Featured Publications
Integrated Single-Cell Atlas of Endothelial Cells of the Human Lung
Schupp JC, Adams TS, Cosme C, Raredon MSB, Yuan Y, Omote N, Poli S, Chioccioli M, Rose KA, Manning EP, Sauler M, DeIuliis G, Ahangari F, Neumark N, Habermann AC, Gutierrez AJ, Bui LT, Lafyatis R, Pierce RW, Meyer KB, Nawijn MC, Teichmann SA, Banovich NE, Kropski JA, Niklason LE, Pe’er D, Yan X, Homer RJ, Rosas IO, Kaminski N. Integrated Single-Cell Atlas of Endothelial Cells of the Human Lung. Circulation 2021, 144: 286-302. PMID: 34030460, PMCID: PMC8300155, DOI: 10.1161/circulationaha.120.052318.Peer-Reviewed Original ResearchConceptsDifferential expression analysisPrimary lung endothelial cellsLung endothelial cellsCell typesMarker genesExpression analysisSingle-cell RNA sequencing dataCross-species analysisVenous endothelial cellsEndothelial marker genesSingle-cell atlasMarker gene setsRNA sequencing dataEndothelial cellsSubsequent differential expression analysisDifferent lung cell typesResident cell typesLung cell typesCellular diversityEndothelial cell typesCapillary endothelial cellsHuman lung endothelial cellsPhenotypic diversityEndothelial diversityIndistinguishable populations
2024
SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data
Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Wei H, Justet A, Adams T, Homer R, Amei A, Rosas I, Kaminski N, Wang Z, Yan X. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 2024, 25: 271. PMID: 39402626, PMCID: PMC11475911, DOI: 10.1186/s13059-024-03416-2.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsGene Expression ProfilingHumansMachine LearningRegression AnalysisRNA-SeqSequence Analysis, RNASingle-Cell AnalysisSoftwareTranscriptomeSingle-cell transcriptomic analysis of human pleura reveals stromal heterogeneity and informs in vitro models of mesothelioma
Obacz J, Valer J, Nibhani R, Adams T, Schupp J, Veale N, Lewis-Wade A, Flint J, Hogan J, Aresu G, Coonar A, Peryt A, Biffi G, Kaminski N, Francies H, Rassl D, Garnett M, Rintoul R, Marciniak S. Single-cell transcriptomic analysis of human pleura reveals stromal heterogeneity and informs in vitro models of mesothelioma. European Respiratory Journal 2024, 63: 2300143. PMID: 38212075, PMCID: PMC10809128, DOI: 10.1183/13993003.00143-2023.Peer-Reviewed Original ResearchMeSH KeywordsGene Expression ProfilingHumansMesotheliomaMesothelioma, MalignantPleuraPleural NeoplasmsConceptsSingle-cell transcriptome atlasSingle-cell levelSingle-cell transcriptome analysisTranscriptome dataTranscriptomic atlasTranscriptomic characterisationMesothelial cellsCell atlasDevelopment of targeted therapiesMalignant mesothelial cellsModel of mesotheliomaUniversal fibroblastsIn vitro model
2023
A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases
Liu W, Deng W, Chen M, Dong Z, Zhu B, Yu Z, Tang D, Sauler M, Lin C, Wain L, Cho M, Kaminski N, Zhao H. A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases. PLOS Genetics 2023, 19: e1010825. PMID: 37523391, PMCID: PMC10414598, DOI: 10.1371/journal.pgen.1010825.Peer-Reviewed Original ResearchConceptsCell typesDisease-associated tissuesWide association studyComplex diseasesCell type proportionsDisease-relevant tissuesReal GWAS dataFunctional genesTranscriptomic dataGWAS dataGenetic dataAssociation studiesNovel statistical frameworkChronic obstructive pulmonary diseaseStatistical frameworkObstructive pulmonary diseaseIdiopathic pulmonary fibrosisBreast cancer riskType proportionsBlood CD8Pulmonary diseasePulmonary fibrosisPredictive biomarkersLung tissueBreast cancerEmergence of division of labor in tissues through cell interactions and spatial cues
