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Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity.

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  1. [1] § Imaging › Statistical Analysis ↔ analysis/pcr.Rmd, lines 20–41 · score 0.53 · glmmTMB, Gaussian, DCq, identity, PCR, Linear

Paper

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The authors' code

R Markdown · 139 lines · 5 KB · CC-BY-4.0 · 1 match

  1. ```{r}
  2. source(here::here("src/init.R"))
  3. ```
  5. <!---------------------------------------------------------->
  6. <!---------------------------------------------------------->
  7. # I. Data
  9. ```{r}
  10. data_dict <- load_data_dict()
  12. (supplementary_data <- load_supplementary_data())
  14. # Use reprocess = TRUE to re-process the data and refit the models (will take a few minutes)
  15. ## Reprocessing requires to provide a model (e.g. model = vasc_model, which is defined right underneath)
  16. # If reprocess = FALSE, the data will be loaded from the processed data (pcr_processed.rds)
  17. (vasc_data <- load_pcr_data(reprocess = FALSE))
  18. ```
  20. <!---------------------------------------------------------->
  21. <!---------------------------------------------------------->
  22. # II. Model fitting
  24. **You can skip this section if you don't want to re-fit the models (e.g. if you
  25. kept `reprocess = FALSE` in I. Data)**
  27. Let's define the model we will fit to each `Gene`'s `DCq` data.
  29. Here, we will fit a simple Linear Model, which is largely similar to running a
  30. t-test between both conditions:
  32. ```{r}
  33. vasc_model <- function(data) {
  34. glmmTMB::glmmTMB(
  35. dcq ~ condition,
  36. family = gaussian("identity"),
  37. data = data,
  38. contrasts = list(condition = "contr.sum")
  39. )
  40. }
  41. ```
  43. Now, let's fit said model to each `Gene`'s data, for a given `Stage` and
  44. `Layer`:
  46. ```{r}
  47. (vasc_data$models <- vasc_data$clean |>
  48. dplyr::group_split(stage, gene) |>
  49. purrr::map_dfr(
  50. \(d) dplyr::summarize(d, mod = pick(condition, dcq) |> vasc_model() |> list(), .by = c(stage, gene)),
  51. .progress = "Fitting models:"
  52. ) |>
  53. dplyr::filter(!has_na_coefs(mod)) |> # Removing models that did not fit properly
  54. dplyr::mutate(fold = purrr::map_dbl(mod, compute_fold_change)) |> # Adding the Fold change
  55. dplyr::select(stage, gene, fold, mod))
  56. ```
  58. <!---------------------------------------------------------->
  59. <!---------------------------------------------------------->
  60. # III. Model analysis
  62. ## Predictions
  64. **You can skip this section if you don't want to re-fit the models (e.g. if you
  65. kept `reprocess = FALSE` in I. Data)**
  67. For each model we fit, we can then extract the CI and p_value for the relevant
  68. contrasts, and use those to establish if a `Gene` was up or down-regulated:
  70. ```{r}
  71. (vasc_data$predictions <- vasc_data$models |>
  72. dplyr::group_split(stage, gene) |>
  73. purrr::map_dfr(\(d) dplyr::mutate(d, get_emmeans_data(mod[[1]])), .progress = "Extracting model predictions:") |>
  74. dplyr::filter(!is.na(p_value)) |>
  75. dplyr::mutate(expression = get_regulation_type(fold, p_value)) |>
  76. dplyr::select(stage, gene, fold, expression, matches("-|/"), LCB, UCB, p_value))
  77. ```
  79. ## Gene regulation timeline
  81. To get a better idea of how each `Gene`'s regulation changes through time, we
  82. can plot a timeline of their expression, split by `Layer` and `Pathway`.
  84. ```{r}
  85. #| fig-width: 12
  86. #| fig-height: 8
  87. (vasc_data$predictions |>
  88. dplyr::left_join(supplementary_data$gene_data$ref, join_by(gene)) |>
  89. dplyr::filter(p_value <= 0.05) |>
  90. dplyr::select(stage, gene, fold, p_value, expression, pathway, effect) |>
  91. dplyr::mutate(
  92. effect = case_when(
  93. stringr::str_detect(expression, "Downregulated") & effect == "Pro" ~ "Anti",
  94. stringr::str_detect(expression, "Downregulated") & effect == "Anti" ~ "Pro",
  95. .default = effect
  96. )
  97. ) |>
  98. make_fold_timeline_plot(facet_rows = "pathway", color_by = "effect", size_boost = 1.5) |>
  99. save_png("vasc_timeline", subfolder = "PCR"))
  100. ```
  102. ## Temporo-functional heatmap
  104. ```{r}
  105. #| fig-height: 8
  106. #| fig-width: 15
  107. heatmap_data <- vasc_data$predictions |>
  108. dplyr::filter(p_value <= 0.05) |>
  109. dplyr::left_join(supplementary_data$gene_data$fx, by = "gene", relationship = "many-to-many") |>
  110. dplyr::select(stage, gene, fold, p_value, fx, effect, abb) |>
  111. dplyr::mutate(tile_label = paste(round(fold, 2), stars.pval(p_value), sep = " "))
  113. heatmap_plot <- make_heatmap(heatmap_data, xaxis = "gene", yaxis = "abb", facet = "stage") +
  114. labs(x = "Gene", y = "Function") +
  115. facet_grid(rows = vars(stage), scales = "free_y", space = "free_y")
  117. save_png(heatmap_plot, "vasc_heatmap", subfolder = "PCR", height = 8, width = 15)
  118. ```
  120. With effects:
  122. ```{r}
  123. #| fig-height: 12
  124. #| fig-width: 16
  125. heatmap_data_effect <- vasc_data$predictions |>
  126. dplyr::filter(p_value <= 0.05) |>
  127. dplyr::left_join(supplementary_data$gene_data$fx, by = "gene", relationship = "many-to-many") |>
  128. dplyr::select(stage, gene, fold, p_value, fx, effect, abb) |>
  129. dplyr::mutate(
  130. tile_label = paste(round(fold, 2), stars.pval(p_value), ifelse(effect == "positive", "➕", "➖"), sep = " ")
  131. )
  133. heatmap_plot_effect <- make_heatmap(heatmap_data_effect, xaxis = "gene", yaxis = "abb", facet = "stage") +
  134. labs(x = "Gene", y = "Function", caption = "➕ Positive effect | ➖ Negative effect") +
  135. facet_grid(rows = vars(stage), scales = "free_y", space = "free_y") +
  136. theme(plot.caption = element_text(hjust = 0.5, vjust = 0.5, face = "bold", size = 16))
  138. save_png(heatmap_plot_effect, "vasc_heatmap_effect", subfolder = "PCR", height = 12, width = 16)
  139. ```

