Presentation
13 March 2024 Early selection of desired biopharmaceutical CHO cell lines using label-free multimodal optical microscopy and machine learning
Jindou Shi, Alexander Ho, Eric J. Chaney, Janet E. Sorrells, Kevin Tan, Aneesh Alex, Remben Talaban, Darold R. Spillman, Marina Marjanovic, Minh Doan, Steve R. Hood, Stephen A. Boppart
Author Affiliations +
Abstract
The biopharmaceutical industry relies on selecting high-performing cell lines to meet quality and manufacturability criteria. However, this process is time- and labor-intensive. To address this, label-free multimodal multiphoton microscopy techniques were employed to characterize biopharmaceutical cell lines in early passages. Using a machine learning-assisted single-cell analysis pipeline, over 95% accuracy for monoclonal cell line classification was achieved in all passages. Additionally, Open Set Recognition allowed the differentiation of desired cell lines in polyclonal pools. The study offers a promising solution to expedite the cell line selection process, reducing time and resources while ensuring the identification of high-performance biopharmaceutical cell lines.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jindou Shi, Alexander Ho, Eric J. Chaney, Janet E. Sorrells, Kevin Tan, Aneesh Alex, Remben Talaban, Darold R. Spillman, Marina Marjanovic, Minh Doan, Steve R. Hood, and Stephen A. Boppart "Early selection of desired biopharmaceutical CHO cell lines using label-free multimodal optical microscopy and machine learning", Proc. SPIE PC12846, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII, PC1284603 (13 March 2024); https://doi.org/10.1117/12.3001298
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KEYWORDS
Machine learning

Optical microscopy

Fluorescence lifetime imaging

Multiphoton fluorescence microscopy

Manufacturing

Monte Carlo methods

Multimodal imaging

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