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Deciphering the ancestral mechanisms of neural regeneration through single-cell analyses of jellyfish rhopalium repair.

Code ↔ Paper

3 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.

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  1. [1] § MATERIALS AND METHODS › Cross-species analysis ↔ code.zip/code/blastp.sh, the whole file · a weak match · score 0.77 · Anolis carolinensis, SAMap, Schmidtea mediterranea, lizard, cross species, rerio
  2. [2] § MATERIALS AND METHODS › Cross-species analysis ↔ code.zip/code/CrossSpecies_main.R, lines 144–213 · score 0.58 · Schmidtea mediterranea, cross species integrative, rerio, Xenopus, mapped, cell
  3. [3] § MATERIALS AND METHODS › Data quality control, integration, and analysis ↔ code.zip/code/CrossSpecies_main.R, lines 215–280 · score 0.56 · FindClusters, FindNeighbors, Seurat, RNA

Paper

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The authors' code

R · 280 lines · 12 KB · CC-BY-4.0 · 2 matches

  1. # ==============================================================================
  2. # Single-Cell RNA-seq Analysis Pipeline for Aurelia
  3. # Main Tasks:
  4. # 1. Subset cell clusters
  5. # 2. Cell stemness analysis (CytoTRACE2)
  6. # 3. Lineage/trajectory inference (Slingshot, Monocle3)
  7. # 4. Cross-species gene mapping & integration
  8. # 5. Visualization of UMAP, trajectories, and gene expression
  9. # ==============================================================================
  10. # Load required libraries
  11. library(Seurat)
  12. library(tidyverse)
  13. library(CytoTRACE2)
  14. library(SeuratDisk)
  15. library(slop)
  16. library(monocle3)
  17. library(writexl)
  18. library(rtracklayer)
  19. library(slingshot)
  20. ZS_nosplit <- read_rds('~/Aurelia/ZS_nosplit.rds')
  21. ZS_nosplit_sub <- subset(ZS_nosplit, RNA_snn_res.0.6 %in%
  22. c(0,1,4,7,8,10,17,18,21,22,28))
  23. set.seed(1234)
  24. ZS_nosplit_down <- ZS_nosplit[, sample(1:ncol(ZS_nosplit), 10000)]
  25. ZS_nosplit_down[['RNA']] <- as(ZS_nosplit_down[['RNA']], Class = 'Assay')
  26. write_rds(ZS_nosplit_down, '~/Aurelia/ZS_nosplit_down.rds')
  27. # ==============================================================================
  28. #Lineage Inference with Slingshot
  29. # ==============================================================================
  30. pdf('~/Aurelia/lineage3.pdf', width = 8, height = 8)
  31. # Run Slingshot trajectory analysis
  32. ZS_nosplit_slingshot <- slop::RunSlingshot(
  33. srt = subset(ZS_nosplit_down, RNA_snn_res.0.6 %in%
  34. c(0,1,4,7,8,10,17,18,21,22,28)),
  35. group.by = "RNA_snn_res.0.6",
  36. start = 1,
  37. reduction = "umap"
  38. )
  39. dev.off()
  41. # Visualize lineages on UMAP
  42. slop::CellDimPlot(
  43. ZS_nosplit_slingshot,
  44. group.by = "RNA_snn_res.0.6",
  45. reduction = "umap",
  46. lineages = paste0("Lineage", 1:3),
  47. lineages_span = 0.1,
  48. lineages_trim = c(0.05, 0.95)
  49. )
  50. # Plot pseudotime for each lineage
  51. umap_limits <- list(
  52. x = range(ZS_nosplit@reductions$[email hidden][,1]),
  53. y = range(ZS_nosplit@reductions$[email hidden][,2])
  54. )
  56. p1 <- FeatureDimPlot(ZS_nosplit_slingshot, features = "Lineage1",
  57. reduction = "umap", pt.size = 0.5) +
  58. xlim(umap_limits$x) + ylim(umap_limits$y)
  60. p2 <- FeatureDimPlot(ZS_nosplit_slingshot, features = "Lineage2",
  61. reduction = "umap", pt.size = 0.5) +
  62. xlim(umap_limits$x) + ylim(umap_limits$y)
  64. p3 <- FeatureDimPlot(ZS_nosplit_slingshot, features = "Lineage3",
