Single-cell epigenomics uncovers heterochromatin instability and transcription factor dysfunction during mouse brain aging.

Code ↔ Paper

14 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 14 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § STAR★METHODS › METHOD DETAILS › Label transfer between snRNA and MERFISH for annotation ↔ MERFISH/transfer_labels_MERFISH.R, the whole file · a weak match · score 0.93 · FindTransferAnchors, TransferData, prediction.score.max, FindClusters, annotated cluster, majority
[2] § STAR★METHODS › METHOD DETAILS › snRNA-seq data processing ↔ RNA/process_RNA.R, lines 44–90 · score 0.91 · high doublet scores, low UMI, SCTransform, low quality, manual filtering, UMAP
[3] § RESULTS › Loss of progenitor populations in aging brains ↔ scratch_scripts/PlotFigure1_RNA.r, lines 1–58 · score 0.79 · caudate putamen, anterior hippocampus, posterior hippocampus, nucleus accumbens, CP, NAC
[4] § RESULTS › Loss of progenitor populations in aging brains ↔ scratch_scripts/plot_scratch.r, lines 1–57 · score 0.78 · caudate putamen, anterior hippocampus, posterior hippocampus, nucleus accumbens, CP, NAC
[5] § STAR★METHODS › METHOD DETAILS › Label transfer between snRNA and MERFISH for annotation ↔ scratch_scripts/transfer_labels.r, lines 44–122 · score 0.75 · FindTransferAnchors, TransferData, CCA, subsampled, Seurat, matched
[6] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Quantifying transposable elements (TEs) accessibility and expression ↔ RNA/TE_analysis_RNA/DESeq2_differential_TEs.R, the whole file · a weak match · score 0.69 · TE subfamilies, transposable element, DESeq2, summing, matrix, age
[7] § STAR★METHODS › METHOD DETAILS › snATAC-seq data processing ↔ scratch_scripts/process_F_M.py, lines 29–64 · score 0.62 · BX, tag, Scrublet, imported, bam, bin
[8] § STAR★METHODS › METHOD DETAILS › snATAC-seq data processing ↔ scratch_scripts/process_combined-Copy1.py, lines 29–64 · score 0.62 · BX, tag, Scrublet, imported, bam, bin
[9] § RESULTS › Heterochromatin destabilization, transposable element activation, and lncRNA dysregulation in aging brains ↔ scratch_scripts/Plot_DAR-Copy1.r, lines 486–525 · score 0.59 · lncRNAs, h3k9me3, chromosomal, overlap, TE, transcriptional
[10] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Quantifying transposable elements (TEs) accessibility and expression ↔ RNA/TE_analysis_RNA/DESeq2_differential_TEs.R, the whole file · a weak match · score 0.55 · DESeq2, SoloTE, TEs, transposable, summing, matrix
[11] § RESULTS › Chromatin accessibility remodeling with aging ↔ scratch_scripts/plotchrom_plot_DEG.r, lines 867–929 · score 0.55 · gene families, log fold change, LogFC, protocadherin, adj
[12] § STAR★METHODS › METHOD DETAILS › MERFISH gene panel design ↔ scratch_scripts/SoloTE_class_todo.r, lines 216–287 · score 0.51 · Gene selection, biological processes, cellular, enriched, age
[13] § STAR★METHODS › METHOD DETAILS › MERFISH gene panel design ↔ scratch_scripts/SoloTE_locus-Copy1.r, lines 207–278 · score 0.51 · Gene selection, biological processes, cellular, enriched, age
[14] § STAR★METHODS › METHOD DETAILS › MERFISH data preprocessing ↔ MERFISH/transfer_labels_MERFISH.R, the whole file · a weak match · score 0.51 · UMAP, transferred, subclass, MERFISH, neighbors, PCA

