfect for small scale magnetic separation and subsequent analysis for weakly magnetic cells.

In Chapter 7, magnetic field flow fractionation (magnetic FFF) was introduced as an analytical/separation apparatus for magnetic particles. An anti-PE magnetic nanoparticle

164

was analysis in an on-off mode to demonstrate the potential of removing non-magnetic contaminants and need-based segmentation of the particles.

8.2. Recommendations

8.2.1. Multiple color fluorescent CTV

The fluorescent option for CTV has greatly improved the image qualities and reduced the sample concentration for CTV, when it is applicable. However, the single-color channel and grey-degree image analysis has limited the differentiation between two cell populations via fluorescent method (such as different fluorescent dye). Here a multiple color CTV is proposed for potentially differentiate and analyzing two cell populations, when the magnetic properties and settling velocity are similar.

Essentially the emission difference between two cell populations is distinguished by a tunable filter. The excitation and dichoric mirror are indented to be kept non-tunable. The excitation may not be optimum for fluorescent dyes, yet the quantum dots could serve as an alternative due to their broad excitation profile. A diagram for potential setup is shown in Figure 8.1.

As the CCD camera cannot differentiate colors, it is impossible to acquire the particles/cells with different fluorescence simultaneously with a single CCD camera.

Though it is still possible to track particle/cells of different fluorescence color within very small time interval, or “quasi-simultaneously”. Figure 8.2 displays the proposed snapshot mechanism of an example with only 2 fluorescence color (red and green) on the particles/cells measured. Although the two sets of the pictures are essentially not taken simultaneously, which makes overlying the corresponding frame of the 2 sets not 165

meaningful, the measured magnetic velocities and settling velocities from the two sets, however, are measured slightly staggered, or “quasi-simultaneously”.

Two sets of liquid crystal tunable filter were found as candidates, and summarized in

Table 8.1. Either of the choice should be feasible for the system setup,

In conclusion, multiple color CTV with tunable filter could be the next step for CTV upgrade. With the capability of differentiation cells with fluorescent color, new dimension of the analysis is available with CTV and it is of value for heterogeneous cell population such as circulating tumor cells.

8.2.2. Large scale magnetic separation

In this dissertation the magnetic separation of several types of intrinsically magnetic cells including genetic modified algae was demonstrated with the MDM. Previously, the magnetic separation of immuno-labeled T-cells with quadrupole magnetic sorter (QMS) has been demonstrated (Tong et al. 2007), too. However, the design of MDM and QMS was not meant for scale-up and large scale magnetic separation is in need especially for genetic engineered algae. Scale-up for the magnetic sorter is in general difficult as even strong magnetic field and field gradient could diminish quickly over distance.

Recently in Chalmers a magnetic wheel sorter has been designed and built as a solution for scalable magnetic sorter (Figure 8.3). The key part of the MWS is a low carbon magnetic wheel that contains 40 neodymium magnet cylinders, which creates a magnetic field around the outer bound of the drum. The space between the cylinders was filled with epoxy to have a smooth surface for the magnetic algae to deposit on. The magnet wheel is driven by a high-torque, low speed, variable DC motor with maximum rotational speed 166

of 0.6 rpm. The wheel is intended to be put on top of a shallow channel with algae suspension with the bottom part submerged into the suspension. The algae would deposit on the cylinder surface while the wheel is turning upwards. On a higher position the cell deposit will be collected by a scraper.

Though the current algae strains are not magnetic enough for the separation with the wheel, it is recommended test the performance of the wheel with cells that binds to magnetic particles (Toh et al. 2014)to serve as a high through put magnetic sorter.

8.2.3. Effect of other iron compound on HeLa

In Chapter 4, Fe(NO3)3 and FAC has been added to the media as iron fortification reagents and their effect on HeLa cell growth and magnetic mobility was measured.

