OSCR

PD-1 regulates CD4<sup>+</sup> T cell-mediated CD8<sup>+</sup> T cell responses in the brain to balance viral control and neuroinflammation.

Code ↔ Paper

7 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 7 matches
  1. [1] § Methods › scRNA-seq whole transcriptome analysis ↔ h173_seurat_for_zenodo.R, lines 45–94 · score 0.95 · LogNormalize, UMAP embeddings, dimension reduction, FindClusters, feature selection, resolution
  2. [2] § Results › T cells are the major cell type expressing PD-1 in the MuPyV-infected brain ↔ figures.r, lines 101–245 · score 0.88 · Csf1r, Cd8a, Cd8b1, Cx3cr1, Mbp, Mobp
  3. [3] § Results › T cells are the major cell type expressing PD-1 in the MuPyV-infected brain ↔ analysis.r, lines 58–118 · score 0.87 · Csf1r, Cd8a, Cd8b1, Cx3cr1, Mbp, Mobp
  4. [4] § Methods › Luminex xMAP multiplexing immunoassays ↔ R Scripts/AnalysisPipeline.R, lines 1–40 · score 0.77 · logistic regression, cleaning pipeline, GitHub, concentrations, Luminex
  5. [5] § Methods › Tissue preparation for MERFISH transcriptomics and analysis ↔ banksy.r, lines 98–129 · score 0.70 · RunPCA, FindClusters, BANKSY, Leiden, spots, neighbours
  6. [6] § Methods › Tissue preparation for MERFISH transcriptomics and analysis ↔ h173_seurat_for_zenodo.R, lines 45–94 · score 0.66 · RunPCA, FindClusters, QC, neighbours, Seurat, filtering
  7. [7] § Results › Transcriptomic analysis of T cells from MuPyV-infected brains of WT and PD-1-deficient mice ↔ figures.r, lines 101–245 · score 0.51 · Cx3cr1, Ifng, MuPyV, Cxcr6, clustering, genes

