OSCR

Cerebrospinal fluid-driven ependymal motile cilia defects are implicated in multiple sclerosis.

Code ↔ Paper

3 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 3 matches
  1. [1] § Materials and methods › Single-cell RNA sequencing bioinformatics ↔ Figure_1_scrnaseq_MAID_analysis.R, lines 123–203 · score 0.88 · LogNormalize, seurat_clusters, variable features, Raw, neighbour, resolution
  2. [2] § Materials and methods › RNA isolation and bulk RNA sequencing of ependymal cells from primary cultures ↔ cilia.r, lines 323–398 · score 0.69 · FindMarkers, DESeq2, GRCr8, Salmon, tximeta, Genome
  3. [3] § Results › Post-mortem tissue analysis of ependymal cells reveals cilia alterations in multiple sclerosis ↔ Figure_1_scrnaseq_MAID_analysis.R, lines 123–203 · score 0.51 · Stacked bar, UMAP, Violin, clusterProfiler, ependymal cell, patients

Paper

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

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The authors' code

R · 221 lines · 7 KB · GPL-3.0 · 2 matches

  1. # Figure 1: Single-cell Analysis of Integrated Ventricles -----------------
  3. # 1. Setup ----------------------------------------------------------------
  4. # Load required libraries
  5. library(Seurat)
  6. library(dplyr)
  7. library(ggplot2)
  8. library(clustree)
  9. library(clusterProfiler)
  10. library(enrichplot)
  11. library(org.Hs.eg.db)
  12. library(AnnotationDbi)
  14. # Set working directory and random seed
  15. set.seed(1)
  18. # 2. Load Data ------------------------------------------------------------
  19. integrated_ventricles <- readRDS("MAID_Integrated_PV_Region_MS_vs_CTRL.RDS")
  22. # 3. Figure 1A: UMAP by CellType ------------------------------------------
  23. DimPlot(
  24. object = integrated_ventricles,
  25. reduction = "umap",
  26. label = TRUE,
  27. pt.size = 0.5,
  28. group.by = "CellType"
  29. )
  32. # 4. Figure 1B: Define & Plot CellGroup ----------------------------------
  34. ## 4.1. Define mapping from CellType → broader CellGroup
  35. cell_group_mapping <- c(
  36. ASTROCYTE = "Astrocytes",
  37. NEURONS_1 = "Neurons",
  38. NEURONS_2 = "Neurons",
  39. GABA_NEURONS = "Neurons",
  40. OLIGOS_1 = "Oligodendrocytes & OPCs",
  41. OLIGOS_2 = "Oligodendrocytes & OPCs",
  42. OLIGOS_3 = "Oligodendrocytes & OPCs",
  43. OPC = "Oligodendrocytes & OPCs",
  44. MACROPHAGE = "Immune Cells",
  45. B_CELL = "Immune Cells",
  46. CYTOTOXIC_T = "Immune Cells",
  47. IMMUNE_1 = "Immune Cells",
  48. IMMUNE_2 = "Immune Cells",
  49. IMMUNE_3 = "Immune Cells",
  50. IMMUNE_4 = "Immune Cells",
  51. IMMUNE_5 = "Immune Cells",
  52. IMMUNE_6 = "Immune Cells",
  53. EPENDYMAL = "Ependymal Cells",
  54. ENDOTHELIAL = "Vascular Cells",
  55. PERICYTE_SMOOTH_MUSCLE = "Vascular Cells"
  56. )
  58. ## 4.2. Assign CellGroup
  59. integrated_ventricles$CellType <- as.character(integrated_ventricles$CellType)
  60. integrated_ventricles$CellGroup <- unname(cell_group_mapping[integrated_ventricles$CellType])
  62. # Verify assignment
  63. table(integrated_ventricles$CellGroup)
  66. ## 4.3. UMAP with custom colors
  67. custom_colors <- c(
  68. "Astrocytes" = "#E41A1C",
  69. "Neurons" = "#377EB8",
  70. "Oligodendrocytes & OPCs" = "#4DAF4A",
  71. "Immune Cells" = "#984EA3",
  72. "Ependymal Cells" = "#FF7F00",
  73. "Vascular Cells" = "#FFFF33"
  74. )
  76. umap_plot <- DimPlot(
  77. object = integrated_ventricles,
  78. reduction = "umap",
  79. label = TRUE,
  80. pt.size = 0.5,
  81. group.by = "CellGroup"
  82. ) +
  83. scale_color_manual(values = custom_colors)
  85. # Save result
  86. ggsave(
  87. filename = "UMAP_CellGroup_CustomColors.png",
  88. plot = umap_plot,
  89. width = 8, height = 6, dpi = 300
  90. )
  93. # 5. Figure 1C: DotPlot of Marker Genes -----------------------------------
  95. features <- c(
  96. "AQP4", "SLC1A2", # Astrocytes
  97. "FOXJ1", "PIFO", # Ependymal
  98. "C1QA", "CD68", # Immune
  99. "SYT1", "SYNPR", # Neurons
  100. "CNP", "MOG", # Oligodendrocytes
  101. "PECAM1", "MCAM" # Vascular
  102. )
  104. DefaultAssay(integrated_ventricles) <- "RNA"
  106. dotplot <- DotPlot(
  107. object = integrated_ventricles,
  108. features = features,
  109. group.by = "CellGroup"
  110. ) +
  111. scale_color_gradient(low = "yellow", high = "red") +
