2025
SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data
Dong M, Su D, Kluger H, Fan R, Kluger Y. SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data. Nature Communications 2025, 16: 2990. PMID: 40148341, PMCID: PMC11950362, DOI: 10.1038/s41467-025-58089-7.Peer-Reviewed Original ResearchConceptsOmics dataSpatial omics dataAnalysis of gene expressionSingle-cell resolutionDownstream analysisCellular statesSpatial interaction modelsGerminal center B cellsGene expressionCommunication machineryOmics technologiesIntercellular interactionsSpatial omics technologiesTumor microenvironmentB cellsSpatial dynamicsHuman tonsilsMacrophage stateSpatial effectsA single-cell atlas of circulating immune cells over the first 2 months of age in extremely premature infants
Olaloye O, Gu W, Gehlhaar A, Sabuwala B, Eke C, Li Y, Kehoe T, Farmer R, Gabernet G, Lucas C, Tsang J, Lakhani S, Taylor S, Tseng G, Kleinstein S, Konnikova L. A single-cell atlas of circulating immune cells over the first 2 months of age in extremely premature infants. Science Translational Medicine 2025, 17: eadr0942. PMID: 40043141, DOI: 10.1126/scitranslmed.adr0942.Peer-Reviewed Original ResearchMeSH KeywordsAdultFemaleFetal BloodHumansInfantInfant, Extremely PrematureInfant, NewbornMaleSingle-Cell AnalysisConceptsExtremely premature infantsFull-term infantsT cellsMonths of lifePremature infantsImmune cellsMemory CD4<sup>+</sup> T cellsCD4<sup>+</sup> T cellsMemory-like T cellsAnalysis of immune cellsNaive CD4<sup>+</sup>Peripheral T cell developmentWeeks of gestationCord blood samplesNatural killer cellsT helper 1B cell receptor sequencesT cell developmentCycling T cellsMonths of ageSingle-cell suspensionsAmount of bloodSusceptibility to infectionCD4<sup>+</sup>Killer cellsSingle cell analysis identifies distinct CD4 + T cells associated with the pathobiology of pediatric obesity related asthma
Thompson D, Wabara Y, Duran S, Reichenbach A, Chen L, Collado K, Yon C, Greally DMed J, Rastogi D. Single cell analysis identifies distinct CD4 + T cells associated with the pathobiology of pediatric obesity related asthma. Scientific Reports 2025, 15: 6844. PMID: 40000680, PMCID: PMC11861978, DOI: 10.1038/s41598-025-88423-4.Peer-Reviewed Original ResearchConceptsCD4+ T cellsT cell subtypesT cellsSteroid resistanceTh1 inflammationObese asthmaTh1 responseCentral memory T cellsObese asthma phenotypeEffector T cellsMemory T cellsObesity-related asthmaCD4+ T cell subtypesPediatric obesity-related asthmaT helper 1Healthy-weight controlsPulmonary function deficitsCD4Th1Asthma phenotypesGlucocorticoid receptorSingle-cell transcriptomicsFunctional deficitsInflammationSteroidsBatch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns
Millard N, Chen J, Palshikar M, Pelka K, Spurrell M, Price C, He J, Hacohen N, Raychaudhuri S, Korsunsky I. Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns. Genome Biology 2025, 26: 36. PMID: 40001084, PMCID: PMC11863647, DOI: 10.1186/s13059-025-03479-9.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsGene Expression ProfilingHumansSequence Analysis, RNASingle-Cell AnalysisSoftwareTranscriptomeConceptsBatch effectsVisualization of gene expression patternsSpatial gene patternsGene expression analysis of cellsGene expression patternsGene expression analysisGene expression levelsGene colocalizationAnalysis of cellsGene patternsTranscriptome analysisLigand-receptor interactionsExpression patternsSpatial transcriptomicsSpatial transcriptomic analysisExpression levelsGenesMultiple samplesSpatial patternsTranscriptomeColocalizationAnatomical contextPatternsCount dataDissecting the role of CAR signaling architectures on T cell activation and persistence using pooled screens and single-cell sequencing
Castellanos-Rueda R, Wang K, Forster J, Driessen A, Frank J, Martínez M, Reddy S. Dissecting the role of CAR signaling architectures on T cell activation and persistence using pooled screens and single-cell sequencing. Science Advances 2025, 11: eadp4008. PMID: 39951542, PMCID: PMC11827634, DOI: 10.1126/sciadv.adp4008.Peer-Reviewed Original ResearchConceptsChimeric antigen receptorT-cell phenotypeT cell responsesT cell activationCAR T cell phenotypesCAR T-cell biologyModulate T cell responsesT cell persistenceCAR-T therapySingle-cell sequencingT cell functionT cell biologyCorrelated in vitroT therapyT cellsAntigen receptorClinical outcomesCD40 costimulationCancer treatmentPhenotypeSignaling domainMembrane-proximal domainCostimulationCD40Screening approachA comprehensive spatio-cellular map of the human hypothalamus
Tadross J, Steuernagel L, Dowsett G, Kentistou K, Lundh S, Porniece M, Klemm P, Rainbow K, Hvid H, Kania K, Polex-Wolf J, Knudsen L, Pyke C, Perry J, Lam B, Brüning J, Yeo G. A comprehensive spatio-cellular map of the human hypothalamus. Nature 2025, 639: 708-716. PMID: 39910307, PMCID: PMC11922758, DOI: 10.1038/s41586-024-08504-8.Peer-Reviewed Original ResearchConceptsGenome-wide association study genesRare deleterious variantsHypothalamic cell typesCell typesSingle-nucleus sequencingBody mass indexTranscription mapDeleterious variantsNeuronal cell typesG protein-coupled receptorsStudy genesBiological functions1Spatial transcriptomicsTranscriptomic identityCellular componentsExpression levelsPro-opiomelanocortin neuronsHuman hypothalamusAssociated with body mass indexPopulation levelMetabolic disordersHypothalamic cellsExpressionNeuronal clustersTranscriptomeThe human and non-human primate developmental GTEx projects
Bell T, Blanchard T, Hernandez R, Linn R, Taylor D, VonDran M, Ahooyi T, Beitra D, Bernieh A, Delaney M, Faith M, Fattahi E, Footer D, Gilbert M, Guambaña S, Gulino S, Hanson J, Hattrell E, Heinemann C, Kreeb J, Leino D, Mcdevitt L, Palmieri A, Pfeiffer M, Pryhuber G, Rossi C, Rasool I, Roberts R, Salehi A, Savannah E, Stachowicz K, Stokes D, Suplee L, Van Hoose P, Wilkins B, Williams-Taylor S, Zhang S, Ardlie K, Getz G, Lappalainen T, Montgomery S, Aguet F, Anderson L, Bernstein B, Choudhary A, Domenech L, Gaskell E, Johnson M, Liu Q, Marderstein A, Nedzel J, Okonda J, Padhi E, Rosano M, Russell A, Walker B, Sestan N, Gerstein M, Milosavljevic A, Borsari B, Cho H, Clarke D, Deveau A, Galeev T, Gobeske K, Hameed I, Huttner A, Jensen M, Jiang Y, Li J, Liu J, Liu Y, Ma J, Mane S, Meng R, Nadkarni A, Ni P, Park S, Petrosyan V, Pochareddy S, Salamon I, Xia Y, Yates C, Zhang M, Zhao H, Conrad D, Feng G, Brady F, Boucher M, Carbone L, Castro J, del Rosario R, Held M, Hennebold J, Lacey A, Lewis A, Lima A, Mahyari E, Moore S, Okhovat M, Roberts V, de Castro S, Wessel B, Zaniewski H, Zhang Q, Arguello A, Baroch J, Dayal J, Felsenfeld A, Ilekis J, Jose S, Lockhart N, Miller D, Minear M, Parisi M, Price A, Ramos E, Zou S. The human and non-human primate developmental GTEx projects. Nature 2025, 637: 557-564. PMID: 39815096, DOI: 10.1038/s41586-024-08244-9.Peer-Reviewed Original ResearchConceptsChromatin accessibility dataFunctional genomic studiesWhole-genome sequencingEffects of genetic variationSpatial gene expression profilesNon-human primatesGenotype-Tissue ExpressionGene expression profilesGenomic studiesGene regulationGenetic dataGenetic variationGenomic researchDonor diversityCommunity engagementHuman evolutionEarly developmental defectsGene expressionCell statesDevelopmental programmeHuman diseasesExpression profilesAdult tissuesDevelopmental defectsSingle-cellTrans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies
Consortium M, Adams M, Streit F, Meng X, Awasthi S, Adey B, Choi K, Chundru V, Coleman J, Ferwerda B, Foo J, Gerring Z, Giannakopoulou O, Gupta P, Hall A, Harder A, Howard D, Hübel C, Kwong A, Levey D, Mitchell B, Ni G, Ota V, Pain O, Pathak G, Schulte E, Shen X, Thorp J, Walker A, Yao S, Zeng J, Zvrskovec J, Aarsland D, Actkins K, Adli M, Agerbo E, Aichholzer M, Aiello A, Air T, Als T, Andersson E, Andlauer T, Arolt V, Ask H, Bäckman J, Badola S, Ballard C, Banasik K, Bass N, Beekman A, Belangero S, Bigdeli T, Binder E, Bjerkeset O, Bjornsdottir G, Børte S, Bränn E, Braun A, Brodersen T, Brückl T, Brunak S, Bruun M, Burmeister M, Buspavanich P, Bybjerg-Grauholm J, Byrne E, Cai J, Campbell A, Campbell M, Campos A, Castelao E, Cervilla J, Chaumette B, Chen C, Chen H, Chen Z, Cichon S, Colodro-Conde L, Corbett A, Corfield E, Couvy-Duchesne B, Craddock N, Dannlowski U, Davies G, de Geus E, Deary I, Degenhardt F, Dehghan A, DePaulo J, Deuschle M, Didriksen M, Dinh K, Direk N, Djurovic S, Docherty A, Domschke K, Dowsett J, Drange O, Dunn E, Eaton W, Einarsson G, Eley T, Elsheikh S, Engelmann J, Benros M, Erikstrup C, Escott-Price V, Fabbri C, Fang Y, Finer S, Frank J, Free R, Gallo L, Gao H, Gill M, Gilles M, Goes F, Gordon S, Grove J, Gudbjartsson D, Gutierrez B, Hahn T, Hall L, Hansen T, Haraldsson M, Hartman C, Havdahl A, Hayward C, Heilmann-Heimbach S, Herms S, Hickie I, Hjalgrim H, Hjerling-Leffler J, Hoffmann P, Homuth G, Horn C, Hottenga J, Hougaard D, Hovatta I, Huang Q, Hucks D, Huider F, Hunt K, Ialongo N, Ising M, Isometsä E, Jansen R, Jiang Y, Jones I, Jones L, Jonsson L, Kanai M, Karlsson R, Kasper S, Kendler K, Kessler R, Kloiber S, Knowles J, Koen N, Kraft J, Kranzler H, Krebs K, Kallak T, Kutalik Z, Lahtela E, Lake M, Larsen M, Lenze E, Lewins M, Lewis G, Li L, Lin B, Lin K, Lind P, Liu Y, MacIntyre D, MacKinnon D, Maher B, Maier W, Marshe V, Martinez-Levy G, Matsuda K, Mbarek H, McGuffin P, Medland S, Meinert S, Mikkelsen C, Mikkelsen S, Milaneschi Y, Millwood I, Molina E, Mondimore F, Mortensen P, Mulsant B, Naamanka J, Najman J, Nauck M, Nenadić I, Nielsen K, Nolt I, Nordentoft M, Nöthen M, Nyegaard M, O'Donovan M, Oddsson A, Oliveira A, Olsen C, Oskarsson H, Ostrowski S, Owen M, Packer R, Palviainen T, Pan P, Pato C, Pato M, Pedersen N, Pedersen O, Peyrot W, Potash J, Preisig M, Preuss M, Quiroz J, Renteria M, Reynolds C, Rice J, Sakaue S, Santoro M, Schoevers R, Schork A, Schulze T, Send T, Shi J, Sigurdsson E, Singh K, Sinnamon G, Sirignano L, Smeland O, Smith D, Sofer T, Sørensen E, Srinivasan S, Stefansson H, Stefansson K, Straub P, Su M, Tadic A, Teismann H, Teumer A, Thapar A, Thomson P, Thørner L, Topaloudi A, Tsai S, Tzoulaki I, Uhl G, Uitterlinden A, Ullum H, Umbricht D, Ursano R, Van der Auwera S, van Hemert A, Veluchamy A, Viktorin A, Völzke H, Walters G, Wang X, Wani A, Weissman M, Wellmann J, Whiteman D, Wildman D, Willemsen G, Williams A, Winsvold B, Witt S, Xiong Y, Zillich L, Zwart J, Team T, Group C, Team E, Team G, Psychiatry H, Project T, Program V, Andreassen O, Baune B, Berger K, Boomsma D, Børglum A, Breen G, Cai N, Coon H, Copeland W, Creese B, Cruz-Fuentes C, Czamara D, Davis L, Derks E, Domenici E, Elliott P, Forstner A, Gawlik M, Gelernter J, Grabe H, Hamilton S, Hveem K, John C, Kaprio J, Kircher T, Krebs M, Kuo P, Landén M, Lehto K, Levinson D, Li Q, Lieb K, Loos R, Lu Y, Lucae S, Luykx J, Maes H, Magnusson P, Martin H, Martin N, McQuillin A, Middeldorp C, Milani L, Mors O, Müller D, Müller-Myhsok B, Okada Y, Oldehinkel A, Paciga S, Palmer C, Paschou P, Penninx B, Perlis R, Peterson R, Pistis G, Polimanti R, Porteous D, Posthuma D, Rabinowitz J, Reichborn-Kjennerud T, Reif A, Rice F, Ricken R, Rietschel M, Rivera M, Rück C, Salum G, Schaefer C, Sen S, Serretti A, Skalkidou A, Smoller J, Stein D, Stein F, Stein M, Sullivan P, Tesli M, Thorgeirsson T, Tiemeier H, Timpson N, Uddin M, Uher R, van Heel D, Verweij K, Walters R, Wassertheil-Smoller S, Wendland J, Werge T, Zwinderman A, Kuchenbaecker K, Wray N, Ripke S, Lewis C, McIntosh A. Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies. Cell 2025, 188: 640-652.e9. PMID: 39814019, PMCID: PMC11829167, DOI: 10.1016/j.cell.2024.12.002.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesCell-type enrichment analysisSingle-cell dataTrans-ancestryAdmixed ancestrySingle-cell analysisFine-mappingPotential repurposing opportunitiesAssociation studiesGene associationsEnrichment analysisReceptor clusteringPolygenic scoresRepurposing opportunitiesPostsynaptic densityCell typesStudies of depressionMedium spiny neuronsAncestryAntidepressant targetSpiny neuronsAmygdala neuronsLociBiological targetsEffective treatment
2024
Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact
Albertson A, Winkler E, Yang A, Buckwalter M, Dingman A, Fan H, Herson P, McCullough L, Perez-Pinzon M, Sansing L, Sun D, Alkayed N. Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact. Stroke 2024, 56: 1082-1091. PMID: 39772596, DOI: 10.1161/strokeaha.124.049001.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBrainCerebrovascular CirculationCerebrovascular DisordersHumansSequence Analysis, RNASingle-Cell AnalysisnipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares
Mattessich M, Reyna J, Aron E, Ay F, Kilmer M, Kleinstein S, Konstorum A. nipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares. Bioinformatics 2024, 41: btaf015. PMID: 39799512, PMCID: PMC11783316, DOI: 10.1093/bioinformatics/btaf015.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsCluster AnalysisComputational BiologyHumansLeast-Squares AnalysisSingle-Cell AnalysisSoftwareConceptsIterative partial least squaresNonlinear iterative partial least squaresDimensionality reductionMultiple co-inertia analysisJoint dimensionality reductionSignificant speed-upUnsupervised learningSingle-cell datasetsMulti-omics dataCo-inertia analysisFeature dimensionsSpeed-upBioconductor packageSingle-cell analysisPartial least squaresLeast squaresRobust approachImplementationHTMLDatasetBioconductorMapping the gene space at single-cell resolution with gene signal pattern analysis
Venkat A, Leone S, Youlten S, Fagerberg E, Attanasio J, Joshi N, Perlmutter M, Krishnaswamy S. Mapping the gene space at single-cell resolution with gene signal pattern analysis. Nature Computational Science 2024, 4: 955-977. PMID: 39706866, DOI: 10.1038/s43588-024-00734-0.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsCell CommunicationComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansSingle-Cell AnalysisTranscriptomeConceptsSingle-cell dataGene spaceGene representationSimulated single-cell dataGene co-expression modulesCell-cell graphCharacterization of genesGene-gene interactionsCo-expression modulesCell-cell communicationCellular state spaceSingle-cell resolutionSingle-cell sequencing analysisSequence analysisGenesBiological tasksSpatial transcriptomicsGraph signal processing approachSignal pattern analysisPattern analysisSignal processing approachComputational methodsTranscriptomeHeterogeneous Cardiac-Derived and Neural Crest–Derived Aortic Smooth Muscle Cells Exhibit Similar Transcriptional Changes After TGFβ Signaling Disruption
