Featured Publications
Pair production in a strong electric field
Kluger Y, Eisenberg J, Svetitsky B, Cooper F, Mottola E. Pair production in a strong electric field. Physical Review Letters 1991, 67: 2427-2430. PMID: 10044423, DOI: 10.1103/physrevlett.67.2427.Peer-Reviewed Original Research
2024
The single-cell opioid responses in the context of HIV (SCORCH) consortium
Ament S, Campbell R, Lobo M, Receveur J, Agrawal K, Borjabad A, Byrareddy S, Chang L, Clarke D, Emani P, Gabuzda D, Gaulton K, Giglio M, Giorgi F, Gok B, Guda C, Hadas E, Herb B, Hu W, Huttner A, Ishmam M, Jacobs M, Kelschenbach J, Kim D, Lee C, Liu S, Liu X, Madras B, Mahurkar A, Mash D, Mukamel E, Niu M, O’Connor R, Pagan C, Pang A, Pillai P, Repunte-Canonigo V, Ruzicka W, Stanley J, Tickle T, Tsai S, Wang A, Wills L, Wilson A, Wright S, Xu S, Yang J, Zand M, Zhang L, Zhang J, Akbarian S, Buch S, Cheng C, Corley M, Fox H, Gerstein M, Gummuluru S, Heiman M, Ho Y, Kellis M, Kenny P, Kluger Y, Milner T, Moore D, Morgello S, Ndhlovu L, Rana T, Sanna P, Satterlee J, Sestan N, Spector S, Spudich S, Tilgner H, Volsky D, White O, Williams D, Zeng H. The single-cell opioid responses in the context of HIV (SCORCH) consortium. Molecular Psychiatry 2024, 1-12. PMID: 38879719, DOI: 10.1038/s41380-024-02620-7.Peer-Reviewed Original ResearchContext of human immunodeficiency virusHuman immunodeficiency virusSubstance use disordersOpioid responseAnimal modelsEffects of substance use disordersOpioid pain medicationsPrevalence of co-morbid conditionsChronic pain syndromesStage of diseaseCell typesAffected cell typesCo-morbid conditionsPain syndromeImmunodeficiency virusPain medicationOpioid addictionIncreased riskRisk factorsHuman cohortsDrug addictionBrain tissue collectionBrain cell typesTissue collectionSingle-cell levelHyperbolic Diffusion Procrustes Analysis for Intrinsic Representation of Hierarchical Data Sets
Lin Y, Kluger Y, Talmon R. Hyperbolic Diffusion Procrustes Analysis for Intrinsic Representation of Hierarchical Data Sets. 2024, 00: 6325-6329. DOI: 10.1109/icassp48485.2024.10446370.Peer-Reviewed Original ResearchHIV-1–infected T cell clones are shared across cerebrospinal fluid and blood during ART
Wang M, Yoon J, Reisert H, Das B, Orlinick B, Chiarella J, Halvas E, Mellors J, Pang A, Barakat L, Fikrig M, Cyktor J, Kluger Y, Spudich S, Corley M, Farhadian S. HIV-1–infected T cell clones are shared across cerebrospinal fluid and blood during ART. JCI Insight 2024, 9: e176208. PMID: 38587074, PMCID: PMC11128194, DOI: 10.1172/jci.insight.176208.Peer-Reviewed Original ResearchConceptsT cell clonesT cell receptorCerebrospinal fluidT cellsHIV-1Infected T-cell clonesCentral memory T cellsCD4 T-cell clonesDetectable HIV RNAMemory T cellsHIV-1 RNAInfected T cellsCNS reservoirsHIV persistenceHIV reservoirHIV RNAHIV cureReservoir cellsPWHTissue compartmentsBloodCNSUninfected controlsCD4Infected cellsGene 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, 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 cellsSequenceThree-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation
