Unified Mouse and Human Kidney Single-Cell Expression Atlas Reveal Commonalities and Differences in Disease States

Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. We analyzed single-cell RNA sequencing data (36 samples) and bulk gene expression data (42 samples) from 18 commonly used mouse kidney disease models. We compared single-nucleus RNA sequencing data from a mouse diabetic kidney disease model with data from patients with diabetic kidney disease and healthy controls. We generated a uniformly processed mouse single-cell atlas containing information for nearly 300,000 cells, identifying all major kidney cell types and states. Our analysis revealed that changes in fractions of cell types are major drivers of differences in bulk gene expression. Although gene expression changes at the single-cell level were mostly model-specific, different disease models showed similar changes when compared at a pathway level. Tensor decomposition analysis highlighted the important changes in proximal tubule cells in disease states. Specifically, we identified important alterations in expression of metabolic and inflammation-associated pathways. The mouse diabetic kidney disease model and patients with diabetic kidney disease shared only a small number of conserved cell type-specific differentially expressed genes, but we observed pathway-level activation patterns conserved between mouse and human diabetic kidney disease samples. This study provides a comprehensive mouse kidney single-cell atlas and defines gene expression commonalities and differences in disease states in mice. The results highlight the key role of cell heterogeneity in driving changes in bulk gene expression and the limited overlap of single-cell gene expression changes between animal models and patients, but they also reveal consistent pathway-level changes. n=6 mouse kidney scRNA-seq samples: n=2 control, n=1 transgenic overexpression of Notch1, n=2 transgenic overexpression of PGC1a, n=1 Esrra KO小鼠模型已被广泛用于阐明肾脏疾病的发病机制，并在药物研发领域发挥关键作用。然而，目前尚未对这类模型开展系统性分析与比较。 我们对18种常用小鼠肾脏疾病模型的单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）数据（36份样本）以及批量基因表达（bulk gene expression）数据（42份样本）进行了整合分析。我们还将小鼠糖尿病肾病模型的单细胞核RNA测序（single-nucleus RNA sequencing, snRNA-seq）数据，与糖尿病肾病患者及健康对照者的对应测序数据进行了比对。 我们生成了经过统一标准化处理的小鼠肾脏单细胞图谱，涵盖近30万个细胞的转录组信息，成功鉴定出肾脏所有主要细胞类型与细胞状态。 分析结果显示，细胞类型占比的变化是驱动批量基因表达谱差异的核心因素。 尽管单细胞水平的基因表达变化大多具有模型特异性，但不同疾病模型在通路层面展现出高度相似的表达改变模式。 张量分解（tensor decomposition）分析揭示了疾病状态下近端肾小管细胞的关键转录组变化。 具体而言，我们鉴定出代谢通路与炎症相关通路的表达发生了显著改变。 小鼠糖尿病肾病模型与糖尿病肾病患者仅共享少量保守的细胞类型特异性差异表达基因（differentially expressed genes, DEGs），但我们观察到小鼠与人类糖尿病肾病样本在通路层面的激活模式具有高度保守性。 本研究构建了一套全面的小鼠肾脏单细胞图谱，明确了小鼠疾病状态下基因表达的共性与差异。 研究结果强调了细胞异质性（cell heterogeneity）在驱动批量基因表达变化中的核心作用，以及动物模型与人类患者间单细胞基因表达变化的有限重叠，但同时也揭示了通路层面的保守改变模式。 本次研究包含6份小鼠肾脏scRNA-seq样本：其中2份为对照样本，1份为Notch1转基因过表达样本，2份为PGC1α转基因过表达样本，1份为Esrra基因敲除（Esrra KO）样本。