2024
Gene trajectory inference for single-cell data by optimal transport metrics
Qu R, Cheng X, Sefik E, Stanley III J, Landa B, Strino F, Platt S, Garritano J, Odell I, Coifman R, Flavell R, Myung P, Kluger Y. Gene trajectory inference for single-cell data by optimal transport metrics. Nature Biotechnology 2024, 1-11. PMID: 38580861, PMCID: PMC11452571, DOI: 10.1038/s41587-024-02186-3.Peer-Reviewed Original ResearchGene dynamicsGene programTrajectory inferenceBiological processesCell-cell graphDynamics of genesCell trajectory inferenceSingle-cell RNA sequencingSingle-cell dataCell state transitionsMyeloid lineage maturationDynamics of biological processesGene distributionRNA sequencingPseudotemporal orderingGene processingTrajectories of cellsGenesActivity of biological processesTechnical noiseGroups of cellsLineage maturationCellsConstruct cellsSequence
2023
Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms
Zyla J, Papiez A, Zhao J, Qu R, Li X, Kluger Y, Polanska J, Hatzis C, Pusztai L, Marczyk M. Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms. Computational And Structural Biotechnology Journal 2023, 21: 4663-4674. PMID: 37841335, PMCID: PMC10568495, DOI: 10.1016/j.csbj.2023.09.035.Peer-Reviewed Original ResearchSingle-cell RNA-seq platformsSingle-cell RNA sequencingBulk RNA-seq dataRNA-seq platformsNumber of transcriptsLow-expression genesRNA-seq dataSingle-cell dataExpression levelsLow sequencing depthDiscordant genesRNA sequencingSequencing technologiesExpression shiftsPathway levelBiological pathwaysGene levelSequencing depthTranscriptomic platformsGenesIndividual cellsSingle cellsRNA integrityPathwayCellsHumanized mouse liver reveals endothelial control of essential hepatic metabolic functions
Kaffe E, Roulis M, Zhao J, Qu R, Sefik E, Mirza H, Zhou J, Zheng Y, Charkoftaki G, Vasiliou V, Vatner D, Mehal W, AlcHepNet, Kluger Y, Flavell R. Humanized mouse liver reveals endothelial control of essential hepatic metabolic functions. Cell 2023, 186: 3793-3809.e26. PMID: 37562401, PMCID: PMC10544749, DOI: 10.1016/j.cell.2023.07.017.Peer-Reviewed Original ResearchConceptsMetabolic functionsSpecies-specific interactionsKey metabolic functionsCell-autonomous mechanismsNon-alcoholic fatty liver diseaseMajor metabolic hubNon-parenchymal cellsMetabolic hubHuman hepatocytesMicroenvironmental regulationHuman diseasesHuman-specific aspectsHuman pathologiesHomeostatic processesSpecies mismatchCholesterol uptakeFatty liver diseaseParacrine mannerHuman immuneBile acid conjugationSinusoidal endothelial cellsHepatic metabolic functionMouse liverEndothelial cellsCells
2022
NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health
Lee P, Benz C, Blood P, Börner K, Campisi J, Chen F, Daldrup-Link H, De Jager P, Ding L, Duncan F, Eickelberg O, Fan R, Finkel T, Furman D, Garovic V, Gehlenborg N, Glass C, Heckenbach I, Joseph Z, Katiyar P, Kim S, Königshoff M, Kuchel G, Lee H, Lee J, Ma J, Ma Q, Melov S, Metis K, Mora A, Musi N, Neretti N, Passos J, Rahman I, Rivera-Mulia J, Robson P, Rojas M, Roy A, Scheibye-Knudsen M, Schilling B, Shi P, Silverstein J, Suryadevara V, Xie J, Wang J, Wong A, Niedernhofer L, Wang S, Anvari H, Balough J, Benz C, Bons J, Brenerman B, Evans W, Gerencser A, Gregory H, Hansen M, Justice J, Kapahi P, Murad N, O’Broin A, Pavone M, Powell M, Scott G, Shanes E, Shankaran M, Verdin E, Winer D, Wu F, Adams A, Blood P, Bueckle A, Cao-Berg I, Chen H, Davis M, Filus S, Hao Y, Hartman A, Hasanaj E, Helfer J, Herr B, Joseph Z, Molla G, Mou G, Puerto J, Quardokus E, Ropelewski A, Ruffalo M, Satija R, Schwenk M, Scibek R, Shirey W, Sibilla M, Welling J, Yuan Z, Bonneau R, Christiano A, Izar B, Menon V, Owens D, Phatnani H, Smith C, Suh Y, Teich A, Bekker V, Chan C, Coutavas E, Hartwig M, Ji Z, Nixon A, Dou Z, Rajagopal J, Slavov N, Holmes D, Jurk D, Kirkland