2024
Single-cell transcriptomic and proteomic analysis of Parkinson’s disease brains
Zhu B, Park J, Coffey S, Russo A, Hsu I, Wang J, Su C, Chang R, Lam T, Gopal P, Ginsberg S, Zhao H, Hafler D, Chandra S, Zhang L. Single-cell transcriptomic and proteomic analysis of Parkinson’s disease brains. Science Translational Medicine 2024, 16: eabo1997. PMID: 39475571, DOI: 10.1126/scitranslmed.abo1997.Peer-Reviewed Original ResearchConceptsProteomic analysisAlzheimer's diseasePrefrontal cortexBrain cell typesGenetics of PDParkinson's diseaseCell-cell interactionsChaperone expressionSingle-nucleus transcriptomesExpressed genesTranscriptional changesPostmortem human brainPostmortem brain tissueDiseased brainSynaptic proteinsSingle-cellDown-regulationBrain cell populationsBrain regionsCell typesNeurodegenerative disordersLate-stage PDParkinson's disease brainsDisease etiologyNeuronal vulnerabilitySingle-Cell Transcriptomic Analyses of Brain Parenchyma in Patients With New-Onset Refractory Status Epilepticus (NORSE)
Hanin A, Zhang L, Huttner A, Plu I, Mathon B, Bielle F, Navarro V, Hirsch L, Hafler D. Single-Cell Transcriptomic Analyses of Brain Parenchyma in Patients With New-Onset Refractory Status Epilepticus (NORSE). Neurology Neuroimmunology & Neuroinflammation 2024, 11: e200259. PMID: 38810181, PMCID: PMC11139018, DOI: 10.1212/nxi.0000000000200259.Peer-Reviewed Original ResearchConceptsNew-onset refractory status epilepticusTemporal lobe epilepsyGABAergic neuronsExcitatory neuronsInfiltrating macrophagesProportion of GABAergic neuronsChronic temporal lobe epilepsyRefractory status epilepticusInhibitory GABAergic neuronsSingle-cell transcriptome analysisDecreased expression of genesDegree of demyelinationImmune disturbancesNeuronal excitabilityImmune dysregulationNew-onsetStatus epilepticusPoor outcomeRefractory epilepsyHealthy childrenMicroglial reactivitySingle-nucleus RNA sequencingNLRP3 inflammasome activationInflammatory responseLobe epilepsy
2023
scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Zhu B, Wang Y, Ku L, van Dijk D, Zhang L, Hafler D, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biology 2023, 24: 292. PMID: 38111007, PMCID: PMC10726524, DOI: 10.1186/s13059-023-03129-y.Peer-Reviewed Original Research
2022
Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19
Unterman A, Sumida TS, Nouri N, Yan X, Zhao AY, Gasque V, Schupp JC, Asashima H, Liu Y, Cosme C, Deng W, Chen M, Raredon MSB, Hoehn KB, Wang G, Wang Z, DeIuliis G, Ravindra NG, Li N, Castaldi C, Wong P, Fournier J, Bermejo S, Sharma L, Casanovas-Massana A, Vogels CBF, Wyllie AL, Grubaugh ND, Melillo A, Meng H, Stein Y, Minasyan M, Mohanty S, Ruff WE, Cohen I, Raddassi K, Niklason L, Ko A, Montgomery R, Farhadian S, Iwasaki A, Shaw A, van Dijk D, Zhao H, Kleinstein S, Hafler D, Kaminski N, Dela Cruz C. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nature Communications 2022, 13: 440. PMID: 35064122, PMCID: PMC8782894, DOI: 10.1038/s41467-021-27716-4.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAgedAntibodies, Monoclonal, HumanizedCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCells, CulturedCOVID-19COVID-19 Drug TreatmentFemaleGene Expression ProfilingGene Expression RegulationHumansImmunity, InnateMaleReceptors, Antigen, B-CellReceptors, Antigen, T-CellRNA-SeqSARS-CoV-2Single-Cell AnalysisConceptsProgressive COVID-19B cell clonesSingle-cell analysisT cellsImmune responseMulti-omics single-cell analysisCOVID-19Cell clonesAdaptive immune interactionsSevere COVID-19Dynamic immune responsesGene expressionSARS-CoV-2 virusAdaptive immune systemSomatic hypermutation frequenciesCellular effectsProtein markersEffector CD8Immune signaturesProgressive diseaseHypermutation frequencyProgressive courseClassical monocytesClonesImmune interactions
