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Extracellular matrix remodelling in degenerative cervical myelopathy.

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Paper

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The authors' code

R · 258 lines · 7.8 KB · no license

  1. #### Packages ####
  2. library("DESeq2")
  3. library("affycoretools")
  4. library("tidyverse")
  5. library("pheatmap")
  6. library("dplyr")
  7. library("biomaRt")
  8. library("AnnotationDbi")
  9. library('org.Hs.eg.db')
  10. library(readxl)
  11. library(ggplot2)
  12. library(readxl)
  13. library(dplyr)
  14. library(ggpubr)
  15. library(gridExtra)
  16. library(keras)
  17. library(tfdatasets)
  18. library(tidyverse)
  19. library(rsample)
  20. library(rcompanion)
  21. library(Rmisc)
  22. library(nnet)
  23. library(ccoptimalmatch)
  24. library(MatchIt)
  25. library(epitools)
  26. library(optmatch)
  27. library(gmodels)
  28. library(ggplot2)
  29. library(ggpubr)
  30. library(tidyverse)
  31. library(broom)
  32. library(AICcmodavg)
  33. library(nnet)
  34. library(performance)
  35. library(abd)
  36. library(EpiStats)
  37. library(knitr)
  38. library(questionr)
  39. library(sjPlot)
  40. library(sjmisc)
  41. library(sjlabelled)
  42. library(tidyverse)
  43. library(finalfit)
  44. library(purrr)
  45. library(clipr)
  46. library(Seurat)
  47. library(dplyr)
  48. library(Matrix)
  49. library(readr)
  50. library(monocle3)
  51. library(SeuratWrappers)
  52. library(SeuratDisk)
  53. library(celldex)
  54. library(SingleR)
  55. library(edgeR)
  56. library(ComplexHeatmap)
  57. library(ggsci)
  58. library(clusterProfiler)
  59. library(circlize)
  60. library(GetoptLong)
  61. library(EnhancedVolcano)
  63. #### Load data ####
  64. tpm <- read.csv("/Users/noah/Desktop/ECM Paper Share/Figure 1/Mouse SEQ/Share_RNAseq/mDCM_combined.csv")
  65. md <- read.csv("/Users/noah/Desktop/ECM Paper Share/Figure 1/Mouse SEQ/Share_RNAseq/SampleList_MouseSeq_DCM.csv")
  67. metadata <- data.frame(md[,1:4])
  69. #### Gene lists ####
  70. matrisome <- read_xlsx("/Users/noah/Desktop/ECM Paper Share/Figure 1/Mouse Seq/matrisome_mouse.xlsx")
  71. matrisome <- lapply(matrisome, as.character)
  72. matrisome <- as.data.frame(matrisome)
  73. matrisome_glyc <- matrisome$ECM_Glycoproteins
  74. matrisome_Coll <- matrisome$Collagens
  75. matrisome_Prot <- matrisome$Proteoglycans
  76. matrisome_Aff <- matrisome$ECM_Affiliated
  77. matrisome_Reg <- matrisome$ECM_Regulators
  78. matrisome_all <- matrisome$All
  80. colnames(matrisome) <- c("GP", "Collagens", "PG", "Affiliated", "Regulators", "Secreted", "All")
  82. matrisome_long <- matrisome[,1:6]
  83. matrisome_long <- matrisome_long %>%
  84. mutate(Row = row_number()) %>%
  85. pivot_longer(
  86. cols = -Row,
  87. names_to = "Class", # New column to store previous column names
  88. values_to = "Gene" # New column to store values
  89. )
  91. bm <- read_xlsx("/Users/noah/Desktop/ECM Paper Share/Figure 1/Mouse SEQ/BM_Genes_mouse.xlsx")
  92. bm_genes <- bm$Symbol
  95. #### Subset to protein-coding genes ####
  96. library(EnsDb.Mmusculus.v79)
  97. edb <- EnsDb.Mmusculus.v79
  98. txtypes <- genes(edb, columns=c("gene_name", "gene_biotype", "tx_biotype", "tx_id"))
  99. protGenes <- genes(edb, filter=GeneBiotypeFilter("protein_coding"))
  100. PG <- protGenes$gene_name
  101. PG2 <- protGenes$gene_id
  103. id_name <- data.frame(PG, PG2)
  105. tpm <- tpm %>% dplyr::filter(gene_id %in% PG2)
  107. #### DEG ####
  108. tpm <- tpm[,-1]
  109. genelist <- tpm[,1:2]
  110. cts <- tpm[,3:20]
  111. rownames(cts) <- tpm[,1]
  113. group_coarse <- c("SHAM","SHAM","12_DCM","4_DCM","4_DCM","SHAM","12_DCM","12_DCM","4_DCM",
  114. "SHAM","SHAM","12_DCM","4_DCM","4_DCM","SHAM","12_DCM","12_DCM","4_DCM")
  116. y <- DGEList(counts = cts, group = group_coarse, genes = genelist)
  117. dds <- DESeqDataSetFromMatrix(
  118. countData = cts,
  119. colData = y$samples,
  120. design = ~group)
  122. dds <- DESeq(dds)
  123. normdds <- vst(dds)
  124. res <- results(dds, contrast=c("group","4_DCM","SHAM"))
  125. res <- data.frame(res)
  126. res$gene_id <- rownames(res)
  127. res$padj <- p.adjust(res$pvalue, method = "BH")
  128. res <- merge(res, genelist, by = "gene_id")
  131. normalized_counts <- data.frame(assay(normdds))
  132. normalized_counts <- counts(dds, normalized=TRUE)
  133. normalized_counts <- data.frame(normalized_counts)
  134. normalized_counts$gene_id <- rownames(normalized_counts)
  135. colnames(genelist) <- c("gene_id", "gene_name")
