PROTEOMIC CHARACTERISATION OF RAT BONE MARROW-DERIVED MESENCHYMAL STROMAL CELLS CULTURED IN ‘STEMNESS’ PROMOTING CONDITIONS.
Morgan Madeline Carlton Bachelor of Biomedical Science, QUT 2015
Tissue Repair and Translational Physiology Program
Institute of Health and Biomedical Innovation
School of Biomedical Sciences Faculty of Health Queensland University of Technology
Submitted in fulfilment of the requirements for the degree of Master of Philosophy (Health)
2018
Keywords
Biological processes Bone marrow-derived mesenchymal stromal cells Bone morphogenetic protein 4 Data independent acquisition Differentiation Fibroblast growth factor 2 Fibronectin Foetal calf serum Gene Ontology Mass spectrometry Mesenchymal stromal cells MetaboAnalyst OFFGEL electrophoresis Protein profiling Proteomics Rat Serum concentration Stemness SWATH acquisition
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. i
Abstract
Mesenchymal stromal cells (MSCs) have the potential to be powerful medical tools for utilisation in clinical practice, for the treatment of numerous ailments including wound healing, tissue regeneration and potentially even spinal injury repair. To maximise the use of MSCs, access to an off-the-shelf MSC therapeutic is highly desirable, however, achieving this outcome is challenging, as MSCs cannot survive, undifferentiated in culture for extended periods of time. Currently, research in this field aims to determine an optimal culture environment for the growth of MSCs, that maintains their stromal phenotype. Evaluation of MSC differentiation and stemness have been investigated using methods of proteomics previously however, advanced protein profiling methods, such as SWATH-MS, have yet to be utilised for this purpose. Therefore, the purpose of this study was to investigate the effect that specific growth factors had on MSCs during culture through the utilisation of SWATH-MS analysis, with specific interest in the maintenance of their undifferentiated state.
Initially, bone marrow-derived MSCs (BM-MSC) were cultured in different concentrations of serum to determine the most suitable concentration to maintain BM- MSC growth without significant interference with the subsequent proteomic analysis methods. Following this, MSCs were cultured, on either fibronectin (FN) coated or uncoated flasks, in condition media supplemented with fibroblast growth factor 2 (FGF2) and/or bone morphogenetic protein 4 (BMP4). The cellular protein was collected and processed for analysis by qualitative and quantitative SWATH-MS analysis.
From this research, a rat BM-MSC protein library containing a total of 943 proteins was developed. Each of these proteins was quantified in each of the treatment conditions and evaluated to determine the most predominant biological processes occurring with regards to differentiation status of the cells. Individual proteins were evaluated to further investigate the effect that the treatments had on the biology of the BM-MSCs. Interestingly, eight proteins were identified to have a significant difference in abundance between the treatment groups. The biological role of these proteins in BM-MSCs were assessed.
ii Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Characterisation of the rat BM-MSC proteome provides insight into the molecular processes that are occurring in the cells when they are stimulated with FGF2 and BMP4. Understanding the effect that these treatments have on the protein profiles of BM-MSCs provides important knowledge required to develop an optimal culture environment for BM-MSCs.
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. iii
Table of Contents
Keywords ...... i Abstract ...... ii Table of Contents ...... iv List of Figures ...... vii List of Tables ...... xi List of Abbreviations ...... xii Statement of Original Authorship ...... xiii Acknowledgements ...... xiv Chapter 1: Literature Review ...... 17 1.1 Background/Introduction ...... 17 1.2 MSCs ...... 17 1.2.1 Stemness ...... 19 1.2.2 Differentiation ...... 20 1.3 Current Culture Conditions ...... 21 1.4 Advances in Culture Conditions ...... 23 1.5 Proteomics in MSC research ...... 26 1.6 Conclusion, perspective and Study Rationale ...... 28 1.6.1 Summary and Implications / Conclusions ...... 28 1.6.2 Perspective and Research Problem ...... 28 1.6.3 Purpose ...... 29 1.6.4 Significance, Scope and Definitions ...... 29 1.6.5 Thesis Outline ...... 29 1.6.6 Hypothesis and Aims ...... 30 Chapter 2: Materials and Methodology ...... 31 2.1 Methodology and Research design ...... 31 2.1.1 Methodology ...... 31 2.1.2 Research design ...... 31 2.2 Materials...... 32 2.2.1 General Reagents ...... 32 2.2.2 General consumables ...... 32 2.2.3 Growth Factors ...... 33 2.2.4 Instrumentation ...... 33 2.2.5 Software ...... 33 2.3 Procedures ...... 34 2.3.1 Cell Culture ...... 34 2.3.1.1 Ethics ...... 34 2.3.1.2 Sample Population ...... 34 2.3.1.3 Cell Collection ...... 34 2.3.1.4 Protein Collection ...... 34 2.3.2 Protein Processing...... 35 2.3.2.1 Acetone Precipitation ...... 35
iv Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
2.3.2.2 Protein Quantification ...... 35 2.3.2.3 Pooled Samples ...... 35 2.3.2.4 FASP ...... 35 2.3.2.5 LDS-PAGE ...... 36 2.3.2.6 In-Gel Digestion ...... 36 2.3.2.7 Iso-electric Focusing ...... 37 2.3.2.8 Peptide Clean Up and Desalting ...... 38 2.3.2.9 iRTs ...... 38 2.3.3 LC-MS/MS ...... 39 2.3.3.1 DDA Mode ...... 39 2.3.3.2 DIA Mode (SWATH-MS) ...... 39 2.4 Analysis ...... 40 2.4.1 DDA-MS Data Processing ...... 40 2.4.2 SWATH-MS Data Processing ...... 40 2.4.3 Statistical analysis ...... 41 2.4.4 Gene Ontology Analysis...... 41 2.4.5 Venn Diagrams ...... 41 Chapter 3: Effect of Serum Concentration on the Proteome of Rat Bone Marrow-derived Mesenchymal Stem Cells...... 43 3.1 Introduction ...... 43 3.2 Experimental Procedures ...... 45 3.2.1 Materials ...... 45 3.2.2 Cells and Culture Conditions...... 45 3.2.3 Protein Digestion ...... 45 3.2.4 Mass Spectrometry ...... 45 3.2.5 Analysis ...... 46 3.3 Results ...... 46 3.3.1 FCS concentration impacts on the number of proteins identified...... 46 3.3.2 Differentiation gene ontologies are more significantly over-represented in the 0 % and 10 % FCS treatments...... 47 3.3.3 The 0 % and 2 % FCS treatments have a higher correlation compared to the 10 % FCS treatment...... 57 3.3.4 Principal component analysis suggests significant similarity between BM-MSCs cultured in 0 % FCS and 2 % FCS...... 57 3.3.5 Individual proteins differ in abundance between the 0 % FCS treatment and the 10 % FCS treatment...... 60 3.3.6 Three contaminating proteins significantly varied in abundance between the 0 % FCS treatment and the 10 % FCS treatment...... 64 3.4 Discussion ...... 66 3.5 Chapter 3 Summary ...... 69 Chapter 4: Multivariate Analysis Reveals that Stimulation with FN, FGF2 and BMP4 Causes Changes in the Protein Profiles of BM-MSCs...... 71 4.1 Introduction ...... 71 4.2 Experimental Procedures ...... 72 4.2.1 Materials ...... 72 4.2.2 Cells and Culture Conditions...... 72 4.2.3 Protein Fractionation ...... 73 4.2.4 Protein Digestion ...... 73 4.2.5 Mass Spectrometry ...... 73 4.2.6 Analysis ...... 73
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. v
4.3 Results ...... 74 4.3.1 Minimal morphological changes were observed between BM-MSCs pre- and post-treatment, or between treatments...... 74 4.3.2 A high correlation exists between the BM-MSCs cultured in each of the treatments...... 77 4.3.3 Subtle changes between the treatment groups can be explained by changes in individual protein abundance...... 79 4.3.3.1 The proteome of BM-MSCs cultured in FGF2+BMP4 is most similar to that of the control BM-MSCs, with only one protein significantly varying in abundance between the two treatments...... 86 4.4 Discussion ...... 92 4.5 Chapter 4 Summary ...... 95 Chapter 5: Analysis of Individual Protein Changes within Stimulated BM- MSCs were Associated with Neuronal Differentiation...... 97 5.1 Introduction ...... 97 5.2 Experimental Procedures ...... 98 5.2.1 Analysis ...... 98 5.3 Results ...... 99 5.3.1 Biologically significant differences in the abundance of 69 proteins were identified when the treated BM-MSC proteomes were compared to the control BM-MSC proteome...... 99 5.3.2 Biologically significant proteins were associated with gene ontologies describing neural, bone and muscle differentiation and development...... 102 5.3.3 Qualitative analysis, using word clouds, identified the major biological themes associated with the proteins with changed abundance in each of the treatment groups...... 107 5.3.4 Eight biologically significant proteins were determined to be statistically significant in the treatment groups compared to the control...... 112 5.4 Discussion ...... 116 5.5 CHAPTER 5 SUMMARY ...... 120 Chapter 6: General Discussion and Conclusions ...... 123 Bibliography ...... 129 Appendices ...... 143
vi Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
List of Figures
Figure 3.1: BM-MSCs cultured in 0 % and 2 % FCS have a very similar protein profile compared to BM-MSCs cultured in 10 % FCS. Venn comparison of the proteins identified using DDA-MS in each of the treatment groups...... 47 Figure 3.2 (Right): The proteins identified in the BM-MSCs cultured with 0 % FCS are associated with biological processes related to development and differentiation, migration and motility, apoptosis and cell death, cellular homeostasis and cell division. Gene Ontology Network of Biological Processes over-represented in the 0 % FCS treatment Group...... 48 Figure 3.3 (Right): The proteins identified in the BM-MSCs cultured with 2 % FCS are associated with biological processes related to development and differentiation, apoptosis and cell death, cellular homeostasis and cell division. Gene Ontology Network of Biological Processes over-represented in the 2 % FCS treatment Group...... 50 Figure 3.4 (Right): The proteins identified in the BM-MSCs cultured with 10 % FCS are associated with biological processes, including those related to development and differentiation, migration, apoptosis and cell death, autophagy, homeostasis and cell division. Gene Ontology Network of Biological Processes over-represented in the 10 % FCS treatment Group...... 52 Figure 3.5: Unique Gene Ontologies are associated with of the treatment groups based on their protein profiles. Venn comparison of Biological Processes GO Terms associated with the proteins detected using DDA-MS in each of the treatment groups...... 55 Figure 3.6: The 0 % FCS treatment has a higher correlation to the 2 % FCS treatment than to the 10 % treatment, however all three treatments have a correlation higher than 0.94. Pearson correlation of A: all replicates, B: treatments using average abundance values...... 57 Figure 3.7: Principal component analysis clusters the 0 % FCS and 2 % FCS treatments together, suggesting similarity between the BM- MSCs cultured in these treatments. A: Principal Component Analysis (PCA), B: Partial Least Squares – Discriminant Analysis (PLS-DA), C: Orthogonal Partial Least Squares – Discriminant Analysis (oPLS-DA), D: Sparse Partial Least Squares – Discriminant Analysis (sPLS-DA)...... 59 Figure 3.8: The Abundance of 105 Proteins Vary Between Two or More Treatments with Different FCS Percentage. One-way analysis of variance (ANOVA) ...... 61 Figure 3.9: The Abundance of 3 Proteins Present in the cRAP Database Significantly Vary Between Two or More of the Different FCS
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. vii
Treatments. One-way analysis of variance (ANOVA) of detected contaminating proteins from the cRAP database...... 64 Figure 3.10: The Abundance of Albumin and Lactotransferrin increase with the Percentage of FCS whilst Keratin (K1C9) and Trypsin decrease. Data are presented as the median abundance units +/- the upper and lower quartiles (n=2) of A) Bovine Albumin; B) Human Keratin; C) Human Lactotransferrin; and D) Porcine Trypsin. Statistical analysis was performed by one-way ANOVA followed by Tukey HSD post hoc test where significance was accepted where p<0.05 = *...... 65 Figure 4.1: Minimal morphological changes are observed between BM- MSCs cultured on flasks coated with FN versus non-coated flasks. Representative images (n=6) of BM-MSCs prior to treatment with growth factors (All images taken are presented in Appendix B). BM- MSCs grown on A: FN coated flasks prior to starvation, B: an uncoated flask prior to starvation, C: FN coated flask after 12 hours of starvation, D: an uncoated flask after 12 hours of starvation...... 75 Figure 4.2: Cells cultured in treatments containing FN and BMP4 exhibited nodule-like clusters after 3 days of treatment. Representative images of BM-MSCs 3 days post treatment (All images taken are presented in Appendix C). BM-MSCs grown on A: an uncoated flask with no supplements added (Control), B: FN coated flask with no supplements (FN), C: FN coated flask supplemented with FGF2 (FN+FGF2), D: FN coated flask supplemented with BMP4 (FN+BMP4), E: FN coated flask supplemented with FGF2 and BMP4 (FN+FGF2+BMP4), F: an uncoated flask supplemented with FGF2 and BMP4 (FGF2+BMP4). Orange arrow indicates nodule-like formations occuring within the cultures...... 76 Figure 4.3: The control treatment is most highly correlated with the FGF2+BMP4 treatment however, all treatment groups exhibit a high level of correlation. Pearson correlation between A: replicates, B: mean replicate abundances...... 78 Figure 4.4: Separation of the different treatment groups is best shown using Sparse Partial Least Squares Discriminant Analysis whereby treatments are clearly separated, particularly those containing FN. A: Principal Component Analysis (PCA), B: Partial Least Squares – Discriminant Analysis (PLS-DA), C: Orthogonal Partial Least Squares – Discriminant Analysis (oPLS-DA), D: Sparse Partial Least Squares – Discriminant Analysis (sPLS-DA)...... 80 Figure 4.5: The Abundances of These 10 Proteins are Responsible for the Separation of Each Treatment Group in the First Component of sPLS-DA Plot. Data are presented as the median log2 abundance with the upper and lower quartiles, where the whiskers represent the most extreme abundance value within 1.5× interquartile range (n = 3). Statistical analysis was performed by one-way ANOVA followed by TukeyHSD posthoc test where significance was accepted where p<0.05 = *, p<0.01 = ** and p<0.001 = ***...... 83
viii Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Figure 4.6: The Abundances of These 10 Proteins are Responsible for the Separation of Each Treatment Group in the Second Component of sPLS-DA Plot. Data are presented as the median log 2 abundance with the upper and lower quartiles, where the whiskers represent the most extreme abundance value within 1.5× interquartile range (n = 3). Statistical analysis was performed by one-way ANOVA followed by TukeyHSD posthoc test where significance was accepted where p<0.05 = *, p<0.01 = ** and p<0.001 = ***...... 85 Figure 4.7: 92 Proteins significantly varied between at least two of the treatment groups. One-way analysis of variance of protein abundances across the six treatment groups...... 87 Figure 4.8: Fibronectin (endoFN) had a significantly lower abundance in the BM-MSCs cultured on a FN coated plate with no additional supplements, than in the BM-MSCs cultured with FN+FGF2, FN+FGF2+BMP4 and FGF2+BMP4...... 91 Figure 5.1: The proteins identified in the BM-MSCs protein library are associated with biological processes related to development and differentiation, migration and motility, apoptosis and cell death, cellular homeostasis and cell division...... 104 Figure 5.2: The word metabolic appears most frequently in the gene ontology analysis of all the treatment groups. A: FN increased, B: FN decreased, C: FN+FGF2 increased, D: FN+FGF2 decreased, E: FN+BMP4 increased, F: FN+BMP4 Decreased, G: FN+FGF2+BMP4 increased, H: FN+FGF2+BMP4 decreased, I: FGF2+BMP4 increased, J: FGF2+BMP4 decreased...... 109 Figure 5.3: Eight proteins are statistically and biologically significant across the 5 treatment groups, compared to the control group. Data are presented as log2 of fold change and log10 of p value. Fold change was calculated as a ration of treatment abundance to control abundance, while p values were obtained using pairwise t-tests. Horizontal red lines indicate p>0.05 and vertical red lines indicate positive and negative fold changes >2. A: FN to Control, B: FN+FGF2 to Control, C: FN+BMP4 to Control, D: FN+FGF2+BMP4 to Control, E: FGF2+BMP4 to Control. Proteins that exhibit both statistical and biological significance are labelled as: UBQL1 – Ubiquilin-1; RS21 – 40S ribosomal protein S21; SLIT3 – Slit homolog protein 3; STML2 – Stomatin-like protein 2, mitochondrial; HEM6 – Oxygen-dependent coproporphyrinogen-III oxidase, mitochondrial; RS11 – 40S ribosomal protein S11; F136A – Potein FAM136A; and NLTP – non-specific lipid transfer protein...... 113 Figure 5.4: The abundances of 8 proteins were statistically and biologically significantly different in one of the treatment groups compared to the control. Data is presented as the median log 2 abundance with the upper and lower quartiles, where the whiskers represent the most extreme abundance value within 1.5× interquartile range (n = 3). Statistical analysis was performed by pairwise t-tests where significance was accepted where p<0.05 = #, along with one-way ANOVA followed by TukeyHSD posthoc test where significance was
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. ix
accepted where p<0.05 = *. Abundances of A: Ubiquilin-1, B: 40S ribosomal protein S21, C: Slit homolog 3 protein, D: Stomatin-like protein 2, mitochondrial, E: Oxygen-dependent coproporphyrinogen- III oxidase, mitochondrial, F: 40S ribosomal protein S11, G: Protein FAM136A and H: Non-specific lipid transfer protein, across all treatment groups...... 115
x Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
List of Tables
Table 1.1: Cell Surface Markers and Transcription Factors Commonly Reported in the Literature as Stem Cell Markers...... 19 Table 3.1: Each treatment had unique gene ontologies associated with processes of differentiation or cell death, as well as shared gene ontologies related to cellular survival and mantainence...... 56 Table 3.2: 100 of the 105 proteins identified as significantly varied between two or more treatments, are significantly varied between the 0 % and 10 % FCS treatment groups...... 61 Table 3.3: Three contaminating proteins significantly vary between the 0 % and 10 % FCS treatment groups...... 64 Table 4.1: Experimental parameters for each treatment condition...... 73 Table 4.2: Top 10 proteins responsible for the separation of treatments in the sPLS plot, per component...... 81 Table 4.3: 20 proteins are significantly varied between the FN and FGF2+BMP4 treatment groups...... 88 Table 5.1: Across the five treatment groups, 69 proteins exhibited a greater than 2-fold difference in abundance to the control group. A positive 2-fold change >2 whereas a negative 2-fold change <0.5...... 99 Table 5.2: Proteins with increased and decreased abundance in the treatment groups compared to the control, associated with specific differentiation gene ontologies...... 105 Table 5.3: Significant proteins with greater than 2-Fold Change in abundance compared to control treatment ...... 114
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List of Abbreviations
Abbreviation Description < Less than > Greater than ACN Acetonitrile ANOVA Analysis of Variance BiNGO Biological Gene Ontology Networks BM-MSC Bone marrow-derived Mesenchymal Stromal Cell BMP4 Bone Morphogenetic Protein 4 BP Biological Process BSA Bovine Serum Albumin CC Cellular Component cRAP Common Repository of Adventitious Proteins DDA Data Dependent Acquisition DTT Dithiothreitol endoFN Endogenously produced FN FA Formic Acid FASP Filter Aided Sample Preparation FBS Foetal Bovine Serum FDR False Discovery Rate FGF2 Fibroblastic Growth Factor 2 FN Purified Human Fibronectin GO Gene Ontology HPLC High Performance Liquid Chromatography IAA Iodoacetamide IPG Immobilised pH Gradient iRTs Index Retention Time Peptides kDa Kilodalton LC Liquid Chromatography LDS-PAGE Lithium Dodecyl-sulfate Poly-acrylamide Gel Electrophoresis MF Molecular Function mM Millimolar MS Mass Spectrometry PBS Phosphate Buffered Saline PCA Principal Component Analysis PLS-DA Partial Least Squares Discriminant Analysis RCF Relative Centrifugal Force STAGE Stop-and-go-extraction STRING Search Tool for the Retrieval of Interacting Genes/Proteins SWATH Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass TOF Time of Flight V Volts
xii Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.
Signature: QUT Verified Signature
Date: ______23/03/2018
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Acknowledgements
When I started my Masters, I was told to think of it as a mountain I was going to climb. At the beginning it seemed like such a daunting task, so, to overcome this, I was told to set my own base camps and take it one step at a time. I was also told that the final leg to the peak would be the most challenging because I would be tired, and my motivation would be wavering. But now having made that final trek, I am standing at the peak of my mountain relishing the knowledge that I have accomplished something great. I would never have been able to conquer my mountain without the love and support of the people around me.
Firstly, I would like to acknowledge Dr Dayle Sampson. He was my first supervisor and he introduced me to the world of research. He coached me through a vacation research scheme and lead me to the base of this mountain. He equipped me with basic skills and gave me a taste of life as a researcher. The journey I took with him was just the beginning of a much larger journey, and it helped me decide that when the time came, I would conquer the mountain.
Next, I want to acknowledge and thank Dr Tony Parker and Dr Daniel Broszczak. In a way, they were my Sherpas. At every obstacle, they were there to give me encouragement and guidance. They taught me so much throughout my Masters and I am confident that without them, I would have fallen into a crevasse or been suffocated in an avalanche (of data!).
I would like to acknowledge and thank Dr Yinghong Zhou and Professor Yin Xiao for their guidance on matters pertaining to the culture of my cells as well as Lingling Chen and Shengfang Wang who were responsible for the culture, treatment and collection of the cells that were used within this research. Finally, I would like to acknowledge Rajesh Gupta and Pawel Sadowski for running the samples on the Mass Spectrometer, as well as CARF for allowing me to use their instruments and facilities.
I want to acknowledge my family, Regina Carlton, Noel Carlton and Daniel Carlton. Thank you for being my oxygen. They gave me the ability to breathe when the mountain air got too thin, especially as I got closer to the peak.
xiv Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
And finally, I want to acknowledge the writer whose words gave me hope and courage throughout this journey, Dr Seuss.
“You’re off to great places! Today is your day! Your mountain is waiting, So… Get on your way!” - Dr Seuss, Oh, The Places You’ll Go!
It’s been a long and winding ascent, but the view from the top is a beautiful one!
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Chapter 1: Literature Review
1.1 INTRODUCTION
This chapter critically analyses the important literature in the field of mesenchymal stromal cell (MSC) research, with a specific interest in the current knowledge about their stemness and differentiation capabilities (Section 1.2), the current culture conditions (Section 1.3), emerging advances to these conditions (Section 1.4) and the use of proteomics and protein profiling as an evaluation method for MSC differentiation (Section 1.5). The final section of this review summarises the key points of the literature (Section 1.6.1), provides an insight into the research problem (Section 1.6.2), and discusses the purpose (Section 1.6.3) and significance of this thesis (Section 1.6.4). Finally, section 1.6.5 describes the outline of the remaining chapters of this thesis and section 1.6.6 discusses the hypothesis and aims for this study.
1.2 MSCs
Mesenchymal stromal cells are a type of progenitor cell that have multipotential differentiation abilities. As a result, MSCs can differentiate into most but not all cell types, including bone, cartilage and adipose (Caplan, 1991). This characteristic gives both researchers and clinicians hope that, through correct manipulation, they can be used to grow and ultimately repair certain tissues within the human body. Unlike pluripotent embryonic stem cells, harvested from embryos, MSCs can be harvested from adult tissues. Specifically, in humans, it is less controversial to harvest MSCs from bone rather than to use embryonic stem cells (S. K. Lee et al., 2013; Robertson, 2010). Furthermore, BM-MSCs are a more reliable stem cell source because unlike embryonic stem cells, they do not develop into teratomas in vivo (Gong et al., 2014).
It is hypothesized that MSCs could become a powerful medical tool and have applications in tissue regeneration (Deng et al., 2017; Yamachika et al., 2012), tissue engineering (Ohgushi, 2014), degenerative disease therapeutics (Guan et al., 2013; Ruiz et al., 2016) and even spinal injury repair (Neirinckx et al., 2015; Park et al., 2012). However, the use of MSCs is limited by their inability to survive, undifferentiated, in vitro for extended periods of time. The use of MSCs as medical
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 17
treatments and in tissue engineering applications relies on whether or not they can be successfully cultured while retaining their ‘stemness’ characteristics. Remaining in the stromal state is specifically important where the MSCs would be used in allografts, due to their immunosuppressive characteristics, i.e. they don’t elicit an immune response within the host, when undifferentiated (Nold et al., 2015).
Aside from their ability to differentiate, MSCs are characterised by their ability to self-renew and proliferate rapidly (Caplan, 1991). The most common source of MSCs documented in the literature are those derived from bone marrow (BM-MSCs) (Baddoo et al., 2003; L. Huang et al., 2015; Mareddy et al., 2010). Although, other sources of MSCs have been used, including cells derived from umbilical cord (L. Huang et al., 2015; B. Zhang et al., 2017), dental pulp (Ba et al., 2017; Eleuterio et al., 2013), the periodontal ligament (Eleuterio et al., 2013; L. Liu et al., 2013; Tran Hle et al., 2014), amniotic fluid (Fei et al., 2013; Roubelakis et al., 2014), adipose tissue (Fotia et al., 2015a; A. Y. Lee et al., 2015), skin (A. Y. Lee et al., 2015; Q. Li et al., 2015) and lung tissue (A. Y. Lee et al., 2015; Rolandsson Enes et al., 2016). MSCs have become the main type of stem cells used for research today because of their abundance and accessibility.
Previous work completed in our laboratory resulted in the observation of two sub-types of BM-MSCs that exhibit different properties with regards to proliferation capacity and differentiation capability (Mareddy et al., 2007). The two sub-types observed by Mareddy, et al. (2007) were termed fast-type MSCs and slow-type MSCs. The fast-type, or true MSCs, were tri-potential, i.e. were able to differentiate into bone, cartilage and adipose tissue, and had an increased ability to proliferate whereas the slow-type MSCs were either bi- or uni-potent and had a significantly lower proliferative ability. In two other studies performed by Mareddy, et al. (2009 and 2010), differences in the proteome and gene expression between each MSC sub-type validated their distinct existence (Mareddy et al., 2009; Mareddy et al., 2010). In a study performed by Hynes, et al. (2014), fast-growing, induced pluripotent stem cells were utilised, however, there is no supporting evidence within the literature to indicate that these two sub-types are recognised in populations of MSCs by the wider academic community (Hynes et al., 2014). This is an avenue of MSC research that could be further investigated, which could lead to understanding the importance of these subtypes and their potential role in regenerative medicine.
18 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
1.2.1 Stemness Stemness is a term used to describe cells that have the characteristics of undifferentiated mesenchymal stromal cells such as increased proliferative and self- renewal capabilities, whilst also retaining their multipotential differentiation capacity. Several cell surface markers have been documented in the literature to determine whether a cell is in a state of stemness prior to their studies. There are six markers (CD29, CD44, CD73, CD90, CD105 and CD166) that are widely accepted as mesenchymal progenitor cell markers however several other markers have also been associated with undifferentiated stem cells, as described in Table 1.1. These novel markers have been identified in few research studies and have not been validated as mesenchymal progenitor cell markers. Therefore, they are not widely used to determine whether MSCs are in a state of stemness.
Table 1.1: Cell Surface Markers and Transcription Factors Commonly Reported in the Literature as Stem Cell Markers. CD Name Gene Species Reference Classification name Widely Accepted Markers of Stemness CD29 Integrin Beta 1 ITGB1 Human, Mouse, (Arufe et al., 2011; Baddoo et al., 2003; Pig Eleuterio et al., 2013; L. Huang et al., 2015; A. Y. Lee et al., 2015; Mareddy et al., 2009; Mareddy et al., 2007; Mareddy et al., 2010; Mindaye et al., 2013; Turnovcova et al., 2009; Vallabhaneni et al., 2015) CD44 CD44 molecule CD44 Human, Mouse, (Arufe et al., 2011; Baddoo et al., 2003; (Indian Blood Group) Pig Eleuterio et al., 2013; L. Huang et al., 2015; A. Y. Lee et al., 2015; S. J. Lee et al., 2014; S. K. Lee et al., 2013; Mareddy et al., 2009; Mareddy et al., 2007; Mareddy et al., 2010; Martino et al., 2014; Mindaye et al., 2013; Ramesh et al., 2012; Roubelakis et al., 2014; Salehinejad et al., 2012; Turnovcova et al., 2009; Vallabhaneni et al., 2015) CD73 Ecto-5’-nucleotidase NT5E Human (Eleuterio et al., 2013; Holley et al., 2015; S. J. Lee et al., 2014; Mareddy et al., 2009; Mareddy et al., 2007; Mareddy et al., 2010; Mindaye et al., 2013; Nold et al., 2015; Ramesh et al., 2012; Roubelakis et al., 2014; Saller et al., 2012; Turnovcova et al., 2009) CD90 Thy-1 THY1 Human, Pig (Arufe et al., 2011; Cappellesso-Fleury et al., 2010; Eleuterio et al., 2013; Holley et al., 2015; L. Huang et al., 2015; A. Y. Lee et al., 2015; S. J. Lee et al., 2014; S. K. Lee et al., 2013; Mareddy et al., 2009; Mareddy et al., 2007; Mareddy et al., 2010; Martino et al., 2014; Mindaye et al., 2013; Nold et al., 2015; Ramesh et al., 2012; Roubelakis et al., 2014; Salehinejad et al., 2012; Saller et al.,
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2012; Turnovcova et al., 2009; Vallabhaneni et al., 2015) CD105 Endoglin ENG Human, Pig (Arufe et al., 2011; Cappellesso-Fleury et al., 2010; Eleuterio et al., 2013; Holley et al., 2015; A. Y. Lee et al., 2015; S. J. Lee et al., 2014; Mareddy et al., 2009; Mareddy et al., 2007; Mareddy et al., 2010; Martino et al., 2014; Mindaye et al., 2013; Nold et al., 2015; Ramesh et al., 2012; Roubelakis et al., 2014; Salehinejad et al., 2012; Saller et al., 2012; Turnovcova et al., 2009; Vallabhaneni et al., 2015) CD166 Activated Leukocyte ALCAM Human (Cappellesso-Fleury et al., 2010; Eleuterio et Cell Adhesion al., 2013; Mareddy et al., 2009; Mareddy et Molecule (ALCAM) al., 2007; Mareddy et al., 2010; Mindaye et al., 2013; Roubelakis et al., 2014; Vallabhaneni et al., 2015) Other Markers CD9 Tetraspanin-29 CD9 Mouse (Baddoo et al., 2003) CD13 Alanyl (Membrane) ANPEP Human (Cappellesso-Fleury et al., 2010; Eleuterio et Aminopeptidase al., 2013; Roubelakis et al., 2014) CD49a Integrin Alpha 1 ITGA1 Human (Cappellesso-Fleury et al., 2010) CD49b Integrin Alpha 2 ITGA2 Human (Cappellesso-Fleury et al., 2010) CD49c Integrin Alpha 3 ITGA3 (Vallabhaneni et al., 2015) CD49f Integrin Alpha 6 ITGA6 (Vallabhaneni et al., 2015) CD54 Intercellular ICAM1 Human (Cappellesso-Fleury et al., 2010) Adhesion Molecule 1 CD59 Complement CD59 Human (Vallabhaneni et al., 2015) Regulatory Protein CD81 Tetraspanin-28 CD81 Mouse (Baddoo et al., 2003) CD106 Vascular Cell VCAM1 Mouse, Human (Baddoo et al., 2003; Cappellesso-Fleury et Adhesion Molecule 1 al., 2010) CD146 Melanoma Cell MCAM Human, Horse (Cappellesso-Fleury et al., 2010; Eleuterio et Adhesion Molecule al., 2013; Martino et al., 2014) CD164 Endolyn CD164 Human (Cappellesso-Fleury et al., 2010) CD271 Nerve Growth Factor NGFR Human (Tsimbouri et al., 2012) Receptor SOX2 Transcription factor SOX2 Human, mouse (Fotia et al., 2015a; Hayashi et al., 2015; SOX2 Takahashi et al., 2007) OCT 3/4 Octamer binding POU5F1 Human, mouse (Fotia et al., 2015a; Hayashi et al., 2015; protein 3/4 Takahashi et al., 2007) c-Myc Transcription factor MYC Human (Takahashi et al., 2007) c-Myc KLF4 Krueppel-like factor KLF4 Human (Takahashi et al., 2007) 4 Nanog Homeobox protein NANOG Human, mouse (Fotia et al., 2015a; Hayashi et al., 2015; nanog Takahashi et al., 2007)
1.2.2 Differentiation MCSs differentiate to replenish different cell types in vivo and have the ability to differentiate into bone (Eom et al., 2014; Mareddy et al., 2007; Wislet-Gendebien et al., 2012), cartilage (De la Fuente et al., 2012; Mareddy et al., 2007; Wislet- Gendebien et al., 2012), adipose (Mareddy et al., 2007; Wislet-Gendebien et al., 2012), neuronal (Eleuterio et al., 2013; Wislet-Gendebien et al., 2012), cardio-
20 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
myogenic (Celebi et al., 2010) and smooth muscle (Wislet-Gendebien et al., 2012) cell types. During differentiation, the MSCs lose their stemness as they undergo morphological and biochemical changes. MSCs can be induced towards a specific lineage during the culturing process by providing them with a specific combination of supplements. For example, a combination of ascorbic acid, β glycerol phosphate and dexamethasone is commonly used to differentiate the MSCs into bone (Eom et al., 2014; Mareddy et al., 2007; Mareddy et al., 2010) while a combination of transforming growth factor β3, ascorbic acid 2-phosphate, sodium pyruvate, proline and insulin- transferrin-selenium (ITS)-plus is used to differentiate MSCs into cartilage (Mareddy et al., 2007; Mareddy et al., 2010). These end phenotypes are verified using a number of assays to determine whether the induced differentiation was successful.
