Biomedical subjects

Shengen Shawn Hu

Publications and source records attributed to Shengen Shawn Hu.

3 recordsLinked to original sources

PubMed2026

Mapping self-associating chromatin hubs identifies Id proteins as key determinants of exhausted CD8+ T cell fate.

Within days of exposure to chronic viral infections, activated CD8+ T cells differentiate into Tcf1-Slamf6loTim3hi exhaustion-prone effector T (TEX_EFF) cells or self-renewing Tcf1+Slamf6hiTim3lo precursor exhausted T (TPEX) cells. Here we showed that early CD8+ TEX cell fates were imprinted by forming subset-specific, self-associating chromatin hubs. Chromatin hub assembly coincided with effector or stemness gene induction and identified the transcription cofactors Id2 and Id3 as key regulators that promoted CD8+ TEX_EFF and CD8+ TPEX cell fates, respectively. Id2 drove CD8+ TEX_EFF cell specification by activating effector genes, while suppressing genes involved in exhaustion and stemness. In contrast, Id3-repressed effector genes but upregulated IL-7Rα and AhR, thereby maintaining the CD8+ TPEX cell pool. Mechanistically, Id2 and Id3 exhibited a distinct impact on the chromatin accessibility landscape in early CD8+ TEX cells by engaging Runx3 and Tcf1 transcription factors along with E proteins. These findings indicated that reshaping chromatin architecture represents a critical means for specifying CD8+ TEX cell fates and ensuring lineage stability.

Animals↗

PubMed2025

PATTY corrects open-chromatin bias for improved bulk and single-cell CUT&Tag profiling.

Precise profiling of epigenomes is essential for better understanding chromatin biology and gene regulation. Cleavage Under Targets & Tagmentation (CUT&Tag) is an efficient epigenomic profiling technique that can be performed on a low number of cells and at the single-cell level. With its growing adoption, CUT&Tag datasets spanning diverse biological systems are rapidly accumulating in the field. CUT&Tag assays use the hyperactive transposase Tn5 for DNA tagmentation. Tn5's preference toward accessible chromatin alters CUT&Tag sequence read distributions in the genome and introduces open-chromatin bias that can confound downstream analysis, an issue more substantial in sparse single-cell data. We show that open-chromatin bias extensively exists in published CUT&Tag datasets, including those generated with recently optimized high-salt protocols. To address this challenge, we present PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a comprehensive computational method that corrects open-chromatin bias in CUT&Tag data by leveraging accompanying ATAC-seq. By integrating transcriptomic and epigenomic data using machine learning and integrative modeling, we demonstrate that PATTY enables accurate and robust detection of occupancy sites for both active and repressive histone modifications, including H3K27ac, H3K27me3, and H3K9me3, with experimental validation. We further develop a single-cell CUT&Tag analysis framework built on PATTY and show improved cell clustering when using bias-corrected single-cell CUT&Tag data compared to using uncorrected data. Beyond CUT&Tag, PATTY sets a foundation for further development of bias correction methods for improving data analysis for all Tn5-based high-throughput assays.

Journal Article↗

PubMed2025

PATTY corrects open chromatin bias for improved bulk and single-cell CUT&Tag profiling.

Precise profiling of epigenomes is essential for better understanding chromatin biology and gene regulation. Cleavage Under Targets & Tagmentation (CUT&Tag) is an efficient epigenomic profiling technique that can be performed on a low number of cells and at the single-cell level. With its growing adoption, CUT&Tag datasets spanning diverse biological systems are rapidly accumulating in the field. CUT&Tag assays use the hyperactive transposase Tn5 for DNA tagmentation. Tn5's preference toward accessible chromatin alters CUT&Tag sequence read distributions in the genome and introduces open chromatin bias that can confound downstream analysis, an issue more substantial in sparse single-cell data. We show that open chromatin bias extensively exists in published CUT&Tag datasets, including those generated with recently optimized high-salt protocols. To address this challenge, we present PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a comprehensive computational method that corrects open chromatin bias in CUT&Tag data by leveraging accompanying ATAC-seq. By integrating transcriptomic and epigenomic data using machine learning and integrative modeling, we demonstrate that PATTY enables accurate and robust detection of occupancy sites for both active and repressive histone modifications, including H3K27ac, H3K27me3, and H3K9me3, with experimental validation. We further develop a single-cell CUT&Tag analysis framework built on PATTY and show improved cell clustering when using bias-corrected single-cell CUT&Tag data compared to using uncorrected data. Beyond CUT&Tag, PATTY sets a foundation for further development of bias correction methods for improving data analysis for all Tn5-based high-throughput assays.

Journal Article↗