A Focal Adhesion Filament Cross-correlation Kit for fast, automated segmentation and correlation of focal adhesions and actin stress fibers in cells

Fibroblasts' secretome from calcified and non-calcified dermis in Pseudoxanthoma elasticum differently contributes to elastin calcification

Pseudoxanthoma elasticum (PXE) is a rare disease characterized by ectopic calcification, however, despite the widely spread effect of pro/anti-calcifying systemic factors associated with this genetic metabolic condition, it is not known why elastic fibers in the same patient are mainly fragmented or highly mineralized in clinically unaffected (CUS) and affected (CAS) skin, respectively. Cellular morphology and secretome are investigated in vitro in CUS and CAS fibroblasts. Here we show that, compared to CUS, CAS fibroblasts exhibit: a) differently distributed and organized focal adhesions and stress fibers; b) modified cell-matrix interactions (i.e., collagen gel retraction); c) imbalance between matrix metalloproteinases and tissue inhibitor of metalloproteinases; d) differentially expressed pro- and anti-calcifying proteoglycans and elastic-fibers associated glycoproteins. These data emphasize that in the development of pathologic mineral deposition fibroblasts play an active role altering the stability of elastic fibers and of the extracellular matrix milieu creating a local microenvironment guiding the level of matrix remodeling at an extent that may lead to degradation (in CUS) or to degradation and calcification (in CAS) of the elastic component. In conclusion, this study contributes to a better understanding of the mechanisms of the mineral deposition that can be also associated with several inherited or age-related diseases (e.g., diabetes, atherosclerosis, chronic kidney diseases).

SFAlab: image-based quantification of mechano-active ventral actin stress fibers in adherent cells

Ventral actin stress fibers (SFs) are a subset of actin SFs that begin and terminate at focal adhesion (FA) complexes. Ventral SFs can transmit forces from and to the extracellular matrix and serve as a prominent mechanosensing and mechanotransduction machinery for cells. Therefore, quantitative analysis of ventral SFs can lead to deeper understanding of the dynamic mechanical interplay between cells and their extracellular matrix (mechanoreciprocity). However, the dynamic nature and organization of ventral SFs challenge their quantification, and current quantification tools mainly focus on all SFs present in cells and cannot discriminate between subsets. Here we present an image analysis-based computational toolbox, called SFAlab, to quantify the number of ventral SFs and the number of ventral SFs per FA, and provide spatial information about the locations of the identified ventral SFs. SFAlab is built as an all-in-one toolbox that besides analyzing ventral SFs also enables the identification and quantification of (the shape descriptors of) nuclei, cells, and FAs. We validated SFAlab for the quantification of ventral SFs in human fetal cardiac fibroblasts and demonstrated that SFAlab analysis i) yields accurate ventral SF detection in the presence of image imperfections often found in typical fluorescence microscopy images, and ii) is robust against user subjectivity and potential experimental artifacts. To demonstrate the usefulness of SFAlab in mechanobiology research, we modulated actin polymerization and showed that inhibition of Rho kinase led to a significant decrease in ventral SF formation and the number of ventral SFs per FA, shedding light on the importance of the RhoA pathway specifically in ventral SF formation. We present SFAlab as a powerful open source, easy to use image-based analytical tool to increase our understanding of mechanoreciprocity in adherent cells.

Grading the Severity of Damage to the Perijunctional Actomyosin Ring and Zonula Occludens-1 of the Corneal Endothelium by Ensemble Learning Methods

Purpose: In many epithelia, including the corneal endothelium, intracellular/extracellular stresses break down the perijunctional actomyosin ring (PAMR) and zonula occludens-1 (ZO-1) at the apical junctions. This study aims to grade the severity of damage to PAMR and ZO-1 through machine learning. Methods: Immunocytochemical images of PAMR and ZO-1 were drawn from recent studies on the corneal endothelium subjected to hypothermia and oxidative stress. The images were analyzed for their morphological (e.g., Hu moments) and textural features (based on gray-level co-occurrence matrix [GLCM] and Gabor filters). The extracted features were ranked by SHapley analysis and analysis of variance. Then top features were used to grade the severity of damage using a suite of ensemble classifiers, including random forest, bagging classifier (BC), AdaBoost, extreme gradient boosting, and stacking classifier. Results: A partial set of features from GLCM, along with Hu moments and the number of hexagons, enabled the classification of damage to PAMR into Control, Mild, Moderate, and Severe with the area under the receiver operating characteristics curve (AUC) = 0.92 and F1 score = 0.77 with BC. In contrast, a bank of Gabor filters provided a partial set of features that could be combined with Hu moments, branch length, and sharpness for the classification of ZO-1 images into four levels with AUC = 0.95 and F1 score of 0.8 with BC. Conclusions: We have developed a workflow that enables the stratification of damage to PAMR and ZO-1. The approach can be applied to similar data during drug discovery or pathophysiological studies of epithelia.

FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking

Cytoskeletal pattern formation and structural dynamics are key to a variety of biological functions and a detailed and quantitative analysis yields insight into finely tuned and well-balanced homeostasis and potential pathological alterations. High content life cell imaging of fluorescently labeled cytoskeletal elements under physiological conditions is nowadays state-of-the-art and can record time lapse data for detailed experimental studies. However, systematic quantification of structures and in particular the dynamics (i.e. frame-to-frame tracking) are essential. Here, an unbiased, quantitative, and robust analysis workflow that can be highly automatized is needed. For this purpose we upgraded and expanded our fiber detection algorithm FilamentSensor (FS) to the FilamentSensor 2.0 (FS2.0) toolbox, allowing for automatic detection and segmentation of fibrous structures and the extraction of relevant data (center of mass, length, width, orientation, curvature) in real-time as well as tracking of these objects over time and cell event monitoring.

The actin cytoskeleton: Morphological changes in pre- and fully developed lung cancer

Actin, a primary component of the cell cytoskeleton can have multiple isoforms, each of which can have specific properties uniquely suited for their purpose. These monomers are then bound together to form polymeric filaments utilizing adenosine triphosphate hydrolysis as a source of energy. Proteins, such as Arp2/3, VASP, formin, profilin, and cofilin, serve important roles in the polymerization process. These filaments can further be linked to form stress fibers by proteins called actin-binding proteins, such as α-actinin, myosin, fascin, filamin, zyxin, and epsin. These stress fibers are responsible for mechanotransduction, maintaining cell shape, cell motility, and intracellular cargo transport. Cancer metastasis, specifically epithelial mesenchymal transition (EMT), which is one of the key steps of the process, is accompanied by the formation of thick stress fibers through the Rho-associated protein kinase, MAPK/ERK, and Wnt pathways. Recently, with the advent of "field cancerization," pre-malignant cells have also been demonstrated to possess stress fibers and related cytoskeletal features. Analytical methods ranging from western blot and RNA-sequencing to cryo-EM and fluorescent imaging have been employed to understand the structure and dynamics of actin and related proteins including polymerization/depolymerization. More recent methods involve quantifying properties of the actin cytoskeleton from fluorescent images and utilizing them to study biological processes, such as EMT. These image analysis approaches exploit the fact that filaments have a unique structure (curvilinear) compared to the noise or other artifacts to separate them. Line segments are extracted from these filament images that have assigned lengths and orientations. Coupling such methods with statistical analysis has resulted in development of a new reporter for EMT in lung cancer cells as well as their drug responses.

Merging of ventral fibers at adhesions drives the remodeling of cellular contractile systems in fibroblasts

Ventral stress fibers (VSFs) are contractile actin fibers dynamically attached to cell-matrix focal adhesions. VSFs are critical in cellular traction force production and migration. VSFs vary from randomly oriented short, thinner fibers to long, thick fibers that span along the whole long axis of a cell. De novo VSF formation was shown to occur by cortical actin mesh condensation or by crosslinking of dorsal stress fibers and transverse arcs at the cell front. However, the formation of long VSFs that extend across the whole cell axis is not well understood. Here, we report a novel phenomenon of VSF merging in migratory fibroblast cells, which is guided by mechanical force balance and contributes to VSF alignment along the long cell axis. The mechanism of VSF merging involves two steps: connection of two ventral fibers by an emerging myosin II bridge at an intervening adhesion and intervening adhesion dissolution. Our data indicate that these two steps are interdependent: slow adhesion disassembly leads to the slowing of the myosin bridge formation. Cellular data and computational modeling show that the contact angle between merging fibers decides successful merging, with shallow angles leading to merge failure. Our data and modeling further show that merging increases the share of uniformly aligned long VSFs, likely contributing to directional traction force production. Thus, we characterize merging as a process for dynamic reorganization of VSFs with functional significance for directional cell migration.

