Automated segmentation and quantification of actin stress fibres undergoing experimentally induced changes

Quantifying cytoskeletal organization from optical microscopy data

The actin cytoskeleton plays a pivotal role in a broad range of physiological processes including directing cell shape and subcellular organization, determining cell mechanical properties, and sensing and transducing mechanical forces. The versatility of the actin cytoskeleton arises from the ability of actin filaments to assemble into higher order structures through their interaction with a vast set of regulatory proteins. Actin filaments assemble into bundles, meshes, and networks, where different combinations of these structures fulfill specific functional roles. Analyzing the organization and abundance of different actin structures from optical microscopy data provides a valuable metric for assessing cell physiological function and changes associated with disease. However, quantitative measurements of the size, abundance, orientation, and distribution of different types of actin structure remains challenging both from an experimental and image analysis perspective. In this review, we summarize image analysis methods for extracting quantitative values that can be used for characterizing the organization of actin structures and provide selected examples. We summarize the potential sample types and metric reported with different approaches as a guide for selecting an image analysis strategy.

Untangling Irregular Actin Cytoskeleton Architectures in Tomograms of the Cell with Struwwel Tracer

In this work, we established, validated, and optimized a novel computational framework for tracing arbitrarily oriented actin filaments in cryo-electron tomography maps. Our approach was designed for highly complex intracellular architectures in which a long-range cytoskeleton network extends throughout the cell bodies and protrusions. The irregular organization of the actin network, as well as cryo-electron-tomography-specific noise, missing wedge artifacts, and map dimensions call for a specialized implementation that is both robust and efficient. Our proposed solution, Struwwel Tracer, accumulates densities along paths of a specific length in various directions, starting from locally determined seed points. The highest-density paths originating from the seed points form short linear candidate filament segments, which are further scrutinized and classified by users via inspection of a novel pruning map, which visualizes the likelihood of being a part of longer filaments. The pruned linear candidate filament segments are then iteratively fused into continuous, longer, and curved filaments based on their relative orientations, gap spacings, and extendibility. When applied to the simulated phantom tomograms of a Dictyostelium discoideum filopodium under experimental conditions, Struwwel Tracer demonstrated high efficacy, with F1-scores ranging from 0.85 to 0.90, depending on the noise level. Furthermore, when applied to a previously untraced experimental tomogram of mouse fibroblast lamellipodia, the filaments predicted by Struwwel Tracer exhibited a good visual agreement with the experimental map. The Struwwel Tracer framework is highly time efficient and can complete the tracing process in just a few minutes. The source code is publicly available with version 3.2 of the free and open-source Situs software package.

Intracellular tension sensor reveals mechanical anisotropy of the actin cytoskeleton

The filamentous actin (F-actin) cytoskeleton is a composite material consisting of cortical actin and bundled F-actin stress fibers, which together mediate the mechanical behaviors of the cell, from cell division to cell migration. However, as mechanical forces are typically measured upon transmission to the extracellular matrix, the internal distribution of forces within the cytoskeleton is unknown. Likewise, how distinct F-actin architectures contribute to the generation and transmission of mechanical forces is unclear. Therefore, we have developed a molecular tension sensor that embeds into the F-actin cytoskeleton. Using this sensor, we measure tension within stress fibers and cortical actin, as the cell is subject to uniaxial stretch. We find that the mechanical response, as measured by FRET, depends on the direction of applied stretch relative to the cell's axis of alignment. When the cell is aligned parallel to the direction of the stretch, stress fibers and cortical actin both accumulate tension. By contrast, when aligned perpendicular to the direction of stretch, stress fibers relax tension while the cortex accumulates tension, indicating mechanical anisotropy within the cytoskeleton. We further show that myosin inhibition regulates this anisotropy. Thus, the mechanical anisotropy of the cell and the coordination between distinct F-actin architectures vary and depend upon applied load.

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.

