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A posttranslational proteomic survey of a single anatomically preserved human 20-week postconception brain.

Code ↔ Paper

10 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 10 matches
  1. [1] § RESULTS AND DISCUSSION › Cortical transcription factors ↔ 1. QC Non mod peptides.R, lines 171–253 · score 0.78 · principal components, cortical regions, transcription factors, insular cortex, primary visual cortex, frontal cortex
  2. [2] § RESULTS AND DISCUSSION › Phosphorylation ↔ 3. Phosphorylation figure.R, lines 95–153 · score 0.76 · predicted phosphorylated, motor cortex, somatosensory cortex, primary visual cortex, Pia Mater, medial
  3. [3] § RESULTS AND DISCUSSION › Phosphorylation ↔ 3. Phosphorylation figure.R, lines 1–52 · score 0.70 · linear model, predicting abundance, phosphorylated peptide abundance, phosphorylation sites, unmodified protein, cortex
  4. [4] § RESULTS AND DISCUSSION › Cortical transcription factors ↔ 1. QC Non mod peptides.R, lines 171–253 · score 0.69 · cortical regions, transcription factors, Insular cortex, Primary Visual Cortex, variance, claustrum
  5. [5] § RESULTS AND DISCUSSION › Ligand:Receptor interactions ↔ 2. RNA_protein_correls.R, lines 93–170 · score 0.68 · L1CAM, RNA protein, CNTN4, ITGB1, LGALS1, PTPRG
  6. [6] § RESULTS AND DISCUSSION › Phosphorylation ↔ 5. Acetylation figure.R, lines 54–128 · score 0.67 · motor cortex, somatosensory cortex, primary visual cortex, Pia Mater, variable, medial
  7. [7] § METHODS › RNA–protein correlations ↔ 0. Table wrangling.R, lines 421–480 · score 0.62 · Pearson correlation, fewer, metadata, PCW, matching, matrix
  8. [8] § RESULTS AND DISCUSSION › Non‐modified proteins ↔ 4. Glycosylation figure.R, lines 119–182 · score 0.56 · multiple modified peptides, unmodified protein abundance, modified peptide abundance, correlations, glycosylation
  9. [9] § METHODS › Ligand–receptor pair prediction ↔ 2. RNA_protein_correls.R, lines 93–170 · score 0.51 · ligand pairs, subplate, receptor, ranks, frontal
  10. [10] § RESULTS AND DISCUSSION › RNA–protein correlations ↔ 2. RNA_protein_correls.R, lines 30–91 · score 0.50 · fold change, prefrontal, BrainSpan, RNA, medial, GO

