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Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation.

Kylie J Trettner, Jeremy Hsieh1, Weikun Xiao2

  • 1Pasadena Polytechnic High School, Pasadena, California 91106, USA.

APL Bioengineering
|April 3, 2024
PubMed
Summary

A new image processing algorithm quantifies cellular viability in 3D cultures without assays. This method accurately tracks cell health and speeds up analysis, improving 3D culture research robustness.

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Area of Science:

  • Cell Biology
  • Biotechnology
  • Bioinformatics

Background:

  • Traditional cell viability assays often provide limited, binary readouts.
  • Recent advances integrate deep learning with image analysis for automated cellular characterization.
  • There is a need for more continuous and detailed viability assessments across various cell culture conditions.

Purpose of the Study:

  • To develop an image processing algorithm for quantifying cellular viability in 3D cultures without assay-based indicators.
  • To validate the algorithm's performance against human experts and demonstrate its utility in tracking therapeutic effects.
  • To establish a foundation for more robust and reproducible 3D culture analysis.

Main Methods:

  • An image processing algorithm was developed to quantify features indicative of cellular viability in 3D cultures.
  • The algorithm's performance was compared to human expert analysis on whole-well images across different culture conditions and time points.
  • A longitudinal study using high-content imaging tracked the impact of a therapeutic agent on pancreatic cancer spheroids.

Main Results:

  • The algorithm demonstrated performance comparable to human experts in assessing cellular viability.
  • It successfully tracked viability at both individual spheroid and whole-well levels during the longitudinal study.
  • The proposed method reduced analysis time by 97% compared to expert evaluation.

Conclusions:

  • The developed image processing algorithm offers a robust, assay-independent method for quantifying cellular viability in 3D cultures.
  • This approach significantly accelerates analysis time and enhances the reproducibility of 3D culture studies.
  • The method is versatile, applicable across different imaging systems, and foundational for advancing biological and clinical research.