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Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods 1 . 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., (© 2023. The Author(s).)

Studying isoform expression at the microscopic level has always been a challenging task. A classical example is kidney, where glomerular and tubulo-interstitial compartments carry out drastically different physiological functions and thus presumably their isoform expression also differs. We aim at developing an experimental and computational pipeline for identifying isoforms at microscopic structure-level. We microdissected glomerular and tubulo-interstitial compartments from healthy human kidney tissues from two cohorts. The two compartments were separately sequenced with the PacBio RS II platform. These transcripts were then validated using transcripts of the same samples by the traditional Illumina RNA-Seq protocol, distinct Illumina RNA-Seq short reads from European Renal cDNA Bank (ERCB) samples, and annotated GENCODE transcript list, thus identifying novel transcripts. We identified 14,739 and 14,259 annotated transcripts, and 17,268 and 13,118 potentially novel transcripts in the glomerular and tubulo-interstitial compartments, respectively. Of note, relying solely on either short or long reads would have resulted in many erroneous identifications. We identified distinct pathways involved in glomerular and tubulo-interstitial compartments at the isoform level, creating an important experimental and computational resource for the kidney research community., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: YG receives personal payment from Eli Lilly and Company, which may have sponsored certain aspects of the study to MK.

To define cellular mechanisms underlying kidney function and failure, the KPMP analyzes biopsy tissue in a multicenter research network to build cell-level process maps of the kidney. This study aimed to establish a single cell RNA sequencing strategy to use cell-level transcriptional profiles from kidney biopsies in KPMP to define molecular subtypes in glomerular diseases. Using multiple sources of adult human kidney reference tissue samples, 22,268 single cell profiles passed KPMP quality control parameters. Unbiased clustering resulted in 31 distinct cell clusters that were linked to kidney and immune cell types using specific cell markers. Focusing on endothelial cell phenotypes, in silico and in situ hybridization methods assigned 3 discrete endothelial cell clusters to distinct renal vascular beds. Transcripts defining glomerular endothelial cells (GEC) were evaluated in biopsies from patients with 10 different glomerular diseases in the NEPTUNE and European Renal cDNA Bank (ERCB) cohort studies. Highest GEC scores were observed in patients with focal segmental glomerulosclerosis (FSGS). Molecular endothelial signatures suggested 2 distinct FSGS patient subgroups with α-2 macroglobulin (A2M) as a key downstream mediator of the endothelial cell phenotype. Finally, glomerular A2M transcript levels associated with lower proteinuria remission rates, linking endothelial function with long-term outcome in FSGS.

Gene expression responses are complex and frequently involve the actions of many genes to effect coordinated patterns. We hypothesized these coordinated responses are evolutionarily conserved and used a comparison of human and mouse gene expression profiles to identify the most prominent conserved features across a set of normal mammalian tissues. Based on data from multiple studies across multiple tissues in human and mouse, 13 gene expression modes across multiple tissues were identified in each of these species using principal component analysis. Strikingly, 1-to-1 pairing of human and mouse modes was observed in 12 out of 13 modes obtained from the two species independently. These paired modes define evolutionarily conserved gene expression response modes (CGEMs). Notably, in this study we were able to extract biological responses that are not overwhelmed by laboratory-to-laboratory or species-to-species variation. Of the variation in our gene expression dataset, 84% can be explained using these CGEMs. Functional annotation was performed using Gene Ontology, pathway, and transcription factor binding site over representation. Our conclusion is that we found an unbiased way of obtaining conserved gene response modes that accounts for a considerable portion of gene expression variation in a given dataset, as well as validates the conservation of major gene expression response modes across the mammals.

Objective: Skin inflammation heralds systemic disease in juvenile myositis, yet we lack an understanding of pathogenic mechanisms driving skin inflammation in this disease. We undertook this study to define cutaneous gene expression signatures in juvenile myositis and identify key genes and pathways that differentiate skin disease in juvenile myositis from childhood‐onset systemic lupus erythematosus (SLE). Methods: We used formalin‐fixed paraffin‐embedded skin biopsy samples from 15 patients with juvenile myositis (9 lesional, 6 nonlesional), 5 patients with childhood‐onset SLE, and 8 controls to perform transcriptomic analysis and identify significantly differentially expressed genes (DEGs; q ≤ 5%) between patient groups. We used Ingenuity Pathway Analysis (IPA) to highlight enriched biologic pathways and validated DEGs by immunohistochemistry and quantitative real‐time polymerase chain reaction. Results: Comparison of lesional juvenile myositis to control samples revealed 221 DEGs, with the majority of up‐regulated genes representing interferon (IFN)–stimulated genes. CXCL10, CXCL9, and IFI44L represented the top 3 DEGs (fold change 23.2, 13.3, and 13.0, respectively; q < 0.0001). IPA revealed IFN signaling as the top canonical pathway. When compared to childhood‐onset SLE, lesional juvenile myositis skin shared a similar gene expression pattern, with only 28 unique DEGs, including FBLN2, CHKA, and SLURP1. Notably, patients with juvenile myositis who were positive for nuclear matrix protein 2 (NXP‐2) autoantibodies exhibited the strongest IFN signature and also demonstrated the most extensive Mx‐1 immunostaining, both in keratinocytes and perivascular regions. Conclusion: Lesional juvenile myositis skin demonstrates a striking IFN signature similar to that previously reported in juvenile myositis muscle and peripheral blood. Further investigation into the association of a higher IFN score with NXP‐2 autoantibodies may provide insight into disease endotypes and pathogenesis. [ABSTRACT FROM AUTHOR]