Development of Separation Methods to Produce Uniform Exosomes Subpopulations Using Field-Flow Fractionation Techniques

DEVELOPMENT OF SEPARATION METHODS TO PRODUCE UNIFORM EXOSOMES SUBPOPULATIONS USING FIELD-FLOW FRACTIONATION TECHNIQUES

by Akram Ashour Ashames

A thesis submitted to the Faculty and the Board of Trustees of the Colorado School of Mines in partial fulfillment of the requirement for the degree of Doctor of Philosophy (Materials Science).

Golden, Colorado

Date ______

Signed: ______Akram A. Ashames

Approved: ______Dr. Kim R. Williams Thesis Advisor

Golden, Colorado

Date ______

Approved: ______Dr. Brian Gorman Professor and Director Materials Science Program

ABSTRACT

Exosomes are nanometer-sized vesicles (30-120 nm) with a defined density range (1.08-

1.22 g/cm3), secreted by most cells in the body, including tumors. Exosomes contain specific proteins, lipids, and functional microRNA (miRNA) and messenger RNA (mRNA) that can be transferred from cell to cell which suggests a role in cell-to-cell communication. Despite the tremendous strides that have been made in the last years toward understanding the properties and function of exosomes, this goal remains a challenging task. Exosomes have a potential role in early diagnosis of cancer but recent reports suggest controversial activities in cancer therapy. Exosomes have an anti-tumorigenic ability by priming the immune system.

However, other studies have demonstrated that exosomes can stimulate tumor growth and metastasis and promote cancer resistance to chemotherapeutic drugs. This paradox may be the result of heterogeneity and contaminants in exosomes preparations and highlights the need for standardized protocols. Ultracentrifugation is the most common isolation technique used for exosomes, but preparations obtained by this method frequently contain other extracellular vesicles such as microvesicles and apoptotic bodies that overlap in densities and sizes with exosomes. This doctoral work seeks to address this challenge by demonstrating the ability of field-flow fractionation (FFF) techniques to be used as an orthogonal approach, along with the primary ultracentrifugation method, to remove non-exosomal byproducts and to produce and characterize homogenous exosomes subpopulations.

This thesis involves three main projects with a general aim to design FFF approaches that have the capability to meet the challenges in exosomes separation and analysis. The first project aims to develop optimized sedimentation FFF (SdFFF) and asymmetric flow FFF

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(AF4) to produce effective mass- and size-uniform exosomes subpopulations from heterogeneous exosomes preparations that were derived from various tumor cell lines (e.g., breast, brain, and ovarian cancers) using ultracentrifugation. Fractions collected at the outlet of the FFF channel are characterized with respect to density and size. The SdFFF, which is an effective mass meff based separation technique, was first used to produce meff uniform

3 fractions. The meff is equal to d Δρ where d is diameter and Δρ is the difference in density between the particle and the carrier liquid. The density distribution of breast cancer ascites exosomes was found to be ~1.01 to 1.18 g/cm3, suggesting that exosomes preparations from ultracentrifugation were contained low density contaminants (in the lower SdFFF fractions) that can be eliminated and separated from exosomes fractions (in the higher SdFFF fractions). The exosomes were also introduced into anAF4 system where they underwent a size separation. AF4 demonstrated the ability to narrow the original size distribution of the exosomes from ~ 30-500 nm to ~ 30-140 nm. Furthermore, each exosomal fraction has a narrow size distribution of approximately ± 10 nm.

While SdFFF and AF4 were independently implemented in the first project, the second project focused on a novel two-dimensional approach wherein both techniques were used to obtain uniform mass and size subpopulations. Exosomes from different cancer cell lines are first separated using SdFFF and fractions with uniform meff were collected. Each SdFFF fraction with uniform meff is then injected into an AF4 channel and a size separation was implemented. Hence, each fraction that is collected at theAF4 outlet has a uniform d and density. An online focusing stage commonly used in AF4 solves the sample dilution problem encountered when conducting two-dimensional separations. Assessment of separated exosome fractions confirmed that the fractions are indeed uniform in density and

size. The average densities of the exosomes fractions after two-dimensional FFF were determined to be between ~ 1.05 to 1.10 g/cm3. These results suggest that non-exosomes components of low densities can be fractionated from exosomes of higher densities. The sizes of exosomes in fractions collected after two-dimensional FFF are determined using dynamic light scattering and ranged from ~ 40 to 80 nm for brain cancer exosomes, ~ 40 to

140 nm for ovarian cancer exosomes and ~ 50 to 145 nm breast ascites exosomes. These ranges, which span the exosomes sizes, represent narrower size distributions than that of the unfractionated exosomes sample.

The third project was designed to improve the sample throughput by scaling up the analytical AF4 channel to a semi-preparative AF4 (sP-AF4) channel. Results from this study showed that the brain cancer-derived exosomes sample volume can be increased up to 250 fold to produce ~ 75 μg of exosomes per fraction using a sP-AF4 channel without affecting the resolution of the separation. This allowed the production of large amounts of size uniform exosomes subpopulations with one sample injection, rather than performing tens of experiments on an analytical channel to collect and concentrate combined fractions.

Characterizing the exosomes fractions collected from the sP-AF4 channel shows that the fractions have distinct sizes that range from ~ 40 to 150 nm. Exosomes fractions also showed an abundance of proteins with molecular weights between ~ 60 and 70 kDa. The presence of common exosomal protein biomarkers including CD9, Hsp/c70, and Hsp90 in each sP-AF4 fraction was confirmed by Western blot analysis. The results showed that most of the fractions contained different relative concentrations of exosomal protein biomarkers, indicating the presence of exosomes.

The data presented in this thesis advances the separation and characterization of exosomes and establishes a route forward towards obtaining purer exosomes preparations and subpopulations. This capability stands to contribute significantly to furthering the understanding of the role of exosomes in intracellular communication, and to assess their potential role and use in cancer diagnosis and progression.

Table of Contents ABSTRACT ...... iii LIST OF FIGURES ...... x LIST OF TABLES ...... xv AKNOWLEDGEMENTS ...... xvi CHAPTER 1 ...... 1 INTRODUCTION ...... 1 1.1. Background on exosomes ...... 2 1.2. Exosome isolation procedure ...... 5 1.3. Analytical and clinical challenges ...... 8 1.4. Motivation for developing flow FFF ...... 9 1.5. Objectives of this thesis ...... 10 CHAPTER 2 ...... 13 FIELD-FLOW FRACTIONATION ...... 13 2.1 Field-flow fractionation ...... 13 2.1.1 General principles ...... 14 2.1.2 Modes of separation ...... 15 2.1.2.1 Normal mode ...... 17 2.1.2.2 Steric mode ...... 17 2.1.2.3 Hyperlayer (lift) mode ...... 18 2.1.3 Normal mode retention theory ...... 18 2.1.4 FFF techniques ...... 22 2.1.4.1 Sedimentation FFF ...... 23 2.1.4.2 Asymmetrical Flow FFF (AF4) ...... 25 2.1.5 Sample Recovery ...... 29 2.1.6 Sample overloading...... 30 2.1.7 FFF optimization ...... 31 2.1.8 Online and offline coupling with other analytical techniques ...... 32 2.1.9 FFF applications for biological separation and analysis ...... 33 2.1.9.1 Proteins ...... 33 2.1.9.2 Nucleic acids ...... 37 2.1.9.3 Vaccines ...... 38 2.1.9.4 Drug delivery ...... 39 2.2 Non-FFF techniques used in this thesis ...... 40

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2.2.1 Dynamic light scattering ...... 40 2.2.2 Thin layer chromatography ...... 42 2.2.3 Sodium dodecyl sulfate polyacrylamide gel electrophoresis ...... 43 2.2.4 Western blot ...... 44 CHAPTER 3 ...... 45 SEPARATION OF TUMOR-DERIVED EXOSOMES BY SEDIMENTATION AND ASYMMETRCIC FLOW FIELD-FLOW FRACTIONATION ...... 45 3.1 Introduction ...... 45 3.2 Experimental ...... 49 3.2.1 Samples ...... 49 3.2.2 Sample preparation ...... 50 3.2.3 Sedimentation field-flow fractionation ...... 51 3.2.4 Asymmetric flow field-flow fractionation ...... 51 3.3 Results and discussion ...... 52 3.3.1 SdFFF of exosomes ...... 52 3.3.2 AF4 of exosomes ...... 60 3.3.2.1 Optimization of AF4 experimental variables ...... 60 3.3.2.1.1 System performance and channel thickness ...... 62 3.3.2.1.2 Flow rates ...... 65 3.3.2.1.2.1 Crossflow rate ...... 65 3.3.2.1.2.2 Channel flow rate ...... 67 3.3.2.1.2.3 Total flow rate ...... 68 3.3.2.1.3 Focusing time ...... 69 3.3.2.1.4 Overloading ...... 71 3.3.2.1.5 Carrier liquid ionic strength ...... 72 3.4 Conclusion ...... 78 CHAPTER 4 ...... 79 TWO-DIMENSIONAL FIELD-FLOW FRACTIONATION SEPARATION OF TUMOR-DERIVED EXOSOMES ...... 79 4.1. Abstract ...... 79 4.2. Introduction ...... 80 4.3. Field-flow fractionation ...... 85 4.4. Experimental ...... 89 4.4.1. Samples ...... 89 4.4.2. Sample preparation ...... 89

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4.4.3. Sedimentation field-flow fractionation ...... 90 4.4.4. Asymmetric flow field-flow fractionation ...... 91 4.4.5. Dynamic light scattering ...... 91 4.4.6. Zeta potential analysis ...... 91 4.5. Results and discussion ...... 92 4.5.1. One-dimensional FFF ...... 92 4.5.2. Online concentration by AF4 ...... 100 4.5.3. Two-dimensional FFF ...... 100 4.6. Conclusion ...... 107 SUPPLEMENTARY INFORMATION ...... 109 LIPID ANALYSIS AND OXIDATION ...... 109 CHAPTER 5 ...... 119 SEMI-PREPARATIVE ASYMMETRIC FLOW FIELD-FLOW FRACTIONATION SEPARATION OF TUMOR-DERIVED EXOSOMES ...... 119 5.1 Introduction ...... 119 5.2 Experimental ...... 121 5.2.1 Exosomes Samples Preparation ...... 121 5.2.2 Nanoparticle standards and carrier liquids ...... 122 5.2.3 AF4 systems ...... 123 5.2.3.1 Analytical scale AF4 system ...... 123 5.2.3.2 sP-AF4 system ...... 123 5.2.4 AF4 experimental conditions ...... 125 5.2.5 Western blot ...... 126 5.3 Results and discussion ...... 127 5.3.1 Effect of sample volume on retention behavior of PSL ...... 127 5.3.2 Separation and characterization of exosomes ...... 130 5.4 Conclusion ...... 138 CHAPTER 6 ...... 139 GENERAL CONCLUSIONS AND FUTURE WORK ...... 139 6.1. General conclusions ...... 139 6.2. Future work ...... 141 REFERENCES ...... 143

LIST OF FIGURES

Figure 1.1 Extracellular vesicles formation and release…..………………………. 3

Figure 1.2 Ultracentrifugation steps to produce a pellet that is mainly exosomes. Adopted from reference………………………………………………. 5 Figure 1.3 Isolation methods commonly used for cancer-derived exosomes. Data obtained from reference 59 …………………………………………….. 8 Figure 2.1 FFF channel and separation mechanism: a) Typical FFF channel, b) injection and relaxation, c) elution, and d) typical FFF fractogram…… 61

Figure 2.2 Common FFF modes of separation: a) Normal, b) Steric and c) Hyperlayer……………………………………………………………… 61

Figure 2.3 Sedimentation FFF system setup………………………………………. 32

Figure 2.4 Different stages experienced by a sample undergoing SdFFF separation………………………………………………………………. 32 Figure 2.5 Asymmetric flow FFF system setup…………………………………… 31

Figure 2.6 Main components of an asymmetric flow FFF channel……………….. 32

Figure 2.7 FI-AFlFFF separation of human plasma lipoproteins obtained from healthy persons (upper) and patients with CAD (lower). Wavelength for detection was λ=610. Reprinted with a permission from reference 113………………………………………………………………………. 22 Figure 2.8 Fractograms representing separation of HDL and LDL by mAsFlFFF (A) and by conventional AsFlFFF (B). Carrier: 8.5 mM phosphate buffer (pH 7.4), 0.02% NaN3, 150 mM NaCl; HDL/LDL (injection volume, 0.08 μl for HDL and 0.17 μl for LDL; injection concentration, 0.058 μg for HDL and 0.77 for LDL). Reprinted with a permission from reference 142………………………………………………………. 21

Figure 2.9 MxHF-FlFFF fractograms of (a) the separation of protein standards (10 μg of each standard) at 280 nm and (b) the separation of HDL and LDL particles from a human plasma sample (50 μL stained with Sudan Black B) at 600 nm. The outflow rate was 1.7 mL/min and the radial flow rate was 1.3 mL/min for both runs. Reprinted with a permission from reference 144……………………….. 22

Figure 3.1 Minimum size that can be separated by commercially available SdFFF system (2700x g) with two retention ratios RL= 3 (red trace) and RL=10 (black trace)……………………………………………………………. 22

Figure 3.2 a) SdFFF fractogram of a mixture of polystyrene latex beads standards with nominal sizes of 46 and 102 nm using a) SdFFF system 2 and b) SdFFF syste1m 1………………………………………………………. 25

Figure 3.3 Superimposed SdFFF fractograms of ovarian cancer-derived exosomes using two different applied field of 1000x g (dotted trace) and 2700x g (solid trace)…………………………………………………………….. 21

Figure 3.4 A plot of carrier liquid densities as a function of effective masses of breast ascites cancer exosomes at four different carrier liquid densities. 22

Figure 3.5 Superimposed SdFFF fractograms of ovarian cancer-derived exosomes (doted trace), breast cancer ascites exosomes (solid trace), and brain cancer exosomes (dashed trace) using applied field of 2700x g………. 25

Figure 3.6 The effective masses of the four collected breast ascites exosomes fractions with an insert of SdFFF fractogram shows the fraction numbers………………………………………………………………… 16 Figure 3.7 Two superimposed AF4 fractograms of a mixture of polystyrene latex beads standards with nominal sizes of 60 and 102 nm………………… 12

Figure 3.8 AF4 fractogram of a mixture of BSA and apoferritin protein standards with nominal sizes of 8 and 11 nm, respectively………………………. 15

Figure 3.9 a) AF4 fractogram of citrate-capped gold nanoparticles, and b) TEM images of two fractions: fraction 1 collected from first peak (8-16 min) and fraction 1 collected from second peak (19-37 min)……………….. 12

Figure 3.10 Superimposed AF4 fractograms of U87 brain cancer exosomes at three different crossflow rates and constant channel flow rate………………. 11

Figure 3.11 Superimposed AF4 fractograms of U87 brain cancer exosomes at five different channel flow rates (0.2, 0.4, 0.6, 0.8, and 1.0 mL/min) and constant crossflow rate of 2.0 mL/min. The void times are 1.85, 1.72, 1.56, 1.41, and 1.34 min, respectively………………………………… 12 Figure 3.12 Superimposed AF4 fractograms of U87 brain cancer exosomes at four different total flow rates and constant crossflow and channel flow rates…………………………………………………………………….. 16

Figure 3.13 Superimposed AF4 fractograms of U87 brain cancer exosomes at three different focusing times………………………………………………... 26

Figure 3.14 Superimposed AF4 fractograms of U87 brain cancer exosomes at four different exosome concentrations……………………………………… 23

Figure 3.15 Superimposed AF4 fractograms of U87 brain cancer exosomes using three different carrier liquid concentrations…………………………… 22

Figure 3.16 Breast cancer ascites exosomes size distribution by DLS……………... 25

Figure 3.17 Superimposed AF4 fractograms of U87 brain cancer and breast ascites exosomes cell lines…………………………………………………….. 22 Figure 3.18 Breast cancer ascites exosomes size distribution using AF4 theory…… 22

Figure 3.19 AF4 fractogram of breast cancer ascites exosomes showing the fractions F2, F4, and F6 that were collected to perform batch mode DLS size measurement. The inserted table above the fractogram summarizes the sizes of these fractions using AF4 theory and DLS…... 21

Figure 3.20 Overlay AF4 fractograms of exosomes (solid line) and microvesicles (dotted line) from same brain cancer cell line…………………………. 22 Figure 4.1 Schema of the two-dimensional field-flow fractionation separation method for cancer-derived exosomes to obtain uniform density and size exosomes subpopulations…………………………………………. 84

Figure 4.2 DLS size distribution of breast ascites exosomes before FFF separation shows a wide size distribution suggesting the preparation contains components of smaller and larger than exosomes……………. 92

Figure 4.3 SdFFF fractogram of a mixture of polystyrene latex beads standards with nominal sizes of 46 ± 2 nm and 102 ± 3 nm……………………… 93

Figure 4.4 a) Superimposed SdFFF fractograms of three exosomes preparations exosomes from brain cancer (dashed line), breast cancer ascites (solid line), and ovarian cancer (dotted line), and b) the effective masses of the four collected exosomes fractions from breast cancer ascites with an insert shows the fraction numbers………………………………….. 95

Figure 4.5 Overlay AF4 fractograms of two runs of a mixture of PSL standards with nominal sizes of 60 ± 4 nm and 102 ± 3 nm……………………… 96

Figure 4.6 a) AF4 overlay fractograms and b) size distributions of exosomes from brain cancer (dotted line), breast cancer ascites (dashed line), and ovarian cancer (solid line)……………………………………………... 98 Figure 4.7 AF4 superimposed fractograms of a) PSL 60 ± 4 nm and b) breast cancer ascites exosomes by injecting the same amounts but different volumes of 0.02 mL (solid line) and 1.00 (dashed line)……………….. 99

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Figure 4.8 AF4 fractograms of uniform meff SdFFF fractions a) brain cancer exosomes and b) breast cancer ascites exosomes with inserts in each fractogram show the SdFFF fraction numbers………………………… 102

Figure 4.9 AF4 fractograms and size distributions (x-axis) of uniform meff breast cancer ascites SdFFF fractions. Two-dimensional FFF fractions 2 (F2), 4 (F4), and 6 (F6) were collected for size measurements by batch mode DLS. Experimental conditions are the same as Figure 4.8……… 103

Figure 4.10 Contour plots of size and density values from Table 4.3 of four cancer ascites exosomes fractions (S1, S2, S3, and S4)………………………. 105

Figure 4S.1 Separation of lipid extracts from breast ascites exosomes fractions by two-dimensional FFF by TLC. Left lane is lipid standard mixture composed of cholesterol (Cho), phosphatidylcholine (PC), phosphatidylethanolamin (PE), and phosphatidylserine (PS)…………. 111

Figure 4S.2 Overlay UV spectra of PE phospholipid standard before (solid line) and after (dotted line) extraction. Conjugated dienes (at 220-230 nm), triens (at ~ 265-270 nm), and tetraenes (at ~ 310-320 nm)……………. 112

Figure 4S.3 Overlay UV spectra of PE phospholipid standard after extraction at day 1 (solid line), day 3 (dotted line), and day 5 (dashed line). The absorbance of conjugated dienes (at 220-230 nm), triens (at ~ 265-270 nm), and tetraenes (at ~ 310-320 nm) was noticeably increased from day 1 to day 5…………………………………………………………... 113 Figure 4S.4 A potassium iodide starch paper to detect peroxide species in water, methanol, and chloroform……………………………………………… 114 Figure 4S.5 A potassium iodide starch paper to detect peroxide species in water before (left) and after (right) treated with sodium thiosulfate. 115 Figure 4S.6 Overlay UV spectra of PE phospholipid standard before extraction (solid line), after extraction with untreated water (dotted line) and with treated water (dashed line)……………………………………………... 115

Figure 4S.7 TLC plate of a blank (lane 1), and separation of PE phospholipid standard extracted with treated water (lane 2), and PE phospholipid standard extracted with untreated water……………………………….. 116 Figure 4S.8 TLC separation of exosomal lipids from fractions F3-2, F3-4, F3-6, F4-2, F4-4, and F4-6 collected from two-dimensional FFF and extracted by Bligh & Dyer method using treated water; and b) two- dimensional FFF fractogram shows the fraction numbers…………….. 117 Figure 5.1 a) A schematic representation of sP=AF4 system with flow direction during the focusing and elution steps, and b) a top view of the sP-AF4 channel and its dimensions…………………………………………….. 124

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Figure 5.2 Superimposed AF4 fractograms of a mixture of polystyrene latex beads standards with nominal sizes of 46 ± 2 nm and 102 ± 3 nm, with different sample injection volumes using a) analytical scale AF4 channel and b) sP-AF4 channel……………………………………… 129

Figure 5.3 Resolution indices obtained from injecting a mixture of polystyrene latex standards with nominal sizes of 46 ± 2 nm and 102 ± 3 nm, with different sample injection volumes using analytical scale AF4 channel and sP-AF4 channel……………………………………………………. 130

Figure 5.4 Effect of sample load on the reproducibility in a) analytical scale AF4 and b) semi-preparative AF4 channels………………………………… 131

Figure 5.5 A sP-AF4 fractogram of standard BSA sample………………………... 132

Figure 5.6 A sP-AF4 fractogram of three runs of blank samples at detector sensitivities of 0.01, 0.005, and 0.002 AUFS and BSA run at detector sensitivity of 0.005…………………………………………………….. 132

Figure 5.7 Superimposed sP-AF4 fractograms using semi-preparative AF4 channel of three exosomes preparations from UPN933 brain cancer (batch 1), breast cancer ascites, and U87 brain cancer………………… 133 Figure 5.8 a) A sP-AF4 fractogram of UPN933 brain cancer exosomes (batch 1) showing the collected fractions from sP-AF4 channel outlet, and b) the average size of each fraction measured by batch mode DLS………….. 134 Figure 5.9 Superimposed sP-AF4 fractograms of UPN933 brain cancer exosomes a) batch 1 and b) batch 2……………………………………………….. 135

Figure 5.10 A sP-AF4 fractogram of UPN933 brain cancer exosomes (batch 2) showing the collected fractions from sP-AF4 channel outlet, with an insert shows a small peak that was observed when turning off the crossflow (fraction 23)…………………………………………………. 136 Figure 5.11 SDS-PAGE analysis of UPN933 brain cancer exosomes (batch 2) fractions collected from sP-AF4 channel outlet using a) Coomassie blue stain and b) Sypro Ruby stain…………………………………….. 137 Figure 5.12 Western blot of collected UPN933 (batch 2) brain cancer exosomes fractions (combined every other fraction) collected from sP-AF4 to confirm the presence of exosomal biomarkers (HSP90, HSP/C70, and CD9…………………………………………………………………….. 138

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LIST OF TABLES

Table 1.1 Size and density ranges of non-exosomal components of exosome preparations…. 3

Table 3.1 The main differences between SdFFF and AF4 features…………………………... 49

Table 3.2 Minimum exosomes size that can be separated by commercially available SdFFF system (2700x g) with two retention ratios RL = 10 and 3………………………… 53

 Table 3.3 Retention levels RL of the different Vc rates of 1.7, 2.0, and 2.7 mL/min, and 67 constant V of 0.2 mL/min………………………………………………………….