Paper

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The authors' code

R · 173 lines · 7.2 KB · no license · 3 matches

  1. library(tidyverse)
  2. library(openxlsx)
  4. master_table <- read_csv("Supplementary tables/S2 Master_table.csv", guess_max = 2000)
  5. glimpse(master_table)
  8. RNA_protein_to_plot <- master_table %>%
  9. select(Gene_symbol, Region, NM_protein_abundance, RPKM) %>%
  10. unique()
  12. ggplot(RNA_protein_to_plot, aes(x = log2(RPKM), y = log2(NM_protein_abundance))) + geom_point() + facet_wrap(~Region)
  15. zero_RNA <- RNA_protein_to_plot %>%
  16. filter(RPKM == 0)
  19. zero_summary <- data.frame(table(zero_RNA$Gene_symbol, zero_RNA$Region)) %>%
  20. arrange(Var2)
  22. ##Create background list for enrichment throughout - all unique genes IDed in the study
  24. protein_list<- data.frame(unique(master_table$Gene_symbol))
  26. write_csv(protein_list, "Input files/background_list.csv")
  30. ##Original brainspan looked at fold changes between regions between the two metrics. Let's try thalamus and prefrontal to start
  32. MD_thal_Frontal_Protein <- RNA_protein_to_plot %>%
  33. select(-RPKM) %>%
  34. filter(Region == "Frontal Cortex" | Region == "Thalamus (medial)") %>%
  35. pivot_wider(names_from = Region, values_from =NM_protein_abundance, values_fn = mean) %>%
  36. mutate(log2FC_Protein = log2(`Frontal Cortex`) - log2(`Thalamus (medial)`)) %>%
  37. select(Gene_symbol, log2FC_Protein)
  41. MD_thal_Frontal_RNA <- RNA_protein_to_plot %>%
  42. select(-NM_protein_abundance) %>%
  43. filter(Region == "Frontal Cortex" | Region == "Thalamus (medial)") %>%
  44. pivot_wider(names_from = Region, values_from =RPKM, values_fn = mean) %>%
  45. mutate(log2FC_RNA = log2(`Frontal Cortex`) - log2(`Thalamus (medial)`)) %>%
  46. select(Gene_symbol, log2FC_RNA) %>%
  47. left_join(MD_thal_Frontal_Protein) %>%
  48. mutate(RNA_diff = ifelse(log2FC_RNA < -1, "Down", ifelse(
  49. log2FC_RNA > 1, "Up", "No"
  50. ))) %>%
  51. mutate(Protein_diff = ifelse(log2FC_Protein < -1, "Down", ifelse(
  52. log2FC_Protein > 1, "Up", "No"
  53. ))) %>%
  54. mutate(RNA_protein_diff = log2FC_RNA - log2FC_Protein) %>%
  55. mutate(diff_colour = ifelse(RNA_protein_diff < -1, "Big", ifelse(
  56. RNA_protein_diff > 1, "Big", "Small"
  57. ))) %>%
  58. mutate(Final_colour = ifelse(RNA_diff == "No" & Protein_diff == "No", "grey", ifelse(
  59. RNA_diff == Protein_diff & diff_colour == "Big", "orange", ifelse(
  60. Protein_diff == "No" & (RNA_diff == "Up" | RNA_diff == "Down"), "blue", ifelse(
  61. RNA_diff == "No" & (Protein_diff == "Up" | Protein_diff == "Down"), "ltblue", ifelse(
  62. diff_colour == "Small" & (RNA_diff == "Up" & Protein_diff == "Up")|(RNA_diff == "Down" & Protein_diff == "Down"), "yellow", ifelse(
  63. diff_colour == "Big" & RNA_diff != Protein_diff, "red", NA
  64. )
  65. )
  66. )
  67. )
  68. )))
  71. plot_colors <- c("grey" = "#FFFFFF", "yellow" = "#ffffbf", "blue" = "#2c7bb6", "ltblue" = "#abd9e9", "red" = "#d7191c", "orange" = "#fdae61")
  73. ggplot(MD_thal_Frontal_RNA, aes(x = log2FC_Protein, y = log2FC_RNA)) + geom_point(aes(color=Final_colour), show.legend = FALSE) +
  74. geom_abline(intercept = 1, slope = 1) +
  75. geom_abline(intercept = -1, slope = 1) + geom_vline(xintercept = -1) + geom_vline(xintercept = 1) +
  76. geom_hline(yintercept = -1) + geom_hline(yintercept = 1) + xlim(-10,10) + ylim(-10,10) +
  77. scale_color_manual(values = plot_colors) +
  78. xlab("log2(fold change protein)") + ylab("log2(fold change RNA)")
  79. ggsave("Figures/Thal_Frontal_RNA_protein.pdf", width = 10, height = 10, units = "cm")
  81. Color_freq <- data.frame(table(MD_thal_Frontal_RNA$Final_colour))
  83. ggplot(Color_freq, aes(x = Var1, y = Freq, fill = Var1)) + geom_bar(stat = "identity", show.legend = FALSE) +
  84. xlab("RNA protein outcome") +
  85. scale_fill_manual(values = plot_colors) + xlab("RNA vs protein outcome") + ylab("Gene count")
