OSCR

Neuronal subtype-specific ribosomal protein mRNA expression.

Code ↔ Paper

19 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 19 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] § MATERIALS AND METHODS › Analysis of aging and stress vulnerability data sets ↔ scripts/Stress_Vulnerability_Dataset_Preprocessing.R, the whole file · a weak match · score 0.83 · FindTransferAnchors, TransferData, prediction score, stress vulnerability, Seurat, confidence
  2. [2] § MATERIALS AND METHODS › RP stability expression analysis ↔ scripts/Figure_2_S2.R, lines 1–61 · score 0.72 · SCTransform, principal component, elbow, neighbor, resolution, PCA
  3. [3] § MATERIALS AND METHODS › Paralog expression analysis ↔ scripts/Figure_4_S4.R, lines 46–106 · score 0.71 · paralog pairs, Rpl7l1, Rpl22l1, Rps27l, Figure 4, subclasses
  4. [4] § MATERIALS AND METHODS › Analysis of aging and stress vulnerability data sets ↔ scripts/Aging_Dataset_Preprocessing.R, lines 112–175 · score 0.70 · FindTransferAnchors, TransferData, Seurat, confidence, prediction, aging
  5. [5] § RESULTS › Subclass-specific ribosomal protein expression patterns are largely stable across aging and stress conditions ↔ scripts/Figure_7D.R, lines 1–39 · score 0.67 · Rpl36al, L6 CT, L4 RSP, Uba52, L2, classification
  6. [6] § MATERIALS AND METHODS › RP stability expression analysis ↔ scripts/Preprocessing_Datasets.R, lines 100–162 · score 0.66 · SCTransform, expression matrix, Seurat, PCA, UMAP, component
  7. [7] § RESULTS › Subclass-specific ribosomal protein expression patterns are largely stable across aging and stress conditions ↔ scripts/Figure_7D.R, lines 1–39 · score 0.66 · L6 CT CTX, L4 RSP ACA, Rpl36al, Uba52, L2, adult
  8. [8] § MATERIALS AND METHODS › Single-cell RNA-seq data analysis ↔ scripts/Preprocessing_Datasets.R, lines 100–162 · score 0.65 · Seurat, ALM, SSs, PCA, Mop, RSPv
  9. [9] § RESULTS › Ribosomal gene expression patterns are a conserved feature of neuronal subtypes across brain regions ↔ scripts/Figure_4_S4.R, lines 1–31 · score 0.62 · L5 NP, Rpl7l1, Rpl22l1, DG, Sst, Lamp5
  10. [10] § MATERIALS AND METHODS › RP expression levels and specificity score calculation ↔ scripts/Figure_3_S3.R, lines 150–232 · score 0.62 · ward.D2, boxplots, linkage, Euclidean, Residuals, metric
  11. [11] § RESULTS › Preferential expression of ribosomal protein paralogs across neuronal subclasses ↔ scripts/Figure_4_S4.R, lines 156–214 · score 0.61 · Rpl7l1, Rpl22l1, Rps27l, S4, neuronal subclass, Figure 4
  12. [12] § RESULTS › Ribosomal proteins exhibit gene-specific and subclass-associated differential expression ↔ scripts/Figure_5_S5.R, lines 162–220 · score 0.60 · intra excitatory, intra inhibitory, S5, underexpression, cluster, scores
  13. [13] § RESULTS › Subclass-specific ribosomal protein expression patterns are largely stable across aging and stress conditions ↔ scripts/Figure_6_Table_2.R, lines 63–122 · score 0.59 · L6 CT CTX, L4 RSP ACA, L2, Sst, thresholds, Scatter
  14. [14] § RESULTS › Subclass-specific ribosomal protein expression patterns are largely stable across aging and stress conditions ↔ scripts/Figure_3_S3.R, lines 61–148 · score 0.58 · L6 CT, L4 RSP, S3, GABAergic, L2, Lamp5
  15. [15] § MATERIALS AND METHODS › Cross-technology concordance analysis ↔ scripts/Figure_6_Table_2.R, lines 63–122 · score 0.57 · concordant RP genes, Discordant RP, Spearman, correlation, log2, Padj
  16. [16] § RESULTS › Preferential expression of ribosomal protein paralogs across neuronal subclasses ↔ scripts/Figure_4_S4.R, lines 46–106 · score 0.57 · Rpl7l1, Rpl22l1, Rps27l, paralogs, genes, subclasses
  17. [17] § MATERIALS AND METHODS › RP expression levels and specificity score calculation ↔ scripts/Figure_7_S6A.R, lines 232–288 · score 0.55 · ward.D2, linkage, distance, Euclidean, metric, clustering
  18. [18] § MATERIALS AND METHODS › Cross-technology concordance analysis ↔ scripts/Figure_7_S6A.R, lines 110–168 · score 0.54 · Discordant RP, Concordant RP, Spearman, correlation, log2, Padj
  19. [19] § RESULTS › Subclass-specific ribosomal protein expression patterns are largely stable across aging and stress conditions ↔ scripts/Figure_8D.R, lines 1–29 · score 0.50 · L5 NP CTX, Rpl15, Rps2, Uba52, Pvalb, Vip

