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Updated: Sep 11, 2025

Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody
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Label-free chimeric antigen receptor T-cell expression analysis using neural networks and statistical distribution

Takehiko Ueda1, Ryo Kobayashi1, Nobuhiro Kasai2

  • 1Solution Planning Department, Healthcare Business Unit, Nikon Corporation, 1-5-20 Nishioi, Shinagawa-ku, Tokyo, 140-8601, Japan.

Biochemical and Biophysical Research Communications
|August 12, 2025
PubMed
Summary

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A new label-free method uses bright-field microscopy and deep learning to predict Chimeric Antigen Receptor T (CAR-T)-cell expression rates. This approach offers accurate, real-time monitoring for CAR-T cell therapy manufacturing and research.

Area of Science:

  • Biotechnology
  • Cellular Biology
  • Immunotherapy

Background:

  • Chimeric antigen receptor T (CAR-T)-cell therapy is a key treatment for blood cancers.
  • Accurate monitoring of CAR expression is vital for CAR-T cell therapy effectiveness and safety.
  • Current methods like flow cytometry are labor-intensive and can affect cell properties.

Purpose of the Study:

  • To develop a novel, label-free method for predicting CAR expression rates in T cells.
  • To establish a practical solution for real-time monitoring in CAR-T cell manufacturing and research.
  • To improve upon the accuracy and efficiency of current CAR expression assessment techniques.

Main Methods:

  • Utilized bright-field microscopy combined with deep learning (convolutional neural networks).
Keywords:
Bright-field imageChimeric antigen receptor T-cellChimeric antigen receptor expression rateConvolutional neural networksLabel-free methodNonstaining methodOptical microscope

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  • Integrated classification score distribution analysis with Gaussian fitting for rate estimation.
  • Validated the method against standard antibody-based flow cytometry measurements.

    • Main Results:

    • Achieved prediction accuracy within 5% of standard flow cytometry measurements across four donors.
    • Reduced the maximum prediction error from 13% to 4.4%.
    • Demonstrated the first successful label-free prediction of CAR expression rates in T cells.

    Conclusions:

    • The novel label-free method provides a practical and accurate approach for monitoring CAR expression.
    • This technique simplifies CAR-T cell analysis, relying only on standard bright-field microscopy.
    • The method is suitable for both research and manufacturing settings, enhancing CAR-T cell therapy development.