OSCR

Region-specific transcriptional signatures of brain aging in the absence of neuropathology at the single-cell level.

Code ↔ Paper

15 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 15 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Comparison to previously published signatures and findings ↔ phase1/development/revamp2024/Revision2_senescence/0_Curate_SenPaths_completed.R, lines 1–44 · score 0.77 · CellAge, SenNet, senescence signatures, HAGR, Unclear, curated
  2. [2] § Results › Non-pathological aging is not fully explained by the proposed senescence signatures ↔ phase1/development/revamp2024/Revision2_senescence/0_Curate_SenPaths_completed.R, lines 1–44 · score 0.75 · Dehkordi_2021_Canonical, Dehkordi_2021_Initiating, SenNet, senescence signatures, cns, aging
  3. [3] § Methods › Comparison to previously published signatures and findings ↔ phase1/development/revamp2024/Revision2_senescence/B_markers/01_marker.R, lines 1–53 · score 0.72 · logfc.threshold, min.pct, senescence pathways, GSEA, CNS, filtered
  4. [4] § Results › Cell-type-specific profiles of aging across multiple human brain regions ↔ phase1/development/revamp2024/Dom/Phase1_astrocyte_subcluster.R, lines 49–137 · score 0.68 · middle temporal gyrus, subventricular zone, expression profiles, entorhinal cortex, brain regions, putamen
  5. [5] § Results › Cell-type-specific profiles of aging across multiple human brain regions ↔ phase1/analyses/compare_discover_replication.ipynb, lines 21–53 · score 0.68 · Middle Temporal Gyrus, Subventricular Zone, Entorhinal cortex, Astro, Oligo, Endo
  6. [6] § Results › Cell-type-specific profiles of aging across multiple human brain regions ↔ phase1/development/analyses/compare_discover_replication_glmpb.ipynb, lines 21–50 · score 0.68 · Middle Temporal Gyrus, Subventricular Zone, Entorhinal cortex, Astro, Oligo, Endo
  7. [7] § Methods › Age-related differential expression and association ↔ phase1/development/analyses/lmm_diffexp.ipynb, lines 72–138 · score 0.66 · mixed model, Sample_id, covariate, statsmodel, Tweedie, zero
  8. [8] § Methods › Brain bank information and sample selection ↔ phase1/development/revamp2024/Revision2_senescence/D_aDEG/1_aDEG_summary.R, lines 1–41 · score 0.66 · middle temporal gyrus, subventricular zone, entorhinal cortex, putamen, SVZ, MTG
  9. [9] § Methods › Brain bank information and sample selection ↔ phase1/development/revamp2024/Revision2_senescence/B_markers/03_viz_markers.R, the whole file · a weak match · score 0.65 · middle temporal gyrus, subventricular zone, entorhinal cortex, putamen, SVZ, MTG
  10. [10] § Results › Cell-type-specific profiles of aging across multiple human brain regions ↔ phase1/development/revamp2024/Revision2_senescence/D_aDEG/1_aDEG_summary.R, lines 1–41 · score 0.61 · middle temporal gyrus, subventricular zone, entorhinal cortex, putamen, SVZ, MTG
  11. [11] § Results › Thousands of genes are differentially expressed across age groups ↔ phase1/development/revamp2024/Monica/P1_GeneLength_Loop_andPlots.R, lines 209–247 · score 0.60 · Wilcoxon Rank Sum, gene length, log10
  12. [12] § Methods › Age-related differential expression and association ↔ phase1/development/analyses/glmm_diffexp.ipynb, lines 157–187 · score 0.60 · mixed model, Sample_id, statsmodel, GLMM, zero, age
  13. [13] § Methods › Isolation of nuclei from the human brain ↔ phase1/development/revamp2024/Monica/P1_GeneLength_Loop_andPlots.R, lines 1–85 · score 0.59 · refdata gex GRCh38, preparation, brain
  14. [14] § Results › Non-pathological aging is not fully explained by the proposed senescence signatures ↔ phase1/development/revamp2024/Revision2_senescence/B_markers/01_marker.R, lines 1–53 · score 0.58 · Dehkordi_2021, Senescence pathways, initiating, canonical, global, cns
  15. [15] § Results › Non-pathological aging is not fully explained by the proposed senescence signatures ↔ phase1/development/revamp2024/Revision2_senescence/1_variable_feat.R, lines 41–108 · score 0.57 · variable features, SenNet, unique genes, senescence, style, dehkordi

