Abstract
Adoptive cell therapies are transforming the treatment of cancer and autoimmunity by enhancing patients’ own immune cells to fight disease. In cell therapy manufacturing, immunomagnetic beads are used to isolate and activate target cells for gene transfer but must be removed downstream to ≤10 beads per 300,000 cells. Current quantification requires time-intensive and error-prone manual counting using brightfield microscopy, while existing automated approaches struggle with variable bead-cell morphology and tedious sample preparation steps. Raman spectroscopy offers rapid, morphology-independent detection using molecular signatures generated by inelastic light scattering. Here, we leverage immunomagnetic beads’ strong Raman signatures to quantify them in area scans from dried samples, achieving single bead resolution and accurate counting of bead clusters with and without cells. Using low power (≤7 mW) and exposure times (≥0.5 s), the average area under 3 signature Raman peaks (1110 cm -1 , 1346 cm -1 , and 1595 cm -1 ) are measured and input to a linear regression model, achieving a mean squared error (MSE) of <0.2 beads. Our results show Raman spectroscopy as a robust, automated approach for bead counting in existing pipelines with potential to improve the safety and throughput of cell therapies.
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Abstract
Adoptive cell therapies are transforming the treatment of cancer and autoimmunity by enhancing patients’ own immune cells to fight disease. In cell therapy manufacturing, immunomagnetic beads are used to isolate and activate target cells for gene transfer but must be removed downstream to ≤10 beads per 300,000 cells. Current quantification requires time-intensive and error-prone manual counting using brightfield microscopy, while existing automated approaches struggle with variable bead-cell morphology and tedious sample preparation steps. Raman spectroscopy offers rapid, morphology-independent detection using molecular signatures generated by inelastic light scattering. Here, we leverage immunomagnetic beads’ strong Raman signatures to quantify them in area scans from dried samples, achieving single bead resolution and accurate counting of bead clusters with and without cells. Using low power (≤7 mW) and exposure times (≥0.5 s), the average area under 3 signature Raman peaks (1110 cm-1, 1346 cm-1, and 1595 cm-1) are measured and input to a linear regression model, achieving a mean squared error (MSE) of <0.2 beads. Our results show Raman spectroscopy as a robust, automated approach for bead counting in existing pipelines with potential to improve the safety and throughput of cell therapies.
Competing Interest Statement
The authors have declared no competing interest.
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