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pmid:33766068

Cerebrospinal fluid cells immune landscape in multiple sclerosis.

Zijian Li · Yongchao Liu · Aili Jia · Yueran Cui · Juan Feng

Journal of translational medicine · 2021 doi:10.1186/s12967-021-02804-7

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
id
pmid:33766068
kind
science-record
content_hash
sha256:c79750e697d0b4654f28467604f9464a5268b8dd975d0fd13904488ced4c0e99
schema
https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T15:41:30Z
updated_at
2026-11-01T22:46:23Z
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paper object · 10 fields
state
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title
Cerebrospinal fluid cells immune landscape in multiple sclerosis.
doi
10.1186/s12967-021-02804-7
pmid
33766068
authors array · 5 items
[0]
Zijian Li
[1]
Yongchao Liu
[2]
Aili Jia
[3]
Yueran Cui
[4]
Juan Feng
journal
Journal of translational medicine
year
2021
abstract
BACKGROUND: Multiple Sclerosis (MS) is a potentially devastating autoimmune neurological disorder, which characteristically induces demyelination of white matter in the brain and spinal cord. METHODS: In this study, three characteristics of the central nervous system (CNS) immune microenvironment occurring during MS onset were explored; immune cell proportion alteration, differential gene expression profile, and related pathways. The raw data of two independent datasets were obtained from the ArrayExpress database; E-MTAB-69, which was used as a derivation cohort, and E-MTAB-2374 which was used as a validation cohort. Differentially expressed genes (DEGs) were identified by the false discovery rate (FDR) value of < 0.05 and |log2 (Fold Change)|> 1, for further analysis. Then, functional enrichment analyses were performed to explore the pathways associated with MS onset. The gene expression profiles were analyzed using CIBERSORT to identify the immune type alterations involved in MS disease. RESULTS: After verification, the proportion of five types of immune cells (plasma cells, monocytes, macrophage M2, neutrophils and eosinophils) in cerebrospinal fluid (CSF) were revealed to be significantly altered in MS cases compared to the control group. Thus, the complement and coagulation cascades and the systemic lupus erythematosus (SLE) pathways may play critical roles in MS. We identified NLRP3, LILRB2, C1QB, CD86, C1QA, CSF1R, IL1B and TLR2 as eight core genes correlated with MS. CONCLUSIONS: Our study identified the change in the CNS immune microenvironment of MS cases by analysis of the in silico data using CIBERSORT. Our data may assist in providing directions for further research as to the molecular mechanisms of MS and provide future potential therapeutic targets in treatment.
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Differentially Expressed Genes (DEGs) between the MS and control groups were determined using the R package ‘limma’.
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sections[4] "Data preprocessing and DEG analysis" paragraph 2
quote
Differentially Expressed Genes (DEGs) between the MS and control groups were determined using the R package ‘limma’.
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The data used to support the findings of this study are available from the corresponding author upon request.
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sections[24] "Data availability" paragraph 2
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The data used to support the findings of this study are available from the corresponding author upon request.
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The expression analysis of the top 20 hub genes with the highest interaction degree
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Identification of core DEGs
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0.002835159
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32
[5]
Down-regulated
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0.000387759
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0.005193712
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[5]
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[5]
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[5]
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2.49E−05
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0.000803783
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25
[5]
Down-regulated
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[0]
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0.000312054
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0.00446939
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23
[5]
Down-regulated
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[5]
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NLRP3
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3.53E−10
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8.93E−07
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21
[5]
Down-regulated
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21
[5]
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The expression of the 8 genes overlapped in top 20 hub genes of derivation cohort and 150 DEGs of validation cohort
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Validation of CNS immune microenvironment alteration
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Table 3
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The expression of the 8 genes overlapped in top 20 hub genes of derivation cohort and 150 DEGs of validation cohort
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fdr_bh
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Differentially Expressed Genes (DEGs) between the MS and control groups were determined using the R package ‘limma’.
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used array · 3 items
[0] object · 3 fields
id
pmid:33766068
type
paper
label
pmid:33766068
[1] object · 3 fields
id
source:pmid:33766068
type
manuscript
label
PMID 33766068 full text
[2] object · 3 fields
id
data:geo:E-MTAB-2374
type
dataset
label
GEO E-MTAB-2374
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-33766068
type
extraction
used array · 1 item
[0]