  65. reduction = "umap", pt.size = 0.5) +
  66. xlim(umap_limits$x) + ylim(umap_limits$y)
  68. # Combine and save lineage plots
  69. pdf('~/Aurelia/reversion/lineage_all.pdf', width = 16, height = 5)
  70. p1 + p2 + p3 +p4
  71. dev.off()
  72. # ==============================================================================
  73. # Re-process Subsetted Data (Normalization, PCA, UMAP)
  74. # ==============================================================================
  75. ZS_nosplit_sub <- JoinLayers(ZS_nosplit_sub)
  76. sceobj_exp <- LayerData(ZS_nosplit_sub, assay = "RNA", layer = 'counts')
  78. # Create new Seurat object
  79. sceobj_new <- CreateSeuratObject(counts = sceobj_exp,
  80. meta.data = [email hidden])
  82. #Split assay by sample for integration
  83. sceobj_new[["RNA"]] <- split(sceobj_new[["RNA"]], f = sceobj_new$orig.ident)
  84. # ==============================================================================
  85. #: Export Cluster2 for Cross-Species Analysis
  86. # ==============================================================================
  87. C2 <- read_rds('~/Aurelia/reversion/cluster2.rds')
  88. C2[['RNA']] <- as(C2[['RNA']], Class = 'Assay')
  89. # Modify gene names to avoid conflicts
  90. C2_exp <- C2@assays$RNA@counts
  91. rownames(C2_exp) <- paste0(stringr::str_replace_all(rownames(C2_exp), 'gene', 'rna'), '.1')
  92. # Create annotated Seurat object
  93. C2 <- CreateSeuratObject(counts = C2_exp, meta.data = [email hidden])
  94. C2 <- NormalizeData(C2)
  95. C2 <- ScaleData(C2)
  96. C2$celltype <- 'C2'
  98. # Export as h5Seurat and h5ad (for Python/scanpy)
  99. SeuratDisk::SaveH5Seurat(C2, filename = "~/Aurelia/reversion/C2_ann.h5Seurat")
  100. SeuratDisk::Convert("~/Aurelia/reversion/C2_ann.h5Seurat", dest = "h5ad")
  101. write_rds(C2, '~/Aurelia/reversion/C2.rds')
  102. # ==============================================================================
  103. # Cross-Species Gene Mapping Functions
  104. # ==============================================================================
  105. trans_species_obj <- function(query_obj, subject_obj, map_gene) {
  106. subject_exp <- subject_obj@assays$RNA$counts
  107. subject_exp_duplicate_new <- subject_exp
  108. for (i in 1:5) {
  109. temp_exp <- subject_exp
  110. rownames(temp_exp) <- paste0(rownames(subject_exp), "-", i)
  111. subject_exp_duplicate_new <- rbind(temp_exp, subject_exp_duplicate_new)
  112. }
  113. map_gene <- map_gene[map_gene$subject_id %in% rownames(subject_exp_duplicate_new), ]
  114. subject_exp_duplicate_new <- subject_exp_duplicate_new[map_gene$subject_id, ]
  115. rownames(subject_exp_duplicate_new) <- map_gene$query_id
  116. subject_obj_new <- CreateSeuratObject(counts = subject_exp_duplicate_new, meta.data = [email hidden])
  117. merge_obj <- merge(query_obj, subject_obj_new)
  118. return(merge_obj)
  119. }
  120. trans_gene_species <- function(species1_vs_species2, species2_vs_species1) {
  121. colnames(species1_vs_species2) <- c(
  122. "query_id", "subject_id", "percent_identity", "alignment_length",
  123. "mismatch_count", "gap_open_count", "q_start", "q_end",
  124. "s_start", "s_end", "e_value", "bit_score"
  125. )
  126. colnames(species2_vs_species1) <- colnames(species1_vs_species2)
  128. best_hits_species1 <- species1_vs_species2 %>%
  129. group_by(query_id) %>%
  130. top_n(4, bit_score) %>%
  131. ungroup()
  133. best_hits_species2 <- species2_vs_species1 %>%
  134. group_by(query_id) %>%
  135. top_n(4, bit_score) %>%
  136. ungroup()
  138. rbh <- inner_join(best_hits_species1, best_hits_species2,