Paper

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The authors' code

R · 90 lines · 3.1 KB · no license · 2 matches

# Load libraries
library(Seurat)
library(dplyr)
# Step 0: Read in Seurat objects
# Make sure to replace these paths with your actual saved RDS paths
fem <- readRDS("../female_RNA/combined_seurat.RDS")
mer <- readRDS("../MERFISH/merfish_seurat.RDS")
# Step 1: Subsample RNA cells to a max of 2000 per annotated cluster
max_cells_per_cluster <- 2000
indices_to_keep <- integer(0)
unique_clusters <- unique(fem$transfer_celltypes)
for (cluster in unique_clusters) {
cluster_indices <- which(fem$transfer_celltypes == cluster)
if (length(cluster_indices) > max_cells_per_cluster) {
max_c = max(max_cells_per_cluster, ceiling(length(cluster_indices)/10))
cat(cluster, max_c, "\n")
sampled_indices <- sample(cluster_indices, max_cells_per_cluster)
indices_to_keep <- c(indices_to_keep, sampled_indices)
} else {
indices_to_keep <- c(indices_to_keep, cluster_indices)
}
}
fem <- subset(fem, cells = colnames(fem)[indices_to_keep])
# Step 2: Run label transfer using CCA
anchors <- FindTransferAnchors(
reference = fem,
query = mer,
dims = 1:45,
reduction = "cca",
features = rownames(mer),
normalization.method = "SCT"
)
predictions <- TransferData(
anchorset = anchors,
refdata = fem$transfer_celltypes,
dims = 1:45
)
mer <- AddMetaData(mer, metadata = predictions)
# Step 3: Subcluster MERFISH data and assign predicted labels per subcluster
allmeta <- list()
for (cl in unique(mer$seurat_clusters)) {
sub <- subset(mer, subset = seurat_clusters == cl)
sub <- NormalizeData(sub)
sub <- FindVariableFeatures(sub, nfeatures = 500)
sub <- ScaleData(sub)
sub <- RunPCA(sub, features = VariableFeatures(sub))
sub <- FindNeighbors(sub, dims = 1:25)
sub <- FindClusters(sub, resolution = 2)
sub <- RunUMAP(sub, reduction = "X_pca", dims = 1:25)
sub$sub_leiden <- paste(cl, sub$seurat_clusters)
# Assign predicted ID based on majority vote in each subcluster
metaf <- [email hidden]
metaf <- metaf[!is.na(sub$predicted.id) & metaf$prediction.score.max > 0.85, ]
predictions_table <- table(metaf$seurat_clusters, metaf$predicted.id)
predictions_table <- predictions_table / rowSums(predictions_table)
predictions_df <- as.data.frame(predictions_table)
new_df <- predictions_df %>%
group_by(Var1) %>%
filter(Freq == max(Freq)) %>%
select(Var1, Var2, Freq) %>%
as.data.frame()
mat <- match(sub$seurat_clusters, new_df$Var1)
sub$predicted_id_ext <- as.character(new_df$Var2[mat])
# Save UMAP and metadata for each subcluster
pdf(paste0(cl, "_sub.pdf"))
print(DimPlot(sub, group.by = "seurat_clusters", label = TRUE))
print(DimPlot(sub, group.by = "subclass_label", label = TRUE))
print(DimPlot(sub, group.by = "predicted_id_ext", label = TRUE))
print(DimPlot(sub, group.by = "predicted.id", label = TRUE))
print(DimPlot(sub, group.by = "age"))
print(FeaturePlot(sub, "log1p_total_counts", max.cutoff = 2000))
dev.off()
meta <- [email hidden][, c("age", "predicted.id", "prediction.score.max",
"predicted_id_ext", "subclass_label", "sub_leiden")]
write.table(meta, paste0(cl, "_sub_meta.txt"), sep = "\t", quote = FALSE)
allmeta[[cl]] <- meta
}

