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

Aug 4, 2026

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Assuring the safety of cell therapy products before patient release is critical, yet compendial sterility testing for bacteria and fungi typically requires 7–14 days. This work develops a rapid, untargeted method to sensitively detect microbial contaminants at low abundance from low-volume manufacturing samples. A long-read sequencing workflow using Oxford Nanopore MinION with 16S and 18S amplicons classifies reads metagenomically to predict microbial species. Extreme gradient boosting (XGBoost) first determines contamination and then validates correct versus misclassified predictions, enabling sterility status decisions. Using an optimized spiked-species-to-sequenced pipeline, microbial samples can be detected down to 10 CFU/mL, including USP <71> organisms present in T-cell cultures.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Abstract

Importance and clinical need

Long-read sequencing approach (16S/18S on MinION)

Metagenomic classification and XGBoost decision logic

Detection performance and limits (down to 10 CFU/mL)

Background: compendial sterility testing and alternatives (USP <71>, BacT/ALERT)

| Clinical Microbiology | Research Article Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing James P. B. Strutt,1 Meenubharathi Natarajan,1 Elizabeth Lee,1 Denise Bei Lin Teo,1 Wei-Xiang Sin,1 Ka-Wai Cheung,1 Marvin Chew,1 Khaing Thazin,1 Paul W. Barone,2 Jacqueline M. Wolfrum,2 Rohan B. H. Williams,1,3,4 Scott A. Rice,4,5 Stacy L. Springs1,2 AUTHOR AFFILIATIONS See affiliation list on p. 15. ABSTRACT Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7–14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect 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 ifa sample is contaminated, and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimized experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 colony-forming units (CFU)/mL using metagenomic classification. Machine learning can be coupled with long-read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, includingthe USP <71> organisms to 10 CFU/mL. IMPORTANCE This research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7–14 days, while our approach can significantly reduce this time. By combining advanced long-read nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process. KEYWORDS T-cells, adventitious agents, machine learning, sterility C ell therapies are increasingly prevalent in the treatment of incurable diseases. For example, chimeric antigen receptor T-cells (CAR-T) are used for the treatment of hematologic malignancies (1) . Ongoing work with human pluripotent stem cells (hPSCs) is targeted to treat Parkinson’s and age-related macular degeneration (AMD) (2), while mesenchymal stromal cells (MSCs) are being developed for immunomodulatory treatments (3) . Compendial sterility methods based on microbial growth are laborious and slow, and faster methods are required to guide clinical management (4) . Rapid testing methodologies could be an important tool for decreasing the time that a patient must wait from initial leukapheresis to application of the cell therapy. Depending on a patient’s current health status, the patient may not be able to afford delays in the application of a potentially lifesaving therapy. Thus, a reduction of release testing time Editor Wujian Ke, Southern Medical University, Guangzhou, China Address correspondence to Stacy L. Springs, ssprings@mit edu will ensure the timely and safe delivery of lifesaving cell therapies leading to improved patient outcomes. Current good manufacturing practice for microbial safety has been developed from experience in recombinant protein manufacturing, where standard practices include three pillars of safety: (i) identifying app

FAQs

Why is rapid sterility testing important for T-cell therapy manufacturing?

Compendial sterility testing for bacteria and fungi can take 7–14 days, delaying clinical release. Faster testing can support timely and safe delivery of cell therapies.

What sequencing and detection strategy was developed in this study?

The method uses Oxford Nanopore Technologies MinION long-read sequencing with 16S and 18S amplicon targets. Reads are classified metagenomically to predict microbial species.

How does the machine learning model contribute to sterility decisions?

An XGBoost algorithm evaluates whether a sample is contaminated and then assesses whether the predicted contaminant is correctly classified or misclassified. The model outputs a final sterility status decision.

Aug 4, 2026

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