OSCR

Refined single-cell profiling captures a CCR5<sup>high</sup> CD4<sup>+</sup> cytotoxic T-cell precursor in multiple sclerosis.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Single cell RNA-sequencing › Filtering, data integration, clustering and DEG analysis ↔ Th17_1_MS_SC_ADT_QC.Rmd, lines 99–137 · score 0.90 · Linear dimensional reduction, Leiden algorithm, highly variable features, log normalised, UMAP, SNN
  2. [2] § Methods › Single cell RNA-sequencing › Filtering, data integration, clustering and DEG analysis ↔ Th17_1_MS_SC_QC.Rmd, lines 477–497 · score 0.84 · Linear dimensional reduction, Leiden algorithm, UMAP, cell cluster, SNN, neighbours
  3. [3] § Results › Natalizumab reduces CCR5high Th17.1 cells with a highly receptive and pre-cytotoxic state in MS ↔ Th17_1_manuscript.Rmd, lines 249–272 · score 0.81 · IL12RB2, IL18R1, CX3CR1, DPP4, NKG7, KLRG1
  4. [4] § Methods › Single cell RNA-sequencing › Data processing and quality control ↔ Th17_1_MS_SC_demultiplexing.Rmd, lines 122–158 · score 0.59 · demultiplexing, ridge, HTOs, demuxmix, doublet, UMIs

