Unveiling Multi‐Scale Architectural Features in Single‐Cell Hi‐C Data Using scCAFE

Single-cell Hi-C (scHi-C) has provided unprecedented insights into the heterogeneity of 3D genome organization. However, its sparse and noisy nature poses challenges for computational analyses, such as chromatin architectural feature identification. Here, scCAFE is introduced, which is a deep learning model for the multi-scale detection of architectural features at the single-cell level. scCAFE provides a unified framework for annotating chromatin loops, TAD-like domains (TLDs), and compartments across individual cells. This model outperforms previous scHi-C loop calling methods and delivers accurate predictions of TLDs and compartments that are biologically consistent with previous studies. The resulting single-cell annotations also offer a measure to characterize the heterogeneity of different levels of architectural features across cell types. This heterogeneity is then leveraged to identify a series of marker loop anchors, demontrating the potential of the 3D genome data to annotate cell identities without the aid of simultaneously sequenced omics data. Overall, scCAFE not only serves as a useful tool for analyzing single-cell genomic architecture, but also paves the way for precise cell-type annotations solely based on 3D genome features.

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Journal
Advanced Science
Published
2025-04-24
DOI
https://doi.org/10.1002/advs.202416432
Citations
1
Primary Topic
Genomics and Chromatin Dynamics
Type
article
Field-Weighted Citation Impact
0.81

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article

Unveiling Multi‐Scale Architectural Features in Single‐Cell Hi‐C Data Using scCAFE

Lingkuan Meng, Hamid Alinejad‐Rokny, Jiecong Lin, Ka‐Chun Wong et al.
1 citations
Advanced Science
Genomics and Chromatin Dynamics
0.81
article

Unveiling Multi‐Scale Architectural Features in Single‐Cell Hi‐C Data Using scCAFE

Lingkuan Meng, Hamid Alinejad‐Rokny, Jiecong Lin, Ka‐Chun Wong, Weidun Xie, Jixiang Yu, Wenjing Ma, Yuchen Wang, Lei Huang, Nanjun Chen, Zhongyu Yao, Fuzhou Wang, Xiangtao Li
article en
1 citations

Abstract

Single-cell Hi-C (scHi-C) has provided unprecedented insights into the heterogeneity of 3D genome organization. However, its sparse and noisy nature poses challenges for computational analyses, such as chromatin architectural feature identification. Here, scCAFE is introduced, which is a deep learning model for the multi-scale detection of architectural features at the single-cell level. scCAFE provides a unified framework for annotating chromatin loops, TAD-like domains (TLDs), and compartments across individual cells. This model outperforms previous scHi-C loop calling methods and delivers accurate predictions of TLDs and compartments that are biologically consistent with previous studies. The resulting single-cell annotations also offer a measure to characterize the heterogeneity of different levels of architectural features across cell types. This heterogeneity is then leveraged to identify a series of marker loop anchors, demontrating the potential of the 3D genome data to annotate cell identities without the aid of simultaneously sequenced omics data. Overall, scCAFE not only serves as a useful tool for analyzing single-cell genomic architecture, but also paves the way for precise cell-type annotations solely based on 3D genome features.

Advanced ScienceVol. 12(23)
Harvard University (US), City University of Hong Kong (HK), Jilin University (CN), UNSW Sydney (AU), Massachusetts General Hospital (US), City University of Hong Kong, Shenzhen Research Institute (CN), Center for Cancer Research (US), University of Hong Kong (HK)
City University of Hong Kong, National Natural Science Foundation of China, University Grants Committee, Innovation and Technology Commission, Innovation and Technology Commission - Hong Kong, Shenzhen Research Institute, City University of Hong Kong
Sustainable cities and communities
Openalex Percentile: Top 29%
Genomics and Chromatin Dynamics
0.81
DOIOpen Access PDFOpenAlex
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