PROJECT SUMMARY The function. relative disease tissues in our body consist of diverse cell t ypes with each cell type specialized to carry out a particular The behavior of a cell is influenced by its surrounding environment within a tissue. Knowledge of the locations of different cells in a tissue is critical for understanding the spatial organization of cell types and pathology.Although single-cell RNA sequencing (scRNA-seq) has made it possible to characterize cell types and states at an unprecedented resolution, the lack of physical relationships among cells has hindered the study of cell-cell communications within tissue context. Recent technology advances in spatial transcriptomics (ST) have enabled gene expression profiling while retaining location information in tissues. A popular ST technology is based on spatial barcoding followed by next-generation sequencing in which transcriptome-wide gene expression is measured in spatially barcoded spots. Data from such ST technologies often include a high- resolution hematoxylin and eosin (H&E)-stained histology image of the tissue section from which the gene expression data are obtained. Although ST is powerful, such data are still expensive to generate. On the other hand, it is relatively cheaper to generate H&E-stained histology images and scRNA-seq data. The main motivation of this project is to leverage information in ST to gain additional knowledge from the relatively easy- to-obtain histology images and scRNA-seq data. Building upon our expertise in statistical genomics, we propose to develop novel machine learning methods to address key computational challenges when performing integrative analysis of ST, histology images, and single cells. Our methods will jointly model gene expression and histology to characterize the spatial organization of tissues and predict spatial gene expression from histology images. The resulting spatial map from these analyses will further enable the spatial mapping of single cells back to tissues. The proposed methods will be applied to public data and data generated from ongoing collaborations in various diseases to evaluate their performance. The successful completion of this project will allow researchers to take advantage of advanced machine learning algorithms to integrate ST, histology, and single-cell data to gain a holistic view of the spatial organization of tissues.