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 effects
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 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 methodsTranscriptomeA complex of the lipid transport ER proteins TMEM24 and C2CD2 with band 4.1 at cell–cell contacts
Johnson B, Iuliano M, Lam T, Biederer T, De Camilli P. A complex of the lipid transport ER proteins TMEM24 and C2CD2 with band 4.1 at cell–cell contacts. Journal Of Cell Biology 2024, 223: e202311137. PMID: 39158698, PMCID: PMC11334333, DOI: 10.1083/jcb.202311137.Peer-Reviewed Original ResearchConceptsPlasma membraneNon-vesicular lipid transferSites of cell contactC-terminus motifsCell contact-dependent signalsContact-dependent signalingCell-cell contactER/PM junctionsTMEM24ER proteinsPM proteinsSynCAM 1Cell adhesion moleculesCellular functionsLipid transferC2CD2Phospholipid transportLipid transportCell contactProteinAdhesion moleculesCalcium homeostasisCellsFamily membersParalogsSingle-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 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, 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 cortexCharting the cellular biogeography in colitis reveals fibroblast trajectories and coordinated spatial remodeling
Cadinu P, Sivanathan K, Misra A, Xu R, Mangani D, Yang E, Rone J, Tooley K, Kye Y, Bod L, Geistlinger L, Lee T, Mertens R, Ono N, Wang G, Sanmarco L, Quintana F, Anderson A, Kuchroo V, Moffitt J, Nowarski R. Charting the cellular biogeography in colitis reveals fibroblast trajectories and coordinated spatial remodeling. Cell 2024, 187: 2010-2028.e30. PMID: 38569542, PMCID: PMC11017707, DOI: 10.1016/j.cell.2024.03.013.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCell CommunicationColitisColitis, UlcerativeFibroblastsGastrointestinal TractHumansIn Situ Hybridization, FluorescenceInflammationMiceConceptsMultiplexed error-robust fluorescence in situ hybridizationFluorescence in situ hybridizationSpatial organizationCell-cell interactionsDiverse cell populationsHealthy gutMouse colitis modelExpression profilesBiogeographyGutNon-immune cellsGut inflammationSpatial remodelingInflammation-associated fibroblastsTissue neighborhoodsInflammation-induced remodelingCell populationsFibroblastsImmune cellsCellsColitis modelUlcerative colitisFibroblastic originStage progressionExpressionSpatial–temporal order–disorder transition in angiogenic NOTCH signaling controls cell fate specification
Kang T, Bocci F, Nie Q, Onuchic J, Levchenko A. Spatial–temporal order–disorder transition in angiogenic NOTCH signaling controls cell fate specification. ELife 2024, 12: rp89262. PMID: 38376371, PMCID: PMC10942579, DOI: 10.7554/elife.89262.Peer-Reviewed Original ResearchMeSH KeywordsCell CommunicationCell DifferentiationEndothelial CellsMorphogenesisSignal TransductionConceptsCell fate specificationFate specificationNotch signalingMorphogenic processesCell-cell communicationComplex morphogenic processesCell fateDynamics of spatial patternsDepletion of fibronectinTip cellsSprout extensionAngiogenic morphogenesisHypoxic micro-environmentCell plasticityCellsComputational analysisPre-existing onesCell patternMicro-environmentSpatial patternsLocal enrichmentMorphogenesisEndothelial cellsAngiogenesis modelFibronectin
2023
Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images
Wang S, Rong R, Zhou Q, Yang D, Zhang X, Zhan X, Bishop J, Chi Z, Wilhelm C, Zhang S, Pickering C, Kris M, Minna J, Xie Y, Xiao G. Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images. Nature Communications 2023, 14: 7872. PMID: 38081823, PMCID: PMC10713592, DOI: 10.1038/s41467-023-43172-8.Peer-Reviewed Original ResearchOptogenetic control of YAP reveals a dynamic communication code for stem cell fate and proliferation
Meyer K, Lammers N, Bugaj L, Garcia H, Weiner O. Optogenetic control of YAP reveals a dynamic communication code for stem cell fate and proliferation. Nature Communications 2023, 14: 6929. PMID: 37903793, PMCID: PMC10616176, DOI: 10.1038/s41467-023-42643-2.Peer-Reviewed Original ResearchConceptsCell fateYAP levelsControlling gene activityCell fate analysisPluripotency regulators Oct4Stem cell fateEffector genesTranscriptional regulationGene activationControl pluripotencyYAP activityNative dynamicsCellular differentiationRegulators Oct4Developmental decision-makingControl proliferationMolecular logicCell behaviorYAPFate analysisDynamic decoderOptogenetic controlOct4 expressionCellsFateDeveloping synthetic tools to decipher the tumor–immune interactome
