Pei-Hsun Wu

This study enhances CAR-T cell migration through synthetic cytokine receptors, reducing metastasis in TNBC mouse models.

VITAL-3D quantifies single-cell drug responses in 2D and 3D cultures, revealing microenvironment-dependent efficacy differences in breast cancer.

This study identifies distinct senescent cell populations in human fibroids and shows their role in ECM remodeling and vascular dysfunction through single-cell RNA sequencing and functional analysis.

The paper identifies two distinct senescent β-cell subpopulations with different functional roles in islet aging and dysfunction using single-cell proteomics and transcriptomics.

This paper introduces DeepBIT, a deep learning method for automated cell migration tracking, enabling high-throughput analysis of cancer cell motility under various conditions and identifying new regulators of migration.

The paper develops a 3D multi-omic pipeline using deep learning to analyze chemotherapy response in pancreatic cancer, identifying molecular signatures of resistance and fibroblast-cancer interactions.

The paper identifies Tbx5 as a gene critical for forming a compartment boundary in the interventricular septum, and shows its disruption leads to congenital heart defects.

The paper identifies and maps senescent cells in postmenopausal ovaries using p16 expression and spatial transcriptomics, revealing a fibro-inflammatory signature associated with aging.

NLTD 2.0 improves color deconvolution in histopathology by handling more than three stains without requiring optimal color vectors, enhancing reliability and accessibility through an ImageJ plugin.

The paper introduces a framework for mathematical modeling of multicellular systems using interpretable cell behavior rules derived from biological knowledge and multi-omics data.

This study identifies functional senescence subtypes in aging human dermal fibroblasts using single-cell morphology and machine learning, showing subtype-specific responses to senotherapies.

The paper develops a machine learning approach to reconstruct complex biological systems from partial faceted datasets, showing successful reconstruction in both mechanical and biological examples.

This study identifies high-motility tumor-associated monocytes (TAMos) that drive macrophage infiltration and cancer cell proliferation via IL-6 and MAPK pathways, suggesting IL-6 as a therapeutic target.

This study introduces qMAP, a deep learning-based workflow for microanatomical mapping of human skin, identifying 10 core processes strongly associated with aging and achieving age prediction accuracy comparable to epigenetic clocks.

This paper uses deep learning to label 17 kidney microstructures in 3D fetal rhesus macaque kidneys, revealing spatial relationships between glomeruli, arteries, and nephrons.

The paper identifies tissue morphology biomarkers in long-term PDAC survivors using digital pathology and machine learning, finding reduced heterogeneity and extra-tumoral space features correlate with better survival.