ChIATAC is an efficient strategy for multi-omics mapping of 3D epigenomes from low-cell inputs

Chai, Haoxi; Tjong, Harianto; Li, Peng; Liao, Wei; Wang, Ping; Wong, Chee-Hong; Ngan, Chew Yee; Wei, Chia‐Lin; Ruan, Yijun

doi:10.1038/s41467-023-35879-5

Cited by 9 publications

(5 citation statements)

References 64 publications

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“…In order to develop a single cell method to simultaneously capture chromatin interactions and epigenomic states, we have previously established a bulk-cell dual-omic method called ChIATAC( 22 ). ChIATAC captures open chromatin loci similar to ATAC-seq( 23 ) and chromatin interactions between those loci simultaneously.…”

Section: Resultsmentioning

confidence: 99%

Tri-omic mapping revealed concerted dynamics of 3D epigenome and transcriptome in brain cells

Chai,

Huang,

Xiong

et al. 2024

Preprint

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Exploring the genomic basis of transcriptional programs has been a longstanding research focus. Here, we report a high-throughput single-cell tri-omic method to capture chromatin accessibility, interaction, and RNA simultaneously (ChAIR). After validating in cultured cells, we applied ChAIR to brain cells across mouse lifespan and delineated the concerted dynamics of 3D-epigenomic architecture and transcription during maturation and aging. Particularly, ultra-long chromatin megacontacts and promoter-associated 3D-epigenomic states are effective in defining cell identity and revealing spatially-resolved anatomic specificity. Importantly, we found that neurons in different brain regions and non-neuronal cells may undergo divergent genomic mechanisms during differentiation and aging. Our results demonstrated ChAIR's robustness of connecting chromatin folding architecture with cellular property and its potential applications to address complex questions in single-cell resolution and spatial specificity.

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Section: Resultsmentioning

confidence: 99%

Tri-omic mapping revealed concerted dynamics of 3D epigenome and transcriptome in brain cells

Chai,

Huang,

Xiong

et al. 2024

Preprint

Self Cite

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“…Micro‐Capture‐C (MCC) 50 and Tiled‐MCC 51 technologies, both of which are based on Micro‐C, were developed for high‐resolution chromatin interaction of specific loci. As needed, other derivative technologies centred on specific factors (e.g., TFs or histones) or specific elements (e.g., promoter or enhancer regions) were established, such as ChIA‐PET, 52 HiChIP, 53 Capture Hi‐C, 54 ChIATAC, 55 and others (Figure 2). Furthermore, the development of single‐cell Hi‐C technology allowed us to detect thousands of chromatin contacts in single cells 56 .…”

Section: Chromosome Conformation Capture and Its‐associated Technologiesmentioning

confidence: 99%

3D genome perspective on cell fate determination, organ regeneration, and diseases

Zhong

Zhang

et al. 2023

Cell Proliferation

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The nucleosome is the fundamental subunit of chromatin. Nucleosome structures are formed by the combination of histone octamers and genomic DNA. Through a systematic and precise process of folding and compression, these structures form a 30‐nm chromatin fibre that is further organized within the nucleus in a hierarchical manner, known as the 3D genome. Understanding the intricacies of chromatin structure and the regulatory mode governing chromatin interactions is essential for unravelling the complexities of cellular architecture and function, particularly in relation to cell fate determination, regeneration, and the development of diseases. Here, we provide a general overview of the hierarchical structure of chromatin as well as of the evolution of chromatin conformation capture techniques. We also discuss the dynamic regulatory changes in higher‐order chromatin structure that occur during stem cell lineage differentiation and somatic cell reprogramming, potential regulatory insights at the chromatin level in organ regeneration, and aberrant chromatin regulation in diseases.

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“…The number of photoconverted cells is at user discretion and can be adjusted to fit the experimental parameters. Multiple sequencing techniques are becoming readily available to produce analyses from low cell inputs [67][68][69]. These approaches provide novel sequencing feasibility to broadly define mechanistic cellular differences within smaller subpopulations.…”

Section: Plos Onementioning

confidence: 99%

A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling

Khatib,

Amanso,

Knippler

et al. 2023

PLoS ONE

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Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype—such as invasiveness, cell:cell interactions, and changes in spatial positioning—with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations.

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ChIATAC is an efficient strategy for multi-omics mapping of 3D epigenomes from low-cell inputs

Cited by 9 publications

References 64 publications

Tri-omic mapping revealed concerted dynamics of 3D epigenome and transcriptome in brain cells

Tri-omic mapping revealed concerted dynamics of 3D epigenome and transcriptome in brain cells

3D genome perspective on cell fate determination, organ regeneration, and diseases

A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling

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