Journal of Shanghai Jiao Tong University (Medical Science) >

2025 , Vol. 45 >Issue 1: 29 - 41

DOI: https://doi.org/10.3969/j.issn.1674-8115.2025.01.004

Basic research

Transcriptional regulatory network analysis of microglia in multiple sclerosis

  • CAI Qiangwei ,
  • SUN Feng ,
  • WU Wenyu ,
  • SHAO Fuming ,
  • GAO Zhengliang ,
  • JIN Shengkai
Expand
  • 1.Institute of Geriatrics, Affiliated Nantong Hospital of Shanghai University, School of Medicine, Shanghai University, Nantong 201613, China
    2.Department of Anesthesiology, Shanghai Gongli Hospital, Naval Military Medical University, Shanghai 200135, China
    3.China-Japan Friendship Medical Research Institute, School of Medicine, Shanghai University, Shanghai 200444, China
    4.Shanghai YangZhi Rehabilitation Hospital, Tongji University School of Medicine, Shanghai 200065, China
GAO Zhengliang, E-mail: zhengliang_gao@tongji.edu.cn
JIN Shengkai, E-mail: jinsk1223@163.com.

Received date: 2024-05-23

  Accepted date: 2024-07-02

  Online published: 2025-01-17

Fold

Abstract

Objective ·To investigate the differential gene expression of microglia in the gray and white matter of multiple sclerosis (MS) using single-nucleus transcriptomic analysis, aiming to explore their roles in disease progression, and identify key transcriptional regulatory networks associated with the disease. Methods ·snRNA-seq data of frozen human brain tissue samples from MS patients and control individuals were obtained from the Gene Expression Omnibus (GEO) database. R language, along with R packages such as Seurat, was employed to identify cell types based on specific cell markers. Microglia were extracted from the identified cell populations and classified based on their anatomical origin, either gray matter or white matter. Dimensionality reduction and clustering techniques were utilized to identify distinct microglial subpopulations with differential characteristics. Differentially expressed genes (DEGs) between the MS and control groups at the subpopulation level were analyzed by using the Seurat package. Gene set enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted on the DEGs to further explore the biological significance of these differences. Monocle3 was used for pseudotime analysis to study dynamic changes in microglia subpopulations during disease progression. Single cell regulatory network inference and clustering (SCENIC) method was applied to analyze transcription factor (TF) regulatory networks, aiming to identify key transcription factors potentially involved in MS regulation. Results ·After quality control, a total of 149 062 nuclei were retained for analysis. Following dimensional reduction and clustering, 12 238 microglia were identified by using key markers, including DOCK8, CSF1R, P2RY12, and CD74. The results of GO and KEGG pathway analysis showed that in gray matter microglia, functions such as endocytosis, ion homeostasis, and lipid localization were downregulated during disease progression, while in white matter microglia, functions such as protein folding, cytoplasmic translation, and response to thermal stimuli were upregulated. SCENIC analysis revealed that the expression of transcription factors such as FLI1, MITF, and FOXP1 was upregulated in MS. Conclusion ·Microglia play a critical role in MS, with white matter microglia being more significantly impacted by MS than their gray matter counterparts. Transcription factors such as FLI1, MITF, and FOXP1 are identified as key regulators involved in disease modulation, with their associated transcriptional regulatory networks playing a central role in disease modulation.

Key words： multiple sclerosis; microglia; single nucleus RNA sequencing; enrichment analysis; transcriptional regulatory network

Cite this article

CAI Qiangwei , SUN Feng , WU Wenyu , SHAO Fuming , GAO Zhengliang , JIN Shengkai . Transcriptional regulatory network analysis of microglia in multiple sclerosis[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025 , 45(1) : 29 -41 . DOI: 10.3969/j.issn.1674-8115.2025.01.004

