OSCR

Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease.

Code ↔ Paper

12 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 12 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Iterative clustering and integration analysis ↔ 02.clustering/R/02h.RNA.integration.R, lines 160–247 · score 0.91 · FindNeighbors, RunUMAP, ElbowPlot, FindClusters, RunPCA, Seurat
  2. [2] § Methods › Preprocessing and nucleus filtering ↔ 01.preprocessing/Python/01f.ATAC.doublet.py, lines 35–43 · score 0.69 · add_tile_matrix, select_features, Scrublet, Doublets, filter, Preprocessing
  3. [3] § Methods › Aging differential gene expression analysis ↔ 04.differential/R/04k.sample_size.R, the whole file · a weak match · score 0.65 · geneCandidate, sizeCal, power, FDR, age, cell
  4. [4] § Methods › Aging differential pathway expression analysis ↔ 04.differential/R/04f.DE_pathway.R, lines 1–36 · score 0.62 · UCell, pathway scoring, limma, KEGG, subclass, age
  5. [5] § Methods › Inference of cCRE–gene links ↔ 05.cCREgenelinks/R/05d.HGcompare.R, lines 44–98 · score 0.62 · human cCRE, gene link, hg38, reciprocal, matched, overlapped
  6. [6] § Methods › Iterative clustering and integration analysis ↔ 02.clustering/R/02a.RNA.pca.R, lines 47–56 · score 0.61 · ElbowPlot, RunPCA, clustering, RNA
  7. [7] § Results › Cell subclass–specific cCRE–gene links in substantia nigra ↔ 05.cCREgenelinks/R/05d.HGcompare.R, lines 1–42 · score 0.60 · cCRE, distal, orthologous, reciprocal, SCENT, S6
  8. [8] § Methods › Cell subclass composition across aging ↔ 04.differential/R/04a.CellProp.R, the whole file · a weak match · score 0.58 · getTransformedProps, limma, Bayes, variable, fit, model
  9. [9] § Results › Cell subclass–specific cCRE–gene links in substantia nigra ↔ 06.PDgene/Shell/06a.liftOver.sh, lines 1–12 · score 0.57 · liftOver, human genome, enhancer, putative, map, mouse
  10. [10] § Methods › Identification of reproducible peaks ↔ 04.differential/R/04h.DAC_pseudobulk.R, lines 114–134 · score 0.57 · sex chromosomes, Peak calling, v2, pseudobulk, ATAC, filtered
  11. [11] § Methods › Identification of reproducible peaks ↔ 03.peakcalling/Shell/03c.naiveoverlap.sh, the whole file · a weak match · score 0.55 · mm10 blacklist, fraction, summits, Genome, overlapped, peak
  12. [12] § Methods › Inference of cCRE–gene links ↔ 05.cCREgenelinks/R/05d.HGcompare.R, lines 1–42 · score 0.53 · cCRE, distal, SCENT, gene

