An unsupervised feature selection by back-propagated weighting the non-Gaussianity score of independence components
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Author list: Modecrua W., Padungwiang P., Krathu W.
Publisher: Nature Research
Publication year: 2019
Journal: Nature Communications (2041-1723)
Volume number: 10
ISSN: 2041-1723
eISSN: 2041-1723
Languages: English-Great Britain (EN-GB)
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Abstract
Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples. ฉ 2019, The Author(s).
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