High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.

Authors

Li Yao, Sagar R Shah, Abdullah Ozer, Yutong Zhu, Xiuqi Pan, Tianyu Xia, Junke Zhang, Alden King-Yung Leung, Meihan Wei, John T Lis, Haiyuan Yu

Published in

Nature biotechnology. Jul 13, 2026. Epub Jul 13, 2026.

Abstract

Single-cell sequencing methods such as scRNA-seq and scATAC-seq have advanced our understanding of individual cellular functions but experimentally adapting genome-wide assays measuring other genomic features to achieve single-cell resolution remains a technical challenge. Here we introduce deep-learning-based deconvolution of tissue profiles with accurate interpretation of locus-specific signals (DeepDETAILS), a quasisupervised framework performing cross-modality deconvolution using scATAC-seq reference libraries for other bulk datasets. DeepDETAILS enables base-pair-resolution mapping of genomic signals across diverse cell types, with great versatility for various omics datasets, including nascent transcript sequencing (such as PRO-cap and PRO-seq) and ChIP-seq for chromatin modifications. Using DeepDETAILS, we generated a compendium of high-resolution nascent transcription and histone modification signals across 39 diverse human tissues and 86 distinct cell types. Furthermore, we applied our compendium to fine-map risk variants associated with primary sclerosing cholangitis, a progressive cholestatic liver disorder, and revealed a potential etiology of the disease.

PMID:
42443492
Bibliographic data and abstract were imported from PubMed on 14 Jul 2026.

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