2025
Transcriptomic Alterations Induced by Tetrahydrocannabinol in SIV/HIV Infection: A Systematic Review
Valizadeh A, Veenhuis R, Bradley B, Xu K. Transcriptomic Alterations Induced by Tetrahydrocannabinol in SIV/HIV Infection: A Systematic Review. International Journal Of Molecular Sciences 2025, 26: 2598. PMID: 40141240, PMCID: PMC11942185, DOI: 10.3390/ijms26062598.Peer-Reviewed Original ResearchConceptsSimian immunodeficiency virus (SIV)-infected macaquesPrevalence of cannabis useSIV-infected macaquesChronic immune activationHIV-infected human cellsEpithelial cell proliferationModulate immune responsesSystematic reviewSIV/HIV infectionHIV infectionPreclinical evidencePathways related to inflammationImmune activationCannabis useMicro-RNA expressionImmunomodulatory roleImmunomodulatory effectsReduce inflammationImmune responseTetrahydrocannabinolComprehensive searchCell proliferationTranscriptomic alterationsMethodological qualityPWHBatch 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 dataIdentification of genes associated with testicular germ cell tumor susceptibility through a transcriptome-wide association study
Ugalde-Morales E, Wilf R, Pluta J, Ploner A, Fan M, Damra M, Aben K, Anson-Cartwright L, Chen C, Cortessis V, Daneshmand S, Ferlin A, Gamulin M, Gietema J, Gonzalez-Niera A, Grotmol T, Hamilton R, Harland M, Haugen T, Hauser R, Hildebrandt M, Karlsson R, Kiemeney L, Kim J, Lessel D, Lothe R, Loveday C, Chanock S, McGlynn K, Meijer C, Nead K, Nsengimana J, Popovic M, Rafnar T, Richiardi L, Rocca M, Schwartz S, Skotheim R, Stefansson K, Stewart D, Turnbull C, Vaughn D, Winge S, Zheng T, Monteiro A, Almstrup K, Kanetsky P, Nathanson K, Wiklund F, Consortium T. Identification of genes associated with testicular germ cell tumor susceptibility through a transcriptome-wide association study. American Journal Of Human Genetics 2025, 112: 630-643. PMID: 39999848, PMCID: PMC11947167, DOI: 10.1016/j.ajhg.2025.01.022.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association studyGenome-wide association studiesAssociation studiesTesticular germ cell tumorsTranscriptome-wide association study signalsGenome-wide association study lociTesticular germ cell tumour susceptibilityTesticular germ cell tumor tissuesFine-mapping analysisGene-disease linksGonadal cell typesEvidence of colocalizationProtein levels accumulationExpression levelsTesticular germ cell tumour riskPrioritized genesFalse discovery rateNeighboring genesGene-diseaseRegulatory featuresGene associationsColocalization analysisProtein patternsGenesNormal testisTranscriptomic landscape of cumulus cells from patients <38 years old with a history of poor ovarian response (POR) treated with platelet-rich plasma (PRP)
Roberts L, Herlihy N, Reig A, Titus S, Garcia-Milian R, Knight J, Yildirim R, Margolis C, Cakiroglu Y, Tiras B, Whitehead C, Werner M, Seli E. Transcriptomic landscape of cumulus cells from patients <38 years old with a history of poor ovarian response (POR) treated with platelet-rich plasma (PRP). Aging 2025, 17: 431-447. PMID: 39976580, PMCID: PMC11892918, DOI: 10.18632/aging.206202.Peer-Reviewed Original ResearchConceptsPlatelet-rich plasmaTreated with platelet-rich plasmaCumulus cellsPoor ovarian responseLive birthsGene expressionIntraovarian injection of autologous platelet-rich plasmaPatients treated with platelet-rich plasmaInjection of autologous platelet-rich plasmaHistory of poor ovarian responseAutologous platelet-rich plasmaPlatelet-rich plasma treatmentDiminished ovarian reserveCell-to-cell signalingRNA sequencing librariesCause of infertilityDifferential expression analysisFalse discovery rate thresholdIntraovarian injectionOvarian reserveFailed implantsSequencing librariesOvarian responseTranscriptomic landscapeRNA sequencingHuman and mouse proteomics reveals the shared pathways in Alzheimer’s disease and delayed protein turnover in the amyloidome
