OSCR

Proteomic profile of hippocampal growth cones through early postnatal development.

Code ↔ Paper

13 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 13 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] § RESULTS › Growth cones of the DG in early postnatal days ↔ Proteomics/scripts/part3_GO_analysis.R, lines 89–149 · score 0.99 · Golgi vesicle transport, Protein DNA complex, RNA catabolic processes, Mitochondrial gene expression, tRNA, RNA metabolism
  2. [2] § RESULTS › Time-regulated proteins associated with neurological disorders ↔ Proteomics/scripts/part5_disease_terms.R, lines 1–62 · score 0.93 · Amyotrophic lateral sclerosis, autism spectrum disorders, epilepsy syndrome, neurological diseases, neurological disorders, regulated proteins
  3. [3] § RESULTS › Hippocampal growth cones in early postnatal days ↔ Proteomics/scripts/part3_GO_analysis.R, lines 89–149 · score 0.85 · Immunoglobulin mediated immune, RNA catabolic processes, RNA splicing, mRNA, GO, cell
  4. [4] § MATERIALS AND METHODS › Proteomic data analysis ↔ Proteomics/scripts/part1_raw_data_processing.R, lines 1–76 · score 0.80 · MaxQuant, potential contaminants, LFQ intensity, raw, reverse, match
  5. [5] § MATERIALS AND METHODS › Confocal imaging and growth cones count ↔ Fluorescent image analysis/main.m, lines 115–154 · score 0.75 · CA3 SLM, inner blade, outer blade, Stx7, ML, GFP
  6. [6] § RESULTS › Hippocampal growth cones in early postnatal days ↔ Proteomics/scripts/part1_raw_data_processing.R, lines 1–76 · score 0.74 · log2 LFQ intensities, log2 transformed, MaxQuant, Raw, contaminations, isolating
  7. [7] § MATERIALS AND METHODS › Proteomic data analysis ↔ Proteomics/scripts/part2_basic_characterisation.R, lines 85–152 · score 0.74 · ShinyGO, KEGG pathway, Disease terms, FDR, genes, proteome
  8. [8] § MATERIALS AND METHODS › Confocal imaging and growth cones count ↔ Fluorescent image analysis/FigureTools/dataIntegratorCounts.m, the whole file · a weak match · score 0.70 · delineated area, picture, mask, Volume, contours, GFP
  9. [9] § RESULTS › Characterization of the critical time window for early postnatal development in the hippocampus ↔ Fluorescent image analysis/main.m, lines 115–154 · score 0.67 · CA3 SLM, inner blade, outer blade, Stx7, GFP, fluorescent
  10. [10] § RESULTS › Subregion specificity of HP-GCs and DG-GCs ↔ Proteomics/scripts/part3_GO_analysis.R, lines 1–87 · score 0.66 · GO term, P3 HP, P1 HP, P5 DG, Uniprot, exploratory
  11. [11] § RESULTS › Characterization of the critical time window for early postnatal development in the hippocampus ↔ Fluorescent image analysis/main.m, lines 67–113 · score 0.62 · normalized volume, inner blade, outer blade, quartile, ML, threshold
  12. [12] § RESULTS › Growth cones of the DG in early postnatal days ↔ Proteomics/scripts/part2_basic_characterisation.R, lines 85–152 · score 0.62 · KEGG pathway, DG GC, HP GC, cardiomyocytes, overlap, proteomic
  13. [13] § RESULTS › Subregion specificity of HP-GCs and DG-GCs ↔ Proteomics/scripts/part2_basic_characterisation.R, lines 198–267 · score 0.61 · DG P5, P3 HP, P1 HP, Uniprot, Upset, overlap

