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CellLoop: Identifying single-cell 3D genome chromatin loops
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DOI：10.1038/s41467-026-71406-y.png)
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Abstract
En 中文
Single-cell 3D genome technologies provide unprecedented views of chromatin architecture, but the extreme sparsity and noise of contact maps limit robust detection of chromatin loops at the individual cell level. Here we present CellLoop, a computational framework for identifying chromatin loops from single-cell contact data by integrating intra-cellular and neighboring inter-cellular contacts through a density-based voting strategy. Applied to Dip-C data from the mouse brain, CellLoop achieves improved loop detection consistent with spatial distances and compartmentalization signals, revealing single cell-specific chromatin loops associated with transcriptional regulation and cell identity. In HiRES embryogenesis data, CellLoop enables finer cell subtype delineation by reducing confounding cell cycle effects. Integration with GAGE-seq and MERFISH data redefines spatial domain functions through chromatin loop dynamics. Together, CellLoop provides a scalable and accurate approach for characterizing chromatin loop variability at single-cell resolution and highlights the utility of 3D genome features in interpreting transcriptional and spatial heterogeneity. CellLoop maps 3D chromatin loops in single cells, revealing how distinct DNA folding patterns shape gene activity, define cell identity, and organize spatial genome domains, providing new insights into cellular diversity and genome function.
Keywords:
Data mining
Genome informatics
Genomics
Science
Humanities and Social Sciences
multidisciplinary
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