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 level
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
2006
Characterizing disease states from topological properties of transcriptional regulatory networks
Tuck DP, Kluger HM, Kluger Y. Characterizing disease states from topological properties of transcriptional regulatory networks. BMC Bioinformatics 2006, 7: 236. PMID: 16670008, PMCID: PMC1482723, DOI: 10.1186/1471-2105-7-236.Peer-Reviewed Original ResearchConceptsTranscriptional regulatory networksRegulatory networksTranscription factorsTranscriptional networksRegulated genesGene deregulationExpression profilesDiseased statesGene regulatory networksCentrality of genesGene expression experimentsGene expression profilesGene expression studiesGene centralityRegulatory linkExpression experimentsExpression studiesGene linksGenesCell typesExpression datasetsGene subsetsDifferential activityNormal cellsRemarkable degree
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 cellsRNA expression patterns change dramatically in human neutrophils exposed to bacteria
Subrahmanyam Y, Yamaga S, Prashar Y, Lee H, Hoe N, Kluger Y, Gerstein M, Goguen J, Newburger P, Weissman S. RNA expression patterns change dramatically in human neutrophils exposed to bacteria. Blood 2001, 97: 2457-2468. PMID: 11290611, DOI: 10.1182/blood.v97.8.2457.Peer-Reviewed Original ResearchMeSH KeywordsCells, CulturedCytokinesDNA, ComplementaryEndopeptidasesEscherichia coliExpressed Sequence TagsGene Expression ProfilingGene Expression RegulationHumansInflammationNeutrophilsOxidoreductasesProtein KinasesReceptors, CytokineRNA, MessengerRNA, RibosomalSpecies SpecificitySubtraction TechniqueTranscription, GeneticTranscriptional ActivationVirulenceYersinia pestisConceptsMembrane trafficking regulatorsRNA expression patternsGene clusterTrafficking regulatorResponse genesGene inductionExpression patternsGene expressionNonpathogenic bacteriaCell typesVariety of stimuliMessenger RNA levelsYersinia pestisDifferent bacteriaVariety of cytokinesNeutrophil physiologyHuman neutrophilsBacteriaGenesActive regulationRNA levelsPestisMRNAActivation responseCellular inflammatory response