OSCR

Human and mouse adrenal glands are characterized by species-specific steroidogenic states and tissue turnover.

Code ↔ Paper

8 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 8 matches
  1. [1] § Methods › Cholesterol balance analysis in bulk scRNA-seq and Visium data ↔ notebooks/human_cortex_endothelium_blood.ipynb, lines 77–154 · score 0.91 · Cyp11a1, Cyp21a2, Cyb5a, Sult2a1, Cyp17a1, Cyp11b1
  2. [2] § Results › Comparison of adult and fetal human adrenal cortex ↔ Adrenal2/AdultMouseHuman/Figures/Figure_AdultMouseHumanSpatialExpression_R.ipynb, lines 545–549 · score 0.80 · CYB5A, SULT2A1, CYP17A1, CYP11B1, HSD3B2, CYP11B2
  3. [3] § Results › Comparison of human and mouse adult adrenal cortex ↔ Adrenal2/AdultMouseHuman/Figures/Figure_AdultMouseHumanSpatialExpression_R.ipynb, lines 545–549 · score 0.77 · LY6D, CYB5A, SULT2A1, CYP17A1, CYP11B1, Ptn
  4. [4] § Results › Comparison of human and mouse adult adrenal cortex ↔ Adrenal2/AdultMouseHuman/Figures/Figure_AdultMouseHumanSpatialExpression_R.ipynb, lines 574–575 · score 0.73 · Ly6d, Sult2a1, Cyp17a1, Cyp11b1, Ptn, VCAN
  5. [5] § Methods › Tissue-pattern identification in high-resolution spatial transcriptomics data ↔ notebooks/human_cortex_endothelium_blood.ipynb, lines 176–184 · score 0.70 · RunUMAP, FindClusters, FindNeighbors, resolution, human
  6. [6] § Results › Steroidogenic cell states within the human adrenal cortex ↔ notebooks/human_cortex_endothelium_blood.ipynb, lines 77–154 · score 0.70 · Cyb5a, Cyp17a1, Cyp11b1, Hsd3b2, Cyp11b2, STAR
  7. [7] § Methods › Tissue-pattern identification in high-resolution spatial transcriptomics data ↔ notebooks/adrenal_mouse.ipynb, lines 20–25 · score 0.57 · FindClusters, FindNeighbors, resolution
  8. [8] § Methods › Cycling cell identification in high-resolution spatial transcriptomics data ↔ notebooks/human_adrenal_medulla.ipynb, lines 317–318 · score 0.51 · cell cycle scores, G2M, CC, genes, adrenal, humans

