Purpose: To model the ultrasound effects on cellular calcium ion (Ca2+) influx for exploring its potential impact on Ca2+ regulated cellular responses to ionizing radiation. Methods: Based on Silvaˈs membrane electrophysiology model and for the ultrasound (30 ∼ 1000mW/cm2) considered, we adopted a linear relation between ultrasound induced cellular membrane strain deltaA/A0 and ultrasound wave amplitude p as deltaA/A0 ∼ p/((Rho*C)* (Rho*C)) (A0 ‐ undisturbed membrane area, deltaA ‐ stress induced area increase, Rho ‐ liquid density, C ‐ sound velocity). The energy density W associated with membrane strain is proportional (deltaA)*(deltaA) while the ultrasound intensity I is proportional to p*p to yield W(I) = kw*I with a linearity coefficient kw. Open channel fraction f0 and rate of exogenous calcium influx qin can be expressed as the following. f0 = 1/(1+a*exp(−fe*kw*I/(kTN))) qin = 4f0*Pmax*VmF*F/(RT)(Caex‐ Cac*exp(2FVm/(RT)))/(1−exp(2FVm /(RT))) (a ‐ probability that a channel is in open state without load, fe ‐ fraction of strain energy used to gate the channel, k ‐ Boltzmann constant, T ‐ temperature, N ‐ area channel density, Pmax — membraneˈs ionic permeability when all channels are open, Vm ‐ membrane potential, F ‐ Faradayˈs constant, R ‐ gas constant, Caex ‐ extracellular calcium concentration, Cac ‐ cytosolic free calcium concentration) Results: A sigmoid relationship between qin and I is obtained, which is due to the Boltzmann character of the mechanosensitive channels. Itˈs shown that a transient rise of qin can be induced at intensities higher than 40 mW/cm2 and stimulation over 1200 mW/cm2 would lead to unphysiological level of intracellular Ca2+. Conclusions: The calcium transient induced by low‐intensity ultrasound has been shown to be comparable to that induced by ionizing radiation reported in literatures. Further investigation is thus needed to examine the potential impact of ultrasound on cellular responses to ionizing radiation, such as bystander effect.
With a diffraction imaging flow cytometer, we have acquired and analyzed the diffraction imaging data from 5 types of cultured cells. A gray level co-occurrence matrix (GLCM) algorithm was applied to extract the interference fringe related textures from the diffraction image data. Six GLCM parameters were chosen and imported into a support vector machine algorithm for automated classification of about 20 cells for each of the 5 cell types. We found that the GLCM based algorithm has the capacity for rapid processing of diffraction images and yield feature parameters for subsequent cell classification except the T- and B-lymphocytes.
Morphological identification is a widespread procedure to assess the presence of apoptosis by visual inspection of the morphological characteristics or the fluorescence images. The procedure is lengthy and results are observer dependent. A quantitative automatic analysis is objective and would greatly help the routine work. We developed an image processing and segmentation method which combined the Otsu thresholding and morphological operators for apoptosis study. An automatic determination method of apoptotic stages of HL-60 cells with fluorescence images was developed. Comparison was made between normal cells, early apoptotic cells and late apoptotic cells about their geometric parameters which were defined to describe the features of cell morphology. The results demonstrated that the parameters we chose are very representative of the morphological characteristics of apoptotic cells. Significant differences exist between the cells in different stages, and automatic quantification of the differences can be achieved.
