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The spatial organization of cell types in tissues fundamentally shapes cellular interactions and function, but the high-throughput spatial mapping of complex tissues remains a challenge. We present сell2location, a principled and versatile Bayesian model that integrates single-cell and spatial transcriptomics to map cell types in situ in a comprehensive manner. We show that сell2location outperforms existing tools in accuracy and comprehensiveness and we demonstrate its utility by mapping two complex tissues. In the mouse brain, we use a new paired single nucleus and spatial RNA-sequencing dataset to map dozens of cell types and identify tissue regions in an automated manner. We discover novel regional astrocyte subtypes including fine subpopulations in the thalamus and hypothalamus. In the human lymph node, we resolve spatially interlaced immune cell states and identify co-located groups of cells underlying tissue organisation. We spatially map a rare pre-germinal centre B-cell population and predict putative cellular interactions relevant to the interferon response. Collectively our results demonstrate how сell2location can serve as a versatile first-line analysis tool to map tissue architectures in a high-throughput manner.

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Genome sequencing of cancers often reveals mosaics of different subclones present in the same tumour1–3. Although these are believed to arise according to the principles of somatic evolution, the exact spatial growth patterns and underlying mechanisms remain elusive4,5. Here, to address this need, we developed a workflow that generates detailed quantitative maps of genetic subclone composition across whole-tumour sections. These provide the basis for studying clonal growth patterns, and the histological characteristics, microanatomy and microenvironmental composition of each clone. The approach rests on whole-genome sequencing, followed by highly multiplexed base-specific in situ sequencing, single-cell resolved transcriptomics and dedicated algorithms to link these layers. Applying the base-specific in situ sequencing workflow to eight tissue sections from two multifocal primary breast cancers revealed intricate subclonal growth patterns that were validated by microdissection. In a case of ductal carcinoma in situ, polyclonal neoplastic expansions occurred at the macroscopic scale but segregated within microanatomical structures. Across the stages of ductal carcinoma in situ, invasive cancer and lymph node metastasis, subclone territories are shown to exhibit distinct transcriptional and histological features and cellular microenvironments. These results provide examples of the benefits afforded by spatial genomics for deciphering the mechanisms underlying cancer evolution and microenvironmental ecology.

Subclonality is a universal feature of cancers yet how clones grow, are spatially organised, differ phenotypically or influence clinical outcome is unclear. To address this, we developed base specific in situ sequencing (BaSISS). In fixed tissues, transcripts harbouring clone-defining mutations are detected, converted into quantitative clone maps and characterised through multi-layered data integration. Applied to 8 samples from key stages of breast cancer progression BaSISS localised 1.42 million genotype informative transcripts across 4.9cm2 of tissue. Microscopic clonal topographies are shaped by resident tissue architectures. Distinct transcriptional, histological and immunological features distinguish coexistent genetic clones. Spatial lineage tracing temporally orders clone features associated with the emergence of aggressive clinical traits. These results highlight the pivotal role of spatial genomics in deciphering the mechanisms underlying cancer progression.

In many cancers programmed cell death ligand 1 (PDL1) expression serves as a biomarker for immunotherapies, but its quantification relies on immunohistochemistry (IHC) and few underlying histopathological patterns are established. Digital pathology, combined with deep learning, can augment histopathological assessment and reveal patterns associated with molecular changes, but the presence of heterogeneity within histopathological images, the scale of billions of pixels and the difficulty in acquiring spatially resolved annotations pose challenges for accurate analysis. Here, we present a weakly supervised learning approach using only slide-level supervision for PDL1 expression prediction based on hematoxylin and eosin (H&E) slides. Our methods, MILTS, extends multiple instance learning paradigm (MIL) with the teacher-student framework (TS), which takes the intra-slide heterogeneity into account by dynamically assigning pseudo-labels to different slide regions and retrieves large amounts of unlabeled instances by distillation of the temporal ensemble model. The approach is evaluated on 9,744 tissue slide images across 20 types of solid tumors from TCGA and CPTAC. Among 9 tumors for PDL1 expression serves as an established biomarker, MILTS achieved a weighted average area under curve of 0.83. Predicted patterns within each slide provide insights into the heterogeneity and help identify morphotypes relevant with PDL1 expression, which include mixed inflammatory stroma with relatively high abundance of eosinophils and a cribriform growth pattern of tumor cells. This study provides a new algorithm for predicting molecular changes from H&E images and provides histological links of PDL1 expression, and thus demonstrates the potential of deep learning in discovering diverse histological patterns.