Paper

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The authors' code

R · 268 lines · 9 KB · no license · 4 matches

  1. library(Seurat)
  2. library(ggplot2)
  3. library(dplyr)
  4. library(scCustomize)
  5. library(RColorBrewer)
  6. library(svglite)
  7. library(tidyr)
  8. library(ggpubr)
  9. library(patchwork)
  10. library(scales)
  11. library(cowplot)
  12. load("data/Smartseq2dataset_seurat_filtered.RData")
  13. seurat_split <- SplitObject(seurat_merged, split.by = "region_label")
  14. RP = read.csv("data/RP.csv")
  15. RP = as.character(RP$x)
  16. RP =gsub("Rack1", "Gnb2l1", x = RP)
  17. subclass_pass <- list(
  18. c("L5 PT CTX","L5 IT CTX","L4/5 IT CTX","L6 IT CTX","L6 CT CTX","L5 NP CTX","Pvalb","Vip","L2/3 IT CTX","Lamp5","Sst","Sst Chodl","Sncg","Car3","L6b CTX"),
  19. c("L4/5 IT CTX","L2/3 IT CTX","Lamp5","L5 IT CTX","L6 IT CTX","Vip","L6 CT CTX","L5 PT CTX","Car3","L5 NP CTX"),
  20. c("Car3","L2/3 IT CTX"),
  21. c("Vip","L5 NP CTX","L4/5 IT CTX","Sst","L6 IT CTX","L5 IT CTX","Lamp5","L2/3 IT CTX","L6 CT CTX","L6b CTX"),
  22. c("L5 PT CTX","L4 RSP-ACA"),
  23. c("L2/3 IT CTX","L6 IT CTX"),
  24. c("Sst","L5 NP CTX","L5 IT CTX","Vip","Lamp5","L4/5 IT CTX","Pvalb","L2/3 IT CTX","L6 IT CTX","L6 CT CTX","L6b CTX","Sncg"),
  25. c("DG","CA1-ProS","CA3","Lamp5","Vip","Sncg","Pvalb","Sst"),
  26. c("Sst","Sst Chodl","Sncg","Vip","L6 CT CTX","L4/5 IT CTX","L5 IT CTX","L2/3 IT CTX","Lamp5","L5 NP CTX","Pvalb","L6 IT CTX","L5 PT CTX"),
  27. c("L2/3 IT CTX","L4/5 IT CTX"),
  28. c("L4/5 IT CTX","L2/3 IT CTX","L5 IT CTX","L2/3 IT PPP","L6 CT CTX","L5 NP CTX","L6 IT CTX","L6b CTX","Sst","Lamp5","Vip","Pvalb")
  29. )
  30. regions_pass_DESeq2 <- c("VISp","VIS","SSs","SSp","RSPv","RSP","MOp","HIP","ALM","AI","ACA")
  31. geneswithparalogs <- c("Rpl7", "Rpl7l1", "Rpl22", "Rpl22l1", "Rps27", "Rps27l")
  32. procesar_seurat <- function(seurat_obj, genes, subclasses, region) {
  33. datos <- FetchData(seurat_obj, c(genes, "subclass_label", "donor_label"))
  34. datos_long <- datos %>%
  35. pivot_longer(cols = all_of(genes), names_to = "gene", values_to = "expression") %>%
  36. mutate(presence = expression > 0)
  37. datos_filtered <- datos_long %>%
  38. filter(subclass_label %in% subclasses)
  39. resultado <- datos_filtered %>%
  40. group_by(subclass_label, donor_label, gene) %>%
  41. summarise(presencia_pct = mean(presence) * 100, .groups = 'drop') %>%
  42. mutate(region = region)
  43. return(resultado)
  44. }
  46. resultados <- lapply(1:length(seurat_split), function(i) {
