Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

Michael Y. Fatemi, Yunrui Lu, Zarif L. Azher, Cyril Sharma, Eric Feng, Alos B. Diallo, Gokul Srinivasan, Grace M. Rosner, Kelli B. Pointer, Brock C. Christensen, Lucas A. Salas, Gregory J. Tsongalis, Scott M. Palisoul, Laurent Perreard, Frederick W. Kolling IV, Louis J. Vaickus, Joshua J. Levy

Abstract: Spatial transcriptomics (ST) enables studying spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. Recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. Elucidating such information from histology alone presents a significant challenge, but if solved can enable spatial molecular analysis at cellular resolution even where ST data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in colorectal cancer whole slide images. A cell-graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA, facilitating the analysis of cellular groupings and gene relationships. We demonstrate that single-cell transcriptional heterogeneity within a spot could be p