Paper

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The authors' code

Jupyter notebook · 460 lines · 11 KB · no license · 3 matches

  1. # %%
  2. source('seurat_helpers.R')
  4. # %%
  5. adrenal_whole = new.env()
  6. adrenal_whole$SR = readRDS('../data/Seurat/adrenal.human.seurat.rds')
  8. # %% [markdown]
  9. # # Mesenchyme, adrenal cortex, kidney
  11. # %%
  12. env_mesenchyme_cortex_kidney = new.env()
  13. env_mesenchyme_cortex_kidney$SR = adrenal_whole$SR[,adrenal_whole$[email hidden]$seurat_clusters %in% c(11,31,22,17,38,20,33,27,14,4,24,0,7,28)]
  15. # %%
  16. plot(DimPlot(env_mesenchyme_cortex_kidney$SR, algorithm=2, reduction = "umap", label=T))
  19. # %%
  20. do_seurat_regress(env_mesenchyme_cortex_kidney)
  22. # %%
  23. dir.create('figures/fig3_cortex/')
  24. markers_txt = 'Lyve1
  25. Egfl7
  26. Cldn5
  27. Sox18
  28. Kdr
  29. Cyp26b1
  30. Flt1
  31. Cavin2
  32. Wt1
  33. Foxd1
  34. Cldn5
  35. Podxl
  36. Mafb
  37. Mki67
  38. Pax8
  39. Pax2
  40. Emx2
  41. Bmp7
  42. Cldn4
  43. Krt19
  44. Rdh10
  45. Gata3
  46. Mal
  47. Pou3f3
  48. Meis1
  49. Meis2
  50. Nr2f1
  51. Nr2f2
  52. Robo1
  53. Robo2
  54. Ncam1
  55. Erbb2
  56. Erbb3
  57. Erbb4
  58. Pdzk1
  59. Lrp2
  60. Smim24
  61. Fgfr3
  62. Spp1
  63. Epha7
  64. Gpx3
  65. Dpp4
  66. '
  68. markers_fig1 = toupper(strsplit(markers_txt, '\n')[[1]])
  69. markers_fig1
  71. for(marker in markers_fig1){
  72. print(marker)
  73. FeaturePlot(env_mesenchyme_cortex_kidney$SR, feature=marker, cols = c("lightgrey", "darkred")) +NoLegend() #pt.size = 0.1,
  74. ggsave(paste0('figures/fig3_cortex/004.cortex_no_recluster.marker.',marker,'.png'), width=5, height=5, dpi=320)
  75. }
  77. # %%
  78. dir.create('figures/fig3_cortex/')
  79. markers_txt = 'Lyve1
  80. Egfl7
  81. Cldn5
  82. Sox18
  83. Kdr
  84. Cyp26b1
  85. Flt1
  86. Cavin2
  87. Wt1
  88. Foxd1
  89. Cldn5
  90. Podxl
  91. Mafb
  92. Mki67
  93. Pax8
  94. Pax2
  95. Emx2
  96. Bmp7
  97. Cldn4
  98. Krt19
  99. Rdh10
  100. Gata3
  101. Mal
  102. Pou3f3
  103. Meis1
  104. Meis2
  105. Nr2f1
  106. Nr2f2
  107. Robo1
  108. Robo2
  109. Ncam1
  110. Erbb2
  111. Erbb3
  112. Erbb4
  113. Pdzk1
  114. Lrp2
  115. Smim24
  116. Fgfr3
  117. Spp1
  118. Epha7
  119. Gpx3
  120. Dpp4
  121. Nr5a1
  122. Fdx1
  123. Star
  124. Nov
  125. Cyp11a1
  126. Ldlr
  127. Sult2a1
  128. Cyb5a
  129. Cyp17a1
  130. Cyp21a2
  131. Cyp11b1
  132. Hsd3b2
  133. Cyp17a1
  134. Cyp11b2
  135. Cyp21a2
  136. Cyp11b1
  137. Hsd3b2
  138. EYA1
  139. SIX2
  140. IRX2
  141. IRX3
  142. PBX2
  143. LHX1
  144. KRT8
  145. SOX9'
  147. markers_fig1 = toupper(strsplit(markers_txt, '\n')[[1]])
  148. markers_fig1
  150. for(marker in markers_fig1){
  151. print(marker)
  152. FeaturePlot(env_mesenchyme_cortex_kidney$SR, feature=marker, cols = c("lightgrey", "darkred")) +NoLegend() #pt.size = 0.1,
  153. ggsave(paste0('figures/fig3_cortex/004.cortex_recluster_hb.marker.',marker,'.png'), width=5, height=5, dpi=320)
  154. }
  156. # %% [markdown]
  157. # # Endothelium
  159. # %%
  160. dir.create('figures/fig5_endothelium')
  162. # %%
  163. env_endothelium = new.env()
  164. env_endothelium$SR = adrenal_whole$SR[,adrenal_whole$[email hidden]$fate=='endothelium']
  167. # %%
  168. DimPlot(env_endothelium$SR, group.by='seurat_clusters', pt.size = 1, label = T)+NoLegend()