Yarbro J, Han X, Dasgupta A, Yang K, Liu D, Shrestha H, Zaman M, Wang Z, Yu K, Lee D, Vanderwall D, Niu M, Sun H, Xie B, Chen P, Jiao Y, Zhang X, Wu Z, Chepyala S, Fu Y, Li Y, Yuan Z, Wang X, Poudel S, Vagnerova B, He Q, Tang A, Ronaldson P, Chang R, Yu G, Liu Y, Peng J. Human and mouse proteomics reveals the shared pathways in Alzheimer’s disease and delayed protein turnover in the amyloidome. Nature Communications 2025, 16: 1533. PMID: 39934151, PMCID: PMC11814087, DOI: 10.1038/s41467-025-56853-3.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseProtein turnoverMouse model of amyloidosisMulti-omics analysisMurine model of Alzheimer's diseaseModel of Alzheimer's diseaseModel of amyloidosisProteome turnoverMouse proteomeGenetic incorporationAD pathwayAmyloid formationBrain proteomeMulti-omicsProteomic strategyAD progressionProteomicsProtein alterationsProteinDisease mechanismsAmyloidPathwayPotential targetMouse brainTurnoverA 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 clustersTranscriptomeTranscriptome signature in the blood of neuromyelitis optica spectrum disorder under steroid tapering
Yamamura R, Kinoshita M, Yasumizu Y, Yata T, Kihara K, Motooka D, Shiraishi N, Sugiyama Y, Beppu S, Murata H, Koizumi N, Sano I, Koda T, Okuno T, Mochizuki H. Transcriptome signature in the blood of neuromyelitis optica spectrum disorder under steroid tapering. Frontiers In Immunology 2025, 16: 1508977. PMID: 39963140, PMCID: PMC11830620, DOI: 10.3389/fimmu.2025.1508977.Peer-Reviewed Original ResearchConceptsNeuromyelitis optica spectrum disorderPeripheral blood mononuclear cellsNeuromyelitis optica spectrum disorder patientsSteroid taperIL-10Steroid dosagePeripheral blood transcriptomic signatureDecreased expressionInterferon signalingTransform treatment strategiesIndicator of disease activityNon-relapsing patientsAnti-AQP4 antibodyDisease activity biomarkersTranscriptomic signaturesBlood mononuclear cellsAdvent of biologicsBlood transcriptomic signaturesAnti-inflammatory pathwayIL-10 signalingRelapsed patientsImmune signaturesSteroid reductionDisease activityActivation biomarkersAge-invariant genes: multi-tissue identification and characterization of murine reference genes
González J, Thrush-Evensen K, Meer M, Levine M, Higgins-Chen A. Age-invariant genes: multi-tissue identification and characterization of murine reference genes. Aging 2025, 17: 170-202. PMID: 39873648, PMCID: PMC11810070, DOI: 10.18632/aging.206192.Peer-Reviewed Original ResearchMeSH KeywordsAgingAnimalsGene Expression ProfilingMaleMiceMice, Inbred C57BLOrgan SpecificityReference StandardsTranscriptomeConceptsRNA-seq datasetsReference genesRNA-seqHallmarks of agingPathway enrichment analysisGenes-thoseCpG islandsShorter transcriptRT-qPCRMolecular functionsExpression studiesGene normalizationTissue-specificEnrichment analysisMouse tissuesGenesMurine tissuesAged tissuesHallmarksYoung organismsLifespanTranscriptionCpGTissuePathwayThe 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-cellProspective validation of ORACLE, a clonal expression biomarker associated with survival of patients with lung adenocarcinoma
