Comparison of Schwann Cells Derived from Peripheral Nerve with Schwann Cells Differentiated from Skin-Derived Precursors

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Comparison of Schwann Cells Derived From Peripheral Nerve With Schwann Cells Differentiated From Skin-derived Precursors

Shaalee Dworski

A thesis submitted in conformity with the requirements for the degree of Master of Science Institute of Medical Science University of Toronto

Comparison of Schwann Cells Derived From Peripheral Nerve With Schwann Cells Differentiated From Skin-derived Precursors

Shaalee Dworski

Master of Science

Institute of Medical Science University of Toronto

2011

Abstract

Schwann cells are the glial cells of the peripheral nervous system. When transplanted into the injured central or peripheral nervous systems they promote repair. Traditionally Schwann cells have been isolated from the sciatic nerve, creating nerve-SC. An alternative Schwann cell source is from the differentiation of skin-derived precursors (SKPs), stem cells found in the skin, to

Schwann cells (SKP-SC). SKP-SC have shown enhanced regenerative ability compared to nerve-

SC. This study compares nerve-SC with SKP-SC at the functional and gene expression level to determine their degree of similarity and find their sources of variance. The functional ability of both Schwann cell types appeared similar. Their gene expression, as assessed by microarray, was similar but not identical. Genes that differed between nerve-SC and SKP-SC may represent differences important to regeneration. The similarity of SKP-SC to nerve-SC supports the use of

SKP-SC for repair, and reasons for enhanced regeneration by SKP-SC are suggested.

Acknowledgements

I would like to thank my supervisor, Dr. Freda Miller, for the opportunity that she has given me, for her ongoing support throughout my journey, and for being an exemplary role model.

Thank you to my committee members Dr. David Kaplan and Dr. Jane Aubin for their guidance, intellectual input, and support.

My sincere appreciation goes out to the past and present members of the laboratories of Dr. Miller and Dr. Kaplan for their guidance, technical help, and friendship, especially Dr. Jeffrey Biernaskie, Dr. Konstantin Feinberg, Karen Jones, Asli Dedeagac, Hiroyuki Jinno, and Smitha Paul.

A heartfelt thank you to my family and friends for their constant encouragement.

Dedicated to Dr. Peter B. Moens.

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Table of Contents

Abstract ...... ii Acknowledgements ...... iii Table of Contents ...... iv Abbreviations ...... vii List of Tables ...... x List of Figures ...... xi List of Appendices ...... xii Chapter 1 Introduction ...... 1 1.1 Schwann cells ...... 1 1.1.1 Endogenous roles ...... 1 1.1.2 Developmental origin...... 2 1.1.3 Markers of Schwann cell developmental stage ...... 3 1.1.3.2 Genes expressed selectively at different stages of Schwann cell development ..... 6 1.1.3.3 Genes expressed during myelination by Schwann cells ...... 7 1.1.3.4 Genes expressed during de-differentiation of Schwann cells ...... 8 1.1.4 Isolation...... 10 1.2 Peripheral nervous system injury and disease ...... 11 1.2.1 Demyelinating diseases ...... 11 1.2.2 Peripheral nerve injury ...... 11 1.2.3 Role of Schwann cells in repair ...... 12 1.2.4 Schwann cell transplantation intervention ...... 14 1.3 Central nervous system injury and disease ...... 14 1.3.1 Demyelinating diseases ...... 14 1.3.2 Spinal cord injury ...... 14 1.3.3 Role of Schwann cells in repair ...... 15 1.3.4 Schwann cell transplantation intervention ...... 16 1.4 Skin-derived precursors...... 16 1.4.1 Endogenous roles ...... 16 1.4.2 Developmental origin...... 17 1.4.3 Isolation...... 17 1.4.4 Multipotency ...... 18 1.4.5 Differentiation to Schwann cells ...... 19 1.5 Skin-derived precursors for peripheral and central nervous system repair ...... 19 1.5.1 Peripheral nerve injury ...... 19 1.5.2 Spinal cord injury ...... 20 1.6 Comparison of nerve-derived Schwann cells with Schwann cells differentiated from skin-derived precursors ...... 21 1.6.1 Neurotrophin production ...... 21 1.6.2 Peripheral nerve injury reparative ability ...... 21 1.6.3 Spinal cord injury reparative ability ...... 22 1.7 Aims and hypothesis ...... 22 Chapter 2 Methods ...... 24 iv

2.1 Cell isolation ...... 24 2.1.1 Nerve-SC isolation ...... 24 2.1.2 Trunk SKPs isolation ...... 25 2.1.3 SKP-SC isolation ...... 25 2.2 Schwann cell verification ...... 26 2.2.1 Immunocytochemistry ...... 26 2.2.2 SCG axon association ...... 27 2.2.3 DRG myelination ...... 28 2.2.4 Sciatic nerve crush and cell transplantation ...... 29 2.2.5 Microscopy ...... 29 2.3 Microarray analysis ...... 29 2.3.1 RNA isolation, purification, and quality analysis ...... 29 2.3.2 Array hybridization ...... 32 2.3.3 Data pre-processing and quality analysis ...... 32 2.3.4 Data analysis ...... 33 2.3.5 Visualization of the data ...... 34 2.3.6 RT-PCR...... 37 2.3.7 Western blot ...... 37 Chapter 3 Results ...... 40 3.1 Schwann cell isolation from sciatic nerve and from SKPs ...... 40 3.1.1 Nerve-SC and SKP-SC express typical Schwann cell markers ...... 40 3.1.2 Nerve-SC and SKP-SC myelinate axons in vitro and in vivo ...... 43 3.2 Microarray comparison of nerve-SC and SKP-SC ...... 51 3.2.1 Quality control of microarrays ...... 51 3.2.2 SKP-SC are more similar to nerve-SC than to trunk SKPs ...... 61 3.2.3 SKP-SC are distinct from nerve-SC ...... 65 3.2.4 Genes that differ between SKP-SC and nerve-SC ...... 74 3.2.6 SKP-SC have higher expression of proliferation genes ...... 84 Chapter 4 Discussion ...... 88 4.1 Overview ...... 88 4.2 Schwann cell developmental stage ...... 88 4.3 Proliferation is enhanced in SKP-SC ...... 89 4.3.1 Pax3...... 89 4.3.2 Cdc2 ...... 90 4.3.3 Id2 ...... 91 4.3.4 Krox-20 ...... 92 4.3.5 Gfap...... 93 4.4 Characteristics important for nerve regeneration ...... 94 4.4.1 Schwann cell migration...... 95 4.4.2 Neurotrophin production ...... 95 4.4.3 Astrocyte interaction ability ...... 96 4.5 Novel markers of Schwann cell developmental stage ...... 98 4.4 Conclusion ...... 99 4.5 Future directions ...... 99

References ...... 101 Appendix I Microarray data for genes that differ between SKP-SC and nerve-SC ...... 115

Abbreviations Adamts4 A disintegrin and metalloproteinase with thrombospondin motifs 4 ALS Amyotrophic lateral sclerosis AP2α Activator protein 2α AU Approximately unbiased Baalc Brain and acute leukemia, cytoplasmic Bdnf Brain-derived neurotrophic factor Bfabp Brain fatty acid-binding protein BP Bootstrap probability Brn1 Brain 1 class III POU-domain protein Brn2 Brain 2 class III POU-domain protein BSA Bovine serum albumin c-Jun Jun proto-oncogene Cdh19 Cadherin 19 Cdh13 Cadherin 13 Cdc2 Cell division control protein 2 homologue Cenpf Centromere protein F CMAP Compound muscle action potential CMT Charcot-Marie-Tooth neuropathy CNS Central nervous system Dhh Desert hedgehog DMEM Dulbecco‟s Modified Eagle Medium DP Dermal papilla DRG Dorsal root ganglia DS Dermal sheath E Embryonic day Ecel1 Endothelin converting enzyme-like 1 ECL Enhanced chemiluminescence EDTA Ethylenediaminetetraacetic acid EGF Epidermal growth factor ELISA Enzyme-linked immunosorbent assays ErbB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) ErbB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) FBS Fetal bovine serum Fgf-1 Acidic fibroblast growth factor Fgf-2 basic fibroblast growth factor GalC Galactocerebroside

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Gap-43 Growth-associated protein 43 Gapdh Glyceraldehyde-3-phosphate dehydrogenase GDC Granular disintegration of the axonal cytoskeleton Gdnf Glial cell line-derived neurotrophic factor Gfap Glial fibrillary acidic protein GFP Green fluorescent protein Grik3 Glutamate receptor, ionotropic, kainate 3 HRP Horseradish peroxidise Id4 Inhibitor of DNA binding 2 Id4 Inhibitor of DNA binding 4 ISE Immature Schwann cell element L1 L1 adhesion molecule LIF Leukemia inhibitor factor Mab21l1 Mab-21-like 1 Mag Myelin-associated glycoprotein Mbp Myelin basic protein MS Multiple sclerosis MSE Myelinating Schwann cell element Nab1 NGFI-A-binding proteins 1 Nab2 NGFI-A-binding proteins 2 Ncad N-cadherin Ncam Neural cell adhesion molecule Necl1 Nectin-like molecule 1 Necl4 Nectin-like molecule 4 Nerve-SC Nerve-derived Schwann cells NFkB Nuclear factor kappa-light-chain-enhancer of activated B cells Ngf Nerve growth factor NOD-SCID Non-obese diabetic, severe combined immunodeficient NT-3 Neurotrophin 3 04 04 lipid antigen Oct6 Octamer-binding transcription factor 6 P0 Myelin protein zero p75 p75 neurotrophin receptor Pax3 Paired box gene 3 PCA Principal Component Analysis PFA Paraformaldehyde PI3K Phosphatodylinositol 3-kinase Pmp22 Peripheral myelin protein 22 PNS Peripheral nervous system viii

Prkcq Protein kinase C, theta Pttg1 Pituitary tumor-transforming 1 RIN RNA integrity number RMA Robust Multichip Analysis rRNA Ribosomal RNA RT-PCR Reverse transcriptase polymerase chain reaction S100β S100 calcium binding protein β SCG Superior cervical ganglia SDS-PAGE Sodium dodecyl sulphate polyacrylamide gel electrophoresis Ski v-ski sarcoma viral oncogene homologue SKPs Skin-derived precursors SKP-SC SKP-derived Schwann cells Sox10 SRY (sex determining region Y) box 10 Sox2 SRY (sex determining region Y) box containing gene 2 Sparcl1 Secreted protein, acidic, cysteine-rich (SPARC)-like 1 ST Sense target TBS-T Tris-buffered saline with 0.05% Tween-20 TCAG The Center for Applied Genomics TNFα Tumor necrosis factor-α Top2A Topoisomerase (DNA) II alpha WT Whole transcript

List of Tables

Table 1. Primers used for reverse-transcriptase PCR to verify microarray genes...... 39 Table 2. Microarray data for genes involved in Schwann cell development...... 83 Table 3. Genes that are higher in SKP-SC...... 115 Table 4. Genes that are higher in nerve-SC...... 125

List of Figures

Figure 1. Markers of Schwann cell development...... 5 Figure 2. Electropherogram detailing the regions that are indicative of RNA quality...... 31 Figure 3. Data analysis commands in R...... 35 Figure 4. Data visualization commands in R...... 36 Figure 5. Rat nerve-SC and SKP-SC are bipolar and express typical Schwann cell markers...... 42 Figure 6. Nerve-SC and SKP-SC align with SCG axons after 10 days in vitro...... 45 Figure 7. Nerve-SC and SKP-SC myelinate DRG axons in vitro...... 48 Figure 8. Nerve-SC and SKP-SC myelinate the regenerating sciatic nerve in vivo...... 50 Figure 9. Bioanalyzer results...... 54 Figure 10. Visual inspection of microarrays shows no flaws...... 57 Figure 11. MvA plots indicate normalization was performed well...... 58 Figure 12. The distribution of signal intensities is similar in all arrays, indicating proper normalization...... 60 Figure 13. SKP-SC have more differences with trunk SKPs than with nerve-SC...... 62 Figure 14. SKP-SC are more similar to nerve-SC than to trunk SKPs...... 64 Figure 15. SKP-SC cluster closer to nerve-SC than to trunk SKPs, yet form a distinct group from nerve-SC...... 67 Figure 16. SKP-SC and nerve-SC have a similar expression pattern of Schwann cell development related genes, that is different from trunk SKPs...... 70 Figure 17. Quantification of the variance between SKP-SC versus nerve-SC and SKP-SC versus trunk SKPs...... 73 Figure 18. Most significantly different genes between SKP-SC and nerve-SC visualized by Volcano plot...... 75 Figure 19. Confirmation of top microarray genes by RT-PCR and Western blot...... 78 Figure 20. Schwann cell stage appears similar between SKP-SC and nerve-SC...... 81 Figure 21. SKP-SC have a higher expression of genes that have a positive effect on proliferation...... 86 Figure 22. Neurotrophin production was not significantly different between SKP-SC and nerve- SC...... 87

List of Appendices

Appendix I: Microarray data for genes that differ between SKP-SC and nerve-SC ...... 115

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Chapter 1 Introduction 1.1 Schwann cells 1.1.1 Endogenous roles Schwann cells are the myelinating cells of the peripheral nervous system (PNS). They wrap large axons in a coating of electrically insulating myelin that increases the velocity of action potentials through saltatory conduction. Axons depend on Schwann cells for myelin, but also for proper nerve formation, maintenance, and repair. Axons instruct Schwann cells to myelinate by releasing neuregulin-1 type III. Axons that release moderate levels of neuregulin become myelinated, with the amount of neuregulin dictating the amount of myelin required. Axons that release a very low level of neuregulin remain unmyelinated and are enveloped in non-myelinating Schwann cells. Axons releasing the threshold level of neuregulin become myelinated. Large diameter axons release more neuregulin than small diameter axons and are therefore ensheathed in more layers of myelin (Michailov et. al., 2004; Taveggia et. al., 2005). Schwann cells deposit myelin on axons and also maintain the unmyelinated areas in between myelin segments, the Nodes of Ranvier. At the nodes there is a high concentration of voltage-gated sodium channels that are needed for saltatory conduction. Apposing Schwann cells cluster sodium channels to form the nodes and potassium channels to form the juxtaparanodes (Joe & Angelides, 1992; Arroyo et. al., 1999; Saito et. al., 2003). Without the myelin coating and nodes, signals would travel very slowly or not at all (Edgar & Garbern, 2004). While the most notable role of Schwann cells is to ensure proper formation and maintenance of myelin structures, other roles include aiding in the proper formation of the nerve during development and maintaining it throughout the life of the animal. During development, Schwann cell precursors promote the survival of developing axons by releasing survival signals (Riethmacher et. al., 1997). They also send signals needed for the proper formation of the connective tissues surrounding the nerve, the perineurium and epineurium (Parmantier et. al., 1999). When mature, axons rely on Schwann cells to maintain proper axon diameter; hypomyelination results in a smaller axon diameter (de Waegh et. al., 1992). Schwann cells also

maintain the rate of slow axonal transport and control neurofilament density and neurofilament phosphorylation (de Waegh et. al., 1992). The role of Schwann cells is beyond nerve formation and maintenance, and extends into nerve repair. They aid in cleaning the injured nerve environment and remodelling it into an environment conducive to axonal regrowth (Vargas & Barres, 2007) (see section 1.2.3). Schwann cells are essential for the proper formation, function and regeneration of the PNS.

1.1.2 Developmental origin The developmental origin of Schwann cells is from the neural crest. Migrating neural crest cells mature in three steps to become Schwann cells. First neural crest cells find axon bundles and become Schwann cell precursors between embryonic day (E) 14-15 in the rat (E12- 13 in the mouse). Schwann cell precursors become immature Schwann cells from E15-17 in the rat (E13-15 in the mouse). Schwann cells that have the intention to myelinate will become pro- myelinating Schwann cells. Postnatally, pro-myelinating Schwann cells begin myelination, while non-myelinating Schwann cells do not. Importantly, the last stage of differentiation is reversible: mature Schwann cells de-differentiate into an immature Schwann cell-like state following nerve injury (Jessen & Mirsky, 2005). Several characteristic changes occur at each stage of Schwann cell development. Schwann cells change their association with axons as they mature. Schwann cell precursors and immature Schwann cells surround bundles of axons. Pro-myelinating Schwann cells begin to establish a 1:1 Schwann cell to axon ratio. This is achieved by radial sorting, which involves the interaction of β1 integrin (expressed on Schwann cells) with laminin (expressed on axons) (Yu et. al., 2005). One mature myelinating Schwann cell myelinates one axon (Jessen & Mirsky, 2005). Conversely, non-myelinating Schwann cells ensheath several small axons at once, forming a Remak bundle. The decision to become a myelinating or non-myelinating Schwann cell is dependent upon the axon which the Schwann cell has enveloped and how much neuregulin-1 type III it receives. Small diameter axons (less than 1 μm) release low levels of neuregulin, and the Schwann cells around them become non-myelinating Schwann cells. Large diameter axons release higher levels of neuregulin and induce the Schwann cell to adopt a myelinating fate (Murray, 1968; Taveggia et. al., 2005).

Another maturation change is the ability to proliferate. Neural crest cells, Schwann cell precursors, and immature Schwann cells all proliferate in vivo. Axonal-derived neuregulin stimulates proliferation in immature Schwann through jun proto-oncogene (c-Jun) (Parkinson et. al., 2004). Only mature Schwann cells (both myelinating and non-myelinating) do not proliferate, as they have exited the cell cycle (Stewart et. al., 1993). Importantly, mature Schwann cells can re-enter the cell cycle and proliferate by de-differentiating into an immature Schwann cell-like state. This occurs when Schwann cells lose their connection to axons, such as after nerve injury and when grown in culture (Mirsky et. al., 2008).

Schwann cells gain the ability to survive in the absence of axons as they mature. Neural crest cells and Schwann cell precursors require axonally derived signals to survive. This is likely to create a proper balance between the number of axons and the number of Schwann cells (Jessen & Mirsky, 2005). Immature Schwann cells can survive through autocrine signalling in the absence of axonal signals. This ability allows de-differentiated Schwann cells (immature-like Schwann cells) to survive and proliferate after axonal injury, a key step in peripheral nerve regeneration (Meier et. al., 1999).

1.1.3 Markers of Schwann cell developmental stage There are characteristic gene expression changes that accompany each stage of Schwann cell development. Various transcription factors are up- and down-regulated to coordinate the maturation of Schwann cells from neural crest cells to mature myelinating or non-myelinating cells. The differentially expressed transcription factors and their target genes can be used as markers to identify the stage of Schwann cell development a cell is in. These factors are summarized in Figure 1.

Neural Schwann Immature Non- Pro- Myelinating De- crest cell Schwann myelinating myelinating Schwann cell differentiated cell precursor cell Schwann cell Schwann cell Schwann cell Sox10 ErbB3 L1 p75 AP2α α4 integrin Ncad Cdh19 Bfabp Dhh P0 Gap-43 S100β Vimentin 04 Gfap Ncam Sox2 Pax3 c-Jun Cyclin D1 Notch Id2/4 α1β1 integrin Galc α7β1 integrin Ran-1 A5E3 NFkB Oct6 Brn1/2 PI3K Krox-20 Nab1/2 Ski Necl4 Pmp22 Mbp Mag Periaxin Connexin-32 Cdc2 Fgf-2

Figure 1. Markers of Schwann cell development. Schwann cell development involves the up- and down-regulation of known genes. Each stage can be typified by a pattern of expressed genes. Genes are grouped by coloured boxes. Genes that are expressed at a given stage are listed under that stage or the coloured box is extended to overlap with all stages in which it is expressed. In the figures of Schwann cell shape, Schwann cells are blue, axons beige, and myelin red. Sox10, SRY (sex determining region Y) box 10; ErbB3, v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian); L1, L1 adhesion molecule; p75, p75 neurotrophin receptor; AP2α, activator protein 2α; Ncad, N-cadherin; Cdh19, cadherin 19; Bfabp, brain fatty acid-binding protein; Dhh, desert hedgehog; P0, myelin protein zero; Gap-43, growth-associated protein 43; S100β, S100 calcium binding protein β; 04, 04 lipid antigen; Gfap, glial fibrillary acidic protein; Ncam, neural cell adhesion molecule; Sox2, SRY- box containing gene 2; Pax3, paired box gene 3; Id2/4, inhibitor of DNA binding 2 and 4; GalC, galactocerebroside; NFkB, nuclear factor kappa-light-chain-enhancer of activated B cells; Oct6, octamer-binding transcription factor 6; Brn1/2, brain 1/2 class III POU-domain protein; PI3K, phosphatodylinositol 3-kinase; Nab1/2, nGFI-A-binding proteins 1 and 2; Ski, v-ski sarcoma viral oncogene homologue; Necl4, nectin-like molecule 4; Pmp22, peripheral myelin protein 22; Mbp, myelin basic protein; Mag, myelin-associated glycoprotein; Cdc2, cell division control protein 2 homologue; Fgf-2, basic fibroblast growth factor.

1.1.3.1 Genes expressed at all stages of Schwann cell development SRY (sex determining region Y) box 10 (Sox10) and v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) (ErbB3) are expressed starting at the neural crest and continue throughout Schwann cell development. Sox10 is a transcription factor that is required for neural crest cells to begin progressing down a Schwann cell lineage. Without it, Schwann cell precursors are absent (Britsch et. al., 2001). ErbB3 is the receptor for neuregulin, whose signalling is critical for Schwann survival and function (Garratt et. al., 2000). Sox10 may aid Schwann cells in receiving neuregulin signals through ErbB3 (Britsch et. al., 2001), thereby making both Sox10 and ErbB3 indispensable for Schwann cell existence. The L1 adhesion molecule (L1) and the p75 neurotrophin receptor (p75) are present at every stage during development except during myelination. They are downregulated in myelinating Schwann cells. L1 is required for axonal adherence (Martini & Schachner, 1986), and p75 is required for Schwann cell migration and death signalling (Bentley & Lee, 2000; Syroid et. al., 2000).

1.1.3.2 Genes expressed selectively at different stages of Schwann cell development Most genes are expressed by Schwann cells at select stages of development. Some genes mark the decision that a neural crest cell has started progression down the Schwann cell lineage. Brain fatty acid-binding protein (Bfabp) (Britsch et. al., 2001), desert hedgehog (Dhh) (Parmantier et. al., 1999), and growth-associated protein 43 (Gap-43) (Curtis et. al., 1992) are not expressed by neural crest cells but are expressed by Schwann cell precursors and immature Schwann cells. Certain factors are only expressed in the very early stages and are absent in later stages. Activator protein 2α (AP2α) is a transcription factor that is expressed in neural crest cells and Schwann cell precursors but is downregulated in immature Schwann cells. It is a positive regulator of remaining at the immature Schwann cell stage and a negative regulator of progressing through the Schwann cell lineage (Stewart et. al., 2001). α4 integrin and N-cadherin (Ncad) have a similar expression pattern and may also play roles in promoting the Schwann cell precursor stage (Jessen & Mirsky, 2005; Wanner et. al., 2006).

There is only one marker that uniquely marks a single Schwann cell stage. Cadherin 19 (Cdh19) was found by in situ hybridization to be expressed only at the Schwann cell precursor stage (Takahashi & Osumi, 2005). The role of Cdh19 in Schwann cells has not yet been elucidated. S100 calcium binding protein β (S100β) is routinely used as a Schwann cell marker. While very low levels can be found in Schwann cell precursors, robust S100β expression begins at the immature Schwann cell stage and continues during myelination, non-myelination, or de- differentiation (Jessen & Mirsky, 2005). One role for S100β is in cell membrane rearrangement (Mbele et. al., 2002). Vimentin has a similar expression pattern, being expressed by Schwann cell precursors and all stages that follow (Triolo et. al., 2006). There are many genes uniquely up-regulated in the immature Schwann cell stage. In general, these genes promote proliferation and prevent myelination. These include c-Jun (Parkinson et. al., 2004), paired box gene 3 (Pax3, Kioussi et. al., 1995), SRY-box containing gene 2 (Sox2, Le et. al., 2005a), Cyclin D1 (Atanasoski et. al., 2001), Notch (Jessen & Mirsky, 2008), and inhibitor of DNA binding 2 and 4 (Id2/4, Stewart et. al., 1997b). These genes may also suppress myelination. For example, Pax3 is a transcription factor that prevents myelination by repressesing myelin basic protein (Mbp) transcription (Kioussi et. al., 1995). A set of genes are expressed during the immature Schwann cells stage and also during the non-myelinating Schwann cell stage. These include Gap-43 (Curtis et. al., 1992), glial fibrillary acidic protein (Gfap, Jessen et. al., 1990), and neural cell adhesion molecule (Ncam) (Martini & Schachner, 1986). Conversely, five genes differentiate immature Schwann cells from non- myelinating Schwann cells: α1β1 integrin (Stewart et. al., 1997a), α7β1 integrin (Previtali et. al.,2003), galactocerebroside (GalC, Jessen et. al., 1987) , Ran-1, and A5E3 (Jessen et. al., 1990) are expressed by non-myelinating Schwann cells but not by immature Schwann cells. Additionally, myelin protein zero (P0) is expressed by immature Schwann cells but not by non- myelinating Schwann cells (Lee et. al., 1997).

1.1.3.3 Genes expressed during myelination by Schwann cells When Schwann cells begin to myelinate, they down-regulate the expression of genes that promote proliferation and up-regulate those that promote myelination. Sox2, c-Jun, and Cyclin

D1, which were expressed during the immature Schwann cell stage, are down-regulated by microRNAs during the progression to myelination (Yun et. al., 2010). To transition from ending proliferation to beginning myelination, the pro-myelinating stage exists. This stage couples the end of proliferation with the up-regulation of genes that promote myelination, such as Krox-20 (Topilko et. al., 1994), octamer-binding transcription factor 6 (Oct6) (Arroyo et. al., 1996), brain 1/2 class III POU-domain protein (Brn1/2, Jaegle et. al., 2003), NGFI-A-binding proteins 1 and 2 (Nab1/2, Le et. al., 2005b), nuclear factor kappa-light-chain-enhancer of activated of B cells (NFkB, Nickols et. al., 2003) and v-ski sarcoma viral oncogene homologue (Ski) (Atanasoski et. al., 2004). Neuregulin signals through phosphatodylinositol 3-kinase (PI3K) to immature Schwann cells to proceed to the pro-myelinating state by activation of NFkB, Oct6, and Brn2 (Maurel & Salzer, 2000). Ski also induces Oct6, and Oct6 induces Ski in return. Ski promotes the expression of the myelin protein genes peripheral myelin protein 22 (Pmp22), P0, and Periaxin. Despite its vast role in myelination, Ski does not interact with Krox-20 (Atanasoski et. al., 2004). Krox-20 is a major regulator of the end of proliferation and the beginning of myelination. Krox- 20 is activated by Oct6 and Brn2 (Ghislain & Charnay, 2006). Krox-20 then suppresses the pro- proliferation signal, c-Jun, and stimulates one of the myelin protein genes, P0 (Parkinson et. al., 2004; 2008, Jang & Svaren, 2009). The activation and activities of Krox-20 require Sox10 (Ghislain & Charnay, 2006; Jang & Svaren, 2009). Myelination requires the up-regulation of myelin component genes, such as Pmp22, P0, Mbp, and myelin-associated glycoprotein (Mag), Griffiths et. al., 1989). Mag is expressed during the process of wrapping an axon in myelin, a time when the myelin is not compact. Mag‟s expression continues and Mbp‟s expression begins when the myelin is compact (Martini & Schachner, 1986). Nectin-like molecule 4 (Necl4) is also required for myelination. It is expressed on Schwann cells and it interacts with nectin-like molecule 1 (Necl1), which is expressed on axons (Maurel et. al., 2007).

