The spectrin is first identified as the main component of erythrocyte membrane skeleton. It is getting growing attention since being found in multiple nonerythroid cells, providing complex mechanical properties and signal interface under the cell membrane. Recent genomics studies have revealed that the spectrin is highly relevant to bone disorders. However, in osteocytes, the important mechanosensors in bone, the role of spectrin is poorly understood. In this research, the role of spectrin in the mechanotransduction of MLO-Y4 osteocytes was studied. Immunofluorescence staining showed that, the spectrins were elaborately organized as a porous network throughout the cytoplasm, and linked with F-actin into a dense layer underlying the cell membrane. AFM results indicate that, the spectrin is pivotal for maintaining the overall elasticity of osteocytes, especially for the cell cortex stiffiness. Disruption of the spectrin network caused obvious softening of osteocytes, and resulted in a significant increase of Ca2+ influx, NO secretion, cell-cell connections and also induced a translocation of eNOS from membrane to cytoplasm. These results indicate that the spectrin network is a global structural support for osteocytes involving in the mechanotransduction process, making it a potential therapeutic target for bone disorders.
We present sensing time-lapse morphogenesis of living bone cells under micro-Fluidic Shear Stress (FSS) by digital holographic (DH) microscopy. To remove the effect of aberrations on quantitative measurement, we propose...
Vibration, especially at low magnitude and high frequency (LMHF), was demonstrated to be anabolic for bone, but how the LMHF vibration signal is perceived by osteocytes is not fully studied. On the other hand, the mechanotransduction of osteocytes under shear stress has been scientists' primary focus for years. Due to the small strain caused by low-magnitude vibration, whether the previous explanation for shear stress will still work for LMHF vibration is unknown. In this study, a finite element method (FEM) model based on the real geometrical shape of an osteocyte was built to compare the mechanical behaviors of osteocytes under LMHF vibration and shear stress. The bio-response of osteocytes to vibration under different frequencies, including the secretion of soluble factors and the concentration of intracellular calcium, were studied. The regulating effect of the conditioned medium (CM) from vibrated osteocytes on osteoblasts was also studied. The FEM analysis result showed the cell membrane deformation under LMHF vibration was very small (with a peak value of 1.09%) as compared to the deformation caused by shear stress (with a peak value of 6.65%). The F-actin stress fibers of osteocytes were reorganized, especially on the nucleus periphery after LMHF vibration. The vibration at 30 Hz has a promoting effect on osteocytes and the osteogenesis of osteoblasts, whereas vibration at 90 Hz was suppressive. These results lead to a conclusion that the bio-response of osteocytes to LMHF vibration is frequency-dependent and is more related to the cytoskeleton on nuclear periphery rather than the membrane deformation.
Digital holographic microscopy (DHM) as a label-free quantitative imaging tool has been widely used to investigate the morphology of living cells dynamically. In the offaxis DHM, the spatial filtering in the frequency spectrum of the hologram is vital to the quality of the reconstructed images. In this paper, we propose an adaptive spatial filtering approach based on convolutional neural networks (CNN) to automatically extracts the optimal shape of frequency components. For achieving robust and precise recognition performance, the net model is trained by using the tens of thousands of frequency spectrums with a variety of specimens and imaging conditions. The experimental results demonstrate that the trained network produce an adaptive spatial filtering window which can accurately select the frequency components of the object term and eliminate the frequency components of the interference terms, especially the coherent noise that overlaps with the object term in the spatial frequency domain. We find that the proposed approach has a fast, robust, and outstanding frequency filtering capability without any manual intervention and initial input parameters compared to previous techniques. Furthermore, the applicability of the proposed method in off-axis DHM for dynamic analysis is demonstrated by real-time monitoring the morphologic changes of living MLO-Y4 cells that are constantly subject to Fluid Shear Stress (FSS).
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