Many other iron reagents could serve as candidate for iron supplementation reagent, among which ferric nitrilotriacetate (FeNTA) is particular of interest. NTA is a weak chelating agent of Fe3+ with stability constant of log Ka = 15.9 (compared to EDTA as

25.0)(Inoue and Kawanishi 1987). Knobel et.al. (Knöbel et al. 2006)tested its effect on the growth rate and ROS generation with human colon tumor cell HT29, and shown only over 1000μM FeNTA starts to suppress cell growth. Klenow et al. (Klenow, Pool-Zobel, and Glei 2009)concurred the same result and tested it on human colorectal adenoma cell line LT 97 and saw an increased growth ranging 10-1000 μM. It will be interesting to see the effect of FeNTA on HeLa cell growth. Other iron reagent of interest include

Fe(EDTA) and ferric-sorbitol-citrate (FSC) (Poljak-Blazi et al. 2011).

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Table 8.1. Candidate tunable filter for multiple color CTV

TOF-SB-VIS CRI-VIS series Aperture 20mm 22mm/35mm Bandwidth Tunable Not tunable, 3 different bandwidth in difffernt products FWHM: 12/24/39nm at 550nm FWHM: 10nm at 550nm (narrow, approximately) Max FWHM: 15/35/60nm Max FWHM:25nm (narrow, (app) approximately) Response <500ms, depends on 50-150ms time wavelength change Tuning FWHM/10 FWHM/8 accuracy Assembly C-mount both sides Mount for optical table and recessed area for other mount. (FWHM: Full width at half maximum)

168

Figure 8.1. Diagram for multiple color CTV

169

Figure 8.2. Timeline for “quasi-simultaneous” measurement with multiple color CTV.

170

variable speed DC motor

½ x 2” Neodymium magnet cylinders

32” V-belt

axel with keyed slot

low friction Steel flux bearing return

slots for belt tightening

Mount for scraper

Figure 8.3. Proto-type of vertical wheel magnetic sorter.

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Appendix A: Detailed deduction of Brownian motion limitation of magnetophoresis

method

As the cells/particles to be measured are suspended in the carrier fluid, the Brownian motion of the cells/particles is inevitable. Here we only consider the magnetic velocity, which is a 1-dimensional situation. Hence the Brownian motion considered here is also 1-

Dimensional.

If we assume:

1) The particles/cells are identical in diameter and magnetic susceptibility;

2) The magnetic field gradient is uniform across the region of interest;

3) The particle/cell interaction is negligible

4) No background flow interference of any sort.

We can conclude that the variance of the magnetic velocity of particles/cells is solely contributed by the Brownian motion. We can consider that, for a particular measurement, the actual motion of any particle/cell i as the sum of a determined velocity generated by the magnetic field, vm and a random velocity generated by Brownian motion, vBi:

vi = vm + vBi (a1)

Note that for each particle vm is the same, but vBi is different.

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Because the mean displacement of Brownian motion is zero, the mean magnetic velocity

equals to the magnetic velocity generated by the magnetic force, or:

vm = v̅ (a2)

The variance of the velocity is represented by the standard deviation:

np np 1 1 2 2 vstd,m = √ ∑(vi − v̅) = √ ∑ VBi = vstd,B (a3) np − 1 np − 1 i=1 i=1

Where is the standard deviation of the magnetic velocity of all particles/cells, is the number of particles, is the standard deviation of Brownian motion velocity of all particles/cells.

The 1-D Brownian motion is analyzed by Einstein in 1956 with a model very similar to the basic diffusion model:

∂ρ ∂2ρ = D (a4) ∂x ∂x2

Where ρ(x, t) is the density of the Brownian motion particles, t is the time, x is the displacement, and D is the massive diffusivity.

Assuming all particles starts from the origin at time t = 0, the diffusion equation has the solution:

x2 ρ0 − ρ(x, t) = e 4Dt (a5) √4πDt

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At any given t, the distribution of displacement is a normal distribution with mean as 0 and standard deviation as √2Dt , or

x̅̅2̅ = 2Dt (a6)

With the analysis of particles in gravity field at dynamic equilibrium, Einstein obtained:

k T D = B (a7) 3πηd

Where kB is the Boltzmann constant, T is the temperature (K), is the dynamic viscosity, d is the diameter of the particles.

