Functional inference of gene regulation using single-cell multi-omics

Kartha, Vinay K.; Duarte, Fabiana M.; Hu, Yan; Ma, Sai; Chew, Jennifer; Lareau, Caleb A.; Earl, Andrew; Burkett, Zachary Daniel; Kohlway, Andrew; Lebofsky, Ronald; Buenrostro, Jason D.

doi:10.1016/j.xgen.2022.100166

Cited by 124 publications

(134 citation statements)

References 88 publications

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“…Single-cell multimodal technologies have huge potential for the study of gene regulation (Chen et al, 2019; Clark et al, 2018; Luo et al, 2022; Ma et al, 2020; Zhu et al, 2019, 2021). In particular, the ability to link epigenomic with transcriptomic changes allows the inference of gene regulatory networks (GRNs)(Aibar et al, 2017; Davidson and Erwin, 2006; Kamimoto et al, 2020; Kartha et al, 2021; Materna and Davidson, 2007). GRNs are able to capture the interplay between TFs, cis-regulatory DNA sequences and the expression of target genes (Garcia-Alonso et al, 2019; Levine and Davidson, 2005; Stadhouders et al, 2018), and can hold predictive power of cell fate transitions and gene perturbations (Kamimoto et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…GRNs are able to capture the interplay between TFs, cis-regulatory DNA sequences and the expression of target genes (Garcia-Alonso et al, 2019; Levine and Davidson, 2005; Stadhouders et al, 2018), and can hold predictive power of cell fate transitions and gene perturbations (Kamimoto et al, 2020). Methods that derive GRNs from single-cell genomics data have been developed (Aibar et al, 2017; Fleck et al, 2021; Kamimoto et al, 2020; Kartha et al, 2021) and applied to the developing fly brain (Janssens et al, 2022) but similar analyses of mammalian development are lacking. In addition, GRN inference relies on accurate TF binding data, yet limited knowledge of TF binding exists for early embryonic development due to limitations in experimental methods such as ChIP-seq or CUT&RUN, which require large numbers of cells (Skene and Henikoff, 2017) and faithful antibodies.…”

Section: Introductionmentioning

confidence: 99%

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Decoding gene regulation in the mouse embryo using single-cell multi-omics

Argelaguet

Lohoff

et al. 2022

Preprint

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Following gastrulation, the three primary germ layers develop into the major organs in a process known as organogenesis. Single-cell RNA sequencing has enabled the profiling of the gene expression dynamics of these cell fate decisions, yet a comprehensive map of the interplay between transcription factors and cis-regulatory elements is lacking, as are the underlying gene regulatory networks. Here we generate a multi-omics atlas of mouse early organogenesis by simultaneously profiling gene expression and chromatin accessibility from tens of thousands of single cells. We develop a computational method to leverage the multi-modal readouts to predict transcription factor binding events in cis-regulatory elements, which we then use to infer gene regulatory networks that underpin lineage commitment events. Finally, we show that these models can be used to generate in silico predictions of the effect of transcription factor perturbations. We validate this experimentally by showing that Brachyury is essential for the differentiation of neuromesodermal progenitors to somitic mesoderm fate by priming cis-regulatory elements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decoding gene regulation in the mouse embryo using single-cell multi-omics

Argelaguet

Lohoff

et al. 2022

Preprint

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“…Only genomic regions annotated as dELS, pELS, dELS, CTCF-bound, or pELS, CTCF-bound in the SCREEN database were used for enrichment analysis. The FigR 40 package was used for peak-to-gene linkage analysis. Optimal matching was used to pair RNA and ATAC cells from the same time points followed by the runGenePeakcorr function to identify peak-gene pairs.…”

Section: Methodsmentioning

confidence: 99%

“…8i, j ). Using our paired RNA and ATAC data, we linked accessible peaks to genes and identified 37,058 putative cis-regulatory elements (CREs) 40 ( Fig 4c , Supplementary Table 6, Methods ). Gene-linked peaks were enriched for enhancer-like signatures (ELS) from the ENCODE candidate CRE database 41 ( Methods , Ext Fig.…”

Section: Introductionmentioning

confidence: 99%

Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programs

Jindal

Adil

Yamaguchi

et al. 2022

Preprint

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Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics enabling high-resolution analysis of cell states while preserving lineage relationships. However, reliance on transcriptional profiling limits their adaptation to an ever-expanding tool kit of multiomic single-cell assays. With CellTag-multi, we present a novel approach for profiling lineage barcodes with single-cell chromatin accessibility without relying on co-assay of transcriptional state, paving the way for truly multiomic lineage tracing. We validate CellTag-multi in mouse hematopoiesis, characterizing transcriptional and epigenomic lineage priming across progenitor cell populations. In direct reprogramming of fibroblasts to endoderm progenitors, we use CellTag-multi to comprehensively link early cell state with reprogramming outcomes, identifying core regulatory programs underlying on-target and off-target reprogramming. Further, we reveal the Transcription Factor (TF) Zfp281 as a novel regulator of reprogramming outcome, biasing cells towards an off-target mesenchymal fate via its regulation of TGF-β signaling. Together, these results establish CellTag-multi as a novel lineage tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.

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“…Recent advances in single cell technologies have allowed profiling of gene expression 12–18 and chromatin accessibility 10 , either separately or in parallel from the same samples 19,20 . While these studies have examined the cell type–specific transcriptional and epigenetic differences between tissues from brain donors with AD and unaffected controls, few have rigorously interrogated the regulatory mechanisms responsible for these alterations 11,21 .…”

Section: Introductionmentioning

confidence: 99%

Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis regulatory elements

Anderson

Rogers

Loupe

et al. 2022

Preprint

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Cell type–specific transcriptional differences between brain tissues from donors with Alzheimer′s disease (AD) and unaffected controls have been well–documented, but few studies have rigorously interrogated the regulatory mechanisms responsible for these alterations. We performed single nucleus multiomics (snRNA–seq + snATAC–seq) on 105,332 nuclei isolated from cortical tissues from 7 AD and 8 unaffected donors to identify candidate cis–regulatory elements (CREs) involved in AD–associated transcriptional changes. We detected 319,861 significant correlations, or links, between gene expression and cell-type specific transposase accessible regions enriched for active CREs. Among these, 40,831 were unique to AD tissues. Validation experiments confirmed the activity of many regions, including several candidate regulators of APP expression. We identified ZEB1 and MAFB as candidate transcription factors playing important roles in AD–specific gene regulation in neurons and microglia, respectively. Microglial inks were globally enriched for heritability of AD risk and previously identified active regulatory regions.

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Functional inference of gene regulation using single-cell multi-omics

Cited by 124 publications

References 88 publications

Decoding gene regulation in the mouse embryo using single-cell multi-omics

Decoding gene regulation in the mouse embryo using single-cell multi-omics

Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programs

Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis regulatory elements

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