Tet2 Controls the Responses of β cells to Inflammation in Autoimmune Diabetes
Rui J, Deng S, Perdigoto AL, Ponath G, Kursawe R, Lawlor N, Sumida T, Levine-Ritterman M, Stitzel ML, Pitt D, Lu J, Herold KC. Tet2 Controls the Responses of β cells to Inflammation in Autoimmune Diabetes. Nature Communications 2021, 12: 5074. PMID: 34417463, PMCID: PMC8379260, DOI: 10.1038/s41467-021-25367-z.Peer-Reviewed Original ResearchConceptsImmune cellsΒ-cellsNOD/SCID recipientsDiabetogenic immune cellsDiabetogenic T cellsBone marrow transplantType 1 diabetesExpression of TET2Human β-cellsIslet infiltratesSCID recipientsMarrow transplantInflammatory pathwaysTransfer of diseaseT cellsInflammatory genesImmune killingPathologic interactionsReduced expressionDiabetesInflammationTET2MiceRecipientsCellsAdverse events induced by immune checkpoint inhibitors
Perdigoto AL, Kluger H, Herold KC. Adverse events induced by immune checkpoint inhibitors. Current Opinion In Immunology 2021, 69: 29-38. PMID: 33640598, PMCID: PMC8122053, DOI: 10.1016/j.coi.2021.02.002.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAutoantibodiesAutoantigensAutoimmune DiseasesAutoimmunityCytotoxicity, ImmunologicDrug-Related Side Effects and Adverse ReactionsGene-Environment InteractionGenetic Predisposition to DiseaseHumansImmune Checkpoint InhibitorsImmunotherapyLymphocyte ActivationNeoplasmsT-LymphocytesConceptsImmune checkpoint inhibitorsCheckpoint inhibitorsAdverse eventsT cellsImmune related adverse eventsEmergence of autoantibodiesRelated adverse eventsAnti-tumor responseAutoreactive T cellsActivated T cellsAutoimmune mechanismsTreatment of cancerAutoimmune diseasesInflammatory responsePredictive valueHost factorsToxic effectsInhibitorsDirect effectOngoing investigationAutoantibodiesCellsAutoimmunityPathogenesisCancerQuantifying the effect of experimental perturbations at single-cell resolution
Burkhardt DB, Stanley JS, Tong A, Perdigoto AL, Gigante SA, Herold KC, Wolf G, Giraldez AJ, van Dijk D, Krishnaswamy S. Quantifying the effect of experimental perturbations at single-cell resolution. Nature Biotechnology 2021, 39: 619-629. PMID: 33558698, PMCID: PMC8122059, DOI: 10.1038/s41587-020-00803-5.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencing datasetsClusters of cellsRNA sequencing datasetsSingle-cell resolutionSingle-cell levelTranscriptomic spaceSequencing datasetsExperimental perturbationsCell populationsGene signatureVertex frequencyDiscrete regionsCellsEffects of perturbationsMultiple conditionsPerturbation responseClustersPopulationPerturbationsLikelihood estimatesGraph signal processing