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 Research
2015
Integrative analysis of 111 reference human epigenomes
Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller M, Amin V, Whitaker J, Schultz M, Ward L, Sarkar A, Quon G, Sandstrom R, Eaton M, Wu Y, Pfenning A, Wang X, ClaussnitzerYaping Liu M, Coarfa C, Alan Harris R, Shoresh N, Epstein C, Gjoneska E, Leung D, Xie W, David Hawkins R, Lister R, Hong C, Gascard P, Mungall A, Moore R, Chuah E, Tam A, Canfield T, Scott Hansen R, Kaul R, Sabo P, Bansal M, Carles A, Dixon J, Farh K, Feizi S, Karlic R, Kim A, Kulkarni A, Li D, Lowdon R, Elliott G, Mercer T, Neph S, Onuchic V, Polak P, Rajagopal N, Ray P, Sallari R, Siebenthall K, Sinnott-Armstrong N, Stevens M, Thurman R, Wu J, Zhang B, Zhou X, Abdennur N, Adli M, Akerman M, Barrera L, Antosiewicz-Bourget J, Ballinger T, Barnes M, Bates D, Bell R, Bennett D, Bianco K, Bock C, Boyle P, Brinchmann J, Caballero-Campo P, Camahort R, Carrasco-Alfonso M, Charnecki T, Chen H, Chen Z, Cheng J, Cho S, Chu A, Chung W, Cowan C, Athena Deng Q, Deshpande V, Diegel M, Ding B, Durham T, Echipare L, Edsall L, Flowers D, Genbacev-Krtolica O, Gifford C, Gillespie S, Giste E, Glass I, Gnirke A, Gormley M, Gu H, Gu J, Hafler D, Hangauer M, Hariharan M, Hatan M, Haugen E, He Y, Heimfeld S, Herlofsen S, Hou Z, Humbert R, Issner R, Jackson A, Jia H, Jiang P, Johnson A, Kadlecek T, Kamoh B, Kapidzic M, Kent J, Kim A, Kleinewietfeld M, Klugman S, Krishnan J, Kuan S, Kutyavin T, Lee A, Lee K, Li J, Li N, Li Y, Ligon K, Lin S, Lin Y, Liu J, Liu Y, Luckey C, Ma Y, Maire C, Marson A, Mattick J, Mayo M, McMaster M, Metsky H, Mikkelsen T, Miller D, Miri M, Mukame E, Nagarajan R, Neri F, Nery J, Nguyen T, O’Geen H, Paithankar S, Papayannopoulou T, Pelizzola M, Plettner P, Propson N, Raghuraman S, Raney B, Raubitschek A, Reynolds A, Richards H, Riehle K, Rinaudo P, Robinson J, Rockweiler N, Rosen E, Rynes E, Schein J, Sears R, Sejnowski T, Shafer A, Shen L, Shoemaker R, Sigaroudinia M, Slukvin I, Stehling-Sun S, Stewart R, Subramanian S, Suknuntha K, Swanson S, Tian S, Tilden H, Tsai L, Urich M, Vaughn I, Vierstra J, Vong S, Wagner U, Wang H, Wang T, Wang Y, Weiss A, Whitton H, Wildberg A, Witt H, Won K, Xie M, Xing X, Xu I, Xuan Z, Ye Z, Yen C, Yu P, Zhang X, Zhang X, Zhao J, Zhou Y, Zhu J, Zhu Y, Ziegler S, Beaudet A, Boyer L, De Jager P, Farnham P, Fisher S, Haussler D, Jones S, Li W, Marra M, McManus M, Sunyaev S, Thomson J, Tlsty T, Tsai L, Wang W, Waterland R, Zhang M, Chadwick L, Bernstein B, Costello J, Ecker J, Hirst M, Meissner A, Milosavljevic A, Ren B, Stamatoyannopoulos J, Wang T, Kellis M. Integrative analysis of 111 reference human epigenomes. Nature 2015, 518: 317-330. PMID: 25693563, PMCID: PMC4530010, DOI: 10.1038/nature14248.Peer-Reviewed Original ResearchConceptsHuman epigenomeHuman diseasesIntegrative analysisReference human genome sequenceDiverse human traitsRoadmap Epigenomics ConsortiumHuman genome sequenceHistone modification patternsRelevant cell typesEpigenomic informationEpigenomic marksDNA accessibilityRegulatory modulesGene regulationEpigenomic studiesGenome sequenceDNA methylationGenetic variationRegulatory elementsCellular differentiationMolecular basisModification patternsEpigenomeHuman traitsCell types
2012
An RNA Profile Identifies Two Subsets of Multiple Sclerosis Patients Differing in Disease Activity
Ottoboni L, Keenan BT, Tamayo P, Kuchroo M, Mesirov JP, Buckle GJ, Khoury SJ, Hafler DA, Weiner HL, De Jager PL. An RNA Profile Identifies Two Subsets of Multiple Sclerosis Patients Differing in Disease Activity. Science Translational Medicine 2012, 4: 153ra131. PMID: 23019656, PMCID: PMC3753678, DOI: 10.1126/scitranslmed.3004186.Peer-Reviewed Original ResearchConceptsGlatiramer acetateDisease activityPatient populationFirst-line disease-modifying treatmentsMultiple sclerosis (MS) patient populationPeripheral blood mononuclear cellsMS patient populationDisease-modifying treatmentsMultiple sclerosis patientsBlood mononuclear cellsSubset of subjectsDisease courseSclerosis patientsMS subjectsMononuclear cellsInflammatory eventsTreatment responseUntreated subjectsAdditional groupHigh expressionTranscriptional signatureSubjectsRNA profilesTreatmentTranscriptional profiles
1992
Gamma delta T-cell receptor repertoire in acute multiple sclerosis lesions.
Wucherpfennig KW, Newcombe J, Li H, Keddy C, Cuzner ML, Hafler DA. Gamma delta T-cell receptor repertoire in acute multiple sclerosis lesions. Proceedings Of The National Academy Of Sciences Of The United States Of America 1992, 89: 4588-4592. PMID: 1374907, PMCID: PMC49128, DOI: 10.1073/pnas.89.10.4588.Peer-Reviewed Original ResearchConceptsGamma delta T cellsDelta T cellsT cellsMultiple sclerosisMS plaquesCentral nervous system inflammatory processesAcute multiple sclerosis lesionsT cell receptor repertoireGamma delta T-cell receptor repertoireHeat shock proteinsAcute MS plaquesMS plaque tissueNormal CNS tissueDistinct lymphocyte populationsT cell populationsMultiple sclerosis lesionsShock proteinsAcute plaquesReactive astrocytesLymphocyte populationsInflammatory processFoamy macrophagesCNS tissueInflammatory sitesQuantitative immunohistochemistry