2025
Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies
Consortium M, Adams M, Streit F, Meng X, Awasthi S, Adey B, Choi K, Chundru V, Coleman J, Ferwerda B, Foo J, Gerring Z, Giannakopoulou O, Gupta P, Hall A, Harder A, Howard D, Hübel C, Kwong A, Levey D, Mitchell B, Ni G, Ota V, Pain O, Pathak G, Schulte E, Shen X, Thorp J, Walker A, Yao S, Zeng J, Zvrskovec J, Aarsland D, Actkins K, Adli M, Agerbo E, Aichholzer M, Aiello A, Air T, Als T, Andersson E, Andlauer T, Arolt V, Ask H, Bäckman J, Badola S, Ballard C, Banasik K, Bass N, Beekman A, Belangero S, Bigdeli T, Binder E, Bjerkeset O, Bjornsdottir G, Børte S, Bränn E, Braun A, Brodersen T, Brückl T, Brunak S, Bruun M, Burmeister M, Buspavanich P, Bybjerg-Grauholm J, Byrne E, Cai J, Campbell A, Campbell M, Campos A, Castelao E, Cervilla J, Chaumette B, Chen C, Chen H, Chen Z, Cichon S, Colodro-Conde L, Corbett A, Corfield E, Couvy-Duchesne B, Craddock N, Dannlowski U, Davies G, de Geus E, Deary I, Degenhardt F, Dehghan A, DePaulo J, Deuschle M, Didriksen M, Dinh K, Direk N, Djurovic S, Docherty A, Domschke K, Dowsett J, Drange O, Dunn E, Eaton W, Einarsson G, Eley T, Elsheikh S, Engelmann J, Benros M, Erikstrup C, Escott-Price V, Fabbri C, Fang Y, Finer S, Frank J, Free R, Gallo L, Gao H, Gill M, Gilles M, Goes F, Gordon S, Grove J, Gudbjartsson D, Gutierrez B, Hahn T, Hall L, Hansen T, Haraldsson M, Hartman C, Havdahl A, Hayward C, Heilmann-Heimbach S, Herms S, Hickie I, Hjalgrim H, Hjerling-Leffler J, Hoffmann P, Homuth G, Horn C, Hottenga J, Hougaard D, Hovatta I, Huang Q, Hucks D, Huider F, Hunt K, Ialongo N, Ising M, Isometsä E, Jansen R, Jiang Y, Jones I, Jones L, Jonsson L, Kanai M, Karlsson R, Kasper S, Kendler K, Kessler R, Kloiber S, Knowles J, Koen N, Kraft J, Kranzler H, Krebs K, Kallak T, Kutalik Z, Lahtela E, Lake M, Larsen M, Lenze E, Lewins M, Lewis G, Li L, Lin B, Lin K, Lind P, Liu Y, MacIntyre D, MacKinnon D, Maher B, Maier W, Marshe V, Martinez-Levy G, Matsuda K, Mbarek H, McGuffin P, Medland S, Meinert S, Mikkelsen C, Mikkelsen S, Milaneschi Y, Millwood I, Molina E, Mondimore F, Mortensen P, Mulsant B, Naamanka J, Najman J, Nauck M, Nenadić I, Nielsen K, Nolt I, Nordentoft M, Nöthen M, Nyegaard M, O'Donovan M, Oddsson A, Oliveira A, Olsen C, Oskarsson H, Ostrowski S, Owen M, Packer R, Palviainen T, Pan P, Pato C, Pato M, Pedersen N, Pedersen O, Peyrot W, Potash J, Preisig M, Preuss M, Quiroz J, Renteria M, Reynolds C, Rice J, Sakaue S, Santoro M, Schoevers R, Schork A, Schulze T, Send T, Shi J, Sigurdsson E, Singh K, Sinnamon G, Sirignano L, Smeland O, Smith D, Sofer T, Sørensen E, Srinivasan S, Stefansson H, Stefansson K, Straub P, Su M, Tadic A, Teismann H, Teumer A, Thapar A, Thomson P, Thørner L, Topaloudi A, Tsai S, Tzoulaki I, Uhl G, Uitterlinden A, Ullum H, Umbricht D, Ursano R, Van der Auwera S, van Hemert A, Veluchamy A, Viktorin A, Völzke H, Walters G, Wang X, Wani A, Weissman M, Wellmann