OSCR

Neuron type-specific translatomes in dorsal hippocampus during early memory consolidation.

Code ↔ Paper

9 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 9 matches
  1. [1] § Methods › Bioinformatics › Differential expression analysis and Gene Ontology analysis ↔ src/Home_Cage_Analysis/HomeCage_Analyses.R, lines 1–81 · score 0.75 · genes enriched, DESeq2, filtering genes, genes identified, vst, matrices
  2. [2] § Methods › Bioinformatics › Differential expression analysis and Gene Ontology analysis ↔ src/DESeq2_Run.R, lines 34–122 · score 0.71 · ComBat seq, DESeq2, batch, variance, vst, matrices
  3. [3] § Methods › Bioinformatics › Weighted gene co-expression network analysis (WGCNA) ↔ src/WGCNA.R, lines 306–356 · score 0.67 · exportNetworkToCytoscape, weighted, edges, exported, nodes, WGCNA
  4. [4] § Methods › Bioinformatics › Machine learning model ↔ src/Home_Cage_Analysis/HomeCage_Analyses.R, lines 189–276 · score 0.65 · log2FC, home cage, fold changes, gradient, filtered, cell
  5. [5] § Methods › Bioinformatics › Quality control and filtering of enriched genes ↔ src/Home_Cage_Analysis/HomeCage_Analyses.R, lines 86–138 · score 0.63 · Aldh1l1, Cx3cr1, Cnp, Gfap, filtering, genes
  6. [6] § Results › Neuron types recruit distinct molecular programs after learning ↔ src/Home_Cage_Analysis/HomeCage_Analyses.R, lines 189–276 · score 0.59 · log2FC, home cage, fold changes, Pairwise, CAMK2A, rows
  7. [7] § Methods › Alignment to the genome and feature count ↔ src/DESeq2_Run.R, lines 34–122 · score 0.56 · ComBat Seq, batch, row, matrix
  8. [8] § Methods › Bioinformatics › Quality control and filtering of enriched genes ↔ src/Home_Cage_Analysis/HomeCage_Analyses.R, lines 86–138 · score 0.54 · Aldh1l1, Cx3cr1, filtering, genes
  9. [9] § Methods › Bioinformatics › Weighted gene co-expression network analysis (WGCNA) ↔ src/WGCNA.R, lines 153–202 · score 0.51 · blockwiseModules, network, power, threshold, WGCNA, genes

