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Stem cells are undifferentiated cells that divide and produce different types of cells. Ordinarily, cells that have differentiated into a specific cell type are post-mitotic—that is, they no longer divide. However, scientists have found a way to reprogram these mature cells so that they “de-differentiate” and return to an unspecialized, proliferative state. These cells are also pluripotent like embryonic stem cells—able to produce all cell types—and are therefore...
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Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning.

Claudia Coronnello1, Maria Giovanna Francipane2,3

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|November 29, 2021
PubMed
Summary

Artificial intelligence (AI) aids in evaluating induced pluripotent stem cells (iPSCs) and their derivatives. AI offers faster, more accurate methods for iPSC quality control, addressing clinical use bottlenecks.

Keywords:
Artificial intelligenceDeep learningInduced pluripotent stem cellsMachine learningQuality controlRegenerative medicine

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Area of Science:

  • Stem Cell Biology
  • Biotechnology
  • Artificial Intelligence

Background:

  • Induced pluripotent stem cell (iPSC) technology offers potential for therapeutic cell and organ generation.
  • Clinical application of iPSCs is hindered by challenges in quality, safety, and standardization.
  • Current methods for iPSC characterization are time-consuming and labor-intensive.

Purpose of the Study:

  • To review recent advancements in artificial intelligence (AI) applications for iPSC evaluation.
  • To highlight AI's role in overcoming bottlenecks in iPSC manufacturing and clinical translation.
  • To explore AI-driven solutions for assessing cell identity and function during iPSC differentiation.

Main Methods:

  • Review of experimental studies utilizing AI for iPSC analysis.
  • Focus on AI-based approaches for quality control and validation of iPSCs and their derivatives.
  • Examination of AI's utility in streamlining iPSC reprogramming and differentiation processes.

Main Results:

  • AI methods show promise in accelerating and improving the accuracy of iPSC characterization.
  • AI can help standardize protocols for cell reprogramming and functional differentiation.
  • AI-based tools may revolutionize the management and application of iPSCs in regenerative medicine.

Conclusions:

  • AI presents a powerful toolkit for enhancing the quality and safety assessment of iPSCs.
  • AI-driven approaches are crucial for overcoming current limitations in iPSC-based therapies.
  • The integration of AI is poised to accelerate the clinical translation of iPSC technology.