1.1.3.4 Genes expressed during de-differentiation of Schwann cells Schwann cells de-differentiate when they lose contact with axons. This occurs after nerve injury and when they are grown in culture in the absence of axons. Both myelinating and non- myelinating Schwann cells de-differentiate and begin proliferation again (Murinson et al. 2005).

Myelinating Schwann cells down-regulate myelin genes (LeBlanc & Poduslo, 1990). The gene expression profile of de-differentiated Schwann cells is similar to that of immature Schwann cells. They re-express key proliferation genes such as c-Jun (Parkinson et. al., 2008), Sox2 (Le et. al., 2005b), and Cyclin D1 (Kim et. al., 2000). Axon adherence genes such as Ncad (Thornton et. al., 2005), Ncam, and L1 (Martini & Schachner, 1988) are re-expressed and α1β1 integrin (Stewart et. al., 1997a) and Necl4 (Zelano et. al., 2009) are up-regulated. They re- express Gap-43 (Curtis et. al., 1992), Gfap (Jessen et. al., 1990), and p75 (Taniuchi et. al., 1986), and up-regulate vimentin (Triolo et. al., 2006). The expression of genes encoding growth factors for neuronal survival, such as basic fibroblast growth factor (Fgf-2) are also up-regulated (Levy et. al., 2007). Transcription factors which may negatively regulate myelin genes are up- regulated, such as Id2 (Le et. al., 2005b; Stewart et. al., 1997) and c-Jun, which down-regulates Krox-20 (Parkinson et. al., 2008). This repression aids in regeneration: lack of Krox-20 results in myelin breakdown, a key step in Wallerian degeneration (Decker et. al.,2006). Although de-differentiated cells acquire an immature Schwann cell-like expression pattern, they are not exactly the same as immature Schwann cells. The surface lipid antigen 04 is expressed by immature Schwann cells but not by de-differentiated Schwann cells (Mirsky et. al., 1990), while Ncad (Thornton et. al., 2005; Wanner et. al., 2006) and α1β1 integrin (Stewart et. al., 1997a) are expressed in de-differentiated cells but not in immature Schwann cells. Even genes that are expressed by both immature Schwann cells and de-differentiated Schwann cells differ in their necessity. The Schwann cells of Cyclin D1 knockout mice (Kim et. al., 2000) or Gfap knockout mice (Triolo et. al., 2006) develop normally but are impaired in Wallerian degeneration. Conversely, ErbB2 knockout mice are not able to develop Schwann cells but are able to regenerate following injury (Atanasoski et. al., 2006). Cyclin D1 and Gfap are not essential for the immature Schwann cell stage but are essential for de-differentiation, while ErbB2 has the opposite requirements. Cell division control protein 2 homologue (Cdc2) is expressed by de-differentiated Schwann cells but has not been explored in immature Schwann cells (Han et. al., 2006). Schwann cells have not been shown to de-differentiate so far as to the Schwann cell precursor stage. The neural crest cell and Schwann cell precursor marker AP2α is not expressed following nerve cut or when Schwann cells are cultured in the absence of axons in vitro (Stewart

et. al., 2001). Additionally, S100β, which is not expressed at the Schwann cell precursor stage, is not down-regulated following injury (Levy et. al., 2007).This supports the hypothesis that when Schwann cells de-differentiate following the loss of axonal contact they only differentiate to an immature Schwann cell like stage, and do not differentiate so far as to the Schwann cell precursor stage.

1.1.4 Isolation The most common source of Schwann cells for studies has been from the peripheral nerve. Schwann cells and their precursors have been isolated from embryonic (Jessen et. al., 1994), neonatal, and adult rodent nerve (Morrissey et. al., 1991), and from adult human nerve (Morrissey et. al., 1991). The most popular method for Schwann cell isolation was published by Morrissey and colleagues in 1991 and requires approximately 6 weeks to obtain a 98% pure population of Schwann cells from adult rat or human nerve. Several variations of this method are used. It is important to note that when isolated Schwann cell are grown in culture in the absence of axons, as is normally done, they de-differentiate into an immature Schwann cell-like state, just as they do during Wallerian degeneration (Mirsky et. al., 2008). The ability to isolate Schwann cells from human nerves is of therapeutic interest. For example, Schwann cells may be used for transplantation into the injured peripheral or central nervous system (see sections 1.2.4 and 1.3.4). However, two downfalls exist. The first is that Schwann cell purification and expansion is a process that takes several weeks. In those weeks between injury and transplantation, the therapeutic advantage of the transplant may significantly diminish (Walsh & Midha, 2009). The second problem with human Schwann cell isolation is the source. Initially, Schwann cells were isolated from post-mortem cadavers (Morrissey et. al., 1991). However, healthy Schwann cells for transplantation should be isolated from a healthy donor which requires the sacrifice of some peripheral nerve, resulting in functional losses (Hood et. al., 2009). An alternative source of Schwann cells is desirable.

1.2 Peripheral nervous system injury and disease 1.2.1 Demyelinating diseases The interactions between Schwann cells and axons are important for the proper function of the nervous system, and perturbations cause several diseases such as Guillain–Barré Syndrome (Hahn, 1998), leprosy (Noon & Lloyd, 2005), and Charcot-Marie-Tooth disease (Meyer zu Horste et. al., 2006). In 1886, Charcot & Marie and Tooth described several neurological disorders that led to muscle weakness and are often inheritable. These diseases have been named Charcot-Marie- Tooth (CMT) neuropathies and include demyelinating diseases that cause reduced nerve conduction velocity (CMT1). Schwann cells are usually affected in these diseases, with mutations in myelin protein genes most common (Meyer zu Horste et. al., 2006). Mutations in myelin protein genes cause demyelination in CMT1 because the protein to lipid relationship in myelin is unbalanced. The most common mutation results in the overexpression of Pmp22, while there are other types where P0, Connexion-32, or Krox-20 are affected (Chance & Fischbeck, 1994; Murakami et al., 1996, Warner et. al., 1998). Not only does improper myelin affect signal conductance, but it also causes axons to degenerate due to inappropriate or missing axon-glia interactions (Kamholz et al., 2000).

1.2.2 Peripheral nerve injury In demyelinating diseases, PNS function slowly declines over time. Following peripheral nerve injury, an abrupt disruption in function is seen. The axon may be crushed or transected due to trauma. Schwann cells play a large role in converting the hostile post-injury environment into one that is favourable for axonal regeneration. After a traumatic injury, Wallerian degeneration occurs. These are steps where the axons and myelin distal to the injury site degenerate and their debris is quickly cleared (Waller, 1850). It is important that the degenerated myelin is cleared because myelin contains inhibitory molecules that prevent axonal growth (He & Koprivica 2004). Timely clearance of debris allows axons the opportunity to grow. In the mammalian PNS, clearance takes 7-14 days. Quick and

complete myelin debris clearance is one important reason why PNS axons regenerate well (compared to central nervous system (CNS) axons) (Vargas & Barres, 2007). Wallerian degeneration begins with the degeneration of the axon. The axolemma degenerates first, forming blebs. Cytoskeletal components of the axoplasm, such as microtubules and neurofilaments, disassemble next in a process called granular disintegration of the axonal cytoskeleton (GDC) (George & Griffin, 1994; Vargas & Barres, 2007). GDC only takes 1 hour to complete once it begins (Beirowski et. al., 2005; Kerschensteiner et. al., 2005), but there is a delay between injury and GDC. In rodents, there is a 1-2 day delay and in humans the delay is up to 7 days (Chaudhry et. al., 1992). Schwann cells de-differentiate in order to re-enter the cell cycle and proliferate (Mirsky et. al., 2008). They phagocytose their own myelin and cease further myelin production. The blood-tissue barrier becomes permeable to macrophages from the circulation to enter and phagocytose the bulk of the myelin and axonal debris (Vargas & Barres, 2007). Myelin debris is cleared by day 30, and axons are seen sprouting on days 10-14 (George & Griffin, 1994). This type of peripheral nerve regeneration is difficult following nerve transections in which there is a long distance for the nerve to regenerate. The Schwann cells of the distal stump lose their regenerative ability the longer they remain denervated. As such, these injuries remain unrepaired and chronic (Walsh & Midha, 2009).

1.2.3 Role of Schwann cells in repair Schwann cells play a major role in peripheral nerve regeneration. They are aware of the injury soon after it happens and act promptly to assist with regeneration. Myelinating Schwann cells are notified of an injury to the axon before that axon even degenerates. A signal is sent from the axon to the ErbB2 receptors of the Schwann cell ensheathing it within minutes after injury (Guertin et. al., 2005). In response to an injury signal, Schwann cells limit myelin production because myelin is inhibitory to axon growth. They down- regulate myelin lipid production within 12 hours (White et. al., 1989) and cease myelin protein production within 48 hours (Trapp et. al., 1988). Myelin separates from the axon starting at Schmidt-Lanterman incisures, followed by separation at the nodes of Ranvier. Myelin debris separates into smaller myelin ovoids that can be degraded more easily (Williams & Hall, 1971).

Schwann cells move to internodes (Liu et. al., 1995), become hypertrophied (Stoll et. al., 1989), and become myelin phagocytosing cells (Hirata & Kawabuchi, 2002). Two to three days after axonal injury, Schwann cells proliferate. They divide longitudinally along the basal lamina creating Büngner Bands, paths for axons to follow as they regrow (Stoll et. al., 1989). Both myelinating and non-myelinating Schwann cells proliferate and assist with regeneration (Murinson et al. 2005). Schwann cells recruit macrophages to the site of injury by releasing chemokines and cytokines, such as tumor necrosis factor-α (TNFα) and leukemia inhibitor factor (LIF) (Shamash et. al., 2002, Banner & Patterson, 1994). Macrophages phagocytose myelin from the endoneurium on day 3, and enter the basal lamina to digest internal myelin on day 5 (Liu et. al., 1995). Macrophages phagocytose most of the myelin debris, but they are not the first myelin phagocytosing cells to act (Vargas & Barres, 2007). Schwann cells are the first phagocytosing cells, degrading their own myelin and phagocytosing extracellular myelin. Myelin debris can be seen in Schwann cells as early as day 2 post injury (Stoll et. al., 1989), while macrophages are first seen between days 3 and 5 (Liu et. al., 1995). Schwann cells aid in clearing the environment of inhibitory myelin signals and also act to improve the environment into one that is conducive to axonal growth. When axons degenerate and Schwann cells lose contact with them, the Schwann cells de-differentiate into an immature Schwann cell-like state. In this state they can proliferate and express factors that are conducive to axonal growth (Mirsky et. al., 2008). They secrete erythropoietin after injury, which is neuroprotective, to prevent axon degeneration. (Hoke & Keswani, 2005). They release neurotrophins to promote axonal growth, such as nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), and glial cell line-derived neurotrophic factor (GDNF). Schwann cells also help guide these newly regenerating axons by secreting the adhesion molecules L1 and Ncam. These factors guide axons through the Büngner Bands (Hirata & Kawabuchi, 2002; Chen et. al., 2007). De-differentiated Schwann cells can re-differentiate into myelinating Schwann cells and myelinate the newly regenerated axons, thereby completing regeneration (Mirsky et. al., 2008).

1.2.4 Schwann cell transplantation intervention The PNS has a self-repair mechanism that works well for short distance regeneration but not for long distance regeneration. When there is a large gap to repair, axonal regrowth takes so long that the Schwann cells lose their regenerative ability (Walsh & Midha, 2009). Transplanting healthy Schwann cells into the site of injury has been one method explored to complete the regeneration of chronically injured nerves. Axon guidance channels filled with Schwann cells may be transplanted to bridge the gap between the proximal and distal ends of the injured axon (Guenard et. al., 1992; Anselin et. al., 1997; Nishiura et. al., 2004). The most obvious source of Schwann cells is from a healthy nerve (nerve-SC) that can be expanded in culture and re-transplanted. Isolation of Schwann cells from the same person who will be receiving them is ideal to decrease the chances of transplant rejection and prevent the need for immunosuppression. However, the isolation of human nerve-SC requires the sacrifice of a healthy nerve, which is counterproductive to the aim, and the proliferation of human nerve-SC in vitro is slow and limited (Hood et. al., 2009). An alternative Schwann cell sources is desired.

1.3 Central nervous system injury and disease 1.3.1 Demyelinating diseases Some CNS axons are myelinated. Schwann cells are not present in the CNS; as such oligodendrocytes myelinate CNS axons. Their myelin is also susceptible to demyelinating diseases, such as amyotrophic lateral sclerosis (ALS), Pelizaeous-Merzbacher disease, and multiple sclerosis (MS). Similar to the PNS, axon-glia interactions are important and demyelination results in axon transection and degeneration (Bjartmar et. al., 2003). Remyelination prior to axon degeneration would aid in repair and prevent axonal loss.

1.3.2 Spinal cord injury Following spinal cord crush or transection, the injured axons degenerate, leaving inhibitory myelin and axonal debris. Wallerian degeneration begins but is not completed. It is slower than in the PNS, taking months to years in mammals (Vargas & Barres, 2007).

Wallerian degeneration begins with axon degeneration, as in the PNS, but the clearance of axonal and myelin debris is lacking (Vargas & Barres, 2007). Axon degeneration is complete by day 3, but axon and myelin debris are still seen at day 90, indicating that Wallerian degeneration was not completed (George & Griffin, 1994). In the PNS, macrophages and Schwann cells clear debris. In the CNS, oligodendrocytes do not clear myelin; instead microglia aim to accomplish this task, being the resident macrophages of the spinal cord (Stoll & Jander, 1999). Circulatory macrophages do not appear until days 18-21 as it is difficult for the macrophages to enter the intact spinal cord due to the blood-brain barrier (George & Griffin, 1994). None of the phagocytosing cells complete debris clearance, and as a result axonal sprouts are not seen (George & Griffin, 1994). This suggests that CNS axons regenerate poorly because of the slow Wallerian degeneration which leaves myelin debris to inhibit axon regeneration (Vargas & Barres, 2007). This is further supported by the observation that the developing CNS loses its ability to regenerate when myelination begins (Vargas & Barres, 2007), and that axons do not grow on CNS myelin in vitro (Schwab & Caroni, 1988; Vanselow et. al., 1990). In contrast, the newt optic nerve is able to complete Wallerian degeneration in 10-14 days, allowing its CNS axons to completely regenerate (Turner & Glaze, 1976). Another barrier to axonal growth is the glial scar. This is a ring of reactive astrocytes that forms around the injury to contain it, but that also acts as a physical barrier to cell migration (Silver & Miller, 2004). Secondary damage also occurs following spinal cord injury. Axons that were not crushed (the spared tissue rim) lose their myelin as a result of the hostile injury environment (Guest et al., 2005).

1.3.3 Role of Schwann cells in repair Schwann cells are not normally present in the CNS. However, they migrate into the CNS following injury. Schwann cell myelin was seen in the spinal cord lesions of human MS (Itoyama et. al., 1983) and spinal cord injury patients (Wang et. al., 1996). After spinal cord injury, Schwann cells myelin is seen on axons in the spared tissue rim (Brook et. al., 1998). Schwann cells migrate into the spinal cord in the de-differentiated state, proliferate there, and then re-differentiate into myelinating Schwann cells (Nagoshi et. al., 2011). Recently it has been

shown that the majority of Schwann cells present in the demyelinated brain are derived from oligodendrocyte precursors in the CNS, and they did not migrate in from the PNS (Zawadzka et. al., 2010). Regardless of their origin, the presence of Schwann cells in the CNS is beneficial and is enhanced when exogenous Schwann cells are transplanted into the site of injury (Ramer et. al., 2004; Hill et. al.,2006; Biernaskie et. al., 2007).

1.3.4 Schwann cell transplantation intervention While Schwann cells are endogenously found in the PNS, they are also able to myelinate CNS axons when transplanted there. Whole nerve grafts from peripheral nerves were first transplanted into the injured spinal cord and found to promote axon growth (Richardson et. al., 1980; David & Aguayo, 1981). It was later found that Schwann cells were the specific cell type promoting the regeneration of CNS axons (Kromer & Cornbrooks, 1985). Schwann cells have been used to treat multiple sclerosis (Halfpenny et. al., 2002) and spinal cord injury (Tetzlaff et. al., 2010). Schwann cells are particularly advantageous to transplant into MS patients because oligodendrocytes are targeted by the immune system in MS, but Schwann cells are not (Halfpenny et. al., 2002). Human Schwann cells have also shown some reparative ability in the injured or demyelinated rodent spinal cord (Guest et. al., 1997; Kohama et. al., 2001).

1.4 Skin-derived precursors 1.4.1 Endogenous roles The skin is an organ that regenerates itself throughout the organism‟s lifetime. The dermis is the middle layer of the skin, as well as the thickest. Skin-derived precursors (SKPs) are stem cells found in the dermis (Fernandes et. al., 2004; Biernaskie et. al., 2009). These stem cells persist into adulthood, as they can be isolated from adult skin (Toma et. al., 2001). In their endogenous environment, the dermis, they instruct hair follicle growth and contribute to maintaining the dermis and repairing it after injury (Biernaskie et. al., 2009). One niche where SKPs are found is at the base of hair follicles, in the dermal papilla (DP) and surrounding the follicle in the dermal sheath (DS) (Fernandes et. al., 2004) The DP is the control center for hair

growth (Jahoda et al., 1984); as such SKPs found in the DP instruct hair growth (Biernaskie et. al., 2009). SKPs can also migrate out of the DP. Upon injury to the dermis, SKPs migrate to the site of injury and differentiate into dermal fibroblasts, thereby replenishing the interfollicular dermis (Biernaskie et. al., 2009).

1.4.2 Developmental origin SKPs have been isolated from the skin of a variety of locations. Our lab has previously determined the developmental origin of SKPs from the whisker pad (facial skin) and dorsal trunk skin. Whisker pad SKPs are neural crest derived, while dorsal trunk SKPs are somite derived (Fernandes et. al., 2004; Jinno et. al., 2010). This correlates with previous data that facial skin is neural crest derived (Couly et. al., 1998), and dorsal trunk skin is somite derived (Mauger, 1972). Despite their different origins, SKPs from the whisker pad and dorsal trunk are very similar. Both populations express the SKPs markers fibronectin, vimentin, nestin and versican, (Jinno et. al., 2010), have similar proliferation and self-renewal rates (Jinno et. al., 2010), and have similar differentiation ability (Toma et. al., 2001; Fernandes et. al., 2004; Jinno et. al., 2010). To further support the similarity of SKPs from different origins, the gene expression profile of SKPs from three separate origins (whisker pad, dorsal trunk, and ventral trunk) are all very similar to each other, and different from another mesenchymal precursor type, bone marrow-derived mesenchymal stromal cells (MSCs) (Jinno et. al., 2010).

1.4.3 Isolation SKPs have been isolated from neonatal and adult whisker pad, dorsal trunk and ventral trunk skin of mice and rats (Toma et. al., 2001; Jinno et. al., 2010), fetal porcine skin (Dyce et. al., 2004), adult canine skin (Valenzuela et. al., 2008), human neonatal and juvenile foreskin (Toma et. al., 2005) and human adult skin (Toma et. al., 2001; Joannides et. al., 2004). The number of SKPs that grow in culture decreases with the age of the skin used (Fernandes et. al., 2004). To isolate SKPs, the skin is dissociated into single cells using enzymatic and mechanical digestion. This single cell suspension is grown in proliferation media containing Fgf-2,

epidermal growth factor (EGF) and B27 supplement. In this medium SKPs grow as floating spheres, while other cells types adhere to the flask. Sequential passaging of the cells allows for a pure population of SKPs in 2-3 weeks (Toma et. al., 2001; Biernaskie et. al., 2006). SKPs can be isolated from a small piece of skin by a simple skin biopsy. Because of this, there are ethical and technical advantages to using SKPs over other stem cell types, such as embryonic stem cells or bone marrow stem cells. The skin for SKPs may be collected from human samples with relative ease and without inflicting major injury to the donor. Additionally, the donor or guardian of the donor can consent.

1.4.4 Multipotency Rodent SKPs have been differentiated into neural, mesodermal, and skeletal progeny. In vitro, they have become neuronal, glial, smooth muscle, adipocyte, myofibroblast, muscle progenitor, smooth muscle, chondrocyte, and osteocyte cell types (Toma et. al., 2001; Joannides et. al., 2004; Yang et. al., 2004; Gingras et. al., 2007; Biernaskie et. al., 2009; Lavoie et. al., 2009; Tolg et. al., in preparation). They have also been differentiated into insulin-producing cells (Guo et. al., 2009), hepatocyte-like cells (Kock et. al., 2009), and primordial germ cell-like cells (Linher et. al., 2009). Similar cell types are seen when naive SKPs are transplanted into different organs and left to differentiate in vivo. When transplanted into back skin, SKPs differentiate into dermal fibroblasts and myofibroblasts in the dermis and adipocytes in the hypodermis (Biernaskie et. al., 2009). SKPs differentiate into osteocytes, chondrocytes, smooth muscle cells and pericytes when transplanted into the fractured tibial bone (Lavoie et. al., 2009). Rodent SKPs differentiate into Schwann cells that are functional and myelinate when transplanted into injured sciatic nerve (McKenzie et. al., 2006) and the injured spinal cord (Biernaskie et. al., 2007). Both rodent and human SKPs differentiated into Schwann cells when transplanted into the brains of shiverer mice (McKenzie et. al., 2006), which have a genetic deficiency in MBP and hypomyelination of the CNS (Dupouey et. al., 1979).

1.4.5 Differentiation to Schwann cells Rodent SKPs differentiate into Schwann cells in vitro and in vivo. In both situations, these SKP-derived Schwann cells (SKP-SC) express typical Schwann cell markers such as Gfap, P0, p75, and S100β. They are functional, as they are able to myelinate dorsal root ganglia (DRG) and brain cerebellar slice cultures in vitro (McKenzie et. al., 2006). To differentiate SKPs into Schwann cells in vitro, SKPs are grown adherently on poly-D- lysine and laminin coated dishes in media containing neuregulin-1β, forskolin, and N2 supplement. After two weeks, colonies of SKP-SC can be seen. These colonies are mechanically isolated and expanded (McKenzie et. al., 2006; Biernaskie et. al., 2006). Rodent SKPs also differentiate into Schwann cells in vivo when transplanted into the injured PNS or CNS (McKenzie et. al., 2006). Human SKPs also differentiated into myelinating Schwann cells in the CNS (McKenzie et. al., 2006). The mechanism of in vivo SKP-SC differentiation from naive SKPs has not been defined, although it likely involves neuregulin released by the axons in the regenerating sciatic nerve and dysmyelinated brain (McKenzie et. al., 2006). Both whisker pad SKPs and dorsal back SKPs differentiate into Schwann cells with similar efficiency (Jinno et. al., 2010). Of note, dorsal trunk SKPs are somite-derived but can still differentiate into Schwann cells, which are neural crest derived during development (Jinno et. al., 2010).

1.5 Skin-derived precursors for peripheral and central nervous system repair 1.5.1 Peripheral nerve injury Schwann cells differentiated from SKPs express typical Schwann cell markers and can myelinate DRG axons in vitro. When transplanted into rodent models of peripheral nerve injury, SKP-SC are able to myelinate axons in vivo and aid in injury repair (McKenzie et. al., 2006; Walsh, et. al., 2009, 2010). The first experiment using SKP-SC transplantation into the PNS used a crush model. Here, the sciatic nerve of a mouse was crushed with forceps (but not transected) and SKP-SC

were immediately transplanted distal to the crush site. SKP-SC were able to myelinate the regenerating axons (McKenzie et. al., 2006). SKP-SC can also be used to repair transected nerves. In an acute model of peripheral injury, SKP-SC were transplanted into an acellular nerve graft and used to bridge a 12 mm gap in the sciatic nerve. SKP-SC promoted axon regeneration, myelination, and electrophysiological recovery (Walsh et. al., 2009). SKP-SC were also able to regenerate chronic peripheral nerve injury. More motor neurons regenerated into the chronically denervated nerve with SKP-SC transplantation compared with media controls, and these regenerated axons were larger. The compound muscle action potential (CMAP) amplitude in the gastrocnemius muscle and its muscle weight were larger with SKP-SC transplantation, suggesting better muscle reinnervation. The reparative ability of SKP-SC approached that of nerves that were immediately sutured and were not chronically denervated (Walsh et. al., 2010).

1.5.2 Spinal cord injury SKPs were first shown to myelinate CNS axons in vivo by McKenzie and colleagues (2006). Naive SKPs transplanted into the brains of shiverer mice (which have poor CNS myelination) were seen myelinating. (McKenzie et. al., 2006) SKPs are also able to myelinate the injured spinal cord. SKP-SC were transplanted into the rat spinal cord 1 week after contusion injury (subacute injury). This injury involves the crushing (but not transection) of the spinal cord, causing axons to degenerate at the site of crush and leave a fluid filled cavity. This cavity is enclosed with the glial scar, a ring of reactive astrocytes that prevents axons and cells from passing. Uncrushed axons around the cavity (the spared tissue rim) remain intact but may lose their myelin as secondary damage. In this model, SKP-SC transplantation resulted in improved functional locomotor recovery and an improved environment within the injured spinal cord over media control. Specifically, they reduced the size of the cavity, created a bridge across it, and increased the size of the spared tissue rim. The glial scar was reduced, allowing SKP-SC to access the spared tissue rim. They were seen myelinated the demyelinated axons of the spared tissue rim. An increase in myelinating endogenous Schwann cells was seen, further aiding in the remyelination of the spared tissue rim.

(Biernaskie et. al., 2007). Similar positive results were also seen when SKP-SC were transplanted into the chronically contused spinal cord (Assinck et. al., in preparation).