Paper

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

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The authors' code

R Markdown · 380 lines · 20 KB · no license · 1 match

  1. ---
  2. title: "Th17_1_MS_SC_Seurat_ADT_QC"
  3. author:
  4. name: Yifan vH
  5. affiliation: ErasmusMC
  6. date: "`r Sys.Date()`"
  7. output:
  8. BiocStyle::html_document:
  9. toc_float: true
  10. ---
  11. # __Dataset information__
  13. For project "Pathogenic features of Th17.1 cells in people with multiple sclerosis", we generated the dataset that contains 15 samples (SRS012-026) including 3 healthy control samples, 6 untreated MS samples, and 6 MS post-natalizumab treatment (TYS) samples. The untreated and post-natalizumab samples are from the same patients, treatment effect nested within group. The 10x Genomics pipeline included RNA layer, ADT layer, TCR-seq, and hashtagging to separate samples (lymphocytes + Th17.1).
  15. <br>
  17. This script is used to load the matrix data from Cell Ranger processing, a quality check to confirm and annotate the ADT proteins and hashtags based on lab records, and generate a seurat object for further processing.
  19. ```{r setup, include=FALSE}
  20. require("knitr")
  21. knitr::opts_chunk$set(
  22. echo = TRUE,
  23. message = TRUE,
  24. warning = TRUE
  25. )
  27. # outDir = "/path/tp/directory/"
  28. figDir = paste0(outDir, "/Figures")
  29. ifelse(!dir.exists(outDir), dir.create(outDir), "Directory Exists")
  30. ifelse(!dir.exists(figDir), dir.create(figDir), "Directory Exists")
  32. knitr::opts_knit$set(root.dir = outDir) # set work directory to your folder that contains data
  33. gc() # free up memory and report the memory usage
  34. options(max.print = .Machine$integer.max, scipen = 999, stringsAsFactors = F, dplyr.summarise.inform = F) # avoid truncated output in R console and scientific notation
  35. ```
  37. # load or install packages
  38. ```{r load packages, echo=TRUE, message=FALSE, warning=FALSE}
  39. ## Install pacman library if not installed
  40. if ( !require("pacman", character.only = TRUE)) {install.packages("pacman", dependencies = TRUE, quiet = TRUE) }
  42. ## Load and install libraries using pacman
  43. pacman::p_load("Azimuth","data.table", "dplyr", "tidyr", "tidyverse","ggplot2", "ggplotify","Matrix", "patchwork", "stringr", "Seurat")
  45. '%notin%' <- Negate('%in%')
  47. # if (!require("BiocManager", quietly = TRUE))
  48. # install.packages("BiocManager")
  49. # BiocManager::install("BiocStyle")
  50. ```
  52. # load matrix data into seurat objects
  54. ```{r load matrix data into seurat objects, echo=TRUE}
  55. #
  56. # # path to the matrix data, one folder for each sample, should contain barcodes.tsv, gene.tsv, and matrix.mtx, can directly read .gz files
  57. # path = "/path/to/raw/data/directory/"
  58. # path = "/home/lety/data/CBBI_Projects/Th17_1_MS_SC/raw/scRNA/" # path to your raw data
  59. #
  60. # # list full path to each folder
  61. # experiment_folders = list.dirs(path, recursive = F, full.names = T)
  62. #
  63. # # obtain sample ids
  64. # sample_id = list.dirs(path, recursive = F, full.names = F)
  65. #
  66. # # create a empty list to reserve space
  67. # Th17_1_MS_SC.raw = list()
  68. #
  69. # # each file consists of "Gene expression" and "Antibody Capture", read each file into a seurat object and store together in the Th17_1_MS_SC.raw list
  70. #
  71. # for (a in 1:length(experiment_folders)) {
