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Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization.

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  1. [1] § Materials and methods › MR using Sc-eQTL data ↔ analysispipeline_main.R, lines 133–189 · score 0.59 · inverse variance weighted, Wald ratio, MR, eQTL, colocalization, SNP

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  1. # Pipeline Overview: Single-cell MR Discovery, Replication, and Colocalization
  3. # ----------------------------
  4. # Step 1: Discovery Mendelian Randomization (MR)
  5. # ----------------------------
  7. ## 1.1 Load necessary libraries and data
  8. library(readxl)
  9. library(dplyr)
  10. library(TwoSampleMR)
  11. library(MRInstruments)
  12. library(writexl)
  14. # 1.1 Load raw single-cell eQTL data and format
  15. dataset1K1K_raw <- read_excel("file_path/dataset1K1K.xlsx")
  16. dataset1K1K <- dataset1K1K_raw %>%
  17. dplyr::filter(pvalue < 1e-5) %>%
  18. dplyr::mutate(
  19. se = abs(`rho correlation coefficient` / qnorm(pvalue/2)),
  20. beta = `rho correlation coefficient`,
  21. pval = pvalue,
  22. cell = `Cell type`,
  23. phenotype = `Gene ID`,
  24. effect_allele = `SNP assessed allele`
  25. ) %>%
  26. dplyr::select(cell, phenotype, SNP, effect_allele, beta, se, pval)
  28. # Save formatted dataset1K1K file for reuse
  29. write_xlsx(dataset1K1K, "file_path/dataset1K1K_formatted.xlsx")
  31. ## 1.2 Load and format outcome GWAS data
  32. outcome_df <- data.table::fread(file = "file_path/outcome.tsv", header = TRUE)
  33. outcome_df <- as.data.frame(outcome_df)
  34. outcome <- format_data(
  35. outcome_df,
  36. type = "outcome",
  37. snp_col = "SNP",
  38. beta_col = "beta",
  39. se_col = "standard_error",
  40. effect_allele_col = "effect_allele",
  41. other_allele_col = "other_allele",
  42. pval_col = "p_value"
  43. )
  45. # Save formatted outcome for reuse
  46. save(outcome, file = "file_path/outcome.RData")
  49. ## 1.3 Run MR for a cell type
  50. cell_type <- "B IN" # manual: set cell_type (`"B IN", "B Mem", "CD4 ET",
  51. #"CD4 NC", "CD4 SOX4", "CD8 ET", "CD8 NC", "CD8 S100B", "DC", "Mono C", "Mono NC",
  52. #"NK", "NK R", "Plasma")
  54. exp_df <- dataset1K1K %>%
  55. dplyr::filter(cell == cell_type)
  57. exp_obj <- format_data(
  58. exp_df,
  59. type = "exposure",
  60. snp_col = "SNP",
  61. beta_col = "beta",
  62. se_col = "se",
  63. effect_allele_col = "effect_allele",
  64. phenotype_col = "phenotype",
  65. pval_col = "pval"
  66. )
  68. har <- harmonise_data(exp_obj, outcome, action = 1)
  69. mr_res_cell_type <- mr(har) %>%
  70. dplyr::filter(method %in% c("Inverse variance weighted", "Wald ratio")) %>%
  71. dplyr::mutate(qval = p.adjust(pval, method = "BH")) %>%
  72. mutate(cell = cell_type)
  74. # (Repeat the above block for each cell type)
  76. # After running for all desired cell types, bind and save combined results:
  77. combined <- dplyr::bind_rows(mget(ls(pattern = "^mr_res_")))
  78. write_xlsx(combined, "file_path/discMR_outcome.xlsx")
  80. # ----------------------------
  81. # Step 2: Replication Mendelian Randomization (MR)
  82. # ----------------------------
  84. ## 2.1 Load previously formatted outcome for replication
  85. load("file_path/outcome.RData")
  87. ## 2.1 Load libraries and replication exposure dataset
  88. library(readr)
  89. library(readxl)
  90. library(dplyr)
  91. library(meta)
  92. library(genetics.binaRies)
  93. library(TwoSampleMR)