Paper

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The authors' code

R · 100 lines · 5 KB · no license · 2 matches

  1. library(tidyverse)
  2. #Build Pathway Lists
  5. #Load in longer gene lists
  6. cellage<-read.delim("./exploration/scripts/Phase1_revision_sen/cellage3.tsv",sep = "\t")#https://genomics.senescence.info/download.html#cellage; downloaded 20260311 - CITE PMID37933854 (HAGR v 5)
  7. cellage%>%
  8. group_by(Type.of.senescence)%>%
  9. summarize(n=n())
  10. dekhordi<-read.csv("./exploration/scripts/Phase1_revision_sen/Dekhordi_2021_genelists.csv")
  12. dekhordi%>%
  13. group_by(pathway)%>%
  14. summarize(n=n())
  16. SenPaths <-list(
  17. "Hu_2021"= c("CDKN2A", "CDKN1A", "CDKN2D", "CASP8", "IL1B", "GLB1", "SERPINE1"), #"Custom senescence signature, expected to incr."
  18. "CellAge_HAGRv5_OncoInduced" = cellage%>%filter(Type.of.senescence == "Oncogene-induced")%>%pull(Gene.symbol),
  19. "CellAge_HAGRv5_Replicative" = cellage%>%filter(Type.of.senescence == "Replicative")%>%pull(Gene.symbol),
  20. "CellAge_HAGRv5_Stress" = cellage%>%filter(Type.of.senescence == "Stress-induced")%>%pull(Gene.symbol),
  21. "CellAge_HAGRv5_Unclear" = cellage%>%filter(Type.of.senescence == "Unclear")%>%pull(Gene.symbol),
  22. "Dehkordi_2021_Canonical" = dekhordi%>%filter(pathway=="Canonical")%>%pull(gene),
  23. "Dehkordi_2021_Response" = dekhordi%>%filter(pathway=="SRP")%>%pull(gene),
  24. "Dehkordi_2021_Initiating"= dekhordi%>%filter(pathway=="SIP")%>%pull(gene),
  25. "Dehkordi_2021_CellAge"= dekhordi%>%filter(pathway=="CellAge")%>%pull(gene),
  26. "Casella_2019_6C"= c("SLCO2B1","CLSTN2","PTCHD4","LINC02154","PURPL"),#instead of up/down --> use 5 genes from 6C which SLCO2B1, CLSTN2 and PTCHD4 mRNAs, as well as LINC02154 and PURPL lncRNAs in the analysis.discriminated scenceses irrespective of CT'
  27. "SenNet_2024_CNS_all"= c("CDKN2A", "CDKN1A", "IL6", "TNF", "H2AX", "IL1A", "IL1B", "GLB1", "SERPINE1", "TGFB1","TP53", "CCL2", "CCL5", "CXCL1", "CXCL8", "MMP12", "CCL3", "HMGB1", "MMP3", "LMNB1", "BCL2", "CCL4", "CDKN2B", "CSF1", "IGF1", "TIMP2", "PLAUR", "SPP1"),
  28. "SenNet_2024_CNS_RNA"= c("CDKN2A", "CDKN1A", "IL6", "TNF", "IL1A","IL1B", "SERPINE1","TGFB1", "TP53", "CCL2", "CCL5", "CXCL1", "CXCL8", "MMP12", "CCL3", "HMGB1", "MMP3", "LMNB1", "BCL2", "CCL4", "CSF1","IGF1", "TIMP2", "PLAUR", "SPP1" ),
  29. "SenNet_2024_CNS_sc"= c("CDKN2A", "CDKN1A", "IL6", "TNF", "IL1A", "IL1B", "SERPINE1","TGFB1", "TP53", "CCL2", "CCL5", "CXCL1", "CXCL8", "MMP12", "CCL3", "HMGB1", "MMP3", "LMNB1", "BCL2", "CCL4", "CSF1","IGF1", "TIMP2", "PLAUR", "SPP1" )
  30. )
  32. #"SenNet_2024_CNS_all": All genes within SenNet consensus that are reported to be in CNS
  33. #"SenNet_2024_CNS_RNA": Genes within SenNet consensus that are reported to be in CNS + detected with ANY RNA-seq modality
