ABSTRACT
Decellularized extracellular matrix (dECM) hydrogels have emerged as promising materials in tissue engineering. The steps to produce dECM hydrogels containing the bioactive epitopes found in the native matrix are often laborious, including the initial harvesting and decellularization of the animal organ. Furthermore, resulting hydrogels often exhibit weak mechanical properties that require the use of additional crosslinkers such as genipin to truly simulate the mechanical properties of the desired study tissue. In this work, we have developed a protocol to readily obtain tens of thin dECM hydrogel cryosections attached to a glass slide as support, to serve as scaffolds for two-dimensional (2D) or three-dimensional (3D) cell culture. Following extensive atomic force microscopy (AFM)-based mechanical characterization of dECM hydrogels crosslinked with increasing genipin concentrations (5 mM, 10 mM, and 20 mM), we provide detailed protocol recommendations for achieving dECM hydrogels of any biologically relevant stiffness. Given that our protocol requires hydrogel freezing, we also confirm that the approach taken can be further used to increase the mechanical properties of the scaffold in a controlled manner exhibiting twice the stiffness in highly crosslinked arrays. Finally, we explored the effect of ethanol-based short- and long-term sterilization on dECM hydrogels, showing that in some situations it may give rise to significant changes in hydrogel mechanical properties that need to be taken into account in experimental design. The hydrogel cryosections produced were shown to be biocompatible and support cell attachment and spreading for at least 72 h in culture. In brief, our proposed method may provide several advantages for tissue engineering: (1) easy availability and reduction in preparation time, (2) increase in the total hydrogel volume eventually used for experiments being able to obtain 15-22 slides from a 250 µL hydrogel) with a (3) reduction in scaffold variability (only a 17.5 ± 9.5% intraslide variability provided by the method), and (4) compatibility with live-cell imaging techniques or further cell characterization of cells.
ABSTRACT
Deep learning architectures for the classification of images have shown outstanding results in a variety of disciplines, including dermatology. The expectations generated by deep learning for, e.g., image-based diagnosis have created the need for non-experts to become familiar with the working principles of these algorithms. In our opinion, getting hands-on experience with these tools through a simplified but accurate model can facilitate their understanding in an intuitive way. The visualization of the results of the operations performed by deep learning algorithms on dermatological images can help students to grasp concepts like convolution, even without an advanced mathematical background. In addition, the possibility to tune hyperparameters and even to tweak computer code further empower the reach of an intuitive comprehension of these processes, without requiring advanced computational and theoretical skills. This is nowadays possible thanks to recent advances that have helped to lower technical and technological barriers associated with the use of these tools, making them accessible to a broader community. Therefore, we propose a hands-on pedagogical activity that dissects the procedures to train a convolutional neural network on a dataset containing images of skin lesions associated with different skin cancer categories. The activity is available open-source and its execution does not require the installation of software. We further provide a step-by-step description of the algorithm and of its functions, following the development of the building blocks of the computer code, guiding the reader through the execution of a realistic example, including the visualization and the evaluation of the results.
