OSCR

Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease.

Code ↔ Paper

8 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 8 matches
  1. [1] § STAR★Methods › Quantification and statistical analysis (STAR Methods) › General annotation ↔ utils/utils.R, lines 191–250 · score 0.99 · CD3E, CD79A, CD8A, CD8B, S100A8, CD1C
  2. [2] § STAR★Methods › Quantification and statistical analysis (STAR Methods) › Normalization and clustering ↔ R/modeling.R, lines 2–63 · score 0.83 · sequencing depth, Seurat package, cell transcriptomic, cell populations, dimensionality, resolutions
  3. [3] § STAR★Methods › Quantification and statistical analysis (STAR Methods) › Cell communication analysis ↔ R/modeling.R, lines 2–63 · score 0.83 · ligand receptor interactions, ligand receptor pairs, inferred communication, communication networks, cell populations, CellChat
  4. [4] § STAR★Methods › Quantification and statistical analysis (STAR Methods) › Normalization and clustering ↔ src/scrublet/scrublet.py, lines 130–252 · score 0.81 · highly variable genes, principal component, log transformed, variance, PCA, dimensionality
  5. [5] § Results › CSF liquid biopsy captures the adaptive immune microenvironment including T cell activity ↔ utils/utils.R, lines 191–250 · score 0.79 · gamma delta, CD8 exhausted, HAVCR2, LAG3, PDCD1, effector
  6. [6] § STAR★Methods › Quantification and statistical analysis (STAR Methods) › Cell communication analysis ↔ R/CellChat_class.R, lines 2–76 · score 0.75 · intercellular communication networks, ligand receptor interactions, ligand receptor pairs, CellChat, cell communication, database
  7. [7] § Results › CNS diseases modulate myeloid profiles in the CSF microenvironment ↔ utils/utils.R, lines 124–189 · score 0.73 · MT RTMs, anti inflammatory, Mreg, DC5, BAMs, DC1
  8. [8] § STAR★Methods › Quantification and statistical analysis (STAR Methods) › scRNA-seq data pre-processing and quality control ↔ notebooks/scRNAseq/01_qc_filtering.ipynb, lines 1–22 · score 0.55 · quality control, quality cells, QC, filters, library, metrics

