ArticleCirculation. Genomic and precision medicine2019
Using Statistical Modeling to Understand and Predict Pediatric Stem Cell Function.
Article in Circulation. Genomic and precision medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Predictive value of multivariate models of quantitative myocardial movement parameters for acute myocardial infarction in elderly patients with coronary artery disease.BMC cardiovascular disorders · 2026Article
- Machine Learning in Identifying Marker Genes for Congenital Heart Diseases of Different Cardiac Cell Types.Life (Basel, Switzerland) · 2024Article
- Single-Cell RNA Sequencing Reveals Distinct Cardiac-Derived Stromal Cell Subpopulations.Journal of cardiovascular development and disease · 2022Article
- Comparative computational RNA analysis of cardiac-derived progenitor cells and their extracellular vesicles.Genomics · 2022Article
- Biomimetic nanovesicle design for cardiac tissue repair.Nanomedicine (London, England) · 2020Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundCongenital heart defects are a leading cause of morbidity and mortality in children, and despite advanced surgical treatments, many patients progress to heart failure. Currently, transplantation is the only effective cure and is limited by donor availability and organ rejection. Recently, cell therapy has emerged as a novel method for treating pediatric heart failure with several ongoing clinical trials. However, efficacy of stem cell therapy is variable, and choosing stem cells with the highest reparative effects has been a challenge.
methodsWe previously demonstrated the age-dependent reparative effects of human c-kit+ progenitor cells (hCPCs) in a rat model of juvenile heart failure. Using a small subset of patient samples, computational modeling analysis showed that regression models could be made linking sequencing data to phenotypic outcomes. In the current study, we used a similar quantitative model to determine whether predictions can be made in a larger population of patients and validated the model using neonatal hCPCs. We performed RNA sequencing from c-kit+ progenitor cells isolated from 32 patients, including 8 neonatal samples. We tested 2 functional parameters of our model, cellular proliferation and chemotactic potential of conditioned media.
resultsInterestingly, the observed proliferation and migration responses in each of the selected neonatal hCPC lines matched their predicted counterparts. We then performed canonical pathway analysis to determine potential mechanistic signals that regulated hCPC performance and identified several immune response genes that correlated with performance. ELISA analysis confirmed the presence of selected cytokines in good performing hCPCs and provided many more signals to further validate.
conclusionsThese data show that cell behavior may be predicted using large datasets like RNA sequencing and that we may be able to identify patients whose c-kit+ progenitor cells exceed or underperform expectations. With systems biology approaches, interventions can be tailored to improve cell therapy or mimic the qualities of reparative cells.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.