2024
CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis
Liu T, Long W, Cao Z, Wang Y, He C, Zhang L, Strittmatter S, Zhao H. CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis. Briefings In Bioinformatics 2024, 26: bbae626. PMID: 39592241, PMCID: PMC11596696, DOI: 10.1093/bib/bbae626.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkersComputational BiologyGene Expression ProfilingGenetic MarkersHumansSingle-Cell AnalysisSoftwareSingle-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 vulnerability
2023
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
2022
A 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 ResearchMeSH KeywordsAnimalsGene Expression ProfilingHumansLipopolysaccharidesMiceRNARNA-SeqSequence Analysis, RNASingle-Cell AnalysisConceptsDE 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