Discrete protein metric (DPM): A new image similarity metric to calculate accuracy of deep learning-generated cell focal adhesion predictions

Understanding cell behaviors can provide new knowledge on the development of different pathologies. Focal adhesion (FA) sites are important sub-cellular structures that are involved in these processes. To better facilitate the study of FA sites, deep learning (DL) can be used to predict FA site morphology based on limited microscopic datasets (e.g., cell membrane images). However, calculating the accuracy score of these predictions can be challenging due to the discrete/point pattern like nature of FA sites. In the present work, a new image similarity metric, discrete protein metric (DPM), was developed to calculate FA prediction accuracy. This metric measures differences in distribution (d), shape/size (s), and angle (a) of FA sites between predicted and ground truth microscopy images. Performance of the DPM was evaluated by comparing it to three other commonly used image similarity metrics: Pearson correlation coefficient (PCC), feature similarity index (FSIM), and Intersection over Union (IoU). A sensitivity analysis was performed by comparing changes in each metric value due to quantifiable changes in FA site location, number, aspect ratio, area, or orientation. Furthermore, accuracy score of DL-generated predictions was calculated using all four metrics to compare their ability to capture variation across samples. Results showed better sensitivity and range of variation for DPM compared to the other metrics tested. Most importantly, DPM had the ability to determine which FA predictions were quantitatively more accurate and consistent with qualitative assessments. The proposed DPM hence provides a method to validate DL-generated FA predictions and has the potential to be used for investigation of other sub-cellular protein aggregates relevant to cell biology.

Statistical parametrization of cell cytoskeleton reveals lung cancer cytoskeletal phenotype with partial EMT signature

Epithelial–mesenchymal Transition (EMT) is a multi-step process that involves cytoskeletal rearrangement. Here, developing and using an image quantification tool, Statistical Parametrization of Cell Cytoskeleton (SPOCC), we have identified an intermediate EMT state with a specific cytoskeletal signature. We have been able to partition EMT into two steps: (1) initial formation of transverse arcs and dorsal stress fibers and (2) their subsequent conversion to ventral stress fibers with a concurrent alignment of fibers. Using the Orientational Order Parameter (OOP) as a figure of merit, we have been able to track EMT progression in live cells as well as characterize and quantify their cytoskeletal response to drugs. SPOCC has improved throughput and is non-destructive, making it a viable candidate for studying a broad range of biological processes. Further, owing to the increased stiffness (and by inference invasiveness) of the intermediate EMT phenotype compared to mesenchymal cells, our work can be instrumental in aiding the search for future treatment strategies that combat metastasis by specifically targeting the fiber alignment process.

A computational method for automated quantification of actin stress fiber alignment in fluorescence images of cultured cells is presented, used to detect changes in stress fiber organization during EMT, with pathways regulating actin dynamics manipulated leading to the discovery of a cytoskeletal phenotype.

Quantitative Biophysical Metrics for Rapid Evaluation of Ovarian Cancer Metastatic Potential

Ovarian cancer is routinely diagnosed long after the disease has metastasized through the fibrous sub-mesothelium. Despite extensive research in the field linking ovarian cancer progression to increasingly poor prognosis, there are currently no validated cellular markers or hallmarks of ovarian cancer that can predict metastatic potential. To discern disease progression across a syngeneic mouse ovarian cancer progression model, here, we fabricated extracellular-matrix mimicking suspended fiber networks: crosshatches of mismatch diameters for studying protrusion dynamics, aligned same diameter networks of varying inter-fiber spacing for studying migration, and aligned nanonets for measuring cell forces. We found that migration correlated with disease, while force-disease biphasic relationship exhibited f-actin stress-fiber network dependence. However, unique to suspended fibers, coiling occurring at tips of protrusions and not the length or breadth of protrusions displayed strongest correlation with metastatic potential. To confirm that our findings were more broadly applicable beyond the mouse model, we repeated our studies in human ovarian cancer cell lines and found that the biophysical trends were consistent with our mouse model results. Altogether, we report complementary high throughput and high content biophysical metrics capable of identifying ovarian cancer metastatic potential on time scale of hours. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].