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.
Computer vision and image-analytical tools are essential in order to study the biology of cells [29]. Several approaches exist for the analysis of filamentous structures [19–21,30–36], consisting in one or several sequential processing steps: pre-processing, filaments network segmentation, and individual fibers extraction. The filaments analysis problem can be solved in many different ways, as illustrated in Fig 2a. These solutions can be roughly organized in at least three main categories (middle layers in Fig 2a and 2b): based on filaments directionality, based on filaments network, and based on single-filaments extraction. The processing path (from the image to output) will naturally determine the computational burden, accuracy and the amount of information to be extracted from the image. The strategy we followed is depicted in Fig 2b. In the first category, the filaments information is directly extracted from the pre-processed image [30,35], sidestepping the foreground/background segmentation step. Segmentation-related errors were avoided at the cost of limiting the model applicability to directionality-related analysis [37] only. Networks-related approaches extract the filaments networks [19] after filtering, allowing to complement orientation information with motion and filaments density analysis [38]. One category of methods for the identification of fiber networks uses template matching [39], where prior knowledge about the target is incorporated into 3D (or 2D) templates. These template-based detection methods are more selective and impose a similarity function. In [40], actin filaments in cryo-ET data sets have been segmented using a stochastic template-based search, which combines a genetic algorithm and a bidirectional expansion strategy. Template matching methods does not resolve the possible template overlap when tracing converging filament branches, thus network junctions could be left undetected. Such problem has been solved in [41] where an active contour based method allowed increasing the segmentation robustness by incorporating prior information about the filament shape. Such approach explicitly models the linear nature of filaments. The overall strategy of the network-based approaches is to extract the whole curvilinear network at once. In contrast, our work aims at providing information at the single-fiber level, where individual filament segments [20,21,32] are extracted. This allows to perform further analysis [11,16,42–46] taking into account fibers position, orientation, and length. However, errors introduced during the different processing stages can accumulate and, unless appropriate computational steps are taken, later analysis (statistical and other) can be compromised.
MC3T3-E1 cells, established as an osteoblastic cell line, were provided from Sigma (99072810). Osteoblasts were grown in alpha-modified minimal essential medium (α-MEM; Life Technologies) supplemented with 10% fetal bovine serum (Life Technologies), 1% L-glutamin (Life Technologies) and 1% penicillin/streptomycin (Life Technologies) in an incubator at 5% CO2 heated at 37°C. The medium was changed twice weekly, and the cells were subcultured into 75 cm2 culture flasks by detaching them gently after a brief PBS rinsing step followed by Trypsin treatment once the cells were reaching subconfluency. For mechanical stimulation, MC3T3-E1 cells were plated into Ibidi μ-slide device (μ-Slide I 0. 4 Luer, ibiTreat from Ibidi) at a concentration of 1. 5 × 104 cells/mL. After overnight culture, the medium was replaced by α-MEM (powder exempt of Bicarbonate, Life Technologies) 10% FBS 1% L-glutamin 1% penicillin/streptomycin 25 mM HEPES (Life Technologies) adjusted to pH 7. 4 for 4 hours in the incubator without CO2 at 37°C. The shear stress was induced by a fluidic system composed of two containers and a pump. This system, depicted in Fig 4a, permits a gravity driven constant flow of culture medium in the chamber containing the osteoblasts. The flow rate was adjusted by setting the container (see Fig 4a nb 4) at the appropriate height above the osteoblasts. In our case, it provided a shear stress between 9 and 12 dyn/cm2. This value corresponds roughly to the one to which endothelial cells are exposed to in our arteries. The direction of the shear stress flow is estimated around ≈ 80° as illustrated in Fig 4b. After stress exposure for 4 hours, the cells were rinsed in phosphate saline buffer (PBS) and chemically fixed with 4% paraformaldehyde (PFA) for 15 minutes. Before immunostaining the cells were rinsed twice in PBS. Eventually, the fixed cells were incubated with PBS 0. 