1.6 Comparison of nerve-derived Schwann cells with Schwann cells differentiated from skin-derived precursors Most studies where Schwann cells are transplanted into an injured or diseased area use Schwann cells obtained from peripheral nerve biopsies (nerve-SC). This is problematic in human studies, as peripheral nerve biopsies are painful and damaging. SKP-SC are a possible alternative source of Schwann cells that are more easily obtained (SKPs require a skin biopsy). It is important to directly compare the characteristics and regenerative ability of SKP-SC and nerve- SC and determine which cell type is superior in terms of regeneration. If they are similar in terms of regenerative ability, SKP-SC have an advantage over nerve-SC in their ease of isolation and the possibility for autologous transplantation (SKPs would be isolated from a patient, differentiated into Schwann cells in vitro and be transplanted back into that same donor, thereby alleviating the need for immunosuppression). SKP-SC and nerve-SC have been directly compared in their ability to repair the rat PNS (Walsh et. al., 2009) and indirectly compared in their ability to repair the rat CNS (Biernaskie et. al., 2007).

1.6.1 Neurotrophin production SKP-SC release higher levels of neurotrophins in culture than nerve-SC. The levels of NGF and neurotrophin 3 (NT-3) compared by enzyme-linked immunosorbent assays (ELISA) were significantly greater in media conditioned by SKP-SC than in media conditioned by nerve- SC. Both SKP-SC and nerve-SC secreted enough neurotrophins to promote similar neurite outgrowth in rat pheochromocytoma (PC12) cells (Walsh et. al., 2009).

1.6.2 Peripheral nerve injury reparative ability SKP-SC had a slight reparative advantage over nerve-SC in an acute model of peripheral nerve injury, the only injury model in which they were directly compared. Acellular nerve grafts were used to bridge a 12 mm gap in the sciatic nerve. When transplanted with SKP-SC or nerve-

SC, the amount of axon regeneration seen was similar. The number of axons, cross-sectional area, and G-ratio were similar. However, SKP-SC showed improved functional regeneration over nerve-SC. CMAP amplitude was higher with SKP-SC transplantation than with nerve-SC transplantation. Nerve conduction velocity appeared to also be higher with SKP-SC than nerve- SC, however this difference did not reach significance. The authors speculated that the improved reparative ability of SKP-SC may be because SKP-SC release higher levels of neurotrophins in culture than nerve-SC (Walsh et. al., 2009).

1.6.3 Spinal cord injury reparative ability Both SKP-SC and nerve-SC have been transplanted into the subacutely and chronically injured spinal cord (Takami et. al., 2002; Biernaskie et. al., 2007; Assinck et. al., in preparation). Both cell types were seen myelinating axons and increase functional recovery. Uniquely, SKP-SC were able to migrate through the astrocytes of the glial scar, as well as reduce the glial scar. This is something not seen with nerve-SC (Lakatos et. al., 2000). This difference is of therapeutic importance as it seems to allow SKP-SC two reparative advantages over nerve- SC: SKP-SC can access axons in the spared tissue rim and myelinate them; and SKP-SC can reduce the glial scar, opening the pathway for axons to regrow into the injury. The difference in astrocyte interaction ability may be due to a difference in maturity. Schwann cell precursors, which are developmentally less mature than Schwann cells, were able to integrate with host astrocytes when transplanted into an ethidium bromide-induced demyelinated region of the rat spinal cord, while nerve-SC were not able to integrate (Woodhoo et. al., 2007). This raises the possibility that the ability of SKP-SC to integrate with astrocytes is due to maturity: perhaps SKP-SC are less mature than nerve-SC.

1.7 Aims and hypothesis SKP-SC may be desirable over nerve-SC due to the ease with which they are obtained and the low severity of damage their removal causes. If SKP-SC are biologically equivalent to nerve-SC then perhaps SKP-SC could be used to treat the same ailments for which nerve-SC are

currently being pursued. This study examines the similarity of SKP-SC and nerve-SC at the functional level and the gene expression level. The aim of this study is to establish the similarities and differences between SKP-SC and nerve-SC. Specifically, these differences will be represented by differing functional abilities and different gene expression profiles. I hypothesize that SKP-SC are similar to nerve-SC but not identical. Previous studies suggest that SKP-SC have advantages over nerve-SC in reparative ability in the sciatic nerve injury model and spinal cord injury model. I hypothesize that the one difference that will be seen is that SKP-SC are less mature than nerve-SC, as suggested by the ability of SKP-SC to interact with astrocytes similarly to Schwann cell precursors (Woodhoo et. al., 2007). Other unexpected differences may be revealed through the gene expression study.

Chapter 2 Methods

2.1 Cell isolation Green fluorescent protein (GFP) expressing Sprague-Dawley rats (Japan SLC) or GFP- negative Sprague Dawley rats (Charles River) were used for all experiments. All animal use was approved by the Animal Care Committee for the Hospital for Sick Children in accordance with the Canadian Council of Animal Care policies.

2.1.1 Nerve-SC isolation Nerve-SC were isolated from the sciatic nerves of 1.5 to 2 month old Sprague Dawley rats. The sciatic nerves of each rat were removed and cleaned of muscle, fat, blood vessels, and the epineurial sheath. Cleaned nerves were cut longitudinally into thin strips. Sciatic nerves were placed in collagenase type XI (1 mg/ml, Sigma) rotating at 37ºC for chemical digestion. After 15 minutes the nerves were triturated with a P1000 pipette, left to digest in collagenase for 5 more minutes, and triturated again. Digested nerves were resuspended in “Schwann cell proliferation media” containing Dulbecco‟s Modified Eagle Medium (DMEM)/F12 (3:1) (Gibco), 1% penicillin/streptomycin (Cambrex), 2% N2 supplement (Invitrogen), 25 ng/ml neuregulin-1β (R&D Systems), and 5 μM forskolin (Sigma). To prevent contamination from dissection, 1 μg/ul Fungizone Antimycotic (Invitrogen) was added (Fungizone was not added in subsequent feedings). One sciatic nerve was plated on one 6 cm petri dish precoated with “Schwann cell coating”: 4 μg/ml laminin (BD Biosciences) and 30 μg/ml poly-D-lysine hydrobromide (Sigma).

Cells were grown at 37ºC at 5% CO2.

Schwann cell proliferation medium was changed every 3-4 days. After 1 week, nerve-SC could be seen growing around the nerve segments. Cells were passaged using 0.25% trypsin- ethylenediaminetetraacetic acid (EDTA) (Gibco) when confluent.

2.1.2 Trunk SKPs isolation SKPs were isolated from the backs of 1.5 to 2 month old Sprague Dawley rats as previously described (Fernandes et. al., 2004). Backs were waxed, cleaned with ethanol, and a patch of skin was removed. The skin was cleaned of fat, fascia, and blood vessels and minced into small pieces a few millimetres in size with razor blades. Minced skin was digested in collagenase type XI (1 mg/ml) rotating at 37ºC for 1.5 to 2 hours. Intermittent mechanical mashing was done with a 10 ml pipette every 40 minutes. Digested skin was triturated with a P1000 pipette and diluted with DMEM to stop digestion. The skin solution was filtered through a 40 μm cell strainer (BD Falcon) to remove undigested skin and collect only single cells. Single cells were grown in “SKPs proliferation media” which contained DMEM/F12 (3:1), 1% penicillin/streptomycin, 2% B27 supplement (Invitrogen), 20 ng/ml EGF, and 40 ng/ml Fgf-2 (BD Bioscience). To prevent contamination from dissection, 1 μg/ul Fungizone Antimycotic was added (Fungizone was not added in subsequent feedings). The cells were seeded at 25 000 to 50

000 cells/ml media in a vented-cap flask. Cells were grown at 37ºC at 5% CO2.

Floating spheres were seen after a few days. SKPs were fed with SKPs proliferation media every 5 days and passaged when spheres were large. To passage SKPs, the spheres were separated from the conditioned media by centrifugation. Conditioned media filtered through a 20 μm syringe filter was saved for feeding. Spheres were digested with 1 mg/ml collagenase for 10 min in a 37ºC water bath, followed by trituration with P1000 and P200 pipettes. Dissociated spheres were resuspended in SKPs proliferation media that contained half DMEM/F12 (3:1) and half conditioned media.

2.1.3 SKP-SC isolation SKP-SC were differentiated from passage 3 trunk SKPs as previously described (McKenzie et. al., 2006; Biernaskie et. al., 2006). SKPs were dissociated to single cells, as during passaging. Single cells were resuspended in “SKPs adherence media” containing DMEM/F12 (3:1), 1% penicillin/streptomycin, 10% fetal bovine serum (FBS, Hyclone), 1% B27 supplement, 1% N2 supplement, 20 ng/ml EGF, and 40 ng/ml Fgf-2. Cells were plated at 25 000 cells/ml in petri dishes that were pre-coated with Schwann cell coating (same as nerve-SC). Cells

were grown at 37ºC at 5% CO2. After 3 days, the media was changed to Schwann cell proliferation media, and this media was replenished every 3-4 days. Schwann cell colonies that could be seen after 2-3 weeks were isolated with cloning cylinders (Corning). Cells were expanded and passaged with trypsin-EDTA when confluent.

2.2 Schwann cell verification To ensure that the nerve-SC and SKP-SC that were isolated were bona fide Schwann cells, their expression of typical Schwann cell markers and their ability to myelinate axons in vitro and in vivo was verified.

2.2.1 Immunocytochemistry To check for the expression of typical Schwann cell markers, immunocytochemistry was performed. Cells were fixed in 4% paraformaldehyde (PFA) for 10 minutes or in 100% methanol (chilled to -20ºC) for 5 minutes at -20ºC. Cells were blocked with 5% normal goat serum (NGS) and permeabilized with 0.1% Triton-X for 30 minutes. Primary antibody was added for 2 hours at room temperature or overnight at 4ºC, and secondary antibody was added for 1 hour at room temperature. Where Hoechst was used, 1:1000 Hoechst 33258 (Sigma) was added for 5 minutes.

The following primary antibodies were used at the stated dilutions: chicken anti-P0 (1:500, Aves Labs), mouse anti-S100β (1:500, Sigma), rabbit anti-Gfap (1:500, Dako), mouse anti-p75 (1:500, Chemicon), mouse anti-Necl4 (1:500, Antibodies Inc.), rabbit anti-βIII tubulin (1:500, Covance), mouse anti-βIII tubulin (1:1000, Covance), chicken anti-GFP (1:500 Millipore), rabbit anti-GFP (1:1000, Abcam), and rat anti-MBP (1:500, Millipore).

The following secondary antibodies were used at the stated dilutions: Alexa 647 goat anti-chicken, Alexa 647 goat anti-rabbit, Alexa 647 goat anti-mouse, Alexa 555 goat anti- chicken, Alex 555 goat anti-rabbit, Alexa 555 goat anti-mouse, Alexa 555 goat anti-rat, Alexa 488 goat anti-chicken, Alexa 488 goat anti-rabbit, Alexa 350 goat anti-mouse (all 1:1000, all Invitrogen), and AMCA anti-rabbit (1:100, Vector Laboratories).

Necl4 was visualized with the Necl1-Fc fusion protein. The Necl4 protein on Schwann cells interacts with Necl1, which is typically expressed on axons (Spiegel et. al., 2007). Necl1

DNA fused to the κ chain of human IgG (Necl1-Fc, gift from Dr. Elior Peles, The Weizmann Institute of Science, Israel) was expressed by human embryonic kidney (HEK) cells, as previously described (Eshed et. al., 2005; Spiegel et. al., 2007). Necl1-Fc was pre-incubated with Cy3-conjugated anti-human Fc (Jackson Laboratories). Cy3 labelled Necl1-Fc was added to Schwann cells.

Schwann cell purity was assessed by immunocytochemistry counts. The number of cells positive for S100β was expressed as a percentage of all cells, as visualized by Hoechst. Five different samples were used for nerve-SC quantification and four for SKP-SC quantification. At least three fields of view were randomly taken for each sample. Percentages are expressed as a mean ± SEM. Significance was assessed by t-test.

2.2.2 SCG axon association Superior cervical ganglia (SCG) were isolated from postnatal day 1 Sprague Dawley rats as previously described (Singh & Miller, 2005). Dissociated cells were grown at a density of 0.5 ganglia per dish on collagen coated Campenot chambers, where the cell bodies grow in a separate compartment than the axons. Both the cell bodies and axons were grown in “SCG media” 2mM L-glutamine (Lonza), 1% penicillin/streptomycin, and 20 ng/ml NGF (Cedarlane) in UltraCULTURE Medium (Lonza). To kill the astrocytes, 0.7% cytosine arabinoside was added, in addition to 3% FBS to help the neurons survive the cytosine arabinoside treatment. SCG media with cytosine arabinoside was replenished on day 3 and 4. On day 5, the cells were washed with SCG media to remove the cytosine arabinoside and FBS. On day 7, GFP expressing nerve-SC or SKP-SC were added to the axonal compartments of the Campenot chambers in SCG media with 10 ng/ml NGF at a concentration of 8 000 cells per compartment. Cells were fed every 2-3 days with SCG media with 10 ng/ml NGF. After 1 week, cells were fixed and immunocytochemistry was performed for βIII tubulin, S100β, GFP, and Hoechst.

The percent of Schwann cell associating with axons was assessed by immunocytochemistry counts. The number of GFP-positive cells aligning with βIII positive axons was expressed as a percentage of all GFP-positive cells. Three different samples were used for nerve-SC quantification and three for SKP-SC quantification. At least three fields of view

were randomly taken for each sample. Percentages are expressed as a mean ± SEM. Significance was assessed by t-test.

2.2.3 DRG myelination In vitro myelination of nerve-SC and SKP-SC was assessed using the dorsal root ganglia (DRG) myelination assay (Eldridge et. al., 1987). DRG were isolated from E15 Sprague-Dawley rats (Charles River) and dissociated with 0.25% trypsin (Gibco). Dissociated ganglia were plated at 300 000 cells/ml on matrigel (VWR) and poly-D-lysine (Sigma) coated chamber slides in “axon growth media” containing 2% B27 supplement, 1% GlutaMAX supplement (Gibco), 50 ng/ml NGF, and 1% penicillin/streptomycin in Neurobasal media (Gibco). Media was changed every other day. To obtain pure cultures of axons, cytosine arabinoside was added to the media for one media change. One week after obtaining pure axons, nerve-SC or SKP-SC were added in “Schwann cell DRG media” containing 1% ITS supplement (Sigma), 1% GlutaMAX supplement, 0.2% bovine serum albumin, 4 g/l D-glucose (Sigma), 50 ng/ml NGF, and 1% penicillin/streptomycin in Basal Media Eagle (BME, Invitrogen). The nerve-SC or SKP-SC were grown in 10% FBS in DMEM (instead of in Schwann cell proliferation media) for 3 days before being used in order to starve them. Schwann cell DRG media was changed every other day. After 6-8 days, Schwann cells were induced to myelinate by changing the media to “myelination media” containing 1% ITS supplement, 1% GlutaMAX supplement, 15% FBS, 4 g/l D-glucose, 50 ng/ml NGF, 1% penicillin/streptomycin, and 50 ug/ml L-ascorbic acid (Sigma) in Basal Media Eagle (BME). Myelination media was changed every other day for 10-14 days. Immunocytochemistry was performed on the cultures at the end point for MBP, βIII tubulin, and GFP.

As a myelination control, some axons did not receive a cytosine arabinoside treatment, leaving the endogenous Schwann cells to myelinate the axons. These wells received axon growth media for 2 days, and then followed the same schedule for Schwann cell DRG media and myelination media.

2.2.4 Sciatic nerve crush and cell transplantation The sciatic nerves of mice were crushed and Schwann cells were transplanted as previously described (McKenzie et. al., 2006) to assess the ability of the transplanted Schwann cells to myelinate axons in vivo. Non-obese diabetic, severe combined immunodeficient (NOD- SCID) mice (Charles River) were used to prevent the rejection of transplanted cells. Mice were anesthetised with isofluorane during the surgery procedure and given analgesics following surgery. The sciatic nerve was crushed with #5 watchmaker forceps for 1 min. GFP-positive rat nerve-SC or SKP-SC were injected into the nerve proximal to the crush site. After 3 weeks mice were perfused with 4% paraformaldehyde. The nerves were fixed for 4 hours in 4% paraformaldehyde and in 30% sucrose overnight. The nerves were mounted in optimal cutting temperature (OCT) compound and cryosectioned into 16 μm thick sections, cut longitudinally. Immunohistochemistry was performed for P0, βIII tubulin, and GFP.

2.2.5 Microscopy All immunocytochemistry was visualized on the Zeiss Axiovert 200 microscope with a spinning disk confocal scan head and Hamamatsu C9100-13 EM-CCD camera. Volocity acquisition software was used.

2.3 Microarray analysis Nerve-SC, SKP-SC, and trunk SKPs were compared by microarray analysis. Three replicates of each cell type were used. Nerve-SC were passaged twice, SKP-SC were passaged 3 times as SKPs and twice as SKP-SC, and trunk SKPs were passaged twice. I prepared the nerve- SC and SKP-SC samples, and Hiroyuki Jinno prepared the trunk SKPs (Jinno et. al., 2010).

2.3.1 RNA isolation, purification, and quality analysis Nerve-SC and SKP-SC were collected with trypsin-EDTA as during passaging. Trunk SKPs were dissociated into single cells with collagenase as during passaging. Cells were lysed in TRIzol Reagent (Invitrogen) following the manufacturer‟s instructions for RNA isolation. RNA was purified with the RNeasy Mini Kit (Qiagen) as per manufacturer‟s instructions. RNA was eluted from the purification tubes in RNase free water. The concentration of RNA was assessed

by the NanoDrop spectrophotometer (Thermo Scientific). RNA was stored at -80°C until it was used for microarray or reverse transcriptase polymerase chain reaction (RT-PCR).

Purified RNA that was to be used for the microarray was analyzed on the Agilent 2100 Bioanalyzer, as per manufacturer‟s instructions. Briefly, the RNA was run on an RNA 6000 Nano LabChip by capillary electrophoresis. A fluorescent dye was added to visualize the RNA. The separation of the RNA into different sized bands was visualized by laser-induced fluorescence and graphed on an electropherogram. A ladder was run as a standard. To determine the RNA concentration of a given sample, the area under the graph was calculated and compared to the standard. To assess the quality of the sample, the Bioanalyzer program attributed an RNA integrity number (RIN) out of 10 to each sample based on the shape of the electropherogram (Figure 2). To obtain a high RIN, the 18S and 28S fragments should have high, distinct peaks, and the rest of the graph should be relatively flat until the marker. Another characteristic of the graph that has been classically used to assess RNA integrity is the ratio of the area under the 28S ribosomal RNA (rRNA) peak to that of the 18S peak. A ratio close to 2 indicates intact RNA.

Figure 2. Electropherogram detailing the regions that are indicative of RNA quality. The Bioanalyzer converts the fluorescence readings of electrophoresed RNA into an electropherogram. The quality of the RNA can be determined by analyzing the regions of the electropherogram. High quality RNA has distinct peaks for the marker, 18S ribosomal RNA (rRNA) fragments, and the 28S rRNA fragment. The 5S region, fast region, inter region, precursor region, and post region should be relatively flat compared to the peaks. A sample with this shape graph has a high RIN out of 10. (Figure from Mueller et. al., 2004, Figure 3).

2.3.2 Array hybridization The microarray was performed by The Center for Applied Genomics (TCAG) at the Hospital for Sick Children. RNA from three biological replicates of SKP-SC and three of nerve- SC were used. The RNA was converted into cDNA by reverse transcription with the Ambion Whole Transcript (WT) Expression Kit (Applied Biosystems), as per kit instructions. The cDNA was hybridized to the GeneChip Rat Gene 1.0 Sense Target (ST) Array (Affymetrix) using the GeneChip Fluidics Station 450 (Affymetrix). The hybridized microarray image was scanned with the GeneChip Scanner 3000 7G (Affymetrix). The intensity of each probe on the microarray was determined from the image, creating a raw probe intensity file.

2.3.3 Data pre-processing and quality analysis TCAG provided me with the raw probe intensity values. I performed the analysis of these values with guidance from Olena Morozova at the University of British Columbia and from Chao Lu of TCAG. Raw probe intensity values were background corrected, normalized with quantile normalization, transformed into the log2 scale, and summarized into probesets using the Robust Multichip Analysis (RMA) algorithm at the gene level in the Affymetrix Expression Console program. For background correction, the background intensities were subtracted from the intensity of each probeset. The background intensity for a given probeset was calculated as a weighted average of the local background intensities of the surrounding sections (the second percentile of all probesets), weighed by proximity of each section to the probe (Gohlmann & Talloen, p82- 83). Quantile normalization was performed to allow each microarray to be compared with the other. To normalize the distribution of probe intensities, the average distribution of signal intensities was calculated for all of the arrays, and the distribution of each array was adjusted so that it mimicked the average distribution. Normalization assumed that the majority of genes were not differentially expressed, there were similar patterns of up- and down-regulated probes, and that the differential expression signal for each probe was independent of the average expression levels (Gohlmann & Talloen, p83-86).

Each background corrected and normalized probe intensity was transformed into a value in the log2 scale. This transformation changes additive effects into multiplicative effects, as well as making the variation between high intensities equal to the variation between low intensities. For example, in the natural scale, a probe intensity up-regulation from 4 to 8 is a increase of 4, and a down-regulation from 4 to 2 is a decrease of 2. However, in the log2 scale, the former is an increase of 1 (doubling) and the latter is a decrease of 1 (halving). In this way, log transformation also changes the distribution of signal intensity densities from one that has many low values around background and few high values to a distribution that more even distribution similar to a normal curve (Gohlmann & Talloen, p79-82).

Summarization was performed last. Each gene had an average of 26 probes scattered across the microarray. Summarization involved combining multiple probes for same gene into a single probeset value using median polish. Probes were weighed equally, except those with values drastically different from other probes in that probeset. These were considered outliers, likely due to a scratch or dust, and were given less weight (Gohlmann & Talloen, p89-90).

To assess the quality of normalization, MvA plots were created in Affymetrix Expression Console which plotted the difference in average intensities between two plots (M) versus (v) the average intensity (A) of one plot (Gohlmann & Talloen, p244). The signal intensities of each array were plotted as a signal histogram and as a box plot in Affymetrix Expression Console to ensure that the distribution of intensities was similar between all arrays.

2.3.4 Data analysis Pre-processed data were analyzed for differentially expressed genes. To reduce the false discovery rate, only probesets corresponding to known genes were used. This core set of 20 412 probesets was analyzed using the Limma package in the statistical analysis program R. The eBayes function in Limma was used to calculate the moderated t-statistics, moderated F-statistic, and log-odds of differential expression by empirical Bayes shrinkage of the standard errors towards a common value (Smyth, 2004). To correct for multiple testing errors and reduce the false discovery rate, the Benjamini & Hochberg procedure was used. This is a step-up correction method, starting with largest p-value (Benjamini & Hochberg, 1995). The commands used in R

are listed in Figure 3. Limma computed the log fold change and B value for each gene. In Excel, log fold change was converted to fold change by the following equation:

2|log fold change| The B value was converted into the probability a gene was differentially expressed between samples by the following equation:

lnB value 1+ lnB value

2.3.5 Visualization of the data Pre-processed data were visualized in several graphical methods. Principal Component Analysis (PCA) was generated in Partek Genomics Suite using trunk SKPs, nerve-SC and SKP- SC data. Spearman rank correlation was generated in Affymetrix Expression Console using trunk SKPs, nerve-SC and SKP-SC data. A cluster dendrogram was generated using trunk SKPs, nerve-SC and SKP-SC data in R with the pvclust package with the commands listed in Figure 4A. Correlation distance and Ward‟s clustering method were used.

Data that was analyzed in R with the eBayes function was visualized. A volcano plot was generated in R to visualize the genes with high significance and log fold change between nerve- SC and SKP-SC (Figure 4B). Heatmaps were generated using the gplots package in R to visualize the relative expression of genes between nerve-SC and SKP-SC (Figure 4C). To ensure that the color key showed the same range of colours for every heatmap, a fake gene was added in heatmapdata.txt with expression values of 1 and 14 (the maximum and minimum values for the color key). A Venn diagram and bar graph were created in Excel to express the number of differentially expressed genes between SKP-SC and nerve-SC, and between SKP-SC and trunk SKPs.

library(limma) # open limma package probesets <- read.table("probesets.txt", row.names=1, header=TRUE) # read pre- processed probeset intensities matrix <- as.matrix(cbind(N=c(1,1,1,0,0,0), S=c(0,0,0,1,1,1))) # create a matrix of probesets. Designate which data correspond to nerve-SC (N) and which correspond to SKP-SC (S) fit <- lmFit(probesets, matrix) # fits a linear model for each probeset cont.matrix <- makeContrasts(NvsS=N-S, levels = matrix) # compare probeset intensities between nerve-SC and SKP-SC fit2 <- contrasts.fit(fit,cont.matrix) # computes estimated coefficients and standard errors for each comparison fit2 <- eBayes(fit2) # perform empirical Bayes shrinkage on the data results <- topTable(fit2, number= nrow(fit2), adjust="BH") # create a list of all genes with a p-value that is adjusted for multiple testing using the Benjamini-Hochberg procedure (BH) write.table(results, file= "results.txt", sep = "\t") # export gene list to a file

Figure 3. Data analysis commands in R. Commands that were implemented in R to analyze the microarray data .The explanation for each command follows it in blue italics after the # symbol.

A) Cluster dendrogram library(pvclust) # open pvclust package probesets <- read.table("probesets.txt", row.names=1, header=TRUE) # read probeset intensities result <- pvclust(probesets, method.dist="cor", method.hclust="ward", nboot=1000) # cluster based on correlation distance and Ward’s clustering method with a bootstrap replication of 1000 plot(result) # view cluster dendrogram

B) Volcano plot names<-read.table("names.txt") # file with gene names volcanoplot(fit2, coef=1, highlight=30, names=names$V1) # volcano plot with names of top 30 genes showing

C) Heatmap library(gplots) # open gplots package x <- read.table("heatmapdata.txt", row.names=1, header=TRUE) # heatpmapdata.txt has gene name and expression values for each array y <- data.matrix(x) # convert data into a matrix colours=c("red","red", "red", "blue", "blue", "blue") # colour coding for groups (nerve-SC are red, SKP-SC are blue) heatmap.2(y, Colv=FALSE, dendrogram="none", ColSideColors=colours, density.info="none", trace="none", col=greenred(256)) # view heatmap

Figure 4. Data visualization commands in R. Commands that were implemented in R to visualize the microarray data by cluster dendrogram (A), volcano plot (B), and heatmap (C). The explanation for each command follows it in blue italics after the # symbol.

