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The transcriptional and regulatory identity of erythropoietin producing cells

Nature Medicine volume 29, pages 1191–1200 (2023)Cite this article

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Abstract

Erythropoietin (Epo) is the master regulator of erythropoiesis and oxygen homeostasis. Despite its physiological importance, the molecular and genomic contexts of the cells responsible for renal Epo production remain unclear, limiting more-effective therapies for anemia. Here, we performed single-cell RNA and transposase-accessible chromatin (ATAC) sequencing of an Epo reporter mouse to molecularly identify Epo-producing cells under hypoxic conditions. Our data indicate that a distinct population of kidney stroma, which we term Norn cells, is the major source of endocrine Epo production in mice. We use these datasets to identify the markers, signaling pathways and transcriptional circuits characteristic of Norn cells. Using single-cell RNA sequencing and RNA in situ hybridization in human kidney tissues, we further provide evidence that this cell population is conserved in humans. These preliminary findings open new avenues to functionally dissect EPO gene regulation in health and disease and may serve as groundwork to improve erythropoiesis-stimulating therapies.

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Fig. 1: Detection and enrichment of renal Epo-producing cells.
Fig. 2: Epo-producing Norn cells are a distinct stromal cell type.
Fig. 3: ATAC-seq profiling of Norn cells identifies genomewide and Epo locus-specific regulatory elements.
Fig. 4: Norn cell molecular signature is conserved in human Epo-producing cells.

Data availability

Mouse single-cell RNA-seq and single-cell ATAC-seq data, and human single-cell RNA-seq data that support the findings of this study, were deposited in the Gene Expression Omnibus (GEO) under accession code GSE193321. Previously published scRNA-seq data that were re-analyzed here are available under the accession codes GSE114530 (ref. 47), GSE129798 (ref. 44), EGAS00001002325GSE155794 (ref. 46) and GSE107585 (ref. 43), as well as from the European Genome-phenome Archive (EGA) under study IDs EGAS00001002325 (ref. 42). We used mouse genome mm10 and human genome Hg38 as reference genomes. ATAC-seq tracks are accessible on UCSC browser https://genome.ucsc.edu/s/bjoert/Mouse%20hypoxia%20kidney%20bulk%20scATAC%2Dseq%20from%20single%20nuclei.

Code availability

Metacell source code can be found at https://github.com/tanaylab/metacell. Source code to identify Norn cells in human single cell atlas can be found at https://github.com/AmitLab/Kidney-Norn-cells-identification. Source code used for transcription factor motif analysis can be found at https://github.com/vanheeringen-lab/gimmemotifs.

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Acknowledgements

We thank members of the Amit laboratory for critical discussions. We thank Y. Kuperman, L. Adler and S. Viukov for assisting with hypoxia experiments. We thank K. Pozyuchenko, G.H. Siloni, C. Padberg and T. Bajanowski for assisting with human kidney samples, and P. Spielmann and T. Knöpfel for technical support. We thank T.-M. Salame, E. Hagai and E. Kopitman for assisting with flow cytometry. This study was funded by European Union European Research Council Advanced (grant no. 101055341-TROJAN-Cell, I.A.); Deutsche Forschungsgemeinschaft (Project-ID 259373024 – TRR 167, I.A.), Israel Science Foundation (ISF; grant no. 1944/22, I.A.), the ISF Israel Precision Medicine Program (607/20, I.A.), Human Frontier Science Program (long-term postdoctoral fellow LT 000230/2019, B.K.K.), European Molecular Biology Organization (postdoctoral fellowship, ALTF 112-2022, A.G.), Villum Fonden (Young Investigator award project no. 00025300, F.R.) and by the Swiss National Science Foundation (grant no. 310030_184813 R.H.W.).

Author information

Author notes

  1. These authors contributed equally: Bjørt K. Kragesteen, Amir Giladi, Eyal David, Shahar Halevi, Fernando Racimo, Eske Willerslev, Chamutal Gur, Roland H. Wenger, Ido Amit.

