OSCR

CHCHD10 Mitigates Alzheimer's Disease-Related Phenotypes in Association With Epigenetic Remodeling in Directly Reprogrammed Neurons.

Code ↔ Paper

22 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 22 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Immunoprecipitation and Proteomics ↔ scripts/data_process/CHCHD10_interactome/CHCHD10_interactome_step1_filter_candidates_06.R, lines 1–70 · score 0.93 · CHCHD10 IP replicates, Candidate interactors, MaxLFQ, Strong candidates, High confidence, bead
  2. [2] § Materials and Methods › Bulk RNA‐Seq & Single‐Cell RNA‐Sequencing Analysis › Single‐Cell RNA‐Seq ↔ scripts/data_process/ROSMAP_scRNAseq/oligo_02.R, lines 55–160 · score 0.89 · APOE genotype, min.cells.feature, FindMarkers, min.pct, Seurat, NC cells
  3. [3] § Materials and Methods › Bulk RNA‐Seq & Single‐Cell RNA‐Sequencing Analysis › Single‐Cell RNA‐Seq ↔ scripts/data_process/ROSMAP_scRNAseq/Astrocytes_02.R, lines 254–371 · score 0.89 · APOE genotype, min.cells.feature, FindMarkers, min.pct, NC cells, subtypes
  4. [4] § Materials and Methods › Immunoprecipitation and Proteomics ↔ scripts/figures/CHCHD10_interactome/Fig_S4_c_enrichment ranking plot.R, lines 64–123 · score 0.84 · High confidence candidates, CHCHD10 IP LFQ, log2 enrichment, Strong candidates, ranking, interactors
  5. [5] § Materials and Methods › Co‐Methylation Network ↔ scripts/figures/Methylation/Fig_S4/Fig_S4_f_improved.meth.network.R, lines 102–140 · score 0.81 · fast greedy, network graph, adjacency matrix, Vertices, igraph, methylation
  6. [6] § Materials and Methods › Bulk RNA‐Seq & Single‐Cell RNA‐Sequencing Analysis › Bulk RNA‐Seq ↔ scripts/data_process/MayoRNAseqADvsNC/DEG_rerun_02.R, lines 41–82 · score 0.81 · Log2 fold changes, clusterProfiler, DESeq2, apeglm, shrinkage, Mayo
  7. [7] § Materials and Methods › Bulk RNA‐Seq & Single‐Cell RNA‐Sequencing Analysis › Single‐Cell RNA‐Seq ↔ scripts/data_process/ROSMAP_scRNAseq/oligo_02.R, lines 55–160 · score 0.80 · v4 v5, LogNormalize, balanced subsampling, Seurat, capping, RNA
  8. [8] § Results › CHCHD10‐Mediated DMRs Overlap With AD Genetic Risk Architecture ↔ scripts/figures/Methylation/Fig_S7/making.300.R, the whole file · a weak match · score 0.77 · ARL17B, LRRC37A2, TAOK2, TBX6, WNT3, GLG1
  9. [9] § Materials and Methods › Bulk RNA‐Seq & Single‐Cell RNA‐Sequencing Analysis › Single‐Cell RNA‐Seq ↔ scripts/data_process/ROSMAP_scRNAseq/Astrocytes_02.R, lines 254–371 · score 0.76 · v4 v5, LogNormalize, balanced subsampling, capping, Seurat, RNA
  10. [10] § Results › CHCHD10 Expression is Reduced in AD and Correlates With Neuropathology ↔ scripts/figures/Inhibitory_Neurons/Fig_02_h_Inhibitory_CHCHD10_vs_Braak.R, lines 101–172 · score 0.70 · inhibitory CHCHD10, Braak stage, inhibitory neurons, CHCHD10 expression, covariates, models
  11. [11] § Results › CHCHD10‐Mediated DMRs Overlap With AD Genetic Risk Architecture ↔ scripts/figures/Fig_07_a.R, lines 21–40 · score 0.67 · ARL17B, LRRC37A2, ABCA7, KANSL1, MAPT, NC