Yamada Y, Zhang F, Kluger Y, Yildirim I. Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation. Entropy 2024, 26: 258. PMID: 38539769, PMCID: PMC10968904, DOI: 10.3390/e26030258.Peer-Reviewed Original ResearchAdversarial trainingPre-trainingAdversarial attacksAdversarial robustnessRobustness of image classifiersModel of human visionComputational model of human visionAdversarial examplesImage classifierWeight initializationDataset settingData augmentationBackground textureSpurious correlationsHuman visionModels of visionImprove robustnessDatasetRobustnessImage formationAttacksComputational modelTrainingShapeNetAdversaryIL-10 constrains sphingolipid metabolism to limit inflammation
York A, Skadow M, Oh J, Qu R, Zhou Q, Hsieh W, Mowel W, Brewer J, Kaffe E, Williams K, Kluger Y, Smale S, Crawford J, Bensinger S, Flavell R. IL-10 constrains sphingolipid metabolism to limit inflammation. Nature 2024, 627: 628-635. PMID: 38383790, PMCID: PMC10954550, DOI: 10.1038/s41586-024-07098-5.Peer-Reviewed Original ResearchActivity of RelCeramide productionVery long chainFatty acid synthesis pathwayCeramide synthase 2Fatty acid homeostasisMetabolic fluxAvailable to cellsRegulatory nodesTranscription factorsCeramide accumulationSynthesis pathwayVLC ceramidesIL-10 deficiencyGene expressionSphingolipid metabolismAcid homeostasisAberrant activationIL-10Cell types1Innate immune cellsInflammatory gene expressionCeramideSignaling resultsGenetic deletion
2023
Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity
Wang M, Patsenker J, Li H, Kluger Y, Kleinstein S. Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity. Nucleic Acids Research 2023, 52: 548-557. PMID: 38109302, PMCID: PMC10810273, DOI: 10.1093/nar/gkad1128.Peer-Reviewed Original ResearchA bedside to bench study of anti-PD-1, anti-CD40, and anti-CSF1R indicates that more is not necessarily better
Djureinovic D, Weiss S, Krykbaeva I, Qu R, Vathiotis I, Moutafi M, Zhang L, Perdigoto A, Wei W, Anderson G, Damsky W, Hurwitz M, Johnson B, Schoenfeld D, Mahajan A, Hsu F, Miller-Jensen K, Kluger Y, Sznol M, Kaech S, Bosenberg M, Jilaveanu L, Kluger H. A bedside to bench study of anti-PD-1, anti-CD40, and anti-CSF1R indicates that more is not necessarily better. Molecular Cancer 2023, 22: 182. PMID: 37964379, PMCID: PMC10644655, DOI: 10.1186/s12943-023-01884-x.Peer-Reviewed Original ResearchConceptsStable diseasePartial responseMacrophage populationsThree-drug regimenUnconfirmed partial responsePhase I trialLimited treatment optionsMonocyte/macrophage populationNon-classical monocytesMurine melanoma modelTreatment-related changesResultsThirteen patientsWorse survivalI trialInflammatory tumorPatient populationTreatment optionsImmune cellsDisease progressionMurine studiesPreclinical modelsResistant melanomaAntigen presentationMurine modelCyTOF analysisMaternal CXCR4 deletion results in placental defects and pregnancy loss mediated by immune dysregulation