2.3.6 RT-PCR RNA was isolated as for microarray analysis. Five different samples of nerve-SC were used (four samples at passage 2, one sample at passage 3). Four different samples of SKP-SC were used (three samples at pass passage 2, one sample at passage 5). To ensure that only RNA was present the samples were treated with DNase I (1 unit/ul, Fermentas) and RiboLock RNase Inhibitor (40 units/ul, Fermentas). RNA was reverse transcribed into cDNA with RevertAid H

Minus M-MuLV Reverse Transcriptase (200 units/ul, Fermentas), Oligo(dT)18 primers (0.5 ug/ul Fermentas), dNTP mix (10mM each, Fermentas), and EDTA (25 mM, Fermentas). cDNA was amplified for the following primers with Taq DNA polymerase (5 units/ul, Fermentas): glyceraldehyde-3-phosphate dehydrogenase (Gapdh); Gfap; glutamate receptor, ionotropic, kainite 3 (Grik3); brain and acute leukemia, cytoplasmic (Baalc); Cdc2; topoisomerase (DNA) II alpha (Top2a); Pax3; secreted protein, acidic, cystein-rich (SPARC)-like 1 (Sparcl1); and endothelin converting enzyme-like 1 (Ecel1). Primer sequences are listed in Table 1. DNA was visualized with ethidium bromide on a 1.5% agarose gel. Each gene was compared between nerve-SC and SKP-SC by RT-PCR at least 3 times.

2.3.7 Western blot Four different samples of nerve-SC were used (two samples at passage 2, one sample at passage 4, and one sample at passage 5). One samples of SKP-SC was used (at passage 8). Cells were lysed in a buffer consisting of 50 mM Tris, 150 mM NaCl, 1% SDS, and 0.5 mM DTT along with a Protease Inhibitor Cocktail Tablet (Roche). Protein concentration was determined using the BCA Protein Assay Kit (Thermo Scientific) and bovine serum albumin (BSA) as a standard. Protein was boiled in sample buffer and equal amounts were subjected to sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) gel then transferred to a 0.45 um nitrocellulose membrane (Bio-Rad). Membranes were blocked with 5% BSA in Tris-buffered saline with 0.05% Tween-20 (TBS-T). Membranes were incubated overnight with a 1:1000 dilution of rabbit anti-Gfap (Dako) in 1% BSA, washed with TBS-T, and were incubated with horseradish peroxidise (HRP)-conjugated goat anti-rabbit (1:5000, Promega). Bands were detected by enhanced chemiluminescence (ECL, Amersham Biosciences). β-actin was used as a

loading control. Membranes were stripped, washed with TBS-T, and re-probed with 1:1000 rabbit anti-β-actin (Santa Cruz) and 1:10 000 HRP-conjugated donkey anti-rabbit (Santa Cruz).

Melting Size of Gene Forward and Reverse Primers Temperature transcript in rat (ºC) (base pairs) 5'-CGTAGACAAAATGGTGAAGGTCGG-3' Gapdh 58 480 5'-AAGCAGTTGGTGGTGCAGGATG-3' 5'-GAAGAGTGGTATCGGTCCAAG-3' Gfap 52 486 5'-CATCTCCACCGTCTTTACCAC-3' 5'-CTACCACTTCATCTTCACCACTC-3' Grik3 62 373 5'-AATCCGTCCAGTTAATCCTTCC-3' 5'-ACCCAATGTCCCAACTCAC-3' Baalc 62 159 5'-TTTACTTCTGTCCATCTGCCG-3' 5'-ATCTTCGAGTTCCTGTCCATG-3' Cdc2 61 486 5'-AGTGTTCTTGTAGTCCTGCAG-3' 5'-CTTTAGCGGAGAGGATTACACG-3' Top2a 61 345 5'-CCACCCTTAGAAGTAGCGATG-3' 5'-GGCCCGAGTGCAGGTCTGGTT-3' Pax3 59 347 5'-GCCCCGATGGAGGCACGAAG-3' 5'-GGGATGACTCTAAGCATGGTG-3' Sparcl1 62 494 5'-TTCGAAGTCCGTACAAGCAG-3' 5'-AACTTCACCGTCTACAACCAG-3' Ecel1 62 356 5'-ACAGAGCATTTATGGACAGGG-3'

Table 1. Primers used for reverse-transcriptase PCR to verify microarray genes. Eight genes were verified by RT-PCR. Gapdh was used to ensure that the amount of RNA in each sample amplified was constant. Gapdh, glyceraldehyde-3-phosphate dehydrogenase; Gfap, glial fibrillary acidic protein; Grik3, glutamate receptor, ionotropic, kainite 3; Baalc, brain and acute leukemia, cytoplasmic; Cdc2, cell division control protein 2 homologue; Top2a, topoisomerase (DNA) II alpha; Pax3, paired box gene 3; Sparcl1, secreted protein, acidic, cystein-rich (SPARC)-like 1; Ecel1, endothelin converting enzyme-like 1.

Chapter 3 Results

3.1 Schwann cell isolation from sciatic nerve and from SKPs Nerve-SC were isolated from the sciatic nerves of 1.5-2 month old GFP-positive Sprague Dawley rats. SKP-SC were differentiated from passage 3 dorsal SKPs from 1.5 to 2 month old GFP-positive Sprague Dawley rats. I verified that the nerve-SC and SKP-SC that I isolated and differentiated expressed typical Schwann cell markers and that they were functional (able to myelinate).

3.1.1 Nerve-SC and SKP-SC express typical Schwann cell markers Nerve-SC and SKP-SC were grown in vitro and immunocytochemistry was performed. Both had a bipolar morphology and expressed the typical Schwann cell markers P0, S100β, Gfap, p75, and Necl4. Additionally, Necl1-Fc, the binding partner of Necl4, was able to bind to the cells, confirming that Necl4 was present on the cell surface (Spiegel et. al., 2007) (Figure 5 A, B). The intensity of staining of all of the proteins appeared uniform and similar between nerve-SC and SKP-SC, except that of Gfap: Gfap staining appeared stronger in some nerve-SC and weaker in others (Figure 5C). In SKP-SC the Gfap staining was uniform but at a level comparable that of the weak nerve-SC staining. To determine the purity of the Schwann cells, the percentage of cells expressing S100β was quantified as a percentage of all cells, as visualized by Hoechst. Five different samples were used for nerve-SC quantification and four for SKP-SC quantification. More than 95% of cells expressed S100β, suggesting a homogenous population with regards to that marker (Figure 5D). There was no significant difference between SKP-SC and nerve-SC as assessed by t-test.

Figure 5. Rat nerve-SC and SKP-SC are bipolar and express typical Schwann cell markers. Nerve-SC were isolated from adult Sprague Dawley rat sciatic nerve. SKP-SC were differentiated from adult Sprague Dawley trunk SKPs. Immunocytochemistry was used to verify that nerve-SC and SKP-SC cultures expressed typical Schwann cell markers. Photomicrographs of nerve-SC double labelled with the Schwann cell markers P0 and S100β (Ai), Gfap and p75 (Aii), and Necl4 and Necl1-Fc (Aiii). Photomicrographs of SKP-SC double labelled with the Schwann cell markers P0 and S100β (Bi), Gfap and p75 (Bii), and Necl4 and Necl1-Fc (Biii). Panels on the left show merged images. Panels on the right show Hoechst nuclear stain to mark all cells in the field. Almost all cells were bipolar and positive for both Schwann cell markers used. (C) Gfap expression in nerve-SC was variable. Some cells expressed low levels of Gfap (arrow) while others expressed high levels (arrow head) despite both cells expressing the same level of P0. The expression of P0, S100β, p75, Necl4, and Necl1-Fc did not vary. Scale bar = 20 um. (D) The percent of S100β positive Schwann cells was quantified to determine the purity of the Schwann cell preparations. The number of cells positive for S100β was expressed as a percentage of all cells, as visualized by Hoechst. Five different samples were used for nerve-SC quantification and four for SKP-SC quantification. At least three fields of view were randomly taken for each sample. Numbers are plotted as a mean ± SEM. There was no significant difference between SKP-SC and nerve-SC as assessed by t-test.

3.1.2 Nerve-SC and SKP-SC myelinate axons in vitro and in vivo The characteristic role of a Schwann cell is to myelinate axons. I ensured that the Schwann cells that I isolated were able to myelinate axons in vitro and in vivo. The ability of SKP-SC to myelinate DRG axons and repair the injured sciatic nerve has been previously published (McKenzie et. al., 2006; Walsh et. al., 2009, 2010). I repeated these experiments to ensure that the SKP-SC and nerve-SC that I derived were functional. A prerequisite of myelination is association with axons. To test this, I added either SKP- SC or nerve-SC to SCG axons in vitro that were prepared by Asli Dedeagac (Miller lab). The SCG axons were isolated from neonatal GFP-negative Sprague Dawley rats and grown in Campenot chambers, where the axons can grow in a different media than the cell bodies (Campenot, 1977). Cytosine arabinoside, which stimulates apoptosis in dividing cells (Grant, 1998), was added to kill endogenous Schwann cells and fibroblasts. When a pure culture of axons was obtained, GFP-positive nerve-SC or SKP-SC were added. After 10 days, cultures were immunostained for βIII tubulin to mark axons, and GFP to mark nerve-SC or SKP-SC. βIII tubulin staining was also observed on nerve-SC and SKP-SC, an observation that our lab has previously seen. This experiment was performed with three different samples of nerve-SC (passage 3) and three different samples of SKP-SC (passage 4). Both SKP-SC and nerve-SC were seen aligning along axons (Figure 6). Nearly all of the cells were seen aligning after 10 days of culture with the axons: 92% of SKP-SC and 98% of nerve-SC were seen associating with axons.

Figure 6. Nerve-SC and SKP-SC align with SCG axons after 10 days in vitro. The ability of nerve-SC and SKP-SC to align with axons was determined in vtiro. Superior cervical ganglia (SCG) from neonatal GFP-negative rats were grown in vitro in Campenot chambers. Cytosine arabinoside was added to remove endogenous Schwann cells, leaving only axons. GFP-positive nerve-SC (A) or SKP-SC (B) were added. Cultures were immunostained after 10 days for GFP, S100β, βIII tubulin, and Hoechst. Almost all nerve-SC and SKP-SC were seen aligning along axons. Arrows indicate cells expressing both S100β and GFP that have a bipolar morphology and are aligning along axons. Arrow heads indicate a GFP-positive cell that is not expressing S100β, is not aligned along axon, and has a fibroblast-like morphology. Scale bar = 25 um. (C) The percent of Schwann cells aligning with axons was quantified. Numbers are plotted as a mean ± SEM. There was no significant difference between SKP-SC and nerve-SC as assessed by t-test.

After the confirmation that the two Schwann cell types could associate with axons, the next test was in vitro myelination. I isolated DRG from GFP-negative E15 rat embryos. To obtain pure axons with no endogenous Schwann cells or fibroblasts, cytosine arabinoside was added. After approximately one week, either GFP-positive nerve-SC or SKP-SC were added to the axons and induced to myelinate. As a positive control for myelination, cytosine arabinoside was not added to some cultures, leaving endogenous Schwann cells to myelinate the axons. After two weeks, cultures were immunostained for MBP to mark myelin, βIII tubulin to mark axons, and GFP to mark added nerve-SC or SKP-SC. Both nerve-SC and SKP-SC were seen myelinating axons (Figure 7). The true test of Schwann cell myelination is in vivo. To test this ability, I transplanted nerve-SC or SKP-SC into the crushed sciatic nerve of a NOD-SCID mouse. The NOD-SCID mouse is immunodeficient and does not reject the transplanted cells. The sciatic nerve of the mouse was crushed with forceps (but not transected), causing the crushed axons to degenerate. Immediately after the crush, nerve-SC or SKP-SC were injected into the nerve proximal to the crush. The nerve was left to regenerate for 3 weeks. In this time, the axons regrew and signalled for nearby Schwann cells to myelinate them. At the end point, the sciatic nerves were sectioned and immunostained for P0, βIII tubulin, and GFP. After 3 weeks the axons appeared to have regenerated across the crush site and were myelinated. The crush site was non-discernable from the uncrushed regions except for the presence of GFP-positive cells. The regenerated axons were seen myelinated by endogenous Schwann cells as well as transplanted nerve-SC or SKP-SC (Figure 8).

Figure 7. Nerve-SC and SKP-SC myelinate DRG axons in vitro. The functional ability of nerve-SC and SKP-SC was determined by an in vitro myelination assay. DRG were isolated from GFP-negative E15 rat embryos. Cytosine arabinoside was added to remove endogenous Schwann cells and fibroblasts, leaving only axons. GFP-positive nerve-SC (B) or SKP-SC (C) were added and myelination was induced. As a control, cytosine arabinoside was not added to some cultures and endogenous Schwann cells of the DRG were induced to myelinate (A). After 10-14 days of myelination, cultures were immunostained for MBP to show myelination, βIII tubulin to show axons, and GFP to show added nerve-SC or SKP-SC. Some endogenous Schwann cells, nerve-SC and SKP-SC were seen myelinating axons. Many Schwann cells in each culture (including control) were not myelinating. Arrows indicate myelinating cells. Scale bar = 10 um.

Figure 8. Nerve-SC and SKP-SC myelinate the regenerating sciatic nerve in vivo. The ability of nerve-SC and SKP-SC to myelinate axons in vivo was assessed in the mouse sciatic nerve crush model. The sciatic nerve of a NOD-SCID mouse was crushed to induce axon death at the crush site. GFP-positive nerve-SC or SKP-SC were transplanted proximal to the crush site immediately after the crush as a source of Schwann cells to myelinate axons as they regenerate. The nerve was left to regenerate for 3 weeks. Nerves were sectioned and immunostained for P0 to show myelin, βIII tubulin to show axons, and GFP to mark transplanted cells. Axons were seen fully regenerated across the crush site and myelinated by both endogenous Schwann cells and transplanted nerve-SC (A) or SKP-SC (B). Arrows indicate transplanted cells myelinating axons. Scale bar = 20 um.

3.2 Microarray comparison of nerve-SC and SKP-SC After verifying that I could isolate functional nerve-SC and SKP-SC, I compared their gene expression profiles by microarray to determine their degree of similarity. The goal was to determine the developmental stage of SKP-SC down the Schwann cell lineage and to identify differentially expressed genes between the two Schwann cell types. SKP-SC were also compared to dorsal trunk SKPs by microarray to determine whether had retained their neural crest-like origins and were more similar to trunk SKPs or if they had followed Schwann cell differentiation and become more similar to nerve-SC. To do this, I prepared 3 biological replicates for microarray analysis from 1.5-2 month old female GFP-negative Sprague Dawley rats. I isolated RNA from passage 2 nerve-SC and passage 2 SKP-SC, and TCAG at The Hospital for Sick Children performed the microarray experiment with the Rat Gene 1.0 ST array. RNA from passage 2 dorsal trunk SKPs was isolated by Hiroyuki Jinno (Miller lab) and the microarray was performed by TCAG (Jinno et. al., 2010). I analyzed microarray data with Affymetrix Expression Console, R, and Partek Genomics Suite. The quality of the microarrays and the pre-processing was verified by several analyses.

3.2.1 Quality control of microarrays RNA was isolated from cultures of trunk SKPs, nerve-SC and SKP-SC using TRIzol reagent (Invitrogen) and purified with the RNeasy Mini Kit (Qiagen). The quality and quantity of purified RNA was assessed on the Agilent 2100 Bioanalyzer. In the Bioanalyzer, the RNA was run by capillary electrophoresis on a RNA 6000 Nano LabChip to separate it by size. The intensity of the different sized bands were detected by laser-induced fluorescence and graphed on an electropherogram. To determine the quality and quantity of the RNA, signal intensities, areas and ratios were compared. To determine the RNA concentration, the area under the graph was read (Preckel et. al., 2005). The RNA was given an RNA integrity number (RIN) out of 10, 1 being degraded RNA and 10 being intact RNA (Mueller et. al., 2004). Another characteristic of the graph that indicates RNA integrity is the ratio of the 28S to 18S rRNA. A ratio close to 2 indicates intact RNA. All of the Schwann cell samples I prepared had an rRNA ratio between 1.8 and 2.0 and a RIN between 9.4 and 10 out of 10, indicating intact RNA that is of suitable quality

for microarray analysis. Figure 9 shows the capillary electrophoreses image of one RNA sample and its corresponding electropherogram and summarizes the bioanalyzer information obtained for the 6 Schwann cell samples. The data for the trunk SKPs was not available.

RNA Concentration rRNA Ratio RNA Integrity Sample RNA Area (ng/ul) (28s/18s) Number N1 306 203 1.8 9.4 N2 241 158 1.8 9.4 N3 707 475 1.9 10 S1 257 171 1.9 10 S2 133 88 1.8 9.4 S3 350 192 2.0 10

Figure 9. Bioanalyzer results. Purified RNA was separated by capillary electrophoresis on the Agilent 2100 Bioanalyzer to determine RNA integrity and concentration. (A) The electrophoresis image for the SKP-SC 3 sample is shown. The RNA is separated by size and detected by laser-induced fluorescence. (B) Electropherogram for the SKP-SC 3 sample. Intensity of fluorescence (FU) is plotted against nucleotide length (nt). The area under the graph was compared to a standard and used to calculate the RNA concentration. The peaks corresponding to the 18S and 28S ribosomal subunits are labeled. A relatively flat area around the peaks is indicative of a intact RNA and is given a high RNA Integrity Number (RIN) of 10 out of 10. Another feature of the graph is the ratio of the 18S and 28S peaks. A ratio near 2 suggests intact RNA. (C) Summary of the bioanalyzer results for all Schwann cell samples. All of the Schwann cell samples had an rRNA ratio between 1.8 and 2.0 and a RIN between 9.4 and 10 out of 10, indicating high quality RNA. The trunk SKPs data was not available.

The RNA was reverse transcribed into cDNA and hybridized to the GeneChip Rat Gene 1.0 ST Array (Affymetrix). Images of the microarray chips were inspected visually for obvious bubbles or scratches that would obscure the data. No obvious flaws were seen, and the control pattern was present (Figure 10). The array data were background corrected, normalized with quantile normalization, and summarized using the Robust Multi-array Average (RMA) algorithm at the gene level in the Affymetrix Expression Console program. The quality of normalization was assessed by MvA analysis, signal intensity histograms, and signal intensity box plots. In all of the analyses, proper normalization was indicated by similar results for all arrays.

MvA plots the difference in average intensities between two plots (M) versus (v) the average intensity (A) of one of those plots (Gohlmann & Talloen, p244). Two arrays at a time are compared to ensure that normalization is consistent across all arrays. Proper normalization is indicated by a horizontal line along y=0 and a correlation value of 1. When nerve-SC and SKP- SC were compared and normalized to each other, the average correlation of all MvA plots was 0.98. In the 3 way comparison of nerve-SC, SKP-SC, and trunk SKPs, the average was 0.93 (Figure 11). These high correlation values suggest that normalization was properly performed.

A signal intensity histogram plots the number of probesets at each range of intensity for each array. The distribution of signal intensities should be similar for all arrays and they should all have a similar shape for well normalized data. This is because most genes are assumed to not be differentially expressed (Gohlmann & Talloen, p247). The histograms of each array closely overlapped, indicating that the distribution of intensities was similar for each and normalization was well performed (Figure 12A). The distribution of low intensity and high intensity signals should also be similar between arrays after normalization (Gohlmann & Talloen, p249). This was the case, as visualized by signal intensity box plot (Figure 12B).

After normalization, the individual probes for each gene were summarized into gene probesets. In total 722 254 probes were summarized into 29 214 probesets. Of these probesets, 27 342 correspond to genes, and the rest to hybridization controls. Only probesets corresponding to annotated genes were used, further reducing the list to 20 412 probesets. Excluding genes that

were not annotated increased the strength of the analysis by reducing the number of false positives occurring in multiple testing procedures because the procedures could be performed fewer times (Gohlmann & Talloen, p119-122).

Figure 10. Visual inspection of microarrays shows no flaws. The RNA from three samples of nerve-SC (A) and three of SKP-SC (B) were converted to cDNA and hybridized to the GeneChip Rat Gene 1.0 ST Array. No obvious bubbles or scratches were observed on the microarrays. The black control pattern was found in the middle of each array, further supporting proper array hybridization.

Figure 11. MvA plots indicate normalization was performed well. MvA plots compare two arrays at a time to assess the quality of data normalization. The difference in average intensities between two plots (M) is plotted versus (v) the average intensity (A) of one plot. Dots indicate individual probesets. A horizontal line along the y=0 line indicates proper normalization and is represented by a correlation value of 1. When only nerve-SC and SKP-SC were compared, the average correlation of all MvA plots was 0.98. In the 3 way comparison of nerve-SC, SKP-SC, and trunk SKPs, the average was 0.93. Both of these high correlation values indicate that normalization was properly performed. A sample MvA plot is shown.

Figure 12. The distribution of signal intensities is similar in all arrays, indicating proper normalization. The raw data for each array was normalized using the RMA algorithm to allow inter-array comparisons. (A) The distribution of signal intensities for each array was plotted as a histogram to assess the quality of normalization. Signal intensity ranges are plotted on the X axis and the frequency of each range is plotted on the Y axis. Each array is represented by a different coloured line. (B) The distribution of signal intensities in the logarithmic scale (Y axis) was plotted as a box plot for each array (X axis). The histograms of each array closely overlap and signal box plots are similar for each array, indicating that the distribution of intensities are similar for each array and data were well normalized.

3.2.2 SKP-SC are more similar to nerve-SC than to trunk SKPs At the gene expression level, SKP-SC were found to be very similar to nerve-SC and different from trunk SKPs. This result was seen in several analyses. Despite the overall similarity of the two groups, there were also slight differences, indicating that SKP-SC were not exactly the same as nerve-SC. The SKP-SC used for microarray were differentiated from dorsal trunk SKPs. To determine whether the SKP-SC were more similar to typical Schwann cells (nerve-SC) or to their cell of origin (trunk SKPs) unbiased clustering algorithms were employed. In unbiased clustering algorithms, SKP-SC clustered closer to nerve-SC than to trunk SKPs. Figure 13 shows a principal component analysis (PCA) of trunk SKPs, SKP-SC, and nerve-SC. PCA graphs each sample as a point in space based on its differences to the other samples. It determines the similarity of samples based on “components” that it creates from differences in gene expression. The X axis represents the component that accounts for the greatest variability between the samples (Ringner, 2008). In this study, the X axis accounted for 46.5 % of the variability. Samples that are distant from each other in the X direction have the greatest difference between them. The three SKP-SC replicates clustered on the right side of the plot close to nerve-SC and away from their parental trunk SKPs. This indicates that SKP-SC have more differences with SKPs than they do with nerve-SC.

Additional variability was seen within each group in the Y, and Z directions, accounting for 13.2% and 11.8% of the differences between samples, respectively. This variability may be due to inherent variability in a cell population. It is important to remember that the X axis accounts for almost half of the variability seen between samples and therefore suggests that the difference between all trunk SKP samples and all Schwann cell samples are greater than the differences between individual samples of trunk SKPs or individual samples of Schwann cells.

Figure 13. SKP-SC have more differences with trunk SKPs than with nerve-SC. The gene expression profiles of SKP-SC, nerve-SC, and trunk SKPs were plotted on a principal component analysis (PCA) graph. This graph plots each sample in a point in space based on its similarity to other samples. The three SKP-SC replicates (blue) clustered closer to the three nerve-SC replicates (red) than to the three trunk SKP replicates (green).

Spearman rank correlation allowed the visualization of the similarities between all of the samples. Here the similarity between two samples was compared. Spearman rank correlation orders the genes based on the magnitude of their expression then compares the order of the genes between two samples. Samples with exactly the same order of genes by magnitude of expression have a perfect correlation of 1 (Causton et. al., p89), expressed as a red rectangle. The weaker the similarity, the further the value is from 1 and the more blue the colour. SKP-SC had more similarities with nerve-SC (Figure 14, bottom right red section) and fewer similarities with trunk SKPs (bottom left and upper right blue sections).

Figure 14. SKP-SC are more similar to nerve-SC than to trunk SKPs. The gene expression data of SKP-SC, nerve-SC, and trunk SKPs were visualized by Spearman rank correlation. Each sample was compared to each other sample and given a similarity rating. Two samples with high similarity are given a red rating. Two samples with lower similarity are given a blue rating. Spearman rank correlation indicates that SKP-SC had a higher correlation with nerve-SC (bottom right red section) and lower correlation with trunk SKPs (bottom left and upper right blue sections). N, nerve-SC; S, SKP-SC, TS, trunk SKPs.

3.2.3 SKP-SC are distinct from nerve-SC PCA and Spearman rank correlation emphasized the similarities between SKP-SC and nerve-SC, and the differences between SKP-SC and trunk SKPs. Hierarchical clustering, another unbiased clustering algorithm, supported these same results, and additionally revealed that SKP- SC and nerve-SC are in fact two distinct groups despite their close similarity.

Hierarchical clustering groups samples based on their similarity with each other. It is visualized by a dendrogram, where samples that are similar to each other share a branch point (Gohlmann & Talloen, p142). The distance between samples was calculated using correlation distance and the samples were clustered using Ward‟s clustering method. Correlation distance calculates the relative distance (instead of absolute difference) to measure data trends. Ward‟s clustering method uses an agglomerative (bottom-up) approach. It starts with all the samples as an individual cluster (n clusters) and groups the two most similar samples together to create n-1 clusters. This is iterated until all of the samples have been grouped into 1 large cluster. Ward‟s method determines similarity by finding the clusters that have the shortest distance between them. It is unique to other clustering methods in that it uses analysis of variance to determine distance, which is ideal for data with more than 100 characteristics to compare, such as microarray data (Ward, 1963; Gohlmann & Talloen, p137).

Hierarchical clustering with correlation distance and Ward‟s clustering method showed that SKP-SC and nerve-SC clustered into one group, separate from trunk SKPs, highlighting their similarity to each other (Figure 15). The relative lengths of the branches in hierarchical clustering indicate the relative degree of similarity of the groups. The branch separating trunk SKPs from nerve-SC and SKP-SC is relatively long, signifying the many differences between the two groups. The branch separating nerve-SC from SKP-SC is very short, signifying the fewer differences between the two groups. The branch lengths highlight the close similarity of the two Schwann cell types and their dissimilarity to trunk SKPs, the non-Schwann cell.

It is important to notice that within the SKP-SC and nerve-SC group there are actually two separate groups: the three SKP-SC samples clustered closer to each other than to the nerve-

SC to create their own group, and vice versa. These data suggest that despite the similarity between SKP-SC and nerve-SC, they are still different in some ways.

The three replicates of each cell type form their own cluster (outlined in different colour boxes in Figure 15). Within each cell type cluster, two replicates are closer to each other than to the third replicate. This is due to normal variation in the biological samples; it is unlikely that three replicates will be equally distinct from each other. It is more informative that the three replicates of one cell type create a branch together, away from the other cell types.

Bootstrapping is a method used to evaluate the correctness of a cluster. The data are re- sampled with some samples missing, and the clusters are recalculated. Re-sampling is performed 1000 times to give an accurate result (Simon et. al., p112). Each cluster is assigned two confidence values: an Approximately Unbiased (AU) p-value and a Bootstrap Probability (BP) value. The AU value is an unbiased p-value calculated by multiscale bootstrap resampling. The BP value is less accurate because it is biased and calculated by bootstrap resampling. An AU or BP value greater than 0.95 means that the p-value was less than 0.05 (Suzuki & Shimodaira, 2009). Bootstrapping was performed 1000 times on the microarray data. The AU and BP values calculated were between 0.95 and 1.0, indicating confidence in the clusters created.

