2024
Single-cell genomics and regulatory networks for 388 human brains
Emani P, Liu J, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee C, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken T, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard J, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman G, Huang A, Jiang Y, Jin T, Jorstad N, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran J, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan A, Riesenmy T, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini K, Wamsley B, Wang G, Xia Y, Xiao S, Yang A, Zheng S, Gandal M, Lee D, Lein E, Roussos P, Sestan N, Weng Z, White K, Won H, Girgenti M, Zhang J, Wang D, Geschwind D, Gerstein M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, Brennand K, Capauto D, Champagne F, Chatzinakos C, Chen H, Cheng L, Chess A, Chien J, 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, Duong D, Eagles N, Edelstein J, Galani K, Girdhar K, Goes F, Greenleaf W, Guo H, Guo Q, Hadas Y, Hallmayer J, Han X, Haroutunian V, He C, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hyde T, Iatrou A, Inoue F, Jajoo A, Jiang L, Jin P, Jops C, Jourdon A, Kellis M, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Li J, Li M, Lin X, Liu S, Liu C, Loupe J, Lu D, Ma L, Mariani J, Martinowich K, Maynard K, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Mukamel E, Nairn A, Nemeroff C, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Pinto D, Pochareddy S, Pollard K, Pollen A, Przytycki P, Purmann C, Qin Z, Qu P, Raj T, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, 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, Wang T, Wang S, Wang Y, Wei Y, Weimer A, Weinberger D, Wen C, Whalen S, Willsey A, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang Y, Ziffra R, Zeier Z, Zintel T. Single-cell genomics and regulatory networks for 388 human brains. Science 2024, 384: eadi5199. PMID: 38781369, DOI: 10.1126/science.adi5199.Peer-Reviewed Original ResearchConceptsSingle-cell genomicsSingle-cell expression quantitative trait locusExpression quantitative trait lociDrug targetsQuantitative trait lociPopulation-level variationSingle-cell expressionCell typesDisease-risk genesTrait lociGene familyRegulatory networksGene expressionCell-typeMultiomics datasetsSingle-nucleiGenomeGenesCellular changesHeterogeneous tissuesExpressionCellsChromatinLociMultiomicsA 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, 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
2023
Lineage specific 3D genome structure in the adult human brain and neurodevelopmental changes in the chromatin interactome
Rahman S, Dong P, Apontes P, Fernando M, Kosoy R, Townsley K, Girdhar K, Bendl J, Shao Z, Misir R, Tsankova N, Kleopoulos S, Brennand K, Fullard J, Roussos P. Lineage specific 3D genome structure in the adult human brain and neurodevelopmental changes in the chromatin interactome. Nucleic Acids Research 2023, 51: 11142-11161. PMID: 37811875, PMCID: PMC10639075, DOI: 10.1093/nar/gkad798.Peer-Reviewed Original ResearchConceptsChromatin interactomeNeural developmentSpecific gene expressionEnhancer-promoter loopsDistinct cell typesGenome compartmentalizationRepressive compartmentGenome architectureFine-scale changesGenome structureChromatin loopsGWAS lociTAD boundariesTranscriptional inactivationActive promotersGene expressionInteractomeGenomeCell typesComplex organDisease mechanismsHuman brainAdult prefrontal cortexAdult human brainNeurodevelopmental processesSTRESS IN A DISH: MODELING THE IMPACT OF COMMON GENETIC VARIATION ON STRESS RESPONSE IN HIPSC-DERIVED NEURONS IN PTSD