pcr.Rmd at commit 21fa4ee, under CC-BY-4.0 · at the source

Overview

Authors: A Rodriguez-Duboc1,2,3, C Racine1, M Basille-Dugay4, D Vaudry1, B Gonzalez1, D Burel1
  1. Univ Rouen Normandie, Inserm, Normandie Univ, CBG UMR 1245, Rouen, F-76000 France
  2. Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
  3. Nasjonalforeningens Demensforskningssenter, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
  4. Univ Rouen Normandie, Inserm, Normandie Univ, NorDiC UMR 1239, Rouen, F-76000 France
Institutions: Inserm (France); Norwegian University of Science and Technology (Norway); Kavli Institute for Systems Neuroscience (Norway); Génomique du cancer et du cerveau; Université de Rouen Normandie (France)
Journal: Cerebellum (London, England), volume 25, issue 3, article 62
Dates: received 17 October 2025; accepted 16 April 2026; published online 28 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s12311-026-02006-1 · PMID 42047887 · PMCID PMC13124948 · OpenAlex W4415308252
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.17.683055
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism)
Keywords: Vascularization, 3D imaging, Cerebellum, Apnea of prematurity, Neurodevelopment, Transcriptomics
MeSH: Angiogenesis*, Apnea*, Cerebellum*, Imaging, Three-Dimensional*, Animals, Animals, Newborn, Disease Models, Animal, Mice, Mice, Inbred C57BL (* major topic)
Topic: Congenital Heart Disease Studies (Epidemiology, Medicine), according to OpenAlex
Funding: Ministère de l’Enseignement supérieur, de la Recherche et de l’Innovation
Citations: cited by 1 paper (Europe PMC); 92 references in the paper