Gravitational effects on fibroblasts' function in relation to wound healing

The spaceflight environment imposes risks for maintaining a healthy skin function as the observed delayed wound healing can contribute to increased risks of infection. To counteract delayed wound healing in space, a better understanding of the fibroblasts' reaction to altered gravity levels is needed. In this paper, we describe experiments that were carried out at the Large Diameter Centrifuge located in ESA-ESTEC as part of the ESA Academy 2021 Spin Your Thesis! Campaign. We exposed dermal fibroblasts to a set of altered gravity levels, including transitions between simulated microgravity and hypergravity. The addition of the stress hormone cortisol to the cell culture medium was done to account for possible interaction effects of gravity and cortisol exposure. Results show a main impact of cortisol on the secretion of pro-inflammatory cytokines as well as extracellular matrix proteins. Altered gravity mostly induced a delay in cellular migration and changes in mechanosensitive cell structures. Furthermore, 20 × g hypergravity transitions induced changes in nuclear morphology. These findings provide insights into the effect of gravity transitions on the fibroblasts' function related to wound healing, which may be useful for the development of countermeasures.

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.

Tracing and tracking filamentous structures across scales: A systematic review

Filamentous structures are ubiquitous in nature, are studied in diverse scientific fields, and span vastly different spatial scales. Filamentous structures in biological systems fulfill different functions and often form dynamic networks that respond to perturbations. Therefore, characterizing the properties of filamentous structures and the networks they form is important to gain better understanding of systems level functions and dynamics. Filamentous structures are captured by various imaging technologies, and analysis of the resulting imaging data addresses two problems: (i) identification (tracing) of filamentous structures in a single snapshot and (ii) characterizing the dynamics (i.e., tracking) of filamentous structures over time. Therefore, considerable research efforts have been made in developing automated methods for tracing and tracking of filamentous structures. Here, we provide a systematic review in which we present, categorize, and discuss the state-of-the-art methods for tracing and tracking of filamentous structures in sparse and dense networks. We highlight the mathematical approaches, assumptions, and constraints particular for each method, allowing us to pinpoint outstanding challenges and offer perspectives for future research aimed at gaining better understanding of filamentous structures in biological systems.

Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms

Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between—or specific shape on—individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D maps of subcellular components. Assuming that the tomogram can be rotated such that the filaments are oriented in a mean direction, the proposed framework first identifies local seed points for candidate filament segments, which are then grown from the seeds using a dynamic programming algorithm. We validate various algorithmic variations of our framework on simulated tomograms that closely mimic the noise and appearance of experimental maps. As we know the ground truth in the simulated tomograms, the statistical analysis consisting of precision, recall, and F1 scores allows us to optimize the performance of this new approach. We find that a bipyramidal accumulation scheme for path density is superior to straight-line accumulation. In addition, the multiplication of forward and backward path densities provides for an efficient filter that lifts the filament density above the noise level. Resulting from our tests is a robust method that can be expected to perform well (F1 scores 0.86–0.95) under experimental noise conditions.

Force-dependent activation of actin elongation factor mDia1 protects the cytoskeleton from mechanical damage and promotes stress fiber repair

Plasticity of cell mechanics underlies a wide range of cell and tissue behaviors allowing cells to migrate through narrow spaces, resist shear forces, and safeguard against mechanical damage. Such plasticity depends on spatiotemporal regulation of the actomyosin cytoskeleton, but mechanisms of adaptive change in cell mechanics remain elusive. Here, we report a mechanism of mechanically activated actin polymerization at focal adhesions (FAs), specifically requiring the actin elongation factor mDia1. By combining live-cell imaging with mathematical modeling, we show that actin polymerization at FAs exhibits pulsatile dynamics where spikes of mDia1 activity are triggered by contractile forces. The suppression of mDia1-mediated actin polymerization increases tension on stress fibers (SFs) leading to an increased frequency of spontaneous SF damage and decreased efficiency of zyxin-mediated SF repair. We conclude that tension-controlled actin polymerization acts as a safety valve dampening excessive tension on the actin cytoskeleton and safeguarding SFs against mechanical damage.

Tracing Filaments in Simulated 3D Cryo-Electron Tomography Maps Using a Fast Dynamic Programming Algorithm

We propose a fast, dynamic programming-based framework for tracing actin filaments in 3D maps of subcellular components in cryo-electron tomography. The approach can identify high-density filament segments in various orientations, but it takes advantage of the arrangement of actin filaments within cells into more or less tightly aligned bundles. Assuming that the tomogram can be rotated such that the filaments can be oriented to be directed in a dominant direction (i.e., the X, Y, or Z axis), the proposed framework first identifies local seed points that form the origin of candidate filament segments (CFSs), which are then grown from the seeds using a fast dynamic programming algorithm. The CFS length l can be tuned to the nominal resolution of the tomogram or the separation of desired features, or it can be used to restrict the curvature of filaments that deviate from the overall bundle direction. In subsequent steps, the CFSs are filtered based on backward tracing and path density analysis. Finally, neighboring CFSs are fused based on a collinearity criterion to bridge any noise artifacts in the 3D map that would otherwise fractionalize the tracing. We validate our proposed framework on simulated tomograms that closely mimic the features and appearance of experimental maps.