Figure 15. SKP-SC cluster closer to nerve-SC than to trunk SKPs, yet form a distinct group from nerve-SC. The gene expression data was clustered using an unbiased clustering algorithm to determine which samples were most similar to each other. Hierarchical clustering was performed using correlation distance and Ward‟s clustering algorithm. SKP-SC and nerve-SC clustered on a separate branch away from trunk SKPs, suggesting that they were more similar to each other than to trunk SKPs. The two Schwann cell types separated into two additional branches: one branch with the three nerve-SC replicates, and one with the three SKP-SC replicates, suggesting that although they were similar to each other, they were still distinct groups. The Approximately Unbiased (AU) p-value and a Bootstrap Probability (BP) values calculated were between 0.95 and 1.0, indicating confidence in the clustering.

Genes that are involved in Schwann cell development can be used to identify a cell type as similar to a Schwann cell or not. Figure 16 is a heatmap of all of the genes involved in various stages of Schwann cell development. The expression pattern of SKP-SC and nerve-SC is similar amongst these genes, and different from trunk SKPs. Additionally, there are more red rectangles in the nerve-SC and SKP-SC groups compared to trunk SKPs, indicating that nerve- SC and SKP-SC have higher expression of these genes.

Figure 16. SKP-SC and nerve-SC have a similar expression pattern of Schwann cell development related genes that is different from trunk SKPs. Genes that are involved in Schwann cell development have been visualized as a heatmap. The expression pattern of SKP-SC and nerve-SC is not only similar amongst these genes, and different from trunk SKPs, but is also higher, as indicated by more red rectangles. A green box represents a lower level of expression, black a moderate level, and red a high level of expression. N, nerve-SC; S, SKP-SC, TS, trunk SKPs.

A quantitative way to express the similarities and differences between the three cell types is by Venn diagram (Figure 17). The Venn diagram compares the number of probesets that are or are not significantly different between two cell types at a time. Of the 20 412 probesets analyzed, 99% of the probesets did not significantly differ between SKP-SC and nerve-SC (p- value >0.054). However, only 69% of probesets did not significantly differ between SKP-SC and trunk SKPs. This supports and quantifies the previous results showing that SKP-SC have more in common with nerve-SC than with trunk SKPs. In addition, the Venn diagram quantifies the difference between SKP-SC and nerve-SC. It depicts the number of probesets that are significantly higher in SKP-SC (39 probesets are significantly greater in SKP-SC by greater than

2 fold in the log2 scale, and 148 are greater by less than 2 fold) and those that are significantly higher in nerve-SC (4 are greater by more than 2 fold, and 34 by less than 2 fold). These 225 probesets are of interest in deciphering the genetic differences between SKP-SC and nerve-SC.

Figure 17. Quantification of the variance between SKP-SC versus nerve-SC and SKP-SC versus trunk SKPs. The microarray data was analyzed for differentially expressed genes using the empirical Bayes function in the Limma Bioconductor package in the program R. All 20 412 probesets were compared and the distribution of differentially expressed and non-differentially expressed genes are shown. (A) A Venn diagram of the 2 way comparison of SKP-SC with nerve-SC (left) and SKP-SC with trunk SKPs (right). Numbers in the intersection represent probesets that did not significantly differ between the two groups. Numbers on the sides represent probesets that were significantly different by more than 2 fold in the log2 scale (number not in brackets) or less than 2 fold (number in brackets). (B) Graphical representation of the data in (A). The number of probesets in each section of the Venn diagram were converted into percentages. 1.1% of probesets significantly differ between SKP-SC and nerve-SC (left bar), and 31% of probesets significantly differ between SKP-SC and trunk SKPs (right bar). There are more genes that differ between SKP-SC and trunk SKPs than between SKP-SC and nerve-SC.

3.2.4 Genes that differ between SKP-SC and nerve-SC Figure 15 revealed that there are differences between SKP-SC and nerve-SC and Figure 17 quantified these differences. It is of interest to identify the 225 genes that differ between SKP- SC and nerve-SC, as they are likely of biological importance and may explain the differences between these two cell types found in other studies, such as in neurotrophin production and the ability to interact with astrocytes (Walsh et. al., 2009; Biernaskie et. al., 2007). A list of these 225 genes is in Appendix I. One way to view the genes that are most significantly differentially expressed between SKP-SC and nerve-SC is by volcano plot (Figure 18). Here, each gene is plotted as a dot based on the fold change in the log2 scale (X axis), and the odds of being differentially expressed in the loge scale (B value) (Y axis) (Gohlmann & Talloen, p243). The top 30 genes (whose names are written in blue) are those that have a large fold change between nerve-SC and SKP-SC and are most likely to be truly differentially expressed (not a false positive). These are potential genes of interest in determining the subtle differences between SKP-SC and nerve-SC.

Figure 18. Most significantly different genes between SKP-SC and nerve-SC visualized by Volcano plot. All genes analyzed in a 2-way comparison of SKP-SC and nerve-SC are plotted as a Volcano plot. Genes are plotted by the log2 fold change between nerve-SC and SKP-SC (X axis) versus the loge odds (B value, the probability that this difference is true, Y axis). Genes are represented by a black dot. The top 30 most significantly differentially expressed genes are named in blue. These are the genes with the highest log fold change between SKP-SC and nerve-SC and the highest probability of being truly different. Genes in the top left area are more highly expressed in SKP-SC than in nerve-SC. Genes in the top right are more highly expressed in nerve-SC.

Top genes were identified by their higher log fold change between nerve-SC and SKP-SC and low p-value. To verify the microarray data, RNA was isolated from cultures of nerve-SC and SKP-SC (different cultures than those used for microarray analysis), reverse transcribed into cDNA, and PCR was performed for 8 top genes of interest. Five different samples of nerve-SC were used (four samples at passage 2, one sample at passage 3). Four different samples of SKP- SC were used (three samples at pass passage 2, one sample at passage 5). Each gene was compared between nerve-SC and SKP-SC by RT-PCR at least 3 times. Gapdh was used as a control to ensure equal amounts of RNA were being compared. Sparcl1, Grik3, Gfap, Ecel1, and Baalc were all confirmed to be higher in nerve-SC than SKP-SC. Pax3, Cdc2, and Top2a were confirmed to be higher in SKP-SC (Figure 19A). Sparcl1and Pax3 are the genes with the largest fold change values: Sparcl1is higher in nerve-SC, and Pax3 is higher in SKP-SC. Pax3 was also the gene with the lowest adjusted p-value. Gfap was further confirmed by Western blot analysis, with β-actin as a loading control (Figure 18B). Four different samples of nerve-SC (two samples at passage 2, one sample at passage 4, and one sample at passage 5) were compared to one sample of SKP-SC (at passage 8).

Log Fold Fold Probability Differentially Gene p-value B value Change Change Expressed Pax3 -3.23 9.40 0.011 5.42 99.6%

Cdc2 -2.45 5.45 0.018 3.59 97.3% Baalc 1.63 3.09 0.029 1.21 77.0% Ecel1 2.56 5.89 0.029 1.17 76.2% Top2a -2.43 5.38 0.033 0.97 72.6%

Grik3 1.60 3.03 0.041 0.64 65.5% Gfap 1.82 3.54 0.047 0.44 60.9% Sparcl1 2.72 6.58 0.072 -0.40 40.2%

Figure 19. Confirmation of top microarray genes by RT-PCR and Western blot. (A) Data for the top eight microarray genes that differ between nerve-SC and SKP-SC in the microarray analysis ordered in increasing order of the p-value. A negative log fold change or fold change means that the gene is more highly expressed in SKP-SC than nerve-SC, while a positive value means the gene is higher in nerve-SC. Log fold change, p-value, and B value were calculated with Limma in R. Fold change was calculated from log fold change, and the chance differentially expressed was calculated from the B value. (B) Heatmap of the top eight microarray genes. (C) Eight top genes were confirmed by reverse-transcriptase PCR on samples different than those used for the microarray. Five different samples of nerve-SC were used (four samples at passage 2, one sample at passage 3). Four different samples of SKP-SC were used (three samples at pass passage 2, one sample at passage 5). Each gene was compared between nerve-SC and SKP-SC by RT-PCR at least 3 times. Gapdh was used as a control to ensure equal amounts of RNA were being compared. (D) Gfap was further confirmed by Western blot. Four different samples of nerve-SC (two samples at passage 2, one sample at passage 4, and one sample at passage 5) were compared to one samples of SKP-SC (at pass passage 8). β-actin was used as a loading control. N, nerve-SC; S, SKP-SC; –RT, no reverse transcriptase added control; Gapdh, glyceraldehyde 3-phosphate dehydrogenase; Sparcl-1, SPARC-like 1; Grik3, glutamate receptor, ionotropic, kainate 3; Gfap, glial fibrillary acidic protein; Ecel1, endothelin converting enzyme-like 1; Baalc, brain and acute leukemia, cytoplasmic; Pax3, paired box 3; Cdc2, cell division control protein 2 homolog; Top2a, topoisomerase (DNA) II alpha.

3.2.5 Schwann cell developmental stage Previous literature suggests that the developmental stage of SKP-SC may be different than that of nerve-SC. SKP-SC appear to be more like Schwann cell precursors than nerve-SC due to their ability to interact with astrocytes (Woodhoo et. al., 2007; Biernaskie et. al., 2007). The microarray data does not support this assumption. The results do not show a vast difference between stage-specific genes, and instead suggest that SKP-SC and nerve-SC are at a similar stage in Schwann cell development. However, several proliferation-related genes are up- regulated in SKP-SC.

Each Schwann cell stage has a defined set of expressed genes (Figure 1).To assess the stage of Schwann cell development that nerve-SC and SKP-SC are in, genes that are known to be expressed at each stage of development were used. When the genes expressed at an individual stage are compared visually between nerve-SC and SKP-SC by heatmap, no obvious difference appears (Figure 20).

To elucidate subtle differences in Schwann cell stage related genes, the data for these genes was reviewed. Table 2 lists all genes relating to Schwann cell development, with the genes most likely to be differentially expressed between the two groups at the top of the list highlighted in yellow. Five stage-related genes had a probability of greater than 50% of being differentially expressed between nerve-SC and SKP-SC: Pax3, Cdc2, Krox-20, Gfap, and Id 2. These genes also had p-values less than 0.055. These genes are not indicative of a particular stage.

Figure 20. Schwann cell stage appears similar between SKP-SC and nerve-SC. The set of genes expressed at each stage of Schwann cell development have been visualized by heatmap. No obvious difference appears between SKP-SC and nerve-SC at the neural crest (A), Schwann cell precursor (B), immature Schwann cell (C), non-myelinating Schwann cell (D), pro-myelinating Schwann cell (E), myelinating Schwann cell (F), or de-differentiated Schwann cell (G) stage. N, nerve-SC; S, SKP-SC.

Log Fold Fold Probability Differentially Gene p-value B Value Change Change Expressed Pax3 -3.23 -9.40 0.011 5.42 99.6% Cdc2 -2.45 -5.45 0.018 3.59 97.3% Krox-20 -1.07 -2.09 0.043 0.57 63.9% Gfap 1.82 3.54 0.047 0.44 60.9% Id2 -1.15 -2.22 0.055 0.12 53.1% α4 integrin 1.07 2.10 0.091 -0.84 30.1% Mag -1.55 -2.92 0.122 -1.49 18.3% NFkB 0.77 1.70 0.136 -1.74 14.9% PI3K 0.48 1.39 0.200 -2.74 6.0% Ncam -0.55 -1.46 0.220 -3.02 4.6% Periaxin -1.15 -2.22 0.223 -3.06 4.5% Fgf-2 0.77 1.71 0.251 -3.46 3.1% Ncam 0.68 1.60 0.254 -3.50 2.9% p75 0.77 1.71 0.254 -3.53 2.9% S100β -0.42 -1.33 0.284 -3.89 2.0% Nab1 -0.32 -1.25 0.361 -4.63 1.0% Connexin-32 0.73 1.66 0.361 -4.63 1.0% Id4 -0.68 -1.60 0.361 -4.64 1.0% Notch 0.41 1.33 0.370 -4.71 0.9% α7 integrin 0.67 1.59 0.397 -4.90 0.7% Notch 0.22 1.16 0.419 -5.04 0.6% ErbB3 0.50 1.41 0.419 -5.04 0.6% β1 integrin 0.20 1.15 0.421 -5.06 0.6% β1 integrin 0.18 1.14 0.458 -5.25 0.5% Sox2 0.72 1.64 0.463 -5.28 0.5% Gap-43 0.31 1.24 0.497 -5.45 0.4% α1 integrin 0.38 1.30 0.510 -5.51 0.4% Nab2 0.21 1.16 0.525 -5.58 0.4% Sox10 0.25 1.19 0.528 -5.60 0.4% Cdh19 0.31 1.24 0.528 -5.60 0.4% c-Jun 0.34 1.27 0.582 -5.80 0.3% Bfabp 0.16 1.12 0.591 -5.84 0.3% Cyclin D1 -0.24 -1.18 0.596 -5.85 0.3% GalC -0.19 -1.14 0.618 -5.92 0.3%

Ncad -0.25 -1.19 0.622 -5.93 0.3% Mbp -0.15 -1.11 0.635 -5.97 0.3% Necl4 0.27 1.21 0.650 -6.01 0.2% PI3K 0.14 1.10 0.659 -6.04 0.2% Notch 0.14 1.10 0.670 -6.06 0.2% Notch 0.14 1.10 0.679 -6.09 0.2% Notch 0.10 1.07 0.690 -6.11 0.2% AP2α 0.14 1.10 0.731 -6.20 0.2% NFkB 0.15 1.11 0.738 -6.21 0.2% L1 0.09 1.06 0.744 -6.23 0.2% Dhh -0.07 -1.05 0.788 -6.30 0.2% Brn2 -0.09 -1.06 0.805 -6.33 0.2% Brn1 0.07 1.05 0.810 -6.34 0.2% Mbp -0.16 -1.11 0.811 -6.34 0.2% P0 0.10 1.07 0.823 -6.35 0.2% Oct6 -0.09 -1.07 0.882 -6.42 0.2% Pmp22 -0.05 -1.03 0.888 -6.43 0.2% AP2α 0.06 1.04 0.889 -6.43 0.2% Vimentin 0.03 1.02 0.892 -6.43 0.2%

Table 2. Microarray data for genes involved in Schwann cell development. Microarray data was collected for genes that are involved in one or more stages of Schwann cell development. Genes are ordered in increasing order of the p-value. Genes with a probability greater than 50% that they are differentially expressed are highlighted in yellow. A negative log fold change or fold change means that the gene is more highly expressed in SKP-SC than nerve- SC, while a positive value means the gene is higher in nerve-SC. Log fold change, p-value, and B value were calculated with Limma in R. Fold change was calculated from log fold change, and the chance differentially expressed was calculated from the B value.

3.2.6 SKP-SC have higher expression of proliferation genes The Schwann cell development gene data suggests that SKP-SC are more proliferative than nerve-SC. To investigate this, microarray data for genes with GO annotations relating to a positive role for proliferation in any cell type were collected. Those genes with greater than 50% probability of being differentially expressed are listed in (Figure 21A). Eleven of the twelve genes have higher expression in SKP-SC. Visual representation of this data as a heatmap in (Figure 21B) shows that all genes, except Mab-21-like 1 (Mab21l1), have a higher expression (brighter red) in SKP-SC.

3.2.7 Neurotrophin production When SKP-SC were compared to nerve-SC in culture, SKP-SC release higher levels of the neurotrophins NGF, NT-3, and BDNF than nerve-SC, as quantified by ELISA (Walsh et. al., 2009). The mRNA expression of these neurotrophins and NT-4 on the microarray were not significantly different (Figure 22).

Log Fold Fold Probability Gene p-value B value Change Change Differentially Expressed

Pax3 -3.23 -9.40 0.011 5.42 99.6% Cyclin A2 -2.58 -6.00 0.018 3.61 97.4% Cdc2 -2.45 -5.45 0.018 3.59 97.3% Mab21l1 1.85 3.60 0.020 2.77 94.1% Cyclin D2 -2.07 -4.20 0.023 2.46 92.1% Cenpf -1.77 -3.42 0.028 1.53 82.2% Fgf1 -1.34 -2.53 0.028 1.51 81.8% Ki67 -2.49 -5.62 0.029 1.35 79.3% Pttg1 -2.00 -4.01 0.029 1.18 76.6% Prkcq -1.65 -3.13 0.033 0.96 72.2% Id2 -1.15 -2.22 0.055 0.12 53.1% Cdh13 -2.54 -5.81 0.057 0.03 50.7%

Figure 21. SKP-SC have a higher expression of genes that have a positive effect on proliferation. (A) Genes that may have a positive effect on proliferation, listed in decreasing order of probability they are differentially expressed. (B) Heatmap of the data in (A) visualizing that all proliferation genes, except Mab21l1, have a higher (brighter red) expression in SKP-SC than nerve-SC. N, nerve-SC; S, SKP-SC. Pax3, paired box 3; Cdc2, cell division control protein 2 homologue; Mab21l1, Mab-21-like 1; Cenpf, centromere protein F; Fgf-1, acidic fibroblast growth factor; Pttg1, pituitary tumor-transforming 1; Prkcq, Protein kinase C, theta; Id2, inhibitor of DNA binding 2; Cdh13, cadherin 13.

Log Fold Fold Probability Differentially Gene p-value B value Change Change Expressed

Ngf -0.90 -1.87 0.079 -0.56 36.3% Ntf3 0.25 1.19 0.415 -5.02 0.7% Bdnf 0.12 1.09 0.807 -6.33 0.2% Ntf4 0.06 1.04 0.832 -6.36 0.2%

Figure 22. Neurotrophin production was not significantly different between SKP-SC and nerve-SC. The mRNA expression does not appear to differ between SKP-SC and nerve-SC. (A) The expression of neurotrophin genes organized in decreasing order of probability that the genes are differentially expressed. (B) Neurotrophin gene expression plotted as a heatmap. N, nerve-SC; S, SKP-SC.

Chapter 4 Discussion

4.1 Overview In this study, nerve-SC from adult rat sciatic nerve were compared with SKP-SC differentiated from adult rat trunk SKPs. The results presented argue that SKP-SC are highly similar to nerve-SC, and that SKP-SC are quite different from their parental trunk SKPs. However, the analyses also revealed small differences between SKP-SC and nerve-SC, suggesting that while the two groups are similar overall, there are slight differences that may have significant biological implications. These differences may explain the differing ability of SKP-SC and nerve-SC to interact with astrocytes. They may also reveal novel genes that are important in Schwann cell development or nerve regeneration.

4.2 Schwann cell developmental stage Nerve-SC prepared from the adult sciatic nerve have been termed de-differentiated Schwann cells, which are in state similar but not exactly the same as immature Schwann cells (Mirsky et. al., 2008). Previous literature suggests that SKP-SC may be like Schwann cell precursors because they are able to interact with the astrocytes of the glial scar (Biernaskie et. al., 2007), similar to Schwann cell precursors (Woodhoo et. al., 2007) while nerve-SC cannot (Lakatos et. al., 2000). However, it is unknown whether the development of SKP-SC from SKPs mirrors the development of nerve-SC from neural crest cells. Considering that SKP-SC were differentiated in vitro in static conditions and in the absence of axons, one may suspect that the steps taken toward becoming Schwann cells were not the same as the steps taken by nerve-SC, which differentiated in dynamic conditions and in the presence of axons. This makes it difficult to determine the exact Schwann cell development stage of SKP-SC because it is unknown what all the possible stages of its development are. One can only be sure that SKP-SC are not in the non-myelinating, pro-myelinating or myelinating stages because these three stages directly involve axon contact, and SKP-SC were grown in an axon-free environment.

One goal of the microarray was to assign a Schwann cell development stage to SKP-SC. Considering that nerve-SC are known to be in an immature Schwann cell-like state, and the microarray did not reveal vast differences between SKP-SC and nerve-SC, it is likely that SKP- SC are also in an immature Schwann cell-like state. Whether the immature Schwann cell-like state of SKP-SC is more similar to immature nerve-SC or de-differentiated nerve-SC has not been determined. What this means functionally is that SKP-SC are able to proliferate, and that given the opportunity they may progress to become myelinating Schwann cells.

4.3 Proliferation is enhanced in SKP-SC The microarray suggested that SKP-SC have advantages in proliferation. Pax3, Cdc2, and Id2 were elevated in SKP-SC, all genes that have been related to proliferation. Support for the role of these genes to enhance proliferation is discussed below. Furthermore, eleven other genes with GO annotations related to increasing proliferation were more highly expressed in SKP-SC.

The proliferative potential of immature Schwann cells has not been directly compared to de-differentiated Schwann cells. Knowing that nerve-SC are in the de-differentiated state, and assuming that SKP-SC are in an immature Schwann cell-like state, it is possible that the different proliferative abilities are characteristic of their different stages. It is conceivable that a cell that was mature and has de-differentiated, as a nerve-SC has, is impaired in proliferation compared to a cell that has never fully matured and has only been a proliferative cell, such as a SKP-SC.

4.3.1 Pax3 Pax3 expression was higher in SKP-SC than nerve-SC. Pax3 is likely increasing proliferation in SKP-SC, and does not appear to be a remnant of the dermomyotome origin of the cells, as discussed below.

To support the role of Pax3 enhancing proliferation in SKP-SC, it has been shown to promote proliferation and prevent differentiation in immature Schwann cells (Kioussi et. al., 1995). It is unclear whether Pax3 is expressed in de-differentiated Schwann cells, who are also at a proliferative stage. In one study, Pax3 was seen in injured nerves and cultured Schwann cells by in situ hybridization (Kioussi et. al., 1995), while in another it was not (Padilla et. al., 1999).

Furthermore, no study has compared the relative expression of Pax3 in immature and de- differentiated Schwann cells. If SKP-SC can in fact be compared to immature Schwann cells, and nerve-SC are de-differentiated Schwann cells, it is possible that the proliferative potential and Pax3 expression differs between the two cell stages.

Elevated Pax3 expression in SKP-SC is likely not due to the developmental origin of SKP-SC compared to nerve-SC. Pax3 is expressed in the dermomyotome. The dermis of dorsal trunk skin is a derivative of the dermomyotome (Goulding et. al., 1991) and trunk SKPs have been shown to be derivatives of somites (Jinno et. al., 2010), possibly of the dermomyotome. The SKP-SC used in this study were differentiated from dermal trunk SKPs, making them likely dermomyotome-derived. The elevated Pax3 expression in SKP-SC may be a remnant of the dermomyotome origin of the cells, a “memory” of the dermomyotome. A similar result was found by Jinno and colleagues (2010): dorsal trunk SKPs expressed higher levels of Hox genes than facial SKPs, a memory of their somite origin. This hypothesis is rejected when the expression of Pax3 in trunk SKPs is examined. The expression of Pax3 in undifferentiated trunk SKPs was lower than in SKP-SC. If Pax3 expression represents a memory of the dermomyotome, then it should be expressed at a similar or higher level in undifferentiated SKPs than SKP-SC. The lower expression of Pax3 in SKPs than SKP-SC suggests that Pax3 expression is not a result of dermomyotome memory.

4.3.2 Cdc2 Cdc2 was found to be up-regulated in SKP-SC compared to nerve-SC. This result was confirmed by RT-PCR. Cdc2 was higher in proliferating Schwann cells than in non-proliferating Schwann cells in the regenerating nerve (Seo et. al., 2006).

Higher levels of Cdc2 may be beneficial to nerve regeneration not only due to enhanced proliferation, but also due to migration advantages. Cdc2 is up-regulated by Schwann cells following injury and is required for Schwann cell migration (Han et. al., 2006). Improved recovery and higher levels of Cdc2 in Schwann cells were found when treadmill training was performed following injury. The advantage in recovery due to treadmill training was absent

when Cdc2 kinase was inhibited (Seo et. al., 2006). This suggests that increased levels of Cdc2 result in better recovery.

4.3.3 Id2 The expression of Id2 was higher in SKP-SC. In vitro, Id2 mRNA is down-regulated when growth is arrested and up-regulated when growth resumes. It is also up-regulated in the presence of cAMP (Stewart et. al., 1997b). Additionally, Id2 is up-regulated following injury (Stewart et. al., 1997; Le et. al., 2005). This defines a role of Id2 as pro-proliferative.

Not only does Id2 promote proliferation, it also prevents progression to the myelination phenotype. The highest expression of Id2 is postnatal at the start of myelination and it decreases as myelination progresses (Stewart et. al., 1997). When myelination is set to begin, a complex of Krox-20, Nab1 and Nab2 represses Id2 transcription (Mager et. al., 2008). Id2 knockdown up- regulates P0 expression (Mager et. al., 2008). This defines the role of Id2 as myelination- inhibitory. In line with this finding, Id2 is up-regulated in a model of demyelination (Giambonini-Brugnoli et. al., 2005).

An alternative role for Id2 has been suggested by Thatikunta and colleagues (1999). Id2 is a negative regulator of bHLH factors from binding to DNA (Stewart et. al., 1997). Because its expression is highest postnatally at the start of myelination (Stewart et. al., 1997), its role may be to bind bHLH factors that are inhibitory to myelin production. By sequestering myelination inhibitory factors, Id2 may promote the start of myelination (Thatikunta et. al., 1999). To support this hypothesis, the study found that cAMP increased both Id2 and P0 levels in vitro, therefore Id2 could not be repressing P0 (Thatikunta et. al., 1999). This assumption was countered with the finding by Mager and colleagues (2008) that Id2 knockdown up-regulates P0 expression. Although P0 and Id2 both increase with cAMP, P0 has a more robust increase with cAMP when Id2 is inhibited (Mager et. al., 2008). Therefore, Id2 is likely not pro-myelination but anti- myelination.

The nuclear localization of Id2 varies during development. Id2 is nuclear only in the Schwann cell precursor stage. Its role at this stage has not been explored. During all other stages and in vitro it is cytoplasmic (Stewart et. al., 1997). It would be of interest to see whether Id2 is

nuclear or cytoplasmic in SKP-SC and nerve-SC. I predict that Id2 would be cytoplasmic in both SKP-SC and nerve-SC as neither of these cells appear to be in the Schwann cell precursor stage.

4.3.4 Krox-20

Pax3, Id2, and Cdc2 all have roles in proliferation (Kioussi et. al., 1995; Stewart et. al., 1997b; Seo et. al., 2006). All three were higher in SKP-SC, and they may be promoting proliferation. Proliferating Schwann cells are in the immature stage and are not myelinating. However, Krox-20, a myelination gene, was higher in SKP-SC. Krox-20 inhibits proliferation by inhibiting c-Jun (Parkinson et. al., 2004). High expression of Krox-20 could be a disadvantage if it makes SKP-SC closer to the myelinating phenotype because this reduces proliferation. However, this does not seem to be the case, as proliferation in SKP-SC appears to be greater than in nerve-SC. Therefore, the up-regulation in Krox-20 is not likely important with regard to proliferation. Krox-20 expression has not yet been confirmed by RT-PCR.