Paper

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The authors' code

R · 186 lines · 9 KB · MIT · 3 matches

  1. library(clusterProfiler)
  2. library(org.Mm.eg.db)
  4. # ensure that you have generated the lists of exclusive proteins (s. part2 line 170 and following)
  6. #### GO term analysis for exclusive protein lists #####
  7. # results for enrichGO are thresholded on the given cutoff values. Only terms that pass both cutoffs are included.
  8. # For an exploratory approach no cutoffs are used in the following analysis to get a better understanding for the dataset.
  9. # pvalue and qvalueCutoff = 1
  11. P1_HP_excl_GO <- enrichGO(gene = P1_HP_exclusive,
  12. OrgDb = org.Mm.eg.db,
  13. keyType = "UNIPROT",
  14. ont = "BP",
  15. pvalueCutoff = 1,
  16. qvalueCutoff = 1,
  17. readable = TRUE)
  18. P1_HP_excl_GO_plot <- dotplot(P1_HP_excl_GO, showCategory = 20) +
  19. ggtitle("P1_HP exclusive") +
  20. theme(plot.title = element_text(hjust = 0.5, face = "bold"))
  21. P1_HP_excl_GO_plot
  22. # ggsave(paste(version, sep = "_", "P1_HP_excl_GO_plot.svg"), plot = P1_HP_excl_GO_plot, device = "svg", unit = "cm", height = 20, width = 17)
  24. P1_DG_excl_GO <- enrichGO(gene = P1_DG_exclusive,
  25. OrgDb = org.Mm.eg.db,
  26. keyType = "UNIPROT",
  27. ont = "BP",
  28. pvalueCutoff = 1,
  29. qvalueCutoff = 1,
  30. readable = TRUE)
  31. P1_DG_excl_GO_plot <- dotplot(P1_DG_excl_GO, showCategory = 20) +
  32. ggtitle("P1_DG exclusive") +
  33. theme(plot.title = element_text(hjust = 0.5, face = "bold"))
  34. P1_DG_excl_GO_plot
  35. # ggsave(paste(version, sep = "_", "P1_DG_excl_GO_plot.svg"), plot = P1_DG_excl_GO_plot, device = "svg", unit = "cm", height = 20, width = 17)
  37. P3_HP_excl_GO <- enrichGO(gene = P3_HP_exclusive,
  38. OrgDb = org.Mm.eg.db,
  39. keyType = "UNIPROT",
  40. ont = "BP",
  41. pvalueCutoff = 1,
  42. qvalueCutoff = 1,
  43. readable = TRUE)
  44. P3_HP_excl_GO_plot <- dotplot(P3_HP_excl_GO, showCategory = 20) +
  45. ggtitle("P3_HP exclusive") +
  46. theme(plot.title = element_text(hjust = 0.5, face = "bold"))
  47. P3_HP_excl_GO_plot
  48. # ggsave(paste(version, sep = "_", "P3_HP_excl_GO_plot.svg"), plot = P3_HP_excl_GO_plot, device = "svg", unit = "cm", height = 20, width = 17)
  50. P3_DG_excl_GO <- enrichGO(gene = P3_DG_exclusive,
  51. OrgDb = org.Mm.eg.db,
  52. keyType = "UNIPROT",
  53. ont = "BP",
  54. pvalueCutoff = 1,
  55. qvalueCutoff = 1,
  56. readable = TRUE)
  57. P3_DG_excl_GO_plot <- dotplot(P3_DG_excl_GO, showCategory = 20) +
  58. ggtitle("P3_DG exclusive") +
  59. theme(plot.title = element_text(hjust = 0.5, face = "bold"))
  60. P3_DG_excl_GO_plot
  61. # ggsave(paste(version, sep = "_", "P3_DG_excl_GO_plot.svg"), plot = P3_DG_excl_GO_plot, device = "svg", unit = "cm", height = 20, width = 17)
  63. P5_HP_excl_GO <- enrichGO(gene = P5_HP_exclusive,
  64. OrgDb = org.Mm.eg.db,
  65. keyType = "UNIPROT",
  66. ont = "BP",
  67. pvalueCutoff = 1,
  68. qvalueCutoff = 1,
  69. readable = TRUE)
  70. P5_HP_excl_GO_plot <- dotplot(P5_HP_excl_GO, showCategory = 20) +
  71. ggtitle("P5_HP exclusive") +
  72. theme(plot.title = element_text(hjust = 0.5, face = "bold"))
  73. P5_HP_excl_GO_plot
  74. # ggsave(paste(version, sep = "_", "P5_HP_excl_GO_plot.svg"), plot = P5_HP_excl_GO_plot, device = "svg", unit = "cm", height = 20, width = 17)
  76. P5_DG_excl_GO <- enrichGO(gene = P5_DG_exclusive,
  77. OrgDb = org.Mm.eg.db,
  78. keyType = "UNIPROT",
  79. ont = "BP",
  80. pvalueCutoff = 1,
  81. qvalueCutoff = 1,
  82. readable = TRUE)
  83. P5_DG_excl_GO_plot <- dotplot(P5_DG_excl_GO, showCategory = 20) +
  84. ggtitle("P5_DG exclusive") +
  85. theme(plot.title = element_text(hjust = 0.5, face = "bold"))
  86. P5_DG_excl_GO_plot
  87. # ggsave(paste(version, sep = "_", "P5_DG_excl_GO_plot.svg"), plot = P5_DG_excl_GO_plot, device = "svg", unit = "cm", height = 20, width = 17)
  89. # generate data frame with top hits based on the dotplot from all GO analyses
  90. df.P1_HP_excl_BP <- P1_HP_excl_GO_plot$data %>%
  91. mutate(Sample = "P1_HP")
  92. df.P1_DG_excl_BP <- P1_DG_excl_GO_plot$data %>%
  93. mutate(Sample = "P1_DG")
  94. df.P3_HP_excl_BP <- P3_HP_excl_GO_plot$data %>%
  95. mutate(Sample = "P3_HP")
  96. df.P3_DG_excl_BP <- P3_DG_excl_GO_plot$data %>%
  97. mutate(Sample = "P3_DG")
  98. df.P5_HP_excl_BP <- P5_HP_excl_GO_plot$data %>%
  99. mutate(Sample = "P5_HP")
