Mechanical activation of lung epithelial cells through the ion channel Piezo1 activates the metalloproteinases ADAM10 and ADAM17 and promotes growth factor and adhesion molecule release.

In the lung, pulmonary epithelial cells undergo mechanical stretching during ventilation. The associated cellular mechanoresponse is still poorly understood at the molecular level. Here, we demonstrate that activation of the mechanosensitive cation channel Piezo1 in a human epithelial cell line (H441) and in primary human lung epithelial cells induces the proteolytic activity of the metalloproteinases ADAM10 and ADAM17 at the plasma membrane. These ADAMs are known to convert cell surface expressed proteins into soluble and thereby play major roles in proliferation, barrier regulation and inflammation. We observed that chemical activation of Piezo1 promotes cleavage of substrates that are specific for either ADAM10 or ADAM17. Activation of Piezo1 also induced the synthesis and ADAM10/17-dependent release of the growth factor amphiregulin (AREG). In addition, junctional adhesion molecule A (JAM-A) was shed in an ADAM10/17-dependent manner resulting in a reduction of cell contacts. Stretching experiments combined with Piezo1 knockdown further demonstrated that mechanical activation promotes shedding via Piezo1. Most importantly, high pressure ventilation of murine lungs increased AREG and JAM-A release into the alveolar space, which was reduced by a Piezo1 inhibitor. Our study provides a novel link between stretch-induced Piezo1 activation and the activation of ADAM10 and ADAM17 in lung epithelium. This may help to understand acute respiratory distress syndrome (ARDS) which is induced by ventilation stress and goes along with perturbed epithelial permeability and release of growth factors.

Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration.

Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and develop effective therapies. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device together with a deep learning tool that we developed for the enhanced-throughput analysis of AxD on microscopic images. The trained convolutional neural network (CNN) sensitively and specifically segmented the features of AxD including axons, axonal swellings, and axonal fragments. Its performance exceeded that of the human evaluators. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected a time-dependent degeneration of axons leading to a decrease in axon area, while axonal swelling and fragment areas increased. Axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network (RNN), we identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration). These findings indicate a morphological heterogeneity of AxD in hemorrhagic stroke. Our EntireAxon platform enables the systematic analysis of axons and AxD in time-lapse microscopy and unravels a so-far unknown intricacy in which AxD can occur in a disease context.