  12. [12] § Materials and Methods › Co‐Methylation Network ↔ scripts/figures/Methylation/Fig_S4/Fig_S4_f_improved.meth.network.R, lines 56–100 · score 0.66 · Euclidean distance matrix, scaled methylation, network, dplyr
  13. [13] § Materials and Methods › Epigenetic and Genetic Relationships Assessment ↔ scripts/figures/Methylation/Fig_06/Fig06_d_final.try.eqtl.R, lines 199–257 · score 0.66 · MetaBeta, linear model, eQTL, AFR, score, EUR
  14. [14] § Materials and Methods › Epigenetic and Genetic Relationships Assessment ↔ scripts/figures/Methylation/Fig_06/Fig_06_b_c_colocalization.R, lines 413–471 · score 0.65 · eQTpLot, GWAS SNPs, eQTL, colocalization, trait, hg38
  15. [15] § Results › CHCHD10 Expression is Reduced in AD and Correlates With Neuropathology ↔ scripts/figures/Inhibitory_Neurons/Fig_02_j_CHCHD10_vs_Age_AD_vs_NC_NOcovariates_inhibitory.R, the whole file · a weak match · score 0.65 · inhibitory neurons, linear models, CHCHD10 expression, covariates, age, death
  16. [16] § Materials and Methods › Pathway Enrichment and Phenotypic Association Analyses ↔ scripts/figures/Methylation/Fig_04/single.go.enrich.apps1vsappsad10.R, lines 227–301 · score 0.60 · ClusterProfiler, Gene symbols, BH, GO, enriched, enrichment
  17. [17] § Materials and Methods › Pathway Enrichment and Phenotypic Association Analyses ↔ scripts/figures/Methylation/Fig_04/PEA.EnrichR.R, lines 46–90 · score 0.60 · GO molecular function, ClusterProfiler, enriched, Phenotypic, Overlapping, methylation
  18. [18] § Results › CHCHD10 Expression is Reduced in AD and Correlates With Neuropathology ↔ scripts/figures/Fig_02_a.R, lines 17–32 · score 0.60 · immune cells, Inhibitory neurons, Excitatory neurons, oligodendrocyte, OPC, astrocyte
  19. [19] § Materials and Methods › Bulk RNA‐Seq & Single‐Cell RNA‐Sequencing Analysis › Per‐Donor Regression (scRNA‐Seq) ↔ scripts/figures/Inhibitory_Neurons/Fig_02_j_CHCHD10_vs_Age_AD_vs_NC_NOcovariates_inhibitory.R, the whole file · a weak match · score 0.60 · linear models, CHCHD10 expression, covariates, fitted, age, logged
  20. [20] § Results › CHCHD10 Expression is Reduced in AD and Correlates With Neuropathology ↔ scripts/figures/Fig_07_a.R, lines 21–40 · score 0.59 · immune cells, Inhibitory neurons, Excitatory neurons, oligodendrocyte, OPC, astrocyte
  21. [21] § Materials and Methods › Bulk RNA‐Seq & Single‐Cell RNA‐Sequencing Analysis › Per‐Donor Regression (scRNA‐Seq) ↔ scripts/figures/Inhibitory_Neurons/Fig_02_h_Inhibitory_CHCHD10_vs_Braak.R, lines 101–172 · score 0.52 · Braak stage, CHCHD10 expression, covariates, logged, models, donor
  22. [22] § Materials and Methods › Integration of Genetic (GWAS/eQTL) and Epigenetic (DMR) Analyses ↔ scripts/figures/Methylation/Fig_06/Fig_06_b_c_colocalization.R, lines 106–147 · score 0.51 · quantitative trait, eQTL, colocalization, SNPs, GWAS

