Fraunhofer Institute for Integrated Circuits IIS, Development Center X-ray Technology, Würzburg, Germany.
Experimental Physics V (Biophysics), Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
PLoS Comput Biol. 2023 Feb 21;19(2):e1010842. doi: 10.1371/journal.pcbi.1010842. eCollection 2023 Feb.
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.
为了治疗退行性疾病，近年来高级治疗药物的重要性有所增加。新开发的治疗策略需要重新思考适当的分析方法。当前的标准缺少对感兴趣产品的完整无菌分析，从而使药物制造工作变得有价值。它们只考虑样品或产品的部分区域，同时还会不可逆地破坏被研究的样本。二维 T1/T2 MR 弛豫度符合这些要求，因此是细胞治疗制造和分类过程中很有前途的过程控制方法。在这项研究中，使用台式磁共振扫描仪进行二维磁共振弛豫度测量。通过开发基于低成本机械臂的自动化平台来提高通量，从而获得大量细胞测量的数据集。使用二维逆拉普拉斯变换进行后处理，然后使用支持向量机（SVM）和优化的人工神经网络（ANN）进行数据分类。经过训练的网络能够以 85%的预测准确率区分未分化和分化的间充质干细胞。为了提高通用性，在 10 个不同细胞系的 354 个独立的、生物学重复的数据集上对 ANN 进行了训练，根据数据组成，预测准确率高达 98%。本研究为 T1/T2 弛豫度作为一种非破坏性细胞分类方法的应用提供了原理证明。它不需要对细胞进行标记，并且可以对每个样本进行整体分析。由于所有测量都可以在无菌条件下进行，因此它可以用作细胞分化的过程控制方法。与其他表征技术相比，这是它的优势，因为大多数技术都是破坏性的或需要某种类型的细胞标记。这些优势突出了该技术在基于患者特定细胞的移植和药物的临床前筛选中的应用潜力。
