Spatial transcriptional mapping of the human nephrogenic program

Nils O. Lindström, Rachel Sealfon, Xi Chen, Riana K. Parvez, Andrew Ransick, Guilherme De Sena Brandine, Jinjin Guo, Bill Hill, Tracy Tran, Albert D. Kim, Jian Zhou, Alicja Tadych, Aaron Watters, Aaron Wong, Elizabeth Lovero, Brendan H. Grubbs, Matthew E. Thornton, Jill A. McMahon, Andrew D. Smith, Seth W. RuffinsChris Armit, Olga G. Troyanskaya, Andrew P. McMahon

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2381-2398.e6
JournalDevelopmental cell
Volume56
Issue number16
DOIs
StatePublished - Aug 23 2021

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Biochemistry, Genetics and Molecular Biology(all)
  • Developmental Biology
  • Cell Biology

Keywords

  • disease
  • human
  • kidney
  • machine-learning
  • nephrogenesis
  • nephron
  • networks
  • registration
  • single-cell
  • spatial

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