OSCR

Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome.

Code ↔ Paper

5 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 5 matches
  1. [1] § MATERIALS AND METHODS › Data preprocessing ↔ analysis_code/Integration_and_Preprocessing.R, lines 1–63 · score 0.85 · LogNormalize, FindClusters, FindNeighbors, dims, workflow, Seurat
  2. [2] § MATERIALS AND METHODS › Cell annotation ↔ analysis_code/Cell_Type_Marker_and_DEG.R, lines 1–78 · score 0.76 · logfc.threshold, FindAllMarkers, min.pct, pos, Seurat, clusters
  3. [3] § MATERIALS AND METHODS › Identification of DEGs in snRNA-seq ↔ analysis_code/Cell_Type_Marker_and_DEG.R, lines 1–78 · score 0.67 · FindMarkers, min.pct, MAST, Seurat, WT, clusters
  4. [4] § MATERIALS AND METHODS › Bootstrap consensus pseudobulk analysis ↔ analysis_code/Bootstrap_pseudobulk.R, lines 1–20 · score 0.54 · Consensus, RNA seq, Bootstrap, seeds, subsamples, pseudobulk
  5. [5] § MATERIALS AND METHODS › CIBERSORTx deconvolution ↔ bulkRNAseq_data.R, lines 344–394 · score 0.50 · HET_F, Null_M, subset, wt, bulk, seq

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 253 lines · 8.2 KB · no license · 2 matches