  2. These authors supervised this work: Chamutal Gur, Roland H. Wenger, Ido Amit.

Authors and Affiliations

  1. Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel

    Bjørt K. Kragesteen, Amir Giladi, Eyal David, Shahar Halevi, Laufey Geirsdóttir, Baoguo Li, Ken Xie, Yonatan Katzenelenbogen, Fadi Sheban, Anna Gurevich-Shapiro, Mor Zada, Truong San Phan, Roberto Avellino, Shuang-Yin Wang, Oren Barboy, Shir Shlomi-Loubaton, Chamutal Gur & Ido Amit

  2. Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, the Netherlands

    Amir Giladi

  3. Oncode Institute, Utrecht, the Netherlands

    Amir Giladi

  4. Institute of Physiology, University of Zurich, Zurich, Switzerland

    Olga M. Lempke, Andreas M. Bapst, Sophie L. Dahl & Roland H. Wenger

  5. Division of Haematology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Anna Gurevich-Shapiro

  6. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Anna Gurevich-Shapiro, Mor Zada, Snir Dekalo & Barak Rosenzweig

  7. Institute of Physiology, University of Duisburg-Essen, Essen, Germany

    Sandra Winning & Joachim Fandrey

  8. Institute for Forensic Medicine, University Hospital Essen, Essen, Germany

    Philipp P. Markwerth

  9. Urology Department, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Snir Dekalo

  10. Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel

    Hadas Keren-Shaul & Merav Kedmi

  11. GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

    Martin Sikora, Thorfinn S. Korneliussen, Fernando Racimo & Eske Willerslev

  12. Department of Medicine, University of Utah, Salt Lake City, UT, USA

    Josef T. Prchal

  13. Department of Urology, Sheba Medical Center, Ramat Gan, Israel

    Barak Rosenzweig

  14. Department of Urology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel

    Vladimir Yutkin

  15. Department of Medicine, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel

    Chamutal Gur

  16. National Centre of Competence in Research ‘Kidney.CH’, University of Zurich, Zurich, Switzerland

    Roland H. Wenger

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  1. Bjørt K. Kragesteen
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  35. Ido Amit
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Contributions

B.K.K. and I.A. conceived the project and designed the experiments. B.K.K. performed the experiments. A.G. and C.G. contributed to experimental and project design. L.G. contributed to experimental and project design and assisted with FACS experiments. A.M.B., Y.K., S.L.D., M.Z., O.B. and S.S.-L., assisted with experiments. A.G.-S., S.D., V.Y., J.F., S.W., P.P.M. and B.R. assisted with human kidney specimens. S.H. and C.G. contributed to the human experimental part. A.G., E.D. and K.X. analyzed the data. R.A. and S.-Y.W. contributed to the analysis. A.M.B., S.S.-L. and O.M.L. performed mRNA-FISH experiments and image analysis. B.L. and A.G. assisted with image analysis. F.R., E.W., J.T.P., M.S. and T.S.K. assisted with analysis. H.K.-S., F.S., T.S.P. and M.K. assisted the 10x multiome experiment. B.K.K., A.G. and I.A. wrote the manuscript.

Corresponding authors

Correspondence to Bjørt K. Kragesteen or Ido Amit.

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All authors declare no competing interests.

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Nature Medicine thanks Katalin Susztak, Tammie Bishop and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Anna Maria Ranzoni, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 QC of single cell data.