Differentiation of MSCs can be validated morphologically and by staining them for various markers. These stains are essential for the determination of the post- differentiation cell type by targeting markers that indicate endpoint differentiation. For instance, Von Kossa stain is used to identify bone differentiation by staining deposits of calcium (Mareddy et al., 2007), whereas, Alcian Blue stain is used to identify cartilaginous differentiation by staining glycoproteins blue (Mareddy et al., 2007). Similarly, Oil Red O staining is used for identification of adipose differentiation by staining lipid vacuoles red (Wislet-Gendebien et al., 2012). Even though it is important to identify endpoint differentiation, these stains are not as beneficial for monitoring the early differentiation processes of MSCs in culture. During the differentiation process, the most important markers would be those that indicate the very first stages of differentiation. Knowledge of these markers and a method for detecting them in culture could potentially allow researchers to suspend the differentiation process, keeping the cells in an undifferentiated state. Distinct markers of early differentiation however have not yet been identified or reported in the literature.
1.3 CURRENT CULTURE CONDITIONS
The media that MSCs are cultured in provides them with the essential nutrients required to facilitate growth, maintain stemness and stimulate differentiation. According to the literature, the media composition is a key player in the survival of the cells and has been targeted as source of manipulation (Gottipamula et al., 2013). Unfortunately, the complex nature of MSCs and the lack of complete understanding of their biology, creates a problem when it comes to the determination of optimal
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 21
culture conditions. Researchers have extensively investigated culture conditions that promote differentiation of MSCs along different cell lineages, whilst only very few studies have been conducted into the optimal conditions for MSC maintenance. Consequently, an optimum environment, containing the right factors for growth, is an area of intense research effort.
In one specific study, Lee, et al. (2015) investigated a number of media for the optimum growth of miniature pig MSCs derived from a number of tissue sources (A. Y. Lee et al., 2015). From the media compositions investigated, they determined, through monitoring the population doubling of the cells, that the addition of F12 and Glutamax to the DMEM media provided the best proliferative capacity, irrespective of the source of the MSCs.
For the growth of human MSCs, DMEM is often preferred, however in a study by Sotiropoulou, et al. (2006), it was determined that α-MEM was better, specifically for culturing human BM-MSCs. In this study, it was also identified that the addition of glutamax or FGF2, or the removal of glucose from the media enhanced growth of the cells (Sotiropoulou et al., 2006). α-MEM has also been deemed the better growth media for adipose derived hMSCs by another research team (Lund et al., 2009).
Serum concentration is another variable that must be considered in cell culture. For human MSCs, 10 % foetal calf serum (FCS) is often used (Baghaei et al., 2017; Binder et al., 2015; Muraglia et al., 2017), however other concentrations, such as 2 % (Muller et al., 2011), 5 % (Binder et al., 2015) and 20% (Nonnis et al., 2016) have been reported. Serum-free alternatives have recently been investigated where cells may be used for clinical application. Alternatives such as human platelet rich plasma (PRP) (Felka et al., 2010; Roubelakis et al., 2014; Van Pham et al., 2016), human platelet lysate (Perez-Ilzarbe et al., 2009) and human serum (Tateishi et al., 2008) have been evaluated to replace FCS. More specifically, PRP has been used to not only, accelerate the healing process of chronic ulcers in diabetic patients, but also to increase the proliferative capacity and migratory capability of amniotic fluid derived MSCs (Roubelakis et al., 2014). Additionally, a number of commercial serum-free media are available (Gottipamula et al., 2013). The downfall of these media is that their ingredients are rarely disclosed, and although they claim to optimise growth, these formulations are not targeted to specific cells.
22 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
1.4 ADVANCES IN CULTURE CONDITIONS
A number of factors have been investigated to prevent the progression of differentiation of MSCs while they are in culture. This is important because even without induction, the MSCs progress towards a differentiated state over time during extended culture periods (Celebi & Elcin, 2009). As the culture environment is the main effector of growth and differentiation, there are three aspects of the culture that have been previously manipulated; external environment factors, growth surface properties and culture medium supplementation.
External environmental factors such as oxygen concentration and temperature have been investigated with regards to MSC survival during culture. A study performed by Stolzing and Scutt (2006) investigated the effect of culturing MSCs at 32 ºC would have on their expansion capacity (Stolzing & Scutt, 2006). They determined that by culturing the cells at this temperature, as compared to the standard 37 ºC, oxidative damage was reduced along with the occurrence of apoptosis. They suggested that culturing MSCs at a lower temperature could prolong the survival of MSCs in long term culture, however further research has not been performed to validate this.
In vivo, MSCs live in an environment where oxygen is limited (Fotia et al., 2015a, 2015b). As a result, the effects of hypoxic culture conditions on the growth and survival of MSCs has been investigated. MSCs cultured under hypoxic conditions (2
% O2) were observed to develop as a more uniform cell population of rapidly self- renewing cells for a prolonged period of time compared to MSCs cultured in normoxic conditions (21% O2) (Saller et al., 2012). This concept was verified in another study where hypoxic culture conditions led to the observation of increased proliferative ability and ‘stemness’ qualities of adipose-derived MSCs (Fotia et al., 2015a). Fotia, et al. (2015a) explained that the hypoxic culture conditions mimic the environment that MSC are generally found in and is responsible for the regulation of Oct-4, Sox-2 and NANOG, all of which are transcription factors necessary for the maintenance of pluripotency (Fotia et al., 2015a).
Other studies have mimicked the in vivo MSC environment during culture by changing the surface properties of culture flasks. Extracellular matrix proteins, binding proteins and different surface types have been evaluated for the culture of MSCs. Vitronectin was found to increase the adhesion of MSCs (Kundu & Putnam, 2006).
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Fibronectin has been observed to increase adhesion and promote migration by Kasten et al. (2014), whereas Faia-Torres et al. (2015) observed that MSCs had increased proliferation at intermediate concentrations and increased osteogenic potential at lower concentrations (Faia-Torres et al., 2015; Kasten et al., 2014). Collagen type-1 (Kundu & Putnam, 2006; Linsley et al., 2013) and fibrinogen (Linsley et al., 2013) have both been evaluated, and determined to enhance osteogenic differentiation of MSCs.
Native extracellular matrix of BM-MSCs successfully preserved the ‘stem’ phenotype of MSCs during culture (Rakian et al., 2015). This method of culture has also been evaluated in the culture of adipose derived mesenchymal stromal cells (Xiong et al., 2015), and was observed to promote stemness in these cells as well.
In a separate study, FGF2 was immobilised to polystyrene flask and used to culture MSCs (Kang et al., 2014). A decreased differential ability towards bone and adipose was observed in the MSCs, along with enhanced adhesion. The lack of differential ability of these cells could render the MSCs unusable in future treatment however, if this effect could be reversed prior to use, immobilised FGF2 could potentially maintain the MSCs is an undifferentiated state. Further research is required to evaluate the use of immobilised FGF2 as a stemness maintenance substrate.
Another way researchers have mimicked the in vivo environment is by culturing MSCs on 3D scaffolds, as compared to 2D surfaces. 3D collagen scaffolds have been observed to support MSC growth (Ramirez-Rodriguez et al., 2017) and when supplemented with ECM, promotes the stemness of MSCs (Antebi et al., 2015). The effect of these 3D scaffolds, has been further manipulated through the addition of conjugated substrates. One study investigated the outcome of MSCs grown on collagen II sponges that had a recombinant protein, containing peptides from both fibronectin and cadherin, immobilised to the surface (Dong et al., 2013). Increased adhesion and migration of the MSCs, along with promotion of chondrogenesis was observed.
Importantly, the substrates that MSCs are cultured on, all have an effect on their growth, however, manipulation of the substrate alone is not sufficient for defining the outcome of the MSC culture (Linsley et al., 2013). Instead, pairing the substrate with the correct growth factors is a more promising method for guiding MSC culture toward a desired outcome.
24 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
A number of growth factors have been evaluated within the literature as to how they affect the culture of MSCs. FGF2 has been frequently used to discourage the differentiation of MSCs and has been added to a number of culture media to achieve this desired effect (Mareddy et al., 2007). One specific study investigated how FGF2 affected murine MSCs induced towards a chondrogenic lineage (Baddoo et al., 2003). Baddoo, et al. (2003) observed that the addition of FGF2 to the growth media of MSCs effectively increased the growth rate (4.5 fold), but only at a concentration of 20 ng / ml, as compared with 5 and 100 ng / ml concentrations. Similarly, Eom, et al. (2014), found that the proliferative capacity of their BM-MSCs, was increased 76-fold compared to the untreated cells, however, growth was suppressed when high doses of FGF2 were used (Eom et al., 2014). Similarly, FGF2 has been observed to increase the proliferation in periodontal ligament cells, whilst also preventing matrix deposition during osteogenic induction of these sells (S. An et al., 2015).
Another growth factor observed to promote stemness in MSCs is bone morphogenetic protein 4 (BMP4). BMP4 has been observed to prevent cell senescence and promote cell growth and proliferation in late passage cells. In a study performed by Liu, et al. (2013), BMP4 treated cells displayed upregulated expression of stemness associated markers, Sox2 and Oct-4, in late passages (L. Liu et al., 2013). Additionally, low doses of BMP4 has been observed to maintain the stemness of adipose-derived MSCs through increases in proliferation and reduction of apoptosis (Vicente Lopez et al., 2011). Conversely, other BMP proteins, such as BMP2, have been identified to enhance the induction of MSCs towards bone (Bilem et al., 2016).
The effect that these factors have on the growth of MSCs can be evaluated using three approaches; a targeted approach where specific markers are used to determine if the MSCs have a ‘stemness’ phenotype; a global approach where all the changes are evaluated to determine if the MSCs are maintaining their ‘stemness’ and; a profiling approach by which the proteins within the MSCs are catalogued and evaluated to determine what is different between a ‘stemness’ phenotype and non-stem phenotype. In terms of monitoring the cells, a profiling approach is beneficial in that it can track the changes in protein levels to determine if the MSCs are moving away from a ‘stemness’ state.
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 25
1.5 PROTEOMICS IN MSC RESEARCH
Proteomics is the study of the protein complement within an environment (e.g. a cell) to investigate what biological processes are occurring at a molecular level (Mareddy et al., 2009). This type of study generates a snapshot of the proteins within a system at a single point in time and provides an overall view of the potential biochemical pathways being utilised by the cells under specific conditions. The data generated in this type of study is extensive and can provide substantial insight into the processes that are occurring within the environment. A proteomics approach for investigation of MSCs could provide detailed information into what processes are occurring while undifferentiated, during differentiation and once they have differentiated, leading to a better understanding of MSC biology.
There is a general trend within the literature regarding the proteomic evaluation of MSCs. Two-dimensional gel electrophoresis has commonly been the first step in proteomic analysis of MSCs, which separates the proteins based on their isoelectric point, as well as their molecular weight. The separated and differentially expressed proteins are then identified using mass spectrometry analysis; most commonly MALDI (Celebi et al., 2010; Celebi & Elcin, 2009; Eleuterio et al., 2013; Mareddy et al., 2009). In a number of cases, western blots have been used to verify the presence of specific proteins (De la Fuente et al., 2012; Eleuterio et al., 2013; Mareddy et al., 2009). In more recent studies, the use of iTRAQ has been utilised (Lei et al., 2015; Peter et al., 2012). All these approaches evaluate the protein composition of the MSCs.
While many studies have focused on the proteomic profiling of MSCs, the key focus has been on the differentiation of MSCs rather than the maintenance of their ‘stem’ profiles. As a result, very little is known about the protein profile of MSCs when they are in a ‘stem’ state as compared to when they have differentiated into chondrocytes (Arufe et al., 2011; De la Fuente et al., 2012), bone (Lei et al., 2015) and adipose tissue (Celebi et al., 2010). Through the use of proteomics methods, the proteome of MSCs sourced from different tissues have been evaluated. Both Huang et al. (2015) and Lee et al. (2015) performed a study in which they compared the protein profile of MSCs from different organs of miniature pigs (L. Huang et al., 2015; A. Y. Lee et al., 2015). Some studies have compared BM-MSCs to lung-derived MSCs (Rolandsson Enes et al., 2017); MSCs derived from periodontal ligament, dental pulp
26 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
and bone marrow (Mrozik et al., 2010); as well as BM-MSCs compared to MSCs from umbilical cord and placenta (G. Li et al., 2009).
Furthermore, proteomic characterisation of BM-MSCs in diseased cohorts, such as scoliosis, has been undertaken. In a study performed by Zhuang et al. (2011), the proteome of BM-MSCs harvested from adolescents with idiopathic scoliosis was evaluated (Zhuang et al., 2011). Among the 41 significantly altered proteins identified in this study, five of those proteins were related to bone development and growth. These findings suggest that impaired osteogenic potential of BM-MSCs may play a significant role in idiopathic scoliosis and could be further investigated to understand the pathogenesis of this disease (Zhuang et al., 2011).
Other research has characterised BM-MSC secretomes (De Boeck et al., 2013; Pires et al., 2016), investigated the effect of TGF beta 1 on BM-MSCs (Wang et al., 2004) and compared MSCs induced down a chondrogenic lineage to chondrocytes (Chiang et al., 2011). Additionally, proteomic analysis of rat BM-MSCs by Zhao et al. (2014) revealed that the surface biology of these cells resembled that of neural lineage cells, suggesting that these cells may be biased towards neural differentiation (Zhao et al., 2014).
A number of studies have identified, and catalogued proteins present in MSCs from different sources. Mindaye, et al. (2013), identified 7753 proteins in human BM- MSCs harvested from six subjects, where only 253 were present in all six donors (Mindaye et al., 2013). In another study, the proteome of MSCs cultured in the presence or absence of FGF2 was evaluated and 1001 proteins were observed (S. K. Lee et al., 2013). These types of studies are largely qualitative in nature and result in large lists of proteins present within samples. Each protein catalogue can be considered as a library of proteins found in MSCs, cultured under specific conditions. The generation of a protein library is commonly the first step in the untargeted form of MS analysis called SWATH-MS (Sequential Windowed Acquisition of all Theoretical fragment ion Mass Spectra), or data independent acquisition.
SWATH-MS is a method of MS that makes it feasible for very complex samples to be analysed as it has the ability to quantify the relative abundance of thousands of peptides in a single experiment (Simburger et al., 2016). This method is particularly useful when targeting low abundant proteins (Basak et al., 2015). SWATH-MS analysis has been used to investigate the proteomes of a number of different cell types,
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 27
such as mesothelioma cell lines (Manfredi et al., 2016), mouse cell lysate (Q. Huang et al., 2015) and yeast cells (Basak et al., 2015) however, to date MSCs have not been analysed using this method.
1.6 CONCLUSION, PERSPECTIVE AND STUDY RATIONALE
1.6.1 Summary and Implications / Conclusions Within the literature, there is a specific focus on the differentiation of MSCs while there is limited knowledge on how to prevent differentiation and maintain stemness. The stem phenotype of MSCs is not well characterised and it is only in recent years that this knowledge gap has started being addressed. In doing so, it has become increasingly evident that the conditions in which MSCs are cultured are responsible for determining their fate and the rate at which they meet this fate. As such, a culture condition that maintains the undifferentiated phenotype of MSCs is being sought after. Using proteomic analysis, biochemical changes of MSCs can be evaluated and the biology of these changes can be better understood. It is important to understand the effect that culture condition have on the MSCs as fully as possible because the biology of MSCs is complex and consequently, the optimal culture conditions will need to reflect this.
1.6.2 Perspective and Research Problem Mesenchymal stromal cells have the potential to be an off-the-shelf treatment for a variety of medical conditions, however, difficulties arise with the cultivation of the stromal cells for long periods of time as they lose their ability to proliferate and self-renew. This results in unguided differentiation of the cells and a loss of ‘stemness’ that prevents the complete utilisation of MSCs for treatment. Currently, there is limited knowledge regarding the culture conditions that maintain the ‘stemness’ capabilities of mesenchymal stromal cells over extended periods of time. However, our research team have identified specific growth factors, FGF2 and BMP4, that appear to maintain the ‘stem’ phenotype and, moreover, are interested in identifying how these growth factors affect the detailed molecular processes, which occur within the cells during culture.
28 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
1.6.3 Purpose The purpose of this study was to evaluate the effect that different culture conditions had on the protein profile of MSCs with specific relation to their stemness. The objectives of this research were as follows:
• To create a protein library database of rat bone marrow-derived mesenchymal stromal cells cultured under a number of standard and test culture conditions.
• To describe how different culture conditions in vitro alter the phenotype of mesenchymal stromal cells, through the analysis of their proteomic profile.
• To identify specific protein changes that may be utilised to evaluate the biological status of BM-MSCs during culture.
1.6.4 Significance, Scope and Definitions The significance of this study lies in the contribution of new knowledge regarding the effects of culture environment on the growth and differentiation of BM- MSCs. This knowledge can be utilised to develop culture conditions that promote the stem phenotype of BM-MSCs and perhaps eventually contribute to the development of an off-the-shelf MSC therapeutic. This work also characterises the protein profiles of BM-MSCs cultured in different serum concentrations and contributes knowledge about how FCS effects the growth of BM-MSCs. This information is important for determining which supplements are required during serum-free culture.
1.6.5 Thesis Outline This thesis outlines the proteomic analysis of mesenchymal stromal cells cultured under ‘stemness’ promoting conditions. In Chapter 1, the relevant literature is critically reviewed, and knowledge gaps are highlighted. This is followed by an outline of the methods utilised within the study in Chapter 2. The results of this work are detailed in Chapter 3, Chapter 4 and Chapter 5. These results describe the effect serum concentration has on the proteome of rat bone marrow-derived mesenchymal stromal cells (Chapter 3); the changes that occur at the protein level of rat MSCs when cultured with fibronectin, fibroblast growth factor 2 and/or bone morphogenetic protein 4 (Chapter 4); and, how the biology of rat MSCs differ when cultured with fibronectin, fibroblast growth factor 2 and/or bone morphogenetic protein 4 (Chapter
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 29
5). In the final chapter of this thesis, a general discussion and summary of the scientific knowledge gained in this thesis is presented (Chapter 6).
1.6.6 Hypothesis and Aims Optimal culture conditions need to be defined if there is any chance of an off- the-shelf MSC therapeutic being developed. This relies on a more comprehensive understanding of the effects that different culture environments have on BM-MSCs. As FGF2 and BMP4 have already displayed stemness promoting abilities in other stem-like cell types, it is possible that these abilities will be similar for MSCs. It was therefore hypothesized that changing the culture environment, through the addition of a substrate, such as Fibronectin, or supplementation with growth factors, specifically FGF2 and/or BMP4, would alter the protein profiles and that these profiles would reflect specific biological processes indicative of a ‘stem’ phenotype. This hypothesis was investigated through the following aims:
Aim 1: To evaluate the effect serum concentration has on the survival of mesenchymal stromal cells.
Aim 2: To identify which proteins were affected by the changes in culture conditions using SWATH acquisition mass spectrometry.
Aim 3: To characterise the biological status of mesenchymal stromal cells cultured under ‘stemness’ promoting conditions.
30 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Chapter 2: Materials and Methodology
This chapter outlines the experimental design used to address the aims and objectives stated in section 1.6.3 of Chapter 1. Section 2.1 discusses the design of the methodology and research. Section 2.2 outlines the chemicals, general consumables and instruments used in this study; section 2.3 outlines the experimental procedures performed for collecting and recording data; section 2.4 outlines the procedures for data analysis; finally, section 2.5 outlines the limitations of the research discussed within this thesis.
2.1 METHODOLOGY AND RESEARCH DESIGN
2.1.1 Methodology The research outlined in this thesis uses a discovery proteomics approach to evaluate the biology of BM-MSCs cultured under different conditions. Typically, a discovery phase research study aims to evaluate a large number of proteins, as opposed to the verification or validation phases whereby a more targeted approach is applied. As such, the methodology utilised herein was chosen for the specific purpose of profiling the BM-MSCs. The project methodology consisted initially of optimisation followed by the creation of an extensive protein library, detection of protein abundance and finally, evaluation of protein abundance to identify significant changes in the proteomes of MSCs cultured in different culture mediums.
2.1.2 Research design The research design of this study used both qualitative and quantitative approaches to achieve the aims outlined in section 1.6.6. Qualitative data in the form of protein presence and absence was used to evaluate the biology of BM-MSCs cultured in each of the treatment conditions. Quantitative data in the form of protein abundance was used to further investigate the biological changes at the molecular level of the BM-MSCs. This was achieved through the production of protein profiles for each of the BM-MSC culture conditions using protein extraction and fractionation, followed by LC-MS/MS. Methods such as Filter-Aided Sample Preparation (FASP) (Wisniewski et al., 2009), isoelectric focusing gel electrophoresis and high- performance liquid chromatography (HPLC) were utilised to separate the proteins
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 31
prior to MS analysis. The data generated was analysed using biostatistical and bioinformatics approaches to assess the effect that culture condition (independent variable) had on the biological status of the BM-MSCs (dependent variable), in an attempt to identify stemness and differentiation.
2.2 MATERIALS
2.2.1 General Reagents The chemicals used throughout the studies detailed in this thesis included: Foetal Bovine Serum purchased from In Vitro Technologies (Noble Park North, Victoria, Australia); Dulbecco’s Modified Eagle Medium (DMEM), penicillin/streptomycin, NuPAGE LDS Sample Buffer (4x), and MOPS SDS Buffer (20x) purchased from Life Technologies (Mulgrave, Victoria, Australia); Iodoacetamide (IAA), ethanol, ammonium sulphate, glycerol, phosphoric acid and MilliQ water (Merck Millipore, Darmstadt, Germany); sodium dodecyl sulfate (SDS) (MP Biomedicals, Solon, Ohio, United States of America); Sequencing grade Trypsin/Lys-C (Promega, Maddison, Wisconsin, United States of America); Tris, Dithiothreitol (DTT) and complete mini EDTA-free protease inhibitor cocktail (Roche Life Sciences, Mannheim, Germany); Formic Acid (FA), Triethyl Ammonium Bicarbonate (TEAB), Urea, Coomassie Brilliant Blue G-250, Acetonitrile (ACN), Thiourea, HPLC water and Mineral oil (Sigma-Aldrich Pty Ltd, Castle Hill, New South Wales, Australia); Hydrochloric Acid (HCL), Acetone, Bovine Serum Albumin (BSA), Acetic Acid (glacial), methanol and Pierce Coomassie (Bradford) Protein Assay Kit (Thermo Fisher Scientific, Scoresby, Victoria, Australia); Dual Colour Precision Plus Molecular weight marker (BioRad, Hercules, California, United States of America); Ampholytes (Agilent Technologies, Mulgrave, Victoria, Australia). Index Retention Time Peptides (iRTs) were purchased from Mimotopes (Clayton, Victoria, Australia).
2.2.2 General consumables The following consumables were used throughout the studies detailed in this thesis: 0.5 mL and 1.5 mL Lo-bind Eppendorf tubes and epT.I.P.S, standard 200 – 300 µL (Eppendorf, Hamburg, Germany); 24 cm Immobiline DryStrip pH 3 - 10 IPG Strip (GE Healthcare, Little Chalfont, United Kingdom); 4-12 % Bis-Tris Precast Gels (Life Technologies); Amicon Ultra-15 3 kDa Centrifugal Filter Devices and Microcon YM- 30 centrifugal Filter Devices (Merck Millipore); Solid phase extraction membrane
32 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Octadecyl C18 (SupelCo, Bellefonte, Pennsylvania, United States of America); 18G Drawing up Needle (TERUMO, Tokyo, Japan); 96 well plates, T75 flasks and 15 mL Falcon Tubes (Thermo Fisher Scientific); glass vials, vial inserts, silicon septa and polypropylene caps (Agilent Technologies).
2.2.3 Growth Factors Purified human fibronectin (FN), recombinant human FGF2 and recombinant human BMP4 were purchased from R&D Systems (Minneapolis, MN, United States of America).
2.2.4 Instrumentation The following instruments were used for the studies described in this thesis: Sanyo Carbon Dioxide Incubator MCO-18AIC (Sanyo, Osaka, Japan), Hybaid Shake n Stack Oven and Invitrogen X Cell Sure Lock Electrophoresis Apparatus (Thermo Fisher Scientific); NikonTS100 microscope (Nikon, Minato, Tokyo, Japan); BioRad Benchmark Plus Plate Reader (BioRad); Eppendorf Concentrator 5301 Rotary Evaporator (Eppendorf); IKA Vortex 3 (LabGear, Milton, Queensland, Australia); SoniClean 120TD water bath sonicator (SoniClean, Stepney, South Australia, Australia);Agilent 3100 OFFGEL Fractionator (Agilent Technologies); Allegra® X- 15R Centrifuge and Microfuge® 18 Centrifuge (Beckman Coulter, Brea, California, United States of America); TripleTOF 5600+ Mass Spectrometer coupled with an Eksigent ekspert 400 nanoLC system (ABSCIEX, Mt Waverley, Victoria, Australia).
2.2.5 Software Several software packages were used throughout the study for data processing and statistical analysis. This software includes Microplate Manager (BioRad, version 5.2), ProteinPilot (ABSCIEX, version 4.5), PeakView (ABSCIEX, version 2.2), MarkerView (version 1.2), Cytoscape (version 3.5.1) and R (version 3.4.0). A web based statistical analysis suite called MetaboAnalyst3.0 (http://www.metaboanalyst.ca/faces/home.xhtml) was also utilised. The databases used in this study included the rat Swiss-Prot database (obtained from http://www.uniprot.org/) and the common Repository of Adventitious Proteins (cRAP) database (obtained from http://www.thegpm.org/crap/).
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 33
2.3 PROCEDURES
2.3.1 Cell Culture 2.3.1.1 Ethics The Queensland University of Technology ethics committee approved the shared use of rat bone marrow samples (ethics number: 1400 000 023) utilised for this research.
2.3.1.2 Sample Population In this study, BM-MSCs were isolated from multiple rats and combined into a single culture prior to being divided into separate culture flasks. In doing this, variability between the treatment groups as a result of BM-MSCs being retrieved from different donors was minimised. All BM-MSCs used in this study were early passage (P1/P2) cells.
2.3.1.3 Cell Collection Bone marrow stromal cells were isolated from the femurs and tibias of 12-week- old male Fischer rats (Animal Resources Centre, WA, Australia) following sacrifice by CO2 asphyxiation. The bones were dissected from their surrounding tissue and the epiphyseal plates removed to allow for marrow collection, via flushing with Dulbecco’s Modified Eagle Medium (DMEM), containing 10 % foetal bovine serum (FBS) and 1 % penicillin/streptomycin (P/S) using a 21G needle. The isolated cells were passed through an 18G needle to create a single cell suspension. All cells were combined into a single culture before being subcultured as per experimental requirements. The cells were cultured in a humidified incubator at 37 °C with 5 %
CO2. On day 2, half of the media was replaced with fresh media to remove non- adherent cells.
2.3.1.4 Protein Collection The conditioned media was collected from the flasks and stored at -80 ºC in 15 mL Falcon Tubes. Lysis buffer containing 4 % sodium dodecyl sulphate (SDS), 0.1 % Tris/HCl (pH 7.6), 0.1 M dithiothreitol (DTT) and protease inhibitor cocktail was added to the culture flasks. Cells were collected using a cell scraper. Cells were vortexed for 2 minutes, heated to 80 ºC for 5 minutes and sonicated for 10 minutes to rupture the cell walls and free the protein. The samples were centrifuged to pellet any cellular debris and insoluble material. The supernatant was collected and the protein
34 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
within it was concentrated using Amicon Ultra-15 3 kDa Centrifugal filter devices by centrifugation at 4000 × g in a swing bucket centrifuge (Allegra® X-15R Centrifuge) for 20 minutes. The volume remaining above the filter contained the protein and was retained. The protein content in each sample was quantified using the Pierce Coomassie (Bradford) Protein Assay kit as per the manufacturer’s instructions.
2.3.2 Protein Processing 2.3.2.1 Acetone Precipitation An acetone precipitation was performed to concentrate the proteins and resuspend them in a buffer more appropriate for the downstream experiments. This was done by combining 1-part sample with seven parts ice-cold acetone and incubation at – 20 °C overnight. The samples were then centrifuged using a Microfuge® 18 Centrifuge at 10,000 × g for 15 minutes at 4 °C. The acetone was removed and the protein pellet air dried for 30 seconds. The pellet was resuspended in 200 µL of 0.05 M triethylammonium bicarbonate (TEAB).
2.3.2.2 Protein Quantification The concentration of protein in each of the samples was quantified using a Bradford Assay as per the manufacturer’s instructions. Briefly, 10 µL of diluted sample was pipetted, in triplicate, into the wells of a 96 well plate, prior to addition of 300 µL of Coomassie Plus Protein Assay Reagent at room temperature, to each well. The plate was covered with alfoil and incubated at room temperature, away from light, for 15 minutes. Using a plate reader and Microplate Manager software, the absorbance of each of the wells were read at 595 nm and their concentrations were calculated. Bovine serum albumin (BSA) was used as a standard.
2.3.2.3 Pooled Samples A pooled sample was created by combining an aliquot of each sample together in a single Eppendorf tube to amount to 1.4 mg of total protein. This pooled sample was fractionated and as per sections 2.3.2.4 - 2.3.2.7 contribute to the creation of a rat BM-MSC protein library.