Combine equation (a6) and (a7), we have:

2k Tt x̅̅2̅ = B (a8) 3πηd

Then standard deviation of the Brownian motion velocity, also the standard deviation of magnetic velocity is:

2k Tt v = v = √ B (a9) std,m std,B 3πηd

The sensitivity of the CTV is basically the question whether the mean magnetic velocity is significantly different from 0. As the velocity measured has a Gaussian distribution, the standard deviation is known (calculated from Brownian motion), and number of particles is large (around 1000), the Z-test is applicable:

The null hypothesis:

vm√n Z = p (a10) vstd,m

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−|Z| p = 2Φ(−|Z|) = 1 + erf ( ) (a11) √2 then we have (1-p) level of confidence to reject the null hypothesis

If we let p = 0.05, we have the minimum of vm:

1.96vstd,m vmin = (a12) √np

Combine equation (a9) and (a12)

1.96 2kBTt vmin = √ (a13) √np 3πηd

The fundamental equation for magnetophoresis method is

Δχd2 v = S (a14) 18η m

Hence Corresponding difference of magnetic susceptibility

35.28 2k Tη √ B Δχmin = 2 (a15) Sm√np 3πd t

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Appendix B: Recipe for the algae media

Preparation 1L of Stock Solutions: to 800mL of deionized water, add amount of salt as indicated in the table. Agitate with magnetic stirring bar until all solid are dissolved. Then bring to 1L with deionized water.

To prepare 1L of sterile medium: to 800mL of deionized water, add 5mL Solution B,

5mL Phosphate Solution, 1mL Iron Solution, and 1mL Trace Metals Solution. 1: adjust

Iron Solution added according to desired concentration (e.g. 8X = 8mL)

Bring to 1L with deionized water. If making media plates, add 15 g of bacto-agar to the solution. Prepare bottle for autoclaving and follow the autoclave protocol. Wait the media to cool down before adding 1mL filtered vitamin B1 stock solution in a biosafety cabinet.

Replenish stock solutions when there is only 100 ml remaining.

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Ferric: EDTA Molar Equivalent Media (FEME)

1 L Stock Solution Final Chemical Concentration Solution B (200X)

NH4Cl 100 g 0.5 g/L

MgSO4.7H2O 4 g 0.02 g/L

CaCl2.2H2O 2 g 0.01 g/L Phosphate Solution (200X)

K2HPO4 288 g 1.44 g/L

KH2PO4 144 g 0.72 g/L Iron Solution (1000X)

Tetrasodium EDTA.2H2O 15.4 g 15.4 mg/L

FeCl3.6H2O 10 g 10 mg/L Trace Metals Solution (1000X) *Add metals in order as listed*

CuSO4.5H2O 80 mg 0.08 mg/L

ZnSO4.7H2O 1.25 g 1.25 mg/L

MnSO4.H2O 380 mg 0.38 mg/L

CoCl2.6H2O 250 mg 0.25 mg/L

Na2MoO4.2H2O 250 mg 0.25 mg/L

H3BO3 1.25 g 1.25 mg/L

Vitamin B1 (1000X)

Thiamine Hydrochloride (Vitamin B1) 100 mg/L 100 μg/L

Modified High salt Media (MHS)

Replace the EDTA iron solution with the following iron citrate solution (100X):

Iron Solution (100X)

Sodium Citrate ∙2H2O 50 g 500 mg/L

FeCl3.6H2O 1 g 10 mg/L

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Manganese Algae Media (MAM)

MHS with 3x iron citrate solution plus 200μM MnSO4.

