Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation

Bonora, Giancarlo; Ramani, Vijay; Singh, Ritambhara; He, Fang; Jackson, Dana L.; Srivatsan, Sanjay; Qiu, Ruolan; Lee, Choli; Trapnell, Cole; Shendure, Jay; Duan, Zhijun; Deng, Xinxian; Noble, William Stafford

doi:10.1101/2020.11.20.390765

Cited by 2 publications

(2 citation statements)

References 91 publications

(163 reference statements)

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“…In parallel, lost interactions between D1 and D2 create two additional separated inactive domains. Collectively, these alterations reproducibly restructure the TAD in cell types where Fat1 and Rex1R genes are both simultaneously active, such as 8-cell mouse embryos and human ESCs (Figure S4B-D) (Bonora et al, 2020;Zhang et al, 2019). Thus, though evolutionarily stable, the ancient TAD has a flexible structure in pluripotent placental mammal cells that physically restricts Fat1 and Rex1R with separate local enhancers.…”

Section: Tad Restructuring In Escs Drives Fat1 and Rex1 To Independently Utilise Local Enhancersmentioning

confidence: 99%

Promoter repression and 3D-restructuring resolves divergent developmental gene expression in TADs

Ringel

Szabo

Chiariello

et al. 2021

Preprint

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Cohesin loop extrusion facilitates precise gene expression by continuously driving promoters to sample all enhancers located within the same topologically-associated domain (TAD). However, many TADs contain multiple genes with divergent expression patterns, thereby indicating additional forces further refine how enhancer activities are utilised. Here, we unravel the mechanisms enabling a new gene, Rex1, to emerge with divergent expression within the ancient Fat1 TAD in placental mammals. We show that such divergent expression is not determined by a strict enhancer-promoter compatibility code, intra-TAD position or nuclear envelope-attachment. Instead, TAD-restructuring in embryonic stem cells (ESCs) separates Rex1 and Fat1 with distinct proximal enhancers that independently drive their expression. By contrast, in later embryonic tissues, DNA methylation renders the inactive Rex1 promoter profoundly unresponsive to Fat1 enhancers within the intact TAD. Combined, these features adapted an ancient regulatory landscape during evolution to support two entirely independent Rex1 and Fat1 expression programs. Thus, rather than operating only as rigid blocks of co-regulated genes, TAD-regulatory landscapes can orchestrate complex divergent expression patterns in evolution.

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Section: Tad Restructuring In Escs Drives Fat1 and Rex1 To Independently Utilise Local Enhancersmentioning

confidence: 99%

Promoter repression and 3D-restructuring resolves divergent developmental gene expression in TADs

Ringel

Szabo

Chiariello

et al. 2021

Preprint

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“…We also align non-co-assay datasets, containing separately sequenced single-cell -omic measurements. Bonora et al generated the first dataset we use, "sciOmics" [15]. This dataset consists of sciRNA-seq, sciATAC-seq, and sciHiC measurements, capturing gene expression, chromatin accessibility, and 3D chromosomal conformation profiles of mouse embryonic stem cells undergoing differentiation.…”

Section: Single-cell Datasets Without 1-1 Correspondencesmentioning

confidence: 99%

Unsupervised integration of single-cell multi-omics datasets with disparities in cell-type representation

Demetçi

Santorella

Sandstede

et al. 2021

Preprint

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Integrated analysis of multi-omics data allows the study of how different molecular views in the genome interact to regulate cellular processes; however, with a few exceptions, applying multiple sequencing assays on the same single cell is not possible. While recent unsupervised algorithms align single-cell multi-omic datasets, these methods have been primarily benchmarked on co-assay experiments rather than the more common single-cell experiments taken from separately sampled cell populations. Therefore, most existing methods perform subpar alignments on such datasets. Here, we improve our previous work Single Cell alignment using Optimal Transport (SCOT) by using unbalanced optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. We show that our proposed method, SCOTv2, consistently yields quality alignments on five real-world single-cell datasets with varying cell-type proportions and is computationally tractable. Additionally, we extend SCOTv2 to integrate multiple (M ≥ 2) single-cell measurements and present a self-tuning heuristic process to select hyperparameters in the absence of any orthogonal correspondence information.Available at:http://rsinghlab.github.io/SCOT.

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Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation

Cited by 2 publications

References 91 publications

Promoter repression and 3D-restructuring resolves divergent developmental gene expression in TADs

Promoter repression and 3D-restructuring resolves divergent developmental gene expression in TADs

Unsupervised integration of single-cell multi-omics datasets with disparities in cell-type representation

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