References

1 BENEDICT R H B, AMATO M P, DELUCA J, et al. Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues[J]. Lancet Neurol, 2020, 19(10): 860-871.
2 BANWELL B, BENNETT J L, MARIGNIER R, et al. Diagnosis of myelin oligodendrocyte glycoprotein antibody-associated disease: international MOGAD Panel proposed criteria[J]. Lancet Neurol, 2023, 22(3): 268-282.
3 KUHLMANN T, MOCCIA M, COETZEE T, et al. Multiple sclerosis progression: time for a new mechanism-driven framework[J]. Lancet Neurol, 2023, 22(1): 78-88.
4 谭红梅, 全超. 多发性硬化疾病修正治疗进展[J]. 重庆医科大学学报, 2024, 49(5): 588-592.
  TAN H M, QUAN C. Advances in disease-modifying therapy for multiple sclerosis[J]. Journal of Chongqing Medical University, 2024, 49(5): 588-592.
5 FAISSNER S, PLEMEL J R, GOLD R, et al. Progressive multiple sclerosis: from pathophysiology to therapeutic strategies[J]. Nat Rev Drug Discov, 2019, 18: 905-922.
6 BORST K, DUMAS A A, PRINZ M. Microglia: immune and non-immune functions[J]. Immunity, 2021, 54(10): 2194-2208.
7 PRINZ M, JUNG S, PRILLER J. Microglia biology: one century of evolving concepts[J]. Cell, 2019, 179(2): 292-311.
8 LUKENS J R, EYO U B. Microglia and neurodevelopmental disorders[J]. Annu Rev Neurosci, 2022, 45: 425-445.
9 DADWAL S, HENEKA M T. Microglia heterogeneity in health and disease[J]. FEBS Open Bio, 2024, 14(2): 217-229.
10 MADORE C, YIN Z, LEIBOWITZ J, et al. Microglia, lifestyle stress, and neurodegeneration[J]. Immunity, 2020, 52(2): 222-240.
11 KENT S A, MIRON V E. Microglia regulation of central nervous system myelin health and regeneration[J]. Nat Rev Immunol, 2024, 24: 49-63.
12 MCNAMARA N B, MUNRO D A D, BESTARD-CUCHE N, et al. Microglia regulate central nervous system myelin growth and integrity[J]. Nature, 2023, 613: 120-129.
13 YONG V W. Microglia in multiple sclerosis: protectors turn destroyers[J]. Neuron, 2022, 110(21): 3534-3548.
14 LLOYD A F, MIRON V E. The pro-remyelination properties of microglia in the central nervous system[J]. Nat Rev Neurol, 2019, 15: 447-458.
15 ZHAO S, UMPIERRE A D, WU L J. Tuning neural circuits and behaviors by microglia in the adult brain[J]. Trends Neurosci, 2024, 47(3): 181-194.
16 SHERAFAT A, PFEIFFER F, REISS A M, et al. Microglial neuropilin-1 promotes oligodendrocyte expansion during development and remyelination by trans-activating platelet-derived growth factor receptor[J]. Nat Commun, 2021, 12: 2265.
17 DONG Y F, D′MELLO C, PINSKY W, et al. Oxidized phosphatidylcholines found in multiple sclerosis lesions mediate neurodegeneration and are neutralized by microglia[J]. Nat Neurosci, 2021, 24: 489-503.
18 LAMPRON A, LAROCHELLE A, LAFLAMME N, et al. Inefficient clearance of myelin debris by microglia impairs remyelinating processes[J]. J Exp Med, 2015, 212(4): 481-495.
19 RAWJI K S, YOUNG A M H, GHOSH T, et al. Niacin-mediated rejuvenation of macrophage/microglia enhances remyelination of the aging central nervous system[J]. Acta Neuropathol, 2020, 139(5): 893-909.
20 HAGAN N, KANE J L, GROVER D, et al. CSF1R signaling is a regulator of pathogenesis in progressive MS[J]. Cell Death Dis, 2020, 11: 904.
21 HWANG D, SEYEDSADR M S, ISHIKAWA L L W, et al. CSF-1 maintains pathogenic but not homeostatic myeloid cells in the central nervous system during autoimmune neuroinflammation[J]. Proc Natl Acad Sci USA, 2022, 119(14): e2111804119.
22 MARZAN D E, BRüGGER-VERDON V, WEST B L, et al. Activated microglia drive demyelination via CSF1R signaling[J]. Glia, 2021, 69(6): 1583-1604.
23 TAHMASEBI F, PASBAKHSH P, MORTEZAEE K, et al. Effect of the CSF1R inhibitor PLX3397 on remyelination of corpus callosum in a cuprizone-induced demyelination mouse model[J]. J Cell Biochem, 2019, 120(6): 10576-10586.
24 VOET S, PRINZ M, VAN LOO G. Microglia in central nervous system inflammation and multiple sclerosis pathology[J]. Trends Mol Med, 2019, 25(2): 112-123.