Paper

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The authors' code

R · 172 lines · 6.9 KB · MIT · 2 matches

  1. # ======================= Oligodendrocytes: normalize-if-needed + DEG (step-logged) =======================
  2. suppressPackageStartupMessages({
  3. library(Seurat)
  4. library(dplyr)
  5. library(tibble)
  6. library(Matrix)
  7. })
  9. # ---------- PATHS (EDIT IF NEEDED) ----------
  10. BASE <- "/home/cxy605/Synapse/ROSMAP_scRNAseq/Oligodendrocytes"
  11. CLIN <- file.path(BASE, "clinical_w_designations.csv")
  12. RDS_PATH <- file.path(BASE, "Oligodendrocytes.rds")
  13. OUT_DIR <- BASE
  15. # ---------- small utils ----------
  16. say <- function(...) cat(sprintf(...), "\n")
  17. guard <- function(step, expr) {
  18. say("\n========== %s ==========", step)
  19. t0 <- proc.time()[3]
  20. val <- tryCatch(expr, error=function(e){ say("ERROR @ %s: %s", step, e$message); stop(e) })
  21. say("OK %s (%.1fs)", step, proc.time()[3]-t0); invisible(val)
  22. }
  24. # v4/v5-safe: does RNA@data look like counts? (if yes -> normalize)
  25. looks_like_counts <- function(obj, assay = "RNA") {
  26. m <- tryCatch(GetAssayData(obj, assay = assay, layer = "data"),
  27. error=function(e) GetAssayData(obj, assay = assay, slot = "data"))
  28. if (is.null(m) || nrow(m) == 0 || ncol(m) == 0) return(TRUE)
  29. set.seed(1)
  30. cs <- sample(seq_len(ncol(m)), size = min(1000, ncol(m)))
  31. gs <- sample(seq_len(nrow(m)), size = min(200, nrow(m)))
  32. v <- as.numeric(m[gs, cs, drop = FALSE])
  33. # counts tend to be integers and can get big; normalized are decimal and <= ~12
  34. frac_integerish <- mean(abs(v - round(v)) < 1e-8, na.rm = TRUE)
  35. maxv <- suppressWarnings(max(v, na.rm = TRUE))
  36. isTRUE(frac_integerish > 0.8 || maxv > 12)
  37. }
  39. # stratified sampling (keep donor>=20 filter and AD/NC proportion)
  40. sample_cells_stratified <- function(seurat_obj, target_size = 50000) {
  41. meta <- [email hidden]
  42. keep_ids <- names(which(table(meta$projid) >= 20))
  43. meta_f <- meta[meta$projid %in% keep_ids, , drop = FALSE]
  44. ad <- rownames(meta_f[meta_f$ad_status == "AD", , drop = FALSE])
  45. nc <- rownames(meta_f[meta_f$ad_status == "NC", , drop = FALSE])
  46. tot <- length(ad) + length(nc)
  47. if (tot == 0) stop("No eligible cells after donor filter.")
  48. ad_prop <- length(ad) / tot; nc_prop <- 1 - ad_prop
  49. n_ad <- min(round(target_size * ad_prop), length(ad))
  50. n_nc <- min(round(target_size * nc_prop), length(nc))
  51. set.seed(123)
  52. c(sample(ad, n_ad), sample(nc, n_nc))
  53. }
  55. run_oligo <- function(rds_path = RDS_PATH, clin_path = CLIN, out_dir = OUT_DIR,
  56. donor_min_cells = 20, cap_per_group = 6000, target_size = 50000,
  57. logfc_thr = 0.25, min_pct = 0.10, min_cells_grp = NULL) {
  59. say("\n================ Oligodendrocytes ================")
  61. # 1) Load
  62. obj <- guard("Load RDS", readRDS(rds_path))
  63. DefaultAssay(obj) <- "RNA"
  65. # 1b) UPGRADE OBJECT to Seurat v5 format
  66. # This ensures the object has the correct slots (Assay5) and avoids the "assay.orig" error.
  67. obj <- guard("UpdateSeuratObject(v4→v5)", UpdateSeuratObject(obj))
  69. # 2) Merge clinical
  70. clinical <- guard("Read clinical", read.csv(clin_path, stringsAsFactors = FALSE))
  71. clinical$projid <- as.character(clinical$projid)
  72. meta <- [email hidden] %>% rownames_to_column("cell_id")
  73. if (!"projid" %in% names(meta)) stop("projid not in metadata; cannot merge clinical.")
  74. meta$projid <- as.character(meta$projid)
  75. meta2 <- guard("Merge meta",
  76. meta %>% left_join(
  77. clinical %>% dplyr::select(
