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pmid:39799354

Ferroptosis-related genes participate in the microglia-induced neuroinflammation of spinal cord injury via NF-κB signaling: evidence from integrated single-cell and spatial transcriptomic analysis.

Siqiao Wang · Li Yang · Zhourui Wu · Chen Li · Shaoke Wang · Zhihui Xiao · Bei Ma · Rongrong Zhu · Liming Cheng

Journal of translational medicine · 2025 doi:10.1186/s12967-025-06095-0

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pmid:39799354
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title
Ferroptosis-related genes participate in the microglia-induced neuroinflammation of spinal cord injury via NF-κB signaling: evidence from integrated single-cell and spatial transcriptomic analysis.
doi
10.1186/s12967-025-06095-0
pmid
39799354
authors array · 9 items
[0]
Siqiao Wang
[1]
Li Yang
[2]
Zhourui Wu
[3]
Chen Li
[4]
Shaoke Wang
[5]
Zhihui Xiao
[6]
Bei Ma
[7]
Rongrong Zhu
[8]
Liming Cheng
journal
Journal of translational medicine
year
2025
abstract
BACKGROUND: Ferroptosis and immune responses are critical pathological events in spinal cord injury (SCI), whereas relative molecular and cellular mechanisms remain unclear. METHODS: Micro-array datasets (GSE45006, GSE69334), RNA sequencing (RNA-seq) dataset (GSE151371), spatial transcriptome datasets (GSE214349, GSE184369), and single cell RNA sequencing (scRNA-seq) datasets (GSE162610, GSE226286) were available from the Gene Expression Omnibus (GEO) database. Through weighted gene co-expression network analysis and differential expression analysis in GSE45006, we identified differentially expressed time- and immune-related genes (DETIRGs) associated with chronic SCI and differentially expressed ferroptosis- and immune-related genes (DEFIRGs), which were validated in GSE151371. Protein-protein interaction and microRNA-mRNA-transcription factor regulatory networks were constructed based on Search Tool for the Retrieval of Interacting Genes (STRING) and NetworkAnalyst, respectively, which were validated by chromatin immunoprecipitation followed by sequencing (ChIP-seq). Cell subclusters and unique features of microglia in SCI were identified by single-cell transcriptomic analysis, which were validated in GSE226286. Spatial expression patterns of DETIRGs and DEFIRGs were validated in brain injury (GSE214349) and SCI (GSE184369). Potential mechanisms underlying neuronal regeneration by neurotrophin-3 (NT3)-chitosan were revealed by transcriptomic analyses in GSE69334. Immune- and ferroptosis-related mechanisms of nanolayered double hydroxide loaded with NT3 (LDH-NT3) were investigated in vivo and in vitro. RESULTS: GBP2, TEC, UNC93B1, PLXNC1, NFATC1, IL10RB, and TLR8 were DETIRGs represented chronic SCI-specific genes and peripheral blood biomarkers. NFKB1 may regulate expression of CYBB and HMOX1 in a unique subcluster of M1 microglia within the middle SCI lesion, establishing links between microglial ferroptosis and neuroinflammation. Reduced inflammatory responses and microglial ferroptosis were potential effects of NT3-chitosan or LDH-NT3 on neuronal regeneration. CONCLUSIONS: A novel subcluster of microglia exhibiting M1 polarization and ferroptosis phenotype was involved in SCI. These microglia may trigger neuroinflammation and induce neuronal degeneration within the middle site of SCI, which might be inhibited by NT3-chitosan or LDH-NT3.
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In the micro-array datasets (GSE45006, GSE69334) and RNA-seq dataset (GSE151371), the “limma” R package was used to identify DEGs [31], and |log2 (FC)|> 1.0 and FDR < 0.05 were the screening criteria for DEG selection [30].
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sections[6] "Differential expression analysis" paragraph 2
quote
In the micro-array datasets (GSE45006, GSE69334) and RNA-seq dataset (GSE151371), the “limma” R package was used to identify DEGs [31], and |log2 (FC)|> 1.0 and FDR < 0.05 were the screening criteria for DEG selection [30].
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All data presented in the study are presented in the manuscript and supplementary files. Further inquiries of datasets are available directed to the corresponding author.
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All data presented in the study are presented in the manuscript and supplementary files. Further inquiries of datasets are available directed to the corresponding author.
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After taking the intersection of 1140 DEGs from GSE45006 and 64 FRGs from PathCards database, 9 FRGs were identified as DEFRGs (Fig. S3a).
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sections[38] "Identification of DEFRGs in SCI" paragraph 2
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After taking the intersection of 1140 DEGs from GSE45006 and 64 FRGs from PathCards database, 9 FRGs were identified as DEFRGs (Fig. S3a).
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After taking the intersection of 1140 DEGs from GSE45006 and 64 FRGs from PathCards database, 9 FRGs were identified as DEFRGs (Fig. S3a).
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title
Expression data from adult wistar female rat 5mm spinal cord tissue
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2026-11-01T20:16:08Z
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In the micro-array datasets (GSE45006, GSE69334) and RNA-seq dataset (GSE151371), the “limma” R package was used to identify DEGs [31], and |log2 (FC)|> 1.0 and FDR < 0.05 were the screening criteria for DEG selection [30].
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[0]
All data presented in the study are presented in the manuscript and supplementary files. Further inquiries of datasets are available directed to the corresponding author.
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minimax
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loaded with NT3 (LDH-NT3) were investigated in vivo and in vitro. RESULTS: GBP2, TEC, UNC93B1, P
[1]
Gs were illustrated by box plots between the rat spinal tissues of sham group and SCI group a