  112. scale_size(range = c(2, 8)) +
  113. theme_minimal() +
  114. theme(
  115. axis.text.x = element_text(angle = 45, hjust = 1),
  116. axis.title = element_blank()
  117. ) +
  118. labs(color = "Avg. Expr.", size = "Pct. Expr.")
  120. print(dotplot)
  123. # 6. Figure 1D: CellGroup Proportions per Patient -------------------------
  125. ## 6.1. Summarize counts & proportions
  126. cell_counts <- [email hidden] %>%
  127. group_by(Patient, CellGroup) %>%
  128. summarise(Count = n(), .groups = "drop") %>%
  129. group_by(Patient) %>%
  130. mutate(Proportion = Count / sum(Count)) %>%
  131. ungroup()
  133. ## 6.2. Rename patients
  134. sample_mapping <- c(
  135. P251 = "HSP1", P252 = "ALS1", P257 = "ALS2", P261 = "ALS3",
  136. P253 = "MS1", P259 = "MS2", P276 = "MS3", P280 = "MS4"
  137. )
  138. cell_counts$Patient <- recode(cell_counts$Patient, !!!sample_mapping)
  140. ## 6.3. Stacked bar plot
  141. bar_plot <- ggplot(cell_counts, aes(x = Patient, y = Proportion, fill = CellGroup)) +
  142. geom_bar(stat = "identity") +
  143. scale_fill_manual(values = custom_colors) +
  144. theme_minimal(base_size = 20) +
  145. labs(
  146. title = "Proportion of Each Cell Group Per Patient",
  147. x = "Patient",
  148. y = "Proportion",
  149. fill = "Cell Group"
  150. ) +
  151. theme(
  152. axis.text.x = element_text(angle = 45, hjust = 1, size = 22),
  153. axis.text.y = element_text(size = 22),
  154. axis.title = element_text(size = 26, face = "bold"),
  155. legend.text = element_text(size = 22),
  156. legend.title= element_text(size = 26, face = "bold"),
  157. plot.title = element_text(size = 30, face = "bold", hjust = 0.5)
  158. )
  160. ggsave("CellGroup_Proportion_Per_Patient.tiff",
  161. plot = bar_plot,
  162. width = 10, height = 8, dpi = 300)
  165. # 7. Reclustering Ependymal Cells -----------------------------------------
  167. # 7.1. Create new Seurat object from raw counts
  168. raw_counts <- GetAssayData(integrated_ventricles, slot = "counts")
  169. new_obj <- CreateSeuratObject(counts = raw_counts)
  171. # 7.2. Transfer metadata
  172. meta_cols <- c("seurat_clusters", "CellType", "Patient", "Group")
  173. for (col in meta_cols) {
  174. new_obj[[col]] <- [email hidden][col, drop = FALSE][colnames(new_obj), ]
  175. }
  177. # 7.3. Subset to ependymal cells
  178. ependymal <- subset(new_obj, subset = CellType == "EPENDYMAL")
  180. # 7.4. Standard preprocessing & clustering
  181. ependymal <- ependymal %>%
  182. NormalizeData(norm.method = "LogNormalize", scale.factor = 1e4) %>%
  183. FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%
  184. ScaleData(features = rownames(.)) %>%
  185. RunPCA(features = VariableFeatures(.)) %>%
  186. FindNeighbors(dims = 1:30) %>%
  187. FindClusters(resolution = 0.75) %>%
  188. RunUMAP(dims = 1:30)
  190. # 7.5. UMAP plots
  191. DimPlot(ependymal, reduction = "umap", label = TRUE, pt.size = 1, group.by = "Group")
  192. ggsave("ependymal_umap.png", width = 12, height = 8, dpi = 300)
  195. # 8. QC Violin Plots ------------------------------------------------------
  197. ## 8.1. Compute percent mito
  198. mito_genes <- grep("^MT-", rownames(ependymal), value = TRUE)
  199. ependymal$percent.mito <-
  200. Matrix::colSums(GetAssayData(ependymal, slot = "counts")[mito_genes, ]) /
  201. Matrix::colSums(GetAssayData(ependymal, slot = "counts")) * 100
  203. meta_data <- [email hidden]
  205. ## 8.2. Plotting function
  206. plot_violin <- function(df, var, label, file) {
  207. p <- ggplot(df, aes_string(x = "Sample", y = var, fill = "Sample")) +
  208. geom_violin(trim = FALSE) +
  209. geom_boxplot(width = 0.2, outlier.shape = NA, fill = "white", alpha = 0.7) +
  210. theme_minimal() +
  211. labs(title = paste(label, "Per Patient"), x = "Patient", y = label) +
  212. theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  213. scale_fill_brewer(palette = "Set3")
  215. ggsave(file, plot = p, width = 8, height = 6, dpi = 300)
  216. }
  218. # Generate QC plots
  219. plot_violin(meta_data, "percent.mito", "Mitochondrial Percentage", "violin_mito_percentage.png")
  220. plot_violin(meta_data, "nCount_RNA", "UMI Count", "violin_UMI_count.png")
  221. plot_violin(meta_data, "nFeature_RNA", "nFeature RNA Count", "violin_nFeature_RNA.png")