  139. by = c("query_id" = "subject_id", "subject_id" = "query_id")) %>%
  140. select(query_id, subject_id)
  141. return(rbh)
  142. }
  144. # ==============================================================================
  145. # Cross-Species Integration
  146. # ==============================================================================
  147. # Xenopus
  148. ROCs_sce <- SeuratDisk::LoadH5Seurat("~/Aurelia/regen/Xenopus/ROCs_sce.h5Seurat")
  149. Au_to_Xp <- read_table('~/Aurelia/regen/maps/XeAu/Au_to_Xe.txt', col_names = F)
  150. Xp_to_Au <- read_table('~/Aurelia/regen/maps/XeAu/Xe_to_Au.txt', col_names = F)
  151. map_gene_Au_Xp <- trans_gene_species(Au_to_Xp, Xp_to_Au)
  152. ROCs_sce$celltype <- 'Xenopus_ROCs'
  153. ROCs_sce$species <- 'Xenopus'
  154. # Hydra
  155. Hy_iCell <- SeuratDisk::LoadH5Seurat("~/Aurelia/regen/Hydra/Hy_iCell.h5Seurat")
  156. Au_to_Hy <- read_table('~/Aurelia/regen/maps/HyAu/Au_to_Hy.txt', col_names = F)
  157. Hy_to_Au <- read_table('~/Aurelia/regen/maps/HyAu/Hy_to_Au.txt', col_names = F)
  158. map_gene_Au_Hy <- trans_gene_species(Au_to_Hy, Hy_to_Au)
  159. Hy_iCell$celltype <- 'Hydra_iCell'
  160. Hy_iCell$species <- 'Hydra'
  161. # Schmidtea mediterranea
  162. Sc_neoblast <- SeuratDisk::LoadH5Seurat("~/Aurelia/regen/Schmidtea_mediterranea/Sc_neoblast.h5Seurat")
  163. Au_to_Sc <- read_table('~/Aurelia/regen/maps/ScAu/Au_to_Sc.txt', col_names = F)
  164. Sc_to_Au <- read_table('~/Aurelia/regen/maps/ScAu/Sc_to_Au.txt', col_names = F)
  165. map_gene_Au_Sc <- trans_gene_species(Au_to_Sc, Sc_to_Au)
  166. Sc_neoblast$celltype <- 'Sc_neoblast'
  167. Sc_neoblast$species <- 'Schmidtea_mediterranea'
  168. # Danio rerio
  169. Danio_rerio_sce_Me <- SeuratDisk::LoadH5Seurat("~/Aurelia/regen/Danio_rerio/Danio_re_Mesenchymal.h5Seurat")
  170. Au_to_Da <- read_table('~/Aurelia/regen/maps/DaAu/Au_to_Da.txt', col_names = F)
  171. Da_to_Au <- read_table('~/Aurelia/regen/maps/DaAu/Da_to_Au.txt', col_names = F)
  172. map_gene_Au_Da <- trans_gene_species(Au_to_Da, Da_to_Au)
  173. Danio_rerio_sce_Me$celltype <- 'Danio_rerio_Mesenchymal'
  174. Danio_rerio_sce_Me$species <- 'Danio_rerio'
  175. # Acoel
  176. Ac_neoblast <- SeuratDisk::LoadH5Seurat("~/Aurelia/regen/Acoel/Ac_neoblast1.h5Seurat")
  177. Au_to_Ac <- read_table('~/Aurelia/regen/maps/AcAu/Au_to_Ac.txt', col_names = F)
  178. Ac_to_Au <- read_table('~/Aurelia/regen/maps/AcAu/Ac_to_Au.txt', col_names = F)
  179. map_gene_Au_Ac <- trans_gene_species(Au_to_Ac, Ac_to_Au)
  180. Ac_neoblast$celltype <- 'Ac_neoblast'
  181. Ac_neoblast$species <- 'Acoel'
  182. # ==============================================================================
  183. # Merge All Species and Integration
  184. # ==============================================================================
  185. query_exp <- C2@assays$RNA$counts
  186. query_exp_duplicate <- query_exp
  187. query_exp_duplicate_new <- query_exp
  188. for (i in 1:5) {
  189. row.names(query_exp_duplicate) <- paste0(row.names(query_exp), "_", i)
  190. query_exp_duplicate_new <- rbind(query_exp_duplicate,query_exp_duplicate_new)
  191. }
  192. identical(colnames(query_exp_duplicate_new),
  193. row.names([email hidden]))
  194. query_obj <- CreateSeuratObject(counts = query_exp_duplicate_new,
  195. meta.data = [email hidden])
  196. trans_Au_Ac_obj <- trans_species_obj(query_obj = query_obj,
  197. subject_obj = Ac_neoblast,
  198. map_gene = map_gene_Au_Ac)
  199. trans_Au_Da_obj <- trans_species_obj(query_obj = query_obj,
  200. subject_obj = Danio_rerio_sce_Me,