  34. #"SenNet_2024_CNS_sc": Genes within SenNet consensus that are reported to be in CNS + detected with sc/snRNA-Seq
  36. saveRDS(SenPaths, file="/data/ADRD/brain_aging/exploration/revision2_senpaths/SenPaths_completed.rds")
  38. ###############################
  39. ###############################
  40. ###############################
  41. names(SenPaths)
  42. poi<-c("CellAge_HAGRv5_OncoInduced","CellAge_HAGRv5_Replicative","CellAge_HAGRv5_Stress","CellAge_HAGRv5_Unclear","Dehkordi_2021_Canonical","Dehkordi_2021_Response","Dehkordi_2021_Initiating","Dehkordi_2021_CellAge","SenNet_2024_CNS_all")
  43. SenPaths_final<-SenPaths[poi]
  44. saveRDS(SenPaths_final, file="/data/ADRD/brain_aging/exploration/revision2_senpaths/SenPaths_completed_final.rds")
  46. ###############################
  47. ###############################
  48. ###############################
  50. library(ComplexUpset)
  51. library(tidyverse)
  53. # 2. Convert the list of gene vectors into a binary presence/absence data frame
  54. # This identifies every unique gene across all lists and checks its presence in each
  55. all_genes <- unique(unlist(SenPaths_final))
  56. upset_data <- data.frame(gene = all_genes)
  58. for (path_name in names(SenPaths_final)) {
  59. upset_data[[path_name]] <- as.integer(all_genes %in% SenPaths_final[[path_name]])
  60. }
  62. # 3. Define the sets to plot
  63. pathway_names <- names(SenPaths_final)
  65. # 4. Create the UpSet Plot
  66. # We use 'n_intersections' to limit to the most frequent overlaps for clarity
  67. p_upset <- upset(
  68. upset_data,
  69. pathway_names,
  70. name = "Senescence Pathway Overlaps",
  71. width_ratio = 0.15, # Adjusts the width of the set size bars on the left
  72. stripes = upset_stripes(
  73. geom = geom_segment(size = 5),
  74. colors = c('grey95', 'white')
  75. ),
  76. base_annotations = list(
  77. 'Intersection size' = intersection_size(
  78. counts = TRUE,
  79. mapping = aes(fill = 'bars_color')
  80. ) + scale_fill_manual(values = c('bars_color' = '#3182bd'), guide = 'none')
  81. ),
  82. matrix = intersection_matrix(
  83. geom = geom_point(size = 3)
  84. ),
  85. set_sizes = (
  86. upset_set_size() +
  87. theme(axis.text.x = element_text(angle = 90)) +
  88. geom_text(aes(label = ..count..), stat = 'count', hjust = -0.1, size = 3)
  89. )
  90. ) +
  91. labs(
  92. title = "Overlap Analysis of Curated Senescence Gene Sets",
  93. caption = "PMID: 37933854 (CellAge v5), Dehkordi 2021, and SenNet 2024 consensus"
  94. ) +
  95. theme_minimal(base_family = "Arial")
  97. # 5. Save the plot
  98. cairo_pdf("/data/ADRD/brain_aging/exploration/revision2_senpaths/misc/SenPaths_UpSet_Overlap_final.pdf", width = 16, height = 10)
  99. print(p_upset)
  100. dev.off()