Figure_1_scrnaseq_MAID_analysis.R at commit 010ea7c, under GPL-3.0 · at the source

Overview

Authors: Maxime Bigotte1, Adam M R Groh1, Elia Afanasiev1, Vincent Wong1, Kevin Lancon2, Moein Yaqubi1, Finn Creeggan1, Airton Sinott1, Junze Pei1, Craig S Moore3, Adil Harroud1,4,5,6, Raphael Schneider7, Philippe Séguéla2, Marc Charabati8, Fiona Tea8, Antoine P Fournier8, Yu Chang Wang5, Jiannis Ragoussis5, Alexandre Prat8, Simon Thebault1, Stephanie Zandee1, Jo Anne Stratton1
ORCID iDs: Maxime Bigotte, Adam M R Groh, Airton Sinott, Craig S Moore, Adil Harroud, Marc Charabati, Antoine P Fournier, Jiannis Ragoussis, Jo Anne Stratton
  1. Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
  2. Department of Neurology and Neurosurgery, Montreal Neurological Institute, Alan Edwards Centre for Research on Pain, McGill University, Montréal, QC, Canada H3A 2B4
  3. Division of BioMedical Sciences, Faculty of Medicine, Memorial University of Newfoundland, St.John's, NL, Canada A1B 3V6
  4. Department of Human Genetics, McGill University, Montréal, QC, Canada H3A 0C7
  5. Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, QC, Canada H3A OC7
  6. Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 171 76, Sweden
  7. St.Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada M5B 1W8
  8. Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM) and Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada H2X 0A9
Institutions: Montreal Neurological Institute and Hospital (Canada); McGill University (Canada); Memorial University of Newfoundland (Canada); Karolinska Institutet (Sweden); St. Michael's Hospital (Canada); Unity Health Toronto; Centre Hospitalier de l’Université de Montréal (Canada); Université de Montréal (Canada)
Journal: Brain : a journal of neurology, volume 149, issue 8, pages 2654-2667
Dates: received 16 July 2025; accepted 26 October 2025; published online 24 November 2025; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/brain/awaf440 · PMID 41277219 · PMCID PMC13431679 · OpenAlex W4416602922
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), multiple sclerosis (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: ependyma, cell culture, neuroinflammation, single-cell RNAseq, epithelial, cerebrospinal fluid barrier
MeSH: Cerebrospinal Fluid*, Cilia*, Ependyma*, Multiple Sclerosis*, Adult, Animals, Encephalomyelitis, Autoimmune, Experimental, Female, Humans, Male, Mice, Mice, Inbred C57BL, Middle Aged (* major topic)
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Multiple Sclerosis Society of Canada (Société Canadienne de la Sclérose en Plaques); Le Grand Portage
Citations: cited by 3 papers (Europe PMC); 92 references in the paper