Seah C, Signer R, Young H, Kozik E, Rusielewicz T, Bader H, Xu C, de Pins A, Breen M, Paull D, Yehuda R, Girgenti M, Brennand K, Huckins L. STRESS IN A DISH: MODELING THE IMPACT OF COMMON GENETIC VARIATION ON STRESS RESPONSE IN HIPSC-DERIVED NEURONS IN PTSD. European Neuropsychopharmacology 2023, 75: s40. DOI: 10.1016/j.euroneuro.2023.08.081.Peer-Reviewed Original ResearchCommon genetic variationGenetic variationStress responseCell typesEQTL associationsTranscriptional stress responseGenomic risk lociTissue-specific mannerChIP-seq datasetsCell type deconvolutionCommon genetic variantsPost-mortem brainsGene expression signaturesHiPSC-derived neuronsTranscription factorsSuch lociCatalog genesRisk lociGenetic studiesExpression signaturesGenetic variantsRegulatory activityGenesEQTLsMechanistic understandingThe functional and evolutionary impacts of human-specific deletions in conserved elements
Xue J, Mackay-Smith A, Mouri K, Garcia M, Dong M, Akers J, Noble M, Li X, Lindblad-Toh K, Karlsson E, Noonan J, Capellini T, Brennand K, Tewhey R, Sabeti P, Reilly S, Andrews G, Armstrong J, Bianchi M, Birren B, Bredemeyer K, Breit A, Christmas M, Clawson H, Damas J, Di Palma F, Diekhans M, Dong M, Eizirik E, Fan K, Fanter C, Foley N, Forsberg-Nilsson K, Garcia C, Gatesy J, Gazal S, Genereux D, Goodman L, Grimshaw J, Halsey M, Harris A, Hickey G, Hiller M, Hindle A, Hubley R, Hughes G, Johnson J, Juan D, Kaplow I, Karlsson E, Keough K, Kirilenko B, Koepfli K, Korstian J, Kowalczyk A, Kozyrev S, Lawler A, Lawless C, Lehmann T, Levesque D, Lewin H, Li X, Lind A, Lindblad-Toh K, Mackay-Smith A, Marinescu V, Marques-Bonet T, Mason V, Meadows J, Meyer W, Moore J, Moreira L, Moreno-Santillan D, Morrill K, Muntané G, Murphy W, Navarro A, Nweeia M, Ortmann S, Osmanski A, Paten B, Paulat N, Pfenning A, Phan B, Pollard K, Pratt H, Ray D, Reilly S, Rosen J, Ruf I, Ryan L, Ryder O, Sabeti P, Schäffer D, Serres A, Shapiro B, Smit A, Springer M, Srinivasan C, Steiner C, Storer J, Sullivan K, Sullivan P, Sundström E, Supple M, Swofford R, Talbot J, Teeling E, Turner-Maier J, Valenzuela A, Wagner F, Wallerman O, Wang C, Wang J, Weng Z, Wilder A, Wirthlin M, Xue J, Zhang X. The functional and evolutionary impacts of human-specific deletions in conserved elements. Science 2023, 380: eabn2253. PMID: 37104592, PMCID: PMC10202372, DOI: 10.1126/science.abn2253.Peer-Reviewed Original ResearchConceptsHuman-specific deletionHuman phenotypic traitsParallel reporterEvolutionary impactDevelopmental genesPhenotypic traitsEvolutionary mechanismsGenomic sequencesNew traitsTranscriptomic datasetsSequence altersRegulatory functionsCell typesRegulatory activityRich resourceDeletionSynaptic functionTraitsBrain developmentGenesSpeciesReporterHumansSequenceExpression
2022
Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders
Seah C, Huckins L, Brennand K. Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders. Biological Psychiatry 2022, 93: 642-650. PMID: 36658083, DOI: 10.1016/j.biopsych.2022.09.033.Peer-Reviewed Original ResearchConceptsStem cell modelCell typesTarget genesGenome-wide association study (GWAS) lociExpression quantitative trait lociGenome-wide association studiesParallel reporter assaysQuantitative trait lociStem cell-derived cell typesPluripotent stem cell modelsComplex polygenic architectureContext-specific mannerPsychiatric disorder riskTrait lociRegulates transcriptionStudy lociGenetic regulationPolygenic architectureCRISPR screensCell modelCausal variantsRegulated expressionPatient-specific humanReporter assaysAssociation studiesUsing Stem Cell Models to Explore the Genetics Underlying Psychiatric Disorders: Linking Risk Variants, Genes, and Biology in Brain Disease