Paper

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The authors' code

R · 393 lines · 15 KB · CC-BY-4.0 · 2 matches

  1. #Load the necessary R packages
  2. rm(list = ls())
  3. library(dplyr)
  4. library(Seurat)
  5. library(patchwork)
  6. library(ggplot2)
  7. library(cowplot)
  8. library(presto)
  9. library(bluster)
  10. library(scran)
  11. library(ape)
  12. library(ggtree)
  13. library(tidyr)
  14. library(biomaRt)
  15. library(scDblFinder)
  16. library(plotly)
  17. library(viridis)
  18. library(sparsevctrs)
  19. library(readxl)
  20. library(RColorBrewer)
  21. library(scCustomize)
  23. ##########
  24. #Load the data
  25. ##########
  26. setwd("C:/Users/vwalter1/OneDrive - Penn State Health/Migration/People/Aron_Lukacher/Data/H173")
  27. cd4Data = readRDS("H173-01_WTA_ST_Seurat.rds") #This is a Seurat object
  28. cd8Data = readRDS("H173-02_WTA_ST_Seurat.rds") #This is a Seurat object
  29. data = merge(x = cd4Data, y = cd8Data, add.cell.ids = c("CD4", "CD8"))
  31. ##########
  32. #Begin QC, as per the Seurat vignette
  33. ##########
  34. #Define mitochondrial gene percentage
  35. data[["percent.mt"]] = PercentageFeatureSet(data, pattern = "^mt-") #Percentage of mitochondrial genes
  37. #Summary statistics used to filter cells
  38. nCountMedian = median(data$nCount_RNA)
  39. nFeatureMedian = median(data$nFeature_RNA)
  40. mitoPctMedian = median(data$percent.mt)
  41. nCountMad = mad(data$nCount_RNA)
  42. nFeatureMad = mad(data$nFeature_RNA)
  43. mitoPctMad = mad(data$percent.mt)
  45. #Define thresholds for QC
  46. highCountRnaThresh = nCountMedian + (3 * nCountMad)
  47. lowFeatureRnaThresh = nFeatureMedian - (2 * nFeatureMad)
  48. highFeatureRnaThresh = nFeatureMedian + (3 * nFeatureMad)
  49. mitoPctThresh = mitoPctMedian + (3 * mitoPctMad)
  51. #Filter data based on thresholds for nFeature_RNA and percent.mt, as defined above
  52. data = subset(data, subset = nFeature_RNA >= lowFeatureRnaThresh & nFeature_RNA <= highFeatureRnaThresh & percent.mt <= mitoPctThresh
  53. & nCount_RNA <= highCountRnaThresh & Sample_Tag %in% c("SampleTag01_mm", "SampleTag02_mm", "SampleTag03_mm", "SampleTag04_mm"))
  54. table(data[[]]$Sample_Tag) #OK
  55. dim(data[[]]) #Now 16009 cells
  57. ##########
  58. #Perform normalization, identify variable features, and perform feature selection
  59. ##########
  60. data = NormalizeData(data, normalization.method = "LogNormalize", scale.factor = 1e5)
  61. data = FindVariableFeatures(data, selection.method = "vst", nfeatures = 2e3)
  62. data = ScaleData(data, features = rownames(data))
  64. ##########
  65. #Perform linear dimension reduction
  66. ##########
  67. data = RunPCA(data, features = VariableFeatures(object = data))
  68. maxDim = 15
  69. data = FindNeighbors(data, reduction = "pca", dims = 1:maxDim)
  70. data = FindClusters(data, random.seed = 12345, resolution = 0.5)
  72. ##########
  73. #UMAP embedding
  74. ##########
  75. data = RunUMAP(data, dims = 1:maxDim)
  77. ##########
  78. #Define plotting colors
  79. ##########
  80. custom_colors = list()
  81. colors_dutch = c(
  82. '#FFC312','#C4E538','#12CBC4','#FDA7DF','#ED4C67',
  83. '#F79F1F','#A3CB38','#1289A7','#D980FA','#B53471',
  84. '#EE5A24','#009432','#0652DD','#9980FA','#833471',