  100. df.P5_DG_excl_BP <- P5_DG_excl_GO_plot$data %>%
  101. mutate(Sample = "P5_DG")
  103. df.Excl_HP_BP_Top20 <- full_join(df.P1_HP_excl_BP, df.P3_HP_excl_BP) %>%
  104. full_join(df.P5_HP_excl_BP)
  105. df.Excl_DG_BP_Top20 <- full_join(df.P1_DG_excl_BP, df.P3_DG_excl_BP) %>%
  106. full_join(df.P5_DG_excl_BP)
  108. HP_Top5 <- c("cellular amino acid metabolic process", "mitochondrial gene expression",
  109. "mitochondrial respiratory chain complex assembly", "organic acid catabolic process",
  110. "tRNA metabolic process",
  111. "mRNA splicing, via spliceosome", "RNA catabolic process", "regulation of cellular protein catabolic process",
  112. "regulation of mRNA stability", "regulation of mRNA processing",
  113. "immunoglobulin mediated immune response", "N-acetylneuraminate metabolic process",
  114. "water-soluble vitamin metabolic process", "sperm-egg recognition",
  115. "transcription initiation from RNA polymerase III promoter")
  116. df.Excl_HP_BP_Top5 <- df.Excl_HP_BP_Top20 %>%
  117. filter(Description %in% HP_Top5)
  118. # add vector with curated terms and filter df accordingly make plots here and compare to paper
  120. Excl_BP_HP_plot <- ggplot(df.Excl_HP_BP_Top5, aes(x = GeneRatio, y = Description, size = Count, color = p.adjust)) +
  121. geom_point() +
  122. scale_size(range = c(1, 5), name = "Count") +
  123. scale_color_gradient(low = "#509e97", high = "grey60", name = "p.adjust") +
  124. facet_grid(rows = vars(Sample), scales = "free") +
  125. theme_minimal() +
  126. theme(strip.background = element_rect(color = "grey90", fill="grey90"))
  127. Excl_BP_HP_plot
  128. # ggsave(paste(version, sep = "_", "Excl_BP_HP.svg"), plot = Excl_BP_HP_plot, device = "svg")
  130. DG_Top5 <- c( "mitochondrial gene expression", "cellular amino acid metabolic process",
  131. "sulfur compound metabolic process", "energy derivation by oxidation of organic compounds",
  132. "mitochondrial respiratory chain complex assembly",
  133. "RNA catabolic process", "positive regulation of cell cycle process",
  134. "protein-DNA complex subunit organization", "rRNA metabolic process",
  135. "nucleus organization",
  136. "positive regulation of neuron projection development", "Golgi vesicle transport",
  137. "mRNA splicing, via spliceosome", "vacuole organization", "dendrite morphogenesis")
  138. df.Excl_DG_BP_Top5 <- df.Excl_DG_BP_Top20 %>%
  139. filter(Description %in% DG_Top5)
  141. Excl_BP_DG_plot <- ggplot(df.Excl_DG_BP_Top5, aes(x = GeneRatio, y = Description, size = Count, color = p.adjust)) +
  142. geom_point() +
  143. scale_size(range = c(1, 5), name = "Count") +
  144. scale_color_gradient(low = "#d62f38", high = "grey60", name = "p.adjust") +
  145. facet_grid(rows = vars(Sample), scales = "free") +
  146. theme_minimal() +
  147. theme(strip.background = element_rect(color = "grey90", fill="grey90"))
  148. Excl_BP_DG_plot
  149. # ggsave(paste(version, sep = "_", "Excl_BP_DG.svg"), plot = Excl_BP_DG_plot, device = "svg")
  151. # Comparison of GO terms between timepoint specific proteins
  152. # make one data frame of GO results for timepoint specific proteins
  153. df.HP_P1_BP <- as.data.frame(P1_HP_excl_GO)
  154. df.HP_P1_BP$condition <- "P1-HP"
  156. df.HP_P3_BP <- as.data.frame(P3_HP_excl_GO)
  157. df.HP_P3_BP$condition <- "P3-HP"
  159. df.HP_P5_BP <- as.data.frame(P5_HP_excl_GO)
  160. df.HP_P5_BP$condition <- "P5-HP"
  162. df.DG_P1_BP <- as.data.frame(P1_DG_excl_GO)
  163. df.DG_P1_BP$condition <- "P1-DG"
  165. df.DG_P3_BP <- as.data.frame(P3_DG_excl_GO)
  166. df.DG_P3_BP$condition <- "P3-DG"
  168. df.DG_P5_BP <- as.data.frame(P5_DG_excl_GO)
  169. df.DG_P5_BP$condition <- "P5-DG"
  171. All_BP <- rbind(df.HP_P1_BP, df.HP_P3_BP, df.HP_P5_BP,
  172. df.DG_P1_BP, df.DG_P3_BP, df.DG_P5_BP)
  174. # extract all results of GO analysis for Top terms for HP and DG
  175. TOP_GO <- unique(c(HP_Top5, DG_Top5))
  177. Top5_BP_plot <- All_BP[All_BP$Description %in% TOP_GO,]
  179. Top_BP_plot <- ggplot(Top5_BP_plot, aes(x = condition, y = Description, size = Count, color = p.adjust)) +
  180. geom_point() +
  181. scale_size(range = c(2, 10), name = "Count") + # Adjust size scale
  182. scale_color_gradient(low = "darkred", high = "orange", name = "p.adjust") + # Color scale for p.adjust
  183. theme_minimal() +
  184. labs(x = NULL, y = NULL)
  185. Top_BP_plot
  186. # ggsave(paste(version, sep = "_", "TOP_BP.svg"), plot = Top_BP_plot, device = "svg")

