The desmin mutation R349P increases contractility and fragility of stem cell-generated muscle micro-tissues.

AIMS: Desminopathies comprise hereditary myopathies and cardiomyopathies caused by mutations in the intermediate filament protein desmin that lead to severe and often lethal degeneration of striated muscle tissue. Animal and single cell studies hinted that this degeneration process is associated with massive ultrastructural defects correlating with increased susceptibility of the muscle to acute mechanical stress. The underlying mechanism of mechanical susceptibility, and how muscle degeneration develops over time, however, has remained elusive. METHODS: Here, we investigated the effect of a desmin mutation on the formation, differentiation, and contractile function of in vitro-engineered three-dimensional micro-tissues grown from muscle stem cells (satellite cells) isolated from heterozygous R349P desmin knock-in mice. RESULTS: Micro-tissues grown from desmin-mutated cells exhibited spontaneous unsynchronised contractions, higher contractile forces in response to electrical stimulation, and faster force recovery compared with tissues grown from wild-type cells. Within 1 week of culture, the majority of R349P desmin-mutated tissues disintegrated, whereas wild-type tissues remained intact over at least three weeks. Moreover, under tetanic stimulation lasting less than 5 s, desmin-mutated tissues partially or completely ruptured, whereas wild-type tissues did not display signs of damage. CONCLUSIONS: Our results demonstrate that the progressive degeneration of desmin-mutated micro-tissues is closely linked to extracellular matrix fibre breakage associated with increased contractile forces and unevenly distributed tensile stress. This suggests that the age-related degeneration of skeletal and cardiac muscle in patients suffering from desminopathies may be similarly exacerbated by mechanical damage from high-intensity muscle contractions. We conclude that micro-tissues may provide a valuable tool for studying the organization of myocytes and the pathogenic mechanisms of myopathies.

Integration of Leaky-Integrate-and-Fire Neurons in Standard Machine Learning Architectures to Generate Hybrid Networks: A Surrogate Gradient Approach.

Up to now, modern machine learning (ML) has been based on approximating big data sets with high-dimensional functions, taking advantage of huge computational resources. We show that biologically inspired neuron models such as the leaky-integrate-and-fire (LIF) neuron provide novel and efficient ways of information processing. They can be integrated in machine learning models and are a potential target to improve ML performance. Thus, we have derived simple update rules for LIF units to numerically integrate the differential equations. We apply a surrogate gradient approach to train the LIF units via backpropagation. We demonstrate that tuning the leak term of the LIF neurons can be used to run the neurons in different operating modes, such as simple signal integrators or coincidence detectors. Furthermore, we show that the constant surrogate gradient, in combination with tuning the leak term of the LIF units, can be used to achieve the learning dynamics of more complex surrogate gradients. To prove the validity of our method, we applied it to established image data sets (the Oxford 102 flower data set, MNIST), implemented various network architectures, used several input data encodings and demonstrated that the method is suitable to achieve state-of-the-art classification performance. We provide our method as well as further surrogate gradient methods to train spiking neural networks via backpropagation as an open-source KERAS package to make it available to the neuroscience and machine learning community. To increase the interpretability of the underlying effects and thus make a small step toward opening the black box of machine learning, we provide interactive illustrations, with the possibility of systematically monitoring the effects of parameter changes on the learning characteristics.

Sparsity through evolutionary pruning prevents neuronal networks from overfitting.

Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained to solve complex tasks was highly increased over the last decades. However, still the networks fail - in contrast to our brain - to develop general intelligence in the sense of being able to solve several complex tasks with only one network architecture. This could be the case because the brain is not a randomly initialized neural network, which has to be trained from scratch by simply investing a lot of calculation power, but has from birth some fixed hierarchical structure. To make progress in decoding the structural basis of biological neural networks we here chose a bottom-up approach, where we evolutionarily trained small neural networks in performing a maze task. This simple maze task requires dynamic decision making with delayed rewards. We were able to show that during the evolutionary optimization random severance of connections leads to better generalization performance of the networks compared to fully connected networks. We conclude that sparsity is a central property of neural networks and should be considered for modern Machine learning approaches.

Durotropic Growth of Pollen Tubes.

To reach the female gametophyte, growing pollen tubes must penetrate different tissues within the pistil, the female reproductive organ of a flower. Past research has identified various chemotropic cues that guide pollen tubes through the transmitting tract of the pistil, which represents the longest segment of its growth path. In addition, physical mechanisms also play a role in pollen tube guidance; however, these processes remain poorly understood. Here we show that pollen tubes from plants with solid transmitting tracts actively respond to the stiffness of the environment. We found that pollen tubes from Nicotiana tabacum and other plant species with a solid or semisolid transmitting tract increase their growth rate in response to an increasing matrix stiffness. By contrast, pollen tubes from Lilium longiflorum and other plant species with a hollow transmitting tract decrease their growth rate with increasing matrix stiffness, even though the forces needed to maintain a constant growth rate remain far below the maximum penetration force these pollen tubes are able to generate. Moreover, when confronted with a transition from a softer to a stiffer matrix, pollen tubes from N. tabacum display a greater ability to penetrate into a stiffer matrix compared with pollen tubes from L. longiflorum, even though the maximum force generated by pollen tubes from N. tabacum (11 µN) is smaller than the maximum force generated by pollen tubes from L. longiflorum (36 µN). These findings demonstrate a mechano-sensitive growth behavior, termed here durotropic growth, that is only expressed in pollen tubes from plants with a solid or semisolid transmitting tract and thus may contribute to an effective pollen tube guidance within the pistil.

