2025
Dissecting the physics of bacterial biofilms with agent-based simulations
Nam K, Li C, Cockx B, Nguyen D, Li Y, Kreft J, Yan J. Dissecting the physics of bacterial biofilms with agent-based simulations. Current Opinion In Solid State And Materials Science 2025, 37: 101228. PMID: 40538632, PMCID: PMC12176386, DOI: 10.1016/j.cossms.2025.101228.Peer-Reviewed Original ResearchVibrio cholerae</i> biofilmsBiofilm developmentExtracellular matrixDevelopment of bacterial coloniesSingle-cell resolutionBacterial communitiesCellular organizationBacterial coloniesBacterial biofilmsBiofilmBiological entitiesDevelopment of modeling approachesOrientational orderPhysical mechanismsExternal perturbationsComplex networksVibrioMechanistic originscMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
Wang G, Zhao J, Lin Y, Liu T, Zhao Y, Zhao H. scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links. Nature Communications 2025, 16: 4994. PMID: 40442129, PMCID: PMC12122792, DOI: 10.1038/s41467-025-60333-z.Peer-Reviewed Original ResearchConceptsDeep learning frameworkSingle-cell multi-omics researchSingle-cell multi-omics dataLearning frameworkMulti-omics dataGenerative adversarial networkSingle-cell technologiesData alignmentSingle-cell resolutionMulti-omics researchDownstream analysisCellular statesOmics datasetsAdversarial networkNeural networkProteomic profilingCorrelated featuresBiological informationOmics perspectiveDiverse datasetsFeature topologyDisease mechanismsCell embeddingData resourcesRelationship inferenceSpatial transcriptomics reveals human cortical layer and area specification
Qian X, Coleman K, Jiang S, Kriz A, Marciano J, Luo C, Cai C, Manam M, Caglayan E, Lai A, Exposito-Alonso D, Otani A, Ghosh U, Shao D, Andersen R, Neil J, Johnson R, LeFevre A, Hecht J, Micali N, Sestan N, Rakic P, Miller M, Sun L, Stringer C, Li M, Walsh C. Spatial transcriptomics reveals human cortical layer and area specification. Nature 2025, 1-11. PMID: 40369074, DOI: 10.1038/s41586-025-09010-1.Peer-Reviewed Original ResearchNeuronal subtypesMid-gestationHuman fetal cortexExcitatory neuron subtypesCortical layersLayer 4 neuronsCortical areasHuman cortical developmentGestational weeksFetal cortexSingle-nucleus RNA sequencingCortical developmentCerebral cortexSingle-cell transcriptomic studiesHuman cerebral cortexDevelopmental time pointsLaminar distributionAreal specificationCortical arealizationTime pointsSubtypesCortexVisual cortexSingle-cell resolutionCytoarchitectural developmentTowards a consensus atlas of human and mouse adipose tissue at single-cell resolution
Loft A, Emont M, Weinstock A, Divoux A, Ghosh A, Wagner A, Hertzel A, Maniyadath B, Deplancke B, Liu B, Scheele C, Lumeng C, Ding C, Ma C, Wolfrum C, Strieder-Barboza C, Li C, Truong D, Bernlohr D, Stener-Victorin E, Kershaw E, Yeger-Lotem E, Shamsi F, Hui H, Camara H, Zhong J, Kalucka J, Ludwig J, Semon J, Jalkanen J, Whytock K, Dumont K, Sparks L, Muir L, Fang L, Massier L, Saraiva L, Beyer M, Jeschke M, Mori M, Boroni M, Walsh M, Patti M, Lynes M, Blüher M, Rydén M, Hamda N, Solimini N, Mejhert N, Gao P, Gupta R, Murphy R, Pirouzpanah S, Corvera S, Tang S, Das S, Schmidt S, Zhang T, Nelson T, O’Sullivan T, Efthymiou V, Wang W, Tong Y, Tseng Y, Mandrup S, Rosen E. Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution. Nature Metabolism 2025, 7: 875-894. PMID: 40360756, DOI: 10.1038/s42255-025-01296-9.Peer-Reviewed Original ResearchConceptsCell annotationSingle-cell dataRegulation of metabolic homeostasisSingle-cell resolutionSingle-cell atlasMouse adipose tissueAdipose tissueMetabolic homeostasisSpecialized cellsPrimary repositoryAnnotationCellsExcess caloriesBionetworkProportion of adipocytesConnective tissueMiceTissueComplex connective tissueAdipocytesHomeostasisDecoding human brain evolution: Insights from genomics
Liu Y, Li M, Segal A, Zhang M, Sestan N. Decoding human brain evolution: Insights from genomics. Current Opinion In Neurobiology 2025, 92: 103033. PMID: 40334295, DOI: 10.1016/j.conb.2025.103033.Peer-Reviewed Original ResearchConceptsHuman brain evolutionNonhuman primatesBrain evolutionHigh-throughput functional screeningSingle-cell resolutionAdvanced cognitive abilitiesGenetic basisCognitive abilitiesFunctional screeningGenetic changesGenetic underpinningsBrain featuresLiving relativesHuman-specific featuresComprehensive atlasGenomic profilingHuman brainFunctional specializationMolecular levelProtocol for characterizing craniopharyngioma subtypes and their microenvironments using single-cell RNA sequencing and immunohistochemistry