Paper

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

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The authors' code

R · 276 lines · 9.2 KB · no license · 5 matches

  1. suppressPackageStartupMessages({
  2. library(DESeq2)
  3. library(dplyr)
  4. library(stringr)
  5. library(ggplot2)
  6. library(rtracklayer)
  7. library(ggrepel)
  8. library(ggpubr)
  9. library(tibble)
  10. })
  12. #FIGURE 1 - CELL TYPE-SPECIFIC TRANSLATOME#####
  13. #Fig 1E
  14. #-------------------------------------------------------------------------------
  15. ##PCA of all genes enriched in neurons
  16. #first get a full list of all genes identified as enriched in all cell types
  17. load("Filtered_gene_cell_Specific.RData")
  18. all_genes <- c(rownames(Camk2a.df), rownames(Pvalb.df), rownames(Sst.df))
  19. all_genes <- unique(all_genes)
  21. #Prepare counts data frame only with Home Cage samples
  22. load("All_counts.RData")
  23. All.HC.samples <- counts_wide %>%
  24. dplyr::select(Geneid, contains("TRAP")) %>%
  25. dplyr::select(Geneid, contains("HC")) %>%
  26. filter(Geneid %in% all_genes) %>%
  27. dplyr::distinct(Geneid, .keep_all = TRUE)
  28. rownames(All.HC.samples) <- All.HC.samples$Geneid
  29. All.HC.samples <- dplyr::select(All.HC.samples, -Geneid)
  31. #Prepare Metadata for DESeq2 run
  32. Metadata_df <- data.frame(
  33. samples = names(All.HC.samples),
  34. Neuron_type = str_replace(names(All.HC.samples), "[0-9]+min_TRAP_HC[0-9]$", ""),
  35. row.names = names(All.HC.samples)
  36. )
  38. my_database_TRAP_all <- DESeqDataSetFromMatrix(countData = as.matrix(All.HC.samples),
  39. colData = Metadata_df,
  40. design = ~ Neuron_type)
  42. keep_TRAP <- rowSums(counts(my_database_TRAP_all) >= 20) >= 5
  43. my_database_TRAP_all <- my_database_TRAP_all[keep_TRAP, ]
  44. my_database_TRAP_all <- estimateSizeFactors(my_database_TRAP_all)
  45. normalized_db_TRAP_all <- vst(my_database_TRAP_all, blind = TRUE)
  47. #PCA all
  48. plotPCA(normalized_db_TRAP_all, intgroup = c("Neuron_type")) +
  49. geom_point(size = 7) +
  50. scale_color_manual(values = c("#b1c6d9","#008631","#ffecec")) +
  51. #geom_text(aes(label = name)) +
  52. xlim(-40,40) +
  53. ylim(-40, 40) +
  54. theme_bw() +
  55. theme(axis.title.x = element_text(size = 24),
  56. axis.text.x = element_text(size = 24),
  57. axis.title.y = element_text(size = 24),
  58. axis.text.y = element_text(size = 24),
  59. legend.text = element_text(size = 20),
  60. legend.title = element_text(size = 24),
  61. legend.position = "bottom",
  62. panel.grid = element_blank())
  65. # -------------------------------------------------------------------------
  66. # CELL MARKERS PLOT
  67. # -------------------------------------------------------------------------
  69. gtf_path <- "C:\\Users\\mauri\\Dropbox\\rMATS_AS_translatome project\\gencode.vM25.annotation.gtf"
  71. # 1) Build mapping table from GTF
  72. gtf <- rtracklayer::import(gtf_path)
  74. gene_map <- as.data.frame(gtf) %>%
  75. filter(type == "gene") %>%
  76. transmute(
  77. Geneid = as.character(gene_id),
  78. gene_name = as.character(gene_name)
  79. ) %>%
  80. distinct() %>%
  81. filter(!is.na(Geneid), !is.na(gene_name))
  83. # If your rownames look like ENSMUSG... .xx, strip version so it matches GTF gene_id