J, Whiteman D, Wildman D, Willemsen G, Williams A, Winsvold B, Witt S, Xiong Y, Zillich L, Zwart J, Team T, Group C, Team E, Team G, Psychiatry H, Project T, Program V, Andreassen O, Baune B, Berger K, Boomsma D, Børglum A, Breen G, Cai N, Coon H, Copeland W, Creese B, Cruz-Fuentes C, Czamara D, Davis L, Derks E, Domenici E, Elliott P, Forstner A, Gawlik M, Gelernter J, Grabe H, Hamilton S, Hveem K, John C, Kaprio J, Kircher T, Krebs M, Kuo P, Landén M, Lehto K, Levinson D, Li Q, Lieb K, Loos R, Lu Y, Lucae S, Luykx J, Maes H, Magnusson P, Martin H, Martin N, McQuillin A, Middeldorp C, Milani L, Mors O, Müller D, Müller-Myhsok B, Okada Y, Oldehinkel A, Paciga S, Palmer C, Paschou P, Penninx B, Perlis R, Peterson R, Pistis G, Polimanti R, Porteous D, Posthuma D, Rabinowitz J, Reichborn-Kjennerud T, Reif A, Rice F, Ricken R, Rietschel M, Rivera M, Rück C, Salum G, Schaefer C, Sen S, Serretti A, Skalkidou A, Smoller J, Stein D, Stein F, Stein M, Sullivan P, Tesli M, Thorgeirsson T, Tiemeier H, Timpson N, Uddin M, Uher R, van Heel D, Verweij K, Walters R, Wassertheil-Smoller S, Wendland J, Werge T, Zwinderman A, Kuchenbaecker K, Wray N, Ripke S, Lewis C, McIntosh A. Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies. Cell 2025, 188: 640-652.e9. PMID: 39814019, PMCID: PMC11829167, DOI: 10.1016/j.cell.2024.12.002.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesCell-type enrichment analysisSingle-cell dataTrans-ancestryAdmixed ancestrySingle-cell analysisFine-mappingPotential repurposing opportunitiesAssociation studiesGene associationsEnrichment analysisReceptor clusteringPolygenic scoresRepurposing opportunitiesPostsynaptic densityCell typesStudies of depressionMedium spiny neuronsAncestryAntidepressant targetSpiny neuronsAmygdala neuronsLociBiological targetsEffective treatment
2024
Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact
Albertson A, Winkler E, Yang A, Buckwalter M, Dingman A, Fan H, Herson P, McCullough L, Perez-Pinzon M, Sansing L, Sun D, Alkayed N. Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact. Stroke 2024, 56: 1082-1091. PMID: 39772596, DOI: 10.1161/strokeaha.124.049001.Peer-Reviewed Original ResearchnipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares
Mattessich M, Reyna J, Aron E, Ay F, Kilmer M, Kleinstein S, Konstorum A. nipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares. Bioinformatics 2024, 41: btaf015. PMID: 39799512, PMCID: PMC11783316, DOI: 10.1093/bioinformatics/btaf015.Peer-Reviewed Original ResearchConceptsIterative partial least squaresNonlinear iterative partial least squaresDimensionality reductionMultiple co-inertia analysisJoint dimensionality reductionSignificant speed-upUnsupervised learningSingle-cell datasetsMulti-omics dataCo-inertia analysisFeature dimensionsSpeed-upBioconductor packageSingle-cell analysisPartial least squaresLeast squaresRobust approachImplementationHTMLDatasetBioconductorCosGeneGate selects multi-functional and credible biomarkers for single-cell analysis