2021
Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study
, , Rouphael N, Maecker H, Montgomery R, Diray-Arce J, Kleinstein S, Altman M, Bosinger S, Eckalbar W, Guan L, Hough C, Krammer F, Langelier C, Levy O, McEnaney K, Peters B, Rahman A, Rajan J, Sigelman S, Steen H, van Bakel H, Ward A, Wilson M, Woodruff P, Zamecnik C, Augustine A, Ozonoff A, Reed E, Becker P, Higuita N, Altman M, Atkinson M, Baden L, Becker P, Bime C, Brakenridge S, Calfee C, Cairns C, Corry D, Davis M, Augustine A, Ehrlich L, Haddad E, Erle D, Fernandez-Sesma A, Hafler D, Hough C, Kheradmand F, Kleinstein S, Kraft M, Levy O, McComsey G, Melamed E, Messer W, Metcalf J, Montgomery R, Nadeau K, Ozonoff A, Peters B, Pulendran B, Reed E, Rouphael N, Sarwal M, Schaenman J, Sekaly R, Shaw A, Simon V. Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study. Science Immunology 2021, 6: eabf3733. PMID: 34376480, PMCID: PMC8713959, DOI: 10.1126/sciimmunol.abf3733.Peer-Reviewed Original ResearchConceptsCOVID-19 cohortProspective longitudinal studyHost immune responseLongitudinal studyCOVID-19Identification of biomarkersHospitalized patientsRespiratory secretionsClinical criteriaDisease progressionImmune responseRadiographic dataImmunologic assaysEffective therapeuticsOptimal timingStudy designBiologic samplingSuch interventionsCohortSeveritySample collectionAssay protocolsPatientsNEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
He L, Davila-Velderrain J, Sumida TS, Hafler DA, Kellis M, Kulminski AM. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Communications Biology 2021, 4: 629. PMID: 34040149, PMCID: PMC8155058, DOI: 10.1038/s42003-021-02146-6.Peer-Reviewed Original ResearchMeSH KeywordsAlzheimer DiseaseApolipoproteins EBinomial DistributionComputational BiologyGene ExpressionGene Expression ProfilingHumansMicrogliaModels, StatisticalSingle-Cell AnalysisConceptsNegative binomial mixed modelsBinomial mixed modelsSingle-cell dataHigh-dimensional integralsLarge sample approximationLaplace approximationCell-level expressionMixed modelsApproximationNebulaSpeed gainData setsOrders of magnitudeMarker gene identificationIntegralsModelOverdispersionFalse positive errors
2017
Podoplanin is a negative regulator of Th17 inflammation
Nylander AN, Ponath GD, Axisa PP, Mubarak M, Tomayko M, Kuchroo VK, Pitt D, Hafler DA. Podoplanin is a negative regulator of Th17 inflammation. JCI Insight 2017, 2: e92321. PMID: 28878118, PMCID: PMC5621890, DOI: 10.1172/jci.insight.92321.Peer-Reviewed Original ResearchConceptsT cellsIL-17IL-17 secretionDistinct cytokine profilesInflammatory gene signatureTh17-polarizing conditionsTh17 cellsCytokine profileCell subsetsInflammatory responseSkin biopsiesMouse modelPDPN expressionMultiple organsSkin diseasesGene signatureInflammationLymphatic systemCLEC-2PDPNRecent dataDifferent subpopulationsCellsTranscriptional profilesShRNA gene