  47. procesar_seurat(seurat_split[[i]], geneswithparalogs, subclass_pass[[i]], names(seurat_split)[i])
  48. })
  50. resultados_finales <- bind_rows(resultados)
  52. results_Rpl7_Rpl7l1 <- resultados_finales %>%
  53. filter(gene %in% c("Rpl7", "Rpl7l1")) %>%
  54. compare_means(presencia_pct ~ gene, data = ., method = "wilcox.test", group.by = "subclass_label")
  56. results_Rpl22_Rpl22l1 <- resultados_finales %>%
  57. filter(gene %in% c("Rpl22", "Rpl22l1")) %>%
  58. compare_means(presencia_pct ~ gene, data = ., method = "wilcox.test", group.by = "subclass_label")
  60. results_Rps27_Rps27l <- resultados_finales %>%
  61. filter(gene %in% c("Rps27", "Rps27l")) %>%
  62. compare_means(presencia_pct ~ gene, data = ., method = "wilcox.test", group.by = "subclass_label")
  64. stat_results_combined <- bind_rows(
  65. results_Rpl7_Rpl7l1 %>% mutate(paralog_pair = "Rpl7 / Rpl7l1"),
  66. results_Rpl22_Rpl22l1 %>% mutate(paralog_pair = "Rpl22 / Rpl22l1"),
  67. results_Rps27_Rps27l %>% mutate(paralog_pair = "Rps27 / Rps27l")
  68. )
  70. data_with_pairs <- resultados_finales %>%
  71. mutate(paralog_pair = case_when(
  72. gene %in% c("Rpl7", "Rpl7l1") ~ "Rpl7 / Rpl7l1",
  73. gene %in% c("Rpl22", "Rpl22l1") ~ "Rpl22 / Rpl22l1",
  74. gene %in% c("Rps27", "Rps27l") ~ "Rps27 / Rps27l"
  75. ))
  77. y_positions <- data_with_pairs %>%
  78. group_by(paralog_pair, subclass_label) %>%
  79. summarise(max_y = max(presencia_pct, na.rm = TRUE), .groups = 'drop')
  81. stat_results_formatted_manual <- stat_results_combined %>%
  82. left_join(y_positions, by = c("paralog_pair", "subclass_label")) %>%
  83. mutate(y.position = max_y + 5) # Ajusta el offset (5) si es necesario
  85. paralog_colors <- c("Rpl7"="#1f78b4", "Rpl7l1"="#a6cee3", "Rpl22"="#33a02c", "Rpl22l1"="#b2df8a", "Rps27"="#e31a1c", "Rps27l"="#fb9a99")
  87. mean_values <- data_with_pairs %>%
  88. group_by(paralog_pair, subclass_label, gene) %>%
  89. summarise(mean_pct = mean(presencia_pct, na.rm = TRUE), .groups = 'drop')
  91. mean_diffs <- mean_values %>%
  92. group_by(paralog_pair, subclass_label) %>%
  93. summarise(
  94. mean_difference = abs(mean_pct[1] - mean_pct[2]),
  95. .groups = 'drop'
  96. )
  98. stat_results_with_diff <- stat_results_formatted_manual %>%
  99. left_join(mean_diffs, by = c("paralog_pair", "subclass_label"))
  101. stat_results_simple_asterisk <- stat_results_with_diff %>%
  102. filter(
  103. p.adj <= 0.05 &
  104. mean_difference >= 5
  105. ) %>%
  106. mutate(label = "*")
  108. p_boxplot <- ggplot(
  109. data_with_pairs,
  110. aes(x = subclass_label, y = presencia_pct, fill = gene, color = gene)
