An Interpretable Multi-instance Learner Decodes Cellular Recruitment from Spatially Resolved Transcriptomics

Thomas M. Menino Convention & Exhibition Center 

The recruitment of various types of cells into the tissue microenvironment and how these cells engage with other cells in the tissues play critical biological roles. However, it is difficult to study these processes on a genome-wide scale using low-throughput experiments. To fill this void, we introduce SPACER, an interpretable and generative multi-instance learning framework, which digests the transcriptomic and spatial modalities of spatially resolved transcriptomics (SRT) data to elucidate the mechanisms of cellular engagement, in a manner that is isomorphic to the tissue spatial architecture. We deployed SPACER to 17 high and 20 low definition whole transcriptomic SRT datasets. SPACER identified tumor cell- and stromal/immune cell-specific features that determined the recruitment of these cells into tumors. SPACER could also map transcriptomic features that were associated with T cell infiltration into cardiomyocytes during myocarditis. SPACER out-performed all benchmark methods in terms of prediction accuracy and recovery of biological signals. Overall, SPACER establishes a new spatially resolved paradigm for studying cellular localization in situ.