| Literature DB >> 34428401 |
Nils O Lindström1, Rachel Sealfon2, Xi Chen2, Riana K Parvez3, Andrew Ransick3, Guilherme De Sena Brandine4, Jinjin Guo3, Bill Hill5, Tracy Tran3, Albert D Kim3, Jian Zhou2, Alicja Tadych6, Aaron Watters7, Aaron Wong7, Elizabeth Lovero7, Brendan H Grubbs8, Matthew E Thornton8, Jill A McMahon3, Andrew D Smith4, Seth W Ruffins3, Chris Armit9, Olga G Troyanskaya10, Andrew P McMahon11.
Abstract
Congenital abnormalities of the kidney and urinary tract are among the most common birth defects, affecting 3% of newborns. The human kidney forms around a million nephrons from a pool of nephron progenitors over a 30-week period of development. To establish a framework for human nephrogenesis, we spatially resolved a stereotypical process by which equipotent nephron progenitors generate a nephron anlage, then applied data-driven approaches to construct three-dimensional protein maps on anatomical models of the nephrogenic program. Single-cell RNA sequencing identified progenitor states, which were spatially mapped to the nephron anatomy, enabling the generation of functional gene networks predicting interactions within and between nephron cell types. Network mining identified known developmental disease genes and predicted targets of interest. The spatially resolved nephrogenic program made available through the Human Nephrogenesis Atlas (https://sckidney.flatironinstitute.org/) will facilitate an understanding of kidney development and disease and enhance efforts to generate new kidney structures.Entities:
Keywords: disease; human; kidney; machine-learning; nephrogenesis; nephron; networks; registration; single-cell; spatial
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Year: 2021 PMID: 34428401 PMCID: PMC8396064 DOI: 10.1016/j.devcel.2021.07.017
Source DB: PubMed Journal: Dev Cell ISSN: 1534-5807 Impact factor: 13.417