A Learning Based Framework for Disease Prediction from Images of Human-Derived Pluripotent Stem Cells of Schizophrenia Patients

Human induced pluripotent stem cells (hiPSCs) have been employed very successfully to identify molecular and cellular features of psychiatric disorders that would be impossible to discover in traditional postmortem studies. Despite the wealth of new available information though, there is still a critical need to establish quantifiable and accessible molecular markers that can be used to reveal the biological causality of the disease. In this paper, we introduce a new quantitative framework based on supervised learning to investigate structural alterations in the neuronal cytoskeleton of hiPSCs of schizophrenia (SCZ) patients. We show that, by using Support Vector Machines or selected Artificial Neural Networks trained on image-based features associated with somas of hiPSCs derived neurons, we can predict very reliably SCZ and healthy control cells. In addition, our method reveals that beta III tubulin and FGF12, two critical components of the cytoskeleton, are differentially regulated in SCZ and healthy control cells, upon perturbation by GSK3 inhibition.

Automated summary

This paper introduces a new quantitative framework based on supervised learning to investigate structural alterations in the neuronal cytoskeleton of schizophrenia patients. By using Support Vector Machines or selected Artificial Neural Networks, it is possible to predict schizophrenia and healthy control cells reliably, and discover differential regulation of two cytoskeleton components in schizophrenia and healthy control cells.