bioRxivDevelopmental BiologyPreprint
Combined analysis of single cell RNA-Seq and ATAC-Seq data reveals putative regulatory toggles operating in native and iPS-derived retina.
Anouk Georges, Arnaud LavergneiD, Michiko MandaiiD, Fanny LepiemmeiD, Latifa Karim, Loic Demeulenaere, Diego Aguilar, Michael Schyns, Sébastien Dupont, Laurent NguyeniD, Jean-Marie Rakic, Masayo TakahashiiD, Michel GeorgesiD, Haruko TakedaiD
Abstract
We report the generation and analysis of single-cell RNA-Seq data (> 38,000 cells) from native and iPSC-derived murine retina at four matched developmental stages spanning the emergence of the major retinal cell types. We combine information from temporal sampling, visualization of 3D UMAP manifolds, pseudo-time and RNA velocity analyses, to show that iPSC-derived 3D retinal aggregates broadly recapitulate the native developmental trajectories. However, we show relaxation of spatial and temporal transcriptome control, premature emergence and dominance of photoreceptor precursor cells, and susceptibility of dynamically regulated pathways and transcription factors to culture conditions in iPSC-derived retina. We generate bulk ATAC-Seq data for native and iPSC-derived murine retina identifying [~]125,000 peaks. We combine single-cell RNA-Seq with ATAC-Seq information and obtain evidence that approximately half the transcription factors that are dynamically regulated during retinal development may act as repressors rather than activators. We propose that sets of activators and repressors with cell-type specific expression constitute "regulatory toggles" that lock cells in distinct transcriptome states underlying differentiation. We provide evidence supporting our hypothesis from the analysis of publicly available single-cell ATAC-Seq data for adult mouse retina. We identify subtle but noteworthy differences in the operation of such toggles between native and iPSC-derived retina particularly for the Etv1, Etv5, Hes1 and Zbtb7a group of transcription factors.
- organism: mouse
- assay: scRNA-seq
- assay: scATAC-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1
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