  169. ggsave('figures/fig5_endothelium/007.endothelium.clusters_labelled.pt1.5x5.square.png', width=5, height=5, dpi=320)
  170. DimPlot(env_endothelium$SR, group.by='seurat_clusters', pt.size = 1, label = F)+NoLegend()
  171. ggsave('figures/fig5_endothelium/007.endothelium.clusters_unlabelled.pt1.5x5.square.png', width=5, height=5, dpi=320)
  173. # %%
  174. do_seurat_regress(env_endothelium)
  176. # %%
  177. with(env_endothelium, {
  178. SR <- FindNeighbors(SR, dims = 1:5)
  179. SR <- FindClusters(SR, resolution = 0.5)
  181. SR <- RunUMAP(SR, dims = 1:8)
  182. plot(DimPlot(SR, reduction = "umap", label=T))
  184. })
  186. # %%
  187. markers1 = toupper(c('PECAM1', 'FLT1', 'CAVIN2', 'PLPP3', 'RAMP2', 'CD24', 'CLDN5', 'Kdr', 'IGFBP1', 'IGFBP2', 'GNG11', 'PLVAP', 'CDH5'))
  188. #'BM2',
  190. for(marker in markers1){
  191. print(marker)
  192. FeaturePlot(env_endothelium$SR, feature=marker, cols = c("lightgrey", "darkred"), pt.size = 1) +NoLegend() #pt.size = 0.1,
  193. ggsave(paste0('figures/fig5_endothelium/004.endothelium.marker.',marker,'.png'), width=5, height=5, dpi=320)
  194. }
  196. # %%
  197. dir.create('figures/fig5_endothelium/reclustered')
  198. markers_txt = 'CXCR4
  199. EFNB2
  200. GJA4
  201. GJA5
  202. MSX1
  203. UNC5B
  204. MKI67
  205. PCNA
  206. ADGRG6
  207. ANXA1
  208. DUSP23
  209. HAPLN3
  210. ACTN1
  211. CCL21
  212. LYVE1
  213. PGM5
  214. PROX1
  215. ACKR3
  216. ACP5
  217. AFP
  218. GATA4
  219. STAB2
  220. B2M
  221. MYL12A
  222. APLNR
  223. EPHB4
  224. NR2F2
  225. TEK
  226. '
  228. markers_fig1 = toupper(strsplit(markers_txt, '\n')[[1]])
  229. markers_fig1
  231. for(marker in markers_fig1){
  232. print(marker)
  233. tryCatch({
  234. FeaturePlot(env_endothelium$SR, feature=marker, cols = c("lightgrey", "darkred")) +NoLegend() #pt.size = 0.1,
  235. ggsave(paste0('figures/fig5_endothelium/reclustered/004.endothelium_recluster.marker.',marker,'.png'), width=5, height=5, dpi=320)
  236. },
  237. error=function(e){print('not found')})
  238. }
  240. # %%
  241. with(env_endothelium, {
  243. Idents(SR) ='seurat_clusters'
  245. SR.markers.AUC <- FindAllMarkers(SR, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25,
  246. test='roc'
  247. )
  248. library(dplyr)
  249. top10.AUC <- SR.markers.AUC %>% group_by(cluster) %>% top_n(n = 5, wt = myAUC)
  250. })
  252. DotPlot(env_endothelium$SR, features = unique(env_endothelium$top10.AUC$gene), group.by='seurat_clusters', cols = c("lightgrey", "darkred"))+
  253. coord_flip()+theme(axis.text.x = element_text(angle = 90, hjust = 1))
  254. ggsave('figures/fig5_endothelium/dotplot_clusters.top5auc.8x10.pdf', width=8,height=10)
  257. # %% [markdown]
  258. # # Blood
  260. # %%
  261. env_blood = new.env()
  262. env_blood$SR = adrenal_whole$SR[,adrenal_whole$[email hidden]$fate %in% c('HSC_and_immune', 'erythroid')]
  264. # %%
  265. dir.create('figures/fig6_blood')
  266. DimPlot(env_blood$SR, group.by='seurat_clusters', pt.size = 1, label = T)+NoLegend()
  267. ggsave('figures/fig6_blood/007.blood.clusters_labelled.pt1.5x5.square.png', width=5, height=5, dpi=320)
  268. DimPlot(env_blood$SR, group.by='seurat_clusters', pt.size = 1, label = F)+NoLegend()
  269. ggsave('figures/fig6_blood/007.blood.clusters_unlabelled.pt1.5x5.square.png', width=5, height=5, dpi=320)