Ren P, Jiang B, Hassab A, Li G, Li W, Assi R, Tellides G. Heterogeneous Cardiac-Derived and Neural Crest–Derived Aortic Smooth Muscle Cells Exhibit Similar Transcriptional Changes After TGFβ Signaling Disruption. Arteriosclerosis Thrombosis And Vascular Biology 2024, 45: 260-276. PMID: 39697172, DOI: 10.1161/atvbaha.124.321706.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAortaAortic AneurysmCell LineageDisease Models, AnimalGene Expression ProfilingHomeobox Protein Nkx-2.5HumansMaleMarfan SyndromeMiceMice, Inbred C57BLMice, KnockoutMuscle, Smooth, VascularMyocytes, Smooth MuscleMyosin Heavy ChainsNeural CrestPhenotypeReceptor, Transforming Growth Factor-beta Type IIReceptors, Transforming Growth Factor betaSignal TransductionSingle-Cell AnalysisTranscription, GeneticTranscriptomeTransforming Growth Factor betaWnt1 ProteinConceptsSmooth muscle cell clustersSmooth muscle cellsAortic smooth muscle cellsNeural crest-derived smooth muscle cellsCardiac derivativesMurine aortic smooth muscle cellsNeural crest originReceptor deletionAortic rootAdult miceNeural crest progenitorsNKX2-5Proximal aortaTranscriptional changesMouse modelTGFB signalingMuscle cellsConditional deletionAdult human aortaEmbryological originIncreased expressionAnalyzed single-cell transcriptomesTGFB receptorsBasal stateAortic homeostasisStatistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation
Bierman R, Dave J, Greif D, Salzman J. Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation. ELife 2024, 12: rp87517. PMID: 39699003, PMCID: PMC11658768, DOI: 10.7554/elife.87517.Peer-Reviewed Original ResearchMeSH Keywords3' Untranslated RegionsAnimalsBrainGene Expression RegulationLiverMicePolyadenylationRNASingle-Cell AnalysisConceptsSubcellular localizationUntranslated regionAlternative poly-adenylationSubcellular RNA localizationCell-type specific regulationLow-throughput studiesUntranslated region lengthRNA subcellular localizationSingle-cell resolutionSpatial transcriptomics techniquesRNA localizationFunction predictionPoly-adenylationTranscriptomic techniquesCellular functionsMouse brainSpecific regulationStatistical frameworkIsoform expressionMouse liverRegulationMiceUntranslatedLocalizationRNASingle-nucleus multi-omics analyses reveal cellular and molecular innovations in the anterior cingulate cortex during primate evolution
Yuan J, Dong K, Wu H, Zeng X, Liu X, Liu Y, Dai J, Yin J, Chen Y, Guo Y, Luo W, Liu N, Sun Y, Zhang S, Su B. Single-nucleus multi-omics analyses reveal cellular and molecular innovations in the anterior cingulate cortex during primate evolution. Cell Genomics 2024, 4: 100703. PMID: 39631404, PMCID: PMC11701334, DOI: 10.1016/j.xgen.2024.100703.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBiological EvolutionCell NucleusChromatinEvolution, MolecularGyrus CinguliHumansMacacaMiceMultiomicsNeuronsPrimatesSingle-Cell AnalysisConceptsChromatin accessibilitySingle-nucleusGene expressionTranscription factor bindingPatterns of gene expressionSingle-nucleus resolutionCell lineage originACC gene expressionPrimate evolutionMulti-omics analysisAnterior cingulate cortexFactor bindingEvolutionary roleFunctional innovationSequence changesMolecular innovationsVon Economo neuronsMolecular regulationMarker genesPublished mouse dataCell typesChromatinMolecular identityHuman originCingulate cortexCosGeneGate selects multi-functional and credible biomarkers for single-cell analysis
Liu T, Long W, Cao Z, Wang Y, He C, Zhang L, Strittmatter S, Zhao H. CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis. Briefings In Bioinformatics 2024, 26: bbae626. PMID: 39592241, PMCID: PMC11596696, DOI: 10.1093/bib/bbae626.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkersComputational BiologyGene Expression ProfilingGenetic MarkersHumansSingle-Cell AnalysisSoftwareComprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating
Liu P, Pan Y, Chang H, Wang W, Fang Y, Xue X, Zou J, Toothaker J, Olaloye O, Santiago E, McCourt B, Mitsialis V, Presicce P, Kallapur S, Snapper S, Liu J, Tseng G, Konnikova L, Liu S. Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating. Briefings In Bioinformatics 2024, 26: bbae633. PMID: 39656848, PMCID: PMC11630031, DOI: 10.1093/bib/bbae633.Peer-Reviewed Original ResearchSingle-cell transcriptomic and proteomic analysis of Parkinson’s disease brains
Zhu B, Park J, Coffey S, Russo A, Hsu I, Wang J, Su C, Chang R, Lam T, Gopal P, Ginsberg S, Zhao H, Hafler D, Chandra S, Zhang L. Single-cell transcriptomic and proteomic analysis of Parkinson’s disease brains. Science Translational Medicine 2024, 16: eabo1997. PMID: 39475571, DOI: 10.1126/scitranslmed.abo1997.Peer-Reviewed Original ResearchConceptsProteomic analysisAlzheimer's diseasePrefrontal cortexBrain cell typesGenetics of PDParkinson's diseaseCell-cell interactionsChaperone expressionSingle-nucleus transcriptomesExpressed genesTranscriptional changesPostmortem human brainPostmortem brain tissueDiseased brainSynaptic proteinsSingle-cellDown-regulationBrain cell populationsBrain regionsCell typesNeurodegenerative disordersLate-stage PDParkinson's disease brainsDisease etiologyNeuronal vulnerabilitySelective utilization of glucose metabolism guides mammalian gastrulation
Cao D, Bergmann J, Zhong L, Hemalatha A, Dingare C, Jensen T, Cox A, Greco V, Steventon B, Sozen B. Selective utilization of glucose metabolism guides mammalian gastrulation. Nature 2024, 634: 919-928. PMID: 39415005, PMCID: PMC11499262, DOI: 10.1038/s41586-024-08044-1.Peer-Reviewed Original ResearchConceptsCellular metabolismMammalian gastrulationHexosamine biosynthetic pathwayTranscription factor networksCellular signaling pathwaysSignaling morphogensGlucose metabolismCellular programmeBiosynthetic pathwayFate acquisitionCell fateHousekeeping natureGenetic mechanismsMesoderm migrationFactor networksERK activationExpression patternsSignaling pathwayDevelopmental processesStem cell modelCell typesSpecialized functionsDevelopmental contextMammalian embryosMouse embryosSDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data
Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Wei H, Justet A, Adams T, Homer R, Amei A, Rosas I, Kaminski N, Wang Z, Yan X. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 2024, 25: 271. PMID: 39402626, PMCID: PMC11475911, DOI: 10.1186/s13059-024-03416-2.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsGene Expression ProfilingHumansMachine LearningRegression AnalysisRNA-SeqSequence Analysis, RNASingle-Cell AnalysisSoftwareTranscriptomeA cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders
Lee A, Ayers L, Kosicki M, Chan W, Fozo L, Pratt B, Collins T, Zhao B, Rose M, Sanchis-Juan A, Fu J, Wong I, Zhao X, Tenney A, Lee C, Laricchia K, Barry B, Bradford V, Jurgens J, England E, Lek M, MacArthur D, Lee E, Talkowski M, Brand H, Pennacchio L, Engle E. A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders. Nature Communications 2024, 15: 8268. PMID: 39333082, PMCID: PMC11436875, DOI: 10.1038/s41467-024-52463-7.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsChromatinEnhancer Elements, GeneticEpigenomicsFemaleHumansMaleMiceMotor NeuronsPedigreeSingle-Cell AnalysisConceptsNon-coding variantsCranial motor neuronsMendelian disordersIn vivo transgenic assayPredictor of enhancer activityCis-regulatory elementsMulti-omic frameworkWhole-genome sequencingEnhanced activityVariant discoveryGenome sequenceChromatin accessibilityPutative enhancersHistone modificationsRegulatory elementsGene expression assaysGene predictionTransgenic assaysEpigenomic profilingMendelian casesExpression assaysMutational enhancementCongenital cranial dysinnervation disordersCell typesFunctional impact
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