  72. # data_dir = experiment_folders[a]
  73. # list.files(data_dir)
  74. # data = Read10X(data.dir = data_dir)
  75. #
  76. # # add in information to filter genes that need to expression in at least one cell
  77. # seurat_object = CreateSeuratObject(counts = data$`Gene Expression`, min.cells = 1,
  78. # assay = "RNA",
  79. # project = sample_id[a])
  80. # protein_assay = data$`Antibody Capture`
  81. #
  82. # # rownames(ADT_assay) = sample_protein # change protein names
  83. # seurat_object[["ADT"]] = CreateAssay5Object(counts = protein_assay)
  84. #
  85. # ## Add prefix to cell IDs (convenient for merging later)
  86. # seurat_object = Seurat::RenameCells(object=seurat_object, add.cell.id = sample_id[a])
  87. #
  88. # Th17_1_MS_SC.raw[[a]] = seurat_object
  89. # rm(data, protein_assay, seurat_object)
  90. # }
  91. #
  92. # names(Th17_1_MS_SC.raw) = sample_id
  93. # rm(path, experiment_folders)
  94. # saveRDS(Th17_1_MS_SC.raw, file = paste0(outDir, "/Th17_1_MS_SC_raw_seurat.rds"))
  96. Th17_1_MS_SC.raw = readRDS(file = paste0(outDir, "/Th17_1_MS_SC_raw_seurat.rds"))
  97. ```
  99. # preprocessing individual unfiltered data
  100. With some base settings to visualize ADT layer in UMAP for annotation
  101. ```{r preprocessing individual data}
  102. #
  103. # for (i in 1:length(Th17_1_MS_SC.raw)){
  104. # ## Normalize RNA data with log normalization
  105. # Th17_1_MS_SC.raw[[i]] = NormalizeData(Th17_1_MS_SC.raw[[i]], normalization.method = "LogNormalize")
  106. #
  107. # ## Normalize ADT data with centered log-ratio (CLR) transformation
  108. # Th17_1_MS_SC.raw[[i]] = NormalizeData(Th17_1_MS_SC.raw[[i]], assay = "ADT", normalization.method = "CLR", margin = 2)
  109. #
  110. # ## identify highly variable features - 2000
  111. # Th17_1_MS_SC.raw[[i]] = FindVariableFeatures(Th17_1_MS_SC.raw[[i]], nfeatures = 2000, selection.method = "vst",
  112. # loess.span = 0.3, clip.max = "auto")
  113. # ## scale data
  114. # Th17_1_MS_SC.raw[[i]] = ScaleData(Th17_1_MS_SC.raw[[i]], features = VariableFeatures(Th17_1_MS_SC.raw[[i]]))
  115. #
  116. # ## linear dimensional reduction with PCA
  117. # Th17_1_MS_SC.raw[[i]] = RunPCA(Th17_1_MS_SC.raw[[i]], features = VariableFeatures(Th17_1_MS_SC.raw[[i]]))
  118. #
  119. # ## find neighbors
  120. # Th17_1_MS_SC.raw[[i]] = FindNeighbors(Th17_1_MS_SC.raw[[i]], reduction = "pca",graph.name = c("NN", "SNN"), dims = 1:30)
  121. #
  122. # ## cluster the cell with leiden algorithm
  123. # Th17_1_MS_SC.raw[[i]] = FindClusters(Th17_1_MS_SC.raw[[i]], cluster.name = "unintegrated_clusters",
  124. # graph.name = "SNN", resolution = 0.8,
  125. # algorithm = 4, method = "igraph")
  126. #
  127. # ## non-linear dimensional reduction to visualize and explore dataset
  128. # Th17_1_MS_SC.raw[[i]] = RunUMAP(Th17_1_MS_SC.raw[[i]], dims = 1:30, reduction = "pca",
  129. # reduction.name = "umap.unintegrated")
  130. #
  131. # ## run azimuth annotation
  132. # Th17_1_MS_SC.raw[[i]] = RunAzimuth(Th17_1_MS_SC.raw[[i]], reference = "pbmcref", umap.name = "ref.umap", query.modality = "RNA")
  133. # }
  134. #
  135. # saveRDS(Th17_1_MS_SC.raw, file = paste0(outDir, "/Th17_1_MS_SC_individualprocessed.rds"))
  136. Th17_1_MS_SC.pro_raw = readRDS(file = paste0(outDir, "/Th17_1_MS_SC_individualprocessed.rds"))
  137. ```
  139. # hashtag & ADT protein visualization
  141. ## hashtags QC per sample
  142. ```{r hashtags QC per sample, fig.width=10, fig.height=10}
  143. for (i in 1:length(Th17_1_MS_SC.pro_raw)){
