Single nuclei RNAseq stratifies multiple sclerosis patients into distinct white matter glial responses

The lack of understanding of the cellular and molecular basis of clinical and genetic heterogeneity in progressive multiple sclerosis (MS) has hindered the search for new effective therapies. Here, to address this gap, we analysed 632,000 single nuclei RNAseq profiles of 156 brain tissue samples, comprising white matter (WM) lesions, normal appearing WM, grey matter (GM) lesions and normal appearing GM from 54 MS patients and 26 controls. We observed the expected changes in overall neuronal and glial numbers previously described within the classical lesion subtypes. We found highly cell type-specific gene expression changes in MS tissue, with distinct differences between GM and WM areas, confirming different pathologies. However, surprisingly, we did not observe distinct gene expression signatures for the classical different WM lesion types, rather a continuum of change. This indicates that classical lesion characterization better reflects changes in cell abundance than changes in cell type gene expression, and indicates a global disease effect. Furthermore, the major biological determinants of variability in gene expression in MS WM samples relate to individual patient effects, rather than to lesion types or other metadata. We identify four subgroups of MS patients with distinct WM glial gene expression signatures and patterns of oligodendrocyte stress and/or maturation, suggestive of engagement of different pathological processes, with an additional more variable regenerative astrocyte signature. The discovery of these patterns, which were also found in an independent MS patient cohort, provides a framework to use molecular biomarkers to stratify patients for optimal therapeutic approaches for progressive MS, significantly advances our mechanistic understanding of progressive MS, and highlights the need for precision-medicine approaches to address heterogeneity among MS patients.

[1]  B. Ueberheide,et al.  Compilation of reported protein changes in the brain in Alzheimer’s disease , 2023, Nature communications.

[2]  G. Castelo-Branco,et al.  Brain matters: unveiling the distinct contributions of region, age, and sex to glia diversity and CNS function , 2023, Acta Neuropathologica Communications.

[3]  L. Alfredsson,et al.  Cross-reactive EBNA1 immunity targets alpha-crystallin B and is associated with multiple sclerosis , 2023, Science advances.

[4]  Junling Liu,et al.  ANGPTL2 binds MAG to efficiently enhance oligodendrocyte differentiation , 2023, Cell & Bioscience.

[5]  J. Saez-Rodriguez,et al.  Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease , 2023, bioRxiv.

[6]  Miguel A. Ibarra-Arellano,et al.  Spatial cell type mapping of multiple sclerosis lesions , 2022, bioRxiv.

[7]  Tuan Leng Tay,et al.  Microglia states and nomenclature: A field at its crossroads , 2022, Neuron.

[8]  L. Wasserman,et al.  Bootstrap , 2022 .

[9]  E. J. Hoekstra,et al.  Natural language processing and modeling of clinical disease trajectories across brain disorders , 2022, medRxiv.

[10]  D. MacManus,et al.  Remyelination varies between and within lesions in multiple sclerosis following bexarotene , 2022, Annals of clinical and translational neurology.

[11]  J. Priller,et al.  Mapping the glial transcriptome in Huntington’s disease using snRNAseq: Selective disruption of glial signatures across brain regions , 2022, bioRxiv.

[12]  Tracy J. Yuen,et al.  Disease-associated oligodendrocyte responses across neurodegenerative diseases. , 2022, Cell reports.

[13]  Markus M. Hilscher,et al.  Spatial and temporal heterogeneity in the lineage progression of fine oligodendrocyte subtypes , 2022, BMC Biology.

[14]  S. Tsirka,et al.  Chronic stress disrupts the homeostasis and progeny progression of oligodendroglial lineage cells, associating immune oligodendrocytes with prefrontal cortex hypomyelination , 2022, Molecular Psychiatry.

[15]  Chun Jimmie Ye,et al.  Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals , 2022, bioRxiv.

[16]  Christian L. Müller,et al.  scCODA is a Bayesian model for compositional single-cell data analysis , 2021, Nature Communications.

[17]  Fabian J Theis,et al.  scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies , 2021, Nature Communications.

[18]  S. Teichmann,et al.  Differential abundance testing on single-cell data using k-nearest neighbor graphs , 2021, Nature Biotechnology.