Table 3.4 Retention levels RL of the different V rates of 0.2, 0.4, 0.6, 0.8, and 1.0 mL/min 68 and constant of 2.0 mL/min…………………………………………………….. Table 3.5 Percentage recovery obtained at different V of 2.2, 3.3, 4.4, and 5.5 mL/min, IN 69 and constant /V ratio…………………………………………………………... Table 3.6 Percentage recovery obtained at different focusing times of 1, 2, and 3 minutes, and constant / ratio………………………………………………………… 70 Table 3.7 Percentage recovery obtained at different dilutions of 10, 25, 50, and 100 times, and constant / ratio…………………………………………………………... 72 Table 3.8 Size measurements by AF4 theory and batch mode DLS of fractions F2, F4, and F6 that were collected from one-dimensional AF4 as shown in Figure 3.17……… 77

Table 4.1 Size and density ranges of exosomes and non-exosomal components of exosomes preparations………………………………………………………………………… 81 Table 4.2 Summary of hydrodynamic diameters of two-dimensional FFF fractions of breast ascites exosomes measured by AF4 and DLS...... 104

Table 4.3 Effective masses (meff), diameters (d), and densities (ρ) of two-dimensional breast ascites exosomes fractions…………………………………………………………. 104

Table 4.4 Zeta potential of unfractionated and two-dimensional FFF fractions of breast cancer ascites exosomes……………………………………………………………. 107

Table 4S.1 Typical lipid composition of mast cell-derived exosomes………………………… 109

Table 5.2 Summary of AF4 optimized experimental used for the separation of PSL………... 125

Table 5.3 Summary of the sP-AF4 optimized experimental conditions used for the separation of BSA………………………………………………………………….. 126

Table 5.4 The sP-AF4 optimized experimental used for the separation of exosomes………... 126

AKNOWLEDGEMENTS

First and foremost, I would like to express my special thanks with sincere appreciation to my advisor, Professor Kim R. Williams. I would never have been able to finish my dissertation without her guidance, support, thoughtful and critical insights, caring, and patience throughout my PhD life. My special thanks and gratitude also go to Professor

Thomas Anchordoquy, Professor Reed Ayers, Professor Matthew Posewitz, and Professor

Ryan Richards for serving on my committee and being incredibly supportive. I am also very grateful to Professor Michael Graner from the Department of Neurosurgery, University of

Colorado Denver - Aurora CO, for his scientific guidance, supplying with exosomes samples, and helping with experimental work. My thanks also go to Tyson Smyth and Jamie Betker from Anchordoquy research group (Skaggs School of Pharmacy and Pharmaceutical

Sciences, University of Colorado Denver, Aurora CO), and Patrick Eduafo from Professor

Posewitz group at Colorado School of Mines for helping with samples and experiments. I would also like to thank all of Professor Williams’ group members including Dr. Ray

Runyon, Dr. Jifang Cheng, Dr. James Oliver, Carmen Bria, Charlie Ponyik, Williams Smith,

Seamus McMullen, and Patrick Skelly for continuous support, fruitful ideas, and scientific help. A special thanks for Jifang, Seamus, and Patrick for their help with experiments. I am also very grateful to Professor David Olson from the Department of Metallurgy and

Materials Science for his thoughtfulness, understanding, and support. I am also glad to thank

Dr. Soheyl Tadjiki from Postnova Analytica Inc. for giving me the opportunity to use the sedimentation field-flow fractionation system for my exosomes separation. My thanks also go to Dr. Edward Dempsey for his technical help. Finally, I would like to thank the Libyan government for financial support, and for all of my friends for being there through all the times. Last but not least, my deep gratitude and love to my parents, my wife and children.

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This dissertation is dedicated to my father Ashour, my mother Warida, my wife Raja, and my

children Mohamed, Feras, and Ranim for their endless support and inspiration.

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CHAPTER 1

INTRODUCTION

Exosomes are small nanometer-sized vesicles of endocytic origin secreted by most cells, including tumors, and have been shown to play a major role in cancer diagnosis and treatment. Cancer is a second leading cause of morbidity and mortality worldwide, with over

8 million deaths and 14 million new diagnosed cases in 2012 1. Advances in nanomedicine have opened up the avenues towards developing novel cancer diagnostic and therapeutic strategies. Different physicochemical parameters (e.g. size, charge, shape) of these nanometer-sized therapeutics can affect their diffusivity, permeability, and interactions with the tumor in the cell 2, 3. Recent reports suggest that exosomes could have a potential role in the field of nanomedicine diagnosis and cancer treatment 4. Studies showed that tumor- derived exosomes can either have anti-tumorigenic or protumorigenic effects 5. Most tumor cells produce exosomes that contain specific antigens that can prime the immune system and recognize and elucidate tumor cells 6, 7. These properties could lead to cancer-derived exosomal vaccines 6, 8-10. Other reports suggested that exosomes may have a role in cancer progression and resistance to chemotherapeutic agents 11, 12. However, to reveal the unique roles of exosomes in cancer there are two hypotheses that need more detailed investigations.

First, exosomes consists of various subpopulations with different properties, cargos, and functions 13. Second, current exosomes isolation techniques produce a complex mixture of exosomes and non-exosomal components. Since exosomes are intrinsically complex, containing components that vary in size, density, morphology, charge, lipid composition, etc., it is difficult to use one separation technique to isolate each component of interest.

This chapter gives an overview on exosomes, their properties, biogenesis, and current

isolation methods. It also highlights the main analytical and clinical challenges for studying

exosomes, and summarizes the objectives of this dissertation.

1.1. Background on exosomes

Extracellular vesicles (EVs) are a group of membrane bound complexes that are secreted

by most cells in released into extracellular environments and bodily fluids such as urine,

saliva, breast milk, and malignant effusions11, 14, 15. The major populations of EVs include

exosomes, microvesicles (MVs, also known as ectosomes, shedding vesicles, or

microparticles), and apoptotic blebs or bodies 16-20. Figure 1.1 illustrates the formation and

release of EVs. Exosomes (panel I) are endocytic vesicles released into the extracellular

environment by fusion of endosomal multivesicular bodies (MVBs) to the cell membrane. At

the early stage of exosomes formation, all the cytosolic components are engulfed within

intraluminal vesicles (ILVs), and the latter are then incorporated into MVBs 16. The fate of

MVBs depends on their mode of trafficking where they can be either transported into

lysosomes and exposed to proteosomal degradation to form degradative MVBs, or trafficked

to the cell membrane to be exocytosed and into the extracellular matrix and release exosomes

upon fusing with the membrane 16, 17, 21. Panel II represents MV’s which are formed by

budding of the intracellular components from with the plasma membrane. During the late

stages of cell apoptosis (death), apoptotic or dead cells release vesicles known as apoptotic

bodies or blebs (panel III) into the extracellular matrix by blebbing of the plasma

membrane16.

Despite distinct modes of formation and release, exosomes, microvesicles, and apoptotic

bodies share some common features in regards of size and density as well as lipid and protein

composition 17. These vesicles differ from each other in other properties and composition as

well. Generally, exosomes are smaller than microvesicles and apoptotic bodies and have

distinct regular cup-like shapes, whereas microvesicles and apoptotic bodies have irregular

shapes. Exosomes are characterized by their specific defined size. Table 1.1 summarizes the

main properties of the different types of the EVs. 16, 17, 22-25. While exosomes contain minute

amounts of intracellular proteins originated from endoplasmic reticulum that can be used as

biomarkers for exosomes, microvesicles and apoptotic bodies’ proteins are commonly

associated with Golgi apparatus, and nucleus 26-28.

Figure 1.1 Extracellular vesicles formation and release.

Table 1 Size and density ranges of non-exosomal components of exosome preparations. Vesicle Apoptotic Microvesicles Exosomes Non-exosomal low density bodies proteins contaminants Size (nm) 100–5000 50-1000 30-100 6-21 Density (g cm-3) 1.16-1.28 > 1.23 1.08-1.22 1.01-1.08

Exosomes are identified in a number of biological cells and fluids such as dendritic cells

(DCs), tumor cells, platelets, urine, blood, ascites fluids, semen, saliva, and breast milk 10, 22,

29-33. Exosomes contain specific proteins, lipids, ribonucleic acids (RNAs) (microRNA

(miRNA), ribosomal RNA (rRNA), messenger RNA (mRNA)), and miRNAs precursors that are capable to block cancer cell proliferation 34-38. Data collected from 146 studies showed that exosomes contain 4,563 proteins, 1639 mRNA, 764 microRNA and 194 lipids 39, 40.

Exosomes also contain distinct protein biomarkers such as tetraspanins (e.g. CD9, CD63,

CD81) and proteins linked to the MVB biogenesis (e.g. HSP70, Alix, TSG101) 41. These biomarkers may allow exosomes to be used as non-invasive tools for monitoring cancer status, and for early diagnosis of cancer. The main lipids in exosomes include phosphatidylcholine, phosphatidylserine, phosphatidylinositol, phosphatidylethanolamine,

GM3 sphingomyelin, and cholesterol 42. Intrinsically, exosomes have net negative charge due to high GM3 lipid content, and have shown the best tumor targetability when they are released in acidic conditions 42.

Although their functions are not fully understood, several reports have shown that exosomes have important physiological roles such as intracellular communication, cellular exchange, and lysosomal degradation 13, 43, 44. However, exosomes are not homogenous with regard to the cargo they carry, suggesting that different subpopulations may serve different purposes, e.g., immune suppression, intercellular communication, molecular transfer 13. Exosomes are capable of inducing primary cytotoxic immune responses in vivo by activation of T lymphocytes because they contain antigen presenting molecules such as

MHC class I and II 7. Hence, they have the ability to recognize and kill cancer cells.

Exosomes derived from dendritic cells (DC) loaded with tumor peptides (synthetic peptides

derived from tumor antigen) were shown to induce potent anti-tumor immune responses in

mice 45, 46. Clinical trials are currently being performed showing promising results to use

cell-free exosome-based anticancer vaccines 47, 48. Recent reports show that exosomes can

only produce 10% cancer eradication, and could potentially be increased 30% by pre-

injecting an anticancer drug such as cyclophosphamide 46.

1.2. Exosome isolation procedure

Ultracentrifugation, sucrose gradient centrifugation, and immunoaffinity capture are the

most common techniques that have been employed for isolating exosomes from non-

exosomal components 43, 49-53. Ultracentrifugation is currently the method of choice for the

isolation of tumor-derived exosomes. This method involves a series of filtration and

centrifugation steps to remove dead cells and large debris 54. The pellet obtained after

10,000 g contains mostly microvesicles and small sized apoptotic bodies 18, 55. A series of

high-speed ultracentrifugation procedures are then carried out to produce an exosome pellet

as illustrated in Figure 1.2.

Figure 1.2 Ultracentrifugation steps to produce a pellet that is mainly exosomes. Adopted from reference 54.

In most cases, impure exosomes are obtained because ultracentrifugation cannot differentiate between exosomes and other vesicular components and lipoproteins and protein aggregates of similar masses 9, 50, 56. Furthermore, this method is time consuming (~

4-5 h), has low sample recovery, and fractions cannot be conveniently collected for further analysis 57. Additionally, aggregation of EVs due to high speed centrifugation has been observed 54, 58.

Ultracentrifugation is crucial as a pre-separation step to remove the majority of non- exosomal components, but it cannot be considered as a final pre-analytical separation stage.

Therefore, complementary “post-ultracentrifugation” steps are required to produce exosome preparations with fewer or no contaminants, and to fractionate exosomes into different fractions of uniform size and density for further analysis.

Sucrose gradient centrifugation is also a used for exosomes isolation 50, 59. The procedure involves a series of centrifugation and ultracentrifugation steps to remove large cellular components. Pellets are then overlaid with 5-35% sucrose gradient to separate exosomes pellets based on their density 23, 54. Although the recovery of exosomes is high, exosomes fractions are cross contaminated with high density lipoproteins (HDL) that have similar density as exosomes 59. Additionally, overlapping densities of different types of EVs may only lead to enrichment rather than isolation 58. The throughput of sucrose gradient centrifugation is also a limitation with procedure times lasting up to 90 h 30, 60.

Immunoaffinity capture (IAC) or affinity chromatography has also been employed to isolate exosomes using antibody coated magnetic beads. The IAC is a liquid chromatography technique that is based on irreversible interaction between the sample and the chromatographic stationary phase. The IAC column is made by loading specific antibodies or

antigens ligands (immunoadsorbents) to create selective affinity media via covalently coupling the primary amine group of the ligand to a chromatographic gel matrix, typically agarose matrix 61. Once the sample of interest is injected, a specific interaction occurs between the ligand and target molecule(s), typically at pH 7.4. These interactions can take place via electrostatic, hydrophobic, van der Waals, and/or hydrogen bonding 62. Once the sample-ligand interaction is completed, a washing step is carried out to remove unreacted contaminants. Exosomes are released and eluted by using a mobile phase consisting of a pH

2 buffer or ethanol 62. This method gives good exosomes and exosome-associated proteins yields that are at least 2-fold higher than ultracentrifugation and sucrose gradient. However, some exosome subpopulations could be lost if specific antibodies are not used 63, 64.

Although ultracentrifugation, sucrose gradient centrifugation and immunoaffinity capture are the most widely used techniques for exosome isolation (Figure 1.3), other methods have been employed. For instance, size exclusion chromatography (SEC) has been used as a pre- centrifugation method for separating large cellular components from the smaller exosomes 65.

However, the separation by SEC is limited by upper the possibility of sample adsorption to the packing material of the SEC column the stationary phase, and shear degradation at high flow rates and sample volume, which leading to excluding these proteins from subsequent analysis 66, 67. To overcome the limitations associated with SEC analysis, a miniaturized frit- inlet asymmetric flow field-flow fractionation (mFI-AF4) was used to fractionate exosomes into size-uniform fractions 68. This AF4 variant can only separate small amounts (in microgram scale) of exosomes resulting in dilute concentrations that are insufficient for conducting further studies. Other isolation methods include ultrafiltration concentrator and commercial kits (ExoQuick©) have shown low exosomes yields and protein impurities 69.

Figure 1.3 Isolation methods commonly used for cancer-derived exosomes. Data obtained from reference 59.

The currently used isolation methods have shown poor exosomes/non-exosomes

resolution resulting in preparations that contain exosomes and other cellular components with

similar sizes and densities such as microvesicles, apoptotic bodies, and other non-exosomal

protein contaminants 20, 44, 50, 70. The co-sedimentation of non-exosomal components in the

exosomes preparations during sample preparation and isolation results from their overlapping

size and/or density with exosomes 14. This highlights the need to develop new separation

techniques that are able to produce more homogenous exosome subpopulations to allow for

comprehensive characterization and further studies.

1.3. Analytical and clinical challenges

The analysis of exosomes preparations is typically performed by monitoring a number of

characteristic parameters of exosomes such as size, density, morphology, zeta potential, and

exosomal proteins biomarkers (e.g. CD9, CD63, CD81, HSP70, Alix, TSG101) 41. Dynamic

light scattering (DLS), nanoparticle tracking analysis (NTA), transmission electron

microscopy (TEM), and scanning electron microscopy (SEM) are the main techniques that

are used for exosomes sizing 71. TEM and SEM are also used to study the shape of exosomes.

Western blot is extensively employed for identifying exosomes biomarkers 72. These methods

will be discussed in more detail in chapter 2.

Size, density, and zeta potential information for exosomes are often reported in the

literature as average values of the entire exosome population 16, 17, 22-25, 73, 74. Several studies

have suggested that exosomes from the same cell type may have different properties that are

overlooked by only reporting average values for the entire exosome population 13, 43, 53.

Therefore, using separation methods to produce homogenous subpopulations of exosomes

can help answer many existing questions about the unique properties and functions of

exosomes. A major separations challenge is the production of exosome preparations that are

free of membrane sheds and non-exosomal protein contaminants 14, 20, 25, 60, 75. Currently, the

established protocols for isolating exosomes are limited 14, 58, 76, 77 and new techniques are

needed 44, 63.

1.4. Motivation for developing flow FFF

Developing a new separation methodology is necessary to produce uniform exosome

subpopulations that allow for detailed characterization of exosome properties and functions.

Field-flow fractionation (FFF) is a family of techniques that is well suited to address

analytical challenges that face exosomes analysis. Due to its separation mechanism and open

channel design, FFF has advantages that include low shear rates (ideal for fragile samples

such as exosomes), low sample loss, tunable analysis speed and resolution, applicability to a

wide range of sizes, and easy fraction collection. The ability of FFF techniques to separate

and characterize analytes based on their physicochemical properties such as size, effective

mass, and charge makes FFF a good technique to be able to separate exosomes from non-

exosomes. To the knowledge of the candidate, only two reports have been published using

symmetric flow FFF to fractionate exosomes for subsequent analyses 68, 78. A miniaturized

frit-inlet AF4 channel, coupled with nanoflow liquid chromatography-tandem mass

spectrometry, was used to fractionate exosomes into size uniform fractions and conduct

proteomic analysis. The reduction of flow rates to microliter scale was advantageous for

better interface with mass spectrometer. However, the exosomal fractions collected from this

miniaturized channel would contain insufficient amounts of exosomes for the subsequent

analyses targeted in this thesis work. Therefore, larger analytical scale and semi-preparative

flow FFF channels that can handle higher sample loads are used. Most of the previous

reported characterization studies have used unfractionated exosomes that may contain

subpopulations with differing properties and functionalities. The characterization of exosome

subpopulations for their specific physicochemical properties such as size, density, zeta

potential, exosomal protein biomarkers, and phospholipid contents has not been reported.

1.5. Objectives of this thesis

The main objectives of this doctoral thesis are to:

1. Develop optimized sedimentation FFF (SdFFF) and asymmetric flow FFF (AF4)

a) to produce effective mass-uniform exosomes subpopulations from heterogeneous

EVs preparations, and characterize each subpopulation with respect to its effective

mass, density and size.

b) to produce size-uniform exosomes subpopulations from heterogeneous EVs

preparations, and characterize each subpopulation with respect to its size and zeta

potential.

2. Develop two-dimensional SdFFF-AF4 separation

Produce density-size homogenous exosomes subpopulations from heterogeneous EVs

preparations, and characterize each with respect to its density, size, zeta potential,

exosomal biomarkers, and phospholipid composition.

3. Increase throughput by using semi-preparative AF4 to producing higher amounts of

size uniform exosome subpopulations, and characterize each subpopulation with

respect to its size, exosomal biomarkers, and phospholipid composition.

Objective 1 (chapter 3) was achieved using the two SdFFF and AF4 techniques. The effect of different experimental conditions on retention behavior of three exosomes cancer cell lines was evaluated. Exosomes that are separated by SdFFF eluted according to differences in effective mass meff. The unfractionated exosomes and the fractions collected at the exit of the SdFFF channel were characterized for effective mass and density using sucrose gradient SdFFF and FFF theory 79. The AF4 technique separates exosomes based on hydrodynamic diameter d. Size uniform fractions were collected and characterized in terms of size, density, and zeta potential using dynamic light scattering (DLS), and sucrose gradient

SdFFF. The use of SdFFF and a conventional AF4 channel and the information obtained from both techniques are new to the field of exosomes.

Objective 2 (chapter 4) was accomplished using a two-dimensional separation approach.

Exosomes preparations from three cancer cell lines were first individually injected into an

SdFFF channel and four meff subpopulations were collected from each cell line. Each subpopulation was then injected into an AF4 channel for separation based solely on d. Ten fractions were then collected from the AF4 channel outlet to produce a total of 40 exosomes subpopulations from each cell line. These subpopulations are uniform in both meff and d (and

hence density) as confirmed by subsequent analyses that were done by DLS and SdFFF. The two-dimensional exosomal fractions were also analyzed with respect to their for phospholipid profile and main exosomal protein biomarkers using thin layer chromatography

(TLC), sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and

Western blot. The use of a two-dimensional separation, and the information obtained are new to the field of exosomes.

Objective 3 (chapter 5) was realized by scaling up the analytical AF4 channel to a semi- preparative AF4 channel to increase throughput by producing fractions with higher amounts of size uniform exosome subpopulations. Characterization of each collected fraction in terms of size, phospholipid profile, and main exosomal protein biomarkers were performed using

DLS, TLC, SDS-PAGE, and Western blot.

CHAPTER 2

FIELD-FLOW FRACTIONATION

This chapter gives an overview of the main techniques that were used in this thesis to

separate and characterize tumor-derived exosomes. This includes field-flow fractionation

(FFF), dynamic light scattering (DLS), sodium dodecyl sulfate polyacrylamide gel

electrophoresis (SDS-PAGE), Western blot, and thin layer chromatography (TLC). FFF was

the main method that was employed in this thesis, and for this reason this chapter will mainly

overview the FFF techniques.

2.1 Field-flow fractionation

Field-flow fractionation (FFF) is a series of techniques that J. Calvin Giddings introduced

in 1966 for the separation and characterization for macromolecules, colloids, and particulates

80. In recent decades, FFF has gained wide use because of its versatility and applicability to

different types of samples, its unique advantages as will be discussed later, and commercial

availability of instruments. The increasing number of research papers on FFF, signifies

recognition of its advantages over other separation techniques especially for biological

applications. Nowadays, FFF techniques are widely applied to many types of samples such as

nanoparticles 81-85, polymers86-89, humic and fulvic substances 90, 91, proteins 92-97,

pharmaceutical and biomedical substances 98-103, cells 104, 105, and chemical mechanical

polishing slurries 106, 107.

2.1.1 General principles

Unlike liquid chromatography (LC) where analytes interact differentially with the

stationary phase of a column packing material, FFF is based on differential interaction with a

field acting orthogonal to the separation axis. Limitations of chromatography for the

separation and characterization of biological materials arise from non-ideal interactions with

the stationary phase which may lead to adsorptive sample loss and a change in their

physiochemical properties 108, 109. The absence of a stationary phase and column packing

material in FFF offers advantageous features, particularly for the separation and

characterization of fragile materials 49, 110.

In general, the FFF separation occurs in an open thin channel that has a ribbon-like

geometry. The FFF channel (~ 100-500 µm thick) is characterized by a high aspect ratio that

generates a parabolic flow profile due to laminar flow conditions. The flow velocities across

the channel thickness vary as a function of distance from the wall. The velocity is at a

maximum at the channel center and gradually decreases until it reaches near zero at the

channel walls. FFF separation typically involves a number of steps including sample

injection, relaxation, elution, and detection and may include fraction collection. Once the

sample is injected, the relaxation step starts where analytes redistribute and form equilibrium

layers near the channel inlet. Each analyte has a characteristic mean equilibrium layer

thickness and will occupy different velocity streamlines of the channel’s parabolic flow. The

time for this relaxation step varies from one analyte to another depending on its

physicochemical properties and interactions with the applied field, and can be estimated

using FFF equations 111. The various FFF techniques arise from the use of different fields to

move analytes into particular positions near the sample accumulation wall. The field-induced

transport is counter-balanced by diffusion that causes particles to move from regions of high

concentration at the accumulation wall. Under steady state conditions, equilibrium sample

clouds are formed with different mean layer thicknesses 1, 2 , 3,... corresponding to the

average position of each analyte in the channel (as shown in Figure 2.1). Since each analyte

occupies different velocity streamlines of the parabolic flow profile, it will elute at a different

retention time tr. Under ideal separation conditions, each component has a unique and thus

a unique tr. The term is proportional to diffusion coefficient D which, in turn, is related to

analyte diameter d. Sample elution is a crucial step, and will be discussed later in more detail.

In the elution step, analytes elute at different times depending on their position in the channel

with analytes positioned near to the channel center eluting before those close to the

accumulation wall. As the analytes elute from the channel outlet and into the detector(s) an

FFF fractogram (plot of detector response versus time) is generated. The FFF channels can

be coupled to various detection systems (online mode) to gain additional information about

the eluting species. An additional step can be added to collect fractions from the detector

outlet for further investigations (offline mode). Figure 2.1 shows a representation of a typical

FFF channel and separation mechanism.

2.1.2 Modes of separation

The relative distribution of sample components across the FFF channel determines the

separation mode of the sample analytes and governs the elution order. The FFF modes of

separation include normal (Brownian), steric, and hyperlayer and are shown in Figure 2.2.

The normal mode is commonly encountered for analytes of submicron size while the steric

and hyperlayer modes are in effect for analytes larger than 1µm.

Figure 2.1 FFF channel and separation mechanism: a) Typical FFF channel, b) injection and relaxation, c) elution, and d) typical FFF fractogram.

Figure 2.2 Common FFF modes of separation: A) Normal, B) Steric and C) Hyperlayer.

2.1.2.1 Normal mode

The normal mode (Figure 2.2 A) is the most widely developed and employed and

governs the elution of most nanoparticles. Since exosomes are nanometer-sized particles, the

normal mode is the separation mechanism used in this work. In this mode, particle diffusion

coefficients govern the separation and determine the elution order. The particles are driven

by an external field towards the accumulation wall to build up a concentration profile that

causes an exponential concentration distribution of analytes at the channel accumulation

wall. Since the analyte diameters d are much smaller than the channel thickness w, the

diffusion coefficient D determines the analyte position in the parabolic flow profile.

Accordingly, small analytes with large D are positioned further away from the accumulation

wall, and are in the region of faster flow velocity, and thus elute before the larger particles

that have a smaller D.

2.1.2.2 Steric mode

The steric mode (Figure 2.2 B) predominates for micron-sized particles ranging from ~1

to 100µm. Unlike the normal mode, the diffusion coefficient D of the particle plays an

insignificant role in the separation mechanism because D ˂˂ d. The position of particles

across the channel governs their retention. The center of mass of large particles is located

further from the accumulation wall than that of smaller particles. This results in large

particles occupying faster flow streamlines and thus large particles elute before the smaller

particles. Resolution and selectivity are affected when particles are eluted by this mode. It

was found that the resolution decreases as the flow rate increases because of increased zone

broadening, even though the selectivity is increased 112, 113.

2.1.2.3 Hyperlayer (lift) mode

Due to the large lift forces that push the particles into a confined region above the

accumulation wall or weak opposing field forces, the particles can be driven away from the

accumulation wall into a faster flow streamlines, which is the case in the hyperlayer mode

(Figure 2.2 C) that occurs when using high flow rates to elute micrometer-sized particles.

