SOX17 Regulates Conversion of Human Fibroblasts Into Endothelial Cells and Erythroblasts by Dedifferentiation Into CD34+ Progenitor Cells

BACKGROUND: The mechanisms underlying the dedifferentiation and lineage conversion of adult human fibroblasts into functional endothelial cells have not yet been fully defined. Furthermore, it is not known whether fibroblast dedifferentiation recapitulates the generation of multipotent progenitors during embryonic development, which give rise to endothelial and hematopoietic cell lineages. Here we established the role of the developmental transcription factor SOX17 in regulating the bilineage conversion of fibroblasts by the generation of intermediate progenitors. METHODS: CD34+ progenitors were generated after the dedifferentiation of human adult dermal fibroblasts by overexpression of pluripotency transcription factors. Sorted CD34+ cells were transdifferentiated into induced endothelial cells and induced erythroblasts using lineage-specific growth factors. The therapeutic potential of the generated cells was assessed in an experimental model of myocardial infarction. RESULTS: Induced endothelial cells expressed specific endothelial cell surface markers and also exhibited the capacity for cell proliferation and neovascularization. Induced erythroblasts expressed erythroid surface markers and formed erythroid colonies. Endothelial lineage conversion was dependent on the upregulation of the developmental transcription factor SOX17, whereas suppression of SOX17 instead directed the cells toward an erythroid fate. Implantation of these human bipotential CD34+ progenitors into nonobese diabetic/severe combined immunodeficiency (NOD-SCID) mice resulted in the formation of microvessels derived from human fibroblasts perfused with mouse and human erythrocytes. Endothelial cells generated from human fibroblasts also showed upregulation of telomerase. Cell implantation markedly improved vascularity and cardiac function after myocardial infarction without any evidence of teratoma formation. CONCLUSIONS: Dedifferentiation of fibroblasts to intermediate CD34+ progenitors gives rise to endothelial cells and erythroblasts in a SOX17-dependent manner. These findings identify the intermediate CD34+ progenitor state as a critical bifurcation point, which can be tuned to generate functional blood vessels or erythrocytes and salvage ischemic tissue

Computational Investigation of Endothelial Heterogeneity During Homeostasis and Inflammation

Endothelial cells (ECs) are a specialized cell type lining all vertebrate blood vessels and form an interface between circulating blood and the neighboring tissue. Understanding the tissue-specific characteristics of endothelial cells could markedly improve our understanding of the organ-specific roles of blood vessel function and the development of vascular disease. Computational approaches statistically modeling the gene expression data provided by high throughput sequencing technologies have been developed to analyze the cellular transcriptome. In our work, we present novel computational methods, namely HeteroPath and Subnetwork Signaling Entropy Analysis (SSEA), to analyze gene expression data and ascertain organ-specific endothelial heterogeneity during homeostasis and inflammation. We hypothesized that characterizing cells from distinct tissues based on the heterogeneity of the molecular signaling would allow for the precise identification of clusters of genes which are uniquely upregulated or uniquely downregulated in each tissue. Using HeteroPath alongside traditional gene set enrichment analysis methods, we demonstrated endothelial transcriptomic heterogeneity. HeteroPath specifically identified organ-specific signaling pathways and provided a comprehensive characterization of EC heterogeneity in the healthy state. We next adopted the RiboTag mRNA isolation technique to directly isolate tissue-specific mRNAs undergoing translation without cell disassociation to understand the nature of the endothelial translatome in vivo. By performing RNA-Sequencing and computationally analyzing the endothelial translatome, we identified specific pathways, transporters, and cell-surface markers expressed in an organ-specific manner. In addition, we found that ECs adopt the characteristics of the tissue by expressing genes typically expressed in the surrounding tissue such as genes associated with synaptic function in the brain endothelium and cardiac contractile genes in the heart endothelium. Once we established the organ-specific endothelial signature during homeostasis, we studied whether this heterogeneity persisted in response to a biological stimulus that induced systemic inflammation. Using differential expression approaches and our novel framework, SSEA, we quantified the organ-specific endothelial gene expression dynamics and found that the progression and resolution of endothelial injury during vascular inflammation in each organ is mediated by distinct endothelial signaling mechanisms. Using these methods and tools, we characterized organ-specific endothelial heterogeneity during homeostasis and inflammation and provided insights regarding the underlying endothelial biology and potential therapeutic targets

A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks

Abstract Background The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues. Results Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database. Conclusions HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes

Additional file 1: of A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks

Figure S1. A) Hierarchical clustering of Endothelial cells from 7 mouse organs Intra- and inter-tissue heterogeneity. Tree plot generated via hierarchical clustering of 500 most variable genes across all distinct tissue endothelial cell samples B) Hierarchical clustering of Neuronal cells from 5 different regions of the mouse forebrain Intra- and inter-tissue heterogeneity. Tree plot generated via hierarchical clustering of 500 most variable genes across all distinct tissue neuronal cell samples. Figure S2. Comparison of statistical power and type-I error rate between HeteroPath, GSEA, and PGSEA for DE Gene Set size of 50 genes. The averaged results of 500 simulations are depicted as function of the sample size on the x-axis, for each of the methods. On the y-axis either the statistical power or the empirical type-I error rate is shown. GSE scores were calculated with each method with respect to two gene sets, one of them differentially expressed (DE) and the other one not. Statistical power and empirical type-I error rates were estimated by performing an ANOVA on the DE and non-DE gene sets, respectively, at a significance level of α = 0.05. Figure S3. Comparison of statistical power and type-I error rate between HeteroPath, GSEA, and PGSEA for DE Gene Set size of 150 genes. The averaged results of 500 simulations are depicted as function of the sample size on the x-axis, for each of the methods. On the y-axis either the statistical power or the empirical type-I error rate is shown. GSE scores were calculated with each method with respect to two gene sets, one of them differentially expressed (DE) and the other one not. Statistical power and empirical type-I error rates were estimated by performing an ANOVA on the DE and non-DE gene sets, respectively, at a significance level of α = 0.05. Figure S4. A) Enriched Wnt Signaling Motifs from Brain endothelial cells The table shows the five most enriched motifs in ChIP-seq peaks and the associated transcription factors. Significance values and significant p-values (p ≤ 0.05) are shown. B) Enriched Oxidative Phosphorylation Motifs from Hippocampal Neurons The table shows the five most enriched motifs in ChIP-seq peaks and the associated transcription factors. Significance values and significant p-values (p ≤ 0.05) are shown. (PPTX 1265 kb