2.3.2.4 FASP Filter aided sample preparation (FASP) was utilised to digest the protein samples using trypsin. Briefly, 20 µg of protein of each sample was diluted in Urea buffer (UB:
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 35
8 M urea; in 0.1 M Tris / HCl, pH 8.5, 0.025 M DTT) and placed in a Microcon YM- 30 centrifugal filter device. The samples were incubated for 1 hour at room temperature then centrifuged at 14,000 × g for 15 minutes at 4 °C (Microfuge® 18 Centrifuge). The proteins were alkylated through the addition of 0.05 M iodoacetamide (IAA) in UB, followed by three washes in UB with centrifugation at 14,000 × g for 15 minutes between washes. The samples were subsequently washed three times in 0.05 M TEAB. Proteins were digested by addition of sequencing grade Trypsin/Lys-C in 0.05 M TEAB, at an enzyme to protein ratio of 1:100, followed by incubation at 37 ºC in a humidified chamber overnight. Finally, the samples were centrifuged, and the peptides collected.
2.3.2.5 LDS-PAGE Lithium dodecyl sulphate polyacrylamide gel electrophoresis (LDS-PAGE) was used for protein separation of the samples based on the electrophoretic mobility of the proteins. An aliquot of the pooled sample containing 30 µg of protein was mixed with 2.5 µl of 4X NuPAGE® LDS sample buffer, 1.0 µL of 100 mM DTT and HPLC water to a final volume of 10 µL then heated at 90 ºC for 5 minutes. The sample was loaded into a 10-well precast 4-12 % Bis-Tris gel and subjected to electrophoresis using diluted 20X MOPS SDS Buffer at 180 V for 60 minutes. The proteins were then visualised using Colloidal Coomassie Stain prepared in-house.
Colloidal Coomassie Stain Visualisation of the proteins was performed using a Colloidal Coomassie Brilliant Blue stain. Initially, any residual buffer was washed from the gel with water. The gel was then submerged in fixative (30 % ethanol, 10 % acetic acid) for 15 minutes. The solution was removed, and the gel was resubmerged in the fixative for a further 15 minutes. The fixative was removed once more, and the gel was stained with a solution containing 17 % ammonium sulphate, 3 % phosphoric acid, 34 % methanol and 0.1 % Coomassie Brilliant Blue G-250, overnight. A solution of 10 % acetic acid and 20 % methanol was used to quickly remove any background staining. To fully de- stain the gel, it was left in a solution of 1 % acetic acid, overnight until it became clear.
2.3.2.6 In-Gel Digestion In-gel digestion is a method used to release proteins from LDS-PAGE gels to allow for identification by mass spectrometry analysis.
36 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Following de-staining, the gel was washed in MilliQ H2O for 15 minutes. Using a scalpel blade, the lane of protein was excised from the gel and divided into 19 sections. Each section was cut into 1 mm3 cubes and placed into a 1.5 mL low-bind tube. A 50 µL volume of 100 mM TEAB and 50 µL of 100 % ACN were added to each of the tubes, which were then incubated for 30 minutes at room temperature, with brief vortexing occurring every 5 minutes. A 500 µL aliquot of 100 % ACN was added to each of the tubes, which were briefly vortexed and incubated until the gel pieces turned opaque. Subsequently, the solution was removed from the tubes and discarded. The samples were reduced by the addition of 50 µL of 10 mM DTT and incubation at 56 °C for 30 minutes. The samples were briefly centrifuged and cooled on ice to room temperature prior to addition of another 500 µL of 100 % ACN prior to incubation at room temperature for 10 minutes. Following the removal of all the liquid, 50 µL of 55 mM IAA was added to the tube. The samples were incubated in the dark, at room temperature for 20 minutes before the addition of 300 µL of 100 % ACN to the tubes. Once the gel pieces became opaque, the liquid was removed from the tubes. 45 µL of 40 ng/µL trypsin (prepared with ice-cold 100 mM TEAB) was added to each of the tubes, which were then placed on ice for 2 hours. A 20 µL aliquot of 100 mM TEAB was overlayed on the gel pieces in each tube before incubating each tube at 37 °C overnight. To halt the digestion, 100 µL of extraction buffer (50 % ACN, 2.5 % FA) was added to each tube, followed by additional incubation at 37 °C for 15 minutes with occasional sonication. The aqueous solution was transferred to a new, labelled tube prior to being dried down using a rotary evaporator. The dried peptides were resuspended in 30 µL of 0.1 % FA (v/v) prior to clean up and desalting using STAGE- Tips.
2.3.2.7 Iso-electric Focusing Iso-electric focusing separates proteins based on their isoelectric point. The proteins migrate through an immobilized pH gradient (IPG) gel under an electric field, to achieve this separation.
The instrument (Agilent 3100 OFFGEL Fractionator) was set up by first removing the protective backing from the IPG strip, with a linear pH gradient of 3 – 10, and placing the strip into the tray gel side facing up. The welled frame was placed onto the gel and clipped into place. Each of the wells received 20 µl of IPG Strip Rehydration Solution (0.48 mL OFFGEL Stock Solution (1.25X; 8.4 M Urea, 2.4 M
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 37
Thiourea, 0.78 M Dithiothreitol, 12 % glycerol + ampholytes) in 0.12 mL MilliQ H2O) was added to each of the wells, ensuring the gel strip was covered. A pair of electrode pads were wetted with the rehydration buffer and placed on either end of the IPG strip. An aliquot of the pooled sample (700 µg of protein) was diluted to 720 µL and mixed with 2.88 mL of the OFFGEL stock solution. After 15 minutes, 150 µL of the sample solution was transferred into each well and the cover seal was placed over the frame. The electrode pads were rewetted with 10 µL of rehydration buffer prior to the tray being assembled on the instrument platform. A 200 µL volume of mineral oil was applied to the anode pads while 400 µL was applied to the cathode pads. After 1 minute, an additional 200 µL of mineral oil was reapplied to both ends of the strip. Both electrodes were attached to the tray and current was applied to the samples for 71 kV.hours. The focused proteins were maintained under 20 µA current until they were subjected to FASP as previously described.
2.3.2.8 Peptide Clean Up and Desalting Stop-and-go-extraction tips (STAGE tips) were utilised to remove any salts and large contaminants from the peptide digests prior to mass spectrometry analysis (Rappsilber et al., 2007). The tips were assembled by plugging the end of a 200 µL pipette tip with C18 solid phase extraction membrane. The tips were conditioned and equilibrated by passing 100 % ACN followed by 0.1 % formic acid (FA) through the tip. The samples were then loaded onto the tip, passed through under positive pressure, then washed with 0.1 % FA. The peptides were collected into a MS vial insert by eluting them from the tip using 80 % ACN in 0.1 % FA. The peptides were dried using a rotary vacuum, prior to resuspension in 2 % ACN in 0.1 % FA.
2.3.2.9 iRTs Index retention time peptides (iRT peptides) were spiked into the resuspended desalted peptide samples as internal calibrants for MS analysis. These peptides have a known retention time during LC-MS/MS and can be used to align SWATH-MS data to the spectra of a reference library or allow for alignment of the data with spectral libraries produced by other researchers in which iRT peptides were also applied (Escher et al., 2012). This is crucial to account for minor shifts in chromatography performance of individual instruments and will ensure a legacy dataset is produced that can easily be used and re-analysed by other researchers globally well into the future.
38 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
2.3.3 LC-MS/MS Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to identify and quantify the proteins present in the rat BM-MSC-derived peptide samples using an Eksigent ekspert 400 nanoLC system coupled to a TripleTOF 5600+ mass spectrometer. This was performed using two modes of acquisition, Data Dependent Acquisition (DDA) and Data Independent Acquisition (DIA), otherwise known as SWATH – MS.
2.3.3.1 DDA Mode The samples were analysed in DDA mode using a 65-minute gradient. The peptides were separated using a C18 nano-LC resolving column (Eksigent Chrom XP C18 CL-120, 3 µm particle size, 120 Å pore size, 75 µm × 150 mm) with a flow rate of 300 nL / minute across a 45-minute linear gradient of decreasing buffer A (0.1 % FA) from 98 % to 60 % and increasing buffer B (0.1 % FA in 100 % ACN) from 2 % to 40 %, followed by increasing buffer B to 95 % within 3 minutes and sustained for 5 minutes to rinse the stationary phase. Column equilibration was performed with 98 % buffer A for 12 minutes.
A peptide identification search, using the generated peptide spectra, was performed using ProteinPilot software and a Swiss-Prot generated, rat protein database. The database contained only reviewed and non-redundant proteins. It was concatenated with the common repository of adventitious proteins (cRAP), which was used to account for any contaminants common in mass spectrometry work.
2.3.3.2 DIA Mode (SWATH-MS) Using the same chromatographic protocol as for DDA analysis, DIA or SWATH-MS analysis was performed. Similar instrument parameters were used; however, the system was tuned to SWATH-MS acquisition mode instead of DDA acquisition mode. SWATH data were imported into the SWATH app within PeakView Software application and ion extraction was performed using the rat BM-MSC protein library to quantify proteins. A false discovery rate (FDR) of 1 % was used to ensure robust results.
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2.4 ANALYSIS
2.4.1 DDA-MS Data Processing The data produced by the LC-MS/MS was in the format of ‘.wiff ‘ and ‘.wiff.scan’ files. These files were uploaded and processed within ProteinPilot™ software, using the paragon method. A concatenated database consisting of the rat Swiss-Prot database and the cRAP database was uploaded to the software. The processed data was saved as a ‘.group’ file. In addition, a second file was saved as an ‘.xls’ file which contained the information regarding the FDR calculation. Using the ProteinPilot™ software, a peptide and protein list was exported as an excel document (.xls). Using these data, a list of confident peptides was created by removing proteins that didn’t meet the FDR cut-off, removing any proteins that aligned with those in the cRAP database and any proteins with less than 2 peptides detected.
2.4.2 SWATH-MS Data Processing The library generated using the DDA data (.group file) was imported into PeakView, where all proteins were imported, including those with shared/common peptides. Next, the SWATH-MS data was imported into the software. The following processing settings were selected:
• 5 peptides per protein
• minimum of 3 and maximum of 5 transitions per peptide
• peptide confidence as per FDR document exported during the library generation
• 1.0 % FDR threshold for ion extraction
• 6-minute XIC extraction window
• 70 (parts per million) XIC width
Two types of files were exported;
• The analysis results (.xls)
• MarkerView (.mkrvw) files for the proteins, peptides and ions.
The protein MarkerView file was opened in the MarkerView™ software and exported as a .txt file. This file contained the protein and corresponding abundance
40 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
data. Finally, the reverse sequence and cRAP proteins were removed, and statistical and bioinformatic analyses were performed on the remaining data.
2.4.3 Statistical analysis SWATH-MS protein abundance data was initially analysed using the MetaboAnalyst website (Xia et al., 2015; Xia & Wishart, 2016). Once the raw abundance data was uploaded to the website, the data was transformed using the generalized log function (glog) and scaled using mean centering. A number of statistical analyses were then explored. Multivariate analyses such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (oPLS-DA) and sparse PLS-DA (sPLS-DA) were performed on the data.
R statistical software (version 3.4.0) was used to perform Pearson correlation analysis, one-way ANOVA, fold change analysis, and pairwise t-tests. It was also utilised to produce boxplots of protein abundances and volcano plots.
2.4.4 Gene Ontology Analysis The proteome data generated was analysed using gene ontology (GO) analysis, whereby the proteins were matched to GO terms categorised as either biological processes (BP), molecular functions (MF) or cellular compartment (CC), using the BiNGO app installed in the open software Cytoscape (Maere et al., 2005). Over- represented terms were evaluated using a hypergeometric statistical test and correcting for multiple testing using FDR. These terms were presented as a visual network of the most influential biological activities occurring in the cells of each treatment group. Word clouds generated from the GO data were also utilised to evaluate which terms were most associated with each of the treatment groups.
2.4.5 Venn Diagrams The Venn diagrams presented in this thesis were generated using the BioVenn web application for comparing and visualising biological lists (Hulsen et al., 2008).
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Chapter 3: Effect of Serum Concentration on the Proteome of Rat Bone Marrow-derived Mesenchymal Stromal Cells.
3.1 INTRODUCTION
Serum is an animal blood product that is used to provide nutrients, hormones, binding proteins and attachment factors to cells during cell culture (Barnes & Sato, 1980). It is commonly used in cellular research, however, there are many downfalls to its use.
Firstly, serum contains a large range of constituents that may or may not be useful in the culture of some cell types. Problems can arise from using such a general supplement as most cell types require a specific combination of nutrients and growth factors for optimal growth, however most of these combinations have yet to be characterised (van der Valk et al., 2010). This results in serum being an ineffective supplement for the growth of certain cells (Brunner et al., 2010). However, for most cell types serum is still frequently used as the primary source of nutrients and growth factors during culture due to the absence of a defined culture medium for that specific cell.
Secondly, it’s constituents are highly variable which can lead to batch-to-batch variation. This can cause several problems during cell culture, especially if specific cells are deprived of a required nutrient or hormone. To control the variation between batches, serum is typically derived from foetal calves. This has raised ethical and animal welfare concerns regarding its collection.
There are ethical concerns related to the collection of serum from foetal calves (van der Valk et al., 2004). It is estimated that 500, 000 L of serum is collected annually (Hodgson, 1995) which equates to near 1, 000, 000 foetal calf deaths (Jochems et al., 2002). It is important that when the serum is collected, the foetuses are unaware of the collection and as such, don’t suffer. There is a lot of debate around foetal awareness, and therefore specific guidelines need to be in place to ensure awareness is absent
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within the animal. As a result, van der Valk et al (2004) produced three safeguards that make up the Fetal Calf Slaughter Welfare Protocol (van der Valk et al., 2004). This, however, is a recommendation and is not enforced, which means it is essential for researchers to purchase FCS from companies that abide by these guidelines.
Additionally, the use of serum in cultures where the cells are destined to be transplanted into a human host is also of concern. Serum has been reported to contain several pathogens that when transplanted into a human, could result in immunological reaction (Spees et al., 2004; Tuschong et al., 2002) and/or rejection of the treatment (Horwitz et al., 2002). Because of this, it’s use is discouraged in cultures that are destined for clinical application. Serum-free culture mediums are now being developed where specific growth factors and hormones are being added individually. Not only does this remove the need for serum, but it also provides researchers with a more customizable cell cultures.
Thus far, a suitable culture environment for BM-MSCs that maintains their stem phenotype has not been characterised, especially not one that is serum-free. Most MSC cultures contain 10 % FCS (Eom et al., 2014; Karnieli et al., 2017; Mareddy et al., 2009), however, research is being conducted to eliminate the need for FCS and improve BM-MSC cultivation for therapeutic use (Ichikawa, 2010; Tan et al., 2015).
Finally, serum contains high abundant proteins that can be problematic with some separation and analysis forms, such as gel electrophoresis and mass spectrometry (Smejkal & Lazarev, 2005). Specifically, in mass spectrometry, these high abundant proteins often mask lower-abundant proteins by dominating the mass spectrum. It is possible to remove these high abundant proteins through depletion methods (Y. An & Goldman, 2013; Jaros et al., 2013), however minimal manipulation of the samples is desired to avoid sample loss. Thus, minimising the use of FCS would decrease the interference of these unwanted high abundant proteins.
Therefore, the research performed in this study was designed to evaluate the effect serum concentration had on the biology of BM-MSCs, in an attempt to minimise its concentration as much as possible in subsequent cultures. BM-MSCs were cultured in 0 %, 2 % and 10 % FCS, and evaluated based on their protein profiles to assess changes in the overall biology of the culture, specifically related to survival and differentiation. It was hypothesised that serum concentration would alter the biology of the BM-MSCs in each of the treatments.
44 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
3.2 EXPERIMENTAL PROCEDURES
Full details of the materials and methods outlined in this chapter are described in Chapter 2. The following are brief summaries of the materials and experimental procedures used for the generation of data presented in section 3.3.
3.2.1 Materials For full details of the materials used in this chapter, please refer to Section 2.2.
3.2.2 Cells and Culture Conditions For full details of the methods used herein, please refer to Section 2.3.1. The cells used in the experiments reported in this chapter were harvested from five rats and pooled, prior to being divided between six T175 flasks. Two days after seeding, complete media changes were performed on day 4 and every 3 days thereafter until 80 % confluence was achieved. The cells were serum starved for 4 hours before being provided with a new media containing either 10 %, 2 % or no FCS. Each serum concentration group was allocated to two T175 flasks, resulting in analysis of two biological replicates per serum concentration. The cells were cultured for a further 24 hours prior to harvest. Protein collection followed the same protocol outlined in section 2.3.1.4.
3.2.3 Protein Digestion Please refer to sections 2.3.2.1, 2.3.2.2, and 2.3.2.4 for full details of this method. Briefly, the harvested protein was precipitated from solution using an acetone precipitation, resuspended in 0.05 M TEAB and quantified using a Bradford Assay. An aliquot of the sample was tryptically digested using the FASP method. The resulting peptides were cleaned using STAGE-Tips.
3.2.4 Mass Spectrometry Please refer to section 2.3.3 for full details of this method. The six samples were run on the TripleTOF 5600+ mass spectrometer first under DDA mode using a 95- minute gradient, then again under the SWATH acquisition mode using a 95-minute gradient. Each sample was injected into and analysed by the mass spectrometer once per analysis mode, therefore only 1 technical replicate for each sample was evaluated.
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 45
3.2.5 Analysis For full details regarding the analyses used in this chapter, please refer to Section 2.4. Briefly, the data was processed as per sections 2.4.1 and 2.4.2. High confidence (0.99) protein lists were generated for all six samples by processing the MS data individually. A basic protein library was also generated by processing all the MS data together. Venn comparisons were used to compare the protein profiles generated using DDA-MS. Gene ontology analysis was performed on the DDA-MS data, whilst statistical analyses were performed on the SWATH-MS data.
3.3 RESULTS
3.3.1 FCS concentration impacts on the number of proteins identified. The DDA data produced a list of proteins identified in each of the treatment groups, where 718 proteins were identified in the 0 % FCS treatment, 713 proteins were identified in the 2 % FCS treatment and 493 proteins were identified in the 10 % FCS treatment (Figure 3.1). It was determined that a total of 466 proteins (58.03 % of all proteins detected) were shared amongst all three treatment groups whilst there were 68 (8.47 %), 68 (8.47 %) and 12 proteins (1.49 %) unique to the 0 %, 2 % and 10 % FCS treatment groups respectively (Figure 3.1). When looking at the similarity between the samples, 79.70 % of all detected proteins are common to the 0 % and 2 % FCS treatments, 59.28 % of proteins are common to the 0 % and 10 % FCS treatments and 58.65 % of proteins are shared between the 2 % and 10 % FCS treatments. Interestingly, the 10 % FCS has less common proteins with the other two treatments however this may be due to only 61.39 % of the total identified proteins being detected in that treatment. This decreased amount of identified proteins are a potential result of ion suppression and masking of lower abundant proteins by high abundant proteins within the samples. It is possible that the differences observed between the sample protein profiles were a result of interference during analysis rather than biological variation.
Initially, these results indicate that there may be a greater similarity between the 0 % and 2 % FCS treatments, however, these data may also suggest that there were more high abundant proteins present in the 10 % FCS treatment which may have masked lower abundant proteins, therefore impacting the depth of analysis. Furthermore, these data are qualitative and only provide information as to whether a
46 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
protein is present or absent in each of the treatment groups. Therefore, even though there are 466 proteins common to all treatments, the abundances of these proteins may differ.
Figure 3.1: BM-MSCs cultured in 0 % and 2 % FCS have a very similar protein profile compared to BM-MSCs cultured in 10 % FCS. Venn comparison of the proteins identified using DDA-MS in each of the treatment groups.
3.3.2 Differentiation gene ontologies are more significantly over-represented in the 0 % and 10 % FCS treatments. Using the protein lists for each of the treatment groups, gene ontology networks were generated (Figure 3.2, Figure 3.3 and Figure 3.4). Specific GO term groups were chosen to assess the survival and stemness maintenance of the cells. Terms related to development and differentiation, migration and motility, apoptosis and cell death, homeostasis and cell division were identified. GO terms associated with proteins identified in the 0 % FCS treatment included: endothelial cell differentiation,
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regulation of cell motility and regulation of cell morphogenesis involved in differentiation. GO terms such as cell-matrix adhesion and positive regulation of apoptotic signalling pathway were associated with the proteins identified in the 2 % FCS treatment while the 10 % FCS was associated with GO terms such as regulation of cell migration, apoptotic cell clearance and ventricular cardiac muscle cell development. Interestingly, osteoblast differentiation and muscle cell differentiation were associated with proteins common to all three treatment groups.
Figure 3.2 (Right): The proteins identified in the BM-MSCs cultured with 0 % FCS are associated with biological processes related to development and differentiation, migration and motility, apoptosis and cell death, cellular homeostasis and cell division. Gene Ontology Network of Biological Processes over-represented in the 0 % FCS treatment Group.
48 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
value
- p
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 49
Figure 3.3 (Right): The proteins identified in the BM-MSCs cultured with 2 % FCS are associated with biological processes related to development and differentiation, apoptosis and cell death, cellular homeostasis and cell division. Gene Ontology Network of Biological Processes over-represented in the 2 % FCS treatment Group.
50 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
value
- p
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 51
Figure 3.4 (Right): The proteins identified in the BM-MSCs cultured with 10 % FCS are associated with biological processes, including those related to development and differentiation, migration, apoptosis and cell death, autophagy, homeostasis and cell division. Gene Ontology Network of Biological Processes over-represented in the 10 % FCS treatment Group.
52 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
value
- p
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A Venn comparison was used to identify which of the GO terms of interest were unique to each of the treatment groups (Figure 3.5). Gene ontologies associated with differentiation, apoptosis and survival were then evaluated to better understand the types of biology occurring in each of the treatment groups (Table 3.1). The 0 % FCS treatment contained proteins associated with 12 differentiation and developmental gene ontologies, as well as proteins associated with apoptosis. Of these 12 terms, five were unique to the 0 % FCS treatment, and another four were common to the 10 % FCS treatment as well. The 2 % FCS treatment was associated with three apoptotic gene ontologies and three differentiation gene ontologies. Of the three apoptotic GOs, one was unique to the 2 % FCS treatment. All the differentiation GOs associated with the 2 % FCS treatment were also associated with the 0 % and 10 % FCS treatments. These gene ontologies were less over-represented in the 2 % FCS treatment as compared to the other two treatments. The 10 % treatment was associated with three apoptotic GOs and eight differentiation and developmental GOs. Of these eight GOs, only one was unique to the 10 % treatment group.
A total of five GO terms were associated with all three treatment groups; three differentiation GOs, one cell division GO, and one cellular homeostasis GO. As previously mentioned, the differentiation GOs were less significantly over-represented in the 2 % FCS treatment followed by the 0 % FCS treatment. However, the cell division and cell homeostasis GO terms were more significantly over-represented in the 2 % treatment.
54 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Figure 3.5: Unique Gene Ontologies are associated with of the treatment groups based on their protein profiles. Venn comparison of Biological Processes GO Terms associated with the proteins detected using DDA-MS in each of the treatment groups.
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 55
nd mantainence. nd
Each treatment had unique gene ontologies associated with processes of differentiation or cell death, as well as shared gene shared as well as death, cell or differentiation of processes with associated ontologies gene unique had treatment Each
: :
1
.
3
Table cellular to ontologiesrelated survival a
56 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
3.3.3 The 0 % and 2 % FCS treatments more closely correlated than to the 10 % FCS treatment. To determine the similarity between the replicates of each of the treatment groups, a Pearson correlation was performed on the SWATH-MS protein abundance data (Figure 3.6). Each of the replicates for each of the treatments were compared to each other to identify similarity between the replicates (Figure 3.6A). All of the replicates were highly correlated as all scores were greater than 0.94. The 10 % replicates clustered together with one 2 % FCS replicate, while the two 0 % replicates clustered with the other 2 % replicate.
The 2 % treatment is most highly correlated with the 0 % treatment (Figure 3.6B), however all treatments have a correlation score greater than 0.97. This suggests that the changes between the treatments are very subtle.
Figure 3.6: The 0 % FCS treatment has a higher correlation to the 2 % FCS treatment than to the 10 % treatment, however all three treatments have a correlation higher than 0.94. Pearson correlation of A: all replicates, B: treatments using average abundance values.
3.3.4 Principal component analysis suggests significant similarity between BM- MSCs cultured in 0 % FCS and 2 % FCS. Multivariate analysis was performed on the SWATH-MS data to evaluate the relationship between each of the treatments. Principal component analysis (PCA) revealed a clustering of samples treated with 0 % and 2 % FCS (Figure 3.7A). Using more supervised methods such as Partial Least Squares Discriminant Analysis (PLS- DA), orthogonal PLS-DA (oPLS-DA) or sparse PLS-DA (sPLS-DA) analysis resulted
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 57
in complete separation of the treatments (Figure 3.7B-D). By increasing the level of supervision in the analyses, the treatments became more distinctly separated. The replicates for each of the treatments were also clustered closer together as the analysis methods became more supervised.
These data support the findings of the Pearson correlation, further contributing to the idea that there is a significant similarity between the 0 % FCS and 2 % FCS treatment groups when evaluation considers all protein changes. However, the treatment groups become less similar as more supervision is applied within the analysis method.
58 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Figure 3.7: Principal component analysis clusters the 0 % FCS and 2 % FCS treatments together, suggesting similarity between the BM-MSCs cultured in these treatments. A: Principal Component Analysis (PCA), B: Partial Least Squares – Discriminant Analysis (PLS-DA), C: Orthogonal Partial Least Squares – Discriminant Analysis (oPLS-DA), D: Sparse Partial Least Squares – Discriminant Analysis (sPLS-DA).
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 59
3.3.5 Individual proteins differ in abundance between the 0 % FCS treatment and the 10 % FCS treatment. To examine the difference between the treatment groups with respects to individual protein abundance, the SWATH-MS data was analysed. SWATH-MS provided the abundances of 997 proteins in each of the treatment group which were analysed using a one-way analysis of variance (ANOVA) (Figure 3.8). It was determined that 105 proteins varied in abundance between at least 2 of the different treatments. A TukeyHSD posthoc test was performed to identify which proteins varied between specific treatments. As described in
Table 3.2, significant differences in protein abundance were exhibited for: 96 proteins between the 0 % and 10 % treatments; 1 protein between the 2 % and 10 % treatments; 1 protein between the 0 % and 2 % treatments. A total of 4 proteins varied between the 0 % and 10 % treatments, where these same 4 proteins also varied between the 2 % and 10 % treatments. As expected, these results demonstrate that there is a larger difference between the 0 % FCS treatment and the 10 % FCS treatment.