Adler M, Moriel N, Goeva A, Avraham-Davidi I, Mages S, Adams T, Kaminski N, Macosko E, Regev A, Medzhitov R, Nitzan M. Emergence of division of labor in tissues through cell interactions and spatial cues. Cell Reports 2023, 42: 112412. PMID: 37086403, PMCID: PMC10242439, DOI: 10.1016/j.celrep.2023.112412.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencingMost cell typesCell-type populationsCell-cell interactionsDistinguishable expression patternsCell population levelSpatial transcriptomics dataCell interactionsLigand-receptor networkMulticellular organismsTranscriptomic dataRNA sequencingInstructive signalsExpression patternsSpecialist cellsCell typesIndividual cellsDivision of laborMultiple functionsTissue environmentSame cellsDifferent functionsPopulation levelCellsDivisionA novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy
Deng W, Li B, Wang J, Jiang W, Yan X, Li N, Vukmirovic M, Kaminski N, Wang J, Zhao H. A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy. Briefings In Bioinformatics 2023, 24: bbac616. PMID: 36631398, PMCID: PMC9851324, DOI: 10.1093/bib/bbac616.Peer-Reviewed Original Research
2022
Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
Raredon M, Yang J, Kothapalli N, Lewis W, Kaminski N, Niklason L, Kluger Y. Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinformatics 2022, 39: btac775. PMID: 36458905, PMCID: PMC9825783, DOI: 10.1093/bioinformatics/btac775.Peer-Reviewed Original ResearchConceptsCell-cell interactionsCell-cell signalingSingle-cell resolutionSingle-cell dataLocal cellular microenvironmentSingle-cell levelSpatial transcriptomics dataCell clustersExtracellular signalingTranscriptomic dataGene expression valuesSpatial transcriptomicsSignaling mechanismCellular microenvironmentNicheExpression valuesSupplementary dataSignalingTranscriptomicsComprehensive visualizationBioinformaticsInteractionCINS: Cell Interaction Network inference from Single cell expression data
Yuan Y, Cosme C, Adams TS, Schupp J, Sakamoto K, Xylourgidis N, Ruffalo M, Li J, Kaminski N, Bar-Joseph Z. CINS: Cell Interaction Network inference from Single cell expression data. PLOS Computational Biology 2022, 18: e1010468. PMID: 36095011, PMCID: PMC9499239, DOI: 10.1371/journal.pcbi.1010468.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBayes TheoremCell CommunicationGene Expression ProfilingLigandsMiceSequence Analysis, RNASingle-Cell AnalysisConceptsCell type interactionsSingle-cell expression dataSingle-cell RNA-seq dataRNA-seq dataScRNA-seq experimentsCell-cell interactionsExpression dataCell typesMouse datasetsNetwork inferenceCell interactionsInteraction predictionNetwork analysisInference pipelineGenesCINSProteinInteractionBayesian network analysisCharacterization of the COPD alveolar niche using single-cell RNA sequencing
Sauler M, McDonough JE, Adams TS, Kothapalli N, Barnthaler T, Werder RB, Schupp JC, Nouws J, Robertson MJ, Coarfa C, Yang T, Chioccioli M, Omote N, Cosme C, Poli S, Ayaub EA, Chu SG, Jensen KH, Gomez JL, Britto CJ, Raredon MSB, Niklason LE, Wilson AA, Timshel PN, Kaminski N, Rosas IO. Characterization of the COPD alveolar niche using single-cell RNA sequencing. Nature Communications 2022, 13: 494. PMID: 35078977, PMCID: PMC8789871, DOI: 10.1038/s41467-022-28062-9.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencingRNA sequencingCell-specific mechanismsChronic obstructive pulmonary diseaseAdvanced chronic obstructive pulmonary diseaseTranscriptomic network analysisSingle-cell RNA sequencing profilesCellular stress toleranceAberrant cellular metabolismStress toleranceRNA sequencing profilesTranscriptional evidenceCellular metabolismAlveolar nicheSequencing profilesHuman alveolar epithelial cellsChemokine signalingAlveolar epithelial type II cellsObstructive pulmonary diseaseSitu hybridizationType II cellsEpithelial type II cellsSequencingCOPD pathobiologyHuman lung tissue samplesSingle-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 interactionsLung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis.