The resulting hydrodynamic forces act on the particles to drive them away from the

accumulation wall while the applied field pushes them towards the accumulation wall. This

results in thin bands of particles distributed in different positions across the channel

thickness. Large particles are focused close to the channel center while smaller particles

position are close to the accumulation wall. As a result, the elution order is similar to the

steric mode where large particles elute before smaller particles. The opposing forces lead to

narrow bands of particles and thus narrow peaks.

2.1.3 Normal mode retention theory

Since exosomes are separated in the normal mode, only normal mode retention theory

will be discussed in this chapter. Retention theory for other FFF modes can be found

elsewhere 111. To understand analyte retention in FFF, it is important to imagine the geometry

of the channel where the spacer is sandwiched between two parallel plates leaving an open

space where the separation takes place. The applied field is perpendicular to the separation

axis with the x-axis of the distance across the channel thickness. Under the influence of the

applied field, particles are moved towards the accumulation wall with a velocity U. This

movement yields a field-induced flux Uc, in the negative x-direction, which is the number of

particles per unit cross-sectional area per unit time. A diffusion-induced transport, in a

positive x-axis direction, acts against the field-induced displacement to yield a diffusive flux

proportional to the concentration gradient dc/dx. The magnitude of each particle diffusive flux is defined by the analyte diffusion coefficient D. The net flux Jx that is determined by the mass transport across the channel and can be described by the equation 111

dc J  Uc  D 2-1 x dx

At the end of the sample relaxation stage, a cloud of particles is formed with each analyte having a characteristic equilibrium distribution as a result of the balance between the two opposing transport-driven processes. At the end of this stage the overall flux equals zero.

When integrating and solving Equation 2-1 and assuming constant D and U, a new equation that describes the concentration profile at equilibrium is obtained 111

c(x)  |U |   exp  x 2.2 c0  D 

where x denotes the distance from the wall and c0 is the analyte concentration at the accumulation wall where x = 0. Another important term is the mean layer thickness ℓ, which corresponds to the average position of the analytes in the channel as previously mentioned.

Here ℓ = D/|U| which shows that ℓ depends on the two opposing forces that act on the analyte, i.e., the external field that moves the analytes toward the accumulation wall expressed by |U|, and the diffusion-driven transport that drives the analyte back away from the accumulation wall expressed by D. Under these conditions, Equation 2-2 can be rewritten in another form that describes the analyte concentration decreasing exponentially with increasing distance from the accumulation wall 86

c(x)  x   exp   . 2.3 c0  

The quantity ℓ can also be expressed in a dimensionless form 86

D    2.4 w |U | w where  is called the retention parameter. The Stokes-Einstein equation can be used to calculate the diffusion coefficient D of a spherical particle in a dilute aqueous solution (D = kT/ƒ), where k is the Boltzmann constant, T is the absolute temperature, and ƒ is the friction coefficient. Additionally, ℓ can be expressed in terms of the ratio between thermal energy

(kT) and the force F that is exerted on the particle by an external field to obtain ℓ = kT/F. The relation between F and  is expressed by 111

kT   2-5 Fw

The term  is an important parameter in FFF for a number of reasons. First, as shown from Equation 2-5 it is dependent on the field strength. As a result, every analyte has a particular . Second, the  value can be translated into physicochemical properties of the sample as will be discussed in sections 2.1.4.1 and 2.1.4.2. This parameter which is always positive can be related to the experimentally measured dimensionless retention ratio R that

0 expresses the relationship between the retention time tr of the analyte and the void time t , which is the time for the carrier liquid or unretained components to travel through the channel 111

t 0 R  2-6 tr

The parameter R is commonly used in separation methods to estimate the degree of the retardation of the analyte in the separation channel relative to the carrier liquid flow rate and the compression caused by the field. The relationship between R and  is expressed by 111

1 R 6(coth 2) 2-7 2

Equation 2-7 shows that retention is dependent on , which explains its importance as a characteristic parameter of each eluting analyte. Equation 2-7 can be approximated to R = 6

– 122 with an accuracy less than 2% for  ˂ 0.2 or R = 6, for low  (˂ 0.02) values with an error of 5%.

For easily evaluating the retention quality, Wahlund introduced a new parameter, retention level RL, which describes the level of retention (e.g. separation efficiency and

114 resolution) a particle can have . The RL can be defined as

1 t R   r 2-8 L R t 0

In this thesis, the parameter RL is used to evaluate the separation efficiency and quality.

The resolution RS, which is the ability of a separation system to separate two zones of different sizes, can be calculated by 114

tr RS  2-9 wb where is the difference in retention times of two peaks and is the average of their baseline widths. This equation applies to Gaussian peaks. For non-Gaussian peaks, the resolution Equation ( ) can be used where widths

are calculated from the width at half the peak height ( ) where are the peak

115 widths at the half peak heights of the peaks 1 and 2, respectively . RS ≥ 1.5 indicates a

baseline separation, whereas RS below 1.5 indicates some degree of peak overlapping, and RS

= 0 means a complete overlap.

By the right choice of retention time, one can optimize the resolution and thus enhance

the separation efficiency. The experimental RL and  can be linked together by the following

approximation 114

1 RL  6 2-10

The quantity RL can be controlled by different experimental parameters such as channel

thickness and field strength.

2.1.4 FFF techniques

Different types of fields can be used in the FFF as long as they interact with some

physicochemical property of the sample 67. The fields include cross-flow (symmetrical

(FlFFF) and asymmetrical (AF4)), temperature gradient (ThFFF), sedimentation (SdFFF),

electrical fields (electrical FFF), gravitation (GrFFF), magnetic (MgFFF), and

dielectrophoretic (DEP-FFF) 116-122. Among these techniques, AF4, SdFFF, and ThFFF are

commercially available from manufacturers such as Postnova Analytics (Landsberg am Lech,

Germany), Wyatt Technology (Santa Barbara, United States), and ConSenxus (Ober-

Hilbershiem, Germany). Since this work utilized SdFFF and AF4, the next sections of this

chapter will only cover these techniques.

2.1.4.1 Sedimentation FFF

As any typical FFF system setup, the SdFFF system is composed of a pump, a separation

channel, a detector, and a data acquisition computer. Other auxiliary components may

include a degasser, autosampler, and fraction collector. The SdFFF system requires one

pump to transport the sample into and through the channel. The SdFFF channel (~ 100-300 μ

in thickness) is placed in a centrifuge basket, and the centrifugal force that results from the

rotation of the channel causes the particles to be pushed toward the accumulation wall. The

latter can be either the inner or the outer wall of the channel depending on the density of the

sample and the carrier liquid. Particles with density smaller than the carrier liquid tend to be

positioned close to the inner wall whereas particles with larger density than the carrier liquid

migrate toward the outer wall of the channel 123. The SdFFF system setup is illustrated in

Figure 2.3.

Figure 2.3 Sedimentation FFF system setup.

Once the sample is injected into the SdFFF channel, the relaxation step is started by

halting the flow and allowing the sample to experience the field and undergo equilibrium

distribution at the accumulation wall. In SdFFF, the relaxation time can be estimated by 111

2 F meff  r 2-12 where meff is the effective mass (particle true mass, m minus buoyant mass, mρ/ρp, where ρ is carrier liquid density and ρp is the particle density), ω is the angular rotation frequency (ω =

2π.rpm/60), and r is the rotor radius of curvature 79, 111.

The centrifugal field can also be expressed in another form which is field strength (or

2 acceleration G), which equals (G = meff ω r / g) where g is the gravitational constant giving G units of gravity. The centrifugal force F can also be related to particle volume Vp, and the difference in density between the particle and carrier liquid Δρ 124

 F  m G V  G  d 3  G 2-13 eff p 6

3 111 where Vp is the volume of the sphere (Vp = πd /6). Substituting F into Equation 2-5, gives

6kT   2-14 πd3Δρω2rw

Taking in consideration the approximation R = 6, the relationship between retention

111 time tr and particle diameter d is given as

πm ω2rw πd3Δρω2rw t  eff t0  t0 2-15 r 36kT 36kT

3 The relationship between tr and d ranks SdFFF amongst the high resolution separation techniques where 10x difference in d translates into 1000x difference in tr. SdFFF can also be used as microbalance for measuring minute effective mass changes in the range from 10-16 to

10-18 g 124. The SdFFF process is shown in Figure 2.3.

Figure 2.4 Different stages experienced by a sample undergoing SdFFF separation.

Unlike ultracentrifugation, the displacement rate of particle bands in SdFFF depends

solely on F whereas with ultracentrifugation, F depends on frictional force which is

proportional to the particle velocity Ʋ and to a friction coefficient f (F ~ fƲ). In this case, the

particle will accelerate until the two opposing forces are equal leading the particle to

sediment 125. Thus, the particle shape and mass can affect F/f and therefore the

ultracentrifugation data interpretation is not as straightforward. Additionally, the

sedimentation velocity v in ultracentrifugation is related to d2 (v = [d2Δρω2 r] / 18µ) where µ

126 is liquid viscosity . This places SdFFF as a higher size selective technique (tr proportional

to d3) than ultracentrifugation.

2.1.4.2 Asymmetrical Flow FFF (AF4)

Similar to SdFFF, the AF4 system is composed of a pump, a separation channel, a

detector, and a data acquisition computer assembly. Other auxiliary components may include

degasser, autosampler, and fraction collector. Unlike SdFFF, AF4 system requires an additional pump that serves to pump the carrier liquid through the channel. The flow entering the channel is split into two flows (crossflow and channel flow) as a result of permeability across a membrane wall 111. By using a valve to regulate back pressure in one of the flow axes, the ratio between the two flowrates can be controlled. The AF4 system setup is illustrated in Figure 2.4.

Figure 2.5 Asymmetric flow FFF system setup.

The AF4 channel assembly is quite different from other FFF techniques. As illustrated in

Figure 2.5, the AF4 channel is composed of two blocks, commonly made of Plexiglas or stainless steel, with porous permeable frit panels that allow the crossflow to exit from the crossflow outlets. An ultrafiltration membrane and a spacer with the FFF channel form are sandwiched between the channel’s two blocks.

The ultrafiltration membrane (accumulation wall) is chosen so that it is permeable to the carrier liquid but not to sample components. Ideally, the membrane must be compatible with both sample and carrier liquid to prevent any unwanted interactions. The selection of the

membrane molecular weight cut-off (MWCO) is important to avoid sample loss through the membrane. Recovery studies to evaluate sample loss can be performed by comparing the peak areas of fractograms obtained with and without an applied crossflow. The latter is accomplished by direct injection of the sample into the detector. The FFF membrane can also serve to selectively filter out the unwanted low MW components.

The spacer, commonly made of Teflon or Mylar, is where the separation takes place. It has a typical thickness between 100 and 500 μm where the lower thickness limit is determined by the membrane compressibility 111, and for this reason the actual channel thickness has to be measured empirically as will be discussed later.

Figure 2.6 Main components of an asymmetric flow FFF channel.

In the AF4, which is a size-based separation, a crossflow is used as an applied field at a right angle to the channel flow. The sample analytes with different diffusion coefficients D are distributed in different flow streamlines of the channel that various flow velocities to

elute at different times. The smaller particles will elute first because they are in the higher stream velocity region (the middle of channel) caused by two opposite forces, field and normal diffusion, where the field force is generated by the crossflow that is perpendicular to the channel flow driving the sample analytes toward the accumulation wall. Diffusion drives the sample components away from the accumulation wall into different velocity regions causing separation by size differences. The parameter D can be calculated from the retention times using the following equation 111

t 0V w 2 D  C 2-16 0 6trV where w is the channel thickness, is the cross-flow rate, V0 the void volume of the channel,

0 tr the retention time, and t the void time, which is the time for unretained analytes to travel along the FFF channel that can be estimated by 111

  bb   w(b z L z2  y) V 0  V  t 0  ln1 C 1 2L  2-17 V V  V 0  C       where is channel flow rate, L is the channel length, and b and are the breadths of the trapezoidal AF4 channel at the inlet and outlet, respectively. The is the length of the cuts of the area of the channel outside the trapezoid geometry.

While the void time is calculated by the previous equation, the retention can also be predicted by 114

w2  V  t  ln1 C B 2-18 r    6D  V 

where B is the value of the expression within the square brackets in the Equation 2-17 that

  defines the channel geometry. From Equation 2-17, it is apparent that the VC V ratio is one

of the main driving forces that controls the retention time. Thus, one can adjust this ratio to

optimize the retention and analysis times.

Based on AF4 theoretical equations, one can use determine the hydrodynamic diameter

114 dH of the analytes from experimental retention times

2kTV 0t d  r 2-19 H  2 0 VC w t

where ƞ is the carrier liquid viscosity.

2.1.5 Sample Recovery

The sample recovery is an important parameter that needs to be considered when

performing an FFF experiment where the sample is subjected to be lost due to either inherent

instability or experimental conditions. Irreversible sample-accumulation wall

interaction/adsorption and/or sample loss through the membrane (in AF4) is commonly

observed if non-optimized conditions are used. Common sources of decreased recoveries in

FFF are carrier liquid, accumulation wall, and field strength. The decrease in the sample

recovery will result in minimizing the sample amount at the detector and the final

concentration of the collected fractions in case of further post-FFF analyses.

Sample recovery is commonly calculated in a form of percentage recovery by dividing

the area under the curve of a regular FFF run with the area under the curve from injecting the

same amount of sample through the channel without the applied field.

2.1.6 Sample overloading

One of the important phenomena that has to take in consideration when carrying out an

FFF experiment is the sample overloading. This refers to the distortion in the sample elution

profile that can be happened by different types of interactions either between sample analytes

(intermolecular interaction) or sample-wall interaction 127, 128. The separation in FFF takes

place in a thin area of the channel close to the accumulation wall (~1% of the channel

thickness). Therefore, the sample amount has to be carefully determined to avoid low sample

detectability due to low concentration or ample overloading phenomena because of high

concentration. The sample amount can also have a significant effect on the different

experimental parameters, and thus on the retention time that is the key parameter to calculate

sample physicochemical properties 111, 127, 129-131. Injecting large sample quantity may cause a

steric effect where sample components have no enough room for equilibrium which may

cause them to prematurely elute due to intermolecular interaction. Additionally, attractive

interaction between particles may result in agglomerating them, and thus increase the

retention time. The decrease of retention time as concentration/mass increases and peak

asymmetry (fronting) are indicative of sample overloading. Conversely, injecting small

sample amounts can affect the sample concentration at the detector which will lead to low

sample concentration in case of collecting fractions for subsequent studies.

Overloading can also be resulted from carrier liquid ionic strength due to the effect of

later on the sample double layer thickness 127, 132. High ionic strength will decrease the

double layer thickness and thus decreasing apparent analyte size which will result in shorter

retention time. Opposite effect can be observed in case of using low ionic strength regimes.

In many cases, post-FFF analyses is needed to be done on the collected fractions.

However, dilution is a challenging issue when a sample injected amount is distributed in

multiple fractions. This can be solved by running multiple runs using identical experimental

conditions, and collect several fractions to be combined to increase the concentration.

Alternatively, semi-preparative AF4 channels are now commercially available, and a large

sample amounts can be injected to overcome this problem as it will be discussed later on in

this thesis. The study of overloading in FFF requires performing multiple runs at different

sample loads and ionic strengths to observe the change in retention time and peak shape.

Unchanged retention time and peak shape is a good sign of absence overloading effect.

2.1.7 FFF optimization

An FFF experiment involves a series of steps and parameters that should be optimized in

order to obtain a desirable separation. The experimental variables can affect other parameters

and features such as retention time, resolution, peak shape and analysis time 133.The main

experimental variables of the FFF include field strength, flow rates, relaxation time and

position, carrier liquid composition and ionic strength, and sample loading.

In addition to these general variables, each FFF technique has its specific experimental

variables that need to be optimized. For instance, the retention in SdFFF is primarily

dependent on the extent of the centrifugal force that is applied on the particle. This force is

considered the main experimental parameter that must be optimized in order to achieve a

desirable separation. The currently available SdFFF instruments have a maximum field

strength of ~ 2700x g (4900 rpm) and ~1000x g (2500 rpm). On the other hand, the choosing

of flow rates in AF4 is critical to obtain reasonable retention time with high resolution.

Higher crossflow rates are required to shorten the analysis time, particularly for

polydispersed samples. However, the risk of sample immobilization as a result of

sample/membrane interaction should be considered. Also, the carrier liquid is a crucial

element in AF4. To achieve optimum retention, certain properties of carrier liquid should be

optimized to avoid undesirable interactions 111. The most critical parameter is the ionic

strength where high ionic strengths could lead to aggregation and adsorption of analytes on

the accumulation wall whereas repulsion might occur if too low ionic strengths are used

leading to reduction in retention and selectivity 111.

The effect of different experimental conditions in SdFFF and AF4 will be discussed in

the chapter 3 as both techniques are optimized for exosomes separation.

2.1.8 Online and offline coupling with other analytical techniques

Equations 2 xx and 2.ydemonstrates the relationship between measured SdFFF and AF4

retention times and physicochemical properties of the analytes. In other words, FFF is both a

separation and characterization technique. Additional information can be obtained from FFF

experiments when FFF is coupled, either online or offline, with detectors and instruments

that measure properties of the eluting sample components. This provides confirmation of the

values calculated using FFF theory and/or orthogonal measurements using a different

analytical technique. The latter is necessary when analyzing complex samples. The most

frequently employed online detectors include ultraviolet-visible (UV–vis), multi-angle light

scattering (MALS), differential refractive index (dRI), viscometry, mass spectrometry,

evaporative light scattering detector (ELSD), fluorescence, dynamic light scattering (DLS),

and inductively coupled plasma mass spectrometry (ICP-MS?). Offline analysis includes

Fourier transform infrared (FTIR) and matrix-assisted laser desorption/ionization time-of-

flight mass spectrometry (MALDI-TOF–MS). Electron microscopy techniques such as

transmission electron microscopy (TEM) and scanning electron microscopy (SEM) are also

widely used for sizing FFF fractions.

2.1.9 FFF applications for biological separation and analysis

The aim of this section is to show the ability of FFF techniques to separate and

characterize biological and biopharmaceutical samples.

2.1.9.1 Proteins

A pre-separation stage is commonly used before protein characterization to accurately

measure different physicochemical properties of proteins such as diffusion coefficient and

protein aggregation. Size exclusion chromatography (SEC) is commonly used as a separation

step for proteins. However, as previously discussed the chromatographic separation of fragile

samples such as proteins may lead to protein denaturation due to the adsorption of analytes

on the packing material of the stationary phase. This leads to a shear stress on the protein

resulting in loss of solubility, dissociation into smaller subunits, or aggregation which can

lead to change of their properties. Since the first paper on protein separation in 1977, FFF has

shown the ability to overcome chromatography’s drawbacks 134. Many publications have

continued to demonstrate flow FFF’s ability to separate and measure the diffusivity of intact

proteins of different origins, with a mass range of 106 in a single run92, 95, 129, 135-137.

Lipoproteins, which are micelle-like macromolecular structures, contain both proteins and

lipids with hydrophilic groups consisting of phospholipids, cholesterol and apolipoproteins

oriented towards the surface and hydrophobic triglyceride and cholesterylester components

located in the core. They are of great interest in the biomedical field because their function

and pathological state depend on their size and shape as well as internal composition.

Historically, many methods were used including analytical ultracentrifugation, gel

electrophoresis, and chromatography. Each of these techniques has its limitations that affect their use for lipoproteins analysis. Different studies have shown the ability of FFF to separate, fractionate, and characterize a variety of lipoproteins and their constituents without altering their original configurations 138-140. Lipoproteins are classified according to their densities as high-density lipoproteins (HDL), low-density lipoproteins (LDL), and very-low- density lipoproteins (VLDL). LDL is known to be responsible for coronary artery disease

(CAD). It is also found that the size and morphology of lipoprotein are linked to CAD. Frit inlet AF4 has shown a capability for the separation and analysis of lipoproteins profiles in healthy persons and CAD patients in a short analysis time 113. In order to selectively identify lipoproteins from other plasma proteins, LDL lipoproteins were stained with Sudan Black B.

This enabled the fractionated lipoproteins to be identified at wavelength of 610 nm. Figure

2.7 shows the control samples of three healthy persons that have similar retention behaviors, except for the control 2 that may suggest to have smaller LDL components. However, the retention times of the LDL from CAD patients were decreased indicating the size decrease, as well as decreasing increasing the HDL level (second peak) with constant size, which is commonly associated with the disease 141.

The separation and characterization of HDL and LDL lipoproteins in miniaturized AF4 is achieved within a shorter analysis time. The geometrical channel dimensions of a conventional AF4 (38 × 2 cm) is scaled down to 11 cm in length and 0.7–0.35 cm in width, while the channel thicknesses kept the same. This allows using smaller sample amounts and lower carrier liquid consumption (Figure 2.8) 142. The resolution of both systems is almost the same. Miniaturized frit inlet AF4 (mFI-AF4) was also used for the separation of proteins standards of carbonic anhydrase (29 kDa), alcohol dehydrogenase (150 kDa), apoferritin

(444 kDa), and thyroglobulin (669 kDa) and protein complex with single strand (ssDNA) oligomers 143. The total channel area was reduced from 4.29 cm2 to 38.8 cm2 and the area of inlet frit was reduced from 4.17 cm2 to 0.52 cm2. This shows a potential applicability of the mFI-AF4 for the fast identification of DNA-protein binding activity.

FFF was also used as a pre-analysis method for lipid profiling. Phospholipids in lipoproteins were analyzed by multiplexed hollow fiber Flow FFF (MxHF-FlFFF) coupled offline with nanoflow liquid chromatography–tandem mass spectrometry (nLC–ESI-MS–

MS) 144. MxHF-FlFFF (also abbreviated as MxHF5), is a cylindrical version of the flow FFF operated with a porous, hollow fiber membrane 145. This FFF technique offers unique advantages over conventional separation methods for the analysis of biological samples. In addition to its advantage in keeping the integrity of natural conformations, as already pointed out, a disposable channel can be constructed to minimize the risk of cross-contamination that

could happen while analyzing different biological samples. Qualitative information on individual phospholipids from the intact high density lipoprotein (HDL) and low density lipoprotein (LDL) of human plasma samples was obtained as shown in the fractograms in

Figure 2.9. The collected fractions were then further analyzed by nLC–ESI-MS–MS after removing plasma proteins and extracting lipids.

Figure 2.8 Fractograms representing separation of HDL and LDL by mAsFlFFF (A) and by conventional AsFlFFF (B). Carrier: 8.5 mM phosphate buffer (pH 7.4), 0.02% NaN3, 150 mM NaCl; HDL/LDL (injection volume, 0.08 μl for HDL and 0.17 μl for LDL; injection concentration, 0.058 μg for HDL and 0.77 for LDL). Reprinted with a permission from reference 142.

The development of FFF techniques and their hyphenation with various detectors and methods have led to invaluable information on proteins. In the last years, FFF/MALDI-TOF

MS has become a new tool for separation and characterization of biomacromolecules 146. The protein profile of two bacterial strains, E. coli and P. putida were obtained 147. The entire analysis including MS measurements took only one hour and without degradation of MALDI peaks. Combination of AF4 with multi angle light scattering (MALS) detector could also provide molecular weight data on proteins as well as monitor protein production and

refolding 148. Another study showed that AF4-MALS can be applied to the size analysis of

green fluorescent protein inclusion bodies (GFPIBs) for determining the effect of culture

conditions such as temperature on GFPIB size distribution 149. The GFPIBs bodies showed

changes in size, depending on temperature during the preparation procedure (37 or 30 °C)

and the induction times at 37 °C.

A comprehensive two-dimensional FFF-LC chromatographic system was successfully

utilized for egg white proteins (10-12% protein content, mainly ovalbumin, ovotransferrin,

ovomucoid, globulins, and lysozyme) separation and characterization 150. This combination

of two separation techniques of different mechanisms, where the AF4 separation depends on

size whereas LC separation depends on hydrophobicity, provided size and hydrophobicity

information of the protein samples.

2.1.9.2 Nucleic acids

Nucleic acids include DNA (deoxyribonucleic acid) and RNA (ribonucleic acid) are

among the main types of biomacromolecules that play essential biological roles; DNA

maintains the genetic information required for protein synthesis while RNA transports this

information to the target site of protein production. Like proteins, the challenge in nucleic

acids analysis is to separate, fractionate, purify, and characterize without altering their

conformation. SdFFF and FlFFF have shown this ability making them as potential tools for

this purpose.

SdFFF was used to separate and characterize fragile λ-DNA molecules with a large

molecular weight range from 106 to 1013 Da to test the ability of SdFFF to separate and

characterize λ-DNA without affecting its conformation 151. The molecular weights and

biological activity remained constant with sample recoveries of over 90%. AF4 was used to

fractionate 700 base pairs of plasmid fragments out of 4600 pairs in a reasonable analysis

time of ~ 30 min using a much thinner (w =120 μm) channel and flow programming elution,

resulted in minimized zone broadening and increased resolution in a shorter time 152. FlFFF

was also used to measure the diffusion coefficients of linear and single- and double-stranded

circular DNA chains having molecular weights ranging from 0.4 x 106 to 4.8 x 106 Da 153.