  84. strip_ensembl_version <- function(x) sub("\\.[0-9]+$", "", x)
  86. marker_genes <- c(
  87. "Camk2a", "Slc17a7",
  88. "Gad1", "Gad2", "Syt2",
  89. "Pvalb", "Sst", "Aldh1l1",
  90. "Gfap", "Cnp", "Cx3cr1"
  91. )
  93. # 2) Convert rownames (Ensembl) -> symbols, then plot as before
  94. markers.df <- RPKM.div.df %>%
  95. rownames_to_column("Geneid") %>%
  96. left_join(gene_map, by = "Geneid") %>%
  97. filter(gene_name %in% marker_genes)
  99. # 3) Long format (tidyr instead of gather)
  100. gather.df <- markers.df %>%
  101. pivot_longer(
  102. cols = HC1_Camk2a:HC9_Sst,
  103. names_to = "sample",
  104. values_to = "normalized_RPKM"
  105. ) %>%
  106. mutate(
  107. geneID = factor(gene_name, levels = marker_genes),
  108. `Cell type` = str_replace(sample, "^.*_", "")
  109. )
  111. ggplot(gather.df, aes(`Cell type`, log2(normalized_RPKM), fill = `Cell type`)) +
  112. geom_boxplot(linewidth = 0.8) +
  113. geom_point() +
  114. geom_hline(yintercept = 0, linewidth = 1) +
  115. scale_fill_manual(values = c("#b1c6d9","#008631","#ffecec")) +
  116. ylab("log2(RPKM TRAP /\n RPKM Total)") +
  117. scale_y_continuous(breaks = seq(-8, 8, by = 2)) +
  118. facet_wrap(~ geneID, nrow = 2) +
  119. theme_bw() +
  120. theme(
  121. axis.title.y = element_text(size = 24),
  122. axis.title.x = element_blank(),
  123. axis.text.y = element_text(size=24),
  124. axis.text.x = element_blank(),
  125. axis.ticks.x = element_blank(),
  126. legend.position = "top",
  127. legend.title = element_text(size = 24),
  128. legend.text = element_text(size = 24),
  129. strip.text = element_text(size = 22),
  130. strip.background = element_blank(),
  131. panel.grid = element_blank()
  132. )
  136. # -----------------------------------------------------------------------
  137. # PRODUCTION OF HOME CAGE DESEQ2 OBJECT
  138. # -----------------------------------------------------------------------
  140. run_pairwise_neuron_type_results <- function(dds,
  141. neuron_col = "Neuron_type",
  142. alpha = 0.05,
  143. cooksCutoff = FALSE,
  144. independentFiltering = FALSE) {
  145. # Make sure the factor exists and is a factor
  146. stopifnot(neuron_col %in% colnames(colData(dds)))
  147. colData(dds)[[neuron_col]] <- factor(colData(dds)[[neuron_col]])
  149. # Fit model once
  150. dds <- DESeq(dds)
  152. # Levels present (e.g., Camk2a, Pvalb, Sst)
  153. lvls <- levels(colData(dds)[[neuron_col]])
  154. lvls <- lvls[lvls %in% unique(as.character(colData(dds)[[neuron_col]]))] # keep used levels
  156. # All pairwise combinations
  157. pairs <- combn(lvls, 2, simplify = FALSE)
  159. # Store results here
  160. res_list <- list()
  162. for (p in pairs) {
  163. a <- p[1]
  164. b <- p[2]
  166. # Name like "Camk2a_vs_Pvalb"
  167. nm <- paste0(a, "_vs_", b)
  169. res <- results(
  170. dds,
  171. contrast = c(neuron_col, a, b),
  172. alpha = alpha,
  173. cooksCutoff = cooksCutoff,
  174. independentFiltering = independentFiltering
  175. )
  177. # Full, unfiltered table; keep Geneid as a column for easy joins later
  178. res_df <- as.data.frame(res) %>%
  179. rownames_to_column("Geneid") %>%
  180. arrange(padj)
  182. res_list[[nm]] <- res_df
  183. }
  185. list(dds = dds, results = res_list, neuron_levels = lvls)
  186. }
  189. out_HC <- run_pairwise_neuron_type_results(