De Sadeleer LJ, McDonough JE, Schupp JC, Yan X, Vanstapel A, Van Herck A, Everaerts S, Geudens V, Sacreas A, Goos T, Aelbrecht C, Nawrot TS, Martens DS, Schols D, Claes S, Verschakelen JA, Verbeken EK, Ackermann M, Decottignies A, Mahieu M, Hackett TL, Hogg JC, Vanaudenaerde BM, Verleden SE, Kaminski N, Wuyts WA. Lung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis. American Journal Of Respiratory And Critical Care Medicine 2022, 205: 60-74. PMID: 34724391, PMCID: PMC8865586, DOI: 10.1164/rccm.202103-0569oc.Peer-Reviewed Original ResearchConceptsFibrotic hypersensitivity pneumonitisIdiopathic pulmonary fibrosisHypersensitivity pneumonitisLung zonesMolecular traitsUnused donor lungsInterstitial lung diseaseLocal disease extentProgression of fibrosisSevere fibrosis groupGene co-expression network analysisCo-expression network analysisExplant lungsDonor lungsLung involvementEndothelial functionLung findingsDisease extentPulmonary fibrosisLung diseaseFibrosis groupLung microenvironmentClinical behaviorDisease progressionBAL samples
2021
A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data
Li H, Zhu B, Xu Z, Adams T, Kaminski N, Zhao H. A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data. BMC Bioinformatics 2021, 22: 524. PMID: 34702190, PMCID: PMC8549347, DOI: 10.1186/s12859-021-04412-0.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsGene Expression ProfilingGene Regulatory NetworksHumansRNA-SeqSequence Analysis, RNASingle-Cell AnalysisConceptsMarkov random field modelRandom field modelMean field-like approximationField modelSpecific DEGsExpectation maximizationSingle-cell sequencing technologiesProtein-coding genesRNA sequencing data setsSingle-cell RNA-seq dataCell-type levelCell typesGibbs samplerSingle-cell RNA sequencing data setsCell-cell networksDifferential expression analysisRNA-seq dataGene network informationStatistical powerType I error ratesDifferent expression levelsMRF modelI error rateModel parametersBiological networks
2020
Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency
Chu JH, Zang W, Vukmirovic M, Yan X, Adams T, DeIuliis G, Hu B, Mihaljinec A, Schupp JC, Becich MJ, Hochheiser H, Gibson KF, Chen ES, Morris A, Leader JK, Wisniewski SR, Zhang Y, Sciurba FC, Collman RG, Sandhaus R, Herzog EL, Patterson KC, Sauler M, Strange C, Kaminski N. Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency. Thorax 2020, 76: 134-143. PMID: 33303696, PMCID: PMC10794043, DOI: 10.1136/thoraxjnl-2019-214301.Peer-Reviewed Original ResearchConceptsWeighted gene co-expression network analysisAlpha-1 antitrypsin deficiencyGene modulesGene co-expression network analysisDifferential gene expression analysisCo-expression network analysisPeripheral blood mononuclear cellsGene expression patternsPBMC gene expression patternsGene coexpression networksAATD individualsGene expression profilesGene expression analysisBronchoalveolar lavageAugmentation therapyClinical variablesAntitrypsin deficiencyGene expression assaysRNA-seqCoexpression networkGene validationExpression analysisExpression assaysWGCNA modulesExpression patternsExpression of SARS-CoV-2 receptor ACE2 and coincident host response signature varies by asthma inflammatory phenotype