Lyu F, Burzynski C, Fang Y, Tal A, Chen A, Kisa J, Agrawal K, Kluger Y, Taylor H, Tal R. Maternal CXCR4 deletion results in placental defects and pregnancy loss mediated by immune dysregulation. JCI Insight 2023, 8: e172216. PMID: 37815869, PMCID: PMC10721256, DOI: 10.1172/jci.insight.172216.Peer-Reviewed Original ResearchConceptsCXCR4-deficient micePlacental vascular developmentNK cellsCxcr4 deletionNormal placental vascular developmentPlacental developmentNK cell dysfunctionNK cell expressionNK cell infiltrationNK cell functionRole of CXCR4Cell functionMaternal-fetal interfaceImmune cell functionEarly placental developmentWt CXCR4Immune dysregulationVascular developmentGiant cell layerImmune toleranceCXCR4 expressionPeripheral bloodPregnancy failureCell infiltrationPregnancy lossEvaluation 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 integrityPathwayCellsIL-6 trans-signaling in a humanized mouse model of scleroderma
Odell I, Agrawal K, Sefik E, Odell A, Caves E, Kirkiles-Smith N, Horsley V, Hinchcliff M, Pober J, Kluger Y, Flavell R. IL-6 trans-signaling in a humanized mouse model of scleroderma. Proceedings Of The National Academy Of Sciences Of The United States Of America 2023, 120: e2306965120. PMID: 37669366, PMCID: PMC10500188, DOI: 10.1073/pnas.2306965120.Peer-Reviewed Original ResearchConceptsBone marrow-derived immune cellsIL-6Human hematopoietic stem cellsImmune cellsT cellsScleroderma skinSoluble IL-6 receptorCD8 T cellsHumanized mouse modelPathogenesis of sclerodermaMesenchymal cellsFibroblast-derived IL-6IL-6 receptorIL-6 signalingT cell activationHuman IL-6Human T cellsExpression of collagenFibrosis improvementPansclerotic morpheaHuman endothelial cellsHumanized miceReduced markersSkin graftsHuman CD4Humanized 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 cellsCellsAutologous humanized PDX modeling for immuno-oncology recapitulates features of the human tumor microenvironment
Chiorazzi M, Martinek J, Krasnick B, Zheng Y, Robbins K, Qu R, Kaufmann G, Skidmore Z, Juric M, Henze L, Brösecke F, Adonyi A, Zhao J, Shan L, Sefik E, Mudd J, Bi Y, Goedegebuure S, Griffith M, Griffith O, Oyedeji A, Fertuzinhos S, Garcia-Milian R, Boffa D, Detterbeck F, Dhanasopon A, Blasberg J, Judson B, Gettinger S, Politi K, Kluger Y, Palucka K, Fields R, Flavell R. Autologous humanized PDX modeling for immuno-oncology recapitulates features of the human tumor microenvironment. Journal For ImmunoTherapy Of Cancer 2023, 11: e006921. PMID: 37487666, PMCID: PMC10373695, DOI: 10.1136/jitc-2023-006921.Peer-Reviewed Original ResearchConceptsHuman tumor microenvironmentTumor microenvironmentTumor-immune interactionsSolid tumorsAdaptive immune activationAdaptive immune populationsIndividual tumor microenvironmentsPatient's hematopoietic systemPatient-derived xenograft tissuesVascular endothelial growth factorBone marrow hematopoietic stemBone marrow aspiratePreclinical drug testingEndothelial growth factorHematopoietic systemAutologous tumorPDX modelingPDX miceImmune activationImmune populationsMarrow aspiratesAutologous systemIndividual patientsLittermate controlsPreclinical predictionsEffector response to necroptotic cell death: an ensemble of immune and stromal cells.