Weizman O, Luyten S, Lu P, Song E, Qin K, Mostaghimi D, Ring A, Iwasaki A. Developing synthetic tools to decipher the tumor–immune interactome. Proceedings Of The National Academy Of Sciences Of The United States Of America 2023, 120: e2306632120. PMID: 37871202, PMCID: PMC10622925, DOI: 10.1073/pnas.2306632120.Peer-Reviewed Original ResearchMeSH KeywordsCell CommunicationHumansHydrolasesImmunologic SurveillanceImmunotherapyNeoplasmsTumor MicroenvironmentConceptsImmune cellsImmune-based therapiesTumor-immune cell interactionsDifferent immunotherapiesRetroviral reportersSensitive tumorsImmune surveillanceTumor subtypesTumor microenvironmentSynthetic Notch receptorCell interactionsCell contactTissue functionTissue locationNotch receptorsVivoOptimal tissue functionCellsComprehensive landscapeImmunotherapyTherapyTumorsImmunogenicitySubtypesEmergence of division of labor in tissues through cell interactions and spatial cues
Adler M, Moriel N, Goeva A, Avraham-Davidi I, Mages S, Adams T, Kaminski N, Macosko E, Regev A, Medzhitov R, Nitzan M. Emergence of division of labor in tissues through cell interactions and spatial cues. Cell Reports 2023, 42: 112412. PMID: 37086403, PMCID: PMC10242439, DOI: 10.1016/j.celrep.2023.112412.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencingMost cell typesCell-type populationsCell-cell interactionsDistinguishable expression patternsCell population levelSpatial transcriptomics dataCell interactionsLigand-receptor networkMulticellular organismsTranscriptomic dataRNA sequencingInstructive signalsExpression patternsSpecialist cellsCell typesIndividual cellsDivision of laborMultiple functionsTissue environmentSame cellsDifferent functionsPopulation levelCellsDivision
2022
Comprehensive 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 visualizationBioinformaticsInteractionInterplay between substrate rigidity and tissue fluidity regulates cell monolayer spreading
Staddon M, Murrell M, Banerjee S. Interplay between substrate rigidity and tissue fluidity regulates cell monolayer spreading. Soft Matter 2022, 18: 7877-7886. PMID: 36205535, PMCID: PMC9700261, DOI: 10.1039/d2sm00757f.Peer-Reviewed Original ResearchConceptsSubstrate rigidityCollective cell motionSubstrate stiffnessTissue fluidityStiff substratesCell-matrix adhesionTraction force generationCell-cell interactionsCell motionTissue morphogenesisSoft elastic matrixCell collectivesEmbryonic developmentCell crawlingCell spreadingCell monolayersSolid tissuesCell behaviorMechanical behaviorPredictive understandingMechanical propertiesCancer invasionCell aggregatesElastic matrixCell propertiesScalable workflow for characterization of cell-cell communication in COVID-19 patients
Lin Y, Loo L, Tran A, Lin D, Moreno C, Hesselson D, Neely G, Yang J. Scalable workflow for characterization of cell-cell communication in COVID-19 patients. PLOS Computational Biology 2022, 18: e1010495. PMID: 36197936, PMCID: PMC9534414, DOI: 10.1371/journal.pcbi.1010495.Peer-Reviewed Original ResearchConceptsCOVID-19 patientsSevere patientsDisease severityDysfunctional immune responseDistinct disease outcomesHigher mortality riskSARS-CoV-2Different disease statesImmune cellsLung tissueDisease outcomeImmune responseMortality riskPatientsCell-cell interactionsPathogenic outcomesCritical symptomsCell-cell interaction patternsDisease statesSeverityOutcomesCellsLungSymptomsDeciphering Spatial Protein–Protein Interactions in Brain Using Proximity Labeling
Mathew B, Bathla S, Williams KR, Nairn AC. Deciphering Spatial Protein–Protein Interactions in Brain Using Proximity Labeling. Molecular & Cellular Proteomics 2022, 21: 100422. PMID: 36198386, PMCID: PMC9650050, DOI: 10.1016/j.mcpro.2022.100422.Peer-Reviewed Original ResearchConceptsProtein-protein interactionsProximity labelingBiological functionsComplex protein-protein interactionsProximity labeling methodProtein-DNA interactionsCell-cell communicationMost biological functionsDistinct cell typesProtein-RNANerve cell functionCell typesBiomolecular complexesCellular levelPhysical interactionProteomeCell functionSpecific subsetProteinSynaptic plasticityComplete catalogLabeling methodRecent advancesPowerful toolAxonal projectionsCINS: Cell Interaction Network inference from Single cell expression data
