Highlights d Identification of fetal-associated endothelial cells and macrophages in HCC d Embryonic-like reprogramming in a subset of FOLR2 + TAM1s d Conserved GRN in mouse embryonically seeded, human fetal-liver, and TAM1 macrophages d Shared onco-fetal ecosystem between human fetal liver and HCC
Recent advances in single-cell open-chromatin and transcriptome profiling have created a challenge of exploring novel applications with a meaningful transformation of read-counts, which often have high variability in noise and drop-out among cells. Here, we introduce UniPath, for representing single-cells using pathway and gene-set enrichment scores by a transformation of their open-chromatin or gene-expression profiles. The robust statistical approach of UniPath provides high accuracy, consistency and scalability in estimating gene-set enrichment scores for every cell. Its framework provides an easy solution for handling variability in drop-out rate, which can sometimes create artefact due to systematic patterns. UniPath provides an alternative approach of dimension reduction of single-cell open-chromatin profiles. UniPath's approach of predicting temporal-order of single-cells using their pathway enrichment scores enables suppression of covariates to achieve correct order of cells. Analysis of mouse cell atlas using our approach yielded surprising, albeit biologically-meaningful co-clustering of cell-types from distant organs. By enabling an unconventional method of exploiting pathway co-occurrence to compare two groups of cells, our approach also proves to be useful in inferring context-specific regulations in cancer cells. Available at https://reggenlab.github.io/UniPathWeb/.
Single-cell RNA-seq (scRNA-seq) of pancreatic islets have reported on α- and β-cell gene expression in mice and subjects of predominantly European ancestry. We aimed to assess these findings in East-Asian islet-cells. 448 islet-cells were captured from three East-Asian non-diabetic subjects for scRNA-seq. Hierarchical clustering using pancreatic cell lineage genes was used to assign cells into cell-types. Differentially expressed transcripts between α- and β-cells were detected using ANOVA and in silico replications of mouse and human islet cell genes were performed. We identified 118 α, 105 β, 6 δ endocrine cells and 47 exocrine cells. Besides INS and GCG, 26 genes showed differential expression between α- and β-cells. 10 genes showed concordant expression as reported in rodents, while FAM46A was significantly discordant. Comparing our East-Asian data with data from primarily European subjects, we replicated several genes implicated in nuclear receptor activations, acute phase response pathway, glutaryl-CoA/tryptophan degradations and EIF2/AMPK/mTOR signaling. Additionally, we identified protein ubiquitination to be associated among East-Asian β-cells. We report on East-Asian α- and β-cell gene signatures and substantiate several genes/pathways. We identify expression signatures in East-Asian β-cells that perhaps reflects increased susceptibility to cell-death and warrants future validations to fully appreciate their role in East-Asian diabetes pathogenesis.
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