Key Points – Myofibroblasts and injury repair–related cell types were exclusively observed in polycystic kidney disease and enriched within the cyst microenvironment. Cyst-associated gene signature of 45 genes with decreased expression further away from the cysts was largely related to inflammation. Communication in low-inflamed cystic microdomains related to cellular signaling, morphogenesis, and inflammation in polycystic kidney disease. Background – Changes in the cyst microenvironment in polycystic kidney disease (PKD) may drive progressive cyst formation. Bulk-cell and single-cell RNA sequencing have advanced our understanding of altered signaling; however, the lack of spatial information has limited our insights into local gene expression and cellular communication near cysts.Methods – We used wild-type and Pkd1-deficient mouse kidneys to generate 10× Genomics Visium Spatial Gene Expression datasets. Using our single-cell mouse kidney atlas and single-cell sequencing data for spot deconvolution, we enhanced resolution and estimated enriched cell types. We analyzed spatial gene expression patterns and used a cyst-centered analysis to identify cyst-associated gene signature. Cell communication near cysts was investigated, identifying key ligand-receptors. Prioritized key factors were validated in tissues.Results – We observed enrichment of fibroblasts, injury repair–related cell types, and diverse immune populations in PKD. Injury repair–related cells were exclusively observed in PKD, predominantly localized within immune cell–dense regions near cysts. These cells collectively contributed to the altered gene expression profile in PKD, including cyst-associated signature genes related to inflammatory processes. Analysis of cellular communication in less-inflamed regions around cysts revealed the involvement of multiple cell types. Key ligand-receptor interactions were associated with cytokine signaling, fibrosis, cellular development, and repair. These included Angpt2, C3, Csf1, Cxcl12, Il34, Gas6, Il16, Mdk, Mif, Ptn, Sfrp2, Spp1, Sdc1, Tnc, Tnfsf12, and Wnt5a. In addition, extracellular matrix (ECM) proteins implicated in immune response, ECM remodeling, cell adhesion, and cell signaling were identified, such as Adam9, Adam10, Col1a1, Col3a1, Col4a2, Lamb2, Lamc1, Efnb1, Efnb2, Thbs1, Thbs2, and Vcam1. Immunohistochemistry confirmed expression of Syndecan-1-Collagen IV, Midkine-Integrin β1, CSF-1, Pleiotrophin, and Tenascin-C in cystic kidneys.Conclusions – Spatial transcriptomics in PKD revealed enrichment of (myo)fibroblasts, immune, and injury repair–related cells near cysts, creating a (pro)inflammatory and (pro)fibrotic niche. Key ligand-receptor and ECM interactions were identified and validated.

We present COAST (consecutive multi-omics alignment of spatial tissues), a method to reliably physically align consecutive tissue sections to produce a unified multi-modal molecular dataset suitable for downstream applications. COAST relies exclusively on the images associated with spatial data, eliminating the need for common molecular features or prior annotations. We demonstrate the effectiveness of COAST using spatial transcriptomics slides from different technologies, tissues, and resolutions, in which it achieves performance comparable to established uni-modal alignment tools. Applying COAST to spatial transcriptomics and metabolomics/lipidomics tissue sections from a mouse model of ischemia-reperfusion injury allowed the investigation of lipid/metabolite features of transcriptionally defined cell types. Overall, COAST offers a streamlined and integrative solution for multi-modal spatial data alignment.

Dystrophinopathies are caused by pathogenic variants in the DMD gene, resulting in partial (Becker) or complete loss (Duchenne) of dystrophin. Becker (BMD) and Duchenne muscular dystrophy (DMD) are characterized by progressive muscle wasting, fatty replacement, fibrosis, and loss of function. To study histopathological changes, we used Visium spatial transcriptomics to profile skeletal muscle biopsies of patients affected by dystrophinopathy (n = 8) and healthy controls (n = 4). We estimated the proportion of cell types and their spatial localization across samples applying a deconvolution strategy using previously published single-nucleus RNA-sequencing data. We identified genes enriched in fat patches and cell types such as fibroadipogenic progenitors (FAPs) in areas of active pathology. Using expression data of ligand–receptor pairs, we highlight cell–cell communications leading to fibrotic and adipogenic lesions. Finally, analysis of gene expression gradients in areas of adjacent muscle and fat, allowed the identification of genes associated with muscle areas committed to becoming fat.

The kidney's cellular diversity is on par with its physiological intricacy; yet identifying cell populations and their markers remains challenging. Here, we created a comprehensive atlas of the healthy adult mouse kidney (MKA: Mouse Kidney Atlas) by integrating 140.000 cells and nuclei from 59 publicly available single-cell and single-nuclei RNA-sequencing datasets from eight independent studies. To harmonize annotations across datasets, we built a hierarchical model of the cell populations. Our model allows the incorporation of novel cell populations and the refinement of known profiles as more datasets become available. Using MKA and the learned model of cellular hierarchies, we predicted previously missing cell annotations from several studies. The MKA allowed us to identify reproducible markers across studies for poorly understood cell types and transitional states, which we verified using existing data from micro-dissected samples and spatial transcriptomics.