Non-destructive classification of unlabeled cells: Combining an automated benchtop magnetic resonance scanner and artificial intelligence

• Published in: PLoS computational biology 2023-02, Vol.19 (2), p.e1010842-e1010842
• Main Authors: Fey, Philipp, Weber, Daniel Ludwig, Stebani, Jannik, Mörchel, Philipp, Jakob, Peter, Hansmann, Jan, Hiller, Karl-Heinz, Haddad, Daniel
• Format: Article
• Language: English
• Subjects: Analysis; Analytical methods; Artificial Intelligence; Artificial neural networks; Automation; Biology and Life Sciences; Cell lines; Cells; Classification; Computer and Information Sciences; Differentiation (biology); Engineering and Technology; Human error; Humans; Labelling; Magnetic resonance; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Manufacturing; Mechanization; Methods; Neural networks; Neural Networks, Computer; Nondestructive testing; Patient package inserts; Physical properties; Physical Sciences; Process control; Process controls; Reproducibility; Research and Analysis Methods; RNA sequencing; Robot arms; Scanners; Spectrum analysis; Support vector machines; Transplants
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1010842

In order to treat degenerative diseases, the importance of advanced therapy medicinal products has increased in recent years. The newly developed treatment strategies require a rethinking of the appropriate analytical methods. Current standards are missing the complete and sterile analysis of the product of interest to make the drug manufacturing effort worthwhile. They only consider partial areas of the sample or product while also irreversibly damaging the investigated specimen. Two-dimensional T1 / T2 MR relaxometry meets these requirements and is therefore a promising in-process control during the manufacturing and classification process of cell-based treatments. In this study a tabletop MR scanner was used to perform two-dimensional MR relaxometry. Throughput was increased by developing an automation platform based on a low-cost robotic arm, resulting in the acquisition of a large dataset of cell-based measurements. Two-dimensional inverse Laplace transformation was used for post-processing, followed by data classification performed with support vector machines (SVM) as well as optimized artificial neural networks (ANN). The trained networks were able to distinguish non-differentiated from differentiated MSCs with a prediction accuracy of 85%. To increase versatility, an ANN was trained on 354 independent, biological replicates distributed across ten different cell lines, resulting in a prediction accuracy of up to 98% depending on data composition. The present study provides a proof of principle for the application of T1 / T2 relaxometry as a non-destructive cell classification method. It does not require labeling of cells and can perform whole mount analysis of each sample. Since all measurements can be performed under sterile conditions, it can be used as an in-process control for cellular differentiation. This distinguishes it from other characterization techniques, as most are destructive or require some type of cell labeling. These advantages highlight the technique's potential for preclinical screening of patient-specific cell-based transplants and drugs.