2.1.9.3 Vaccines

In the last years, new types of vaccines have been launched that are based on so-called

virus-like particles (VLPs) 154. Comparing with traditional recombinant protein vaccines,

VLPs were revealed to have stronger and longer immune responses. These self-assembling

proteins mimic the overall structure of virus particles but they do not contain infectious

genetic material 155. VLPs are currently used for vaccination, drug delivery, and gene

delivery 156. The VLPs’ features have attracted scientists to carry out many investigations to

produce and characterize VLPs. Their size is one of the important parameters affecting their

properties and for this reason, it is crucial to accurately determine their size and size

distribution. Thus, the availability of a reliable technique for this purpose has become a need

where many of known methods could cause VLPs aggregation during the analysis. Lately,

AF4 coupled with MALS detector has shown to be a potent tool for the characterization of

VLPs without causing aggregation 157. The capability of FFF coupled with MALS detection

to investigate VLPs size and size distribution was studied and compared with DLS and TEM

results. The measured sizes were in good agreement with DLS and TEM. Furthermore, it was

possible to observe changes in the quaternary structure which is important in many

manufacturing procedures in the process control of assembly/disassembly-related processes.

2.1.9.4 Drug delivery

Many of new drug delivery systems that have been either on the market or under

investigation aim to overcome the major drawbacks of traditional systems such as side

effects, efficacy, and patient compliance. One of the approaches to achieve these criteria is to

use pharmaceutical carriers that transport the pharmaceutical active ingredient to a specific

pathological site in a higher dose and without damaging healthy cells. Several drug carriers

such as liposomes, nano-associates, nanoparticles, drug polymer-conjugates, and polymeric

micelles have been developed.

In order to achieve the desired biological performance, a drug carrier must possess

multiple required properties such as the right size and molecular weight that are important for

determining the permeability and solubility of the carrier to and in different biological

barriers and fluids. AF4 has become the workhorse for novel pharmaceutical delivery

systems. In a recent study, AF4 was used to characterize the size of drug-loaded polymeric

core/shell nanoparticles, a lipid core of lecithin and a polymeric shell of Pluronic 158. The

successful separation and analysis of carriers that have broad size (dH = 100-600 nm) and

bimodal size distributions could overcome the limitation of other techniques such as DLS,

which is not suitable for populations with sizes that differ by less than two times in size as

noted later 159. It was also difficult to accurately determine the size distributions of these

carriers by TEM or cryo-TEM because where only few particles could be counted. AF4 was

also used to monitor the stability of two batches of these carriers during storage. The two

batches were subjected to different storage conditions for 70 days and samples were taken

from each at various storage times to be analyzed by AF4. It was found that the band area of

the drug-loaded particles decreased as the storage time increased, and disappeared after 70

days.

2.2 Non-FFF techniques used in this thesis

In this study, a number of techniques have been used for the characterization of exosome

fractions that were collected from FFF channels. These techniques include dynamic light

scattering (DLS), thin layer chromatography (TLC), and sodium dodecyl

sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and Western blot.

2.2.1 Dynamic light scattering

Dynamic light scattering (DLS) is a sizing technique in which the sample is illuminated

by a laser beam, and the light scattering fluctuations that arise from the Brownian motion of

particles is recorded in the form of intensity 160. The rate at which these intensity fluctuations

occur depends on the diffusion coefficient D of the particles. Small particles (or high D) have

faster Brownian motion causing the intensity to fluctuate more rapidly than large particles

(low D) that have slower Brownian motions.

The intensity of signal at time t is compared to the intensity at a short interval later (t+δt), and a correlation between the intensities of two signals is calculated. If the signal at t is compared with signals at t+2δt, t+3δt, t+4δ etc., the correlation of a signal arriving from a random source will decrease with time until t = ∞ where the correlation equals zero. The signal of large particles will decay slowly and thus the correlation will continue for a long time. Conversely, the signal of small particles decay rapidly and the correlation decay quickly. This can be related by the following Equation 161

I(t)I(t ) g( ) 2-19 I 2 where g(τ) is the autocorrelation function, I(t) is the intensity at time t, and I(t + τ) is the intensity shifted by a delay time of τ.

At τ → 0, I(t) = I(t+τ); = , where :is the average of the squared scattering intensity.

By computing the intensity correlation function g(τ), one can obtain the diffusion coefficient of the particles D. The Stokes-Einstein equation assumes spherical particles and relates D to the hydrodynamic diameter dH of the particle by

kT D 3dH 2-20

DLS analysis can be done by either batch or online mode. In batch mode, the entire sample is placed in a cuvette typically without prior separation to measure the average D. In this mode, the sample volume, concentration, and timescale of the measurement are well controlled. However, this operation mode yields average D values for the whole sample making it suitable for monodisperse samples. For multimodal samples, distinct populations

have to differ in size by at least 2x in order to achieve size resolution 159. Additionally,

polydisperse samples that have continuous size distribution, the results bias towards larger

sizes (lower D values) due to the large scattering contribution of large particles to the total

scattering signal because of the inherent relationship between intensity I and diameter d (I α

d6) 162. In the online mode, the DLS detection system is coupled to a separation system such

as FFF for simultaneously determination of D. The pre-separation stage provides more

monodispersed populations, and thus more accurate determination of D and D distribution.

However, sample dilution and the weak detector signals are the major drawbacks of this

mode.

2.2.2 Thin layer chromatography

Thin layer chromatography (TLC) is a simple and inexpensive analytical tool that is

widely used for lipids separation and characterization. Two modes of TLC include adsorption

(or normal) and reversed modes are generally used. Adsorption is the most used mode in

which silica gel (unequivocally the absolutely dominant phase), alumina, is used as a

stationary phase and a mixture of hexane and diethyl ether, sometimes with formic acid, is

used as a mobile phase. Lipids with different retention properties will elute at different times

based on their polarities. Generally, polar lipids such as sphingolipids are separated on the

lower half of the stationary phase while nonpolar lipids such as cholesterol migrate to unique

positions in the upper half. Recently, high-performance TLC (HPTLC) using a very uniform

and small particle size silica gel reveals excellent separations and short analysis time.

HPTLC is fast and relatively inexpensive method, and useful for complex lipids particularly

apolar compounds.

In this thesis, fractions collected from the FFF channels were analyzed for phospholipid

compositions. The lipids were first extracted by the Bligh & Dyer method 163, using a

mixture of solvents that included chloroform, methanol, and water (1:1:1, by volume) and

1% ethanoic acid in methanol. Two phases will then be generated; the organic phase

containing lipids and the aqueous phase containing water-soluble components. After

collecting the (lower organic) phase, the sample can then be dried using nitrogen. It can then

be kept in a mixture of chloroform and ethanol until further analysis or storage.

2.2.3 Sodium dodecyl sulfate polyacrylamide gel electrophoresis

Electrophoresis is a separation method used to separate charged molecules such as

proteins based on their mobility in an electric field 164. A sodium dodecyl sulfate (SDS),

which is an anionic surfactant with a negative charge in a wide pH range, is used to denature

the proteins by binding with protein hydrocarbon chain creating a uniform charge to mass

ratio 165. The length of the SDS molecule with bind equally to all peptides chains. The

polyacrylamide gel is used as a support medium, and molecules with different sizes have

different migration velocities along the gel with larger molecules migrating slower than

smaller molecules. When an electrical field is applied, the SDS-bound protein analytes will

migrate along the gel towards the positively charged electrode causing a separation based on

molecular weight. Protein standards of known mass are run simultaneously with the sample

of interest to compare their migration and identify the unknown proteins. The use of standard

colored molecular weight markers enables the identification of proteins of interest. Since

proteins are invisible, dyes are used to stain different bands of proteins in the gel. In this

thesis, two types of dyes were used Coomassie Blue and Sypro Ruby, which have detection

limits of ~ 10 and 2 ng, respectively. Finally, gels can be imaged using a suitable

chemiluminescence instrument.

2.2.4 Western blot

Western blot is a commonly used analytical method to identify specific proteins in a

given complex biological sample 166. The technique involves three main steps: (1) separating

the proteins based on their molecular weight using SDS-PAGE, (2) transferring the separated

proteins from the gel into a membrane, and (3) probing the antigen of the protein of interest

with a specific antibody and visualize it 167.

In this thesis, Western blot was used to identify the exosomal protein biomarkers in

fractions collected from the FFF channels using the protocol described by Graner et al 168.

The exosomes fractions were first solubilized and lysed in RIPA buffer 10x to extract

proteins. The exosome lysates from each fraction were then subjected into SDS-PAGE to

separate the proteins based on molecular weight. The gel was transferred into a nitrocellulose

membrane that was then washed in TBST at pH 7.4 (10mM TrisHCl, 100 mM NaCl, 0.1%

Tween). A 5% non-fat dry milk (NFDM) in TBST was then used as a blocking agent to block

all the non-protein binding areas on the nitrocellulose membrane in order to prevent

unspecific reactions between antibodies and membrane surface. A primary antibody was then

added and left at 4°C overnight. Probes contained antibodies against HSP90, HSP/HSC70,

CD9, and CD63 were used in this thesis. Membrane was then washed with TBST three times

for five minutes each. A secondary antibody was then added and left for 1 h at room

temperature. Dyes are used to stain different bands of proteins to be imaged using a suitable

chemiluminescence instrument.

CHAPTER 3

SEPARATION OF TUMOR-DERIVED EXOSOMES BY SEDIMENTATION AND ASYMMETRCIC FLOW FIELD-FLOW FRACTIONATION

3.1 Introduction

Exosomes are tiny vesicles (30-120 nm) of endocytic origin released into the extracellular

environment by all cells and fluids including tumors 17. Exosomes have a density range

between 1.08 and 1.22 g/cm3 and contain specific proteins, lipids, RNAs, and micro-RNA

(miRNAs) precursors 34-38. Exosomes also contain distinct protein biomarkers such as

tetraspanins (e.g. CD9, CD63, CD81) and proteins linked to the MVB biogenesis (e.g.

HSP70, Alix, TSG101) 41, making them as non-invasive tool for monitoring cancer status 169.

Despite the extensive studies on exosomes in the recent years, knowledge about their

unique properties and functions are still ambiguous and controversial 170. For instance,

exosomes have shown to have a role in altering cancer cell proliferation by either promoted

or inhibited 171, 172. This can be linked to the fact that published data have been obtained

from either bulk exosomes preparations that may contain various subpopulations of different

functions or from impure preparations that contain exosomes and non-exosomes of

contracted behaviors 63, 70. The lack of established protocol for producing pure exosomes

preparations with minimum non-exosomal components is recognized 14, 20. The limitations of

the current exosomes isolation techniques such as ultracentrifugation and sucrose gradient

that are incapable of producing exosomes preparations that free from non-exosomal

components such as microvesicles, apoptotic bodies, and low molecular weight plasma

proteins. These contaminants co-sediment with exosomes during isolation procedures

because of the overlap in size and/or density with exosomes 14. Microvesicles range in size

from ~ 50 to 1000 nm and density > 1.23 g/cm3, whereas apoptotic bodies range in size from

~ 100 to 5000 nm and density from 1.16 to 1.24 g/cm3 16, 22. Developing a new separation methods has, therefore, become a need to obtain “pure” exosomes preparations and subpopulations to study their properties and function.

Field-flow fractionation (FFF) is a series of versatile separation techniques that separates samples based on their physicochemical properties such as size and density 80. The theory and applications of FFF have already been discussed in chapter 2 of this thesis, and can also be found elsewhere 86, 111. The separation in FFF takes place in a thin open a ribbon-like chamber, and is based on differential interaction with field acting along an axis orthogonal to that of the mobile liquid. The FFF techniques have showed the ability of FFF to separate a wide range of samples of different properties such as particulates, macromolecules, and supramacromolecular structures 173. The FFF possesses the potential to be one of the workhorses in the field of exosomes separation and characterization. Various types of applied fields are used in the FFF, provided they have the ability to interact with some physicochemical property of the sample 67. A variety of fields have been employed as an applied field 111. For instance, cross-flow is used as an applied field for symmetrical (FlFFF) and asymmetrical flow FFF (AF4), centrifugal force is for sedimentation FFF (SdFFF), temperature gradient for thermal FFF (ThFFF), and electrical field for electrical FFF

(electrical FFF) 116-119. AF4, SdFFF, and ThFFF are commercially-available.

Since the FFF experimental conditions can be varied to optimize different parameters such as resolution, analysis time, sample recovery, etc., this chapter aims to study the influence of different experimental variables of SdFFF and AF4 such as field strength, flow rates, and carrier liquid on the retention behavior of different tumor-derived exosomes. The

effect of each experimental conditions and the separation efficiency are primarily assessed in

0 terms of retention level RL, which can be defined according to the Equation RL tr t ,

0 where tr is the retention time and t is the void time (the time of unretained components to

0 114 travel through the channel) where tr and t can be obtained empirically . The parameter RL can also be defined as (RL = 1/R), where R is the retention ratio that is commonly used in separation methods to estimate the degree of the retardation of the analyte in the separation channel relative to the carrier liquid flow rate and the compression caused by the field. The quantity RL can be controlled by experimental parameters such as applied field and channel thickness to tune other experimental parameters and conditions such as analysis time, resolution, and sample recovery. RL preferably ranges between 5 and 40 for an efficient separation and reasonable resolution RS (RS ~ 1.5), where RL <5 can lead to poor resolution whereas RL > above 40 can deteriorate the peak symmetry, where the deviation of peak shape

114 from ideality is indicative of existence unwanted reactions . However, the RL can sometimes be sacrificed (<5) to improve sample recovery or due to applied field limits. In this study, the RL is used to evaluate the efficiency of FFF systems to retain exosomes.

The separation channel in SdFFF is inserted into a centrifuge basket, and the centrifugal force F that results from the rotation of the channel causes the particles to be moved toward

2 111 the accumulation wall ( F meff  r ) , where meff is the effective mass (particle true mass, m minus buoyant mass, mρ/ρp, where ρ is carrier liquid density and ρp is the particle density),

ω is the angular rotation frequency (ω = 2π.rpm/60), and r is the rotor radius of curvature.

The F is counteracted by a diffusion-induced movement that forces the sample to move back

3 toward the center of the channel. Therefore, the separation is based on meff (meff = d Δρ), where d is diameter and Δρ is the difference in density between the particle and the carrier

liquid density. In SdFFF, if two particles have same size but different density, they elute at different times because their meff are different. Therefore, exosomes and non-exosomal components of similar sizes but different densities can be separated by SdFFF.

The main experimental parameters that must be optimized in SdFFF in order to achieve a desirable separation (e.g. resolution, reasonable analysis time, and sample recovery) are the field strength, the density of the carrier liquid, the channel thickness, the flow rate, and the temperature. The field strength has a major impact on the retention time, resolution, and peak shape. Currently available SdFFF instruments have a maximum field strength of 2700x g.

The separation in AF4 is size-based where particles are subjected into two opposed forces, which are crossflow that acts on the particle to drive it toward the accumulation wall and the diffusion-driven transport that moves it back from the accumulation wall 116. The main experimental variables that affect the quality and efficiency of separation include flow rates, focusing time, carrier liquid ionic strength, and sample load. These variables can affect other parameters and features such as retention time, resolution, band broadening, peak shape and analysis time 133. In this work, different parameters were tested to investigate their effect on exosomes retention. Table 3.1 shows the main differences between SdFFF and AF4.

Miniaturized frit-inlet asymmetric flow field-flow fractionation (mFI-AF4) was used to separate exosomes from human neural stem cells 68. Although, this special AF4 variant showed a potential to separate exosomes based on size, mFI-AF4 is limited by the minute quantities amounts (in microgram scale) that can be produced. In order to carry out further investigation on exosomes from the fractions collected from mFI-AF4 channel outlet multiple runs are need to increase to exosomes concentration for subsequent studies.

Additionally, one of the limitations of FFF, especially by new FFF users, is the extensive

work that FFF procedures needs to optimize experimental conditions 174. A study of the

effect of different experimental conditions on the retention behavior of exosomes is,

therefore, needed to address this issue.

Table 3.1 The main differences between SdFFF and AF4 features.

Feature SdFFF AF4 Separation mechanism Effective mass-based separation Size-based separation Size range From 25 nm to 100 µm depending From 1 nm 100 µm depending on on sample density and max applied AF4 membrane minimum pore size centrifugal field and channel thickness Interaction May happen at high centrifugal field May happen due to membrane and/or high crossflow

3 Selectivity More selective (tr α d ) Less selective (tr α d)

In this study, sedimentation FFF (SdFFF) and asymmetric flow FFF (AF4) were

individually used for two purposes: optimizing the SdFFF and AF4 experimental conditions

and collecting exosome fractions for subsequent analyses and separation by two-dimensional

FFF approach (chapter 4). To the best of author’s knowledge, SdFFF has never been

employed for the separation of exosomes into meff uniform subpopulations. Although AF4

has been employed for exosomes separation, the effect of different AF4 experimental

conditions on the exosomes retention has not been investigated.

3.2 Experimental

This section describes the experimental conditions and methods used in this study.

3.2.1 Samples

Exosomes used in this study were obtained from three different cell lines and fluids: 1)

ascites fluid from breast cancer patients; 2) grade IV glioma or glioblastoma multiform

(GBM) cell line (U87) exosomes, which is the most common primary malignant brain tumor

from cancer patients; and 3) ovarian cancer exosomes from diagnosed patients and is a

composite of many different donors. All exosome samples were obtained from research

laboratory of Dr. Thomas J. Anchordoquy (Skaggs School of Pharmacy and Pharmaceutical

Sciences, University of Colorado Denver, Aurora CO 80045) and the research laboratory of

Dr. Michael Graner group (Department of Neurosurgery, University of Colorado Denver -

Aurora CO 80045). The exosomes samples are stored at -80 ̊C to keep their integrity.

Polystyrene Latex (PSL) standards with nominal sizes of 46 ± 2nm, 60 ± 4nm, and 102 ±

3nm were purchased from Thermo Scientific (Fremont, CA). FL-70 surfactant was purchased

from Fisher Scientific (Fair Lawn, NJ), and the bactericide sodium azide (≥ 99% NaN3) from

Sigma (St. Louis, MO) were used as a carrier liquid for PSL. Phosphate buffer saline (1x

PBS) was used as a carrier liquid for exosomes. PBS was prepared by dissolving 8.0 g NaCl

(99.7%), 1.44 g Na2HPO4 (99.7%), 0.20 gKCl (99.7%), and 0.24 g KH2PO4 (99.3%) in 1.0 L

of deionized water, and the pH was adjusted to 7.40 (± 0.02) using HCl.

3.2.2 Sample preparation

All exosome samples used in this study were prepared by the same procedures. The

isolation of exosomes from cell culture conditioning medium was carried out according to

the commonly used protocol developed by Thery et al. (2006) 54. It involves a series of

centrifugation steps of increased strengths (300-10,000x g) and times (10-30 min) at 4 °C to

remove large cells, dead cells, and large cellular debris 54. After discarding the pellet in each

step, supernatant obtained after 30 min of centrifugation at 10,000x g was subjected to a

high-speed ultracentrifugation procedure (100,000x g) for overnight at 4 °C. The pellet was

vortexed to re-suspended in 3 mL 1x PBS (162.7 mM, pH 7.4) by vortex, and spun at

100,000x g for 3 hours at 4 °C. After removing the supernatant, the exosome-containing

pellet was re-suspended in 1x PBS and stored at -80 °C for further separation by FFF.

3.2.3 Sedimentation field-flow fractionation

Two SdFFF instruments with two different maximum field strengths of ~1000x g (2500

rpm) and ~ 2700x g (4900 rpm) were used in this study to examine their ability to separate

exosomes. The first SdFFF system was Model S101 Fractionator from FFFractionation, Inc.

(Salt Lake City, UT, USA). It has a channel breadth of 1.0 cm, channel thickness (w) of

0.0127 cm, and tip-to-tip length of 90.0 cm. The rotor radius of the channel was 15.1 cm. The

experimental conditions were: 2500 rpm (1000x g); 45 s sample injection (100 µL); 1.0

mL/min flow rate and; 22 min stop-flow time; channel void volume (V0), 3.16 mL; the rotor

radius (r), 10.2 cm;. The carrier liquid was 162.7 mM PBS. Detection was achieved with a

UV-vis detector model 486 from Waters (Milford, MA) with a set wavelength of 280 nm.

The second SdFFF instrument was CF2000 system from Postnova Analytics (Salt Lake City,

UT). Experimental conditions were: 4900 rpm or 2700x g; 36 s sample injection (100 µL);

1.0 mL/min flow rate; 13 min stop-flow time; measured channel thickness (w), 200 µm; tip to

tip channel length, 59.8 cm; channel void volume (V0), 2.39 mL; the rotor radius (r), 10.2 cm.

3.2.4 Asymmetric flow field-flow fractionation

The AF4 instrument was built-in-house and consists of two pumps, one pump from

Shimadzu model LC-6A (Columbia, MD) for sample injection and the other pump is Model

590 from Waters (Milford, MA) for pumping the carrier liquid into the channel. Different

experimental conditions were tested, as it will be mentioned below where relevant. The

optimized AF4 experimental conditions were: sample injection flow rate, 0.2 mL/min for 30

s; focusing time, 2 min; channel flow of 0.2 mL/min; cross flow ( c), 2.0 mL/min. The

trapezoidal channel was 28.0 cm long from tip to tip with breadths of 2.0 cm and 0.5 cm. The

lengths of the triangles at both the inlets and outlets are 3.0 and 0.5 cm, respectively. A

regenerated cellulose ultrafiltration membrane with 30 kDa cutoff from Microdyn-Nadir

(Rheingaustr, Germany) was used as an accumulation wall. PBS 1x was used as a carrier

liquid. Detection was achieved with a UV detector model 486 from Waters (Milford, MA)

with a set wavelength of 280 nm.

3.3 Results and discussion

Exosomes were separated into meff-uniform subpopulations by SdFFF and d-uniform

subpopulations by AF4.

3.3.1 SdFFF of exosomes

The minimum size limit of the SdFFF system is determined by the effective mass meff

(Δρ.d3) and the instrument configuration 175. The two SdFFF systems that were used in this

work have a maximum rpm of 2500 (~ 1000x g) and 4900 (~ 2700x g) with a rotor radii of

15.1 and 10.2 cm, respectively. Figure 3.1 shows two plots that relate Δρ to d in order to

determine the minimum hydrodynamic diameter (dmin) that can be retained using the two

SdFFF systems using two retention ratios RL of 3 (acceptable) and 10 (good).

Data presented in Figure 3.1 are summarized in Table 3.2 to show the ability of

commercial available SdFFF system 2 (4900 rpm, 2700x g) to retain exosomes with a

retention ratio RL = 3.

Figure 3.1 Minimum size that can be separated by commercially available SdFFF system

(2700x g) with two retention ratios RL= 3 (red trace) and RL=10 (black trace).

The ability of both systems to separate a mixture of polystyrene latex (PSL) beads standards with different nominal sizes of 46, 60, and 102 nm, as declared by the manufacturer, and a density of 1.05 g/cm3, were checked. As shown in Figure 3.2a, SdFFF system 2 was able to retain the PSL 102 nm with an RL ~ 5.0. However, the PSL 46 nm was not resolved from the void peak because of the small d (i.e. small meff). On the other hand, a mixture of PSL with nominal sizes of 60 and 102 nm was injected into SdFFF system 1

(Figure 3.2b).

Table 3.2 Minimum exosomes size that can be separated by two SdFFF systems of 1000x g and 2700x g centrifugal forces with two retention ratios RL = 10 and 3. Exosome d (nm) by SdFFF d (nm) by SdFFF Retention level min min density system 1 (1000x g) system 2 (2700x g) R = 10 152.0 95.2 (ρ = 1.08 g/cm3) L RL = 3 105.6 66.0 R = 10 114.0 71.1 (ρ = 1.22 g/cm3) L RL = 3 78.8 49.3

The peak of PSL 60 that has a lower meff than the system threshold was not observed, whereas PSL 102 that has larger meff was partially resolved from the void peak with a very poor RL of ~ 1.0. A higher centrifugal force is, therefore, is required to retain the PSL 60 and improve the resolution of PSL 102 from the void peak. These results showed that system 2 gave better RL than system 1 for the separation of PSL 100.

Figure 3.2 a) SdFFF fractogram of a mixture of polystyrene latex beads standards with nominal sizes of 46 ± 2 nm and 102 ± 3 nm using a) SdFFF system 2 and b) SdFFF system 1.

3.3.1.1 Effect of experimental variables

The retention in SdFFF is dependent on the extent of the centrifugal driving force that is

applied on the particle. This force has a significant effect on the retention, and is considered

as the most influential parameter that must be optimized in order to achieve a desirable

separation. The effect of the two field strengths on the exosome retention were examined.