J, Lagnado A, Tchkonia T, Abraham K, Dibattista A, Fridell Y, Howcroft T, Jhappan C, Montes V, Prabhudas M, Resat H, Taylor V, Kumar M, Suryadevara V, Cigarroa F, Cohn R, Cortes T, Courtois E, Chuang J, Davé M, Domanskyi S, Enninga E, Eryilmaz G, Espinoza S, Gelfond J, Kirkland J, Kuchel G, Kuo C, Lehman J, Aguayo-Mazzucato C, Meves A, Rani M, Sanders S, Thibodeau A, Tullius S, Ucar D, White B, Wu Q, Xu M, Yamaguchi S, Assarzadegan N, Cho C, Hwang I, Hwang Y, Xi J, Adeyi O, Aliferis C, Bartolomucci A, Dong X, DuFresne-To M, Ikramuddin S, Johnson S, Nelson A, Niedernhofer L, Revelo X, Trevilla-Garcia C, Sedivy J, Thompson E, Robbins P, Wang J, Aird K, Alder J, Beaulieu D, Bueno M, Calyeca J, Chamucero-Millaris J, Chan S, Chung D, Corbett A, Gorbunova V, Gowdy K, Gurkar A, Horowitz J, Hu Q, Kaur G, Khaliullin T, Lafyatis R, Lanna S, Li D, Ma A, Morris A, Muthumalage T, Peters V, Pryhuber G, Reader B, Rosas L, Sembrat J, Shaikh S, Shi H, Stacey S, Croix C, Wang C, Wang Q, Watts A, Gu L, Lin Y, Rabinovitch P, Sweetwyne M, Artyomov M, Ballentine S, Chheda M, Davies S, DiPersio J, Fields R, Fitzpatrick J, Fulton R, Imai S, Jain S, Ju T, Kushnir V, Link D, Ben Major M, Oh S, Rapp D, Rettig M, Stewart S, Veis D, Vij K, Wendl M, Wyczalkowski M, Craft J, Enninful A, Farzad N, Gershkovich P, Halene S, Kluger Y, VanOudenhove J, Xu M, Yang J, Yang M. NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health. Nature Aging 2022, 2: 1090-1100. PMID: 36936385, PMCID: PMC10019484, DOI: 10.1038/s43587-022-00326-5.Peer-Reviewed Original ResearchConceptsSenescence-associated secretory phenotypeSenescent cellsSecretory phenotypeMulti-omics datasetsStable growth arrestHuman lifespanDiverse rolesGrowth arrestProinflammatory senescence-associated secretory phenotypeHuman tissuesPhenotypeMetabolic changesCellsHuman healthLifespanPhysiological healthCommon Coordinate Framework
2021
Detection of differentially abundant cell subpopulations in scRNA-seq data
Zhao J, Jaffe A, Li H, Lindenbaum O, Sefik E, Jackson R, Cheng X, Flavell RA, Kluger Y. Detection of differentially abundant cell subpopulations in scRNA-seq data. Proceedings Of The National Academy Of Sciences Of The United States Of America 2021, 118: e2100293118. PMID: 34001664, PMCID: PMC8179149, DOI: 10.1073/pnas.2100293118.Peer-Reviewed Original ResearchMeSH KeywordsAgingB-LymphocytesBrainCell LineageCOVID-19CytokinesDatasets as TopicDendritic CellsGene Expression ProfilingGene Expression RegulationHigh-Throughput Nucleotide SequencingHumansMelanomaMonocytesPhenotypeRNA, Small CytoplasmicSARS-CoV-2Severity of Illness IndexSingle-Cell AnalysisSkin NeoplasmsT-LymphocytesTranscriptomeConceptsDA subpopulationsIll COVID-19 patientsImmune checkpoint therapyCOVID-19 patientsSingle-cell RNA sequencing analysisCheckpoint therapyBrain tissueCell subpopulationsRNA sequencing analysisTime pointsSubpopulationsDiseased individualsDistinct phenotypesOriginal studyCell typesAbundant subpopulationSequencing analysisCellsDA measuresPhenotypeImportant differencesNonrespondersPatientsTherapy
2013
TrAp: a tree approach for fingerprinting subclonal tumor composition
Strino F, Parisi F, Micsinai M, Kluger Y. TrAp: a tree approach for fingerprinting subclonal tumor composition. Nucleic Acids Research 2013, 41: e165-e165. PMID: 23892400, PMCID: PMC3783191, DOI: 10.1093/nar/gkt641.Peer-Reviewed Original ResearchConceptsGenome-wide experimentsEvolutionary relationshipsMutational profileSequencing technologiesMixed cell populationsSilico analysisTumor samplesCell subpopulationsEvolutionary frameworkNumber of subpopulationsSingle cellsEvolutionary pathCell populationsCollective signalClonal compositionMetastatic potentialNumerous cellsTumor karyotypeComputational approachSubpopulationsCellsMixed subpopulationsAbundanceDistinct metastasesTumor composition