Brennand K. Using Stem Cell Models to Explore the Genetics Underlying Psychiatric Disorders: Linking Risk Variants, Genes, and Biology in Brain Disease. American Journal Of Psychiatry 2022, 179: 322-328. PMID: 35491564, DOI: 10.1176/appi.ajp.20220235.Commentaries, Editorials and LettersConceptsRisk variantsFunctional genomic studiesCell typesDiverse cell typesPatient-specific variantsStem cell modelGenomic studiesSignificant lociStem cell-based approachesGenetic studiesExciting questionsCell-based approachesEngineering strategiesGenetic profileNovel therapeutic interventionsCell modelPluripotent stem cell-based approachesVariantsComplex interplayGenetic riskCRISPRGenesLociBiologyTherapeutic interventions
2021
Applying stem cells and CRISPR engineering to uncover the etiology of schizophrenia
Michael Deans P, Brennand K. Applying stem cells and CRISPR engineering to uncover the etiology of schizophrenia. Current Opinion In Neurobiology 2021, 69: 193-201. PMID: 34010781, PMCID: PMC8387340, DOI: 10.1016/j.conb.2021.04.003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsCell type-specific fashionStem cell biologyType-specific fashionDisease-associated variantsNeural cell typesCommon genetic variantsMore genesCell biologyCRISPR engineeringGene manipulationGene targetsCRISPR technologyMolecular geneticsInvaluable advancesCell typesHiPSC technologyGenetic variantsStem cellsIndividual variantsEtiology of diseasePolygenic disorderVariantsComplex interactionsRecent advancesEtiology of schizophreniaChapter 30 Functional genomics of psychiatric disease risk using genome engineering
Garcia M, Powell S, LaMarca E, Fernando M, Cohen S, Fang G, Akbarian S, Brennand K. Chapter 30 Functional genomics of psychiatric disease risk using genome engineering. 2021, 711-734. DOI: 10.1016/b978-0-12-823577-5.00021-0.ChaptersRisk variantsDisease-relevant cell typesPsychiatric disease riskPluripotent stem cellsFunctional genomicsGenetic risk architectureGenome engineeringComplex geneticsCRISPR engineeringHiPSC modelsCell typesFunctional explorationStem cellsFunctional impactRisk architecturePossible new avenuesGenetic findingsGenetic screeningNew insightsNovel therapeutic approachesFundamental roleNew avenuesDisease pathophysiologyEpigenomeVariants
2020
Integration of CRISPR-engineering and hiPSC-based models of psychiatric genomics
Matos MR, Ho SM, Schrode N, Brennand KJ. Integration of CRISPR-engineering and hiPSC-based models of psychiatric genomics. Molecular And Cellular Neuroscience 2020, 107: 103532. PMID: 32712198, PMCID: PMC7484226, DOI: 10.1016/j.mcn.2020.103532.Peer-Reviewed Original ResearchConceptsPenetrant rare variantsDisease-associated variantsNeuronal cell typesPluripotent stem cellsGenomic engineeringFunctional characterizationComplex geneticsCRISPR engineeringCRISPR technologyIsogenic comparisonsPsychiatric genomicsCell typesGenetic variantsStem cellsIndividual variantsCommon variantsPolygenic disorderRare variantsVariantsComplex interplayGenomicsGenetic riskPleiotropyCRISPRGeneticsCell Type-Specific In Vitro Gene Expression Profiling of Stem Cell-Derived Neural Models
Gregory JA, Hoelzli E, Abdelaal R, Braine C, Cuevas M, Halpern M, Barretto N, Schrode N, Akbalik G, Kang K, Cheng E, Bowles K, Lotz S, Goderie S, Karch CM, Temple S, Goate A, Brennand KJ, Phatnani H. Cell Type-Specific In Vitro Gene Expression Profiling of Stem Cell-Derived Neural Models. Cells 2020, 9: 1406. PMID: 32516938, PMCID: PMC7349756, DOI: 10.3390/cells9061406.Peer-Reviewed Original ResearchConceptsCell type-restricted expressionDisease-associated interactionsGene expression profilingHiPSC-derived motor neuronsHuman-induced pluripotent stem cellsPluripotent stem cellsCell-type specific perturbationsImmortalized cell linesRibosomal proteinsGenomic studiesExpression profilingMolecular mechanismsOff-target RNAMouse tissuesCell typesStem cellsPrimary mouse astrocytesExperimental replicatesCell linesMixed speciesMouse astrocytesExpressionMotor neuronsRiboTagCellsModeling the complex genetic architectures of brain disease