0_Curate_SenPaths_completed.R at commit 6313c71, no license · at the source

Overview

Authors: Monica E. Mesecar1, Megan F. Duffy1, Dominic J. Acri1, Jinhui Ding2, Rebekah G. Langston1, Syed I. Shah3, Mike A. Nalls3,4, Xylena Reed1,4, Sonja W. Scholz5,6, D. Thad Whitaker1, Pavan K. Auluck7, Stefano Marenco7, Alex R. DeCasien8, J. Raphael Gibbs2, Mark R. Cookson1,4
ORCID iDs: Sonja W. Scholz
  1. Cell Biology and Gene Expression Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health,Bethesda, MD USA
  2. Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health,Bethesda, MD USA
  3. DataTecnica LLC, Washington, DC USA
  4. Center for Alzheimer’s and Related Dementias, National Institutes of Health,Bethesda, MD USA
  5. Neurodegenerative Diseases Research Section, National Institute of Neurological Disorders and Stroke,Bethesda, MD USA
  6. Department of Neurology, Johns Hopkins University Medical Center,Baltimore, MD USA
  7. Human Brain Collection Core, Division of Intramural Research, National Institute of Mental Health, NIH,Bethesda, MD USA
  8. Computational and Evolutionary Neurogenomics Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health,Bethesda, MD USA
Institutions: National Institutes of Health (United States); National Institute of Neurological Disorders and Stroke (United States); Johns Hopkins Bayview Medical Center (United States)
Journal: npj aging, volume 12, issue 1, article 100
Dates: received 29 August 2025; accepted 16 April 2026; published online 13 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41514-026-00391-9 · PMID 42129273 · PMCID PMC13396485 · OpenAlex W7161064970
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), histology / microscopy (modality), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Genetics, Neurology, Neuroscience
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute on Aging (ZO1 AG000535, 1ZIAAG000539-01); NINDS (1ZIANS003154); National Institute of Mental Health (ZIC MH002903)
Citations: cited by 2 papers (Europe PMC); 144 references in the paper

Abstract

As age is a significant risk factor for multiple neurodegenerative diseases, investigating normal brain aging may help identify molecular events contributing to increased disease risk over time. Single-nucleus RNA sequencing (snRNA-seq) enables analysis of gene expression changes within specific cell-types, offering insights into the molecular mechanisms underlying aging. However, most brain aging snRNA-seq datasets use age-matched controls from studies focused on pathology and sample cortical regions. Therefore, there is a need to investigate non-pathological aging within brain regions vulnerable to age-related diseases. We report a snRNA-seq study of 6 young (20–30 years) and 7 aged (60–85 years) individuals encompassing four different brain regions: the entorhinal cortex, middle temporal gyrus, subventricular zone, and putamen. We captured over 150,000 nuclei representing 10 broad cell-types. Region- and cell-type-specific differential expression analyses identified over 8000 age-associated genes. Notably, within a given cell-type, most of these associations were region-specific. Functional enrichment analyses of gene sets for each cell-type-region subgroup reflected multiple hallmarks of aging, including: proteostasis, interactions with cytokines, vesicular trafficking, metabolism, inflammation, metal ion homeostasis, and cellular senescence. Overall, our findings suggest that unique cell-types exhibit distinct transcriptional aging profiles both at the cell-type level and across different brain regions.

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

Repositories

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neurogenetics/adrd_brain_aging

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 6313c7143f529cce75cd70980841e8b60dd38cc9, 24 September 2026
Languages: Jupyter (106), Python (68), R (31), Shell (11)
Size: 235 files, 216 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (pyproject.toml, uv.lock), tests, 106 notebooks
Not found: license file, CITATION.cff, continuous integration, documentation
Tools: pandas (35 files), Matplotlib (31 files), NumPy (31 files), Scanpy (25 files), seaborn (25 files), anndata (23 files), tidyverse (23 files), statsmodels (15 files), Seurat (11 files), ggplot2 (8 files), scikit-learn (7 files), Harmony (4 files), Numba (4 files), SciPy (4 files), circlize (2 files), ComplexHeatmap (2 files), PyTorch (2 files), reshape2 (2 files), broom (1 file), cowplot (1 file), data.table (1 file), edgeR (1 file), ggpubr (1 file), glmmTMB (1 file), patchwork (1 file), Plotly (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
69 files
At the source: github.com/neurogenetics/ADRD_Brain_Aging/tree/main/phase1

