Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin bundles, traditional approaches to filament detection and tracing have failed in these cases. In this study, we introduce BundleTrac, an effective method to trace hundreds of filaments in a bundle. A comparison between BundleTrac and manually tracing the actin filaments in a stereocilium showed that BundleTrac accurately built 326 of 330 filaments (98.8%), with an overall cross-distance of 1.3 voxels for the 330 filaments. BundleTrac is an effective semi-automatic modeling approach in which a seed point is provided for each filament and the rest of the filament is computationally identified. We also demonstrate the potential of a denoising method that uses a polynomial regression to address the resolution and high-noise anisotropic environment of the density map

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

Quantitative Analysis Techniques for Assessing Organelle Organization and Dynamics in Individual Cells

In biomedical optics and microscopy, the organization and morphology of organelles have been widely studied. In spite of novel imaging techniques, there is still a lack of quantitative tools to easily measure cellular characteristics from image data. Previous studies have explored multiple approaches to assess organelle organization and alignment, resulting in complicated and extensive algorithms that are both subject to multiple steps of image processing and influenced by non-cellular artifacts. In this thesis, a technique called the Modified Blanket Method (MBM) is introduced to quantify organelle organization through measurements of fractal dimension (FD) on a pixel-by-pixel basis. With the use of simulated fractal clouds, it is demonstrated that the MBM is capable of accurately and rapidly quantify FD, having a higher sensitivity to a wider range of FD values compared to previous methods. Furthermore, the MBM could differentiate mitochondrial organization of radiation-resistant A549 lung cancer cells at different time points post-radiation. In later experiments, the MBM is combined with similar computational techniques to quantify fiber alignment and nuclear shape through measurements of directional variance (DV) and nuclear aspect ratio (NAR). The simultaneous use of these tools demonstrated that the organization and alignment of mitochondria and actin of NIH 3T3 cells treated with L-buthionine-sulfoximine (BSO) change over time, having different nuclear shapes as well. It is then concluded the this set of computational tools is capable of providing significant cellular data, which could potentially be employed to understand the cellular dynamics of multiple pathological conditions such as diabetes, Alzheimer’s, Leigh’s syndrome, and myopathy, all of which are known to be influenced by dysfunctional organelles

Functional and Computational Validation of a Tissue Engineered Model of Human Type 2 Long QT Syndrome

Type 2 Long QT Syndrome (LQT2) refers to a channelopathy in which a dysfunctional IKr current results in a prolonged QT interval potentially causing increased risk of arrhythmic events. Patient derived induced pluripotent stem cells (hiPSCs) have allowed for the creation of cardiomyocytes that can replicate such pathologies in vitro. Using iPSCs, A422T LQT2 cardiomyocytes were differentiated and seeded onto engineered heart slices (EHS) in order to form a more physiologically relevant model of the disease compared to current monolayer culture methods. These LQT2 EHS accurately exhibited the expected electrophysiological phenotype, such that action potentials were longer than those of control EHS at various pacing rates. The establishment of this model allowed for two functional characterizations. First, using S1S2 pacing, a quantitative measure of LQT2 EHS refractory periods was measured and was observed to be correlated with action potential duration. Second, LQT2 EHS were dosed with a highly specific IKr blocking drug, E-4031, revealing dose dependent action potential elongation. While rate-dependent action potential elongation was observed, it was generally not found to be statistically significant. Finally, the data collected from the LQT2 EHS was used to constrain an existing computational model of iPSC derived cardiomyocytes as a proof-of-concept demonstration

A Robust Actin Filaments Image Analysis Framework.

The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a 'cartoon' part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the 'cartoon' image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts grown in two different conditions: static (control) and fluid shear stress. The proposed methodology exhibited higher sensitivity values and similar accuracy compared to state-of-the-art methods

A Robust Actin Filaments Image Analysis Framework

The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a 'cartoon' part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the 'cartoon' image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts grown in two different conditions: static (control) and fluid shear stress. The proposed methodology exhibited higher sensitivity values and similar accuracy compared to state-of-the-art methods