Fernando MB, Ahfeldt T, Brennand KJ. Modeling the complex genetic architectures of brain disease. Nature Genetics 2020, 52: 363-369. PMID: 32203467, PMCID: PMC7909729, DOI: 10.1038/s41588-020-0596-3.Peer-Reviewed Original ResearchConceptsGenetic architectureComplex genetic architectureFunctional validation studiesRelevant disease biologyIntersection of genomicsComplex genetic diseasesCombination of genesPluripotent stem cellsGene perturbationsIsogenic comparisonsMolecular mechanismsPhenotypic drug discoveryCell typesGenetic diseasesFunctional consequencesGenetic backgroundRisk variantsStem cellsCRISPRDisease biologyDrug discoveryRare variantsConfer riskGenetic diagnosisVariantsA computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles
Sey NYA, Hu B, Mah W, Fauni H, McAfee JC, Rajarajan P, Brennand KJ, Akbarian S, Won H. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nature Neuroscience 2020, 23: 583-593. PMID: 32152537, PMCID: PMC7131892, DOI: 10.1038/s41593-020-0603-0.Peer-Reviewed Original ResearchConceptsChromatin interaction profilesH-MAGMARisk genesMost risk variantsGenome-wide association studiesCell typesGene regulatory relationshipsRelevant target genesCell-type specificitySingle nucleotide polymorphism associationsBrain cell typesDisease-relevant tissuesInteraction profilesGenomic annotationsNearest geneTarget genesRegulatory relationshipsAssociation studiesBiological pathwaysGenesRisk variantsDevelopmental windowBiological mechanismsNeurodegenerative disordersHuman brain tissueIntegrating CRISPR Engineering and hiPSC-Derived 2D Disease Modeling Systems
Rehbach K, Fernando MB, Brennand KJ. Integrating CRISPR Engineering and hiPSC-Derived 2D Disease Modeling Systems. Journal Of Neuroscience 2020, 40: 1176-1185. PMID: 32024766, PMCID: PMC7002154, DOI: 10.1523/jneurosci.0518-19.2019.Peer-Reviewed Original ResearchConceptsHuman induced pluripotent stem cellsMajor brain cell typesDual Perspectives CompanionBrain cell typesNeuronal maturityPsychiatric disordersHuman neuronsDisease riskStudy designBrain organoidsIntradonor variabilityDisease modelsHuman neurodevelopmentInduced pluripotent stem cellsNeural differentiationDiseaseStem cellsCell typesPluripotent stem cellsHuman diseasesEfficient neural differentiationInduction strategyPatient-specific cellsDisease modelingCells
2019
Examining the relationship between astrocyte dysfunction and neurodegeneration in ALS using hiPSCs
Halpern M, Brennand KJ, Gregory J. Examining the relationship between astrocyte dysfunction and neurodegeneration in ALS using hiPSCs. Neurobiology Of Disease 2019, 132: 104562. PMID: 31381978, PMCID: PMC6834907, DOI: 10.1016/j.nbd.2019.104562.Peer-Reviewed Original ResearchConceptsAmyotrophic lateral sclerosisAstrocyte dysfunctionNeurodegenerative diseasesRole of astrocytesNon-cell autonomous mechanismsFatal neurodegenerative diseaseRisk-associated genesAstrocytic dysfunctionNeural cell typesAstrocyte functionDisease onsetDisease progressionMotor neuronsLateral sclerosisTherapeutic interventionsDysfunctionDisease initiationGenetic factorsPotential targetProgressionAutonomous mechanismsDiseaseStem cellsNeurodegenerationCell types
2018
Expression-based drug screening of neural progenitor cells from individuals with schizophrenia