Abstract

Multiple sclerosis (MS) is a disorder of the CNS in which autoreactive immune cells migrate through a damaged blood–brain barrier, resulting in focal demyelinating lesions. Beyond focal lesions, there are also diffuse ‘surface-in’ gradients of pathology in MS, wherein damage is most severe directly adjacent to CSF-contacting surfaces, such as the subpial and periventricular areas. This observation suggests that toxic factors within MS CSF contribute to the emergence and/or evolution of surface-in gradients. Directly separating the CSF from the periventricular parenchyma are ependymal cells—a glial epithelium—that are equipped with tufts of motile cilia, which are critical for circulating CSF solutes and regulating local fluid flow. While damage to ependymal cilia has the potential to drastically modify CSF homeostasis and thus contribute to the damage of CSF exposed regions, these motile cellular structures have yet to be investigated in the context of MS.

We first conducted single-cell RNA sequencing of fresh human periventricular brain tissue containing ependymal cells from patients with MS and non-MS disease controls. We subsequently collected CSF from patients with MS and exposed cultured rodent ependymal cells to this CSF to evaluate the impact on ependymal ciliary function. To complement our direct evaluation of cilia in the context of MS, we also confirmed whether cilia were altered in an animal model of MS, experimental autoimmune encephalomyelitis (EAE), and designed a novel transgenic animal model to evaluate the cellular and behavioural effect(s) of adult ependymal ciliary disruption.

Single-cell RNA sequencing analysis of human ependymal cells in MS demonstrated large-scale dysregulation of ciliary genes, and in situ stains of MS brain tissue confirmed a loss of ependymal cilia. Exposure of ependymal cells to MS CSF led to transcriptional modification of ciliary gene and protein expression and reduced ciliary beating frequency. Likewise, analysis of ependymal cells in EAE demonstrated altered cilia gene and protein expression. We showed that IFNγ, which is elevated in MS CSF, could alter cilia protein expression and motility. Lastly, conditional knockout of Ccdc39 in ependymal cells of adult mice led to transient ventricular enlargement, increased periventricular microglial density and alterations in nesting behaviour.

These data suggest that motile cilia in ependymal cells are dysregulated in CNS autoimmunity. More importantly, they suggest that ependymal cilia disruption could play a role in periventricular pathology formation in MS and be associated with behavioural deficits underlying non-motor symptomatology.

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 3 matches between paragraphs and lines of code.

stratton-lab/Bigotte-2025-Cilia

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 010ea7ca3a3a19fa1a040c5d5ab3c03b37bcdf03, 11 July 2025
Languages: R (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: license file
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: clusterProfiler (2 files), ggplot2 (2 files), Seurat (2 files), tidyverse (2 files), data.table (1 file), DESeq2 (1 file), patchwork (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
3 files
At the source: github.com/stratton-lab/Bigotte-2025-Cilia

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;
  • 2 scripts, each with its path and the digest of its content;
  • 3 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

Data links

Data availability

The code used to conduct all bioinformatic analyses is available at https://github.com/stratton-lab/Bigotte-2025-Cilia. Single-cell RNA MOG35-55-EAE, single-cell RNA human MS, and in vitro bulk RNA datasets are publicly available under the GEO accession numbers GSE254863, GSE301585, and GSE301791, respectively, at https://www.ncbi.nlm.nih.gov/geo/. These datasets are also publicly available at https://singlocell.openscience.mcgill.ca/display?dataset=RNA_Hu_Nervous_MSCTRL_2025.