Liu T, Long W, Cao Z, Wang Y, He C, Zhang L, Strittmatter S, Zhao H. CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis. Briefings In Bioinformatics 2024, 26: bbae626. PMID: 39592241, PMCID: PMC11596696, DOI: 10.1093/bib/bbae626.Peer-Reviewed Original ResearchSingle-cell analysis reveals a subpopulation of adipose progenitor cells that impairs glucose homeostasis
Wang H, Du Y, Huang S, Sun X, Ye Y, Sun H, Chu X, Shan X, Yuan Y, Shen L, Bi Y. Single-cell analysis reveals a subpopulation of adipose progenitor cells that impairs glucose homeostasis. Nature Communications 2024, 15: 4827. PMID: 38844451, PMCID: PMC11156882, DOI: 10.1038/s41467-024-48914-w.Peer-Reviewed Original ResearchConceptsAdipose progenitor cellsT2D patientsProgenitor cellsDiphtheria toxin A expressionHeterogeneous stromal cellsGlycemic disturbancesAdipose tissueInfluence of obesityGlucose homeostasisVisceral adipose tissueHuman visceral adipose tissueImpaired glucose homeostasisType 2 diabetesHunter-killer peptidesRegulating glucose homeostasisSingle-cell analysisAPC functionStromal cellsA expressionMetabolic homeostasisAdipocyte lipolysisT2D developmentPatientsT2DBioactive proteinsSingle-cell analysis reveals transcriptional dynamics in healthy primary parathyroid tissue
Venkat A, Carlino M, Lawton B, Prasad M, Amodio M, Gibson C, Zeiss C, Youlten S, Krishnaswamy S, Krause D. Single-cell analysis reveals transcriptional dynamics in healthy primary parathyroid tissue. Genome Research 2024, 34: 837-850. PMID: 38977309, PMCID: PMC11293540, DOI: 10.1101/gr.278215.123.Peer-Reviewed Original ResearchCell statesMitochondrial transcript abundanceParathyroid glandsHuman parathyroidCell-cell communication analysisRNA expression analysisSingle-cell analysisTranscriptional dynamicsTranscript abundanceExpression dynamicsRNA transcriptomeEpithelial cell statesCell abundanceExpression analysisPseudotime analysisModeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets
Patil A, Schug J, Liu C, Lahori D, Descamps H, Consortium T, Naji A, Kaestner K, Faryabi R, Vahedi G. Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets. Cell Reports Medicine 2024, 5: 101535. PMID: 38677282, PMCID: PMC11148720, DOI: 10.1016/j.xcrm.2024.101535.Peer-Reviewed Original ResearchSingle-cell transcriptomic measurementsGene expression of single cellsGene signatureSingle-cell analysisTranscriptional outputTranscriptomic measurementsUnique gene signatureNon-diabetic organ donorsAnalysis of isletsGene expressionAutoantibody-positive donorsPrediction of T1DHuman isletsBeta cellsCell typesT1D developmentSingle cellsGenesType 1 diabetes progressionCellsT1D onsetType 1 diabetesIsletsImmune cellsNon-diabeticsSingle-Cell Analysis Reveals a Subset of High IL-12p40-Secreting Dendritic Cells within Mouse Bone Marrow-Derived Macrophages Differentiated with M-CSF.
