Epigenetic atlas of aging kidneys with single cell resolution
Technological breakthroughs are generating big data sets to assemble molecular maps of kidneys and other tissues. These approaches aim to assign molecular features to cells residing in defined microenvironment and anatomic structures. As such, molecular maps link intracellular processes to kidney pathology. For example, a transcriptional map in primate brain identified a disease-specific spatiotemporal enrichment of gene expression patterns, linking them to neurodevelopmental disorders. This pioneering research underscores the translational value of generating tissue maps that can identify molecular events driving pathologic changes that cannot otherwise be detected by either histology or molecular assays when used alone. Epigenetic changes drive kidney aging and associated chronic kidney disease (CKD). These pathologic processes most likely start in individual cells with altered epigenetic landscapes. The ability to detect early epigenetic events in single cells that become dominant and contribute later to tissue fibrosis and decline in nephron function will advance epigenetic studies in kidneys and become a novel sensitive diagnostic tool. Due to the lack of efficient technologies, new methods are needed to map epigenetic events in kidney cells in the context of histological features. The in situ Epigenetic Visualization Assay, that we have recently developed, will bridge this technological gap and provide the means to begin the mapping of epigenetic events in kidney tissues with single-cell and allelic resolution that will allow early detection of culprit aging cells within the structural context of the tissue. As such, EVA adds a critical new epigenetic dimension to better understand molecular bases of functional heterogeneity in the nephron. Previous microscopy-based single-cell epigenetic methods (6) yield qualitative rather than quantitative data and work in a small fraction of cells (<1%). Efficient single cell epigenetic assays that preserve spatial information about cell position are needed to better understand epigenetic processes driving aging and associated kidney diseases. Our single cell EVA allows for quantitative analysis of epigenetic changes at genomic loci within a context of intact tissues. Combined with automated imaging, and multiplexed with transcriptome analysis, we propose to adapt EVA for mapping epigenetic features in the kidneys at aging-related and control genes with single cell resolution, that will provide a fundamentally new epigenetic view of aging kidney.
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