Figure 3.3 shows an SdFFF fractogram of ovarian cancer-derived exosomes using two

SdFFF systems 1 and 2 of centrifugal forces of 1000x g and 2700x g, respectively. From the

position of the void peaks of both runs, it is obvious that system 2 was able to retain more

exosomes than system 1 since the former has stronger centrifugal force than the latter. In

both systems, the peaks started eluting before the void time. This can be explained by the

presence of non-exosomal components that have lower meff than exosomes. Additionally,

since exosomes have continuous size distribution, exosomes of the sizes lower than 66.0 and

49.3 nm and density of lower than 1.08 g/cm3 cannot be retained by systems 1 and 2,

respectively. The peak obtained by the system 1 was broader than the one obtained by system

2. Since the flow rate was constant (1 mL/min) in both systems, this peak broadening is

likely due to the strong field that is exerted on exosomes separated by system 2.

Another parameter that has an effect on the retention time tr is the channel thickness w

where tr is proportional to w, and thus using thicker spacer results in longer tr. Since the

channel thicknesses of the two systems are also different (w = 0.0127 cm for system 1 and

0.0200 cm for system 2), tr was affected. In the case of system 2, using a thicker spacer (w =

0.0500 cm), the tr and thus dmin would be decreased by 26%, from 49.3 to 36.3 nm (RL = 3)

3 and from 66.0 to 48.6 nm (RL = 10) by assuming ρ = 1.15 g/cm . Since the spacers were

already placed in the systems by the manufactures, it was impractical to be replaced.

Figure 3.3 Superimposed SdFFF fractograms of ovarian cancer-derived exosomes using two different applied field of 1000x g (dotted trace) and 2700x g (solid trace).

The SdFFF system 2 that has a maximum rpm of 4900 (~ 2700x g) was the selected

system to be used for the separation of exosomes into meff uniform fractions, and to

characterize the unfractionated samples and fractions in terms of effective mass and density.

This system has the ability of retaining exosomes with higher RL than system 1.

3.3.1.2 Measuring the density by sucrose gradient SdFFF

The density of the sample can be determined by sucrose gradient SdFFF by increasing or

decreasing the density of the carrier liquid to modify the retention time 79. As mentioned

above (section 3.1), the density differences Δρ between the sample and the carrier liquid has

a significant effect on retention time 79. Since the sample density cannot be changed, the

density of the carrier liquid can be increased by adding sucrose or decreased by adding

ethanol 111. The addition of ethanol can deteriorate the integrity of exosomes and exclude them from subsequent studies. For this reason, the density of the carrier liquid was increased by adding sucrose 79. Four concentrations of sucrose were used to obtain carrier liquid densities of 1.00, 1.02, 1.03, and 1.04 g/cm3. The densities of the carrier liquid were then plotted as a function of the calculated effective masses from the experimental retention times of each run, and a linear function was obtained as shown in Figure 3.4. The y-intercept of the extrapolated line where the effective mass equals zero gives the exosomes density of 1.10 ±

0.02 g/cm3, which span the literature density range of exosomes (1.08-1.22 g/cm3). Since the increase in carrier liquid density leads to a shorter tr, only four data points were obtained where in further increase of carrier liquid density results in co-elution of exosomes and void peaks with RL ~ 0.

Figure 3.4 A plot of carrier liquid densities as a function of effective masses of breast ascites cancer exosomes at four different carrier liquid densities.

3.3.1.3 Separation of various cancer-derived exosome cell lines by SdFFF

The optimized SdFFF conditions were used to individually separate three cancer-derived

exosome cell line preparations from brain, ovarian, and breast cancer cells using SdFFF

system 2. The SdFFF fractograms of the three cell lines are superimposed and shown in

Figure 3.5. From the position of the void peak, it is apparent that ascites and brain cancer

exosomes were retained. Ovarian cancer exosomes, on the other hand, began eluting before

-18 the void time. This suggests the presence of low meff (< 9.7 x 10 g based on max of 4900

rpm) components that are not impacted by the applied centrifugal field. These low meff

species may be smaller (6-21 nm) and lower have a lower density (1.01-1.08 g/cm3) than

exosomes or other cellular components.

Due to this small size and density, the non-exosomal contaminants tend to have a mean layer thickness that is in the center of the channel where the flow velocity is 1.5 times faster than channel flow 111. Taking as an example, a 46.00 nm particle with a density of 1.01 g/cm3 would have a calculated mean thickness (Equation 2-15) of 58.58 µm assuming performing the experiment at room temperature, and use 4900 rpm and a rotor radius of 0.151 m. This corresponds to an equal position that is 31 % of the channel thickness (187 µm spacer). This was confirmed by our calculations for the sedimentation velocity (U) and diffusion coefficient (D) using SdFFF equations 111 showed that the non-exosomal contaminants have

U range from 6.035 x 10-10 to 5.914 x 10-8 cm/s and D range from 7.603 x 10-9 to 2.172 x 10-9 cm2/s. The U values are higher than the corresponding values for exosomes whereas D values are smaller than the diffusion coefficient range of exosomes which is 1.637 x 10-7 to 4.092 x

10-8 cm2/s. The average size for each of the four fractions collected from SdFFF was measured by DLS and the meff for each fraction was back calculated using SdFFF equations.

Figure 3.6 shows the effective masses of the four collected fractions of the breast cancer ascites exosomes which confirms the effective mass separation of theses exosomes.

The meff data presented in Figure 3.6 were then transformed into average density values

3 of each exosomes fractions using the equation (meff = d * Δρ) using size information from

AF4 as it will be discussed later on in this chapter. The density distribution of breast cancer ascites exosome fractions were found to range from 1.01 to 1.18 g/cm3. This suggest that the lower fractions contain low density non-exosomal protein contaminants whereas the higher fractions contain more exosomes.

Figure 3.6 The effective masses of the four collected breast ascites exosomes fractions with an insert of SdFFF fractogram shows the fraction numbers.

3.3.2 AF4 of exosomes

The purpose of this section is to study the effect of different AF4 experimental variables

such as flow rates (crossflow, channel flow, and total flow rates), carrier liquid type and ionic

strength, focus time, and sample load on the retention behavior of exosomes in one-

dimensional AF4.

3.3.2.1 Optimization of AF4 experimental variables

The choosing of flow rates in AF4 is critical to obtain reasonable retention level (RL ≥ 3-

5) and resolution (RS = ≥ 1.0-1.5). Higher crossflow rates are required to shorten the analysis

times particularly for polydisperse samples. However, this increases the risk of sample

immobilization as a result of sample/membrane interaction. Additionally, to achieve

optimum retention, certain properties of carrier liquid such as ionic strength and pH should be optimized to avoid undesirable interactions 111. High carrier liquid ionic strength could lead to aggregation and adsorption of analytes on the accumulation wall, whereas low ionic strengths can cause repulsion both leading to a reduction in retention and size separation selectivity 111. In this work, it is desired that exosomes fractions from FFF are suspended in an aqueous liquid that is suitable for subsequent studies to maintain the integrity and function of the biological exosomes. Therefore, a carrier liquid with a pH ~ 7.4 was used. Focusing time is also a key parameter that influences sample recovery where long focusing time can cause sample loss through the membrane or by undesirable sample-membrane interactions. A short focusing time may lead to incomplete sample equilibrium, and result in a premature tr and thus poor resolution.

The membrane is an essential component of AF4 system where it is important to be permeable to the carrier liquid and not to the sample. The AF4 membrane which is considered as an accumulation wall can also serve to selectively filter out the unwanted low

MW components that may co-sediment with exosomes sample during sample preparation by ultracentrifugation. A membrane must be compatible with both sample and carrier liquid to prevent any unwanted interactions. In the proposed work, regenerated cellulose membrane, which has been commonly used for biological samples, was used as an accumulation wall 176.

Two molecular weight cut-offs (MWCO) of 10 and 30 kDa were tested to study the effect of membrane MWCO on the recovery of exosomes. Recovery was determined by comparing the peak areas of two experiments involving FFF analysis and direct injection of the sample into the detector.

3.3.2.1.1 System performance and channel thickness

Due to variations in AF4 parameters, one has to check the performance of the AF4

system using standards of known sizes. Also, the channel thickness of the AF4 system is

frequently less than the nominal spacer thickness because of the compressibility of the

membrane when it is sandwiched between the two AF4 channel blocks. For this reason, the

channel thickness needs to be accurately determined to obtain accurate values of different

parameters and physicochemical properties such as retention ratio, retention parameter,

diffusion coefficient and diameter 111. This can be achieved by carrying out an AF4 run of a

standard of known size such as PSL beads or protein standards such as bovine serum albumin

(BSA) and apoferritin. The actual channel thickness can then be back calculated from

retention time and the nominal standard sizes using AF4 equations. In this work, the channel

thickness was found to be compressed by ~ 25% from 250 µm (nominal thickness) into 188 ±

3 µm (calculated thickness) using different standards of know sizes, which are PSL beads,

BSA, apoferritin, and gold nanoparticles.

First, the performance of the AF4 system was evaluated by injecting a mixture of

polystyrene latex (PSL) standards with nominal diameters of 60 and 102 ± 2 nm as declared

by manufacturer. These two sizes were chosen because they span the exosomes size range.

Figure 3.7 shows the AF4 fractogram of two runs of PSL mixture. Both PSL standard beads

were well retained and resolved. Also, the AF4 system revealed an excellent repeatability as

shown in the superimposed fractograms of two AF4 runs.

Since PSL cannot be retained using FL-70 as a carrier liquid, the AF4 system

performance was also tested using a mixture of two protein standards which are bovine

serum albumin (BSA) and apoferritin using the same carrier liquid that will be used for

exosomes, which is PBS 1x. BSA has a molecular weight of 66 kDa with a hydrodynamic

diameter of  8 nm while apoferritin has a molecular weight of 443 kDa with a

hydrodynamic diameter of  11 nm. Figure 3.8 shows an AF4 fractogram of the BSA and

apoferritin. The two peaks were not completely resolved (RS ~ 0.95), and RL of 3.3 and 3.4

for BSA and apoferritin, respectively.

Figure 4.7 Overlay AF4 fractograms of two runs of a mixture of PSL standards with nominal sizes of 60 ± 4 nm and 102 ± 3 nm.

The performance of the AF4 system was also checked to test its ability to separate citrate-

capped gold nanoparticles (GNPs) that was synthesized by a method as described by

Rouhana et al. (2007) 177. The optimized AF4 flow rates were 3.0 mL/min for crossflow and

0.2 mL/min for the channel flow was. After conditioning the AF4 channel with FL-70 as a

carrier liquid for 48 hours, the channel was flushed with DI water for overnight. DI water

was then used as a carrier liquid to avoid the undesirable interactions between FL-70 and

sample and/or membrane. The membrane was a regenerated cellulose of 5 kDa MWCO. Two peaks were observed at ~ 12 and 26 min (Figure 3.9).

Figure 3.8 AF4 fractogram of a mixture of BSA and apoferritin protein standards with nominal sizes of 8 and 11 nm, respectively.

Two fractions were collected: fraction 1 (8-16 min) and fraction 2 (19-37 min) for further size analysis with TEM. The diameters obtained by TEM were 9.2 ± 1.6 nm and 31.4 ± 9.7

0  2 0 nm for fractions 1 and 2, respectively (Figure 3.8 b). AF4 theory ( d H 2kTV tr VC w t ) was also used to calculate the size of the two fractions 114. Sizes from both AF4 and TEM are in a good agreement. While AF4 measures the hydrodynamic diameter which includes the particle size and the hydrated layer, TEM measures the real size of the particle since the hydrated layer is dried out during the drying step. Thus, by subtracting the diameter obtained by TEM from the hydrodynamic diameter from AF4, the thickness of the hydrated layer is ~

5.0 nm.

3.3.2.1.2 Flow rates

   In AF4, there are three flow rates (crossflow Vc , channel flow V , and total flow VIN )

that can affect the resolution and retention level, and thus need to be optimized.

3.3.2.1.2.1 Crossflow rate

Crossflow is the applied field in AF4, and is considered to be the key experimental

parameter that needs to be optimized. Sample recovery, resolution, retention ratio, and

 analysis time are affected byVc . In this section, the effect of on these parameters is

examined. Figure 3.10 shows superimposed AF4 fractograms of U87 brain cancer exosomes

at three different crossflow rates (1.2, 1.7, and 2.7 mL/min) and constant channel flow rate of

0.2 mL/min. As expected, the retention time is shifted to longer tr as increases. However,

the peak height and the area under the curve (AUC) become smaller. This can be explained

by the compression of the sample on the accumulation wall causing the sample to be

adsorbed on the membrane and/or sample loss through the membrane pores as a result of

higher . Sample recovery can be better related to another parameter, which is crossflow

velocity Ʋc (ƲC = /bL, where b and L are the channel breadth and length, respectively).

Figure 3.10 Superimposed AF4 fractograms of U87 brain cancer exosomes at three different crossflow rates and constant channel flow rate.

As shown in Table 3.3, the retention levels RL of the different rates were 2.0, 2.3, and

2.6 for of 1.7, 2.0, and 2.7 mL/min, respectively. Although the = 2.7 mL/min gave the

better retention level, it also gave lower sample recovery. Since one of the objectives of this

work was to produce exosome subpopulations to be further studied, recovery needs to be

maximized. For this reason, RL can be sacrificed to increase recovery of exosomes for

 subsequent studies. Thus, Vc = 2.0 mL/min was selected as the optimized crossflow rate.

Table 3.3 Retention levels RL of the different rates of 1.7, 2.0, and 2.7 mL/min, and constant V of 0.2 mL/min. t (min) R (mL/min) V (mL/min) r L 1.2 0.2 3.8 2.0 2.0 0.2 4.2 2.3 2.7 0.2 4.8 2.6

3.3.2.1.2.2 Channel flow rate

After optimizing the crossflow rate to be 2.0 mL/min, this rate was kept constant while

varying the channel flow rate V from 0.2 to 1.0 mL/min. As the increases, the tr becomes

shorter as expected, which deteriorates the RL as shown in Figure 3.11 and Table 3.4.

Table 3.4 Retention levels RL of the different V rates of 0.2, 0.4, 0.6, 0.8, and 1.0  mL/min and constantVc of 2.0 mL/min.

0 (mL/min) V (mL/min) / tr (min) t (min) RL 2.0 0.2 66.0 5.0 1.8 2.8 2.0 0.4 5.0 4.1 1.7 2.4 2.0 0.6 3.3 3.5 1.6 2.2 2.0 0.8 2.5 3.0 1.4 2.1 2.0 1.0 2.0 2.8 1.3 2.1

3.3.2.1.2.3 Total flow rate

In order to keep resolution Rs ≥ 1.5, it is important to optimize the various AF4 flow rates

  (crossflow , channel flow V , and total flow VIN ). This can be achieved by increasing the

and while keeping the /V ratio constant, so the sample mean layer thickness ℓ

114   narrows without affecting the tr . The increase inVIN , which equals ( +V ), will increase

and while keeping their ratio constant and thus tr. However, the increase of will

result in decreasing the band broadening because of the increase of , which leads to

enhancing the resolution. By improving the resolution by increasing the sample recovery

will decrease which can lead to decrease the amount of the sample that goes to the detector,

and therefore producing diluted fractions, which deleteriously impacts any subsequent

analysis. Figure 3.12 shows overlaid AF4 fractograms obtained at the same / ratio of 10

but different values of 2.2, 3.3, 4.4, and 5.5 mL/min.

As expected, the tr did not change, but the % recovery was remarkably decreased from

76% to 11% as shown in Table 3.5. Since the best sample recovery was obtained at = 2.2

mL/min, it was chosen as the optimized value.

Figure 3.12 Superimposed AF4 fractograms of U87 brain cancer exosomes at four different total flow rates and constant crossflow and channel flow rates.

Based on the different experiments to optimize the three flow rates, the optimized flow

   rates are Vc = 2.0 mL/min, V = 0.2 mL/min, and V IN = 2.2 mL/min. These flow rates were

chosen to optimize other AF4 conditions such as focusing time, carrier liquid ionic strength,

and overloading.

Table 3.5 Percentage recovery obtained at different of 2.2, 3.3, 4.4, and 5.5 mL/min, and constant /V ratio.

 V / ratio tr (min) % recovery 2.0 0.2 10 4.92 ± 0.01 76% 3.0 0.3 10 5.02 ± 0.01 53% 4.0 0.4 10 5.01 ± 0.01 19% 5.0 0.5 10 5.06 ± 0.01 11%

3.3.2.1.3 Focusing time

Focusing procedure in AF4 is an essential and crucial step where the sample analytes are

subjected to two opposing flows from both the channel inlet and outlet ports. This allows for

analytes to be concentrated into a single band to decrease band broadening and increase resolution before starting the elution step. The focusing time has to be optimized to avoid any undesirable sample-membrane interactions and/or sample loss through the membrane

(low sample recovery) that could be happened when using long focusing time. On the other hand, short focusing time is not recommended to avoid premature elution when the sample analytes do not reach and establish equilibrium and/or to avoid low sample concentration at the detector. Three different focusing times of 1, 2, and 3 minutes were tested. The results are shown in Figure 3.13 and table 3.6.

Table 3.6 Percentage recovery obtained at different focusing times of 1, 2, and 3 minutes,   and constant Vc /V ratio.

Focusing time / ratio % Recovery 1 min 2 0.2 10 76% 2 min 2 0.2 10 76% 3 min 2 0.2 10 74%

Figure 3.13 Superimposed AF4 fractograms of U87 brain cancer exosomes at three different focusing times.

As shown in Figure 3.13, from the peak shape there was no indication of undesirable

interaction between the sample and the membrane. However, the sample recovery was

insignificantly decreased from ~76% to ~74% as the focusing time was changed from 1 to 2

minutes (Table 3.6). Also, 1 and 2 minutes focusing times give identical peak shape and

sample recovery. To minimize the analysis time, 1 min focusing time was chosen as an

optimized time.

3.3.2.1.4 Overloading

The sample amount that is injected into the AF4 channel is critical, and can have a

remarkable effect on the separation quality in terms of sample recovery and resolution 111.

Since the separation takes place in a very small region of the channel close to the

accumulation wall, it is recommended to avoid any steric effects when injecting a large

sample quantity. Steric effects can occur when the sample components do not have a room to

reach equilibrium during the focusing time which can result in undesirable sample-sample

interactions and may cause sample analytes to aggregate or repulse. This can lead to

shortened retention times leading to poor resolution. A Decrease in the retention time as well

as peak fronting are signs for sample overloading. In this study, four different sample

concentrations were tested to study the sample overloading (Figure 3.13).

As shown in Figure 3.14, there was no sign of sample overloading where the retention

time and the peak shape were not changed. One the other hand, the use of low sample

concentrations decreases the detector signal, which is a problematic in case of subsequent

analyses. The sample recovery decreased from 76% to 42% as decreasing the sample

concentration as shown in Table 3.7. Therefore, 1:10 dilution was chosen as the optimum

concentration because it gives the better exosomes recovery and thus more exosomes reaches

the detector which is important for the successful of post-FFF studies.

Figure 3.14 Superimposed AF4 fractograms of U87 brain cancer exosomes at four different exosome concentrations. Table 3.7 Percentage recovery obtained at different dilutions of 10, 25, 50, and 100   times, and constant Vc /V ratio.

Dilution / ratio % Recovery 10x 2 0.2 10 76% 25x 2 0.2 10 52% 50x 2 0.2 10 48% 100x 2 0.2 10 42%

3.3.2.1.5 Carrier liquid ionic strength

The carrier liquid ionic strength can have undesirable effects on sample retention. The

electrical double layer thickness of the sample can be either increased or decreased at low or

high ionic strength, which influences retention time. At low ionic strength, the electrostatic

repulsion between particles is decreased which results in decreasing of the retention time.

Conversely, at high ionic strength sample analytes tend to be closer to the membrane of the

FFF channel due to their increased apparent size and decreased diffusion coefficient, leading to increase of the retention time and thus the measured size 112. For biological samples, the carrier liquid has to be carefully chosen in order to preserve the sample integrity and function during and after AF4 procedure. Additionally, post-AF4 in vitro and in vivo analyses require the fractions that are collected from AF4 to be compatible with human body. PBS of different concentrations and ionic strengths are typically used as a carrier liquid in AF4 for the separation of biological sample 178. Three different ionic strengths of 162.7 mM (PBS 1x),

16.27 mM (PBS 0.1x), and 1.627 mM (PBS 0.01x) were tested to study their effect on exosomes retention.

Figure 3.15 Superimposed AF4 fractograms of U87 brain cancer exosomes using three different carrier liquid concentrations.

As shown in Figure 3.15, the retention time was decreased as the ionic strength decreases. A peak fronting was also observed as the ionic strength decreases indicating of

undesirable interaction that leads to a premature tr. An ionic strength of 0.01 mMol (1x PBS)

was chosen as an optimum ionic strength because it gives tr as expected with AF4 theory.

3.3.2.2 Exosomes separation and characterization by AF4

The size distribution of breast ascites exosomes was measured before performing AF4

separation using dynamic light scattering (DLS) as shown in Figure 3.16. The exosomes had

a wide size distribution range from ~ 30 to ~ 500 nm which suggest presence of exosomes,

and exosomes aggregates and/or other non-exosomal components of larger size than

exosomes.

Figure 3.16 Breast cancer ascites exosomes size distribution by DLS.

Figure 3.17 shows AF4 fractograms of two exosomes cell lines from breast cancer ascites

and brain cancer. From the position of the void peak, the two exosomes cell lines were well

retained with RL ~ 2.8. The breast ascites exosomes showed a main peak at ~ 5 min with a

shoulder at ~ 6 min and a small peak at ~ 8 min. The shoulder and the small peak likely

represent either exosome aggregates or larger non-exosomal components. Although both

exosomes preparations from two cell lines were prepared with identical protocol, the AF4

0 t

results showed two different AF4 retention profiles, which suggests different properties and behavior.

Figure 3.17 Superimposed AF4 fractograms of U87 brain cancer and breast ascites exosomes cell lines. The x-axis of the AF4 fractograms was transformed into size distribution curve to show the size range of the Breast cancer ascites exosomes as it shown in Figure 3.18.

Figure 3.18 Breast cancer ascites exosomes size distribution using AF4 theory.

The breast cancer ascites exosomes size distribution (Figure 3.18) demonstrated the ability of AF4 to narrow the size distribution of the exosomes from ~ 30-500 nm before AF4 into ~ 30-170 nm after AF4. Three fractions (2, 4, and 6) were also collected from AF4 to measure their size using batch mode DLS as shown in Figure 3.19.

The size measurements from both DLS and AF4 (Table 3.8) confirmed the size-based separation by AF4. A good agreement between the sizes measurements from both techniques, and all the fractions span the size range of exosomes. The error bars from DLS show that

DLS gives slightly larger sizes than AF4 where DLS is known to bias towards larger sizes due to the large scattering contribution of large particles to the total scattering and to inherent relationship between intensity I and diameter d (I α d6) 162.

Table 3.8 Size measurements by AF4 theory and batch mode DLS of fractions F2, F4, and F6 that were collected from one-dimensional AF4 as shown in Figure 3.17.

Fraction dH (nm) by FFF theory dH (nm) by DLS, n = 10 2 59 ±1 68 ±7 4 87 ±1 85 ±5 6 101 ±1 104 ±14

The Af4 separation is also performed on two samples from same cell line (brain cancer), the first sample is microvesicles, pelleted at 10,000 g centrifugation step, and second sample is exosomes that pelleted at 100,000 g ultracentrifugation stage, as already proven in a number of studies 18, 44, 55. As shown from Figure 3.20, the microvesicles sample showed a small peak at ~ 8 min which is corresponded to exosomes. When the crossflow is turned off at 30 min, a large peak is observed indicating presence of large vesicles. Similarly, a peak corresponding to large vesicles at ~ 30-52 min is also observed when exosomes injecting exosomes sample. These results indicates that both sample were not purely isolated by ultracentrifugation. The exosomes preparation is largely cross-contaminated with microvesicles suggesting that these sample may have different biological effects.

Figure 3.20 Overlay AF4 fractograms of exosomes (solid line) and microvesicles (dotted line) from same brain cancer cell line

3.4 Conclusion

Two FFF techniques were successfully used to separate and characterize exosomes from

different cancer cell lines and fluids. SdFFF was used for the first time to separate exosomes

based on their effective mass. Results from this study showed that exosomes fractions

collected from the SdFFF channel outlet were homogenous in effective mass, and thus in

density. The densities of exosomes fraction span the density range for exosomes reported in

literature (1.08-1.22 g/cm3). The effective mass based separation of SdFFF enables

separating two particles of same size but different density, which can be beneficial for

exosomes preparations from ultracentrifugation that commonly contain exosomes non-

exosomal components of overlapped sizes and densities. The development of SdFFF system

with higher centrifugal field strength could enhance the separation efficiency of this

technique to separate exosomes of low meff.