Hughes L, Altun O, Nevin J, Wang M, Kluger Y, Pelorosso F, Leighton J, Rothlin C, Ghosh S. Effector response to necroptotic cell death: an ensemble of immune and stromal cells. The Journal Of Immunology 2023, 210: 72.39-72.39. DOI: 10.4049/jimmunol.210.supp.72.39.Peer-Reviewed Original ResearchApoptotic cell deathCell deathGene expression programsEffector responsesNecroptotic cell deathSingle-cell levelMyofibroblast transitionExpression programsStromal cellsTissue renewalTranscriptomic changesCellular corpsesInflammatory bowel diseaseExcessive inflammatory responseInfluence of TGFChemo-genetic approachNon-resolving inflammationMolecular pathwaysResolution of inflammationEssential roleMajor stromal cellsBowel diseaseInflammatory responseInjury modelHelminth infectionsSpectral top-down recovery of latent tree models
Aizenbud Y, Jaffe A, Wang M, Hu A, Amsel N, Nadler B, Chang J, Kluger Y. Spectral top-down recovery of latent tree models. Information And Inference A Journal Of The IMA 2023, 12: 2300-2350. PMID: 37593361, PMCID: PMC10431953, DOI: 10.1093/imaiai/iaad032.Peer-Reviewed Original ResearchLatent tree modelsHigh-dimensional dataLaplacian matrixFiedler vectorRandom wayTree structureObserved nodesGraphical modelsTerminal nodesTree modelTerms of runtimeNumber of samplesModerate sizeConquer approachRandom subsetCertain conditionsSimilar accuracyPrevious methodsSmall subtreesCommon approachModelHigh probabilityOnly observationScientific domainsNon-random waySpatial epigenome–transcriptome co-profiling of mammalian tissues
Zhang D, Deng Y, Kukanja P, Agirre E, Bartosovic M, Dong M, Ma C, Ma S, Su G, Bao S, Liu Y, Xiao Y, Rosoklija G, Dwork A, Mann J, Leong K, Boldrini M, Wang L, Haeussler M, Raphael B, Kluger Y, Castelo-Branco G, Fan R. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 2023, 616: 113-122. PMID: 36922587, PMCID: PMC10076218, DOI: 10.1038/s41586-023-05795-1.Peer-Reviewed Original ResearchConceptsGene expressionSingle-cell resolutionChromatin accessibilityJoint profilingHistone modificationsGene regulationCellular statesEpigenetic mechanismsCentral dogmaSpatial transcriptomeTranscriptional phenotypeCell statesOmics informationSpatial transcriptomicsEpigenetic primingMammalian tissuesEpigenomeMolecular biologyTissue architectureCell dynamicsMechanistic relationshipDifferential rolesNew insightsMouse brainProfilingIntegrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations
Ren J, Qu R, Rahman N, Lewis J, King A, Liao X, Mirza F, Carlson K, Huang Y, Gigante S, Evans B, Rajendran B, Xu S, Wang G, Foss F, Damsky W, Kluger Y, Krishnaswamy S, Girardi M. Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations. Blood Advances 2023, 7: 445-457. PMID: 35947128, PMCID: PMC9979716, DOI: 10.1182/bloodadvances.2022008168.Peer-Reviewed Original ResearchConceptsCutaneous T-cell lymphomaMalignant CTCL cellsDiverse transcriptomic profilesT cellsSingle-cell RNACTCL cellsDevelopment of CTCLIntegrated transcriptomeT-cell receptor sequencingT cell exhaustion phenotypeCommon antigenic stimulusPeripheral blood CD4Transcriptomic profilesGene expressionT-cell lymphomaIntegrative analysisPotential therapeutic targetProliferation advantageLimited diversityBlood CD4Blood involvementMutation levelsExhaustion phenotypeWorse prognosisAntigenic stimulusThe age of bone marrow dictates the clonality of smooth muscle-derived cells in atherosclerotic plaques
Kabir I, Zhang X, Dave J, Chakraborty R, Qu R, Chandran R, Ntokou A, Gallardo-Vara E, Aryal B, Rotllan N, Garcia-Milian R, Hwa J, Kluger Y, Martin K, Fernández-Hernando C, Greif D. The age of bone marrow dictates the clonality of smooth muscle-derived cells in atherosclerotic plaques. Nature Aging 2023, 3: 64-81. PMID: 36743663, PMCID: PMC9894379, DOI: 10.1038/s43587-022-00342-5.Peer-Reviewed Original ResearchConceptsAtherosclerotic plaquesBone marrowSmooth muscle-derived cellsSMC progenitorsAtherosclerotic plaque cellsSmooth muscle cell progenitorsPredominant risk factorCause of deathNovel therapeutic strategiesTNF receptor 1Muscle-derived cellsAged bone marrowAged BMEffect of agePlaque burdenAged miceRisk factorsTumor necrosisTherapeutic strategiesPlaque cellsMyeloid cellsReceptor 1Integrin β3Cell progenitorsAtherosclerosis
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