  271. # %%
  272. markers1 = toupper(c('GATA1', 'RGS10', 'SPINK2', 'CYTL1', 'BLVRB', 'FAM8A1', 'UROD', 'HBM', 'CPOX', 'HEMGN', 'ALAS2', 'BPGM', 'SRGN', 'AIF1', 'LAPTM5', 'TYROBP', 'CD74', 'FCER1G', 'HCST'))
  273. #'BM2',
  275. for(marker in markers1){
  276. print(marker)
  277. FeaturePlot(env_blood$SR, feature=marker, cols = c("lightgrey", "darkred"), pt.size = 1) +NoLegend() #pt.size = 0.1,
  278. ggsave(paste0('figures/fig6_blood/004.blood.marker.',marker,'.png'), width=5, height=5, dpi=320)
  279. }
  281. # %%
  282. with(env_blood, {
  284. Idents(SR) ='seurat_clusters'
  286. SR.markers.AUC <- FindAllMarkers(SR, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25,
  287. test='roc'
  288. )
  289. library(dplyr)
  290. top10.AUC <- SR.markers.AUC %>% group_by(cluster) %>% top_n(n = 5, wt = myAUC)
  291. })
  293. DotPlot(env_blood$SR, features = unique(env_blood$top10.AUC$gene), group.by='seurat_clusters', cols = c("lightgrey", "darkred"))+
  294. coord_flip()+theme(axis.text.x = element_text(angle = 90, hjust = 1))
  295. ggsave('figures/fig6_blood/dotplot_clusters.top5auc.8x10.pdf', width=8,height=10)
  297. # %%
  298. do_seurat_regress(env_blood)
  300. # %%
  301. dir.create('figures/fig6_blood/')
  302. dir.create('figures/fig6_blood/reclustered/')
  303. DimPlot(env_blood$SR, group.by='seurat_clusters', pt.size = 1, label = T)+NoLegend()
  304. ggsave('figures/fig6_blood/reclustered/007.blood_recluster.clusters_labelled.pt1.5x5.square.png', width=5, height=5, dpi=320)
  305. DimPlot(env_blood$SR, group.by='seurat_clusters', pt.size = 1, label = F)+NoLegend()
  306. ggsave('figures/fig6_blood/reclustered/007.blood_recluster.clusters_unlabelled.pt1.5x5.square.png', width=5, height=5, dpi=320)
  308. # %%
  309. with(env_blood, {
  310. SR <- FindNeighbors(SR, reduction = "umap", dims=1:2)
  311. SR <- FindClusters(SR, resolution = 0.1) #, pc_use=1:15
  312. plot(DimPlot(SR, reduction = "umap", label=T))
  313. })
  315. # %%
  316. dir.create('figures/fig6_blood/reclustered')
  317. markers_txt = 'FCN1
  318. LYZ
  319. RETN
  320. S100A8
  321. S100A12
  322. C1QA
  323. C1QB
  324. C1QC
  325. HLA-DRA
  326. STAB1
  327. CD3E
  328. CD24
  329. IL2RB
  330. IL32
  331. KLRC1
  332. LCK
  333. TXK
  334. KLRB1
  335. BLK
  336. CD19
  337. IGHD
  338. RAG2
  339. AZU1
  340. CD34
  341. PRTN3
  342. SPINK2
  343. GP9
  344. HBD
  345. ITGA2B
  346. KIF2A
  347. RUFY1
  348. VCL
  349. CSF2RB
  350. IL1RL1
  351. KIT
  352. LMO4
  353. CNRIP1
  354. CSF2RB
  355. CTNNBL1
  356. GATA1
  357. STAT5A
  358. CD44
  359. CD48
  360. MPO
  361. SFN
  362. HMOX1
  363. SLC48A1
  364. HBZ
  365. '
  367. markers_fig1 = toupper(strsplit(markers_txt, '\n')[[1]])
  368. markers_fig1
  370. for(marker in markers_fig1){
  371. print(marker)
  372. tryCatch({
  373. FeaturePlot(env_blood$SR, feature=marker, cols = c("lightgrey", "darkred")) +NoLegend() #pt.size = 0.1,
  374. ggsave(paste0('figures/fig6_blood/reclustered/004.blood_recluster.marker.',marker,'.png'), width=5, height=5, dpi=320)
  375. },
  376. error=function(e){print('not found')})
  377. }
  379. # %%
  380. SR = env_6tp$SR[,sample(1:ncol(env_6tp$SR), 10000)]
  382. dir.create('figures/pagoda/')
  384. library(pagoda2)
  385. library(igraph)
  387. p2 <- basicP2proc(SR@assays$RNA@counts, n.cores = 1)