  78. projid, ad_status, braaksc, msex, educ, age_death,
  79. apoe_genotype, cts_mmse30_first_ad_dx, cogdx,
  80. cts_mmse30_lv, age_first_ad_dx
  81. ),
  82. by = "projid"
  83. ) %>% column_to_rownames("cell_id")
  84. )
  85. [email hidden] <- meta2
  86. say("Cells per diagnosis:"); print(table(obj$ad_status, useNA = "ifany"))
  87. if (any(is.na(obj$ad_status))) stop("Missing ad_status after merge.")
  89. # 3) Normalize if needed (your earlier check indicated counts in data)
  90. need_norm <- looks_like_counts(obj, "RNA")
  91. say("RNA 'data' looks like counts? %s", as.character(need_norm))
  92. if (need_norm) {
  93. obj <- guard("NormalizeData(LogNormalize)", NormalizeData(
  94. obj, assay = "RNA", normalization.method = "LogNormalize",
  95. scale.factor = 1e4, verbose = FALSE))
  96. }
  98. # 4) If huge, stratified sample to target_size (keeps AD/NC balance)
  99. if (ncol(obj) > target_size) {
  100. cells <- guard(sprintf("Stratified sample to %d", target_size),
  101. sample_cells_stratified(obj, target_size))
  102. obj <- guard("Subset to sampled cells", subset(obj, cells = cells))
  103. say("Post-sampling cells: %d", ncol(obj))
  104. } else {
  105. say("No sampling needed.")
  106. }
  108. # 5) Donor filter (>= donor_min_cells)
  109. keep_ids <- names(which(table(obj$projid) >= donor_min_cells))
  110. obj <- guard(sprintf("Donor filter (>=%d cells)", donor_min_cells),
  111. subset(obj, subset = projid %in% keep_ids))
  112. tbl <- table(obj$ad_status)
  113. say("After donor filter AD/NC: %s", paste(paste(names(tbl), tbl, sep="="), collapse=" | "))
  114. if (length(tbl) < 2 || any(tbl < 10)) stop("Insufficient AD/NC cells after donor filter.")
  116. # 6) Balanced subsampling (cap per group)
  117. Idents(obj) <- obj$ad_status
  118. gAD <- WhichCells(obj, idents = "AD")
  119. gNC <- WhichCells(obj, idents = "NC")
  120. set.seed(123)
  121. gADs <- if (length(gAD) > cap_per_group) sample(gAD, cap_per_group) else gAD
  122. gNCs <- if (length(gNC) > cap_per_group) sample(gNC, cap_per_group) else gNC
  123. obj <- guard(sprintf("Subset balanced (cap=%d)", cap_per_group),
  124. subset(obj, cells = c(gADs, gNCs)))
  125. Idents(obj) <- obj$ad_status
  126. nAD <- sum(Idents(obj) == "AD")
  127. nNC <- sum(Idents(obj) == "NC")
  128. say("Entering DEG AD=%d / NC=%d", nAD, nNC)
  130. # 7) DEG (fully unfiltered)
  131. if (is.null(min_cells_grp)) min_cells_grp <- 1L # ≥1 expressing cell per group
  133. markers <- guard("FindMarkers(wilcox)",
  134. FindMarkers(
  135. obj, ident.1 = "AD", ident.2 = "NC",
  136. test.use = "wilcox",
  137. logfc.threshold = 0, # no FC prefilter
  138. min.pct = 0, # no pct prefilter
  139. min.cells.group = min_cells_grp,
  140. min.cells.feature = 1,
  141. assay = "RNA",
  142. slot = "data",
  143. verbose = TRUE
  144. )
  145. )
  146. say("Markers found: %d rows", nrow(markers))
  148. # 8) Save outputs
  149. out_csv <- file.path(out_dir, "DEG_oligodendrocytes_AD_vs_NC_unfiltered.csv")
  150. out_deg_rds <- file.path(out_dir, "DEG_oligodendrocytes_AD_vs_NC_unfiltered.rds")
  151. out_obj_rds <- file.path(out_dir, "Oligodendrocytes_normalized_balanced.rds")
  153. write.csv(markers, out_csv, row.names = TRUE)
  154. saveRDS(markers, out_deg_rds)
  155. say("WROTE: %s", out_csv)
  156. say("WROTE: %s", out_deg_rds)
  158. saveRDS(obj, out_obj_rds)
  159. say("Saved normalized object: %s", out_obj_rds)
  160. }
  162. # ---------- RUN ----------
  163. try(run_oligo(
  164. RDS_PATH, CLIN, OUT_DIR,
  165. donor_min_cells = 20,
  166. cap_per_group = 6000,
  167. target_size = 50000,
  168. logfc_thr = 0,
  169. min_pct = 0,
  170. min_cells_grp = 1
  171. ))
  172. say("\nAll done.")