25 SEN M K, MAHNS D A, COORSSEN J R, et al. The roles of microglia and astrocytes in phagocytosis and myelination: insights from the cuprizone model of multiple sclerosis[J]. Glia, 2022, 70(7): 1215-1250.
26 JAKIMOVSKI D, BITTNER S, ZIVADINOV R, et al. Multiple sclerosis[J]. Lancet, 2024, 403(10422):183-202.
27 FRISCHER J M, BRAMOW S, DAL-BIANCO A, et al. The relation between inflammation and neurodegeneration in multiple sclerosis brains[J]. Brain, 2009, 132(5): 1175-1189.
28 ABSINTA M, MARIC D, GHARAGOZLOO M, et al. A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis[J]. Nature, 2021, 597: 709-714.
29 CLARK I C, GUTIéRREZ-VáZQUEZ C, WHEELER M A, et al. Barcoded viral tracing of single-cell interactions in central nervous system inflammation[J]. Science, 2021, 372(6540): eabf1230.
30 SCHIRMER L, VELMESHEV D, HOLMQVIST S, et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis[J]. Nature, 2019, 573: 75-82.
31 J?KEL S, AGIRRE E, MENDANHA FALC?O A, et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis[J]. Nature, 2019, 566: 543-547.
32 KISS M G, MINDUR J E, YATES A G, et al. Interleukin-3 coordinates glial-peripheral immune crosstalk to incite multiple sclerosis[J]. Immunity, 2023, 56(7): 1502-1514.e8.
33 MIEDEMA A, GERRITS E, BROUWER N, et al. Brain macrophages acquire distinct transcriptomes in multiple sclerosis lesions and normal appearing white matter[J]. Acta Neuropathol Commun, 2022, 10(1): 8.
34 HAO Y, HAO S, ANDERSEN-NISSEN E, et al. Integrated analysis of multimodal single-cell data[J]. Cell, 2021, 184(13): 3573-3587.e29.
35 HAFEMEISTER C, SATIJA R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression[J]. Genome Biol, 2019, 20(1): 296.
36 KORSUNSKY I, MILLARD N, FAN J, et al. Fast, sensitive and accurate integration of single-cell data with Harmony[J]. Nat Meth, 2019, 16: 1289-1296.
37 HU C, LI T, XU Y, et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data[J]. Nucleic Acids Res, 2023, 51(d1): D870-D876.
38 WU T, HU E, XU S, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data[J]. Innovation (Camb), 2021, 2(3): 100141.
39 YU G, WANG L G, HAN Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287.
40 TRAPNELL C, CACCHIARELLI D, GRIMSBY J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells[J]. Nat Biotechnol, 2014, 32: 381-386.
41 QIU X J, HILL A, PACKER J, et al. Single-cell mRNA quantification and differential analysis with Census[J]. Nat Meth, 2017, 14: 309-315.
42 QIU X J, MAO Q, TANG Y, et al. Reversed graph embedding resolves complex single-cell trajectories[J]. Nat Meth, 2017, 14: 979-982.
43 CAO J Y, SPIELMANN M, QIU X J, et al. The single-cell transcriptional landscape of mammalian organogenesis[J]. Nature, 2019, 566: 496-502.
44 AIBAR S, GONZáLEZ-BLAS C B, MOERMAN T, et al. SCENIC: single-cell regulatory network inference and clustering[J]. Nat Meth, 2017, 14: 1083-1086.
45 H?NZELMANN S, CASTELO R, GUINNEY J. GSVA: gene set variation analysis for microarray and RNA-seq data[J]. BMC Bioinformatics, 2013, 14: 7.
46 RITCHIE M E, PHIPSON B, WU D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res, 2015, 43(7): e47.
47 KOOI E J, STRIJBIS E M, VAN DER VALK P, et al. Heterogeneity of cortical lesions in multiple sclerosis: clinical and pathologic implications[J]. Neurology, 2012, 79(13): 1369-1376.
48 SINGHAL T, O′CONNOR K, DUBEY S, et al. Gray matter microglial activation in relapsing vs progressive MS: a[F-18]PBR06-PET study[J]. Neurol Neuroimmunol Neuroinflamm, 2019, 6(5): e587.
49 FAN L Y, YANG J, LIU R Y, et al. Integrating single-nucleus sequence profiling to reveal the transcriptional dynamics of Alzheimer′s disease, Parkinson's disease, and multiple sclerosis[J]. J Transl Med, 2023, 21(1): 649.
50 ZINATIZADEH M R, SCHOCK B, CHALBATANI G M, et al. The Nuclear Factor Kappa B (NF-kB) signaling in cancer development and immune diseases[J]. Genes Dis, 2021, 8(3): 287-297.
Options
Outlines

/

〈 〉