Paper

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The authors' code

R · 98 lines · 7.4 KB · no license · 3 matches

  1. suppressPackageStartupMessages(library("dplyr"))
  2. suppressPackageStartupMessages(library("Seurat"))
  3. suppressPackageStartupMessages(library("patchwork"))
  4. suppressPackageStartupMessages(library("ggplot2"))
  5. suppressPackageStartupMessages(library("cowplot"))
  6. suppressPackageStartupMessages(library("reshape2"))
  7. suppressPackageStartupMessages(library("ggpubr"))
  8. suppressPackageStartupMessages(library("stringr"))
  9. library("biomaRt")
  11. # Load RNA data to get all gene names
  12. input <- "./rds/RNA/cluster/redoround2/RNA.combined.allen.integration.final.rds"
  13. RNA <- readRDS(input)
  14. all_genes <- rownames(RNA)
  16. # Basic function to convert mouse to human gene names
  17. human <- useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", host = "https://dec2021.archive.ensembl.org")
  18. mouse <- useEnsembl("ensembl", dataset = "mmusculus_gene_ensembl", host = "https://dec2021.archive.ensembl.org")
  19. mouse2human <- getLDS(attributes = c("ensembl_gene_id","external_gene_name", "hsapiens_homolog_associated_gene_name", "hsapiens_homolog_orthology_type"),
  20. filters = "external_gene_name", values = all_genes,
  21. mart = mouse,
  22. attributesL = c("ensembl_gene_id","external_gene_name", "mmusculus_homolog_associated_gene_name", "mmusculus_homolog_orthology_type"),
  23. martL = human)
  24. write.csv(mouse2human, file="./rds/ATAC/after_integra/GRN/SCENT/VShuman/mouse2human_gene_conversion.csv", row.names=FALSE)
  27. # reciprocal cCREs
  28. all_peak <- read.table("/projects/ps-renlab2/kaw033/WK_aging/peak_calling/after_integra/final/SN_integra.final.peak.srt.bed")
  29. colnames(all_peak) <- c("chrom","start","end","peak")
  31. reciprocal_peak <- read.table("/projects/ps-renlab2/kaw033/WK_aging/ldsc/after_integra/celltype/raw/SN_integra.final.peak.srt.reciprocalToHg38.bed")
  32. colnames(reciprocal_peak) <- c("chrom","start","end","Mpeak")
  33. length(intersect(all_peak$peak, reciprocal_peak$Mpeak))
  34. reciprocal_peak %>% mutate(Hpeak = paste0(chrom, ":", start, "-", end)) -> reciprocal_peak
  36. # get human pdc from Yang
  37. human_peak <- read.table("rds/ATAC/after_integra/GRN/SCENT/VShuman/YangLi_cCRE/Table S6 – List of cCREs in bed format")
  38. colnames(human_peak) <- c("chrom","start","end","cCRE")
  39. human_peak$peak <- gsub("cCRE", "peak", human_peak$cCRE)
  41. human_pdc <- read.table("rds/ATAC/after_integra/GRN/SCENT/VShuman/YangLi_cCRE/Table S12 - Summary of gene-cCRE correlations.txt", header=TRUE)
  42. human_pdc %>% left_join(human_peak, by=c("distal_cCRE"="peak")) %>% dplyr::select(chrom, start, end, distal_cCRE, gene, proximal_cCRE) -> human_pdc_bed
  44. # get mouse pdc
  45. ATAC_subclass_forCicero <- read.csv("rds/ATAC/after_integra/GRN/cicero/subclass_list.csv", header=FALSE)
  46. scent_pdc_all <- read.table(file="/projects/ps-renlab2/kaw033/WK_aging/rds/ATAC/after_integra/GRN/SCENT/peak_gene_link/SN_all.SCENT.bedpe")
  47. colnames(scent_pdc_all) <- c("chr1","start1","end1","chrom","start","end","pdc","score","V9","V10")
  49. scent_pdc_all %>% tidyr::separate(pdc, into=c("gene","peak"), sep="[|]", remove=FALSE) -> scent_pdc_all
  50. scent_pdc_all$Hpeak <- reciprocal_peak$Hpeak[match(scent_pdc_all$peak, reciprocal_peak$Mpeak)]
  51. mouse2human %>% dplyr::select(Gene.name, Human.gene.name) %>% rename(gene=Gene.name, Hgene=Human.gene.name) -> mouse2human_rename
  52. scent_pdc_all_gene <- scent_pdc_all %>% left_join(mouse2human_rename, by=join_by("gene"=="gene"))
  53. #scent_pdc_all$Hgene <- mouse2human$Human.gene.name[match(scent_pdc_all$gene, mouse2human$Gene.name)]
  54. scent_pdc_all_gene$Hgene2UP <- toupper(scent_pdc_all_gene$gene)
  55. scent_pdc_all_gene %>% mutate(Hgene2UPvsHgene = ifelse(Hgene2UP == Hgene, TRUE, FALSE), Hgene_final=ifelse(is.na(Hgene) | Hgene=="", Hgene2UP, Hgene)) -> scent_pdc_all_gene