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

Focal adhesions (FAs) and associated actin stress fibers (SFs) form a complex mechanical system that mediates bidirectional interactions between cells and their environment. This linked network is essential for mechanosensing, force production and force transduction, thus directly governing cellular processes like polarization, migration and extracellular matrix remodeling. We introduce a tool for fast and robust coupled analysis of both FAs and SFs named the Focal Adhesion Filament Cross-correlation Kit (FAFCK). Our software can detect and record location, axes lengths, area, orientation, and aspect ratio of focal adhesion structures as well as the location, length, width and orientation of actin stress fibers. This enables users to automate analysis of the correlation of FAs and SFs and study the stress fiber system in a higher degree, pivotal to accurately evaluate transmission of mechanocellular forces between a cell and its surroundings. The FAFCK is particularly suited for unbiased and systematic quantitative analysis of FAs and SFs necessary for novel approaches of traction force microscopy that uses the additional data from the cellular side to calculate the stress distribution in the substrate. For validation and comparison with other tools, we provide datasets of cells of varying quality that are labelled by a human expert. Datasets and FAFCK are freely available as open source under the GNU General Public License.

Expression of cell type incongruent alpha-cardiac actin 1 subunit in medulloblastoma reveals a novel mechanism for cancer cell survival and control of migration

Background

Alterations in actin subunit expression have been reported in multiple cancers, but have not been investigated previously in medulloblastoma.

Methods

Bioinformatic analysis of multiple medulloblastoma tumor databases was performed to profile ACTC1 mRNA levels. Western blot was used to verify protein expression in established medulloblastoma cell lines. Immunofluorescence microscopy was performed to assess ACTC1 localization. Stable cell lines with ACTC1 overexpression were generated and shRNA knockdown of ACTC1 was accomplished. We used PARP1 cleavage by Western blot as a marker of apoptosis and cell survival was determined by FACS viability assay and colony formation. Cell migration with overexpression or knockdown of ACTC1 was determined by the scratch assay. Stress fiber length distribution was assessed by fluorescence microscopy.

Results

ACTC1 mRNA expression is highest in SHH and WNT medulloblastoma among all subgroups. ACTC1 protein was confirmed by Western blot in SHH subgroup and Group 3 subgroup cell lines with the lowest expression in Group 3 cells. Microscopy demonstrated ACTC1 co-localization with F-actin. Overexpression of ACTC1 in Group 3 cells abolished the apoptotic response to Aurora kinase B inhibition. Knockdown of ACTC1 in SHH cells and in Myc overexpressing SHH cells induced apoptosis, impaired colony formation, and inhibited migration. Changes in stress fiber length distribution in medulloblastoma cells are induced by alterations in ACTC1 abundance.

Conclusions

Alpha-cardiac actin (ACTC1) is expressed in SHH medulloblastoma. Expression of this protein in medulloblastoma modifies stress fiber composition and functions in promoting resistance to apoptosis induced by mitotic inhibition, enhancing cell survival, and controlling migration.

Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review

Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.

TRIP6 is required for tension at adherens junctions

Hippo signaling mediates influences of cytoskeletal tension on organ growth. TRIP6 and LIMD1 have each been identified as being required for tension-dependent inhibition of the Hippo pathway LATS kinases and their recruitment to adherens junctions, but the relationship between TRIP6 and LIMD1 was unknown. Using siRNA-mediated gene knockdown, we show that TRIP6 is required for LIMD1 localization to adherens junctions, whereas LIMD1 is not required for TRIP6 localization. TRIP6, but not LIMD1, is also required for the recruitment of vinculin and VASP to adherens junctions. Knockdown of TRIP6 or vinculin, but not of LIMD1, also influences the localization of myosin and F-actin. In TRIP6 knockdown cells, actin stress fibers are lost apically but increased basally, and there is a corresponding increase in the recruitment of vinculin and VASP to basal focal adhesions. Our observations identify a role for TRIP6 in organizing F-actin and maintaining tension at adherens junctions that could account for its influence on LIMD1 and LATS. They also suggest that focal adhesions and adherens junctions compete for key proteins needed to maintain attachments to contractile F-actin.

