Circulation. Genomic and precision medicine · 2019
Using Statistical Modeling to Understand and Predict Pediatric Stem Cell Function.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Shoja-Taheri Farnaz, George Alex, Agarwal Udit, Platt Manu O, Gibson Greg, Davis Michael E
Аннотация
BACKGROUND: Congenital 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.
METHODS: We 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.
RESULTS: Interestingly, 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.
CONCLUSIONS: These 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.
METHODS: We 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.
RESULTS: Interestingly, 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.
CONCLUSIONS: These 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.