  145. print(Th17_1_MS_SC.pro_raw[[i]]@project.name)
  147. Idents(Th17_1_MS_SC.pro_raw[[i]]) = "predicted.celltype.l2"
  149. ## plot the top two hashtags - decipher between th17.1 and lympho
  150. DefaultAssay(Th17_1_MS_SC.pro_raw[[i]]) = "ADT"
  151. print(rownames(Th17_1_MS_SC.pro_raw[[i]]))
  153. # pdf(file = paste0(figDir, "/ADT/ADT_hashtag_QC_", Th17_1_MS_SC.pro_raw[[i]]@project.name, ".pdf"), width = 15, height = 10)
  155. p1 = FeaturePlot(Th17_1_MS_SC.pro_raw[[i]],
  156. features =rownames(Th17_1_MS_SC.pro_raw[[i]][["ADT"]])[1:2],
  157. reduction = "umap.unintegrated", ncol = 2,
  158. label = T, label.size = 4, repel = T,
  159. cols = c("lightgrey","darkgreen"))
  161. DefaultAssay(Th17_1_MS_SC.pro_raw[[i]]) = "RNA"
  162. p2 = FeaturePlot(Th17_1_MS_SC.pro_raw[[i]],
  163. features = c("CD4","CD8A","CD19","FCGR3A"),
  164. reduction = "umap.unintegrated", ncol = 2,
  165. label = T, label.size = 4, repel = T,
  166. cols = c("lightgrey","darkgreen"))
  168. plot(p1 / p2)
  170. # dev.off()
  171. }
  172. ```
  174. ## QC ADT protein and its corresponding RNA level per sample
  175. ```{r ADT per sample QC, fig.width=10, fig.height=10}
  177. adt_proteins = c("CD4.1","CD8","CD194","CD20","CD183","CD196")
  178. adt_genes = c("CD4","CD8A","CCR4","MS4A1","CXCR3","CCR6") # gene names and protein names not the same
  180. for (i in 1:length(Th17_1_MS_SC.pro_raw)){
  182. print(Th17_1_MS_SC.pro_raw[[i]]@project.name)
  184. sample = Th17_1_MS_SC.pro_raw[[i]]@project.name
  186. Idents(Th17_1_MS_SC.pro_raw[[i]]) = "predicted.celltype.l2"
  188. DefaultAssay(Th17_1_MS_SC.pro_raw[[i]]) = "ADT"
  189. plot1 = FeaturePlot(Th17_1_MS_SC.pro_raw[[i]],
  190. features = rownames(Th17_1_MS_SC.pro_raw[[i]][["ADT"]])[3:8],
  191. reduction = "umap.unintegrated", slot="data", keep.scale = "all",
  192. label = T, label.size = 4, repel = T,
  193. cols = c("lightgrey","darkgreen"))
  194. plot1 = plot1 + plot_layout(ncol = 3)
  196. DefaultAssay(Th17_1_MS_SC.pro_raw[[i]]) = "RNA"
  197. plot2 = FeaturePlot(Th17_1_MS_SC.pro_raw[[i]], features = adt_genes,
  198. reduction = "umap.unintegrated", slot="data", keep.scale = "all",
  199. label = T, label.size = 4, repel = T,
  200. cols = c("lightgrey","blue"))
  201. plot2 = plot2 + plot_layout(ncol = 3)
  204. # pdf(file = paste0(figDir, "/ADT/",sample, "_ADT_protein_QC.pdf"), width = 15, height = 20)
  206. plot(plot1 / plot2)
  208. # dev.off()
  210. }
  212. ```
  214. # annotate ADT layer
  215. Only in SRS_012 sample, the order of the two hashtags were reversed.
  216. ```{r}
  217. # # list of protein labels
  218. # sample_protein = c("Lympho", "Th17.1", "CD4.1","CD8","CD194","CD20","CD183","CD196")
  219. #
  220. # for (i in 1:length(Th17_1_MS_SC.raw)){
  221. #
  222. # if (Th17_1_MS_SC.raw[[i]]@project.name == "SRS_012"){
  223. # rownames(Th17_1_MS_SC.raw[[i]][["ADT"]]) = c("Th17.1","Lympho", "CD4.1","CD8","CD194","CD20","CD183","CD196")
  224. # }
  225. # else{
  226. # rownames(Th17_1_MS_SC.raw[[i]][["ADT"]]) = sample_protein
  227. # }
  228. # }
  229. ```
  231. # merge all samples and add metadata information to seurat object
  233. | Sample_name | Disease. | Treatment | Patient_id| Condition | Condition_id | Therapy_response | sex | age |
  234. | :---------- | :-------------- | :-------- | :-------- | :-------- | :----------- | :--------------- | :-- | :---|
  235. | SRS_012 | healthy_control | None | 8 | HC | HC_3 | none | F | 50 |