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Appendix C: Matlab code for the MDM simulation

%this program is recreated from Lee Moore's TFM simulation on Maple by Wei %Xue %This program's purpose is to calculate trajectories for labeled cells to %determine recovery and deposition location in the TFM channel. %An input of frequency vs. mobility from CTV is required. %This program will read in stored B data from a Magneto output file (modified with Excel). %With matrix manipulation x and y force components are obtained. %THIS PROGRAM GIVES THE DEPOSITION PATTERN AND RUNS FROM BOTTOM UP. %IT WILL RUN FASTER THAN A TOP DOWN PROGRAM WHEN Fcapture <= 0.5. function capture_rate = MDM_v2()

%RBC physical parameters: density difference compared with water (kg/m3), and cell radius (m). delrho = 95; Rcell = 3.85 * 10^(-6);

%Give the total flow rate for the experiment in mL/min. Qml_min = 1/60;

%Reads in external file of mobility versus frequency obtained by CTV. Units of mobility are in mm3/TAs. mobDATA = importdata('c:/Users/Wei Xue/exp/MDM/MDM simulation/deoxyrbc.txt');

%//plot(mobDATA);

%Give the directory path to the magnetic field data file. %This file will be *.txt format and contain 1 column and 10,000 rows.

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%The following commands read B2 data stored in a (space delimited) formatted text file created by EXCEL. %The EXCEL source file contains values of B arranged in a single column with no text header or empty rows at the top in the format: %Bx1y1, Bx1y2 .. Bx1yn, .. Bxmy1, Bxmy2 .. Bxmyn; further x ranges from a negative value to 0, taking advantage of symmetry. magDATA = importdata('c:/Users/Wei Xue/exp/MDM/MDM simulation/IESoutput.txt');

%For the large TFM (Mk I) system. The following defines the area of the trajectory analysis. %The x limits define where the field gradients drop to less than 1% of the maximum, while the y-limits define the upper and lower walls of the flow channel. Gravity and the direction of fluid flow are along x; the force causing magnetic deposition is along y. xlo = -2.5/1000; xhi = 2.5/1000; ylo = 0.005*25.4/1000; yhi = ylo + 0.010*25.4/1000; height = yhi - ylo; ycenter = (ylo + yhi)/2; width = 0.25*25.4/1000; AR = width/height; c = width/2; b = height/2;

%The following converts volumetric flow rate from ml/min to m^3/sec. Q = Qml_min/(1e6 * 60); vmean = Q/(width * height);

%Assignment of constants. M is the number of x elements and N is the number of y elements. %The ranges of x and y correspond to the domain of the magnetic field, which must be larger than the area of the trajectory analysis. %Note that xmax = 0, so that the domain is reflected across the line of symmetry. xmin = -2.6/1000; xmax = 0/1000; Nx = 250; ymin = 0.10/1000; ymax = 0.40/1000; Ny = 40; deltax = (xmax - xmin)/(Nx - 1); deltay = (ymax - ymin)/(Ny-1);

%The following subroutine calculates the mean mobility of the data read into "mobDATA". % //productsum = sum(mobDATA(:,1).*mobDATA(:,2)); freqsum = sum(mobDATA(:,2)); % //meanm = productsum/freqsum;

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%This subroutine converts vector MagData(1D) containing field values from Magneto output to an array, B (2D). %The number of rows in the array corresponds to the number of y elements and the number of columns corresponds to the number of x elements. B = zeros(Ny, Nx); for i1 = 1: Nx B(:,i1) = magDATA((Ny*(i1-1)+1):(Ny*i1)); end

%This section copies and squares B values stored in matrix B across the plane of symmetry. The values of B are in T. BB = B.*B; for i2 = 1: Nx BB(:, Nx+i2) = B(:,(Nx-i2+1)).*B(:,(Nx-i2+1)); end