60 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Figure 3.8: The Abundance of 105 Proteins Vary Between Two or More Treatments with Different FCS Percentage. One-way analysis of variance (ANOVA)
Table 3.2: 100 of the 105 proteins identified as significantly varied between two or more treatments, are significantly varied between the 0 % and 10 % FCS treatment groups. Protein Accession p Value F Value -LOG10(p) TukeyHSD† 10 %-0 %, 2 %-10 Elongation Factor 2 (EF2) P05197 0.022624 17.25201 1.645435 % Endoplasmin (ENPL) Q66HD0 0.04827 9.814579 1.316321 10 %-0 % 10 %-0 %, 2 %-10 AP-2 Complex Subunit beta (AP2B1) P62944 0.016946 21.23622 1.770941 % PDZ and LIM Domain Protein 5 (PDLI5) Q62920 0.041424 11.02923 1.38275 2 %-10 % 10 %-0 %, 2 %-10 T-Complex Protein 1 Subunit Gamma (TCPG) Q6P502 0.004614 52.62557 2.335967 % Elongation Factor 1-gamma (EF1G) Q68FR6 0.045996 10.18447 1.337277 60S Ribosomal Protein L4 (RL4) P50878 0.030756 13.78046 1.512068 10 %-0 % Creatine Kinase B-type (KCRB0) P07335 0.037093 11.98636 1.430705 2 %-0 % Transgelin-2 (TAGL2) Q5XFX0 0.04334 10.65707 1.363107 10 %-0 % Plasminogen Activator Inhibitor 1 (PAI1) P20961 0.010344 30.09679 1.985328 10 %-0 % Malate Dehydrogenase, mitochondrial P04636 0.0187 19.79082 1.728152 10 %-0 % (MDHM) 10 %-0 %, 2 %-10 Eukaryotic Initiation Factor 4A-II (IF4A2) Q5RKI1 0.002102 89.92267 2.677444 % Cofilin-1 (COF1) P45592 0.047866 9.878112 1.319968 40S Ribosomal Protein S4, X Isoform (RS4X) P62703 0.048884 9.719651 1.310832 PDZ and LIM Domain Protein (PDLI1) P52944 0.024879 16.10106 1.604171 10 %-0 % Annexin A4 (ANXA4) P55260 0.013916 24.42686 1.856488 10 %-0 % 26S Proteasome Regulatory Subunit 8 (PRS8) P62198 0.010536 29.71173 1.97734 10 %-0 % 60S Acidic Ribosomal Protein P0 (RLA0) P19945 0.005768 45.13652 2.238952 10 %-0 % Nucleolin (NUCL) P13383 0.003682 61.40394 2.43388 10 %-0 % Major Vault Protein (MVP) Q62667 0.020607 18.45664 1.685994 10 %-0 % 60S Ribosomal Protein L3 (RL3) P21531 0.038948 11.5548 1.409518 10 %-0 % Phosphoglycerate Mutase 1 (PGAM1) P25113 0.032309 13.28697 1.490682 10 %-0 %
Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions. 61
Alpha-2-Macroglobulin (A3MG) P06238 0.004898 50.50841 2.309973 10 %-0 % 40S Ribosomal Protein S2 (RS2) P27952 0.020208 18.71848 1.694486 10 %-0 % Galectin-1 (LAG1) P11762 0.026257 15.47964 1.580756 10 %-0 % Ectonucleotide Pyrophosphatase/Phosphodiesterase Family Q924C3 0.003688 61.33788 2.433195 10 %-0 % Member 1 (ENPP1) 40S Ribosomal Protein SA (RSSA) P38983 0.017551 20.71065 1.755705 10 %-0 % 26S Proteasome non-ATPase Regulatory B0BN93 0.001861 97.62844 2.730161 10 %-0 % Subunit 13 (PSD13) Leucine-rich Repeat-containing Protein 59 Q5RJR8 0.018821 19.69947 1.725351 10 %-0 % (LRC59) Lysosome-associated Membrane P14562 0.037161 11.96988 1.429908 10 %-0 % Glycoprotein 1 (LAMP1) Proteosome Subunit Alpha type-1 (PSA1) P18420 0.013458 25.0119 1.871024 10 %-0 % Ras-related Protein Rap-1b (RAP1B) Q62636 0.035313 12.43594 1.452067 10 %-0 % 60S Ribosomal Protein L15 (RL15) P61314 0.014724 23.46968 1.831983 10 %-0 % 60S Ribosomal Protein L6 (RL6) P21533 0.025865 15.65095 1.587295 10 %-0 % NAD(P)H Dehydrogenase [Quinone] 1 (NQO1) P05982 0.028817 14.45865 1.540357 10 %-0 % Plasminogen Activator Inhibitor 1 RNA- Q6AXS5 0.030894 13.73513 1.510133 10 %-0 % Binding Protein (PAIRB) 60S Ribosomal Protein L13a (RL13A) P35427 0.019151 19.45508 1.717797 10 %-0 % 60S Ribosomal Protein L9 (RL9) P17077 0.016332 21.80249 1.786967 10 %-0 % 40S Ribosomal Protein S12 (RS12) P63324 0.014123 24.1729 1.850076 10 %-0 % Thioredoxin-Dependent Peroxide Reductase, Q9Z0V6 0.042426 10.8311 1.372366 10 %-0 % Mitochondrial (PRDX3) Eukaryotic Translation Initiation Factor 3 B5DFC8 0.022312 17.42639 1.651465 10 %-0 % Subunit C (EIF3C) Prothymosin Alpha (PTMA) P06302 0.002499 79.95119 2.602209 10 %-0 % 40S Ribosomal Protein S18 (RS18) P62271 0.035861 12.2936 1.44538 10 %-0 % 60S Ribosomal Protein L18 (RL18) P12001 0.024022 16.51714 1.619392 10 %-0 % Histone H2B Type 1 (H2B1) Q00715 0.048404 9.793758 1.315121 10 %-0 % 60S Ribosomal Protein L13 (RL13) P41123 0.042779 10.76328 1.368773 10 %-0 % Microtubule-Associated Protein Q66HR2 0.049772 9.585895 1.303019 10 %-0 % RP/EB/Family Member 1 (MARE1) 60S Ribosomal Protein L12 (RL12) P23358 0.015926 22.19697 1.797902 10 %-0 % Lysosome Membrane Protein 2 (SCRB2) P27615 0.013674 24.73228 1.864117 10 %-0 % 60S Ribosomal Protein L23a (RL23A) P62752 0.00035 300.7909 3.456501 10 %-0 % Ubiquitin Carboxyl-Terminal Hydrolase Q00981 0.01156 27.83996 1.937052 10 %-0 % Isozyme L1 (UCHL1) Histone H1.4 (H14) P15865 0.004081 57.23584 2.389218 10 %-0 % Alpha-1 -Inhibitor 3 (A1I3) P14046 0.035708 12.33295 1.447235 10 %-0 % 60S Ribosomal Protein L14 (RL14) Q63507 0.00756 37.44294 2.121506 10 %-0 % ATP Synthase Subunit D, Mitochondrial P31399 0.00634 42.28957 2.197919 10 %-0 % (ATP5H) 40S Ribosomal Protein S17 (RS17) P04644 0.03378 12.85442 1.471341 10 %-0 % Cytochrome C Oxidase Subunit 4 Isoform 1, P10888 0.042883 10.74332 1.367712 10 %-0 % Mitochondrial (COX41) 60S Ribosomal Protein L32 (RL32) P62912 0.00546 46.87715 2.262824 10 %-0 % 60S Ribosomal Protein L26 (RL26) P12749 0.00092 157.0285 3.036024 10 %-0 % 60S Ribosomal Protein L24 (rL24) P83732 0.012124 26.92264 1.91636 10 %-0 % Voltage-Dependent Anion-Selective Channel Q9R1Z0 0.021083 18.15465 1.676061 10 %-0 % Protein 3 (VDAC3) 60S Ribosomal Protein L27a (RL27A) P18445 0.019158 19.4503 1.717648 10 %-0 % 60S Acidic Ribosomal Protein P2 (RLA2) P02401 0.023425 16.8217 1.630312 10 %-0 % Histone H3.3 (H33) P84245 0.017167 21.04076 1.765316 10 %-0 % Serine-Threonine Kinase Receptor-Associated Q5XIG8 0.02032 18.64359 1.692069 10 %-0 % Protein (STRAP) Adenylate Kinase Isoenzyme 1 (KAD1) P39069 0.017537 20.72234 1.756048 10 %-0 % Alpha-1-Macroglobulin (A1M) Q63041 0.001114 138.1162 2.953267 10 %-0 % 60S Ribosomal Protein L28 (RL28) P17702 0.00157 109.5562 2.804177 10 %-0 % 40S Ribosomal Protein S26 (RS26) P62856 0.01434 23.91256 1.843436 10 %-0 % 60S Ribosomal Protein L31 (RL31) P62902 0.017624 20.64876 1.753887 10 %-0 % 40S Ribosomal Protein S23 (RS23) P62268 0.047641 9.91397 1.322018 10 %-0 % 40S Ribosomal Protein S20 (RS20) P60868 0.001592 108.5168 2.798052 10 %-0 % Dual Specificity Mitogen-Activated Protein Q01986 0.033643 12.89344 1.47311 10 %-0 % Kinase 1 (MP2K1) GTP-binding Protein SAR1b (SAR1B) Q5HZY2 0.018593 19.87296 1.73066 10 %-0 % Protein CYR61 (CYR61) Q9ES72 0.029035 14.37834 1.537071 10 %-0 % 60S Ribosomal Protein L22 (RL22) P47198 0.043779 10.57567 1.35873 10 %-0 % Proteolipid Protein 2 (plp2) Q6P742 0.028953 14.4086 1.538311 10 %-0 % Lysophospholipid Acyltransferase 5 (MBOA5) Q5FVN0 0.041752 10.96357 1.379327 10 %-0 %
62 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Glia Maturation Factor Beta (GMFB) Q63228 0.030454 13.88129 1.516352 10 %-0 % Guanine Nucleotide-Binding Protein P54311 0.013128 25.45349 1.881786 10 %-0 % G(I)/G(S)/G(T) Subunit Beta-1 (GBB1) V-Type Proton ATPase 16 kDa Proteolipid P63081 0.025792 15.683 1.588512 10 %-0 % Subunit (VATL) Myotrophin (MTPN) P62775 0.009477 31.99494 2.023332 10 %-0 % C-1-Tetrahydrofolate Synthase, Cytoplasmic P27653 0.000309 326.835 3.510339 10 %-0 % (C1TC) Microtubule-Associated Protein 6 (MAP6) Q63560 0.047316 9.966189 1.324992 10 %-0 % Matrix Gla Protein (MGP) P08494 0.026052 15.56842 1.584153 10 %-0 % 60S Ribosomal Protein L35 (RL35) P17078 0.01652 21.62476 1.781979 10 %-0 % Glutathione S-Transferase Mu 5 (GSTM5) Q9Z1B2 0.013481 24.98164 1.870281 10 %-0 % GrpE Protein Homolog 1, Mitochondrial P97576 0.025514 15.80749 1.593214 10 %-0 % (GRPE1) P2X Purinoceptor 4 (P2RX4) P51577 0.029917 14.06487 1.524082 10 %-0 % Unconventional Myosin-Ib (MYO1B) Q05096 0.019789 19.0026 1.703577 10 %-0 % MOB Kinase Activator 1A (MOB1A) Q3T1J9 0.02705 15.14604 1.567829 10 %-0 % Membrane-Associated Progesterone P70580 0.029089 14.35873 1.536266 10 %-0 % Receptor Component 1 (PGRC1) 72kDa Type IV Collagenase (MMP2) P33436 0.003849 59.57612 2.41467 10 %-0 % Very-long-chain Enoyl-CoA Reductase (TECR) Q64232 0.037381 11.917 1.427346 10 %-0 % Mitochondrial Carnitine/Acylcarnitine Carrier P97521 0.0078 36.63877 2.107913 10 %-0 % Protein (MCAT) Fructose-Bisphosphate Aldolase C (ALDOC) P09117 0.018503 19.94222 1.732768 10 %-0 % Small Ubiquitin-related Modifier 2 (SUMO2) P61959 0.034355 12.69383 1.464013 10 %-0 % Leptin Receptor Gene-Related Protein Q9JLS8 0.04504 10.34921 1.346397 10 %-0 % (OBRG) Pituitary Tumor-Transforming Gene 1 Q6P767 0.008707 33.94255 2.060151 10 %-0 % Protein-Interacting Protein (PTTG) Dynein Light Chain 2, Cytoplasmic (DLY2) Q78P75 0.031042 13.68665 1.508056 10 %-0 % Coatomer Subunit Gamma-2 (COPG2) D4ABY2 0.015064 23.09269 1.822072 10 %-0 % Serum Albumin (ALBU) P02770 0.015585 22.54069 1.807284 10 %-0 % Coronin-1A (COR1A) Q91ZN1 0.040563 11.20579 1.391865 10 %-0 % Histone H3.1 (H31) Q6LED0 0.017662 20.61713 1.752956 10 %-0 % Histone H1.1 (H11) D4A3K5 0.046427 10.11218 1.333234 10 %-0 % †Where TukeyHSD is blank indicates non-signifcance.
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3.3.6 Three contaminating proteins significantly varied in abundance between the 0 % FCS treatment and the 10 % FCS treatment. To identify whether contaminants varied between each of the treatment groups, a one-way ANOVA was performed on the cRAP proteins detected within the samples (Figure 3.9). It was determined that 3 proteins significantly varied between at least two of the treatment groups. Using a TukeyHSD posthoc test, it was determined that all three proteins varied significantly between 0 % FCS treatment and the 10 % treatment. These proteins were bovine serum albumin, human keratin and human lactotransferrin (Table 3.3).
Figure 3.9: The Abundance of 3 Proteins Present in the cRAP Database Significantly Vary Between Two or More of the Different FCS Treatments. One-way analysis of variance (ANOVA) of detected contaminating proteins from the cRAP database.
Table 3.3: Three contaminating proteins significantly vary between the 0 % and 10 % FCS treatment groups. Protein Accession # p Value F Value -Log10(p) TukeyHSD Serum Albumin, Bovine (ALBU) P02769 0.020108 18.78493 1.696623 10 %-0 % Keratin, type I cytoskeletal 9, Human (K1C9) P35527 0.014964 23.20204 1.824962 10 %-0 % Lactotransferrin, Human (TRFL) P02788 0.047252 9.976485 1.325576 10 %-0 %
64 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
As expected, bovine albumin was significantly increased specifically in the 10 % FCS treatment compared to the 0 % treatment (p<0.05) (Figure 3.10A). The increase in this protein however was not proportional to the increase in serum concentration. The 0 % treatment had albumin present at the time of collection even though there was no serum in the treatment media. Human keratin decreased in abundance as serum concentration increased. Specifically, the 0 % treatment had a significantly greater amount of human keratin as compared to the 10 % treatment (p<0.05) (Figure 3.10B). Human Lactotransferrin abundance was significantly greater in the 10 % treatment as compared to the 0 % treatment (p<0.05) (Figure 3.10C). The amount of trypsin in each of the treatment groups would be expected to be the same, as the same amount was added to each treatment. No significant difference was observed between the treatment groups (Figure 3.10D).
Figure 3.10: The Abundance of Albumin and Lactotransferrin increase with the Percentage of FCS whilst Keratin (K1C9) and Trypsin decrease. Data are presented as the median abundance units +/- the upper and lower quartiles (n=2) of A) Bovine Albumin; B) Human Keratin; C) Human Lactotransferrin; and D) Porcine Trypsin. Statistical analysis was performed by one-way ANOVA followed by Tukey HSD post hoc test where significance was accepted where p<0.05 = *.
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3.4 DISCUSSION
Optimal culture conditions have been developed for several cell lines where serum has been replaced by specific growth factors, nutrients, hormones and substrates (Hedlund & Miller, 1994). However, serum continues to be the primary source of nutrients in the culture of MSCs (Karnieli et al., 2017). In this chapter, the effect that serum concentration had on the proteome of mesenchymal stromal cells was investigated. This was performed as an optimisation step to determine whether serum could be removed from the culture conditions without a) significant disruption to the biology of the cells and b) interference with downstream analysis methods. From this study, it was determined that 2 % FCS could be a suitable concentration of serum for the culture of BM-MSCs for subsequent proteomics analysis. This was due to the proteome of BM-MSCs cultured in 2 % FCS being associated with: less differentiation GO terms; a greater number of significantly over-represented terms, such as cell division and cellular homeostasis; and, fewer GO terms associated with apoptosis or cell death (Table 3.1). Moreover, interference in the detection of proteins in the 2 % FCS treatment was not evident. As a result of this research, 2 % FCS was used in all subsequent cultures investigated and discussed within this thesis.
Initially, a subset of proteins present in the BM-MSCs from each of the serum treatment groups were identified using DDA-MS. The proteins detected from the cells exposed to the three serum concentrations were compared to evaluate the similarity of the protein profiles for each of the treatment groups (Figure 3.1). The increase in the number of proteins common to the 0 % and 2 % FCS treatment groups compared to the 10 % FCS treatment suggested that there was a substantial similarity in biology between these two groups. However, the dissimilarity of the 10 % FCS treatment may be due to it having fewer proteins detected during DDA-MS analysis. Gene Ontology analysis of the protein profiles was performed to further explore the similarities between the 0 % and 2 % FCS treatments by evaluation of the associated biological processes (Figure 3.2 – 3.4). Comparisons between the gene ontologies associated with each of the treatment groups revealed that the shared biology between the 0 % and 2 % FCS treatments was greater than the shared biology of any other two groups (Figure 3.5). Quantitative analysis of the protein abundances further supported these findings. For instance, Pearson correlation and PCA both revealed a similarity between the 0 % and 2 % FCS treatments (Figure 3.6 and Figure 3.7A). ANOVA analysis
66 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
revealed that the greatest difference was between the 0 % and 10 % treatments (Figure 3.8).
The specific purpose of this experiment was to evaluate whether serum could be removed from the culture medium without causing the BM-MSCs to differentiate or die over the course of the experiment. In response to this, analysis of BM-MSC biology using GO term analysis, revealed terms associated with cell death and differentiation which were further investigated (Table 3.1). The 0 % FCS treatment was associated with several differentiation GO terms suggesting that the BM-MSC biology was changing from a more stem phenotype, towards a differentiated state. Several different types of differentiation were described by these GO terms. This may be explained by various cells within the same culture differentiating down separate lineage pathways. Analysis by flow cytometry may provide useful in evaluating this hypothesis (Lecht et al., 2014). The 10 % FCS treated BM-MSCs were associated with terms relating to differentiation towards cardiac muscle, skeletal muscle and bone. Whereas, BM-MSCs cultured in the 2 % FCS treatment had no unique differentiation GO terms associated with them, they were associated with differentiation processes such as muscle cell differentiation, striated muscle cell differentiation and osteoblast differentiation, which were also associated with BM-MSCs cultured in the other two treatments. This commonality may suggest that BM-MSCs have an inherent potential for muscle cell and bone differentiation. The over-representation of these terms was less significant in the 2 % treatment compared to the 0 % and 10 % FCS treatment groups suggesting that both insufficient and excessive serum may push the BM-MSCs towards a differentiated state or cause spontaneous differentiation to occur more quickly. Other GO terms that were common between the three groups were cell division and cellular homeostasis. Unlike the shared differentiation GO terms, these terms were more significantly over-represented in the 2 % FCS treatment group, suggesting that 2 % FCS provides the cells with enough nutrients to maintain important cellular processes that promote the survival of BM-MSCs.
The final category of GO terms that were evaluated were those associated with cell death and apoptosis. All three treatments were associated with GO terms in this category to some extent, however, only one apoptotic GO term was associated with the 2 % FCS treatment. Importantly, with regard to functional analysis an assessment
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of BM-MSC survival using a cell viability assay would also have assisted in determination of the most effective serum concentration.
Other methods for determination of the biological effect of serum concentration on the BM-MSCs include migration assays, proliferation assays, and differentiation stains or assays. These analyses could be used to validate the proteomic data and should be pursued in future experiments. In addition, it is further recommended that future proteomics studies also investigate the effect of other serum concentrations, such as 0.1 %, 5 %, 8 % and 15 %, as well as evaluate the effectiveness of commercially available serum-free media supplements. Zhang et al. (2017) determined that for short term (24h) preservation of adipose derived stromal cells, 5 % human serum was optimal. Furthermore, Binder et al. (2015) examined 1 % and 5 % FCS for osteogenic differentiation with hypoxia and determined that 5 % FCS increased the calcium deposition by MSCs induced towards bone (Binder et al., 2015). As a result, Binder et al. (2015) suggested that standard culture conditions may not be appropriate for osteogenic differentiation.
Another avenue explored in this study was whether the concentration of serum used in the culture would interfere with the analytical approach used to examine BM- MSC differentiation. Protein mass spectrometry is susceptible to ion suppression from the presence of high abundant proteins. Therefore, it was hypothesized that the use of 10 % FCS would provide a source of high abundant serum proteins that would limit the depth of analysis of the BM-MSC proteome. The data presented in this chapter would seem to support this hypothesis, as there were less proteins detected in the 10 % FCS samples using DDA-MS as compared to the 0 % and 2 % FCS treatment groups. Further consideration of the results may suggest that the apparent lower abundance might be due to ion suppression. As such, it is reasonable to question how much of the BM-MSC biology is hidden as a result of an incomplete protein profile from the 10 % FCS treatment. A separate study, performed by Nonnis, et al. (2016), evaluated the effect that serum concentration had on the detection of secreted proteins from human MSCs and determined that 10 % FCS resulted in half as many proteins detected compared to either 0 % or 5 % FCS (Nonnis et al., 2016). Nonnis et al. concluded that 10 % FCS contained a large amount of contaminating bovine protein that resulted in undetected, lower abundance proteins (Nonnis et al., 2016). These conclusions are reinforced by the results of this study, further supporting the idea that
68 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
10 % serum contains too many high abundant proteins that interfere with mass spectrometry analyses.
To work around this issue in the context where high concentrations of serum are still utilised, high abundant protein depletion methods may be applied, such as commercially available depletion spin columns (Sundberg et al., 2015). Future studies regarding this topic may involve the evaluation of higher serum concentrations on instrument interference, or perhaps re-performing the current analysis with the use of high abundant protein depletion to more clearly assess the impact on protein detection. Re-performing the current study using high abundant protein depletion should be performed and the effect of serum concentration on BM-MSCs be re-evaluated. The removal of the high abundant proteins present in the samples may reveal information on the biology of the BM-MSCs that was not uncovered in the current analysis.
This work contributes knowledge about the effects that low serum has on the growth and survival of BM-MSCs and takes into consideration the effect that high abundant proteins such as those present in serum have on the depth of proteomic analyses. Since the challenge of this work was to balance the effect that serum concentration had on the biology of BM-MSCs and the analytical approach, it was concluded that 2 % FCS was the most suitable serum concentration.
3.5 CHAPTER 3 SUMMARY
The research discussed in this chapter was performed as an optimization for further mass spectrometry analyses of BM-MSCs cultured in serum-containing media. Qualitative analysis of the BM-MSCs cultured with different serum concentrations revealed that the 2 % FCS treatment group had less GO terms associated with differentiation and apoptosis, and more significant over-representation of the terms cell division and cellular homeostasis and was determined to be the most suitable concentration of serum for the planned future experiments. The 10 % FCS treatment group had less proteins detected using DDA-MS than the 2 % or 0 % FCS treatment groups which may suggest high abundant protein interference. However, validation of this through future experimental work is required.
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Chapter 4: Stimulation with FN, FGF2 and/or BMP4 Variously Influences BM-MSC Protein Profiles.
4.1 INTRODUCTION
The successful growth and maintenance of MSCs is reliant on the culture conditions used. Currently, a culture environment that optimises the growth of MSCs whilst maintaining their stem characteristics is undefined. However, numerous studies have begun to investigate the roles of specific growth factors and substrates that could potentially lead to the formulation of such a culture environment.
Fibronectin (FN) is a protein that is often utilised in cell culture as a substrate for cellular adhesion. For example, regarding the culture of MSCs, FN has been observed to increase adhesion and promote migration (Kasten et al., 2014). Furthermore, at specific concentrations (213 ng/ml – 48 ng/ml), FN has also been observed to promote proliferation (Faia-Torres et al., 2015). Importantly, while fibronectin is commonly present in serum (Brunner et al., 2010), it is also secreted by MSCs themselves (Faia-Torres et al., 2015).
Fibroblast growth factor 2 (FGF2) is the most commonly studied member of the fibroblastic growth factor family and has been observed to enhance stemness properties in MSCs from several sources (Eom et al., 2014; Sukarawan et al., 2014). It has been observed to increase proliferation, decrease collagen alpha 1 expression and prevent mineralized matrix deposition in periodontal ligament cells differentiating towards bone (S. An et al., 2015). FGF2 is also known to enhance the growth of BM- MSCs (Baddoo et al., 2003; Eom et al., 2014) as well as maintain the differentiation potential of BM-MSCs (Solchaga et al., 2005). It is found in serum (Brunner et al., 2010) and is therefore present to some extent in most cell cultures.
Bone morphogenetic protein 4 (BMP4) is a protein of the TGFβ family. It has previously been observed to increase proliferation in periodontal ligament cells whilst also preventing differentiation through cell cycle arrest (L. Liu et al., 2013). Moreover,
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BMP4 has been observed to maintain the stemness properties of adipose-derived MSCs and protect their multipotential ability (Vicente Lopez et al., 2011).
Each of these proteins exhibit specific stemness promoting abilities in MSCs or MSC-like cells when cultured individually. The aim of this research was to investigate the combined effect of these growth factors and substrates on the BM-MSC proteome and evaluate the changes in biological status they elicit. It has been hypothesised that the biological status of the BM-MSCs will be altered by the addition of these specific growth factors such that their stem characteristics will be preserved.
4.2 EXPERIMENTAL PROCEDURES
Full details of the materials and methods outlined in this chapter are described in Chapter 2. The following are brief summaries of the materials and experimental procedures used for the generation of data presented in section 4.33.3.
4.2.1 Materials For full details of the materials used in this chapter, please refer to section 2.2.
4.2.2 Cells and Culture Conditions Cells from the initial pool, containing BM-MSCs harvested from three rats, were seeded into six T75 tissue culture flasks. The cells were grown until 80 % confluence was achieved with media changes occurring every three days. The cells were serum starved for 12 hours prior to the transfer of BM-MSCs from each T75 flask into 3 T25 flasks with a seeding density of 2.9x105 cells/flask. Twelve of the eighteen flasks were coated with fibronectin prior to cell seeding as described below. The cells were provided with new ‘treatment’ media supplemented with either 5 ng/mL of FGF2, 10 ng/mL of BMP4 or both (L. Liu et al., 2013). A summary of the different treatments is outlined in Table 4.1. The cells were grown in the supplemented media for 72 hours prior to protein collection. Protein collection was performed as outlined in section 2.3.1.4.
Fibronectin Coating Fibronectin was diluted to 1 ng/mL in PBS, at 4 ºC prior to the addition of 1 mL of the diluted FN to each of the twelve T25 flasks. The flasks were left to incubate for 4 hours at room temperature. Prior to cell seeding, the flasks were rinsed twice using PBS.
72 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Table 4.1: Experimental parameters for each treatment condition. Parameters (+ or -) Sample Name Replicates BM-MSCs FN Coating FGF2 BMP4 Control + - - - 3 FN + + - - 3 FN+FGF2 + + + - 3 FN+BMP4 + + - + 3 FN+FGF2+BMP4 + + + + 3 FGF2+BMP4 + - + + 3
4.2.3 Protein Fractionation For full details of the protein fractionation methods used in this chapter, please refer to sections 2.3.2.5 and 2.3.2.7. Briefly, an aliquot of the pooled proteins was fractionated using LDS-PAGE. The remaining pooled proteins were subjected to OFF- GEL fractionation.
4.2.4 Protein Digestion For full details of the protein digestion methods, please refer to sections 2.3.2.4 and 2.3.2.6. The OFF-GEL fractions and raw protein samples were digested using the FASP method, whilst the LDS-PAGE separated samples were digested using the in- gel digestion method. All digested samples were desalted and cleaned using STAGE tips and spiked with iRT peptides prior to mass spectrometry analysis.
4.2.5 Mass Spectrometry For full details of the mass spectrometry methods used in this chapter, please refer to section 2.3.3. The fractionated samples were run on the TripleTOF 5600+ mass spectrometer first under DDA mode using a 65-minute gradient. The individual treatment samples were run on the same machine under SWATH acquisition mode using the same 65-minute gradient.
4.2.6 Analysis For full details regarding the analyses of data reported in this chapter, please refer to Section 2.4. Briefly, the data was processed as per sections 2.4.1 and 2.4.2. The DDA-MS data was used to develop the rat BM-MSC protein library (Appendix A), whilst gene ontology and statistical analyses were performed on the SWATH-MS
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data. During the processing of the SWATH-MS data, the samples were calibrated using the iRT peptides in the RT calibration menu of the software.
4.3 RESULTS
4.3.1 Minimal morphological changes were observed between BM-MSCs pre- and post-treatment, or between treatments. The cells were imaged at three time-points; prior to starvation (Figure 4.1A-B), after starvation (Figure 4.1C-D) and three days post-treatment (Figure 4.2). The cells exhibited a spindle shape with minimal difference in morphology between cells for each of the time-points prior to treatment. A slight increase in cell density was perhaps observed in the +FN culture after starvation (Figure 4.1C) although this was not formally measured.
Post-treatment, the cells did not exhibit any discernible change in morphology between the treatment groups or compared to the pre-treated cells (Figure 4.2). Some clustering of the BM-MSCs was observed in each treatment except the FN only group. This clustering was specifically evident in the FN+BMP4 and FN+FGF2+BMP4 treatment groups where nodule-like formations were observed (Figure 4.2D and E).
74 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Figure 4.1: Minimal morphological changes are observed between BM-MSCs cultured on flasks coated with FN versus non-coated flasks. Representative images (n=6) of BM-MSCs prior to treatment with growth factors (All images taken are presented in Appendix B). BM-MSCs grown on A: FN coated flasks prior to starvation, B: an uncoated flask prior to starvation, C: FN coated flask after 12 hours of starvation, D: an uncoated flask after 12 hours of starvation. Scale bar = 100 µm
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Figure 4.2: Cells cultured in treatments containing FN and BMP4 exhibited nodule-like clusters after 3 days of treatment. Representative images of BM-MSCs 3 days post treatment (All images taken are presented in Appendix C). BM-MSCs grown on A: an uncoated flask with no supplements added (Control), B: FN coated flask with no supplements (FN), C: FN coated flask supplemented with FGF2 (FN+FGF2), D: FN coated flask supplemented with BMP4 (FN+BMP4), E: FN coated flask supplemented with FGF2 and BMP4 (FN+FGF2+BMP4), F: an uncoated flask supplemented with FGF2 and BMP4 (FGF2+BMP4). Orange arrow indicates nodule- like formations occuring within the cultures. Scale bar = 100 µm
76 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
4.3.2 A high correlation exists between the BM-MSCs cultured in each of the treatments. A Pearson correlation was performed on the protein abundance data to determine the similarity of each of the treatment groups (Figure 4.3). Firstly, each of the replicates for all the treatment groups were compared to one another (Figure 4.3A). While the correlation between these samples was high, having scores greater than 0.92, replicates cultured with the same treatment were clustered with replicates from other treatments. This suggests that differences between the cells of different treatments were very subtle. To investigate the similarity between the treatments, the average abundances for each treatment were compared using Pearson correlation (Figure 4.3B). The treatments had correlation scores that were 0.98 or greater, indicating that they were highly correlated. This was expected, as the BM-MSCs were all harvested from the same population. The Control and FGF2+BMP4 treatments were the most highly correlated and were clustered together. The FN+FGF2 treatment had the lowest correlation to all the other treatment groups. Taken together, these data indicate that the replicates and treatments are all highly correlated, with only subtle differences being responsible for the changes in correlation.
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Figure 4.3: The control treatment is most highly correlated with the FGF2+BMP4 treatment however, all treatment groups exhibit a high level of correlation. Pearson correlation between A: replicates, B: mean replicate abundances.
78 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
4.3.3 Subtle changes between the treatment groups can be explained by changes in individual protein abundance. Multivariate analysis methods were used to evaluate the similarities of each of the treatment groups. Principal component analysis (PCA) resulted in overlap of all treatment groups (Figure 4.4A). This was not surprising as PCA is an unsupervised method and the treatments have a high correlation. Partial Least Squares Discriminant Analysis (PLS-DA) is a more supervised method of analysis that resulted in near complete separation of the treatments within Component 1 (Figure 4.4B). There was a small overlap between the FN and FN+FGF2 treatments. An orthogonal partial least squares discriminant analysis (oPLS-DA) was performed, where complete separation of the treatments was observed (Figure 4.4C). Finally, a more selectively-supervised method was utilised, a sparse partial least squares discriminant analysis (sPLS-DA) (Figure 4.4D). This method separated the treatments in both Component 1 and Component 2. The treatments that were cultured on FN coated flasks appeared lower and to the left side of the plot whilst those that were cultured on uncoated flasks were plotted in the upper-right corner. The FN+BMP4 treatment was distinctly separated from the other treatments in Component 1.
These data indicate that there is great similarity between each of the treatment groups when looked at as a whole, however, when analysed using a subset of proteins, differences between the treatments becomes clearer. Based on sPLS-DA, the FN+BMP4 treatment has a greater difference to the other treatments and FN plays a role in separating the treatments.
Table 4.2 outlines the 10 proteins that the sPLS-DA used to separate the treatments in each component. The abundance scores for each of the 10 proteins in component one were plotted for each of the treatment groups, as observed in Figure 4.5. Each of the 10 proteins were found to have a lower abundance in the FN+BMP4 treatment. This explains the isolation of the FN+BMP4 treatment in the sPLS-DA plot.
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Figure 4.4: Separation of the different treatment groups is best shown using Sparse Partial Least Squares Discriminant Analysis whereby treatments are clearly separated, particularly those containing FN. A: Principal Component Analysis (PCA), B: Partial Least Squares – Discriminant Analysis (PLS-DA), C: Orthogonal Partial Least Squares – Discriminant Analysis (oPLS-DA), D: Sparse Partial Least Squares – Discriminant Analysis (sPLS-DA).
80 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Table 4.2: Top 10 proteins responsible for the separation of treatments in the sPLS plot, per component.
Abundance Scores Accession Protein Name # Component Component 1 2 O88280 Slit Homolog 3 Protein (SLIT3) 0.75956
Q3B8Q2 Eukaryotic Initiation Factor 4A-III (IF4A3) 0.41384
P85970 Actin-Related Protein 2/3 Complex Subunit 2 (ARPC2) 0.32758
P27952 40S Ribosomal Protein S2 (RS2) 0.24749
P63081 V-Type Proton ATPase 16 kDa Proteolipid Subunit (VATL) 0.17548
P62890 60S Ribosomal Protein L30 (RL30) 0.15584
P09895 60S Ribosomal Protein L5 (RL5) 0.12412
Q66HD0 Endoplasmin (ENPL) 0.097159
O89046 Coronin-1B (COR1B) 0.05749
Q68FP1 Gelsolin (GELS) 0.0028406
P14141 Carbonic Anhydrase 3 (CAH3) 0.50685
P05370 Glucose-6-Phosphate 1-Dehydrogenase (G6PD) 0.49577
P70645 Bleomycin Hydrolase (BLMH) 0.45907 Q3MIE4 Synaptic Vesicle Membrane Protein VAT-1 Homolog (VAT1) 0.32729
P04642 L-lactate dehydrogenase A chain (LDHA) 0.2623
P42123 L-lactate dehydrogenase B chain (LDHB) 0.12034
P62959 Histidine triad nucleotide-binding protein 1 (HINT1) 0.098621
P11980-2 Pyruvate Kinase PKM, Isoform M2 (KPYM) 0.071588
P11980 Pyruvate Kinase PKM (KPYM) 0.071588
Q9QZK5 Serine protease HTRA1 (HTRA1) -0.11341
P13084 Nucleophosmin (NPM) -0.25164
The Slit homolog 3 protein (SLIT3) exhibited significantly lower abundance in the FN+BMP4 treatment group compared to all other treatments (p<0.05) (Figure 4.5A). Actin-related Protein 2/3 complex, subunit 2 (ARPC2) was significantly decreased in the FN+BMP4 treatment compared to the control group (p=0.021), as well as compared to the FN+FGF2 (p=0.049) and FGF2+BMP4 treatment groups (p=0.013) (Figure 4.5B). Similarly, 40S ribosomal protein S2 (RS2) abundance was significantly decreased in the FN+BMP4 treatment as compared to the control (p=0.038) and FGF2+BMP4 treatment groups (p=0.018) (Figure 4.5C). Eukaryotic initiation factor 4A-III (IF4A3) and 60S ribosomal protein L5 (RL5) abundance was significantly lower in the FN+BMP4 treatment compared to the control group only (p<0.05) (Figure 4.5D-E). V-type Proton ATPase 16kDa proteolipid subunit (VATL), 60S ribosomal protein L30 (RL30) and Endoplasmin (ENPL) had a significantly decreased abundance in the FN+BMP4 treatment as compared to the FGF2+BMP4
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treatments (p<0.05) (Figure 4.5F-H). Coronin-1B (COR1B) and Gelsolin (GELS) was not significantly different between the treatments (Figure 4.5I & J).