Bridges K, Pizzurro G, Khunte M, Chen M, Salvador Rocha E, Alexander A, Bass V, Kellman L, Baskaran J, Miller-Jensen K. Single-Cell Analysis Reveals a Subset of High IL-12p40-Secreting Dendritic Cells within Mouse Bone Marrow-Derived Macrophages Differentiated with M-CSF. The Journal Of Immunology 2024, 212: 1357-1365. PMID: 38416039, DOI: 10.4049/jimmunol.2300431.Peer-Reviewed Original ResearchBone marrow-derived macrophagesDendritic cellsCell-to-cell heterogeneitySingle-cell RNA sequencing dataRNA sequencing dataSingle-cell analysisIL-12p40Sequence dataExpression of IL12BInnate immune functionProduction of IL-12Bone marrow-derived macrophage culturesMurine bone marrow-derived macrophagesSurface marker expressionAcute inflammatory responseMarrow-derived macrophagesGene encoding IL-12p40Secretion assayIL12B expressionReporter miceDC lineageIL-12GenesProinflammatory cytokinesM-CSF
2023
Thinking process templates for constructing data stories with SCDNEY
Cao Y, Tran A, Kim H, Robertson N, Lin Y, Torkel M, Yang P, Patrick E, Ghazanfar S, Yang J. Thinking process templates for constructing data stories with SCDNEY. F1000Research 2023, 12: 261. DOI: 10.12688/f1000research.130623.1.Peer-Reviewed Original Research
2022
Longitudinal 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 therapyTherapyTranslational opportunities of single-cell biology in atherosclerosis
de Winther M, Bäck M, Evans P, Gomez D, Goncalves I, Jørgensen H, Koenen R, Lutgens E, Norata G, Osto E, Dib L, Simons M, Stellos K, Ylä-Herttuala S, Winkels H, Bochaton-Piallat M, Monaco C. Translational opportunities of single-cell biology in atherosclerosis. European Heart Journal 2022, 44: 1216-1230. PMID: 36478058, PMCID: PMC10120164, DOI: 10.1093/eurheartj/ehac686.Peer-Reviewed Original ResearchConceptsSingle-cell biologyDisease developmentSingle-cell technologiesSingle-cell resolutionHuman biological processesSingle-cell analysisBiological processesCellular subpopulationsCardiovascular diseaseCell communityAtherosclerosis pathologyCardiovascular disease samplesTranslational opportunitiesBiologyTherapeutic strategiesInternational collaborative effortTreat diseasesField of cardiovascular diseaseClinical impactClinically relevant featuresAnalysis of atherosclerotic plaquesLineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling
Chan J, Zaidi S, Love J, Zhao J, Setty M, Wadosky K, Gopalan A, Choo Z, Persad S, Choi J, LaClair J, Lawrence K, Chaudhary O, Xu T, Masilionis I, Linkov I, Wang S, Lee C, Barlas A, Morris M, Mazutis L, Chaligne R, Chen Y, Goodrich D, Karthaus W, Pe'er D, Sawyers C. Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling. Science 2022, 377: 1180-1191. PMID: 35981096, PMCID: PMC9653178, DOI: 10.1126/science.abn0478.Peer-Reviewed Original ResearchConceptsFibroblast growth factor receptorProstate cancerLineage plasticityJanus kinaseGenetically engineered mouse modelsCastration-resistant diseaseFibroblast growth factor receptor signalingTumor cell statesGrowth factor receptorSingle-cell analysisMetastatic diseaseStratify patientsIncreased JAK/STATAntiandrogen resistanceEpithelial populationsDrug resistanceFactor receptorClinical trialsInhibitor treatmentMouse modelInflammatory signalingGene expressionCell statesMurine organoidsMolecular mechanismsFrom COVID to fibrosis: lessons from single-cell analyses of the human lung
Justet A, Zhao AY, Kaminski N. From COVID to fibrosis: lessons from single-cell analyses of the human lung. Human Genomics 2022, 16: 20. PMID: 35698166, PMCID: PMC9189802, DOI: 10.1186/s40246-022-00393-0.Peer-Reviewed Original ResearchConceptsSingle-cell RNA-sequencing technologySingle-cell RNA sequencingRNA-sequencing technologyGene expression patternsMonocyte-derived macrophage populationSingle-cell analysisCell populationsLung diseaseCellular phenotypesRNA sequencingExpression patternsGene expressionAberrant repairMultiple tissuesPulmonary fibrosisMechanisms of diseaseFibrotic interstitial lung diseaseLife-threatening complicationsProgressive lung diseaseCOVID-19 pneumoniaInterstitial lung diseaseParenchymal lung diseaseAcute viral diseaseMacrophage populationsNovel cellSingle-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19
