scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Zhu B, Wang Y, Ku L, van Dijk D, Zhang L, Hafler D, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biology 2023, 24: 292. PMID: 38111007, PMCID: PMC10726524, DOI: 10.1186/s13059-023-03129-y.Peer-Reviewed Original ResearchSingle-cell analysis reveals inflammatory interactions driving macular degeneration
Kuchroo M, DiStasio M, Song E, Calapkulu E, Zhang L, Ige M, Sheth A, Majdoubi A, Menon M, Tong A, Godavarthi A, Xing Y, Gigante S, Steach H, Huang J, Huguet G, Narain J, You K, Mourgkos G, Dhodapkar R, Hirn M, Rieck B, Wolf G, Krishnaswamy S, Hafler B. Single-cell analysis reveals inflammatory interactions driving macular degeneration. Nature Communications 2023, 14: 2589. PMID: 37147305, PMCID: PMC10162998, DOI: 10.1038/s41467-023-37025-7.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsHumansMacular DegenerationMiceNeurodegenerative DiseasesNeurogliaRetinaSingle-Cell AnalysisConceptsAge-related macular degenerationMacular degenerationNeurodegenerative diseasesNeurodegenerative conditionsLate-stage age-related macular degenerationPossible new therapeutic targetsPostmortem human retinaProgressive multiple sclerosisNew therapeutic targetsEarly phaseSingle-nucleus RNA sequencingInflammatory interactionsMultiple sclerosisInterleukin-1βDisease progressionControl retinasTherapeutic approachesGlial populationsGlial stateTherapeutic targetDisease pathogenesisRetinal diseasesAlzheimer's diseaseDiseaseHuman retina