Biology subjects

Chou, E.

Publications and source records attributed to Chou, E..

4 recordsLinked to original sources

bioRxiv2026

Multiscale, Survey-and-Target Optical Super-Resolution Histology

Super-resolution microscopy reveals fine subcellular tissue architecture, yet extending this capability to histological screening requires balancing spatial resolution with imaging area, axial access, throughput, and instrumental complexity. Here we present multiscale optical super-resolution histology (MOSH), an integrated optical-computational platform that combines tissue-scale, extended-depth-of-focus super-resolution surveys with coordinate-guided three-dimensional interrogation of selected targets. MOSH leverages stationary multifocal excitation, continuous sample translation, tunable axial refocusing, and parallelized reconstruction within a shared spatial-coordinate framework in a conventional epi-fluorescence setting. This survey-and-target strategy preserves tissue-scale context, directs volumetric acquisition to selected subcellular structures, and generates virtual H&E images that retain resolution-enhanced structural information. Across cultured cells, cytological preparations, conventional tissue sections, and intact unsectioned biopsies, MOSH achieved approximately twofold resolution enhancement, continuous lateral coverage beyond the native field of view, and tunable axial survey ranges up to 20 m. Bridging tissue-scale histological screening and targeted, quantitative nanoscale interrogation, MOSH establishes a scalable framework for multiscale analysis of biological and clinical specimens.

bioengineering↗

bioRxiv2026

TFBSpedia: a comprehensive human and mouse transcription factor binding sites database

Mapping the genomic locations and patterns of transcription factor binding sites (TFBS) is essential for understanding gene regulation and advancing treatments for diseases driven by DNA modifications, including epigenetic changes and sequence variants. Although several TFBS databases exist, no study has systematically benchmarked these databases across different sequencing technologies and computational algorithms. In this study, we addressed this gap by constructing a TFBS database that integrates all available ENCODE cell line ATAC-seq and Cistrome Data Browser ChIP-seq datasets, comprising 11.3 million human and 1.87 million mouse TFBS. We also integrated previously published TFBS resources (Factorbook, Unibind, RegulomeDB, and ENCODE_footprint) and found each contains a substantial fraction of unique TFBS predictions, highlighting significant discrepancies among existing resources. To assess the accuracy of the combined TFBS regions, we assembled ten independent genomic annotation datasets for evaluation and found that TFBS regions predicted by multiple databases are more likely to represent true and biologically meaningful binding sites. For each predicted TFBS region, we define two scores: the confidence score reflects prediction reliability, while the importance score represents biological functional relevance. Finally, we introduce TFBSpedia, a lightweight and efficient search engine that enables rapid retrieval of TFBS regions and comprehensive annotation information across the integrated databases.

bioinformatics↗

bioRxiv2022

Mechanical stimuli activate gene expression via a cell envelope stress sensing pathway

In tissues with mechanical function, the regulation of remodeling and repair processes is often controlled by mechanosensitive mechanisms; damage to the tissue structure is detected by changes in mechanical stress and strain, stimulating matrix synthesis and repair. While this mechanoregulatory feedback process is well recognized in animals and plants, it is not known whether such a process occurs in bacteria. In Vibrio cholerae, antibiotic-induced damage to the load-bearing cell wall promotes increased signaling by the two-component system VxrAB, which stimulates cell wall synthesis. Here we show that changes in mechanical stress and strain within the cell envelope are sufficient to stimulate VxrAB signaling in the absence of antibiotics. We applied mechanical forces to individual bacteria using three distinct loading modalities: extrusion loading within a microfluidic device, compression, and hydrostatic pressure. In all three cases, VxrAB signaling, as indicated by a fluorescent protein reporter, was increased in cells submitted to greater magnitudes of mechanical loading, hence diverse forms of mechanical stimuli activate VxrAB signaling. Mechanosensitivity of VxrAB signaling was lost following removal of the VxrAB stimulating endopeptidase ShyA, suggesting that VxrAB may not be directly sensing mechanical forces, but instead relies on other factors including lytic enzymes in the periplasmic space. Our findings suggest that mechanical signals play an important role in regulating cell wall homeostasis in bacteria. Significance StatementBiological materials with mechanical function (bones, muscle, etc.) are often maintained through mechanosensitive mechanisms, in which damage-induced reductions in stiffness stimulate remodeling and repair processes that restore mechanical function. Here we show that a similar process can occur in bacteria. We find that mechanical stresses in the bacterial cell envelope (the primary load-bearing structure in bacteria) regulate signaling of a two-component system involved in cell wall synthesis. These findings suggest that the mechanical stress state within the cell envelope can contribute to cell wall homeostasis. Furthermore, these findings demonstrate the potential to use mechanical stimuli to regulate gene expression in bacteria.

microbiology↗

bioRxiv2022

Data Fusion by Matrix Completion for Exposome Target Interaction Prediction

Human exposure to toxic chemicals presents a huge health burden and disease risk. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research. In this study, we implemented a novel matrix completion algorithm named coupled matrix-matrix completion (CMMC) for predicting exposome-target interactions, which exploits the vast amount of accumulated data regarding chemical exposures and their molecular targets. Our approach achieved an AUC of 0.89 on a benchmark dataset generated using data from the Comparative Toxicogenomics Database. Our case study with bisphenol A (BPA) and its analogues shows that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Overall, our results demonstrate the feasibility and promise of computational predicting environmental chemical-target interactions to efficiently prioritize chemicals for further study.

pharmacology and toxicology↗