Reproduced under the paper's license (CC BY-NC), 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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 22 authors, 6 keywords, 13 MeSH terms, 5 funders, 91 references.

Cite

This paper

Bigotte, M., Groh, A. M. R., Afanasiev, E., Wong, V., Lancon, K., Yaqubi, M., Creeggan, F., Sinott, A., Pei, J., Moore, C. S., Harroud, A., Schneider, R., Séguéla, P., Charabati, M., Tea, F., Fournier, A. P., Wang, Y. C., Ragoussis, J., Prat, A., . . . Stratton, J. A. (2026). Cerebrospinal fluid-driven ependymal motile cilia defects are implicated in multiple sclerosis. Brain : a journal of neurology, 149(8), 2654-2667. https://doi.org/10.1093/brain/awaf440

BibTeX

@article{bigotte2026cerebrospinal,
author = {Bigotte, Maxime and Groh, Adam M R and Afanasiev, Elia and Wong, Vincent and Lancon, Kevin and Yaqubi, Moein and Creeggan, Finn and Sinott, Airton and Pei, Junze and Moore, Craig S and Harroud, Adil and Schneider, Raphael and Séguéla, Philippe and Charabati, Marc and Tea, Fiona and Fournier, Antoine P and Wang, Yu Chang and Ragoussis, Jiannis and Prat, Alexandre and Thebault, Simon and Zandee, Stephanie and Stratton, Jo Anne},
title = {{Cerebrospinal fluid-driven ependymal motile cilia defects are implicated in multiple sclerosis}},
journal = {Brain : a journal of neurology},
year = {2026},
month = aug,
volume = {149},
number = {8},
pages = {2654--2667},
publisher = {Oxford University Press},
issn = {0006-8950},
doi = {10.1093/brain/awaf440},
url = {https://doi.org/10.1093/brain/awaf440},
pmid = {41277219},
pmcid = {PMC13431679}
}

RIS

TY - JOUR
AU - Bigotte, Maxime
AU - Groh, Adam M R
AU - Afanasiev, Elia
AU - Wong, Vincent
AU - Lancon, Kevin
AU - Yaqubi, Moein
AU - Creeggan, Finn
AU - Sinott, Airton
AU - Pei, Junze
AU - Moore, Craig S
AU - Harroud, Adil
AU - Schneider, Raphael
AU - Séguéla, Philippe
AU - Charabati, Marc
AU - Tea, Fiona
AU - Fournier, Antoine P
AU - Wang, Yu Chang
AU - Ragoussis, Jiannis
AU - Prat, Alexandre
AU - Thebault, Simon
AU - Zandee, Stephanie
AU - Stratton, Jo Anne
TI - Cerebrospinal fluid-driven ependymal motile cilia defects are implicated in multiple sclerosis
T2 - Brain : a journal of neurology
J2 - Brain
PY - 2026
DA - 2026/08/01
VL - 149
IS - 8
SP - 2654
EP - 2667
SN - 0006-8950
PB - Oxford University Press
DO - 10.1093/brain/awaf440
UR - https://doi.org/10.1093/brain/awaf440
LA - en
ER -

CSL-JSON

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[9] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: DESeq2, clusterProfiler, Seurat, 4 other tools, mouse, cellular / molecular, 1 reference
[10] doi:10.1186/s44342-026-00076-5 [code]
Exploratory strain-associated patterns of antiviral transcriptional responses to Zika virus exposure in developing human neural tissue.
Journal: Genomics & informatics
In common: DESeq2, Seurat, data.table, 3 other tools, genetics / omics, 3 references

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