  86. ggsave("Figures/Thal_frontal_RNA_freq.pdf", width = 10, height = 10, units = "cm")
  91. write_csv(MD_thal_Frontal_RNA, "Intermediate files/Thal_vs_frontal_for_GO.csv")
  93. ##GO analysis in STRING with defaults for enrichment and all proteins detected as background set
  96. ##Red category proteins - String output file for Uniprot keyword - only 4 sig terms
  98. string_red_thal_RNA <- read_tsv("Intermediate files/Thal_FC_red.tsv") %>%
  99. mutate(colour = "red")
  101. point_colour <- c("red" = "#d7191c")
  102. ggplot(string_red_thal_RNA, aes(x = strength, y = reorder(`term description`, strength), size = `observed gene count`)) +
  103. geom_point(aes(color = colour)) + theme_bw() + xlim(0,0.5) + scale_color_manual(values = point_colour)
  104. ggsave("Figures/Thal_FC_GO_red.pdf", width = 20, height = 10, units = "cm")
  108. ##String output file for Molecular Function for blue, FC down
  110. string_blue_thal_RNA <- read_tsv("Intermediate files/Thal_Blue_down_MF.tsv") %>%
  111. mutate(colour = "blue")
  112. head(string_blue_thal_RNA)
  114. point_colour <- c("blue" = "#2c7bb6")
  115. ggplot(string_blue_thal_RNA, aes(x = strength, y = reorder(`term description`, strength), size = `observed gene count`)) +
  116. geom_point(aes(color = colour)) + xlim(0,1.2) + theme_bw() + scale_color_manual(values = point_colour)
  117. ggsave("Figures/Thal_FC_GO_blue.pdf", width = 20, height = 10, units = "cm")
  122. ##Shiva's data
  124. RGC_pairs <- read_csv("Input files/thalamus_RGC_PFC_GW22.csv") %>%
  125. select(target, ligand.complex, receptor.complex,aggregate_rank)
  127. Subplate_pairs <- read_csv("Input files/thalamus_subplate_PFC_GW22.csv") %>%
  128. select(target,ligand.complex, receptor.complex,aggregate_rank)
  131. both_pairs <- RGC_pairs %>%
  132. full_join(Subplate_pairs) %>%
  133. filter(aggregate_rank < 0.05) %>%
  134. select(-c(aggregate_rank, target)) %>%
  135. pivot_longer(c(ligand.complex,receptor.complex), names_to = "Type", values_to = "Gene_symbol") %>%
  136. unique() %>%
  137. left_join(MD_thal_Frontal_RNA)
  139. library(ggrepel)
  140. plot_colors <- c("ligand.complex" = "#af8dc3", "receptor.complex" = "#7fbf7b")
  141. ggplot(both_pairs, aes(x = log2FC_Protein, y = log2FC_RNA)) + geom_point(aes(color=Type)) + geom_abline(intercept = 1, slope = 1) +
  142. geom_abline(intercept = -1, slope = 1) + geom_vline(xintercept = -1) + geom_vline(xintercept = 1) +
  143. geom_hline(yintercept = -1) + geom_hline(yintercept = 1) + xlim(-5,5) + ylim(-5,5) +
  144. scale_color_manual(values = plot_colors) +
  145. geom_text_repel(aes(label = Gene_symbol), max.overlaps = 5)
  146. ggsave("Figures/Receptor_ligand_all.pdf", width = 15, height = 10, units = "cm")
  148. ## Looking at this plot and which ligands/receptors fall in the "right" places, focus in on a number
  150. focus_ligand <- c( "L1CAM", "CNTN4", "LGALS1")
  151. focus_receptor <- c( "CD9","PTPRG", "ITGB1" )
  152. focus_pairs <- RGC_pairs %>%
  153. full_join(Subplate_pairs) %>%
  154. filter(aggregate_rank < 0.05) %>%
  155. filter(ligand.complex %in% focus_ligand) %>%
  156. filter(receptor.complex %in% focus_receptor) %>%
  157. mutate(pair_num = rank(receptor.complex))%>%
  158. select(-c(aggregate_rank, target)) %>%
  159. pivot_longer(c(ligand.complex,receptor.complex), names_to = "Type", values_to = "Gene_symbol") %>%
  160. unique() %>%
  161. left_join(MD_thal_Frontal_RNA)
  163. ggplot(focus_pairs, aes(x = log2FC_Protein, y = log2FC_RNA)) + geom_point(aes(color=as.factor(pair_num), shape = Type), size = 3) + geom_abline(intercept = 1, slope = 1) +
  164. geom_abline(intercept = -1, slope = 1) + geom_vline(xintercept = -1) + geom_vline(xintercept = 1) +
  165. geom_hline(yintercept = -1) + geom_hline(yintercept = 1) + xlim(-4,4) + ylim(-4,4) +
  166. scale_color_brewer(palette = "RdYlBu") +
  167. geom_text_repel(aes(label = Gene_symbol), max.overlaps = 20)
  170. ggsave("Figures/Receptor_ligand_pairs.pdf", width = 15, height = 10, units = "cm")