In another contradiction, Krox-20 promotes myelination by inducing P0 transcription, while Pax3 prevents myelination by repressesing MBP transcription (Kioussi et. al., 1995; Jang & Svaren, 2009). The cells are grown in the absence of axons so they could not be myelinating. Additionally, the expression of P0 and MBP appears similar between nerve-SC and SKP-SC, suggesting that the effects of Krox-20 and Pax3 on promoting and inhibiting myelination, respectively, are either counteracted by each other or not significantly occurring.

One solution to this apparent paradox involves the culture conditions. Krox-20 is induced by cAMP, in as little as 2 μM forskolin (Zorich 1999). The cells used for the microarray were grown with 5 μM forskolin. While one may expect that cAMP would up-regulate Krox-20 equally in both nerve-SC and SKP-SC, it is possible that SKP-SC are more sensitive to cAMP and therefore up-regulate Krox-20 to a higher degree. However, the up-regulation of Krox-20 is not inhibiting the proliferation. To further support this, Krox-20 in SKP-SC should inactivate proliferation in neuregulin (Parkinson 2001, 2004). This did not happen to SKP-SC, as many proliferation genes are higher in SKP-SC than nerve-SC.

The Schwann cells for the microarray were cultured with N2 supplement. N2 contains 0.002 μM (0.63 mg/L) progesterone (N2 Supplement Technical Resources). Progesterone stimulates Krox-20, in as low as 0.001 μM (Guennoun et. al., 2001; Mercier et. al., 2001). However, N2 was diluted in media to 2%. The effect of progesterone increasing Krox-20 expression was likely not seen.

One last possible explanation for Krox-20 expression to be higher in SKP-SC is because it has two regulatory elements. One regulatory element is used during the immature Schwann cell stage, termed the immature Schwann cell element (ISE), and the other is used when Schwann cells myelinate, termed the myelinating Schwann cell element (MSE). While the role of the ISE has not been elucidated, it has been shown that Krox-20 is expressed at the immature Schwann cell stage, albeit to a lower level than when myelinating (Ghislain et. al., 2002). The Krox-20 expression seen in proliferating SKP-SC and nerve-SC is likely due to the ISE, as these cells are in an immature Schwann cell-like state. Krox-20 expression may be higher in SKP-SC because their use of the ISE is enhanced.

Mab21l1 was grouped with the pro-proliferation genes, which all had higher expression in SKP-SC, but it was the only one to be more highly expressed in nerve-SC. It is possible that MAB21l1 expression is indicating that nerve-SC are neural crest-derived, while SKP-SC are not. Mab21l1 was found to be expressed by neural crest cells in Caenorhabditis elegans (Mariani et. al., 1999). The SKP-SC used in this study were differentiated from somite-derived trunk SKPs, which expressed low levels of Mab21l1 compared to neural crest-derived facial SKPs. (Jinno et. al., 2010). Mab21l1 may not be displaying its role as a pro-proliferation gene in Schwann cells; it may be a memory of the neural crest lineage of nerve-SC.

4.3.5 Gfap The microarray indicated that there was higher Gfap expression in nerve-SC than SKP- SC, and this was confirmed by RT-PCR and Western blot. This analysis indicates an overall increase in Gfap within the population of nerve-SC. Immunocytochemical analysis reveals that this increase in Gfap is likely not due to the whole population of nerve-SC having higher Gfap expression. Rather, single cells in the population have high Gfap expression. Gfap differs

amongst the population of nerve-SC, as detected immunocytochemically: some cells have higher apparent expression levels of Gfap, while others have weaker Gfap that is comparable to that seen in SKP-SC. The higher level of Gfap seen in the microarray analysis is therefore probably not characteristic of the population. Instead, it is likely due to a subpopulation of high-Gfap expressing cells.

Gfap is expression begins at E18 in the rat and is expressed by immature Schwann cells, non-myelinating Schwann cells, and de-differentiated Schwann cells, but it is not expressed by Schwann cell precursors or myelinating Schwann cells (Jessen et. al., 1990). The higher expression of this gene in nerve-SC does not suggest that nerve-SC are in any particular stage, only that they are not Schwann cell precursors or myelinating Schwann cells.

Gfap is not essential for Schwann cell development, but is required following injury. Gfap knockout mice are impaired in Wallerian degeneration due to reduced Schwann cell proliferation. The impairment in proliferation may be because Gfap is an intermediate filament, and cytoskeleton reorganization is crucial for mitosis (Triolo et. al., 2006). This suggests that the increased Gfap expression observed in a sub-population of nerve-SC may be beneficial to their proliferation. However, many other proliferation-related genes are lower in nerve-SC, suggesting that Gfap is not exerting its role on proliferation.

One possible explanation for differential Gfap expression in nerve-SC is the previous identity of the Schwann cell. Gfap expression may be different in de-differentiated Schwann cells that came from myelinating Schwann cells versus those that came from non-myelinating Schwann cells. Gfap was continually expressed by non-myelinating Schwann cells, while myelinating Schwann cells had to re-express it (Jessen et. al., 1990). Differential Gfap expression has not previously been explored in the Schwann cell field.

4.4 Characteristics important for nerve regeneration Nerve-SC and SKP-SC had a similar ability to associate with axons in vitro, and were both able to myelinate axons in vitro and in vivo. Their functional ability appears similar. This supports the use of SKP-SC as an alternative to nerve-SC for regeneration and repair.

4.4.1 Schwann cell migration SKP-SC have previously been shown to myelinate PNS axons in vivo and to aid in nerve regeneration in the acute and chronic situation (McKenzie et. al., 2006; Walsh et. al. 2009, 2010). The in vivo myelination experiment in the current study was in the acute situation and is most similar to that published by McKenzie and colleagues (2006). Two differences exist between the current study and the published one. In the published study SKP-SC were differentiated from neonatal skin and were transplanted distal to the crush site in the sciatic nerve. In the current study SKP-SC were differentiated from adult skin and they were transplanted proximal to the crush site.

The current study builds on the previous study by showing that SKP-SC from adult skin are also able to re-myelinate the injured nerve. As well, it suggests another similarity between SKP-SC and nerve-SC: the ability to receive attractive signals and migrate from the proximal to the distal end of the crush. After an injury, Schwann cells from the proximal end migrate distally to aid in regeneration (Torigoe et. al., 1996). Both SKP-SC and nerve-SC cells were seen myelinating axons distal to the crush site. These cells could have spread into the distal end during injection, or migrated from the proximal to the distal end, or both. Being able to migrate from the proximal to distal ends requires the ability to receive attractive signals. Because the nerve-SC and SKP-SC were transplanted proximal to the crush site, this experiment suggests that both cell types may be able to receive migration signals, an important characteristic for a regenerating cell.

4.4.2 Neurotrophin production Schwann cells release neurotrophins following injury to promote axon growth (Chen et. al., 2007). Walsh and colleagues (2009) found that SKP-SC release more neurotrophins than nerve-SC. This was found by the protein analysis ELISA. In the current study, microarray comparison of neurotrophin mRNA did not confirm these results: the gene expression of neurotrophins appeared similar between nerve-SC and SKP-SC. This opposition may be reconciled since one analysis compared mRNA while the other compared protein. It is possible

that although there are similar amounts of neurotrophin mRNAs in the cells, SKP-SC translate this mRNA to protein more often than nerve-SC. The discrepancy between mRNA expression and protein expression emphasizes the need for protein-based assays to completely identify all of the differences between SKP-SC and nerve-SC.

4.4.3 Astrocyte interaction ability The ability to interact with astrocytes in the glial scar suggests that SKP-SC are similar to Schwann cell precursors which are able to interact with astrocytes (Woodhoo et. al., 2007). However, it is unlikely that SKP-SC are in a Schwann cell precursor-like state, considering the microarray results that SKP-SC are highly similar to nerve-SC, and the literature on cultured Schwann cells being in an immature Schwann cell-like state. Therefore, the ability of SKP-SC to interact with astrocytes may be due to two possibilities: 1) SKP-SC express surface proteins that facilitate astrocyte interactions that nerve-SC do not express, or 2) SKP-SC do not express surface proteins that inhibit astrocyte interaction, while nerve-SC do express them. There are microarray data to support the second possibility, specifically that nerve-SC express higher levels of Sparcl1 which may prevent astrocyte interaction, while SKP-SC express a lower level of Sparcl1.

Sparcl1 was the gene with the highest fold change up-regulation in nerve-SC. The higher level of Sparcl1 expression in nerve-SC compared to SKP-SC was confirmed by semi- quantitative RT-PCR. Sparcl1 expression in nerve-SC may be detrimental by preventing the ability of the Schwann cells transplanted into the injured spinal cord to migrate through the glial scar, as well as by reducing the ability of axons to migrate through Schwann cell bands.

Sparcl1 has been shown to have anti-adhesive properties in radial glia, dermal fibroblasts, and endothelial cells (Gongidi et. al., 2004; Sullivan et. al., 2008; Girard & Springer, 1996). Sparcl1 is expressed by radial glia in the upper cortical plate. During development neurons migrating towards the upper cortical plate cease migration once they reach this area due to the anti-adhesive properties of Sparcl1 (Gongidi et. al., 2004). Similarly, dermal fibroblasts expressing Sparcl1 cannot migrate because of an inability to adhere (Sullivan et. al., 2008).

Endothelial cells cannot attach and spread in the presence of Sparcl1 (Girard & Springer, 1996). These studies suggest that Sparcl1 prevents adhesion and migration of cells.

The increased Sparcl1 expression in nerve-SC may contribute to their inability to interact with the glial scar. Adhesion is a requirement of migration, so too much anti-adhesive Sparcl1 expression may prevent the nerve-SC from migrating through the glial scar. Additionally, Sparcl1 may be preventing axon regeneration. Up-regulation of Sparcl1 in astrocytes following injury may contribute to failed axon regeneration due to a migration inability (Mendis et. al., 1996).

An opposite consequence of Sparcl1 has been suggested, but does not appear to be occurring in these cells. Sparcl1 has been implicated in reducing reactive astrocytes, as seen in the glial scar. Sparcl1 null mice showed increased Gfap expression in their brains (Weaver et. al., 2010). This was also seen in a disintegrin and metalloproteinase with thrombospondin motifs 4 (Adamts4) null mice, Adamts4 being the protein that cleaves Sparcl1, releasing a peptide similar to Sparc (Weaver et. al., 2010). These results suggest that Sparcl1 expression reduces reactive gliosis. This would represent an advantage to nerve-SC having increased Sparcl1 expression, in that they would reduce the reactive astrocytes and the glial scar when transplanted into the injured spinal cord. This result was not seen in previous studies transplanting nerve-SC into the injured spinal cord (Lakatos et. al., 2000), perhaps because Sparcl1 was not cleaved.

Fgf-1 was also up-regulated in SKP-SC. While there are many roles of Fgf-1, one possible role in SKP-SC may be to aid in astrocyte interaction. In a study by Lee and colleagues (2008) the spinal cord of rats were transected and repaired with nerve segments (containing Schwann cells) in fibrin glue. Nerve segments alone caused a characteristic glial scar response with high levels of inhibitory proteoglycan. However, when these nerve segments were supplemented with Fgf1 there was reduced glial scarring and reduced inhibitory proteoglycan expression (Lee et. al., 2008). Fgf1 has also shown the ability to promote axon growth and revascularization, and functional recovery in humans (Wu et. al., 2008).

4.5 Novel markers of Schwann cell developmental stage The differences in gene expression between immature Schwann cells and de- differentiated Schwann cells have been explored previously. This study suggests additional genes that may merit further study. As discussed above, several proliferation-related genes differed between SKP-SC and nerve-SC. These differences may also be present between immature Schwann cells and de-differentiated Schwann cells. Similarly, Gfap expression may differ between de-differentiated Schwann cells depending whether they are derived from myelinating or non-myelinating Schwann cells. Another gene that may be of interest is Grik3. Grik3 has not been specifically studied in Schwann cells. As an ionotropic glutamate receptor, it forms the ion channel pore when glutamate is bound. As a kainite receptor, it is permeable to sodium and potassium ions. Potassium has a role in Schwann cell proliferation following Wallerian degeneration. Hyperpolarization of the resting membrane potential of rabbit Schwann cells (increased potassium) induced proliferation in vitro (Chiu & Wilson, 1989). Additionally, potassium channel activity increased during Wallerian degeneration in the myelinating Schwann cells of the rabbit (Chiu & Wilson, 1988). This suggests that Grik3 may be up-regulated in de-differentiated Schwann cells. Indeed, nerve-SC had a higher expression of Grik3 than SKP-SC in the microarray analysis and as confirmed by RT-PCR. Grik3 may be differentially expressed by immature and de-differentiated Schwann cells. Overall, the microarray did not find a set of genes for one Schwann cell stage that differed between SKP-SC and nerve-SC. It did reveal other genes, such as those involved in proliferation and astrocyte interaction. These genes may be important in the regenerative capacity of each cell type. Possible advantages of SKP-SC have been suggested. These support the use of SKP-SC for treatments that are currently using nerve-SC.

In addition to reparative advantages, the use of SKP-SC is also advantageous in terms of its source. SKPs are isolated from skin, which is far more accessible and less damaging than the peripheral nerves required for nerve-SC isolation. The potential of autotransplantation also exists, whereby SKPs are isolated from the skin of a patient, differentiated into SKP-SC in vitro, and the SKP-SC are transplanted back into the same person. This prevents the need for

immunosuppression and the many complications associated with it. In a similar fashion, persons with PNS demyelinating diseases could have their Schwann cells replaced by SKP-SC differentiated from their own skin. The SKPs could be treated by gene therapy prior to differentiation to repair the gene mutation causing the demyelinating disease. SKP-SC may also be useful for CNS demyelinating diseases, such as to replace lost astrocytes in MS. Schwann cells are not targeted by MS (Halfpenny et. al., 2002) and the skin is a theoretically indefinite cell source due its regenerative ability.

4.4 Conclusion This study has shown that at an mRNA level SKP-SC are very similar to nerve-SC and less similar to the SKPs that they have been differentiated from. This supports the use of SKP- SC for treatments that are currently using nerve-SC. At the same time, it has shown that despite their similarity, SKP-SC and nerve-SC are distinct cell types. The hypothesis that SKP-SC are less mature than nerve-SC was rejected, as the expression of developmentally specific genes was similar. Instead of a difference in maturity, this study has created a list of genes that differ between SKP-SC and nerve-SC. The genes warrant future study as they may reveal previously unidentified genes that are important in nerve regeneration and Schwann cell development.

4.5 Future directions This study has taken an unbiased approach to identify genes that differ between nerve-SC and SKP-SC. Some of the most interesting and potentially biologically significant genes have been discussed. The differential expression of these genes may be due to inherent differences in SKP-SC and nerve-SC due to their origins. Alternatively, they may be due to differences between immature Schwann cells and de-differentiated Schwann cells. It would be of interest to examine the expression of these genes during Schwann cell development. They may reveal new stage-specific markers as well as insights into the abilities of cells from each stage. Specifically, the data presented here argue that there may be a difference in proliferative ability between immature Schwann cells and de-differentiated Schwann cells. Genes of interest must first be confirmed by RT-PCR and Western blot. Immunocytochemistry would also be useful to address

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whether the elevated/reduced gene expression is global among all cells in a sample or whether it is restricted to sub-populations within the sample, such as was found with Gfap.

The differentially expressed genes are interesting to explore in relation to Schwann cell developmental stage, but also in relation to regeneration. The functional relevance of the differentially expressed genes could reveal advantages and disadvantages in reparative ability. Specifically, genes that may be related to the ability to interact with astrocytes, such as Sparcl1, could be over expressed in SKP-SC to see if this impairs their ability to interact with astrocytes. Conversely, Sparcl1 could be inhibited by silencing RNA in nerve-SC to see if this improves their ability to interact with astrocytes. These studies would suggest ways to improve regeneration by reducing or increasing the expression of key genes.

This study was performed with rat cells. The translatability of these results to human cells is of ultimate therapeutic interest. SKP-SC from human skin and nerve-SC from human nerve could be compared in a similar fashion to the present study. This would be of particular interest because it is less invasive to obtain Schwann cells from the skin than from the nerve. One hurdle to this study is that currently the differentiation of SKP-SC from human skin is uncertain, and any SKP-SC that may be produced are in low yield. Examining the expression profile of SKP-SC may suggest a unique molecular switch that needs to be turned on or off in human SKP-SC in order to achieve successful differentiation.

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Appendix I Microarray data for genes that differ between SKP-SC and nerve- SC

Table 3. Genes that are higher in SKP-SC. Genes with a p-value less than 0.05, or a fold change greater than 2, or a probability of being differentially expressed greater than 50%. Table is organized in increasing order of fold change.

Log Probability Fold p- B Nerve- Nerve- Nerve- SKP- SKP- SKP- Gene Fold Differentially Change value value SC 1 SC 2 SC 3 SC 1 SC 2 SC 3 Change Expressed Pax3 -3.23 -9.40 0.011 5.42 99.6% 6.82 6.67 6.63 10.07 9.35 10.39 LOC691979 -2.97 -7.82 0.018 3.55 97.2% 7.43 6.55 6.22 9.33 9.78 9.98 LOC691979 -2.95 -7.73 0.019 3.34 96.6% 7.53 6.64 6.29 9.38 9.87 10.07 Cdkn3 -2.90 -7.47 0.018 3.94 98.1% 6.98 6.31 6.19 9.02 9.22 9.93 Elovl7 -2.89 -7.42 0.020 2.94 95.0% 8.40 7.14 7.21 10.15 10.68 10.60 LOC691979 -2.87 -7.32 0.020 3.11 95.7% 7.60 6.73 6.33 9.37 9.87 10.04 Sucnr1 -2.81 -7.01 0.020 3.04 95.4% 8.27 7.72 7.16 10.35 11.07 10.17 Rrm2 -2.67 -6.35 0.028 1.47 81.4% 8.36 7.33 6.84 10.14 9.74 10.65 Ccna2 -2.58 -6.00 0.018 3.61 97.4% 8.38 7.52 7.41 10.13 10.28 10.66 Il1rl1 -2.58 -5.97 0.175 -2.30 9.1% 6.06 6.17 6.09 6.74 9.65 9.66 Rrm2 -2.57 -5.96 0.026 1.71 84.7% 8.33 7.43 6.92 10.06 9.74 10.59 Cdh13 -2.54 -5.81 0.057 0.03 50.7% 9.85 9.00 8.24 11.91 10.73 12.07 Mmp3 -2.53 -5.79 0.124 -1.56 17.4% 6.64 6.45 6.07 7.31 9.60 9.86 Depdc1 -2.50 -5.66 0.018 3.61 97.4% 6.27 5.74 5.40 7.94 8.28 8.69 Mki67 -2.49 -5.62 0.029 1.35 79.3% 9.24 8.03 7.69 10.66 10.66 11.11 Pbk -2.48 -5.59 0.020 3.21 96.1% 6.67 5.94 6.11 8.20 8.82 9.14 Cenpk -2.46 -5.50 0.018 4.13 98.4% 5.78 5.84 5.49 7.63 8.52 8.34 Cdc2 -2.45 -5.45 0.018 3.59 97.3% 6.73 6.01 6.26 8.56 8.50 9.27 Top2a -2.43 -5.38 0.033 0.97 72.6% 8.16 7.23 6.80 9.75 9.32 10.40 Bub1 -2.41 -5.32 0.018 3.52 97.1% 7.38 6.81 7.01 9.15 9.26 10.03 Cd55 -2.39 -5.26 0.071 -0.36 41.1% 8.32 8.17 7.98 10.89 11.42 9.34 Nusap1 -2.37 -5.19 0.024 2.27 90.6% 7.87 7.05 6.82 9.39 9.36 10.10 Sgol2 -2.36 -5.14 0.026 1.68 84.3% 7.05 6.04 5.65 8.33 8.75 8.75 Aspn -2.35 -5.08 0.432 -5.12 0.6% 6.44 8.93 5.52 6.65 12.15 9.14 Dtl -2.32 -4.98 0.025 2.07 88.8% 7.42 6.94 6.32 8.86 9.08 9.68 Thy1 -2.31 -4.95 0.243 -3.34 3.4% 7.44 8.32 6.95 8.08 11.55 10.00 Tpx2 -2.29 -4.90 0.024 2.28 90.7% 8.19 7.15 7.18 9.52 9.78 10.12

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Cd3g -2.28 -4.85 0.018 3.47 97.0% 6.37 5.98 5.59 7.95 8.65 8.18 Gria2 -2.28 -4.85 0.036 0.85 70.0% 5.71 5.72 5.81 8.71 8.27 7.08 Ect2 -2.22 -4.67 0.029 1.37 79.8% 7.34 6.32 6.26 8.49 8.75 9.34 RGD1562646 -2.15 -4.45 0.061 -0.07 48.2% 5.99 5.23 5.07 7.27 7.01 8.49 Ckap2 -2.15 -4.44 0.026 1.79 85.7% 7.46 7.27 6.38 8.89 9.09 9.57 Prr11 -2.13 -4.38 0.028 1.47 81.3% 6.89 6.47 5.98 7.96 8.92 8.85 Ns5atp9 -2.13 -4.38 0.029 1.29 78.4% 7.77 7.11 7.33 8.83 9.67 10.10 Ndc80 -2.11 -4.32 0.020 3.04 95.4% 6.64 5.87 6.10 8.14 8.09 8.70 rCG_50757 -2.09 -4.25 0.177 -2.34 8.8% 7.22 7.90 7.89 9.99 10.94 8.35 Kif11 -2.09 -4.24 0.020 2.65 93.4% 7.34 6.91 6.83 8.83 8.84 9.67 Ccnd2 -2.07 -4.20 0.023 2.46 92.1% 9.18 9.91 9.72 11.39 11.47 12.17 Ttk -2.07 -4.19 0.020 2.69 93.6% 5.85 5.27 5.44 7.17 7.51 8.08 Pon1 -2.07 -4.19 0.023 2.41 91.8% 5.60 5.52 4.76 7.09 7.24 7.74 Plk4 -2.05 -4.15 0.024 2.11 89.2% 7.14 6.26 6.47 8.31 8.62 9.09 Nuf2 -2.05 -4.13 0.018 4.49 98.9% 6.09 5.97 5.83 7.73 7.91 8.40 Ccne2 -2.03 -4.09 0.020 2.68 93.6% 6.53 6.12 6.41 7.82 8.66 8.69 Ccnb1 -2.03 -4.08 0.026 1.80 85.8% 7.41 6.76 6.77 8.63 8.80 9.58 Mfap4 -2.01 -4.02 0.294 -4.00 1.8% 7.83 7.75 7.45 8.27 11.85 8.93 Mastl -2.00 -4.01 0.026 1.88 86.7% 7.00 6.18 6.27 8.19 8.31 8.96 Pttg1 -2.00 -4.01 0.029 1.18 76.6% 8.00 7.46 7.01 8.96 9.91 9.60 03-Sep -2.00 -4.01 0.029 1.17 76.4% 8.32 7.56 7.29 9.96 9.22 9.99 Hmmr -2.00 -3.99 0.026 1.87 86.7% 6.59 5.90 5.86 7.85 7.85 8.63 Frzb -1.98 -3.95 0.361 -4.63 1.0% 6.82 6.48 6.66 7.04 11.26 7.61 Casc5 -1.98 -3.93 0.024 2.18 89.8% 7.54 6.84 6.99 8.94 8.77 9.59 LOC690102 -1.97 -3.92 0.020 2.70 93.7% 8.19 7.50 7.75 9.41 10.17 9.78 Fam111a -1.97 -3.91 0.020 2.92 94.9% 6.29 5.88 5.84 7.81 7.65 8.45 Rab38 -1.96 -3.89 0.020 2.79 94.2% 7.82 8.36 7.46 9.61 10.01 9.89 Kif18a -1.95 -3.88 0.029 1.34 79.2% 6.31 5.94 5.71 7.64 7.58 8.61 Anln -1.95 -3.87 0.020 2.71 93.8% 8.24 7.54 7.44 9.50 9.60 9.99 Gpr37 -1.94 -3.84 0.037 0.77 68.4% 6.92 6.26 6.04 7.78 8.37 8.89 Cep55 -1.94 -3.84 0.027 1.65 83.8% 5.98 5.91 5.76 7.55 7.41 8.52 Nuak1 -1.91 -3.76 0.055 0.11 52.8% 8.53 7.93 7.67 9.22 10.16 10.49 Anln -1.91 -3.76 0.020 2.85 94.5% 8.48 7.82 7.73 9.71 9.85 10.19 Drp2 -1.91 -3.75 0.018 4.16 98.5% 7.66 7.50 7.04 10.41 8.13 8.94 Kif20a -1.90 -3.73 0.050 0.33 58.1% 7.26 6.56 6.42 8.41 8.20 9.34 Plk1 -1.90 -3.73 0.026 1.95 87.6% 8.21 7.66 7.46 9.44 9.44 10.15 RGD1307201 -1.89 -3.70 0.020 2.68 93.6% 8.27 7.81 7.77 9.37 10.03 10.11 Ckap2 -1.88 -3.69 0.018 3.42 96.8% 7.57 7.31 7.12 8.95 9.09 9.62 RGD1308541 -1.87 -3.65 0.029 1.37 79.7% 6.83 6.39 6.04 7.91 8.13 8.83