Camiolo M, Gauthier M, Kaminski N, Ray A, Wenzel SE. Expression of SARS-CoV-2 receptor ACE2 and coincident host response signature varies by asthma inflammatory phenotype. Journal Of Allergy And Clinical Immunology 2020, 146: 315-324.e7. PMID: 32531372, PMCID: PMC7283064, DOI: 10.1016/j.jaci.2020.05.051.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAngiotensin-Converting Enzyme 2AsthmaBetacoronavirusBiomarkersBronchiBronchoalveolar Lavage FluidCohort StudiesCoronavirus InfectionsCOVID-19EosinophilsFemaleGene Expression ProfilingHumansInterferon Type IInterferon-gammaMaleMiddle AgedPandemicsPeptidyl-Dipeptidase APneumonia, ViralProtein Interaction MappingReceptors, VirusRisk FactorsSARS-CoV-2Severity of Illness IndexT-LymphocytesTranscriptomeUnited StatesConceptsCoronavirus disease 2019Severe coronavirus disease 2019Subset of patientsDisease 2019Risk factorsBronchial epitheliumAcute respiratory syndrome coronavirus 2 infectionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectionSevere acute respiratory syndrome coronavirus 2Syndrome coronavirus 2 infectionType 2 inflammatory biomarkersAcute respiratory syndrome coronavirus 2Receptor ACE2SARS-CoV-2 receptor ACE2Respiratory syndrome coronavirus 2Asthma inflammatory phenotypesLarge asthma cohortsLower peripheral bloodT cell-activating factorCoronavirus 2 infectionEnzyme 2 (ACE2) expressionHistory of hypertensionDiagnosis of asthmaBronchoalveolar lavage lymphocytesT cell recruitment
2019
Elevated CO2 regulates the Wnt signaling pathway in mammals, Drosophila melanogaster and Caenorhabditis elegans
Shigemura M, Lecuona E, Angulo M, Dada LA, Edwards MB, Welch LC, Casalino-Matsuda SM, Sporn PHS, Vadász I, Helenius IT, Nader GA, Gruenbaum Y, Sharabi K, Cummins E, Taylor C, Bharat A, Gottardi CJ, Beitel GJ, Kaminski N, Budinger GRS, Berdnikovs S, Sznajder JI. Elevated CO2 regulates the Wnt signaling pathway in mammals, Drosophila melanogaster and Caenorhabditis elegans. Scientific Reports 2019, 9: 18251. PMID: 31796806, PMCID: PMC6890671, DOI: 10.1038/s41598-019-54683-0.Peer-Reviewed Original ResearchConceptsLarge-scale transcriptomic studyAvailable transcriptomic datasetsCell linesWnt pathway genesOrganismal functionDrosophila melanogasterElevated CO2Different tissue originsTranscriptomic studiesBronchial cell lineCO2 elevationTranscriptomic datasetsGenomic responsesHuman bronchial cell linePathway genesGene expressionDifferent tissuesGenesHigh CO2Tissue originMammalsSkeletal musclePathwayCaenorhabditisMelanogasterIntegrating multiomics longitudinal data to reconstruct networks underlying lung development
Ding J, Ahangari F, Espinoza CR, Chhabra D, Nicola T, Yan X, Lal CV, Hagood JS, Kaminski N, Bar-Joseph Z, Ambalavanan N. Integrating multiomics longitudinal data to reconstruct networks underlying lung development. American Journal Of Physiology - Lung Cellular And Molecular Physiology 2019, 317: l556-l568. PMID: 31432713, PMCID: PMC6879899, DOI: 10.1152/ajplung.00554.2018.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAnimals, NewbornChildChild, PreschoolDNA MethylationEpigenesis, GeneticFemaleGene Expression ProfilingGene Expression Regulation, DevelopmentalGene Regulatory NetworksHigh-Throughput Nucleotide SequencingHumansImmunity, InnateInfantInfant, NewbornLungMaleMiceMice, Inbred C57BLMicroRNAsOrganogenesisProteomicsPulmonary AlveoliRNA, MessengerSingle-Cell AnalysisTranscriptomeConceptsSingle-cell RNA-seq dataLung developmentDynamic regulatory networksOmics data setsRNA-seq dataIndividual cell typesHuman lung developmentRegulatory networksDNA methylationLaser capture microdissectionEpigenetic changesExpression trajectoriesKey pathwaysCell typesActive pathwaysCapture microdissectionRegulatorKey eventsInnate immunityNew insightsSpecific key eventsPathwayComprehensive understandingProteomicsMethylationBAL Cell Gene Expression Is Indicative of Outcome and Airway Basal Cell Involvement in Idiopathic Pulmonary Fibrosis