Conventional AF4 system was also showed an ability to separate exosomes from

different tumor cell lines and fluids into size-uniform subpopulations. The use of this AF4

variant enabled enhancing the exosomes high throughput than the previously used

miniaturized AF4 variant. The study reported for the first time the effect of different

experimental conditions on the retention behavior of exosomes. The results of this study will

contribute in FFF method development for separating exosomes, thereby making the

implementation of FFF techniques for the study of exosomes a less effort and time task,

especially for new FFF users.

CHAPTER 4

TWO-DIMENSIONAL FIELD-FLOW FRACTIONATION SEPARATION OF TUMOR- DERIVED EXOSOMES

4.1. Abstract

Exosomes are nanovesicles secreted by most biological cells and fluids and contain

specific lipid and protein profiles as well as functional microRNA (miRNA) and messenger

RNA (mRNA) that can be transferred from cell to cell which implicated in cell-to-cell

communication. They have also been found that exosomes play an important role to regulate

immune system function. In this study, two-dimensional field-flow fractionation was

developed for separating and characterizing cancer-derived exosomes into fractions with

uniform size and density. Common isolation techniques produce exosome preparations that

contain non-exosomal contaminants such as microvesicles and apoptotic bodies with

overlapping in densities and sizes which likely contribute in different exosome functions.

The different cell lines and fluids including breast cancer, brain cancer, ovarian and breast

cancer ascites cancer were first separated using sedimentation field-flow

fractionation(SdFFF) and fractions with uniform effective mass meff were collected. The meff

is equal to d3Δρ where d is diameter and Δρ is the difference in density between the particle

3 and the carrier liquid density. Each SdFFF fraction with uniform meff (or d Δρ) was then

injected into an asymmetrical flow field-flow fractionation (AF4) channel and separated

based on size. Hence, each fraction that is collected at the AF4 outlet has a uniform d and

meff. The online focusing stage, commonly used in AF4, solves the sample dilution problem

encountered when conducting two-dimensional separations. 2D-FFF of various tumor-

derived exosomes allowed the production of various exosome subpopulations that are

uniform in size and density. Assessment of separated exosome fractions in terms of density

and size confirmed that the fractions are indeed uniform in density and size. The meff of

exosomes fractions from SdFFF was determined to be 4.6 x 10-18, 9.7 x 10-18, 1.4 x 10-18, and

-18 2.4 x 10 g which proved the meff separation of exosomes was successful. The sizes of

exosomes in fractions collected after two-dimensional FFF were determined using batch

mode dynamic light scattering (DLS) and ranged from ~ 40 to 80 nm for brain cancer

exosomes, ~ 40 to 140 nm for ovarian cancer exosomes and ~ 50 to 145 nm breast ascites

exosomes. These ranges represented narrower size distributions than that of the

unfractionated exosomes sample. The densities of the two-dimensional fractions were

calculated from size and effective mass data. Results showed that most of the exosome

fractions were uniform in size and density and span the size and density range of exosomes

whereas some fractions contained non-exosomal contaminants that co-sediment with

exosomes during the ultracentrifugation step prior to FFF.

4.2. Introduction

Exosomes are nanosized vesicles produced by late endosomes of most living cells and

exported to the blood via a form of exocytosis, as described in chapter 1 of this thesis 29, 179,

180. Exosomes range in size from 30 to 120 nm 24 and in density from 1.08 to 1.22 g/cm3 23,

179. They are found in biological cells and fluids such as dendritic cells (DCs), tumor cells,

platelets, urine, blood and breast milk 29. Cancer-derived exosomes contain specific lipids

and a variety of nucleic acids, particularly microRNA (miRNA) and messenger RNA

(mRNA), which make the exosomes transfer their contents to the recipient cells and induce

phenotypic changes 13, 181, 182. Although their functions are not fully understood, recent

reports have shown that exosomes have important physiological roles such as intracellular

communication, cellular exchange, and lysosomal degradation 13, 43, 44. Exosomes have

recently been found to be biomarkers for a number of diseases such as cancer and tuberculosis 183, 184. However, exosomes are not homogenous with regard to the contents they carry 13 suggesting that different subpopulations are designed for different purposes. It has also been suggested that like other virus-sized vaccine delivery systems, exosome size could be one of the driving forces that determine their biological performance 185 and hence warrant further investigation.

Biological materials are intrinsically complex containing components that are similar in one or more properties such as size, density, morphology, charge, chemical composition, architecture, etc. This complexity makes it difficult to use one separation method to isolate a single component such as exosomes. The major limitation in the study of exosomes’ behavior and function is the limited separation techniques for producing exosome preparations that are free of membrane sheds, non-exosomal protein contaminants and cellular debris. This restricts accurate assessment of exosome properties 25. Furthermore, published research suggests that exosomes may be comprised of different subpopulations with different functionalities 63. Currently, there is no established protocol for isolating and purifying exosomes 14, 76, 77. The similarity in size and buoyant density between exosomes and other non-exosomal species such as large protein aggregates and microvesicles that co-sediment with exosomes during ultracentrifugation (Table 4.1) 43, 50-53, show that size is not the only factor for exosome isolation and purification. Density also has to be considered to minimize the contamination of exosomes preparations with other non-exosomal contaminants that co- purify with exosomes and to produce uniform exosome subpopulations that may contain different amounts and types of RNAs, proteins, lipids, etc. Thus, it becomes a necessity to

develop new “post-ultracentrifugation” separation methodologies to allow for detailed

investigation of exosome unique properties and function.

Table 4.1 Size and density ranges of exosomes and non-exosomal components of exosome preparations. Vesicle Apoptotic Microves Exosomes Non-exosomal low density bodies icles proteins contaminants Size (nm) 50–5000 50-1000 30-100 6-21 Density (g/cm3) 1.16-1.28 > 1.23 1.08-1.22 1.01-1.08

Ultracentrifugation, with or without a sucrose gradient, and immunoaffinity capture are

the most common techniques that have been employed for isolating and separating exosomes

from non-exosomal components 43, 50-53. These methods have shown to have poor size and

density separation as they produce exosome preparations that contain other cellular

components with similar sizes and densities44. A main drawback is that fractions cannot be

conveniently obtained from ultracentrifugation system for further analysis of distinct

subpopulations. Furthermore, this method is time consuming and produces impure pellets in

most cases 56. Nevertheless, ultracentrifugation remains the traditional and reliable method

for isolating exosomes. However, this technique is crucial as a pre-separation step to remove

the majority of non-exosomal components but it cannot be considered as a final pre-

analytical isolation step. Therefore, complementary “post-ultracentrifugation” separation

steps are required to produce exosome preparations with fewer non-exosomal contaminants

and to fractionate exosomes into uniform fractions for further studies to investigate their

properties and behaviors.

Immunoaffinity has also been employed for exosome isolation using magnetic beads

coated with specific monoclonal antibodies to capture specific exosomal surface proteins 186.

This method leads to produce good protein yields (~ 200 μg/cm3) of exosomes but some exosome subpopulations could be lost if specific antibodies were not used 63. Additionally, this method isolates only antigen-positive exosome subpopulations which leads to incomplete isolation of exosomes 187. Furthermore, exosomes are likely to lose function during their release from magnetic beads exosomes, possibly because of the interaction with the packing material of the chromatographic stationary phase 188.

Although ultracentrifugation and immunoaffinity are the most widely used techniques for exosome isolation, other methods have been employed. For instance, size exclusion chromatography (SEC) has been used as a pre-ultracentrifugation method to remove large non-exosomal components that elute in the void volume 65, 189, 190. Recently, SEC was also combined with ultracentrifugation and ultrafiltration to isolate exosomes from human plasma

191. The void volume containing large components was discarded and collecting fractions that contained exosomes and other smaller non-exosomal components to be subjected into ultracentrifugation. The use of SEC prior ultracentrifugation showed the removal of non- exosomal plasma proteins and small components. However, the possibility of nonspecific interactions between pretentious components and the packing material of the stationary phase of SEC column could affect the resolution. This more likely to occurs when injecting large volumes and may also lead to protein denaturation and thus excluding exosomal proteins from subsequent analysis. Other isolation methods such as using an ultrafiltration concentrator with nanomembranes and commercially available kits such as ExoQuick™

(System Biosciences), Total Exosome Isolation (Invitrogen), Exo-spin™ (Cambridge

Bioscience) and ExoMir™ (Bioo Scientific) have shown poor specificity due to co-

participation of non-exosomal components as well as low exosome recovery (less than ~ 100 ug/cm3) and protein impurities69.

In this study, a two-dimensional approach is used to fractionate exosomes into different subpopulations that are uniform in size and density. First, exosome preparation that was previously isolated by ultracentrifugation was separated by sedimentation FFF (SdFFF) based on their effective mass (meff). Four fractions (S1, S2, S3, and S4) were collected from the SdFFF channel outlet. Each fraction was then separated according to size by asymmetric flow FFF (AF4). Four runs of AF4 were carried out (one run per fraction) and several fractions were collected from each run for subsequent analyses of density, size, zeta potential, exosomal biomarkers and phospholipid composition. Figure 4.1 illustrates the schema of the two-dimensional field-flow fractionation separation method for cancer-derived exosomes to obtain uniform density and size exosome subpopulations. To the best of authors’ knowledge, this approach has not been used in the field of exosomes

Figure 4.1 Schema of the two-dimensional field-flow fractionation separation method for cancer-derived exosomes to obtain uniform density and size exosome subpopulations.

4.3. Field-flow fractionation

Field-flow fractionation (FFF) has been successfully used to separate and characterize a

wide range of materials such as proteins, nanoparticles, polysaccharides, macromolecules,

microgels, and polymers 67. FFF has gained popularity due to inherent strengths and

advantageous features over chromatography that include low shear rates (ideal for fragile

samples) because of the absence of stationary phase, low sample loss, tunable analysis speed,

applicability to a wide range of sizes, better resolved fractions and high sample recovery 124.

Depending on the type of field that is used, the FFF family of techniques can separate

analytes based on physicochemical properties including hydrodynamic radius, density,

surface composition, and surface charge 67, 111, 192. In general, the FFF separation occurs in

an open thin channel that has a ribbon-like geometry. The FFF channel (~200 µm thick) is

characterized by a high aspect ratio that generates a parabolic flow profile due to laminar

flow conditions with flow velocities decreasing gradually from the maximum at the channel

center to near zero at the channel walls. Separation takes place by applying an external field

to distribute particles in different velocity streamlines of the channel’s parabolic flow.

Common FFF fields include crossflow, centrifugal, and temperature gradient giving rise

to the techniques of flow, sedimentation, and thermal FFF (FlFFF, SdFFF, and ThFFF,

respectively). Each FFF field can separate based on different analyte properties such as size

and effective mass 124, 193. For instance, the centrifugal force applied in SdFFF is

proportional to the effective mass meff which in turn is proportional to the analyte diameter to

the third power or d3 and the density difference between analyte and carrier liquid Δρ

3 Δρ.d ), where d is the diameter of the particle. The retention time tr equation for SdFFF is

given in Eq. 1 192.

πmeffrω2w πd3 Δρrω2w t  t 0  t 0 4.1 r 36kT 36kT where k is the Boltzmann constant, T is the absolute temperature, t0 is the void time (time of unretained components), w is the channel thickness, ω is the angular rotation frequency, and r is the rotor radius. This equation shows that larger meff (i.e. larger Δρ and d) result in larger

3 tr. The relationship of tr d places SdFFF among the highest size resolution methods where a 10x difference in d translates into a 1000x difference in tr. The relaxation time can be estimated using the equation .

In AF4, the retention time is directly related to hydrodynamic diameter and therefore the time axis of the fractogram can be easily transformed into a size axis using the following equation 192

πdV w2 t  c t 0 4.2 r 2kTV 0

  0 where Vc is the cross flow rate, V is the void volume.

The above equations can be used to estimate size and density of exosomes by firstly calculating the size from AF4 (Equation 2) or other independent technique and then use this information to calculate the density for each exosome subpopulation using Equation 1.

Furthermore, one of the key features of FFF is the simplicity of collecting fractions from the detector outlet at any time interval. Thus, exosome fractions collected from this two- dimensional FFF separation can be further characterized for different physicochemical properties such as shape and surface charge as well as conducting in vitro and in vivo trials to study exosome behavior.

AF4 involves a series of steps that are injection, focusing, and elution. Focusing is a crucial step happened by pumping an opposing stream of carrier liquid from the channel outlet simultaneously with a flow coming from the inlet 111. During this step, the analyte components are focused in a position close to the channel inlet forming a thin band to establish an equilibrium of size and concentration along the channel thickness before starting the elution.

Flow field-flow fractionation (FlFFF) has also been used to fractionate exosomes from human neural stem cells into size-uniform fractions and study the proteomics of each fraction68. However, the frit-inlet flow FFF variant that was gave poor resolution than the conventional channel when injecting large volumes. Additionally, the miniaturized channel could only separate small amounts of exosomes (in the microgram range) that are insufficient for conducting further studies. Recently, AF4 coupled on-line with ultraviolet (UV), dynamic light scattering (DLS), multi-angle light scattering (MALS) detectors and offline with DLS and transmission electron microscopy (TEM) was successfully used for separation and size characterization of non-labeled B16-F10 exosomes from an aggressive mouse melanoma cell line 194. Hyphenating AF4 with these different detectors enabled differentiating between different exosome batches that were collected several weeks apart from the same source cells.

Dilution is a challenging issue one may face when conducting two or more separation procedures consecutively in most elution-based techniques due to compounding dilution results from each separation step. This restricts performing two-dimensional separation since analytes may become too dilute to be detected or useful for subsequent studies. Using AF4, this dilution problem can be overcome by exploiting the online focusing procedure that is

used prior to the elution step to enrich analyte concentration before the elution step starts. As mentioned earlier, online concentration in AF4 is done by introducing two opposite flows from the channel inlet and outlet to focus the analytes in a thin band near to the inlet before the elution step starts. Normally, the focusing time is determined by the time it takes to focus

20-40 µl of injected sample into a narrow starting band. This procedure can be extended to longer times to allow large volumes of dilute samples to be concentrated as well as focused.

This advantageous feature allowed injection of up to 1 L of highly dilute sample solution into a 1.3 mL volume flow FFF channel 195. Online focusing cannot be carried out in SdFFF. For this reason, the two-dimensional FFF approach utilized in this study involved SdFFF as a first separation step followed by AF4.

In this work, exosome preparations isolated by differential ultracentrifugation contained different non-exosomal components that overlap in size and density with exosomes. To separate these components from the exosomes, exosome pellets prepared by ultracentrifugation were firstly injected into an SdFFF channel for separation based on effective mass. Fractions were collected from the SdFFF channel outlet at different time intervals for additional separation and characterization. This fractionation step allowed obtaining exosome subpopulations that are homogenous in meff. The collected fractions were then injected into an AF4 channel for further separation based solely on hydrodynamic size.

Fractions were then collected from the AF4 channel outlet for additional studies. The fractions collected from this novel two-dimensional FFF separation approach are uniform in density and size and contain fewer amounts of non-exosomal components such as membrane sheds, microvesicles, and other cellular debris.

4.4. Experimental

This section describes the experimental conditions and methods used in this study.

4.4.1. Samples

Exosomes used in this study were obtained from three different cell lines: 1) ascites fluid

from breast cancer patients; 2) grade IV glioma or glioblastoma multiform (GBM) cell line

(U87) exosomes, which is the most common primary malignant brain tumor from cancer

patients; and 3) ovarian cancer exosomes from ovarian cancer patients and is a composite of

many different donors. All exosome samples were obtained from Dr. Thomas J.

Anchordoquy group (Skaggs School of Pharmacy and Pharmaceutical Sciences, University

of Colorado Denver, Aurora CO 80045) and Dr. Michael Graner group (Department of

Neurosurgery, University of Colorado Denver - Anschutz Medical Campus, Aurora CO

80045). Exosomes preparations were kept in phosphate buffer saline (1x PBS) at -80 °C until

use. Polystyrene Latex (PSL) standards with nominal sizes of 46 ± 2nm, 60 ± 4nm, and 102 ±

3nm were purchased from Thermo Scientific (Fremont, CA). FL-70, a surfactant that is used

as a carrier liquid for PSL was purchased from Fisher Scientific (Fair Lawn, NJ), and sodium

azide (≥ 99%) from Sigma (St. Louis, MO). The PBS that was used as a carrier liquid for

exosomes was prepared by dissolving 8.0 g NaCl (99.7%), 1.44 g Na2HPO4 (99.7%), 0.20 g

KCl (99.7%), and 0.24 g KH2PO4 (99.3%) in 1.0 L of deionized water, and the pH was

adjusted to 7.40 (± 0.02) using HCl (37%).

4.4.2. Sample preparation

All exosome samples from the different cell lines used in this study were prepared by the

same procedures. The isolation of exosomes from cell culture conditioning medium was

carried out according to the commonly used protocol 54 that involves a series of

centrifugation steps of increased speeds (300-10,000 g) and times (10-30 min) at 4 °C to

remove large cells, dead cells, and large cellular debris. After discarding the pellet in each

step, supernatant obtained after 30 min of centrifugation at 10,000 g was subjected to a high-

speed ultracentrifugation procedure (100,000 g) for overnight at 4 °C. The pellet was then re-

suspended in 3 mL PBS (pH 7.4) and spun at 100,000 g for 3 hours at 4 °C. After removing

the supernatant, the exosome-containing pellet was re-suspended in PBS and stored at -80 °C

for further separation by FFF.

4.4.3. Sedimentation field-flow fractionation

The SdFFF instrument (CF2000) used in this study was from Postnova Analytics (Salt

Lake City, UT). The experimental conditions for PSL separation were 4900 rpm or 2700x g,

V = 1.0 mL/min flow rate, and the sample injected into the channel at 0.2 mL/min for 30 s

and relaxed for 22 min. The carrier liquid was deionized distilled water containing 0.01%

(v/v) FL-70 and 0.02% (w/v) sodium azide (NaN3) with a pH of 9.90. The detector was set at

a wavelength of 254 nm. Experimental conditions for exosomes separation were: 4900 rpm

or 2700x g, 36 s sample injection, 1.0 mL/min flow rate, 0.10 mL sample volume, 13 min

stop-flow time, measured channel thickness (w) 200 µm, tip to tip channel length 59.8 cm;

channel void volume (V0), 2.39 mL; and the rotor radius (r), 10.2 cm;. The carrier liquid was

Sodium phosphate saline (PBS) 1x and UV detector set at 280 nm as a detection system.

Fractions were collected at 1 min intervals starting at .

4.4.4. Asymmetric flow field-flow fractionation

The AF4 instrument was built-in-house and consists of two pumps, one pump from

Shimadzu, model LC-6A (Columbia, MD) for sample injection and the other pump is Model

590 from Waters (Milford, MA) for pumping the carrier liquid through the system. The AF4

experimental conditions were: sample injection flow rate, 0.2 mL/min for 5.5 min; focusing

time, 3 min; channel flow of 0.2 mL/min; cross flow ( c), 2.0 mL/min. The trapezoidal

channel was 28.0 cm long from tip to tip with breadths of 2.0 cm and 0.5 cm. The lengths of

the triangles at both the inlets and outlets are 3.0 and 0.5 cm, respectively. The channel

thickness was measured empirically to be 188 µm. A regenerated cellulose ultrafiltration

membrane with 30 kDa from Microdyn-Nadir (Rheingaustr, Germany) was used as an

accumulation wall. PBS 1x was used as a carrier liquid and UV detector model 486 from

Waters (Milford, MA) set at 280 nm was used as a detection system.

4.4.5. Dynamic light scattering

The sizes of unfractionated and fractionated exosomes were measured using the Zetasizer

Nano series (Malvern, Worcestershire, UK). After suspending the exosomes sample in 2 mL

PBS. Experiments consisted of 10 runs per measurement and all experiments were done

in triplicate. The reported data are Z-average values in nm.

4.4.6. Zeta potential analysis

The zeta potential values for unfractionated and fractionated exosomes were determined

with Zetasizer Nano series apparatus (Malvern, Worcestershire, UK) equipped with a 633

nm red diode laser. Experiments consisted of 10 runs per measurement and all experiments

were done in triplicate.

4.5. Results and discussion

The size of exosomes was evaluated by batch mode DLS before and after carrying out the

FFF separation to assess the ability of FFF of narrowing the size distribution of exosomes.

Figure 4.2 shows the DLS size distribution of breast cancer ascites exosomes with a wide

size distribution ranges from ~ 20 to 500 nm. This suggested that exosome preparations

contained not only exosomes (30-120 nm) but also other components that are smaller and

larger than exosomes (see Table 4.1).

Figure 4.2 DLS size distribution of breast ascites exosomes before FFF separation shows a wide size distribution suggesting the preparation contains components of smaller and larger than exosomes.

4.5.1. One-dimensional FFF

SdFFF was first performed to collect fractions of uniform meff that were then be subjected

to a size separation by AF4. The SdFFF system was optimized using a mixture of 46 and 102

nm polystyrene latex (PSL) beads standards. These sizes were selected because they span the size range of exosomes. For SdFFF, these PSL beads that have an average density of 1.05 g/cm3 show also different effective masses. Using the maximum rpm of 4900, the 100 nm PS particle was readily retained and resolved from the void peak but the 46 nm PSL was eluted with the void peak because of its low meff (Figure 4.3). Based on Equation 1, the calculated minimum PS latex bead (Δρ = 0.05 g/cm3) size that can be separated by SdFFF is ~ 55 nm.

To resolve PSL 46 nm from the void peak, other parameters should be changed. This can be done by either decreasing the carrier density (i.e. increasing Δρ), lowering the system temperature, and/or increasing the channel thickness 111. While carrier liquid density has a great effect on retention time, temperature and channel thickness have a very little impact.

The decrease in carrier liquid density can be achieved by adding ethanol 111. However, in this study it was not possible to add ethanol as ethanol will affect the structure of the exosomes by dissolving the lipid membrane.

Figure 4.3 SdFFF fractogram of a mixture of polystyrene latex beads standards with nominal sizes of 46 ± 2 nm and 102 ± 3 nm.

After optimizing the SdFFF conditions, three exosome cell line preparations derived from brain, ovarian, and breast cancer cells were individually separated by SdFFF. The SdFFF fractograms of the three cell lines are superimposed and shown in Figure 4.4a. From the position of the void peak, it is apparent that ascites and brain cancer exosomes were retained.

Components of the ovarian cancer exosome sample, on the other hand, began eluting before

-18 the void time. This suggests the presence of low meff (< 9.7 x 10 g based on max of 4900 rpm) components that are not impacted by the applied centrifugal field. These low meff species may be smaller (6-21 nm) and lower in density (1.01-1.08 g/cm3) than exosomes or other cellular components. Due to this small size and density, the non-exosomal contaminants tend to have a mean layer thickness that is in the center of the channel where the flow velocity is 1.5 times faster than channel flow 111. Taking as an example, a 46 nm particle with a density of 1.01 g/cm3 would have a calculated (from Eq. 2) mean layer thickness of 58.58 µm assuming 4900 rpm at room temperature and a rotor radius of 0.151 m. This corresponds to an equal position that is 31 % of the channel thickness and a flow velocity of a 187 µm spacer is used. This was confirmed by our calculations for the sedimentation velocity (U) and diffusion coefficient (D) using SdFFF equations 111. It was shown that the non-exosomal contaminants have U range from 6.035 x 10-10 to 5.914 x 10-8 cm/s and D range from 7.603 x 10-9 to 2.172 x 10-9 cm2/s. The U values are higher than the corresponding values for exosomes whereas D values are smaller than the diffusion coefficient range of exosomes which is 1.637 x 10-7 to 4.092 x 10-8 cm2/s. These findings confirm that ovarian cancer exosomes may have more low density non-exosomal components than ascites breast cancer and brain cancer exosomes used in this study. The average size for each of the four fractions collected from SdFFF was measured by DLS and

the meff for each fraction was back calculated using Equation 4.1. Figure 4.4b shows the meff

of the four collected fractions which confirms the effective mass separation of exosomes. a

The performance of AF4 was also assessed using a mixture of 60 ± 4nm and 102 ± 3nm

PSL beads as stated by the manufacturer. As it can be seen from the AF4 fractogram (Figure

4.5), a baseline resolution between the two PSL bead standards was obtained. One of the AF4 parameters that can vary uncontrollably is the channel thickness w due to changes in pH and ionic strength of the carrier liquid as well as the compression caused by the channel walls 111.

The channel thickness was determined experimentally from the retention times of PLS 111. A channel thickness of 188 µm was determined in this manner and this w was used in subsequent calculation of exosome size.