82 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Figure 4.5: The Abundances of These 10 Proteins are Responsible for the Separation of Each Treatment Group in the First Component of sPLS-DA Plot. Data are presented as the median log2 abundance with the upper and lower quartiles, where the whiskers represent the most extreme abundance value within 1.5× interquartile range (n = 3). Statistical analysis was performed by one-way ANOVA followed by TukeyHSD posthoc test where significance was accepted where p<0.05 = *, p<0.01 = ** and p<0.001 = ***.
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The abundances for each of the 10 proteins in component 2 were plotted for each of the treatment groups as detailed in Figure 4.6. Carbonic anhydrase 3 (CAH3) exhibited a significantly decreased abundance in the FN treatment compared to all other treatment groups (p<0.05) (Figure 4.6A). Glucose-6-phosphate 1- dehydrogenase (G6PD) had significantly decreased abundance in the FN+FGF2 treatment compared to all other treatment groups except the FN treatment (p<0.05) (Figure 4.6B). Synaptic vesicle membrane protein VAT1 homolog (VAT1) had a significantly decreased abundance in the FN treatment group compared to both the control (p=0.045) and the FGF2+BMP4 treatment groups (p=0.039) (Figure 4.6C). The abundance of Pyruvate Kinase PKM (KPYM) was significantly lower in the FN treatment groups as compared to the FGF2+BMP4 treatment group (p=0.033) (Figure 4.6D). Nucleophosmin (NPM) abundances were significantly decreased in the FN+FGF2 treatment compared to the FN+BMP4 (p=0.022) and FGF2+BMP4 treatment groups (p=0.032) (Figure 4.6E). Bleomycin hydrolase (BLMH) has a significantly decreased abundance in the FN+FGF2 treatment compared to the FGF2+BMP4 treatment group (p=0.038) (Figure 4.6F). Finally, there was no significant difference in abundance of serine protease HTRA1 (HTRA1), L-lactate dehydrogenase, chain A (LDHA), L-lactate dehydrogenase, chain B (LDHB) or histidine triad nucleotide-binding protein 1 (HINT1) between any of the treatment groups (Figure 4.6G-J).
Taken together, these data indicate that even though the treatments are highly correlated, subtle differences can be explained by changes in the abundance of specific proteins. The 20 proteins described here are a subset of the proteins that vary between the treatment groups and are responsible for the separation of the treatments using the selectively-supervised sPLS-DA method.
84 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Figure 4.6: The Abundances of These 10 Proteins are Responsible for the Separation of Each Treatment Group in the Second Component of sPLS- DA Plot. Data are presented as the median log 2 abundance with the upper and lower quartiles, where the whiskers represent the most extreme abundance value within 1.5× interquartile range (n = 3). Statistical analysis was performed by one-way ANOVA followed by TukeyHSD posthoc test where significance was accepted where p<0.05 = *, p<0.01 = ** and p<0.001 = ***.
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4.3.3.1 The proteome of 89BM-MSCs cultured in FGF2+BMP4 is most similar to that of the control BM-MSCs, with only one protein significantly varying in abundance between the two treatments. To determine which proteins varied between each of the treatments, a one-way ANOVA on the abundance data was performed (Figure 4.7). It was determined that 92 proteins varied significantly between two or more of the treatment groups. A TukeyHSD posthoc test was used to determine which of the 92 proteins varied between each of the treatment groups. These proteins are detailed in Table 4.3.
Based on the TukeyHSD posthoc test, the treatments that had the most protein abundance variation were the FN and FGF2+BMP4 treatments with 20 proteins exhibiting significant variation between these two treatment groups. Additionally, 17 proteins exhibited significant abundance variation between the FGF2+BMP4 and the FN+FGF2+BMP4 treatments, followed by 16 proteins with significant abundance variation between the FN+BMP4 and FGF2+BMP4 treatments. Alternatively, the two treatments with the least amount of proteins with significant abundance variation were the control and FGF2+BMP4 treatments, with only 1 significant protein. Of the 92 proteins, 16 proteins were determined to be non-significant by the posthoc test.
Interestingly, of the 20 proteins selected for the sPLS-DA in section 4.3.3, only 14 were determined to have significant abundance variation between the treatment groups. Coronin-1B was initially considered significant by the ANOVA, however was considered non-significant by the TukeyHSD posthoc test.
86 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Figure 4.7: 92 Proteins significantly varied between at least two of the treatment groups. One-way analysis of variance of protein abundances across the six treatment groups.
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Table 4.3: 20 proteins are significantly varied between the FN and FGF2+BMP4 treatment groups.
Protein Name (Protein ID) Accession p Value F Value -LOG10(p) TukeyHSD† FN+BMP4-Control, FN+BMP4-FN, FN+BMP4- Slit Homolog 3 Protein (SLIT3) O88280 0.000 10.743 3.376 FN+FGF2, FN+FGF2+BMP4-FN+BMP4, FGF2+BMP4-FN+BMP4 FN+FGF2-Control, FN+BMP4-FN+FGF2, Glucose-6-Phosphate 1- P05370 0.001 9.130 3.051 FN+FGF2+BMP4-FN+FGF2, FGF2+BMP4- Dehydrogenase (G6PD) FN+FGF2 FN-Control, FN+BMP4-FN, FN+FGF2+BMP4- Carbonic Anhydrase 3 (CAH3) P14141 0.001 8.366 2.883 FN, FGF2+BMP4-FN, FGF2+BMP4-FN+FGF2 FN+BMP4-Control, FN+FGF2+BMP4-Control, 40S Ribosomal Protein S7 P62083 0.002 7.247 2.615 FN+FGF2+BMP4-FN, FN+FGF2+BMP4- (RS7) FN+FGF2 FN+FGF2+BMP4-Control, FN+FGF2+BMP4- Heterogeneous Nuclear Q8VHV7 0.003 7.057 2.567 FN+FGF2, FN+FGF2+BMP4-FN+BMP4, Ribonucleoprotein H (HNRH1) FGF2+BMP4-FN+FGF2+BMP4 40S Ribosomal Protein S21 FN+FGF2-Control, FN+FGF2-FN, P05765 0.004 6.266 2.356 (RS21) FN+FGF2+BMP4-FN+FGF2 GTP-binding Protein SAR1b FN+BMP4-FN, FN+BMP4-FN+FGF2, Q5HZY2 0.004 6.254 2.352 (SAR1B) FN+FGF2+BMP4-FN+BMP4 Cytochrome C Oxidase Subunit FGF2+BMP4-FN, FGF2+BMP4-FN+BMP4, P11240 0.004 6.238 2.348 5A, Mitochondrial (COX5A) FGF2+BMP4-FN+FGF2+BMP4 Ornithine Aminotransferase, FN+FGF2-FN, FN+BMP4-FN+FGF2, P04182 0.005 6.161 2.326 Mitochondrial (OAT) FN+FGF2+BMP4-FN+FGF2 Palmitoyl-protein FN+FGF2+BMP4-Control, FN+FGF2+BMP4- P45479 0.007 5.547 2.149 Thioesterase 1 (PPT1) FN+BMP4, FGF2+BMP4-FN+FGF2+BMP4 FGF2+BMP4-Control, FGF2+BMP4-FN, Heat Shock Protein Beta-1 P42930 0.007 5.518 2.140 FGF2+BMP4-FN+BMP4, FGF2+BMP4- (HSPB1) FN+FGF2+BMP4 Actin-Related Protein 2/3 FN+BMP4-Control, FN+BMP4-FN+FGF2, P85970 0.008 5.331 2.083 Complex Subunit 2 (ARPC2) FGF2+BMP4-FN+BMP4 FN+FGF2-FN, FN+FGF2+BMP4-FN, Fibronectin (FINC) P04937 0.009 5.215 2.047 FGF2+BMP4-FN FN-Control, FN+FGF2-Control, Biglycan (PGS1) P47853 0.009 5.184 2.038 FN+FGF2+BMP4-Control FN+FGF2-FN, FN+BMP4-FN+FGF2, Reticulon-3, Isoform 2 (RTN3) Q6RJR6-2 0.010 5.126 2.020 FGF2+BMP4-FN+BMP4 FN+FGF2-FNFN+BMP4- Lysosomal Acid Phosphatase P20611 0.010 5.034 1.990 FN+FGF2FN+FGF2+BMP4- (PPAL) FN+FGF2FGF2+BMP4-FN+FGF2 FGF2+BMP4-FN, FGF2+BMP4- Calmodulin-3 (CALM3) P0DP31 0.011 4.997 1.979 FN+FGF2+BMP4 FN+BMP4-FNFN+BMP4- Adapter Molecule CRK (CRK) Q63768 0.011 4.941 1.961 FN+FGF2FN+FGF2+BMP4- FN+BMP4FGF2+BMP4-FN+BMP4 PDZ and LIM Domain Protein P52944 0.011 4.899 1.947 FGF2+BMP4-FN (PDLI1) Caveolin-1 (CAV1) P41350 0.011 4.886 1.943 FN+BMP4-FN+FGF2, FGF2+BMP4-FN+FGF2 60S Ribosomal Protein L30 P62890 0.012 4.860 1.935 FGF2+BMP4-FN+BMP4 (RL30) Lysosome-associated Membrane Glycoprotein 1 P14562 0.012 4.820 1.922 FN-Control, FN+BMP4-FN (LAMP1) Macrophage Migration P30904 0.014 4.645 1.864 FN+FGF2+BMP4-FN+FGF2 Inhibitory Factor (MIF) Amyloid Beta A4 Protein, P08592-2 0.014 4.643 1.864 FN-Control, FGF2+BMP4-FN Isoform APP695 (A4) Synaptic Vesicle Membrane Protein VAT-1 Homolog Q3MIE4 0.014 4.627 1.858 FN-Control, FGF2+BMP4-FN (VAT1) Tropomyosin Alpha-1 Chain, Isoform 2 (Smooth Muscle) P04692-2 0.014 4.611 1.853 FGF2+BMP4-FN, FGF2+BMP4-FN+FGF2 (TPM1) Tropomyosin Alphaa-1 Chain, Isoform 7 (Fibroblast 5a) P04692-7 0.014 4.611 1.853 FGF2+BMP4-FN, FGF2+BMP4-FN+FGF2 (TPM1)
88 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Coatomer Subunit Beta P23514 0.014 4.582 1.843 FN-Control, FN+BMP4-Control (COPB) Actin-related Protein 2/3 O88656 0.015 4.557 1.835 FN+BMP4-Control, FGF2+BMP4-FN+BMP4 Complex Subunit 1B (ARC1B) Proteasome Subunit Beta FN+FGF2+BMP4-Control, FN+FGF2+BMP4- P40307 0.015 4.539 1.829 Type-2 (PSB2) FN+FGF2, FGF2+BMP4-FN+FGF2+BMP4 Myosin Regulatory Light Chain FGF2+BMP4-FN, FGF2+BMP4-FN+FGF2, P13832 0.015 4.492 1.813 RLC-A (MRLCA) FGF2+BMP4-FN+FGF2+BMP4 Tropomyosin Alpha-3 Chain Q63610 0.016 4.417 1.788 FGF2+BMP4-FNFGF2+BMP4-FN+FGF2 (TPM3) Tropomyosin Alpha-3 Chain, Q63610-2 0.016 4.417 1.788 FGF2+BMP4-FN, FGF2+BMP4-FN+FGF2 Isoform 2 (Tpm3_v3) (TPM3) Actin-Related Protein 2 (ARP2) Q5M7U6 0.017 4.393 1.779 Nucleophosmin (NPM) P13084 0.017 4.381 1.775 FN+BMP4-FN+FGF2FGF2+BMP4-FN+FGF2 KDEL Motif-Containing Protein Q566E5 0.018 4.311 1.751 FGF2+BMP4-FN+BMP4 2 (KDEL2) Ras-related Protein Rab-10 FN+FGF2+BMP4-Control, FGF2+BMP4- P35281 0.018 4.299 1.747 (RAB10) FN+FGF2+BMP4 Eukaryotic Initiation Factor Q3B8Q2 0.019 4.245 1.728 FN+BMP4-Control 4A-III (IF4A3) Protein S100-A4 (S10A4) P05942 0.019 4.239 1.726 FGF2+BMP4-FN+FGF2+BMP4 40S Ribosomal Protein SA P38983 0.019 4.221 1.720 FN+BMP4-FN+FGF2FN+FGF2+BMP4-FN+FGF2 (RSSA) Proteasome Subunit Beta FN-Control, FN+FGF2+BMP4-FN, FGF2+BMP4- Q9JHW0 0.019 4.220 1.720 Type-7 (PSB7) FN Procollagen C-Endopeptidase O08628 0.019 4.216 1.718 FN+FGF2-FN, FGF2+BMP4-FN Enhancer 1 (PCOC1) 40S Ribosomal Protein S2 P27952 0.019 4.212 1.717 FN+BMP4-Control, FGF2+BMP4-FN+BMP4 (RS2) Heterogenous Nuclear FN+FGF2+BMP4-FN+FGF2, FGF2+BMP4- Ribonucleoprotein H2 Q6AY09 0.020 4.166 1.700 FN+FGF2+BMP4 (HNRH2) Glutathione S-Transferase P FN+FGF2+BMP4-FN+BMP4, FGF2+BMP4- P04906 0.020 4.162 1.699 (GSTP1) FN+BMP4 Fatty Acid Synthase (FAS) P12785 0.020 4.157 1.697 Astrocytic Phosphoprotein Q5U318 0.020 4.133 1.689 FGF2+BMP4-FN+FGF2+BMP4 PEA-15 (PEA15) Coronin-1B (COR1B) O89046 0.021 4.098 1.677 Tropomyosin Alpha-1 Chain, Isoform 6 (Fibroblast TM-2) P04692-6 0.021 4.098 1.676 FGF2+BMP4-FN, FGF2+BMP4-FN+FGF2 (TPM1) Equilibrative Nucleoside O54698 0.021 4.075 1.668 FN+FGF2-FN Transporter 1 (S29A1) Bleomycin Hydrolase (BLMH) P70645 0.022 4.050 1.659 FGF2+BMP4-FN+FGF2 Q5U312- Ankycorbin Isoform 2 (RAI14) 0.022 4.031 1.653 FN+FGF2-FN, FN+FGF2+BMP4-FN 2 Eukaryotic Translation Initiation Factor 2 Subunit 1 P68101 0.023 3.994 1.639 FN+BMP4-Control, FGF2+BMP4-FN+BMP4 (IF2A) Serpin H1 (SERPH) P29457 0.024 3.923 1.614 FN+BMP4-Control, FN+FGF2+BMP4-Control Ferritin Light Chain 1 (FRIL1) P02793 0.025 3.879 1.598 FGF2+BMP4-FN+FGF2+BMP4 Protein S100-A10 (S10AA) P05943 0.025 3.877 1.597 FGF2+BMP4-FN+FGF2+BMP4 Connective Tissue Growth Q9R1E9 0.026 3.854 1.589 Factor (CTGF) Collagen Alpha-2(I) (CO1A2) P02466 0.027 3.789 1.564 FN+FGF2-FN Numb-like Protein (NUMBL) A1L1I3 0.030 3.690 1.528 FN-Control RNA-binding Motif, Single- stranded-interacting Protein 1 Q5PQP1 0.030 3.689 1.527 FN+FGF2+BMP4-Control, FN+FGF2+BMP4-FN (RBMS1) Coiled-coil Domain-containing Q6QD51 0.030 3.661 1.517 Protein 80 (CCD80) T-Complex Protein 1 Subunit Q5XIM9 0.033 3.560 1.479 Beta (TCPB) Acetocaetyl-CoA Synthetase Q9JMI1 0.035 3.502 1.457 FGF2+BMP4-FN+FGF2 (AACS) Thy-1 Membrane Glycoprotein P01830 0.035 3.495 1.454 FN+BMP4-FN+FGF2, FGF2+BMP4-FN+FGF2 (THY1) Cytosol Aminopeptidase Q68FS4 0.035 3.492 1.453 (AMPL) Tropomyosin Beta Chain P58775-2 0.036 3.478 1.447 FGF2+BMP4-FN+FGF2 (TPM2)
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60S Ribosomal Protein L13a P35427 0.036 3.464 1.442 FN+BMP4-FN+FGF2 (RL13A) Endoplasmin (ENPL) Q66HD0 0.037 3.444 1.434 FGF2+BMP4-FN+BMP4 Pyruvate Kinase PKM, Isoform P11980-2 0.037 3.425 1.427 FGF2+BMP4-FN M2 (KPYM) Pyruvate Kinase PKM (KPYM) P11980 0.037 3.425 1.427 FGF2+BMP4-FN Diphosphomevalonate Q62967 0.039 3.374 1.407 Decarboxylase (MVD1) 40S Ribosomal Protein S4, X P62703 0.039 3.372 1.407 FGF2+BMP4-FN+BMP4 Isoform (RS4X) Protein ERGIC-53 (LMAN1) Q62902 0.039 3.371 1.406 FN+BMP4-FN+FGF2 60S Acidic Ribosomal Protein P02401 0.040 3.363 1.403 FGF2+BMP4-FN+FGF2+BMP4 P2 (RLA2) Dolichyk- diphosphooligosaccharide-- Q641Y0 0.040 3.357 1.400 FN+FGF2-FN Protein Glycosyltransferase 48 kDa (OST48) Clathrin Heavy Chain 1 (CLH1) P11442 0.040 3.354 1.399 FGF2+BMP4-FN+FGF2+BMP4 60S Ribosomal Protein L5 P09895 0.041 3.311 1.383 FN+BMP4-Control (RL5) Nucleobindin-2 (NUCB2) Q9JI85 0.042 3.294 1.376 FGF2+BMP4-FN, FGF2+BMP4-FN+BMP4 Brain Acid Soluble Protein 1 Q05175 0.042 3.290 1.374 FGF2+BMP4-FN+FGF2+BMP4 (BASP1) Oxygen-dependent Coproporphyrinogen-III Q3B7D0 0.043 3.279 1.370 Oxidase, Mitochondrial (HEM6) Unconventional Myosin-ID Q63357 0.043 3.270 1.367 (MYO1D) 40S Ribosomal Protein S12 P63324 0.044 3.252 1.359 FN+FGF2-Control (RS12) V-Type Proton ATPase 16 kDa P63081 0.044 3.249 1.358 FGF2+BMP4-FN+BMP4 Proteolipid Subunit (VATL) S-phase Kinase-associated Q6PEC4 0.044 3.234 1.352 FGF2+BMP4-FN+FGF2+BMP4 Protein 1 (SKP1) Small Ubiquitin-related Q5XIF4 0.045 3.216 1.345 Modifier 3 (SUMO3) 60S Ribosomal Protein L18 P12001 0.045 3.214 1.344 (RL18) Serine/Threonine-protein Phosphatase 2A Catalytic P63331 0.046 3.198 1.338 FGF2+BMP4-FN+FGF2 Subunit Alpha Isoform (PP2AA) Fatty Acid-Binding Protein, P07483 0.046 3.195 1.337 heart (FABPH) Filamin-C (FLNC) D3ZHA0 0.046 3.187 1.334 40S Ribosomal Protein S3a P49242 0.047 3.164 1.324 FN+BMP4-FN+FGF2 (RS3A) Receptor of Activated Protein P63245 0.049 3.122 1.308 C Kinase (RACK1) RNA Binding Motif Protein, X- D4AE41 0.050 3.107 1.301 linked-like-1 (RMXL1) †Where TukeyHSD is blank indicates non-significance.
90 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
It was also interesting that endogenously produced fibronectin (endoFN) abundance varied between the FN treatment and the FN+FGF2, FN+FGF2+BMP4 and FGF2+BMP4 treatments. To investigate the changes in abundance between the treatments, the abundances of the endoFN were plotted for each of the treatment groups (Figure 4.8). endoFN had the lowest abundance in the BM-MSCs cultured on a FN coated plate without additional growth factors. Conversely, it had an increased abundance in the three treatments that contained FGF2. The control treatments and the FN+BMP4 treatments exhibited a similar abundance of endoFN.
Figure 4.8: Fibronectin (endoFN) had a significantly lower abundance in the BM- MSCs cultured on a FN coated plate with no additional supplements, than in the BM-MSCs cultured with FN+FGF2, FN+FGF2+BMP4 and FGF2+BMP4. (** p<0.01, * p<0.05)
These data indicate that 76 proteins significantly differ in abundance between the treatment groups where 20 proteins differ significantly between the FN and the FGF2+BMP4 treatments. Only 1 protein differs significantly between the control and FGF2+BMP4 treatments. This suggests that culturing BM-MSCs on FN results in a change in the protein profiles of the cells and subsequently alters the biology of the BM-MSCs. Also, these data suggest that stimulation with FGF2 increases the production of endogenous FN by the BM-MSCs.
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4.4 DISCUSSION
A number of recent studies have investigated the effects that different culture conditions have had on the growth of MSCs to provide knowledge that could contribute to the development of an optimal culture environment (L. Liu et al., 2013; Rakian et al., 2015; Roubelakis et al., 2014). In the study reported in this chapter, the changes to the BM-MSC proteome, when cultured on flasks coated with fibronectin, and/or supplemented with FGF2 and/or BMP4 were examined. This analysis was performed using SWATH-MS in order to detect changes in protein abundance between the BM-MSCs cultured in the presence of FN, FGF2 and/or BMP4. From this research, it was determined that: FN substrate was important for enabling a change in BM-MSC protein abundance; minimal differences were observed in the protein profiles of MSCs cultured in FGF2+BMP4 compared to the control; and, endogenous fibronectin (endoFN) is produced by BM-MSCs stimulated with FGF2.
Many studies have explored the idea of supplementation of growth factors into the culture medium of MSCs to enhance characteristics such as migration, proliferation and differentiation potential (Berendsen & Olsen, 2015; Cui et al., 2014; F. Zhang et al., 2015). Most of these studies rely on morphological changes, and cell culture based assays, whereas this research primarily used a proteomics approach. The purpose of this research was to stimulate the growth and proliferation of the BM-MSCs without stimulation of differentiation processes. The results of this study were expected to provide knowledge that would assist with the development of a culture medium that would enhance the long-term culture of MSCs, for future development of an off-the- shelf MSC therapeutic treatment option.
In this research, FN was evaluated as a substrate within the culture of BM-MSCs. It was observed to significantly affect the proteome of the BM-MSCs. Initially, a perceived increase in cell density in the cultures containing FN suggested that FN may maintain the growth of the BM-MSCs during serum starvation (Figure 4.1). However, quantitative analysis of this observation, using proliferation assays and cell counting was not performed. Subsequent statistical analyses of the BM-MSC protein profiles suggested that FN greatly changed the proteomes of the BM-MSCs. Pearson correlation (Figure 4.3) and sPLS analysis (Figure 4.4) distinctly separated the FN containing treatments from the FN negative treatments, indicating that the presence of FN within the culture, alters the protein profiles of the BM-MSCs and changes their
92 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
biology. This was further investigated by evaluating the abundances of individual proteins.
One-way ANOVA accompanied by TukeyHSD posthoc test, was used to identify proteins that significantly varied in abundance between the treatment groups (Figure 4.7 and Table 4.3). Using this analysis method, a similarity between the protein profiles of the MSCs that were stimulated with both FGF2 and BMP4 in the presence of 2 % FCS and the MSCs that were only stimulated with 2 % FCS, was identified. Of the 943 proteins detected in each of the treatments, only one protein varied significantly between the Control and FGF2+BMP4 treatments, Heat Shock Binding Protein β1 (HSPB1) (Table 4.3). HSPB1 acts as a molecular chaperone for denatured proteins (Kampinga et al., 1994), and has previously been identified in human BM-MSCs stimulated with Transforming Growth Factor-β1 (Wang et al., 2004). The two treatments that varied the most were the FN only treatment and the FGF2+BMP4 treatment, with a total of 20 proteins with different abundances.
Fibronectin was identified to be one of the proteins that varied in abundance between the treatment groups, and is known to be endogenously (endoFN) produced by BM-MSCs (Chen et al., 2007). Interestingly, it was revealed in this study that in the presence of FGF2, the abundance of endoFN is increased compared to FN coated cultures alone (Figure 4.7 and Figure 4.8). Indeed, FGF2 has previously been observed to stimulate FN expression in fibroblasts (Tang et al., 2007). Furthermore, in a study conducted by Faia-Torres et al. (2014), endogenously produced FN was observed in BM-MSCs grown on a FN gradient of 213 ng/cm2 to 48 ng/cm2 (Faia- Torres 2014). An increase in endoFN production was observed towards the middle of the gradient, where increased BM-MSC proliferation and adhesion was also observed. These results suggest that the presence of FN doesn’t supress production of endoFN. This contrasts with the results of the study presented herein, as endoFN abundance is observed to decrease in the treatment containing FN alone (Figure 4.8). This contrast may be due to the extremely small concentration of FN used in this study, 0.04 ng/cm2, as compared to the concentrations used by Faia-Torres et. al (2014). The effect of FN concentration on the production of endoFN should be further investigated, as these results of this research suggest that FN is important for BM-MSC growth.
Another interesting observation was the formation of nodule-like cell clusters of the BM-MSCs cultured with both FN and BMP4 (Figure 4.2). These could potentially
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be explained by over-confluence of the cells; however, similar nodules have been previously observed during osteogenesis (Woll et al., 2006). It is possible that these cell clusters are the precursors of osteocytic nodules and could suggest that bone differentiation is occurring. To evaluate this hypothesis, techniques such as histochemical staining for collagen type I (Yang et al., 2014) or calcium deposits (Tseng et al., 2012), and ALP activity assays (Birmingham et al., 2012) could be used.
The lack of morphological analysis techniques, such as staining for differentiation, as well as proliferation and migration assays, is one of the biggest limitations of this research. These techniques would have greatly enhanced this research, and future studies should utilise these methods. Additionally, using SWATH analysis also introduces another limitation to this work, as the depth of analysis relies of the depth of the library that is used. The library used in this research contained 943 rat proteins, which is only a fraction of the 8020 known and reviewed rat proteins contained in the Swiss-Prot rat database. It is possible to increase the depth of the library by using additional fractionation methods. For example, different types of chromatographic fractionation would increase the depth of the library by selecting proteins based on different characteristics. Performing these experiments in the future would allow for the current rat BM-MSC library to be updated.
Other future experiments that should be performed could include investigation of the protein profiles of BM-MSCs cultured in FGF2 or BMP4 alone, and subsequent comparison of these to the FGF2+BMP4 treatment group protein profile. This would be particularly interesting in the case of BMP4 as previous research has observed significant osteoblastic differentiation capacity in BM-MSCs cultured with 2 % FCS and BMP4 (Cordonnier et al., 2011). Additionally, evaluation of the concentration of FN, FGF2 and BMP4 on the protein profiles of BM-MSCs; determination of the combined effect of supplementation with growth factors and culture in a hypoxic environment; and, investigation of other preformed matrices in conjunction with growth factors during culture, would all contribute knowledge towards identifying a suitable culture environment to maintain BM-MSC stemness. Finally, future studies that validate the findings presented herein should also be performed using BM-MSCs of individual rats or different rat populations to ensure the observed protein changes are reproducible.
94 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
4.5 CHAPTER 4 SUMMARY
In this chapter, the effects of FN, FGF2 and BMP4 on the protein profiles of BM-MSCs were evaluated. It was revealed that FN, used as an immobilised substrate has a significant effect on the protein complement of BM-MSCs. It was also observed that stimulation with FGF2 resulted in increased abundance of endogenously expressed fibronectin. In addition, stimulation with FGF2 and BMP4, alone, revealed a protein profile similar to that of BM-MSCs with minimal stimulation and indicates the importance of using a preformed matrix. Further studies into culture with FN, FGF2 and BMP4 should be undertaken, including the evaluation of individual protein changes between stimulated and unstimulated BM-MSCs. In doing so, a more specific insight into biological processes occurring in the BM-MSCs due to culture with FN, FGF2 and/or BMP4 could be achieved.
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96 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Chapter 5: Combinations of FN and Growth Factors Changes the Abundance of Proteins Associated with Bone and Neuronal Differentiation in BM-MSCs.
5.1 INTRODUCTION
In Chapter 4, differences between protein profiles of the BM-MSCs cultured in each of the treatment conditions were evaluated at a global level. In this chapter, changes in specific proteins are reported. The aim of the research reported in this chapter was to further evaluate the biology occurring within BM-MSCs cultured in FN, FGF2 and/or BMP4 through gene ontology analysis. To do this, specific proteins that differed between the BM-MSCs in culture with growth factors and the control group, where minimal stimulation occurred, were identified. Proteins were selected for analysis based on their biological and statistical significance using fold-change and pairwise t-tests. Significant proteins were analysed by evaluating their associated GO terms.
Fold-change is used as a determinant for biological significance in a number of research areas, including: transcriptomics (K. Li et al., 2015; Spanu et al., 2014); genomics (Yu et al., 2014; Zheng et al., 2014); and proteomics (Fang et al., 2014). In transcriptomics, a 2-fold change is commonly used as a threshold for biological significance (H. Li et al., 2012; L. Lu et al., 2015; Romine et al., 2014), however, in proteomics, fold change cut off thresholds vary. Some researchers have previously used a 1.2-fold change cut off (Alm et al., 2006; L. Li et al., 2012; Serang et al., 2013), while other researchers have used a 2-fold change cut off (Fang et al., 2014; L. M. Lu et al., 2015; Rider et al., 2011; Velez et al., 2014). Within this study, the fold change threshold for predicted biological significance was set at 2.
Differentiation of BM-MSCs into other cell lineages is undesirable in cases where BM-MSCs are cultured for extended periods of time. Therefore, specific
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emphasis has been put on understanding the biology of differentiating BM-MSCs and determining ways to monitor this process. Bone is the most common lineage for BM- MSC differentiation, and has a promising future in tissue engineering and bone healing applications. Therefore, the differentiation of BM-MSCs into bone has been evaluated extensively. As a result of this, a number of techniques for the evaluation of bone differentiation have been developed. For example, bone differentiation can be evaluated by staining for calcium deposits secreted by the BM-MSCs (Chiu et al., 2014) or through alkaline phosphatase (ALP) activity assays (Santos et al., 2015). Specific proteins, such as collagen type 1 (Cai et al., 2012), osteocalcin (Santos et al., 2015) and osteopontin (Egusa et al., 2005) can also be used to assess whether BM- MSCs are differentiating towards bone.
Similarly, neuronal differentiation has attracted significant research interest, as understanding the mechanisms behind it, and being able to manipulate them, may lead to using BM-MSCs for outcomes such as spinal cord injury repair (Y. Zhao et al., 2017). Neuronal differentiation of BM-MSCs can be evaluated through morphological analyses, where BM-MSCs adopt a neuron-like shape with rounded bodies and multiple processes that resemble axons or dendrites (Takeda & Xu, 2015). Otherwise, proteins such as nestin and β tubulin III are known indicators of neuronal differentiation (Egusa et al., 2005) and can also be used to identify neuronal differentiation.