Paper

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The authors' code

R · 250 lines · 10 KB · no license · 3 matches

  1. suppressPackageStartupMessages({
  2. library(Seurat)
  3. library(tidyverse)
  4. library(magrittr)
  5. library(scRepertoire)
  6. library(patchwork)
  7. library(rscrublet) # devtools::install_github("iaaaka/Rscrublet")
  8. library(Matrix)
  9. library(glue)
  10. library(ggpubr)
  11. library(cowplot)
  12. library(ggExtra)
  13. library(harmony)
  14. })
  16. # -------------------------------
  18. # function to run R scrublet on a Seurat object
  19. # assumes you have RNA counts on your object (@assays$RNA@counts)
  20. # returns the same Seurat object with a "doublet_score" metadata column
  22. run_scrublet <- function(seu_obj){
  23. count_matrix = t(as(seu_obj@assays$RNA@counts,'dgTMatrix'))
  24. scrr = scrub_doublets(E_obs = count_matrix,
  25. expected_doublet_rate=0.06,
  26. min_counts=3,
  27. min_cells=3,
  28. min_gene_variability_pctl=85,
  29. n_prin_comps=30)
  31. scrr=call_doublets(scrr)
  32. #plot_doublet_histogram(scrr)
  33. seu_obj$doublet_score = scrr$doublet_scores_obs
  34. seu_obj
  35. }
  37. # -------------------------------
  39. # function to load a CellRanger library
  40. # input: the expression directories and T/BCR if any
  41. # also project and sample names to label/identify the object
  42. # sp parameter is H or m to indicate mouse or human
  43. # run scrublet can be skipped with the run_scrubblet arg
  44. # it returns a Seurat object
  46. load_seurat <- function(gex_dir, tcr_dir = NULL, bcr_dir = NULL, project, sample, sp = "H", run_scrublet = TRUE){
  48. print(paste0("Processing sample ", sample, "..."))
  50. #### create Seurat object -----------------
  52. obj <- Seurat::CreateSeuratObject(
  53. Read10X(
  54. data.dir = gex_dir,
  55. strip.suffix = TRUE),
  56. min.cells = 5,
  57. min.features = 5
  58. )
  60. # add metadata to object
  61. obj$project <- project
  62. obj$sample <- sample
  63. # compute mitochondrial percentage
  64. obj[["percent.mt"]] <- ifelse(sp == "H", PercentageFeatureSet(obj, pattern = "^MT-"), PercentageFeatureSet(obj, pattern = "^mt-"))
  65. # rename cells, in this case by appending the sample id
  66. obj <- RenameCells(obj, add.cell.id = paste0(project, "_", sample))
  68. print(obj)
  70. #### add TCR info (metadata) -----------------
  72. if (!is.null(tcr_dir)){
  73. if (file.exists(glue::glue("{tcr_dir}filtered_contig_annotations.csv"))){
  74. print(paste0("Sample ", sample, " has TCRs!"))
  75. print(paste0("Reading ", glue::glue("{tcr_dir}filtered_contig_annotations.csv")))
  76. tcr <- read.csv(glue::glue("{tcr_dir}filtered_contig_annotations.csv"))
  77. tcr <- scRepertoire::combineTCR(tcr, samples = project, ID = sample)