[19]  Ji Eun Lee,et al.  Primary Cilia in Glial Cells: An Oasis in the Journey to Overcoming Neurodegenerative Diseases , 2021, Frontiers in Neuroscience.

[20]  Mark D. Robinson,et al.  Doublet identification in single-cell sequencing data using scDblFinder , 2021, F1000Research.

[21]  D. Reich,et al.  A lymphocyte–microglia–astrocyte axis in chronic active multiple sclerosis , 2021, Nature.

[22]  D. MacManus,et al.  Safety and efficacy of bexarotene in patients with relapsing-remitting multiple sclerosis (CCMR One): a randomised, double-blind, placebo-controlled, parallel-group, phase 2a study , 2021, The Lancet Neurology.

[23]  H. Vihinen,et al.  Mitochondrial dysfunction compromises ciliary homeostasis in astrocytes , 2021, bioRxiv.

[24]  B. Erickson,et al.  Magnetic Resonance Imaging Correlates of Multiple Sclerosis Immunopathological Patterns , 2021, Annals of neurology.

[25]  Zachary D. Wallen,et al.  Comparison study of differential abundance testing methods using two large Parkinson disease gut microbiome datasets derived from 16S amplicon sequencing , 2021, BMC Bioinformatics.

[26]  Ariel J. Levine,et al.  Confronting false discoveries in single-cell differential expression , 2021, Nature Communications.

[27]  C. Langefeld,et al.  A practical solution to pseudoreplication bias in single-cell studies , 2021, Nature Communications.

[28]  R. Reynolds,et al.  CSF parvalbumin levels reflect interneuron loss linked with cortical pathology in multiple sclerosis , 2021, Annals of clinical and translational neurology.

[29]  P. Kind,et al.  Selective vulnerability of inhibitory networks in multiple sclerosis , 2021, Acta Neuropathologica.

[30]  Helena L. Crowell,et al.  muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data , 2020, Nature Communications.

[31]  Yu Chen Chang,et al.  Demyelination Regulates the Circadian Transcription Factor BMAL1 to Signal Adult Neural Stem Cells to Initiate Oligodendrogenesis. , 2020, Cell reports.

[32]  Raphael Gottardo,et al.  Integrated analysis of multimodal single-cell data , 2020, Cell.

[33]  C. Stadelmann,et al.  Remyelination in multiple sclerosis: from basic science to clinical translation , 2020, The Lancet Neurology.

[34]  S. Peddada,et al.  Analysis of compositions of microbiomes with bias correction , 2020, Nature Communications.

[35]  J. Marioni,et al.  MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data , 2020, Genome Biology.

[36]  G. Comi,et al.  Siponimod (BAF312) Activates Nrf2 While Hampering NFκB in Human Astrocytes, and Protects From Astrocyte-Induced Neurodegeneration , 2020, Frontiers in Immunology.

[37]  Yuxin Zhou,et al.  Diverged morphology changes of astrocytic and neuronal primary cilia under reactive insults , 2020, Molecular Brain.

[38]  S. Ourselin,et al.  Efficacy of three neuroprotective drugs in secondary progressive multiple sclerosis (MS-SMART): a phase 2b, multiarm, double-blind, randomised placebo-controlled trial , 2020, The Lancet Neurology.

[39]  P. Durrenberger,et al.  Meningeal inflammation changes the balance of TNF signalling in cortical grey matter in multiple sclerosis , 2019, Journal of Neuroinflammation.

[40]  John C. Marioni,et al.  Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender , 2019, bioRxiv.

[41]  Kamil Slowikowski,et al.  Fast, sensitive, and accurate integration of single cell data with Harmony , 2019, Nature Methods.

[42]  Kyle A. Martin,et al.  Oligodendrocyte precursor cells present antigen and are cytotoxic targets in inflammatory demyelination , 2019, Nature Communications.

[43]  D. Centonze,et al.  Safety and efficacy of opicinumab in patients with relapsing multiple sclerosis (SYNERGY): a randomised, placebo-controlled, phase 2 trial , 2019, The Lancet Neurology.

[44]  Richard Reynolds,et al.  Neuronal vulnerability and multilineage diversity in multiple sclerosis , 2019, Nature.

[45]  Samuel Demharter,et al.  Joint analysis of heterogeneous single-cell RNA-seq dataset collections , 2019, Nature Methods.

