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

Nickolas Fularczyk, Jessica Di Re, Laura Stertz, Consuelo Walss-Bass, Fernanda Laezza, Demetrio Labate

Research output: Contribution to journalArticlepeer-review

Abstract

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 βIII tubulin and FGF12, two critical components of the cytoskeleton, are differentially regulated in SCZ and healthy control cells, upon perturbation by GSK3 inhibition.

Original languageEnglish (US)
Pages (from-to)513-523
Number of pages11
JournalNeuroinformatics
Volume20
Issue number2
DOIs
StatePublished - Apr 2022

Keywords

  • Convolutional neural networks
  • Fluorescence microscopy
  • Human induced pluripotent stem cells
  • Image processing
  • PI3k/GSK3 pathway
  • Schizophrenia
  • Statistical matrices

ASJC Scopus subject areas

  • Software
  • General Neuroscience
  • Information Systems

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