  236. | SRS_013 | healthy_control | None | 7 | HC | HC_1 | none | F | 31 |
  237. | SRS_014 | MS_patient | None | 1 | MS_UNTR | MS_UNTR_1 | responder | F | 30 |
  238. | SRS_015 | MS_patient | TYS | 1 | MS_TYS | MS_TYS_1 | responder | F | 30 |
  239. | SRS_016 | MS_patient | None | 2 | MS_UNTR | MS_UNTR_2 | responder | M | 38 |
  240. | SRS_017 | MS_patient | TYS | 2 | MS_TYS | MS_TYS_2 | responder | M | 38 |
  241. | SRS_018 | MS_patient | None | 3 | MS_UNTR | MS_UNTR_3 | non-responder | F | 30 |
  242. | SRS_019 | MS_patient | TYS | 3 | MS_TYS | MS_TYS_3 | non-responder | F | 30 |
  243. | SRS_020 | MS_patient | None | 4 | MS_UNTR | MS_UNTR_4 | responder | F | 49 |
  244. | SRS_021 | MS_patient | TYS | 4 | MS_TYS | MS_TYS_4 | responder | F | 49 |
  245. | SRS_022 | MS_patient | None | 5 | MS_UNTR | MS_UNTR_5 | non-responder | F | 49 |
  246. | SRS_023 | MS_patient | TYS | 5 | MS_TYS | MS_TYS_6 | non-responder | F | 49 |
  247. | SRS_024 | healthy_control | None | 9 | HC | HC_2 | none | M | 36 |
  248. | SRS_025 | MS_patient | None | 6 | MS_UNTR | MS_UNTR_6 | non-responder | M | 39 |
  249. | SRS_026 | MS_patient | TYS | 6 | MS_TYS | MS_TYS_6 | non-responder | M | 39 |
  251. <br>
  253. ```{r merge seurat object and add metadata, echo=TRUE}
  255. # # merge all samples into a single Seurat object - this way to add in the project name
  256. # Th17_1_MS_SC = merge(Th17_1_MS_SC.raw[[1]],
  257. # y = c(Th17_1_MS_SC.raw[[2]],Th17_1_MS_SC.raw[[3]],Th17_1_MS_SC.raw[[4]],
  258. # Th17_1_MS_SC.raw[[5]],Th17_1_MS_SC.raw[[6]],Th17_1_MS_SC.raw[[7]],
  259. # Th17_1_MS_SC.raw[[8]],Th17_1_MS_SC.raw[[9]],Th17_1_MS_SC.raw[[10]],
  260. # Th17_1_MS_SC.raw[[11]], Th17_1_MS_SC.raw[[12]],Th17_1_MS_SC.raw[[13]],
  261. # Th17_1_MS_SC.raw[[14]],Th17_1_MS_SC.raw[[15]]),
  262. # project = "Th17_1_MS_SC_project")
  263. #
  264. # # join all layers
  265. # Th17_1_MS_SC[['RNA']] <- SeuratObject::JoinLayers(object = Th17_1_MS_SC[['RNA']])
  266. # Th17_1_MS_SC[['ADT']] <- SeuratObject::JoinLayers(object = Th17_1_MS_SC[['ADT']])
  267. #
  268. # ## Add experimental condition for each sample
  269. # [email hidden] <- [email hidden] %>%
  270. # dplyr::mutate(condition = case_when(orig.ident %in% c("SRS_012", "SRS_013", "SRS_024") ~ "HC",
  271. # orig.ident %in% c("SRS_015", "SRS_017", "SRS_019",
  272. # "SRS_021", "SRS_023", "SRS_026") ~ "MS_TYS",
  273. # orig.ident %in% c("SRS_014", "SRS_016", "SRS_018",
  274. # "SRS_020", "SRS_022", "SRS_025") ~ "MS_UNTR"),
  275. #
  276. # disease = case_when(orig.ident %in% c("SRS_012", "SRS_013", "SRS_024") ~ "HC",
  277. # orig.ident %in% c("SRS_015", "SRS_017", "SRS_019",
  278. # "SRS_021", "SRS_023", "SRS_026",
  279. # "SRS_014", "SRS_016", "SRS_018",
  280. # "SRS_020", "SRS_022", "SRS_025") ~ "MS"),
  281. #
  282. # treatment = case_when(orig.ident %in% c("SRS_012", "SRS_013", "SRS_024",
  283. # "SRS_014", "SRS_016", "SRS_018",
  284. # "SRS_020", "SRS_022", "SRS_025") ~ "None",
  285. # orig.ident %in% c("SRS_015", "SRS_017", "SRS_019",
  286. # "SRS_021", "SRS_023", "SRS_026") ~ "TYS"),
  287. #
  288. # condition_id = case_when(orig.ident%in%c("SRS_012") ~ "HC_3",
  289. # orig.ident%in%c("SRS_013") ~ "HC_1",
  290. # orig.ident%in%c("SRS_014") ~ "MS_UNTR1",
  291. # orig.ident%in%c("SRS_015") ~ "MS_TYS_1",
  292. # orig.ident%in%c("SRS_016") ~ "MS_UNTR2",
  293. # orig.ident%in%c("SRS_017") ~ "MS_TYS_2",
  294. # orig.ident%in%c("SRS_018") ~ "MS_UNTR3",
  295. # orig.ident%in%c("SRS_019") ~ "MS_TYS_3",
