Objective To establish individually expandable primary fibroblast and keratinocyte cultures from 3‐mm skin punch biopsies for patient‐derived in vitro skin models to investigate of small fiber pathology. Methods We obtained 6‐mm skin punch biopsies from the calf of two patients with small fiber neuropathy (SFN) and two healthy controls. One half (3 mm) was used for diagnostic intraepidermal nerve fiber density (IENFD). From the second half, we isolated and cultured fibroblasts and keratinocytes. Cells were used to generate patient‐derived full‐thickness three‐dimensional (3D) skin models containing a dermal and epidermal component. Cells and skin models were characterized morphologically, immunocyto‐ and ‐histochemically (vimentin, cytokeratin (CK)‐10, CK 14, ki67, collagen1, and procollagen), and by electrical impedance. Results Distal IENFD was reduced in the SFN patients (2 fibers/mm each), while IENFD was normal in the controls (8 fibers/mm, 7 fibers/mm). Two‐dimensional (2D) cultured skin cells showed normal morphology, adequate viability, and proliferation, and expressed cell‐specific markers without relevant difference between SFN patient and healthy control. Using 2D cultured fibroblasts and keratinocytes, we obtained subject‐derived 3D skin models. Morphology of the 3D model was analogous to the respective skin biopsy specimens. Both, the dermal and the epidermal layer carried cell‐specific markers and showed a homogenous expression of extracellular matrix proteins. Interpretation Our protocol allows the generation of disease‐specific 2D and 3D skin models, which can be used to investigate the cross‐talk between skin cells and sensory neurons in small fiber pathology.
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.
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