  389. go.env <- p2.generate.human.go(p2)
  392. p2$clusters$PCA$seurat_cluster = as.factor([email hidden]$seurat_cluster)
  393. names(p2$clusters$PCA$seurat_cluster) = rownames([email hidden])
  395. p2$embeddings$PCA$tSNE = as.matrix(SR@reductions$[email hidden])
  396. #p2$embeddings$PCA = as.matrix(p2$embeddings$[email hidden])
  398. p2$clusters$PCA$timepoint = as.factor([email hidden]$orig.ident)
  399. names(p2$clusters$PCA$timepoint) = rownames([email hidden])
  401. p2$embeddings$PCA$tSNE = as.matrix(SR@reductions$[email hidden])
  402. #p2$embeddings$PCA = as.matrix(p2$embeddings$[email hidden])
  405. n.cores=1
  407. cat('Calculating hdea...\n')
  408. hdea <- p2$getHierarchicalDiffExpressionAspects(type='PCA',clusterName='multilevel',z.threshold=3, n.cores = n.cores)
  410. extraWebMetadata = NULL
  411. app.title = 'adrenal_sr_10K'
  412. metadata.forweb <- list();
  413. metadata.forweb$timepoint <- p2.metadata.from.factor(p2$clusters$PCA$timepoint,displayname='timepoint')
  414. metadata.forweb$leiden <- p2.metadata.from.factor(p2$clusters$PCA$seurat_cluster,displayname='seurat_cluster')
  415. metadata.forweb$multilevel <- p2.metadata.from.factor(p2$clusters$PCA$multilevel,displayname='multilevel')
  416. metadata.forweb <- c(metadata.forweb, extraWebMetadata)
  417. genesets <- hierDiffToGenesets(hdea)
  418. appmetadata = list(apptitle=app.title)
  419. cat('Making KNN graph...\n')
  420. #p2$makeGeneKnnGraph(n.cores=n.cores)
  421. p2w = make.p2.app(p2, additionalMetadata = metadata.forweb, geneSets = genesets, dendrogramCellGroups = p2$clusters$PCA$multilevel, show.clusters=F, appmetadata = appmetadata)
  423. p2w$serializeToStaticFast(binary.filename = 'figures/pagoda/adrenal.sampled_10K_cells.bin')
  425. # %%
  426. rm(p2, p2w)
  427. gc()
  429. # %%
  430. unique(env_6tp$[email hidden]$orig.ident)
  431. unique(env_6tp$[email hidden]$timepoint)
  433. # %%
  434. env_6tp$[email hidden]$orig.ident2 = env_6tp$[email hidden]$orig.ident
  435. unique(env_6tp$[email hidden]$orig.ident)
  436. env_6tp$[email hidden]$orig.ident2[env_6tp$[email hidden]$orig.ident2=='R53a_w00'] = 'R53a_w95'
  437. env_6tp$[email hidden]$orig.ident2[env_6tp$[email hidden]$orig.ident2=='R53b_w00'] = 'R53b_w115'
  438. env_6tp$[email hidden]$orig.ident2[env_6tp$[email hidden]$orig.ident2=='R53c_w00'] = 'R53c_w95'
  439. env_6tp$[email hidden]$timepoint = sapply(strsplit(env_6tp$[email hidden]$orig.ident2, '_'), `[`, 2)
  440. env_6tp$[email hidden]$orig.ident = env_6tp$[email hidden]$orig.ident2
  441. env_6tp$[email hidden]$orig.ident2 = NULL
  443. # %%
  444. env_6tp$[email hidden]$timepoint = substr(env_6tp$[email hidden]$timepoint,1,nchar(env_6tp$[email hidden]$timepoint)-1)
  445. env_6tp$[email hidden]$timepoint = gsub('w','',env_6tp$[email hidden]$timepoint)
  447. # %%
  448. head(env_6tp$[email hidden])
  449. SR = env_6tp$SR
  450. [email hidden]$fate[[email hidden]$fate=='blood2'] = 'erythroid'
  451. [email hidden]$fate[[email hidden]$fate=='blood1'] = 'HSC_and_immune'
  452. [email hidden]$fate[[email hidden]$fate=='progenitors'] = 'intermediate_mesoderm'
  453. [email hidden]$fate[[email hidden]$fate=='other'] = 'melanocytes'
  454. DimPlot(SR, group.by = 'fate')
  456. saveRDS(SR, file='/mnt/data/artem/rdata/Adrenal/adrenal.human.seurat.rds')
  458. # %%
  459. DimPlot(SR, group.by = 'timepoint')