  190. dds = my_database_TRAP_all,
  191. neuron_col = "Neuron_type",
  192. alpha = 0.05,
  193. cooksCutoff = FALSE,
  194. independentFiltering = FALSE
  195. )
  197. # Your results list:
  198. # out_HC$results$Camk2a_vs_Pvalb
  199. # out_HC$results$Camk2a_vs_Sst
  200. # out_HC$results$Pvalb_vs_Sst
  203. #-------------------------------------------------------------------------------
  204. # VENN DIAGRAM OF HOME CAGE SAMPLES
  205. #-------------------------------------------------------------------------------
  208. Venn_df <- list(Camk2a = rownames(Camk2a.df),
  209. Pvalb = rownames(Pvalb.df),
  210. Sst = rownames(Sst.df))
  213. attributes(Venn_df) <- list(names = names(Venn_df),
  214. row.names = 1:7000, #Random number much larger than the total number of a cell type-specific gene list
  215. class = 'data.frame')
  217. #Venn diagram
  218. ggVennDiagram(Venn_df, label_alpha = 0,
  219. set_color = "midnightblue",
  220. label_size = 8,
  221. label_percent_digit = 1,
  222. set_size = 8) +
  223. scale_fill_gradient(low = "#F4FAFE",
  224. high = "#4981BF")
  226. # ------------------------------------------
  227. # COMPARISON OF CAMK2A-TO-INTERNEURON DEGs
  228. # ------------------------------------------
  230. common_genes <- Reduce(intersect, list(rownames(Camk2a.df),
  231. rownames(Pvalb.df),
  232. rownames(Sst.df)))
  234. res_Camk2a_vs_Pvalb <- out_HC$results$Camk2a_vs_Pvalb
  235. res_Camk2a_vs_Sst <- out_HC$results$Camk2a_vs_Sst
  236. res_Pvalb_vs_Sst <- out_HC$results$Pvalb_vs_Sst
  238. df.final <- tibble(Geneid = common_genes) %>%
  239. left_join(
  240. res_Camk2a_vs_Pvalb %>% select(Geneid, Camk2avsPV = log2FoldChange),
  241. by = "Geneid"
  242. ) %>%
  243. left_join(
  244. res_Camk2a_vs_Sst %>% select(Geneid, Camk2avsSst = log2FoldChange),
  245. by = "Geneid"
  246. ) %>%
  247. left_join(
  248. res_Pvalb_vs_Sst %>% select(Geneid, padj_PvalbvsSst = padj),
  249. by = "Geneid"
  250. ) %>%
  251. mutate(
  252. sig_PvalbvsSst = if_else(!is.na(padj_PvalbvsSst) & padj_PvalbvsSst < 0.05, "yes", "no")
  253. )
  255. gene.markers <- c("ENSMUSG00000024617.16" ,"ENSMUSG00000005716.16", "ENSMUSG00000004366.4")
  257. ggplot(df.final, aes(Camk2avsPV, Camk2avsSst)) +
  258. geom_point(data = df.final, aes(fill = sig_PvalbvsSst), size = 3, shape = 21) +
  259. scale_fill_manual(values = c("yes" = "darkblue", "no" = "gray")) +
  260. geom_smooth(method = 'lm', colour = "black", linewidth = 1, alpha = 0.2) +
  261. geom_vline(xintercept = 0, linetype = "dashed", linewidth = 1) +
  262. geom_hline(yintercept = 0, linetype = "dashed", linewidth = 1) +
  263. xlim(c(-3,3)) + ylim (c(-3,3)) +
  264. xlab(label = "log2FC - Camk2a/Pvalb") + ylab(label = "log2FC - Camk2a/Sst") +
  265. theme_classic() +
  266. theme(axis.title.x = element_text(size = 20),
  267. axis.text.x = element_text(size = 20),
  268. axis.title.y = element_text(size = 20),
  269. axis.text.y = element_text(size = 20),
  270. legend.text = element_text(size = 18),
  271. legend.title = element_text(size = 18),
  272. legend.position = "bottom") +
  273. stat_cor(method = "spearman", size = 7, label.x = -3, label.y = 2.5, cor.coef.name = "rho") +
  274. geom_label_repel(aes(label = ifelse(Geneid %in% gene.markers, row.names(df.final), '')),
  275. max.overlaps = Inf, box.padding = 1
  276. )