2% Triton X100 for 20 minutes, and exposed to Alexa Fluor 568 phalloidin (Molecular Probes) for 1 hour at room temperature. Finally the cells were rinsed again with PBS before a quick water rinse and coverage with cover glass (by using polyvinylalcohol (Sigma) ). A static osteoblast culture, in the same ibidi device, was performed and actin stained as control. Imaging was performed with an inverted Axiovert 200M system with a 40x Plan-Neofluor (Carl Zeiss, Oberkochen, Germany). We used a motorized platform (MS-2000, Applied Scientific Instrumentation, with NanoDrive controller; Mad City Labs, Madison, WI) to scan the sample. Acquisition was performed using a CoolSnap HQ2 camera (PhotoMetrics, Tucson, AZ) and the Multi-Dimensional Acquisition module of the software Metamorph (Molecular Devices, Sunnyvale, CA). The problem of separating an image into different semantic constituent parts, ‘textures’ and ‘cartoon’, can be addressed by several approaches. Variational calculus [24,25], and sparse multi-source separation [23] are among the most popular methods and their success varies depending on the image nature. In this work, we follow the Starck’s [23] image decomposition which is based on the Basis-Pursuit denoising (BPDN) algorithm. The basic idea behind this algorithm is to choose two appropriate dictionaries, one for the representation of ‘textures’, and the other for the ‘cartoon’ parts. Both dictionaries are to be designed such that each leads to sparse representations over the images it is serving, while yielding non-sparse representations on the other content type. In our problem, we model an imaged (actin) cytoskeleton f as a combination of two sources plus some additive noise: f = u f + v a + η (1) being va the background artifacts related content, and uf the filamentous elements; the noise is represented by η. In [23] the sparse source separation problem, for the image f, has been defined as: min λ a, λ f ‖ f - D a λ a - D f λ f ‖ 2 2 + γ ‖ λ a ‖ p + γ ‖ λ f ‖ p + δ (∇ λ a + ‖ λ a ‖ 1) (2) with γ > 0, δ ≥ 0 and p ∈ {0,1}. The obtained sparse coefficients λf provides the fibers-related content uf = Df λf, referred to as fibers image in the rest of the paper, and λa provides the artifacts related content ua = Da λa, referred to as artifacts image; the norm p ∈ {0,1} determines the reguralizer type and the parameter γ regulates the solution coefficients. The reconstructed image f ^ = u f + v a is an approximation of f involving artifacts-related and fiber-related dictionaries, Da and Df respectively; the reminder f - f ^ is usually related to noise η. The definition of the dictionaries Da and Df is very much related to the nature of the different contents present in the image, therefore we considered different dictionaries based on fast transforms. For the artifacts related content we used an undecimated wavelet transform (UDWT) for modeling the dictionary Da, whereas for modeling the fibers dictionary Df, we used the curvelets transform. The latter being, as discussed in the introduction section, a natural choice for modeling curvilinear structures, while the wavelets transform allows modeling artifacts present in the images. Note that, in [23] the image component has been modelled by ridgelets and the texture (artefact) component by a Discrete Cosine Transform (DCT). Ridgelets has been also used in [50] to represent global lines in images. Our implementation of the image decomposition is based on the MCALab library provided in [54], running a maximum of 100 iterations. The parameter γ (in Eq 2) was linearly decremented during the iterations and initialized accordingly. For all reported experiments, we set p = 0 (in Eq 2), namely a ℓ0-norm for the model definition, and δ = 3. Results of the the image decomposition are illustrated in Fig 5. We refer the reader to [50] for more details on the solution algorithm, including initialization and updates of the γ parameter (in Eq 2), and to the Supporting Information (SN. 1. 1 in S1 Text) for a discussion on the impact of the different parameters values on the decomposition results. For the filaments network segmentation, we opted for a multi-scale line detector based on a structural element of different orientations and widths, representing the scales. The multi-scale line detector basically analyze each pixel’s neighborhood of uE at different scales by evaluating (according to a score) if such pixel is part of a line of certain width. Such evaluation is performed by computing a ‘line response’ for the width evaluation, but also for a length evaluation that will be used in the next processing step for individual lines segments detection, as illustrated in Fig 7. The line response takes place at each pixel in the image, in a (discrete) set of possible orientations between 0 and 180 degrees, considering linear elements of size s ∈ [1, W], with W the expected fibers width (this parameter is related to the magnification of the image). Then, the final multi-scale line response of each pixel (x, y) of the enhanced fibers image, uE, is provided as [34]: u G (x, y) = 1 W + 1 ∑ s = 1 W max θ ∈ { 0, ⋯, 180 } R (u E (x, y); θ, s, W) ∀ (x, y) ∈ Ω (4) where R (.), applied on the fibers enhanced image uE, provides a score indicating how likely is certain point to be the center of a line passing though it with a certain width and a certain direction, by analyzing the neighboring points. The reader is referred to [34] for the details on how R (.) is computed. In our implementation, based on the source code provided by [34], we retained the advantages of the multi-scale