Machine-learning based detection of adventitious microbes in T-cell therapy cultures using long read sequencing

Background Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, rapid sterility tests for bacteria and fungi take weeks. The goal of this work was to develop an approach for the rapid and sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of T-cell therapies. Methods We developed a Nanopore-based MinION long read sequencing methodology using 16S and 18S amplicon sequencing on USP<71> organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated and second, determine whether the predicted contaminant is genuine. The model was used to make a final decision on the sterility status of the input sample. Results An optimised experimental and bioinformatics pipeline starting from engineered spiked samples through to sequenced reads allowed for the detection of microbial samples at 10 CFU using metagenomic classification. Conclusion Machine learning can be coupled with long read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP<71> organisms to 10 CFU. Graphical Abstract