2024
Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data
Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino F, Lee D, Fullard J, Hoffman G, Roussos P, Wang Y, Wang X, Pinto D, Wang S, Zhang C, consortium P, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. Science Advances 2024, 10: eadh2588. PMID: 38781336, PMCID: PMC11114236, DOI: 10.1126/sciadv.adh2588.Peer-Reviewed Original ResearchConceptsHuman brain transcriptome dataBrain transcriptomic dataRNA-seqTranscriptome dataCell-type gene expressionGene expressionCell-type proportionsSingle-cell dataMultiple brain disordersBrain cell typesCell deconvolution methodsPostmortem brainsRNA sequencingBrain disordersBrain developmentSchizophreniaEQTLAlzheimer's diseaseCell typesOrganoid samplesBrainBiological applications
2020
Cell Lineage Tracing and Cellular Diversity in Humans
Abyzov A, Vaccarino FM. Cell Lineage Tracing and Cellular Diversity in Humans. Annual Review Of Genomics And Human Genetics 2020, 21: 101-116. PMID: 32413272, DOI: 10.1146/annurev-genom-083118-015241.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsLineage tracingLineage mapCell lineage mapCell lineage tracingDNA methylation statusComplex biological processesMultiple cell typesMulticellular organismsCellular diversityMitochondrial DNALineage hierarchyCell lineagesBiological processesNatural variationCell typesMethylation statusLineagesNoncancerous cellsRecent studiesFetal developmentGeneral conceptual designCellsOrganismsHumansDNA
2018
Comprehensive functional genomic resource and integrative model for the human brain
Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, Clarke D, Gu M, Emani P, Yang YT, Xu M, Gandal MJ, Lou S, Zhang J, Park JJ, Yan C, Rhie SK, Manakongtreecheep K, Zhou H, Nathan A, Peters M, Mattei E, Fitzgerald D, Brunetti T, Moore J, Jiang Y, Girdhar K, Hoffman GE, Kalayci S, Gümüş ZH, Crawford GE, Roussos P, Akbarian S, Jaffe A, White K, Weng Z, Sestan N, Geschwind D, Knowles J, Gerstein M, Ashley-Koch A, Crawford G, Garrett M, Song L, Safi A, Johnson G, Wray G, Reddy T, Goes F, Zandi P, Bryois J, Jaffe A, Price A, Ivanov N, Collado-Torres L, Hyde T, Burke E, Kleiman J, Tao R, Shin J, Akbarian S, Girdhar K, Jiang Y, Kundakovic M, Brown L, Kassim B, Park R, Wiseman J, Zharovsky E, Jacobov R, Devillers O, Flatow E, Hoffman G, Lipska B, Lewis D, Haroutunian V, Hahn C, Charney A, Dracheva S, Kozlenkov A, Belmont J, DelValle D, Francoeur N, Hadjimichael E, Pinto D, van Bakel H, Roussos P, Fullard J, Bendl J, Hauberg M, Mangravite L, Peters M, Chae Y, Peng J, Niu M, Wang X, Webster M, Beach T, Chen C, Jiang Y, Dai R, Shieh A, Liu C, Grennan K, Xia Y, Vadukapuram R, Wang Y, Fitzgerald D, Cheng L, Brown M, Brown M, Brunetti T, Goodman T, Alsayed M, Gandal M, Geschwind D, Won H, Polioudakis D, Wamsley B, Yin J, Hadzic T, De La Torre Ubieta L, Swarup V, Sanders S, State M, Werling D, An J, Sheppard B, Willsey A, White K, Ray M, Giase G, Kefi A, Mattei E, Purcaro M, Weng Z, Moore J, Pratt H, Huey J, Borrman T, Sullivan P, Giusti-Rodriguez P, Kim Y, Sullivan P, Szatkiewicz J, Rhie S, Armoskus C, Camarena A, Farnham P, Spitsyna V, Witt H, Schreiner S, Evgrafov O, Knowles J, Gerstein M, Liu S, Wang D, Navarro F, Warrell J, Clarke D, Emani P, Gu M, Shi X, Xu M, Yang Y, Kitchen R, Gürsoy G, Zhang J, Carlyle B, Nairn A, Li M, Pochareddy S, Sestan N, Skarica M, Li Z, Sousa A, Santpere G, Choi J, Zhu Y, Gao T, Miller D, Cherskov A, Yang M, Amiri A, Coppola G, Mariani J, Scuderi S, Szekely A, Vaccarino F, Wu F, Weissman S, Roychowdhury T, Abyzov A. Comprehensive functional genomic resource and integrative model for the human brain. Science 2018, 362 PMID: 30545857, PMCID: PMC6413328, DOI: 10.1126/science.aat8464.Peer-Reviewed Original ResearchConceptsQuantitative trait lociCell type proportionsComprehensive functional genomics resourceExpression quantitative trait lociFunctional genomics resourcesSingle-cell expression profilesGene regulatory networksFurther quantitative trait lociPsychENCODE ConsortiumGenomic resourcesComprehensive online resourceRegulatory networksKey genesCross-population variationExpression profilesMolecular mechanismsCell typesGenesAdult brainPolygenic risk scoresStudy variantsChromatinSplicingGenetic riskInterpretable deep learning model