transfer_labels_MERFISH.R at commit 43e7a41, no license · at the source

OverviewAuthors: Maria Luisa Amaral1,2, Sainath Mamde1, Michael Miller3, Xiaomeng Hou3, Jessica Arzavala4, Julia Osteen4, Nicholas D. Johnson4, Elizabeth Walker Smoot3, Qian Yang3, Emily Eisner3, Qiurui Zeng5,6, Cindy Tatiana Báez-Becerra4, Jacqueline Olness3, Joseph Colin Kern3, Jonathan Rink4, Ariana Barcoma4, Silvia Cho4, Stella Cao4, Nora Emerson4, Jasper Lee4and 10 other authorsJackson Willier4, Timothy Loe1, Henry Jiao3, Songpeng Zu1, Quan Zhu3, Sebastian Preissl3,7,8,9,10, Allen Wang3, Joseph R. Ecker5,11, Maria Margarita Behrens4, Bing Ren1,12,13,14
ORCID iDs: Jessica Arzavala, Joseph R. Ecker, Bing Ren
14 affiliationsDepartment of Cellular and Molecular Medicine, School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
Center for Epigenomics, School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
CIBSS – Centre for Integrative Biological Signaling Studies, University of Freiburg, Freiburg, Germany
Department of Pharmacology and Toxicology, Institute of Pharmaceutical Sciences, University of Graz, 8010 Graz, Austria
Field of Excellence BioHealth, University of Graz, Graz, Austria
Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
New York Genome Center, New York, NY 10013, USA
Departments of Genetics and Development, Biochemistry and Molecular Biophysics, and Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
Lead contact
Institutions: University of California San Diego (United States); Salk Institute for Biological Studies (United States); University of Graz (Austria); University of Freiburg (Germany); Howard Hughes Medical Institute (United States); Columbia University Irving Medical Center (United States); New York Genome Center (United States)
Journal: Cell reports, volume 45, issue 3, article 117073
Dates: published online 12 March 2026; in print 24 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.celrep.2026.117073 · PMID 41824460 · PMCID PMC13189690 · OpenAlex W7135077805
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Machine learning
Keywords: Aging, Transposable elements, Heterochromatin, Epigenome, Mouse Brain, Single-cell, Cp: Neuroscience, Cp: Genomics
MeSH: Aging*, Brain*, Epigenomics*, Heterochromatin*, Single-Cell Analysis*, Transcription Factors*, Animals, Male, Mice, Mice, Inbred C57BL, Neurons (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute on Aging; NIA NIH HHS (R01 AG066018)
Citations: cited by 8 papers (Europe PMC); 74 references in the paper
Research resources: RRID:SCR_018093
AbstractThe abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

luisajamaral/aging_mouse_brain_code

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 43e7a41eefdc9017722f05a326f336f13685f23c, 21 April 2025

Languages: R (124), Python (24), Shell (3)

Size: 158 files, 151 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: ggplot2 (109 files), tidyverse (79 files), data.table (69 files), pheatmap (63 files), Seurat (55 files), pandas (23 files), NumPy (22 files), edgeR (17 files), Scanpy (16 files), clusterProfiler (11 files), reshape2 (10 files), patchwork (9 files), DESeq2 (7 files), broom (5 files), Matplotlib (5 files), pysam (5 files), SciPy (5 files), anndata (4 files), seaborn (3 files), cowplot (2 files), ggpubr (2 files), reticulate (2 files), rpy2 (2 files), BEDTools (1 file), lme4 (1 file), NetworkX (1 file), Pingouin (1 file), SAMtools (1 file), Snakemake (1 file), Subread (featureCounts) (1 file), xarray (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