117

Nek2 -1.86 -3.63 0.026 1.75 85.2% 6.88 6.46 6.18 7.96 8.28 8.86 Nek2 -1.86 -3.63 0.026 1.75 85.2% 6.88 6.46 6.18 7.96 8.28 8.86 Mybl1 -1.86 -3.63 0.020 2.83 94.4% 6.67 6.54 6.57 8.03 8.94 8.39 Dpt -1.86 -3.63 0.286 -3.93 1.9% 7.19 7.68 6.69 7.30 10.62 9.21 Tm4sf1 -1.86 -3.62 0.069 -0.30 42.6% 8.93 9.08 8.39 11.42 9.94 10.62 Prc1 -1.85 -3.61 0.033 0.95 72.2% 7.94 7.22 7.30 9.25 8.86 9.91 Serpinf1 -1.85 -3.60 0.413 -5.00 0.7% 6.84 8.90 6.66 7.18 11.41 9.37 Elmo1 -1.84 -3.58 0.027 1.63 83.6% 8.10 7.22 7.24 9.50 9.03 9.54 Cdca3 -1.82 -3.53 0.026 2.01 88.2% 6.97 6.32 6.29 8.08 8.20 8.77 Rad51 -1.80 -3.48 0.029 1.21 77.1% 6.35 6.16 5.74 7.53 7.63 8.48 Slitrk6 -1.77 -3.42 0.289 -3.95 1.9% 5.87 5.97 6.87 8.56 9.24 6.23 Slc6a15 -1.77 -3.42 0.322 -4.32 1.3% 5.47 5.25 5.64 7.48 8.93 5.27 Cenpf -1.77 -3.42 0.028 1.53 82.2% 7.22 6.54 6.72 8.21 8.50 9.08 Calr4 -1.77 -3.40 0.098 -0.98 27.3% 7.05 6.79 6.09 9.08 8.53 7.62 Rpl36a -1.76 -3.39 0.189 -2.57 7.1% 11.70 12.08 12.05 12.26 12.94 12.62 Gdpd2 -1.74 -3.35 0.055 0.16 54.1% 7.05 7.05 6.97 6.95 6.98 6.84 Ccnb2 -1.73 -3.32 0.029 1.27 78.1% 7.42 6.80 6.94 8.31 8.80 9.26 Gldn -1.73 -3.32 0.028 1.55 82.4% 9.69 10.16 9.68 11.90 11.80 11.03 Ccnb2 -1.71 -3.28 0.029 1.18 76.4% 7.44 6.82 6.94 8.30 8.79 9.25 Dscc1 -1.70 -3.25 0.029 1.21 77.0% 6.16 5.74 5.85 7.53 7.14 8.18 Hist2h4 -1.70 -3.25 0.018 3.95 98.1% 10.30 10.28 10.37 11.94 11.74 12.36 Dbf4 -1.69 -3.23 0.083 -0.67 33.8% 6.42 5.47 6.19 7.00 8.02 8.14 Gsta4 -1.69 -3.22 0.055 0.15 53.7% 9.01 7.93 8.58 10.02 9.89 10.67 LOC689296 -1.69 -3.22 0.024 2.36 91.4% 7.09 6.61 6.72 8.06 8.76 8.66 Chp2 -1.66 -3.16 0.011 5.56 99.6% 6.02 6.09 6.16 7.60 7.91 7.74 RGD1304693 -1.66 -3.16 0.024 2.25 90.5% 6.11 5.77 5.67 7.25 7.32 7.96 Melk -1.65 -3.14 0.060 -0.03 49.2% 6.15 5.59 5.54 7.06 7.07 8.10 Prkcq -1.65 -3.13 0.033 0.96 72.2% 8.56 8.58 8.60 10.54 9.57 10.59 RGD1310335 -1.65 -3.13 0.029 1.22 77.2% 5.69 5.77 5.63 7.13 6.94 7.96 Gldc -1.65 -3.13 0.050 0.34 58.3% 7.54 7.13 7.22 9.63 8.50 8.70 Slc9a3 -1.64 -3.11 0.088 -0.77 31.6% 9.01 8.34 7.57 10.21 9.62 10.00 Trib2 -1.62 -3.08 0.157 -1.95 12.4% 8.84 9.33 8.14 9.70 10.27 11.21 Aspm -1.61 -3.04 0.026 1.92 87.2% 7.32 6.68 6.75 8.34 8.36 8.88 LOC681994 -1.59 -3.02 0.427 -5.09 0.6% 5.37 5.78 5.39 5.71 9.62 5.99 Ddah1 -1.59 -3.02 0.053 0.23 55.8% 10.39 10.72 9.61 11.94 12.04 11.51 Sgol1 -1.59 -3.01 0.029 1.30 78.6% 6.30 6.11 6.14 7.64 7.35 8.34 Fancd2 -1.59 -3.01 0.023 2.43 91.9% 6.94 6.89 6.48 8.22 8.13 8.73 Cenpe -1.58 -2.99 0.051 0.28 57.0% 6.59 6.40 6.26 7.76 7.55 8.68 Mmp16 -1.57 -2.98 0.124 -1.56 17.4% 5.81 6.04 6.42 6.90 8.52 7.57

118

Tbx15 -1.57 -2.98 0.331 -4.41 1.2% 7.36 8.72 6.49 7.83 10.36 9.09 Pir -1.56 -2.96 0.310 -4.17 1.5% 8.42 8.42 8.48 8.31 9.17 7.96 Cdc42ep3 -1.55 -2.93 0.027 1.59 83.1% 8.03 7.39 8.26 9.58 9.49 9.28 Hist2h3c2 -1.55 -2.93 0.026 1.98 87.9% 11.66 11.29 10.83 12.75 12.72 12.98 Hist2h3c2 -1.55 -2.93 0.026 1.98 87.9% 11.66 11.29 10.83 12.75 12.72 12.98 Crabp1 -1.55 -2.93 0.319 -4.28 1.4% 9.04 9.24 8.14 9.07 10.01 11.99 Dck -1.55 -2.93 0.018 4.18 98.5% 8.05 8.02 7.84 9.26 9.63 9.67 Mag -1.55 -2.92 0.122 -1.49 18.3% 10.20 9.37 8.56 10.84 10.76 11.17 Nefm -1.55 -2.92 0.303 -4.09 1.7% 8.14 7.88 7.83 10.29 10.43 7.77 RGD1564767 -1.54 -2.90 0.023 2.40 91.7% 9.37 8.62 9.06 10.41 10.60 10.66 Kcna1 -1.54 -2.90 0.261 -3.62 2.6% 8.88 7.63 7.77 10.92 9.15 8.81 Npal1 -1.53 -2.89 0.106 -1.16 23.9% 8.02 7.53 7.42 8.89 8.67 10.01 Ccdc80 -1.53 -2.88 0.033 0.96 72.4% 6.47 6.81 6.81 8.18 8.72 7.75 Tyrp1 -1.52 -2.87 0.368 -4.69 0.9% 6.51 6.97 6.75 10.26 7.65 6.87 Ptplad2 -1.50 -2.84 0.033 0.94 72.0% 6.60 6.27 5.84 8.15 7.51 7.57 Dusp15 -1.50 -2.84 0.189 -2.58 7.1% 9.60 9.04 8.13 10.94 9.63 10.71 Gstk1 -1.50 -2.82 0.011 5.41 99.6% 7.56 7.69 7.53 9.13 9.16 8.97 Bub1b -1.49 -2.82 0.079 -0.54 36.9% 6.19 5.63 5.88 7.06 7.01 8.11 Psat1 -1.49 -2.82 0.056 0.09 52.3% 9.92 9.84 9.03 10.69 11.23 11.34 Cdca2 -1.49 -2.80 0.051 0.30 57.5% 6.73 6.35 6.21 7.63 7.61 8.50 S100a3 -1.48 -2.80 0.049 0.37 59.2% 6.34 6.42 6.33 7.70 7.37 8.47 Asf1b -1.48 -2.78 0.020 2.83 94.4% 7.20 7.01 7.14 8.51 8.31 8.95 Myo1b -1.47 -2.77 0.120 -1.47 18.7% 7.92 8.52 7.03 9.60 9.17 9.11 Mthfd2 -1.47 -2.77 0.046 0.46 61.4% 7.72 8.34 7.54 8.96 9.68 9.36 Mlf1ip -1.46 -2.75 0.080 -0.60 35.5% 4.84 4.95 4.68 6.22 5.67 6.97 Adamts20 -1.45 -2.73 0.075 -0.45 38.9% 10.17 9.63 9.45 11.04 10.77 11.79 Hmgb2 -1.45 -2.73 0.026 1.97 87.8% 9.67 9.30 9.49 10.61 10.87 11.31 Spc25 -1.45 -2.72 0.029 1.26 77.9% 7.31 6.84 6.54 8.29 8.12 8.62 Racgap1 -1.44 -2.71 0.064 -0.17 45.7% 7.19 7.02 6.84 8.25 8.01 9.11 Kif2c -1.44 -2.71 0.053 0.22 55.5% 6.66 6.35 6.48 7.49 7.78 8.53 Brip1 -1.43 -2.70 0.079 -0.55 36.7% 6.57 5.93 6.00 7.33 7.25 8.22 Dok6 -1.43 -2.70 0.382 -4.80 0.8% 6.30 5.62 6.16 8.28 8.60 5.49 Icoslg -1.43 -2.69 0.117 -1.41 19.7% 9.74 9.01 8.42 10.40 10.16 10.89 Mad2l1 -1.43 -2.69 0.063 -0.15 46.2% 6.84 6.89 6.79 7.90 7.95 8.95 Pole2 -1.42 -2.68 0.060 -0.05 48.8% 5.74 5.72 5.34 6.58 6.86 7.63 H2afz -1.42 -2.68 0.029 1.25 77.8% 9.25 9.80 9.80 10.67 11.27 11.19 Kif20b -1.42 -2.68 0.123 -1.54 17.7% 4.91 4.49 4.60 5.53 5.80 6.93 Pole -1.42 -2.67 0.042 0.59 64.3% 7.10 7.17 6.88 8.36 8.04 9.01 Lrrtm4 -1.41 -2.65 0.171 -2.25 9.5% 8.37 8.22 7.34 9.50 8.64 10.01

119

RGD1564263 -1.40 -2.64 0.083 -0.67 33.9% 6.09 5.66 6.23 6.79 7.47 7.93 Hist1h2bp -1.38 -2.61 0.020 2.83 94.4% 8.70 8.44 8.61 9.81 9.81 10.29 Cmc1 -1.38 -2.61 0.046 0.46 61.3% 5.50 5.68 5.81 6.93 7.58 6.62 Hmgb2 -1.38 -2.60 0.027 1.64 83.8% 9.87 9.54 9.74 10.78 11.01 11.50 Rasgef1a -1.37 -2.59 0.270 -3.72 2.4% 8.01 6.73 6.45 9.33 7.57 8.42 Bard1 -1.37 -2.59 0.067 -0.26 43.7% 7.13 6.90 6.87 7.88 8.17 8.98 Has2 -1.37 -2.59 0.247 -3.39 3.3% 8.92 9.03 7.47 10.11 10.36 9.06 Tbx15 -1.36 -2.57 0.298 -4.02 1.8% 6.79 7.99 6.59 7.32 9.38 8.75 Omd -1.36 -2.57 0.340 -4.47 1.1% 3.60 4.45 3.46 3.88 6.63 5.08 Kif23 -1.36 -2.56 0.069 -0.33 41.9% 7.00 6.59 6.46 7.70 7.80 8.63 Aurkb -1.35 -2.55 0.113 -1.29 21.6% 7.63 7.04 7.15 8.21 8.32 9.35 Gsta4 -1.35 -2.55 0.115 -1.32 21.0% 9.12 8.03 8.74 9.79 9.68 10.48 RGD1310784 -1.35 -2.55 0.062 -0.11 47.3% 4.79 4.35 4.27 5.49 5.59 6.37 Ophn1 -1.35 -2.55 0.289 -3.95 1.9% 7.48 7.13 7.41 7.30 6.97 7.38 Dhfr -1.34 -2.53 0.018 3.52 97.1% 7.39 7.01 7.00 8.41 8.44 8.57 Il6 -1.34 -2.53 0.351 -4.55 1.0% 4.72 5.04 5.04 5.33 7.99 5.50 Fgf1 -1.34 -2.53 0.028 1.51 81.8% 7.21 7.54 6.94 8.52 8.34 8.85 Pik3cb -1.34 -2.53 0.189 -2.57 7.1% 6.66 6.53 6.38 7.08 8.82 7.68 Tmem97 -1.33 -2.51 0.154 -1.92 12.8% 8.47 8.67 8.12 9.03 10.48 9.73 Hist1h1a -1.33 -2.51 0.024 2.28 90.7% 8.65 8.08 8.18 9.60 9.51 9.78 Ccne1 -1.32 -2.50 0.106 -1.17 23.6% 6.92 7.09 6.45 7.78 7.83 8.81 RGD1561797 -1.32 -2.49 0.079 -0.52 37.2% 9.64 9.47 9.49 10.21 11.34 10.99 Atad2 -1.32 -2.49 0.026 1.70 84.5% 7.57 7.28 7.33 8.41 8.64 9.08 Hjurp -1.32 -2.49 0.094 -0.90 28.9% 7.19 6.76 6.57 8.13 7.63 8.72 Hmgb2 -1.31 -2.49 0.029 1.24 77.5% 9.17 8.84 8.97 10.03 10.15 10.74 Khdrbs3 -1.31 -2.49 0.181 -2.40 8.3% 6.30 6.63 6.47 7.05 8.69 7.60 Stil -1.31 -2.48 0.035 0.90 71.0% 6.62 6.64 6.08 7.57 7.60 8.11 Cenpm -1.31 -2.48 0.026 1.71 84.7% 7.58 7.25 7.23 8.31 8.92 8.74 Dusp4 -1.31 -2.47 0.283 -3.87 2.0% 8.49 7.89 7.88 8.23 9.44 10.51 Serpine2 -1.31 -2.47 0.309 -4.16 1.5% 8.44 9.02 8.48 9.78 11.28 8.79 Diaph3 -1.30 -2.47 0.054 0.20 55.0% 7.38 7.13 6.82 8.00 8.40 8.84 Ghr -1.30 -2.47 0.189 -2.57 7.1% 9.25 8.43 8.67 10.63 10.42 9.21 Hmgb2 -1.30 -2.47 0.031 1.09 74.9% 8.82 8.50 8.64 9.67 9.80 10.40 Ckap2l -1.30 -2.46 0.027 1.67 84.1% 6.80 6.36 6.67 7.87 7.65 8.22 Arhgdib -1.30 -2.46 0.415 -5.01 0.7% 6.14 6.85 5.83 6.20 9.40 7.13 Wisp1 -1.30 -2.46 0.117 -1.38 20.1% 8.51 8.37 7.84 9.58 10.11 8.94 Iqgap3 -1.30 -2.46 0.101 -1.05 26.0% 8.35 7.87 7.65 9.08 8.84 9.85 RGD1563546 -1.29 -2.45 0.024 2.20 90.0% 5.92 5.79 5.50 6.79 7.27 7.04 Tf -1.29 -2.45 0.027 1.60 83.2% 7.75 7.18 7.10 8.73 8.51 8.67

120

LOC680615 -1.29 -2.45 0.027 1.59 83.1% 7.30 7.07 7.62 8.54 8.42 8.90 Wee1 -1.28 -2.43 0.018 3.75 97.7% 7.21 7.26 7.43 8.39 8.67 8.69 Mcm10 -1.28 -2.43 0.097 -0.96 27.8% 7.21 7.17 6.77 8.13 7.89 8.97 Kif18b -1.28 -2.43 0.122 -1.52 18.0% 8.17 7.60 7.67 8.87 8.62 9.79 Tyms -1.28 -2.42 0.020 2.92 94.9% 7.41 7.31 7.15 8.38 8.49 8.82 Slc29a4 -1.27 -2.41 0.346 -4.52 1.1% 8.16 7.23 6.91 7.28 9.62 9.22 Ube2t -1.27 -2.41 0.029 1.36 79.6% 6.68 6.39 6.28 7.36 7.83 7.97 Gsta3 -1.26 -2.40 0.509 -5.51 0.4% 7.58 8.76 6.98 7.23 11.06 8.82 Pcna -1.26 -2.39 0.020 2.87 94.6% 9.59 9.57 9.70 10.62 11.12 10.88 Smoc2 -1.26 -2.39 0.020 2.77 94.1% 7.56 7.87 7.42 8.79 8.80 9.03 Gsta4 -1.26 -2.39 0.079 -0.57 36.0% 8.24 7.55 7.94 8.94 8.89 9.67 Mmp13 -1.25 -2.38 0.522 -5.57 0.4% 8.96 8.44 7.57 7.83 11.86 9.02 LOC689399 -1.25 -2.38 0.026 1.73 85.0% 7.93 7.27 7.67 8.80 8.97 8.86 Epyc -1.25 -2.37 0.364 -4.66 0.9% 5.05 5.08 4.91 7.34 6.82 4.62 LOC682649 -1.24 -2.37 0.020 3.20 96.1% 10.98 10.70 10.83 11.97 11.99 12.29 Prss12 -1.24 -2.37 0.147 -1.85 13.5% 10.14 9.71 8.92 11.20 10.51 10.79 Hist2h2bb -1.24 -2.37 0.024 2.30 90.8% 9.21 9.17 9.17 10.53 10.10 10.66 RGD1561963 -1.24 -2.36 0.167 -2.17 10.2% 10.24 9.98 9.07 11.13 10.51 11.37 Il34 -1.24 -2.36 0.178 -2.35 8.7% 9.05 8.29 7.57 9.26 9.56 9.81 Tacc3 -1.23 -2.35 0.134 -1.72 15.2% 6.47 6.19 6.24 7.20 7.09 8.31 Lum -1.23 -2.35 0.646 -6.00 0.2% 8.10 10.45 6.12 7.26 11.57 9.53 Mt1a -1.23 -2.34 0.345 -4.51 1.1% 10.83 10.32 9.41 11.23 12.64 10.37 Cenpt -1.21 -2.32 0.059 -0.02 49.5% 7.57 7.45 7.25 8.53 8.25 9.13 Arhgap11a -1.21 -2.32 0.099 -1.01 26.7% 7.35 7.06 6.77 7.83 8.15 8.83 Gen1 -1.21 -2.31 0.122 -1.50 18.3% 6.13 6.11 5.70 6.76 6.93 7.87 Pdlim1 -1.21 -2.31 0.235 -3.23 3.8% 8.54 8.56 8.09 9.10 10.65 9.08 Brca1 -1.21 -2.31 0.096 -0.93 28.3% 6.61 6.32 6.16 7.42 7.14 8.16 Sdccag3 -1.20 -2.30 0.251 -3.44 3.1% 10.52 10.51 10.38 10.65 10.74 10.66 Cnih2 -1.20 -2.30 0.139 -1.77 14.5% 10.46 10.36 9.51 10.82 11.51 11.60 Asns -1.20 -2.30 0.318 -4.27 1.4% 9.21 9.88 8.33 10.00 11.42 9.61 Dlgap5 -1.20 -2.30 0.183 -2.44 8.0% 6.83 6.38 6.56 7.38 7.35 8.65 Nefl -1.20 -2.30 0.417 -5.03 0.6% 11.02 10.35 10.13 12.87 12.27 9.97 Ogn -1.20 -2.30 0.672 -6.07 0.2% 7.02 9.96 6.61 6.30 11.60 9.29 Ednra -1.20 -2.30 0.491 -5.42 0.4% 7.98 8.45 7.48 7.62 11.23 8.65 Slc12a2 -1.20 -2.29 0.111 -1.25 22.3% 9.65 9.26 9.05 10.85 9.92 10.79 RGD1309701 -1.19 -2.29 0.254 -3.53 2.8% 6.60 7.41 6.98 7.18 8.94 8.46 Tpmt -1.19 -2.29 0.024 2.22 90.2% 7.16 7.49 7.35 8.26 8.67 8.65 Mcm5 -1.19 -2.28 0.178 -2.36 8.6% 7.47 7.21 6.85 8.39 7.67 9.05 Rad51c -1.19 -2.28 0.029 1.41 80.3% 6.94 6.93 6.65 7.76 7.96 8.35

121

Kif15 -1.19 -2.27 0.111 -1.25 22.3% 7.27 7.00 7.23 8.00 8.03 9.02 LOC684611 -1.18 -2.27 0.122 -1.52 18.0% 4.79 5.27 4.79 5.71 5.91 6.77 Mgst3 -1.18 -2.26 0.026 1.82 86.1% 8.63 8.61 9.04 10.02 10.07 9.72 Rfc4 -1.17 -2.26 0.111 -1.27 22.0% 6.46 6.80 6.22 7.10 8.11 7.80 Lpl -1.17 -2.26 0.283 -3.87 2.0% 6.62 6.88 5.71 6.67 7.64 8.41 Mthfd2 -1.17 -2.25 0.055 0.12 52.9% 8.08 8.65 8.15 9.15 9.79 9.45 Plod2 -1.17 -2.25 0.154 -1.93 12.7% 7.86 8.52 7.84 9.22 9.81 8.70 Kntc1 -1.17 -2.24 0.052 0.26 56.4% 6.71 6.41 6.19 7.42 7.39 8.00 Mt2A -1.17 -2.24 0.449 -5.21 0.5% 9.33 9.58 9.03 9.85 12.37 9.23 Nav3 -1.16 -2.24 0.205 -2.82 5.6% 9.89 9.93 8.70 10.20 10.77 11.04 Mt1a -1.16 -2.24 0.361 -4.64 1.0% 10.95 10.50 9.55 11.30 12.72 10.48 E2f7 -1.16 -2.24 0.118 -1.44 19.2% 7.44 7.15 7.03 8.19 7.91 9.00 Hist1h2bh -1.16 -2.24 0.028 1.46 81.2% 5.67 5.46 5.17 6.37 6.61 6.80 St6galnac2 -1.16 -2.23 0.340 -4.47 1.1% 7.97 7.17 7.77 7.43 9.41 9.55 Hist1h2bl -1.16 -2.23 0.026 1.71 84.6% 11.90 11.42 11.43 12.62 12.71 12.90 Mcm4 -1.16 -2.23 0.028 1.48 81.5% 9.29 9.36 9.20 10.34 10.19 10.79 Gstm2 -1.16 -2.23 0.424 -5.07 0.6% 4.89 6.40 5.06 5.68 8.14 6.00 Id2 -1.15 -2.22 0.055 0.12 53.1% 11.23 10.65 10.40 11.81 11.89 12.03 Ctsc -1.15 -2.22 0.026 1.74 85.1% 10.02 9.88 9.88 10.96 10.87 11.41 Prx -1.15 -2.22 0.223 -3.06 4.5% 10.35 9.59 9.39 11.35 10.14 11.29 Foxm1 -1.15 -2.21 0.067 -0.25 43.7% 8.41 8.17 8.41 9.30 9.14 9.98 Ndst3 -1.14 -2.21 0.352 -4.56 1.0% 5.94 5.76 5.79 6.21 8.44 6.26 Tspan13 -1.14 -2.20 0.224 -3.08 4.4% 8.16 7.88 8.32 8.52 10.13 9.12 Hist1h2bl -1.13 -2.19 0.036 0.84 69.8% 9.96 9.37 9.35 10.55 10.75 10.78 LOC691215 -1.13 -2.19 0.159 -2.01 11.8% 4.09 4.10 3.88 3.86 4.17 3.93 Bloc1s2 -1.13 -2.19 0.024 2.13 89.3% 9.65 9.55 9.70 10.55 11.04 10.69 Rgs4 -1.13 -2.19 0.115 -1.32 21.1% 7.53 8.07 7.39 8.69 9.28 8.41 Plekhk1 -1.13 -2.18 0.048 0.40 60.0% 6.46 6.28 5.97 7.09 7.29 7.70 Cyb5a -1.12 -2.18 0.097 -0.95 27.8% 7.97 8.17 8.78 9.25 9.80 9.24 RGD1559690 -1.12 -2.18 0.041 0.62 65.1% 6.64 6.21 6.34 7.21 7.53 7.82 Hist1h1b -1.12 -2.17 0.084 -0.71 33.0% 8.36 7.94 8.06 9.28 8.76 9.66 Col12a1 -1.11 -2.16 0.056 0.09 52.3% 9.83 9.60 9.13 10.76 10.76 10.37 RGD1310185 -1.11 -2.16 0.029 1.40 80.1% 5.93 5.63 5.89 6.66 7.20 6.93 Mcm6 -1.11 -2.16 0.106 -1.14 24.2% 7.83 7.81 7.60 8.65 8.46 9.45 Prelp -1.11 -2.16 0.247 -3.39 3.3% 7.81 7.93 7.55 8.00 9.73 8.88 Tmem117 -1.10 -2.15 0.047 0.44 60.7% 7.62 7.60 7.45 8.72 9.00 8.26 Nav3 -1.10 -2.15 0.206 -2.84 5.5% 8.67 8.81 7.54 9.06 9.57 9.70 Kcnk1 -1.10 -2.15 0.330 -4.39 1.2% 8.20 7.91 7.29 10.07 8.64 7.99 Snrpf -1.10 -2.14 0.116 -1.37 20.2% 6.64 7.10 6.64 7.34 8.29 8.04

122

Hist1h2bl -1.10 -2.14 0.026 1.69 84.5% 12.00 11.59 11.55 12.67 12.82 12.94 Csrp2 -1.10 -2.14 0.254 -3.50 2.9% 9.71 8.92 8.07 9.57 10.19 10.25 Luzp5 -1.10 -2.14 0.116 -1.37 20.2% 6.61 6.82 6.68 7.55 7.41 8.43 Frmd3 -1.09 -2.14 0.168 -2.18 10.1% 9.53 8.74 8.25 10.07 9.80 9.94 Pcsk2 -1.09 -2.13 0.029 1.22 77.1% 6.87 6.69 7.02 7.65 8.04 8.17 RGD1565672 -1.08 -2.12 0.065 -0.21 44.9% 7.49 7.10 6.86 7.94 8.22 8.53 LOC682650 -1.08 -2.12 0.132 -1.69 15.5% 8.30 8.22 7.98 9.81 9.26 8.68 Troap -1.08 -2.11 0.097 -0.96 27.7% 8.57 8.25 8.11 9.36 8.96 9.85 Fbln2 -1.08 -2.11 0.264 -3.63 2.6% 7.20 7.04 7.07 8.07 9.15 7.33 LOC681849 -1.08 -2.11 0.358 -4.60 1.0% 10.42 10.16 8.20 10.78 10.72 10.52 RGD1563551 -1.08 -2.11 0.298 -4.02 1.8% 6.64 5.50 6.10 6.56 8.12 6.79 Prim2 -1.07 -2.11 0.111 -1.27 21.9% 8.32 7.79 7.44 8.87 8.66 9.24 Il16 -1.07 -2.10 0.124 -1.56 17.4% 9.89 9.61 9.06 10.89 10.17 10.73 Pgbd5 -1.07 -2.10 0.090 -0.83 30.4% 8.30 7.87 7.90 9.41 8.61 9.27 LOC501224 -1.07 -2.10 0.481 -5.37 0.5% 5.63 5.96 4.95 5.71 8.41 5.62 Palmd -1.07 -2.10 0.275 -3.79 2.2% 10.87 10.01 9.24 10.73 11.64 10.94 Rps27 -1.07 -2.09 0.159 -2.00 11.9% 5.30 5.12 5.56 5.81 6.97 6.40 Egr2 -1.07 -2.09 0.043 0.57 63.9% 9.63 9.50 9.45 10.37 10.42 10.99 Fbxo5 -1.07 -2.09 0.059 -0.01 49.7% 6.76 6.74 6.93 7.65 7.64 8.33 Ncapd2 -1.06 -2.09 0.079 -0.58 35.9% 8.13 7.99 7.62 8.94 8.62 9.37 Fhl1 -1.06 -2.08 0.056 0.07 51.8% 7.32 7.95 7.41 7.03 7.22 6.75 Nrg1 -1.06 -2.08 0.223 -3.07 4.4% 6.26 6.45 6.37 7.08 8.30 6.87 Trim59 -1.06 -2.08 0.200 -2.74 6.0% 6.97 6.72 7.15 7.35 7.94 8.72 Penk1 -1.06 -2.08 0.275 -3.80 2.2% 8.52 8.81 8.33 9.29 10.63 8.91 Ier3 -1.06 -2.08 0.171 -2.25 9.5% 9.02 9.52 9.85 10.63 10.95 9.98 Synpr -1.05 -2.08 0.285 -3.90 2.0% 6.93 6.96 7.22 9.16 7.77 7.34 RGD1310788 -1.05 -2.07 0.054 0.17 54.3% 8.42 7.82 7.73 8.96 9.06 9.10 RGD1561797 -1.05 -2.07 0.075 -0.45 38.9% 9.63 9.42 9.39 10.14 10.97 10.49 Cyp4f4 -1.05 -2.07 0.032 1.05 74.0% 5.91 5.48 5.44 6.84 6.61 6.51 Tox -1.05 -2.07 0.114 -1.32 21.2% 8.90 8.63 8.11 9.22 9.88 9.69 Cldn19 -1.04 -2.06 0.339 -4.46 1.1% 12.01 10.96 10.52 12.37 11.32 12.94 Rps27a -1.04 -2.05 0.106 -1.16 23.9% 9.13 9.34 9.90 10.15 10.64 10.71 Slco2a1 -1.04 -2.05 0.395 -4.89 0.7% 7.09 7.20 6.48 6.85 9.30 7.73 Angpt1 -1.04 -2.05 0.361 -4.64 1.0% 5.59 5.55 4.85 5.31 7.52 6.27 RGD1307315 -1.03 -2.04 0.200 -2.75 6.0% 7.72 7.69 7.89 9.48 8.83 8.09 Otub2 -1.03 -2.04 0.029 1.34 79.3% 8.45 8.43 8.30 9.19 9.71 9.37 Bckdhb -1.03 -2.04 0.038 0.75 67.9% 6.86 6.79 6.68 7.60 7.65 8.16 Hist1h2bn -1.03 -2.04 0.024 2.32 91.0% 11.49 11.30 11.21 12.19 12.45 12.44 Akt3 -1.03 -2.04 0.275 -3.78 2.2% 8.84 8.78 7.57 9.52 8.83 9.91

