Single-cell RNA Sequencing of Peripheral Blood Mononuclear Cells in Patients with Single Ventricle/Hypoplastic Left Heart Syndrome
Qu, H.-Q.; Ostberg, K.; Slater, D. J.; Wang, F.; Snyder, J.; Hou, C.; Connolly, J. J.; March, M.; Glessner, J. T.; Kao, C.; Hakonarson, H.
Show abstract
BackgroundSingle ventricle and hypoplastic left heart syndrome (SV/HLHS) patients require lifelong medical monitoring and management to address potential complications and optimize their health. The consequence of SV/HLHS had detrimental effects on multiple organ systems, including on peripheral blood mononuclear cells (PBMCs) and can weaken the immune system, exacerbating the risk of infection and various cardiovascular complications. MethodsUsing single-cell RNA sequencing (scRNA-seq), we studied PBMCs from 33 pediatric patients (10 females and 23 males) with SV/HLHS. By a pair-wide study design, the SV/HLHS patients were compared to 33 controls without heart diseases. ResultsFour cell types account for the top 62% cumulative importance of disease effects on gene expression in different cell types, i.e., [T cells, CD4+, Th1/17], [T cells, CD4+, TFH], [NK cells], and [T cells, CD4+, Th2]. Significant sex differences were observed in [T cells, CD4+, TFH], with less prominent effects in female patients. A total of 6659 genes in different cell types were significantly differentially expressed (DE). Hierarchical clustering by WGCNA analysis of the DE genes revealed that DE genes in NK cells are most closely related to those in SV/HLHS. A total of 822 genes showed cell specific DE with opposite directions in different cell types, highlighting overrepresented MYC and IFN-{gamma} activity in T cell and NK cell populations, as well as underrepresentation in monocytes and Treg cells. ConclusionThis study elucidates the complex transcriptome landscape in PBMCs in patients with SV/HLHS, emphasizing the differential impacts on various cell types. New insights are gained into the precise modulation of MYC and IFN-{gamma} activity in SV/HLHS, which may help balance immune responses and reduce harmful inflammation, and promote effective tissue repair and infection control.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease 93%
- Development of Small Molecule MEIS Inhibitors that modulate HSC activity 92%
- Alterations in genes associated with cytosolic RNA sensing in whole blood are associated with coronary microvascular disease in SLE 92%
Similar papers in this journal
- Specific methylation marks in promoter regions are associated to the pathogenic process of Chronic Chagas disease Cardiomyopathy by modifying transcription factor binding patterns 93%
- Transcriptome and Functions of Granulocytic Myeloid-Derived Suppressor Cells Determine their Association with Disease Severity of COVID-19 93%
- Lasting alterations in monocyte and dendritic cell subsets in individuals after hospitalization for COVID-19 92%
Similar papers in this journal
Similar papers in this journal
- Left ventricular transcriptome and extracellular vesicle-derived miRNAs in porcine donation after circulatory death (DCD) hearts undergoing prolonged working mode ex situ perfusion 92%
- Advanced Hemodynamic and Cluster Analysis for Identifying Novel RV function subphenotypes in Patients with Pulmonary Hypertension 90%
- Genome-wide association study reveals two novel genetic loci associated with chronic lung allograft dysfunction 89%
Similar papers in this journal
- The effect of sex and underlying disease on the genetic association of QT interval and sudden cardiac death 91%
- microRNA Expression Levels Change in Neonatal Patients During and After Exposure to Cardiopulmonary Bypass 91%
- Fabry Cardiomyopathy: Myocardial Fibrosis, Inflammation and Down-Regulation of Mannose-6-Phosphate Receptors cause Low accessibility to Enzyme Replacement Therapy 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.