  78. obj <- scRepertoire::combineExpression(
  79. tcr,
  80. obj,
  81. proportion = FALSE,
  82. cloneTypes = c(Single = 1, Small = 10, Medium = 100, Large = 1000, Hyperexpanded = 10000)
  83. )
  84. }
  85. else {print(paste0(glue::glue("{tcr_dir}filtered_contig_annotations.csv"), " does not exist!"))}
  86. }
  87. else {print(paste0("Sample ", sample, " does NOT have TCRs!"))}
  90. #### add BCR info (metadata) -----------------
  92. if (!is.null(bcr_dir)){
  93. if (file.exists(glue::glue("{bcr_dir}filtered_contig_annotations.csv"))){
  94. print(paste0("Sample ", sample, " has BCRs!"))
  95. print(paste0("Reading ", glue::glue("{bcr_dir}filtered_contig_annotations.csv")))
  96. bcr <- read.csv(glue::glue("{bcr_dir}filtered_contig_annotations.csv"))
  97. bcr <- scRepertoire::combineBCR(bcr, samples = project, ID = sample)
  98. obj <- scRepertoire::combineExpression(
  99. bcr,
  100. obj,
  101. proportion = FALSE,
  102. cloneTypes = c(Single = 1, Small = 10, Medium = 100, Large = 1000, Hyperexpanded = 10000)
  103. )
  104. }
  105. else {print(paste0(glue::glue("{bcr_dir}filtered_contig_annotations.csv"), " does not exist!"))}
  106. }
  107. else {print(paste0("Sample ", sample, " does NOT have BCRs!"))}
  109. #### run scrublet (R port, from function I created) -----------------
  111. if (run_scrublet){
  113. print(paste0("Running Scrubblet for sample ", sample, "..."))
  114. obj <- run_scrublet(obj)
  116. }
  117. else {print(paste0("Skipping Scrubblet for sample ", sample))}
  119. print("Done!")
  121. obj
  122. }
  124. # -------------------------------
  126. # COLOR PALETTE DEFINITION
  128. pal_lv2 <- list(
  129. "B cells" = "#a0cfd9",
  130. "Plasma cells" = "#578797",
  131. "CD8 T cells" = "#4f8a65",
  132. "CD4 T cells" = "#9cd379",
  133. "NK cells" = "#b7b5e2",
  134. "Macrophages" = "#d96f6f",
  135. "Monocytes" = "#fcbe81",
  136. "DC" = "#fad57f",
  137. "Non-immune" = "#94735e"
  138. )
  140. pal_disease <- list(
  141. "Brain met" = "#456c2c",
  142. "Brain Metastasis" = "#456c2c",
  143. "Glioblastoma" = "#788fa3",
  144. "Inflammatory" = "#7d1517",
  145. "Lymphoma" = "#cc8630",
  146. "Healthy" = "black"
  147. )
  149. alt_pal_lv2 <- list(
  150. "B cells" = "#88CCEE",
  151. "Plasma cells" = "#332288",
  152. "CD8 T cells" = "#CC6677",
  153. "CD4 T cells" = "#AA4499",
  154. "NK cells" = "#882255",
  155. "Macrophages" = "#117733",
  156. "Monocytes" = "#44AA99",
  157. "DC" = "#DDCC77",
  158. "Non-immune" = "black"
  159. )
  161. pal_myeloid <- list('BAMs' = '#d7cd95',
  162. 'Microglia-like' = '#cc4566',
  163. 'Macrophages anti-inflammatory'='#b5da4c',
  164. 'Macrophages MT-RTM-like' = '#5d262a',
  165. 'Macrophages proliferative' = '#dabb43',
  166. 'Monocytes intermediate' = '#dc4733',
  167. 'Monocytes classical' = '#778632',
