Characterizing tissue composition through combined analysis of single-cell morphologies and transcriptional states

Abstract

Advances in spatial transcriptomics technologies enable optical profiling of morphological and transcriptional modalities from the same cells within tissues. Here, we present multi-modal structured embedding (MUSE), an approach to deeply characterize tissue heterogeneity through analysis of combined image and transcriptional single-cell measurements. We demonstrate that MUSE can discover cellular subpopulations missed by either modality as well as compensate for modality-specific noise. MUSE identified biologically meaningful cellular subpopulations and stereotyped spatial patterning within heterogeneous mouse cortex brain tissues, profiled by seqFISH+ or STARmap technologies. MUSE provides a framework for combining multi-modal single-cell data to reveal deeper insights into the states, functions and organization of cells in complex biological tissues.