Biswas D, Liu Y, Herrero J, Wu Y, Moore D, Karasaki T, Grigoriadis K, Lu W, Veeriah S, Naceur-Lombardelli C, Magno N, Ward S, Frankell A, Hill M, Colliver E, de Carné Trécesson S, East P, Malhi A, Snell D, O’Neill O, Leonce D, Mattsson J, Lindberg A, Micke P, Moldvay J, Megyesfalvi Z, Dome B, Fillinger J, Nicod J, Downward J, Szallasi Z, Hackshaw A, Jamal-Hanjani M, Kanu N, Birkbak N, Swanton C. Prospective validation of ORACLE, a clonal expression biomarker associated with survival of patients with lung adenocarcinoma. Nature Cancer 2025, 6: 86-101. PMID: 39789179, PMCID: PMC11779643, DOI: 10.1038/s43018-024-00883-1.Peer-Reviewed Original ResearchConceptsLung adenocarcinomaStage I diseaseClinicopathological risk factorsSurvival of patientsResponse to treatmentRNA sequencing dataI diseaseSequence dataMetastatic clonesNeedle biopsyIndividual tumorsLung expressionTranscription signalsPrognostic informationWhole exomeExpressed genesChemotherapy sensitivityProspective validationSurvival associationsTranscriptomic heterogeneityHuman tumorsEvolutionary measuresChromosomal instabilityRisk factorsNatural historycSTAR analysis identifies endothelial cell cycle as a key regulator of flow-dependent artery remodeling
Deng H, Rukhlenko O, Joshi D, Hu X, Junk P, Tuliakova A, Kholodenko B, Schwartz M. cSTAR analysis identifies endothelial cell cycle as a key regulator of flow-dependent artery remodeling. Science Advances 2025, 11: eado9970. PMID: 39752487, PMCID: PMC11698091, DOI: 10.1126/sciadv.ado9970.Peer-Reviewed Original ResearchConceptsShear stressCell cycle-dependent kinasesHigh shear stressLow shear stressOscillatory shear stressPhysiological shear stressFluid shear stressCell cycle arrestRegulatory networksTranscriptomic statesResponse to drug treatmentCycle arrestCell cycleEndothelial cell cycleDisease susceptibilityRegulatory mechanismsVessel behaviorCDK2Endothelial cellsIn vitroStressRegulationVascular endothelial cellsRemodelingCellsPrenatal stress alters mouse offspring dorsal striatal development and placental function in sex-specific ways
Maurer S, Hing B, Lussier S, Radhakrishna S, Davis J, Abbott P, Michaelson J, Stevens H. Prenatal stress alters mouse offspring dorsal striatal development and placental function in sex-specific ways. Journal Of Psychiatric Research 2025, 182: 149-160. PMID: 39809011, DOI: 10.1016/j.jpsychires.2024.12.048.Peer-Reviewed Original ResearchConceptsPrenatal stressAutism spectrum disorderSex-specific wayDorsal striatumNeurodevelopmental disordersGABAergic populationEffects of prenatal stressStriatal GABAergic neuronsReversal learningVentral forebrain regionsBrain changesSpectrum disorderForebrain regionsStriatal developmentGABAergic neuronsBehavioral assaysBrain developmentPlacental transcriptomeStriatumCognitionDevelopmental originsDisordersPlacental differencesPlacental functionPlacenta function
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 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 homeostasisNeuroinflammatory history results in overlapping transcriptional signatures with heroin exposure in the nucleus accumbens and alters responsiveness to heroin in male rats