  168. 'DC mreg' = '#924026',
  169. 'DC1' = '#a38c73',
  170. 'DC2' = '#cf8238',
  171. 'DC5' = '#554825',
  172. 'pDC' = '#d48980'
  173. )
  175. pal_t <- list('CD4 CM' = '#61c271',
  176. 'CD4 IFN Response' = '#6946c9',
  177. 'CD4 Naive' = '#a1dc49',
  178. 'CD4 T helper' = '#c454ca',
  179. 'CD4 T reg' = '#cca83e',
  180. 'CD4 Th17' = '#4a2c70',
  181. 'CD8 Cytotoxic' = '#cad09a',
  182. 'CD8 EM' = '#d64b83',
  183. 'CD8 Exhausted' = '#7fd5cf',
  184. 'CD8 Pre-exhausted' = '#d75332',
  185. 'NK-gd' = '#6c82c4',
  186. 'T cells Proliferative' = '#556931'
  187. )
  189. pal_clones <- c("#F0F921", "#F69441", "#CA4778", "#7D06A5", "#0D0887")
  191. # gene expression
  192. pal_gene_exp <- c("#ADD8E633", "#E46726")
  194. pal_type <- list(
  195. "exp both" = "forestgreen",
  196. "exp AT" = "orange1",
  197. "exp BT" = "darkblue",
  198. "NE" = "black"
  199. )
  201. pal_xenium <- list(
  202. "B and Plasma cells" = "#a0cfd9",
  203. "T cells" = "#9cd379",
  204. "Macrophages" = "#d96f6f",
  205. "Microglia" = "#fad57f",
  206. "Non-immune" = "#94735e",
  207. "Neutrophils" = "purple4",
  208. "Tumor" = 'grey'
  209. )
  211. pal_k_l <- list("IGKC+" = "#d6604d", "IGKC-" = "#4393c3", `NA` = "black")
  214. # -------------------------------
  217. marker_genes <- list(
  218. "CD4 Naive/CM" = c("CD4", "ANXA1", "PASK", "SELL", "LEF1", "NOSIP", "CCR7", "TCF7", "ACTN1", "FOXP1", "KLF2", "ITGA6", "CD8A-", "CD8B-", "GZMK-"),
  219. "CD4 Effector/Mem" = c("CD4", "ZNF683", "KLRB1", "PRDM1", "CX3CR1", "EOMES", "KLRG1", "TNFSF13B", "GZMK", "CCL5", "CCL4", "NKG7", "CD69", "ITGAE", "CD8A-", "CD8B-"),
  220. "T helper" = c("CD4", "CXCR3", "GATA3", "RORC", "RORA", "IL17F", "IL17A", "CCR6", "CXCR6", "IFNG", "IL4", "IL6ST", "CXCR5", "CXCL13", "PDCD1", "CD8A-", "CD8B-"),
  221. "CD4 IFN response" = c("CD4", "IFI16", "IFI35", "IFI44", "IFI44L", "IFI6", "IFIH1", "IFIT1", "IFIT2", "IFIT3", "IFIT5", "ISG15", "CD8A-", "CD8B-"),
  222. "CD4 Proliferative" = c("CD4", "MKI67", "TOP2A", "STMN1", "UBE2C", "PCLAF", "CENPF", "CDK1", "CD8A-", "CD8B-"),
  223. "T reg" = c("IL32", "CCR7", "LEF1", "TCF7", "FOXP3", "CTLA4", "IL2RA", "ICOS", "TIGIT", "TOX2", "IKZF2", "GATA3", "CD28", "CD8A-", "CD8B-"),
  224. "Gamma Delta" = c("TRGC1", "TRGC2", "TRDC", "CD8A-", "CD8B-", "CD4-"),
  225. # "MAIT" = c("KLRB1, IL7R", "SLC4A10"),
  226. "CD8 Naive/CM" = c("CD4-", "ANXA1", "PASK", "SELL", "LEF1", "NOSIP", "CCR7", "TCF7", "ACTN1", "FOXP1", "KLF2", "ITGA6", "CD8A", "CD8B", "GZMK-"),
  227. "CD8 Mem" = c("CD8A", "CD8B", "ZNF683", "KLRB1", "PRDM1", "CX3CR1", "EOMES", "KLRG1", "TNFSF13B", "CD4-"),
  228. "CD8 Cytotoxic" = c("CD8A", "CD8B", "GZMK", "GZMH", "CCL5", "CCL4", "CD69", "PRF1", "ITGAE", "CD4-", "CST7", "GZMA", "CCL4L2", "CTSW", "GZMH", "GZMM", "HLA-C"),