2. RNA_protein_correls.R at commit 5b18ec8, no license · at the source

Overview

Authors: S. Bandiera1, H. Bogetofte2, P. Jensen2, R. Hussain1, S. A. Nischal1, L. Rihova1, G. J. Clowry3, M. R. Larsen2, Z. Molnár1, B. C. Carlyle1,4
ORCID iDs: Z. Molnár
  1. Department of Physiology, Anatomy & Genetics University of Oxford Oxford UK
  2. Department of Biochemistry and Molecular Biology University of Southern Denmark Odense M Denmark
  3. Newcastle University Biosciences Institute and Centre for Transformative Neuroscience Newcastle upon Tyne UK
  4. Kavli Institute for Nanoscience Discovery University of Oxford Oxford UK
Institutions: University of Oxford (United Kingdom); University of Southern Denmark (Denmark); Newcastle University (United Kingdom)
Journal: Journal of anatomy, article 10.1111/joa.70170
Dates: received 14 August 2025; accepted 21 April 2026; published online 3 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/joa.70170 · PMID 42071286 · PMCID PMC13399142 · OpenAlex W7160066020
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging
Keywords: brain development, development, posttranslational modifications, proteomics, thalamocortical connections
Topic: Advanced Proteomics Techniques and Applications (Spectroscopy, Chemistry), according to OpenAlex
Funding: Wellcome Trust (099175/Z/12/Z); Alzheimer’s Research UK (ARUK‐SRF2022A‐012); BD2 ‐ Breakthrough Discoveries for Thriving with Bipolar Disorder (DG240508); Medical Research Council (MR/R006237/1, MR/W029073/1); RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) (BB/X008711/1); Einstein Stiftung Berlin; Lundbeck Foundation (R336‐2020‐1113, R380‐2021‐1425); Danish Agency of Higher Education and Science infrastructure (5229‐00012B)
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

Progress in defining the proteome of the developing human brain has lagged behind our understanding of the adult human brain, primarily due to challenges in tissue acquisition and in preservation of anatomical structure during experimental processing. Single‐cell transcriptomics alone is an excellent resource for defining cellular identity, but has limited capacity to trace neuronal connectivity because proteins, the active molecules in interactions, may be transported significant distances from cell bodies and their site of synthesis. There are numerous protein‐mediated transient interactions between cellular elements in the developing brain, such as between migrating cortical neurons and subplate, and thalamic projections and cortical progenitors. Anatomical approaches have identified specific cell populations that interact, allowing us to characterize the transient and dynamically changing early circuits. Proteomic data generation is now essential for ligand–receptor pair prediction and validation. Upon receipt of a single, exceptionally well‐preserved 20 postconception week human brain hemisphere, we conducted fine dissections of 18 anatomically distinct brain regions, including the pia mater. These samples underwent in‐depth analysis of both the total and posttranslationally modified proteomes, with the aim of creating a reference resource for investigators studying this critical stage of neurodevelopment. Here, we have presented an overview of the resulting dataset, compared the proteomic profiles across regions, and highlighted examples of variable posttranslational modifications within individual proteins. As expected, non‐modified protein profiles revealed substantial differences across brain regions and structures. For instance, pia mater and thalamus were enriched for proteins involved in transcription and chromatin organization, which may suggest a higher proportion of dividing cells and/or significant epigenetic regulation in these areas at this developmental stage. In contrast, the cortical and hippocampal proteome reflected active synaptogenesis and cytoskeletal remodeling. While interregional differences in phosphorylated and acetylated peptides largely mirrored those observed in the non‐modified proteome with respect to gene ontology categories, the glycosylated peptidome of the pia mater was markedly distinct. This divergence is driven by the secretion of extracellular matrix proteins and the region's intimate association with the basement membrane of the pia. Finally, by integrating our proteomic data with publicly available single‐cell RNA sequencing datasets from the same developmental stage, we identified high‐confidence ligand–receptor pairs (e.g., L1CAM:CD9, CNTN4:PTPRG, LGALS1:ITGB1) likely involved in thalamocortical interactions.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

