A Whole Genome-Wide Arrayed CRISPR Screen in Primary Organ Fibroblasts to Identify Regulators of Kidney Fibrosis.

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.

Dual-color metal-induced and Förster resonance energy transfer for cell nanoscopy.

We report a novel method, dual-color axial nanometric localization by metal--induced energy transfer, and combine it with Förster resonance energy transfer (FRET) for resolving structural details in cells on the molecular level. We demonstrate the capability of this method on cytoskeletal elements and adhesions in human mesenchymal stem cells. Our approach is based on fluorescence-lifetime-imaging microscopy and allows for precise determination of the three-dimensional architecture of stress fibers anchoring at focal adhesions, thus yielding crucial information to understand cell-matrix mechanics. In addition to resolving nanometric structural details along the z-axis, we use FRET to gain precise information on the distance between actin and vinculin at focal adhesions.

X-Ray Micro- and Nanodiffraction Imaging on Human Mesenchymal Stem Cells and Differentiated Cells.

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.

Mechanotransduction: use the force(s).

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.

The filament sensor for near real-time detection of cytoskeletal fiber structures.

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.