  111. ) +
  112. geom_boxplot(outlier.shape = NA, alpha = 0.6, width = 0.8, linewidth = 0.7) +
  114. stat_pvalue_manual(
  115. stat_results_simple_asterisk,
  116. x = "subclass_label",
  117. label = "label",
  118. tip.length = 0.01
  119. ) +
  120. facet_wrap(~ paralog_pair, scales = "free_x", nrow = 1) +
  121. scale_fill_manual(values = paralog_colors) +
  122. scale_color_manual(values = paralog_colors) +
  123. scale_y_continuous(limits = c(NA, 105), expand = expansion(mult = c(0, 0.05))) +
  124. labs(
  125. x = NULL,
  126. y = "% of Expressing Cells",
  127. fill = "Gene",
  128. color = "Gene"
  129. ) +
  130. guides(fill = guide_legend(nrow = 1)) +
  131. theme_classic() +
  132. theme(
  133. legend.position = "top",
  134. panel.spacing.x = unit(0.5, "lines"),
  135. axis.title = element_text(size = 10),
  136. axis.text.y = element_text(size = 7),
  137. axis.text.x = element_text(angle = 45, hjust = 1, size = 7),
  138. legend.title = element_text(size = 10),
  139. legend.text = element_text(size = 7),
  140. strip.text = element_text(face = "bold.italic", size = 10)
  141. )
  143. print(p_boxplot)
  144. dir.create("Figuras/Figura_4", recursive = TRUE, showWarnings = FALSE)
  145. output_dir <- "Figuras/Figura_4/"
  146. output_filename <- "Fig_4A.svg"
  148. ggsave(
  149. filename = file.path(output_dir, output_filename),
  150. plot = p_boxplot,
  151. width = 7.5,
  152. height = 3,
  153. units = "in"
  154. )
  156. features_ordered <- c("Rpl22", "Rpl22l1", "Rpl7", "Rpl7l1", "Rps27", "Rps27l")
  157. DefaultAssay(seurat_merged) <- "RNA"
  158. dot_plot_agregado <- DotPlot(
  159. seurat_merged,
  160. features = features_ordered,
  161. group.by = "subclass_label",
  162. scale = FALSE, dot.min = 0.25, scale.min = 65, col.max = 1.25,
  163. dot.scale = 2
  164. ) +
  165. labs(
  166. y = "Neuronal Subclass",
  167. x = NULL,
  168. color = "Average Expression",
  169. size = "Percentage Expression"
  170. ) +
  171. scale_colour_gradient2(
  172. low = "blue",
  173. mid = "lightgray",
  174. high = "red",
  175. midpoint = 0.75,
  176. limits = c(NA, 1.0),
  177. oob = scales::squish
  178. ) +
  179. guides(
  180. colour = guide_colorbar(
  181. barheight = unit(2.5, "lines"),
  182. barwidth = unit(0.5, "lines")
  183. ),
  184. size = guide_legend(
  185. keyheight = unit(0.5, "lines")
  186. )
  187. ) +
  188. theme_classic() +
  189. theme(
  190. axis.text.x = element_text(angle = 45, hjust=1, size = 7),
  191. axis.title = element_text(size = 10),
  192. axis.text.y = element_text(size = 7),
