Histopathological-Based Analysis of Human Kidney Spatial Transcriptomics Data: Toward Precision Pathology.

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.

High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing.

Emerging spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ sequencing (dRNA HybISS, Cartana part of 10xGenomics) to compare male and female healthy mouse kidneys and the male kidney injury and repair timecourse. A pre-selected panel of 200 genes is used to identify cell state dynamics patterns during injury and repair. We develop a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting dataset allows us to resolve 13 kidney cell types within distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. At late timepoints after injury, C3+ leukocytes are enriched near pro-inflammatory, failed-repair proximal tubule cells. Integration of snRNA-seq dataset from the same injury and repair samples also allows us to impute the spatial localization of genes not directly measured by dRNA HybISS.

Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy.

A large-scale multimodal atlas that includes major kidney regions is lacking. Here, we employed simultaneous high-throughput single-cell ATAC/RNA sequencing (SHARE-seq) and spatially resolved metabolomics to profile 54 human samples from distinct kidney anatomical regions. We generated transcriptomes of 446,267 cells and chromatin accessibility profiles of 401,875 cells and developed a package to analyze 408,218 spatially resolved metabolomes. We find that the same cell type, including thin limb, thick ascending limb loop of Henle and principal cells, display distinct transcriptomic, chromatin accessibility, and metabolomic signatures, depending on anatomic location. Surveying metabolism-associated gene profiles revealed non-overlapping metabolic signatures between nephron segments and dysregulated lipid metabolism in diseased proximal tubule (PT) cells. Integrating multimodal omics with clinical data identified PLEKHA1 as a disease marker, and its in vitro knockdown increased gene expression in PT differentiation, suggesting possible pathogenic roles. This study highlights previously underrepresented cellular heterogeneity underlying the human kidney anatomy.