part3_GO_analysis.R at commit 7d0a610, under MIT · at the source

Overview

Authors: Maike Krause1,2, Kamilla Aase Kronberg1, Paulo J B Girão1, Lara Sophie Strohmeier1, Giulia Quattrocolo1,2
ORCID iDs: Maike Krause, Giulia Quattrocolo
  1. Kavli Institute for Systems Neuroscience and Centre for Algorithms of the Cortex, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
  2. Mohn Research Center for the Brain, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
Institutions: Norwegian University of Science and Technology (Norway)
Journal: Development (Cambridge, England), volume 153, issue 12, article dev205342
Dates: received 3 November 2025; accepted 25 May 2026; published online 22 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1242/dev.205342 · PMID 42252990 · PMCID PMC13354955 · OpenAlex W4416109154
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.10.687591
Status: code verified
Categories: genetics / omics (modality), mouse (organism), developmental (subfield)
Methods: Statistics, Evoked potentials
Keywords: Early postnatal development, Mouse, Growth cones, Proteomics, Local translation, Hippocampus
MeSH: Growth Cones*, Hippocampus*, Proteome*, Proteomics*, Animals, Dentate Gyrus, Mice, Mice, Inbred C57BL, Neurodevelopment (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: Kavli Foundation; Norges Forskningsrad (332640, 223262, 324305); Norwegian University of Science and Technology; Norges Teknisk-Naturvitenskapelige Universitet (295721); Trond Mohn stiftelse (2021TMT04); Norges Forskningsråd (223262, 324305, 332640)
Citations: not cited yet (Europe PMC); 106 references in the paper

Abstract

Axonal growth cones rely on local proteomic changes to interpret guidance cues while navigating towards their synaptic targets. Yet the developmental dynamics of their proteome remain poorly understood. By isolating growth cones from the whole mouse hippocampus and dentate gyrus at early postnatal days, we performed a systematic characterization of their proteome. We show that growth cones from both subregions share a proteomic signature at postnatal day (P) 1, dominated by energy metabolism and protein synthesis, before taking distinct developmental trajectories. In fact, hippocampal growth cones undergo a rapid transition, from RNA-related processes to synapse formation. Dentate gyrus growth cones, however, maintain a similar proteomic profile at P3 and P5, suggesting a prolonged exploratory behaviour, consistent with later ingrowth of entorhinal cortex fibres. At these early timepoints, hippocampal and dentate gyrus growth cones are characterized by several temporally regulated proteins, suggesting dynamic and different developmental trajectories. Our dataset provides a comprehensive proteomic resource for understanding growth cone function during hippocampal circuit formation and insights into the molecular mechanisms underlying region-specific developmental timelines.