Zenodo 14040699

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (14 files), ComplexHeatmap (3 files), data.table (2 files), ggplot2 (2 files), circlize (1 file), ggpubr (1 file), Seurat (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
31 files
At the source:

neurogenetics/saha

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 0ced41731fb3200fd16d1e415b33d088ad42b695, 13 May 2026
Languages: R (32)
Size: 83 files, 32 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (DESCRIPTION), tests, documentation, 1 notebook
Not found: CITATION.cff, continuous integration
Tools: tidyverse (15 files), ComplexHeatmap (3 files), Seurat (3 files), ggplot2 (2 files), circlize (1 file), data.table (1 file), ggpubr (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
34 files
At the source: github.com/neurogenetics/SAHA/tree/pre-release

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

Tracing map

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What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 129 scripts, each with its path and the digest of its content;
  • 15 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

Raw single-nucleus RNA sequencing data are available in the NIMH Data Archive associated with the collection “Human Brain Collection Core genomics data in postmortem brain of psychiatric disorders #3151” (https://nda.nih.gov/edit_collection.html?id=3151); experiment ID 2370: “snRNA_brain_aging.” De-identified individual level meta-data used as either selection criteria and/or covariates in analysis can be found in Supplementary Data file 1. Summary-level results and processed single-cell objects are available on Zenodo (10.5281/zenodo.7803697) and in Supplementary Data files 8, 12-13. Associated code used for analysis and plot generation are available on GitHub (https://github.com/neurogenetics/ADRD_Brain_Aging/tree/main/phase1).

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 3 keywords, 3 funders, 144 references.

Cite

This paper

Mesecar, M. E., Duffy, M. F., Acri, D. J., Ding, J., Langston, R. G., Shah, S. I., Nalls, M. A., Reed, X., Scholz, S. W., Thad Whitaker, D., Auluck, P. K., Marenco, S., DeCasien, A. R., Raphael Gibbs, J., & Cookson, M. R. (2026). Region-specific transcriptional signatures of brain aging in the absence of neuropathology at the single-cell level. npj aging, 12(1), 100. https://doi.org/10.1038/s41514-026-00391-9

BibTeX

@article{mesecar2026region,
author = {Mesecar, Monica E. and Duffy, Megan F. and Acri, Dominic J. and Ding, Jinhui and Langston, Rebekah G. and Shah, Syed I. and Nalls, Mike A. and Reed, Xylena and Scholz, Sonja W. and Thad Whitaker, D. and Auluck, Pavan K. and Marenco, Stefano and DeCasien, Alex R. and Raphael Gibbs, J. and Cookson, Mark R.},
title = {{Region-specific transcriptional signatures of brain aging in the absence of neuropathology at the single-cell level}},
journal = {npj aging},
year = {2026},
month = may,
volume = {12},
number = {1},
pages = {100},
publisher = {Nature Publishing Group},
issn = {2731-6068},
doi = {10.1038/s41514-026-00391-9},
url = {https://doi.org/10.1038/s41514-026-00391-9},
pmid = {42129273},
pmcid = {PMC13396485}
}

RIS

TY - JOUR
AU - Mesecar, Monica E.
AU - Duffy, Megan F.
AU - Acri, Dominic J.
AU - Ding, Jinhui
AU - Langston, Rebekah G.
AU - Shah, Syed I.
AU - Nalls, Mike A.
AU - Reed, Xylena
AU - Scholz, Sonja W.
AU - Thad Whitaker, D.
AU - Auluck, Pavan K.
AU - Marenco, Stefano
AU - DeCasien, Alex R.
AU - Raphael Gibbs, J.
AU - Cookson, Mark R.
TI - Region-specific transcriptional signatures of brain aging in the absence of neuropathology at the single-cell level
T2 - npj aging
J2 - NPJ Aging
PY - 2026
DA - 2026/05/13
VL - 12
IS - 1
SP - 100
SN - 2731-6068
PB - Nature Publishing Group
DO - 10.1038/s41514-026-00391-9
UR - https://doi.org/10.1038/s41514-026-00391-9
LA - en
ER -

CSL-JSON

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[8] doi:10.1016/j.celrep.2026.117073 [code]
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Journal: Cell reports
In common: edgeR, anndata, broom, 14 other tools, genetics / omics, cellular / molecular, 4 references
[9] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: Harmony, anndata, circlize, 16 other tools, genetics / omics, cellular / molecular, 2 references
[10] doi:10.1038/s42003-026-10957-8 [code]
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Journal: Communications biology
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