  56. scent_pdc_all_gene %>% filter(Hgene2UPvsHgene!=TRUE) %>% dplyr::select(gene, Hgene, Hgene2UP, Hgene_final) %>% distinct() -> scent_pdc_gene_conversion_check
  58. scent_pdc_all_gene %>% filter(is.na(Hpeak)) -> scent_pdc_noReciprocal_peak
  59. scent_pdc_all_gene %>% filter(!is.na(Hpeak)) -> scent_pdc_withReciprocal_peak
  61. scent_pdc_withReciprocal_peak %>% dplyr::select(Hpeak, Hgene_final, Hgene2UP, gene, pdc) %>% tidyr::separate(Hpeak, into=c("chrom", "start", "end"), sep="[:-]", remove=FALSE) %>% dplyr::select(chrom, start, end, Hpeak, Hgene_final,Hgene2UP, gene, pdc) %>% distinct() -> scent_pdc_withReciprocal_peak_bed
  63. bedtoolsr::bt.intersect(scent_pdc_withReciprocal_peak_bed, human_pdc_bed, wao=TRUE) %>% filter(V9 != ".") %>% dplyr::select(V5, V6, V7, V13) -> scent_human_overlap_gene_check
  64. write.csv(scent_human_overlap_gene_check, file="./rds/ATAC/after_integra/GRN/SCENT/VShuman/scent_human_overlap_gene_check.csv", row.names=FALSE)
  66. bedtoolsr::bt.intersect(scent_pdc_withReciprocal_peak_bed, human_pdc_bed, wao=TRUE) %>% filter(V9 != ".") %>% mutate(match=ifelse(V5 == V13 | V6 == V13, TRUE, FALSE)) %>% filter(match==TRUE) %>%
  67. mutate(human_peak_coord=paste0(V9, ":", V10, "-", V11)) %>% dplyr::select(V8, V4, V12, human_peak_coord, V13) %>% rename(PDC=V8, peak_in_hg38=V4, human_ccre=V12, human_gene=V13) %>% distinct() -> scent_human_overlap_final
  68. write.csv(scent_human_overlap_final, file="./rds/ATAC/after_integra/GRN/SCENT/VShuman/scent_human_overlap_final.csv", row.names=FALSE)
  71. # for each cell type
  72. scent_pdc_list <- list()
  73. scent_pdc_overlap_list <- list()
  74. for (subclass_label_id in ATAC_subclass_forCicero$V1) {
  75. subclass_name <- gsub(" ", "_", subclass_label_id)
  76. file <- file.path("/projects/ps-renlab2/kaw033/WK_aging/", "rds/ATAC/after_integra/GRN/SCENT/peak_gene_link/", paste0(subclass_name, ".SCENT.bedpe"))
  77. scent_pdc_celltype<- read.table(file)
  78. colnames(scent_pdc_celltype) <- c("chr1","start1","end1","chrom","start","end","pdc","score","V9","V10")
  79. scent_pdc_celltype %>% tidyr::separate(pdc, into=c("gene","peak"), sep="[|]", remove=FALSE) -> scent_pdc_celltype
  81. scent_pdc_celltype$Hpeak <- reciprocal_peak$Hpeak[match(scent_pdc_celltype$peak, reciprocal_peak$Mpeak)]
  83. scent_pdc_celltype_gene <- scent_pdc_celltype %>% left_join(mouse2human_rename, by=join_by("gene"=="gene"))
  84. #scent_pdc_celltype$Hgene <- mouse2human$Human.gene.name[match(scent_pdc_celltype$gene, mouse2human$Gene.name)]
  85. scent_pdc_celltype_gene$Hgene2UP <- toupper(scent_pdc_celltype_gene$gene)
  86. scent_pdc_celltype_gene %>% mutate(Hgene2UPvsHgene = ifelse(Hgene2UP == Hgene, TRUE, FALSE), Hgene_final=ifelse(is.na(Hgene) | Hgene=="", Hgene2UP, Hgene)) -> scent_pdc_celltype_gene
  87. scent_pdc_list[[subclass_name]] <- scent_pdc_celltype
  89. scent_pdc_celltype_gene %>% filter(is.na(Hpeak)) -> scent_pdc_celltype_noReciprocal_peak
  90. scent_pdc_celltype_gene %>% filter(!is.na(Hpeak)) -> scent_pdc_celltype_withReciprocal_peak
  92. scent_pdc_celltype_withReciprocal_peak %>% dplyr::select(Hpeak, Hgene_final, Hgene2UP, gene, pdc) %>% tidyr::separate(Hpeak, into=c("chrom", "start", "end"), sep="[:-]", remove=FALSE) %>% dplyr::select(chrom, start, end, Hpeak, Hgene_final,Hgene2UP, gene, pdc) %>% distinct() -> scent_pdc_celltype_withReciprocal_peak_bed
  94. bedtoolsr::bt.intersect(scent_pdc_celltype_withReciprocal_peak_bed, human_pdc_bed, wao=TRUE) %>% filter(V9 != ".") %>% mutate(match=ifelse(V5 == V13 | V6 == V13, TRUE, FALSE)) %>% filter(match==TRUE) %>%
  95. mutate(human_peak_coord=paste0(V9, ":", V10, "-", V11)) %>% dplyr::select(V8, V4, V12, human_peak_coord, V13) %>% rename(PDC=V8, peak_in_hg38=V4, human_ccre=V12, human_gene=V13) %>% distinct() -> scent_celltype_human_overlap_final
  96. write.csv(scent_celltype_human_overlap_final, file=paste0("./rds/ATAC/after_integra/GRN/SCENT/VShuman/scent_human_overlap_final_", subclass_name, ".csv"), row.names=FALSE)
  97. scent_pdc_overlap_list[[subclass_name]] <- scent_celltype_human_overlap_final
  98. }