  85. '#EA2027','#006266','#1B1464','#5758BB','#6F1E51')
  86. colors_spanish = c(
  87. '#40407a','#706fd3','#f7f1e3','#34ace0','#33d9b2',
  88. '#2c2c54','#474787','#aaa69d','#227093','#218c74',
  89. '#ff5252','#ff793f','#d1ccc0','#ffb142','#ffda79',
  90. '#b33939','#cd6133','#84817a','#cc8e35','#ccae62')
  91. custom_colors$discrete = c(colors_dutch, colors_spanish)
  92. custom_colors$cell_cycle = setNames(
  93. c('#45aaf2', '#f1c40f', '#e74c3c', '#7f8c8d'),
  94. c('G1', 'S', 'G2M', '-'))
  96. ##########
  97. #UMAP plot by sample tag
  98. ##########
  99. umap3 = DimPlot(data, reduction = "umap", label = T, raster = F, split.by = "Sample_Tag",
  100. cols = custom_colors$discrete, ncol = 2)
  101. #pdf()
  102. umap3
  103. #dev.off()
  105. ##########
  106. #Feature plots for genes of interest by sample tag
  107. ##########
  108. setwd("C:/Users/vwalter1/OneDrive - Penn State Health/Migration/People/Aron_Lukacher/Gene_sets")
  109. geneList1 = as.data.frame(read_excel("gene_list_76_20250410.xlsx"))
  110. geneList2 = as.data.frame(read_excel("Ren_NIHMS1643105-supplement-Table_1_VW.xlsx", sheet = "Fig3A_FigS3C_genes"))
  111. geneList3 = as.data.frame(read_excel("genelist_36_20250428.xlsx"))
  112. geneList4 = c("Cxcl9", "Il12rb1", "Irf7", "Socs1", "Runx2", "Nr3c1")
  113. geneList5 = c("Ccl3", "Ccl4", "Trgc2", "Trgv2")
  114. geneList6 = c("Cd8a", "Cd8b1", "Cd4")
  115. geneList7 = c("Birc5", "Cd160", "Ebi3", "Itgal", "Itgb7", "Klf4", "Mki67",
  116. "Malat1", "Stmn1", "Tnfrsf18")
  118. #Use scCustomize to split the above feature plot by Sample Tag
  119. moreGenes = unique(c(geneList1$Gene, geneList3$Gene, geneList4, geneList5, geneList6, geneList7))
  120. moreGenes = sort(intersect(moreGenes, rownames(data)))
  121. dataST1 = subset(data, subset = Sample_Tag == "SampleTag01_mm")
  122. dataST2 = subset(data, subset = Sample_Tag == "SampleTag02_mm")
  123. dataST3 = subset(data, subset = Sample_Tag == "SampleTag03_mm")
  124. dataST4 = subset(data, subset = Sample_Tag == "SampleTag04_mm")
  126. #pdf()
  127. for (i in 1:length(moreGenes))
  128. {
  129. tempFeature1 = FeaturePlot(dataST1, moreGenes[i]) +
  130. scale_colour_gradientn(colours = c("gray85", rev(brewer.pal(n = 11, name = "Spectral"))), limits = c(0, 9)) +
  131. scale_x_continuous(limits = c(-10, 10)) +
  132. scale_y_continuous(limits = c(-8, 5)) +
  133. labs(title = moreGenes[i], subtitle = "SampleTag01_mm") +
  134. theme(
  135. plot.title = element_text(hjust = 0.5),
  136. plot.subtitle = element_text(hjust = 0.5)
  137. )
  138. tempFeature2 = FeaturePlot(dataST2, moreGenes[i]) +
  139. scale_colour_gradientn(colours = c("gray85", rev(brewer.pal(n = 11, name = "Spectral"))), limits = c(0, 9)) +
  140. scale_x_continuous(limits = c(-10, 10)) +
  141. scale_y_continuous(limits = c(-8, 5)) +
  142. labs(title = moreGenes[i], subtitle = "SampleTag02_mm") +
  143. theme(
  144. plot.title = element_text(hjust = 0.5),
  145. plot.subtitle = element_text(hjust = 0.5)
  146. )
  147. tempFeature3 = FeaturePlot(dataST3, moreGenes[i]) +
  148. scale_colour_gradientn(colours = c("gray85", rev(brewer.pal(n = 11, name = "Spectral"))), limits = c(0, 9)) +
  149. scale_x_continuous(limits = c(-10, 10)) +
  150. scale_y_continuous(limits = c(-8, 5)) +