  95. scBloodNL_cell_type <- read_delim(
  96. "file_path/cell_type_expression_eQTLsFDR-ProbeLevel.txt", delim = "\t") %>%
  97. dplyr::filter(PValue < 1e-5) # manual: set cell_type ("B", "CD4", "CD8", "DC",
  98. #"Mono", "NK")
  100. ## 2.2 Identify significant genes from discovery MR
  101. discMR <- read_excel("file_path/discMR_outcome.xlsx")
  102. sig_genes <- discMR %>% filter(cell %in% cell_types, qval < 0.05) %>%
  103. pull(exposure) # manual: set cell_types ("B IN", "B Mem", "Plasma" for "B",
  104. #"CD4 ET", "CD4 NC", "CD4 SOX4" for "CD4", "CD8 ET", "CD8 NC", "CD8 S100B" for "CD8",
  105. #"DC" for "DC", "Mono C", "Mono NC" for "Mono", "NK", "NK R" for "NK")
  107. ## 2.3 Subset replication exposure and export for manual formatting
  108. subset_cell_type <- scBloodNL_cell_type %>% dplyr::filter(HGNCName %in% sig_genes)
  109. write_xlsx(subset_cell_type, "file_path/subset_cell_type.xlsx")
  110. # manual: keep required columns, format column names and add beta_i, se_i, beta_ii, se_ii and save
  112. rep_cell_type <- read_excel("file_path/subset_cell_type.xlsx")
  114. df1 <- rep_cell_type %>% dplyr::select(HGNCName, SNP, beta_i, se_i) %>%
  115. dplyr::mutate(obs = row_number())
  116. df2 <- rep_cell_type %>% dplyr::select(HGNCName, SNP, beta_ii, se_ii) %>%
  117. rename("beta_i" = "beta_ii", "se_i" = "se_ii") %>% dplyr::mutate(obs = row_number())
  118. meta_df <- bind_rows(df1, df2) %>% arrange(obs)
  120. ma_rep <- metagen(TE = beta_i, seTE = se_i, data = meta_df, common = TRUE,
  121. random = FALSE, sm = "MD", backtransf = FALSE, subgroup = obs)
  122. rep_cell_type <- bind_cols(rep_cell_type,
  123. data.frame(beta = ma_rep$TE.fixed.w, se = ma_rep$seTE.fixed.w))
  125. clumped <- ld_clump_local(
  126. tibble(rsid = rep_cell_type$SNP, pval = rep_cell_type$PValue),
  127. plink_bin = "file_path/plink_win64_20231211/plink.exe",
  128. bfile = "file_path/Eur_ldref/EUR",
  129. clump_r2 = 0.1, clump_p = 1, clump_kb = 10000
  130. )
  131. rep_cell_type <- rep_cell_type %>% dplyr::filter(SNP %in% clumped$rsid)
  133. ## 2.4 Format and run replication MR
  134. rep_cell_type <- format_data(
  135. rep_cell_type,
  136. type = "exposure",
  137. snp_col = "SNP",
  138. beta_col = "beta",
  139. se_col = "se",
  140. effect_allele_col = "effect_allele",
  141. phenotype_col = "HGNCName",
  142. pval_col = "PValue"
  143. )
  144. har_rep <- harmonise_data(rep_cell_type, outcome, action = 1)
  145. mr_cell_type <- mr(har_rep) %>%
  146. dplyr::filter(method %in% c("Inverse variance weighted", "Wald ratio")) %>%
  147. dplyr::mutate(qval = p.adjust(pval, method = "BH")) %>%
  148. mutate(cell = cell_type)
  150. # (Repeat the above block for each cell type)
  152. # After running for all desired cell types, bind and save combined results:
  153. combined <- dplyr::bind_rows(mget(ls(pattern = "^mr_")))
  154. write_xlsx(combined, "file_path/repMR_outcome.xlsx")
  156. # ----------------------------
  157. # Step 3: Colocalization Analysis
  158. # ----------------------------
  160. ## 3.1 Format outcome for colocalisation
  161. outcome_df <- data.table::fread(file = "file_path/outcome.tsv", header = TRUE) %>%
  162. dplyr::rename("snp" = "SNP", "se" = "standard_error", "pval" = "p_value") %>%
  163. dplyr::mutate(varbeta = se^2) %>%
  164. dplyr::select(snp, beta, varbeta, pval)
  165. outcome <- as.list(outcome_df)
  166. outcome$type <- "cc"
  167. outcome$s <- p #manual: add proportion of cases in outcome population
  169. ## 3.2 Subset eQTLs for finer cell and matching genes