  1. #!/usr/bin/env Rscript
  3. suppressPackageStartupMessages({
  4. library(Seurat)
  5. library(MAST)
  6. library(presto)
  7. library(dplyr)
  8. library(tidyr)
  9. library(stringr)
  10. })
  12. # ============================================================
  13. # Purpose
  14. # ============================================================
  15. # This script does TWO things for BOTH datasets (unsorted & sorted):
  16. #
  17. # (A) Cell-type marker discovery (cluster markers):
  18. # JoinLayers() -> FindAllMarkers(only.pos=TRUE, min.pct=0.25, logfc.threshold=0.25)
  19. #
  20. # (B) Differential expression (DEG) with MAST, performed WITHIN each group of cells:
  21. # - Within each seurat cluster (and additionally within each cell_type and neu_type)
  22. # - For each cluster/type group: compare condition pairs using FindMarkers(test.use="MAST")
  23. #
  24. # Notes:
  25. # - The script assumes the integrated Seurat objects already contain metadata:
  26. # seurat_clusters, group, and (optionally) cell_type and neu_type.
  27. # - For unsorted: comparisons are Null_M vs WT_M, Het_F vs WT_F, WT_F vs WT_M
  28. # - For sorted: comparisons are Het-Positive vs WT, Het-Negative vs WT
  29. #
  30. # Output:
  31. # - Markers: one CSV for FindAllMarkers per dataset
  32. # - DEGs: one RDS + one long CSV per dataset (contains all clusters/types + comparisons)
  34. # ============================================================
  35. # 1) Config
  36. # ============================================================
  38. INTEGRATED_UNSORTED_RDS <- "outputs/integration/unsorted_harmony_integrated.rds"
  39. INTEGRATED_SORTED_RDS <- "outputs/integration/sorted_harmony_integrated.rds"
  41. OUT_DIR <- "outputs/deg_and_markers"
  42. dir.create(OUT_DIR, showWarnings = FALSE, recursive = TRUE)
  44. # Marker discovery parameters (must match your workflow)
  45. MARKER_PARAMS <- list(
  46. only.pos = TRUE,
  47. min.pct = 0.25,
  48. logfc.threshold = 0.25
  49. )
  51. # DEG parameters (must match your workflow intent)
  52. DEG_PARAMS <- list(
  53. assay = "RNA",
  54. test.use = "MAST",
  55. logfc.threshold = 0,
  56. min.pct = 0.1,
  57. max.cells.per.ident = Inf
  58. )
  60. # For DEG, we run within each "grouping variable"
  61. # You requested: seurat_clusters + cell_type + neu_type
  62. GROUPING_VARS <- c("seurat_clusters", "cell_type", "neu_type")
  64. # Dataset-specific comparisons (ident.1 vs ident.2) based on "group" metadata
  65. UNSORTED_COMPARISONS <- list(
  66. list(name = "Null_M_vs_WT_M", ident.1 = "Null_M", ident.2 = "WT_M"),
  67. list(name = "Het_F_vs_WT_F", ident.1 = "Het_F", ident.2 = "WT_F"),
  68. list(name = "WT_F_vs_WT_M", ident.1 = "WT_F", ident.2 = "WT_M")
  69. )
  71. SORTED_COMPARISONS <- list(
  72. list(name = "Het-Positive_vs_WT", ident.1 = "Het-Positive", ident.2 = "WT"),
  73. list(name = "Het-Negative_vs_WT", ident.1 = "Het-Negative", ident.2 = "WT")
  74. )
  76. # ============================================================
  77. # 2) Helper functions
  78. # ============================================================
  80. assert_has_meta <- function(seu, cols) {
  81. missing <- cols[!cols %in% colnames([email hidden])]
  82. if (length(missing) > 0) {
  83. stop("Missing required metadata columns: ", paste(missing, collapse = ", "))
  84. }
  85. }
  87. safe_joinlayers <- function(seu) {
  88. # JoinLayers is needed for Seurat v5 multi-layer assays.
  89. # If object is already joined or JoinLayers is unavailable, this will error.
  90. # For a GitHub-ready script, we keep it simple: try and move on if it fails.
  91. out <- tryCatch(JoinLayers(seu), error = function(e) seu)
  92. out
  93. }
  95. run_find_all_markers <- function(seu, out_csv, marker_params) {
  96. seu <- safe_joinlayers(seu)
  97. markers <- FindAllMarkers(
  98. seu,
  99. only.pos = marker_params$only.pos,
  100. min.pct = marker_params$min.pct,
  101. logfc.threshold = marker_params$logfc.threshold
  102. )
  103. write.csv(markers, out_csv, row.names = FALSE)
  104. invisible(markers)
  105. }
  107. run_deg_within_groups <- function(seu,
  108. grouping_var,
  109. comparisons,
  110. out_rds,
  111. out_csv,
  112. deg_params) {
  113. assert_has_meta(seu, c("group", grouping_var))
  115. # Ensure identities are set to the condition/group variable for FindMarkers
  116. Idents(seu) <- "group"
  118. group_levels <- unique([email hidden][[grouping_var]])
  119. group_levels <- group_levels[!is.na(group_levels)]
  121. all_results <- list()
  122. idx <- 1
  124. for (g in group_levels) {
  125. # Subset to one cluster/cell_type/neu_type group
  126. sub_obj <- subset(seu, subset = get(grouping_var) == g)
  128. # Skip if too small or missing identities (kept minimal; not over-engineered)
  129. present_idents <- unique(as.character(sub_obj$group))
  130. present_idents <- present_idents[!is.na(present_idents)]
  132. for (cmp in comparisons) {
  133. if (!(cmp$ident.1 %in% present_idents && cmp$ident.2 %in% present_idents)) next
  135. deg <- FindMarkers(
  136. sub_obj,
  137. ident.1 = cmp$ident.1,
  138. ident.2 = cmp$ident.2,
  139. assay = deg_params$assay,
  140. test.use = deg_params$test.use,
  141. logfc.threshold = deg_params$logfc.threshold,
  142. min.pct = deg_params$min.pct,
  143. max.cells.per.ident = deg_params$max.cells.per.ident
  144. )
  146. # Standardize to a tidy data.frame with metadata columns
  147. deg_df <- as.data.frame(deg)
  148. deg_df$gene <- rownames(deg_df)
  149. rownames(deg_df) <- NULL
  151. deg_df$grouping_var <- grouping_var
  152. deg_df$group_value <- as.character(g)
  153. deg_df$comparison <- cmp$name
  154. deg_df$ident.1 <- cmp$ident.1
  155. deg_df$ident.2 <- cmp$ident.2
  157. all_results[[idx]] <- deg_df
  158. idx <- idx + 1
  159. }
  160. }
  162. if (length(all_results) == 0) {
  163. warning("No DEG results produced for grouping_var=", grouping_var, " (no valid groups/comparisons found).")
  164. saveRDS(list(), out_rds)
  165. write.csv(data.frame(), out_csv, row.names = FALSE)
  166. return(invisible(data.frame()))
  167. }
  169. merged <- dplyr::bind_rows(all_results)
  171. saveRDS(all_results, out_rds)
  172. write.csv(merged, out_csv, row.names = FALSE)
  174. invisible(merged)
  175. }
  177. run_dataset <- function(seu,
  178. dataset_name,
  179. comparisons,
  180. marker_params,
  181. deg_params,
  182. grouping_vars,
  183. out_dir) {
  184. message("============================================================")
  185. message("Dataset: ", dataset_name)
  187. # ---------- (A) Cluster markers ----------
  188. marker_out_csv <- file.path(out_dir, paste0(dataset_name, "_FindAllMarkers.csv"))
  189. message("[A] Running FindAllMarkers -> ", marker_out_csv)
  190. run_find_all_markers(seu, marker_out_csv, marker_params)
  192. # ---------- (B) DEG within seurat_clusters/cell_type/neu_type ----------
  193. message("[B] Running DEG with MAST within grouping variables...")
  194. for (gv in grouping_vars) {
  195. # If optional annotations are missing, skip silently to keep script clean
  196. if (!gv %in% colnames([email hidden])) {
  197. message(" - Skipping ", gv, " (not found in meta.data)")
  198. next
  199. }
  201. out_rds <- file.path(out_dir, paste0(dataset_name, "_DEG_", gv, ".rds"))
  202. out_csv <- file.path(out_dir, paste0(dataset_name, "_DEG_", gv, ".csv"))
  203. message(" - Grouping: ", gv, " -> ", basename(out_csv))
  205. run_deg_within_groups(
  206. seu = seu,
  207. grouping_var = gv,
  208. comparisons = comparisons,
  209. out_rds = out_rds,
  210. out_csv = out_csv,
  211. deg_params = deg_params
  212. )
  213. }
  215. message("Done: ", dataset_name)
  216. invisible(TRUE)
  217. }
  219. # ============================================================
  220. # 3) Run: unsorted + sorted (same workflow)
  221. # ============================================================
  223. stopifnot(file.exists(INTEGRATED_UNSORTED_RDS))
  224. stopifnot(file.exists(INTEGRATED_SORTED_RDS))
  226. unsorted_seu <- readRDS(INTEGRATED_UNSORTED_RDS)
  227. sorted_seu <- readRDS(INTEGRATED_SORTED_RDS)
  229. # 'group' must exist for DE comparisons
  230. assert_has_meta(unsorted_seu, c("group", "seurat_clusters"))
  231. assert_has_meta(sorted_seu, c("group", "seurat_clusters"))
  233. run_dataset(
  234. seu = unsorted_seu,
  235. dataset_name = "unsorted",
  236. comparisons = UNSORTED_COMPARISONS,
  237. marker_params = MARKER_PARAMS,
  238. deg_params = DEG_PARAMS,
  239. grouping_vars = GROUPING_VARS,
  240. out_dir = OUT_DIR
  241. )
  243. run_dataset(
  244. seu = sorted_seu,
  245. dataset_name = "sorted",
  246. comparisons = SORTED_COMPARISONS,
  247. marker_params = MARKER_PARAMS,
  248. deg_params = DEG_PARAMS,
  249. grouping_vars = GROUPING_VARS,
  250. out_dir = OUT_DIR
  251. )
  253. message("All finished.")

