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High-throughput platform for label-free sorting of 3D spheroids using deep learning.

Claudia Sampaio da Silva1,2, Julia Alicia Boos2, Jonas Goldowsky1

  • 1Automated Sample Handling Group, CSEM SA Centre Suisse d'Electronique et de Microtechnique, Neuchâtel, Switzerland.

Frontiers in Bioengineering and Biotechnology
|December 24, 2024
PubMed
Summary

A new automated platform sorts 3D spheroids using label-free imaging and machine learning, enabling scalable tissue engineering. This technology ensures spheroid homogeneity for bioprinting, advancing regenerative medicine for liver diseases.

Keywords:
3D bioprintingautomationhigh-throughput sortingmachine learningmulti-cellular spheroidstissue engineeringtransfer learning

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Area of Science:

  • Regenerative Medicine
  • Biotechnology
  • Tissue Engineering

Background:

  • End-stage liver disease is a growing global health concern, with organ transplant shortages limiting treatment options.
  • Tissue engineering offers a promising solution by creating functional tissues and organs in vitro.
  • Bioprinting with 3D biological models like multicellular spheroids enhances tissue construct mimicry of in vivo organ function, but lacks scalable spheroid production methods.

Purpose of the Study:

  • To develop a fully automated platform for high-throughput, label-free sorting of 3D spheroids.
  • To enable large-scale production of homogeneous spheroids for tissue fabrication.
  • To advance regenerative medicine by improving the scalability and standardization of tissue engineering.

Main Methods:

  • A custom-built, automated platform featuring a compact microscope and fluidic systems for high-throughput spheroid handling.
  • Label-free brightfield image analysis combined with machine learning for spheroid classification based on bioprinting compatibility.
  • Transfer learning applied to biological applications with limited datasets to assess spheroid viability and morphology.

Main Results:

  • Efficient sorting of both mono-cellular and multi-cellular liver spheroids was achieved.
  • The sorting process demonstrated preservation of spheroid viability and functionality.
  • The platform successfully classified spheroids for bioprinting applications using morphological analysis without fluorescent labels.

Conclusions:

  • The developed automated platform enables high-throughput, label-free spheroid sorting, addressing a key bottleneck in tissue engineering.
  • Machine learning classification of spheroids ensures homogeneity, crucial for standardized and scalable tissue fabrication.
  • This technology supports the advancement of regenerative medicine, particularly for liver tissue engineering and bioprinting applications.