05d.HGcompare.R at commit 91c29fc, no license · at the source

Overview

Authors: Kangli Wang1, Weikun Xia1, Yingli Gu1, Songpeng Zu1, Qian Yang2, Maria Luisa Amaral1, Yaozhi Wang1, Allen Wang2, Xiang-Dong Fu3, William C Mobley4, Bing Ren1,2
  1. Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, California 92093, USA
  2. Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, California 92093, USA
  3. Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
  4. Department of Neuroscience, University of California San Diego, La Jolla, California 92093, USA
Institutions: University of California San Diego (United States); Westlake University (China)
Journal: Genome research, volume 36, issue 4, pages 849-864
Dates: received 23 June 2025; accepted 2 March 2026; published online April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1101/gr.281113.125 · PMID 41781332 · PMCID PMC13138337 · OpenAlex W7133566881
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
MeSH: Aging*, Parkinson Disease*, Substantia Nigra*, Animals, Gene Expression Profiling, Mice, Microglia, Multiomics, Oligodendroglia, Single-Cell Analysis, Single-Cell Gene Expression Analysis, Transcriptome (* major topic)
Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex
Funding: Michael J. Fox Foundation for Parkinson&apos;s Research; National Institutes of Health SIG (S10 OD026929); NIH HHS (S10 OD026929); MJFF; Aligning Science Across Parkinson&apos;s (ASAP-020566)
Citations: not cited yet (Europe PMC); 124 references in the paper