Prasse A, Binder H, Schupp JC, Kayser G, Bargagli E, Jaeger B, Hess M, Rittinghausen S, Vuga L, Lynn H, Violette S, Jung B, Quast K, Vanaudenaerde B, Xu Y, Hohlfeld JM, Krug N, Herazo-Maya JD, Rottoli P, Wuyts WA, Kaminski N. BAL Cell Gene Expression Is Indicative of Outcome and Airway Basal Cell Involvement in Idiopathic Pulmonary Fibrosis. American Journal Of Respiratory And Critical Care Medicine 2019, 199: 622-630. PMID: 30141961, PMCID: PMC6396865, DOI: 10.1164/rccm.201712-2551oc.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisAirway basal cellsChronic obstructive pulmonary diseaseObstructive pulmonary diseasePulmonary diseaseBAL cellsBasal cellsPulmonary fibrosisControl subjectsCell gene expressionIndependent IPF cohortsNine-gene signatureIPF cohortDerivation cohortClinical parametersRetrospective studyUnivariate analysisUnpredictable courseCell involvementDiscovery cohortGene expressionHealthy volunteersCox modelStage IIIFatal diseaseLCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data
Sun J, Herazo-Maya JD, Wang JL, Kaminski N, Zhao H. LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data. Statistical Applications In Genetics And Molecular Biology 2019, 18: 20170060. PMID: 30759070, DOI: 10.1515/sagmb-2017-0060.Peer-Reviewed Original Research
2018
The DNA repair transcriptome in severe COPD
Sauler M, Lamontagne M, Finnemore E, Herazo-Maya JD, Tedrow J, Zhang X, Morneau JE, Sciurba F, Timens W, Paré PD, Lee PJ, Kaminski N, Bossé Y, Gomez JL. The DNA repair transcriptome in severe COPD. European Respiratory Journal 2018, 52: 1701994. PMID: 30190272, PMCID: PMC6422831, DOI: 10.1183/13993003.01994-2017.Peer-Reviewed Original ResearchMeSH KeywordsAgedDNA DamageDNA RepairFemaleGene Expression ProfilingHumansImmunohistochemistryLungMaleMiddle AgedPulmonary Disease, Chronic ObstructiveTranscriptomeConceptsDNA damage toleranceDNA repairInadequate DNA repairSevere chronic obstructive pulmonary diseaseChronic obstructive pulmonary diseaseRepair pathwaysGene correlation network analysisIntegrative genomics approachNucleotide excision repair pathwayDNA repair pathwaysGene Set Enrichment AnalysisExcision repair pathwayGlobal transcriptomic profilesDNA repair genesDNA repair responseCorrelation network analysisCOPD severityGenomic approachesLung tissue transcriptomeTranscriptomic differencesTranscriptomic changesTranscriptomic patternsRNA sequencingTissue transcriptomesTranscriptomic profilesRegularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome
Sun J, Herazo-Maya J, Molyneaux PL, Maher TM, Kaminski N, Zhao H. Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome. Biometrics 2018, 75: 69-77. PMID: 30178494, DOI: 10.1111/biom.12964.Peer-Reviewed Original ResearchConceptsJoint latent class modelLongitudinal biomarkersExtensive simulation studyLatent class modelLongitudinal submodelJoint modeling methodSurvival submodelLikelihood approachSimulation studyClass modelEvent outcomesLatent classesModeling methodMembership modelRandom effectsModeling approachClassSubmodelsJoint analysisModelBootstrapUnique trajectoriesNovel biological insightsInference