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Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE195523 | GEO | 2023/08/09

REPOSITORIES: GEO

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Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets

Project description:The functional heterogeneity of β-cells is important for metabolism and diseases, but the nature and mechanism of such variation at molecular level remain elusive. Here we explore this question by comparing both single cell RNA-seq and single cell ATAC-seq maps of healthy and type II diabetic (T2D) human islets. We dissect the T2D-associated single cell trajectory and identified signature genes and enhancers in β-cells. We also map the 3D genome of both α- and β-cells with low-input eHi-C approach to unravel the cell type-specific gene regulatory circuits. Strikingly, more than one third of the T2D signature genes show significant intra-donor heterogeneity at single cell level; these genes are functionally distinct from other signature genes that are largely invariant among cells from the same individual but differentially expressed between donors, suggesting different roles in T2D pathogenesis. Importantly, we identified consistent intra-donor variations at both transcriptomic and epigenomic levels, which strongly support the heterogenous β-cell states and transcription programs. Finally, we construct the disease-signature regulatory networks and pinpointed HNF1A as one of the top transcription factors governing T2D-associated β-cell heterogeneity. Taken together, we provide the first multi-omic characterization of β-cell heterogeneity and reveals its connection to T2D.

2023-08-10 | GSE234754 | GEO

Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets(scRNASeq)

2023-08-09 | GSE195986 | GEO

Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets(scATACSeq)

2023-08-09 | GSE194401 | GEO

Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets (Hi-C)

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Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets

Project description:Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets

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Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets

Project description:Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets

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Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets(scRNASeq)

Project description:Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets(scRNASeq)

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Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets(scATACSeq)

Project description:Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets(scATACSeq)

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Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets (Hi-C)

Project description:Single cell multi-omics analysis deconstructs diabetes-associated β-cell heterogeneity in human islets (Hi-C)

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Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity.

Project description:Integrative analysis of multi-omics layers at single cell level is critical for accurate dissection of cell-to-cell variation within certain cell populations. Here we report scCAT-seq, a technique for simultaneously assaying chromatin accessibility and the transcriptome within the same single cell. We show that the combined single cell signatures enable accurate construction of regulatory relationships between cis-regulatory elements and the target genes at single-cell resolution, providing a new dimension of features that helps direct discovery of regulatory patterns specific to distinct cell identities. Moreover, we generate the first single cell integrated map of chromatin accessibility and transcriptome in early embryos and demonstrate the robustness of scCAT-seq in the precise dissection of master transcription factors in cells of distinct states. The ability to obtain these two layers of omics data will help provide more accurate definitions of "single cell state" and enable the deconvolution of regulatory heterogeneity from complex cell populations.

| S-EPMC6349937 | biostudies-literature

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