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Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

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Single-Cell Sorting of Immunophenotyped Mesenchymal Stem Cells from Human Exfoliated Deciduous Teeth
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Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.

Marziyeh Mousazadeh1, Atieh Jahangiri-Manesh1, Hossein Soltaninejad2

  • 1Department of Nanobiotechnology, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.

Iranian Journal of Medical Sciences
|June 4, 2026
PubMed
Summary

Machine learning (ML) enhances stem cell sorting accuracy and efficiency for regenerative medicine and therapies. This review categorizes ML strategies using visual and non-visual data for improved stem cell identification and application.

Keywords:
Artificial intelligenceCell biologyMachine learningStem cells

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Regenerative Medicine

Background:

  • Stem cells are vital for regenerative medicine, drug discovery, and cell therapies.
  • Accurate stem cell identification and sorting are crucial for research and clinical success.
  • Traditional sorting methods face limitations in speed, scalability, cost, and accuracy.

Purpose of the Study:

  • To comprehensively review machine learning (ML)-driven strategies for stem cell sorting.
  • To categorize ML approaches based on data types (visual and non-visual), ML techniques, and stem cell types.
  • To discuss historical development, current applications, and future directions in automated stem cell sorting.

Main Methods:

  • Exploration of ML techniques applied to image and video processing for stem cell classification.
  • Analysis of non-visual data processing methods using ML for stem cell analysis and separation.
  • Categorization of reviewed studies based on input data, ML algorithms, stem cell types, objectives, and performance metrics.

Main Results:

  • ML-based methods offer rapid, automated, and highly accurate stem cell classification compared to traditional techniques.
  • Both visual (image/video) and non-visual data processing using ML are effective for stem cell sorting.
  • The review provides a structured overview of diverse ML applications across various stem cell types.

Conclusions:

  • ML significantly advances stem cell sorting, improving efficiency and accuracy for research and clinical applications.
  • Emerging automated systems and software solutions promise further integration of ML in stem cell technologies.
  • Future directions include refining ML algorithms and developing scalable, cost-effective automated stem cell sorters.