  193. legend.title = element_text(size = 10),
  194. legend.text = element_text(size = 7)
  195. )
  197. print(dot_plot_agregado)
  199. output_dir <- "Figuras/Figura_4/"
  200. output_filename <- "Fig_4B.svg"
  202. ggsave(
  203. filename = file.path(output_dir, output_filename),
  204. plot = dot_plot_agregado,
  205. width = 7.5,
  206. height = 2.5,
  207. units = "in"
  208. )
  210. dir.create("Figuras/Figura_S4", recursive = TRUE, showWarnings = FALSE)
  211. output_dir <- "Figuras/Figura_S4/DotPlots_Individuales_SinEscalar_SVG"
  212. if (!dir.exists(output_dir)) {
  213. dir.create(output_dir)
  214. }
  216. geneswithparalogs <- c("Rpl7", "Rpl7l1", "Rpl22", "Rpl22l1", "Rps27", "Rps27l")
  218. dotplots_sin_leyenda <- list()
  219. for (i in seq_along(seurat_split)) {
  220. dot_plot <- DotPlot(
  221. seurat_split[[i]],
  222. features = geneswithparalogs,
  223. group.by = "subclass_label",
  224. scale = FALSE, dot.min = 0.25, scale.min = 65, col.max = 1.25,
  225. dot.scale = 4
  226. ) +
  227. labs(title = regions_pass_DESeq2[i]) +
  228. scale_colour_gradient2(low = "blue", mid = "lightgray", high = "red", midpoint = 0.75) +
  229. theme(
  230. axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
  231. axis.text.y = element_text(size = 8),
  232. plot.title = element_text(size = 10, face = "bold"),
  233. legend.position = "none"
  234. )
  235. dotplots_sin_leyenda[[i]] <- dot_plot
  236. }
  238. legend_plot <- DotPlot(
  239. seurat_split[[1]],
  240. features = geneswithparalogs,
  241. group.by = "subclass_label",
  242. scale = FALSE, dot.min = 0.25, scale.min = 65, col.max = 1.25,
  243. dot.scale = 4
  244. ) +
  245. scale_colour_gradient2(low = "blue", mid = "lightgray", high = "red", midpoint = 0.75) +
  246. theme(
  247. legend.text = element_text(size = 8),
  248. legend.title = element_text(size = 8),
  249. legend.key.size = unit(0.4, "cm"),
  250. legend.spacing.y = unit(0.2, "cm")
  251. )
  253. legend <- cowplot::get_legend(legend_plot)
  254. ncol <- 3
  255. n_plots <- length(dotplots_sin_leyenda)
  256. total_slots <- ceiling((n_plots + 1) / ncol) * ncol
  257. n_fillers <- total_slots - (n_plots + 1)
  258. dotplots_con_leyenda <- c(dotplots_sin_leyenda, rep(list(NULL), n_fillers), list(legend))
  259. final_plot <- cowplot::plot_grid(plotlist = dotplots_con_leyenda, ncol = ncol)
  261. ggsave(
  262. filename = file.path(output_dir, "DotPlots_Combined_LegendRight.svg"),
  263. plot = final_plot,
  264. width = 7.5,
  265. height = ceiling(total_slots / ncol) * 3.5,
  266. units = "in"
  267. )