Unterman A, Sumida TS, Nouri N, Yan X, Zhao AY, Gasque V, Schupp JC, Asashima H, Liu Y, Cosme C, Deng W, Chen M, Raredon MSB, Hoehn KB, Wang G, Wang Z, DeIuliis G, Ravindra NG, Li N, Castaldi C, Wong P, Fournier J, Bermejo S, Sharma L, Casanovas-Massana A, Vogels CBF, Wyllie AL, Grubaugh ND, Melillo A, Meng H, Stein Y, Minasyan M, Mohanty S, Ruff WE, Cohen I, Raddassi K, Niklason L, Ko A, Montgomery R, Farhadian S, Iwasaki A, Shaw A, van Dijk D, Zhao H, Kleinstein S, Hafler D, Kaminski N, Dela Cruz C. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nature Communications 2022, 13: 440. PMID: 35064122, PMCID: PMC8782894, DOI: 10.1038/s41467-021-27716-4.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAgedAntibodies, Monoclonal, HumanizedCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCells, CulturedCOVID-19COVID-19 Drug TreatmentFemaleGene Expression ProfilingGene Expression RegulationHumansImmunity, InnateMaleReceptors, Antigen, B-CellReceptors, Antigen, T-CellRNA-SeqSARS-CoV-2Single-Cell AnalysisConceptsProgressive COVID-19B cell clonesSingle-cell analysisT cellsImmune responseMulti-omics single-cell analysisCOVID-19Cell clonesAdaptive immune interactionsSevere COVID-19Dynamic immune responsesGene expressionSARS-CoV-2 virusAdaptive immune systemSomatic hypermutation frequenciesCellular effectsProtein markersEffector CD8Immune signaturesProgressive diseaseHypermutation frequencyProgressive courseClassical monocytesClonesImmune interactions
2021
Emerging Single-cell Approaches to Understand HIV in the Central Nervous System
Corley MJ, Farhadian SF. Emerging Single-cell Approaches to Understand HIV in the Central Nervous System. Current HIV/AIDS Reports 2021, 19: 113-120. PMID: 34822063, PMCID: PMC8613726, DOI: 10.1007/s11904-021-00586-7.Peer-Reviewed Original ResearchConceptsCentral nervous systemSingle-cell sequencing methodsSingle-cell approachesStudy of genomesSingle-cell technologiesSingle-cell analysisSingle-cell levelSingle-cell studiesNervous systemMolecular identitySequencing methodsRare infected cellsViral establishmentCentral nervous system tissueInfected cellsNervous system tissueAutopsy brainsCNS reservoirsCSF cellsHIV StudyHIVTranslational studiesCNS perturbationsCNS studiesTissue compartmentsSingle-cell analysis of prostaglandin E2-induced human decidual cell in vitro differentiation: a minimal ancestral deciduogenic signal†
Stadtmauer DJ, Wagner G. Single-cell analysis of prostaglandin E2-induced human decidual cell in vitro differentiation: a minimal ancestral deciduogenic signal†. Biology Of Reproduction 2021, 106: 155-172. PMID: 34591094, PMCID: PMC8757638, DOI: 10.1093/biolre/ioab183.Peer-Reviewed Original ResearchMeSH KeywordsCell DifferentiationCell Line, TransformedCells, CulturedCyclic AMPCyclic AMP-Dependent Protein KinasesDeciduaDinoprostoneEndometriumFemaleFibroblastsGene ExpressionGenome-Wide Association StudyHumansMedroxyprogesterone AcetatePregnancyReceptors, Prostaglandin E, EP2 SubtypeSequence Analysis, RNASingle-Cell AnalysisConceptsPlacental mammalsCore gene regulatory networkCyclic AMP/protein kinase A (cAMP/PKA) pathwayProtein kinase A (PKA) pathwaySenescence-associated genesProgesterone-dependent activationGenome-wide studiesSingle-cell transcriptomicsGene regulatory networksProgesterone-dependent inductionMembrane-permeable cAMPKinase A PathwaySingle-cell analysisUse of PGE2Outgroup taxaCellular statesRegulatory networksPKA axisGene expressionDecidual genesPKA activationPGE2 receptor 2Progestin-dependent inductionA PathwayAdenylyl cyclase activationSingle-cell analysis by mass cytometry reveals metabolic states of early-activated CD8+ T cells during the primary immune response