  151. labs(title = moreGenes[i], subtitle = "SampleTag03_mm") +
  152. theme(
  153. plot.title = element_text(hjust = 0.5),
  154. plot.subtitle = element_text(hjust = 0.5)
  155. )
  156. tempFeature4 = FeaturePlot(dataST4, moreGenes[i]) +
  157. scale_colour_gradientn(colours = c("gray85", rev(brewer.pal(n = 11, name = "Spectral"))), limits = c(0, 9)) +
  158. scale_x_continuous(limits = c(-10, 10)) +
  159. scale_y_continuous(limits = c(-8, 5)) +
  160. labs(title = moreGenes[i], subtitle = "SampleTag04_mm") +
  161. theme(
  162. plot.title = element_text(hjust = 0.5),
  163. plot.subtitle = element_text(hjust = 0.5)
  164. )
  165. print(CombinePlots(plots = list(tempFeature1, tempFeature2, tempFeature3, tempFeature4), ncol = 2))
  166. }
  167. #dev.off()
  169. ##########
  170. #Define helper functions used to create dot plots
  171. ##########
  172. prct_helper = function(object, genes)
  173. {
  174. counts = object[['RNA']]@counts
  175. ncells = ncol(counts)
  176. if(genes %in% row.names(counts))
  177. {
  178. return(sum(counts[genes,]>0)/ncells)
  179. } else
  180. {
  181. return(NA)
  182. }
  183. }
  185. mean_helper = function(object, genes)
  186. {
  187. expVals = object@assays$RNA@data
  188. if(genes %in% row.names(expVals))
  189. {
  190. return(sparse_mean(expVals[genes,]))
  191. } else
  192. {
  193. return(NA)
  194. }
  195. }
  197. PrctCellExpringGene = function(object, genes, group.by = "all")
  198. {
  199. if(group.by == "all")
  200. {
  201. prct = unlist(lapply(genes, prct_helper, object = object))
  202. result = data.frame(Markers = genes, Cell_proportion = prct)
  203. return(result)
  204. }
  206. else {
  207. list = SplitObject(object, group.by)
  208. factors = names(list)
  210. results = lapply(list, PrctCellExpringGene, genes = genes)
  211. for(i in 1:length(factors))
  212. {
  213. results[[i]]$Feature = factors[i]
  214. }
  215. combined = do.call("rbind", results)
  216. return(combined)
  217. }
  218. }
  220. MeanGeneExp = function(object, genes, group.by = "all")
  221. {
  222. if(group.by == "all")
  223. {
  224. meanVal = unlist(lapply(genes, mean_helper, object = object))
  225. result = data.frame(Markers = genes, MeanExp = meanVal)
  226. return(result)
  227. }
  229. else {
  230. list = SplitObject(object, group.by)
  231. factors = names(list)
  233. results = lapply(list, MeanGeneExp, genes = genes)
  234. for(i in 1:length(factors))
  235. {
  236. results[[i]]$Feature = factors[i]
  237. }
  238. combined = do.call("rbind", results)
  239. return(combined)
  240. }
  241. }
  243. ##########
  244. #Make dot plots
  245. ##########
  246. setwd("C:/Users/vwalter1/OneDrive - Penn State Health/Migration/People/Aron_Lukacher/Gene_sets")
  247. dotPlotGeneInfo = as.data.frame(read_excel("Gene_list_dot plot_3_20250929_VW.xlsx"))
  248. dotPlotGenes = dotPlotGeneInfo$Gene
  250. #Remove Maf from dotPlotGenes - this was done on 20250721
  251. dotPlotGenes = setdiff(dotPlotGenes, "Maf")
  253. #Add a second numeric version of Sample_Tag to the metadata that groups CD4 and CD8 cells together
  254. numSampleTag2 = (1 * as.numeric(data[[]]$Sample_Tag == "SampleTag01_mm")) +
  255. (3 * as.numeric(data[[]]$Sample_Tag == "SampleTag02_mm")) +