  170. disc_hits <- read_excel("file_path/discMR_outcome.xlsx") %>%
  171. filter(cell == cell_type, qval < 0.05) # manual: set cell_type (`"B IN", "B Mem",
  172. #"CD4 ET", "CD4 NC", "CD4 SOX4", "CD8 ET", "CD8 NC", "CD8 S100B", "DC", "Mono C",
  173. #"Mono NC", "NK", "NK R", "Plasma")
  174. rep_hits <- read_excel("file_path/repMR_outcome.xlsx") %>%
  175. filter(cell == cell_type, qval < 0.05) # manual: set cell_types
  176. #("B" for "B IN","B Mem","Plasma","CD4" for "CD4 ET","CD4 NC","CD4 SOX4",
  177. # "CD8" for "CD8 ET","CD8 NC","CD8 S100B","DC" for "DC",
  178. # "Mono" for "Mono C","Mono NC","NK" for "NK","NK R")
  179. matching_genes <- intersect(disc_hits$exposure, rep_hits$exposure)
  181. dataset1K1K <- read_excel("file_path/dataset1K1K_formatted.xlsx", col_names = TRUE)
  182. eqtl_df <- dataset1K1K %>% filter(cell == cell_type, phenotype %in% matching_genes)
  183. # manual: set cell_type (`"B IN", "B Mem", #"CD4 ET", "CD4 NC", "CD4 SOX4", "CD8 ET",
  184. #"CD8 NC", "CD8 S100B", "DC", "Mono C", "Mono NC", "NK", "NK R", "Plasma")
  186. eqtl_df$varbeta <- eqtl_df$se^2
  187. eqtl_df$MAF <- MAF #MAF values of SNPs can be obtained from Ensembl
  188. eqtl_df <- eqtl_df %>%
  189. rename("gene" = "phenotype", "snp" = "SNP", "position" = "Position")
  192. ## 3.3a Colocalization using coloc.abf
  193. library(coloc)
  195. # Get unique gene values
  196. unique_genes <- unique(eqtl_df$gene)
  198. # Initialize a list to store colocalization results
  199. results_list <- list()
  201. # Initialize an empty dataframe to store the posterior probabilities
  202. pp_df_abf <- data.frame(
  203. Gene = character(),
  204. PP_H0 = numeric(),
  205. PP_H1 = numeric(),
  206. PP_H2 = numeric(),
  207. PP_H3 = numeric(),
  208. PP_H4 = numeric(),
  209. stringsAsFactors = FALSE
  210. )
  212. for (gene in unique_genes) {
  213. # Subset dataframe for the current gene and convert to list
  214. gene_subset <- as.list(eqtl_df[eqtl_df$gene == gene, ])
  216. # Add scalars to the list
  217. gene_subset$type <- "quant"
  218. gene_subset$N <- s # manual: set sample size (CD4 NC=463528,
  219. #CD4 ET=61786, CD4 SOX4=4065, CD8 ET=205077, CD8 NC=133482, CD8 S100B=34528,
  220. #DC=8690, Plasma=3625, Mono C=38233, Mono NC=15166, B Mem=48023, B IN=82068,
  221. #NK=159820, NK R=9677)
  223. # Run colocalization analysis using coloc.abf()
  224. coloc_result <- coloc.abf(gene_subset, outcome, MAF = NULL,
  225. p1 = 1e-04, p2 = 1e-04, p12 = 1e-05)
  227. # Store the coloc_result for that gene in results_list
  228. results_list[[gene]] <- coloc_result
  230. # Check if the coloc_result object has row names
  231. if (length(rownames(coloc_result)) == 0) {
  232. print(paste("No colocalization results found for gene:", gene))
  233. } else {
  234. # Extract the posterior probabilities from the coloc_result object
  235. pp_h0 <- coloc_result$PP.H0.abf
  236. pp_h1 <- coloc_result$PP.H1.abf
  237. pp_h2 <- coloc_result$PP.H2.abf
  238. pp_h3 <- coloc_result$PP.H3.abf
  239. pp_h4 <- coloc_result$PP.H4.abf
  241. # Create a row for the current gene in the pp_df_abf dataframe
  242. gene_row <- data.frame(
  243. Gene = gene,
  244. PP_H0 = pp_h0,
  245. PP_H1 = pp_h1,
  246. PP_H2 = pp_h2,
  247. PP_H3 = pp_h3,
  248. PP_H4 = pp_h4,
  249. stringsAsFactors = FALSE
  250. )
  252. # Append the gene_row to the pp_df_abf dataframe
  253. pp_df_abf <- rbind(pp_df_abf, gene_row)
  254. }
  255. }
  257. pp_df_abf$cell <- cell_type
