OSCR

Risperidone regulates the expression of schizophrenia-related genes in the forebrain of adult male mice.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Spatial patterns of gene expression in the forebrain ↔ preprocessing/functions/umap_feature_expression_plot.R, lines 195–263 · score 0.68 · Adora2a, Ppp1r2, Drd1, Drd2, Ecel1, Gad2
  2. [2] § Results › Spatial patterns of gene expression in the forebrain ↔ preprocessing/risperidone/ris-umap-genes.R, the whole file · a weak match · score 0.67 · Adora2a, Ppp1r2, Drd1, Drd2, Ecel1, Gad2
  3. [3] § Methods › Cluster identification and marker validation ↔ preprocessing/functions/functions-spatial-data.R, lines 71–132 · score 0.66 · Principal component, Seurat, UMAP, neighbor, variable
  4. [4] § Methods › Cluster identification and marker validation ↔ preprocessing/old-scripts/deseq-test-analysis.R, lines 392–476 · score 0.64 · variance stabilizing transformation, log2 fold change, Clusters
  5. [5] § Methods › Differential gene expression analysis ↔ preprocessing/functions/statistics-functions-v2.R, lines 186–234 · score 0.57 · log2 fold change, quantile normalization, assignments, clustering resolution, transcript
  6. [6] § Methods › Peak processing, validation, and custom reference generation ↔ preprocessing/old-scripts/reduction-peaks.R, lines 333–414 · score 0.52 · amplitude, log10, coverage, BAM, summit, ratio
  7. [7] § Results › Enrichment analysis ↔ preprocessing/clozapine/supplementaryAnalysis_paper_03.12.2025.R, the whole file · a weak match · score 0.50 · Cacna1i, Bhlhe40, Phactr3, Homer1, Kalrn, Olig2