%This section computes d(BB)/dx from BB and stores in matrix FX. A 5 pt interpolating formula is used. FX = zeros(Ny, 2*Nx-1); for jx= 1:Ny for kx = 1:(2*Nx - 1) if kx == 1 FX(jx, kx) = 1/(12 * deltax) * (-25 * BB(jx, kx) + 48 * BB(jx, kx+1) - 36*BB(jx, kx+2) +16*BB(jx,kx+3) - 3 * BB(jx,kx+4)); elseif kx == 2 FX(jx, kx) = 1/(12 * deltax) * (-3 * BB(jx, kx-1) - 10 * BB(jx,kx) + 18 * BB(jx,kx +1) - 6*BB(jx,kx+2) + BB(jx,kx+3)) ; elseif kx == 2*Nx - 2 FX(jx,kx) = 1/(12 * deltax) * (-BB(jx,kx-3) +6 * BB(jx,kx-2) -18 * BB(jx,kx-1) + 10 * BB(jx,kx) + 3 * BB(jx,kx+1)); elseif kx == 2 * Nx - 1 FX(jx,kx) = 1/(12 * deltax) * (3 * BB(jx,kx-4) - 16 * BB(jx,kx-3) + 36 * BB(jx,kx-2) - 48 * BB(jx,kx-1) + 25 * BB(jx,kx)); else FX(jx,kx) =1/(12 * deltax) * ( BB(jx,kx-2) - 8 * BB(jx,kx- 1) + 8 * BB(jx,kx+1) - BB(jx,kx+2) ); end end end 197

%This section computes d(BB)/dy from BB and stores in matrix FY. FY = zeros(Ny, 2*Nx-1); for jy =1: Ny for ky=1:(2*Nx - 1) if jy == 1 FY(jy, ky) = 1/(12* deltay) * (-25 * BB(jy, ky) + 48 * BB(jy+1, ky) - 36*BB(jy +2, ky) +16*BB(jy+3,ky) - 3 * BB(jy+4,ky)); elseif jy == 2 FY(jy,ky) = 1/(12* deltay) * (-3 * BB(jy - 1, ky) - 10 * BB(jy,ky) + 18 * BB(jy + 1,ky) - 6*BB(jy+ 2,ky)+ BB(jy + 3,ky)) ; elseif jy == Ny - 1 FY(jy,ky) = 1/(12* deltay) * (-BB(jy -3,ky) + 6 * BB(jy - 2,ky) -18 * BB(jy - 1,ky) + 10 * BB(jy,ky) + 3 * BB(jy+1,ky)); elseif jy == Ny FY(jy,ky) = 1/(12* deltay) * (3 * BB(jy - 4,ky) - 16 * BB(jy - 3,ky) + 36 * BB(jy - 2,ky) - 48 * BB(jy -1,ky) + 25 * BB(jy,ky)); else FY(jy,ky) = 1/(12* deltay) * ( BB(jy - 2,ky) - 8 * BB(jy - 1,ky) + 8 * BB(jy +1,ky) - BB(jy + 2,ky) ); end end end

%The following calculates the absolute value of the gradient of B2 along x, and stores in array FXX. %//FXX = abs(FX);

%The following statements plot the magnetic field data. %//mesh(BB)%;mesh(FY);mesh(FX);mesh(FXX);

%The following section finds the mean value of the dB2/dy in some defined area, usually the spatail domain of the channel. Nxlo = round((xlo-xmin)/deltax + 1); Nxhi = round((xhi-xmin)/deltax + 1); Nylo = round((ylo-ymin)/deltay + 1); Nyhi = round((yhi-ymin)/deltay + 1); ydersum = sum(sum(FY(Nylo:Nyhi, Nxlo:Nxhi)));

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maxyder = min(min(FY(Nylo:Nyhi, Nxlo:Nxhi)));%note the max meaning absolute value. FY value are negatvie

meanyder = ydersum/((Nyhi - Nylo + 1)*(Nxhi - Nxlo + 1)); area = (xhi-xlo)*(yhi-ylo); fprintf('mean and max y-derivative (T2/m)= %4.2f %4.2f \n', meanyder, maxyder ); fprintf('area where the y-derivative is evaluated (m2)= %4.8f\n', area);

%This subroutine computes an estimated value of the x derivative of B2 from the array FX using 2D linear interpolation, %for any point between the array locations. function P3 = fxapprox(x,y) k =floor((x - xmin)/deltax) + 1; j =floor ((y-ymin)/deltay) + 1; if j<1 %the ode solver somehow use the value out of the matrix dimention %so estimated the "out of region" value equals to the boundary value j = 1; end if k<1 k = 1; end if j>39 j = 39; end if k>498 k = 498; end x1 = xmin + (k-1)*deltax; x2 = xmin + (k)*deltax; y1 = ymin + (j-1)*deltay; y2 = ymin+(j)*deltay; P1 = FX(j,k) + (x-x1)/(x2 - x1) * (FX(j, k +1) - FX(j,k)); P2 = FX(j + 1, k) + (x-x1)/(x2 - x1) * (FX(j + 1, k + 1) - FX(j + 1, k)); P3 =P1 + (y - y1)/(y2 - y1) * (P2 - P1); end