a, b, QC for mouse renal MARS-seq showing number of reads and genes per plate, respectively. Box plots presents the third quartile (top of the box), median (centre lines) and first quartile (bottom of the box) of measurements. The whiskers represent 1.5 times the interquartile range from the top or bottom of the box. From n = 22 independent experiments, kidneys from n = 52 mice (33 males, 19 females), cells were sorted into 384 well plates. Nu,ber of QC cells per plate are as follows: AB9040 = 103, AB9041 = 122, AB9042 = 103, AB9043 = 107, AB9044 = 109, AB9045 = 124, AB9046 = 180, AB9047 = 177, AB9202 = 132, AB9203 = 12, AB9204 = 101, AB9205 = 28, AB9206 = 11, AB9207 = 16, AB9208 = 19, AB9209 = 182, AB9210 = 180, AB9211 = 203, AB9212 = 168, AB9213 = 211, AB9214 = 253, AB9215 = 249, AB9216 = 196, AB9217 = 198, AB9218 = 214, AB9219 = 182, AB9220 = 197, AB9221 = 174, AB9316 = 118, AB9317 = 116, AB9453 = 365, AB9454 = 298, AB9455 = 361, AB9456 = 361, AB9464 = 176, AB10289 = 12, AB10290 = 21, AB10291 = 91, AB10292 = 34, AB10293 = 28, AB10344 = 279, AB10345 = 299, AB10346 = 263, AB10347 = 259, AB10448 = 127, AB10449 = 35, AB10450 = 103, AB10451 = 327, AB10452 = 321, AB10453 = 342, AB10454 = 365, AB10455 = 361, AB10460 = 64, AB10461 = 291, AB10765 = 339, AB10766 = 318, AB10767 = 328, AB10768 = 277, AB10769 = 319, AB10770 = 297, AB10771 = 329, AB10772 = 301, AB10926 = 369, AB10927 = 362, AB10928 = 361, AB10929 = 357, AB11060 = 360, AB11061 = 355, AB11062 = 365, AB11063 = 364, AB11064 = 352, AB11065 = 330, AB11066 = 360, AB11306 = 356, AB11307 = 344, AB11308 = 335, AB11309 = 329, AB11310 = 320, AB11311 = 324, AB11312 = 353, AB11313 = 333, AB11409 = 16, AB11410 = 11, AB11411 = 26, AB11412 = 20, AB11413 = 43, AB11414 = 17, AB11415 = 67, AB11416 = 137, AB11630 = 295, AB11631 = 297, AB11632 = 326, AB11633 = 318, AB11634 = 285, AB11635 = 241, AB11636 = 293, AB11637 = 282, AB11638 = 279, AB11639 = 278, AB11640 = 269, AB11641 = 241, AB12023 = 260, AB12024 = 234, AB12025 = 333, AB12026 = 268, AB12027 = 185, AB12028 = 164, AB12029 = 335, AB12030 = 306, AB12031 = 315, AB12032 = 150, AB12033 = 50, AB12034 = 55, AB12035 = 9, AB12890 = 299, AB12891 = 292, AB12892 = 320, AB12893 = 311, AB12894 = 338, AB12895 = 336, AB13561 = 246, AB13714 = 323, AB13715 = 315, AB13716 = 288, AB13717 = 280, AB13718 = 327, AB13719 = 323, AB13720 = 260, AB13721 = 256, AB13722 = 257, AB13723 = 307, AB14027 = 322, AB14028 = 289, AB14029 = 345, AB14030 = 325, AB14031 = 333, AB14032 = 335, AB14033 = 302, AB14034 = 328, AB14035 = 342, AB14036 = 329, AB14037 = 338, AB14038 = 334, AB14039 = 300, AB14040 = 287, AB14041 = 204, AB14042 = 231, AB14043 = 177, AB14044 = 203. c, QC for 10x multiome mRNA showing number of reads, genes per plate and percentage mitochondria. N = 3,861 cells pooled from 5 mice (3 males, 2 females) exposed to hypoxia 0.1% CO, 4 hours. Each cells has a mean read of 14,537. d, QC for mouse renal 10x multiome ATAC data showing number of reads and genes per plate. N = 3,306 cells pooled from 5 mice (3 males, 2 females) exposed to hypoxia 0.1% CO, 4 hours, in one experiment. Per cell we obtained 10,666 ATAC median high-quality fragments. e, QC for human renal RNA data derived from 10x scRNAseq showing number of reads, genes, and percentage mitochondria per cell, per patient. Box plots presents the third quartile (top of the box), median (centre lines) and first quartile (bottom of the box) of measurements. The whiskers represent 1.5 times the interquartile range from the top or bottom of the box. N = 3 independent experiments, each experiment using kidney from different individual n = 3 biologically independent samples: human sample 1 = 8185 cells, human sample 2 = 3707 cells, human sample 3 = 6358 cells.