Abstract

Parkinson's disease (PD) is a prevalent neurodegenerative disorder predominantly affecting individuals over 60. Its motor symptoms stem from the deterioration of dopaminergic neurons within the substantia nigra. Despite aging being a significant risk factor, the specific mechanisms linking aging and PD pathology remain unclear. Leveraging advancements in single-cell genomics, this study utilizes single-nucleus multiome sequencing to capture transcriptomic and epigenetic profiles from 40,125 cells across the lifespan of the mouse substantia nigra. Our analysis pinpoints age-associated changes at a cell type–specific level, revealing a subset of genes that increasingly express with age and are enriched in PD-related pathways, notably in oligodendrocytes at late aging stages. Integration with five public PD single-cell RNA-seq data sets highlights 85 genes consistently differentially expressed with aging and PD. Key genes such as Hsp90aa1 and Hsp90ab1 are upregulated at late aging stages in oligodendrocytes, microglia, and glutamatergic neurons. Additionally, Apoe in microglia and genes related to protein folding in oligodendrocytes are upregulated at late aging stages, whereas genes involved in myelination are downregulated at early aging stages in oligodendrocyte. Our multiomic atlas underscores the substantial regulatory network changes during aging that may predispose to PD, providing valuable insights for furthering understanding of PD pathogenesis and potential therapeutic targets.

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 12 matches between paragraphs and lines of code.

wkl1990/SN_aging

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 91c29fc00dcdd1b537fa6e3e77c044a85dc1a5c9, 22 April 2026
Languages: R (33), Python (9), Shell (3)
Size: 47 files, 45 scripts
Software Heritage: not archived
Found in: the text, “Data access”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (29 files), Seurat (24 files), patchwork (22 files), ggplot2 (21 files), ggpubr (21 files), reshape2 (21 files), cowplot (20 files), NumPy (8 files), data.table (6 files), Monocle 3 (5 files), pandas (5 files), BEDTools (3 files), ComplexHeatmap (3 files), anndata (2 files), circlize (2 files), clusterProfiler (2 files), edgeR (2 files), limma (2 files), reticulate (2 files), SciPy (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
46 files
At the source: github.com/wkl1990/SN_aging

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;
  • 45 scripts, each with its path and the digest of its content;
  • 12 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

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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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 12 MeSH terms, 5 funders, 122 references.

Cite

This paper

Wang, K., Xia, W., Gu, Y., Zu, S., Yang, Q., Amaral, M. L., Wang, Y., Wang, A., Fu, X.-D., Mobley, W. C., & Ren, B. (2026). Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease. Genome research, 36(4), 849-864. https://doi.org/10.1101/gr.281113.125

BibTeX

@article{wang2026single,
author = {Wang, Kangli and Xia, Weikun and Gu, Yingli and Zu, Songpeng and Yang, Qian and Amaral, Maria Luisa and Wang, Yaozhi and Wang, Allen and Fu, Xiang-Dong and Mobley, William C and Ren, Bing},
title = {{Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease}},
journal = {Genome research},
year = {2026},
month = apr,
volume = {36},
number = {4},
pages = {849--864},
publisher = {Cold Spring Harbor Laboratory Press},
issn = {1088-9051},
doi = {10.1101/gr.281113.125},
url = {https://doi.org/10.1101/gr.281113.125},
pmid = {41781332},
pmcid = {PMC13138337}
}

RIS

TY - JOUR
AU - Wang, Kangli
AU - Xia, Weikun
AU - Gu, Yingli
AU - Zu, Songpeng
AU - Yang, Qian
AU - Amaral, Maria Luisa
AU - Wang, Yaozhi
AU - Wang, Allen
AU - Fu, Xiang-Dong
AU - Mobley, William C
AU - Ren, Bing
TI - Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease
T2 - Genome research
J2 - Genome Res
PY - 2026
DA - 2026/04/07
VL - 36
IS - 4
SP - 849
EP - 864
SN - 1088-9051
PB - Cold Spring Harbor Laboratory Press
DO - 10.1101/gr.281113.125
UR - https://doi.org/10.1101/gr.281113.125
LA - en
ER -

CSL-JSON

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"container-title": "Genome research",
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