  201. map_gene = map_gene_Au_Da)
  202. trans_Au_Hy_obj <- trans_species_obj(query_obj = query_obj,
  203. subject_obj = Hy_iCell,
  204. map_gene = map_gene_Au_Hy)
  205. trans_Au_Xe_obj <- trans_species_obj(query_obj = query_obj,
  206. subject_obj = ROCs_sce,
  207. map_gene = map_gene_Au_Xp)
  208. trans_Au_Sc_obj <- trans_species_obj(query_obj = query_obj,
  209. subject_obj = Sc_neoblast,
  210. map_gene = map_gene_Au_Sc)
  211. trans_Au_La_obj <- trans_species_obj(query_obj = query_obj,
  212. subject_obj = Sc_neoblast,
  213. map_gene = map_gene_Au_Sc)
  215. C2$species <- 'AU'
  216. merge_all_species <- merge(C2,
  217. list(trans_Au_Ac_obj,
  218. trans_Au_Da_obj,
  219. trans_Au_Hy_obj,
  220. trans_Au_Xe_obj,
  221. trans_Au_Sc_obj))
  222. merge_all_species <- JoinLayers(merge_all_species)
  223. merge_all_species <- NormalizeData(merge_all_species) %>% ScaleData()
  224. Idents(merge_all_species) <- 'species'
  225. merge_all_species_exp <- AverageExpression(merge_all_species,assays = 'RNA')$RNA
  226. write.csv(merge_all_species_exp,'~/Aurelia/regen/merge_all_species_exp.csv')
  227. write_rds(merge_all_species,file = '~/Aurelia/regen/merge_all_species.rds')
  228. # ==============================================================================
  229. # Integration (Harmony, CCA, RPCA)
  230. # ==============================================================================
  231. sce <- read_rds('~/Aurelia/regen/merge_all_species.rds')
  232. sce <- JoinLayers(sce)
  233. sce <- CreateSeuratObject(sce@assays$RNA, meta.data = [email hidden])
  234. sce[["RNA"]] <- split(sce[["RNA"]], f = sce$species)
  236. sce <- NormalizeData(sce)
  237. sce <- FindVariableFeatures(sce)
  238. sce <- ScaleData(sce)
  239. sce <- RunPCA(sce)
  241. # Harmony
  242. sce <- IntegrateLayers(object = sce, method = HarmonyIntegration,
  243. orig.reduction = "pca", new.reduction = "integrated.harmony")
  244. sce <- RunUMAP(sce, reduction = "integrated.harmony", dims = 1:30, reduction.name = "umap.harmony")
  246. # CCA
  247. sce_cca <- IntegrateLayers(object = sce, method = CCAIntegration,
  248. orig.reduction = "pca", new.reduction = "integrated.cca")
  249. sce_cca <- RunUMAP(sce_cca, reduction = "integrated.cca", dims = 1:30, reduction.name = "umap.cca")
  251. # RPCA
  252. sce_rpca <- IntegrateLayers(object = sce, method = RPCAIntegration,
  253. orig.reduction = "pca", new.reduction = "integrated.rpca")
  254. sce_rpca <- RunUMAP(sce_rpca, reduction = "integrated.rpca", dims = 1:30, reduction.name = "umap.rpca")
  256. # Clustering
  257. sce <- FindNeighbors(sce, reduction = "integrated.harmony", dims = 1:30)
  258. sce <- FindClusters(sce, resolution = c(0.2, 0.5, 1))
  260. # Plot UMAP
  261. pdf('~/Aurelia/regen/merge_all_species_umap.pdf', width = 10, height = 10)
  262. DimPlot(sce, group.by = 'species')
  263. DimPlot(sce_cca, reduction = 'umap.cca', group.by = 'species')
  264. DimPlot(sce_rpca, reduction = 'umap.rpca', group.by = 'species')
  265. dev.off()
  267. write_rds(sce_cca, '~/Aurelia/regen/merge_all_species.rds')
  269. # Export results
  270. exp <- AverageExpression(sce)
  271. write_rds(exp$RNA, '~/Aurelia/regen/exp.rds')
  272. writexl::write_xlsx(
  273. list(map_gene_Au_Sc = map_gene_Au_Sc,
  274. map_gene_Au_Ac = map_gene_Au_Ac,
  275. map_gene_Au_Da = map_gene_Au_Da,
  276. map_gene_Au_Hy = map_gene_Au_Hy,
  277. map_gene_Au_Xp = map_gene_Au_Xp),
  278. path = '~/Aurelia/regen/all_species_mapping_gene.xlsx'
  279. )
  280. # ==============================================================================