Carlyle-Lab/PCW20_PTM_Proteomics

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 5b18ec88a6a0ce25d524cceef4a7ef569a39d8fa, 14 August 2025
Languages: R (6)
Size: 217 files, 6 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), broom (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
7 files
At the source: github.com/Carlyle-Lab/PCW20_PTM_Proteomics

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;
  • 6 scripts, each with its path and the digest of its content;
  • 10 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

Mass spectrometry raw files are available on PRIDE with reference PXD065788. Processed quant files are available as supplementary data to this manuscript. Code and input data are available on github at: https://github.com/Carlyle‐Lab/PCW20_PTM_Proteomics (https://github.com/Carlyle-Lab/PCW20_PTM_Proteomics).

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, pages, dates, 10 authors, 5 keywords, 8 funders, 60 references.

Cite

This paper

Bandiera, S., Bogetofte, H., Jensen, P., Hussain, R., Nischal, S. A., Rihova, L., Clowry, G. J., Larsen, M. R., Molnár, Z., & Carlyle, B. C. (2026). A posttranslational proteomic survey of a single anatomically preserved human 20-week postconception brain. Journal of anatomy, 10.1111/joa.70170. https://doi.org/10.1111/joa.70170

BibTeX

@article{bandiera2026posttranslational,
author = {Bandiera, S. and Bogetofte, H. and Jensen, P. and Hussain, R. and Nischal, S. A. and Rihova, L. and Clowry, G. J. and Larsen, M. R. and Molnár, Z. and Carlyle, B. C.},
title = {{A posttranslational proteomic survey of a single anatomically preserved human 20-week postconception brain}},
journal = {Journal of anatomy},
year = {2026},
month = may,
pages = {10.1111/joa.70170},
publisher = {Wiley},
issn = {0021-8782},
doi = {10.1111/joa.70170},
url = {https://doi.org/10.1111/joa.70170},
pmid = {42071286},
pmcid = {PMC13399142}
}

RIS

TY - JOUR
AU - Bandiera, S.
AU - Bogetofte, H.
AU - Jensen, P.
AU - Hussain, R.
AU - Nischal, S. A.
AU - Rihova, L.
AU - Clowry, G. J.
AU - Larsen, M. R.
AU - Molnár, Z.
AU - Carlyle, B. C.
TI - A posttranslational proteomic survey of a single anatomically preserved human 20-week postconception brain
T2 - Journal of anatomy
J2 - J Anat
PY - 2026
DA - 2026/05/03
SP - 10.1111/joa.70170
SN - 0021-8782
PB - Wiley
DO - 10.1111/joa.70170
UR - https://doi.org/10.1111/joa.70170
LA - en
ER -

CSL-JSON

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"title": "A posttranslational proteomic survey of a single anatomically preserved human 20-week postconception brain",
"container-title": "Journal of anatomy",
"author": [
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"family": "Bandiera",
"given": "S."
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{
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"page": "10.1111/joa.70170",
"DOI": "10.1111/joa.70170",
"PMID": "42071286",
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"ISSN": "0021-8782",
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"issued": {
"date-parts": [
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3
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}

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