Paper

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The authors' code

R · 267 lines · 10 KB · no license · 1 match

  1. umap_feature_expression_plot <- function(ldopa_integrate,
  2. spatial_data,
  3. type_data,
  4. peak_id,
  5. samples,
  6. plot_title = "UMAP Visualization with Feature Expression", # Nowy argument dla tytułu
  7. min_percentile = 0.01,
  8. max_percentile = 0.99,
  9. normalization = TRUE,
  10. low_color = "gray99",
  11. high_color = "red",
  12. na_color = "grey99",
  13. point_size = 1,
  14. alpha = 1,
  15. save_to_png = FALSE, # Nowy argument: czy zapisać do PNG
  16. file_path = NULL, # Nowy argument: ścieżka do zapisu
  17. width = 8, # Opcjonalny argument: szerokość wykresu
  18. height = 6, # Opcjonalny argument: wysokość wykresu
  19. dpi = 300) { # Opcjonalny argument: rozdzielczość
  21. # Wyciągnij koordynaty UMAP oraz barcodes
  22. umap_coordinates <- ldopa_integrate@reductions$[email hidden]
  23. umap_table <- data.frame(
  24. barcode = rownames(umap_coordinates),
  25. X = umap_coordinates[, 1], # Oś X
  26. Y = umap_coordinates[, 2] # Oś Y
  27. )
  29. # Przygotowanie tabeli ekspresji
  30. expression_table <- spatial_data[[type_data]]$data[peak_id, ] %>%
  31. as.data.frame() %>%
  32. dplyr::rename(expression = ".") %>%
  33. rownames_to_column("sample_barcode") %>%
  34. separate("sample_barcode", c("sample", "barcode"), sep = "_") %>%
  35. left_join(., spatial_data$bcs_information, by = c("barcode", "sample")) %>%
  36. filter(sample %in% samples) %>%
  37. mutate(
  38. max_perc = as.numeric(quantile(expression, probs = c(max_percentile), na.rm = TRUE)),
  39. min_perc = as.numeric(quantile(expression, probs = c(min_percentile), na.rm = TRUE)),
  40. expression = ifelse(expression > max_perc, max_perc, expression),
  41. expression = ifelse(expression < min_perc, min_perc, expression),
  42. expression = if (normalization == TRUE) {
  43. (expression - min(expression, na.rm = TRUE)) / (max(expression, na.rm = TRUE) - min(expression, na.rm = TRUE))
  44. } else {
  45. expression
  46. },
  47. barcode = paste(sample, barcode, sep = "_")
  48. ) %>%
  49. dplyr::select(barcode, expression)
  51. # Merge dwóch tabel
  52. merged_table <- dplyr::left_join(umap_table, expression_table, by = "barcode")
  54. # Sprawdzenie poprawności mergu
  55. all_matched <- nrow(merged_table) == nrow(umap_table)
  56. missing_matches <- sum(is.na(merged_table$expression))
  57. if (!all_matched || missing_matches > 0) {
  58. cat("Uwaga: Niektóre wiersze nie zostały dopasowane. Niedopasowane wiersze:", missing_matches, "\n")
  59. }
  61. # Tworzenie wykresu
  62. plot <- ggplot(merged_table, aes(x = X, y = Y, fill = expression)) +
  63. geom_point(shape = 21, size = point_size, alpha = alpha, stroke = 0) + # Punkty bez obramówki
  64. scale_fill_gradient(low = low_color, high = high_color, na.value = na_color) + # Skala kolorów
  65. labs(
  66. title = plot_title, # Użycie nowego argumentu dla tytułu
  67. x = "UMAP 1",
  68. y = "UMAP 2",
  69. fill = "Expression"
  70. ) +
  71. theme_minimal() +
  72. theme(
  73. panel.grid.major = element_blank(),
  74. panel.grid.minor = element_blank(),
  75. panel.background = element_blank(),
  76. axis.line = element_line(colour = "black")
  77. )
  79. # Opcjonalny zapis do pliku
  80. if (save_to_png) {
  81. # Ustawienie domyślnej ścieżki, jeśli nie została podana
  82. if (is.null(file_path)) {
  83. file_path <- getwd()
  84. }
  86. # Upewnienie się, że ścieżka istnieje
  87. if (!dir.exists(file_path)) {
  88. dir.create(file_path, recursive = TRUE)
  89. cat("Utworzono katalog:", file_path, "\n")
  90. }
  92. # Przygotowanie bezpiecznej nazwy pliku
  93. safe_title <- stringr::str_replace_all(plot_title, "[: ]", "_")
  94. safe_title <- stringr::str_replace_all(safe_title, "_+", "_")
  95. file_name <- paste0(safe_title, ".png")
  96. full_path <- file.path(file_path, file_name)
  98. # Dostosowanie motywu do białego tła
  99. plot_to_save <- plot + theme(
  100. panel.background = element_rect(fill = "white", color = NA),
  101. plot.background = element_rect(fill = "white", color = NA)
  102. )
  104. # Zapis wykresu
  105. ggsave(filename = full_path, plot = plot_to_save, width = width, height = height, dpi = dpi)
  106. cat("Wykres zapisano do pliku:", full_path, "\n")
  107. }
  109. return(plot)
  110. }
  113. #
  114. # # Wywołanie funkcji z Twoimi danymi
  115. # plot <- umap_feature_expression_plot(
  116. # ldopa_integrate = ldopa_integrate,
  117. # spatial_data = ldopa_st_data,
  118. # type_data = "quantile_normalize_resolution_1",
  119. # peak_id = "ldopa-peak-17758",
