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

摘要

Author summaryStem cells are of great importance for scientific and clinical research. Modern approaches seek to treat degenerative diseases with stem cell therapies. For these applications, it is of central interest to ensure that the cells have developed into the cell or tissue type required for the respective treatment. To this end, a small number of cells or tissues are typically sacrificed to perform routine staining or genetic analysis, and the results are generalized to the entire sample. This study presents an alternative method based on magnetic resonance that is capable of classifying cells in the entirety of the culture. Since only lightweight and mobile components were included, this application is highly flexible and can be used in a routine laboratory environment. With the help of a self-developed robotic platform a large database of cell-related measurements was created. Using an artificial neural network, it was possible to accurately follow the developmental process of mesenchymal stromal cells and distinguish between differentiated and undifferentiated cells with an accuracy of 85. Furthermore, it was possible to distinguish between multiple different cell lines with an accuracy of up to 98 depending on the sample composition. 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 T-1 / T-2 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 T-1 / T-2 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.