Extended Data Fig. 2 Identification and validation of Norn cell markers.

a, Label transfer from Park et al.43 to MARS-seq meta-cell clusters. Each coloured circle denotes a meta-cell. Colour scale denotes z-score of each gene signature across all meta-cells. Collecting duct i., collecting duct intercalated; collecting duct t., collecting duct transient; collecting duct p., collecting duct principal; Distal conv. Tub., distal convoluted tubule. b, Bubble plot depicting enrichment of selected marker genes used for cell labels. Coloured circles denote expression intensity (log2 fold change over the mean). Size of circle represents the percentage of cells expressing the gene within cell type. c, Plot depicts quantification of double positive cells in mRNA FISH imaging of hypoxic (0.1%, 4 hours) kidney sections. N = 1 biological specimen (female), n = 3 independent experiments. The mean is shown. Quantification of cells are shown on top of the plot. Y-axis represents the percentage of Epo expressing cells that are double positive for tested Norn cell marker expression (x-axis). X-axis is showing Norn cell markers tested. d, Schematic of experimental procedure and representative gating strategy using CD73 to enrich for Norn cells in wildtype C57BL/6J mice. Linage depletion using CD31 (endothelial), CD326 (epithelial), and CD45 (immune) markers, and enrichment of CD73 positive cells.

Extended Data Fig. 3 Extended ATAC analysis.

a, UMAP showing cell type clusters based on 10x multiome RNA using the Seurat tool71. Cell label transfer based on supplementary table 1. b, UMAP showing cell type clusters based on 10x multiome ATAC using Seurat tool. c, Heatmap representing sub clustering of selected ATAC-seq regions, and their associated genes.

Extended Data Fig. 4 Transcription-factor motif analysis.

Transcription-factor motif analysis showing enrichment of motifs in accessible regions n = 5,844 peaks Norn specific ATAC-enhancer peaks vs n = 360380 total background sequences. P-value is calculated using cumulative binomial distributions.

Extended Data Fig. 5 SCENIC analysis of Norn cells identifies Tcf21 regulome.

a, Heatmap representing TF regulome enrichment from MARS-seq dataset derived from normoxic kidneys. b, Heatmap representing TF regulome enrichment in our MARS-seq dataset derived from hypoxic kidneys. c, Tcf21 regulome: List of downstream targets of Tcf21.

Extended Data Fig. 6 Identification of Norn cells across development and species.

a, Prediction of identified renal cell types, including Norn cells, in adult mouse kidney MARS-seq data (as shown in Fig. 4a). Heatmap showing sum log2 of normalised UMIs. b–g Heatmaps representing predicted cell types, with red box highlighting Norn-like cells: (b) Norn enriched MARS-seq renal dataset (7.4% Norn cells), (c) C57BL/6J adult mice renal scRNA-seq44 (0.1% Norn cells), (d) E18.5 foetal kidney scRNA-seq data45 (7.6% Norn cells, n = 3 individuals), (e) E18.5 Foxd1 enriched foetal kidney scRNA seq data46 (19.9% Norn cells), (f) adult human kidney scRNA seq data (3% Norn cells, n = 12 individuals)42, and (g) foetal week 18 human kidney scRNA seq data (18.8% Norn cells, n = 1 individual)47.

Extended Data Fig. 7 Conservation of Norn cell markers across development and species.

a–e: Scatter plot depicting global log2 size-normalised gene expression in Norn cells versus all other cell types. Green circles represent genes shown in ‘selected’ bar plot. Bar plots showing log2 fold change of Norn cells versus all other cell types in selected genes, receptors, and TFs. a, C57BL/6J adult mice renal scRNA-seq44. b, E18.5 unenriched mouse foetal kidney scRNA-seq data45. c, E18.5 Foxd1 enriched foetal kidney scRNA seq data46. d, Adult human unenriched kidney scRNA seq data42. e, Foetal week 18 human unenriched kidney scRNA seq data (18.8% Norn cells)47. f, Plot showing quantification of double positive cells in mRNA FISH imaging from hypoxic human kidney. N = 3 independent experiments from n = 1 biological specimen (female). The mean is shown. Y-axis is showing percentage of EPO expressing cells that are double positive for tested Norn cell marker expression (x-axis). X-axis is presenting Norn cell markers tested.

Supplementary information

Reporting Summary

Supplementary Table

1. Gene markers for renal cell types. 2. Top 50 genes for renal cell types. 3. Differential expression between Norn cells versus stromal cells. 4. Differential expression in Norn cells in normoxia versus hypoxia. 5. Cell type-specific ATAC peaks. 6. qPCR analysis. 7. RNA in situ hybridization quantification.

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Kragesteen, B.K., Giladi, A., David, E. et al. The transcriptional and regulatory identity of erythropoietin producing cells. Nat Med 29, 1191–1200 (2023). https://doi.org/10.1038/s41591-023-02314-7

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