Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlas
The use of single-cell RNA sequencing in clinical and translational research is limited by cross-species differences and the challenge of identifying cell-type-specific, targetable molecular changes in individual patients. Here, we created an Integrated Single-cell Kidney Atlas including over one million cells from 140 samples, defining more than 70 conserved cell states in human and rodent models. We developed CellSpectra, a computational tool that quantifies changes in gene expression coordination across cellular functions, which we applied to kidney and lung cancer data. This tool powers our Patient-Level Single-Cell Functional Profiling Report, which highlights cell-type-specific changes in pathway gene expression coordination in individuals. Our cross-species atlas facilitates the selection of a rodent model that closely reflects the cellular and pathway-level signatures observed in a patient sample, advancing the application of single-cell methodologies in clinical precision medicine. Finally, by using experimental models, we demonstrate how our informatics approach can be applied for the potential selection of suitable therapeutics. Overall design: Mouse: To obtain a more heterogenous collection of mouse models we generated snRNA-seq data from mouse disease models and controls incuding a podocyte specific G2NA APOL1 risk allele mouse and samples prepared following folic acid injection (250 mg/kg body weight in 300 mM NaHCO3) sacrificed 7 days after disease induction. Human: New generated human data was based on biopsy samples in a biobank from an observational cohort. No interventions were part of this investigation. A total of 5 kidney biopsy specimen from patients with kidney disease were processed (more clinical metadata in manuscript supplements). ***Human raw data were not provided due to privacy concerns***