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Multi-omics integration provides biological insight and prioritizes potential drug targets in multiple sclerosis progression.

Code ↔ Paper

2 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 2 matches
  1. [1] § Results › Integrative multi-omics validation pinpoints key proteins ↔ run_scRNA_DEA.R, lines 1–43 · score 0.77 · oligodendrocyte precursor cell, endothelial cells, single cell, RNA, astrocyte, neuron
  2. [2] § Methods › Statistical analysis › Validating proteins using causal evidence from MR ↔ run_MR.R, lines 54–119 · score 0.57 · Wald ratio, error, IVW, MR, exposure, R2

Paper

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

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The authors' code

R · 112 lines · 2.8 KB · no license · 1 match

  1. #!/usr/bin/env Rscript
  3. suppressPackageStartupMessages({
  4. library(Seurat)
  5. library(data.table)
  6. })
  8. # =========================
  9. # Inputs (edit here)
  10. # =========================
  11. seurat_rds <- "data/scRNA/ms_single_cell.rds"
  12. outdir <- "results/scRNA_DE"
  14. # cell types to keep (edit to your study)
  15. cell_types_to_keep <- c(
  16. "microglial cell",
  17. "oligodendrocyte",
  18. "oligodendrocyte precursor cell",
  19. "astrocyte",
  20. "endothelial cell",
  21. "neuron",
  22. "pericyte",
  23. "progenitor cell"
  24. )
  26. # target genes (Ensembl IDs in your example)
  27. target_genes <- c(
  28. "ENSG00000197959", # DNM3 (example)
  29. "ENSG00000075292", # ZNF638
  30. "ENSG00000174227", # PIGG
  31. "ENSG00000135636" # DYSF
  32. )
  34. # disease labels in metadata
  35. group_var <- "disease"
  36. case_level <- "multiple sclerosis"
  37. ctrl_level <- "normal"
  39. # FindMarkers settings (minimal & robust)
  40. min_pct <- 0.1
  41. test_use <- "wilcox"
  43. dir.create(outdir, recursive = TRUE, showWarnings = FALSE)
  45. # =========================
  46. # Load Seurat object
  47. # =========================
  48. obj <- readRDS(seurat_rds)
  50. # Required metadata checks
  51. meta_cols <- colnames([email hidden])
  52. stopifnot("cell_type" %in% meta_cols)
  53. stopifnot(group_var %in% meta_cols)
  55. # Subset cell types of interest
  56. obj <- subset(obj, subset = cell_type %in% cell_types_to_keep)
  58. # Keep only target genes present in object
  59. target_present <- intersect(target_genes, rownames(obj))
  60. if (length(target_present) == 0) {
  61. stop("None of target_genes found in Seurat object rownames().")
  62. }
  64. # =========================
  65. # Differential expression per cell type
  66. # =========================
  67. res_list <- list()
  69. for (ct in cell_types_to_keep) {
  71. sub <- subset(obj, subset = cell_type == ct)
  73. # check both groups exist
  74. groups <- unique(as.character([email hidden][[group_var]]))
  75. if (!(case_level %in% groups && ctrl_level %in% groups)) {
  76. message("[SKIP] ", ct, ": missing one of groups (", ctrl_level, ", ", case_level, ")")
  77. next
  78. }
  80. # run DE only on target genes (fast + focused)
  81. mk <- FindMarkers(
  82. object = sub,
  83. group.by = group_var,
  84. ident.1 = case_level,
  85. ident.2 = ctrl_level,
  86. features = target_present,
  87. logfc.threshold = 0,
  88. min.pct = min_pct,
  89. test.use = test_use
  90. )
  92. if (nrow(mk) == 0) next
  94. mk$gene <- rownames(mk)
  95. mk$cell_type <- ct
  97. # FDR
  98. mk$p_val_adj_fdr <- p.adjust(mk$p_val, method = "fdr")
  100. res_list[[ct]] <- mk
  101. }
  103. res <- data.table::rbindlist(res_list, fill = TRUE)
  105. # Save
  106. data.table::fwrite(res, file.path(outdir, "MS_vs_normal_DE_target_genes.tsv"), sep = "\t")
  108. # Also save significant results only
  109. res_sig <- res[p_val_adj_fdr < 0.05]
  110. data.table::fwrite(res_sig, file.path(outdir, "MS_vs_normal_DE_target_genes_sig.tsv"), sep = "\t")
  112. message("[DONE] Wrote results to: ", outdir)

