2021
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks
Sehanobish A, Ravindra N, Van Dijk D. Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. Proceedings Of The AAAI Conference On Artificial Intelligence 2021, 35: 4864-4873. DOI: 10.1609/aaai.v35i6.16619.Peer-Reviewed Original ResearchSARS-CoV-2 infectionSingle-cell omics dataCOVID-19 severitySingle-cell RNA sequencing datasetsCell typesRNA sequencing datasetsSARS-CoV-2Transcriptomic patternsSequencing datasetsOmics dataCellular determinantsCellular understandingIndividual cellsBronchoalveolar lavage fluid samplesInfected cellsSevere COVID-19Lavage fluid samplesCOVID-19Lung organoidsDisease statesInfectionTranscriptomeCellsSeverityFluid samplesQuantifying 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
2020
Transcriptomic and clonal characterization of T cells in the human central nervous system
Pappalardo JL, Zhang L, Pecsok MK, Perlman K, Zografou C, Raddassi K, Abulaban A, Krishnaswamy S, Antel J, van Dijk D, Hafler DA. Transcriptomic and clonal characterization of T cells in the human central nervous system. Science Immunology 2020, 5 PMID: 32948672, PMCID: PMC8567322, DOI: 10.1126/sciimmunol.abb8786.Peer-Reviewed Original ResearchConceptsCentral nervous systemCSF of patientsT cellsCerebrospinal fluidMultiple sclerosisImmune surveillanceNervous systemCSF T cellsHuman central nervous systemHealthy human donorsT cell activationImmune dysfunctionNeuroinflammatory diseasesCytotoxic capacityHealthy donorsHealthy individualsCell activationHuman donorsTissue adaptationPatientsClonal characterizationExpression of genesCellsSurveillanceFurther characterizationLearning General Transformations of Data for Out-of-Sample Extensions
Amodio M, van Dijk D, Wolf G, Krishnaswamy S. Learning General Transformations of Data for Out-of-Sample Extensions. 2023 IEEE 33rd International Workshop On Machine Learning For Signal Processing (MLSP) 2020, 00: 1-6. PMID: 34557339, PMCID: PMC8456777, DOI: 10.1109/mlsp49062.2020.9231660.Peer-Reviewed Original Research
2015
Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage
van Dijk D, Dhar R, Missarova AM, Espinar L, Blevins WR, Lehner B, Carey LB. Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage. Nature Communications 2015, 6: 7972. PMID: 26268986, PMCID: PMC4557116, DOI: 10.1038/ncomms8972.Peer-Reviewed Original ResearchConceptsTranscriptional stress responseDNA damageDNA damage responseIsogenic populationsRNA polymerase fidelityDamage responseTranscriptional differencesIsogenic cellsEnvironmental stressCell variabilityStress responseGrowth ratePolymerase fidelityCulture conditionsPolymerase error rateOxidative stressCellsSuch cellsSame environmentTranscriptomeTransposonSubpopulationsTranscriptsGenotypicStress