  136. normalized_counts <- merge(normalized_counts, genelist, by = "gene_id")
  138. up <- dplyr::filter(res, padj <0.05 & log2FoldChange <0)
  139. down <- dplyr::filter(res, padj <0.05 & log2FoldChange >0)
  141. #### BM heatmap ####
  143. library(GetoptLong)
  144. x = normalized_counts %>% dplyr::filter(gene_name %in% matrisome_all)
  145. tpm <- x[!duplicated(x$gene_name),]
  146. rownames(tpm) <- tpm$gene_name
  147. tpm <- na.omit(tpm)
  148. x <- tpm[,2:19]
  149. rownames(x) <- tpm$gene_name
  150. x <- x[rowSums(x[])>0,]
  151. x <- as.matrix(x)
  152. x <- t(scale(t(x), scale = TRUE, center = TRUE))
  155. order <- c("M1_4W", "M2_4W", "M3_4W", "F4_4W", "F5_4W", "F6_4W",
  156. "M1_12W", "M2_12W", "M3_12W", "F1_12W", "F2_12W", "F3_12W",
  157. "M1_SHAM", "M2_SHAM", "M3_SHAM", "F1_SHAM", "F2_SHAM", "F3_SHAM")
  159. group <- factor(group_coarse, levels = c("4_DCM", "12_DCM", "SHAM"))
  161. row_split <- matrisome_long$Class[match(rownames(x), matrisome_long$Gene)]
  163. Heatmap(x,
  164. name = "Expression",
  165. column_order = order,
  166. show_row_names = FALSE,
  167. cluster_columns = FALSE,
  168. cluster_column_slices = FALSE,
  169. column_split = group,
  170. row_split = row_split,
  171. show_column_dend = FALSE,
  172. show_column_names = FALSE,
  173. column_title = c("DCM-4w", "DCM-12w", "Sham"))
  176. #### Barplots ####
  178. ## Filter and prep data
  179. as_plot <- normalized_counts %>% dplyr::filter(gene_name %in% fibr | gene_name %in% ra_genes | gene_name %in% ba_genes)
  180. as_plot <- data.frame(as_plot)
  181. as_plot <- t(as_plot)
  182. as_plot_b <- data.frame(as_plot)
  183. as_plot_b$animals <- rownames(as_plot_b)
  184. write_csv(as_plot_b, "toplotgp5.csv")
  186. as_plot <- normalized_counts %>% dplyr::filter(gene_name %in% bm_genes)
  187. as_plot <- data.frame(as_plot)
  188. as_plot <- t(as_plot)
  189. as_plot_b <- data.frame(as_plot)
  190. as_plot_b$animals <- rownames(as_plot_b)
  191. write_csv(as_plot_b, "toplotgp6.csv")
  193. #### PCA ####
  194. vsd <- vst(dds, blind=FALSE)
  195. plotPCA(vsd, intgroup=c("group"))
  198. x <- normalized_counts[,2:19]
  199. x<- x[rowSums(x[])>300,]
  200. xa <- as.matrix(x)
  201. xb <- t(xa)
  202. xb <- xb[, colSums(xb != 0) > 0]
  203. pca <- t(xa)
  204. pca <- data.frame(pca)
  205. pca$group_coarse <- c("SHAM","SHAM","12_DCM","4_DCM","4_DCM","SHAM","12_DCM","12_DCM","4_DCM",
  206. "SHAM","SHAM","12_DCM","4_DCM","4_DCM","SHAM","12_DCM","12_DCM","4_DCM")
  207. pcax <- subset(pca, select = -c(group_coarse))
  208. pca_res <- prcomp(xb, scale. = TRUE)
  209. library(ggfortify)
  210. autoplot(pca_res, data = pca, colour = c("group_coarse"), size = 3) + theme_bw() +
  211. scale_color_npg() + scale_fill_npg() + guides(size = "none")
  215. #### Volcano plots ####
  216. res <- res %>%
  217. mutate(Expression = case_when(log2FoldChange >= log(2) & padj <= 0.05 ~ "Up-regulated",
  218. log2FoldChange <= -log(2) & padj <= 0.05 ~ "Down-regulated",
  219. TRUE ~ "Unchanged"))
  220. top <- 12
  221. top_genes <- bind_rows(
  222. res %>%
  223. dplyr::filter(Expression == 'Up-regulated') %>%
  224. arrange(padj, desc(abs(log2FoldChange))) %>%
  225. head(top),
  226. res %>%
  227. dplyr::filter(Expression == 'Down-regulated') %>%
  228. arrange(padj, desc(abs(log2FoldChange))) %>%
  229. head(top))
  230. res <- na.omit(res)
  232. ggplot(res, aes(log2FoldChange, -log(padj, 10))) +
  233. geom_point(aes(color = Expression), size = 2/5) +
  234. scale_color_manual(values = c("dodgerblue3", "gray50", "firebrick3")) +
  235. xlab(expression("log"[2]*"FC")) +
  236. ylab(expression("-log"[10]*"PValue")) +
  237. theme_pubr() + theme(legend.position="none") +
  238. geom_label_repel(data = top_genes, mapping = aes(log2FoldChange, -log(padj,10),
  239. label = Gene_name.x),size = 6,max.overlaps = 30)
  241. #### GO ####
  242. library(org.Mm.eg.db)
  243. gene_list <- dplyr::filter(res, padj <0.05, log2FoldChange <0)
  244. entrez_ids <- bitr(gene_list$gene_id, fromType = "ENSEMBL", toType = "ENTREZID", OrgDb = org.Mm.eg.db)
  246. ego <- enrichGO(gene = entrez_ids$ENTREZID,
  247. OrgDb = org.Mm.eg.db,
  248. keyType = "ENTREZID",
  249. ont = "CC",
  250. pAdjustMethod = "BH",
  251. pvalueCutoff = 0.05,
  252. qvalueCutoff = 0.05,
  253. readable = TRUE)
  255. ego_simplified <- simplify(ego, cutoff = 0.8, by = "p.adjust", select_fun = min)
  257. barplot(ego_simplified, showCategory = 5) + theme(legend.position="none") +
  258. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())

MouseSeq_DCM_Github.R at commit 37af289, no license · at the source

Overview

Authors: Noah D Poulin1,2,3, Sydney Brockie3, Koby Baranes1,2, James Hong3, Cindy Zhou3, Sarah Sadat3, Mark R Kotter1,2, Michael G Fehlings3,4
ORCID iDs: Koby Baranes, Michael G Fehlings
  1. Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
  2. Welcome-MRC Cambridge Stem Cell Institute, Cambridge CB2 0AW, UK
  3. Division of Genetics and Development, Krembil Research Institute, University Health Network, Toronto, Canada M5T 2S8
  4. Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Canada M5T 1P5
Institutions: University Health Network (Canada); Wellcome/MRC Cambridge Stem Cell Institute (United Kingdom); University of Cambridge (United Kingdom); Krembil Research Institute; University of Toronto (Canada)
Journal: Brain communications, volume 8, issue 4, article fcag239
Dates: received 14 October 2025; accepted 10 April 2026; published online 23 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag239 · PMID 42494494 · PMCID PMC13392463 · OpenAlex W7170142812
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), human (organism), mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: blood–spinal cord barrier, matrisome, chronic compression, astrogliosis, fibrosis
Topic: Cervical and Thoracic Myelopathy (Surgery, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 48 references in the paper

Abstract

Degenerative cervical myelopathy (DCM), a type of spinal cord injury triggered by chronic compression from degenerative changes of the spine, induces pathophysiological changes similar to traumatic spinal cord injury, including reactive gliosis, demyelination and neuron loss. However, the effects of chronic spinal cord compression on extracellular matrix composition and organization remain uncharacterized.

