2022
Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19
Unterman A, Sumida TS, Nouri N, Yan X, Zhao AY, Gasque V, Schupp JC, Asashima H, Liu Y, Cosme C, Deng W, Chen M, Raredon MSB, Hoehn KB, Wang G, Wang Z, DeIuliis G, Ravindra NG, Li N, Castaldi C, Wong P, Fournier J, Bermejo S, Sharma L, Casanovas-Massana A, Vogels CBF, Wyllie AL, Grubaugh ND, Melillo A, Meng H, Stein Y, Minasyan M, Mohanty S, Ruff WE, Cohen I, Raddassi K, Niklason L, Ko A, Montgomery R, Farhadian S, Iwasaki A, Shaw A, van Dijk D, Zhao H, Kleinstein S, Hafler D, Kaminski N, Dela Cruz C. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nature Communications 2022, 13: 440. PMID: 35064122, PMCID: PMC8782894, DOI: 10.1038/s41467-021-27716-4.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAgedAntibodies, Monoclonal, HumanizedCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCells, CulturedCOVID-19COVID-19 Drug TreatmentFemaleGene Expression ProfilingGene Expression RegulationHumansImmunity, InnateMaleReceptors, Antigen, B-CellReceptors, Antigen, T-CellRNA-SeqSARS-CoV-2Single-Cell AnalysisConceptsProgressive COVID-19B cell clonesSingle-cell analysisT cellsImmune responseMulti-omics single-cell analysisCOVID-19Cell clonesAdaptive immune interactionsSevere COVID-19Dynamic immune responsesGene expressionSARS-CoV-2 virusAdaptive immune systemSomatic hypermutation frequenciesCellular effectsProtein markersEffector CD8Immune signaturesProgressive diseaseHypermutation frequencyProgressive courseClassical monocytesClonesImmune interactions
2018
Manifold learning-based methods for analyzing single-cell RNA-sequencing data
Moon K, Stanley J, Burkhardt D, van Dijk D, Wolf G, Krishnaswamy S. Manifold learning-based methods for analyzing single-cell RNA-sequencing data. Current Opinion In Systems Biology 2018, 7: 36-46. DOI: 10.1016/j.coisb.2017.12.008.Peer-Reviewed Original ResearchSingle-cell RNA-sequencing dataSingle-cell RNA sequencing technologyRNA sequencing technologyRNA-sequencing dataThousands of cellsGene regulationCellular statesPhenotypic diversityCellular developmentGene interactionsSequencing technologiesGene expressionSeq dataUnderlying biological signalManifold learning-based methodsSingle experimentBiological signalsRecent advancesDiversityDeeper insightRegulationExpression
2016
Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators
van Dijk D, Sharon E, Lotan-Pompan M, Weinberger A, Segal E, Carey LB. Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators. Genome Research 2016, 27: 87-94. PMID: 27965290, PMCID: PMC5204347, DOI: 10.1101/gr.212316.116.Peer-Reviewed Original ResearchConceptsTranscription factorsGene regulatory logicPromoter DNA sequencesGene expression outputActive transcription factorTarget gene expressionGene expression profilesMaximum promoter activityTranscriptional activatorExpression outputRegulatory logicDNA sequencesGene expressionPromoter activityIntracellular signalsExpression profilesTF moleculesActivity of thousandsActivator siteLocal poolAbsolute expressionTF concentrationPromoterKey mediatorExpression
2015
Noise in gene expression is coupled to growth rate
Keren L, van Dijk D, Weingarten-Gabbay S, Davidi D, Jona G, Weinberger A, Milo R, Segal E. Noise in gene expression is coupled to growth rate. Genome Research 2015, 25: 1893-1902. PMID: 26355006, PMCID: PMC4665010, DOI: 10.1101/gr.191635.115.Peer-Reviewed Original ResearchConceptsGene expression noiseExpression noiseGene expressionGlobal changeLower growth rateDifferent growth ratesDifferent cell cycle stagesAverage gene expressionNutrient-poor conditionsEnvironmental conditionsCell cycle stageOverall high variabilityGrowth rateMost promotersCell cycle heterogeneityCellular regulationPromoter featuresDisplay elevated levelsExpression variabilityBiological functionsExpression distributionPhenotypic implicationsAsynchronous populationIdentical cellsExpression values
2014
Probing the effect of promoters on noise in gene expression using thousands of designed sequences
Sharon E, van Dijk D, Kalma Y, Keren L, Manor O, Yakhini Z, Segal E. Probing the effect of promoters on noise in gene expression using thousands of designed sequences. Genome Research 2014, 24: 1698-1706. PMID: 25030889, PMCID: PMC4199362, DOI: 10.1101/gr.168773.113.Peer-Reviewed Original ResearchConceptsExpression noiseGene expressionMean expression levelTranscription factorsPromoter sequencesExpression levelsGene expression noisePromoter DNA sequencesMore transcription factorsNucleosome-disfavoring sequencesHigher expression noiseDifferent promoter sequencesNonspecific DNA bindingOne-dimensional slidingCellular functionsHigh-throughput methodNative promoterDNA sequencesDNA bindingSynthetic promotersPromoterIdentical cellsTarget siteSequence
2013
Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise
Dadiani M, van Dijk D, Segal B, Field Y, Ben-Artzi G, Raveh-Sadka T, Levo M, Kaplow I, Weinberger A, Segal E. Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise. Genome Research 2013, 23: 966-976. PMID: 23403035, PMCID: PMC3668364, DOI: 10.1101/gr.149096.112.Peer-Reviewed Original ResearchConceptsPromoter dynamicsExpression variabilityPromoter transitionsSingle-cell time-lapse microscopyInactive stateSequence changesNucleosome-disfavoring sequencesCis-regulatory elementsProcess of transcriptionActive stateNumber of transcriptsTime-lapse microscopyCell populationsTranscriptional noiseTranscriptional dynamicsSite resultsTranscription factorsDNA sequencesGene expressionMean expressionIdentical populationsIndividual cellsSequence resultsExpression levelsTranscripts