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
2023
Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis
Jourdon A, Wu F, Mariani J, Capauto D, Norton S, Tomasini L, Amiri A, Suvakov M, Schreiner J, Jang Y, Panda A, Nguyen C, Cummings E, Han G, Powell K, Szekely A, McPartland J, Pelphrey K, Chawarska K, Ventola P, Abyzov A, Vaccarino F. Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis. Nature Neuroscience 2023, 26: 1505-1515. PMID: 37563294, PMCID: PMC10573709, DOI: 10.1038/s41593-023-01399-0.Peer-Reviewed Original ResearchConceptsIdiopathic autism spectrum disorderCortical neuron subtypesAutism spectrum disorderEarly cortical developmentCortical organoidsCortical plateExcitatory neuronsCortical developmentRare formNeuron subtypesUnaffected fatherASD pathogenesisForebrain organoidsEarly neurogenesisRare variantsIdiopathic autismRisk genesTranscriptomic alterationsNeuronsProbandsSingle-cell transcriptomicsForebrain developmentSpectrum disorderTranscriptomic changesAlterations
2022
Analysis of somatic mutations in 131 human brains reveals aging-associated hypermutability
Bae T, Fasching L, Wang Y, Shin JH, Suvakov M, Jang Y, Norton S, Dias C, Mariani J, Jourdon A, Wu F, Panda A, Pattni R, Chahine Y, Yeh R, Roberts RC, Huttner A, Kleinman JE, Hyde TM, Straub RE, Walsh CA, Urban A, Leckman J, Weinberger D, Vaccarino F, Abyzov A, Walsh C, Park P, Sestan N, Weinberger D, Moran J, Gage F, Vaccarino F, Gleeson J, Mathern G, Courchesne E, Roy S, Chess A, Akbarian S, Bizzotto S, Coulter M, Dias C, D’Gama A, Ganz J, Hill R, Huang A, Khoshkhoo S, Kim S, Lee A, Lodato M, Maury E, Miller M, Borges-Monroy R, Rodin R, Zhou Z, Bohrson C, Chu C, Cortes-Ciriano I, Dou Y, Galor A, Gulhan D, Kwon M, Luquette J, Sherman M, Viswanadham V, Jones A, Rosenbluh C, Cho S, Langmead B, Thorpe J, Erwin J, Jaffe A, McConnell M, Narurkar R, Paquola A, Shin J, Straub R, Abyzov A, Bae T, Jang Y, Wang Y, Molitor C, Peters M, Linker S, Reed P, Wang M, Urban A, Zhou B, Zhu X, Pattni R, Serres Amero A, Juan D, Lobon I, Marques-Bonet T, Solis Moruno M, Garcia Perez R, Povolotskaya I, Soriano E, Antaki D, Averbuj D, Ball L, Breuss M, Yang X, Chung C, Emery S, Flasch D, Kidd J, Kopera H, Kwan K, Mills R, Moldovan J, Sun C, Zhao X, Zhou W, Frisbie T, Cherskov A, Fasching L, Jourdon A, Pochareddy S, Scuderi S. Analysis of somatic mutations in 131 human brains reveals aging-associated hypermutability. Science 2022, 377: 511-517. PMID: 35901164, PMCID: PMC9420557, DOI: 10.1126/science.abm6222.Peer-Reviewed Original ResearchConceptsTranscription factorsSomatic mutationsPutative transcription factorEnhancer-like regionSingle nucleotide mutationsWhole-genome sequencingGene regulationSomatic duplicationGenome sequencingDamaging mutationsBackground mutagenesisMutationsHypermutabilityClonal expansionMotifDiseased brainPotential linkVivo clonal expansionMutagenesisGenesDuplicationSequencingRegulation
2020
SCELLECTOR: ranking amplification bias in single cells using shallow sequencing
Sarangi V, Jourdon A, Bae T, Panda A, Vaccarino F, Abyzov A. SCELLECTOR: ranking amplification bias in single cells using shallow sequencing. BMC Bioinformatics 2020, 21: 521. PMID: 33183232, PMCID: PMC7663899, DOI: 10.1186/s12859-020-03858-y.Peer-Reviewed Original ResearchConceptsMultiple displacement amplificationShallow sequencingSingle-cell platformsSingle-cell sequencingCoverage sequencing dataSingle cellsHuman neuronal cellsMosaic mutationsAmount of DNAAmplification qualityCell sequencingCoverage sequencingHigh-coverage dataSequencing dataHaplotype informationPhi29 polymeraseDNA damageIndividual cellsNeuronal cellsSequencingAmplification biasAllelic imbalancePresence of sitesMutationsFragment length