☆ 4.3 Article

Chromatin accessibility maps provide evidence of multilineage gene priming in hematopoietic stem cells

EPIGENETICS & CHROMATIN (2021)

Journal

EPIGENETICS & CHROMATIN

Volume 14, Issue 1, Pages -

Publisher

BMC

DOI: 10.1186/s13072-020-00377-1

Keywords

Funding

NIH [R01HL115158, DK100917, R01AG062 879]
NIH/NHLBI [F31HL144115]
CIRM SCILL grant via San Jose State University [TB1-01195]
CIRM Training grant [TG2-01157]
UCSC Genomic Sciences Graduate Training Program from NIH/NHGRI [NIH T32 HG008345]
UCSC IMSD award from NIH/NIGMS [R25GM058903]
Baskin School of Engineering
Ken and Glory Levy Fund for RNA Biology
CIRM Shared Stem Cell Facilities [CL1-00506]
CIRM Major Facilities [FA1-00617-1]

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Abstract

The study found that regulatory elements controlling fate decisions were primed in hematopoietic stem cells, and compared chromatin accessibility of different cell types to provide new insights into the regulation of stem cell multipotency and identify functional drivers of lineage fate.

Hematopoietic stem cells (HSCs) have the capacity to differentiate into vastly different types of mature blood cells. The epigenetic mechanisms regulating the multilineage ability, or multipotency, of HSCs are not well understood. To test the hypothesis that cis-regulatory elements that control fate decisions for all lineages are primed in HSCs, we used ATAC-seq to compare chromatin accessibility of HSCs with five unipotent cell types. We observed the highest similarity in accessibility profiles between megakaryocyte progenitors and HSCs, whereas B cells had the greatest number of regions with de novo gain in accessibility during differentiation. Despite these differences, we identified cis-regulatory elements from all lineages that displayed epigenetic priming in HSCs. These findings provide new insights into the regulation of stem cell multipotency, as well as a resource to identify functional drivers of lineage fate.

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Simultaneous deep generative modelling and clustering of single-cell genomic data

Qiao Liu, Shengquan Chen, Rui Jiang, Wing Hung Wong

Summary: Recent advances in single-cell technologies have enabled large-scale profiling of chromatin accessibility at the single-cell level, but computational analysis faces challenges due to high dimensionality and sparsity of data. Researchers propose a computational tool, scDEC, based on deep generative neural networks for scATAC-seq analysis, showing superior performance in multiple datasets and experimental settings. The generative power of scDEC helps infer cell trajectory and state during differentiation, potentially revealing cell types and within-cell-type variations.

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