  229. "CD8 IFN response" = c("CD8A", "CD8B", "IFI16", "IFI35", "IFI44", "IFI44L", "IFI6", "IFIH1", "IFIT1", "IFIT2", "IFIT3", "IFIT5", "ISG15", "CD4-"),
  230. "CD8 Exhausted" = c("CD8A", "CD8B", "HAVCR2", "LAG3", "PDCD1", "TIGIT", "TOX", "TOX2", "LAYN", "CTLA4", "CD4-"),
  231. "CD8 Proliferative" = c("CD8A", "CD8B", "MKI67", "TOP2A", "STMN1", "UBE2C", "PCLAF", "CENPF", "CDK1", "CD4-"),
  232. # "ILC" = c("KIT", "NCR1", "KLRG1"),
  233. "NK" = c("NCAM1", "FCGR3A", "CX3CR1", "GNLY", "KLRC2", "KLRD1", "KLRC3", "KLRK1", "KLRC1", "NKG7", "XCL2", "KLRB1", "PRF1", "TRDC"),
  234. # "Immature B cell" = c("MS4A1", "CD79A", "CD19", "RAG1", "RAG2", "CD3E-", "CD3G-", "CD3D-", "CD4-", "CD8A-", "CD8B-"),
  235. "Naive B cell" = c("MS4A1", "IGHD", "IGHM", "CCR7", "SELL", "TCL1A", "CD79A", "VPREB3", "FCRL1", "NIBAN3", "CD79B", "HVCN1", "CD72", "FCER2", "CD83", "CD19", "CD3E-", "CD3G-", "CD3D-", "CD4-", "CD8A-", "CD8B-"),
  236. "Memory B cell" = c("CD79A", "MS4A1", "CD27", "TNFRSF13B", "ITGAX", "PRDM1", "CD24", "BANK1", "CD74", "HLA-DRA", "IGHA1", "BLK", "SPIB", "P2RX5", "IGHA2", "CD37", "CD3E-", "CD3G-", "CD3D-", "CD4-", "CD8A-", "CD8B-"),
  237. "Plasma cells" = c("MZB1", "SDC1", "IGHG1", "JCHAIN", "IGHA1", "IGHG3", "IGLC3", "IGLC1", "IGHGP", "DERL3", "IGHG4", "XBP1", "IRF4", "CD3E-", "CD3G-", "CD3D-", "CD4-", "CD8A-", "CD8B-"),
  238. "Monocytes" = c("CD14", "S100A8", "S100A9", "LYZ", "VCAN", "FCN1"),
  239. "M1 Macrophages" = c("HLA-DPB1", "HLA-DPA1", "HLA-DQA1", "HLA-DQB1", "HLA-DQA2", "HLA-DMA", "HLA-DRB5", "HLA-DRB1", "HLA-DRA", "HLA-DMB", "HLA-DQB2", "APOE", "APOC1", "CD68", "C1QA","C1QB", "C1QC","CCL2","IL1B","CCL4","CCL7","CCL8","NFKB","CD40", "CXCL2", "CXCL3", "CXCL9", "CXCL10","CXCL11","IDO1","NFKBIA", "TNF","CXCL8","G0S2","IL6","INHBA", "CD14-", "LYZ-", "VCAN-", "FCN1-"),
  240. "M2 Macrophages" = c("APOE", "APOC1", "CD68", "C1QA","C1QB", "C1QC","CD68", "SELENOP", "MRC1", "CCL18","CD163", "CD209", "ARG1", "IL10", "CD274","CHIT1", "RNASE1", "TREM2", "IL10", "ITGA4", "LGALS9", "MARCO", "TGFB2", "TGFB1", "CSF1R", "CSF1", "SPP1","TREM2", "CD14-", "LYZ-", "VCAN-", "FCN1-"),
  241. "Myeloid proliferative" = c("CD68", "CD163", "MKI67", "TOP2A", "STMN1", "UBE2C", "PCLAF", "CENPF", "CDK1"),
  242. "Alveolar macrophages" = c("GPNMB", "SPP1", "CTSB", "C1QC", "C1QB", "APOC1", "APOE", "GLUL", "C1QA", "HMOX1", "FTL", "FN1", "PLTP", "MARCO", "CD163", "CD68", "CTSL", "TREM2", "TMIGD3", "FCGRT", "CTSD"),
  243. "pDC" = c("IL3RA", "IRF7", "LILRA4", "IRF8", "JCHAIN", "GZMB"),
  244. "DC1" = c("CLEC9A", "XCR1", "IDO1", "CLNK", "ZNF366"),
  245. "DC2" = c("CD1C", "FCER1A", "CLEC10A"),
  246. "DC3" = c("CD1C", "S100A8", "S100A9", "ANXA1"),
  247. "DC4" = c("ITGAX", "FCGR3A", "SERPINA1", "LILRB2", "SIGLEC10"),
  248. "DC5" = c("AXL", "SIGLEC6", "CD22", "DAB2"),
  249. "Mesothelial cells" = c("UPK3B", "KRT7", "CDH2", "PECAM1", "PRG4")
  250. )

