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 AnalysisSoftwareTranscriptome
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
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 analysisFrom COVID to fibrosis: lessons from single-cell analyses of the human lung
Justet A, Zhao AY, Kaminski N. From COVID to fibrosis: lessons from single-cell analyses of the human lung. Human Genomics 2022, 16: 20. PMID: 35698166, PMCID: PMC9189802, DOI: 10.1186/s40246-022-00393-0.Peer-Reviewed Original ResearchConceptsSingle-cell RNA-sequencing technologySingle-cell RNA sequencingRNA-sequencing technologyGene expression patternsMonocyte-derived macrophage populationSingle-cell analysisCell populationsLung diseaseCellular phenotypesRNA sequencingExpression patternsGene expressionAberrant repairMultiple tissuesPulmonary fibrosisMechanisms of diseaseFibrotic interstitial lung diseaseLife-threatening complicationsProgressive lung diseaseCOVID-19 pneumoniaInterstitial lung diseaseParenchymal lung diseaseAcute viral diseaseMacrophage populationsNovel cellCharacterization 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 interactions
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
Single-Cell Transcriptional Archetypes of Airway Inflammation in Cystic Fibrosis.
Schupp JC, Khanal S, Gomez JL, Sauler M, Adams TS, Chupp GL, Yan X, Poli S, Zhao Y, Montgomery RR, Rosas IO, Dela Cruz CS, Bruscia EM, Egan ME, Kaminski N, Britto CJ. Single-Cell Transcriptional Archetypes of Airway Inflammation in Cystic Fibrosis. American Journal Of Respiratory And Critical Care Medicine 2020, 202: 1419-1429. PMID: 32603604, PMCID: PMC7667912, DOI: 10.1164/rccm.202004-0991oc.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAirway ResistanceCystic FibrosisFemaleHumansInflammationMaleMiddle AgedSingle-Cell AnalysisTranscriptional ActivationConceptsCF lung diseaseHealthy control subjectsImmune dysfunctionLung diseaseCystic fibrosisControl subjectsSputum cellsAbnormal chloride transportLung mononuclear phagocytesInnate immune dysfunctionDivergent clinical coursesImmune cell repertoireMonocyte-derived macrophagesCF monocytesAirway inflammationClinical courseProinflammatory featuresCell survival programInflammatory responseTissue injuryCell repertoireImmune functionTranscriptional profilesAlveolar macrophagesMononuclear phagocytesSARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues
Ziegler C, Allon S, Nyquist S, Mbano I, Miao V, Tzouanas C, Cao Y, Yousif A, Bals J, Hauser B, Feldman J, Muus C, Wadsworth M, Kazer S, Hughes T, Doran B, Gatter G, Vukovic M, Taliaferro F, Mead B, Guo Z, Wang J, Gras D, Plaisant M, Ansari M, Angelidis I, Adler H, Sucre J, Taylor C, Lin B, Waghray A, Mitsialis V, Dwyer D, Buchheit K, Boyce J, Barrett N, Laidlaw T, Carroll S, Colonna L, Tkachev V, Peterson C, Yu A, Zheng H, Gideon H, Winchell C, Lin P, Bingle C, Snapper S, Kropski J, Theis F, Schiller H, Zaragosi L, Barbry P, Leslie A, Kiem H, Flynn J, Fortune S, Berger B, Finberg R, Kean L, Garber M, Schmidt A, Lingwood D, Shalek A, Ordovas-Montanes J, Network H, Banovich N, Barbry P, Brazma A, Desai T, Duong T, Eickelberg O, Falk C, Farzan M, Glass I, Haniffa M, Horvath P, Hung D, Kaminski N, Krasnow M, Kropski J, Kuhnemund M, Lafyatis R, Lee H, Leroy S, Linnarson S, Lundeberg J, Meyer K, Misharin A, Nawijn M, Nikolic M, Ordovas-Montanes J, Pe’er D, Powell J, Quake S, Rajagopal J, Tata P, Rawlins E, Regev A, Reyfman P, Rojas M, Rosen O, Saeb-Parsy K, Samakovlis C, Schiller H, Schultze J, Seibold M, Shalek A, Shepherd D, Spence J, Spira A, Sun X, Teichmann S, Theis F, Tsankov A, van den Berge M, von Papen M, Whitsett J, Xavier R, Xu Y, Zaragosi L, Zhang K. SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues. Cell 2020, 181: 1016-1035.e19. PMID: 32413319, PMCID: PMC7252096, DOI: 10.1016/j.cell.2020.04.035.