oligo_02.R at commit 8b1d187, under MIT · at the source

Overview

Authors: Teresa M Thomas1, Ching‐Yao Yang1, Kexin Zhang1, Liam Wetzel1, Dina Bugybayeva1, Julia Ferguson1, Md Mahmudul Hasan1, William Samsa1, Nandita Patil1, Anshul Dash1, Tarini Gowda1, Xiongwei Zhu1, Feixiong Cheng2,3,4, Tian Liu1
ORCID iDs: Md Mahmudul Hasan, Feixiong Cheng, Tian Liu
  1. Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
  2. Cleveland Clinic Genome Center, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio, USA
  3. Department of Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio, USA
  4. Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
Institutions: Case Western Reserve University (United States); Cleveland Clinic (United States); Cleveland Clinic Lerner College of Medicine (United States)
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), volume 13, issue 51, article e76205
Dates: received 22 December 2025; accepted 11 June 2026; published online 25 June 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/advs.76205 · PMID 42348392 · PMCID PMC13337126 · OpenAlex W7165925188
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, Machine learning
Keywords: biology, chromatin, cpg site, dna methylation, epigenetics, epigenome, epigenomics, expression quantitative trait loci, methylation, reprogramming
MeSH: Alzheimer Disease*, Epigenesis, Genetic*, Mitochondrial Proteins*, Neurons*, Cellular Reprogramming, DNA Methylation, Humans, Phenotype (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: NIA NIH HHS (R01 AG076448, R01 AG086365, R33 AG083003, P30 AG072959, R01 AG092462, R21 AG070299, RF1 AG082211, R01 AG084250, R01 AG092591, R21 AG095389, U01 AG073323, R01 AG066707, R01 AG082118, R01AG086365 to T.L R21AG070299 to T.L R21AG095389 to T.L R03AG084948 to T.L, R03 AG084948); NINDS NIH HHS (RF1NS133812, RF1 NS133812); Alzheimer's Association award (ALZDISCOVERY-1051936)
Citations: cited by 1 paper (Europe PMC); 110 references in the paper

