2024
Mapping the gene space at single-cell resolution with gene signal pattern analysis
Venkat A, Leone S, Youlten S, Fagerberg E, Attanasio J, Joshi N, Perlmutter M, Krishnaswamy S. Mapping the gene space at single-cell resolution with gene signal pattern analysis. Nature Computational Science 2024, 4: 955-977. PMID: 39706866, DOI: 10.1038/s43588-024-00734-0.Peer-Reviewed Original ResearchConceptsSingle-cell dataGene spaceGene representationSimulated single-cell dataGene co-expression modulesCell-cell graphCharacterization of genesGene-gene interactionsCo-expression modulesCell-cell communicationCellular state spaceSingle-cell resolutionSingle-cell sequencing analysisSequence analysisGenesBiological tasksSpatial transcriptomicsGraph signal processing approachSignal pattern analysisPattern analysisSignal processing approachComputational methodsTranscriptomeSupervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Hodgson L, Li Y, Iturria-Medina Y, Stratton J, Wolf G, Krishnaswamy S, Bennett D, Bzdok D. Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression. Communications Biology 2024, 7: 591. PMID: 38760483, PMCID: PMC11101463, DOI: 10.1038/s42003-024-06273-8.Peer-Reviewed Original ResearchConceptsGene programAlzheimer's diseaseLate-onset Alzheimer's diseaseAD risk lociCell type-specificSingle-nucleus RNA sequencingRisk lociAD brainAlzheimer's disease progressionSnRNA-seqRNA sequencingAD pathophysiologySignaling cascadesTranscriptome modulationProgressive neurodegenerative diseaseCell-typeGWASNeurodegenerative diseasesNeuronal lossGlial cellsTranscriptomeLociGenesPseudo-trajectoriesDisease progression
2023
Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations
Girardi M, Ren J, Qu R, Rahman N, Lewis J, King A, Liao X, Mirza F, Carlson K, Huang Y, Gigante S, Evans B, Rajendran B, Xu S, Wang G, Foss F, Damsky W, Kluger Y, Krishnaswamy S. Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations. Blood Advances 2023, 7: 445-457. PMID: 35947128, PMCID: PMC9979716, DOI: 10.1182/bloodadvances.2022008168.Peer-Reviewed Original ResearchMeSH KeywordsCD4-Positive T-LymphocytesHumansLymphoma, T-Cell, CutaneousReceptors, Antigen, T-CellSkin NeoplasmsTranscriptomeConceptsCutaneous T-cell lymphomaMalignant CTCL cellsDiverse transcriptomic profilesT cellsSingle-cell RNACTCL cellsDevelopment of CTCLIntegrated transcriptomeT-cell receptor sequencingT cell exhaustion phenotypeCommon antigenic stimulusPeripheral blood CD4Transcriptomic profilesGene expressionT-cell lymphomaIntegrative analysisPotential therapeutic targetProliferation advantageLimited diversityBlood CD4Blood involvementMutation levelsExhaustion phenotypeWorse prognosisAntigenic stimulus
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply