Featured Publications
Normalizing and denoising protein expression data from droplet-based single cell profiling
Mulè M, Martins A, Tsang J. Normalizing and denoising protein expression data from droplet-based single cell profiling. Nature Communications 2022, 13: 2099. PMID: 35440536, PMCID: PMC9018908, DOI: 10.1038/s41467-022-29356-8.Peer-Reviewed Original ResearchConceptsProtein expression dataSingle-cell profiling methodsExpression dataSingle-cell profilingOligo-conjugated antibodiesTechnical noiseProtein populationCITE-seqCellular heterogeneityComprehensive dissectionDownstream analysisCell profilingDSBsSingle cellsProtein levelsProtein expressionCell populationsOpen-source R packageProfiling methodProtein countsEmpty dropletsR packageComputational analysisCellsBiological variationBroad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus
Kotliarov Y, Sparks R, Martins A, Mulè M, Lu Y, Goswami M, Kardava L, Banchereau R, Pascual V, Biancotto A, Chen J, Schwartzberg P, Bansal N, Liu C, Cheung F, Moir S, Tsang J. Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus. Nature Medicine 2020, 26: 618-629. PMID: 32094927, PMCID: PMC8392163, DOI: 10.1038/s41591-020-0769-8.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAdolescentAdultAgedAged, 80 and overAntibody FormationB-LymphocytesChildChild, PreschoolCohort StudiesFemaleGene Expression ProfilingHumansInfluenza VaccinesInfluenza, HumanLupus Erythematosus, SystemicMaleMiddle AgedTranscriptomeVaccinationYellow FeverYellow Fever VaccineYoung AdultConceptsDisease activityVaccine responsivenessAutoimmune disease activityBlood transcriptional signaturesYellow fever vaccinationSystemic lupus erythematosusClinical quiescenceFever vaccinationLupus erythematosusCancer immunotherapyBaseline predictorsDisease outcomeHealthy subjectsImmune responseI IFNHealthy individualsVaccinationTranscriptional signatureImmune variationBaseline statePatientsExtent of activationBiological basisSurface proteinsInfection responseCancer prognosis with shallow tumor RNA sequencing
Milanez-Almeida P, Martins A, Germain R, Tsang J. Cancer prognosis with shallow tumor RNA sequencing. Nature Medicine 2020, 26: 188-192. PMID: 32042193, DOI: 10.1038/s41591-019-0729-3.Peer-Reviewed Original ResearchConceptsCancer prognosisTumor RNA-seq dataTumor RNA sequencingPrediction of outcomeTypes of cancerClinical outcomesRNA sequencingAdverse outcomesRelative riskDisease outcomeOutcome predictionTumor RNA-seqPersonalized oncologyTranscriptional signatureCancer1–3Molecular pathwaysOutcomesPrognosisLongitudinal analysisTranscriptional pathwaysCancerA crowdsourcing approach for reusing and meta-analyzing gene expression data
Shah N, Guo Y, Wendelsdorf K, Lu Y, Sparks R, Tsang J. A crowdsourcing approach for reusing and meta-analyzing gene expression data. Nature Biotechnology 2016, 34: 803-806. PMID: 27323300, PMCID: PMC6871002, DOI: 10.1038/nbt.3603.Peer-Reviewed Original Research
2022
Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses
Hagan T, Gerritsen B, Tomalin LE, Fourati S, Mulè MP, Chawla DG, Rychkov D, Henrich E, Miller HER, Diray-Arce J, Dunn P, Lee A, Levy O, Gottardo R, Sarwal M, Tsang J, Suárez-Fariñas M, Sékaly R, Kleinstein S, Pulendran B. Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses. Nature Immunology 2022, 23: 1788-1798. PMID: 36316475, PMCID: PMC9869360, DOI: 10.1038/s41590-022-01328-6.Peer-Reviewed Original ResearchMeSH KeywordsAdultAntibodies, ViralAntibody FormationGene Expression ProfilingHumansImmunity, InnateVaccinationVaccinesConceptsAntibody responseDay 1Vaccine-induced antibodiesYellow fever vaccineHuman immune responseMechanisms of immunityB cell activationTranscriptional atlasFever vaccineDifferent vaccinesSystems vaccinologyImmune responseMost vaccinesDay 7Cell activationInnate immunityVaccineVaccinationImmunityCommon predictorsMolecular signaturesResponsePlasmablastsInterferonAntibodies
2021
Pre-existing chromatin accessibility and gene expression differences among naive CD4+ T cells influence effector potential
Rogers D, Sood A, Wang H, van Beek J, Rademaker T, Artusa P, Schneider C, Shen C, Wong D, Bhagrath A, Lebel M, Condotta S, Richer M, Martins A, Tsang J, Barreiro L, François P, Langlais D, Melichar H, Textor J, Mandl J. Pre-existing chromatin accessibility and gene expression differences among naive CD4+ T cells influence effector potential. Cell Reports 2021, 37: 110064. PMID: 34852223, DOI: 10.1016/j.celrep.2021.110064.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencingGene expression differencesCell receptor signalingChromatin accessibilityLineage choiceTCR signal strengthCell chromatinTranscriptional differencesRNA sequencingExpression differencesReceptor signalingLandscape differencesEffector potentialEffector lineagesThymic developmentCellsNaive CD4Self-peptide MHCChromatinCognate antigenLineagesGenesSignalingTCR interactionsKey driversAn immune-based biomarker signature is associated with mortality in COVID-19 patients
Abers MS, Delmonte OM, Ricotta EE, Fintzi J, Fink DL, de Jesus AAA, Zarember KA, Alehashemi S, Oikonomou V, Desai JV, Canna SW, Shakoory B, Dobbs K, Imberti L, Sottini A, Quiros-Roldan E, Castelli F, Rossi C, Brugnoni D, Biondi A, Bettini LR, D’Angio’ M, Bonfanti P, Castagnoli R, Montagna D, Licari A, Marseglia GL, Gliniewicz EF, Shaw E, Kahle DE, Rastegar AT, Stack M, Myint-Hpu K, Levinson SL, DiNubile MJ, Chertow DW, Burbelo PD, Cohen JI, Calvo KR, Tsang JS, Consortium N, Su HC, Gallin JI, Kuhns DB, Goldbach-Mansky R, Lionakis MS, Notarangelo LD. An immune-based biomarker signature is associated with mortality in COVID-19 patients. JCI Insight 2021, 6: e144455. PMID: 33232303, PMCID: PMC7821609, DOI: 10.1172/jci.insight.144455.Peer-Reviewed Original ResearchMeSH KeywordsAdrenal Cortex HormonesAdultAgedAnti-Bacterial AgentsAntibodies, Monoclonal, HumanizedAntiviral AgentsAzithromycinBiomarkersCalgranulin BCase-Control StudiesChemokine CCL2Chemokine CXCL9COVID-19Enzyme InhibitorsFemaleFerritinsGene Expression ProfilingHumansHydroxychloroquineImmunologic FactorsInterferon Type IInterferon-gammaInterleukin-1 Receptor-Like 1 ProteinInterleukin-10Interleukin-15Interleukin-2Interleukin-6LactoferrinLipocalin-2MaleMatrix Metalloproteinase 9Middle AgedMultivariate AnalysisNF-kappa BPrognosisReceptors, Tumor Necrosis Factor, Type ISARS-CoV-2Severity of Illness IndexVascular Endothelial Growth Factor Receptor-1ConceptsType I IFNI IFNSevere acute respiratory syndrome coronavirus 2Whole blood transcriptional signaturesAcute respiratory syndrome coronavirus 2Respiratory syndrome coronavirus 2Immune-based biomarkersCOVID-19 patientsSyndrome coronavirus 2Eventual disease outcomeTissue-resident cellsCoronavirus disease 2019COVID-19Type II IFNInflammatory signatureIL-10Clinical outcomesMultivariable analysisIL-15Aforementioned biomarkersCell subsetsCoronavirus 2IL-1αSoluble biomarkersInflammatory response
2017
Transcriptional Response of Respiratory Epithelium to Nontuberculous Mycobacteria
