SMART researchers develop fast advanced method to detect microbial contamination in cell cultures
SMART CAMP Senior Research Engineer Shruthi Pandi Chelvam using the UV absorbance spectrometer to measure the absorbance spectra of cell culture samples
Researchers from the Critical Analytics for Manufacturing Personalized-Medicine (CAMP), an Interdisciplinary Research Group (IRG) of the Singapore-MIT Alliance for Research and Technology (SMART), in collaboration with the Massachusetts Institute of Technology (MIT), ASTAR Skin Research Labs (ASRL), and the National University of Singapore (NUS), have developed a novel method for rapid and automated detection of microbial contamination in cell therapy products (CTPs).
The approach uses ultraviolet (UV) absorbance spectroscopy to scan cell culture fluids, combined with machine learning to identify contamination patterns. This new technique provides a simple yes/no contamination result within 30 minutes using a small sample volume—far quicker and more resource-efficient than traditional sterility tests that can take up to 14 days.
This rapid testing is particularly valuable for critically ill patients who urgently need cell therapies, as delays due to lengthy sterility assessments can be life-threatening.
Cell therapy holds promise for treating cancers, inflammatory conditions, and degenerative diseases by restoring or replacing damaged cells. However, current sterility testing methods are labor-intensive and slow, requiring microbial growth in enrichment media, and are highly dependent on skilled personnel. Even rapid microbiological methods (RMMs) still require several days and complex procedures.
In contrast, SMART CAMP’s method is non-invasive, label-free, and compatible with automation. It avoids staining and cell extraction, requires no incubation or enrichment steps, and does not rely on specialised equipment—resulting in lower costs and greater efficiency. These features make it well-suited for early-stage contamination monitoring during manufacturing.
“This method enables continuous safety checks during production and allows timely corrective actions when contamination is suspected. RMMs can then be used more selectively, reducing costs and accelerating production,” said Shruthi Pandi Chelvam, Senior Research Engineer at SMART CAMP and first author of the study, published in Scientific Reports.
“Cell therapy manufacturing is traditionally laborious and prone to variability. By integrating machine learning and automation, we aim to streamline this process and reduce contamination risk. Our method enables scheduled, automated sampling and early detection without manual intervention,” added Prof Rajeev Ram, Principal Investigator at SMART CAMP, MIT Professor, and corresponding author of the paper.
Future work will expand the method’s ability to detect a wider range of microbial contaminants relevant to cGMP settings and CTP manufacturing, and validate it across more cell types beyond mesenchymal stem cells (MSCs). The approach may also be adapted for microbial quality control in food and beverage production.
This research is supported by the National Research Foundation (NRF) Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.
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