Featured Publications
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 ResearchiDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects
Liu Y, Zhao J, Adams T, Wang N, Schupp J, Wu W, McDonough J, Chupp G, Kaminski N, Wang Z, Yan X. iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects. BMC Bioinformatics 2023, 24: 318. PMID: 37608264, PMCID: PMC10463720, DOI: 10.1186/s12859-023-05432-8.Peer-Reviewed Original ResearchTranscriptomics of bronchoalveolar lavage cells identifies new molecular endotypes of sarcoidosis
Vukmirovic M, Yan X, Gibson KF, Gulati M, Schupp JC, DeIuliis G, Adams TS, Hu B, Mihaljinec A, Woolard TN, Lynn H, Emeagwali N, Herzog EL, Chen ES, Morris A, Leader JK, Zhang Y, Garcia JGN, Maier LA, Collman RG, Drake WP, Becich MJ, Hochheiser H, Wisniewski SR, Benos PV, Moller DR, Prasse A, Koth LL, Kaminski N. Transcriptomics of bronchoalveolar lavage cells identifies new molecular endotypes of sarcoidosis. European Respiratory Journal 2021, 58: 2002950. PMID: 34083402, PMCID: PMC9759791, DOI: 10.1183/13993003.02950-2020.Peer-Reviewed Original ResearchMeSH KeywordsBronchoalveolar LavageBronchoalveolar Lavage FluidHumansSarcoidosisSarcoidosis, PulmonaryTranscriptomeConceptsWeighted gene co-expression network analysisGene co-expression network analysisCo-expression network analysisGene expression programsGene expression patternsDistinct transcriptional programsImmune response pathwaysIon Torrent ProtonMicroarray expression datasetsExpression programsTranscriptional programsPhenotypic traitsGene modulesResponse pathwaysRNA sequencingMolecular endotypesExpression patternsGene expressionHilar lymphadenopathyOrgan involvementGenomic researchMechanistic targetExpression datasetsT helper type 1T cell immune responsesNoninvasive Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma
Yan X, Chu JH, Gomez J, Koenigs M, Holm C, He X, Perez MF, Zhao H, Mane S, Martinez FD, Ober C, Nicolae DL, Barnes KC, London SJ, Gilliland F, Weiss ST, Raby BA, Cohn L, Chupp GL. Noninvasive Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma. American Journal Of Respiratory And Critical Care Medicine 2015, 191: 1116-1125. PMID: 25763605, PMCID: PMC4451618, DOI: 10.1164/rccm.201408-1440oc.Peer-Reviewed Original ResearchConceptsHistory of intubationNitric oxide levelsOxide levelsClinical phenotypeMost subjectsHigher bronchodilator responseNormal lung functionBlood of patientsCohort of childrenLogistic regression analysisSputum gene expressionBlood of childrenAirway transcriptomeMilder asthmaPathophysiologic heterogeneityPrebronchodilator FEV1Steroid requirementsLung functionBronchodilator responseGene expressionPhenotype of diseaseAsthmaBlood samplesGene signatureIntubation
2023
A 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
Lung 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
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 populationsSingle-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma
Chen A, Diaz-Soto MP, Sanmamed MF, Adams T, Schupp JC, Gupta A, Britto C, Sauler M, Yan X, Liu Q, Nino G, Cruz CSD, Chupp GL, Gomez JL. Single-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma. Respiratory Research 2021, 22: 122. PMID: 33902571, PMCID: PMC8074196, DOI: 10.1186/s12931-021-01709-9.Peer-Reviewed Original ResearchConceptsPeripheral blood mononuclear cellsSevere asthmaEffector T cellsBlood mononuclear cellsT cellsHealthy controlsPoly IDendritic cellsMononuclear cellsUnstimulated peripheral blood mononuclear cellsInterferon responseTLR3 agonist poly IImpaired interferon responseMain cell subsetsNatural killer cellsPro-inflammatory profilePro-inflammatory pathwaysC stimulationCyTOF profilingHigh CD8Cell typesEffector cellsKiller cellsCell subsetsMain cell typesMicroRNA miR-24-3p reduces DNA damage responses, apoptosis, and susceptibility to chronic obstructive pulmonary disease
Nouws J, Wan F, Finnemore E, Roque W, Kim SJ, Bazan IS, Li CX, Sköld C, Dai Q, Yan X, Chioccioli M, Neumeister V, Britto CJ, Sweasy J, Bindra RS, Wheelock ÅM, Gomez JL, Kaminski N, Lee PJ, Sauler M. MicroRNA miR-24-3p reduces DNA damage responses, apoptosis, and susceptibility to chronic obstructive pulmonary disease. JCI Insight 2021, 6: e134218. PMID: 33290275, PMCID: PMC7934877, DOI: 10.1172/jci.insight.134218.Peer-Reviewed Original ResearchConceptsCellular stress responseStress responseHomology-directed DNA repairDNA damage responseProtein BRCA1Damage responseCellular stressDNA repairProtein BimCOPD lung tissueLung epithelial cellsCellular responsesExpression arraysEpithelial cell apoptosisDNA damageChronic obstructive pulmonary diseaseBRCA1 expressionCell apoptosisApoptosisEpithelial cellsCritical mechanismMicroRNAsRegulatorObstructive pulmonary diseaseIncreases Susceptibility
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 patterns
2019
Transcriptional regulatory model of fibrosis progression in the human lung
McDonough JE, Ahangari F, Li Q, Jain S, Verleden SE, Herazo-Maya J, Vukmirovic M, DeIuliis G, Tzouvelekis A, Tanabe N, Chu F, Yan X, Verschakelen J, Homer RJ, Manatakis DV, Zhang J, Ding J, Maes K, De Sadeleer L, Vos R, Neyrinck A, Benos PV, Bar-Joseph Z, Tantin D, Hogg JC, Vanaudenaerde BM, Wuyts WA, Kaminski N. Transcriptional regulatory model of fibrosis progression in the human lung. JCI Insight 2019, 4 PMID: 31600171, PMCID: PMC6948862, DOI: 10.1172/jci.insight.131597.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisAdvanced fibrosisAlveolar surface densityFibrosis progressionLung fibrosisHuman lungDynamic Regulatory Events MinerExtent of fibrosisIPF lungsPulmonary fibrosisControl lungsIPF tissueB lymphocytesFibrosisLungLinear mixed-effects modelsMixed-effects modelsGene expression changesSystems biology modelsDifferential gene expression analysisGene expression analysisProgressionGene expression networksRNA sequencingBiology modelsIntegrating 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 understandingProteomicsMethylation