2016
AKT isoforms modulate Th1‐like Treg generation and function in human autoimmune disease
Kitz A, de Marcken M, Gautron AS, Mitrovic M, Hafler DA, Dominguez-Villar M. AKT isoforms modulate Th1‐like Treg generation and function in human autoimmune disease. EMBO Reports 2016, 17: 1169-1183. PMID: 27312110, PMCID: PMC4967959, DOI: 10.15252/embr.201541905.Peer-Reviewed Original ResearchMeSH KeywordsAutoimmune DiseasesBiomarkersCell DifferentiationCytokinesForkhead Transcription FactorsGene Expression ProfilingGene SilencingHumansImmunomodulationInterferon-gammaPhenotypePhosphatidylinositol 3-KinasesProtein IsoformsProto-Oncogene Proteins c-aktSignal TransductionT-Lymphocyte SubsetsT-Lymphocytes, RegulatoryTranscriptomeConceptsAutoimmune diseasesIFNγ secretionHuman TregsGenome-wide gene expression approachUntreated relapsing-remitting MS patientsRelapsing-remitting MS patientsImmune suppressive functionHuman autoimmune diseasesT helper 1Inflammatory cytokines IFNγTreg suppressor functionNovel treatment paradigmEffector phenotypeMS patientsTreg generationCytokines IFNγHelper 1Multiple sclerosisTreatment paradigmSuppressive functionTregsVivo modelDiseaseSecretionSuppressor function
2015
Sodium chloride inhibits the suppressive function of FOXP3+ regulatory T cells
Hernandez AL, Kitz A, Wu C, Lowther DE, Rodriguez DM, Vudattu N, Deng S, Herold KC, Kuchroo VK, Kleinewietfeld M, Hafler DA. Sodium chloride inhibits the suppressive function of FOXP3+ regulatory T cells. Journal Of Clinical Investigation 2015, 125: 4212-4222. PMID: 26524592, PMCID: PMC4639983, DOI: 10.1172/jci81151.Peer-Reviewed Original ResearchMeSH KeywordsAdoptive TransferAnimalsAntibodies, NeutralizingAutoimmunityCD4-Positive T-LymphocytesCells, CulturedCoculture TechniquesColitisCytokinesForkhead Transcription FactorsGene Expression ProfilingGenes, ReporterGraft vs Host DiseaseHeterograftsHumansImmediate-Early ProteinsInflammationInterferon-gammaLeukocytes, MononuclearMaleMiceProtein Serine-Threonine KinasesRNA InterferenceRNA, Small InterferingSodium ChlorideSodium Chloride, DietaryT-Lymphocytes, RegulatoryConceptsHigh-salt dietTreg functionIFNγ secretionCD4 effector cellsHuman Treg functionRegulatory T cellsAdoptive transfer modelAnti-IFNγ antibodyHost disease modelType 1 diabetesInduction of proinflammatoryTreg pathwayExperimental colitisXenogeneic graftEffector cellsMultiple sclerosisProinflammatory responseT cellsTregsMurine modelSuppressive activitySuppressive functionSerum/glucocorticoid-regulated kinaseAutoimmunityGlucocorticoid-regulated kinaseFunctional inflammatory profiles distinguish myelin-reactive T cells from patients with multiple sclerosis
Cao Y, Goods BA, Raddassi K, Nepom GT, Kwok WW, Love JC, Hafler DA. Functional inflammatory profiles distinguish myelin-reactive T cells from patients with multiple sclerosis. Science Translational Medicine 2015, 7: 287ra74. PMID: 25972006, PMCID: PMC4497538, DOI: 10.1126/scitranslmed.aaa8038.Peer-Reviewed Original ResearchMeSH KeywordsAdultCase-Control StudiesFemaleGene Expression ProfilingHumansInflammationMaleMiddle AgedMultiple SclerosisMyelin SheathT-LymphocytesConceptsMyelin-reactive T cellsMultiple sclerosisT cellsHealthy controlsT cell librariesT helper cell 17Antigen-specific T cellsGene signatureMore IL-10More proinflammatory cytokinesAutoreactive T cellsIL-10 productionHuman autoimmune diseasesGranulocyte-macrophage colony-stimulating factorProduction of interferonColony-stimulating factorMyelin antigensTh17 cellsIL-10Inflammatory profileInterleukin-17Proinflammatory cytokinesAutoimmune diseasesDisease progressionHealthy subjects