  296. # orig.ident%in%c("SRS_020") ~ "MS_UNTR4",
  297. # orig.ident%in%c("SRS_021") ~ "MS_TYS_4",
  298. # orig.ident%in%c("SRS_022") ~ "MS_UNTR5",
  299. # orig.ident%in%c("SRS_023") ~ "MS_TYS_5",
  300. # orig.ident%in%c("SRS_024") ~ "HC_2",
  301. # orig.ident%in%c("SRS_025") ~ "MS_UNTR6",
  302. # orig.ident%in%c("SRS_026") ~ "MS_TYS_6"),
  303. #
  304. # patient_id = case_when(orig.ident%in%c("SRS_012") ~ "8",
  305. # orig.ident%in%c("SRS_013") ~ "7",
  306. # orig.ident%in%c("SRS_014") ~ "1",
  307. # orig.ident%in%c("SRS_015") ~ "1",
  308. # orig.ident%in%c("SRS_016") ~ "2",
  309. # orig.ident%in%c("SRS_017") ~ "2",
  310. # orig.ident%in%c("SRS_018") ~ "3",
  311. # orig.ident%in%c("SRS_019") ~ "3",
  312. # orig.ident%in%c("SRS_020") ~ "4",
  313. # orig.ident%in%c("SRS_021") ~ "4",
  314. # orig.ident%in%c("SRS_022") ~ "5",
  315. # orig.ident%in%c("SRS_023") ~ "5",
  316. # orig.ident%in%c("SRS_024") ~ "9",
  317. # orig.ident%in%c("SRS_025") ~ "6",
  318. # orig.ident%in%c("SRS_026") ~ "6"),
  319. #
  320. # therapy_response = case_when(orig.ident%in%c("SRS_012") ~ "none",
  321. # orig.ident%in%c("SRS_013") ~ "none",
  322. # orig.ident%in%c("SRS_014") ~ "responder",
  323. # orig.ident%in%c("SRS_015") ~ "responder",
  324. # orig.ident%in%c("SRS_016") ~ "responder",
  325. # orig.ident%in%c("SRS_017") ~ "responder",
  326. # orig.ident%in%c("SRS_018") ~ "non-responder",
  327. # orig.ident%in%c("SRS_019") ~ "non-responder",
  328. # orig.ident%in%c("SRS_020") ~ "responder",
  329. # orig.ident%in%c("SRS_021") ~ "responder",
  330. # orig.ident%in%c("SRS_022") ~ "non-responder",
  331. # orig.ident%in%c("SRS_023") ~ "non-responder",
  332. # orig.ident%in%c("SRS_024") ~ "none",
  333. # orig.ident%in%c("SRS_025") ~ "non-responder",
  334. # orig.ident%in%c("SRS_026") ~ "non-responder"),
  335. #
  336. # sex = case_when(orig.ident%in%c("SRS_012") ~ "F",
  337. # orig.ident%in%c("SRS_013") ~ "F",
  338. # orig.ident%in%c("SRS_014") ~ "F",
  339. # orig.ident%in%c("SRS_015") ~ "F",
  340. # orig.ident%in%c("SRS_016") ~ "F",
  341. # orig.ident%in%c("SRS_017") ~ "M",
  342. # orig.ident%in%c("SRS_018") ~ "M",
  343. # orig.ident%in%c("SRS_019") ~ "F",
  344. # orig.ident%in%c("SRS_020") ~ "F",
  345. # orig.ident%in%c("SRS_021") ~ "F",
  346. # orig.ident%in%c("SRS_022") ~ "F",
  347. # orig.ident%in%c("SRS_023") ~ "F",
  348. # orig.ident%in%c("SRS_024") ~ "M",
  349. # orig.ident%in%c("SRS_025") ~ "M",
  350. # orig.ident%in%c("SRS_026") ~ "M"),
  351. #
  352. # age = case_when(orig.ident%in%c("SRS_012") ~ "50",
  353. # orig.ident%in%c("SRS_013") ~ "31",
  354. # orig.ident%in%c("SRS_014") ~ "30",
  355. # orig.ident%in%c("SRS_015") ~ "30",
  356. # orig.ident%in%c("SRS_016") ~ "38",
  357. # orig.ident%in%c("SRS_017") ~ "38",
  358. # orig.ident%in%c("SRS_018") ~ "30",
  359. # orig.ident%in%c("SRS_019") ~ "30",
  360. # orig.ident%in%c("SRS_020") ~ "49",
  361. # orig.ident%in%c("SRS_021") ~ "49",
  362. # orig.ident%in%c("SRS_022") ~ "49",
  363. # orig.ident%in%c("SRS_023") ~ "49",
  364. # orig.ident%in%c("SRS_024") ~ "36",
  365. # orig.ident%in%c("SRS_025") ~ "39",
  366. # orig.ident%in%c("SRS_026") ~ "39"))
  367. #
  368. # head([email hidden])
  369. #
  370. # saveRDS(Th17_1_MS_SC, file = paste0(outDir, '/Th17_1_MS_SC_merged_seurat.rds'))
  371. ```
  373. # Session Info
  374. ```{r}
  375. # remove everything from environment at the end
  376. rm(list=ls())
  377. sessionInfo()
  378. ```

