Inferring Super-Resolution Tissue Architecture by Integrating Spatial Transcriptomics with Histology
David Zhang, PhD
Assistant Professor
Department of Biostatistics and Genetics | UNC-Chapel Hill
WHEN:聽Wednesday, April 2, 2025, from 3:30 to 4:30 p.m.
WHERE:聽Hybrid | 2001 9I制作厂免费 College Avenue, Room 1140;
NOTE:聽David Zhang will be presenting from Chapel Hill
Abstract
Spatial transcriptomics (STs) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Despite the availability of many ST platforms, none of them provides a comprehensive solution. An ideal ST platform should achieve single-cell resolution, cover the whole transcriptome, and be cost-effective. While such ST data are difficult to collect physically using existing platforms, they can be constructed in silico using innovative machine learning algorithms. Here we present iStar, a generative computer vision model that integrates low-resolution ST measurements with high-resolution histology images to construct spatial gene expression data at near-single-cell-resolution. The resulting model not only enhances gene expression resolution but also enables gene expression prediction in tissue sections where only histology images are available. The application of iStar to healthy and diseased samples across diverse datasets demonstrates its efficacy in facilitating scientific inquiries and performing clinical tasks, including tissue segmentation, cell type inference, cancer detection, and tumor microenvironment analysis, all with state-of-the-art accuracy and efficiency. Using this tool, researchers and physicians can detect and analyze diseases such as cancer with unprecedented precision and depth.
Speaker Bio
David Zhang is an Assistant Professor of Biostatistics and Genetics at the University of North Carolina, Chapel Hill. His research centers around the development of novel machine learning and AI frameworks for analyzing high-dimensional, multi-modal, and multi-scale data, especially those originating from spatial omics, computational pathology, and medical imaging. The overarching goal of his research is to harness the power of AI to answer the most pressing scientific inquiries and clinical needs.