Figure 4.5 Overlay AF4 fractograms of two runs of a mixture of PSL standards with nominal sizes of 60 ± 4 nm and 102 ± 3 nm.

The AF4 experimental conditions which are crossflow, channel flow rate, focusing time, and the type and ionic strength of the carrier liquid were first optimized for the separation of

exosomes (see chapter 3). The carrier liquid should be carefully chosen and optimized since the type of carrier liquid has to maintain the integrity and biological function of the exosomes for further studies. Different concentrations of PBS were tested. The carrier ionic strength was also optimized to achieve optimum retention 111. Ionic strength can affect retention time, overloading, aggregation, sample recovery, and the apparent membrane cutoff because intermolecular repulsion can lead to increase the effective volume and thus decrease in sample’s double layer thickness. The PBS ionic strength had a distinct effect on retention behavior with the retention time of breast ascites exosomes increasing with the ionic strength.

The optimized AF4 conditions for exosomes were Vc = 2.0 mL/min, Vout = 0.2 mL/min, 30- kDa MWCO regenerated cellulose membrane, and 0.02 mL exosome sample was injected into the channel at 0.2 mL/min for 30 s. The optimum focusing time was 1 min. The carrier liquid was PBS and 0.01 M PBS with pH 7.40 was found to be the optimum concentration.

The UV detector was set at a wavelength of 280 nm.

Using the optimized AF4 condition, three different exosome cell lines were individually separated by AF4. As shown in Figure 4.6a, all exosome cell lines were well retained and showed different fractograms corresponding to different size distributions (Figure 4.6b). The shoulders in the ovarian and breast ascites cancer exosomes suggest the presence of larger components such as exosome aggregates and/or cellular debris. Based on calculations of size distribution (Figure 4.6b) and the defined exosome size range (30 – 120 nm), the main peak between ~ 3 and 7 min is attributed to exosomes. The size distribution shows sizes ranging from ~ 40 to 80 nm for brain cancer exosomes and ~ 45 to 150 nm for ovarian and breast ascites exosomes. These sizes agree with previously reported exosome size ranges 29, 179, 180,

196. Brain cancer exosomes showed a narrow size distribution with no indication of

aggregates. Conversely, ovarian and breast ascites exosomes had wider size distribution with possible aggregates or larger non-exosomal components of sizes from 100 to 150 nm. These findings demonstrate that the size distributions of exosomes by AF4 narrower that size from

~ 10 to 500 nm before AF4 to ~ 40 to 150 nm.

Figure 4.6 a) AF4 overlay fractograms and b) size distributions of exosomes from brain cancer (dotted line), breast cancer ascites (dashed line), and ovarian cancer (solid line) .

Figure 4.7 AF4 superimposed fractograms of a) PSL 60 ± 4 nm and b) breast cancer ascites exosomes by injecting the same amounts but different volumes of 0.02 mL (solid line) and 1.00 (dashed line).

4.5.2. Online concentration by AF4

Two dilutions of PS 60 nm beads suspensions (in FL-70/NaN3 carrier liquid) containing

the same amount of PS but different dilutions (20 µL and 1 mL) were injected into an AF4

channel to test the effectiveness of online concentration. Experimental conditions were the

same except for injection and focusing times. The injection time was increased 11 fold to 5.5

min when injecting the 1 mL volume. Two injection loops of different volumes were used,

which in turn needed different injection times as the injection flow rate was kept constant at

0.2 mL/min. This was needed to avoid undesirable interaction between sample and

membrane if higher flow rate were used 111. The AF4 fractograms obtained from a 1 mL

sample loading and a regular 20 µL injection with stop-flow focusing are superimposed in

Figure 4.7a.

4.5.3. Two-dimensional FFF

The optimum focusing time was determined to be 3 min (data not shown). Both PS

concentrations gave identical retention profiles demonstrating the success of online focusing.

This demonstrates the ability of the AF4 membrane to filter out the suspending liquid in the

sample while retaining the PS beads. The same online concentration procedures were also

tested on breast ascites exosomes to examine its effectiveness on exosomes. In this case, the

retention times are comparable for both exosome runs with a minor difference in peak areas

between the two runs (Figure 4.7b).

As mentioned earlier, exosome preparations from ultracentrifugation contains exosomes

and other non-exosomal components of varied sizes and densities 43, 50-53. To obtain more

uniform exosomal subpopulations with no or fewer non-exosomal contaminants, more than

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one non-invasive technique is needed to separate based on key exosome features.

Additionally, it is required to use a non-invasive method that permits easy fraction collection for further analysis. Thereby, two-dimensional separation can be used to achieve the goal of obtaining homogenous exosome subpopulations with few contaminants. FFF, with its various techniques and its unique advantages of open channel that maintains the integrity of exosomal components for further studies, is qualified for achieving this goal.

Each 1.0 mL fraction collected from the SdFFF channel outlet had exosomes with homogenous meff. Each fraction was then injected into an AF4 channel for further separation based on size. The focusing procedure allowed for online concentration before starting the elution step. AF4 fractograms of brain cancer exosome fractions show similar retention times and peak shapes (Figure 4.8a). They only differ in peak intensity due to differences in concentration. The similar behaviors between fractions are likely due to exosomes with different sizes and densities eluting simultaneously during the SdFFF separation.

Interestingly, breast ascites exosome fractions show different AF4 retention profiles (Figure

4.8b). There is main peak at ~ 3 min, a shoulder at ~ 4 min, and a small peak at ~ 6 min

(Figure 4.6). This shoulder is possibly due to presence of exosomal aggregates or non- exosomal components with similar meff as exosomes but different size. According to AF4 calculations, the shoulder has a size range from ~ 120 to 140 nm, which is out of exosome typical size range (Figure 4.9b). It is only possible to observe this in AF4 since the AF4 separates solely by size. This suggests that exosomes with similar densities can have different size distributions.

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Figure 4.8 AF4 fractograms of uniform meff SdFFF fractions a) brain cancer exosomes and b) breast cancer ascites exosomes with inserts in each fractogram show the SdFFF fraction numbers.

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The different fractions from this new two-dimensional separation were further analyzed

for size, density, and zeta potential by DLS, SdFFF, and zeta potential analyzer. The size

distribution for breast ascites exosomes had a range of ~ 46 to 120 nm with a shoulder

ranging in diameter from ~ 120 to 140 nm (Figure 4.9). Fractions F2, F4, and F6 were

analyzed by DLS to obtain size data. Table 4.2 shows the summary of size information that

obtained from above techniques. DLS and AF4 size averages are within relative agreement

except for F2. The slightly higher DLS averages are likely due to the bias of DLS towards

larger size particles.

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Table 4.2 Summary of hydrodynamic diameters of two-dimensional FFF fractions of breast ascites exosomes measured by AF4 and DLS. Size by AF4 Size by DLS TEM (nm) (nm) (nm) Fraction 2 59 ± 1 68 ± 7 46 ± 9 Fraction 4 87 ± 1 85 ± 5 74 ±8 Fraction 6 111 ± 1 104 ± 14 91 ±6

Once the average size of each fraction was known, tr from SdFFF can be used to

determine the density using Equation 4.1. Size information from DLS was used to back

calculate the density of the collected fractions from meff. The average d of fractions 2, 4, and

6 of breast cancer ascites exosomes that were collected from the two-dimensional FFF were

used to calculate the effective masses and densities. Results showed that the density of

fractions were different proving our initial hypothesis about the possible density

heterogeneity of exosome subpopulations. Table 4.3 summarizes the meff, ρ, and d of

fractions (S1, S2, S3, and S4) of breast ascites exosomes (also see Figure 4.1).

Table 4.3 Effective masses (meff), diameters (d), and densities (ρ) of two-dimensional breast ascites exosome fractions.

-18 3 Fraction Average meff (g), x 10 Average d (nm) Average ρ (g/cm )

S1 0.7 ± 1.2 57.10 ± 27.40 1.05 ± 0.04

S2 1.1 ± 1.2 56.50 ± 31.20 1.07 ± 0.06

S3 1.5 ± 1.2 56.20 ± 29.50 1.09 ± 0.08

S4 1.8 ± 1.2 56.70 ± 28.50 1.10 ± 0.08

The average meff, and thus ρ, are calculated from the average tr of each fraction. The ±

ranges for each fraction that are shown in Table 4.3 do not represent the uncertainty of the

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values. The ranges are obtained from the minimum and maximum tr of each fraction, and therefore the ranges represents the meff and ρ distributions of each fraction. Taking this in account, the density distribution of all the fractions ranges from ~ 1.01 to 1.18 g/cm3. This suggests that the fraction S1 has more non-exosomes of lower density. This was expected since this fraction was collected from the void peak that contains analytes that unretained by the SdFFF system.

The size and density data from Table 4.3 are then plotted for all the fractions to generate two-dimensional contour plots that relate size, densities and concentration in order to evaluate the purity of exosome fractions (Figure 4.10).

Figure 4.10 Contour plots of size and density values from Table 4.3 of four cancer ascites exosome fractions (S1, S2, S3, and S4).

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From the contour plots of the four fractions, it was clear that there are generally more non-exosomes than exosomes, which explains the difficulty of separation of exosome preparations because they contain more non-exosomes than exosomes. Based on average densities, fractions S3 and S4 contain more exosomes than fractions S1 and S3. Also, the relative exosomes/non-exosomes vary from one fraction to another. These findings can be used to determine the exosomes fractions that only contain exosomes to be subjected into further investigations to study the exosomes properties and functions.

Zeta potential measurements were also conducted to test the charge uniformity of the two-dimensional FFF fractions as well as unfractionated exosomes (before two-dimensional

FFF separation). The zeta potential plays a role in tumor targeting and uptake, blood brain barrier diffusivity, and drug resistance of nanometer-sized particles like exosomes 197-200.

Exosomes have been reported to have a zeta potential range of -11 ±5 mV 73, 74. Zeta potential has also an effect on the biological pharmacokinetics of nanosystems and nanoparticles phagocytosis and uptake 201-207. The analysis of the electronegativity of breast ascites exosomes fractions before and after the two-dimensional FFF separation showed that zeta potential values of unfractionated was -9.1 ±0.6 mV. The zeta potential of the two- dimensional FFF fractions were ranged from 2.4 ±0.2 to 3.9 ±0.3 mV. Although the zeta potential of the two-dimensional FFF fractions were lower than the unfractionated exosomes, all the fractions were uniform in zeta potential (Table 4.4). These findings suggest that exosome subpopulations would biologically behave differently from the unfractionated exosomes i.e. the pharmacokinetics of different exosome fractions could be different.

Additionally, the exosome fraction preparations will remain reasonably stable. This

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encourages further characterization of the surface charge of exosomes and its role in

biogenesis and behavior of exosomes.

Table 4.4 Zeta potential of unfractionated and two-dimensional FFF fractions of breast cancer ascites exosomes.

Exosome fraction ζ potential (mV) AF4 of Fraction S1 Fraction 1-2 - 3.1 ± 0.2 Fraction 1-4 - 3.9 ± 0.3 Fraction 1-6 - 3.4 ± 0.3 AF4 of Fraction S2 Fraction 2-2 - 3.0 ± 0.2 Fraction 2-4 - 3.3 ± 0.3 Fraction 2-6 - 3.5 ± 0.3 AF4 of Fraction S3 Fraction 3-2 - 2.4 ± 0.2 Fraction 3-4 - 3.1 ± 0.2 Fraction 3-6 - 3.5 ± 0.2 AF4 of Fraction S4 Fraction 4-2 - 2.7 ± 0.3 Fraction 4-4 - 3.0 ± 0.2 Fraction 4-6 - 3.0 ± 0.2 Unfractionated exosomes - 9.1 ± 0.6

4.6. Conclusion

This work confirmed that the current common exosome isolation protocols that is based

on ultracentrifugation produce exosome preparations that contain exosomes and non-

exosomes which are heterogeneous in size and density. This diversity strongly supports that

exosome preparations contain different subpopulations of various properties and functions. In

this study, “post” ultracentrifugation separation of exosomes from different cancer cell lines

were successfully carried out using two field-flow fractionation subtechniques that separate

based on effective mass and size. Characterizing the collected fractions showed that there are

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generally more non-exosomes than exosomes. This explains the difficulty of obtaining pure exosomes by one single method. The presence of exosomes in the two-dimensional FFF fractions was unambiguously proven by size and density. The data obtained from exosome fractions produced by two-dimensional FFF separation showed that each fraction was uniform in size and density as well as charge suggesting that each subpopulation would behave differently depending on the cargo it carries.

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SUPPLEMENTARY INFORMATION

LIPID ANALYSIS AND OXIDATION

4S.1 Introduction

This supplementary information aims to present the results of lipid analysis that have

been performed on breast ascites cancer exosomes fractions collected from two-dimensional

field-flow fractionation (chapter 4).

The main lipids in exosomes include sphingomyelin, cholesterol,

phosphatidylethanolamine (PE), phosphatidylcholine (PC), phosphatidylserine (PS),

phosphatidylinositol (PI), and ganglioside GM3 208. However, no studies have revealed the

lipid composition of every exosomal subpopulation. Lipid composition of exosomes may

condition their fate and function in vivo 209, 210 211. Table 1 summarizes the different lipids in

exosomes.

Table 4S.1 Typical lipid composition of mast cell-derived exosomes 209.

Exosomal lipid Mole % Lyso-phosphatidylcholine 5.6 Sphingomyelin 12 Disaturated phosphatidylcholine 5.2 Phosphatidylcholine (other species) 21 Disaturated phosphatidylethanolamine 3.5 Phosphatidylethanolamine (other species) 18 Phosphatidylserine + phosphatidylinositol 14.4 Lyso-bis phosphatidic acid (BMP) 0.6 Cholesterol 14.1 Diglyceride 5.6

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4S.2 Experimental

This section describes the experimental conditions and methods used in this study.

4S.2.1 Lipid extraction

In this study, the Bligh & Dyer extraction method was used to extract lipids from

exosomes fractions 163, which has successfully used for extract lipids in exosomes 212. 400 µL

of each exosomes fraction was first suspended in 400 µL methanol, transferred to a 1.5 mL

microcentrifuge tube, and sonicated for 20 min for complete lysis. 400 µL chloroform was

then added to solubilize lipids, and 400 µL water for phase separation. The bottom organic

phase contains the extracted lipids. The organic phase was then transferred into a new

microcentrifuge tube, blow down using a stream of nitrogen gas, and resuspended in 100 µL

of chloroform:methanol (1:1 by volume) until further analysis.

4S.2.2 Lipid separation

The lipid extracts were then separated by high performance thin layer chromatography

(HP-TLC). The inner side of the TLC chamber was first lined with paper towel, and a 50 mL

of the eluent methyl acetate:isopropanol:chloroform:methanol:KCl (0.25%, 25:25:25:10:4

v/v/v/v/v) was added to the TLC chamber and left for 45 min. Two horizontal lines were

drawn at 1 cm (origin) and 5 cm (solvent front) from the bottom of the TLC plate, and

marked on the edge of the plate. A 4 µL of lipid standard mixture composed of cholesterol

(Cho), phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylserine

(PS), and exosomes lipid extracts were then spotted on origin line the plate and allowed to

dry for 15 min. The TLC plate was then put in the chamber and allow to run to a solvent

front, and allowed to dry in the dark for 45 min. The TLC plate was then dipped facedown

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into a round glass bowl that contained the developer 2',7'-dichlorofluorescein solution for 15

seconds. The plate was finally allowed to turn yellow in the dark, visualized under UV, and

imaged using GE Luminescent Image Analyzer.

4S.2.3 Results and discussion

Figure 4S.1 shows the separation of lipid extracts from 8 breast ascites exosomes

fractions (F1 to F8) by two-dimensional FFF by TLC. The lipid standards were well resolved

from each other, while all the exosomal lipids were migrated into the solvent front.

Cho

Standards F1 F2 F3 F4 F5 F6 F7 F8 Figure 4S.1 Separation of lipid extracts from breast ascites exosomes fractions by two- dimensional FFF by TLC. Left lane is lipid standard mixture composed of cholesterol (Cho), phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylserine (PS).

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It was hypothesized that all the exosomal lipids were oxidized during extraction procedure. Conjugated dienes, trienes, and tetraenes are the major lipid oxidative products, and can be detected spectrophotometrically at UV wavelengths of ~ 220-230 nm for dienes, ~

265-270 nm for triens, and ~ 310-320 nm for tetraenes 213-215. To study the possibility of lipid oxidation during extraction procedure, the absorbance spectra of PE lipid standard was measured before and after extraction. As shown in Figure 4S.2, three peaks were observed at wavelengths corresponding to dienes, triens, and tetraenes.

Dienes

Trienes

Tetraenes

The absorbance intensity was increased by time from day 1 to day 3 as shown in Figure

4S.2 indicating an increase of the oxidized products.

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Figure 4.S3 Overlay UV spectra of PE phospholipid standard after extraction at day 1 (solid line), day 3 (dotted line), and day 5 (dashed line). The absorbance of conjugated dienes (at 220-230 nm), triens (at ~ 265-270 nm), and tetraenes (at ~ 310-320 nm) was noticeably increased from day 1 to day 5.

It was then hypothesized that the oxidation was occurring during the extraction procedures by extraction solvent system that was composed of chloroform, methanol, and water. To test the presence of peroxide species in each solvent, a test paper contains potassium iodide (KI) and starch was immersed into each solvent. The change of the paper color into blue is indicative of presence peroxide H2O2 (KI is oxidized by peroxide and converted into I2 which turns the starch indicator from colorless into blue.) As shown in

Figure 4S.4, the test paper was only changed into dark blue when immersed in water indicating the presence of peroxide species. Methanol and chloroform did not show presence of peroxide species. This confirms that water was the source of oxidation of lipids.

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Figure 4S.4 A potassium iodide starch paper to detect peroxide species in water, methanol, and chloroform.

The H2O2 present in water was quantified spectrophotometrically by adding Amplex Red

(AR, sometimes called Ampliflu Red), which is a colorless compound that forms resorufin (a pink dye) when is oxidized by H2O2 that can be measured by UV/Vis spectrometer at 570 nm

216 to determine its concentration . The concentration of H2O2 from two water sources was found to be 172.4 and 153.0 nM.

To break down the peroxide species, sodium thiosulfate (Na2S2O3) was used to convert

H2O2 to non-oxidative products as illustrated in this reaction

Na2S2O3(aq) + 4H2O2(aq) → Na2SO4(aq) + H2SO4(aq) + 3H2O(l)

To test the effectiveness of peroxide breakdown by sodium thiosulfate, a test paper contains KI and starch was immersed into untreated and treated water. As shown in Figure

4S.5, the test paper was only changed into dark blue when immersed in untreated water but remained unchanged in treated water, indicating the breakdown of peroxides

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Figure 4S.5 A potassium iodide starch paper to detect peroxide species in water before (left) and after (right) treated with sodium thiosulfate.

The UV spectra was then obtained for PE before and after extraction with treated and untreated water as shown in Figure 4S.6. The absence of conjugated dienes, trienes, and tetraenes peaks in the UV spectra of PE extracted with treated water indicates the successful removal of peroxides species from the treated water, and thus the blocking of lipid oxidation.

Figure 4.S6 Overlay UV spectra of PE phospholipid standard before extraction (solid line), after extraction with untreated water (dotted line) and with treated water (dashed line).

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TLC was then performed on PE standard that was extracted with treated and untreated water (Figure 4S.7). The PE extracted with untreated water was migrated into the solvent front indicating the formation of oxidized products. On the other hand, the PE extracted with treated water did not migrate to solvent front indicating the absence of oxidized products and the successful of peroxide breakdown by thiosulfate.

Figure 4S.7 TLC plate of a blank (lane 1), and separation of PE phospholipid standard extracted with treated water (lane 2), and PE phospholipid standard extracted with untreated water.

The lipids of exosomes fractions from two-dimensional FFF (chapter 4) were then extracted by Bligh & Dyer method using treated water, and separated by TLC as shown in

Figure 4S.8. Unlike using untreated water (Figure 4S.1), the main phospholipid classes did not migrate into the solvent front and were clearly separated. The fractions showed to contain PC, PE, and cholesterol. The results are inconclusive since the concentration of exosomes in the collected fractions were too low.

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Figure 4.S8 a) TLC separation of exosomal lipids from fractions F3-2, F3-4, F3-6, F4-2, F4- 4, and F4-6 collected from two-dimensional FFF and extracted by Bligh & Dyer method using treated water; and b) two-dimensional FFF fractogram shows the fraction numbers.

Results from this work showed that lipids were oxidized during the extraction procedures due to the presence of oxidative peroxide species in water used in the extraction. The breakdown of peroxide species by thiosulfate successfully blocked the oxidation of lipids.

The TLC results of separating lipids from exosomal fractions collected from two- dimensional FFF separation showed that fractions had different phospholipid/cholesterol

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profiles. However, the results are inconclusive as the exosomal fractions are too diluted.

Future work must involve increasing the exosomes concentration in the collected fractions by using semi-preparative AF4 system.

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CHAPTER 5

SEMI-PREPARATIVE ASYMMETRIC FLOW FIELD-FLOW FRACTIONATION SEPARATION OF TUMOR-DERIVED EXOSOMES

5.1 Introduction

Extracellular vesicles (EVs) are defined as lipid bilayer membranous compartments that

are secreted to the extracellular environment by most living healthy and cancerous cells 21.

This simple definition does not reflect the complex composition, properties, and functions

these vesicles possess 5, 16, 20. The main types of EVs are exosomes, microvesicles, and

apoptotic bodies. This classification is based on physical features (size, density and

morphology), mode of biogenesis and release (endosomal origin, membrane shedding, and

fragments associated with cell death), and molecular composition (proteins, protein

biomarkers, nucleic acids e.g. RNAs, lipids) 21, 217. The EVs overlap in some properties,

particularly size and density, which complicate their isolation and subsequent investigation of

their specific functions 21, 58, 170.

Among EV types, exosomes have attracted a special attention because of their possible

role in intracellular communication, the specific biomarkers they carry, and their

characteristic size (30-120 nm) they possess 20, 34-38, 169. Size plays a crucial role in exosomes

behavior, cellular uptake, bioavailability, biodistribution, toxicity, and transport 218.

Additionally, exosomes subpopulations of different sizes may have different cargos such as

protein biomarkers and RNAs 18. Therefore, separating exosomes into size-uniform

subpopulations can allow for detailed investigation of the composition and function of each

subpopulation 57, 124. Nevertheless, the availability of sufficiently quantities of isolated

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exosomes is critical for successful post-separation studies 219. Asymmetric flow field-flow fractionation (AF4) has shown the ability to separate biological sample components based solely on their diffusion coefficient (and hence size) without affecting their integrity and function 68. Moreover, the ability to easily collect fractions from the AF4 channel outlet is an advantage over non-elution methods 124. However, the relatively high sample dilution of an elution-based separation systems results in low sample concentration in the collected fractions. Increasing the injected sample volume or mass may deteriorate the resolution and cause non-ideal interactions that impacts the retention time and the reproducibility 111, 127-131.

In order to overcome the low concentration in the collected fractions, a concentration step is required before performing post-AF4 studies. This can be done by using conventional size

AF4 channel by performing replicate runs, collecting multiple fractions, and combining the corresponding fractions. However, the risk of sample loss and increased analysis time is disadvantageous 220. An alternative approach is to scale up the channel size by using a semi- preparative (sP) sP-AF4 channel. This would allow larger sample amount to be separated, and the throughput enhanced, without affecting resolution and reproducibility. In many post-

FFF studies, the exosomes amounts in the collected fractions are crucial for successful results. For instance, RNAs profiling requires a minimum inputs of 2.5–5 μg of total RNA per sample 221. For mi-RNA profiling, which represents ~ 0.01% of the total RNA mass, obtaining an input of 2.5–5 μg is a challenging task 222. The typical injection volume using conventional AF4 channel is 0.02 mL (contains ~ 12.00 μg of exosomes based on protein contents), approximately 100 runs are needed in order to yield ~ 5.00 μg of miRNA per fraction. The fractions have to be combined and concentrated down before subsequent mi-

RNA profiling. On the other hand, scaling up the injection volume into 2.00 mL using sP-

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AF4 channel, only one run is needed to yield the same amount of miRNA per fraction

without performing any post-AF4 concentration step.

Reports on using sP-AF4 are limited and studies about the effect of sample load on the

separation quality, particularly the resolution, of sP-AF4 are not available 223. In this thesis,

the sP-AF4 was used for the separation of primary brain cancer-derived exosomes to obtain

high-throughput exosomal fraction that are uniform in size. To the best of author’s

knowledge, this AF4 variant has not been used for the separation of exosomes.

5.2 Experimental

This section describes the experimental conditions and methods used in this study.

