Biology subjects

Hu, S. S.

Publications and source records attributed to Hu, S. S..

5 recordsLinked to original sources

bioRxiv2025

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. Tn5s 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.

bioinformatics↗

bioRxiv2025

Super-resolution microscopy reveals distinct epigenetic states regulated by estrogen receptor activity

Changes in gene expression regulated by ligand-dependent transcription factors such as estrogen receptor- (ER) involves the recruitment of coactivators including p300 that acetylates histone H3 at lysine 27 (H3K27ac). While H3K27ac marks active enhancers, the detailed chromatin architecture of enhancers remains unclear. Using super-resolution microscopy, we reveal distinct structural states of H3K27ac modified chromatin in response to ER activation. In estradiol (E2)-treated cells, H3K27ac modified chromatin adopts open, elongated structures, while ER inhibition induces compact, spherical H3K27ac modified chromatin conformations. Using MED1, a core subunit of the Mediator complex that bridges transcription factors with RNA polymerase II (Pol II), we demonstrate that larger H3K27ac structures are preferentially associated with active enhancers, whereas more compact structures show reduced MED1 association, consistent with a less active or inactive state. A constitutively active ER mutation linked to endocrine therapy resistance in breast cancer maintains open chromatin states independent of ligand, suggesting sustained transcriptional activity. Our findings provide the first direct visualization of H3K27ac associated chromatin structural dynamics, challenging the assumption that H3K27ac modification alone is sufficient to lead to enhancer activation. By demonstrating that H3K27ac architecture is dynamically regulated by ER, we establish a new paradigm for understanding epigenetic regulation and highlight potential therapeutic targets for endocrine therapy resistant cancers.

cancer biology↗

bioRxiv2025

Mapping Hand Function with Simultaneous Brain-Spinal Cord Functional MRI

INTRODUCTIONHand motor control depends on intricate brain-spinal cord interactions that regulate muscle activity. Hand function can be disrupted by injury to the brain, spinal cord, and peripheral nerves leading to weakness and impaired coordination. Functional MRI (fMRI) can map motor-related neural activity and potentially characterize the mechanisms underlying hand weakness and diminished coordination. Although brain motor control has been extensively studied, spinal cord mechanisms remain less explored. Here we use simultaneous brain-spinal cord fMRI to map neural activity related to hand strength and dexterity across the central nervous system using force matching and finger tapping tasks. This study pioneers the use of simultaneous brain-spinal cord fMRI to comprehensively map hand function, offering novel insights into coordinated motor processing across the central nervous system. METHODSWe performed simultaneous brain-spinal cord fMRI in 28 right-handed healthy volunteers (age: 40.0 {+/-} 13.8 years, 14 females, 14 males) using a 3T GE SIGNA Premier scanner equipped with a 21-channel head-neck coil. Participants performed a force-matching task at 10%, 20%, and 30% of maximum voluntary contraction using a hand dynamometer. For the finger tapping task, participants completed button-presses at 1 Hz with a 5-button response pad for three task levels: single-digit response with the second digit only (low), single-digit response with all digits in a sequential order (medium), single-digit response with all digits in a random order (high). Visual cues and feedback were provided during the tasks. Brain and spinal cord images were processed separately using FSL and the Spinal Cord Toolbox, with motion correction, physiological noise filtering, and spatial normalization to standard templates. Subject level activity maps were generated and entered into group level analyses to explore both activations and deactivations. For the brain, we used a mixed effect design with a voxelwise threshold of Z score > 3.10 and cluster threshold of p < 0.05. For the spinal cord, we used a fixed effect design with a voxelwise threshold of Z score > 1.64 and cluster threshold of p < 0.05. Region of interest (ROI) analyses were conducted to examine localized changes in activation across task levels RESULTSBoth tasks elicited activation in motor and sensory regions of the brain and spinal cord, with graded responses in the left primary motor (M1), left primary sensory (S1) cortex, and right spinal cord gray matter across task levels. Deactivation of the right M1 and S1 was also present for both tasks. Deactivation of the left spinal cord gray matter was present in the high task level of the force matching task. The ROI analysis findings complemented the group level activity maps. DISCUSSIONOur study provides a detailed map of brain-spinal cord interactions in hand function, revealing graded neural activation and inhibition patterns across motor and sensory regions. Interhemispheric inhibition, reflected in right M1 deactivation, likely restricts extraneous motor output during unilateral tasks. For force matching, the deactivation of the left ventral and dorsal horns of the spinal cord, provides the first evidence that the inhibition of motor areas during a unilateral motor task extends to the spinal cord. Whether this inhibition results from direct descending modulation from the brain or interneuronal inhibition in the cord remains to be interrogated. These findings expand our understanding of central motor control mechanisms and could inform rehabilitation strategies for individuals with motor impairments. CONCLUSIONSOur simultaneous brain-spinal cord fMRI approach provides novel insights into the neural coordination of hand function, enhancing our understanding of motor control and its modulation. This approach may offer a foundation for studying motor dysfunction in conditions such as stroke, spinal cord injury, and neurodegenerative diseases.

neuroscience↗

bioRxiv2023

HILAMA: High-dimensional multi-omic mediation analysis with latent confounding

MotivationThe increasingly available multi-omic datasets have posed both new opportunities and challenges to the development of quantitative methods for discovering novel mechanisms in biomedical research. One natural approach to analyzing such datasets is mediation analysis originated from the causal inference literature. Mediation analysis can help unravel the mechanisms through which exposure(s) exert the effect on outcome(s). However, existing methods fail to consider the case where (1) both exposures and mediators are potentially high-dimensional and (2) it is very likely that some important confounding variables are unmeasured or latent; both issues are quite common in practice. To the best of our knowledge, however, no methods have been developed to address these challenges with statistical guarantees. ResultsIn this article, we propose a new method for HIgh-dimensional LAtent-confounding Mediation Analysis, abbreviated as "HILAMA", that considers both high-dimensional exposures and mediators, and more importantly, the possible existence of latent confounding variables. HILAMA achieves false discovery rate (FDR) control under finite sample size for multiple mediation effect testing. The proposed method is evaluated through extensive simulation experiments, demonstrating its improved stability in FDR control and superior power in finite sample size compared to existing competitive methods. Furthermore, our method is applied to the proteomics-radiomics data from ADNI, identifying some key proteins and brain regions relating to Alzheimers disease. The results show that HILAMA can effectively control FDR and provide valid statistical inference for high dimensional mediation analysis with latent confounding variables. AvailabilityThe R package HILAMA is publicly available at https://github.com/Cinbo-Wang/HILAMA. Contactcinbo_w@sjtu.edu.cn

bioinformatics↗

bioRxiv2021

Accurate estimation of intrinsic biases for improved analysis of chromatin accessibility sequencing data using SELMA

Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data.

bioinformatics↗