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%The following subroutine computes an estimated value of Fy using 2D linear interpolation on the matrix FY. function P3 = fyapprox(x,y) k =floor((x - xmin)/deltax) + 1; j = floor ((y-ymin)/deltay) + 1; if j<1 j = 1; end if k<1 k = 1; end if j>39 j = 39; end if k>498 k=498; end x1 = xmin + (k-1)*deltax; x2 = xmin + (k)*deltax; y1 = ymin + (j-1)*deltay; y2 = ymin+(j)*deltay; P1 = FY(j,k) + (x-x1)/(x2 - x1) * (FY(j, k +1) - FY(j,k)); P2= FY(j + 1, k) + (x-x1)/(x2 - x1) * (FY(j + 1, k + 1) - FY(j + 1, k)); P3=P1 + (y - y1)/(y2 - y1) * (P2 - P1); end

%Some physical properties including fluid at 23 C. mu0 = 4*pi*10^(-7); g = 9.80665; viscosity = 0.93/1000;

%Evaluates the exponent used in the Purday velocity profile equation. %This is necessary for finding wmean/wmax for both velocity profile equations. n = fsolve(@Purday,30); function F = Purday(n)

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F = n*(n+1) - 2*c^2/b^2; end %Maximum and average velocity in m/s. vmax = 3/2 * (n+1)/n * vmean;

%The following subroutine calculates axial velocity in m/s using the Takahashi-Gill equation. %It still requres vmax calculated from the Purday equation. function gillw_result = gillw (y,z) Y = (y-ycenter)/b; Z = z/c; term = (cosh(3^0.5*AR*Z)-1)/(cosh(3^0.5*AR)-1); gillw_result = vmax * (1-Y^2) * (1-term); end

%The following subroutine finds the mean x fluid velocity at a given y by averaging across the range of z. function ubar_result = ubar(y) vsum = 0.0; Nz = 200; for i = 1:Nz z1 = -c + width*(2*i-1)/(2*Nz); vsum = gillw(y,z1) + vsum; end ubar_result = vsum/Nz; end

%The following subroutine calculates the x-velocity as a function of y using the infinite parallel plate approximation. function result_xvelocity = xvelocity(y) result_xvelocity = -4*vmax/height^2 * (y - ylo)*(y - yhi); end

%The following compares velocity estimates from the 3 methods. fprintf('From Takahashi-Gill equation, Average velocity at y = ycenter: %0.8f\n', ubar(ycenter)); fprintf('From Takahashi-Gill equation, velocity at z = 0, y = ycenter: %0.8f\n', gillw(ycenter,0)); fprintf('From infinite parallel plate approximation, velocity at z = 0, y = ycenter: %0.8f\n', xvelocity(ycenter));

%Sedimentation rate for no wall/lift effects. Usediment = 2 * g * (Rcell)^2 * delrho / (9 * viscosity); 201

fprintf('Sedimentation velocity is %0.10f\n', Usediment);

%THe following section is adopted from subroutine "Deposit" form Lee Moore %This section propagates trajectories at various heights of the flow cell for various mobilities read in from CTV. %When a trajectory reaches the accumulation surface, its position along x is determined as an array element in AX. %The terminii are weighted by initial velocity and the frequency of the mobility increment and summed in each AX element. %The sum of all velocity and frequency products propagated is called the "feedsum". %Each AX[l] divided by feedsum gives normalized recovery in each x- position increment. %The sum of AX elements divided by feedsum gives the fractional deposition.