linear response image, uG, and at the same time introduced some modifications to obtain the final binary image, uB of the fibers-related pixels. For the latter, we applied on the uG gray-scale image a local thresholding algorithm to separate background and fibers pixels. For this purpose, we made use of the Wellner’s adaptive thresholding [55] were a median filtering provides an estimated local threshold. The final retained local threshold is a percentage b of the estimated threshold. The smaller b is the more line segments candidates are retained in the final binary image, representing a set of edge segments, which are clean, contiguous, i. e. , connected, chains of edge pixels. Fig 8 illustrates the output of this step. Note that the obtained edges have a width larger than 1 pixel. Several complex approaches of line grouping, such as the on in [56], have been proposed within the computer vision community. However, for our purpose of fibers extraction, we developed a simple algorithm capable of iteratively extracting continuous linear segments (denoted as filament segments) by connecting the fixed-length segments extracted in the previous step. We first associate to a given filament segment all overlapping fixed-length segments oriented in the same direction (same θ). By repeating this process, all the overlapping segments with the same orientation will be combined into a longer straight-line segment. We then connect segments according to their orientation difference up to a ‘curvature’ threshold θ < Tθ. In addition, when merging (i. e. connecting) the k-th and the i-th segments (Fk⋃Fi), we discard all the pixels that are beyond the connection (intersection) point. The above described procedure is detailed in Algorithm (1), and illustrated in Fig 9. Algorithm 1 Fibers extraction θi: Indicates the orientation of extracted fiber Fi δ (Fi): Indicates the length of extracted fiber Fi Input: Tθ as angle merging tolerance (F, θ) ← List of fixed-length extracted segments tuples (Line segmentation section) for ξ = 0⋯Tθ 0 for i = 1⋯ do S ← { k ∈ N + | k > i, - ξ ≤ θ k - θ i ≤ ξ, OverlapEndPoints (Fk, Fi) } if S ≠ ∅ then k ← S (1) (F k, θ k) ← (F k ⋃ F i, δ (F k) θ k + δ (F i) θ i δ (F k) + δ (F i) ) end if end for end for Output: list of individual fibers (F, θ) Note: Fk⋃Fi combines the segments Fk and Fi discarding all the pixels located beyond the connection (intersection) point.
The cytoskeleton plays an important role in numerous physiological and pathological processes, its morphological characteristics are therefore of prime importance to understand numerous basic cellular phenomena, such as cellular adaptation to physical or chemical stress. However, cytoskeleton quantification and analysis is far from being straightforward, and sophisticated algorithms are required to fulfill the task. In this work we present a processing framework that efficiently detects cytoskeletal fibers and quantifies its morphological characteristics such as the number of filaments it contains, their length and orientation. The proposed model was tested on images of osteoblasts cultivated in shear stress and static (control) conditions. The detection of highly oriented actin fibers in shear stress cultivated cells corroborates what one would expect in such a condition, i. e. an alignment of the fibers with the direction of the flow. In addition, our algorithm successfully detected the perinuclear actin cap, a structure difficult to detect by other state-of-the-art methods, and that seems to play an important role in mechano- transduction [11,58]. It was also shown that the model separated filaments and imaging-related artifacts very efficiently, even in the presence of heavy blurring, a step that endowed the model with high sensitive detection capabilities. The proposed framework can be extended to extract 3D meshwork of actin filaments. Indeed, after the image decomposition and filament enhancements steps, a method such as the multiple Stretching Open Active Contours (SOACs) of [61] could be used for fibers tracing.
We propose a novel actin filaments cytoskeleton analysis framework that allows extracting quasi-straight individual fibers in a robust manner, and provides their respective position, orientation, and length as output. The proposed framework is defined as a three-steps processing sequence, that can explicitly cope with high-throughput imaging related issues, such as noise/artifacts presence and heavy blurring, and can similarly process artifacts-free and well-focused images.
cell motility
medicine and health sciences
actin filaments
classical mechanics
engineering and technology
signal processing
mechanical stress
osteoblasts
materials science
connective tissue cells
cellular structures and organelles
cytoskeleton
materials by structure
fibers
research and analysis methods
contractile proteins
actins
imaging techniques
animal cells
proteins
connective tissue
biological tissue
physics
shear stresses
biochemistry
cytoskeletal proteins
cell biology
anatomy
image processing
biology and life sciences
cellular types
physical sciences
A Robust Actin Filaments Image Analysis Framework