CrossSpecies_main.R, under CC-BY-4.0 · at the source

Overview

Authors: Yongxue Li1,2, Fanghan Wang1,2, Tingting Sun1,2, Xiaoran Ma1, Zhangyi Yu3, Ziyue Xu4, Wenhui Wang5, Yixuan Xing5, Saijun Peng1, Lei Wang1,2, Jianmin Zhao1,2, Zhijun Dong1,2
ORCID iDs: Yongxue Li, Fanghan Wang, Tingting Sun, Xiaoran Ma, Zhangyi Yu, Ziyue Xu, Zhijun Dong
  1. Muping Coastal Environment Research Station, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, Shandong 264003, China
  2. University of Chinese Academy of Sciences, Beijing 100049, China
  3. College of Bio-Medicine and Health, Huazhong Agricultural University, Wuhan, Hubei 430070, China
  4. Department of Ocean Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China
  5. School of Life Science, Yantai University, Yantai, Shandong 264005, China
Institutions: Chinese Academy of Sciences (China); Yantai Institute of Coastal Zone Research (China); University of Chinese Academy of Sciences (China); Huazhong Agricultural University (China); Hong Kong University of Science and Technology (Hong Kong SAR China); Yantai University (China)
Journal: Science advances, volume 12, issue 21, article eaeb8034
Dates: received 26 August 2025; accepted 14 April 2026; published online 20 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.aeb8034 · PMID 42160431 · PMCID PMC13189125 · OpenAlex W7161815617
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (organism), cellular / molecular (subfield)
Methods: Statistics
MeSH: Nerve Regeneration*, Scyphozoa*, Single-Cell Analysis*, Animals, Gene Regulatory Networks, Regeneration, Tretinoin, Wnt Signaling Pathway (* major topic)
Topic: Marine Invertebrate Physiology and Ecology (Paleontology, Earth and Planetary Sciences), according to OpenAlex
Citations: not cited yet (Europe PMC); 77 references in the paper

