2024
A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex
Huuki-Myers L, Spangler A, Eagles N, Montgomery K, Kwon S, Guo B, Grant-Peters M, Divecha H, Tippani M, Sriworarat C, Nguyen A, Ravichandran P, Tran M, Seyedian A, Hyde T, Kleinman J, Battle A, Page S, Ryten M, Hicks S, Martinowich K, Collado-Torres L, Maynard K, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, Bhattacharya A, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clarke D, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Fullard J, Galani K, Galeev T, Gandal M, Gaynor S, Gerstein M, Geschwind D, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Haroutunian V, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Hoffman G, Huang Y, Huuki-Myers L, Hwang A, Hyde T, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu C, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Margolis M, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollard K, Pollen A, Pratt H, Przytycki P, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Roussos P, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Sestan N, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Voloudakis G, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Weinberger D, Wen C, Weng Z, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex. Science 2024, 384: eadh1938. PMID: 38781370, PMCID: PMC11398705, DOI: 10.1126/science.adh1938.Peer-Reviewed Original ResearchConceptsRNA sequencing dataCell type compositionGene expression platformSpatial transcriptomics technologiesAnterior-posterior axisCell-cell interactionsTranscriptome mapExpression platformHuman dorsolateral prefrontal cortexTranscriptomic technologiesSingle-cellCell typesPrefrontal cortexMolecular organizationDorsolateral prefrontal cortexHuman prefrontal cortex
2021
Analysis framework and experimental design for evaluating synergy-driving gene expression
Schrode N, Seah C, Deans P, Hoffman G, Brennand K. Analysis framework and experimental design for evaluating synergy-driving gene expression. Nature Protocols 2021, 16: 812-840. PMID: 33432232, PMCID: PMC8609447, DOI: 10.1038/s41596-020-00436-7.Peer-Reviewed Original ResearchConceptsRaw read countsPluripotent stem cell-derived neuronsRNA sequencing experimentsRNA sequencing datasetsStem cell-derived neuronsDifferential expression analysisCell-derived neuronsComplex genetic disorderNon-additive interactionsGenetic risk variantsChemical perturbagensBioinformatics skillsExpression analysisSequencing datasetsGene expressionTranscriptomic effectsSequencing experimentsComputational pipelineRead countsRisk variantsCareful experimental designCombinatorial manipulationGenetic variantsComplex diseasesPerturbation studiesMolecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets
Neff R, Wang M, Vatansever S, Guo L, Ming C, Wang Q, Wang E, Horgusluoglu-Moloch E, Song W, Li A, Castranio E, Julia T, Ho L, Goate A, Fossati V, Noggle S, Gandy S, Ehrlich M, Katsel P, Schadt E, Cai D, Brennand K, Haroutunian V, Zhang B. Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets. Science Advances 2021, 7: eabb5398. PMID: 33523961, PMCID: PMC7787497, DOI: 10.1126/sciadv.abb5398.Peer-Reviewed Original ResearchMeSH KeywordsAlzheimer DiseaseAmyloid beta-PeptidesAnimalsBrainHumansMiceRNASequence Analysis, RNATau ProteinsConceptsAlzheimer's diseaseMouse modelAD mouse modelDiverse pathophysiologic mechanismsTau-mediated neurodegenerationMajor molecular subtypesSpecific mouse modelsPathophysiologic mechanismsHuman trialsMolecular subtypesImmune activityHeterogeneous diseaseAD cohortAD subtypesBrain regionsSynaptic signalingMolecular subtypingSubtype heterogeneityDiseaseCommon formPrecision medicineMultiscale network analysisDevastating diseaseMolecular heterogeneitySubtypes
2020
Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease
Wang M, Li A, Sekiya M, Beckmann ND, Quan X, Schrode N, Fernando MB, Yu A, Zhu L, Cao J, Lyu L, Horgusluoglu E, Wang Q, Guo L, Wang YS, Neff R, Song WM, Wang E, Shen Q, Zhou X, Ming C, Ho SM, Vatansever S, Kaniskan HÜ, Jin J, Zhou MM, Ando K, Ho L, Slesinger PA, Yue Z, Zhu J, Katsel P, Gandy S, Ehrlich ME, Fossati V, Noggle S, Cai D, Haroutunian V, Iijima KM, Schadt E, Brennand KJ, Zhang B. Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease. Neuron 2020, 109: 257-272.e14. PMID: 33238137, PMCID: PMC7855384, DOI: 10.1016/j.neuron.2020.11.002.Peer-Reviewed Original ResearchConceptsLate-onset Alzheimer's diseaseAlzheimer's diseaseKey regulatorPluripotent stem cell-derived neuronsRNAi-based knockdownStem cell-derived neuronsNovel therapeutic targetNext-generation therapeutic agentsCell-derived neuronsKey brain regionsIntegrative network analysisMulti-omics dataComplex molecular interactionsMulti-omics profilingNCH-51Neuronal impairmentGene subnetworksDisease-related processesCortical areasTherapeutic targetDrosophila modelNeuropathological phenotypeBrain regionsTherapeutic agentsMolecular mechanismsFunctional annotation of rare structural variation in the human brain
Han L, Zhao X, Benton ML, Perumal T, Collins RL, Hoffman GE, Johnson JS, Sloofman L, Wang HZ, Stone MR, Brennand K, Brand H, Sieberts S, Marenco S, Peters M, Lipska B, Roussos P, Capra J, Talkowski M, Ruderfer D. Functional annotation of rare structural variation in the human brain. Nature Communications 2020, 11: 2990. PMID: 32533064, PMCID: PMC7293301, DOI: 10.1038/s41467-020-16736-1.Peer-Reviewed Original Research
2017
Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains
Hoffman GE, Hartley BJ, Flaherty E, Ladran I, Gochman P, Ruderfer DM, Stahl EA, Rapoport J, Sklar P, Brennand KJ. Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains. Nature Communications 2017, 8: 2225. PMID: 29263384, PMCID: PMC5738408, DOI: 10.1038/s41467-017-02330-5.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAntigens, SurfaceAutopsyBrainCase-Control StudiesChildDNA Copy Number VariationsFemaleHumansInduced Pluripotent Stem CellsLinear ModelsMaleNanog Homeobox ProteinNestinNeural Stem CellsNeuronsOctamer Transcription Factor-3ProteoglycansRNA, MessengerSchizophreniaSequence Analysis, RNASOXB1 Transcription FactorsStage-Specific Embryonic AntigensSynapsinsTranscriptomeYoung Adult