Mechanotransduction - how cells sense physical forces and translate them into biochemical and biological responses - is a vibrant and rapidly-progressing field, and is important for a broad range of biological phenomena. This forum explores the role of mechanotransduction in a variety of cellular activities and highlights intriguing questions that deserve further attention.

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length, and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.

Adult human mesenchymal stem cells show structural rearrangements of their cytoskeletal network during mechanically induced differentiation toward various cell types. In particular, the alignment of acto-myosin fibers is cell fate-dependent and can serve as an early morphological marker of differentiation. Quantification of such nanostructures on a mesoscopic scale requires high-resolution imaging techniques. Here, we use small- angle x-ray scattering with a spot size in the micro- and submicrometer range as a high-resolution and label-free imaging technique to reveal structural details of stem cells and differentiated cell types. We include principal component analysis into an automated empirical analysis scheme that allows the local characterization of oriented structures. Results on freeze-dried samples lead to quantitative structural information for all cell lines tested: differentiated cells reveal pronounced structural orientation and a relatively intense overall diffraction signal, whereas naive human mesenchymal stem cells lack these features. Our data support the hypothesis of stem cells establishing ordered structures along their differentiation process.

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Dual-color metal-induced energy transfer (dcMIET) imaging simultaneously resolves the axial localization of two distinct intracellular structures with nanometer accuracy. Combined with Förster resonance energy transfer, dcMIET is a powerful method to elucidate cellular architecture on the nanometer scale, also in living cells.

We generalize the SiZer of Chaudhuri and Marron (J. Amer. Statist. Assoc. 94 (1999) 807-823; Ann. Statist. 28 (2000) 408-428) for the detection of shape parameters of densities on the real line to the case of circular data. It turns out that only the wrapped Gaussian kernel gives a symmetric, strongly Lipschitz semi-group satisfying "circular" causality, that is, not introducing possibly artificial modes with increasing levels of smoothing. Some notable differences between Euclidean and circular scale space theory are highlighted. Based on this, we provide an asymptotic theory to make inference about the persistence of shape features. The resulting circular mode persistence diagram is applied to the analysis of early mechanically-induced differentiation in adult human stem cells from their actin-myosin filament structure. As a consequence, the circular SiZer based on the wrapped Gaussian kernel (WiZer) allows the verification at a controlled error level of the observation reported by Zemel et al. (Nat. Phys. 6 (2010) 468-473): Within early stem cell differentiation, polarizations of stem cells exhibit preferred directions in three different microenvironments.

Kidney fibrosis presents a hallmark of chronic kidney disease. With ever-increasing patient numbers and limited treatment options available, novel strategies for therapeutic intervention in kidney disease are warranted. Fibrosis commonly results from a wound healing response to repeated or chronic tissue damage, irrespective of the underlying etiology, and can occur in virtually any solid organ or tissue. In order to identify targets relevant for kidney fibrosis, we aimed to employ CRISPR screening in primary human kidney fibroblasts. We demonstrate that CRISPR technology can be applied in primary kidney fibroblasts and can furthermore be used to conduct arrayed CRISPR screening using a high-content imaging readout in a whole genome-wide manner. Hits coming out of this screen were validated using orthogonal approaches and present starting points for validation of novel targets relevant to kidney disease.

Despite recent efforts to understand homeostasis in epithelial tissues, there are many unknowns surrounding this steady state. It is considered to be regulated by mechanoresponse, but unlike for single cells, this remains heavily debated for tissues. Here, we show that changes in matrix stiffness induce a non-equilibrium transition from tubular to squamous Madin-Darby Canine Kidney II tissues. Nonetheless, despite different cell morphologies and densities, all homeostatic tissues display equivalent topologies, which, hence, must be actively targeted and regulated. On the contrary, the mechanoresponse induces dramatic changes in the large-scale organization of the colonies. On stiff gels, this yields an unreported cooperative state of motile cells displaying higher densities than in the arrested homeostatic state. This suggests a more complex relation between cell density and motility than previously anticipated. Our results unequivocally relate the mechanosensitive properties of individual cells to the evolving macroscopic structures, an effect that could be important for understanding the emergent pathologies of living tissues.