  120. # samples = c(samples_saline, samples_ldopa),
  121. # min_percentile = 0.00,
  122. # max_percentile = 1,
  123. # normalization = TRUE,
  124. # low_color = "gray99",
  125. # high_color = "red",
  126. # na_color = "grey99",
  127. # point_size = 1,
  128. # alpha = 1
  129. # )
  130. #
  131. # # Wyświetlenie wykresu
  132. # print(plot)
  135. umap_gene_expression_plot <- function(spatial_data,
  136. type_data,
  137. ncol,
  138. gene,
  139. ldopa_integrate,
  140. samples,
  141. min_percentile = 0.01,
  142. max_percentile = 0.99,
  143. normalization = TRUE,
  144. low_color = "gray95",
  145. high_color = "red",
  146. na_color = "grey99",
  147. point_size = 1,
  148. alpha = 1,
  149. save_to_png = FALSE, # Nowy argument: czy zapisać do PNG
  150. file_path = NULL, # Nowy argument: ścieżka do zapisu
  151. width = 8, # Opcjonalny argument: szerokość wykresu
  152. height = 6, # Opcjonalny argument: wysokość wykresu
  153. dpi = 300, # Opcjonalny argument: rozdzielczość
  154. ...) {
  156. # Wyciągnięcie wektora peak_id dla danego genu
  157. vector_peak <- spatial_data[[type_data]]$annotate %>%
  158. dplyr::filter(gene_name == gene) %>%
  159. dplyr::select(peak_id) %>%
  160. pull(peak_id)
  162. print(paste("Peaki dla genu", gene, ":", paste(vector_peak, collapse = ", ")))
  164. # Lista do przechowywania wykresów
  165. plot_list <- list()
  167. # Iteracja po każdym peak_id
  168. for (peak in vector_peak) {
  169. # Tworzenie tytułu wykresu
  170. plot_title <- paste(gene, peak, sep = ": ")
  172. # Generowanie wykresu UMAP
  173. plot <- umap_feature_expression_plot(
  174. ldopa_integrate = ldopa_integrate,
  175. spatial_data = spatial_data,
  176. type_data = type_data,
  177. peak_id = peak,
  178. samples = samples,
  179. plot_title = plot_title,
  180. min_percentile = min_percentile,
  181. max_percentile = max_percentile,
  182. normalization = normalization,
  183. low_color = low_color,
  184. high_color = high_color,
  185. na_color = na_color,
  186. point_size = point_size,
  187. alpha = alpha,
  188. save_to_png = save_to_png, # Przekazanie argumentu
  189. file_path = file_path, # Przekazanie argumentu
  190. width = width, # Przekazanie argumentu
  191. height = height, # Przekazanie argumentu
  192. dpi = dpi # Przekazanie argumentu
  193. )
  195. # Dodanie wykresu do listy
  196. plot_list[[peak]] <- plot
  198. # Opcjonalne wyświetlenie wykresu
  199. print(plot_title)
  200. print(plot)
  201. }
  203. # Organizacja wykresów w siatkę
  204. combined_plot <- patchwork::wrap_plots(plot_list, ncol = ncol, guides = "collect") +
  205. patchwork::plot_annotation(title = paste("UMAP Plots dla Genu:", gene))
  207. # Wyświetlenie połączonego wykresu
  208. print(combined_plot)
  210. # Opcjonalny zapis połączonego wykresu
  211. if (save_to_png) {
  212. # Ustawienie domyślnej ścieżki, jeśli nie została podana
  213. if (is.null(file_path)) {
  214. file_path <- getwd()
  215. }
  217. # Upewnienie się, że ścieżka istnieje
  218. if (!dir.exists(file_path)) {
  219. dir.create(file_path, recursive = TRUE)
  220. cat("Utworzono katalog:", file_path, "\n")
  221. }
  223. # Przygotowanie bezpiecznej nazwy pliku
  224. safe_title <- gsub("[/:*?\"<>|\\s]", "_", paste("Combined_", gene, sep = ""))
  225. file_name <- paste0(safe_title, ".png")
  226. full_path <- file.path(file_path, file_name)
  228. # # Zapis połączonego wykresu
  229. # ggsave(filename = full_path, plot = combined_plot, width = width, height = height, dpi = dpi)
  230. # cat("Połączony wykres zapisano do pliku:", full_path, "\n")
  231. }
  233. # Zwrócenie listy wykresów oraz połączonego wykresu
  234. return(list(individual_plots = plot_list, combined_plot = combined_plot))
  235. }
  239. umap_gene_expression_plot(
  240. spatial_data = risperidone_st_data_half,
  241. type_data = "quantile_normalize_resolution_0.4",
  242. ncol = 2,
  243. gene = "Gad2",
  244. ldopa_integrate = risperidone_integrate_half,
  245. samples = c(samples_saline, samples_risperidone),
  246. min_percentile = 0.01,
  247. max_percentile = 0.99,
  248. normalization = TRUE,
  249. low_color = "gray95",
  250. high_color = "red",
  251. na_color = "grey99",
  252. point_size = 1.5,
  253. alpha = 1,
  254. save_to_png = TRUE, # Włączenie zapisu do PNG
  255. file_path = "./results/risperidone/umap-gene-expression", # Podanie ścieżki do katalogu
  256. width = 10, # Szerokość wykresu w calach
  257. height = 8, # Wysokość wykresu w calach
  258. dpi = 300 # Rozdzielczość
  259. )
  262. # Definiowanie wektora z nazwami genów
  263. genes <- c("Drd1", "Drd2", "Adora2a", "Ppp1r1b", "Ppp1r2", "Gad2", "Ecel1", "Gfra1")