utils.R at commit a3ef4fd, no license · at the source

Overview

Authors: Paula Nieto1,2, Svenja Klinsing3,4,5, Ginevra Caratù1, Mareike Dettki3, Domenica Marchese1, Katharina J. Weber5,6,7,8, Samuel Morabito1, Patricia Lorden1, Irene Ruano1, Katharina Imkeller5,6,7,8, M. Angels Velasco9, Silvia Vidal9, Juan L. Melero10, Philipp Euskirchen11,12, Marcus Czabanka5,7,8,13, Karl H. Plate5,6,7,8, Patrick N. Harter14, Anna Pascual-Reguant1, Joachim P. Steinbach3,5,7,8, Holger Heyn1,15,16, Pia S. Zeiner3,4,5,7,8, Juan C. Nieto1
16 affiliations
  1. Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
  2. Universitat Pompeu Fabra (UPF), Barcelona, Spain
  3. Goethe University Frankfurt, University Hospital, Dr. Senckenberg Institute of Neurooncology, Frankfurt, Germany
  4. Goethe University Frankfurt, University Hospital, Department of Neurology, Frankfurt, Germany
  5. Goethe University Frankfurt, University Hospital, University Cancer Center (UCT), Frankfurt, Germany
  6. Goethe University Frankfurt, University Hospital, Institute of Neurology (Edinger-Institute), Frankfurt, Germany
  7. Goethe University Frankfurt, Frankfurt Cancer Institute (FCI), Frankfurt, Germany
  8. German Cancer Research Center (DKFZ) Heidelberg, Germany and German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt, Germany
  9. Biomedical Research Institut Sant Pau (IIB Sant Pau), Barcelona, Spain
  10. Omniscope Inc., Barcelona, Spain
  11. Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin und Humboldt Universität zu Berlin, Department of Neuropathology, Berlin, Germany
  12. German Cancer Consortium (DKTK), partner site Berlin, a partnership between DKFZ and Charité - Universitätsmedizin Berlin, Berlin, Germany
  13. Goethe University Frankfurt, University Hospital, Department of Neurosurgery, Frankfurt, Germany
  14. Center for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany
  15. University of Barcelona (UB), Barcelona, Spain
  16. ICREA, Barcelona, Spain
Institutions: Universitat Pompeu Fabra (Spain); Centro Nacional de Análisis Genómico (Spain); Goethe University Frankfurt (Germany); University Hospital Frankfurt (Germany); German Cancer Research Center (Germany); Frankfurt Cancer Institute (Germany); Deutsches Konsortium für Translationale Krebsforschung (Germany); Institut de Recerca Sant Pau (Spain); Humboldt-Universität zu Berlin (Germany); Charité - Universitätsmedizin Berlin (Germany); Institut für Neuropathologie (Germany); Ludwig-Maximilians-Universität München (Germany); Universitat de Barcelona (Spain)
Journal: Cell reports. Medicine, volume 7, issue 3, article 102651
Dates: received 13 August 2025; accepted 3 February 2026; published online 6 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.xcrm.2026.102651 · PMID 41794040 · PMCID PMC13006398 · OpenAlex W7134135627
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: leptomeningeal disease, cerebrospinal fluid, liquid biopsy, single-cell RNA sequencing, T cell receptor sequencing, CNS lymphoma, brain metastasi, glioblastoma, tumor immune microenvironment
MeSH: Brain Neoplasms*, Glioblastoma*, Meningeal Neoplasms*, Female, Humans, Lymphoma, Male, Receptors, Antigen, T-Cell, T-Lymphocytes, Tumor Microenvironment (* major topic)
Topic: Brain Metastases and Treatment (Pulmonary and Respiratory Medicine, Medicine), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG); Goethe University; Frankfurt research; Mildred Scheel Career Center Frankfurt; Deutsche Krebshilfe; Ministry of Higher Education; HMWK; Frankfurt Cancer Institute; Dr. Senckenberg Foundation; Spanish Universities Ministry; Goethe University Frankfurt
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

