Spatial transcriptional mapping of the human nephrogenic program

Creators: Lindström, Nils O.¹; Sealfon, Rachel^{2, 3}; Chen, Xi^{2, 3}; Parvez, Riana K.¹; Ransick, Andrew¹; De Sena Brandine, Guilherme⁴; Guo, Jinjin¹; Hill, Bill⁵; Tran, Tracy¹; Kim, Albert D.¹; Zhou, Jian^{2, 3}; Tadych, Alicja³; Watters, Aaron⁶; Wong, Aaron⁶; Lovero, Elizabeth⁶; Grubbs, Brendan H.¹; Thornton, Matthew E.¹; McMahon, Jill A.¹; Smith, Andrew D.⁴; Ruffins, Seth W.¹; Armit, Chris⁷; Troyanskaya, Olga G.^{8, 3}; McMahon, Andrew P.¹

1. University of Southern California
2. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
3. Princeton University
4. Molecular and Computational Biology, Division of Biological Sciences, University of Southern, Los Angeles, CA 90089, USA.
5. MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK.
6. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
7. MRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK; BGI Hong Kong, 26/F, Kings Wing Plaza 2, 1 On Kwan Street, Shek Mun, NT, Hong Kong.
8. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA; Department of Computer Science, Princeton University, Princeton, NJ, USA. Electronic address: ogt@genomics.princeton.edu.

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.

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Acknowledgement

We thank current and past members of the McMahon Lab for helpful discussions and feedback. We would also like to thank Melissa L. Wilson (Department of Preventive Medicine, University of Southern California) and Family Planning Associates for coordinating fetal tissue collection. B.H. and C.A. wish to thank Richard Baldock of the MRC Human Genetics Unit, IGMM, University of Edinburgh for supporting this project and providing financial support enabling travel for C.A. to visit USC and N.O.L. at the outset of the project. We thank Friedhelm Hildebrandt for discussion and informational sources on known KD/CAKUT genes and Pietro Cippa for helpful discussions on GWAS studies. Funding: Work in A.P.M.’s laboratory was supported by grants from the National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (DK054364, DK110792) and the California Institute for Regenerative Medicine (LA1-06536). Work in OT’s laboratory was supported by NIH/NIDDK grants U24DK100845, UGDK114907, and U2CDK114886 and NIH grant UH3TR002158 to O.G.T. O.G.T. is a senior fellow of the Genetic Networks program of the Canadian Institute for Advanced Research (CIFAR).

Contributions

N.O.L., R.S., X.C., C.A., O.G.T., and A.P.M. wrote the manuscript. N.O.L., R.S., X.C., C.A., B.H., A.S., O.G.T., and A.P.M. designed the experiments. N.O.L., R.S., X.C., R.P., A.R., G.D.S.B., B.H., J.C., T.T., A. Watters, A. Wong, B.G., M.T., J.A., and S.R. performed experiments and analyses and helped with computational tools and web resources.

Conflict of Interest

A.P.M. is a scientific advisor on kidney research at Novartis, TRESTLE Therapeutics, eGENESIS, and IVIVA Medical.

Supplemental Material

Document S1. Figures S1–S11.
Table S1. Differential gene expression in whole week 14 kidneys, related to Figures 3 and S2. Differential gene expression analyses between all cells in replicate 1 and replicate 2 of the week 14 human fetal cortex cell isolations as shown in Figure S2A.
Table S2. Differential gene expression during nephrogenesis, related to Figure 3. Differential gene expression analyses between cells in clusters 1–18 as shown in Figure 3A.
Table S3. Differential gene expression between mapped transcriptomes, related to Figure 4. Differential gene expression analyses between cells mapping to patterns 1–8 as shown in Figure 4D.
Table S4. KD/CAKUT genes expression and functionally related gene prediction, related to Figure 5. Sheet 1, 272 KD/CAKUT genes from literature. Sheet 2, 199 KD/CAKUT genes are active in at least one cell type (expressed in >10% cells). Normalized gene expression value per gene per cell type is shown. Sheet 3, summary for connectivity of KD/CAKUT genes in each of the 18 cell type-specific functional networks. Sheet 4, network-based prediction of cell type-specific genes that are functionally related to KD/CAKUT, validated against GWAS.
Table S5. Genome-scale data sets used for generating gene functional networks, related to Figure 5. Sheet 1. Dataset Name, Title, Description, and Version download link for each dataset used in generating the gene functional networks.
Document S2. Article plus supplemental information.

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