human_cortex_endothelium_blood.ipynb at commit b17096b, no license · at the source

Overview

Authors: Maria E Kastriti1, Denis Maksimov2, Julia Krupinova3, Marina Utkina4, Dmitry Beltsevich4, Anna Roslyakova4, Olga Glazova2,3, Samira Kaziakhmedova4, Alina Ryabova4, Anna Kuznetsova4, Anastasia Shcherbakova4, Ekaterina Bondarenko4, Zoia Antysheva2, Eugene Albert2, Lilia Urusova4, Anastasia Lapshina4, Anastassia Chevais4, Ekaterina Avsievich3, Valentin Trofimov4, Galina Melnichenko4
and 10 other authorsNatalya Mokrysheva4, Ivan Dedov4, Lukas Englmaier5, Julian Petersen6, Oleg Gusev7,8,9, Andreas Heinzel10, Rainer Oberbauer10, Pavel Volchkov2,3,11,12, Peter V Kharchenko1,13, Igor Adameyko1,14
ORCID iDs: Maria E Kastriti, Denis Maksimov, Marina Utkina, Alina Ryabova, Anastasia Shcherbakova, Ekaterina Bondarenko, Anastassia Chevais, Ekaterina Avsievich, Lukas Englmaier, Julian Petersen, Oleg Gusev, Rainer Oberbauer, Pavel Volchkov, Peter V Kharchenko, Igor Adameyko
14 affiliations
  1. Medical University of Vienna, Center for Brain Research, Department of Neuroimmunology, Vienna, Austria
  2. Lomonosov Moscow State University, Moscow, Russia
  3. Moscow Clinical Scientific Center named after A.S. Loginov, Moscow, Russia
  4. Endocrinology Research Centre, Moscow, Russia
  5. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
  6. Department of Orthodontics, University of Leipzig Medical Center, Leipzig, Germany
  7. Intractable Disease Research Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan
  8. Life Improvement by Future Technologies (LIFT) Center, Moscow, Russia
  9. Federal Center of Brain Research and Neurotechnologies, Моscow, Russia
  10. Department of Nephrology, Internal Medicine III, Medical University of Vienna, Vienna, Austria
  11. National Medical Research Centre of Cardiology named after Academician E.I. Chazov, Moscow, Russia
  12. Almazov National Medical Research Centre, St Petersburg, Russia
  13. Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
  14. Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Sweden
Institutions: Medical University of Vienna (Austria); Lomonosov Moscow State University (Russia); Moscow Clinical Scientific Center (Russia); Endocrinology Research Center (Russia); Austrian Academy of Sciences (Austria); CeMM Research Center for Molecular Medicine (Austria); University Hospital Leipzig (Germany); Leipzig University (Germany); Juntendo University (Japan); Federal Almazov North-West Medical Research Centre (Russia); National Medical Research Center of Cardiology (Russia); Harvard University (United States); Karolinska Institutet (Sweden)
Journal: Nature genetics, volume 58, issue 9, pages 2270-2283
Dates: received 9 February 2024; accepted 1 August 2026; published online 8 September 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41588-026-02737-1 · PMID 42711427 · PMCID PMC13553300 · OpenAlex W7211966900
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: Transcriptomics, Stem cells
MeSH: Adrenal Glands*, Adrenal Cortex, Adult, Aldosterone, Animals, Cell Proliferation, Female, Humans, Hydrocortisone, Male, Mice, Species Specificity, Transcriptome, Wnt4 Protein, Zona Glomerulosa (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Vienna Science and Technology Fund (Wiener Wissenschafts-, Forschungs- und Technologiefonds) (WWTF Genomics based immunologic risk stratification, grant number 10.47379/LS20081)); Riksbankens Jubileumsfond (Stiftelsen Riksbankens Jubileumsfond) (none); Novo Nordisk Fonden (Novo Nordisk Foundation) (NNF17OC0026874)
Citations: cited by 1 paper (Europe PMC); 78 references in the paper