Purpose: To develop a new interpolation method for accurate 3D reconstruction of cell morphology from laser scanning confocal microscope (LSCM) image data. Methods: Current techniques are based on the assumption that pixel intensity or contour shapes of images change linearly in the interpolation direction. Gray-value and position of the pixel in interpolated image slice are obtained through weighted average calculation with gray-values and distances of corresponding pixels in two adjacent original image slices, only information from adjacent image slices is considered, often fail to meet the need of 3D reconstruction for cells because of the complex cell morphology. The new method interpolates cellular organelle contours in polar coordinate system. Coordinate system origin is chosen to be the mass center weighted by pixel intensity instead of conventional geometric center, contour points of the organelle is sampled by their angles first and fitted with uniform cubic B-spline to perform interpolation. For complex organelle structures such as branched nuclei, a special method combining morphological information and corner detection technique based on curvature scale space has been developed to solve the contour division and related problems. New method was applied to confocal images of 130 different cells acquired with an LSCM system (LSM510, Zeiss), sampling step was set as 0.5 μm in longitudinal direction, pixel size in horizontal plane was 0.07 μm and the resolution was 512×512. Marching cubes algorithm was used for 3D reconstruction. Results: Experiments showed that reconstructed 3D images with new method have much smoother and more valid organelle surfaces for both cytoplasm and nucleus than those from conventional methods. Conclusions: The new interpolation method can significantly improve the quality of 3D reconstruction and serve as a valid and effective tool for quantitative study of 3D cell morphology in radiation biology and other areas of life science.*support by NSFC- 81171342. Supported by the National Science Foundation of China (NSFC- 81171342)
Motion blur (MB) presents a significant challenge for obtaining high-contrast image data from biological cells with a polarization diffraction imaging flow cytometry (p-DIFC) method. A new p-DIFC experimental system has been developed to evaluate the MB and its effect on image analysis using a time-delay-integration (TDI) CCD camera. Diffraction images of MCF-7 and K562 cells have been acquired with different speed-mismatch ratios and compared to characterize MB quantitatively. Frequency analysis of the diffraction images shows that the degree of MB can be quantified by bandwidth variations of the diffraction images along the motion direction. The analytical results were confirmed by the p-DIFC image data acquired at different speed-mismatch ratios and used to validate a method of numerical simulation of MB on blur-free diffraction images, which provides a useful tool to examine the blurring effect on diffraction images acquired from the same cell. These results provide insights on the dependence of diffraction image on MB and allow significant improvement on rapid biological cell assay with the p-DIFC method.
The two major subtypes of human T cells, CD4+ and CD8+, play important roles in adaptive immune response by their diverse functions. To understand the structure–function relation at the single cell level, we isolated 2483 CD4+ and 2450 CD8+ T cells from fresh human splenocytes by immunofluorescent sorting and investigated their morphologic relations to the surface CD markers by acquisition and analysis of cross-polarized diffraction image (p-DI) pairs. A deep neural network of DINet-R has been built to extract 2560 features across multiple pixel scales of a p-DI pair per imaged cell. We have developed a novel algorithm to form a matrix of Pearson correlation coefficients by these features for selection of a support cell set with strong morphologic correlation in each subtype. The p-DI pairs of support cells exhibit significant pattern differences between the two subtypes defined by CD markers. To explore the relation between p-DI features and CD markers, we divided each subtype into two groups of A and B using the two support cell sets. The A groups comprise 90.2% of the imaged T cells and classification of them by DINet-R yields an accuracy of 97.3 ± 0.40% between the two subtypes. Analysis of depolarization ratios further reveals the significant differences in molecular polarizability between the two subtypes. These results prove the existence of a strong structure–function relation for the two major T cell subtypes and demonstrate the potential of diffraction imaging flow cytometry for accurate and label-free classification of T cell subtypes.
Purpose: Accurate study of 3D cell morphology with confocal imaging method requires correction of the z‐axis aberration. A fast method with fluorospheres has been developed to achieve this goal. Methods: Fluorospheres with nominal diameter of 10um(+−2%) were used for this study. The z‐stack images were obtained using Zeiss‐410 CLSM system with the stepsize of 0.4 um in air along the z‐axis. Ideally, a sphere should be shown as a disk with diameter d in each image of the stack. But the measured data showed significant deviation in d vs z relation from the expected one due to the optical aberration by light refraction at various index‐mismatched interfaces. To correct this aberration, the following algorithm were developed and used to fit the measured data and obtain a rescaling factor for accurate 3D reconstruction along z‐axis. We define three parameters, 1. Covariance between theoretical and experimental data of the disk diameter arrays (Pcov), the maximum of which means the optimal match with the theoretical model; 2. Eccentricity of the largest disk which is in the middle layers and less influenced by the PSF (Pecc); 3. The mean eccentricity of all the stacks measured (Pmecc). These parameters were utilized and weighted with factors of a, b and c, respectively, in the objective function (F). F = a*Pcov + b*Pecc + c*Pmecc Results: 29 images were acquired for the fluorosphere as the measured image stack. After the data fitting and optimization, diameters in all images were corrected to obtain the rescaling factor (f=0.862), and the aberration was reduced significantly in the reconstructed 3D image of the fluorosphere. Conclusions: It is shown that the above method can significantly improve the quality of the reconstructed 3D image of the fluorosphere from the confocal images. Additional test results will be presented for evaluation of the methodˈs effectiveness.
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