2017
Human induced pluripotent stem cells for modelling neurodevelopmental disorders
Ardhanareeswaran K, Mariani J, Coppola G, Abyzov A, Vaccarino FM. Human induced pluripotent stem cells for modelling neurodevelopmental disorders. Nature Reviews Neurology 2017, 13: 265-278. PMID: 28418023, PMCID: PMC5782822, DOI: 10.1038/nrneurol.2017.45.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsEmbryonic stem cellsNeurodevelopmental disordersPluripotent stem cellsBrain developmentStem cellsAbnormal brain developmentBrain cell typesDopaminergic neuronsCortical neuronsUnique genetic signatureEarly developmentKey PointsHumanHiPSC modelsSomatic cellsDisordersGenetic signaturesGenetic studiesAltered trajectoryCell typesAdult cellsNeuronsUnknown facetsCellsDrug discoveryHiPSCs
2007
Astroglial Cells in Development, Regeneration, and Repair
Vaccarino FM, Fagel DM, Ganat Y, Maragnoli ME, Ment LR, Ohkubo Y, Schwartz ML, Silbereis J, Smith KM. Astroglial Cells in Development, Regeneration, and Repair. The Neuroscientist 2007, 13: 173-185. PMID: 17404377, DOI: 10.1177/1073858406298336.Peer-Reviewed Original Research In PressConceptsFibroblast growth factor receptorAstroglial cellsGenetic fate mappingCell divisionLineage studiesGrowth factor receptorPostnatal CNSEmbryonic CNSMain cellular componentsFate mappingNeuronal differentiationCellular componentsCell typesInjury-induced increaseFactor receptorNeurogenic nichePerinatal injuryCerebral cortexYoung miceCellsOligodendrocytesNeuronsDifferent rolesCNSNiche
2000
Stem Cells and Neuronal Progenitors and Their Diversity in the CNS: Are Time and Place Important?
Vaccarino F. Stem Cells and Neuronal Progenitors and Their Diversity in the CNS: Are Time and Place Important? The Neuroscientist 2000, 6: 338-352. DOI: 10.1177/107385840000600508.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsDifferent cell typesFibroblast growth factorStem cellsTranscription factorsEarly transcriptional regulatorCell typesHelix transcription factorHomeodomain transcription factorPattern of expressionMultilineage progenitor cellsTranscriptional regulatorsFounder cellsCellular repertoireExtracellular signalsProper assemblyNeuronal progenitorsPositional specificationBasic fibroblast growth factorBody axesImmediate progenyProgenitor cellsGrowth factorRegulatorCellsCNS domainsThe subcellular localization of OTX2 is cell-type specific and developmentally regulated in the mouse retina
Baas D, Bumsted KM, Martinez JA, Vaccarino FM, Wikler KC, Barnstable CJ. The subcellular localization of OTX2 is cell-type specific and developmentally regulated in the mouse retina. Brain Research 2000, 78: 26-37. PMID: 10891582, DOI: 10.1016/s0169-328x(00)00060-7.Peer-Reviewed Original ResearchMeSH Keywords3T3 CellsAnimalsAntibodiesBlotting, WesternCell NucleusCytoplasmGene Expression Regulation, DevelopmentalHomeodomain ProteinsHumansMiceMice, Inbred StrainsNerve Tissue ProteinsOtx Transcription FactorsPC12 CellsPigment Epithelium of EyeRabbitsRatsRetinal Ganglion CellsRetinal Rod Photoreceptor CellsTeratocarcinomaTrans-ActivatorsTransfectionTumor Cells, CulturedConceptsSubcellular localizationTranscription factorsHomeodomain-containing proteinCell fate determinationHomeodomain transcription factorCytoplasm of rodsFate determinationCell fateOtx2 proteinSubcellular distributionOtx2Retinal pigment epithelial cellsCell typesRod photoreceptorsPigment epithelial cellsRetinal developmentCytoplasmCell linesAdult eyesEpithelial cellsCentral nervous systemImmature rodsProteinCellsDifferential distribution