152 files

ATAC/TE_analysis_ATAC/run_DESeq2_TE.R, R, 52 lines
ATAC/TE_analysis_ATAC/run_featureCounts_on_TEs.sh, Shell, 22 lines
ATAC/find_hotspots_smooth.R, R, 130 lines
ATAC/find_motifs.sh, Shell, 33 lines
ATAC/plot_hotspots.R, R, 37 lines
ATAC/plot_motifs.R, R, 74 lines
ATAC/preprocess/get_TSS_enrichment_barplot.R, R, 70 lines
ATAC/preprocess/run_snakemake.sh, Shell, 17 lines
ATAC/process_ATAC.py, Python, 152 lines
ATAC/split_female_ATAC_bams.py, Python, 39 lines
ATAC/split_male_ATAC_bams.py, Python, 73 lines
MERFISH/transfer_labels_MERFISH.R, R, 90 lines, 2 matches
RNA/GO_GSEA.R, R, 78 lines
RNA/TE_analysis_RNA/DESeq2_differential_TEs.R, R, 84 lines, 2 matches
RNA/TE_analysis_RNA/create_combined_Seurat_subfamily_level.R, R, 77 lines
RNA/TE_analysis_RNA/run_SoloTE_on_10x_output.py, Python, 19 lines
RNA/process_RNA.R, R, 174 lines, 1 match
scratch_scripts/01.run.ABC.py, Python, 369 lines
scratch_scripts/ABC.r, R, 308 lines
scratch_scripts/ABC_bar.r, R, 650 lines
scratch_scripts/ABC_redo.r, R, 250 lines
scratch_scripts/Aggregate_motif_plots.r, R, 168 lines
scratch_scripts/All_modality_celltype_prop.r, R, 936 lines
scratch_scripts/DEG_MAST_latent.r, R, 259 lines
scratch_scripts/DESeq2.r, R, 516 lines
scratch_scripts/Figure2.r, R, 1,918 lines
scratch_scripts/Figure4.py, Python, 222 lines
scratch_scripts/Figure6.py, Python, 368 lines
scratch_scripts/Gene_length.r, R, 214 lines
scratch_scripts/PlotFigure1.r, R, 711 lines
scratch_scripts/PlotFigure1_RNA.r, R, 276 lines, 1 match
scratch_scripts/Plot_DAR-Copy1.r, R, 817 lines, 1 match
scratch_scripts/Plot_DAR-Copy2.r, R, 614 lines
scratch_scripts/Plot_DAR.r, R, 594 lines
scratch_scripts/Plot_GSEA.r, R, 425 lines
scratch_scripts/Plot_MERFISH_bar.r, R, 722 lines
scratch_scripts/Plot_age_ct_heatmaps.r, R, 304 lines
scratch_scripts/Plot_fig1.r, R, 1,330 lines
scratch_scripts/Run_DEG_rm_rpl.r, R, 622 lines
scratch_scripts/SoloTE_class_todo.r, R, 646 lines, 1 match
scratch_scripts/SoloTE_locus-Copy1.r, R, 629 lines, 1 match
scratch_scripts/SoloTE_locus-Copy2.r, R, 578 lines
scratch_scripts/SoloTE_locus.r, R, 590 lines
scratch_scripts/SoloTE_subfamily.r, R, 884 lines
scratch_scripts/TEs_ATAC_plot.r, R, 331 lines
scratch_scripts/attempt_lmm_DE.r, R, 404 lines
scratch_scripts/celltype_annotation_scratch.r, R, 734 lines
scratch_scripts/celltype_frac.r, R, 166 lines
scratch_scripts/celltype_fractions.r, R, 166 lines
scratch_scripts/chromplot.r, R, 1,096 lines
scratch_scripts/clusterprofiler_kegg.r, R, 356 lines
scratch_scripts/combine_sub_celltypes.r, R, 341 lines
scratch_scripts/combined_plot_meta.r, R, 276 lines
scratch_scripts/commands_for_deeptools.r, R, 16 lines
scratch_scripts/create_Seurat.r, R, 448 lines
scratch_scripts/do_everything.r, R, 327 lines
scratch_scripts/filtering_RNA.r, R, 636 lines
scratch_scripts/get_5k_bins.py, Python, 36 lines
scratch_scripts/get_D12MSN_split.r, R, 98 lines
scratch_scripts/get_DAR_and_homer_loop.py, Python, 214 lines
scratch_scripts/get_DAR_and_homer_parallel.py, Python, 207 lines
scratch_scripts/get_DARs_yng_old.py, Python, 366 lines
scratch_scripts/get_L2rough_annot.r, R, 92 lines
scratch_scripts/get_annotation_breakdown_plot.r, R, 75 lines
scratch_scripts/get_annotations.r, R, 347 lines
scratch_scripts/get_cicero_connected_bed.r, R, 130 lines
scratch_scripts/get_clustered_DARs.r, R, 183 lines
scratch_scripts/get_enrichR_result.r, R, 89 lines
scratch_scripts/get_final_clusters_from_subclustering.r, R, 584 lines
scratch_scripts/get_gene_nearby_motif.r, R, 523 lines
scratch_scripts/get_genes_only_MAST_latent_Frozen.r, R, 561 lines
scratch_scripts/get_heatmap_GO.r, R, 563 lines
scratch_scripts/get_linked_deg_peaks.r, R, 129 lines
scratch_scripts/get_mtx_subcluster.py, Python, 182 lines
scratch_scripts/get_rachel_annot.r, R, 52 lines
scratch_scripts/get_region_DAR_overlap.r, R, 602 lines
scratch_scripts/get_region_edgeR.r, R, 328 lines
