Skip to main content

REVIEW article

Front. Neurosci.
Sec. Neurodevelopment
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1524577
This article is part of the Research Topic Advancing Neurodevelopmental Disorder Models with Human iPSC and Multi-Omics Integration View all articles

Harnessing the Potential of hiPSC, Functional Assays, and Machine Learning for Neurodevelopmental Disorders

Provisionally accepted
Ziqin Yang Ziqin Yang 1,2Nicole A. Teaney Nicole A. Teaney 1,2Elizabeth D. Buttermore Elizabeth D. Buttermore 1,3Mustafa Sahin Mustafa Sahin 1,2,3Wardiya Afshar-Saber Wardiya Afshar-Saber 1,2*
  • 1 Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States
  • 2 FM Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States
  • 3 Human Neuron Core, Boston Children's Hospital, Boston, MA, United States, Boston, United States

The final, formatted version of the article will be published soon.

    Neurodevelopmental disorders (NDDs) affect 4.7% of the global population and are associated with delays in brain development and a spectrum of impairments that can lead to lifelong disability and even mortality. Identification of biomarkers for accurate diagnosis and medications for effective treatment are lacking, in part due to the historical use of preclinical model systems that do not translate well to the clinic for neurological disorders, such as rodents and heterologous cell lines. Human-induced pluripotent stem cells (hiPSCs) are a promising in vitro system for modeling NDDs, providing opportunities to understand mechanisms driving NDDs in human neurons. Functional assays, including patch clamping, multielectrode array, and imaging-based assays, are popular tools employed with hiPSC disease models for disease investigation. Recent progress in machine learning (ML) algorithms also presents unprecedented opportunities to advance the NDD research process. In this review, we compare two-dimensional and three-dimensional hiPSC formats for disease modeling, discuss the applications of functional assays, and offer insights on incorporating ML into hiPSC-based NDD research and drug screening.

    Keywords: hiPSC, Neurodevelopmental disorders, patch clamping, MEA, Voltage Imaging, calcium imaging, machine learning, Translational research

    Received: 07 Nov 2024; Accepted: 19 Dec 2024.

    Copyright: © 2024 Yang, Teaney, Buttermore, Sahin and Afshar-Saber. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Wardiya Afshar-Saber, Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, 02115, Massachusetts, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.