Multiparametric Analysis of Cell Shape Demonstrates that β-PIX Directly Couples YAP Activation to Extracellular Matrix Adhesion.

Mechanical signals from the extracellular matrix (ECM) and cellular geometry regulate the nuclear translocation of transcriptional regulators such as Yes-associated protein (YAP). Elucidating how physical signals control the activity of mechanosensitive proteins poses a technical challenge, because perturbations that affect cell shape may also affect protein localization indirectly. Here, we present an approach that mitigates confounding effects of cell-shape changes, allowing us to identify direct regulators of YAP localization. This method uses single-cell image analysis and statistical models that exploit the naturally occurring heterogeneity of cellular populations. Through systematic depletion of all human kinases, Rho family GTPases, GEFs, and GTPase activating proteins (GAPs), together with targeted chemical perturbations, we found that β-PIX, a Rac1/Ccd42 GEF, and PAK2, a Rac1/Cdc42 effector, drive both YAP activation and cell-ECM adhesion turnover during cell spreading. Our observations suggest that coupling YAP to adhesion dynamics acts as a mechano-timer, allowing cells to rapidly tune gene expression in response to physical signals

Size-tunable nanoneedle arrays for influencing stem cell morphology, gene expression and nuclear membrane curvature

High-aspect-ratio nanostructures have emerged as versatile platforms for intracellular sensing and biomolecule delivery. Here, we present a microfabrication approach in which a combination of reactive ion etching protocols was used to produce high-aspect-ratio, nondegradable silicon nanoneedle arrays with tip diameters that can be finely tuned between 20 and 700 nm. We used these arrays to guide the long-term culture of human mesenchymal stem cells (hMSCs). Notably, we used the nanoneedle tip diameter to control the morphology, nuclear size and F-actin alignment of interfaced hMSCs, and to regulate the expression of nuclear lamina genes, Yes-associated protein (YAP) target genes and focal adhesion genes. These topography-driven changes were attributed to signaling by Rho-family GTPase pathways, differences in the effective stiffness of the nanoneedle arrays and the degree of nuclear membrane impingement, with the latter clearly visualized using focused-ion beam scanning electron microscopy (FIB-SEM). Our approach to design high-aspect-ratio nanostructures will be broadly applicable to design biomaterials and biomedical devices used for long-term cell stimulation and monitoring

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

Visualizing cellular imaging data using PhenoPlot

Visualization is essential for data interpretation, hypothesis formulation and communication of results. However, there is a paucity of visualization methods for image-derived data sets generated by high-content analysis in which complex cellular phenotypes are described as high-dimensional vectors of features. Here we present a visualization tool, PhenoPlot, which represents quantitative high-content imaging data as easily interpretable glyphs, and we illustrate how PhenoPlot can be used to improve the exploration and interpretation of complex breast cancer cell phenotypes