2024
Single-cell transcriptomic and proteomic analysis of Parkinson’s disease brains
Zhu B, Park J, Coffey S, Russo A, Hsu I, Wang J, Su C, Chang R, Lam T, Gopal P, Ginsberg S, Zhao H, Hafler D, Chandra S, Zhang L. Single-cell transcriptomic and proteomic analysis of Parkinson’s disease brains. Science Translational Medicine 2024, 16: eabo1997. PMID: 39475571, DOI: 10.1126/scitranslmed.abo1997.Peer-Reviewed Original ResearchConceptsProteomic analysisAlzheimer's diseasePrefrontal cortexBrain cell typesGenetics of PDParkinson's diseaseCell-cell interactionsChaperone expressionSingle-nucleus transcriptomesExpressed genesTranscriptional changesPostmortem human brainPostmortem brain tissueDiseased brainSynaptic proteinsSingle-cellDown-regulationBrain cell populationsBrain regionsCell typesNeurodegenerative disordersLate-stage PDParkinson's disease brainsDisease etiologyNeuronal vulnerabilitySingle-Cell Transcriptomic Analyses of Brain Parenchyma in Patients With New-Onset Refractory Status Epilepticus (NORSE)
Hanin A, Zhang L, Huttner A, Plu I, Mathon B, Bielle F, Navarro V, Hirsch L, Hafler D. Single-Cell Transcriptomic Analyses of Brain Parenchyma in Patients With New-Onset Refractory Status Epilepticus (NORSE). Neurology Neuroimmunology & Neuroinflammation 2024, 11: e200259. PMID: 38810181, PMCID: PMC11139018, DOI: 10.1212/nxi.0000000000200259.Peer-Reviewed Original ResearchConceptsNew-onset refractory status epilepticusTemporal lobe epilepsyGABAergic neuronsExcitatory neuronsInfiltrating macrophagesProportion of GABAergic neuronsChronic temporal lobe epilepsyRefractory status epilepticusInhibitory GABAergic neuronsSingle-cell transcriptome analysisDecreased expression of genesDegree of demyelinationImmune disturbancesNeuronal excitabilityImmune dysregulationNew-onsetStatus epilepticusPoor outcomeRefractory epilepsyHealthy childrenMicroglial reactivitySingle-nucleus RNA sequencingNLRP3 inflammasome activationInflammatory responseLobe epilepsy
2023
scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Zhu B, Wang Y, Ku L, van Dijk D, Zhang L, Hafler D, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biology 2023, 24: 292. PMID: 38111007, PMCID: PMC10726524, DOI: 10.1186/s13059-023-03129-y.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 therapyTherapyDissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain
Marsh SE, Walker AJ, Kamath T, Dissing-Olesen L, Hammond TR, de Soysa TY, Young AMH, Murphy S, Abdulraouf A, Nadaf N, Dufort C, Walker AC, Lucca LE, Kozareva V, Vanderburg C, Hong S, Bulstrode H, Hutchinson PJ, Gaffney DJ, Hafler DA, Franklin RJM, Macosko EZ, Stevens B. Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain. Nature Neuroscience 2022, 25: 306-316. PMID: 35260865, DOI: 10.1038/s41593-022-01022-8.Peer-Reviewed Original ResearchConceptsSingle-cell sequencing experimentsCell type diversitySingle-cell sequencingRNA-sequencing datasetsGene expression changesGene expression signaturesVivo gene expressionTranscriptional profilesGene expressionExpression changesSequencing experimentsGlial signaturesDownstream analysisExpression signaturesTissue typesSingle cell suspensionsOptimal cell yieldIntact tissueHuman tissuesCell yieldEnzymatic dissociationHuman samplesTissue digestionPostmortem human samplesTissueSingle-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
Single cell immunophenotyping of the skin lesion erythema migrans Identifies IgM memory B cells
Jiang R, Meng H, Raddassi K, Fleming I, Hoehn KB, Dardick KR, Belperron AA, Montgomery RR, Shalek AK, Hafler DA, Kleinstein SH, Bockenstedt LK. Single cell immunophenotyping of the skin lesion erythema migrans Identifies IgM memory B cells. JCI Insight 2021, 6: e148035. PMID: 34061047, PMCID: PMC8262471, DOI: 10.1172/jci.insight.148035.Peer-Reviewed Original ResearchConceptsMemory B cellsErythema migransB cellsEM lesionsIgM memory B cellsLyme diseaseB-cell receptor sequencingSkin infection siteCell receptor sequencingEarly Lyme diseaseLocal antigen presentationSkin immune responsesB cell populationsSingle-cell immunophenotypingMHC class II genesUninvolved skinImmune cellsSpirochetal infectionAntigen presentationCell immunophenotypingT cellsImmune responseIsotype usageAntibody productionInitial signsNEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
He L, Davila-Velderrain J, Sumida TS, Hafler DA, Kellis M, Kulminski AM. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Communications Biology 2021, 4: 629. PMID: 34040149, PMCID: PMC8155058, DOI: 10.1038/s42003-021-02146-6.Peer-Reviewed Original ResearchConceptsNegative binomial mixed modelsBinomial mixed modelsSingle-cell dataHigh-dimensional integralsLarge sample approximationLaplace approximationCell-level expressionMixed modelsApproximationNebulaSpeed gainData setsOrders of magnitudeMarker gene identificationIntegralsModelOverdispersionFalse positive errors