Floris G, Zanda M, Dabrowski K, Daws S. Neuroinflammatory history results in overlapping transcriptional signatures with heroin exposure in the nucleus accumbens and alters responsiveness to heroin in male rats. Translational Psychiatry 2024, 14: 500. PMID: 39702361, PMCID: PMC11659471, DOI: 10.1038/s41398-024-03203-4.Peer-Reviewed Original ResearchConceptsResponse to heroinNucleus accumbensHeroin exposureOpioid use disorderSelf-administrationHeroin self-administrationMesolimbic dopamine systemOpioid-induced behaviorsMolecular neuroadaptationsDopamine systemOpioid rewardHeroin intakeBehavioral effectsUse disorderPsychiatric researchLipopolysaccharide (LPS)-induced neuroinflammationPreclinical modelsBehavioral ramificationsSensitivity to opioidsOpioid heroinHeroinMale ratsAccumbensHeightened immune responseNeuroinflammationThe dynamics of hematopoiesis over the human lifespan
Li H, Côté P, Kuoch M, Ezike J, Frenis K, Afanassiev A, Greenstreet L, Tanaka-Yano M, Tarantino G, Zhang S, Whangbo J, Butty V, Moiso E, Falchetti M, Lu K, Connelly G, Morris V, Wang D, Chen A, Bianchi G, Daley G, Garg S, Liu D, Chou S, Regev A, Lummertz da Rocha E, Schiebinger G, Rowe R. The dynamics of hematopoiesis over the human lifespan. Nature Methods 2024, 22: 422-434. PMID: 39639169, PMCID: PMC11908799, DOI: 10.1038/s41592-024-02495-0.Peer-Reviewed Original ResearchConceptsHematopoietic stem cellsHematopoietic stemProgenitor cellsClassification of acute myeloid leukemiaDifferentiation of hematopoietic stem cellsAssociated with poor prognosisAcute myeloid leukemiaHuman hematopoietic stemWave of hematopoiesisGene expression networksMyeloid leukemiaPoor prognosisLineage outputMultilineage capacityDynamics of hematopoiesisCell ontogenyStem cellsLineage primingFate decisionsModel organismsTranscriptomic statesExpression networksHuman lifespanTranscriptional programsHematopoiesisSingle-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 vulnerabilitySDePER: 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 ResearchMulti-omics profiling of DNA methylation and gene expression alterations in human cocaine use disorder
Zillich E, Belschner H, Avetyan D, Andrade-Brito D, Martínez-Magaña J, Frank J, Mechawar N, Turecki G, Cabana-Domínguez J, Fernàndez-Castillo N, Cormand B, Montalvo-Ortiz J, Nöthen M, Hansson A, Rietschel M, Spanagel R, Witt S, Zillich L. Multi-omics profiling of DNA methylation and gene expression alterations in human cocaine use disorder. Translational Psychiatry 2024, 14: 428. PMID: 39384764, PMCID: PMC11464785, DOI: 10.1038/s41398-024-03139-9.Peer-Reviewed Original ResearchConceptsCocaine use disorderUse disorderAlternative splicingHuman prefrontal cortexProfiling of DNA methylationBrodmann area 9Differential alternative splicingDeregulated biological processesPostmortem brain tissueMulti-omics approachCocaine intakeMulti-omics studiesPrefrontal cortexBrain alterationsMulti-omics profilingGene expression alterationsArea 9Fatty acid metabolismReceptor-targeting drugsSpliced transcriptsEpigenome-wideDNA methylationNeuronal morphogenesisAS changesDrug repositioning analysisShear stress is uncoupled from atheroprotective KLK10 in atherosclerotic plaques
Zhou Z, Korteland S, Tardajos-Ayllon B, Wu J, Chambers E, Weninck J, Simons M, Dunning M, Schenkel T, Diagbouga M, Wentzel J, Fragiadaki M, Evans P. Shear stress is uncoupled from atheroprotective KLK10 in atherosclerotic plaques. Atherosclerosis 2024, 398: 118622. PMID: 39413592, DOI: 10.1016/j.atherosclerosis.2024.118622.Peer-Reviewed Original ResearchConceptsShear stressPhysiological shear stressComputational fluid dynamicsDiseased arteriesHealthy arteriesGene Ontology termsFluid dynamicsSingle-cell RNA sequencingApoptotic gene expressionEffect of physiological shear stressPromote vascular homeostasisOntology termsRNA sequencingFunctional enrichmentEndothelial cellsGene expressionRisk of cardiovascular complications
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