  256. (2 * as.numeric(data[[]]$Sample_Tag == "SampleTag03_mm")) +
  257. (4 * as.numeric(data[[]]$Sample_Tag == "SampleTag04_mm"))
  258. data = AddMetaData(object = data, metadata = numSampleTag2, col.name = "numSampleTag2")
  260. dotPlotPcts = PrctCellExpringGene(object = data, genes = dotPlotGenes, group.by = "numSampleTag2")
  261. dotPlotPcts = dotPlotPcts[order(as.numeric(dotPlotPcts$Feature), dotPlotPcts$Markers),]
  262. dotPlotPcts$Feature = as.numeric(dotPlotPcts$Feature)
  264. dotPlotMeans = MeanGeneExp(object = data, genes = dotPlotGenes, group.by = "numSampleTag2")
  265. dotPlotMeans = dotPlotMeans[order(as.numeric(dotPlotMeans$Feature), dotPlotMeans$Markers),]
  266. dotPlotMeans$Feature = as.numeric(dotPlotMeans$Feature)
  268. dotPlotData = dotPlotPcts
  269. dotPlotData$MeanExp = dotPlotMeans$MeanExp
  271. dotPlotTitle = "Expression by Cell Type"
  272. pctDotPlot = ggplot(data = dotPlotData, aes(x = Feature, y = Markers,
  273. size = Cell_proportion, color = MeanExp)) +
  274. geom_point() +
  275. #scale_color_gradient(low = "gray", high = "red") +
  276. scale_colour_gradientn(colours = c("gray85", rev(brewer.pal(n = 11, name = "Spectral"))), limits = c(0, 5)) +
  277. theme_bw() +
  278. ylab("") +
  279. xlab("") +
  280. scale_x_continuous(breaks = c(0:10), name = "Cell Type") +
  281. scale_y_discrete(limits = rev(dotPlotGenes)) +
  282. labs(title = dotPlotTitle, x = "Cell Type", y = "Gene",
  283. size = "Proportion", color = "Mean Expression") +
  284. theme(axis.text.x = element_text(size = 12), axis.title.x = element_text(size = 14),
  285. axis.text.y = element_text(size = 12, face = "italic"), axis.title.y = element_text(size = 14),
  286. plot.title = element_text(size = 14))
  288. #pdf()
  289. print(pctDotPlot)
  290. #dev.off()
  292. ##########
  293. #Stacked barplots
  294. ##########
  295. tableSamplesByClusters2 = [email hidden] %>%
  296. group_by(numSampleTag2, seurat_clusters) %>%
  297. summarize(count = n()) %>%
  298. spread(seurat_clusters, count, fill = 0) %>%
  299. ungroup() %>%
  300. mutate(total_cell_count = rowSums(.[c(2:ncol(.))])) %>%
  301. dplyr::select(c('numSampleTag2', 'total_cell_count', everything())) %>%
  302. arrange(factor(numSampleTag2, levels = levels([email hidden]$numSampleTag2)))
  304. groupLabels2 = [email hidden] %>%
  305. group_by(numSampleTag2) %>%
  306. tally()
  308. barPlot2 = tableSamplesByClusters2 %>%
  309. dplyr::select(-c('total_cell_count')) %>%
  310. reshape2::melt(id.vars = 'numSampleTag2') %>%
  311. mutate(sample = factor(numSampleTag2, levels = levels([email hidden]$numSampleTag2))) %>%
  312. ggplot(aes(numSampleTag2, value)) +
  313. geom_bar(aes(fill = variable), position = 'fill', stat = 'identity') +
  314. geom_text(
  315. data = groupLabels2,
  316. aes(x = numSampleTag2, y = Inf, label = paste0('n = ', format(n, big.mark = ',', trim = TRUE)), vjust = -1),
  317. color = 'black', size = 2.8
  318. ) +
  319. scale_fill_manual(name = 'Cluster', values = custom_colors$discrete) +
  320. scale_y_continuous(name = 'Percentage [%]', labels = scales::percent_format(), expand = c(0.01,0)) +
  321. coord_cartesian(clip = 'off') +
  322. theme_bw() +
  323. theme(
  324. legend.position = 'left',
  325. plot.title = element_text(hjust = 0.5),
