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 Research
2022
Characterization 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 ResearchConceptsMarkov 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 networksIntegrated transcriptomic analysis of human tuberculosis granulomas and a biomimetic model identifies therapeutic targets
Reichmann MT, Tezera LB, Vallejo AF, Vukmirovic M, Xiao R, Reynolds J, Jogai S, Wilson S, Marshall B, Jones MG, Leslie A, D'Armiento JM, Kaminski N, Polak ME, Elkington P. Integrated transcriptomic analysis of human tuberculosis granulomas and a biomimetic model identifies therapeutic targets. Journal Of Clinical Investigation 2021, 131 PMID: 34128839, PMCID: PMC8321576, DOI: 10.1172/jci148136.Peer-Reviewed Original ResearchConceptsTherapeutic targetTB granulomasHuman TB diseaseHuman tuberculosis granulomasNoninfectious granulomatous diseasesPathological host responsesSarcoidosis lymph nodesInflammatory immune responseSphingosine kinase 1 inhibitionInflammatory mediator secretionPotential therapeutic targetHuman TB granulomasKinase 1 inhibitionHuman cell culture modelsInfected granulomasTB diseaseLymph nodesTB outcomesTuberculosis granulomasStandard treatmentSphingosine kinase 1Granulomatous diseaseLaser capture microdissectionMediator secretionExtensive infection
2020
Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis
Adams TS, Schupp JC, Poli S, Ayaub EA, Neumark N, Ahangari F, Chu SG, Raby BA, DeIuliis G, Januszyk M, Duan Q, Arnett HA, Siddiqui A, Washko GR, Homer R, Yan X, Rosas IO, Kaminski N. Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis. Science Advances 2020, 6: eaba1983. PMID: 32832599, PMCID: PMC7439502, DOI: 10.1126/sciadv.aba1983.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisVascular endothelial cellsIPF lungsPulmonary fibrosisChronic obstructive pulmonary disease (COPD) lungsFatal interstitial lung diseaseEndothelial cellsInterstitial lung diseaseCell populationsIPF myofibroblastsMyofibroblast fociNonsmoker controlsLung diseaseCOPD lungsBasaloid cellsSingle-cell atlasInvasive fibroblastsMacrophage populationsLungStromal cellsEpithelial cellsFibrosisCellular populationsDevelopmental markersSingle-cell RNA-seqAssessment of viral RNA in idiopathic pulmonary fibrosis using RNA-seq
Yin Q, Strong MJ, Zhuang Y, Flemington EK, Kaminski N, de Andrade JA, Lasky JA. Assessment of viral RNA in idiopathic pulmonary fibrosis using RNA-seq. BMC Pulmonary Medicine 2020, 20: 81. PMID: 32245461, PMCID: PMC7119082, DOI: 10.1186/s12890-020-1114-1.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisViral RNA expressionViral infectionPulmonary fibrosisPathogenesis of IPFLung biopsy samplesRNA expressionHerpes virus infectionLow-level evidenceReal-time RT-PCRAcute exacerbationControl lungsLung tissueVirus infectionBiopsy samplesSelect virusesPossible associationInfectionRT-PCRStatistical differenceNext-generation sequencingViral RNAFibrosisVirusRNA-seq resultsPlatform 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