Figure_4_S4.R at commit 1b2356a, no license · at the source

Overview

Authors: Joaquín Garat1, Sofía Niño-Rivero2, Patricia Lagos2, Andrés Di Paolo1, Pablo Smircich3,4, José Sotelo-Silveira1,5
ORCID iDs: Joaquín Garat, Sofía Niño-Rivero, Patricia Lagos, Andrés Di Paolo, Pablo Smircich, José Sotelo-Silveira
  1. Departamento de Genómica, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo 11600, Uruguay
  2. Unidad Académica de Fisiología, Facultad de Medicina, Universidad de la República, Montevideo 11800, Uruguay
  3. Laboratorio de Bioinformática, Departamento de Genómica, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo 11600, Uruguay
  4. Sección Genómica Funcional, Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay
  5. Departamento de Biología Celular y Molecular, Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay
Institutions: Instituto de Investigaciones Biológicas Clemente Estable (Uruguay); Universidad de la República de Uruguay (Uruguay)
Journal: RNA (New York, N.Y.), volume 32, issue 7, pages 1077-1100
Dates: received 13 January 2026; accepted 22 March 2026; published online July 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1261/rna.080954.126 · PMID 41956739 · PMCID PMC13271003 · OpenAlex W7152431529
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity
Keywords: ribosome, scRNA-seq, transcriptomics, neuron
MeSH: Neurons*, Ribosomal Proteins*, RNA, Messenger*, Animals, Cerebral Cortex, Gene Expression Regulation, Hippocampus, Mice, Single-Cell Gene Expression Analysis, Transcriptome (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

Current understanding recognizes that ribosomal proteins (RPs) have regulatory roles beyond their canonical structural functions in translation, raising the question of how their expression is organized across cell types. Given the diversity of neuronal cell types, understanding RP gene expression at the neuronal subtype level is an important and previously inaccessible question. Here, leveraging advances in single-cell transcriptomics, we analyzed single-cell RNA-seq data sets from the mouse cerebral cortex and hippocampus to examine RP mRNA expression across neuronal subtypes. We observed distinct RP mRNA expression profiles between excitatory and inhibitory neurons and found that higher Rps27 transcript levels in inhibitory neurons corresponded to increased RPS27 protein abundance. Beyond excitatory-inhibitory differences, RP mRNA expression further segregated across well-defined neuronal subclasses, with 59 of 84 RP genes differentially expressed, including enrichment of Rpl21 in Lamp5 and Rps27 in Vip interneurons. These patterns were consistent across cortical regions and reproducible across two independent single-cell technologies (Smart-seq2 and 10x Genomics). Analysis of aging- and stress-associated data sets revealed stable RP expression signatures, with limited phenotype-linked changes. Together, we present a comprehensive atlas of ribosomal protein gene expression at neuronal subclass resolution, revealing robust subclass-specific transcriptional signatures, suggesting an underestimated regulatory layer.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 19 matches between paragraphs and lines of code.

joagarat/RP_scRNAseq_Neurons_Code

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1b2356a2eb8ae044bda31b13a9c732513203add6, 17 March 2026
Languages: R (20), Python (2)
Size: 29 files, 22 scripts
Software Heritage: not archived
Found in: “DATA DEPOSITION”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (18 files), ggplot2 (17 files), Seurat (17 files), patchwork (12 files), data.table (11 files), DESeq2 (8 files), circlize (4 files), ComplexHeatmap (4 files), cowplot (2 files), ggpubr (2 files), anndata (1 file), h5py (1 file), lme4 (1 file), lmerTest (1 file), Matplotlib (1 file), nlme (1 file), NumPy (1 file), pandas (1 file), reticulate (1 file), Scanpy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
23 files
At the source: github.com/joagarat/RP_scRNAseq_Neurons_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;
  • 22 scripts, each with its path and the digest of its content;
  • 19 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 deposition

