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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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.
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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.).
All authors declare no competing interests.
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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.
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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