Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions

Xiong, Haiqing; Luo, Yingjie; Wang, Qianhao; Yu, Xianhong; He, Aibin

doi:10.1038/s41592-021-01129-z

Cited by 68 publications

(57 citation statements)

References 79 publications

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“…Our integrated analysis strategy represents an analytical alternative to technological solutions that aim to simultaneously profile multiple modalities in single cells. Despite substantial recent progress 22,23 , it is not currently feasible to simultaneously profile the genome-wide binding patterns of six different histone modifications in the same cell, particularly when there is a partial overlap in their localization. Similarly, future extensions of CUT&Tag enabling simultaneous measurements of RNA and protein levels are likely to result in decreased per-modality data quality, as has been previously described 28,29 .…”

Section: Discussionmentioning

confidence: 99%

“…We emphasize that cell surface protein levels represent one of multiple potential modalities that can be utilized for ‘mega-omics’ integration. For example, two recent pioneering approaches (Paired-Tag 22 , and CoTECH 23 ) introduced combinatorial indexing-based approaches for simultaneous scCUT&Tag and scRNA-seq in single cells, enabling the integration of additional multi-omic technologies (i.e. Paired-Seq) via transcriptomic measurements.…”

Section: Discussionmentioning

confidence: 99%

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Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

Zhang

Srivastava

Mimitou

et al. 2021

Preprint

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New technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization. However, the sparsity of these measurements and the challenge of integrating multiple binding maps represent significant challenges. Here we introduce scCUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce scChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states, and identify extensive and cell type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

Zhang

Srivastava

Mimitou

et al. 2021

Preprint

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“…This technique was used to reveal tissue lineages at the molecular level in early mouse embryos (Peng et al, 2019). Single-cell sequencing is also able to generate high-resolution epigenomic data, such as the DNA methylome, and to reveal chromatin occupancy, resolving high-dimension chromatin structures Xiong et al, 2021).…”

Section: Technical Advancesmentioning

confidence: 99%

The rise of developmental biology in China

et al. 2021

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The developmental biology community is growing rapidly in China.Here, we will discuss the past, present and future of developmental biology in China to gain an overview. Brief information about the related societies and current government funding support will be given. Research spotlights and several technical advances during the last decade or so will be presented. Lastly, the current trends in developmental biology studies in China will be discussed.

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“…Here we introduce 'bridge integration', which performs integration of single-cell datasets measuring different modalities by leveraging a separate dataset where both modalities are simultaneously measured as a molecular 'bridge'. The multi-omic bridge dataset, which can be generated by a diverse set of technologies [23][24][25][26][27][28][29][30][31][32] (Fig. 1a), helps to translate information between disparate measurements, resulting in robust integration without requiring any limiting biological assumptions.…”

Section: Introductionmentioning

confidence: 99%

Dictionary learning for integrative, multimodal, and scalable single-cell analysis

Hao

Stuart

Kowalski

et al. 2022

Preprint

208

163

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Mapping single-cell sequencing profiles to comprehensive reference datasets represents a powerful alternative to unsupervised analysis. Reference datasets, however, are predominantly constructed from single-cell RNA-seq data, and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to harmonize single-cell datasets across modalities by leveraging a multi-omic dataset as a molecular bridge. Each cell in the multi-omic dataset comprises an element in a 'dictionary', which can be used to reconstruct unimodal datasets and transform them into a shared space. We demonstrate that our procedure can accurately harmonize transcriptomic data with independent single cell measurements of chromatin accessibility, histone modifications, DNA methylation, and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to substantially improve computational scalability, and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach aims to broaden the utility of single-cell reference datasets and facilitate comparisons across diverse molecular modalities. Availability: Installation instructions, documentations, and vignettes are available at http://www.satijalab.org/seurat

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Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions

Cited by 68 publications

References 79 publications

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

The rise of developmental biology in China

Dictionary learning for integrative, multimodal, and scalable single-cell analysis

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