Th17_1_MS_SC_ADT_QC.Rmd at commit cf93553, no license · at the source

Overview

Authors: Fabiënne van Puijfelik1,2, Jasper Rip1,2, Yifan van Hasselt1,2, Annet F. Wierenga-Wolf1,2, Sophie Shyfrin1,2, Anna C. Pelser1,2, Marie-Jose Melief1,2, Romy A.M. Klein Kranenbarg2,3,4, Eric M. Bindels5, Harmen J.G. van de Werken1, Chaja M.J. van Ansenwoude6,7, Helga E. de Vries6,7, Willem A. Dik1,8, Janet de Beukelaar4, Ide Smets2,3, Beatrijs H. Wokke2,3, Joost Smolders1,2,3,9, Marvin M. van Luijn1,2
ORCID iDs: Fabiënne van Puijfelik, Romy A.M. Klein Kranenbarg, Eric M. Bindels, Joost Smolders
  1. Department of Immunology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
  2. MS Center ErasMS, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
  3. Department of Neurology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
  4. Department of Neurology, Albert Schweitzer Hospital, Dordrecht, the Netherlands
  5. Department of Hematology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
  6. Department of Molecular Cell Biology and Immunology, Amsterdam Neuroscience, Amsterdam Institute for Infection and Immunity, Amsterdam UMC Location Vrije Universiteit, Amsterdam, the Netherlands
  7. MS Center Amsterdam, Amsterdam UMC Location Vrije Universiteit, Amsterdam, the Netherlands
  8. Laboratory Medical Immunology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
  9. Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, the Netherlands
Institutions: Erasmus MC (Netherlands); Albert Schweitzer Ziekenhuis (Netherlands); Amsterdam Neuroscience (Netherlands); Amsterdam UMC Location Vrije Universiteit Amsterdam (Netherlands); Netherlands Institute for Neuroscience (Netherlands)
Journal: EBioMedicine, volume 129, article 106324
Dates: received 2 September 2025; accepted 23 May 2026; published online 6 June 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.ebiom.2026.106324 · PMID 42250325 · PMCID PMC13264205 · OpenAlex W7163751304
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), multiple sclerosis (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Multiple sclerosis (MS), CD4+ T cell, Cytotoxic T lymphocytes (CTL), Th17.1 cells, CCR5, Single-cell RNA sequencing (scRNA-seq)
MeSH: CD4-Positive T-Lymphocytes*, Multiple Sclerosis*, Receptors, CCR5*, Single-Cell Analysis*, T-Lymphocytes, Cytotoxic*, Adult, Biomarkers, Female, Gene Expression Profiling, Humans, Immunophenotyping, Male, Natalizumab, Th17 Cells, Transcriptome (* major topic)
Topic: Chemokine receptors and signaling (Oncology, Medicine), according to OpenAlex
Funding: Horizon Europe; Multiple Sclerosis Research Foundation; Netherlands Organisation for Health Research and Development
Citations: not cited yet (Europe PMC); 52 references in the paper
Research resources: RRID:AB_2869473, RRID:CVCL_U985

Abstract

Background: Decades of immunological and genetic studies highlight CD4+ T cells as initiators of central nervous system (CNS) pathology in individuals who develop multiple sclerosis (MS). A low-frequency CD4+ T-cell population termed T helper 17.1 (Th17.1) has been argued to play a central role during MS onset due to differences in abundance, yet little is known about their functional heterogeneity and distinctive mode of action in MS.

Methods: Features that distinguished Th17.1 from Th17 and Th1 cells in the blood were explored using spectral flow cytometry. We focused on the brain-homing properties of Th17.1 in MS by analysing paired blood and cerebrospinal fluid (CSF) as well as blood before and after natalizumab treatment. Identified Th17.1 effector signatures were further analysed using purified subsets for single-cell transcriptomics and corresponding in vitro stimulation assays.

Findings: Our screens reveal an CCR5high Th17.1 cluster that is enriched in CSF, but also selectively reduced in post-versus pre-natalizumab blood samples from people with MS. The phenotypical and transcriptional signature of this Th17.1 cluster matched and pointed to a highly receptive and pre-cytotoxic state. Indeed, particularly CCR5high Th17.1 cells upregulated cytolytic proteins by strongly responding to IL-12, while secretion of these proteins was only found when adding both IL-12 and IL-18 in vitro.

Interpretation: We show that low-frequency, circulating Th17.1 cells harbour a unique MS-associated CCR5high cluster with enhanced cytotoxic and CNS-infiltrating potential. This CD4+ cytotoxic T-cell precursor could be a promising target for future research aimed at monitoring disease-relevant immune perturbations for precision medicine in MS.

Funding: 10.13039/501100001826ZonMw (09150171910036), 10.13039/501100003000Stichting MS Research (19–1075 MS, 23–490 g MS), European Union’s Horizon Europe Research and Innovation Actions (101137235; BEHIND-MS) and the 10.13039/501100007352Swiss State Secretariat for Education, Research and Innovation (SERI), Erasmus MC Foundation (10502/02).

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 4 matches between paragraphs and lines of code.

YFWang-YvH/Refined-single-cell-profiling-captures-a-CCR5high-CD4-cytotoxic-T-cell-precursor-in-MS

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cf9355357c42ebec235789a25c6fb2096678d642, 5 May 2026
Languages: R (6)
Size: 8 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 5 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (6 files), tidyverse (6 files), ggplot2 (5 files), patchwork (5 files), data.table (4 files), igraph (3 files), limma (3 files), reticulate (3 files), car (2 files), DESeq2 (2 files), lme4 (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/YFWang-YvH/Refined-single-cell-profiling-captures-a-CCR5high-CD4-cytotoxic-T-cell-precursor-in-MS

Code availability

The code described in the methods for the single-cell sequencing analysis is available on GitHub (https://github.com/YFWang-YvH/Refined-single-cell-profiling-captures-a-CCR5high-CD4-cytotoxic-T-cell-precursor-in-MS).

