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InSituPy -- A framework for histology-guided, multi-sample analysis of single-cell spatial transcriptomics data

Wirth, J.; Chernysheva, A.; Lemke, B.; Giray, I.; Egea Lavandera, A.; Steiger, K.

2025-03-12 bioinformatics
10.1101/2025.03.07.641860 bioRxiv
Show abstract

Single-cell spatial transcriptomics (scST) methodologies allow, in combination with histological stainings, an unprecedented view on disease progression. To comprehensively analyze scST data, bioinformatic analysis frameworks need to integrate the diverse set of data modalities and, just as importantly, enable the joint analysis of multiple datasets from clinical or experimental cohorts together with its corresponding metadata. Here, we present the InSituPy framework to comprehensively analyze single-cell spatial transcriptomic data from a multi-sample level down to the cellular and subcellular level. The framework contains analysis workflows for the integration of image data as well as pathological and biological expert knowledge. Increasing the accessibility of the data for non-bioinformaticians, the framework opens new ways of generating hypotheses, especially in the context of translational research.

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