umap_feature_expression_plot.R at commit 03f0a59, no license · at the source

Overview

Authors: Magdalena Ziemiańska1, Mateusz Zięba2, Anna Radlicka-Borysewska1, Łukasz Szumiec1, Sławomir Gołda1, Małgorzata Borczyk2, Marcin Piechota2, Michał Korostyński2, Jan Rodriguez Parkitna1
  1. Department of Molecular Neuropharmacology, Maj Institute of Pharmacology, Polish Academy of Sciences, Krakow, Poland
  2. Laboratory of Pharmacogenomics, Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
Institutions: Maj Institute of Pharmacology (Poland); Polish Academy of Sciences (Poland)
Journal: Frontiers in molecular neuroscience, volume 19, article 1844705
Dates: received 1 April 2026; accepted 12 May 2026; published online 29 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnmol.2026.1844705 · PMID 42293048 · PMCID PMC13260059 · OpenAlex W7162765275
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), schizophrenia / psychosis (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing
Keywords: antipsychotics, forebrain, gene expression, mouse, risperidone, schizophrenia, spatial transcriptomics
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 79 references in the paper
Research resources: RRID:SCR_001575, RRID:SCR_002105, RRID:SCR_014583, 000 Sequencing System RRID:SCR_016387, RRID:SCR_018555, 100 RRID:SCR_019715, RRID:SCR_020214, RRID:SCR_023571, RRID:SCR_025848

Abstract

Introduction: Risperidone is a widely used antipsychotic that reduces psychotic symptoms through modulation of monoaminergic signaling. At the cellular level, however, its effects extend beyond receptor antagonism and induce spatially discrete transcriptional responses across forebrain structures, particularly in the basal ganglia and frontal cortex.

Methods: We applied sequencing-based spatial transcriptomics to the forebrain of male C57BL/6N mice following acute risperidone treatment (0.5 mg/kg, i.p.). Unsupervised clustering of spatial transcriptomic profiles accurately delineated cortical divisions, cortical layers, and basal ganglia subregions. Differential expression analysis within anatomically defined clusters, performed using a customized statistical framework, revealed transcriptional patterns that were highly structure-specific and distinct between cortical and basal ganglia regions.

Results: Acute risperidone treatment significantly affected 95 transcripts across 12 brain regions. The most prominent transcriptional changes were concentrated in ventral forebrain structures, including the olfactory tubercle (25 differentially expressed transcripts), diagonal band nucleus (22), corpus callosum and commissures (13), and lateral septal nucleus (9). Importantly, 18 of these 95 genes have previously been implicated in schizophrenia, including Olig2, Smpd3, and Cacna1i.