Nanoindentation of mesenchymal stem cells using atomic force microscopy: effect of adhesive cell-substrate structures

The procedure commonly adopted to characterize cell materials using atomic force microscopy neglects the stress state induced in the cell by the adhesion structures that anchor it to the substrate. In several studies, the cell is considered as made from a single material and no specific information is provided regarding the mechanical properties of subcellular components. Here we present an optimization algorithm to determine separately the material properties of subcellular components of mesenchymal stem cells subjected to nanoindentation measurements. We assess how these properties change if the adhesion structures at the cell-substrate interface are considered or not in the algorithm. In particular, among the adhesion structures, the focal adhesions and the stress fibers were simulated. We found that neglecting the adhesion structures leads to underestimate the cell mechanical properties thus making errors up to 15%. This result leads us to conclude that the action of adhesion structures should be taken into account in nanoindentation measurements especially for cells that include a large number of adhesions to the substrate.

Coarse-grained elastic network modelling: A fast and stable numerical tool to characterize mesenchymal stem cells subjected to AFM nanoindentation measurements

The knowledge of the mechanical properties is the starting point to study the mechanobiology of mesenchymal stem cells and to understand the relationships linking biophysical stimuli to the cellular differentiation process. In experimental biology, Atomic Force Microscopy (AFM) is a common technique for measuring these mechanical properties. In this paper we present an alternative approach for extracting common mechanical parameters, such as the Young's modulus of cell components, starting from AFM nanoindentation measurements conducted on human mesenchymal stem cells. In a virtual environment, a geometrical model of a stem cell was converted in a highly deformable Coarse-Grained Elastic Network Model (CG-ENM) to reproduce the real AFM experiment and retrieve the related force-indentation curve. An ad-hoc optimization algorithm perturbed the local stiffness values of the springs, subdivided in several functional regions, until the computed force-indentation curve replicated the experimental one. After this curve matching, the extraction of global Young's moduli was performed for different stem cell samples. The algorithm was capable to distinguish the material properties of different subcellular components such as the cell cortex and the cytoskeleton. The numerical results predicted with the elastic network model were then compared to those obtained from hertzian contact theory and Finite Element Method (FEM) for the same case studies, showing an optimal agreement and a highly reduced computational cost. The proposed simulation flow seems to be an accurate, fast and stable method for understanding the mechanical behavior of soft biological materials, even for subcellular levels of detail. Moreover, the elastic network modelling allows shortening the computational times to approximately 33% of the time required by a traditional FEM simulation performed using elements with size comparable to that of springs.

CytoSeg 2.0: automated extraction of actin filaments

MOTIVATION

Actin filaments (AFs) are dynamic structures that substantially change their organization over time. The dynamic behavior and the relatively low signal-to-noise ratio during live-cell imaging have rendered the quantification of the actin organization a difficult task.

RESULTS

We developed an automated image-based framework that extracts AFs from fluorescence microscopy images and represents them as networks, which are automatically analyzed to identify and compare biologically relevant features. Although the source code is freely available, we have now implemented the framework into a graphical user interface that can be installed as a Fiji plugin, thus enabling easy access by the research community.

AVAILABILITY AND IMPLEMENTATION

CytoSeg 2.0 is open-source software under the GPL and is available on Github: https://github.com/jnowak90/CytoSeg2.0.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RHOA-mediated mechanical force generation through Dectin-1

Dendritic cell-associated C-type lectin 1 (Dectin-1, also known as CLEC7A) is an innate immune pattern recognition receptor that recognizes β-glucan on the Candida albicans cell wall. Recognition of β-glucan by immune cells leads to phagocytosis, oxidative burst, cytokine and chemokine production. We looked for specific mechanisms that coordinate phagocytosis downstream of Dectin-1 leading to actin reorganization and internalization of fungus. We found that stimulation of Dectin-1 by soluble β-glucan leads to mechanical force generation and areal contraction in Dectin-1-transfected HEK-293 cells and M1 macrophages. With inhibitor studies, we found this force generation is a spleen tyrosine kinase (SYK)-independent, but SRC family kinase (SFK)-dependent process mediated through the RHOA-ROCK-myosin light chain (MLC) pathway. We confirmed activation of RHOA downstream of Dectin-1 using activity assays and stress fiber formation. Through phagocytosis assays, we found direct evidence for the importance of RHOA-ROCK-MLC signaling in the process of phagocytosis of C. albicans.

