Machine learning-driven label-free cell sorting for CAR-T cell manufacturing

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      Background & Aim

      We have developed a label-free high throughput machine learning-driven cell sorter based on cells’ morphological information. The cytometer acquires cell-specific waveform signals containing morphologic information from a defined training set. After a supervised machine learning model is developed from the training set, the cell of interest can be classified and sorted based on the scores obtained by the machine learning model. We investigated that the label-free cell classification and sorting of CAR-T cells to improve manufacturing process.
      CAR-T therapy has shown innovative therapeutic effects in leukemia treatment. However, many clinical studies have been conducted and the clinical results indicated that quality control for the therapeutic cell production process is always an issue for any cell therapy manufacturing process. Also, an ideal CAR-T cell population or “High-Quality CAR-T” with regard to a T cell subset, phenotype, and CAR construct is under investigation. Detailed analysis of CAR-T cells administered to CLL patients have been conducted and the results revealed the important features of clinically active CAR-T such as its glycolysis status, early memory phenotype or exhaustion low profile.

      Methods, Results & Conclusion

      To avoid CAR-T manufacturing failure and/or improve the CAR-T quality, it is necessary to collect enough healthy and functional T cells from the patient by leukapheresis in the manufacturing process. We investigated that our label-free cytometer can discriminate healthy T cells from samples after hemolysis process, wherein CD3 positive T cells and other cells were used as a training set to develop the T cell classifier model. The accuracy of the label-free discrimination of T cells was found to be very high. This label-free classification may allow us to perform the quality control of CAR-T manufacturing without the use of surface markers and at the minimal sample loss.
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