  258. pp_df_abf_cell_type <- pp_df_abf
  260. # After running for all desired cell types, bind and save combined results:
  261. combined <- dplyr::bind_rows(mget(ls(pattern = "^pp_df_abf_")))
  262. write_xlsx(combined, "file_path/colabf_outcome.xlsx")
  265. ## 3.3b Colocalization using coloc.susie for Gene IDs with >1 eQTL as IV
  266. remotes::install_github("chr1swallace/coloc")
  268. library(coloc)
  269. library(data.table)
  270. library(TwoSampleMR)
  272. # Identify overlapping SNPs with outcome
  273. eqtl_df <- eqtl_df %>% rename(SNP = snp)
  274. outcome <- data.table::fread(file = "file_path/outcome.tsv", header = TRUE)
  275. common_snps <- intersect(eqtl_df$SNP, outcome$SNP)
  276. outcome <- outcome[outcome$SNP %in% common_snps, ]
  278. # Mismatch correction
  279. # Identify allele mismatches and correct them
  280. mismatched_snps <- eqtl_df %>%
  281. inner_join(outcome, by = "SNP") %>%
  282. filter(effect_allele.x != effect_allele.y)
  284. if (nrow(mismatched_snps) > 0) {
  285. for (i in 1:nrow(mismatched_snps)) {
  286. snp <- mismatched_snps$SNP[i]
  288. # Update effect allele to match outcome and flip beta
  289. eqtl_df <- eqtl_df %>%
  290. mutate(
  291. effect_allele = ifelse(SNP == snp, outcome$effect_allele[outcome$SNP == snp], effect_allele),
  292. beta = ifelse(SNP == snp, -beta, beta)
  293. )
  295. cat("Corrected allele mismatch for SNP:", snp, "\n")
  296. }
  297. }
  299. # Write the list of common SNPs
  300. write.table(common_snps, "locus_snps.txt", row.names = FALSE, col.names = FALSE, quote = FALSE)
  301. system("file_path/plink_win64_20231211/plink.exe --bfile file_path/Eur_ldref/EUR --extract locus_snps.txt --keep-allele-order --out locus_ld --r square --write-snplist")
  303. # Read LD matrix and SNP names
  304. ld <- read.table("locus_ld.ld", header = FALSE)
  305. ldnames <- fread("locus_ld.snplist", header = FALSE)
  307. # Create LD matrix
  308. ld.mat <- matrix(as.vector(data.matrix(ld)), nrow = nrow(ldnames), ncol = nrow(ldnames))
  310. # Name rows and columns
  311. colnames(ld.mat) <- ldnames$V1
  312. rownames(ld.mat) <- ldnames$V1
  314. # Prepare Dataset1 from eqtl_df
  315. Dataset1 <- list(
  316. beta = eqtl_df$beta,
  317. varbeta = eqtl_df$se^2,
  318. snp = eqtl_df$SNP,
  319. type = "quant",
  320. MAF = eqtl_df$MAF,
  321. N = s, # manual: set sample size (CD4 NC=463528,
  322. #CD4 ET=61786, CD4 SOX4=4065, CD8 ET=205077, CD8 NC=133482, CD8 S100B=34528,
  323. #DC=8690, Plasma=3625, Mono C=38233, Mono NC=15166, B Mem=48023, B IN=82068,
  324. #NK=159820, NK R=9677)
  325. LD = ld.mat
  326. )
  328. # Prepare Dataset2 from outcome_sub
  329. Dataset2 <- list(
  330. beta = outcome$beta,
  331. varbeta = outcome$se^2,
  332. snp = outcome$SNP,
  333. type = "cc",
  334. N = s, # manual: set sample size
  335. LD = ld.mat
  336. )
  338. # Run SuSiE fine-mapping on both traits
  339. S1 <- runsusie(Dataset1)
  340. S2 <- runsusie(Dataset2)
  342. # Perform coloc.susie
  343. susie_res <- coloc.susie(S1, S2)
  345. # Extract rsid and PP.H4.abf
  346. susie_df <- data.frame(
  347. rsid = susie_res$summary$hit1,
  348. PP_H4_abf = susie_res$summary$`PP.H4.abf`,
  349. stringsAsFactors = FALSE
  350. )
  352. susie_df$cell <- cell_type
  353. susie_df_cell_type <- susie_df
  355. # After running for all desired cell types, bind and save combined results:
  356. combined <- dplyr::bind_rows(mget(ls(pattern = "^susie_df_")))
  357. write_xlsx(combined, "file_path/colsus_outcome.xlsx")

