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Quantification of cardiomyocyte contraction is usually obtained by measuring globally cell shortening from the displacement of cell extremities. We developed a correlation-based optical flow method, which correlates the whole-cell temporal pattern with a precise quantification of the intracellular strain wave at the sarcomeres level. A two-dimensional image correlation analysis of cardiomyocytes phase-contrast images was developed to extract local cell deformations from videomicroscopy time-lapse sequences. Test images, synthesized from known intensity displacement fields, were first used to validate the method. Intracellular strain fields were then computed from videomicroscopy time-lapse sequences of single adult and neonatal cardiomyocytes. The propagation of the sarcomeres contraction–relaxation wave during cell contraction has been successfully quantified. The time-varying patterns of intracellular displacement were obtained accurately, even when cardiomyocyte bending occurred in pace with contraction. Interestingly, the characterization of the successive 2D displacement fields show a direct quantification of the variation with time of intracellular strains anywhere in the cell. The proposed method enables a quantitative analysis of cardiomyocyte contraction without requiring wave tracking with the use of fluorescent calcium probes. Thus, our algorithmic approach provides a fast and efficient tool for analyzing the correlation between global cell dynamical behavior and mechanosensitive intracellular processes. © 2008 International Society for Advancement of Cytometry
INTRACELLULAR Ca2+ waves play an integral part in the process of excitation-contraction coupling within mammalian cardiomyocytes. Ca2+ waves might spontaneously arise from a triggering pulse of Ca2+ released from the sarcoplasmic reticulum, which under certain circumstances propagates throughout the cell (1–3). The qualitative and quantitative characterization of Ca2+ waves, including amplitude, frequency, and propagation velocity, is thus a central issue for understanding both normal and pathological contractile cell behavior, such as the correlation of spontaneous Ca2+ waves with arrhythmic electrical activity (4–6). Despite obvious limitations, such analyses are usually conducted on isolated single cardiac cells. Indeed, within the myocardium, the cardiomyocytes are arranged in a complex network. Thus, correlating accurately forces and length changes with intracellular Ca2+ waves is rather difficult. Monitoring Ca2+ waves has then been made possible by using fast confocal microscopy techniques, which image the intensity variation of calcium fluorescent probes such as Fluo-3 (7, 8). In addition, such probes have been proven to be highly valuable for analyzing the dynamic of intracellular Ca2+ components, like ryanodine receptors (9, 10).
On the other hand, the direct quantification of sarcomere mechanical status becomes highly valuable to get insights into the regulation of cardiac muscle contraction mediated by the mechanosensitivity of biochemical processes or molecular interactions, which depend on the sarcomere length or on actin-myosin filaments spacing. Sarcomeres length measurements are usually based on laser diffraction techniques (11), but this approach is not well suited for the analysis of the spatio-temporal variation of the sarcomeres length at the cell level, especially when the dynamic of the cardiomyocyte contraction wave has to be characterized.
To overcome these limitations, we developed an optical flow approach aiming at characterizing the spatio-temporal variation of intracellular strain fields associated with sarcomeres contraction–relaxation over the course of cardiomyocyte contraction. Even though optical flow methods are rather widely applied in several engineering or bioengineering fields, only a few attempts have yet been made to apply them to single cell dynamics analysis (12–14), mostly because animal cells are highly deformable objects. Among the rather large spectrum of optical flow methods implementations (15, 16), Image Correlation Method (ICM) (17–19) seemed to us a quite relevant approach for characterizing intracellular strain fields during cardiomyocyte contraction. Indeed, the highly structured and anisotropic organization of the cell sarcomeres defines a very precise and repetitive spatial pattern, which may be used in a rather discriminating way by ICM when looking for a similarity between two successive patches of the region of interest (ROI) within cell images. Thus, we were expecting that such a priori optimality can be quantitatively assessed by developing an image-processing algorithm that takes benefit of the specific grey level patterns generated by phase contrast observation of contracting cardiac cells.
In this article, we propose and validate a complete procedure which provides an accurate estimation of the 2D displacement fields at any cardiomyocyte location from the analysis of time-lapse sequences of single cell contraction recorded using phase contrast and differential interface contrast (DIC) videomicroscopy. An essential step in this procedure is to find the displacement field, which optimizes the prediction of the light intensity pattern which will be observed when considering two successive images of the sequence. The first part of the article focuses on the method implementation. Then, our method reliability and accuracy is assessed by analyzing different sets of synthesized pair of images, constructed from known intensity displacement fields. The second part of the article is devoted to the analysis of experimental time-lapse sequences showing different spatio-temporal patterns of spontaneously contracting adult rat cardiomyocytes. For each considered experiment, the spatial distribution of intracellular strain fields has been characterized at several times during the cardiomyocyte contraction. In the discussion section, the robustness of our approach is discussed by considering less textured contracting objects. For this purpose, image sequences of spontaneously contracting neonatal cardiomyocytes have been analyzed, since at this stage of development, the cytoskeleton of these cells lacks the regular sarcomere organization present in adult cells.
