Learning relationships between histone modifications in single cells

Recent advances have enabled mapping of histone marks in single cells, but most methods are constrained to profile only one histone mark per cell. Here we present an integrated statistical and experimental framework, scChIX (single-cell chromatin immunocleavage and unmixing), to map multiple histone marks in single cells. scChIX multiplexes two histone marks together in single cells, then computationally deconvolves the signal using training data from respective histone mark profiles. This framework learns the cell type-specific correlation structure between histone marks, and therefore does not require a priori assumptions of their genomic distributions. Applying scChIX to two active marks during in vitro macrophage differentiation, we find H3K4me1 dynamics preceding H3K36me3. Modeling these dynamics enables integrated analysis of chromatin velocity during differentiation. Overall, scChIX reveals unique biological insights by leveraging multimodal analysis between histone modifications in single cells.