Deep Learning-Powered Colloidal Digital SERS for Precise Monitoring of Cell Culture Media

Abstract

Maintaining consistent quality in biomanufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) often fall short in achieving rapid and accurate monitoring of small-molecule critical process parameters and critical quality attributes. Surface-enhanced Raman spectroscopy (SERS) holds great promise but faces challenges like intensity fluctuations, compromising reproducibility. Herein, we propose a deep learning-powered colloidal digital SERS platform. This innovation converts SERS spectra into binary “ON/OFF” signals based on defined intensity thresholds, which allows single-molecule event visualization and reduces false positives. Through integration with deep learning, this platform enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks. Furthermore, we demonstrate its accuracy and reproducibility for studying AMBIC 1.1 mammalian cell culture media. These results highlight its rapidity, accuracy, and precision, paving the way for widespread adoption and scale-up as a novel PAT tool in biomanufacturing and diagnostics.