  326. text = element_text(size = 16),
  327. panel.grid.major = element_blank(),
  328. panel.grid.minor = element_blank(),
  329. axis.title.x = element_blank(),
  330. axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
  331. plot.margin = margin(t = 20, r = 0, b = 0, l = 0, unit = 'pt')
  332. )
  334. #pdf()
  335. barPlot2
  336. #dev.off()
  338. ##########
  339. #Differential expression analyses
  340. ##########
  341. Idents(data) = "Sample_Tag"
  342. cd4DeResults = FindMarkers(data, ident.1 = "SampleTag01_mm", ident.2 = "SampleTag03_mm")
  343. cd8DeResults = FindMarkers(data, ident.1 = "SampleTag02_mm", ident.2 = "SampleTag04_mm")
  345. ##########
  346. #Volcano plot for CD4
  347. ##########
  348. cd4DeResults = cd4DeResults[-which(cd4DeResults$p_val == 0),]
  349. volcanoData = data.frame(
  350. logFC = as.numeric(cd4DeResults$avg_log2FC),
  351. logPval = -log10(cd4DeResults$p_val_adj))
  352. rownames(volcanoData) = rownames(cd4DeResults)
  353. comparison = "Sample Tag 1 vs. Sample Tag 3"
  355. fcCutoff = 1
  356. pvalCutoff = -log10(0.05)
  357. absFcVal = max(abs(volcanoData$logFC))
  359. #pdf("")
  360. fcPosPoints1 = intersect(which(volcanoData$logFC > 0), which(volcanoData$logPval > pvalCutoff))
  361. fcPosPoints2 = intersect(which(volcanoData$logFC > fcCutoff), which(volcanoData$logPval > pvalCutoff))
  362. fcPosPoints3 = setdiff(which(volcanoData$logFC > fcCutoff), which(volcanoData$logPval > pvalCutoff))
  364. fcNegPoints1 = intersect(which(volcanoData$logFC < 0), which(volcanoData$logPval > pvalCutoff))
  365. fcNegPoints2 = intersect(which(volcanoData$logFC < -fcCutoff), which(volcanoData$logPval > pvalCutoff))
  366. fcNegPoints3 = setdiff(which(volcanoData$logFC < -fcCutoff), which(volcanoData$logPval > pvalCutoff))
  368. plot(volcanoData$logFC, volcanoData$logPval, axes = F, xlab = "", ylab = "", main = "", type = "n",
  369. xlim = c(-absFcVal, absFcVal), ylim = c(0, 12))
  370. abline(h = pvalCutoff, col = "gray", lty = 2, lwd = 2)
  371. abline(v = -fcCutoff, col = "gray", lty = 2, lwd = 2)
  372. abline(v = fcCutoff, col = "gray", lty = 2, lwd = 2)
  373. points(volcanoData$logFC, volcanoData$logPval, pch = 19, col = "black")
  375. points(volcanoData$logFC[fcPosPoints1], volcanoData$logPval[fcPosPoints1], pch = 19, col = "pink", cex = 1.25)
  376. points(volcanoData$logFC[fcPosPoints2], volcanoData$logPval[fcPosPoints2], pch = 19, col = "red", cex = 1.25)
  377. points(volcanoData$logFC[fcPosPoints3], volcanoData$logPval[fcPosPoints3], pch = 19, col = "lightsalmon", cex = 1.25)
  379. points(volcanoData$logFC[fcNegPoints1], volcanoData$logPval[fcNegPoints1], pch = 19, col = "lightblue", cex = 1.25)
  380. points(volcanoData$logFC[fcNegPoints2], volcanoData$logPval[fcNegPoints2], pch = 19, col = "blue", cex = 1.25)
  381. points(volcanoData$logFC[fcNegPoints3], volcanoData$logPval[fcNegPoints3], pch = 19, col = "lightgreen", cex = 1.25)
  383. axis(side = 1, cex.axis = 1.25)
  384. axis(side = 2, cex.axis = 1.25)
  385. mtext(text = expression("log"[2]*"(Fold Change)"), side = 1, line = 2.5, cex = 1.25)
  386. mtext(text = expression("-log"[10]*"(Bonferroni p-Value)"), side = 2, line = 2.5, cex = 1.25)
  387. mtext(text = comparison, side = 3, cex = 1.25, line = 2.5)
  388. #dev.off()