Abstract

Apnea of prematurity (AOP) affects 50% of preterm infants causing intermittent hypoxia (IH), which can lead to long-term neurodevelopmental deficits. Cerebellar abnormalities have been observed in AOP but the relationship between vascular alterations and neural development remains unclear. This study investigates how IH affects cerebellar angiogenesis using a murine model of AOP. We developed an innovative 3D imaging workflow combining Imaris and VesselVio software to quantitatively analyze cerebellar vascularization at different postnatal stages (P4, P8, P12, P21, and P70). We correlated these results with a transcriptomic analysis of 23 angiogenesis-related genes in the same stages to uncover the associated molecular pathways. We found that IH induced significant vascular changes, particularly at P4, with a global increase in vascular-network dimensions. By P8, the vascular network normalized, but genes were downregulated in all pathways studied. After P12, at the end of the IH protocol, transcriptional regulations had varied but persisted long-term. Moreover, differential analysis showed distinct effects on superficial versus deep vascular networks, allowing for a more precise understanding of remodeling patterns throughout development. Overall, transcriptomic changes were associated with morphological alterations in a time-dependent manner, suggesting a multiphasic IH response through development with lasting effects. Key regulations included VEGF, angiopoietin, and matrix metalloprotease signaling. These findings demonstrate that IH disrupts cerebellar angiogenesis in parallel with neurogenesis, potentially contributing to the neurodevelopmental deficits observed in AOP. Thus, the interconnected nature of angio- and neurogenesis during cerebellar development makes it crucial to take vascular aspects into account in therapeutic approaches to neurodevelopmental disorders.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12311-026-02006-1.

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 1 match between paragraphs and lines of code.

agalic-rd/vasc-aop](https:

License: none: the authors keep all their rights
State: the link is dead, verified on 30 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link is dead
  • 30 September 2026: the link is dead
At the source: github.com/agalic-rd/Vasc-AoP](https:/github.com/agalic-rd/Vasc-AoP)

Zenodo 15319299

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), easystats (3 files), emmeans (2 files), patchwork (2 files), car (1 file), ggplot2 (1 file), glmmTMB (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
15 files
At the source: doi.org/10.5281/zenodo.15319299](https:/doi.org/10.5281/zenodo.15319299)

agalic-rd/vasc-aop

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 21fa4ee05bbfa8817611bc1609f69dfbf200b8e0, 28 April 2026
Languages: R (13)
Size: 184 files, 13 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, CITATION.cff, environment (renv.lock), 3 notebooks
Not found: tests, continuous integration, documentation
Tools: tidyverse (8 files), easystats (3 files), emmeans (2 files), patchwork (2 files), car (1 file), ggplot2 (1 file), glmmTMB (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
15 files
At the source: github.com/agalic-rd/Vasc-AoP/tree/v1.0

The paper's code and data availability statement is in the Data section.

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;
  • 26 scripts, each with its path and the digest of its content;
  • 1 match 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

The raw datasets, analyses and code supporting this work can be accessed via the GitHub repository available at [https://github.com/agalic-rd/Vasc-AoP](https:/github.com/agalic-rd/Vasc-AoP), also referenced on Zenodo under: [https://doi.org/10.5281/zenodo.15319299](https:/doi.org/10.5281/zenodo.15319299).

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 6 keywords, 9 MeSH terms, 1 funder, 88 references.

Cite

This paper

Rodriguez-Duboc, A., Racine, C., Basille-Dugay, M., Vaudry, D., Gonzalez, B., & Burel, D. (2026). Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity. Cerebellum (London, England), 25(3), 62. https://doi.org/10.1007/s12311-026-02006-1

BibTeX

@article{rodriguezduboc2026innovative,
author = {Rodriguez-Duboc, A and Racine, C and Basille-Dugay, M and Vaudry, D and Gonzalez, B and Burel, D},
title = {{Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity}},
journal = {Cerebellum (London, England)},
year = {2026},
month = apr,
volume = {25},
number = {3},
pages = {62},
publisher = {Springer Science+Business Media},
issn = {1473-4222},
doi = {10.1007/s12311-026-02006-1},
url = {https://doi.org/10.1007/s12311-026-02006-1},
pmid = {42047887},
pmcid = {PMC13124948}
}

RIS

TY - JOUR
AU - Rodriguez-Duboc, A
AU - Racine, C
AU - Basille-Dugay, M
AU - Vaudry, D
AU - Gonzalez, B
AU - Burel, D
TI - Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity
T2 - Cerebellum (London, England)
J2 - Cerebellum
PY - 2026
DA - 2026/04/28
VL - 25
IS - 3
SP - 62
SN - 1473-4222
PB - Springer Science+Business Media
DO - 10.1007/s12311-026-02006-1
UR - https://doi.org/10.1007/s12311-026-02006-1
LA - en
ER -

CSL-JSON

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"container-title": "Cerebellum (London, England)",
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