2024
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling
Lee H, Emani P, Gerstein M. Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling. Journal Of Chemical Information And Modeling 2024 PMID: 39576762, DOI: 10.1021/acs.jcim.4c01116.Peer-Reviewed Original ResearchBinding affinity predictionAffinity predictionMeta-modelMeta-modeling approachLigand-protein binding affinityState-of-the-art deep learning toolsState-of-the-artBinding affinityDeep learning modelsDeep learning toolsMolecular descriptorsInclusion of featuresVirtual screeningBase modelDatabase scalabilityGeneralization capabilityDiverse modeling approachesTraining databaseApplication benchmarksDrug ligandsLearning modelsLigandPhysicochemical propertiesLearning toolsDevelopment effortsThe cell-type underpinnings of the human functional cortical connectome
Zhang X, Anderson K, Dong H, Chopra S, Dhamala E, Emani P, Gerstein M, Margulies D, Holmes A. The cell-type underpinnings of the human functional cortical connectome. Nature Neuroscience 2024, 1-11. PMID: 39572742, DOI: 10.1038/s41593-024-01812-2.Peer-Reviewed Original ResearchSingle-nucleus RNA sequencing dataCell-type distributionRNA sequencing dataMicroarray dataSingle-nucleusIn vivo organizationCortical sheetCortical connectomeAllen Human Brain AtlasCell typesFunctional magnetic resonance imagingCellular fingerprintsCortical tissue samplesHuman Brain AtlasOrganization of cortexFunctional organization of cortexCellular compositionBrain atlasesFunctional organizationCellsEnriched cellsAssociative territoryFunctional propertiesTissue samplesSpatial topographyGENCODE 2025: reference gene annotation for human and mouse
Mudge J, Carbonell-Sala S, Diekhans M, Martinez J, Hunt T, Jungreis I, Loveland J, Arnan C, Barnes I, Bennett R, Berry A, Bignell A, Cerdán-Vélez D, Cochran K, Cortés L, Davidson C, Donaldson S, Dursun C, Fatima R, Hardy M, Hebbar P, Hollis Z, James B, Jiang Y, Johnson R, Kaur G, Kay M, Mangan R, Maquedano M, Gómez L, Mathlouthi N, Merritt R, Ni P, Palumbo E, Perteghella T, Pozo F, Raj S, Sisu C, Steed E, Sumathipala D, Suner M, Uszczynska-Ratajczak B, Wass E, Yang Y, Zhang D, Finn R, Gerstein M, Guigó R, Hubbard T, Kellis M, Kundaje A, Paten B, Tress M, Birney E, Martin F, Frankish A. GENCODE 2025: reference gene annotation for human and mouse. Nucleic Acids Research 2024, gkae1078. PMID: 39565199, DOI: 10.1093/nar/gkae1078.Peer-Reviewed Original ResearchGene annotationLong-read transcriptome sequencingMulti-genome alignmentsRibo-Seq experimentsUCSC Genome BrowserState-of-the-art proteomicsGenome browserRibo-seqSpecies genomesMouse genomeTranscriptome sequencingGENCODEGenomeAnnotation workflowAnnotationSequencePangenomeMiceGenesetsState-of-the-artUCSCProteomicsTranscriptionGenesSpeciesA variational graph-partitioning approach to modeling protein liquid-liquid phase separation
Wang G, Warrell J, Zheng S, Gerstein M. A variational graph-partitioning approach to modeling protein liquid-liquid phase separation. Cell Reports Physical Science 2024, 5: 102292. DOI: 10.1016/j.xcrp.2024.102292.Peer-Reviewed Original ResearchREliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers
Cameron C, Seager S, Sigworth F, Tagare H, Gerstein M. REliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers. Communications Biology 2024, 7: 1421. PMID: 39482410, PMCID: PMC11528043, DOI: 10.1038/s42003-024-07045-0.Peer-Reviewed Original ResearchConceptsCryo-EM usersParticle identificationParticle pickingLow signal-to-noise ratioAchievable resolutionSignal-to-noise ratioState-of-the-art computational algorithmsInteger linear programmingParticle setManual interventionHigh-quality particlesGraph problemsParticle locationMultiple pickersParticlesEpitope-anchored contrastive transfer learning for paired CD8+ T cell receptor–antigen recognition