Cell_Type_Marker_and_DEG.R at commit 1917bf2, no license · at the source

Overview

Authors: Yan Li1,2,3, Ashley G. Anderson1,3, Guantong Qi1,2, Sih-Rong Wu4, Jean-Pierre Revelli1,3, Hu Chen3,5, Zhandong Liu3,5, Huda Y. Zoghbi1,3,4,5,6
ORCID iDs: Yan Li, Ashley G. Anderson, Jean-Pierre Revelli, Hu Chen, Zhandong Liu, Huda Y. Zoghbi
  1. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
  2. Genetics and Genomics program, Baylor College of Medicine, Houston, TX, USA
  3. Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, USA
  4. Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
  5. Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
  6. Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX, USA
Institutions: Baylor College of Medicine (United States); Texas Children's Hospital (United States); Howard Hughes Medical Institute (United States)
Journal: Science advances, volume 12, issue 24, article eaeb4265
Dates: received 13 August 2025; accepted 1 May 2026; published online 10 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aeb4265 · PMID 42268975 · PMCID PMC13251834 · OpenAlex W7164207471
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity
MeSH: Cell Nucleus*, Rett Syndrome*, Transcriptome*, Animals, Disease Models, Animal, Female, Gene Expression Profiling, Hippocampus, Male, Methyl-CpG-Binding Protein 2, Mice, Mosaicism, Mutation, Single-Cell Gene Expression Analysis (* major topic)
Journal subjects: Neuroscience, Diseases and Disorders
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIH (1S10OD016167); Howard Hughes Medical Institute (HHMI); NINDS (R01NS057819, F32NS122920-01A1); Eunice Kennedy Shriver National Institute of Child Health & Human Development (P50 HD103555)
Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