Leptomeningeal disease (LMD) is a devastating manifestation of advanced cancer, marked by rapid neurological decline and limited treatment options. Immune profiling in central nervous system (CNS) neoplasms, including LMD, is critical for understanding disease biology and guiding therapy. Here, we use single-cell RNA and T cell receptor (TCR) sequencing of cerebrospinal fluid (CSF) from patients with CNS lymphoma (CNSL), brain metastases (BrMs), and glioblastoma (GB), alongside deep TCR sequencing of blood and spatial transcriptomics of brain lesions. We uncover distinct, disease-specific CSF immune landscapes: CNSL-associated LMD shows clonal T cell expansion, while BrMs and GB are enriched in blood-derived and resident-like myeloid cells. Spatial analysis confirms transcriptional similarities between CSF and tumor microenvironments. Longitudinal sampling reveals dynamic immune changes and emerging resistant clones. These findings establish the CSF as an immune-active compartment reflecting disease-specific features and highlight the value of CSF liquid biopsy for immune monitoring and therapeutic stratification in LMD.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

cellxgene.cziscience.com/collections/573e2e06-8af0-4d96-bfdd-7d64a4bb9c21

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: cellxgene.cziscience.com/collections/573e2e06-8af0-4d96-bfdd-7d64a4bb9c21

Single-Cell-Genomics-Group-CNAG-CRG/CSF

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a3ef4fdf01a450f9604fe762428d1159e688a960, 5 March 2026
Languages: Jupyter (7), R (1)
Size: 12 files, 8 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, environment (environments/environment_CSF_python.yml, environments/environment_CSF_R.yml), 4 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (5 files), Seurat (3 files), anndata (1 file), cowplot (1 file), ggpubr (1 file), Harmony (1 file), PyTorch Lightning (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), patchwork (1 file), PyTorch (1 file), reshape2 (1 file), Scanpy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
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9 files
At the source: github.com/Single-Cell-Genomics-Group-CNAG-CRG/CSF

swolock/scrublet

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 67f8ecbad14e8e1aa9c89b43dac6638cebe38640, 28 December 2020
Languages: Python (8), Jupyter (6)
Size: 20 files, 14 scripts
Software Heritage: archived
Found in: the text, “Key resources table”
Holds: README, license file, environment (requirements.txt, setup.py, old_versions/v0.1/requirements.txt, old_versions/v0.1/setup.py), 6 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (12 files), NumPy (10 files), SciPy (9 files), scikit-learn (8 files), pandas (4 files), UMAP (4 files), NetworkX (2 files), scikit-image (2 files), Numba (1 file), Scanpy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
16 files
At the source: github.com/swolock/scrublet

immunogenomics/presto

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: b5df6ee6097eb62522f2e557aa93ac21bab2d05f, 20 September 2026
Languages: JavaScript (15), R (14), C++ (2)
Size: 148 files, 31 scripts
Software Heritage: archived
Found in: the text, “Key resources table”
Holds: README, environment (DESCRIPTION), tests, continuous integration, documentation, 2 notebooks
Not found: license file, CITATION.cff
Tools: tidyverse (4 files), Seurat (3 files), DESeq2 (2 files), SingleCellExperiment (2 files), broom (1 file), data.table (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
32 files
At the source: github.com/immunogenomics/presto

jinworks/CellChat

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 75253cd0c9e68410e6e721a6d3a0419a1d7e358f, 4 March 2026
Languages: R (19), C++ (2)
Size: 178 files, 21 scripts
Software Heritage: not archived
Found in: the text, “Key resources table”
Holds: README, license file, environment (DESCRIPTION), documentation, 9 notebooks
Not found: CITATION.cff, tests, continuous integration
Tools: tidyverse (9 files), patchwork (6 files), Seurat (4 files), ComplexHeatmap (3 files), cowplot (3 files), ggplot2 (3 files), SingleCellExperiment (3 files), igraph (2 files), reshape2 (2 files), reticulate (2 files), circlize (1 file), Plotly (1 file), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
23 files
At the source: github.com/jinworks/CellChat

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:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 74 scripts, each with its path and the digest of its content;
  • 8 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

