DEGs between SKPs and SFBs.

<p>(A) The numbers of DEGs.(B) Scattered plot of DEGs.</p

Comparison of the Transcriptomes of Mouse Skin Derived Precursors (SKPs) and SKP-Derived Fibroblasts (SFBs) by RNA-Seq

<div><p>Skin-derived precursors (SKPs) from dermis possess the capacities of self-renewal and multipotency. <i>In vitro</i> and <i>in vivo</i> studies demonstrated that they can differentiate into fibroblasts. However, little is known about the molecular mechanism of the differentiation of SKPs into fibroblasts. Here we compare the transcriptomes of mouse SKPs and SKP-derived fibroblasts (SFBs) by RNA-Seq analysis, trying to find differences in gene expression between the two kinds of cells and then elucidate the candidate genes that may play important roles in the differentiation of SKPs into fibroblasts. A total of 1971 differentially expressed genes (DEGs) were identified by RNA-Seq, which provided abundant data for further analysis. Gene Ontology enrichment analysis revealed that genes related to cell differentiation, cell proliferation, protein binding, transporter activity and membrane were significantly enriched. The most significantly up-regulated genes <i>Wnt4</i>, <i>Wisp2</i> and <i>Tsp-1</i> and down-regulated genes <i>Slitrk1</i>, <i>Klk6</i>, <i>Agtr2</i>, <i>Ivl</i>, <i>Msx1</i>, <i>IL15</i>, <i>Atp6v0d2</i>, <i>Kcne1l</i> and <i>Thbs4</i> may play important roles in the differentiation of SKPs into fibroblasts. KEGG analysis showed that DEGs were significantly enriched in the TGF-β signaling pathway, Wnt signaling pathway and Notch signaling pathway, which have been previously proven to regulate the differentiation and self-renewal of various stem cells. These identified DEGs and pathways could facilitate further investigations of the detailed molecular mechanisms, making it possible to take advantage of the potential therapeutic applications of SKPs in skin regeneration in the future.</p></div

Classification of total raw reads.

<p>After filtering the only adaptor sequences, containing N sequences and low quality sequences, the RNA-Seq libraries of SKPs and SFBs generated over 19 million clean reads each, and the percentage of clean reads among raw tags in each library ranged from 95.95% to 98.38%.</p

Morphology of SKPs and fibroblasts.

<p>(A) SKPs exhibited sphere-like structure in suspension culture.(B) PFBs were typically stellate or spindle shaped.(C) SFBs had the same morphology as PFBs.</p

GO functional classification(WEGO) of DEGs.

<p>The results were summarized in three main domains: biological process, cellular component and molecular function. In the three main domains, 28, 10 and 13 functional groups were identified respectively.</p

Correlation between RNA-Seq and qRT-PCR data of selected genes.

<p>Pearson correlation coefficient(r = 0.960) was used to determine the similarity in gene expression pattern between RNA-Seq and qRT-PCR.</p

Summary of mapping results(mapping to reference genes).

<p>Summary of mapping results(mapping to reference genes).</p

Validation of RNA-Seq results and comparison of gene expression between SFBs and PFBs by qRT-PCR.

<p>(A) Down-regulated genes.(B) Up-regulated genes. Fold changes shown are((SFBs or PFBs gene expression level)/(SKPs gene expression level)). Error bars represent SE; * represents statistically significant.</p

Software and Databases.

<p>Software and Databases.</p

Characterization of SKPs and SFBs by immunocytochemistry.

<p>SKP spheres expressed SOX2(A), nestin(D), vimentin(G) and fibronectin(J), did not express collagen 1(M); SFBs expressed vimentin(I), fibronectin(L) and collagen 1(O), did not express SOX2(C) or nestin(F). The same results were observed with PFBs(B, E, H, K and N). Scale bars, 25 μm(A-C, J-L); 50 μm(D-I, M-O).</p