2025
Medications for opioid use disorder shape immune responses during chronic HIV infection
Collora J, Steinhauser S, Davenport T, Lin D, Eshetu A, Zeidi S, Kim R, Frank C, Kluger Y, Springer S, Ho Y. Medications for opioid use disorder shape immune responses during chronic HIV infection. Cell Reports Medicine 2025, 6: 102159. PMID: 40460829, PMCID: PMC12208330, DOI: 10.1016/j.xcrm.2025.102159.Peer-Reviewed Original ResearchConceptsOpioid use disorderTumor necrosis factorImmune responseHIV infectionAssociated with HIV replicationCytotoxic T cell populationsRisk of opioid use disorderUse disorderPeripheral blood immune cellsI interferonChronic HIV infectionRNA expressionPartial agonist buprenorphineT cell populationsShape immune responsesBlood immune cellsType I interferonImprove immune responsesAgonist buprenorphineAgonist methadoneHIV expressionAntagonist naltrexoneHIV replicationImmune profileImmune cellsSupervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction
Wang M, Patsenker J, Li H, Kluger Y, Kleinstein S. Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. PLOS Computational Biology 2025, 21: e1012153. PMID: 40163503, PMCID: PMC12013870, DOI: 10.1371/journal.pcbi.1012153.Peer-Reviewed Original ResearchConceptsSupervised fine-tuningImmune responseMolecular basis of antigen recognitionSARS-CoV-2 spike proteinLanguage modelSARS-CoV-2 vaccinesAdaptive immune responsesSpecific predictionsMolecular basisSpike proteinAntibody-based therapeuticsFine-tuningAntibody-antigen specificitySpecific to antigensInfluenza hemagglutininVaccine designAntigen recognitionModel embeddingsImmune functionLanguage model embeddingsSARS-CoV-2AntibodiesAntigenInfluenzaVaccineSIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data
Dong M, Su D, Kluger H, Fan R, Kluger Y. SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data. Nature Communications 2025, 16: 2990. PMID: 40148341, PMCID: PMC11950362, DOI: 10.1038/s41467-025-58089-7.Peer-Reviewed Original ResearchConceptsOmics dataSpatial omics dataAnalysis of gene expressionSingle-cell resolutionDownstream analysisCellular statesSpatial interaction modelsGerminal center B cellsGene expressionCommunication machineryOmics technologiesIntercellular interactionsSpatial omics technologiesTumor microenvironmentB cellsSpatial dynamicsHuman tonsilsMacrophage stateSpatial effects
2024
HIV-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 cells
2023
A 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 analysisIL-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 predictionsSpatial 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
Girardi M, 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. 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 FrameworkLongitudinal single-cell analysis of a patient receiving adoptive cell therapy reveals potential mechanisms of treatment failure
Qu R, Kluger Y, Yang J, Zhao J, Hafler D, Krause D, Bersenev A, Bosenberg M, Hurwitz M, Lucca L, Kluger H. Longitudinal single-cell analysis of a patient receiving adoptive cell therapy reveals potential mechanisms of treatment failure. Molecular Cancer 2022, 21: 219. PMID: 36514045, PMCID: PMC9749221, DOI: 10.1186/s12943-022-01688-5.Peer-Reviewed Original ResearchConceptsAdoptive cell therapySingle-cell analysisDepth single-cell analysisSingle-cell RNAACT productsDisease progressionT-cell receptor sequencingCell therapyFamily genesFeatures of exhaustionMultiple tumor typesCell expansionGenesNew clonotypesTIL preparationsClonal cell expansionCytokine therapyTreatment failureSerial bloodClonesEffector functionsSerial samplesTumor typesCellular therapyTherapyMitochondrial Stress Induces an HRI-eIF2α Pathway Protective for Cardiomyopathy
Zhu S, Nguyen A, Pang J, Zhao J, Chen Z, Liang Z, Gu Y, Huynh H, Bao Y, Lee S, Kluger Y, Ouyang K, Evans SM, Fang X. Mitochondrial Stress Induces an HRI-eIF2α Pathway Protective for Cardiomyopathy. Circulation 2022, 146: 1028-1031. PMID: 36154620, PMCID: PMC9523491, DOI: 10.1161/circulationaha.122.059594.Peer-Reviewed Original ResearchZero-preserving imputation of single-cell RNA-seq data
Linderman GC, Zhao J, Roulis M, Bielecki P, Flavell RA, Nadler B, Kluger Y. Zero-preserving imputation of single-cell RNA-seq data. Nature Communications 2022, 13: 192. PMID: 35017482, PMCID: PMC8752663, DOI: 10.1038/s41467-021-27729-z.Peer-Reviewed Original Research
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 differencesNonrespondersPatientsTherapyGraph of graphs analysis for multiplexed data with application to imaging mass cytometry
Lin YE, Shnitzer T, Talmon R, Villarroel-Espindola F, Desai S, Schalper K, Kluger Y. Graph of graphs analysis for multiplexed data with application to imaging mass cytometry. PLOS Computational Biology 2021, 17: e1008741. PMID: 33780435, PMCID: PMC8032202, DOI: 10.1371/journal.pcbi.1008741.Peer-Reviewed Original Research
2018
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network
Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology 2018, 18: 24. PMID: 29482517, PMCID: PMC5828433, DOI: 10.1186/s12874-018-0482-1.Peer-Reviewed Original ResearchConceptsDeep neural networksPersonalized treatment recommendationsTreatment recommendationsNeural networkTreatment optionsPatient covariatesRecommender systemsSurvival methodsCox proportional hazards modelDifferent treatment optionsProportional hazards modelSurvival modelsExtensive feature engineeringIndividual treatment recommendationsPrior medical knowledgeSet of patientsLinear Cox proportional hazards modelsPatient characteristicsClinical studiesPatient featuresSurvival timeFeature engineeringHazards modelArt survival modelsTreatment effectiveness
2017
Gating mass cytometry data by deep learning
Li H, Shaham U, Stanton KP, Yao Y, Montgomery RR, Kluger Y. Gating mass cytometry data by deep learning. Bioinformatics 2017, 33: 3423-3430. PMID: 29036374, PMCID: PMC5860171, DOI: 10.1093/bioinformatics/btx448.Peer-Reviewed Original ResearchRemoval of batch effects using distribution-matching residual networks
Shaham U, Stanton KP, Zhao J, Li H, Raddassi K, Montgomery R, Kluger Y. Removal of batch effects using distribution-matching residual networks. Bioinformatics 2017, 33: 2539-2546. PMID: 28419223, PMCID: PMC5870543, DOI: 10.1093/bioinformatics/btx196.Peer-Reviewed Original ResearchConceptsMeasurement errorNovel deep learning approachRandom measurement errorMultivariate distributionsResidual neural networkDeep learning approachNovel biological technologiesMaximum mean discrepancyPhysical phenomenaResidual networkNeural networkLearning approachSystematic componentSupplementary dataSystematic errorsMean discrepancyScRNA-seq datasetsBatch effectsErrorNetworkStatistical analysis
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