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Updated: Apr 13, 2026

TChIP-Seq: Cell-Type-Specific Epigenome Profiling

TChIP-Seq: Cell-Type-Specific Epigenome Profiling

Published on: January 23, 2019

PACells identifies phenotype-associated cell states from single-cell chromatin accessibility profiles.

Jiao Hua¹, Qiongyu Sheng¹, Shutong Xiao¹

¹School of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang Province 150001, China.

American Journal of Human Genetics

|April 12, 2026

Summary

PACells links bulk and single-cell ATAC-seq data to identify disease-driving cell states. This framework improves prediction of cells and molecular signatures linked to clinical phenotypes and gene mutations.

Keywords:

cell states chromatin accessibility clinical phenotypes data integration

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Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Identifying cell states driving disease is key for understanding pathogenesis and developing targeted therapies.
Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) provides high-resolution insights into cellular regulatory landscapes.
Linking bulk ATAC-seq clinical data with scATAC-seq cellular data remains a challenge.

Purpose of the Study:

To present PACells, a novel computational framework for integrating bulk and single-cell ATAC-seq data.
To identify critical cell states associated with clinical phenotypes at single-cell resolution.
To enhance the understanding of disease mechanisms and identify potential therapeutic targets.

Main Methods:

PACells framework development for linking bulk ATAC-seq clinical data with scATAC-seq data.
Benchmarking PACells against existing methods for cell prediction and molecular signature identification.
Application of PACells to Alzheimer disease and glioblastoma datasets.
Extension of PACells to transcriptomics data for melanoma immunotherapy outcomes.

Main Results:

PACells demonstrates superior performance in predicting cells and molecular signatures associated with disease and gene mutations compared to other methods.
The framework successfully identified clinical cell states and regulatory elements relevant to Alzheimer disease.
PACells discerned cell states linked to poor survival in glioblastoma.
The extended PACells framework showed utility in analyzing melanoma transcriptomics data for immunotherapy outcomes.

Conclusions:

PACells provides a powerful tool for dissecting cellular heterogeneity and its link to clinical phenotypes using ATAC-seq data.
The framework advances the identification of disease-specific cell states and regulatory elements, paving the way for precision medicine.
PACells has broad applicability across different diseases and data modalities, including transcriptomics.