Area and Geometry Dependence of Cell Migration in Asymmetric Two-State Micropatterns

Microstructured surfaces provide a unique framework to probe cell migration and cytoskeletal dynamics in a standardized manner. Here, we report on the steady-state occupancy probability of cells in asymmetric two-state microstructures that consist of two fibronectin-coated adhesion sites connected by a thin guidance cue. In these dumbbell-like structures, cells transition between the two sites in a repeated and stochastic manner, and average dwell times in the respective microenvironments are determined from the cell trajectories. We study the dynamics of human breast carcinoma cells (MDA-MB-231) in these microstructures as a function of area, shape, and orientation of the adhesion sites. On square adhesive sites with different areas, we find that the occupancy probability ratio is directly proportional to the ratio of corresponding adhesion site areas. These asymmetries are well captured by a simple model for the stochastic nonlinear dynamics of the cells, which reveals generic features of the motion. Sites of equal area but different shape lead to equal occupancy if shapes are isotropic (e.g., squared or circular). In contrast, an asymmetry in the occupancy is induced by anisotropic shapes like rhombi, triangles, or rectangles that enable motion in the direction perpendicular to the transition axis. Analysis of the two-dimensional motion of cells between two rectangles with orthogonal orientation suggests that cellular transition rates depend on the cell polarization induced by anisotropic micropatterns. Taken together, our results illustrate how two-state micropatterns provide a dynamic migration assay with distinct dwell times and relative cell occupancy as readouts, which may be useful to probe cell-microenvironment interactions.

Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX

Studies of how individual semi-flexible biopolymers and their network assemblies change over time reveal dynamical and mechanical properties important to the understanding of their function in tissues and living cells. Automatic tracking of biopolymer networks from fluorescence microscopy time-lapse sequences facilitates such quantitative studies. We present an open source software tool that combines a global and local correspondence algorithm to track biopolymer networks in 2D and 3D, using stretching open active contours. We demonstrate its application in fully automated tracking of elongating and intersecting actin filaments, detection of loop formation and constriction of tilted contractile rings in live cells, and tracking of network deformation under shear deformation.

The Role of Palladin in Podocytes

Background Podocyte loss and effacement of interdigitating podocyte foot processes are the major cause of a leaky filtration barrier and ESRD. Because the complex three-dimensional morphology of podocytes depends on the actin cytoskeleton, we studied the role in podocytes of the actin bundling protein palladin, which is highly expressed therein.Methods We knocked down palladin in cultured podocytes by siRNA transfection or in zebrafish embryos by morpholino injection and studied the effects by immunofluorescence and live imaging. We also investigated kidneys of mice with podocyte-specific knockout of palladin (PodoPalld-/- mice) by immunofluorescence and ultrastructural analysis and kidney biopsy specimens from patients by immunostaining for palladin.Results Compared with control-treated podocytes, palladin-knockdown podocytes had reduced actin filament staining, smaller focal adhesions, and downregulation of the podocyte-specific proteins synaptopodin and α-actinin-4. Furthermore, palladin-knockdown podocytes were more susceptible to disruption of the actin cytoskeleton with cytochalasin D, latrunculin A, or jasplakinolide and showed altered migration dynamics. In zebrafish embryos, palladin knockdown compromised the morphology and dynamics of epithelial cells at an early developmental stage. Compared with PodoPalld+/+ controls, PodoPalld-/- mice developed glomeruli with a disturbed morphology, an enlarged subpodocyte space, mild effacement, and significantly reduced expression of nephrin and vinculin. Furthermore, nephrotoxic serum injection led to significantly higher levels of proteinuria in PodoPalld-/- mice than in controls. Kidney biopsy specimens from patients with diabetic nephropathy and FSGS showed downregulation of palladin in podocytes as well.Conclusions Palladin has an important role in podocyte function in vitro and in vivo.