Breast Cancer Cells Adapt Contractile Forces to Overcome Steric Hindrance.

Cell migration through the extracellular matrix is governed by the interplay between cell-generated propulsion forces, adhesion forces, and resisting forces arising from the steric hindrance of the matrix. Steric hindrance in turn depends on matrix porosity, matrix deformability, cell size, and cell deformability. In this study, we investigate how cells respond to changes in steric hindrance that arise from altered cell mechanical properties. Specifically, we measure traction forces, cell morphology, and invasiveness of MDA-MB 231 breast cancer cells in three-dimensional collagen gels. To modulate cell mechanical properties, we either decrease nuclear deformability by twofold overexpression of the nuclear protein lamin A or we introduce into the cells stiff polystyrene beads with a diameter larger than the average matrix pore size. Despite this increase of steric hindrance, we find that cell invasion is only marginally inhibited, as measured by the fraction of motile cells and the mean invasion depth. To compensate for increased steric hindrance, cells employ two alternative strategies. Cells with higher nuclear stiffness increase their force polarity, whereas cells with large beads increase their net contractility. Under both conditions, the collagen matrix surrounding the cells stiffens dramatically and carries increased strain energy, suggesting that increased force polarity and increased net contractility are functionally equivalent strategies for overcoming an increased steric hindrance.

CameraTransform: A Python package for perspective corrections and image mapping.

Camera images and video recordings are simple and non-invasive tools to investigate animals in their natural habitat. Quantitative evaluations, however, often require an exact reconstruction of object positions, sizes, and distances in the image. Here, we provide an open source software package to perform such calculations. Our approach allows the user to correct for perspective distortion, transform images to "bird's-eye" view projections, or transform image-coordinates to real-world coordinates and vice versa. The extrinsic camera parameters that are necessary to perform such image corrections and transformations (elevation, tilt/roll angle, and heading of the camera) are obtained from the image using contextual information such as a visible horizon, GPS coordinates of landmarks, known object sizes, or images of the same object obtained from different viewing angles. All mathematical operations are implemented in the Python package CameraTransform. The performance of the implementation is evaluated using computer-generated synthetic images with known camera parameters. Moreover, we test our algorithm on images of emperor penguin colonies, and demonstrate that the camera tilt and roll angles can be estimated with an error of less than one degree, and the camera elevation with an error of less than 5%. The CameraTransform software package simplifies camera matrix-based image transformations and the extraction of quantitative image information. An extensive documentation and usage examples in an ecological context are provided at http://cameratransform.readthedocs.io.

The role of focal adhesion anchoring domains of CAS in mechanotransduction.

CAS is a docking protein, which was shown to act as a mechanosensor in focal adhesions. The unique assembly of structural domains in CAS is important for its function as a mechanosensor. The tension within focal adhesions is transmitted to a stretchable substrate domain of CAS by focal adhesion-targeting of SH3 and CCH domain of CAS, which anchor the CAS protein in focal adhesions. Mechanistic models of the stretching biosensor propose equal roles for both anchoring domains. Using deletion mutants and domain replacements, we have analyzed the relative importance of the focal adhesion anchoring domains on CAS localization and dynamics in focal adhesions as well as on CAS-mediated mechanotransduction. We confirmed the predicted prerequisite of the focal adhesion targeting for CAS-dependent mechanosensing and unraveled the critical importance of CAS SH3 domain in mechanosensing. We further show that CAS localizes to the force transduction layer of focal adhesions and that mechanical stress stabilizes CAS in focal adhesions.

Measuring mechanical properties in cells: three easy methods for biologists.

The mechanism by which cells sense stresses and transmit them throughout the cytoplasm and the cytoskeleton (CSK) and by which these mechanical signals are converted into biochemical signaling responses is not clear. Specifically, there is little direct experimental evidence on how intracellular CSK structural elements in living cells deform and transmit stresses in response to external mechanical forces. Existing theories have invoked various biophysical and biochemical mechanisms to explain how cells spread, deform, divide, move, and change shape in response to mechanical inputs, but rigorous tests in cells are lacking. The lack of data and understanding is preventing the identification of mechanisms and sites of mechano-regulation in cells. Here, we introduce and describe three unique and easy methods for biologists to determine mechanical properties and signaling events in cells.