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 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 lossIL-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 brainProfilingThe 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
Zero-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
2019
Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data
Linderman GC, Rachh M, Hoskins JG, Steinerberger S, Kluger Y. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nature Methods 2019, 16: 243-245. PMID: 30742040, PMCID: PMC6402590, DOI: 10.1038/s41592-018-0308-4.Peer-Reviewed Original Research
2001
Genomic and proteomic analysis of the myeloid differentiation program
Lian Z, Wang L, Yamaga S, Bonds W, Beazer-Barclay Y, Kluger Y, Gerstein M, Newburger P, Berliner N, Weissman S. Genomic and proteomic analysis of the myeloid differentiation program. Blood 2001, 98: 513-524. PMID: 11468144, DOI: 10.1182/blood.v98.3.513.Peer-Reviewed Original ResearchConceptsNew transcription factorMammalian cell typesMurine bone marrow cellsGene expression patternsDominant-negative retinoic acid receptorGene expression changesTranscription regulatory factorsMyeloid differentiation programRetinoic acid receptorsTranscription factorsProteomic analysisDifferentiation programArray hybridizationDifferential displayExpression patternsExpression changesPromyelocytic cell lineRegulatory factorsMyeloid differentiationLevels of mRNACell typesMolecular levelExtensive catalogueMicroM retinoic acidBone marrow cells