Zhang Y, Wang Z, Jiang Y, Littler D, Gerstein M, Purcell A, Rossjohn J, Ou H, Song J. Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor–antigen recognition. Nature Machine Intelligence 2024, 1-15. DOI: 10.1038/s42256-024-00913-8.Peer-Reviewed Original ResearchPeptide-major histocompatibility complexT cellsEpitope-specific T cellsImmune responseResidue-level interactionsPredicted binding strengthSpike-specific immune responsesTCR-based immunotherapyTumor-associated antigensT cell antigen recognitionPredicted binding specificityAdaptive immune responsesTCR cross-reactivityTCR repertoireCross-reactivityBinding specificityAutoimmune diseasesImmunodominant epitopesContact residuesAntigen recognitionHistocompatibility complexTCRImmunotherapyDistance matrixT-cell receptor-antigen recognitionSpatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues
Bai Z, Zhang D, Gao Y, Tao B, Zhang D, Bao S, Enninful A, Wang Y, Li H, Su G, Tian X, Zhang N, Xiao Y, Liu Y, Gerstein M, Li M, Xing Y, Lu J, Xu M, Fan R. Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues. Cell 2024, 187: 6760-6779.e24. PMID: 39353436, DOI: 10.1016/j.cell.2024.09.001.Peer-Reviewed Original ResearchRNA biologyWhole-transcriptome sequencingMicroRNA regulatory networkSplicing dynamicsDeterministic barcodingRNA speciesRNA processingRNA variantsFFPE tissuesRegulatory networksTranscriptome sequencingSpliced isoformsNon-malignant cellsTumor clonal architecturesClonal architectureGene expressionCellular dynamicsRNAArchival formalin-fixed paraffin-embedded tissueMalignant subclonesFormalin-fixed paraffin-embedded (FFPEFFPE samplesParaffin-embedded (FFPEBiologyHuman lymphomasPredicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs
Song T, Cosatto E, Wang G, Kuang R, Gerstein M, Min M, Warrell J. Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs. Bioinformatics 2024, 40: ii111-ii119. PMID: 39230702, PMCID: PMC11373608, DOI: 10.1093/bioinformatics/btae383.Peer-Reviewed Original ResearchConceptsGene expressionSpatial gene expressionSpatial transcriptomics technologiesTissue histology imagesExpressed genesGene activationTranscriptomic technologiesMolecular underpinningsGraph neural networksState-of-the-artSpatial expressionGenesTissue architectureExpressionHistological imagesNeural networkFast, sensitive detection of protein homologs using deep dense retrieval
Hong L, Hu Z, Sun S, Tang X, Wang J, Tan Q, Zheng L, Wang S, Xu S, King I, Gerstein M, Li Y. Fast, sensitive detection of protein homologs using deep dense retrieval. Nature Biotechnology 2024, 1-13. PMID: 39123049, DOI: 10.1038/s41587-024-02353-6.Peer-Reviewed Original ResearchProtein language modelsRemote homologsProtein homologsProtein sequence comparisonsAlignment-based approachesWell-characterized proteinsPSI-BLASTSuperfamily levelProtein evolutionSequence comparisonProtein sequencesHomologyProteinSensitivity compared to previous methodsSensitive detectionHMMERSuperfamilyStructural informationSequenceLeveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach
Frank M, Ni P, Jensen M, Gerstein M. Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2320510121. PMID: 39110734, PMCID: PMC11331094, DOI: 10.1073/pnas.2320510121.Peer-Reviewed Original ResearchConceptsProtein phase transitionsAssociated with reduced gene expressionProtein structure predictionAlzheimer's disease-related proteinsDisease-related proteinsAlzheimer's diseaseProtein sequencesSequence variantsStructure predictionAmyloid aggregatesProtein designGene expressionAge-related diseasesNatural defense mechanismsSoluble stateProteinDefense mechanismsBiophysical featuresAlzheimerSequenceAmyloidVariantsExpressionLanguage modelComputational frameworkRepresenting core gene expression activity relationships using the latent structure implicit in Bayesian networks
Gao J, Gerstein M. Representing core gene expression activity relationships using the latent structure implicit in Bayesian networks. Bioinformatics 2024, 40: btae463. PMID: 39051682, PMCID: PMC11316617, DOI: 10.1093/bioinformatics/btae463.Peer-Reviewed Original ResearchTranscriptional regulatory networksGene regulatory networksCo-expression networkGene expression activityChIP-seqGene conservationCluster genesSupplementary dataRegulatory networksBiological networksClearer clusteringCo-expressionExpression activityBioinformaticsGenesBiomedical studiesConservationExpressionClustersMolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations
Tang X, Tran A, Tan J, Gerstein M. MolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations. Bioinformatics 2024, 40: i357-i368. PMID: 38940177, PMCID: PMC11256921, DOI: 10.1093/bioinformatics/btae260.Peer-Reviewed Original ResearchConceptsTransformer encoderDownstream tasksLanguage modelBiomedical textSelf-supervised pre-trainingExplicit 3D representationRepresentation improves performanceDeep learning modelsRepresentation of moleculesContrastive learningSupervisory signalExtract embeddingsRepresentation capabilityJoint representationBiomedical domainPre-trainingTextual dataLearning modelsMolecular representationsModel weightsJupyter NotebookStep-by-step guidanceEncodingProperty predictionStructural informationTranscriptional determinism and stochasticity contribute to the complexity of autism-associated SHANK family genes
Lu X, Ni P, Suarez-Meade P, Ma Y, Forrest E, Wang G, Wang Y, Quiñones-Hinojosa A, Gerstein M, Jiang Y. Transcriptional determinism and stochasticity contribute to the complexity of autism-associated SHANK family genes. Cell Reports 2024, 43: 114376. PMID: 38900637, PMCID: PMC11328446, DOI: 10.1016/j.celrep.2024.114376.Peer-Reviewed Original ResearchSHANK family genesFamily genesLong-read sequencingCDNA captureTranscript structureDeleterious variantsGenomic studiesAbundant mRNAsTranscriptional dysregulationStochastic transcriptionStudies of neuropsychiatric disordersCausative genesTranscriptional profilesTranscriptional determinantsTranscriptomePostmortem brain tissueAutism spectrum disorderShank3 transcriptsTranscriptionGenesGenomeSHANK3Neuropsychiatric disordersSpectrum disorderAutism modelThe single-cell opioid responses in the context of HIV (SCORCH) consortium
Ament S, Campbell R, Lobo M, Receveur J, Agrawal K, Borjabad A, Byrareddy S, Chang L, Clarke D, Emani P, Gabuzda D, Gaulton K, Giglio M, Giorgi F, Gok B, Guda C, Hadas E, Herb B, Hu W, Huttner A, Ishmam M, Jacobs M, Kelschenbach J, Kim D, Lee C, Liu S, Liu X, Madras B, Mahurkar A, Mash D, Mukamel E, Niu M, O’Connor R, Pagan C, Pang A, Pillai P, Repunte-Canonigo V, Ruzicka W, Stanley J, Tickle T, Tsai S, Wang A, Wills L, Wilson A, Wright S, Xu S, Yang J, Zand M, Zhang L, Zhang J, Akbarian S, Buch S, Cheng C, Corley M, Fox H, Gerstein M, Gummuluru S, Heiman M, Ho Y, Kellis M, Kenny P, Kluger Y, Milner T, Moore D, Morgello S, Ndhlovu L, Rana T, Sanna P, Satterlee J, Sestan N, Spector S, Spudich S, Tilgner H, Volsky D, White O, Williams D, Zeng H. The single-cell opioid responses in the context of HIV (SCORCH) consortium. Molecular Psychiatry 2024, 1-12. PMID: 38879719, DOI: 10.1038/s41380-024-02620-7.Peer-Reviewed Original ResearchContext of human immunodeficiency virusHuman immunodeficiency virusSubstance use disordersOpioid responseAnimal modelsEffects of substance use disordersOpioid pain medicationsPrevalence of co-morbid conditionsChronic pain syndromesStage of diseaseCell typesAffected cell typesCo-morbid conditionsPain syndromeImmunodeficiency virusPain medicationOpioid addictionIncreased riskRisk factorsHuman cohortsDrug addictionBrain tissue collectionBrain cell typesTissue collectionSingle-cell levelSingle-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, PMCID: PMC11365579, 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 tissuesExpressionCellsChromatinLociMultiomicsCross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain
Wen C, Margolis M, Dai R, Zhang P, Przytycki P, Vo D, Bhattacharya A, Matoba N, Tang M, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker R, Chatzinakos C, Clarke D, Pratt H, Peters M, Gerstein M, Daskalakis N, Weng Z, Jaffe A, Kleinman J, Hyde T, Weinberger D, Bray N, Sestan N, Geschwind D, Roeder K, Gusev A, Pasaniuc B, Stein J, Love M, Pollard K, Liu C, Gandal M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, 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, Clement A, Collado-Torres L, Cooper G, Crawford G, 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, Gaynor S, 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, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, 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 S, Lou S, Loupe J, Lu D, Ma S, Ma L, 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, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollen A, 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, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Voloudakis G, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, 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 C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain. Science 2024, 384: eadh0829. PMID: 38781368, DOI: 10.1126/science.adh0829.