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- MATERIALS AND METHODS
- LITERATURE CITED
In this study, a correlation-based optical flow method has been developed to analyze time-lapse sequences of contracting cardiomyocytes and to quantify originally spatio-temporal variations of intracellular strains generated at a sarcomere scale. The proposed image processing method allowed a precise tracking of the strain wave traveling along the cardiac cell by analyzing the correlation of grey level patterns between consecutive pairs of images.
While belonging to a class of image processing methods rather widely used for analyzing objects motions from image sequences (16), few applications have been reported in the context of cell dynamics to date (12–14). Indeed, the association of the usually limited contrast of videomicroscopy images with large cell deformations defines a challenging framework for implementation of optical flow methods. Thus, in order to obtain a 2D vector representation of image pixels displacements, regularization conditions are usually added to constrain the search for the optimum displacement field solution. The method developed here presents substantial improvements compared with the method used in other studies (13, 30), since it avoids the need for choosing regularization constraints. In fact, our method takes benefit of the landmarks provided by the sarcomeres structural organization, and thus provides a precise characterization of cardiomyocyte contraction and deformation without requiring the coupling of phase contrast observation with intracellular calcium imaging by fluorescent probes. In addition, there is no need for linking specific markers, such as microbeads, on the cell surface to track complex cell distortions (31–33).
Our approach also differs from other optical flow approaches developed for analyzing oscillatory dynamics of cell protrusions (12) or tissue contractions (34), which model the flow by a linear transformation. As a result, one gets a compact and global measurement of the biological object deformation from the time evolution of motion descriptors associated with the different types of singular points, characterizing the velocity field computed between two consecutive images (19).
In the present study, we were looking for a simple and efficient way to link observations made at the whole-cell level to dynamical features exhibited at the sarcomere level. The proposed approach appears rather powerful and satisfactory since the integration of the displacement fields computed at this sub-cellular scale are in excellent agreement with the global measurements of cardiomyocyte shortening obtained by tracking the motion of both cell extremities. As a consequence, we were able to accurately identify the amplitude and spatio-temporal variations of the strains wave that characterize successive sarcomere contraction/relaxation.
The accuracy of the method already insures that displacements of a few microns can be reliably detected. However, our algorithm can still be extended to allow sub-pixel accuracy. The temporal resolution of the method obviously depends on the sampling rate of image acquisition, and can take benefit of the ultra-fast cameras already used to image intracellular calcium dynamics.
Finally, we would like to discuss possible implications of our image analysis approach. First, we can notice that the propagation of the negative longitudinal strains Exx,t (Fig. 9), corresponding to a localized intracellular compression, can be viewed as a mirror image of the underlying propagating peak of cytosolic calcium that drives the cell-contracting twitch (35).
Figure 9. Scan-line of the propagation pattern in seq1 of the axial large strain component Exx,t of the intracellular strain field computed by ICM along a cardiomyocyte section crossing the middle of the cell from one extremity to the other. The successive profiles shown at time t = 0.99 s t = 1.21 s and t = 1.65 s can be viewed as mirror images of the underlying profile of intracellular concentrations which would have been obtained if tracking calcium wave propagation with the aid of fluorescent probes. Parameters for the ICM computations are as in Figure 7.
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Second, we might wonder if such similarity-based optical flow approach still remains reliable when analyzing mechanical deformation of cells, which do not exhibit regular and organized grey-level intensity patterns when observed by phase contrast or DIC microscopy. As a reply, we considered the contraction of cardiomyocytes taken at an earlier developmental stage. Although rat neonatal cardiomyocytes lack the well-organized band-like arrangement observable in adult cells, our ICM approach still successfully quantified the rather complicated cell dynamics, including phases of centripetal contraction and generation of localized intracellular shear strain.
In conclusion, the proposed approach offers significant improvements to the classical ways of quantifying single cardiomyocyte deformations dynamics and using either force transducers, lasers diffraction techniques or fluorescent probes. By providing a direct and noninvasive access to intracellular strain at the sarcomere scale, this image processing method provides information on both active and passive mechanical properties of cardiac cell in a noninvasive way, i.e. without requiring the use of external probes, as it is the case in micromanipulation experiments like MTC experiments (36). More fundamentally, this optical flow method might also help to investigate the influence of mechanosensitive biochemical processes, depending on sarcomere stretching or interfilament spacing (37), which control the contractility and efficiency of cardiomyocyte contraction (38).