The single-cell RNA-seq and ATAC data sets reanalyzed in this study are publicly available. The data sets from Yao et al. (2021) and Jin et al. (2025) were sourced from the Neuroscience Multi-omic (NeMO) Archive under the data set identifiers dat-jb2f34y and dat-61kfys3, respectively. The data set from Hing et al. (2024) is available in the Gene Expression Omnibus (GEO) database under accession number GSE240975 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE240975). The single-nucleus ATAC-seq data set from Zu et al. (2023) is available in GEO under accession number GSE246791 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE246791). Complete results of the pseudobulk differential expression analyses of ribosomal protein (RP) genes derived from the Smart-seq2 and 10x Genomics data sets are provided in Supplemental Data S1 (http://www.rnajournal.org/lookup/suppl/doi:10.1261/rna.080954.126/-/DC1) and S2 (http://www.rnajournal.org/lookup/suppl/doi:10.1261/rna.080954.126/-/DC1), respectively. Differential expression results for aging-associated (18 vs. 2 months) and stress-associated phenotypes are provided in Supplemental Data S3 (http://www.rnajournal.org/lookup/suppl/doi:10.1261/rna.080954.126/-/DC1) and S5 (http://www.rnajournal.org/lookup/suppl/doi:10.1261/rna.080954.126/-/DC1), respectively. Z-score difference values (GABAergic minus glutamatergic) for each RP gene across data sets, used for cross-data set concordance analyses, are provided in Supplemental Data S4 (http://www.rnajournal.org/lookup/suppl/doi:10.1261/rna.080954.126/-/DC1). Raw and normalized quantification data from the immunofluorescence experiments shown in Figure 9 and Supplemental Figure S9 (http://www.rnajournal.org/lookup/suppl/doi:10.1261/rna.080954.126/-/DC1) are provided in Supplemental Data S6 (http://www.rnajournal.org/lookup/suppl/doi:10.1261/rna.080954.126/-/DC1). The raw confocal microscopy images generated for this study are available from the corresponding author upon reasonable request. All custom scripts used for data processing, analysis, and figure generation have been deposited in a public GitHub repository, available at https://github.com/joagarat/RP_scRNAseq_Neurons_Code.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 10 MeSH terms, 78 references.

Cite

This paper

Garat, J., Niño-Rivero, S., Lagos, P., Di Paolo, A., Smircich, P., & Sotelo-Silveira, J. (2026). Neuronal subtype-specific ribosomal protein mRNA expression. RNA (New York, N.Y.), 32(7), 1077-1100. https://doi.org/10.1261/rna.080954.126

BibTeX

@article{garat2026neuronal,
author = {Garat, Joaquín and Niño-Rivero, Sofía and Lagos, Patricia and Di Paolo, Andrés and Smircich, Pablo and Sotelo-Silveira, José},
title = {{Neuronal subtype-specific ribosomal protein mRNA expression}},
journal = {RNA (New York, N.Y.)},
year = {2026},
month = jun,
volume = {32},
number = {7},
pages = {1077--1100},
publisher = {Cold Spring Harbor Laboratory Press},
issn = {1355-8382},
doi = {10.1261/rna.080954.126},
url = {https://doi.org/10.1261/rna.080954.126},
pmid = {41956739},
pmcid = {PMC13271003}
}

RIS

TY - JOUR
AU - Garat, Joaquín
AU - Niño-Rivero, Sofía
AU - Lagos, Patricia
AU - Di Paolo, Andrés
AU - Smircich, Pablo
AU - Sotelo-Silveira, José
TI - Neuronal subtype-specific ribosomal protein mRNA expression
T2 - RNA (New York, N.Y.)
J2 - RNA
PY - 2026
DA - 2026/06/16
VL - 32
IS - 7
SP - 1077
EP - 1100
SN - 1355-8382
PB - Cold Spring Harbor Laboratory Press
DO - 10.1261/rna.080954.126
UR - https://doi.org/10.1261/rna.080954.126
LA - en
ER -

CSL-JSON

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"title": "Neuronal subtype-specific ribosomal protein mRNA expression",
"container-title": "RNA (New York, N.Y.)",
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"family": "Garat",
"given": "Joaquín"
},
{
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"given": "Sofía"
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{
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"given": "Patricia"
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{
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],
"container-title-short": "RNA",
"volume": "32",
"issue": "7",
"page": "1077-1100",
"DOI": "10.1261/rna.080954.126",
"PMID": "41956739",
"PMCID": "PMC13271003",
"ISSN": "1355-8382",
"publisher": "Cold Spring Harbor Laboratory Press",
"URL": "https://doi.org/10.1261/rna.080954.126",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
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]
}
}

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