Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
San Diego Institute of Science, Altos Labs, San Diego, CA, USA.
Nature. 2023 Jul;619(7970):585-594. doi: 10.1038/s41586-023-05769-3. Epub 2023 Jul 19.
Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.
理解肾脏疾病依赖于定义细胞类型和状态的复杂性、它们的相关分子特征以及组织邻域内的相互作用。在这里,我们应用了多种单细胞和单核检测(超过 40 万个核或细胞)和空间成像技术,对广泛的健康参考肾脏(45 个供体)和患病肾脏(48 个患者)进行了研究。这提供了一个包含 51 种主要细胞类型的高分辨率细胞图谱,其中包括罕见的和以前未描述的细胞群体。多组学方法提供了详细的转录组谱、调控因子和跨越整个肾脏的空间定位。我们还定义了 28 种在肾脏损伤中改变的肾单位和间质的细胞状态,包括有丝分裂、适应性(成功或适应性修复)、过渡和退行性状态。分子特征允许使用空间转录组学在损伤邻域中定位这些状态,而大规模的 3D 成像分析(约 120 万个邻域)提供了与活跃免疫反应的相应联系。这些分析定义了与损伤时间过程和小生境相关的生物学途径,包括上皮修复的特征,这些特征预测了与肾功能下降相关的适应性不良状态。这个整合的多模态空间人类肾脏细胞图谱代表了一个全面的细胞状态、邻域、与结果相关的特征和公开的交互式可视化的基准。