source:pmid:33766068
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-E-MTAB-2374
type
accession_resolution
used array · 1 item
[0]
data:geo:E-MTAB-2374
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
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dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
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snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:33766068
parent_hash
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fork_of
null
gaps array · 8 items
[0]
asset:data:geo:E-MTAB-2374 licence not read
[1]
asset:data:geo:E-MTAB-2374 not downloaded
[2]
asset:data:geo:E-MTAB-69 licence not read
[3]
asset:data:geo:E-MTAB-69 not downloaded
[4]
execution.not_run
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review.human_critique
[6]
review.independent_reader
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review.machine_critique
assays array · 1 item
[0] object · 11 fields
role
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differential_expression
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f2132e96ae6c7a4e
required_operations array · 3 items
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normalise_counts
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differential_expression_fit
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fdr_bh
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Differentially Expressed Genes (DEGs) between the MS and control groups were determined using the R package ‘limma’.
order_basis
analysis_only
id
assay:1
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33766068", "kind": "science-record", "revision": 8, "content_hash": "sha256:c79750e697d0b4654f28467604f9464a5268b8dd975d0fd13904488ced4c0e99", "created_at": "2026-11-01T15:41:30Z", "updated_at": "2026-11-01T22:46:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Cerebrospinal fluid cells immune landscape in multiple sclerosis.", "doi": "10.1186/s12967-021-02804-7", "pmid": "33766068", "authors": ["Zijian Li", "Yongchao Liu", "Aili Jia", "Yueran Cui", "Juan Feng"], "journal": "Journal of translational medicine", "year": 2021, "abstract": "BACKGROUND: Multiple Sclerosis (MS) is a potentially devastating autoimmune neurological disorder, which characteristically induces demyelination of white matter in the brain and spinal cord.\nMETHODS: In this study, three characteristics of the central nervous system (CNS) immune microenvironment occurring during MS onset were explored; immune cell proportion alteration, differential gene expression profile, and related pathways. The raw data of two independent datasets were obtained from the ArrayExpress database; E-MTAB-69, which was used as a derivation cohort, and E-MTAB-2374 which was used as a validation cohort. Differentially expressed genes (DEGs) were identified by the false discovery rate (FDR) value of < 0.05 and |log2 (Fold Change)|> 1, for further analysis. Then, functional enrichment analyses were performed to explore the pathways associated with MS onset. The gene expression profiles were analyzed using CIBERSORT to identify the immune type alterations involved in MS disease.\nRESULTS: After verification, the proportion of five types of immune cells (plasma cells, monocytes, macrophage M2, neutrophils and eosinophils) in cerebrospinal fluid (CSF) were revealed to be significantly altered in MS cases compared to the control group. Thus, the complement and coagulation cascades and the systemic lupus erythematosus (SLE) pathways may play critical roles in MS. We identified NLRP3, LILRB2, C1QB, CD86, C1QA, CSF1R, IL1B and TLR2 as eight core genes correlated with MS.\nCONCLUSIONS: Our study identified the change in the CNS immune microenvironment of MS cases by analysis of the in silico data using CIBERSORT. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:E-MTAB-2374"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:E-MTAB-2374"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33766068", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-021-02804-7", "sha256": "sha256:074bddb815229e289831de7ebc4e2e8cf5387f68b499fdda5e0b1991ccf45cbf", "bytes": 56104, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12967-021-02804-7", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7995713", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:E-MTAB-2374", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=E-MTAB-2374", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "E-MTAB-2374", "version": null, "note": "The data used to support the findings of this study are available from the corresponding author upon request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data used to support the findings of this study are available from the corresponding author upon request.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": null, "resolution_checked_at": "2026-10-31T18:28:58Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:E-MTAB-69", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=E-MTAB-69", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "E-MTAB-69", "version": null, "note": "The data used to support the findings of this study are available from the corresponding author upon request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data used to support the findings of this study are available from the corresponding author upon request.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": null, "resolution_checked_at": "2026-10-31T18:29:02Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "asset:table:pmid:33766068:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:0a01b477c06e665427c8ef294ee9062b787da1b636ef94e8c75c2981ea5635d8", "bytes": 1842, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#Tab1", "version": null, "note": "The expression analysis of the top 20 hub genes with the highest interaction degree", "structure": {"pmcid": "PMC7995713", "section_index": 11, "section_title": "Identification of core DEGs", "row_count": 21, "column_count": 6}, "table": {"label": "Table 1", "caption": "The expression analysis of the top 20 hub genes with the highest interaction degree", "rows": [["Gene symbol", "LogFC", "P.Value", "Adj. 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