[46]  Fabian J Theis,et al.  PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells , 2019, Genome Biology.

[47]  Dheeraj Malhotra,et al.  Altered human oligodendrocyte heterogeneity in multiple sclerosis , 2019, Nature.

[48]  G. Castelo-Branco,et al.  Disease-specific oligodendrocyte lineage cells arise in multiple sclerosis , 2018, Nature Medicine.

[49]  Gabriel E. Hoffman,et al.  dream: Powerful differential expression analysis for repeated measures designs , 2018, bioRxiv.

[50]  H. Lassmann Multiple Sclerosis Pathology. , 2018, Cold Spring Harbor perspectives in medicine.

[51]  Fabian J Theis,et al.  SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.

[52]  G. Su,et al.  Activin receptors regulate the oligodendrocyte lineage in health and disease , 2018, Acta Neuropathologica.

[53]  S. Hauser,et al.  Clemastine fumarate as a remyelinating therapy for multiple sclerosis (ReBUILD): a randomised, controlled, double-blind, crossover trial , 2017, The Lancet.

[54]  Casper W. Berg,et al.  glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling , 2017, R J..

[55]  Stylianos E. Antonarakis,et al.  MBV: a method to solve sample mislabeling and detect technical bias in large combined genotype and sequencing assay datasets , 2017, Bioinform..

[56]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[57]  Peter Bankhead,et al.  QuPath: Open source software for digital pathology image analysis , 2017, Scientific Reports.

[58]  Gennady Korotkevich,et al.  Fast gene set enrichment analysis , 2016, bioRxiv.

[59]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[60]  Arcadi Navarro,et al.  The European Genome-phenome Archive of human data consented for biomedical research , 2015, Nature Genetics.

[61]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[62]  Tracy J. Yuen,et al.  M2 microglia and macrophages drive oligodendrocyte differentiation during CNS remyelination , 2013, Nature Neuroscience.

[63]  Shinichi Nakagawa,et al.  A general and simple method for obtaining R2 from generalized linear mixed‐effects models , 2013 .

[64]  A. Cardona,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[65]  Helga Thorvaldsdóttir,et al.  Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..

[66]  C. ffrench-Constant,et al.  Regulatory Mechanisms that Mediate Tenascin C-Dependent Inhibition of Oligodendrocyte Precursor Differentiation , 2010, The Journal of Neuroscience.

[67]  Davis J. McCarthy,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[68]  Gordon K. Smyth,et al.  Testing significance relative to a fold-change threshold is a TREAT , 2009, Bioinform..

[69]  B. Hoffman,et al.  Gadd45 in stress signaling , 2008, Journal of molecular signaling.

[70]  A. Gelman Scaling regression inputs by dividing by two standard deviations , 2008, Statistics in medicine.

[71]  I. Ziabreva,et al.  Mitochondrial defects in acute multiple sclerosis lesions , 2008, Brain : a journal of neurology.

[72]  C. ffrench-Constant,et al.  Adhesion molecules in the regulation of CNS myelination. , 2007, Neuron glia biology.

[73]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[74]  G. Assmann,et al.  Expression of ATP binding cassette‐transporter ABCG1 prevents cell death by transporting cytotoxic 7β‐hydroxycholesterol , 2007, FEBS letters.

[75]  Christine Stadelmann,et al.  Extensive Cortical Remyelination in Patients with Chronic Multiple Sclerosis , 2007, Brain pathology.

[76]  J. Parisi,et al.  Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination , 2000, Annals of neurology.

[77]  B. Trapp,et al.  Pathogenesis of tissue injury in MS lesions , 1999, Journal of Neuroimmunology.

[78]  H. Lassmann,et al.  The demyelinating potential of antibodies to myelin oligodendrocyte glycoprotein is related to their ability to fix complement , 1991, Journal of Neuroimmunology.

[79]  P. Hanson,et al.  Explorer Mitochondrial changes within axons in multiple sclerosis , 2016 .

[80]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[81]  A. Fornace,et al.  Gadd45 in stress signaling, cell cycle control, and apoptosis. , 2013, Advances in experimental medicine and biology.

[82]  D. Hafler,et al.  Protective and therapeutic role for alphaB-crystallin in autoimmune demyelination. , 2007, Nature.