HomeCage_Analyses.R at commit 8057321, no license · at the source

Overview

Authors: Mauricio M Oliveira1, Olivia Mosto1, Robert Carney1, Wendy J Liu1, Maggie Mamcarz1, Emmanuel Makinde1, Emily H Lu1, Karen S A Ruiz1,2, Carson C Schultz1, Catherine Leckie1, Thomas J Carew1, Eric Klann1
ORCID iDs: Mauricio M Oliveira, Olivia Mosto, Wendy J Liu, Karen S A Ruiz, Thomas J Carew, Eric Klann
  1. Center for Neural Science, New York University, New York City, NY USA
  2. Department of Molecular Biology, Massachusetts General Hospital, Harvard University, Boston, MA USA
Institutions: New York University (United States); Harvard University (United States); Massachusetts General Hospital (United States)
Journal: Nature communications, volume 17, issue 1, article 7897
Dates: received 18 December 2025; accepted 3 June 2026; published online 23 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74455-5 · PMID 42336862 · PMCID PMC13443933 · OpenAlex W7165687568
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), mouse (organism), cognitive (subfield)
Methods: Statistics, Machine learning, Preprocessing, Evoked potentials, Smoothing, state filtering, decompositions, Spectral & time-frequency
Keywords: Molecular neuroscience, Fear conditioning
MeSH: Hippocampus*, Memory Consolidation*, Neurons*, Protein Biosynthesis*, Animals, Male, Memory, Long-Term, Mice, Mice, Inbred C57BL, RNA, Messenger (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Brain and Behavior Research Foundation (Brain & Behavior Research Foundation) (33631); U.S. Department of Health & Human Services | National Institutes of Health (NIH) (1R01MH120300-01A1); NIMH NIH HHS (R01 MH120300)
Citations: not cited yet (Europe PMC); 67 references in the paper

Abstract

The 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 9 matches between paragraphs and lines of code.

Oli-Mauricio/Oliveira_etal_2026

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8057321d8567e51db24120cfd0ef28846eaccaae, 6 February 2026
Languages: R (5)
Size: 10 files, 5 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), DESeq2 (3 files), ggplot2 (3 files), ggpubr (2 files), caret (1 file), clusterProfiler (1 file), edgeR (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/Oli-Mauricio/Oliveira_etal_2026

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-74455-5.

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  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

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:

Read it in the paper: doi.org/10.1038/s41467-026-74455-5.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 2 keywords, 10 MeSH terms, 3 funders, 65 references.

Cite

This paper

Oliveira, M. M., Mosto, O., Carney, R., Liu, W. J., Mamcarz, M., Makinde, E., Lu, E. H., Ruiz, K. S. A., Schultz, C. C., Leckie, C., Carew, T. J., & Klann, E. (2026). Neuron type-specific translatomes in dorsal hippocampus during early memory consolidation. Nature communications, 17(1), 7897. https://doi.org/10.1038/s41467-026-74455-5

BibTeX

@article{oliveira2026neuron,
author = {Oliveira, Mauricio M and Mosto, Olivia and Carney, Robert and Liu, Wendy J and Mamcarz, Maggie and Makinde, Emmanuel and Lu, Emily H and Ruiz, Karen S A and Schultz, Carson C and Leckie, Catherine and Carew, Thomas J and Klann, Eric},
title = {{Neuron type-specific translatomes in dorsal hippocampus during early memory consolidation}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7897},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74455-5},
url = {https://doi.org/10.1038/s41467-026-74455-5},
pmid = {42336862},
pmcid = {PMC13443933}
}

RIS

TY - JOUR
AU - Oliveira, Mauricio M
AU - Mosto, Olivia
AU - Carney, Robert
AU - Liu, Wendy J
AU - Mamcarz, Maggie
AU - Makinde, Emmanuel
AU - Lu, Emily H
AU - Ruiz, Karen S A
AU - Schultz, Carson C
AU - Leckie, Catherine
AU - Carew, Thomas J
AU - Klann, Eric
TI - Neuron type-specific translatomes in dorsal hippocampus during early memory consolidation
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/23
VL - 17
IS - 1
SP - 7897
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74455-5
UR - https://doi.org/10.1038/s41467-026-74455-5
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-74455-5",
"type": "article-journal",
"title": "Neuron type-specific translatomes in dorsal hippocampus during early memory consolidation",
"container-title": "Nature communications",
"author": [
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"family": "Oliveira",
"given": "Mauricio M"
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{
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{
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{
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{
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{
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"given": "Eric"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7897",
"DOI": "10.1038/s41467-026-74455-5",
"PMID": "42336862",
"PMCID": "PMC13443933",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
"date-parts": [
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23
]
]
}
}

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