Abstract

Mitochondrial dysfunction and chromatin dysregulation are interconnected contributors to neuronal vulnerability in Alzheimer's disease (AD), yet the molecular mechanisms linking these processes remain poorly understood. CHCHD10, a mitochondrial intermembrane space protein, has been implicated in neurodegenerative disorders, but its role in AD has not been defined. Here, we identify CHCHD10 as a previously unrecognized modulator of neuronal epigenomic stability in AD. Using direct fibroblast‐to‐neuron reprogramming, which preserves patient‐specific epigenetic signatures, we show that AD neurons recapitulate genome‐wide hypomethylation patterns observed in postmortem AD cortex. CHCHD10 expression is significantly reduced in AD neurons and across multiple human brain datasets, including single‐cell and bulk RNA sequencing, proteomics, and human cortical tissue analyses. Restoration of CHCHD10 in AD neurons reduces amyloid‐β and insoluble tau accumulation while reversing AD‐associated differentially methylated regions across CpG islands, promoters, and regulatory elements. CHCHD10‐responsive methylation changes overlap with those observed in human AD brain regions and colocalize with significant AD loci and cortex‐specific eQTL loci, including MAPT and ABCA7. Finally, we identify KATNAL2 as a CHCHD10‐responsive effector whose loss enhances tau phosphorylation and seeding, whereas its restoration mitigates tau pathology. Together, these findings support a CHCHD10‐associated neuroprotective pathway linking mitochondrial dysfunction, epigenomic instability, and tau pathology in AD.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 22 matches between paragraphs and lines of code.

tliu203/CHCHD10-Alleviates-Alzheimer-s-Disease-Pathogenesis-by-Modulating-Epigenetic-Landscape-

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8b1d1877f0cf70f6c8b8cd2b6f091013dbd251ff, 1 June 2026
Languages: R (40)
Size: 56 files, 40 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (36 files), ggplot2 (25 files), Seurat (9 files), clusterProfiler (7 files), pheatmap (5 files), broom (3 files), circlize (3 files), patchwork (3 files), glmnet (2 files), igraph (2 files), lme4 (2 files), randomForest (2 files), Stan (2 files), ComplexHeatmap (1 file), DESeq2 (1 file), Plotly (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
42 files
At the source: github.com/tliu203/CHCHD10-Alleviates-Alzheimer-s-Disease-Pathogenesis-by-Modulating-Epigenetic-Landscape-.git

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

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 40 scripts, each with its path and the digest of its content;
  • 22 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

Datasets cited

Data Availability Statement

Publicly available datasets used in this study include human postmortem brain single‐cell RNA‐seq data from the ROSMAP Synapse repository from the AD Knowledge Portal (syn2580853). Processed scRNA‐seq data by cluster were downloaded under synapse ID syn52293433. Bulk RNA‐seq data from the ROSMAP cohort (Synapse ID: syn8456629), and cortex‐specific eQTL data from the MetaBrain consortium (https://www.metabrain.nl/cis‐eqtls.html (https://www.metabrain.nl/cis-eqtls.html)). AD GWAS summary statistics were obtained from the GWAS Catalog (GCST90027158). All custom scripts used for methylation processing, genomic windowing, GWAS–eQTL integration, and statistical analyses are available from the corresponding author upon reasonable request. Processed DNA methylation data, including normalized methylation values, count matrices, and associated sample metadata, are provided in the Supplementary Materials and deposited in GEO under accession GSE328184 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE328184). The code used for analysis and figure generation is available at GitHub: https://github.com/tliu203/CHCHD10‐Alleviates‐Alzheimer‐s‐Disease‐Pathogenesis‐by‐Modulating‐Epigenetic‐Landscape‐.git (https://github.com/tliu203/CHCHD10-Alleviates-Alzheimer-s-Disease-Pathogenesis-by-Modulating-Epigenetic-Landscape-.git). Raw data are partially available due to an unrecoverable data storage error that occurred after the processed datasets were generated.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 10 keywords, 8 MeSH terms, 3 funders, 110 references.