scratch_scripts/get_region_overlap_plots.r, R, 536 lines
scratch_scripts/get_rna_heatmaps.r, R, 389 lines
scratch_scripts/get_smooth20_scratch.r, R, 181 lines
scratch_scripts/get_smoothed_table.r, R, 176 lines
scratch_scripts/get_subsampled_cells_forsignac.r, R, 159 lines
scratch_scripts/plotMA.r, R, 908 lines
scratch_scripts/plotTEs.r, R, 791 lines
scratch_scripts/plot_D12MSN.r, R, 61 lines
scratch_scripts/plot_DAR_chrm_plot.r, R, 250 lines
scratch_scripts/plot_DAR_heat.r, R, 177 lines
scratch_scripts/plot_DEG_heat-Gaba.r, R, 1,323 lines
scratch_scripts/plot_DEG_heat.r, R, 1,574 lines
scratch_scripts/plot_Family_heats.r, R, 129 lines
scratch_scripts/plot_GO.r, R, 126 lines
scratch_scripts/plot_GO_region.r, R, 617 lines
scratch_scripts/plot_GSEApy.r, R, 450 lines
scratch_scripts/plot_RNA_and_ATAC_heats.r, R, 1,139 lines
scratch_scripts/plot_bar_num_DEGs.r, R, 128 lines
scratch_scripts/plot_celltype_Female_agg.r, R, 161 lines
scratch_scripts/plot_clustered_DARs.r, R, 318 lines
scratch_scripts/plot_combined_motifs--final.r, R, 715 lines
scratch_scripts/plot_combined_motifs.r, R, 233 lines
scratch_scripts/plot_doublet_scores.py, Python, 128 lines
scratch_scripts/plot_enrichr_result.r, R, 141 lines
scratch_scripts/plot_female.r, R, 181 lines
scratch_scripts/plot_female_volcano.r, R, 248 lines
scratch_scripts/plot_genOn.r, R, 239 lines
scratch_scripts/plot_genome_ontology.r, R, 206 lines
scratch_scripts/plot_heatmap_scratch.r, R, 2,020 lines
scratch_scripts/plot_liknked_motifs.r, R, 311 lines
scratch_scripts/plot_linked_GO.r, R, 176 lines
scratch_scripts/plot_linked_genes_GO_BP.r, R, 117 lines
scratch_scripts/plot_locus.r, R, 339 lines
scratch_scripts/plot_locus_TE.r, R, 137 lines
scratch_scripts/plot_male.r, R, 293 lines
scratch_scripts/plot_male_volcano.r, R, 286 lines
scratch_scripts/plot_marker_peaks_gene_heatmap.r, R, 179 lines
scratch_scripts/plot_motif.r, R, 234 lines
scratch_scripts/plot_motif_chromvar.r, R, 134 lines
scratch_scripts/plot_motifs.r, R, 145 lines
scratch_scripts/plot_motifs_dotplot.r, R, 463 lines
scratch_scripts/plot_motifs_female.r, R, 268 lines
scratch_scripts/plot_num_DARs_eCDF.r, R, 398 lines
scratch_scripts/plot_num_deg_top_genes.r, R, 379 lines
scratch_scripts/plot_pie_peak_call.r, R, 374 lines
scratch_scripts/plot_region_motifs.r, R, 340 lines
scratch_scripts/plot_same_direction_genes-redo_newDARs.r, R, 519 lines
scratch_scripts/plot_same_direction_genes.r, R, 519 lines
scratch_scripts/plot_scratch.r, R, 149 lines, 1 match
scratch_scripts/plot_sex_correlation.r, R, 141 lines
scratch_scripts/plot_subfamily.r, R, 241 lines
scratch_scripts/plot_subfamily_bars.r, R, 102 lines
scratch_scripts/plotchrom_plot_DEG.r, R, 1,324 lines, 1 match
scratch_scripts/plotvolcano.r, R, 50 lines
scratch_scripts/pot_moptifs.r, R, 271 lines
scratch_scripts/prepare_methyl.py, Python, 78 lines
scratch_scripts/process_F_M.py, Python, 1,170 lines, 1 match
scratch_scripts/process_RNA.r, R, 100 lines
scratch_scripts/process_combined-Copy1.py, Python, 2,275 lines, 1 match
scratch_scripts/process_combined.py, Python, 1,346 lines
scratch_scripts/region_specific_RPM_heatmaps.r, R, 749 lines
scratch_scripts/retry_from_clustering.r, R, 650 lines
scratch_scripts/run_DAR_bg_and_homer-parallel.py, Python, 218 lines
scratch_scripts/run_DAR_bg_and_homer.py, Python, 232 lines
scratch_scripts/run_DEG_MAST.r, R, 652 lines
scratch_scripts/sample_DEG_heatmap.r, R, 305 lines
scratch_scripts/scratchD12.py, Python, 145 lines
scratch_scripts/split_bams_female.py, Python, 111 lines
scratch_scripts/split_bams_female_RNA.py, Python, 161 lines
scratch_scripts/split_bams_male.py, Python, 246 lines
scratch_scripts/subsample_allen.py, Python, 112 lines
scratch_scripts/subsample_umap.r, R, 379 lines
scratch_scripts/top_TEs.r, R, 444 lines
scratch_scripts/transfer_labels.r, R, 205 lines, 1 match
README.md, Text, 14 lines

At the source: github.com/luisajamaral/aging_mouse_brain_code

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;
151 scripts, each with its path and the digest of its content;
14 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.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: luisajamaral/aging_mouse_brain_code
it says that the data are available on request

Read it in the paper: doi.org/10.1016/j.celrep.2026.117073.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 30 authors, 8 keywords, 11 MeSH terms, 2 funders, 74 references, 1 RRID.

Cite

This paper

Amaral, M. L., Mamde, S., Miller, M., Hou, X., Arzavala, J., Osteen, J., Johnson, N. D., Smoot, E. W., Yang, Q., Eisner, E., Zeng, Q., Báez-Becerra, C. T., Olness, J., Kern, J. C., Rink, J., Barcoma, A., Cho, S., Cao, S., Emerson, N., . . . Ren, B. (2026). Single-cell epigenomics uncovers heterochromatin instability and transcription factor dysfunction during mouse brain aging. Cell reports, 45(3), 117073. https://doi.org/10.1016/j.celrep.2026.117073

BibTeX

@article{amaral2026single, author = {Amaral, Maria Luisa and Mamde, Sainath and Miller, Michael and Hou, Xiaomeng and Arzavala, Jessica and Osteen, Julia and Johnson, Nicholas D. and Smoot, Elizabeth Walker and Yang, Qian and Eisner, Emily and Zeng, Qiurui and Báez-Becerra, Cindy Tatiana and Olness, Jacqueline and Kern, Joseph Colin and Rink, Jonathan and Barcoma, Ariana and Cho, Silvia and Cao, Stella and Emerson, Nora and Lee, Jasper and Willier, Jackson and Loe, Timothy and Jiao, Henry and Zu, Songpeng and Zhu, Quan and Preissl, Sebastian and Wang, Allen and Ecker, Joseph R. and Behrens, Maria Margarita and Ren, Bing}, title = {{Single-cell epigenomics uncovers heterochromatin instability and transcription factor dysfunction during mouse brain aging}}, journal = {Cell reports}, year = {2026}, month = mar, volume = {45}, number = {3}, pages = {117073}, publisher = {Cell Press}, issn = {2211-1247}, doi = {10.1016/j.celrep.2026.117073}, url = {https://doi.org/10.1016/j.celrep.2026.117073}, pmid = {41824460}, pmcid = {PMC13189690} }

RIS

TY - JOUR AU - Amaral, Maria Luisa AU - Mamde, Sainath AU - Miller, Michael AU - Hou, Xiaomeng AU - Arzavala, Jessica AU - Osteen, Julia AU - Johnson, Nicholas D. AU - Smoot, Elizabeth Walker AU - Yang, Qian AU - Eisner, Emily AU - Zeng, Qiurui AU - Báez-Becerra, Cindy Tatiana AU - Olness, Jacqueline AU - Kern, Joseph Colin AU - Rink, Jonathan AU - Barcoma, Ariana AU - Cho, Silvia AU - Cao, Stella AU - Emerson, Nora AU - Lee, Jasper AU - Willier, Jackson AU - Loe, Timothy AU - Jiao, Henry AU - Zu, Songpeng AU - Zhu, Quan AU - Preissl, Sebastian AU - Wang, Allen AU - Ecker, Joseph R. AU - Behrens, Maria Margarita AU - Ren, Bing TI - Single-cell epigenomics uncovers heterochromatin instability and transcription factor dysfunction during mouse brain aging T2 - Cell reports J2 - Cell Rep PY - 2026 DA - 2026/03/12 VL - 45 IS - 3 SP - 117073 SN - 2211-1247 PB - Cell Press DO - 10.1016/j.celrep.2026.117073 UR - https://doi.org/10.1016/j.celrep.2026.117073 LA - en ER -

CSL-JSON

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