Kono T, Matsuda T, Fujimoto M, Taki Y, Sakuma I, Hashimoto N, Nakamura Y, Horiguchi K, Higuchi Y, Onodera A, Miki T, Tanaka T. Protocol for characterizing craniopharyngioma subtypes and their microenvironments using single-cell RNA sequencing and immunohistochemistry. STAR Protocols 2025, 6: 103760. PMID: 40238632, PMCID: PMC12022695, DOI: 10.1016/j.xpro.2025.103760.Peer-Reviewed Original ResearchSIMVI 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 effectsSpatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq
Li H, Bao S, Farzad N, Qin X, Fung A, Zhang D, Bai Z, Tao B, Fan R. Spatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq. Nature Protocols 2025, 1-35. PMID: 40119005, DOI: 10.1038/s41596-025-01145-9.Peer-Reviewed Original ResearchNear single-cell resolutionControl cell identityTargeting histone modificationsSingle-cell resolutionTn5 transposaseAccessible chromatinEpigenomic landscapeIn situ reverse transcriptionHistone modificationsCell identityTranscriptomic landscapeSequencing protocolSequencing methodsGene transcriptionEpigenomeOligonucleotide barcodesTranscriptomeDiverse biological activitiesIntact tissue sectionsReverse transcriptionTranscriptionBiological activityPrimary antibodyTagmentationTissue pixelsMetabolic rewiring in skin epidermis drives tolerance to oncogenic mutations
Hemalatha A, Li Z, Gonzalez D, Matte-Martone C, Tai K, Lathrop E, Gil D, Ganesan S, Gonzalez L, Skala M, Perry R, Greco V. Metabolic rewiring in skin epidermis drives tolerance to oncogenic mutations. Nature Cell Biology 2025, 27: 218-231. PMID: 39762578, PMCID: PMC11821535, DOI: 10.1038/s41556-024-01574-w.Peer-Reviewed Original ResearchConceptsWild-type cellsOxidative tricarboxylic acid cycleOncogenic mutationsTricarboxylic acid cycleSingle-cell resolutionMutant phenotypeMutant cellsMutant epidermisCell competitionMetabolic rewiringAcid cycleCellular redoxStem cellsMutant modelsSkin epithelial stem cellsEpidermal stem cellsContribution of glucoseEpithelial stem cellsCytosolic redoxRedox ratioMetabolic stateMutationsHrasG12VSkin epidermisCells
2024
Mapping 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 ResearchConceptsSingle-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 methodsTranscriptomeStatistical 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 ResearchConceptsSubcellular 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 liverRegulationMiceUntranslatedLocalizationRNAStatistical 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 DOI: 10.7554/elife.87517.2.Peer-Reviewed Original ResearchSubcellular 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 liverRegulationNXPH1MiceUntranslatedLocalizationComprehensive 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 ResearchMapping cellular interactions from spatially resolved transcriptomics data
Zhu J, Wang Y, Chang W, Malewska A, Napolitano F, Gahan J, Unni N, Zhao M, Yuan R, Wu F, Yue L, Guo L, Zhao Z, Chen D, Hannan R, Zhang S, Xiao G, Mu P, Hanker A, Strand D, Arteaga C, Desai N, Wang X, Xie Y, Wang T. Mapping cellular interactions from spatially resolved transcriptomics data. Nature Methods 2024, 21: 1830-1842. PMID: 39227721, DOI: 10.1038/s41592-024-02408-1.Peer-Reviewed Original ResearchInferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Steach H, Viswanath S, He Y, Zhang X, Ivanova N, Hirn M, Perlmutter M, Krishnaswamy S. Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning. Lecture Notes In Computer Science 2024, 14758: 235-252. DOI: 10.1007/978-1-0716-3989-4_15.Peer-Reviewed Original ResearchSingle-cell resolutionMetabolic networksStructure of metabolic networksBiological processesGlobal metabolic networkMetabolic stateMeasure gene expressionGenomic informationTranscriptomic dataTranscriptome dataPost-translationallyEpigenetic modificationsMultimodal regulationGene expressionSingle-cellTissue scaleBiological featuresCellsTranscriptomeMetabolomicsTranscriptionFlux ratesMultiomicsScRNAseqBiology