analysispipeline_main.R at commit 1d96949, under Apache-2.0 · at the source

Overview

Authors: Tian-long Gao1, Shan Geng1, Jia Chen1, Liu Yang1, Yun-juan Nie1, Haitao Yu1,2,3, Gao-shang Chai1,2,3
ORCID iDs: Tian-long Gao, Gao-shang Chai
  1. Department of Fundamental Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu 214122 P. R. China
  2. MOE Medical Basic Research Innovation Center for Gut Microbiota and Chronic Diseases, School of Medicine, Jiangnan University, Wuxi, Jiangsu China
  3. Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi, 214122 China
Institutions: Jiangnan University (China); Wuxi Fourth People's Hospital (China)
Journal: Translational psychiatry, volume 16, issue 1, article 449
Dates: received 2 January 2026; accepted 12 June 2026; published online 23 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04199-9 · PMID 42331779 · PMCID PMC13538538 · OpenAlex W7165545338
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: Pharmacogenomics, Molecular neuroscience
MeSH: Alzheimer Disease*, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Multiomics, Quantitative Trait Loci (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (82401671, 81601121, 82571630, BK20231050, BK20211238); China Postdoctoral Science Foundation (2020M651491); Government of Jiangsu Province; Jiangnan University; Fundamental Research Funds for the Central Universities (JUSRP123070)
Citations: not cited yet (Europe PMC); 67 references in the paper