h173_seurat_for_zenodo.R, under CC-BY-4.0 · at the source

Overview

Authors: Arrienne B Butic1, Elia Afanasiev2, Samantha A Spencer1, Mofida Abdelmageed3, Anirban Paul3, Kalynn M Alexander1, Katelyn N Ayers1, Todd D Schell1, Matthew D Lauver1, Ge Jin1, Samantha M Borys4, Rachel Y Kang5, ChaeMin Kim5, Elizabeth A Proctor5, Laurent Brossay4, Jo Anne A Stratton2, Vonn Walter6, Aron E Lukacher1
ORCID iDs: Samantha A Spencer, Kalynn M Alexander, Katelyn N Ayers, Matthew D Lauver, Ge Jin, Samantha M Borys, Rachel Y Kang, ChaeMin Kim, Elizabeth A Proctor, Laurent Brossay, Jo Anne A Stratton, Vonn Walter, Aron E Lukacher
  1. Department of Cell and Biological Systems, Pennsylvania State University College of Medicine, Hershey, PA USA
  2. Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC Canada
  3. Department of Neuroscience and Experimental Therapeutics, Pennsylvania State University College of Medicine, Hershey, PA USA
  4. Division of Biology and Medicine, Department of Molecular Microbiology & Immunology, Brown University, Providence, RI USA
  5. Department of Neurology, Center for Brain, Immunology, and Glia, Brain Institute, University of Virginia, Charlottesville, VA USA
  6. Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA USA
Institutions: Pennsylvania State University (United States); Montreal Neurological Institute and Hospital (Canada); McGill University (Canada); Brown University (United States); University of Virginia (United States)
Journal: Nature communications, volume 17, issue 1, article 9839
Dates: received 26 November 2025; accepted 3 August 2026; published online 17 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-76762-3 · PMID 42744829 · PMCID PMC13578448 · OpenAlex W4416867851
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.26.690770
Status: code verified
Categories: human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Infection, Central nervous system infections, Cellular immunity, T cells
MeSH: Brain*, CD4-Positive T-Lymphocytes*, CD8-Positive T-Lymphocytes*, Leukoencephalopathy, Progressive Multifocal*, Neuroinflammatory Diseases*, Programmed Cell Death 1 Receptor*, Animals, Female, Humans, JC Virus, Mice, Mice, Inbred C57BL, Mice, Knockout (* major topic)
Topic: Polyomavirus and related diseases (Oncology, Medicine), according to OpenAlex
Funding: NIAID NIH HHS (R01 AI173163, R01 AI046709); NIA NIH HHS (R01 AG072513, RF1 AG072602); U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (R35NS127217); NINDS NIH HHS (R35 NS127217)
Citations: not cited yet (Europe PMC); 71 references in the paper
Research resources: RRID:SCR_014601, The dplyr RRID:SCR_016708, tidyr RRID:SCR_017102, RRID:SCR_021123, the Flow Cytometry Core RRID:SCR_021134, the Comparative Medicine Imaging Core RRID:SCR_023179

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

elizabethproctor/Luminex-Data-Cleaning

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b1fa88f4b03aa8a444fdef59ad14d6607eff1ab9, 30 July 2023
Languages: R (2)
Size: 6 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Luminex xMAP multiplexing immunoassays”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), glmnet (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/elizabethproctor/Luminex-Data-Cleaning

stratton-lab/alexander-2025-cxcr

License: EUPL-1.2
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: beed74e6f228608d1660fe8be75ffc7c1d661492, 24 June 2025
Languages: R (3)
Size: 4 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: license file
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (3 files), tidyverse (3 files), data.table (2 files), ggplot2 (2 files), patchwork (2 files), reticulate (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/stratton-lab/alexander-2025-cxcr

Zenodo 21179919

License: eupl-1.2
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (3 files), tidyverse (3 files), data.table (2 files), ggplot2 (2 files), patchwork (2 files), reticulate (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
4 files
At the source: doi.org/10.5281/zenodo.21179919

Zenodo 21243082

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 1 file, 1 script
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (1 file), ggplot2 (1 file), patchwork (1 file), Plotly (1 file), reshape2 (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source:

Zenodo 21076315

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), glmnet (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
4 files
At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-76762-3.

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:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 11 scripts, each with its path and the digest of its content;
  • 7 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

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-76762-3.

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, 18 authors, 4 keywords, 13 MeSH terms, 4 funders, 71 references, 6 RRIDs.