2023
Mapping the gene space at single-cell resolution with gene signal pattern analysis
Venkat A, Damo M, Joshi N, Krishnaswamy S. Mapping the gene space at single-cell resolution with gene signal pattern analysis. The Journal Of Immunology 2023, 210: 251.03-251.03. DOI: 10.4049/jimmunol.210.supp.251.03.Peer-Reviewed Original ResearchGene-gene relationshipsGene spaceSingle-cell RNA sequencing analysisGene signalsSingle-cell resolutionRNA sequencing analysisMelanoma patient samplesScRNA-seq analysisTranscriptional programsCellular heterogeneityGene representationSequencing analysisCell typesSingle cellsPattern analysisCell subtypesCell subpopulationsMultiscale viewEffector functionsCellsMouse modelComputational methodsGenesCell spacePatient samplesSpatial epigenome–transcriptome co-profiling of mammalian tissues
Zhang D, Deng Y, Kukanja P, Agirre E, Bartosovic M, Dong M, Ma C, Ma S, Su G, Bao S, Liu Y, Xiao Y, Rosoklija G, Dwork A, Mann J, Leong K, Boldrini M, Wang L, Haeussler M, Raphael B, Kluger Y, Castelo-Branco G, Fan R. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 2023, 616: 113-122. PMID: 36922587, PMCID: PMC10076218, DOI: 10.1038/s41586-023-05795-1.Peer-Reviewed Original ResearchConceptsGene expressionSingle-cell resolutionChromatin accessibilityJoint profilingHistone modificationsGene regulationCellular statesEpigenetic mechanismsCentral dogmaSpatial transcriptomeTranscriptional phenotypeCell statesOmics informationSpatial transcriptomicsEpigenetic primingMammalian tissuesEpigenomeMolecular biologyTissue architectureCell dynamicsMechanistic relationshipDifferential rolesNew insightsMouse brainProfiling
2022
Translational opportunities of single-cell biology in atherosclerosis
de Winther M, Bäck M, Evans P, Gomez D, Goncalves I, Jørgensen H, Koenen R, Lutgens E, Norata G, Osto E, Dib L, Simons M, Stellos K, Ylä-Herttuala S, Winkels H, Bochaton-Piallat M, Monaco C. Translational opportunities of single-cell biology in atherosclerosis. European Heart Journal 2022, 44: 1216-1230. PMID: 36478058, PMCID: PMC10120164, DOI: 10.1093/eurheartj/ehac686.Peer-Reviewed Original ResearchConceptsSingle-cell biologyDisease developmentSingle-cell technologiesSingle-cell resolutionHuman biological processesSingle-cell analysisBiological processesCellular subpopulationsCardiovascular diseaseCell communityAtherosclerosis pathologyCardiovascular disease samplesTranslational opportunitiesBiologyTherapeutic strategiesInternational collaborative effortTreat diseasesField of cardiovascular diseaseClinical impactClinically relevant featuresAnalysis of atherosclerotic plaquesComprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
Raredon M, Yang J, Kothapalli N, Lewis W, Kaminski N, Niklason L, Kluger Y. Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinformatics 2022, 39: btac775. PMID: 36458905, PMCID: PMC9825783, DOI: 10.1093/bioinformatics/btac775.Peer-Reviewed Original ResearchConceptsCell-cell interactionsCell-cell signalingSingle-cell resolutionSingle-cell dataLocal cellular microenvironmentSingle-cell levelSpatial transcriptomics dataCell clustersExtracellular signalingTranscriptomic dataGene expression valuesSpatial transcriptomicsSignaling mechanismCellular microenvironmentNicheExpression valuesSupplementary dataSignalingTranscriptomicsComprehensive visualizationBioinformaticsInteractionA Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data
Zhu B, Li H, Zhang L, Chandra SS, Zhao H. A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data. Briefings In Bioinformatics 2022, 23: bbac166. PMID: 35514182, PMCID: PMC9487630, DOI: 10.1093/bib/bbac166.Peer-Reviewed Original ResearchConceptsDE genesSeq dataSingle-cell RNA sequencing technologyDifferential expressionSingle-cell RNA-seq dataIdentification of genesRNA sequencing technologySpecific differential expressionSingle-cell resolutionRNA-seq dataMarkov random field modelMarkov random field model-based approachSimilar cell typesNovel statistical modelRandom field modelComplex biological systemsBiological pathwaysGene detectionGenesCell typesStatistical modelMouse datasetsField modelBiological systemsReal data
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