Obesity-instructed TREM2^high macrophages identified by comparative analysis of diabetic mouse and human kidney at single cell resolution

Abstract

Mouse models are a tool for studying the mechanisms underlying complex diseases; however, differences between species pose a significant challenge for translating findings to patients. Here, we used single-cell transcriptomics and orthogonal validation approaches to provide cross-species taxonomies, identifying shared broad cell classes and unique granular cellular states, between mouse and human kidney. We generated cell atlases of the diabetic and obese kidney using two different mouse models, a high-fat diet (HFD) model and a genetic model (BTBR ob/ob), at multiple time points along disease progression. Importantly, we identified a previously unrecognized, expanding Trem2high macrophage population in kidneys of HFD mice that matched human TREM2high macrophages in obese patients. Taken together, our cross-species comparison highlights shared immune and metabolic cell-state changes.

High Resolution Slide-seqV2 Spatial Transcriptomics Enables Discovery of Disease-Specific Cell Neighborhoods and Pathways

“…Among many insights, this work revealed multiple macrophage subsets including C1QB+LYVE1+ macrophages in human adult kidneys. A unique TREM2+ subset was expanded with age in kidneys of diabetic and obese mice and humans 20 , mirroring obesity-associated macrophages in other tissues 48 . Based on these results, we hypothesized that these specialized macrophage populations might be localized in disease-related microenvironments, and that we could define these cell neighborhoods using high resolution spatial transcriptomics.…”

“…Several studies using single cell (sc-) or single nucleus (sn-) RNA sequencing of human and mouse kidney in health and disease have been performed [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] , and have used spatial validation methods including immunofluorescence microscopy, fluorescence in-situ hybridization, and targeted panels of a few dozen RNA probes or antibodies. However, these spatial methods are hampered by relatively low throughput [22][23][24][25][26][27][28] .…”

“…To date, efforts to spatially capture transcriptomewide profiles in kidney cells in situ have been limited in resolution [29][30][31] . Given that several cell types in spatially restricted areas are the hypothesized drivers of injury as a result of ischemia and inflammation [32][33][34][35][36] , diabetic conditions 20,[37][38][39][40] , and genetic disorders [41][42][43][44][45][46][47] , high resolution spatial transcriptomics in the kidney could catalyze mechanistic and therapeutic insights.…”

“…To understand the way in which cells act in concert in the kidney, we employed Slide-seqV2, a high resolution method for unbiased spatial transcriptomics, with a feature (or bead) size of 10µm most commonly capturing 1-2 cells/feature, and no more than 3 cells/feature 1,2 . Leveraging this near-single cell spatial resolution and single cell atlases 20 , we developed SlideSeqV2 working protocols and analysis pipelines for human and mouse kidney tissue. Furthermore, we compared tissue from healthy and diseased kidneys, because the side-by-side analysis allowed us to measure changes in the organization of cellular neighborhoods, thus taking full advantage of the high spatial resolution.…”

“…The arrays were positioned at different locations in each tissue cross-section to cover as much area in each 3mm array as possible. Following library preparation, we mapped cell types using our recently developed single cell reference 20 (Methods). Cell type classifications of Slide-seqV2 features were confirmed based on established markers of gene expression (Fig S1).…”