Readhead B, Hartley BJ, Eastwood BJ, Collier DA, Evans D, Farias R, He C, Hoffman G, Sklar P, Dudley JT, Schadt EE, Savić R, Brennand KJ. Expression-based drug screening of neural progenitor cells from individuals with schizophrenia. Nature Communications 2018, 9: 4412. PMID: 30356048, PMCID: PMC6200740, DOI: 10.1038/s41467-018-06515-4.Peer-Reviewed Original ResearchConceptsNeural progenitor cellsHiPSC neural progenitor cellsCell typesCancer cell linesGene expression differencesProgenitor cellsDisease-associated genesPatient-specific platformPluripotent stem cellsTranscriptional responseExpression differencesTranscriptional signatureTranscriptomic signaturesStem cellsCell linesDependent mannerDrug discoveryDrug screeningCellsNeuropsychiatric disordersSchizophreniaBest treatmentDrugsDiscoveryGenes
2017
The Importance of Non-neuronal Cell Types in hiPSC-Based Disease Modeling and Drug Screening
Gonzalez D, Gregory J, Brennand K. The Importance of Non-neuronal Cell Types in hiPSC-Based Disease Modeling and Drug Screening. Frontiers In Cell And Developmental Biology 2017, 5: 117. PMID: 29312938, PMCID: PMC5742170, DOI: 10.3389/fcell.2017.00117.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsProspects for Modeling Abnormal Neuronal Function in Schizophrenia Using Human Induced Pluripotent Stem Cells
Prytkova I, Brennand K. Prospects for Modeling Abnormal Neuronal Function in Schizophrenia Using Human Induced Pluripotent Stem Cells. Frontiers In Cellular Neuroscience 2017, 11: 360. PMID: 29217999, PMCID: PMC5703699, DOI: 10.3389/fncel.2017.00360.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsInhibitory GABAergic neuronsAbnormal neuronal functionStem cellsPluripotent stem cellsGABAergic neuronsDopaminergic neuronsNetwork dysfunctionSZ pathologyDifficult disorderPatient-derived pluripotent stem cellsGlial culturesAnimal modelsNeuronal functionNetwork pathologyPatient-specific mannerHuman induced pluripotent stem cellsMolecular dysfunctionCo-culture techniqueInduced pluripotent stem cellsDysfunctionHigh rateDifferent cell typesNeuronsPathologyCell typesEvaluating Synthetic Activation and Repression of Neuropsychiatric-Related Genes in hiPSC-Derived NPCs, Neurons, and Astrocytes
Ho S, Hartley B, Flaherty E, Rajarajan P, Abdelaal R, Obiorah I, Barretto N, Muhammad H, Phatnani H, Akbarian S, Brennand K. Evaluating Synthetic Activation and Repression of Neuropsychiatric-Related Genes in hiPSC-Derived NPCs, Neurons, and Astrocytes. Stem Cell Reports 2017, 9: 615-628. PMID: 28757163, PMCID: PMC5550013, DOI: 10.1016/j.stemcr.2017.06.012.Peer-Reviewed Original ResearchConceptsSynthetic activationRisk genesCell typesModulation of transcriptionNeuropsychiatric risk genesCommon single nucleotide variantsCas9 fusion proteinsEndogenous expression levelsNeural cell typesPluripotent stem cell-derived neural progenitor cellsRare copy number variationsCopy number variationsSingle nucleotide variantsNeural progenitor cellsGene functionFunctional annotationGenetic studiesGenesRisk variantsProgenitor cellsExpression levelsTranscriptionRepressionPositional effectsProtein
2016
Neural organoids for disease phenotyping, drug screening and developmental biology studies
Hartley B, Brennand K. Neural organoids for disease phenotyping, drug screening and developmental biology studies. Neurochemistry International 2016, 106: 85-93. PMID: 27744003, PMCID: PMC5389930, DOI: 10.1016/j.neuint.2016.10.004.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsHuman induced pluripotent stem cellsNeural organoidsCell-extracellular matrix interactionsCell typesDevelopmental biology studiesSpecific physiological functionsCell-cell interactionsInduced pluripotent stem cellsNervous system cell typesCentral nervous system cell typesPluripotent stem cellsCell replacement therapyBiology studiesCurrent biomedical researchDifferentiation protocolsPhysiological functionsComplex tissuesMatrix interactionsNovel technological platformStem cellsDisease mechanismsSpatial organizationUnknown disease mechanismsToxicity assaysHeterogeneous tissues