Abstract

The adult adrenal cortex undergoes constant renewal, yet underlying human-specific mechanisms remain poorly understood. Here we generated single-cell and spatial transcriptomic atlases of adult human and mouse adrenal glands, leveraging single-cell-resolution spatial data and a rare clonal mosaic case for lineage inference. In humans, we identified age-associated zona glomerulosa (ZG) cell states with direct cortisol synthesis capacity and sex-specific differences in inferred cholesterol balance. Cross-species comparison revealed conserved aldosterone-producing ZG but notable divergence in zona fasciculata markers, absence of zona reticularis homologs in mice and differential SHH–WNT4 signaling in proliferating cells. We uncovered human WT1− capsule-to-ZG transition and vascular smooth muscle cell-to-steroidogenic transitions supported by mosaic lineage evidence. We revealed dispersed proliferating cortical SF1+EZH2+ cells throughout the human cortex in contrast with ZG restriction in mice. Taken together, our data expand the centripetal renewal model and establish a comparative framework for human adrenocortical biology.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

artem-artemov/adrenal

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: b17096bfee5acce26c43c61d164f2743870a3852, 27 April 2021
Languages: Jupyter (8), R (3)
Size: 18 files, 11 scripts
Software Heritage: archived
Found in: “Data availability”
Holds: README, 8 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: igraph (3 files), tidyverse (3 files), ggplot2 (2 files), Seurat (2 files), cowplot (1 file), Matplotlib (1 file), Monocle 3 (1 file), pandas (1 file), pheatmap (1 file), Scanpy (1 file), scVelo (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
6 files
At the source: github.com/artem-artemov/adrenal

MaksimovDenis/adrenalglandscatlas

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 5eb548cf9eaa330819fe6441bebefdacbb2dfbf1, 9 July 2026
Languages: R (80), Jupyter (37)
Size: 183 files, 117 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 117 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ComplexHeatmap (1 file), ggplot2 (1 file), patchwork (1 file), Seurat (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
1 file
At the source: gitlab.com/MaksimovDenis/adrenalglandscatlas

Zenodo 20258071

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
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Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
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At the source:

Code availability

Source code is available via GitLab at https://gitlab.com/MaksimovDenis/adrenalglandscatlas and via Zenodo at https://doi.org/10.5281/zenodo.20258071(ref. 78).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

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  • 6 scripts, each with its path and the digest of its content;
  • 8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

Sequenced reads of mouse scRNA-seq and Visium HD samples, h5 files with gene counts in individual cells or spots and all data related to Visium, Visium HD or Stereo-Seq spatial transcriptomics (coordinate matrices, slide images and so on) were deposited under the Gene Expression Omnibus (GEO) accession no. GSE253852 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE253852). Sequenced reads of human scRNA-seq, Visium and Stereo-seq spatial transcriptomics were deposited under European Genome–phenome Archive (EGA) controllable accession no. EGAC00001003383 (http://www.ebi.ac.uk/ena/data/view/EGAC00001003383). The EGA provides a managed access framework in which human data are archived under controlled conditions and released only to approved applicants through a Data Access Committee (DAC) review process. Researchers wishing to access the raw data may submit a data access request to the DAC via the EGA portal (https://ega-archive.org/datasets/EGAC00001003383), describing their intended use. Requests are evaluated only against the original ethical framework and, if approved according to the ethical criteria, access is granted subject to a Data Use Agreement that legally binds the recipient to the approved terms of use. Controlled access via the EGA is not intended to restrict scientific use, but to ensure that human raw sequencing data cannot be repurposed in ways that would violate donor privacy or applicable ethical regulations. The contact details for the DAC and expected response timelines are available on the EGA dataset page. We provide interactive access to the data via CellxGene74–76 (detailed list and links in Supplementary Information) and PDF versions of all Extended Data figures and Supplementary figures are available via figshare at https://adameykolab.hifo.meduniwien.ac.at/cellxgene_public/filecrawl/2026_NatGen_Kastriti_Maksimov (ref. 77). Human fetal scRNA-seq data were obtained from the following sources: Kameneva et al.22 processed Seurat object via the link from github: https://github.com/artem-artemov/adrenal (http://pklab.med.harvard.edu/artem/adrenal/data/Seurat/adrenal.human.seurat.scrublet.rds); Jansky et al.23 counted matrix and annotation from: https://adrenal.kitz-heidelberg.de/developmental_programs_NB_viz/; Kildisiute et al.24 processed Seurat object from: https://www.neuroblastomacellatlas.org/, Han et al.25 samples counted matrices from GEO accession no. GSE134355 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134355). Previously published mouse adult scRNA-seq sample count matrices were downloaded from GEO accession nos. GSE134355 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134355) and GSE161751 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161751) (refs. 25,31). Normal adrenal gland bulk RNA-seq data from the Genotype-Tissue Expression cohort15 were obtained via the recount3 v1.0.7R package65. Adrenocortical carcinoma bulk RNA-seq and metadata were downloaded from the TCGA portal: https://portal.gdc.cancer.gov/projects/TCGA-ACC.

