CellLoop: Identifying single-cell 3D genome chromatin loops

Abstract 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 resolution 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. When integrated with GAGE-seq and MERFISH in the mouse cortex, CellLoop redefines spatial domain functions through chromatin loop dynamics. These findings establish CellLoop as a scalable and accurate approach for dissecting chromatin loop dynamics at single-cell resolution, and demonstrate the utility of 3D genome features in interpreting transcriptional and spatial cell state heterogeneity.