The research in this chapter uses gene ontologies to assess the biology of proteins that have a biologically significant change in protein abundance and uses this information to comment on the potential differentiation status of the BM-MSCs. In doing so, elucidation of the treatment with the most promising, stemness-promoting abilities was sought.
5.2 EXPERIMENTAL PROCEDURES
Full details of the methods outlined in this chapter are described in Chapter 2. The following are brief summaries of the materials and experimental procedures used for the generation of data presented in section 5.3.
5.2.1 Analysis For full details of the analyses performed in this chapter, please refer to section 2.4. All statistical analyses reported in this chapter were performed in R. Protein fold
98 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
change was calculated by comparison of the average protein abundance in the treatment group to that of the control group. Pairwise t-tests were also performed in R and used to generate volcano plots detailed in section 5.3.4. Word cloud analysis was performed using the gene ontology data described in section 5.3.3.
5.3 RESULTS
5.3.1 Biologically significant differences in the abundance of 69 proteins were identified when the treated BM-MSC proteomes were compared to the control BM-MSC proteome. Fold change is often used to evaluate biologically significant changes within a cellular environment (Fang et al., 2014; S. J. Liu et al., 2015). The fold change of each protein in each of the treatment groups was calculated and compared to that of the control group. Any proteins that had a fold change greater than or equal to two were considered to be biologically significant (Table 5.1). A total of 69 proteins had a greater-than-2-fold change in abundance compared to the control across the five treatment groups. Statistical significance was not yet evaluated.
Table 5.1: Across the five treatment groups, 69 proteins exhibited a greater than 2-fold difference in abundance to the control group. A positive 2-fold change >2 whereas a negative 2-fold change <0.5. Protein Fold Change Protein Name (Protein ID) ID FN FN+FGF2 FN+BMP4 FGF2+BMP4 FN+FGF2+BMP4 P11915 Non-specific lipid-transfer protein (NLTP) 2.6961 2.8013 2.1839 3.4244 3.9156
Q6AYK6 Calcyclin-binding protein (CYBP) 2.5945 2.7167 2.8558 3.8174
Q64194 Lysosomal acid lipase/cholesteryl ester hydrolase 2.3445 2.3103 (LICH) P21396 Amine oxidase [flavin-containing] A (AOFA) 2.2224 3.332 2.8723 3.111
B0BNG0 ER membrane protein complex subunit 2 (EMC2) 2.1442
B0BN94 Protein FAM136A (F136A) 2.0702 2.8488 2.8294
O08730 Glycogenin-1 (GLYG) 2.0352
Q9JJW3 Up-regulated during skeletal muscle growth protein 0.49889 5 (USMG5) Q63524 Transmembrane emp24 domain-containing protein 2 0.47628 (TMED2) P41499 Tumor necrosis factor ligand superfamily member 11 0.46407 0.40872 0.41722 (PTN11) P60522 Gamma-aminobutyric acid receptor-associated 0.4624 protein-like 2 (GBRL2) Q3B7D0 Oxygen-dependent Coproporphyrinogen-III Oxidase, 0.44069 0.29202 0.1527 Mitochondrial (HEM6) Q76LD0 Chordin-like protein 1 (CRDL1) 0.40394
P60123 RuvB-like 1 (RUVB1) 0.38493
Q62991 Sec1 family domain-containing protein 1 (SCFD1) 0.37555
P02466 Collagen Alpha-2(I) chain (CO1A2) 0.3492 5.7499
Q9JLA3 UDP-glucose:glycoprotein glucosyltransferase 1 0.31757 0.47782 0.4785 (UGGG1)
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Protein Fold Change Protein Name (Protein ID) ID FN FN+FGF2 FN+BMP4 FGF2+BMP4 FN+FGF2+BMP4
Q5I0P2 Glycine cleavage system H protein, mitochondrial 0.31313 0.3528 0.29477 (GCSH) Q68FS1 Cytosolic Fe-S cluster assembly factor NUBP2 0.30604 0.4312 (NUBP2) Q62967 Diphosphomevalonate Decarboxylase (MVD1) 0.27893
A1L1I3 Numb-like Protein (NUMBL) 0.20132 0.39652
Q66H59 N-acetylneuraminate lyase (NPL) 0.15659 0.41905
Q9JJP9 Ubiquilin-1 (UBQL1) 0.074763 0.38694 0.39454
P02454 Collagen alpha-1(I) chain (CO1A1) 17.825
P13941 Collagen alpha-1(III) chain (CO3A1) 2.5776
P62282 40S ribosomal protein S11 (RS11) 2.2856 2.8679
Q6RJR6-2 Reticulon-3, Isoform 2 (RTN3) 2.0498 0.49253
P22062 Protein-L-isoaspartate(D-aspartate) O-methyltransferase (PIMT) 0.48886
P11030 Acyl-CoA-binding protein (ACBP) 0.44669
Q5U2U2 Crk-like protein (CRKL) 0.42494
P21913 Succinate dehydrogenase [ubiquinone] iron-sulfur subunit, 0.40541 mitochondrial (SDHB) B0BN18 Prefoldin subunit 2 (PFD2) 0.36477
P20611 Lysosomal Acid Phosphatase (PPAL) 0.087931
P05765 40S Ribosomal Protein S21 (RS21) 0.082464 0.39416
O35824 DnaJ homolog subfamily A member 2 (DNJA2) 3.7865
Q63768 Adapter Molecule CRK (CRK) 3.6445
P58405-2 Striatin-3 (STRN) 2.1745 2.1117 A1L108 Actin-related protein 2/3 complex subunit 5-like protein (ARP5L) 2.1441 2.3702 4.3707
Q63797 Proteasome activator complex subunit 1 (PSME1) 0.49833 0.28625
Q6UPE1 Electron transfer flavoprotein-ubiquinone oxidoreductase, mitochondrial 0.49113 (ETFD) Q4V8C7 Interferon-inducible double-stranded RNA-dependent protein kinase activator 0.48917 A (PRKRA) Q9JM53 Apoptosis-inducing factor 1, mitochondrial (AIFM1) 0.46843
Q5U2Q7 Eukaryotic peptide chain release factor subunit 1 (ERF1) 0.46447
Q5XIG8 Serine-threonine kinase receptor-associated protein (STRAP) 0.45902
Q9JK72 Copper chaperone for superoxide dismutase (CCS) 0.45577
Q62733 Lamina-associated polypeptide 2, isoform beta (LAP2) 0.43832
Q5XIF4 Small Ubiquitin-related Modifier 3 (SUMO3) 0.40417
Q4TU93 C-type mannose receptor 2 (MRC2) 0.39885
P12368 cAMP-dependent protein kinase type II-alpha regulatory subunit (KAP2) 0.39421
Q923V8 Selenoprotein F (SEP15) 0.36218
Q4FZT0 Stomatin-like protein 2, mitochondrial (STML2) 0.11586
O88280 Slit Homolog 3 Protein (SLIT3) 0.076779 0.47123
Q6IFV1 Keratin, type 1 cytoskeletal 14 (K1C14) 2.3602
P83565 39S ribosomal protein L40, mitochondrial (RM40) 2.3424
Q6IFU7 Keratin, type 1 cytoskeletal 42 (K1C42) 2.2947
P63170 Dynein light chain 1, cytoplasmic (DYL1) 0.49662
Q8K3F3 Protein phosphatase 1 regulatory subunit 14 B (PP14B) 0.47512 P70541 Translation initiation factor eIF-2B subunit gamma (EI2BG) 0.45901 0.49775 Q63396 Activated RNA polymerase II transcriptional coactivator p15 (TCP4) 0.43799 0.36211
P07483 Fatty Acid-Binding Protein, heart (FABPH) 0.41813
Q5PQJ6 Pyrroline-5-carboxylat reductase 3 (P5CR3) 3.0801
100 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Protein Fold Change Protein Name (Protein ID) ID FN FN+FGF2 FN+BMP4 FGF2+BMP4 FN+FGF2+BMP4
P28075 Proteasome subunit beta type-5 (PSB5) 0.46245
P13437 3-ketoacyl-CoA thiolase, mitochondrial (THIM) 0.44108
Q4FZU4 ADAMTS-like protein 4 (ATL4) 0.43755
P62634 Cellular nucleic acid-binding protein (CNBP) 0.43151
P83883 60S Ribosomal protein L36a (RL36A) 0.41627
Q9Z2J4 Nexilin (NEXN) 0.33212 Q5PQP1 RNA-binding Motif, Single-stranded-interacting Protein 1 (RBMS1) 0.32033 Q71UF4 Histone binding protein RBBP7 (RBBP7) 0.17258
The FN, FN+FGF2 and the FN+FGF2+BMP4 treatments each had 7 proteins that exhibited a more than 2-fold increase compared to the control group, whereas the FN+BMP4 and FGF2+BMP4 treatments both had 8 proteins that exhibited a more than 2-fold increase in abundance compared to the control. In addition, the FN treatment had 16 proteins that exhibited a more than 2-fold decrease in abundance compared to the control group. Finally, the FN+FGF2, FN+BMP4, FGF2+BMP4 and FN+FGF2+BMP4 treatments had 13, 19, 9 and 12 proteins, respectively, that exhibited a greater than 2-fold decrease in abundance compared to the control group (Table 5.1).
More specifically, non-specific lipid-transfer protein (NLTP) had a greater-than- 2-fold increase in protein abundance in all five treatment groups whilst, calcyclin- binding protein (CYBP) and amine oxidase [flavin-containing] A (AOFA) had a greater-than-2-fold increase in protein abundance in four of the treatment groups (Table 5.1).
Interestingly, collagen alpha-2(I) chain (CO1A2) exhibited a 2.86-fold decrease in protein abundance in the FN treatment, whereas it had a 5.75-fold increase in abundance in the FN+FGF2 treatment. Similarly, reticulon-3, isoform 2 (RET3) was observed to have a 2-fold increase in protein abundance in the FN+FGF2 treatment and a 2-fold decrease in abundance in the FN+BMP4 treatment compared to the control group (Table 5.1).
Ubiquilin-1 (UBQL1) exhibited a 13.38-fold decrease in abundance in the FN treatment, while 40S ribosomal protein S21 (RS21) exhibited a 12.13-fold decrease in the FN+FGF2 treatment. Furthermore, the slit homolog 3 protein (SLIT3) exhibited a 13.02-fold decrease in protein abundance in the FN+BMP4 treatment group.
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Conversely, collagen alpha-1(I) chain (CO1A1) exhibited a 17.83-fold increase in protein abundance in the FN+FGF2 treatment compared to the control (Table 5.1).
5.3.2 Proteins that exhibit biologically significant differences in abundance were associated with GO terms describing neural, bone and muscle differentiation and development. Gene ontology analysis was performed on the protein library generated using DDA-MS to identify which biological processes were represented in each of the BM- MSC cultures examined. Gene ontology terms related to apoptosis and cell death, development and differentiation, migration and motility, cell division and homeostasis were highlighted (Figure 5.1). These GOs were investigated to assess cell survival/growth as well as evaluation of potential differentiation due to stimulation by the different treatments.
Gene ontology analysis was used to identify which GO terms, specifically differentiation related, were associated with the proteins that were either greater than 2-fold increased, or greater than 2-fold decreased in abundance in each of the treatment groups (Table 5.2). This was performed by searching for the changed abundance proteins within the GO data. GO terms of interest were selected (Appendix D) with those related to differentiation and development further investigated (Table 5.2). As a result of this analysis, the proteins with biologically significant abundance changes can be used to evaluate differentiation biological processes that are occurring in the cells of different treatment groups.
A total of 10 proteins with altered abundances were associated with GO terms related to neuronal development. These were: numb-like protein (NUMBL); chordin- like protein 1 (CRDL1); tumor necrosis factor ligand superfamily member 11 (PTN11); transmembrane emp24 domain-containing protein 2 (TMED2); collagen- alpha-1(III) chain (CO3A1); Acyl-CoA binding protein (ACBP); slit homolog 3 protein (SLIT3); apoptosis-inducing factor 1, mitochondrial (AIFM1); translation initiation factor eif-2B subunit gamma (EI2BG); and dynein light chain 1, cytoplasmic (DYL1). Of these proteins, 4 were decreased in the FN treatment and a different set of 4 were also decreased in the FN+BMP4 treatment. PTN11 and ACBP were decreased in the FN+FGF2 treatment, EI2BG and SLIT3 were decreased in the FN+FGF2+BMP4 treatment, and DYL1 and EI2BG were decreased in the
102 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
FGF2+BMP4 treatment. The only proteins that were increased in abundance were CO3A1, in the FN+FGF2 treatment.
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104 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Table 5.2: Proteins with increased and decreased abundance in the treatment groups compared to the control, associated with specific differentiation gene ontologies. GO Term Treatments and Protein IDs † FN FN+FGF2 FN+BMP4 FN+FGF2+BMP4 FGF2+BMP4 ↑ ↓ ↑ ↓ ↑ ↓ ↑ ↓ ↑ ↓ nervous system development NUMBL CO3A1 PTN11 SLIT3 EI2BG DYL1 CRDL1 ACBP NUMBL SLIT3 EI2BG PTN11 PTN11 TMED2 AIFM1 central nervous system NUMBL CO3A1 PTN11 SLIT3 EI2BG DYL1 development PTN11 ACBP NUMBL SLIT3 EI2BG PTN11 neurogenesis NUMBL CO3A1 PTN11 SLIT3 EI2BG EI2BG CRDL1 ACBP NUMBL SLIT3 PTN11 PTN11 AIFM1 gliogenesis PTN11 PTN11 PTN11 EI2BG EI2BG ACBP neuron differentiation NUMBL PTN11 SLIT3 SLIT3 CRDL1 NUMBL PTN11 PTN11 AIFM1 neuron development NUMBL PTN11 SLIT3 SLIT3 PTN11 NUMBL PTN11 generation of neurons NUMBL CO3A1 PTN11 SLIT3 SLIT3 CRDL1 NUMBL PTN11 PTN11 AIFM1 heart development CYBP PTN11 CO3A1 PTN11 CYBP PTN11 CYBP NEXN CYBP TMED2 CRKL cardiovascular system CO1A2 CO1A1 CRKL development TMED2 CO1A2 CO3A1 cardiac muscle tissue CYBP CYBP CYBP NEXN CYBP development muscle cell differentiation CYBP CYBP CYBP NEXN CYBP striated muscle cell NEXN development
striated muscle cell CYBP CYBP CYBP NEXN CYBP differentiation
muscle cell development NEXN muscle structure development CYBP CO3A1 CYBP CYBP NEXN CYBP
ossification CRDL1 CO1A1 MRC2 RS11 RS11 osteoblast differentiation CO1A1 MRC2 RS11 RS11 epithelium development TMED2 ACBP ATL4 K1C14
epithelial cell differentiation ATL4 K1C14
gonad development SLIT3 SLIT3
lung development LICH LICH
† Proteins with decreased abundance are displayed in italics. Arrows indicate increase ↑ or decrease ↓ in protein abundance relative to the control.
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CO3A1 is also associated with heart development. In the FN+FGF2 group, its increase is accompanied by an increase in collagen alpha-1(I) chain (CO1A1) and collagen alpha-2(I) chain (CO1A2), and a decrease in PTN11 and crk-like protein (CRKL), which are all associated with heart development. PTN11 is also decreased in the FN and FN+BMP4 treatments. TMED2 and CO1A2 are also decreased in FN treatment group. Calcyclin-binding protein (CYBP), a heart development protein, is also associated with GO terms relating to muscle cell differentiation, and exhibited an increased protein abundance in all treatments except the FN+FGF2 treatment. Interestingly, within FN+FGF2+BMP4 treatment, alongside an increase in CYBP abundance, nexilin (NEXN), which is also associated with differentiation towards muscle, exhibited a decreased protein abundance.
CO1A1 and 40S ribosomal protein S11 (RS11) are associated with bone development GO terms, and are observed to be increased in abundance in the FN+FGF2 treatment. RS11 is also increased in the FN+FGF2+BMP4 treatment. This may suggest an increase in bone development in the BM-MSCs of these treatments. Conversely, other proteins associated with these GO terms, CRDL1 and c-type mannose receptor 2 (MRC2), are decreased in abundance in the FN and FN+BMP4 treatments respectively, perhaps suggesting that bone differentiation processes are decreased in BM-MSCs in these treatments.
Proteins associated with epithelial differentiation are decreased in the FN, FN+FGF2 and FN+FGF2+BMP4 treatment groups, and increased in the FGF2+BMP4 treatment.
Overall, this suggests that processes involved in neural differentiation of BM- MSCs are suppressed in all treatment groups; heart development processes are decreased in the FN treatment; muscle differentiation processes are increased in FN, FN+BMP4 and FGF2+BMP4 treatments are increased; bone differentiation processes are increased in FN+FGF2 and FN+FGF2+BMP4 treatments but decreased in the FN and FN+BMP4 treatments; and epithelial differentiation is increased in the FGF2+BMP4 treatments but decreased in the FN, FN+FGF2 and the FN+FGF2+BMP4 treatment groups.
106 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
5.3.3 Qualitative analysis, using word clouds, identified the major biological themes associated with the proteins with changed abundance in each of the treatment groups. A word cloud was produced for each of the 2-fold increased/decreased GO lists (Figure 5.2) to ascertain the major biological themes presented by the GO data for each treatment. Words that were not explanatory, for example ‘increase’, ‘decrease’, ‘regulation of’, and ‘cellular’ were removed to enable greater clarity of the major underlying terms. This type of analysis assesses the frequency of words in each body of text to help determine the most important ideas or terms. This approach to GO term analysis was used to provide insight into the predominant processes occurring within the cells of each treatment group based on key words.
To analyse word clouds, the size of each word and the presence or absence of specific words were compared to determine what gene ontology categories were over- represented. The term ‘Metabolic’ was the predominant word in all the word clouds (Figure 5.2). Similarly, the term ‘development’ was also large in all the word clouds. The term ‘Catabolic’ was larger in all the word clouds associated with decreased abundance proteins (Figure 5.2B, D, F, H and J). The term ‘Organization’ was more prevalent in the word clouds with proteins of increased abundance in the FN+FGF2 (Figure 5.2C), FN+BMP4 Figure 5.2E) and FGF2+BMP4 (Figure 5.2I) treatments compared to the word clouds of decreased proteins (Figure 5.2D, F and J). This term is of approximately equal size in both of the FN+FGF2+BMP4 word clouds (Figure 5.2G-H) and smaller in the FN word cloud for increased abundant proteins (Figure 5.2A). The term ‘Actin’ is only present in the word clouds of treatments that contain BMP4 and only in the decreased protein abundance word clouds (Figure 5.2F, H and J).
To assess potential differentiation occurring in the BM-MSCs, terms related to specific differentiated states were evaluated. For example, terms such as ‘heart’, ‘cardiac’ or ‘vascular’ were identified in all the word clouds except the one for decreased abundant proteins in the FGF2+BMP4 treatment (Figure 5.2A-I). Proteins that were decreased in abundance in each of the treatments, as well as the proteins increased in the FN+FGF2 had terms such as ‘neuron’, ‘nervous’, ‘dendrite’ or ‘neurogenesis’ identified in their corresponding word clouds (Figure 5.2B-D, F, H and J).
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For the word clouds of increased abundant proteins in the FN+FGF2 and FN+FGF2+BMP4 treatments (Figure 5.2C and G), as well as the decreased abundant proteins in the FN+BMP4 treatment (Figure 5.2F), terms associated with bone, such as ‘osteoblast’ and ‘ossification’, were present. The term ‘striated’ appeared in the word clouds for the proteins that increased in abundance in the FN+BMP4, FN+FGF2+BMP4 and FGF2+BMP4 treatments (Figure 5.2E, G and I), as well as the proteins that decreased in abundance in the FN+FGF2+BMP4 treatment (Figure 5.2H). Interestingly, the term ‘muscle’ appeared in the same word clouds, as well as in the word cloud for proteins that increased in abundance in the FN treatment (Figure 5.2A, E, and G-I).
Taken together, these data indicate that the major biological themes associated with proteins that were either increased or decreased in abundance varies between the treatments. Metabolism was a major theme in all treatments, whereas themes associated with differentiation were specific to certain treatments.
108 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
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110 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
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5.3.4 Eight proteins that exhibited biologically significant differences in abundance were also determined to be statistically significant in the treatment groups compared to the control. Volcano plots were generated for each of the treatment groups to assess both biological significance and statistical significance of their identified proteins (Figure 5.3). T-tests were used to determine statistical significance between each of the treatment groups and the control group. Statistical significance was accepted if the protein’s p-value was less than 0.05. Fold change was used as a determinant of biological significance, where a fold change greater than two was accepted as biologically significant.
In total, eight proteins were determined to be statistically and biologically significant over the five treatment groups. A single protein was determined to be significant in the FN treatment, Ubiquilin-1 (UBQL1) (Figure 5.3A), while 40S ribosomal protein S21 (RS21) was determined to be statistically and biologically significant in the FN+FGF2 treatment (Figure 5.3B). UBQL1 exhibited a 13.38-fold decrease in abundance. Likewise, RS21 also exhibited a decrease in protein abundance with a fold change of 12.13 (Table 5.3). Both the FN+BMP4 and the FN+FGF2+BMP4 treatments exhibited three proteins that were found to be statistically and biologically significant (Figure 5.3C and Figure 5.3D). Slit 3 homolog protein (SLIT3); stomatin-like protein 2, mitochondrial (STML2); and oxygen-dependent coproporphyrinogen-III oxidase, mitochondrial (HEM6) were statistically and biologically significant for the FN+BMP4 treatment (Figure 5.3C). SLIT3, STML2 and HEM6 all exhibited decreased protein abundance compared to the control with fold changes of 13.02, 8.63 and 6.54, respectively (Table 5.3). Similarly, the FN+FGF2+BMP4 treatment also produced three statistically and biologically significant proteins including: 40s ribosomal protein S11 (RS11); protein FAM136A (F136A); and non-specific lipid-transfer protein (NLTP) (Figure 5.3D). All three of these proteins increased in protein abundance, where RS11 exhibited a fold change of 2.87, F136A exhibited a fold change of 2.83 and NLTP exhibited a fold change of 3.92 (Table 5.3). Interestingly, none of the proteins in the FGF2+BMP4 treatment were determined to be statistically and biologically significant compared to the control conditions (Figure 5.3E).
Taken together, these data indicated that of the 943 proteins detected, only 8 proteins exhibited a statistical and biological difference due to the treatment compared
112 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
to the control group. This suggests that the cell’s response to the treatments, specifically in a 2 % FCS background, is subtle.
Figure 5.3: Eight proteins are statistically and biologically significant across the 5 treatment groups, compared to the control group. Data are presented as log2 of fold change and log10 of p value. Fold change was calculated as a ration of treatment abundance to control abundance, while p values were obtained using pairwise t-tests. Horizontal red lines indicate p>0.05 and vertical red lines indicate positive and negative fold changes >2. A: FN to Control, B: FN+FGF2 to Control, C: FN+BMP4 to Control, D: FN+FGF2+BMP4 to Control, E: FGF2+BMP4 to Control. Proteins that exhibit both statistical and biological significance are labelled as: UBQL1 – Ubiquilin-1; RS21 – 40S ribosomal protein S21; SLIT3 – Slit homolog protein 3; STML2 – Stomatin-like protein 2, mitochondrial; HEM6 – Oxygen-dependent coproporphyrinogen-III oxidase, mitochondrial; RS11 – 40S ribosomal protein S11; F136A – Potein FAM136A; and NLTP – non-specific lipid transfer protein.
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Table 5.3: Significant proteins with greater than 2-Fold Change in abundance compared to control treatment
Protein Accession Protein Name Treatment Fold Change Increased/ p Value ID # Decreased
UBQL1 Q9JJP9 Ubiquilin-1 FN 13.37551 ↓ 0.011768
RS21 P05765 40S Ribosomal Protein S21 FN+FGF2 12.12643 ↓ 0.011698
SLIT3 O88280 Slit Homolog 3 Protein FN+BMP4 13.02433 ↓ 0.000464
STML2 Q4FZT0 Stomatin-like Protein 2, FN+BMP4 8.631162 ↓ 0.001676 mitochondrial
HEM6 Q3B7D0 Oxygen-dependent FN+BMP4 6.548964 ↓ 0.00613 Coproporphyrinogen-III Oxidase, mitochondrial
RS11 P62282 40s Ribosomal Protein S11 FN+FGF2+ 2.870927 ↑ 0.000064 BMP4
F136A B0BN94 Protein FAM136A FN+FGF2+ 2.82937 ↑ 0.028611 BMP4
NLTP P11915 Non-specific Lipid-transfer FN+FGF2+ 3.915583 ↑ 0.034041 Protein BMP4
The abundances of each of the eight significant proteins in each of the treatment groups were plotted to assess the changes in abundance between all treatments (Figure 5.4). Ubiquilin-1 (UBQL1) significantly decreased in the FN treatment (p<0.05) compared to the control group (Figure 5.4A). Similarly, the 40S ribosomal protein S21 (RS21) exhibited a significantly decreased abundance in the FN+FGF2 treatment compared to the control, FN and FN+FGF2+BMP4 treatments (Figure 5.4B). Slit homolog protein 3 (SLIT3), stomatin-like protein 2 (STML) and oxygen-dependent coproporphyrinogen-III oxidase (HEM6) all significantly decreased in the FN+BMP4 treatment, compared to the control (Figure 5.4C-E). Interestingly, SLIT3 also significantly decreased in the FN+BMP4 treatment compared to all other treatment groups. However, 40S ribosomal protein S11 (RS11), protein FAM136A (F136A) and non-specific lipid transfer protein (NLTP) all significantly increased in abundance in the FN+FGF2+BMP4 treatment (Figure 5.4F-H). Taken together, these data indicate that treatment with different combinations of FN, FGF2 and BMP4 result in increased and decreased abundance of different proteins, suggesting that the biological processes occurring within BM-MSCs in one treatment differ from those occurring in BM-MSCs treated with a different combination of supplements.
114 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
Figure 5.4: The abundances of 8 proteins were statistically and biologically significantly different in one of the treatment groups compared to the control. Data is presented as the median log 2 abundance with the upper and lower quartiles, where the whiskers represent the most extreme abundance value within 1.5× interquartile range (n = 3). Statistical analysis was performed by pairwise t-tests where significance was accepted where p<0.05 = #, along with one-way ANOVA followed by TukeyHSD posthoc test where significance was accepted where p<0.05 = *. Abundances of A: Ubiquilin-1, B: 40S ribosomal protein S21, C: Slit homolog 3 protein, D: Stomatin-like protein 2, mitochondrial, E: Oxygen-dependent coproporphyrinogen- III oxidase, mitochondrial, F: 40S ribosomal protein S11, G: Protein FAM136A and H: Non- specific lipid transfer protein, across all treatment groups.
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5.4 DISCUSSION
In the analysis described in this chapter, changes in individual protein abundance between treatment groups and the control were investigated. A total of 69 proteins were identified as biologically significant across the treatment groups compared to the control (Table 5.1). These proteins were used to determine what the predominant biological processes were occurring within the BM-MSCs cultured in each of the treatment groups, by evaluating the gene ontologies they are associated with (Table 5.2). Using this method, it was determined that neural differentiation processes were suppressed by all treatment groups, as several proteins associated with neural development exhibited decreased abundance in each of the treatment groups.
Processes of ossification, or bone differentiation were found to increase in treatments that contained both FN and FGF2 as a result of increased abundances of collagen alpha-1(I) and 40S ribosomal protein S11 (RS11) (Table 5.2). Conversely, treatments containing FN and FGF2 were found to have decreased epithelial differentiation processes due to a decrease in protein abundance of acyl-coA-binding protein (ACBP) and ADAMTS-like protein 4 (ATL4). Increased abundance of calcyclin-binding protein (CYBP) in FN, FN+BMP4 and FGF2+BMP4 treatment suggested increased muscle differentiation of the BM-MSCs, however, decreased abundance of nexilin (NEXN) in the FN+FGF2+BMP4 treatment suggested decreased muscle differentiation. Interestingly, CRKL, a protein associated with the FGF2 signalling pathway (Ornitz & Itoh, 2015), exhibited a decreased abundance in the FN+FGF2 treatment.
Word cloud analysis (Figure 5.2) revealed the major themes associated with each of the treatments. All five treatments exhibited decreased abundance for proteins associated with neural differentiation. However, FN+BMP4 exhibited decreased abundance for proteins associated with bone differentiation, whilst FN+FGF2 and FN+FGF2+BMP4 exhibited increased abundance for proteins associated with bone differentiation. Additionally, all treatments containing BMP4 exhibited an increase in abundance for proteins associated with muscle differentiation. This suggests that while all treatments possibly supress neural differentiation, FN+FGF2 may also promote bone differentiation; FN+BMP4 and FGF2+BMP4 may promote muscle differentiation; and, FN+FGF2+BMP4 may promote both bone and muscle differentiation. In saying this, the word clouds only provide a qualitative overview of
116 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
the GO analysis and can easily be over-interpreted. Therefore, evaluation of the functions of individual proteins may provide better insight into the changed biology of the BM-MSCs.
Eight proteins were observed to exhibit a statistically and biologically significant difference between the treatment groups and the control (Figure 5.3). Ubiquilin-1 (UBQL1) is a ubiquitin-like protein involved in endoplasmic-reticulum-associated protein degradation (ERAD) (Lim et al., 2009) and exhibited a 13.38-fold decrease in BM-MSCs cultured on FN, with no other supplements (Figure 5.4A). UBQL1 has been found to protect against oxidative stress and increase the response to ischemic stroke in mice through the removal of damaged proteins (Y. Liu et al., 2014). Similarly, UBQL1 is known to remove beta-amyloid, a protein that is over abundant in the brain in Alzheimer’s disease, through amyloid precursor protein (APP) manipulation (Takalo et al., 2013). Additionally, UBQL1 was found to be down regulated in the neural cell line ReNCell VM (Hoffrogge et al., 2006). Thus, the decrease in abundance of UBQL1 exhibited by the FN treatment suggests reduced protein degradation by the BM-MSCs. This could suggest that the BM-MSCs may have the potential to differentiate into a neural lineage cell. However, it is likely that the UBQL1 reduction may more be related to BM-MSC survival, which was supported by the GO data (Appendix D, page 153).
40S Ribosomal Protein S21 (RS21) is a small cytosolic ribosomal subunit important for protein production (Collatz et al., 1977) and was observed to decrease in abundance by 12.13-fold in the FN+FGF2 treatment group (Figure 5.4B). This protein has been observed to increase the expression of genes responsible for cell motility and matrix biosynthesis in bacillus subtilis (Takada et al., 2014), as well as impair photosynthesis and sugar-response when knocked down in the plant, Arabidopsis thaliana (Morita-Yamamuro et al., 2004). The specific functional role of this protein in BM-MSC stemness and differentiation has yet to be investigated. Specific investigation into its effect on migration and matrix production should be investigated in the future.
Slit homolog protein 3 (SLIT3) is a developmental protein that plays a role in axon guidance (Knoll et al., 2003; Patel et al., 2001). SLIT3 is has also been observed in lung development (Doi et al., 2009) and has been used in tissue engineering to promote angiogenesis (Paul et al., 2013). It exhibited a 13.02-fold decrease in
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abundance in BM-MSCs cultured on FN and supplemented with BMP4 (Figure 5.4C). In accordance with the gene ontology data, a decrease in the abundance of SLIT3 in the FN+BMP4 treatment suggests that neural differentiation and angiogenic processes may be decreased in the BM-MSCs of this culture. Perhaps this is an indicator that other differentiation pathways are being favoured, such as bone or cartilage.