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In the micro-array datasets (GSE45006, GSE69334) and RNA-seq dataset (GSE151371), the “limma” R package was used to identify DEGs [31], and |log2 (FC)|> 1.0 and FDR < 0.05 were the screening criteria for DEG selection [30].
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39799354", "kind": "science-record", "revision": 4, "content_hash": "sha256:f3390eaab7438385483f5505b7b3e9a319dad9166c5e3009e65fc8f7a5ed1445", "created_at": "2026-11-01T20:30:06Z", "updated_at": "2026-11-01T22:55:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Ferroptosis-related genes participate in the microglia-induced neuroinflammation of spinal cord injury via NF-κB signaling: evidence from integrated single-cell and spatial transcriptomic analysis.", "doi": "10.1186/s12967-025-06095-0", "pmid": "39799354", "authors": ["Siqiao Wang", "Li Yang", "Zhourui Wu", "Chen Li", "Shaoke Wang", "Zhihui Xiao", "Bei Ma", "Rongrong Zhu", "Liming Cheng"], "journal": "Journal of translational medicine", "year": 2025, "abstract": "BACKGROUND: Ferroptosis and immune responses are critical pathological events in spinal cord injury (SCI), whereas relative molecular and cellular mechanisms remain unclear.\nMETHODS: Micro-array datasets (GSE45006, GSE69334), RNA sequencing (RNA-seq) dataset (GSE151371), spatial transcriptome datasets (GSE214349, GSE184369), and single cell RNA sequencing (scRNA-seq) datasets (GSE162610, GSE226286) were available from the Gene Expression Omnibus (GEO) database. Through weighted gene co-expression network analysis and differential expression analysis in GSE45006, we identified differentially expressed time- and immune-related genes (DETIRGs) associated with chronic SCI and differentially expressed ferroptosis- and immune-related genes (DEFIRGs), which were validated in GSE151371. Protein-protein interaction and microRNA-mRNA-transcription factor regulatory networks were constructed based on Search Tool for the Retrieval of Interacting Genes (STRING) and NetworkAnalyst, respectively, which were validated by chromatin immunoprecipitation followed by sequencing (ChIP-seq). Cell subclusters and unique features of microglia in SCI were identified by single-cell transcriptomic analysis, which were validated in GSE226286. Spatial expression patterns of DETIRGs and DEFIRGs were validated in brain injury (GSE214349) and SCI (GSE184369). Potential mechanisms underlying neuronal regeneration by neurotrophin-3 (NT3)-chitosan were revealed by transcriptomic analyses in GSE69334. Immune- and ferroptosis-related mechanisms of nanolayered double hydroxide loaded with NT3 (LDH-NT3) were investigated in vivo and in vitro.\nRESULTS: GBP2, TEC, UNC93B1, PLXNC1, NFATC1, IL10RB, and TLR8 were DETIRGs represented chronic SCI-specific genes and peripheral blood biomarkers. NFKB1 may regulate expression of CYBB and HMOX1 in a unique subcluster of M1 microglia within the middle SCI lesion, establishing links between microglial ferroptosis and neuroinflammation. Reduced inflammatory responses and microglial ferroptosis were potential effects of NT3-chitosan or LDH-NT3 on neuronal regeneration.\nCONCLUSIONS: A novel subcluster of microglia exhibiting M1 polarization and ferroptosis phenotype was involved in SCI. These microglia may trigger neuroinflammation and induce neuronal degeneration within the middle site of SCI, which might be inhibited by NT3-chitosan or LDH-NT3.", "claims": [{"id": "claim-method", "statement": "In the micro-array datasets (GSE45006, GSE69334) and RNA-seq dataset (GSE151371), the “limma” R package was used to identify DEGs [31], and |log2 (FC)|> 1.0 and FDR < 0.05 were the screening criteria for DEG selection [30].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39799354", "locator": "sections[6] \"Differential expression analysis\" paragraph 2", "quote": "In the micro-array datasets (GSE45006, GSE69334) and RNA-seq dataset (GSE151371), the “limma” R package was used to identify DEGs [31], and |log2 (FC)|> 1.0 and FDR < 0.05 were the screening criteria for DEG selection [30]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data presented in the study are presented in the manuscript and supplementary files. Further inquiries of datasets are available directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39799354", "locator": "sections[58] \"notes\" paragraph 1", "quote": "All data presented in the study are presented in the manuscript and supplementary files. Further inquiries of datasets are available directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After taking the intersection of 1140 DEGs from GSE45006 and 64 FRGs from PathCards database, 9 FRGs were identified as DEFRGs (Fig. S3a).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39799354", "locator": "sections[38] \"Identification of DEFRGs in SCI\" paragraph 2", "quote": "After taking the intersection of 1140 DEGs from GSE45006 and 64 FRGs from PathCards database, 9 FRGs were identified as DEFRGs (Fig. S3a)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "After taking the intersection of 1140 DEGs from GSE45006 and 64 FRGs from PathCards database, 9 FRGs were identified as DEFRGs (Fig. S3a).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39799354"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE151371"], "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:GSE151371"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39799354", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-025-06095-0", "sha256": "sha256:603e15dedba808a0918670369de79614952bbb3eb15d36955df1a12d80ccb557", "bytes": 103598, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12967-025-06095-0", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11725224", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE151371", "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=GSE151371", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE151371", "version": null, "note": "All data presented in the study are presented in the manuscript and supplementary files. 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