BMC BiologyBioinformaticsPeer-reviewed journal article
Systematic single-cell analysis reveals dynamic control of transposable element activity orchestrating the endothelial-to-hematopoietic transition
Cong FengiD, Ruxiu Tie, Saige Xin, Yuhao CheniD, Sida LiiD, Yifan CheniD, Xiaotian HuiD, Yincong ZhouiD, Yongjing LiuiD, Yueming HuiD, Yanshi HuiD, Hang PaniD, Zexu Wu, Haoyu ChaoiD, Shilong ZhangiD, Qingyang NiiD, Jinyan HuangiD, Wenda LuoiD, He HuangiD, Ming CheniD
Abstract
BackgroundThe endothelial-to-hematopoietic transition (EHT) process during definitive hematopoiesis in vertebrate is highly conserved. Stage-specific expression of transposable elements (TEs) has been detected during zebrafish EHT and may promote hematopoietic stem cell formation by activating inflammatory signaling. However, little is known about how TEs contribute to the EHT process in human and mouse. ResultsWe reconstructed the single-cell EHT trajectories of human and mouse, and resolved the dynamic expression patterns of TEs during EHT. Most TEs presented a transient co-upregulation pattern along the conserved EHT trajectories. Enhanced TE activation was tightly associated with the temporal relaxation of epigenetic silencing systems. TE products can be sensed by multiple pattern recognition receptors, triggering inflammatory signaling to facilitate the emergence of hematopoietic stem cells. Furthermore, we observed that hypoxia-related signals were enriched in cells with higher TE expression. Additionally, we constructed the hematopoietic cis-regulatory network of accessible TEs and identified potential enhancers derived by TEs, which may boost the expression of specific EHT marker genes. ConclusionsOur study provides a systematic vision on how TEs are dynamically controlled to promote the hematopoietic fate decision through transcriptional and cis-regulatory networks, and pre-train the immunity of nascent hematopoietic stem cells.
- organism: human
- organism: mouse
- assay: scRNA-seq
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 7
- FWCI
- 2.16
- Field percentile
- top 12%
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