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

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 contaminant...

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Main Authors: Strutt, James P. B., Natarajan, Meenubharathi, Lee, Elizabeth, Teo, Denise Bei Lin, Sin, Wei-Xiang, Cheung, Ka-Wai, Chew, Marvin, Thazin, Khaing, Barone, Paul W., Wolfrum, Jacqueline M., Williams, Rohan B. H., Rice, Scott A., Springs, Stacy L.
Other Authors: Singapore-MIT Alliance for Research and Technology
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/173925
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1739252024-03-07T15:30:26Z Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing Strutt, James P. B. Natarajan, Meenubharathi Lee, Elizabeth Teo, Denise Bei Lin Sin, Wei-Xiang Cheung, Ka-Wai Chew, Marvin Thazin, Khaing Barone, Paul W. Wolfrum, Jacqueline M. Williams, Rohan B. H. Rice, Scott A. Springs, Stacy L. Singapore-MIT Alliance for Research and Technology Singapore Centre for Environmental Life Sciences and Engineering Medicine, Health and Life Sciences Adventitious agents Machine learning 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 if a 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, including the 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. Ministry of Education (MOE) National Research Foundation (NRF) Published version This research is supported by the National Research Foundation, Prime Minister’s Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program, through Singapore MIT Alliance for Research and Technology (SMART): Critical Analytics for Manufacturing Personalised-Medicine (CAMP) Inter-Disciplinary Research Group. Financial support from National Research Foundation and Ministry of Education Singapore under its Research Centre of Excellence Program was provided through Singapore Centre for Environmental Life Sciences Engineering. 2024-03-06T06:28:53Z 2024-03-06T06:28:53Z 2023 Journal Article Strutt, J. P. B., Natarajan, M., Lee, E., Teo, D. B. L., Sin, W., Cheung, K., Chew, M., Thazin, K., Barone, P. W., Wolfrum, J. M., Williams, R. B. H., Rice, S. A. & Springs, S. L. (2023). Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing. Microbiology Spectrum, 11(5), e0135023-. https://dx.doi.org/10.1128/spectrum.01350-23 2165-0497 https://hdl.handle.net/10356/173925 10.1128/spectrum.01350-23 37646508 2-s2.0-85175792876 5 11 e0135023 en Microbiology Spectrum © 2023 Strutt et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Medicine, Health and Life Sciences
Adventitious agents
Machine learning
spellingShingle Medicine, Health and Life Sciences
Adventitious agents
Machine learning
Strutt, James P. B.
Natarajan, Meenubharathi
Lee, Elizabeth
Teo, Denise Bei Lin
Sin, Wei-Xiang
Cheung, Ka-Wai
Chew, Marvin
Thazin, Khaing
Barone, Paul W.
Wolfrum, Jacqueline M.
Williams, Rohan B. H.
Rice, Scott A.
Springs, Stacy L.
Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing
description 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 if a 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, including the 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.
author2 Singapore-MIT Alliance for Research and Technology
author_facet Singapore-MIT Alliance for Research and Technology
Strutt, James P. B.
Natarajan, Meenubharathi
Lee, Elizabeth
Teo, Denise Bei Lin
Sin, Wei-Xiang
Cheung, Ka-Wai
Chew, Marvin
Thazin, Khaing
Barone, Paul W.
Wolfrum, Jacqueline M.
Williams, Rohan B. H.
Rice, Scott A.
Springs, Stacy L.
format Article
author Strutt, James P. B.
Natarajan, Meenubharathi
Lee, Elizabeth
Teo, Denise Bei Lin
Sin, Wei-Xiang
Cheung, Ka-Wai
Chew, Marvin
Thazin, Khaing
Barone, Paul W.
Wolfrum, Jacqueline M.
Williams, Rohan B. H.
Rice, Scott A.
Springs, Stacy L.
author_sort Strutt, James P. B.
title Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing
title_short Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing
title_full Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing
title_fullStr Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing
title_full_unstemmed Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing
title_sort machine learning-based detection of adventitious microbes in t-cell therapy cultures using long-read sequencing
publishDate 2024
url https://hdl.handle.net/10356/173925
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