2014
Monoallelic expression of the human FOXP2 speech gene
Adegbola AA, Cox GF, Bradshaw EM, Hafler DA, Gimelbrant A, Chess A. Monoallelic expression of the human FOXP2 speech gene. Proceedings Of The National Academy Of Sciences Of The United States Of America 2014, 112: 6848-6854. PMID: 25422445, PMCID: PMC4460484, DOI: 10.1073/pnas.1411270111.Peer-Reviewed Original ResearchMeSH KeywordsApraxiasComparative Genomic HybridizationFemaleForkhead Transcription FactorsGene Expression ProfilingGene Expression Regulation, DevelopmentalGenes, X-LinkedHumansPolymorphism, Single NucleotideReverse Transcriptase Polymerase Chain ReactionSequence Analysis, DNASequence DeletionSpeechX Chromosome InactivationConceptsRandom monoallelic expressionMonoallelic expressionAllele-specific expressionNumber of genesHuman Mendelian disordersForkhead box P2 (FOXP2) geneP2 geneAutosomal genesMore genesAutosomal genomeX chromosomeGene expressionHaploinsufficiency phenotypeMendelian disordersGenesDevelopmental verbal dyspraxiaFOXP2 mutationsIntriguing possibilityFOXP2 geneExpressionRecent descriptionMutationsVerbal dyspraxiaAutosomesGenomeSystems Immunology Reveals Markers of Susceptibility to West Nile Virus Infection
Qian F, Goel G, Meng H, Wang X, You F, Devine L, Raddassi K, Garcia MN, Murray KO, Bolen CR, Gaujoux R, Shen-Orr SS, Hafler D, Fikrig E, Xavier R, Kleinstein SH, Montgomery RR. Systems Immunology Reveals Markers of Susceptibility to West Nile Virus Infection. MSphere 2014, 22: 6-16. PMID: 25355795, PMCID: PMC4278927, DOI: 10.1128/cvi.00508-14.Peer-Reviewed Original ResearchConceptsWest Nile virus infectionVirus infectionMyeloid dendritic cellsMarker of susceptibilityPotential therapeutic strategySeverity of infectionSevere neurological diseaseOlder patientsAcute infectionDendritic cellsCXCL10 expressionDetectable yearsImmunity-related genesStratified cohortWNV infectionTherapeutic strategiesPathogenic mechanismsAnimal studiesNeurological diseasesDisease severityVivo infectionPredictive signatureInfectionProminent alterationsPrimary cellsTreg Cells Expressing the Coinhibitory Molecule TIGIT Selectively Inhibit Proinflammatory Th1 and Th17 Cell Responses
Joller N, Lozano E, Burkett PR, Patel B, Xiao S, Zhu C, Xia J, Tan TG, Sefik E, Yajnik V, Sharpe AH, Quintana FJ, Mathis D, Benoist C, Hafler DA, Kuchroo VK. Treg Cells Expressing the Coinhibitory Molecule TIGIT Selectively Inhibit Proinflammatory Th1 and Th17 Cell Responses. Immunity 2014, 40: 569-581. PMID: 24745333, PMCID: PMC4070748, DOI: 10.1016/j.immuni.2014.02.012.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCell ProliferationCells, CulturedCytokinesEosinophilsFibrinogenForkhead Transcription FactorsGene Expression ProfilingGene Expression RegulationImmunosuppression TherapyLymphocyte ActivationMiceMice, Inbred C57BLMice, KnockoutMice, TransgenicReceptors, ImmunologicRespiratory HypersensitivityTh1-Th2 BalanceT-Lymphocyte SubsetsT-Lymphocytes, RegulatoryConceptsTreg cell subsetsTh2 cell responsesTreg cellsCell subsetsCell responsesProinflammatory T helper 1T effector cell proliferationTreg cell-mediated suppressionFibrinogen-like protein 2Allergic airway inflammationT regulatory (Treg) cellsTh2 cytokine productionSuppression of Th1T helper 1Effector cell proliferationTreg signature genesProinflammatory Th1TIGIT expressionAirway inflammationTh17 cellsRegulatory cellsHelper 1Cytokine productionT cellsImmune response
