bioRxivCell Biology
Multidimensional Single-Nuclei RNA-Seq Reconstruction of Adipose Tissue Reveals Adipocyte Plasticity Underlying Thermogenic Response
C. A. Biagi, S. S. Cury, C. P. Alves, N. Rabhi, W. A. Silva, S. R. Farmer, R. F. Carvalho, M. L. Batista
Abstract
Adipose tissue has been classified based on its morphology and function as white, brown, or beige / brite. It plays an essential role as a regulator of systemic metabolism through paracrine and endocrine signals. Recently, multiple adipocyte subtypes have been revealed using RNA sequencing technology, going beyond simply defined morphology but by their cellular origin, adaptation to metabolic stress, and plasticity. Here, we performed an in-depth analysis of publicly available single-nuclei RNAseq from adipose tissue and utilized a workflow template to characterize adipocyte plasticity, heterogeneity, and secretome profiles. The reanalyzed dataset led to the identification of different subtypes of adipocytes including three subpopulations of thermogenic adipocytes and provided a characterization of distinct transcriptional profiles along the adipocyte trajectory under thermogenic challenges. This study provides a useful resource for further investigations regarding mechanisms related to adipocyte plasticity and trans-differentiation. HighlightsMultidimensional transcriptome analysis at single-nucleus resolution recovers nuclei of cell types in adipose tissue Adaptative thermogenic response results in 3 distinct mature adipose cell types Single-nuclei transcriptomic-based secretome analysis reveals adipose cell-type-specific genes The in vivo trajectory of adipocyte plasticity for thermogenic response reveals sets of trans-differentiation genes Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/431320v2_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@291767org.highwire.dtl.DTLVardef@1bfa3b1org.highwire.dtl.DTLVardef@93e051org.highwire.dtl.DTLVardef@6bef5e_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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