Cite

This paper

Butic, A. B., Afanasiev, E., Spencer, S. A., Abdelmageed, M., Paul, A., Alexander, K. M., Ayers, K. N., Schell, T. D., Lauver, M. D., Jin, G., Borys, S. M., Kang, R. Y., Kim, C., Proctor, E. A., Brossay, L., Stratton, J. A. A., Walter, V., & Lukacher, A. E. (2026). PD-1 regulates CD4&lt;sup&gt;+&lt;/sup&gt; T cell-mediated CD8&lt;sup&gt;+&lt;/sup&gt; T cell responses in the brain to balance viral control and neuroinflammation. Nature communications, 17(1), 9839. https://doi.org/10.1038/s41467-026-76762-3

BibTeX

@article{butic2026pd,
author = {Butic, Arrienne B and Afanasiev, Elia and Spencer, Samantha A and Abdelmageed, Mofida and Paul, Anirban and Alexander, Kalynn M and Ayers, Katelyn N and Schell, Todd D and Lauver, Matthew D and Jin, Ge and Borys, Samantha M and Kang, Rachel Y and Kim, ChaeMin and Proctor, Elizabeth A and Brossay, Laurent and Stratton, Jo Anne A and Walter, Vonn and Lukacher, Aron E},
title = {{PD-1 regulates CD4\&lt;sup\&gt;+\&lt;/sup\&gt; T cell-mediated CD8\&lt;sup\&gt;+\&lt;/sup\&gt; T cell responses in the brain to balance viral control and neuroinflammation}},
journal = {Nature communications},
year = {2026},
month = aug,
volume = {17},
number = {1},
pages = {9839},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76762-3},
url = {https://doi.org/10.1038/s41467-026-76762-3},
pmid = {42744829},
pmcid = {PMC13578448}
}

RIS

TY - JOUR
AU - Butic, Arrienne B
AU - Afanasiev, Elia
AU - Spencer, Samantha A
AU - Abdelmageed, Mofida
AU - Paul, Anirban
AU - Alexander, Kalynn M
AU - Ayers, Katelyn N
AU - Schell, Todd D
AU - Lauver, Matthew D
AU - Jin, Ge
AU - Borys, Samantha M
AU - Kang, Rachel Y
AU - Kim, ChaeMin
AU - Proctor, Elizabeth A
AU - Brossay, Laurent
AU - Stratton, Jo Anne A
AU - Walter, Vonn
AU - Lukacher, Aron E
TI - PD-1 regulates CD4&lt;sup&gt;+&lt;/sup&gt; T cell-mediated CD8&lt;sup&gt;+&lt;/sup&gt; T cell responses in the brain to balance viral control and neuroinflammation
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/08/17
VL - 17
IS - 1
SP - 9839
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76762-3
UR - https://doi.org/10.1038/s41467-026-76762-3
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-76762-3",
"type": "article-journal",
"title": "PD-1 regulates CD4&lt;sup&gt;+&lt;/sup&gt; T cell-mediated CD8&lt;sup&gt;+&lt;/sup&gt; T cell responses in the brain to balance viral control and neuroinflammation",
"container-title": "Nature communications",
"author": [
{
"family": "Butic",
"given": "Arrienne B"
},
{
"family": "Afanasiev",
"given": "Elia"
},
{
"family": "Spencer",
"given": "Samantha A"
},
{
"family": "Abdelmageed",
"given": "Mofida"
},
{
"family": "Paul",
"given": "Anirban"
},
{
"family": "Alexander",
"given": "Kalynn M"
},
{
"family": "Ayers",
"given": "Katelyn N"
},
{
"family": "Schell",
"given": "Todd D"
},
{
"family": "Lauver",
"given": "Matthew D"
},
{
"family": "Jin",
"given": "Ge"
},
{
"family": "Borys",
"given": "Samantha M"
},
{
"family": "Kang",
"given": "Rachel Y"
},
{
"family": "Kim",
"given": "ChaeMin"
},
{
"family": "Proctor",
"given": "Elizabeth A"
},
{
"family": "Brossay",
"given": "Laurent"
},
{
"family": "Stratton",
"given": "Jo Anne A"
},
{
"family": "Walter",
"given": "Vonn"
},
{
"family": "Lukacher",
"given": "Aron E"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "9839",
"DOI": "10.1038/s41467-026-76762-3",
"PMID": "42744829",
"PMCID": "PMC13578448",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-76762-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
17
]
]
}
}

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

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