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

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;
  • 4 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

Due to patient privacy reasons, only processed demultiplexed Th17.1-purified Seurat objects from the single-cell sequencing data reported in this study are deposited at the publicly accessible data repository platform DataverseNL (https://doi.org/10.34894/GNSVFB). Other experimental data will be made available from the corresponding author upon reasonable request.

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

Data sharing statement

Data supporting our findings will be made available by the corresponding author upon reasonable request, beginning at publication with no end date. Single-cell RNAseq data are available at the DataverseNL platform (see Data availability in the Methods section).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 18 authors, 6 keywords, 15 MeSH terms, 3 funders, 51 references, 2 RRIDs.

Cite

This paper

van Puijfelik, F., Rip, J., van Hasselt, Y., Wierenga-Wolf, A. F., Shyfrin, S., Pelser, A. C., Melief, M.-J., Klein Kranenbarg, R. A., Bindels, E. M., van de Werken, H. J., van Ansenwoude, C. M., de Vries, H. E., Dik, W. A., de Beukelaar, J., Smets, I., Wokke, B. H., Smolders, J., & van Luijn, M. M. (2026). Refined single-cell profiling captures a CCR5&lt;sup&gt;high&lt;/sup&gt; CD4&lt;sup&gt;+&lt;/sup&gt; cytotoxic T-cell precursor in multiple sclerosis. EBioMedicine, 129, 106324. https://doi.org/10.1016/j.ebiom.2026.106324

BibTeX

@article{vanpuijfelik2026refined,
author = {van Puijfelik, Fabiënne and Rip, Jasper and van Hasselt, Yifan and Wierenga-Wolf, Annet F. and Shyfrin, Sophie and Pelser, Anna C. and Melief, Marie-Jose and Klein Kranenbarg, Romy A.M. and Bindels, Eric M. and van de Werken, Harmen J.G. and van Ansenwoude, Chaja M.J. and de Vries, Helga E. and Dik, Willem A. and de Beukelaar, Janet and Smets, Ide and Wokke, Beatrijs H. and Smolders, Joost and van Luijn, Marvin M.},
title = {{Refined single-cell profiling captures a CCR5\&lt;sup\&gt;high\&lt;/sup\&gt; CD4\&lt;sup\&gt;+\&lt;/sup\&gt; cytotoxic T-cell precursor in multiple sclerosis}},
journal = {EBioMedicine},
year = {2026},
month = jun,
volume = {129},
pages = {106324},
publisher = {Elsevier},
issn = {2352-3964},
doi = {10.1016/j.ebiom.2026.106324},
url = {https://doi.org/10.1016/j.ebiom.2026.106324},
pmid = {42250325},
pmcid = {PMC13264205}
}

RIS

TY - JOUR
AU - van Puijfelik, Fabiënne
AU - Rip, Jasper
AU - van Hasselt, Yifan
AU - Wierenga-Wolf, Annet F.
AU - Shyfrin, Sophie
AU - Pelser, Anna C.
AU - Melief, Marie-Jose
AU - Klein Kranenbarg, Romy A.M.
AU - Bindels, Eric M.
AU - van de Werken, Harmen J.G.
AU - van Ansenwoude, Chaja M.J.
AU - de Vries, Helga E.
AU - Dik, Willem A.
AU - de Beukelaar, Janet
AU - Smets, Ide
AU - Wokke, Beatrijs H.
AU - Smolders, Joost
AU - van Luijn, Marvin M.
TI - Refined single-cell profiling captures a CCR5&lt;sup&gt;high&lt;/sup&gt; CD4&lt;sup&gt;+&lt;/sup&gt; cytotoxic T-cell precursor in multiple sclerosis
T2 - EBioMedicine
J2 - EBioMedicine
PY - 2026
DA - 2026/06/06
VL - 129
SP - 106324
SN - 2352-3964
PB - Elsevier
DO - 10.1016/j.ebiom.2026.106324
UR - https://doi.org/10.1016/j.ebiom.2026.106324
LA - en
ER -

CSL-JSON

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