Discussion: Together, these findings indicate that acute treatment with risperidone in male mice exerts pronounced molecular effects in medial and ventral forebrain regions with high oligodendrocyte and glial cell abundance. Moreover, enrichment analysis points to a molecular convergence between risperidone-induced transcription and genetic pathways implicated in schizophrenia.

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 7 matches between paragraphs and lines of code.

macs3-project/MACS

License: BSD-3-Clause
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: c5443190e3edfeb301cc94acf450e2b2c026a223, 25 September 2026
Languages: Python (75), C/C++ (17), C (16), Jupyter (4), Shell (2)
Size: 365 files, 114 scripts
Software Heritage: archived
Found in: the end of the paper
Holds: README, license file, environment (pyproject.toml, requirements.txt, setup.py), tests, continuous integration, documentation, 4 notebooks
Not found: CITATION.cff
Tools: NumPy (26 files), pandas (6 files), anndata (4 files), SciPy (4 files), Matplotlib (3 files), PyTorch (1 file), Scanpy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
116 files
At the source: github.com/macs3-project/MACS

ippas/ifpan-janrod-spatial

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 03f0a59895f28cd124562d4e07b397f41684a177, 1 September 2026
Languages: R (204), Python (23), Shell (8)
Size: 286 files, 235 scripts
Software Heritage: not archived
Found in: the end of the paper
Holds: README, environment (.devcontainer/devcontainer.json, preprocessing/dockerfiles/Dockerfile.peaks, preprocessing/dockerfiles/Dockerfile.r4.0.3-seurat4-package-version-spatial, preprocessing/dockerfiles/Dockerfile.r4.0.3-seurat4-package-version-spatial-update, preprocessing/dockerfiles/Dockerfile.r4.0.3-seurat4-shiny, preprocessing/dockerfiles/Dockerfile.r4.0.3-seurat4-spatial, preprocessing/dockerfiles/Dockerfile.r4.3.0-seurat5, preprocessing/dockerfiles/Dockerfile.rstudio, preprocessing/dockerfiles/Dockerfile.shiny)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (81 files), circlize (28 files), ComplexHeatmap (28 files), pandas (21 files), patchwork (17 files), scikit-learn (15 files), Matplotlib (12 files), NumPy (12 files), edgeR (11 files), ggplot2 (11 files), anndata (6 files), Seurat (4 files), BEDTools (3 files), SciPy (3 files), igraph (2 files), limma (2 files), pysam (2 files), SAMtools (2 files), seaborn (2 files), cowplot (1 file), data.table (1 file), DESeq2 (1 file), psych (1 file), reshape2 (1 file), Scanpy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
236 files
At the source: github.com/ippas/ifpan-janrod-spatial

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;
  • 349 scripts, each with its path and the digest of its content;
  • 7 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 availability statement

The RNAseq dataset generated for this study can be found in the Sequence Read Archive, https://www.ncbi.nlm.nih.gov/, under accession number PRJNA1143882.

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, pages, dates, 9 authors, 7 keywords, 79 references, 9 RRIDs.

Cite

This paper

Ziemiańska, M., Zięba, M., Radlicka-Borysewska, A., Szumiec, Ł., Gołda, S., Borczyk, M., Piechota, M., Korostyński, M., & Rodriguez Parkitna, J. (2026). Risperidone regulates the expression of schizophrenia-related genes in the forebrain of adult male mice. Frontiers in molecular neuroscience, 19, 1844705. https://doi.org/10.3389/fnmol.2026.1844705

BibTeX

@article{ziemianska2026risperidone,
author = {Ziemiańska, Magdalena and Zięba, Mateusz and Radlicka-Borysewska, Anna and Szumiec, Łukasz and Gołda, Sławomir and Borczyk, Małgorzata and Piechota, Marcin and Korostyński, Michał and Rodriguez Parkitna, Jan},
title = {{Risperidone regulates the expression of schizophrenia-related genes in the forebrain of adult male mice}},
journal = {Frontiers in molecular neuroscience},
year = {2026},
month = may,
volume = {19},
pages = {1844705},
publisher = {Frontiers Media SA},
issn = {1662-5099},
doi = {10.3389/fnmol.2026.1844705},
url = {https://doi.org/10.3389/fnmol.2026.1844705},
pmid = {42293048},
pmcid = {PMC13260059}
}