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAlveolar Epithelial CellsAngiotensin-Converting Enzyme 2AnimalsBetacoronavirusCell LineCells, CulturedChildCoronavirus InfectionsCOVID-19EnterocytesGoblet CellsHIV InfectionsHumansInfluenza, HumanInterferon Type ILungMacaca mulattaMiceMycobacterium tuberculosisNasal MucosaPandemicsPeptidyl-Dipeptidase APneumonia, ViralReceptors, VirusSARS-CoV-2Serine EndopeptidasesSingle-Cell AnalysisTuberculosisUp-RegulationConceptsSARS-CoV-2Interferon-stimulated genesAirway epithelial cellsCell subsetsSingle-cell RNA sequencing datasetsRNA sequencing datasetsSARS-CoV-2 receptor ACE2Human interferon-stimulated genesTransmembrane serine protease 2Human airway epithelial cellsEpithelial cellsSevere acute respiratory syndrome coronavirus clade 2SARS-CoV-2 spike proteinType II pneumocytesSerine protease 2Clade 2Putative targetsNon-human primatesSpecific cell subsetsCo-expressing cellsDisease COVID-19ACE2 expressionLung injuryLung type II pneumocytesAbsorptive enterocytesCollagen-producing lung cell atlas identifies multiple subsets with distinct localization and relevance to fibrosis
Tsukui T, Sun KH, Wetter JB, Wilson-Kanamori JR, Hazelwood LA, Henderson NC, Adams TS, Schupp JC, Poli SD, Rosas IO, Kaminski N, Matthay MA, Wolters PJ, Sheppard D. Collagen-producing lung cell atlas identifies multiple subsets with distinct localization and relevance to fibrosis. Nature Communications 2020, 11: 1920. PMID: 32317643, PMCID: PMC7174390, DOI: 10.1038/s41467-020-15647-5.Peer-Reviewed Original ResearchConceptsCollagen-producing cellsSitu hybridization showDisease-relevant phenotypesCell atlasDistinct localizationExpression of CTHRC1Fibrotic lungsDifferent compartmentsPulmonary fibrosisDistinct anatomical localizationCellsCTHRC1Murine lungFibroblastsIdiopathic pulmonary fibrosisAdoptive transfer experimentsLocalizationSubpopulationsComplex architectureTransfer experimentsFibroblastic fociPathologic fibrosisPathologic scarringScleroderma patientsSimilar heterogeneityPlatform Effects on Regeneration by Pulmonary Basal Cells as Evaluated by Single-Cell RNA Sequencing
Greaney AM, Adams TS, Raredon M, Gubbins E, Schupp JC, Engler AJ, Ghaedi M, Yuan Y, Kaminski N, Niklason LE. Platform Effects on Regeneration by Pulmonary Basal Cells as Evaluated by Single-Cell RNA Sequencing. Cell Reports 2020, 30: 4250-4265.e6. PMID: 32209482, PMCID: PMC7175071, DOI: 10.1016/j.celrep.2020.03.004.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencingBasal marker expressionBasal cellsChronic pulmonary diseaseRat tracheal epitheliumPulmonary diseaseRNA sequencingCell-based therapiesRat tracheaAir-liquid interfaceTissue graftMarker expressionTracheal epitheliumRegenerative outcomesTracheaEpithelial progenitorsDifferential outcomesEpitheliumOutcomesWhole organPopulation level
2019
Single-cell connectomic analysis of adult mammalian lungs
Raredon MSB, Adams TS, Suhail Y, Schupp JC, Poli S, Neumark N, Leiby KL, Greaney AM, Yuan Y, Horien C, Linderman G, Engler AJ, Boffa DJ, Kluger Y, Rosas IO, Levchenko A, Kaminski N, Niklason LE. Single-cell connectomic analysis of adult mammalian lungs. Science Advances 2019, 5: eaaw3851. PMID: 31840053, PMCID: PMC6892628, DOI: 10.1126/sciadv.aaw3851.Peer-Reviewed Original ResearchConceptsTissue homeostasisMammalian lungSingle-cell RNA sequencing techniquesAdult mammalian lungRNA sequencing techniquesCell-cell interactionsSequencing techniquesKey pathwaysAlveolar type IFunctional roleCell typesCell populationsRegenerative medicineHomeostatic mechanismsHomeostasisFine architectureFunctional lung tissueIncomplete understandingMajor roleType ITissueRegulationPathwayAlveolar cell populationsDistal lungIntegrating 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