Yuan Y, Cosme C, Adams TS, Schupp J, Sakamoto K, Xylourgidis N, Ruffalo M, Li J, Kaminski N, Bar-Joseph Z. CINS: Cell Interaction Network inference from Single cell expression data. PLOS Computational Biology 2022, 18: e1010468. PMID: 36095011, PMCID: PMC9499239, DOI: 10.1371/journal.pcbi.1010468.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBayes TheoremCell CommunicationGene Expression ProfilingLigandsMiceSequence Analysis, RNASingle-Cell AnalysisConceptsCell type interactionsSingle-cell expression dataSingle-cell RNA-seq dataRNA-seq dataScRNA-seq experimentsCell-cell interactionsExpression dataCell typesMouse datasetsNetwork inferenceCell interactionsInteraction predictionNetwork analysisInference pipelineGenesCINSProteinInteractionBayesian network analysisMapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment
Bridges K, Miller-Jensen K. Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment. Frontiers In Immunology 2022, 13: 885267. PMID: 35572582, PMCID: PMC9096838, DOI: 10.3389/fimmu.2022.885267.Peer-Reviewed Original ResearchMeSH KeywordsCell CommunicationGene Expression ProfilingSequence Analysis, RNASingle-Cell AnalysisTumor MicroenvironmentConceptsScRNA-seqHigh-throughput transcriptional profilingCell-cell communication networksSingle-cell RNA sequencingCell-cell communicationSingle-cell technologiesCell-cell interactionsTumor microenvironmentTranscriptional profilingInteraction networksRNA sequencingBiological systemsNetwork analysisComputational approach
2021
Three-dimensional transistor arrays for intra- and inter-cellular recording
Gu Y, Wang C, Kim N, Zhang J, Wang T, Stowe J, Nasiri R, Li J, Zhang D, Yang A, Hsu L, Dai X, Mu J, Liu Z, Lin M, Li W, Wang C, Gong H, Chen Y, Lei Y, Hu H, Li Y, Zhang L, Huang Z, Zhang X, Ahadian S, Banik P, Zhang L, Jiang X, Burke P, Khademhosseini A, McCulloch A, Xu S. Three-dimensional transistor arrays for intra- and inter-cellular recording. Nature Nanotechnology 2021, 17: 292-300. PMID: 34949774, PMCID: PMC8994210, DOI: 10.1038/s41565-021-01040-w.Peer-Reviewed Original ResearchConceptsTransistor arraysCellular networksField-effect transistor arraysField-effect transistorsMuscle tissue constructsTissue constructsConduction pathElectrical behaviorSensing accuracyElectrogenic cellsImpulse generatorCellular interfaceBehavior of single cellsRecordings of transmembrane potentialsStandard patch clampCell-cell interactionsVelocityArrayCellular physiologyPatch clampIntracellular recordingsTransistorsConduction velocityTransmembrane potentialSupermeres are functional extracellular nanoparticles replete with disease biomarkers and therapeutic targets
Zhang Q, Jeppesen DK, Higginbotham JN, Graves-Deal R, Trinh VQ, Ramirez MA, Sohn Y, Neininger AC, Taneja N, McKinley ET, Niitsu H, Cao Z, Evans R, Glass SE, Ray KC, Fissell WH, Hill S, Rose KL, Huh WJ, Washington MK, Ayers GD, Burnette DT, Sharma S, Rome LH, Franklin JL, Lee YA, Liu Q, Coffey RJ. Supermeres are functional extracellular nanoparticles replete with disease biomarkers and therapeutic targets. Nature Cell Biology 2021, 23: 1240-1254. PMID: 34887515, PMCID: PMC8656144, DOI: 10.1038/s41556-021-00805-8.Peer-Reviewed Original ResearchConceptsSmall extracellular vesiclesTherapeutic targetExtracellular vesiclesHepatic lipidsCardiovascular diseaseCetuximab resistanceMultiple cancersAlzheimer's diseaseDiseaseLactate secretionDisease biomarkersBiomarkersExtracellular RNAIntense investigationHuman diseasesGreater uptakeVivoCancerSecretionMitochondrial complex II in intestinal epithelial cells regulates T cell-mediated immunopathology
Fujiwara H, Seike K, Brooks MD, Mathew AV, Kovalenko I, Pal A, Lee HJ, Peltier D, Kim S, Liu C, Oravecz-Wilson K, Li L, Sun Y, Byun J, Maeda Y, Wicha MS, Saunders TL, Rehemtulla A, Lyssiotis CA, Pennathur S, Reddy P. Mitochondrial complex II in intestinal epithelial cells regulates T cell-mediated immunopathology. Nature Immunology 2021, 22: 1440-1451. PMID: 34686860, PMCID: PMC9351914, DOI: 10.1038/s41590-021-01048-3.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCase-Control StudiesCell CommunicationCells, CulturedColitisColonCytotoxicity, ImmunologicDisease Models, AnimalElectron Transport Complex IIEpithelial CellsFemaleGraft vs Host DiseaseHumansImmunity, MucosalIntestinal MucosaMice, Inbred BALB CMice, Inbred C57BLMice, TransgenicMitochondriaOxidative PhosphorylationSuccinic AcidT-LymphocytesConceptsGenetic experimental approachesCell-intrinsic featuresMetabolic flux studiesIntestinal epithelial cellsOxidative phosphorylationDisease severityT cell-mediated immunopathologyT cell-mediated colitisIntestinal epithelial cell damageProtein analysisSuccinate dehydrogenaseCell-mediated immunopathologyInflammatory bowel diseaseEpithelial cell damageHuman clinical samplesSuccinate levelsEpithelial cellsCritical roleSDHAHost diseaseBowel diseaseComplementary chemicalIntestinal diseaseT cellsMetabolic alterations
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