5.2.1 Exosomes Samples Preparation

The main exosomes preparations that were used in this study were obtained from

astrocytic oligodendroglioma primary brain cancer cell line UPN933. Other exosomes were

derived from glioblastoma multiform (GBM) cell line (U87), which is the most common

primary malignant brain tumor from cancer patients, and ascites fluid of breast cancer

patients. UPN933 and U87 exosomes were prepared in the research laboratory of the Dr.

Michael Graner group (Department of Neurosurgery, University of Colorado Denver -

Aurora CO 80045), whereas breast cancer ascites exosomes were obtained from the research

laboratory of Dr. Thomas J. Anchordoquy (Skaggs School of Pharmacy and Pharmaceutical

Sciences, University of Colorado Denver, Aurora CO).

UPN933 cells were surgically harvested and grown in a “stem cell” Neurobasal-A

medium that consists of fetal calf serum-free medium from Life Technologies (Gaithersburg,

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MD), Epidermal Growth Factor (EGF) and basic Fibroblast Growth Factor (bFGF), and N-2

and B27 supplements. The cells grow as balls (so-called “neurospheres”), which is a sign of

them having stem cell-like properties. The medium is harvested every 3 days for 2-3 weeks,

~ 500 mL are collected and spun at 500x g to remove cells. The medium was then stored in

the cold room (4 °C).The exosomes preparation step commences with the medium being

subjected to centrifugation at 20,000x g for 30 min at 4 °C, and the supernatant containing

microvesicles (MVs) was removed and filtered through a 0.22 μm filter to remove any

remaining components that are larger than exosomes. The filtrate was then concentrated to

~50 mL using centrifugal concentrators with 100 kDa cut-off membranes. The concentrated

retentate was then centrifuged at 200,000x g for 2 hours, at 4 °C to pellet exosomes. The

supernatant was then discarded and the exosomes pellet was resuspended in ~1 mL of

phosphate buffered saline (1x PBS). The suspension was pulled 5 times through a 21 gauge

needle, followed by a 25 gauge needle to break up clumped exosomes and strip denatured

proteins off the vesicles. The resuspended material was then filtered through a 0.45 μm filter

to remove debris that may co-sediment with exosomes. The exosome-containing pellet was

re-suspended in 1x PBS and stored at -80 °C for further FFF separation. Breast cancer ascites

and U87 brain cancer exosomes were prepared in the same manner as UPN9333 exosomes.

5.2.2 Nanoparticle standards and carrier liquids

Polystyrene latex (PSL) standards with nominal sizes of 46 ± 2 nm and 102 ± 3 nm were

purchased from Thermo Scientific (Fremont, CA). The carrier liquid for PSL separation was

0.01% (v/v) FL-70 surfactant (Fisher Scientific, Fair Lawn, NJ) and 0.002% (w/v) NaN3,

(Sigma, St. Louis, MO). Phosphate buffer saline (1x PBS) was the carrier liquid for

exosomes. The PBS was prepared by dissolving 8.0 g NaCl (99.7%), 1.44 g Na2HPO4

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(99.7%), 0.20 g KCl (99.7%), and 0.24 g KH2PO4 (99.3%) in 1.0 L of deionized water, and

the pH was adjusted to 7.40 (± 0.02) using HCl (37%).

5.2.3 AF4 systems

Two AF4 systems were used in this work, an analytical scale AF4 and a semi-preparative

AF4 (sP-AF4). The geometrical channel dimensions of a conventional AF4 is scaled up by

increasing the breadths of the channel from 2.0 and 0.5 to 10.0 and 2.0 cm while the channel

thicknesses kept the same, which allows using larger sample amounts.

5.2.3.1 Analytical scale AF4 system

The analytical scale AF4 instrument (AF2000, Postnova Analytics, Salt Lake City, UT) is

connected to a UV-Vis detector model (SPD-20A, Shimadzu, Japan) set at 280 nm. The AF4

channel has a trapezoidal shape and is 27.6 cm long from tip to tip with breadths of 2.0 cm

and 0.5 cm. The lengths of the triangles at the inlets and outlets are 3.4 and 1.0 cm,

respectively. As mentioned in chapter 3, the channel thickness w is frequently less than the

nominal spacer thickness because of the compressibility of the membrane when it is

sandwiched between the two AF4 channel blocks. Thus, the w was measured empirically by

carrying out an AF4 run of standard samples of known size and back calculate the w from tr

using Equation 2.19. The actual w is determined using PSL and BSA standards, and found to

be 280 ±2 µm (nominal w = 350 nm). A 30 kDa cutoff regenerated cellulose ultrafiltration

membrane (Microdyn-Nadir, Rheingaustr, Germany) was used as an accumulation wall.

5.2.3.2 sP-AF4 system

The sP-AF4 instrument consisted of two pumps (Shimadzu LC-6A, Columbia, MD). The

first pump weeps sample into the channel and constitutes the carrier liquid flow entering the

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inlet of the channel. This carrier liquid flow is subsequently split into a crossflow and

channel flow with magnitudes that are dependent on the relative back pressure in the two

flow paths. The second pump provides the opposing flow which is used during the sample

focusing stage to form a narrow sample band with equilibrated analyte components prior to

starting the separation process. A schematic illustration of the sP-AF4 setup is shown in

Figure 5.1a.

. Figure 5.1 a) A schematic representation of sP-AF4 system with flow direction during the focusing and elution steps, and b) a top view of the sP-AF4 channel and its dimensions.

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The sP-AF4 channel (Postnova Analytics, Salt Lake City, Utah) is a trapezoidal channel

(Figure 5.1b) that was 27.6 cm long from tip to tip with breadths of 10.0 cm and 2.0 cm. The

lengths of the triangles at both the inlet and outlet are 3.0 and 1.0 cm, respectively. The

channel thickness was measured empirically, as described in the previous section, and found

to be 292 µm. A regenerated cellulose ultrafiltration membrane with 30 kDa cutoff

(Microdyn-Nadir, Rheingaustr, Germany) was used as an accumulation wall. Sample elution

was monitored using a Spectra 100 UV-vis detector (Spectra Physics, San Jose, CA) set at

280 nm.

5.2.4 AF4 experimental conditions

A mixture of PSL beads of nominal sizes of 46 ± 2 nm and 102 ± 3 nm were used to

evaluate the effect of sample load on resolution RS using analytical AF4 and sP-AF4 systems.

The optimized experimental conditions are summarized in Table 5.1.

Table 5.1 The AF4 optimized experimental used for the separation of PSL. Parameter Analytical AF4 sP-AF4 Cross flow ( c), mL/min 0.5 2.0 Channel flow mL/min 1.0 4.0

c/ ratio 0.5 0.5 Injection/focusing flow rate, mL/min 7.0 (0.02 mL sample) 9.0 (1.25 mL sample) 12.0 (1.25 mL sample) 12.6 (2.0 mL sample) 15.6 (2.0 mL sample) 27.0 (5.0 mL sample) 30.0 (5.0 mL sample) Injection/focusing time, min 0.3 0.3 Carrier liquid 0.01% FL-70 0.01% FL-70 0.002% NaN3 0.002% NaN3 UV-Vis wavelength, nm 254 254

Before separating exosomes by sP-AF4, a new 30 kDa cutoff regenerated cellulose

ultrafiltration membrane is installed to avoid any possible cross contamination from previous

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runs. The membrane is conditioned by flush the system overnight at flow rate of 0.5 mL/min

with the carrier liquid (BPS 1x). The performance of sP-AF4 system is then tested by

carrying out triplicate runs of a standard bovine serum albumin (BSA) using optimized

conditions that are summarized in Table 5.2.

Table 5.2 The sP-AF4 optimized experimental conditions used for the separation of BSA. sP-AF4 experimental conditions BSA

Cross flow ( c), mL/min 4.1 Channel flow mL/min 0.5

c/ ratio 8.0 Injection/focusing flow rate, mL/min 9.0 (1.25 mL sample) Injection/focusing time, min 0.3 Carrier liquid 1x PBS UV-Vis wavelength, nm 280

Table 5.3 summarizes the sP-AF4 optimized conditions that are used for exosomes

separation.

Table 5.3 The sP-AF4 optimized experimental used for the separation of exosomes. Parameter sP-AF4 Analytical AF4 Cross flow ( c), mL/min 5.0 2.0 Channel flow mL/min 0.5 0.2

c/ ratio 10.0 10.0 Injection/focusing flow rate, mL/min 8.0 (1.0 mL sample) 4.0 (0.02 mL sample) Injection/focusing time, min 0.3 0.3 Carrier liquid 1x PBS 1x PBS UV-Vis wavelength, nm 280 280

5.2.5 Western blot

Exosomes fractions collected from sP-AF4 channel outlet were first lysed with a 12.5%

Tris-HCl (pH 7.4) lysis buffer and centrifuged at 500 g for 10 min to remove non-exosomal

components 54. Proteins from each fraction were then separated by SDS-PAGE, and the

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proteins were transferred from gels onto nitrocellulose membrane. The membranes were

incubated with 5% nonfat dry milk (NFDM) in Tris buffered saline with Tween (TBST) 10x

for 1 h, and then incubated with 5% NFDM in TBST and the primary antibody overnight at

4°C. The nitrocellulose membrane was then washed with TBST three times for five minutes

each, and incubated with 5% NFDM and the equivalent secondary antibodies for 1 h at room

temperature. Chemiluminescence was used to confirm the presence of exosomal biomarkers

including HSP90, HSP/HSC70, CD9, and CD63, which were then visualized and imaged

using FluorChem Q imaging system from Cell Biosciences.

5.3 Results and discussion

The ability of the sP-AF4 to separate exosomes was assessed by first studying the effect

of sample volume on the retention behavior of PSL standards with nominal sizes that span

the size range of exosomes. Similarly, the effect of sample volume on retention behavior of

PSL using an analytical scale AF4 channel was evaluated. The sP-AF4 was then used to

separate exosomes into size uniform fractions to be analyzed in terms of size, main proteins

composition, and presence of exosomal protein biomarkers.

5.3.1 Effect of sample volume on retention behavior of PSL

0 The separation efficiency is evaluated in terms of retention level RL ( RL tr /t ) which has

114 a direct influence on resolution . As previously mentioned in chapter 2, the resolution RS is

a measure of the ability of a separation system to separate two zones of different sizes 111.

This can be calculated by using Equation 2-9 ( ), where is the difference in

retention times of two peaks and is the average of their baseline widths. This equation

applies to Gaussian peaks. For non-Gaussian peaks, the resolution Equation

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( ) can be used where widths are calculated from the width at half the peak height ( ) where are the peak widths at the half peak

115 heights of the peaks 1 and 2, respectively . RS ≥ 1.5 indicates a baseline separation, whereas RS below 1.5 indicates some degree of peak overlapping, and RS = 0 means a complete overlap.

Four different sample volumes (0.02, 1.25, 2.00, and 5.00 mL) of the same concentration were individually injected into an analytical scale AF4 channel, and the resulting fractograms are shown in Figure 5.2a. The RL was ~ 2 and 4 for the PSL 46 nm and 102 nm, respectively.

Similarly, three sample volumes (1.25, 2.00, and 5.00 mL) of the same concentration were individually injected into a sP-AF4 channel as shown in Figure 5.2b. The retention level RL was ~ 9 and 22 for the PSL 46 nm and 102 nm, respectively. The RL was ~ 5 times higher in the sP-AF4 channel than the analytical scale AF4 channel. However, the analysis time in analytical channel was shorter, as expected, due to the smaller channel volume.

The retention time and the peak shape were impacted when the sample volume was increased using analytical scale AF4 channel (Figure 5.2a), indicating an overloading effect.

As mentioned in section 3.3.2.1.4 of this thesis, the sample amount can have a major effect on the separation quality in terms of sample recovery and resolution 111. In this case, due to injecting a large sample volume a steric effect was likely happening where the analytes had insufficient room to reach equilibrium during the focusing step, resulting in undesirable intermolecular interactions, which likely led to short retention time and poor resolution.

The resolution RS of the PSL 46 and 102 nm peaks from both channels are shown in

Figure 5.3. The resolution in analytical AF4 was affected as increasing the sample volume where best resolution (RS = 0.99 ± 0.2) was observed for 0.02 mL sample volume, and it

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decreased by ~ 30% (RS = 0.76 ± 0.2) when injecting 5.00 mL. Conversely, the resolution in semi-preparative channel was not affected (RS = 0.98 ± 0.2 for 1.25 mL, RS = 0.99 ± 0.1 for

2.00 mL, and RS = 1.01 ± 0.1 for 1.25 mL.

0 t

b 1.25 mL 2.00 mL

5.00 mL Detector signalDetector

0 0 10 20 30 40 t Time (min)

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The effect of sample load on the reproducibility was also examined by injecting 1.25 mL

of PSL mixture into both channels. As shown in Figure 5.4, the tr and peak shape were

affected in analytical AF4 (Figure 5.4a). This suggests that intermolecular and/or sample-

membrane interactions may happen due to overloading. Conversely, reproducibility and peak

shape were not impacted in sP-AF4 (Figure 5.4b). The bigger channel size allowed enough

room to analytes to reach equilibrium with no sign of undesirable interactions.

5.3.2 Separation and characterization of exosomes

Figure 5.5 shows a sP-AF4 fractogram of BSA using 1x PBS as a carrier liquid. To

exclude the possibility of cross contamination, any residual BSA was removed from the

membrane by turning the crossflow off after completing the elution. A very small peak was

observed at 28-30 min indicating the presence of small amounts of unrecovered BSA.

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Channel cleaning was then performed by flushing the system with the carrier liquid at 1.0 mL/min for overnight. Control runs were then performed by injecting a blank sample (1x

PBS) using same experimental conditions that were used to separate the BSA sample but varying the detector sensitivity to allow detecting any minute amounts of BSA before injecting exosomes. Figure 5.6 shows overlaid fractograms of blank runs at different detector sensitivities (0.01, 0.005, and 0.002 AUFS) and BSA run using identical experimental conditions. The baseline at both 16-18 min (BSA peak) and 28-30 min (residual BSA confirming the successful of the cleaning procedures. a

0 t

Figure 5.4 Effect of sample load on the reproducibility in a) analytical scale AF4 and b) semi-preparative AF4 channels.

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0 t V off c

Figure 5.5 A sP-AF4 fractogram of standard BSA sample.

0 t Vc off Figure 5.6 A sP-AF4 fractogram of three runs of blank samples at detector sensitivities of 0.01, 0.005, and 0.002 AUFS and BSA run at detector sensitivity of 0.005.

The exosomes were separated with a RL of ~ 3.0 which is a reasonable level of retention,

 taking in account that RL could be improved by increasing the crossflow rate VC while

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keeping the channel flow constant V . However, this can impact the sample recovery and thus affect subsequent studies.

Three exosomes cell lines were then individually injected into a sP-AF4 channel for separation based solely on size. Figure 5.7 shows overlay AF4 fractograms of the three exosomes preparations.

0 t

Figure 5.7 Superimposed sP-AF4 fractograms using semi-preparative AF4 channel of three exosomes preparations from UPN933 brain cancer (batch 1), breast cancer ascites, and U87 brain cancer.

The three exosomes preparations showed different retention behaviors with unimodal, bimodal, and trimodal for U87 brain cancer, UPN933 brain cancer (batch 1), and breast cancer ascites, respectively, which suggest presence of various populations of different sizes that could be either larger exosomes, exosomal aggregates, or larger non-exosomal components. The size measurements of UPN933 exosomes was then done by dynamic light scattering (DLS). Factions were collected at 1 min interval (Figure 5.8a) for further analysis

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by batch mode DLS. Results showed that fractions 2 to 7 have size range from ~ 37 to 111 ±

2 nm which span the size range of exosomes (30-120 nm), whereas fraction 1 (~ 16 ± 1 nm) and fraction 9 (~ 207 ± 1 nm) were outside exosomes size range (Figure 5.8b).

Figure 5.8 a) sP-AF4 fractogram of UPN933 brain cancer exosomes (batch 1) showing the collected fractions from sP-AF4 channel outlet, and b) the average size of each fraction measured by batch mode DLS.

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The batch 2 of the UPN933 brain cancer exosomes were also separated by sP-AF4. The retention behavior of batch 2 was slightly different from batch 1 as shown in Figure 5.9 where the small peak that appeared in batch 1 was merged with the main peak.

t Figure 5.9 Superimposed sP-AF4 fractograms of UPN933 brain cancer exosomes a) batch 1 and b) batch 2.

Fractions were collected from the separation of fractogram of batch 2 of UPN933 brain cancer exosomes (Figure 5.10) for further biochemical analysis to study their exosomal protein biomarkers using SDS-PAGE and Western blot.

Proteins in each fraction were then separated by SDS-PAGE using Coomassie brilliant blue-stain to develop the gel that was obtained after SDS-PAGE. The results demonstrated differences in protein profiles between fractions (Figure 5.11a). Fractions 4-9 showed an abundance of albumin (~66 kDa), which is typically seen in exosomes and tumor cells that make their own. The 70-75 kDa bands may be enriched in fractions 4-13. Bands at ~ 53 kDa and 73 kDa in fractions 3-13 correspond to exosomal biomarkers CD63 and HSP70, respectively. Sypro Ruby stain, which is more sensitive than Coomassie blue, was then used to investigate if minute amounts of proteins were present. Even loading only 1 μL of the

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high-protein fractions, there were still significantly more proteins than some of the later fractions, but there is better resolution. It seems that there are some of bands between fractions 6 and 10 still unresolved.

Figure 5.10 sP-AF4 fractogram of UPN933 brain cancer exosomes (batch 2) showing the collected fractions from sP-AF4 channel outlet, with an insert shows a small peak that was observed when turning off the crossflow (fraction 23).

Western blot was then performed on collected fractions (combined every other fraction) to confirm the presence of exosomal biomarkers. After separating the proteins by SDS-

PAGE (12.5% Tris-HCl), the fractions were Western blotted, and probed with antibodies against exosomes biomarkers (HSP90, HSP/HSC70, and CD9), as shown in Figure 5.12.

Fractions showed different relative concentrations where fractions 9-14 revealed abundant CD9, whereas fractions 9-10 seem to have more concentrated HSP90 than other fractions. Although fractions 1 and 2 from the void peak of sP-AF4 (unretained components) did not show the presence of HSP90, HSP/70 and a minute concentration of CD9 were surprisingly observed in these fractions suggesting presence of exosomes of low sizes that

136

co-eluted with the void peak. Since HSP90 is a biomarker that associates with cancer and exosomes can be produced by healthy and cancer person, the small exosomes that co-eluted with the void peak seem to be from healthy cells 11, 14, 15, 224. Thus, more proteomic analyses are needed to confirm this hypothesis.

Figure 5.11 SDS-PAGE analysis of UPN933 brain cancer exosomes (batch 2) fractions collected from sP-AF4 channel outlet using a) Coomassie blue stain and b) Sypro Ruby stain.

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Figure 5.12 Western blot of collected UPN933 (batch 2) brain cancer exosomes fractions (combined every other fraction) collected from sP-AF4 to confirm the presence of exosomal biomarkers (HSP90, HSP/C70, and CD9.

5.4 Conclusion

Results from this study showed that sample volume can be increased to up to 250 fold

into a semi-preparative AF4, compared to analytical scale AF4, without affecting the

resolution. This allowed producing high throughput size uniform exosomes subpopulations

by one injection, rather than performing tens of experiments to collect and concentrate the

fractions, provided that identical experimental conditions are used. Characterizing the

exosomes fractions that collected from the sP-AF4 channel showed that the fractions have

distinct sizes, and a number of exosomal protein biomarkers were identified. This would

allow further studies to investigate other properties and functions of the exosomes

subpopulations (e.g. RNAs and mi-RNA profiling) that need concentrated fractions for

successful analysis.

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CHAPTER 6

GENERAL CONCLUSIONS AND FUTURE WORK

6.1. General conclusions

The work presented in this thesis involved three main projects aimed to use field-flow

fractionation (FFF) techniques to develop new separation strategies to separate exosomes

into more uniform and pure subpopulations.

The first project (chapter 3) studied the ability of two FFF techniques, sedimentation FFF

(SdFFF) and asymmetric flow FFF (AF4), to separate and characterize tumor-derived

exosomes. The SdFFF was used for the first time to separate exosomes based on their

effective mass. Results from this project showed that exosomes fractions that were collected

from the SdFFF were homogenous in effective mass. The densities of exosomes fractions

that were obtained from effective masses span the density range for exosomes reported in

literature (1.08-1.22 g/cm3). Since the effective mass based separation of SdFFF enables

separating two particles of same size but different density, this approach was beneficial for

exosomes preparations from ultracentrifugation that commonly contain exosomes and non-

exosomal components of overlapped sizes and densities. The currently available commercial

SdFFF system could separate exosomes with a retention level from ~ 1 to 2, where low

effective mass exosomes were not completely separated because of the centrifugal field

threshold of the SdFFF system. The retention level and the ability of SdFFF system to

separate exosomes can be enhanced the by developing of an SdFFF system with higher

centrifugal field strength to separate exosomes of low effective masses.

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The conventional AF4 system was also showed a successful ability to produce size- uniform exosomes subpopulations with good separation levels ranging from ~ 2 to 5. The study reported for the first time the effect of key experimental AF4 conditions such as flow rates, focusing time, and carrier liquid ionic strength on the retention behavior of exosomes.

Results of this study will contribute in FFF method development for separating exosomes, thereby making the implementation of FFF techniques for the study of exosomes a less effort and time task, especially for new FFF users.

The second project (chapter 4) aimed to combine the abilities of both SdFFF and AF4 techniques to produce more uniform subpopulations with narrow effective mass and size distributions and fewer non-exosomal components. The results obtained from this project confirmed the previously acknowledged problem of the current common exosomes isolation protocols of producing exosomes preparations that contain exosomes and non-exosomes which are heterogeneous in size and density. Characterizing the collected fractions from this

SdFFF-AF4 two-dimensional separation approach showed that there were generally more non-exosomes than exosomes in “exosomes” preparations from ultracentrifugation. This explains the difficulty of obtaining pure exosomes by one single method. The data obtained from exosomes fractions produced by two-dimensional FFF separation showed that each fraction was uniform in size and density as well as charge suggesting that each subpopulation would behave differently.

Lastly, the objective of the third project (chapter 5) was to test the capability of AF4 to produce high-throughput size uniform exosomal fraction by using a semi-preparative AF4

(sP-AF4) channel by increasing the sample volume without impacting the separation resolution. Results from this project showed that sample volume can be increased to up to

140

250 fold into a semi-preparative AF4, compared to analytical scale AF4, without affecting

the resolution. Fractions collected from sP-AF4 were uniform in size with good separation

levels ranging from ~ 2 to 10. This allowed producing high throughput size uniform

exosomes subpopulations by one injection, rather than performing tens of experiments to

collect and concentrate the fractions, provided that identical experimental conditions are

used. Characterizing the collected exosomes fractions showed that the fractions have distinct

sizes, and display a number of common exosomal protein biomarkers including CD9,

Hsp/c70, and Hsp90 which indicates the presence of exosomes. This would allow further

studies to investigate other properties and functions of the exosomes subpopulations (e.g. mi-

RNA profiling) that need concentrated fractions for successful analysis.

6.2. Future work

The work presented in this thesis will advance understanding of exosomes to assess their

potential role and use in cancer progression and resistance to chemotherapeutic agents, as

well as their use as cell specific drug carriers. A number of possible benefits could be gained

from the results of this dissertation that showed the ability of FFF techniques to produce pure

and concentrated exosome subpopulations for further analysis and use. This will allow better

testing and determining the exosome subpopulation(s) that specifically target tumor cells.

Consequently, loading these exosome with anticancer agents would contribute in reducing

their dosage and thus side effects and increasing their efficacy. The possible use of these

exosomes labeled with fluorescent probe could also be beneficial in early diagnosing of

cancer. Finally, determining the zeta potential of exosome subpopulations at different pH

ranges could be beneficial for determining their effect on cellular uptake and targetability.

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Several future directions to allow better insights on the properties and behavior of exosomes by performing in-depth post-FFF analyses on exosomal subpopulations on their specific behaviors by studying their uptake and internalization, migration, chemotherapeutic resistance. Furthermore, detailed lipidomics, RNAs profiling, surface analysis would allow for understanding the composition and function of exosomes that are still ambiguous.

Finally, the FFF methods that were developed in this thesis specifically for exosomes will be useful for other type of separations involving vesicles and biological particles to allow detailed information that could be otherwise unachievable.

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Figure 2.7: License number (3543700111619) from Elsevier Limited, The Boulevard,Langford Lane, Kidlington,Oxford,OX5 1GB,UK

Figure 2.8: License number (3542600684498) from Elsevier Limited, The Boulevard,Langford Lane, Kidlington,Oxford,OX5 1GB,UK

Figure 2.9: License number (3542601230726) from Elsevier Limited, The Boulevard,Langford Lane, Kidlington,Oxford,OX5 1GB,UK

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