Matsuyama M, Martins A, Shallom S, Kamenyeva O, Kashyap A, Sampaio E, Kabat J, Olivier K, Zelazny A, Tsang J, Holland S. Transcriptional Response of Respiratory Epithelium to Nontuberculous Mycobacteria. American Journal Of Respiratory Cell And Molecular Biology 2017, 58: 241-252. PMID: 28915071, PMCID: PMC5806000, DOI: 10.1165/rcmb.2017-0218oc.Peer-Reviewed Original ResearchConceptsCholesterol biosynthesisUpregulation of genesRespiratory epitheliumGene expression signaturesCiliary genesTranscriptional responseRNA sequencingEpithelial cell infectionResponse genesInflammatory response genesHost responseCytokine/chemokine productionRespiratory epithelial cell culturesEpithelial cell culturesPulmonary nontuberculous mycobacteria (NTM) diseaseExpression signaturesMajor host responsesCytokines/chemokinesGenesRespiratory epithelial cellsCiliary functionNontuberculous mycobacteria diseaseCell infectionMultiplicity of infectionBiosynthesis
2016
Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling
Narayanan M, Martins A, Tsang J. Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling. PLOS Computational Biology 2016, 12: e1005016. PMID: 27438699, PMCID: PMC4954693, DOI: 10.1371/journal.pcbi.1005016.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyGene Expression ProfilingHumansMacrophagesModels, BiologicalModels, StatisticalRNA, MessengerSingle-Cell AnalysisConceptsSingle-cell expression levelsExpression levelsNovel biological informationSingle cellsKey inflammatory genesExpression variationGene expressionCellular heterogeneityBiological informationRandom poolSingle populationHuman macrophagesBiological conditionsCell populationsGenesMultiplexed technologiesK cellsInflammatory genesCellsBiological varianceQuantifying differencesSensitive technologyContinuous variationRobust inferenceProfilingEffects of Systemically Administered Hydrocortisone on the Human Immunome
Olnes M, Kotliarov Y, Biancotto A, Cheung F, Chen J, Shi R, Zhou H, Wang E, Tsang J, Nussenblatt R. Effects of Systemically Administered Hydrocortisone on the Human Immunome. Scientific Reports 2016, 6: 23002. PMID: 26972611, PMCID: PMC4789739, DOI: 10.1038/srep23002.Peer-Reviewed Original ResearchMeSH KeywordsAdultB-Lymphocyte SubsetsCluster AnalysisDose-Response Relationship, DrugFemaleFlow CytometryGene Expression ProfilingHumansHydrocortisoneImmunophenotypingInfusions, IntravenousKiller Cells, NaturalLymphocyte CountLymphocyte SubsetsMaleMiddle AgedSignal TransductionT-Lymphocyte SubsetsTime FactorsTranscriptomeYoung AdultConceptsSystemic corticosteroid administrationNK cell numbersHigh-dimensional flow cytometryEffect of corticosteroidsNatural killer cellsT cell subsetsEffects of systemicallyImmune system parametersAdministration of hydrocortisoneNF-κB signalingPaucity of dataHuman immunomeIntravenous hydrocortisoneCorticosteroid administrationLymphocyte subsetsNK cellsKiller cellsCell subsetsHC infusionsT cellsT lymphocytesHealthy humansGlucocorticoid receptorLow dosesCorticosteroids
2015
Lineage relationship of CD8+ T cell subsets is revealed by progressive changes in the epigenetic landscape
Crompton J, Narayanan M, Cuddapah S, Roychoudhuri R, Ji Y, Yang W, Patel S, Sukumar M, Palmer D, Peng W, Wang E, Marincola F, Klebanoff C, Zhao K, Tsang J, Gattinoni L, Restifo N. Lineage relationship of CD8+ T cell subsets is revealed by progressive changes in the epigenetic landscape. Cellular & Molecular Immunology 2015, 13: 502-513. PMID: 25914936, PMCID: PMC4947817, DOI: 10.1038/cmi.2015.32.Peer-Reviewed Original ResearchConceptsHistone modificationsDynamic gene expression programsHistone H3 lysine 4Global gene expression profilingChIP-seq approachH3 lysine 4Gene expression programsT cell differentiationActivation of genesStem cellsGene expression profilingT cell metabolismEpigenetic landscapeLysine 4Expression programsGene expression signaturesEpigenetic mechanismsExpression profilingGene expressionLineage relationshipsCell differentiationT cell ontogenyGenomic landscapeMemory stem cellsExpression signatures