%setting constants %Nxelements is the number of bins across the horizontal domain (along x axis) where cells may deposit. %Ny is the number of divisions along the channel height (along y) that are the trajectory seeds. %The total number of trajectory seeds is Ny * Nmob. %xlimit is the fractional axial length of the channel for allowing that the particle escaped the channel without depositing. %ylimit is the fractional height of the channel allowing that the cell deposited. %freqmin is a means to eliminate some of the mobility-frequency pairs in the CTV data %that have very low frequencies and will contribute little to the calculation of the capture rate. %If the program runs sufficiently fast, set this to a low value or even 0.

Nxelements = 50; Ny = 51; xlimit = 0.01; ylimit = 0.02; freqmin = 1.0e-09;

%Sets arrays to 0. AX = zeros(Nxelements,1); Xmid = zeros(Nxelements,1);%x position in mm

%initialize mobility and frequency( get rid of low frequency) frequency = mobDATA(:,2); freq_filter = frequency>=freqmin; 202

frequency = frequency(freq_filter); mobility = mobDATA(:,1)/1e09; mobility = mobility(freq_filter); velx = zeros(Ny, 1); %This loop calculates velocity at differnt starting point %Starting position of trajectories is varied over channel height for each mobility read in. %jv starts from the lowest number so that the corresponding starting %position is out of the deposition region for jv=ceil(ylimit*Ny+1/2):Ny lambda = 1/Ny * (jv-1/2); y0 = ylo + lambda*height; velx(jv) = ubar(y0); end

%feedsum is to normorlize recovery data. feedsum = sum(frequency*sum(velx)); lcount = [];

%This outermost loop reads in mobilites and frequencies from CTV file. %Moblity units are converted to m3/TAs. Starting position of trajectories is varied over channel height for each mobility read in. %Statement of differential equations "diff" depends on mobility. for im=1:size(mobility,1); if mobility(im) <=0 mobility(im) = 1.0e-07/1e09; end

%This loop varies starting position of trajectory along y, which changes one of the initial conditions, y0. for jv=ceil(ylimit*Ny+1/2):Ny lambda = 1/Ny * (jv-1/2); y0 = ylo + lambda*height; posInit = [xlo y0]; %time invertal is set an arbitary large enough number. tspan = [0 300];

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%computes the trajectory and find deposit position if any options = odeset('Events',@events,'Initialstep', 1/(60*Qml_min)); [t,pos,te,ye,ie] = ode23(@cellmovement, tspan, posInit, options); if pos(end,2)<=ylo+ylimit*(yhi-ylo) l= floor((pos(end,1)-xlo)/(xhi-xlo) * Nxelements)+1; if l<=0 lcount(end+1) = l;l = 1;end %failproof and record of fail l, and the less recored in lcount the better AX(l) = AX(l) + frequency(im) * velx(jv); else continue; end end display(im);%loop index, monitor the running display(lcount); end

%computing the capture_rate and cumulative frequency. depositsum = sum(AX); AXnorm = AX/feedsum; for h=1:Nxelements Xmid(h) = (h-1/2)/Nxelements * (xhi - xlo) + xlo; end AXcum = zeros(Nxelements,1); AXcum(1) = AXnorm(1); for h = 2:Nxelements AXcum(h) = AXcum(h-1)+AXnorm(h); end

capture_rate = depositsum/feedsum; display(lcount); export(dataset([Xmid AXnorm AXcum]), 'XLSfile', 'deposit');

%the velocity of the cell function dpos = cellmovement(~,pos) xpos = pos(1); ypos = pos(2);

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dpos = [fxapprox(xpos,ypos)/(2*mu0)*mobility(im) + Usediment + ubar(ypos); fyapprox(xpos,ypos)/(2*mu0) * mobility(im)]; end

%boundary to stop solving ODE. function [value,isterminal,direction] = events(~,pos) % Locate the time when height passes through zero in a % decreasing direction and stop integration. value = [(pos(2)-ylimit*(yhi-ylo)-ylo); xhi-pos(1)-xlimit*(xhi- xlo);(pos(2)-yhi);pos(1)-xlo];%value to detect passing 0 isterminal = [1;1;1;1]; % Stop the integration direction = [0;0;0;0]; % Any direction end end

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