Cite

This paper

Thomas, T. M., Yang, C., Zhang, K., Wetzel, L., Bugybayeva, D., Ferguson, J., Hasan, M. M., Samsa, W., Patil, N., Dash, A., Gowda, T., Zhu, X., Cheng, F., & Liu, T. (2026). CHCHD10 Mitigates Alzheimer's Disease-Related Phenotypes in Association With Epigenetic Remodeling in Directly Reprogrammed Neurons. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 13(51), e76205. https://doi.org/10.1002/advs.76205

BibTeX

@article{thomas2026chchd10,
author = {Thomas, Teresa M and Yang, Ching‐Yao and Zhang, Kexin and Wetzel, Liam and Bugybayeva, Dina and Ferguson, Julia and Hasan, Md Mahmudul and Samsa, William and Patil, Nandita and Dash, Anshul and Gowda, Tarini and Zhu, Xiongwei and Cheng, Feixiong and Liu, Tian},
title = {{CHCHD10 Mitigates Alzheimer's Disease-Related Phenotypes in Association With Epigenetic Remodeling in Directly Reprogrammed Neurons}},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2026},
month = jun,
volume = {13},
number = {51},
pages = {e76205},
publisher = {Wiley},
issn = {2198-3844},
doi = {10.1002/advs.76205},
url = {https://doi.org/10.1002/advs.76205},
pmid = {42348392},
pmcid = {PMC13337126}
}

RIS

TY - JOUR
AU - Thomas, Teresa M
AU - Yang, Ching‐Yao
AU - Zhang, Kexin
AU - Wetzel, Liam
AU - Bugybayeva, Dina
AU - Ferguson, Julia
AU - Hasan, Md Mahmudul
AU - Samsa, William
AU - Patil, Nandita
AU - Dash, Anshul
AU - Gowda, Tarini
AU - Zhu, Xiongwei
AU - Cheng, Feixiong
AU - Liu, Tian
TI - CHCHD10 Mitigates Alzheimer's Disease-Related Phenotypes in Association With Epigenetic Remodeling in Directly Reprogrammed Neurons
T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
J2 - Adv Sci (Weinh)
PY - 2026
DA - 2026/06/25
VL - 13
IS - 51
SP - e76205
SN - 2198-3844
PB - Wiley
DO - 10.1002/advs.76205
UR - https://doi.org/10.1002/advs.76205
LA - en
ER -

CSL-JSON

{
"id": "10.1002/advs.76205",
"type": "article-journal",
"title": "CHCHD10 Mitigates Alzheimer's Disease-Related Phenotypes in Association With Epigenetic Remodeling in Directly Reprogrammed Neurons",
"container-title": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)",
"author": [
{
"family": "Thomas",
"given": "Teresa M"
},
{
"family": "Yang",
"given": "Ching‐Yao"
},
{
"family": "Zhang",
"given": "Kexin"
},
{
"family": "Wetzel",
"given": "Liam"
},
{
"family": "Bugybayeva",
"given": "Dina"
},
{
"family": "Ferguson",
"given": "Julia"
},
{
"family": "Hasan",
"given": "Md Mahmudul"
},
{
"family": "Samsa",
"given": "William"
},
{
"family": "Patil",
"given": "Nandita"
},
{
"family": "Dash",
"given": "Anshul"
},
{
"family": "Gowda",
"given": "Tarini"
},
{
"family": "Zhu",
"given": "Xiongwei"
},
{
"family": "Cheng",
"given": "Feixiong"
},
{
"family": "Liu",
"given": "Tian"
}
],
"container-title-short": "Adv Sci (Weinh)",
"volume": "13",
"issue": "51",
"page": "e76205",
"DOI": "10.1002/advs.76205",
"PMID": "42348392",
"PMCID": "PMC13337126",
"ISSN": "2198-3844",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/advs.76205",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
25
]
]
}
}

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