run_scRNA_DEA.R at commit d2cb5ff, no license · at the source

Overview

Authors: Yuan Jiang1, Jinyu Xiao2,1, Ingrid Kockum1, Pernilla Stridh1, Qianwen Liu1, Tomas Olsson1, Lars Alfredsson3,1, Xia Jiang1,2
ORCID iDs: Yuan Jiang, Ingrid Kockum, Xia Jiang
  1. Department of Clinical Neuroscience, The Karolinska Neuroimmunology and Multiple Sclerosis Centre, Centre for Molecular Medicine, Karolinska Institutet, Stockholm, 171 77 Sweden
  2. Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041 Chengdu, China
  3. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, 17177 Sweden
Institutions: Karolinska Institutet (Sweden); Sichuan University (China); West China Medical Center of Sichuan University (China)
Journal: Journal of neuroinflammation, volume 23, issue 1, article 198
Dates: received 5 November 2025; accepted 27 May 2026; published online 2 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s12974-026-03895-z · PMID 42231304 · PMCID PMC13261964 · OpenAlex W7163237096
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, Preprocessing
Keywords: Progressive multiple sclerosis, Protein, Drug targets, Omics
MeSH: Disease Progression*, Multiple Sclerosis*, Genome-Wide Association Study, Humans, Multiomics, Proteomics (* major topic)
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: Karolinska Institute
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

Introduction: Current therapies for multiple sclerosis (MS) primarily reduce relapse rates and delay disability by targeting inflammation, while have limited efficacy against disease progression driven by neurodegenerative processes. We sought to identify and validate proteins for MS progression by integrating a large genome-wide association study (GWAS) of MS progression with large-scale protein quantitative trait loci data from blood and brain.

Methods: We conducted proteome-wide association studies (PWAS) to nominate proteins; applied summary-data Mendelian randomization and colocalization to evaluate association; and performed functional annotation (pathway enrichment, drug-target mapping, and protein-protein interaction networks) to prioritize therapeutic potential. Additionally, we performed external validation through bulk and cell-type-specific expression analyses and prioritized protein evaluation. The final key proteins were determined by triangulating evidence across all these streams.

Results: We identified 48 genetically prioritized proteins. Functional annotation prioritized 14 with therapeutic potential and highlighted 13 non-MS drugs for repurposing. Triangulation of evidence with multi-omics external validation highlighted six key proteins: RRM2B (a Cladribine target), CBR1, and ETFA, which are linked to existing drugs; DNM3 (a GWAS-implicated locus), CAB39L, and NMRAL1, which emerged as validated novel proteins providing biological insight into MS progression.

Conclusion: Our multi-omics integration prioritizes proteins implicated in MS progression, providing mechanistic insights into neurodegeneration and a foundation for future therapeutic exploration in progressive MS.

Supplementary Information: The online version contains supplementary material available at 10.1186/s12974-026-03895-z.

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 2 matches between paragraphs and lines of code.

Yuanjiang1110/MS-progression-drug-targets

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d2cb5ff554886c9155b88c851c89cc742426c410, 4 March 2026
Languages: R (3), Shell (3)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (3 files), Seurat (1 file)
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/Yuanjiang1110/MS-progression-drug-targets

Code availability

The R scripts and analytical pipelines used to generate the results of this study are available on GitHub at: [https://github.com/Yuanjiang1110/MS-progression-drug-targets].

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;
  • 2 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 availability

All the genetic and QTL data used in this study were from the publicly accessible summary statistics and can be accessed through the corresponding references presented in the main text.

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, 4 keywords, 6 MeSH terms, 1 funder, 51 references.

Cite

This paper

Jiang, Y., Xiao, J., Kockum, I., Stridh, P., Liu, Q., Olsson, T., Alfredsson, L., & Jiang, X. (2026). Multi-omics integration provides biological insight and prioritizes potential drug targets in multiple sclerosis progression. Journal of neuroinflammation, 23(1), 198. https://doi.org/10.1186/s12974-026-03895-z

BibTeX

@article{jiang2026multi,
author = {Jiang, Yuan and Xiao, Jinyu and Kockum, Ingrid and Stridh, Pernilla and Liu, Qianwen and Olsson, Tomas and Alfredsson, Lars and Jiang, Xia},
title = {{Multi-omics integration provides biological insight and prioritizes potential drug targets in multiple sclerosis progression}},
journal = {Journal of neuroinflammation},
year = {2026},
month = jun,
volume = {23},
number = {1},
pages = {198},
publisher = {BMC},
issn = {1742-2094},
doi = {10.1186/s12974-026-03895-z},
url = {https://doi.org/10.1186/s12974-026-03895-z},
pmid = {42231304},
pmcid = {PMC13261964}
}

RIS

TY - JOUR
AU - Jiang, Yuan
AU - Xiao, Jinyu
AU - Kockum, Ingrid
AU - Stridh, Pernilla
AU - Liu, Qianwen
AU - Olsson, Tomas
AU - Alfredsson, Lars
AU - Jiang, Xia
TI - Multi-omics integration provides biological insight and prioritizes potential drug targets in multiple sclerosis progression
T2 - Journal of neuroinflammation
J2 - J Neuroinflammation
PY - 2026
DA - 2026/06/02
VL - 23
IS - 1
SP - 198
SN - 1742-2094
PB - BMC
DO - 10.1186/s12974-026-03895-z
UR - https://doi.org/10.1186/s12974-026-03895-z
LA - en
ER -

CSL-JSON

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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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