Abstract

The rhopalium, an early specialized centralized nervous structure in medusozoans, exhibits a remarkable regenerative capacity after removal, offering a unique model for investigating the ancestral regulatory logic of neural regeneration. However, the cellular and molecular mechanisms underlying the process remain unknown. Here, we defined distinct stages of rhopalium regeneration in the jellyfish Aurelia coerulea and constructed a stage-specific cell landscape and gene regulatory networks. Notably, we identified a population of wound-induced cells that emerges rapidly postinjury and activates Wnt signaling to initiate blastema formation; cross-species comparisons revealed that this regulatory mechanism is conserved across metazoans. We further identified that retinoic acid signaling is essential for photoreceptor regeneration and ocelli reconstruction. The findings presented herein uncover the ancestral regulatory mechanisms governing neural regeneration in the early centralized nervous system, providing insights into the common evolutionary origins of neural repair across organisms.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

Zenodo 19466460

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Monocle 3 (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
2 files
At the source: doi.org/10.5281/zenodo.19466460

The paper's code and data availability statement is in the Data section.

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data, code, and materials availability

Sequencing data have been deposited in the NCBI under BioProject accession number PRJNA1224934. All other data needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. The processed files and code are publicly available in Zenodo at https://doi.org/10.5281/zenodo.19466460. This study did not generate new materials.

Reproduced under the paper's license (CC BY-NC), 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 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 8 MeSH terms, 72 references.

Cite

This paper

Li, Y., Wang, F., Sun, T., Ma, X., Yu, Z., Xu, Z., Wang, W., Xing, Y., Peng, S., Wang, L., Zhao, J., & Dong, Z. (2026). Deciphering the ancestral mechanisms of neural regeneration through single-cell analyses of jellyfish rhopalium repair. Science advances, 12(21), eaeb8034. https://doi.org/10.1126/sciadv.aeb8034

BibTeX

@article{li2026deciphering,
author = {Li, Yongxue and Wang, Fanghan and Sun, Tingting and Ma, Xiaoran and Yu, Zhangyi and Xu, Ziyue and Wang, Wenhui and Xing, Yixuan and Peng, Saijun and Wang, Lei and Zhao, Jianmin and Dong, Zhijun},
title = {{Deciphering the ancestral mechanisms of neural regeneration through single-cell analyses of jellyfish rhopalium repair}},
journal = {Science advances},
year = {2026},
month = may,
volume = {12},
number = {21},
pages = {eaeb8034},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aeb8034},
url = {https://doi.org/10.1126/sciadv.aeb8034},
pmid = {42160431},
pmcid = {PMC13189125}
}

RIS

TY - JOUR
AU - Li, Yongxue
AU - Wang, Fanghan
AU - Sun, Tingting
AU - Ma, Xiaoran
AU - Yu, Zhangyi
AU - Xu, Ziyue
AU - Wang, Wenhui
AU - Xing, Yixuan
AU - Peng, Saijun
AU - Wang, Lei
AU - Zhao, Jianmin
AU - Dong, Zhijun
TI - Deciphering the ancestral mechanisms of neural regeneration through single-cell analyses of jellyfish rhopalium repair
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/05/20
VL - 12
IS - 21
SP - eaeb8034
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aeb8034
UR - https://doi.org/10.1126/sciadv.aeb8034
LA - en
ER -

CSL-JSON

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{
"family": "Zhao",
"given": "Jianmin"
},
{
"family": "Dong",
"given": "Zhijun"
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "21",
"page": "eaeb8034",
"DOI": "10.1126/sciadv.aeb8034",
"PMID": "42160431",
"PMCID": "PMC13189125",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aeb8034",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
20
]
]
}
}

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