Source code is available via GitLab at https://gitlab.com/MaksimovDenis/adrenalglandscatlas and via Zenodo at https://doi.org/10.5281/zenodo.20258071(ref. 78).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 30 authors, 2 keywords, 15 MeSH terms, 3 funders, 75 references.

Cite

This paper

Kastriti, M. E., Maksimov, D., Krupinova, J., Utkina, M., Beltsevich, D., Roslyakova, A., Glazova, O., Kaziakhmedova, S., Ryabova, A., Kuznetsova, A., Shcherbakova, A., Bondarenko, E., Antysheva, Z., Albert, E., Urusova, L., Lapshina, A., Chevais, A., Avsievich, E., Trofimov, V., . . . Adameyko, I. (2026). Human and mouse adrenal glands are characterized by species-specific steroidogenic states and tissue turnover. Nature genetics, 58(9), 2270-2283. https://doi.org/10.1038/s41588-026-02737-1

BibTeX

@article{kastriti2026human,
author = {Kastriti, Maria E and Maksimov, Denis and Krupinova, Julia and Utkina, Marina and Beltsevich, Dmitry and Roslyakova, Anna and Glazova, Olga and Kaziakhmedova, Samira and Ryabova, Alina and Kuznetsova, Anna and Shcherbakova, Anastasia and Bondarenko, Ekaterina and Antysheva, Zoia and Albert, Eugene and Urusova, Lilia and Lapshina, Anastasia and Chevais, Anastassia and Avsievich, Ekaterina and Trofimov, Valentin and Melnichenko, Galina and Mokrysheva, Natalya and Dedov, Ivan and Englmaier, Lukas and Petersen, Julian and Gusev, Oleg and Heinzel, Andreas and Oberbauer, Rainer and Volchkov, Pavel and Kharchenko, Peter V and Adameyko, Igor},
title = {{Human and mouse adrenal glands are characterized by species-specific steroidogenic states and tissue turnover}},
journal = {Nature genetics},
year = {2026},
month = sep,
volume = {58},
number = {9},
pages = {2270--2283},
publisher = {Nature Portfolio},
issn = {1061-4036},
doi = {10.1038/s41588-026-02737-1},
url = {https://doi.org/10.1038/s41588-026-02737-1},
pmid = {42711427},
pmcid = {PMC13553300}
}

RIS

TY - JOUR
AU - Kastriti, Maria E
AU - Maksimov, Denis
AU - Krupinova, Julia
AU - Utkina, Marina
AU - Beltsevich, Dmitry
AU - Roslyakova, Anna
AU - Glazova, Olga
AU - Kaziakhmedova, Samira
AU - Ryabova, Alina
AU - Kuznetsova, Anna
AU - Shcherbakova, Anastasia
AU - Bondarenko, Ekaterina
AU - Antysheva, Zoia
AU - Albert, Eugene
AU - Urusova, Lilia
AU - Lapshina, Anastasia
AU - Chevais, Anastassia
AU - Avsievich, Ekaterina
AU - Trofimov, Valentin
AU - Melnichenko, Galina
AU - Mokrysheva, Natalya
AU - Dedov, Ivan
AU - Englmaier, Lukas
AU - Petersen, Julian
AU - Gusev, Oleg
AU - Heinzel, Andreas
AU - Oberbauer, Rainer
AU - Volchkov, Pavel
AU - Kharchenko, Peter V
AU - Adameyko, Igor
TI - Human and mouse adrenal glands are characterized by species-specific steroidogenic states and tissue turnover
T2 - Nature genetics
J2 - Nat Genet
PY - 2026
DA - 2026/09/08
VL - 58
IS - 9
SP - 2270
EP - 2283
SN - 1061-4036
PB - Nature Portfolio
DO - 10.1038/s41588-026-02737-1
UR - https://doi.org/10.1038/s41588-026-02737-1
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41588-026-02737-1",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
8
]
]
}
}

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