Here, we analyse untreated post-mortem human tissue and a mouse model of chronic spinal cord compression using immunohistochemical and transcriptomic approaches to assess chondroitin sulphate proteoglycan (CSPG) and fibrosis-related matrix deposition.

In human post-mortem tissue, astrogliosis, CSPG accumulation and fibrotic collagen deposition were elevated in the DCM cases (n = 7) compared to controls (n = 5), though gliosis and fibrosis-related matrix deposition were not significantly associated with the degree of cord compression. The mouse model, however, demonstrated more distinct border-forming astrocyte phenotypes. Transcriptional and histological markers of vascular and interstitial fibrosis were also significantly increased in the mouse model.

These findings demonstrate that astroglial CSPG deposition and fibrotic scarring occur in DCM, albeit potentially in a more diffuse pattern than in traumatic spinal cord injury. Moreover, this study supports previous observations of vascular fibrotic thickening in DCM.

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

Repository

Its files are read in the Code ↔ Paper reader above.

noahdavidpoulin/ECM-in-DCM

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 37af289a2069a470e8fe9ab6f3382060685a1df1, 30 January 2026
Languages: R (1)
Size: 1 file, 1 script
Software Heritage: not archived
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (1 file), circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), DESeq2 (1 file), easystats (1 file), edgeR (1 file), ggplot2 (1 file), ggpubr (1 file), Monocle 3 (1 file), pheatmap (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
1 file
At the source: github.com/noahdavidpoulin/ECM-in-DCM

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;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Transcriptomic data generated in this study are available as raw counts. Code associated with data analysis is available at https://github.com/noahdavidpoulin/ECM-in-DCM.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 47 references.

Cite

This paper

Poulin, N. D., Brockie, S., Baranes, K., Hong, J., Zhou, C., Sadat, S., Kotter, M. R., & Fehlings, M. G. (2026). Extracellular matrix remodelling in degenerative cervical myelopathy. Brain communications, 8(4), fcag239. https://doi.org/10.1093/braincomms/fcag239

BibTeX

@article{poulin2026extracellular,
author = {Poulin, Noah D and Brockie, Sydney and Baranes, Koby and Hong, James and Zhou, Cindy and Sadat, Sarah and Kotter, Mark R and Fehlings, Michael G},
title = {{Extracellular matrix remodelling in degenerative cervical myelopathy}},
journal = {Brain communications},
year = {2026},
month = jul,
volume = {8},
number = {4},
pages = {fcag239},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag239},
url = {https://doi.org/10.1093/braincomms/fcag239},
pmid = {42494494},
pmcid = {PMC13392463}
}

RIS

TY - JOUR
AU - Poulin, Noah D
AU - Brockie, Sydney
AU - Baranes, Koby
AU - Hong, James
AU - Zhou, Cindy
AU - Sadat, Sarah
AU - Kotter, Mark R
AU - Fehlings, Michael G
TI - Extracellular matrix remodelling in degenerative cervical myelopathy
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/07/23
VL - 8
IS - 4
SP - fcag239
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag239
UR - https://doi.org/10.1093/braincomms/fcag239
LA - en
ER -

CSL-JSON

{
"id": "10.1093/braincomms/fcag239",
"type": "article-journal",
"title": "Extracellular matrix remodelling in degenerative cervical myelopathy",
"container-title": "Brain communications",
"author": [
{
"family": "Poulin",
"given": "Noah D"
},
{
"family": "Brockie",
"given": "Sydney"
},
{
"family": "Baranes",
"given": "Koby"
},
{
"family": "Hong",
"given": "James"
},
{
"family": "Zhou",
"given": "Cindy"
},
{
"family": "Sadat",
"given": "Sarah"
},
{
"family": "Kotter",
"given": "Mark R"
},
{
"family": "Fehlings",
"given": "Michael G"
}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "4",
"page": "fcag239",
"DOI": "10.1093/braincomms/fcag239",
"PMID": "42494494",
"PMCID": "PMC13392463",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag239",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
23
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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