Similarly, stomatin-like protein 2, mitochondrial (STML2) also exhibited an 8.63-fold decrease in abundance in the FN+BMP4 treatment (Figure 5.4C). STML2 has been observed in human oesophageal squamous cell carcinoma as a regulator of cell growth and cell adhesion and may play a significant role in tumorigenesis (L. Zhang et al., 2006). It has also been observed to have a protective effect in neurons and may be a potential therapeutic for Parkinson’s diseases (Zanon et al., 2017). This protein has yet to be investigated in BM-MSCs. However, this provides an avenue for future work, as it would be interesting to evaluate its effect on the growth and adhesion of BM-MSCs in response to specific extracellular matrix and growth factor combinations.
Additionally, oxygen dependent coproporphyrinogen-III oxidase, mitochondrial (HEM6), a protein involved in heme biosynthesis (Elder & Evans, 1978), was also decreased in the FN+BMP4 treatment with a fold change of 6.55 (Figure 5.4C). Aside from its role in protoporphyrin IX biosynthesis (F. Li et al., 1986; Macieira et al., 2003), other biological roles of HEM6 are unknown. Perhaps this protein should be investigated in regard to energy production and cell survival, due to its mitochondrial location within the cell.
Growth of BM-MSCs on FN, and stimulation with FGF2 and BMP4 increased the abundance of three proteins. 40S Ribosomal Protein S11 (RS11), protein FAM136A (F136A) and non-specific lipid-transfer protein (NLTP). RS11 is a small cytosolic ribosomal subunit and was observed to have an increased protein abundance of 2.87-fold (Figure 5.4D). Interestingly, RS11 has been observed to have a 3-fold increase in hMSCs after induction towards bone (Foster et al., 2005). This suggests that perhaps the BM-MSCs, in the study described herein, were beginning to differentiate towards bone, as a result of stimulation with FGF2 and BMP4, whilst grown on a FN substrate. RS11 has also been observed to decrease in abundance in human breast carcinoma cells that underwent apoptosis (Nadano et al., 2001). Perhaps,
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an increase in RS11 in the BM-MSCs in the FN+FGF2+BMP4 treatment suggests that the cells are thriving, as opposed to undergoing apoptosis.
F136A is a mitochondrial protein of unknown function and it was observed to increase in abundance by 2.83-fold (Figure 5.4D). Very little is known about protein F136A, however, a nonsense mutation of the gene encoding the F136A protein has been associated with familial Meniere’s disease (Requena et al., 2015), a disease of the inner ear caused by fluid accumulation. This protein was also identified in research that examined the development of zebra fish gastrointestinal tract, however its function was not evaluated (Stuckenholz et al., 2009). Future studies investigating this protein are needed to determine its function, specifically in BM-MSCs.
NLTP increased in abundance in all five treatments however, it only significantly increased in the FN+FGF2+BMP4 treatment, by 3.91-fold (Figure 5.4D). This protein is involved in lipid transfer (Schroeder et al., 2003), however, hasn’t been specifically investigated in BM-MSCs. NLTP is known to bind to caveolin-1 (Zhou et al., 2004), which is a protein that has been suggested to supress osteogenesis in human MSCs through the PI3k/Akt pathway (Baker et al., 2015). However, the effect that NLTP has on osteogenesis is unknown. Interestingly, this pathway is stimulated by both FN via focal adhesion kinases, and FGF2 via receptor tyrosine kinases (Ornitz & Itoh, 2015; Yousif, 2014).
The role that each of these eight proteins have within MSCs should be investigated, specifically their effect on the differentiation status of BM-MSCs. This could be achieved using overexpression or knock out studies, through lentiviral vectors and short hairpin RNA. Additionally, phosphoproteomics could be utilised to evaluate the role of these proteins in cell signalling, specifically NLTP. Other future studies that investigate the changes in expression of each of these proteins through RT-PCR methods would also be beneficial as it is unclear whether a change in the transcription of these proteins is wholly responsible for their changed protein abundance. Additionally, it would be of interest to evaluate the effect that increased FN and growth factor concentration would have on the abundance of these proteins.
Based on these eight proteins, each of the treatment groups stimulated different changes in the biology of the BM-MSCs. These data suggest that FN alone may disrupt protein degradation through interference of the ERAD pathway, which is crucial to maintain stemness and cell viability. In addition, FN+BMP4 seemed to suppress neural
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lineage differentiation, which is also important to maintain stemness, however, it may also indicate a transition down a different lineage pathway. Furthermore, FN+FGF2+BMP4 promotes bone differentiation, which would not be ideal for maintaining stemness. However, FGF2+BMP4 alone seems to maintain the BM-MSCs in a state similar to that of minimal stimulation. Taken together this indicates that FN was important for mediating the biological effects of FGF2 and BMP4. Due to limited knowledge on the functions of RS21, it is challenging to evaluate the changes to BM- MSC biology that the FN+FGF2 treatment induced, however, GO analysis would suggest that perhaps bone differentiation was partially induced. These biological processes should be further investigated in BM-MSCs to validate the effect each of the treatments have on these cells along with more in-depth functional analysis of the proteins with a biologically and statistically significant difference in abundance between the treatment and the control.
5.5 CHAPTER 5 SUMMARY
Through the investigation of individual protein abundance variations between BM-MSCs cultured with FN, FGF2 and/or BMP4, and BM-MSCs cultured with minimal stimulation, it was determined that different combinations of the supplements result in different changes to the biology of the cells. BM-MSCs cultured in the presence of both FGF2 and BMP4, on an uncoated flask most closely resembled BM- MSCs that were minimally stimulated and could be a promising start for developing an optimal culture condition for maintenance of BM-MSC stemness. However, FN or other extracellular matrix proteins may be important for enabling manipulation of BM- MSC biology using various growth factors. These biological changes require further investigation and validation, which unfortunately exceed the scope of this research.
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Chapter 6: General Discussion and Conclusions
Mesenchymal stromal cells remain a hopeful candidate for clinical application, however, the therapeutic use of BM-MSCs is hindered by the use of FCS in sub- optimal culture conditions that don’t maintain their stem qualities (Gu et al., 2016). Research outlined within this thesis aimed to determine whether a lower concentration of FCS could be used in culture of BM-MSCs for proteomic research purposes, and evaluated the effect that fibronectin, fibroblast growth factor 2 and/or bone morphogenetic protein 4 had on the biology of BM-MSCs. These specific growth factors were chosen based on previous studies where they exhibited stemness promoting abilities in MSC-like cells (S. An et al., 2015; L. Liu et al., 2013).
Foetal calf serum remains one of the challenges of using BM-MSCs in clinical application, primarily because contains pathogens, harmful to humans (Spees et al., 2004; Tuschong et al., 2002). However, it is difficult to remove FCS completely from cultures of BM-MSCs at present because there is still limited knowledge on what conditions are optimal for their growth. To investigate the effect of different culture conditions on the growth of BM-MSCs, FCS needs to be present in a concentration that will keep the cells viable for the course of the research without greatly influencing changes to their biology. In time, as more information is gathered about which supplements promote BM-MSC growth, and an optimal culture condition becomes closer to fruition, it may be possible to remove the FCS completely. In the research outlined within this thesis, it was determined that 2 % FCS maintains the survival of BM-MSCs, and additionally, exhibits limited interference with proteomic analysis methods. Simply, lower amounts of serum result in increased depth of proteomic analysis, as supported by previous research (Nonnis et al., 2016).
Optimal culture conditions for the growth and maintenance of BM-MSCs are required for them to be utilised as an off-the-shelf therapeutic. These conditions need to suppress differentiation of the BM-MSCs and promote stemness. Both FGF2 and BMP4 have individually been observed to increase stemness qualities in stem-like cells previously, however, they had not been evaluated in BM-MSCs when
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supplemented together until now. The research outlined within evaluated the effect that each of these growth factors had on the biology of BM-MSCs, cultured in flasks coated with FN, based on changes to their proteome. It was determined that different combinations of the three supplements each had a different effect on the biology of the cells.
Initially, BM-MSCs that were grown on FN coated flasks, seemed to have increased growth during serum starvation. The FN present within serum, and therefore present in the culture medium, seemed to have minimal effect on the biology of the BM-MSCs compared to the use of FN as a substrate, which seemed to dramatically change the biology of the BM-MSCs. This emphasizes the idea that all aspects of the culture environment need to be considered when attempting to develop optimal culture conditions. In addition, it highlights a number of potential areas for future research investigating the effect of different substrates on the growth of BM-MSCs, in conjunction with other growth factors, including how such factors are presented to the cells.
Each of the treatment conditions investigated in this study exhibited different effects on the protein profiles of the BM-MSCs. For example, the BM-MSCs grown on FN coated flasks that were supplemented with both FGF2 and BMP4 exhibited increases in abundance of proteins associated with bone differentiation whereas supplementation of BM-MSCs with FGF2 and BMP4 without the presence of FN exhibited biology more like BM-MSCs cultured in only 2 % FCS. Investigation into individual protein changes revealed eight proteins that had biologically and statistically changed abundances between the treatment groups and the control. Of these eight proteins, five have unknown functional roles within BM-MSCs. To make the most of the research presented within, further studies are required to evaluate the role of these proteins. More specifically, the role of RS21 in migration and matrix production/deposition in BM-MSCs should be evaluated as previous research has observed that this protein increases motility and matrix biosynthesis in bacillus subtilis (Takada et al., 2014). Additionally, the role of STML2 in BM-MSC growth and adhesion should be further investigated, as it has been observed to increase these properties in specific cancers (L. Zhang et al., 2006). HEM6 is a known heme biosynthesis protein and could potentially play a role in energy production, due to its mitochondrial location. The limited research surrounding the protein F136A means
124 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
that it’s functional role is unknown not only in BM-MSCs, but most cell types. It is a mitochondrial protein and therefore may have a role in energy production, however future research is required to determine this. Finally, NLTP should be evaluated with regards to bone differentiation of BM-MSCs, as it is known to bind caveolin-1, a protein that has been suggested to suppress osteogenesis (Zhou et al., 2004). Determination of the functional roles of these five proteins will enhance the understanding of how the treatments investigated in this research change the biology of BM-MSCs.
Additionally, another outcome of this research is the generation of a rat BM- MSC protein spectral library that can be used for future SWATH-MS analysis. The use of iRTs within this study will allow for this library to be utilised in future research, as they account for minor shifts in chromatography and instrument variation. These peptides also allow for concatenation of this library with other BM-MSC spectral libraries that also contain iRTs.
The research outlined in this thesis evaluated the effect that culture conditions had on the BM-MSCs using a proteomics approach. Other methods should also be utilised to evaluate different aspects of the biology that FN, FGF2 and BMP4 influence. Characteristics such as cell viability (Zeng et al., 2017), cell proliferation (F. Zhang et al., 2015), migration (Kasten et al., 2014), transcription rate (Almalki & Agrawal, 2016) and differentiation capacity (Alimperti et al., 2014) should also be evaluated through further research. Importantly, when considering new culture conditions, when the time comes for the BM-MSCs to be differentiated, they must maintain the ability to do so into the desired lineage. A culture condition that limits the differentiation potential of the BM-MSCs is not optimal. More in-depth proteomic analyses of defined culture medium effects on BM-MSC biology are required before an optimal culture condition can be achieved. Cell signalling could also be evaluated through phosphoproteomic approaches (Humphrey et al., 2015).
Within this research, the culture medium was not characterised and therefore another interesting avenue of research would be to perform proteomic and metabolomic analysis on the culture medium. A number of proteins are secreted from the cells and could be potential indicators of either differentiation or stemness, which may allow for monitoring of the biological state of the BM-MSCs without harming the culture.
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Furthermore, evaluation of the effect that the culture conditions had on the biology of the BM-MSCs was only performed over a short culture period. It would be interesting to determine their effects in longer term culture as well as over multiple passages. This could be achieved through investigation of the biological effect at different time points and would hopefully provide insight as to whether the cells can be sustained for long periods of time within the specific culture conditions. In addition, the effect that the time of stimulation has on the BM-MSCs could be evaluated. What happens to the BM-MSCs if they are only stimulated until confluent compared to if they are continuously stimulated with growth factors? Moreover, validation of the findings presented within this thesis should also be performed. Western blot analysis, staining for differentiation markers, ELIZA and flow cytometry are all possible methods for achieving this.
Alternate evaluation methods may provide a different perspective on which proteins should be targeted. Even though fold change is commonly used to determine biologically significant changes in protein abundance, perhaps using magnitude of abundance change as a determinant of biological significance would provide useful information.
A number of limitations impacted this research. Access to rat BM-MSCs was limited, and therefore the effect of FCS was only evaluated in 2 replicates, while the effect of FN, FGF2 and BMP4 was evaluated in 3 replicates. Additionally, this meant that only 3 concentrations of FCS were investigated. Ideally, the effect of 5% and 8% FCS would have been evaluated as well. Furthermore, cell viability wasn’t performed, and therefore, cell survival was evaluated based on proteins related to apoptosis and cell death. Only 1 technical replicate was evaluated for each of the samples. Additional technical replicates, or replication of this study, would increase the statistical robustness of data. Finally, individual protein changes were not evaluated in the FCS concentration study, and therefore the depth of the analysis is less than that of the subsequent study. Perhaps investigation of individual protein changes would have enhanced the understanding of the effect that serum had on the BM-MSCs.
Overall, the findings of this research suggest that 2 % FCS is suitable for culturing BM-MSCs in a minimally stimulated environment, for research purposes; FN suppresses neural lineage differentiation; the combination of FN, FGF2 and BMP4 results in bone differentiation of BM-MSCs in short term culture; and stimulation with
126 Proteomic Characterisation of Rat Bone Marrow-Derived Mesenchymal Stromal Cells Cultured in ‘Stemness’ Promoting Conditions.
FGF2 and BMP4 in the absence of FN, maintains the stemness of BM-MSCs. However, validation and further study is required to better understand the functional effects that these conditions have on the BM-MSCs. The work presented within this thesis, has contributed knowledge towards the effect FN, FGF2 and BMP4 have on BM-MSCs which will assist future research into the development of optimal culture conditions for therapeutic use.
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Appendices
Appendix A: The protein library created using DDA-MS analysis contained 943 rat BM-MSC proteins. This library was created using the DDA-MS data obtained from a pooled BM-MSC protein isolate that was fractionated via iso-electric focusing and LDS-page separation.
Accession # Gene Accession # Gene Accession # Gene Accession # Gene A0JPJ7 OLA1_RAT P19234 NDUV2_RAT P63039 CH60_RAT Q63584 TMEDA_RAT A0JPM9 EIF3J_RAT P19511 AT5F1_RAT P63074 IF4E_RAT Q63598 PLST_RAT A0JPQ9 CHID1_RAT P19804 NDKB_RAT P63081 VATL_RAT Q63610 TPM3_RAT A1L108 ARP5L_RAT P19944 RLA1_RAT P63086 MK01_RAT Q63610-2 TPM3_RAT A1L1I3 NUMBL_RAT P19945 RLA0_RAT P63102 1433Z_RAT Q63617 HYOU1_RAT A2RUW1 TOLIP_RAT P20070 NB5R3_RAT P63159 HMGB1_RAT Q63644 ROCK1_RAT A7VJC2 ROA2_RAT P20280 RL21_RAT P63170 DYL1_RAT Q63692 CDC37_RAT B0BN18 PFD2_RAT P20611 PPAL_RAT P63174 RL38_RAT Q63716 PRDX1_RAT B0BN85 SGT1_RAT P20717 PADI2_RAT P63245 RACK1_RAT Q63768 CRK_RAT B0BN93 PSD13_RAT P20909-6 COBA1_RAT P63259 ACTG_RAT Q63769 SRPX_RAT B0BN94 F136A_RAT P20961 PAI1_RAT P63324 RS12_RAT Q63797 PSME1_RAT B0BNA7 EIF3I_RAT P21396 AOFA_RAT P63326 RS10_RAT Q63798 PSME2_RAT B0BNE5 ESTD_RAT P21531 RL3_RAT P63331 PP2AA_RAT Q63945-2 SET_RAT B0BNF1 SEPT8_RAT P21533 RL6_RAT P67779 PHB_RAT Q64057 AL7A1_RAT B0BNG0 EMC2_RAT P21670 PSA4_RAT P68101 IF2A_RAT Q64060 DDX4_RAT B0BNM1 NNRE_RAT P21775 THIKA_RAT P68182 KAPCB_RAT Q64119 MYL6_RAT B0K020 CISD1_RAT P21913 SDHB_RAT P68255 1433T_RAT Q64122 MYL9_RAT B0K025 OSTC_RAT P22062 PIMT_RAT P68511 1433F_RAT Q64194 LICH_RAT B2GUZ5 CAZA1_RAT P22734 COMT_RAT P69897 TBB5_RAT Q641X3 HEXA_RAT B2GV06 SCOT1_RAT P22985 XDH_RAT P70470 LYPA1_RAT Q641Y0 OST48_RAT B2GV24 UFL1_RAT P23358 RL12_RAT P70490 MFGM_RAT Q641Y8 DDX1_RAT B2RYF7 WASH1_RAT P23514 COPB_RAT P70541 EI2BG_RAT Q641Z6 EHD1_RAT B2RYG6 OTUB1_RAT P23928 CRYAB_RAT P70580 PGRC1_RAT Q64303 PAK2_RAT B2RYW9 FAHD2_RAT P23965 ECI1_RAT P70615 LMNB1_RAT Q64361 LXN_RAT B2RZ37 REEP5_RAT P24049 RL17_RAT P70645 BLMH_RAT Q64428 ECHA_RAT B2RZ78 VPS29_RAT P24050 RS5_RAT P80254 DOPD_RAT Q64537 CPNS1_RAT B3GNI6-2 SEP11_RAT P24268 CATD_RAT P81155 VDAC2_RAT Q64654 CP51A_RAT B4F7E8 NIBL1_RAT P24368 PPIB_RAT P81795 IF2G_RAT Q66H59 NPL_RAT B5DEH2 ERLN2_RAT P25086 IL1RA_RAT P81799 NAGK_RAT Q66H80 COPD_RAT B5DF91 ELAV1_RAT P25113 PGAM1_RAT P82471 GNAQ_RAT Q66H94 FKBP9_RAT B5DFN2 SAHH2_RAT P25235 RPN2_RAT P82995 HS90A_RAT Q66HA6 ARL8B_RAT D3Z8L7 RRAS_RAT P26051 CD44_RAT P83565 RM40_RAT Q66HA8 HS105_RAT D3ZAF6 ATPK_RAT P26284 ODPA_RAT P83732 RL24_RAT Q66HD0 ENPL_RAT D3ZHA0 FLNC_RAT P26772 CH10_RAT P83868 TEBP_RAT Q66HF1 NDUS1_RAT D3ZTX0 TMED7_RAT P27274 CD59_RAT P83883 RL36A_RAT Q66HF9 LRRF1_RAT D3ZW55 ITPA_RAT P27321 ICAL_RAT P84079 ARF1_RAT Q66HG4 GALM_RAT D4A1R8 CPNE1_RAT P27605 HPRT_RAT P84100 RL19_RAT Q66HL2 SRC8_RAT
Appendices 143
D4AE41 RMXL1_RAT P27615 SCRB2_RAT P84245 H33_RAT Q66HR2 MARE1_RAT E9PU28 IMDH2_RAT P27653 C1TC_RAT P84817 FIS1_RAT Q66X93 SND1_RAT F1LMZ8 PSD11_RAT P27867 DHSO_RAT P85108 TBB2A_RAT Q68A21 PURB_RAT F1LQ48 HNRPL_RAT P27952 RS2_RAT P85125 PTRF_RAT Q68FP1-2 GELS_RAT F2Z3T4 MBNL2_RAT P28023 DCTN1_RAT P85515 ACTZ_RAT Q68FQ0 TCPE_RAT G3V9R8 HNRPC_RAT P28073 PSB6_RAT P85834 EFTU_RAT Q68FR6 EF1G_RAT O08557 DDAH1_RAT P28075 PSB5_RAT P85845 FSCN1_RAT Q68FR9-2 EF1D_RAT O08628 PCOC1_RAT P28480 TCPA_RAT P85968 6PGD_RAT Q68FS1 NUBP2_RAT O08629 TIF1B_RAT P28648 CD63_RAT P85970 ARPC2_RAT Q68FS4 AMPL_RAT O08651 SERA_RAT P29147 BDH_RAT P85971 6PGL_RAT Q68FU3 ETFB_RAT O08730 GLYG_RAT P29266 3HIDH_RAT P85972 VINC_RAT Q68FX0 IDH3B_RAT O35115 FHL2_RAT P29314 RS9_RAT P85973 PNPH_RAT Q68FY0 QCR1_RAT O35142 COPB2_RAT P29315 RINI_RAT P86252 PURA_RAT Q6AXR4 HEXB_RAT O35217 MINP1_RAT P29410 KAD2_RAT P97536 CAND1_RAT Q6AXS5 PAIRB_RAT O35244 PRDX6_RAT P29411 KAD3_RAT P97584 PTGR1_RAT Q6AXT5 RAB21_RAT O35263 PA1B3_RAT P29419 ATP5I_RAT P97615 THIOM_RAT Q6AY09 HNRH2_RAT O35264 PA1B2_RAT P29457 SERPH_RAT P97629 LCAP_RAT Q6AY23 P5CR2_RAT O35303-6 DNM1L_RAT P29975 AQP1_RAT P97697 IMPA1_RAT Q6AY25 TMED3_RAT O35509 RB11B_RAT P30009 MARCS_RAT P97700 M2OM_RAT Q6AY61 PRS23_RAT O35567 PUR9_RAT P30121 TIMP2_RAT P97852 DHB4_RAT Q6AY63 NUDT5_RAT O35760 IDI1_RAT P30349 LKHA4_RAT Q00238 ICAM1_RAT Q6AY84 SCRN1_RAT O35763 MOES_RAT P30427-3 PLEC_RAT Q00438 PTBP1_RAT Q6AYC4 CAPG_RAT O35783 CALU_RAT P30904 MIF_RAT Q00657 CSPG4_RAT Q6AYD3 PA2G4_RAT O35796 C1QBP_RAT P30919 ASPG_RAT Q00715 H2B1_RAT Q6AYE2 SHLB1_RAT O35814 STIP1_RAT P31000 VIME_RAT Q00981 UCHL1_RAT Q6AYH5 DCTN2_RAT O35824 DNJA2_RAT P31044 PEBP1_RAT Q01205 ODO2_RAT Q6AYK6 CYBP_RAT O35828 CORO7_RAT P31232 TAGL_RAT Q02253 MMSA_RAT Q6AYK8 EIF3D_RAT O35854 BCAT2_RAT P31399 ATP5H_RAT Q02589 ADPRH_RAT Q6AYT3 RTCB_RAT O35952 GLO2_RAT P31643 SC6A6_RAT Q03344 ATIF1_RAT Q6AYZ1 TBA1C_RAT O35964 SH3G1_RAT P31977 EZRI_RAT Q04462 SYVC_RAT Q6AZ50 ATG3_RAT O35987 NSF1C_RAT P32089 TXTP_RAT Q05030 PGFRB_RAT Q6B345 S10AB_RAT O54698 S29A1_RAT P32551 QCR2_RAT Q05175 BASP1_RAT Q6IFU7 K1C42_RAT O54975 XPP1_RAT P33436 MMP2_RAT Q05962 ADT1_RAT Q6IFU8 K1C17_RAT O55012-2 PICAL_RAT P34058 HS90B_RAT Q05982 NDKA_RAT Q6IFV1 K1C14_RAT O55096 DPP3_RAT P34064 PSA5_RAT Q06647 ATPO_RAT Q6IG01 K2C1B_RAT O55171 ACOT2_RAT P34067 PSB4_RAT Q07009 CAN2_RAT Q6IRK9 CBPQ_RAT O70199 UGDH_RAT P35213 1433B_RAT Q07205 IF5_RAT Q6IUR5 NENF_RAT O70351 HCD2_RAT P35281 RAB10_RAT Q07258 TGFB3_RAT Q6MG60 DDAH2_RAT O70593 SGTA_RAT P35427 RL13A_RAT Q07266-2 DREB_RAT Q6MG61 CLIC1_RAT O88280 SLIT3_RAT P35434 ATPD_RAT Q07936 ANXA2_RAT Q6NYB7 RAB1A_RAT O88453 SAFB1_RAT P35435 ATPG_RAT Q07984 SSRD_RAT Q6P502 TCPG_RAT O88600 HSP74_RAT P35465 PAK1_RAT Q08163 CAP1_RAT Q6P686 OSTF1_RAT O88656 ARC1B_RAT P35565 CALX_RAT Q08290 CNN1_RAT Q6P6Q2 K2C5_RAT O88677 BTG3_RAT P35704 PRDX2_RAT Q09073 ADT2_RAT Q6P6R2 DLDH_RAT O88761 PSMD1_RAT P36201 CRIP2_RAT Q10728-2 MYPT1_RAT Q6P6T4 EMAL2_RAT O88767 PARK7_RAT P36506 MP2K2_RAT Q1JU68 EIF3A_RAT Q6P6V0 G6PI_RAT O88989 MDHC_RAT P36972 APT_RAT Q2PQA9 KINH_RAT Q6P742 PLP2_RAT O89046 COR1B_RAT P37285 KLC1_RAT Q32PX7 FUBP1_RAT Q6P767 PTTG_RAT