2012
An RNA Profile Identifies Two Subsets of Multiple Sclerosis Patients Differing in Disease Activity
Ottoboni L, Keenan BT, Tamayo P, Kuchroo M, Mesirov JP, Buckle GJ, Khoury SJ, Hafler DA, Weiner HL, De Jager PL. An RNA Profile Identifies Two Subsets of Multiple Sclerosis Patients Differing in Disease Activity. Science Translational Medicine 2012, 4: 153ra131. PMID: 23019656, PMCID: PMC3753678, DOI: 10.1126/scitranslmed.3004186.Peer-Reviewed Original ResearchConceptsGlatiramer acetateDisease activityPatient populationFirst-line disease-modifying treatmentsMultiple sclerosis (MS) patient populationPeripheral blood mononuclear cellsMS patient populationDisease-modifying treatmentsMultiple sclerosis patientsBlood mononuclear cellsSubset of subjectsDisease courseSclerosis patientsMS subjectsMononuclear cellsInflammatory eventsTreatment responseUntreated subjectsAdditional groupHigh expressionTranscriptional signatureSubjectsRNA profilesTreatmentTranscriptional profiles
2010
Multidimensional analysis of the frequencies and rates of cytokine secretion from single cells by quantitative microengraving
Han Q, Bradshaw EM, Nilsson B, Hafler DA, Love JC. Multidimensional analysis of the frequencies and rates of cytokine secretion from single cells by quantitative microengraving. Lab On A Chip 2010, 10: 1391-1400. PMID: 20376398, PMCID: PMC3128808, DOI: 10.1039/b926849a.Peer-Reviewed Original Research
2008
Genetic Analysis of Human Traits In Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines
Choy E, Yelensky R, Bonakdar S, Plenge RM, Saxena R, De Jager PL, Shaw SY, Wolfish CS, Slavik JM, Cotsapas C, Rivas M, Dermitzakis ET, Cahir-McFarland E, Kieff E, Hafler D, Daly MJ, Altshuler D. Genetic Analysis of Human Traits In Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines. PLOS Genetics 2008, 4: e1000287. PMID: 19043577, PMCID: PMC2583954, DOI: 10.1371/journal.pgen.1000287.Peer-Reviewed Original ResearchMeSH KeywordsCell LineDNA, MitochondrialDrug ResistanceGene Expression ProfilingGenetic VariationHerpesvirus 4, HumanHumansLymphocytesPhenotypeQuantitative Trait LociRNA, MessengerConceptsLymphoblastoid cell linesBiological noiseGenome-wide significanceInternational HapMap ProjectDrug responseCell linesGenotype-phenotype relationshipsIndividual mRNAsEQTL SNPsGenetic analysisGene expressionHapMap projectHuman cellsHuman traitsNon-genetic factorsQTLMetabolic stateModel systemGenesMRNA levelsBaseline growth ratesSpurious associationsGrowth ratePharmacogenetic experimentsEQTLs
2004
Gene expression profiling in MS: what is the clinical relevance?
De Jager PL, Hafler DA. Gene expression profiling in MS: what is the clinical relevance? The Lancet Neurology 2004, 3: 269. PMID: 15099539, DOI: 10.1016/s1474-4422(04)00731-8.Peer-Reviewed Original ResearchGene ExpressionGene Expression ProfilingHumansMultiple SclerosisOligonucleotide Array Sequence Analysis