Image created by Dr. Michael J. Miller
Ultraviolet light “fingerprints” on cell cultures and machine learning can now provide a definitive yes/no contamination assessment within 30 minutes.
Researchers from the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) interdisciplinary research group of the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, in collaboration with MIT, A*STAR Skin Research Labs, and the National University of Singapore, have developed a novel method that can quickly and automatically detect and monitor microbial contamination in cell therapy products (CTPs) early on during the manufacturing process. By measuring ultraviolet light absorbance of cell culture fluids and using machine learning to recognize light absorption patterns associated with microbial contamination, this preliminary testing method aims to reduce the overall time taken for sterility testing and, subsequently, the time patients need to wait for CTP doses. This is especially crucial where timely administration of treatments can be life-saving for terminally ill patients.
Cell therapy represents a promising new frontier in medicine, especially in treating diseases such as cancers, inflammatory diseases, and chronic degenerative disorders by manipulating or replacing cells to restore function or fight disease. However, a major challenge in CTP manufacturing is quickly and effectively ensuring that cells are free from contamination before being administered to patients.
Existing sterility testing methods, based on microbiological methods, are labor-intensive and require up to 14 days to detect contamination, which could adversely affect critically ill patients who need immediate treatment. While advanced techniques such as rapid microbiological methods (RMMs) can reduce the testing period to seven days, they still require complex processes such as cell extraction and growth enrichment mediums, and they are highly dependent on skilled workers for procedures such as sample extraction, measurement, and analysis. This creates an urgent need for new methods that offer quicker outcomes without compromising the quality of CTPs, meet the patient-use timeline, and use a simple workflow that does not require additional preparation.
In a paper titled “Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products,” published in the journal Scientific Reports, SMART CAMP researchers described how they combined UV absorbance spectroscopy to develop a machine learning-aided method for label-free, noninvasive, and real-time detection of cell contamination during the early stages of manufacturing.
This method offers significant advantages over both traditional sterility tests and RMMs, as it eliminates the need for staining of cells to identify labelled organisms, avoids the invasive process of cell extraction, and delivers results in under half-an-hour. It provides an intuitive, rapid “yes/no” contamination assessment, facilitating automation of cell culture sampling with a simple workflow. Furthermore, the developed method does not require specialized equipment, resulting in lower costs.
“This rapid, label-free method is designed to be a preliminary step in the CTP manufacturing process as a form of continuous safety testing, which allows users to detect contamination early and implement timely corrective actions, including the use of RMMs only when possible contamination is detected. This approach saves costs, optimizes resource allocation, and ultimately accelerates the overall manufacturing timeline,” says Shruthi Pandi Chelvam, senior research engineer at SMART CAMP and first author of the paper.
“Traditionally, cell therapy manufacturing is labor-intensive and subject to operator variability. By introducing automation and machine learning, we hope to streamline cell therapy manufacturing and reduce the risk of contamination. Specifically, our method supports automated cell culture sampling at designated intervals to check for contamination, which reduces manual tasks such as sample extraction, measurement, and analysis. This enables cell cultures to be monitored continuously and contamination to be detected at early stages,” says Rajeev Ram, the Clarence J. LeBel Professor in Electrical Engineering and Computer Science at MIT, a principal investigator at SMART CAMP, and the corresponding author of the paper.
Moving forward, future research will focus on broadening the application of the method to encompass a wider range of microbial contaminants, specifically those representative of current good manufacturing practices environments and previously identified CTP contaminants. Additionally, the model’s robustness can be tested across more cell types apart from MSCs. Beyond cell therapy manufacturing, this method can also be applied to the food and beverage industry as part of microbial quality control testing to ensure food products meet safety standards.
Reference
Pandi Chelvam, S., Ng, A.J., Huang, J. et al. Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products. Sci Rep 15, 7631 (2025). https://doi.org/10.1038/s41598-024-83114-y
Abstract
We demonstrate the feasibility of machine-learning aided UV absorbance spectroscopy for in-process microbial contamination detection during cell therapy product (CTP) manufacturing. This method leverages a one-class support vector machine to analyse the absorbance spectra of cell cultures and predict if a sample is sterile or contaminated. This label-free technique provides a rapid output (< 30 minutes) with minimal sample preparation and volume (< 1 mL). Spiking of 7 microbial organisms into mesenchymal stromal cells supernatant aliquots from 6 commercial donors showed that contamination events could be detected at low inoculums of 10 CFUs with mean true positive and negative rates of 92.7% and 77.7% respectively. The true negative rate further improved to 92% after excluding samples from a single donor with anomalously high nicotinic acid. In cells spiked with 10 CFUs of E. coli, contamination was detected at the 21-hour timepoint, demonstrating comparable sensitivity to compendial USP < 71 > test (~ 24 hours). We hypothesize that spectral differences between nicotinic acid and nicotinamide in the UV region are the underlying mechanisms for contamination detection. This approach can be deployed as a preliminary test during different CTP manufacturing stages, for real-time, continuous culture monitoring enabling early detection of microbial contamination, assuring safety of CTP.