Integrating Histopathology-Based Analysis with Spatial Transcriptomics of Human Kidneys: Towards Precision Pathology

Background: During spatial transcriptomic (ST) data analysis, computationally-annotated clusters are often superimposed on a histological image without any consideration of the tissue morphology by standard light microscopic pathologic evaluation, thereby ignoring important information that can help interpretation of the ST data Methods: We conduct a histopathological-based analysis (by a practicing renal pathologist) of spatial transcriptomics (10X genomics, Visium) on 4 human kidney samples with CKD corresponding as closely as possible to how a kidney biopsy is interpreted in clinical practice Results: We first validated the relevance of this pathological-based clustering by showing that the DEGs and cell composition of each cluster were those expected in relation to the structure analyzed. By examining each sector independently, we showed in particular that ST data can be used to confirm the diagnosis of different structures (TLS, tumor). We also showed how this strategy could be used to: identify potential diagnostic biomarkers, determine cell composition, or study molecular pathways specific to a lesion. We performed a comparative molecular analysis of healthy and pathological glomerular and tubular segments. We could identify several known cellular and molecular mechanisms behind kidney lesions in CKD and were able to identify potential candidate genes in glomeruli (FXYD5, CXCL12) and tubules (TMSB4X, NQO1, MGST1, SQSTM1) possibly involved in disease progression or acting as defense mechanisms. Finally, we show that this pathological-based clustering could be transferred to study other human kidney ST Visium data (nephrectomy sample or needle kidney biopsies). This last result implies that our pathological clustering is potentially not restricted to the analysis of a single slide, and could be projected coherently onto another slide, paving the way for the constitution of spatial molecular banks of lesions and structure as slide analysis progresses Conclusion: In conclusion, we show the feasibility of a morphological-based approach to interpreting ST data, helping to improve our understanding of kidney lesions in CKD, at both cellular and molecular levels. This work introduces a method to combine traditional histopathology with ST data to pave the way for the future of molecular microscopy and precision pathology Funding: Private Foundation Support