2024
Single-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
2022
CINS: 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
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 typesSingle-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics
Muus C, Luecken M, Eraslan G, Sikkema L, Waghray A, Heimberg G, Kobayashi Y, Vaishnav E, Subramanian A, Smillie C, Jagadeesh K, Duong E, Fiskin E, Torlai Triglia E, Ansari M, Cai P, Lin B, Buchanan J, Chen S, Shu J, Haber A, Chung H, Montoro D, Adams T, Aliee H, Allon S, Andrusivova Z, Angelidis I, Ashenberg O, Bassler K, Bécavin C, Benhar I, Bergenstråhle J, Bergenstråhle L, Bolt L, Braun E, Bui L, Callori S, Chaffin M, Chichelnitskiy E, Chiou J, Conlon T, Cuoco M, Cuomo A, Deprez M, Duclos G, Fine D, Fischer D, Ghazanfar S, Gillich A, Giotti B, Gould J, Guo M, Gutierrez A, Habermann A, Harvey T, He P, Hou X, Hu L, Hu Y, Jaiswal A, Ji L, Jiang P, Kapellos T, Kuo C, Larsson L, Leney-Greene M, Lim K, Litviňuková M, Ludwig L, Lukassen S, Luo W, Maatz H, Madissoon E, Mamanova L, Manakongtreecheep K, Leroy S, Mayr C, Mbano I, McAdams A, Nabhan A, Nyquist S, Penland L, Poirion O, Poli S, Qi C, Queen R, Reichart D, Rosas I, Schupp J, Shea C, Shi X, Sinha R, Sit R, Slowikowski K, Slyper M, Smith N, Sountoulidis A, Strunz M, Sullivan T, Sun D, Talavera-López C, Tan P, Tantivit J, Travaglini K, Tucker N, Vernon K, Wadsworth M, Waldman J, Wang X, Xu K, Yan W, Zhao W, Ziegler C. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics. Nature Medicine 2021, 27: 546-559. PMID: 33654293, PMCID: PMC9469728, DOI: 10.1038/s41591-020-01227-z.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAlveolar Epithelial CellsAngiotensin-Converting Enzyme 2Cathepsin LCOVID-19Datasets as TopicDemographyFemaleGene Expression ProfilingHost-Pathogen InteractionsHumansLungMaleMiddle AgedOrgan SpecificityRespiratory SystemSARS-CoV-2Sequence Analysis, RNASerine EndopeptidasesSingle-Cell AnalysisVirus InternalizationConceptsSingle-cell RNA-sequencing studiesRNA-sequencing studiesSpecific expression patternsExpression programsKey immune functionsExpression patternsSARS-CoV-2 entry genesSpecific expressionAlveolar type 2 cellsMolecular pathwaysLung parenchyma samplesCoronavirus disease 2019 (COVID-19) transmissionDifferent tissuesCellular entryGenesRespiratory epithelial cellsAirway secretory cellsSecretory cellsTumor necrosis factorEntry genesExpression levelsType 2 cellsEpithelial cellsGut tissueSpecific subset
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
BAL 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 disease
2017
Transcriptome profiles in sarcoidosis and their potential role in disease prediction
Schupp JC, Vukmirovic M, Kaminski N, Prasse A. Transcriptome profiles in sarcoidosis and their potential role in disease prediction. Current Opinion In Pulmonary Medicine 2017, 23: 487-492. PMID: 28590292, PMCID: PMC5637542, DOI: 10.1097/mcp.0000000000000403.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsGene Expression ProfilingGenome-Wide Association StudyHumansInterferon-gammaSarcoidosisSequence Analysis, RNASignal TransductionTh1 CellsTranscriptomeConceptsGenome-wide expression studiesWide expression studiesTranscriptome profilesTranscriptomic dataRNA sequencingExpression studiesGene expressionMolecular mechanismsLarge prospective followTh1 immune responseTranscriptomeNonnecrotizing granulomasProspective followSystemic diseaseDisease progressionTreatment outcomesImmune responseSarcoidosisPotential roleControl tissuesProgressive sarcoidosisKey roleDiseaseTranscriptomicsGranulomas