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 13 matches between paragraphs and lines of code.

Quattrocolo-Lab/krause-et-al-2026

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7d0a61090d1642f2a4d7c830a3fc0ad964d37970, 7 April 2026
Languages: MATLAB (23), R (5)
Size: 33 files, 28 scripts
Software Heritage: not archived
Found in: the text, “Proteomic data analysis”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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30 files
At the source: github.com/Quattrocolo-Lab/krause-et-al-2026

gaospecial/ggVennDiagram

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1312fac6076e7ac033d3188e2eb6153df3b35bdc, 10 January 2026
Languages: R (26)
Size: 83 files, 26 scripts
Software Heritage: not archived
Found in: the text, “Proteomic data analysis”
Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 6 notebooks
Not found: CITATION.cff
Tools: ggplot2 (9 files), tidyverse (8 files), cowplot (1 file), Plotly (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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28 files
At the source: github.com/gaospecial/ggVennDiagram

mbojan/alluvial

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4003444cb43c3b2bf8961951912563cd73c81629, 5 May 2026
Languages: R (11)
Size: 45 files, 11 scripts
Software Heritage: archived
Found in: the text, “Proteomic data analysis”
Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 2 notebooks
Not found: CITATION.cff
Tools: tidyverse (4 files), ggplot2 (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/mbojan/alluvial

Tracing map

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What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 65 scripts, each with its path and the digest of its content;
  • 13 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

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Versions

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Version 2, 28 September 2026

  • Publisher: n/a → The Company of Biologists

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 9 MeSH terms, 6 funders, 105 references.

Cite

This paper

Krause, M., Kronberg, K. A., Girão, P. J. B., Strohmeier, L. S., & Quattrocolo, G. (2026). Proteomic profile of hippocampal growth cones through early postnatal development. Development (Cambridge, England), 153(12), dev205342. https://doi.org/10.1242/dev.205342

BibTeX

@article{krause2026proteomic,
author = {Krause, Maike and Kronberg, Kamilla Aase and Girão, Paulo J B and Strohmeier, Lara Sophie and Quattrocolo, Giulia},
title = {{Proteomic profile of hippocampal growth cones through early postnatal development}},
journal = {Development (Cambridge, England)},
year = {2026},
month = jun,
volume = {153},
number = {12},
pages = {dev205342},
publisher = {The Company of Biologists},
issn = {0950-1991},
doi = {10.1242/dev.205342},
url = {https://doi.org/10.1242/dev.205342},
pmid = {42252990},
pmcid = {PMC13354955}
}

RIS

TY - JOUR
AU - Krause, Maike
AU - Kronberg, Kamilla Aase
AU - Girão, Paulo J B
AU - Strohmeier, Lara Sophie
AU - Quattrocolo, Giulia
TI - Proteomic profile of hippocampal growth cones through early postnatal development
T2 - Development (Cambridge, England)
J2 - Development
PY - 2026
DA - 2026/06/22
VL - 153
IS - 12
SP - dev205342
SN - 0950-1991
PB - The Company of Biologists
DO - 10.1242/dev.205342
UR - https://doi.org/10.1242/dev.205342
LA - en
ER -

CSL-JSON

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"title": "Proteomic profile of hippocampal growth cones through early postnatal development",
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}
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"container-title-short": "Development",
"volume": "153",
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"page": "dev205342",
"DOI": "10.1242/dev.205342",
"PMID": "42252990",
"PMCID": "PMC13354955",
"ISSN": "0950-1991",
"publisher": "The Company of Biologists",
"URL": "https://doi.org/10.1242/dev.205342",
"language": "en",
"issued": {
"date-parts": [
[
2026,
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22
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]
}
}

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[9] doi:10.3390/ijms27104466 [code]
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[10] doi:10.1016/j.xcrm.2026.102766 [code]
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