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide association study lociSplicing quantitative trait lociQuantitative trait lociSplicing regulationCross-ancestryTrait lociAssociation studiesRegulatory elementsCellular contextHuman brainTranscriptome regulationCoexpression networkRisk genesAutism spectrum disorderGenesCellular heterogeneityComprehensive landscapeSpectrum disorderIsoformsSplicingIncreased cellular heterogeneityLociNeuronal maturationRegulationA 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 cortexMassively parallel characterization of regulatory elements in the developing human cortex
Deng C, Whalen S, Steyert M, Ziffra R, Przytycki P, Inoue F, Pereira D, Capauto D, Norton S, Vaccarino F, Pollen A, Nowakowski T, Ahituv N, Pollard 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, Khullar S, 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. Massively parallel characterization of regulatory elements in the developing human cortex. Science 2024, 384: eadh0559. PMID: 38781390, DOI: 10.1126/science.adh0559.Peer-Reviewed Original ResearchConceptsGene regulatory elementsRegulatory elementsRegulation of enhancer activityCharacterization of regulatory elementsCis-regulatory activityNeuronal developmentPrimary cellsEnhanced activityGene regulationHuman neuronal developmentNucleotide changesEnhancer sequencesSequence basisUpstream regulatorComprehensive catalogHuman cellsDeveloping cortexSequenceVariantsOrganoidsCellsCerebral organoidsCortexHuman cortexNucleotideSingle-cell multi-cohort dissection of the schizophrenia transcriptome
Ruzicka W, Mohammadi S, Fullard J, Davila-Velderrain J, Subburaju S, Tso D, Hourihan M, Jiang S, Lee H, Bendl J, Voloudakis G, Haroutunian V, Hoffman G, Roussos P, Kellis M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, 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, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, 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, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, 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, 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, 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, Rozowsky J, Ruth M, 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, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, 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. Single-cell multi-cohort dissection of the schizophrenia transcriptome. Science 2024, 384: eadg5136. PMID: 38781388, DOI: 10.1126/science.adg5136.Peer-Reviewed Original ResearchConceptsGenetic risk factorsRisk factorsTranscriptional changesHeterogeneity of schizophreniaNeuronal cell statesSchizophrenia pathophysiologySingle-cell dissectionExcitatory neuronsEffective therapySchizophrenia transcriptomicsCortical cytoarchitectureSingle-cell atlasGenomic variantsCell groupsHuman prefrontal cortexMolecular pathwaysSchizophreniaTranscriptional alterationsTranscriptomic changesPrefrontal cortexCell statesAlterationsTherapyPathophysiologyDissectionUsing a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements
Pratt H, Andrews G, Shedd N, Phalke N, Li T, Pampari A, Jensen M, Wen C, Consortium P, Gandal M, Geschwind D, Gerstein M, Moore J, Kundaje A, Colubri A, Weng Z. Using a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements. Science Advances 2024, 10: eadj4452. PMID: 38781344, PMCID: PMC11114231, DOI: 10.1126/sciadv.adj4452.Peer-Reviewed Original ResearchConceptsEpigenetic dataCell-type-specific gene regulationCis-regulatory elementsComprehensive atlasGenetic variants associated with psychiatric disordersLineage-specific transcription factorsBrain cell typesMammalian elementsPsychENCODE ConsortiumNoncoding regionsEvolutionary historyGene regulationRegulatory elementsSequence mutationsTranscription factorsSequence syntaxRegulatory informationPrimate-specific sequencesBinding sitesHuman traitsCell typesFunctional implicationsPsychiatric disordersSequenceFetal brain development