RIS

TY - JOUR
AU - Ziemiańska, Magdalena
AU - Zięba, Mateusz
AU - Radlicka-Borysewska, Anna
AU - Szumiec, Łukasz
AU - Gołda, Sławomir
AU - Borczyk, Małgorzata
AU - Piechota, Marcin
AU - Korostyński, Michał
AU - Rodriguez Parkitna, Jan
TI - Risperidone regulates the expression of schizophrenia-related genes in the forebrain of adult male mice
T2 - Frontiers in molecular neuroscience
J2 - Front Mol Neurosci
PY - 2026
DA - 2026/05/29
VL - 19
SP - 1844705
SN - 1662-5099
PB - Frontiers Media SA
DO - 10.3389/fnmol.2026.1844705
UR - https://doi.org/10.3389/fnmol.2026.1844705
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fnmol.2026.1844705",
"type": "article-journal",
"title": "Risperidone regulates the expression of schizophrenia-related genes in the forebrain of adult male mice",
"container-title": "Frontiers in molecular neuroscience",
"author": [
{
"family": "Ziemiańska",
"given": "Magdalena"
},
{
"family": "Zięba",
"given": "Mateusz"
},
{
"family": "Radlicka-Borysewska",
"given": "Anna"
},
{
"family": "Szumiec",
"given": "Łukasz"
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{
"family": "Gołda",
"given": "Sławomir"
},
{
"family": "Borczyk",
"given": "Małgorzata"
},
{
"family": "Piechota",
"given": "Marcin"
},
{
"family": "Korostyński",
"given": "Michał"
},
{
"family": "Rodriguez Parkitna",
"given": "Jan"
}
],
"container-title-short": "Front Mol Neurosci",
"volume": "19",
"page": "1844705",
"DOI": "10.3389/fnmol.2026.1844705",
"PMID": "42293048",
"PMCID": "PMC13260059",
"ISSN": "1662-5099",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnmol.2026.1844705",
"language": "en",
"issued": {
"date-parts": [
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2026,
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29
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]
}
}

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In common: SAMtools, edgeR, anndata, 18 other tools, genetics / omics, cellular / molecular
[5] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: SAMtools, edgeR, limma, 17 other tools, mouse, cellular / molecular, 1 reference
[6] doi:10.1016/j.cpblue.2026.100007 [code]
An integrated single-cell and spatial proteotranscriptomics atlas of fibroblast-driven immunoregulation within the human adult oral cavity.
Journal: Cell press blue
In common: SAMtools, edgeR, limma, 18 other tools
[7] doi:10.1186/s13059-026-04177-w [code]
Genomic sequence evolution underlying human neocortical interareal diversification.
Journal: Genome biology
In common: pysam, BEDTools, SAMtools, 16 other tools, genetics / omics, mouse, cellular / molecular
[8] doi:10.1126/sciadv.aeg3223 [code]
The extreme diversity of retinal amacrine cells has deep evolutionary roots.
Journal: Science advances
In common: psych, anndata, DESeq2, 17 other tools, genetics / omics, cellular / molecular
[9] doi:10.1038/s41586-026-10512-9 [code]
Astrocyte glucocorticoid receptor signalling restricts neuronal plasticity.
Journal: Nature
In common: pysam, BEDTools, SAMtools, 14 other tools, mouse, cellular / molecular, 1 reference
[10] doi:10.1038/s44318-026-00818-9 [code]
FAM134B-mediated ER-phagy degrades APP and suppresses Alzheimer's disease pathology.
Journal: The EMBO journal
In common: BEDTools, limma, anndata, 16 other tools, mouse, cellular / molecular

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