144 Appendices
O89049 TRXR1_RAT P37397 CNN3_RAT Q3B7D0 HEM6_RAT Q6P791 LTOR1_RAT P00406 COX2_RAT P37996 ARL3_RAT Q3B8Q2 IF4A3_RAT Q6P799 SYSC_RAT P00507 AATM_RAT P38062 MAP2_RAT Q3KR86 MIC60_RAT Q6P7A9 LYAG_RAT P00787 CATB_RAT P38650 DYHC1_RAT Q3MIE4 VAT1_RAT Q6P7B0 SYWC_RAT P01041 CYTB_RAT P38652 PGM1_RAT Q3SWU3 HNRDL_RAT Q6P7C7 GPNMB_RAT P01830 THY1_RAT P38659 PDIA4_RAT Q3T1I4 PRRC1_RAT Q6P7P5 BZW1_RAT P02401 RLA2_RAT P38983 RSSA_RAT Q3T1J1 IF5A1_RAT Q6P7Q4 LGUL_RAT P02454 CO1A1_RAT P39052-4 DYN2_RAT Q3T1J9 MOB1A_RAT Q6P7S1 ASAH1_RAT P02466 CO1A2_RAT P39069 KAD1_RAT Q3T1K5 CAZA2_RAT Q6P9T8 TBB4B_RAT P02793 FRIL1_RAT P40112 PSB3_RAT Q497B0 NIT2_RAT Q6P9U3 COMD3_RAT P04182 OAT_RAT P40241 CD9_RAT Q498E0 TXD12_RAT Q6PCU2 VATE1_RAT P04256 ROA1_RAT P40307 PSB2_RAT Q498U4 SARNP_RAT Q6PDU1 SRSF2_RAT P04550 PTMS_RAT P40329 SYRC_RAT Q499N6 UBXN1_RAT Q6PDU7 ATP5L_RAT P04631 S100B_RAT P41123 RL13_RAT Q4AEF8 COPG1_RAT Q6PDV7 RL10_RAT P04636 MDHM_RAT P41350 CAV1_RAT Q4FZT0 STML2_RAT Q6PEC1 TBCA_RAT P04642 LDHA_RAT P41498 PPAC_RAT Q4FZT6 H2A3_RAT Q6PEC4 SKP1_RAT P04644 RS17_RAT P41499 PTN11_RAT Q4FZT9 PSMD2_RAT Q6Q0N1 CNDP2_RAT P04692-2 TPM1_RAT P41542 USO1_RAT Q4FZU2 K2C6A_RAT Q6QD51 CCD80_RAT P04692-6 TPM1_RAT P41562 IDHC_RAT Q4FZU4 ATL4_RAT Q6RJR6-2 RTN3_RAT P04692-7 TPM1_RAT P41565 IDHG1_RAT Q4FZU6 ANXA8_RAT Q6RUV5 RAC1_RAT P04762 CATA_RAT P41740 ANPRC_RAT Q4FZY0 EFHD2_RAT Q6TEK3 VKORL_RAT P04764 ENOA_RAT P42123 LDHB_RAT Q4G061 EIF3B_RAT Q6TEK4 VKOR1_RAT P04785 PDIA1_RAT P42676 NEUL_RAT Q4KLF8 ARPC5_RAT Q6TUG0 DJB11_RAT P04797 G3P_RAT P42930 HSPB1_RAT Q4KM49 SYYC_RAT Q6UPE1 ETFD_RAT P04897 GNAI2_RAT P43244 MATR3_RAT Q4KM73 KCY_RAT Q6URK4 ROA3_RAT P04905 GSTM1_RAT P45479 PPT1_RAT Q4KMA2 RD23B_RAT Q6URK4-2 ROA3_RAT P04906 GSTP1_RAT P45592 COF1_RAT Q4QQR9 MEMO1_RAT Q6VBQ5 MYADM_RAT P04937 FINC_RAT P45953 ACADV_RAT Q4QQT4 2AAB_RAT Q6VV72 IF1A_RAT P05065 ALDOA_RAT P46413 GSHB_RAT Q4QQW4 HDAC1_RAT Q6W3E9 P4HA3_RAT P05197 EF2_RAT P46462 TERA_RAT Q4QQW8 PLBL2_RAT Q711G3 IAH1_RAT P05369 FPPS_RAT P46844 BIEA_RAT Q4QRB4 TBB3_RAT Q71TY3 RS27_RAT P05370 G6PD_RAT P47198 RL22_RAT Q4TU93 MRC2_RAT Q71UF4 RBBP7_RAT P05426 RL7_RAT P47727 CBR1_RAT Q4V7C6 GUAA_RAT Q75Q41 TOM22_RAT P05539 CO2A1_RAT P47819 GFAP_RAT Q4V7C7 ARP3_RAT Q76LD0 CRDL1_RAT P05708 HXK1_RAT P47853 PGS1_RAT Q4V8C7 PRKRA_RAT Q794E4 HNRPF_RAT P05712 RAB2A_RAT P47875 CSRP1_RAT Q4V8H8 EHD2_RAT Q794F9 4F2_RAT P05765 RS21_RAT P47942 DPYL2_RAT Q505J8 SYFA_RAT Q7M0E3 DEST_RAT P05942 S10A4_RAT P48004-2 PSA7_RAT Q561S0 NDUAA_RAT Q7M767 UB2V2_RAT P05943 S10AA_RAT P48037 ANXA6_RAT Q566E5 KDEL2_RAT Q7TP40 PCNP_RAT P05964 S10A6_RAT P48500 TPIS_RAT Q5BJK8 GOLI4_RAT Q7TP47 HNRPQ_RAT P05982 NQO1_RAT P48679 LMNA_RAT Q5BJP3 UFM1_RAT Q7TPB1 TCPD_RAT P06238 A2MG_RAT P48721 GRP75_RAT Q5BK20 HN1L_RAT Q7TPJ0 SSRA_RAT P06302 PTMA_RAT P49088 ASNS_RAT Q5BK81 PTGR2_RAT Q7TQ94 NIT1_RAT P06685 AT1A1_RAT P49134 ITB1_RAT Q5EAJ6 IKIP_RAT Q80W92 VAC14_RAT P06760 BGLR_RAT P49242 RS3A_RAT Q5EAJ6-2 IKIP_RAT Q80WE1 FMR1_RAT P06761 GRP78_RAT P49432 ODPB_RAT Q5EB81 NB5R1_RAT Q80Z29 NAMPT_RAT P07150 ANXA1_RAT P49911 AN32A_RAT Q5FVH0 C1QT5_RAT Q811A3-2 PLOD2_RAT P07151 B2MG_RAT P50137 TKT_RAT Q5FVH2 PLD3_RAT Q8CFN2 CDC42_RAT
Appendices 145
P07153 RPN1_RAT P50398 GDIA_RAT Q5FVM4 NONO_RAT Q8CG45 ARK72_RAT P07323 ENOG_RAT P50399 GDIB_RAT Q5FVQ4 MLEC_RAT Q8CGU6 NICA_RAT P07335 KCRB_RAT P50408 VATF_RAT Q5HZV9 PP1R7_RAT Q8K3F3 PP14B_RAT P07483 FABPH_RAT P50475 SYAC_RAT Q5HZY2 SAR1B_RAT Q8R431 MGLL_RAT P07632 SODC_RAT P50503 F10A1_RAT Q5I0D1 GLOD4_RAT Q8R491 EHD3_RAT P07824 ARGI1_RAT P50878 RL4_RAT Q5I0D7 PEPD_RAT Q8VHF5 CISY_RAT P07861 NEP_RAT P51583 PUR6_RAT Q5I0E7 TMED9_RAT Q8VHK7 HDGF_RAT P07895 SODM_RAT P51607 RENBP_RAT Q5I0G4 GARS_RAT Q8VHQ7 SYTL4_RAT P07943 ALDR_RAT P51635 AK1A1_RAT Q5I0P2 GCSH_RAT Q8VHV7 HNRH1_RAT P08010 GSTM2_RAT P52296 IMB1_RAT Q5M7U6 ARP2_RAT Q8VIF7 SBP1_RAT P08081-2 CLCA_RAT P52555 ERP29_RAT Q5M7W5 MAP4_RAT Q91Y81 SEPT2_RAT P08082 CLCB_RAT P52925 HMGB2_RAT Q5M827 PIR_RAT Q91ZW6-2 TMLH_RAT P08461 ODP2_RAT P52944 PDLI1_RAT Q5M9G3 CAPR1_RAT Q920A6 RISC_RAT P08494 MGP_RAT P53534 PYGB_RAT Q5PPL3 NSDHL_RAT Q920J4 TXNL1_RAT P08503 ACADM_RAT P53812-2 PIPNB_RAT Q5PQJ6 P5CR3_RAT Q920L2 SDHA_RAT P08592-2 A4_RAT P54001 P4HA1_RAT Q5PQP1 RBMS1_RAT Q920P6 ADA_RAT P08699 LEG3_RAT P54311 GBB1_RAT Q5QD51-2 AKA12_RAT Q923V8 SEP15_RAT P09117 ALDOC_RAT P54313 GBB2_RAT Q5RJR2 TWF1_RAT Q923W4 HDGR3_RAT P09456 KAP0_RAT P54690 BCAT1_RAT Q5RJR8 LRC59_RAT Q924C3 ENPP1_RAT P09495 TPM4_RAT P54921 SNAA_RAT Q5RK30 SBDS_RAT Q924S5 LONM_RAT P09527 RAB7A_RAT P55053 FABP5_RAT Q5RKI0 WDR1_RAT Q925G0 RBM3_RAT P09895 RL5_RAT P55260 ANXA4_RAT Q5RKI1 IF4A2_RAT Q99J82 ILK_RAT P0C5E3 PALLD_RAT P55770 NH2L1_RAT Q5SGE0 LPPRC_RAT Q99ML5 PCYOX_RAT P0C5H9 MANF_RAT P56571 ES1_RAT Q5U1X1 ORN_RAT Q99MZ8 LASP1_RAT P0DMW1 HS71B_RAT P56574 IDHP_RAT Q5U211 SNX3_RAT Q99N27 SNX1_RAT P0DP31 CALM3_RAT P58366 ANKH_RAT Q5U2Q7 ERF1_RAT Q99NA5 IDH3A_RAT P10111 PPIA_RAT P58405-2 STRN3_RAT Q5U2R7 MESD_RAT Q99PD6 TGFI1_RAT P10688 PLCD1_RAT P58775-2 TPM2_RAT Q5U2U2 CRKL_RAT Q99PF5 FUBP2_RAT P10719 ATPB_RAT P60123 RUVB1_RAT Q5U2Z3 NP1L4_RAT Q9EPB1 DPP2_RAT P10760 SAHH_RAT P60522 GBRL2_RAT Q5U300 UBA1_RAT Q9EPC6 PROF2_RAT P10860 DHE3_RAT P60711 ACTB_RAT Q5U301 AKAP2_RAT Q9EPH8 PABP1_RAT P10888 COX41_RAT P60868 RS20_RAT Q5U312-2 RAI14_RAT Q9EQS0 TALDO_RAT P10960 SAP_RAT P60901 PSA6_RAT Q5U318 PEA15_RAT Q9EQX9 UBE2N_RAT P11030 ACBP_RAT P61078 UB2D3_RAT Q5U367 PLOD3_RAT Q9ER34 ACON_RAT P11232 THIO_RAT P61107 RAB14_RAT Q5XFW8 SEC13_RAT Q9ERE6 MPRIP_RAT P11240 COX5A_RAT P61149 FGF1_RAT Q5XFX0 TAGL2_RAT Q9ES21 SAC1_RAT P11442 CLH1_RAT P61314 RL15_RAT Q5XHY5 SYTC_RAT Q9ES40 PRAF3_RAT P11598 PDIA3_RAT P61354 RL27_RAT Q5XHZ0 TRAP1_RAT Q9ES72 CYR61_RAT P11762 LEG1_RAT P61589 RHOA_RAT Q5XI07 LPP_RAT Q9ESN0 NIBAN_RAT P11883 AL3A1_RAT P61751 ARF4_RAT Q5XI22 THIC_RAT Q9EST6 AN32B_RAT P11884 ALDH2_RAT P61765 STXB1_RAT Q5XI32 CAPZB_RAT Q9HB97 PARVA_RAT P11915 NLTP_RAT P61805 DAD1_RAT Q5XI68 NC2B_RAT Q9JHL4 DBNL_RAT P11980 KPYM_RAT P61972 NTF2_RAT Q5XI72 IF4H_RAT Q9JHU5 ARFP1_RAT P11980-2 KPYM_RAT P61980 HNRPK_RAT Q5XI73 GDIR1_RAT Q9JHW0 PSB7_RAT P12001 RL18_RAT P61983 1433G_RAT Q5XI78 ODO1_RAT Q9JI03 CO5A1_RAT P12007 IVD_RAT P62076 TIM13_RAT Q5XIC1 GMPPA_RAT Q9JI85 NUCB2_RAT P12075 COX5B_RAT P62083 RS7_RAT Q5XIF4 SUMO3_RAT Q9JI92 SDCB1_RAT P12368 KAP2_RAT P62138 PP1A_RAT Q5XIF6 TBA4A_RAT Q9JID2 GNA11_RAT
146 Appendices
P12711 ADHX_RAT P62142 PP1B_RAT Q5XIG8 STRAP_RAT Q9JJ19 NHRF1_RAT P12785 FAS_RAT P62193 PRS4_RAT Q5XIH7 PHB2_RAT Q9JJ54-4 HNRPD_RAT P13084 NPM_RAT P62198 PRS8_RAT Q5XII0 EPDR1_RAT Q9JJM9-3 SEPT5_RAT P13086 SUCA_RAT P62243 RS8_RAT Q5XIM5 CDV3_RAT Q9JJP9 UBQL1_RAT P13221 AATC_RAT P62246 RS15A_RAT Q5XIM9 TCPB_RAT Q9JJW3 USMG5_RAT P13264 GLSK_RAT P62250 RS16_RAT Q5XIU9 PGRC2_RAT Q9JK11-4 RTN4_RAT P13264-2 GLSK_RAT P62260 1433E_RAT Q60587 ECHB_RAT Q9JK72 CCS_RAT P13383 NUCL_RAT P62268 RS23_RAT Q62627 PAWR_RAT Q9JLA3 UGGG1_RAT P13437 THIM_RAT P62271 RS18_RAT Q62632 FSTL1_RAT Q9JLJ3 AL9A1_RAT P13471 RS14_RAT P62278 RS13_RAT Q62636 RAP1B_RAT Q9JLT0 MYH10_RAT P13596 NCAM1_RAT P62282 RS11_RAT Q62658 FKB1A_RAT Q9JLZ1 GLRX3_RAT P13676 ACPH_RAT P62332 ARF6_RAT Q62667 MVP_RAT Q9JM53 AIFM1_RAT P13697 MAOX_RAT P62425 RL7A_RAT Q62698 DC1L2_RAT Q9JMB5 ADRM1_RAT P13803 ETFA_RAT P62628 DLRB1_RAT Q62703 RCN2_RAT Q9JMI1 AACS_RAT P13832 MRLCA_RAT P62630 EF1A1_RAT Q62733 LAP2_RAT Q9JMJ4 PRP19_RAT P13852 PRIO_RAT P62634 CNBP_RAT Q62736 CALD1_RAT Q9QUL6 NSF_RAT P13941 CO3A1_RAT P62703 RS4X_RAT Q62745 CD81_RAT Q9QUR2 DCTN4_RAT P14046 A1I3_RAT P62718 RL18A_RAT Q62764 YBOX3_RAT Q9QW07-2 PLCB4_RAT P14141 CAH3_RAT P62738 ACTA_RAT Q62785 HAP28_RAT Q9QWN8 SPTN2_RAT P14408 FUMH_RAT P62744 AP2S1_RAT Q62786 FPRP_RAT Q9QXQ0 ACTN4_RAT P14562 LAMP1_RAT P62749 HPCL1_RAT Q62812 MYH9_RAT Q9QXU8 DC1L1_RAT P14604 ECHM_RAT P62752 RL23A_RAT Q62826 HNRPM_RAT Q9QZA2 PDC6I_RAT P14668 ANXA5_RAT P62755 RS6_RAT Q62839 GOGA2_RAT Q9QZK5 HTRA1_RAT P14841 CYTC_RAT P62775 MTPN_RAT Q62868 ROCK2_RAT Q9QZR6 SEPT9_RAT P15178 SYDC_RAT P62804 H4_RAT Q62871-3 DC1I2_RAT Q9R063 PRDX5_RAT P15650 ACADL_RAT P62815 VATB2_RAT Q62881 NOL3_RAT Q9R064 GORS2_RAT P15791 KCC2D_RAT P62828 RAN_RAT Q62902 LMAN1_RAT Q9R1E9 CTGF_RAT P15865 H14_RAT P62832 RL23_RAT Q62908 CSRP2_RAT Q9R1J8 P3H1_RAT P15999 ATPA_RAT P62845 RS15_RAT Q62920 PDLI5_RAT Q9R1Z0 VDAC3_RAT P16036 MPCP_RAT P62850 RS24_RAT Q62940 NEDD4_RAT Q9WTT6 GUAD_RAT P16086 SPTN1_RAT P62853 RS25_RAT Q62952-2 DPYL3_RAT Q9WTV5 PSMD9_RAT P16446 PIPNA_RAT P62856 RS26_RAT Q62967 MVD1_RAT Q9WUH4 FHL1_RAT P16617 PGK1_RAT P62859 RS28_RAT Q62991 SCFD1_RAT Q9WVB1 RAB6A_RAT P16636 LYOX_RAT P62864 RS30_RAT Q63009 ANM1_RAT Q9WVC0 SEPT7_RAT P16638-2 ACLY_RAT P62870 ELOB_RAT Q63016 LAT1_RAT Q9WVH8 FBLN5_RAT P16975 SPRC_RAT P62890 RL30_RAT Q63072 BST1_RAT Q9WVK7 HCDH_RAT P17046 LAMP2_RAT P62898 CYC_RAT Q63081 PDIA6_RAT Q9Z0V5 PRDX4_RAT P17074 RS19_RAT P62902 RL31_RAT Q63083 NUCB1_RAT Q9Z0V6 PRDX3_RAT P17077 RL9_RAT P62907 RL10A_RAT Q63184 E2AK2_RAT Q9Z0W7 CLIC4_RAT P17078 RL35_RAT P62909 RS3_RAT Q63228 GMFB_RAT Q9Z142 TMM33_RAT P17164 FUCO_RAT P62912 RL32_RAT Q63279 K1C19_RAT Q9Z1A6 VIGLN_RAT P17220 PSA2_RAT P62914 RL11_RAT Q63321 PLOD1_RAT Q9Z1B2 GSTM5_RAT P17425 HMCS1_RAT P62919 RL8_RAT Q63347 PRS7_RAT Q9Z1E1 FLOT1_RAT P17764 THIL_RAT P62944 AP2B1_RAT Q63355 MYO1C_RAT Q9Z1H9 PRDBP_RAT P18395 CSDE1_RAT P62959 HINT1_RAT Q63357 MYO1D_RAT Q9Z1P2 ACTN1_RAT P18418 CALR_RAT P62961 YBOX1_RAT Q63377 AT1B3_RAT Q9Z1W6 LYRIC_RAT P18420 PSA1_RAT P62963 PROF1_RAT Q63396 TCP4_RAT Q9Z1X1 ESYT1_RAT P18421 PSB1_RAT P62966 RABP1_RAT Q63413 DX39B_RAT Q9Z1Z9 PDLI7_RAT
Appendices 147
P18422 PSA3_RAT P62982 RS27A_RAT Q63450 KCC1A_RAT Q9Z270 VAPA_RAT P18445 RL27A_RAT P62994 GRB2_RAT Q63507 RL14_RAT Q9Z2G8 NP1L1_RAT P18484 AP2A2_RAT P63004 LIS1_RAT Q63524 TMED2_RAT Q9Z2J4 NEXN_RAT P18645 GALE_RAT P63018 HSP7C_RAT Q63525 NUDC_RAT Q9Z2L0 VDAC1_RAT P18757 CGL_RAT P63025 VAMP3_RAT Q63544 SYUG_RAT Q9Z2Q1 SC31A_RAT P19132 FRIH_RAT P63029 TCTP_RAT Q63569 PRS6A_RAT Q9Z339 GSTO1_RAT
P19139 CSK21_RAT P63036 DNJA1_RAT Q63570 PRS6B_RAT
148 Appendices
-
N
: : FN
F
-
A
MSCs MSCs cultured
-
MSCs grown on:
-
Photos of BM
: uncoated flasks after 12 hours starvation. hours 12 after flasks uncoated :
W
-
S
MSCs grown on uncoated flasks before starvation; starvation; before flasks uncoated on grown MSCs
-
coated flasks.
-
BM
: :
M
-
G
hour starvation; and and starvation; hour
-
Minimal Minimal morphological changes are observed between BM
:
B
: FN coated flasks after 12 after flasks coated FN :
Appendix on flasks coated with FN versus non starvation; before flasks coated R µm 100 = Scale
Appendices 149
: : FN
FN F:
O
- -
like clusters after 3 -
L: L: FN coated flask with BMP4; M
-
nd BMP4 exhibited nodule
: uncoated flasks with no stimulation (control); D (control); stimulation no with flasks uncoated : C
- A
FGF2 with anduncoated Q: flask BMP4. -
I: I: FN coated flask with FGF2; J
-
MSCs grown on: grown MSCs -
Images of BM of Images
Cells Cells cultured in treatments containing FN a
: :
C
coated coated flasks with no stimulation; G Appendix Appendix treatment. of days and flask P and FGF2 with BMP4; coated µm 100 = Scale
150 Appendices
Appendix D: GO Terms of interest and associated proteins that were increased or decreased in abundance by greater than 2-fold in each treatment.
GO Term Treatments FN FN+FGF2 FN+BMP4 FN+FGF2+BMP4 FGF2+BMP4 ↑ ↓ ↑ ↓ ↑ ↓ ↑ ↓ ↑ ↓
head development NUMBL CO1A1 PTN11 SLIT3 EI2BG DYL1 PTN11 CO3A1 ACBP NUMBL SLIT3 EI2BG PTN11 cell differentiation CYBP NUMBL CO1A1 PTN11 CYBP SLIT3 CYBP EI2BG CYBP EI2BG CRDL1 CO3A1 ACBP SEP15 RS11 SLIT3 K1C14 PTN11 RS11 NUMBL ATL4 MRC2 NEXN PTN11 AIFM1 developmental NLTP NUMBL CO1A1 PPAL CYBP SLIT3 NLTP EI2BG NLTP DYL1 process CYBP CO1A2 CO1A2 PTN11 LICH SEP15 CYBP SLIT3 CYBP EI2BG LICH SCFD1 NLTP CRKL NLTP NUMBL RS11 ATL4 K1C14 CRDL1 CO3A1 ACBP MRC2 NEXN PTN11 RS11 PIMT PTN11 TMED2 LICH AIFM1 PRKRA system development CYBP NUMBL CO1A1 PPAL CYBP SLIT3 CYBP EI2BG CYBP DYL1 LICH CO1A2 CO1A2 PTN11 LICH NUMBL SLIT3 EI2BG CRDL1 CO3A1 CRKL PTN11 NEXN PTN11 ACBP LICH TMED2 AIFM1 PRKRA
anatomical structure CYBP NUMBL CO1A1 PPAL CYBP SLIT3 CYBP EI2BG CYBP DYL1
development LICH CO1A2 CO1A2 PTN11 LICH SEP15 SLIT3 K1C14 EI2BG SCFD1 CO3A1 CRKL NUMBL ATL4 CRDL1 ACBP PTN11 NEXN PTN11 LICH TMED2 AIFM1 PRKRA multicellular organism CYBP NUMBL CO1A1 PPAL CYBP SLIT3 CYBP EI2BG CYBP DYL1 development LICH CO1A2 CO1A2 PTN11 LICH NUMBL SLIT3 EI2BG CRDL1 CO3A1 CRKL PTN11 NEXN PTN11 ACBP LICH
TMED2 AIFM1 Development and Differentiation and Development PRKRA nervous system NUMBL CO3A1 PTN11 SLIT3 EI2BG DYL1 development CRDL1 ACBP NUMBL SLIT3 EI2BG PTN11 PTN11 TMED2 AIFM1 cell development NUMBL PTN11 SLIT3 EI2BG EI2BG PTN11 SEP15 SLIT3 NUMBL ATL4 PTN11 NEXN animal organ CYBP NUMBL CO1A1 PTN11 CYBP SLIT3 CYBP EI2BG CYBP DYL1 development LICH CO1A2 CO1A2 CRKL LICH NUMBL SLIT3 EI2BG CRDL1 CO3A1 ACBP PTN11 NEXN PTN11 LICH TMED2 PRKRA cellular CYBP NUMBL CO1A1 PTN11 CYBP SLIT3 CYBP EI2BG CYBP EI2BG developmental LICH SCFD1 CO3A1 ACBP LICH SEP15 RS11 SLIT3 K1C14 process CRDL1 RS11 NUMBL ATL4 PTN11 MRC2 NEXN PTN11 LICH AIFM1 central nervous NUMBL CO3A1 PTN11 SLIT3 EI2BG DYL1 system development PTN11 ACBP NUMBL SLIT3 EI2BG PTN11
Appendices 151
single-organism NLTP NUMBL CO1A1 PPAL CYBP SLIT3 NLTP EI2BG NLTP DYL1 developmental CYBP CO1A2 CO1A2 PTN11 LICH SEP15 CYBP SLIT3 CYBP EI2BG process LICH SCFD1 NLTP CRKL NLTP NUMBL RS11 ATL4 K1C14 CRDL1 CO3A1 ACBP MRC2 NEXN PTN11 RS11 PIMT PTN11 TMED2 LICH AIFM1 PRKRA neurogenesis NUMBL CO3A1 PTN11 SLIT3 EI2BG EI2BG CRDL1 ACBP NUMBL SLIT3 PTN11 PTN11 AIFM1 gliogenesis PTN11 PTN11 PTN11 EI2BG EI2BG ACBP brain development NUMBL CO3A1 PTN11 SLIT3 EI2BG DYL1 PTN11 ACBP NUMBL SLIT3 EI2BG PTN11 cell morphogenesis LICH NUMBL PTN11 LICH SLIT3 SLIT3 SCFD1 NUMBL PTN11 PTN11 LICH reproductive structure PTN11 PTN11 SLIT3 SLIT3 development TMED2 PTN11
reproductive process PTN11 PTN11 SLIT3 SLIT3 TMED2 SEP15 PTN11 neuron differentiation NUMBL PTN11 SLIT3 SLIT3 CRDL1 NUMBL PTN11 PTN11 AIFM1 neuron development NUMBL PTN11 SLIT3 SLIT3 PTN11 NUMBL PTN11 developmental PTN11 PTN11 SLIT3 SLIT3 process involved in TMED2 SEP15 reproduction PTN11 cell morphogenesis NUMBL PTN11 SLIT3 SLIT3 involved in PTN11 NUMBL differentiation PTN11
reproductive system PTN11 PTN11 SLIT3 SLIT3 development TMED2 PTN11
anatomical structure LICH NUMBL CO1A1 PTN11 LICH SLIT3 SLIT3 morphogenesis CO1A2 CO1A2 CRKL NUMBL SCFD1 CO3A1 PTN11 PTN11 LICH TMED2 PRKRA generation of neurons NUMBL CO3A1 PTN11 SLIT3 SLIT3 CRDL1 NUMBL PTN11 PTN11 AIFM1 reproduction PTN11 PTN11 SLIT3 SLIT3 TMED2 SEP15 PTN11 neuron projection NUMBL PTN11 SLIT3 SLIT3 development PTN11 NUMBL PTN11 circulatory system CYBP CO1A2 CO1A1 PTN11 CYBP PTN11 CYBP NEXN CYBP development PTN11 CO1A2 CRKL TMED2 CO3A1 cellular component LICH NUMBL PTN11 LICH SLIT3 SLIT3 morphogenesis SCFD1 NUMBL PTN11 PTN11 LICH heart development CYBP PTN11 CO3A1 PTN11 CYBP PTN11 CYBP NEXN CYBP TMED2 CRKL
152 Appendices
regulation of metal UBQL1 ACBP UBQL1 UBQL1 ion transport positive regulation of NUMBL CRKL CRK NUMBL cell morphogenesis involved in differentiation
positive regulation of NUMBL CO1A1 CRKL CRK NUMBL cell differentiation
regulation of cell NUMBL CRKL CRK NUMBL morphogenesis
metencephalon PTN11 PTN11 PTN11 development negative regulation of PTN11 CO3A1 PTN11 PTN11 multicellular STRAP organismal process
positive regulation of NUMBL CRKL CRK NUMBL cell development
regulation of cell NUMBL CO3A1 CRKL CRK NUMBL development
regulation of NUMBL CO1A1 PTN11 CRK NUMBL developmental PTN11 CO3A1 CRKL PTN11 process STRAP lateral ventricle NUMBL ACBP NUMBL development
regulation of cell NUMBL CRKL CRK NUMBL morphogenesis involved in differentiation
regulation of cell NUMBL CO1A1 CRKL CRK NUMBL differentiation CO3A1 STRAP positive regulation of NUMBL CO1A1 CRKL CRK NUMBL developmental process Bergmann glial cell PTN11 PTN11 PTN11 differentiation
cerebellum PTN11 PTN11 PTN11 development
regulation of NUMBL CRKL CRK NUMBL anatomical structure morphogenesis
epithelium TMED2 ACBP ATL4 K1C14 development tissue development CYBP TMED2 CO1A1 ACBP CYBP CYBP ATL4 CYBP CO3A1 NEXN K1C14 blood vessel CO1A2 CO1A1 CRKL development TMED2 CO1A2 CO3A1 vasculature CO1A2 CO1A1 CRKL development TMED2 CO1A2 CO3A1 cardiovascular system CO1A2 CO1A1 CRKL development TMED2 CO1A2 CO3A1
Appendices 153
regulation of nervous NUMBL CO3A1 NUMBL system development
regulation of NUMBL CO1A1 NUMBL multicellular CO3A1 STRAP organismal development
multi-organism TMED2 SEP15 reproductive process
positive regulation of NUMBL NUMBL dendrite development
positive regulation of NUMBL NUMBL nervous system development
ossification CRDL1 CO1A1 MRC2 RS11 RS11 regulation of neuron NUMBL NUMBL projection development positive regulation of NUMBL NUMBL neuron projection development
positive regulation of NUMBL NUMBL neurogenesis
regulation of NUMBL CO3A1 NUMBL neurogenesis
positive regulation of NUMBL NUMBL dendrite morphogenesis regulation of dendrite NUMBL NUMBL morphogenesis
regulation of neuron NUMBL NUMBL differentiation
regulation of dendrite NUMBL NUMBL development
positive regulation of NUMBL NUMBL neuron differentiation
positive regulation of NUMBL CO1A1 NUMBL multicellular organismal process
gonad development SLIT3 SLIT3
placenta development TMED2
gland development CRKL
respiratory tube LICH LICH LICH development
osteoblast CO1A1 MRC2 RS11 differentiation RS11
lung development LICH LICH LICH
tissue remodelling LICH LICH LICH
muscle cell CYBP CYBP CYBP NEXN CYBP differentiation
154 Appendices
striated muscle cell NEXN development
muscle cell NEXN development muscle structure CYBP CO3A1 CYBP CYBP NEXN CYBP development
cardiac muscle tissue CYBP CYBP CYBP NEXN CYBP development
striated muscle cell CYBP CYBP CYBP NEXN CYBP differentiation
epithelial cell ATL4 K1C14 differentiation
neural nucleus DYL1 development
midbrain DYL1 development substantia nigra DYL1 development
regulation of ARP5L ARP5L ARP5L anatomical structure size regulation of cell CRK shape response to nerve CRK growth factor
cellular response to CRK nerve growth factor stimulus
regulation of NLTP UBQL1 NLTP PIMT NLTP UBQL1 NLTP THIM NLTP UBQL1 programmed cell RUVB1 AIFM1 ATL4 death PRKRA regulation of cell NLTP UBQL1 NLTP PIMT CYBP UBQL1 NLTP THIM NLTP UBQL1 death CYBP RUVB1 NLTP AIFM1 CYBP ATL4 CYBP PRKRA regulation of NLTP UBQL1 NLTP PIMT NLTP UBQL1 NLTP THIM NLTP UBQL1 apoptotic process RUVB1 AIFM1 ATL4 PRKRA regulation of UBQL1 UBQL1 THIM UBQL1 apoptotic signalling PRKRA
pathway regulation of oxidative UBQL1 UBQL1 UBQL1 stress-induced intrinsic apoptotic signalling pathway
regulation of oxidative UBQL1 UBQL1 UBQL1
stress-induced cell Apoptosis and Cell Death Cell and Apoptosis death autophagy UBQL1 UBQL1 UBQL1 GBRL2 regulation of intrinsic UBQL1 UBQL1 UBQL1 apoptotic signalling PRKRA pathway
vacuole organization UBQL1 PPAL UBQL1 UBQL1
apoptotic process RTN3 SLIT3 SLIT3 DYL1 AIFM1 THIM RTN3 ATL4
Appendices 155
programmed cell RTN3 PPAL SLIT3 SLIT3 DYL1 death AIFM1 THIM RTN3 ATL4 cell death RTN3 PPAL SLIT3 SLIT3 DYL1 AIFM1 THIM RTN3 ATL4 negative regulation of CYBP PIMT CYBP CYBP THIM CYBP cell death
negative regulation of PIMT THIM programmed cell death negative regulation of PIMT THIM apoptotic process
positive regulation of NLTP NLTP NLTP AIFM1 NLTP ATL4 NLTP cell death PRKRA
positive regulation of NLTP NLTP NLTP AIFM1 NLTP ATL4 NLTP programmed cell PRKRA death positive regulation of NLTP NLTP NLTP AIFM1 NLTP ATL4 NLTP apoptotic process PRKRA
regulation of cardiac PIMT muscle cell apoptotic process
negative regulation of PIMT striated muscle cell apoptotic process
negative regulation of PIMT cardiac muscle cell apoptotic process
regulation of striated PIMT muscle cell apoptotic process
aging NLTP NLTP PIMT CYBP NLTP NLTP CYBP CO3A1 NLTP CYBP CYBP K1C14 positive regulation of PRKRA apoptotic signalling pathway
regulation of cysteine- AIFM1 type endopeptidase activity involved in apoptotic process
regulation of neuron AIFM1 apoptotic process
regulation of neuron AIFM1 death
negative regulation of THIM mitochondrial membrane permeability involved in apoptotic process
156 Appendices
negative regulation of THIM apoptotic signalling pathway
negative regulation of THIM mitochondrial outer membrane permeabilization involved in apoptotic signalling pathway
regulation of THIM mitochondrial outer membrane permeabilization involved in apoptotic signalling pathway
apoptotic THIM mitochondrial changes
positive regulation of MVD1 CRKL PRKRA CNBP cell proliferation
regulation of growth RUVB1 PTN11 CRK SLIT3 SLIT3 PTN11 PTN11 RBBP7 cell proliferation LICH NUMBL ACBP LICH NUMBL LICH cell division NUMBL NUMBL RUVB1 regulation of cell CRK SLIT3 SLIT3 growth RBBP7 Proliferation and Growth and Proliferation positive regulation of CRK cell growth
single-organism NLTP SCFD1 NLTP ACBP NLTP STML2 NLTP NLTP DYL1 localization TMED2 FABPH
cellular localization NLTP SCFD1 CO1A1 NLTP STML2 NLTP THIM NLTP DYL1 TMED2 NLTP SUMO3 single-organism NLTP TMED2 NLTP NLTP STML2 NLTP NLTP DYL1 cellular localization
regulation of NLTP CO1A1 CRK STRAP NLTP NEXN NLTP locomotion NLTP NLTP CO3A1 regulation of cell NLTP CO1A1 CRK STRAP NLTP NEXN NLTP
motility NLTP NLTP
CO3A1 regulation of cell NLTP CO1A1 CRK STRAP NLTP NEXN NLTP migration NLTP NLTP Migration CO3A1 negative regulation of NLTP NLTP CRK STRAP NLTP NLTP cell motility CO3A1 NLTP
negative regulation of NLTP NLTP NLTP STRAP NLTP NLTP cell migration CO3A1
negative regulation of NLTP NLTP CRK STRAP NLTP NLTP locomotion CO3A1 NLTP
establishment of NLTP SCFD1 CO1A1 ACBP NLTP STML2 NLTP THIM NLTP DYL1 localization GBRL2 NLTP MRC2 FABPH TMED2 RTN3 CCS RTN3
Appendices 157
regulation of NLTP UBQL1 CO1A1 PTN11 CRK UBQL1 NLTP THIM NLTP UBQL1 localization SCFD1 NLTP ACBP NLTP PTN11 NEXN RUVB1 CO3A1 STRAP PTN11 regulation of cellular NLTP SCFD1 NLTP PTN11 NLTP PTN11 NLTP THIM NLTP localization RUVB1 ACBP PTN11 localization NLTP SCFD1 CO1A1 PTN11 NLTP STML2 ARP5L THIM NLTP DYL1 GBRL2 NLTP ACBP ARP5L MRC2 NLTP ARP5L FABPH PTN11 RTN3 SUMO3 TMED2 PTN11 CCS RTN3 positive regulation of CO1A1 CRK cell motility
positive regulation of CO1A1 CRK cell migration
positive regulation of CO1A1 CRK locomotion
regulation of smooth CRK muscle cell migration
homeostatic process LICH PTN11 PTN11 LICH STML2 FABPH ACBP PTN11 LICH
STRAP tissue homeostasis LICH PTN11 PTN11 LICH PTN11 LICH STRAP
Homeostasis anatomical structure LICH PTN11 PTN11 LICH PTN11 homeostasis LICH STRAP cellular homeostasis ACBP STML2
† Proteins with decreased abundance are displayed in italics. Arrows indicate increase ↑ or decrease ↓ in protein abundance relative to the control.
158 Appendices