123

Mfap3l -1.03 -2.04 0.099 -1.00 26.8% 9.10 9.07 8.64 10.27 9.51 10.11 Ccdc99 -1.03 -2.04 0.093 -0.87 29.6% 7.17 7.15 6.66 7.76 7.86 8.43 Fem1a -1.02 -2.03 0.177 -2.34 8.8% 8.72 8.63 8.99 8.79 8.33 8.95 Lmo7 -1.02 -2.03 0.106 -1.15 24.1% 6.78 6.64 6.30 7.13 7.72 7.95 Efna5 -1.02 -2.03 0.365 -4.66 0.9% 5.80 6.06 5.98 5.97 8.25 6.68 Atp7a -1.02 -2.03 0.088 -0.79 31.3% 9.70 9.74 9.91 9.48 9.66 9.69 Ryr3 -1.02 -2.03 0.463 -5.28 0.5% 7.26 6.54 6.15 6.30 7.62 9.11 Hmcn1 -1.02 -2.03 0.125 -1.58 17.0% 6.78 6.53 6.68 7.09 8.07 7.90 Vax2 -1.01 -2.02 0.029 1.36 79.6% 7.77 7.59 7.72 8.75 8.43 8.93 Cdc20 -1.01 -2.02 0.312 -4.20 1.5% 8.74 8.36 7.97 9.49 8.39 10.24 Abat -1.01 -2.02 0.117 -1.40 19.7% 7.55 7.40 7.38 8.57 7.90 8.89 Ly96 -1.01 -2.01 0.383 -4.81 0.8% 7.87 8.12 7.77 8.25 10.33 8.20 Cntf -1.01 -2.01 0.242 -3.32 3.5% 8.56 8.35 8.61 10.31 9.50 8.75 Elovl4 -1.01 -2.01 0.154 -1.92 12.8% 7.23 7.20 8.08 8.48 8.80 8.26 Il1rl2 -1.01 -2.01 0.288 -3.94 1.9% 6.56 6.73 6.54 6.68 8.52 7.65 Idi1 -1.01 -2.01 0.165 -2.12 10.7% 8.20 8.07 7.87 8.42 9.20 9.53 Cenpn -1.01 -2.01 0.036 0.82 69.4% 8.16 8.05 7.84 8.80 8.97 9.31 Jag1 -1.00 -2.01 0.149 -1.87 13.3% 7.58 8.28 7.92 8.81 9.42 8.57 Creg1 -1.00 -2.01 0.057 0.03 50.6% 6.52 6.48 6.26 7.75 7.47 7.06 Zfp367 -1.00 -2.01 0.080 -0.61 35.2% 6.61 6.88 6.71 7.77 7.32 8.12 Rps27a -1.00 -2.00 0.106 -1.14 24.2% 9.14 9.26 9.82 10.06 10.55 10.62 Smc4 -1.00 -2.00 0.029 1.15 76.0% 9.05 8.80 8.73 9.69 9.77 10.11 RGD1309522 -1.00 -1.99 0.058 0.02 50.4% 7.66 7.61 7.57 8.19 8.80 8.83 Fam83d -0.99 -1.98 0.035 0.87 70.5% 7.58 7.67 7.37 8.38 8.39 8.81 Sat2 -0.98 -1.98 0.033 0.95 72.2% 8.11 8.00 8.14 9.35 9.05 8.80 Abhd10 -0.98 -1.98 0.035 0.88 70.7% 8.49 8.20 8.17 9.15 9.12 9.53 Ccdc111 -0.97 -1.96 0.026 1.76 85.4% 6.00 5.81 5.62 6.69 6.75 6.90 Vamp5 -0.97 -1.96 0.026 1.85 86.5% 9.01 8.72 8.70 9.67 9.75 9.91 Hist1h2ail -0.96 -1.95 0.029 1.23 77.4% 11.71 11.22 11.38 12.34 12.41 12.45 Hsd17b7 -0.95 -1.94 0.027 1.59 83.1% 7.68 7.94 7.77 8.55 8.89 8.81 LOC679840 -0.95 -1.93 0.023 2.42 91.8% 10.45 10.27 10.17 11.18 11.29 11.29 Pcca -0.95 -1.93 0.040 0.66 65.8% 7.32 7.55 7.54 8.17 8.69 8.40 Lonrf2 -0.95 -1.93 0.029 1.38 79.9% 7.36 7.15 7.24 8.41 7.99 8.20 Usp1 -0.94 -1.92 0.027 1.62 83.5% 7.94 8.09 8.04 8.82 8.89 9.19 Fam82a -0.94 -1.91 0.024 2.23 90.3% 7.01 6.92 6.71 7.77 7.87 7.81 Fuca2 -0.93 -1.90 0.032 1.03 73.7% 6.89 6.95 6.57 7.78 7.57 7.84 Apip -0.93 -1.90 0.029 1.26 77.9% 8.20 8.04 7.97 8.77 9.15 9.07 Hmgn2 -0.92 -1.89 0.028 1.46 81.1% 10.32 10.04 9.99 10.92 11.02 11.16 Gtse1 -0.91 -1.87 0.032 1.01 73.4% 7.20 7.01 6.96 7.84 7.86 8.20

124

Hmgn2 -0.91 -1.87 0.036 0.84 69.8% 10.32 10.11 10.10 10.90 11.02 11.33 Hist2h3c2 -0.90 -1.87 0.045 0.51 62.4% 12.84 12.38 12.40 13.35 13.43 13.55 Kpna2 -0.90 -1.87 0.029 1.25 77.7% 10.62 10.73 10.83 11.54 11.50 11.85 Cdca8 -0.89 -1.86 0.055 0.11 52.8% 6.44 6.35 6.33 7.04 7.15 7.60 Hist1h4b -0.88 -1.84 0.030 1.13 75.6% 10.94 10.69 10.60 11.66 11.73 11.48 Cab39l -0.88 -1.84 0.055 0.13 53.3% 9.71 9.38 9.43 10.32 10.19 10.65 Hist1h4b -0.87 -1.83 0.054 0.18 54.5% 11.78 11.45 11.60 12.40 12.29 12.74 Kpna2 -0.87 -1.82 0.030 1.14 75.7% 10.64 10.77 10.83 11.53 11.48 11.83 Usp46 -0.86 -1.82 0.036 0.82 69.4% 7.68 7.90 7.57 8.41 8.73 8.60 Folh1 -0.86 -1.81 0.056 0.10 52.4% 7.11 6.88 6.87 7.73 7.61 8.10 RGD1560092 -0.86 -1.81 0.043 0.56 63.7% 7.08 6.85 7.25 8.05 7.97 7.76 Tiparp -0.85 -1.80 0.040 0.65 65.7% 9.64 9.49 9.51 10.53 10.52 10.14 Raver2 -0.83 -1.78 0.024 2.10 89.1% 6.58 6.66 6.58 7.56 7.39 7.37 Mpst -0.81 -1.75 0.051 0.28 57.0% 9.32 9.09 9.05 9.80 9.91 10.18 Fkbp3 -0.81 -1.75 0.028 1.44 80.9% 10.39 10.37 10.57 11.14 11.34 11.28 Dnajc6 -0.80 -1.74 0.044 0.53 62.9% 7.11 6.70 6.98 7.80 7.70 7.68 Hexb -0.80 -1.74 0.041 0.62 65.1% 8.70 8.70 8.58 9.62 9.50 9.24 Tmem48 -0.78 -1.71 0.052 0.27 56.6% 9.16 9.17 9.08 9.66 10.08 10.00 Olr1541 -0.76 -1.69 0.054 0.18 54.4% 2.42 2.32 2.35 2.87 3.26 3.21 Dars -0.75 -1.68 0.038 0.76 68.2% 8.76 8.84 8.80 9.47 9.44 9.73 RGD1565514 -0.73 -1.66 0.029 1.38 79.9% 5.07 5.21 4.88 5.03 5.71 4.96 Gstm7 -0.73 -1.66 0.037 0.78 68.6% 7.94 7.98 8.04 8.66 8.61 8.88 Atp6v1d -0.72 -1.64 0.053 0.21 55.3% 9.58 9.65 9.55 10.28 10.51 10.15 Tmem126a -0.71 -1.64 0.049 0.36 58.9% 7.21 7.31 7.44 7.88 8.11 8.09 Gmnn -0.71 -1.63 0.039 0.69 66.6% 6.94 7.05 6.96 7.62 7.60 7.84 Clspn -0.66 -1.58 0.053 0.21 55.3% 6.56 6.63 6.61 7.18 7.18 7.43 Atp1a1 -0.63 -1.55 0.038 0.73 67.5% 9.90 9.87 9.77 10.48 10.49 10.48 Hist2h2ac -0.63 -1.55 0.051 0.29 57.1% 10.90 10.87 10.82 11.54 11.37 11.57 Alpl -0.63 -1.55 0.056 0.07 51.7% 7.34 7.54 7.60 8.11 8.13 8.12 Hist2h2ac -0.62 -1.53 0.057 0.04 51.1% 10.92 10.91 10.84 11.54 11.37 11.60 Clybl -0.60 -1.52 0.056 0.07 51.7% 6.78 6.64 6.59 7.23 7.31 7.26 Cul4b -0.59 -1.50 0.055 0.15 53.7% 9.49 9.39 9.43 9.38 9.75 9.48

125

Table 4. Genes that are higher in nerve-SC. Genes with a p-value less than 0.05, or a fold change greater than 2, or a probability of being differentially expressed greater than 50%. Table is organized in decreasing order of log fold change.

Log Probability Fold p- B Nerve- Nerve- Nerve- SKP- SKP- SKP- Gene Fold Differentially Change value value SC 1 SC 2 SC 3 SC 1 SC 2 SC 3 Change Expressed Sparcl1 2.72 6.58 0.072 -0.40 40.2% 12.87 11.85 11.57 9.99 9.97 8.16 RGD1564709 2.68 6.43 0.165 -2.12 10.7% 11.72 11.47 9.52 8.59 9.18 6.89 Ecel1 2.56 5.89 0.029 1.17 76.2% 9.11 10.22 8.52 6.94 6.81 6.42 Cldn11 2.50 5.66 0.250 -3.43 3.1% 12.18 12.04 11.34 11.48 9.16 7.43 C7 2.43 5.39 0.295 -4.00 1.8% 6.89 10.29 5.98 4.97 5.88 5.02 RGD1564195 2.25 4.76 0.096 -0.93 28.3% 10.19 10.79 10.15 9.37 7.48 7.53 Grin2b 2.20 4.61 0.050 0.33 58.1% 10.16 10.28 9.92 8.82 7.08 7.84 Pde1c 2.18 4.53 0.026 1.96 87.7% 10.05 10.21 10.96 8.68 8.23 7.77 Myof 2.16 4.48 0.029 1.31 78.7% 11.55 11.64 11.72 9.56 10.15 8.70 Btc 1.97 3.91 0.069 -0.33 41.9% 9.99 10.23 11.29 8.88 8.82 7.90 Stmn4 1.95 3.87 0.018 3.80 97.8% 8.08 8.58 8.03 6.45 5.96 6.41 Mab21l1 1.85 3.60 0.020 2.77 94.1% 8.27 9.15 8.78 7.07 6.85 6.74 Cadm2 1.83 3.55 0.342 -4.49 1.1% 11.04 11.00 11.13 11.02 9.62 7.04 Gfap 1.82 3.54 0.047 0.44 60.9% 9.37 9.61 8.87 7.97 7.61 6.80 Thbs4 1.72 3.28 0.303 -4.08 1.7% 8.48 10.66 7.95 8.18 7.31 6.45 Olfml1 1.70 3.25 0.183 -2.47 7.8% 9.87 9.94 9.93 9.32 7.06 8.26 Ebf2 1.66 3.16 0.186 -2.52 7.4% 6.60 8.25 6.15 5.27 5.51 5.24 Baalc 1.63 3.09 0.029 1.21 77.0% 10.45 10.22 10.24 9.24 8.19 8.59 Dnm3 1.60 3.03 0.094 -0.88 29.3% 10.94 10.78 11.02 10.15 9.17 8.62 Grik3 1.60 3.03 0.041 0.64 65.5% 8.58 8.42 8.36 7.47 6.77 6.33 Psd3 1.59 3.02 0.237 -3.25 3.7% 7.75 7.64 8.95 6.66 7.47 5.43 Ptprn 1.55 2.93 0.020 2.65 93.4% 9.78 10.29 10.16 8.22 8.67 8.70 Prg4 1.54 2.92 0.249 -3.41 3.2% 6.99 7.95 5.48 5.31 5.47 5.00 Cacna1a 1.52 2.86 0.159 -2.00 11.9% 10.28 10.16 9.03 8.94 8.22 7.75 H2afy2 1.51 2.85 0.018 4.35 98.7% 8.93 8.97 8.63 7.48 7.20 7.32 Tmod1 1.50 2.82 0.111 -1.26 22.0% 9.39 9.45 10.29 8.54 8.58 7.53 RGD1308448 1.45 2.74 0.343 -4.49 1.1% 5.70 8.21 5.27 4.95 5.11 4.76 Gda 1.43 2.69 0.165 -2.13 10.7% 9.65 9.89 10.27 8.14 9.45 7.94 Qprt 1.40 2.64 0.055 0.13 53.2% 9.23 9.30 9.84 8.56 7.85 7.74 Shh 1.40 2.63 0.189 -2.58 7.1% 9.74 10.07 9.85 8.90 9.15 7.43 Hs6st1 1.37 2.58 0.099 -1.00 26.9% 11.83 11.95 11.76 10.96 9.72 10.76 Sspn 1.37 2.58 0.051 0.29 57.3% 9.74 9.89 9.98 9.06 8.06 8.38 Trim47l 1.36 2.57 0.018 4.01 98.2% 8.48 8.56 8.53 7.34 7.20 6.94

126

Prtfdc1 1.36 2.57 0.305 -4.12 1.6% 9.80 9.70 9.61 6.81 9.01 9.21 RGD1561817 1.35 2.55 0.133 -1.70 15.5% 9.49 9.58 10.60 8.13 8.47 9.00 Cxcl10 1.35 2.55 0.333 -4.42 1.2% 7.19 9.41 6.85 6.47 6.86 6.06 G6pd 1.34 2.53 0.245 -3.36 3.4% 11.62 11.71 11.64 11.53 11.63 11.11 Adamts2 1.33 2.52 0.049 0.37 59.2% 9.91 10.57 10.29 9.34 8.80 8.62 Scn3b 1.33 2.52 0.024 2.18 89.8% 7.09 7.00 7.32 6.01 5.46 5.94 Olfml2b 1.33 2.52 0.079 -0.57 36.2% 12.02 11.83 11.52 11.06 10.02 10.28 Sv2c 1.33 2.51 0.029 1.28 78.2% 10.39 10.48 10.98 9.43 8.98 9.45 Cadm3 1.33 2.51 0.252 -3.47 3.0% 12.10 12.15 12.12 11.97 10.63 9.80 Sema4f 1.31 2.49 0.199 -2.73 6.1% 8.05 8.06 8.86 7.85 6.90 6.29 Cd200 1.31 2.48 0.129 -1.63 16.4% 9.25 9.19 8.79 8.47 7.70 7.12 Diras2 1.31 2.48 0.235 -3.23 3.8% 9.86 8.92 10.55 8.36 7.92 9.12 Cyp3a9 1.30 2.46 0.205 -2.81 5.7% 8.72 8.67 9.17 8.36 6.62 7.68 Plagl1 1.29 2.44 0.461 -5.27 0.5% 8.57 11.16 7.64 7.48 8.50 7.54 Ugt1a5 1.27 2.42 0.185 -2.50 7.6% 10.01 9.50 9.26 8.90 8.57 7.48 Nkd2 1.26 2.40 0.207 -2.86 5.4% 9.95 10.43 10.73 9.73 9.40 8.19 Pfkfb4 1.25 2.37 0.108 -1.21 23.0% 10.11 10.33 11.21 9.55 9.26 9.10 Mmp17 1.21 2.32 0.106 -1.18 23.5% 8.80 9.15 9.67 8.47 7.79 7.71 Axl 1.19 2.29 0.111 -1.25 22.3% 10.92 11.11 11.50 10.58 9.78 9.59 Btbd16 1.19 2.28 0.024 2.29 90.8% 7.66 7.95 8.16 6.87 6.66 6.68 RGD1564387 1.18 2.27 0.108 -1.22 22.9% 12.34 12.14 12.34 11.75 10.67 10.84 Nupr1 1.18 2.27 0.270 -3.72 2.4% 8.28 9.10 9.43 6.78 8.27 8.22 B3gnt5 1.18 2.26 0.169 -2.22 9.8% 8.16 8.19 7.42 7.13 7.02 6.08 RGD1565687 1.18 2.26 0.157 -1.96 12.4% 8.89 8.47 7.68 7.47 7.17 6.86 RGD1306866 1.17 2.25 0.026 1.76 85.3% 9.44 9.26 9.18 7.88 8.05 8.43 Slitrk5 1.16 2.23 0.117 -1.42 19.5% 8.16 7.91 7.13 6.90 6.44 6.39 Pltp 1.16 2.23 0.335 -4.44 1.2% 8.60 9.99 7.56 7.58 7.80 7.30 RGD1565772 1.15 2.22 0.039 0.69 66.7% 9.85 9.51 9.97 8.67 8.91 8.30 Hspb1 1.15 2.22 0.283 -3.87 2.0% 8.78 9.07 8.89 7.96 8.68 6.64 Lrrc17 1.15 2.21 0.185 -2.51 7.5% 7.14 8.41 7.56 6.71 6.13 6.84 Fam38b 1.13 2.20 0.053 0.23 55.8% 9.88 9.72 9.80 8.20 8.83 8.97 Prr5l 1.13 2.19 0.180 -2.39 8.4% 7.65 7.67 8.11 7.11 7.02 5.91 Lims2 1.12 2.18 0.208 -2.87 5.4% 9.55 9.48 9.08 9.07 7.59 8.08 Rftn2 1.11 2.16 0.069 -0.32 42.0% 8.64 8.46 9.24 7.89 7.65 7.47 Arhgap20 1.11 2.15 0.221 -3.05 4.5% 9.11 8.74 9.01 6.94 8.36 8.25 Hspa2 1.10 2.14 0.062 -0.12 46.9% 10.62 10.89 10.35 9.91 9.26 9.40 Col6a3 1.10 2.14 0.274 -3.77 2.3% 9.44 9.72 8.46 7.38 8.77 8.20 Cthrc1 1.09 2.13 0.123 -1.54 17.7% 8.95 9.44 9.17 7.99 8.66 7.63 Thbs2 1.09 2.13 0.107 -1.19 23.3% 12.88 13.01 12.83 12.41 11.61 11.41

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Ttc7 1.09 2.13 0.031 1.08 74.6% 9.48 9.52 9.45 8.77 8.24 8.17 Angptl2 1.09 2.13 0.181 -2.39 8.4% 12.61 12.49 13.08 12.32 11.12 11.47 Dach2 1.09 2.13 0.320 -4.30 1.3% 5.22 5.22 5.19 4.98 5.14 4.75 Cdh1 1.09 2.13 0.308 -4.15 1.5% 9.97 9.84 9.78 9.92 8.58 7.82 Itga4 1.07 2.10 0.091 -0.84 30.1% 10.16 10.44 9.98 9.61 8.83 8.92 Arhgap4 1.07 2.10 0.066 -0.23 44.3% 6.74 6.89 6.57 6.76 6.64 6.86 Cd97 1.07 2.10 0.120 -1.47 18.7% 10.89 10.96 11.47 9.73 9.84 10.56 Fxyd1 1.06 2.08 0.159 -2.01 11.8% 12.52 12.25 12.71 11.93 11.56 10.81 Stac2 1.06 2.08 0.024 2.14 89.5% 8.73 8.51 8.86 7.73 7.48 7.73 Dclk2 1.05 2.08 0.183 -2.47 7.8% 9.45 9.72 10.22 9.24 8.15 8.84 Abcg3l2 1.05 2.07 0.057 0.02 50.6% 10.29 10.39 9.95 9.26 9.42 8.81 Gjc3 1.05 2.07 0.230 -3.14 4.1% 11.94 11.45 11.66 11.44 9.96 10.51 Pcdh1 1.05 2.06 0.114 -1.31 21.2% 11.35 11.34 11.32 10.89 9.99 9.99 Sema3e 1.04 2.06 0.317 -4.27 1.4% 7.89 8.11 9.52 7.38 8.09 6.92 Tuba4a 1.04 2.06 0.254 -3.52 2.9% 9.26 10.02 9.93 9.12 9.13 7.83 Lpar3 1.04 2.05 0.075 -0.46 38.8% 9.35 9.29 9.41 8.71 8.37 7.86 Sox6 1.04 2.05 0.276 -3.80 2.2% 8.13 8.05 8.99 7.90 7.67 6.49 Gpr83 1.03 2.04 0.160 -2.04 11.5% 7.69 8.38 7.28 7.01 6.63 6.61 Matn2 1.03 2.04 0.269 -3.69 2.4% 11.41 11.35 11.95 11.27 9.65 10.69 RGD1562618 1.03 2.04 0.256 -3.56 2.8% 10.67 10.89 11.57 10.79 9.38 9.87 RGD1565430 1.02 2.03 0.582 -5.81 0.3% 5.27 5.27 5.27 5.12 5.05 5.05 Afaf 1.02 2.02 0.218 -2.99 4.8% 6.98 6.59 6.47 5.11 5.46 6.43 RGD1565844 1.01 2.02 0.521 -5.56 0.4% 6.56 6.35 6.34 6.16 6.28 6.14 Clca4l 1.01 2.02 0.387 -4.83 0.8% 10.94 11.50 10.74 10.83 10.62 8.70 Olfml2a 1.01 2.01 0.239 -3.27 3.7% 13.02 13.14 12.23 12.36 11.17 11.83 LOC691900 1.01 2.01 0.155 -1.94 12.5% 10.14 9.78 9.86 9.34 8.31 9.11 Depdc2 1.01 2.01 0.185 -2.49 7.6% 10.04 9.88 10.17 9.72 8.50 8.85 Atp1b2 1.00 2.00 0.244 -3.35 3.4% 10.25 10.08 9.24 9.50 8.42 8.64 Txnip 1.00 2.00 0.320 -4.30 1.3% 11.39 11.65 12.03 11.75 9.84 10.47 Ano4 1.00 2.00 0.080 -0.62 35.0% 9.48 9.40 9.62 8.49 8.91 8.09 Pragmin 0.98 1.97 0.051 0.32 57.9% 8.35 8.77 8.78 7.92 7.55 7.50 RGD1562037 0.98 1.97 0.029 1.25 77.7% 10.01 10.25 9.99 9.30 8.87 9.16 Hcn4 0.95 1.93 0.028 1.52 82.1% 7.62 7.85 8.03 6.99 6.79 6.88 Plekhg1 0.94 1.92 0.048 0.42 60.3% 10.16 9.86 10.44 9.29 9.09 9.24 RGD1309285 0.93 1.91 0.026 1.76 85.3% 11.37 11.42 11.59 10.52 10.37 10.69 Cd24 0.92 1.89 0.020 2.79 94.2% 8.82 8.81 8.90 7.84 8.02 7.92 Alk 0.90 1.86 0.038 0.75 67.9% 9.89 9.79 9.59 8.96 8.62 8.99 Adfp 0.85 1.80 0.029 1.35 79.4% 9.57 9.57 9.67 8.70 8.61 8.95 Sdc4 0.83 1.78 0.028 1.53 82.2% 11.76 11.52 11.58 10.84 10.68 10.85

128

Bpgm 0.80 1.74 0.026 1.82 86.0% 10.59 10.63 10.77 9.94 9.84 9.81 Setbp1 0.79 1.73 0.029 1.29 78.4% 8.24 8.21 8.31 7.59 7.50 7.31 Prelid2 0.78 1.72 0.055 0.13 53.4% 8.86 8.61 8.74 8.11 7.72 8.03 Scn1b 0.77 1.71 0.044 0.53 63.0% 9.71 9.77 9.98 9.01 9.21 8.92 Zfp862 0.77 1.70 0.032 1.02 73.4% 8.15 8.19 8.29 7.61 7.33 7.39 Ric8b 0.76 1.70 0.056 0.08 51.9% 10.00 10.06 9.93 9.32 8.98 9.41 Usp54 0.75 1.68 0.026 1.89 86.9% 10.16 10.18 10.24 9.46 9.47 9.40 Dpf3 0.71 1.64 0.032 1.03 73.7% 7.34 7.13 7.27 6.59 6.49 6.53 N5 0.69 1.61 0.047 0.45 61.1% 8.64 8.36 8.41 7.77 7.78 7.79 Map1b 0.68 1.60 0.056 0.08 51.9% 11.44 11.57 11.64 10.88 11.00 10.71 Plcd1 0.62 1.53 0.056 0.08 52.0% 12.08 11.97 12.11 11.40 11.36 11.54