YAP Signaling Regulates the Cellular Uptake and Therapeutic Effect of Nanoparticles.

Interactions between living cells and nanoparticles are extensively studied to enhance the delivery of therapeutics. Nanoparticles size, shape, stiffness, and surface charge are regarded as the main features able to control the fate of cell-nanoparticle interactions. However, the clinical translation of nanotherapies has so far been limited, and there is a need to better understand the biology of cell-nanoparticle interactions. This study investigates the role of cellular mechanosensitive components in cell-nanoparticle interactions. It is demonstrated that the genetic and pharmacologic inhibition of yes-associated protein (YAP), a key component of cancer cell mechanosensing apparatus and Hippo pathway effector, improves nanoparticle internalization in triple-negative breast cancer cells regardless of nanoparticle properties or substrate characteristics. This process occurs through YAP-dependent regulation of endocytic pathways, cell mechanics, and membrane organization. Hence, the study proposes targeting YAP may sensitize triple-negative breast cancer cells to chemotherapy and increase the selectivity of nanotherapy.

Correction of AFM data artifacts using a convolutional neural network trained with synthetically generated data.

AFM microscopy from its nature produces outputs with certain distortions, inaccuracies and errors given by its physical principle. These distortions are more or less well studied and documented. Based on the nature of the individual distortions, different reconstruction and compensation filters have been developed to post-process the scanned images. This article presents an approach based on machine learning - the involved convolutional neural network learns from pairs of distorted images and the ground truth image and then it is able to process pairs of images of interest and produce a filtered image with the artifacts removed or at least suppressed. What is important in our approach is that the neural network is trained purely on synthetic data generated by a simulator of the inputs, based on an analytical description of the physical phenomena causing the distortions. The generator produces training samples involving various combinations of the distortions. The resulting trained network seems to be able to autonomously recognize the distortions present in the testing image (no knowledge of the distortions or any other human knowledge is provided at the test time) and apply the appropriate corrections. The experimental results show that not only is the new approach better or at least on par with conventional post-processing methods, but more importantly, it does not require any operator's input and works completely autonomously. The source codes of the training set generator and of the convolutional neural net model are made public, as well as an evaluation dataset of real captured AFM images.