Levine L, Hiam-Galvez K, Marquez D, Tenvooren I, Madden M, Contreras D, Dahunsi D, Irish J, Oluwole O, Rathmell J, Spitzer M. Single-cell analysis by mass cytometry reveals metabolic states of early-activated CD8+ T cells during the primary immune response. Immunity 2021, 54: 829-844.e5. PMID: 33705706, PMCID: PMC8046726, DOI: 10.1016/j.immuni.2021.02.018.Peer-Reviewed Original ResearchConceptsImmune responseMetabolic stateMass cytometrySingle-cell metabolic analysisSingle-cell resolutionChimeric antigen receptor TDistinct metabolic statesSingle-cell analysisAdvanced lymphoma patientsMetabolic protein expressionListeria monocytogenes infectionImmune cell populationsPrimary immune responseMetabolic proteinsCell signalingOxidative phosphorylationMetabolic regulationLymphoma patientsMemory TMonocytogenes infectionEffector TMetabolic regulatorMetabolic analysisMetabolic adaptationEffector functionsColorectal Cancer Stem Cell States Uncovered by Simultaneous Single‐Cell Analysis of Transcriptome and Telomeres
Wang H, Gong P, Chen T, Gao S, Wu Z, Wang X, Li J, Marjani SL, Costa J, Weissman SM, Qi F, Pan X, Liu L. Colorectal Cancer Stem Cell States Uncovered by Simultaneous Single‐Cell Analysis of Transcriptome and Telomeres. Advanced Science 2021, 8: 2004320. PMID: 33898197, PMCID: PMC8061397, DOI: 10.1002/advs.202004320.Peer-Reviewed Original ResearchConceptsCancer stem cellsCancer epithelial cellsTumor-initiating cellsCancer stem cell stateEpithelial cellsStem cell stateStem cellsRare cancer stem cellsSimultaneous single-cell analysisHeterogeneity of CSCsSingle-cell analysisNumber variation patternsHippo/YAPDepth transcriptomeSmart-seq2Phylogenetic relationshipsNormal stem cellsCell statesNonproliferative natureColorectal cancerDisplay plasticityDormant stateShort telomeresTelomerase activityTranscriptome
2020
A single-cell analysis of the molecular lineage of chordate embryogenesis
Zhang T, Xu Y, Imai K, Fei T, Wang G, Dong B, Yu T, Satou Y, Shi W, Bao Z. A single-cell analysis of the molecular lineage of chordate embryogenesis. Science Advances 2020, 6: eabc4773. PMID: 33148647, PMCID: PMC7673699, DOI: 10.1126/sciadv.abc4773.Peer-Reviewed Original ResearchConceptsCell typesEmbryonic cell lineagesSingle-cell RNA sequencing analysisAsymmetric cell divisionWild-type embryosCell lineage differentiationMouse cell typesOnset of gastrulationSingle-cell datasetsRNA sequencing analysisSingle-cell analysisMolecular lineagesFate transformationChordate embryogenesisEarly embryogenesisConvergent differentiationMother cellsNotochord lineageCell divisionTranscription factorsLineage developmentMaster regulatorLineage differentiationGene pathwaysCell lineagesMulti-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells
Marczyk M, Patwardhan GA, Zhao J, Qu R, Li X, Wali VB, Gupta AK, Pillai MM, Kluger Y, Yan Q, Hatzis C, Pusztai L, Gunasekharan V. Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells. Cancers 2020, 12: 2551. PMID: 32911681, PMCID: PMC7563413, DOI: 10.3390/cancers12092551.Peer-Reviewed Original ResearchTriple-negative breast cancer cellsCancer cellsBreast cancer cellsStress response genesMulti-omics landscapeCell population compositionDrug-induced cell deathMulti-omics investigationsCell linesBCL2 family inhibitorsSingle-cell analysisChromatin accessibilityGenome structureMDA-MB-231 triple-negative breast cancer cellsChromatin structureMethylation stateResponse genesFamily inhibitorsCell deathTNBC cell linesNumber variationsDefense mechanismsResistance mechanismsNew therapeutic strategiesGenes
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