Data and code availability

• Raw single-cell RNA and TCR sequencing data (fastq files) of CSF samples are deposited in GEO (GSE286518 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE286518)). • Xenium spatial transcriptomics raw data are deposited in Zenodo (https://doi.org/10.5281/zenodo.14510199). • Access and exploration of processed CSF data are available through the CELLxGENE portal (https://cellxgene.cziscience.com/collections/573e2e06-8af0-4d96-bfdd-7d64a4bb9c21). • All code, scripts, and notebooks related to this publication are available on GitHub (https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/CSF). • All data and code are publicly available as of the date of publication. Additional information is available upon reasonable request to the lead contact.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 22 authors, 9 keywords, 10 MeSH terms, 11 funders, 62 references.

Cite

This paper

Nieto, P., Klinsing, S., Caratù, G., Dettki, M., Marchese, D., Weber, K. J., Morabito, S., Lorden, P., Ruano, I., Imkeller, K., Velasco, M. A., Vidal, S., Melero, J. L., Euskirchen, P., Czabanka, M., Plate, K. H., Harter, P. N., Pascual-Reguant, A., Steinbach, J. P., . . . Nieto, J. C. (2026). Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease. Cell reports. Medicine, 7(3), 102651. https://doi.org/10.1016/j.xcrm.2026.102651

BibTeX

@article{nieto2026integrative,
author = {Nieto, Paula and Klinsing, Svenja and Caratù, Ginevra and Dettki, Mareike and Marchese, Domenica and Weber, Katharina J. and Morabito, Samuel and Lorden, Patricia and Ruano, Irene and Imkeller, Katharina and Velasco, M. Angels and Vidal, Silvia and Melero, Juan L. and Euskirchen, Philipp and Czabanka, Marcus and Plate, Karl H. and Harter, Patrick N. and Pascual-Reguant, Anna and Steinbach, Joachim P. and Heyn, Holger and Zeiner, Pia S. and Nieto, Juan C.},
title = {{Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease}},
journal = {Cell reports. Medicine},
year = {2026},
month = mar,
volume = {7},
number = {3},
pages = {102651},
publisher = {Elsevier},
issn = {2666-3791},
doi = {10.1016/j.xcrm.2026.102651},
url = {https://doi.org/10.1016/j.xcrm.2026.102651},
pmid = {41794040},
pmcid = {PMC13006398}
}

RIS

TY - JOUR
AU - Nieto, Paula
AU - Klinsing, Svenja
AU - Caratù, Ginevra
AU - Dettki, Mareike
AU - Marchese, Domenica
AU - Weber, Katharina J.
AU - Morabito, Samuel
AU - Lorden, Patricia
AU - Ruano, Irene
AU - Imkeller, Katharina
AU - Velasco, M. Angels
AU - Vidal, Silvia
AU - Melero, Juan L.
AU - Euskirchen, Philipp
AU - Czabanka, Marcus
AU - Plate, Karl H.
AU - Harter, Patrick N.
AU - Pascual-Reguant, Anna
AU - Steinbach, Joachim P.
AU - Heyn, Holger
AU - Zeiner, Pia S.
AU - Nieto, Juan C.
TI - Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease
T2 - Cell reports. Medicine
J2 - Cell Rep Med
PY - 2026
DA - 2026/03/06
VL - 7
IS - 3
SP - 102651
SN - 2666-3791
PB - Elsevier
DO - 10.1016/j.xcrm.2026.102651
UR - https://doi.org/10.1016/j.xcrm.2026.102651
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.xcrm.2026.102651",
"type": "article-journal",
"title": "Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease",
"container-title": "Cell reports. Medicine",
"author": [
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"family": "Nieto",
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{
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{
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{
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{
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"family": "Euskirchen",
"given": "Philipp"
},
{
"family": "Czabanka",
"given": "Marcus"
},
{
"family": "Plate",
"given": "Karl H."
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{
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}
}

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

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