Abstract
The application of spatial transcriptomics (ST) technologies is booming and has already yielded important insights across many different tissues and disease models. In nephrology, ST technologies have helped to decipher the cellular and molecular mechanisms at work in kidney diseases and have allowed the recent creation of spatially anchored human kidney atlases in healthy and diseased kidney tissues. During ST data analysis, the obtained computationally annotated clusters are often superimposed on a histologic image without their initial identification being based on the morphologic and spatial analyses of the tissues and lesions. In this study, we conduct a histopathologic-based analysis of ST data on a human kidney sample corresponding as closely as possible to the reality of the interpretation of a kidney biopsy sample in a health care or research context. This study shows the feasibility of a morphology-based approach to interpreting ST data, helping to improve our understanding of the lesion phenomena at work in chronic kidney disease at both the cellular and the molecular level. Finally, we show that our newly identified pathology-based clusters can be accurately projected onto other slides from nephrectomy or needle biopsy samples. They thus serve as a reference for analyzing other kidney tissues, paving the way for the future of molecular microscopy and precision pathology.
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