报告时间：2023年12月20日(周三)15：00-16:00

报告地点：中国人民大学明德主楼1016

报告嘉宾：Mingyao Li, PhD

报告主题：Integrating spatial transcriptomics with histology to infer super-resolution tissue architecture

报告摘要

Integrating spatial transcriptomics with histology to infer super-resolution tissue architecture

The rapid development of spatial transcriptomics (ST) technologies has made it possible to measure gene expression within the original tissue contexts. The applications of ST have enabled researchers to characterize spatial gene expression patterns, study cell-cell communications, and resolve the spatiotemporal order of cellular development, which have transformed our understanding of the functional organization of tissues. Previous studies have shown that gene expression patterns are correlated with histological features, suggesting that gene expression can be predicted from histology images. However, these existing methods do not fully utilize the rich cellular information provided by high-resolution histology images. In this talk, I will present methods that we recently developed that aim to integrate gene expression with histology to computationally reconstruct ST data that cover the entire transcriptome with near-single-cell resolution. Through comprehensive analysis of diverse datasets generated from both diseased and normal tissues, we show that our super-resolution gene prediction is accurate and useful for different applications in tissue architecture inference.

作者简介

Dr. Li received her PhD in Biostatistics from the University of Michigan in 2005. She was trained as a statistical geneticist, but since she joined the faculty at the University of Pennsylvania in 2006, she has gradually transitioned her research from traditional statistical genetics to statistical genomics with the goal of having a deeper understanding of the molecular mechanism of human disease. The central theme of her current research is to use statistical and machine learning methods to understand cellular heterogeneity in human-disease-relevant tissues, to characterize gene expression diversity across cell types, to study the patterns of cell state transition and crosstalk of various cells using data generated from single-cell and spatial transcriptomics studies, and to translate these findings into clinics. More recently, she expanded her expertise into computational pathology, which is critical when processing and analyzing spatial transcriptomics data. In addition to methods development, she is also interested in collaborating with researchers seeking to identify complex disease susceptibility genes and acting cell types. At UPenn, she serves as the Director of the Statistical Center for Single-Cell and Spatial Genomics. She also chairs the Graduate Program in Biostatistics. She is an elected member of the International Statistical Institute, a Fellow of the American Statistical Association, and a Fellow of the American Association for the Advancement of Science.