Mapping cellular interactions from spatially resolved transcriptomics data

Cell–cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia’s power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand–receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.

James received Bachelor’s degrees in Biology and Chemistry from the University of North Carolina at Chapel Hill. He joined the University of Texas Southwestern Medical Center as a graduate student in 2019, and is currently mentored by Dr. Yang Xie and Dr. Tao Wang at the Quantitative Biomedical Research Center (QBRC), under the Health Data Science concentration of the Peter O'Donnell Jr. School of Public Health. He has worked on projects and authored publications involving computational immunology and spatial transcriptomics