Abstract

Alzheimer’s disease (AD) is a multifactorial neurodegenerative condition in which accumulating genetic and molecular evidence implicates dysregulation of peripheral immune processes in disease pathogenesis. Nevertheless, the contribution of distinct peripheral immune cell subsets and associated gene regulatory landscapes to AD risk remains incompletely defined. To address this gap, we integrated single-cell expression quantitative trait loci (sc‑eQTL) data from the OneK1K cohort with AD GWAS summary statistics. We systematically interrogated immune cell-specific genes for their contributions to AD risk by integrating genetic causal inference with Bayesian colocalization analyses, and identified 24 eGenes that passed both the MR significance threshold (P < 0.05) and the criterion for strong shared genetic signals (PP.H4 > 0.8). Notable candidates included GATS, HLA-DOB, HLA-DQA1, PM20D1, and others, with each gene demonstrating a cell-type-specific association restricted to its corresponding immune cell type, such as monocytes, CD8 + T cells, or B cells. Independent peripheral blood single-cell transcriptomic data further supported disease-associated shifts in cell-type-specific expression patterns in AD. Phenome-wide association studies (PheWAS) indicated limited associations with off-target traits, indicating a favorable safety profile for therapeutic intervention, with the exceptions of B4GALNT3, PM20D1, and CNN2. Integration of immune gene targets with pharmacological databases yielded three candidate compound, including NSC321521 (targeting HLA-DQA1), phenoxybenzamine (targeting GSTP1), and rimexolone (targeting BIN1). Among these compounds, Predicted blood-brain barrier permeability was observed only for phenoxybenzamine and rimexolone, with docking studies indicating stable interactions, such as those between NSC321521 and HLA-DQA1, phenoxybenzamine and GSTP1, and rimexolone and BIN1. This integrative approach highlights key immune‑cell‑specific genes involved in AD and proposes repurposable drugs with central nervous system potential, paving the way for more targeted immunomodulatory strategies in AD.

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DeepVasc-Lab/3-step_sceQTLMR

License: Apache-2.0
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Size: 3 files, 1 script
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
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Tools: data.table (1 file), tidyverse (1 file)
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Version 2, 28 September 2026

  • Funding: added National Natural Science Foundation of China: 82401671, 81601121, 82571630, BK20231050, BK20211238; China Postdoctoral Science Foundation: 2020M651491; Government of Jiangsu Province; Jiangnan University; Fundamental Research Funds for the Central Universities: JUSRP123070

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 6 MeSH terms, 67 references.

Cite

This paper

Gao, T.-l., Geng, S., Chen, J., Yang, L., Nie, Y.-j., Yu, H., & Chai, G.-s. (2026). Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization. Translational psychiatry, 16(1), 449. https://doi.org/10.1038/s41398-026-04199-9

BibTeX

@article{gao2026immune,
author = {Gao, Tian-long and Geng, Shan and Chen, Jia and Yang, Liu and Nie, Yun-juan and Yu, Haitao and Chai, Gao-shang},
title = {{Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization}},
journal = {Translational psychiatry},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {449},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04199-9},
url = {https://doi.org/10.1038/s41398-026-04199-9},
pmid = {42331779},
pmcid = {PMC13538538}
}

RIS

TY - JOUR
AU - Gao, Tian-long
AU - Geng, Shan
AU - Chen, Jia
AU - Yang, Liu
AU - Nie, Yun-juan
AU - Yu, Haitao
AU - Chai, Gao-shang
TI - Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/06/23
VL - 16
IS - 1
SP - 449
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04199-9
UR - https://doi.org/10.1038/s41398-026-04199-9
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41398-026-04199-9",
"type": "article-journal",
"title": "Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Gao",
"given": "Tian-long"
},
{
"family": "Geng",
"given": "Shan"
},
{
"family": "Chen",
"given": "Jia"
},
{
"family": "Yang",
"given": "Liu"
},
{
"family": "Nie",
"given": "Yun-juan"
},
{
"family": "Yu",
"given": "Haitao"
},
{
"family": "Chai",
"given": "Gao-shang"
}
],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "449",
"DOI": "10.1038/s41398-026-04199-9",
"PMID": "42331779",
"PMCID": "PMC13538538",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-04199-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
23
]
]
}
}

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