Rett syndrome (RTT) is an X-linked neurological disorder caused by MECP2 mutations, creating distinct cellular environments in females (mosaic) versus males (nonmosaic). Despite female patients representing most cases, how mosaicism contributes molecularly to RTT pathogenesis, particularly in presymptomatic stages, remains poorly understood. To address this question, we profiled hippocampal transcriptomes of young female and male RTT mice using bulk and single-nucleus RNA sequencing. We identified a core disease signature of consistently dysregulated genes only in MeCP2− cells across RTT models. Moreover, we uncovered non–cell autonomous effects exclusively in female MeCP2+ excitatory neurons, suggesting that these circuits are more vulnerable early in the mosaic RTT environment. The single-nuclei data also revealed an underappreciated MeCP2− interneuron subtype that had the most transcriptional dysregulation in both male and female RTT hippocampi. Together, these data highlight the different effects of MeCP2 loss on excitatory and inhibitory circuits between the mosaic and nonmosaic environments in early RTT pathogenesis.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

Zenodo 18462624

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (7 files), Seurat (5 files), ggplot2 (4 files), DESeq2 (2 files), pheatmap (2 files), reshape2 (2 files), circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), ggpubr (1 file), Harmony (1 file), limma (1 file), rstatix (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
9 files
At the source: dx.doi.org/10.5281/zenodo.18462624

yanl54/mecp2_hippocampus_signature

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1917bf21d2e6276e213a79a16da1ec50893463d1, 2 February 2026
Languages: R (8)
Size: 9 files, 8 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (7 files), Seurat (5 files), ggplot2 (4 files), DESeq2 (2 files), pheatmap (2 files), reshape2 (2 files), circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), ggpubr (1 file), Harmony (1 file), limma (1 file), rstatix (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/YanL54/Mecp2_hippocampus_signature/tree/v1.0.1

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 16 scripts, each with its path and the digest of its content;
  • 5 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.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate any new materials. Next generation sequencing data generated in this paper are deposited in GEO (Gene Expression Omnibus) under BioProject PRJNA1284340 with accession number GSE301254 (snRNA-seq data were directly deposited through SRA to the same BioProject). Processed differentially expressed genes are available in the Supplementary tables. Code for preprocessing and downstream analysis was deposited on Zenodo (DOI: 10.5281/zenodo.18462624 (http://dx.doi.org/10.5281/zenodo.18462624)).

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, 8 authors, 14 MeSH terms, 4 funders, 79 references.

Cite

This paper

Li, Y., Anderson, A. G., Qi, G., Wu, S.-R., Revelli, J.-P., Chen, H., Liu, Z., & Zoghbi, H. Y. (2026). Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome. Science advances, 12(24), eaeb4265. https://doi.org/10.1126/sciadv.aeb4265

BibTeX

@article{li2026single,
author = {Li, Yan and Anderson, Ashley G. and Qi, Guantong and Wu, Sih-Rong and Revelli, Jean-Pierre and Chen, Hu and Liu, Zhandong and Zoghbi, Huda Y.},
title = {{Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome}},
journal = {Science advances},
year = {2026},
month = jun,
volume = {12},
number = {24},
pages = {eaeb4265},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aeb4265},
url = {https://doi.org/10.1126/sciadv.aeb4265},
pmid = {42268975},
pmcid = {PMC13251834}
}

RIS

TY - JOUR
AU - Li, Yan
AU - Anderson, Ashley G.
AU - Qi, Guantong
AU - Wu, Sih-Rong
AU - Revelli, Jean-Pierre
AU - Chen, Hu
AU - Liu, Zhandong
AU - Zoghbi, Huda Y.
TI - Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/06/10
VL - 12
IS - 24
SP - eaeb4265
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aeb4265
UR - https://doi.org/10.1126/sciadv.aeb4265
LA - en
ER -

CSL-JSON

{
"id": "10.1126/sciadv.aeb4265",
"type": "article-journal",
"title": "Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome",
"container-title": "Science advances",
"author": [
{
"family": "Li",
"given": "Yan"
},
{
"family": "Anderson",
"given": "Ashley G."
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{
"family": "Qi",
"given": "Guantong"
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{
"family": "Wu",
"given": "Sih-Rong"
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{
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{
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{
"family": "Liu",
"given": "Zhandong"
},
{
"family": "Zoghbi",
"given": "Huda Y."
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "24",
"page": "eaeb4265",
"DOI": "10.1126/sciadv.aeb4265",
"PMID": "42268975",
"PMCID": "PMC13251834",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aeb4265",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
10
]
]
}
}

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