Polarized diffraction images (p-DIs) can provide a wealth of information about the morphologies of scatterers, making them a valuable tool for use in a variety of applications, including the characterization of biological cells and tissues. However, most studies on biological cells have chiefly relied on qualitative analysis, which is achieved through the analysis of patterns of p-DIs for cell clustering. Although qualitative analysis can provide major insights into the morphologies and characteristics of cells, it may not always provide accurate quantitative data about the sizes and shapes of cells, which is critical for some applications. Although quantitative studies on the refractive indices and sizes of cells have been conducted, these investigations have typically been based on the assumption that cells are perfectly spherical. Accordingly, this assumption may not hold true for many types of cells that exhibit non-spherical shapes. Based on a texture analysis of p-Dis, this study conducted quantitative analyses on the characteristic parameters of yeast monomers and budding yeast, where the results are shown to be consistent with those obtained using traditional microscopy methods. This approach can provide insights into the quantitative analysis of non-spherical cells based on light-scattering techniques.
A systematic study on p-DIs with scattering angles of 60°?120° and azimuth angles of 150°?210° of 1197 yeast monomers and budding yeast was conducted using optical models established based on the discrete dipole approximation theory (DDA). Excluding the assumption that the two short aixs of yeast cells are equal, all parameters of these optical models were obtained through microscopy. The experimental p-DIs of 25000 Ale and Lager yeasts were obtained using polarization diffraction imaging flow cytometry. The Fourier spectrum and gray-level co-occurrence matrix (GLCM) parameters of all p-Dis, including those derived from a simulation and experiment, and the depolarization coefficient of p-DIs of yeast monomer were calculated. A regression model was used to establish the quantitative relationship between image feature and cellular structural parameters, such as the sizes of the short aixs of yeasts, the aspect ratio of yeast monomer under different short axis sizes, and short axis ratio of bud yeast to mother yeast. In addition, a statistical correlation between characteristic parameters of yeast cells and GLCM parameters was investigated, and a support vector machine (SVM) classifier was trained based on simulated p-DIs to classify the yeast monomers and budding yeasts in the GLCM parameter space.
This study finds a significant statistical correlation between GLCM parameters and short axis ratio of bud yeast to mother yeast (Fig.6). Therefore, for a statistical analysis of budding rates, the SVM model shows an accuracy of as high as 98.1% [Fig.7(a)]. Moreover, the budding rates of yeasts as calculated by the SVM classifier and microscopic count method are found to be highly consistent [Fig.7(b)]. Further analysis shows that the adjusted R2 as determined by the multiple regression equation is 0.86, indicating that the regression model has very high statistical significance and good predictability. In addition, a power law relationship derived from the nonlinear least squares fit between the normalized spatial frequency along the θ direction and short axis of yeasts is obtained with an R2 value of 0.9986 and narrow 95% prediction interval, indicating that the precision and reliability are satisfactory [Fig.9(a)]. With the aid of this power law relationship, the statistics of the short axis size distribution of yeast monomer based on experimental p-DIs are realized with an error of 7.4% [Fig.9(b)]. The correlations between the aspect ratio of yeast monomer under different short axis sizes and the depolarization coefficients were also analyzed. We find that when the short axis sizes vary in the range of 5?8 μm, the changing trends of the depolarization coefficient with the aspect ratio of yeast monomer under different short axis sizes are consistent and assumed to be a Gaussian function (Fig.10). These results indicate that polarized diffraction imaging technology has promise in terms of quantitative analysis of the structural parameters of non-spherical cell models.
A comprehensive numerical and experimental study on the polarized diffraction characteristics of yeast cells is conducted. We develop a yeast cell structural parameter prediction model based on texture features extracted from p-DIs. This prediction model can accurately and rapidly predict yeast cell structural parameters such as short axis size, aspect ratio, and bud size based on given p-DIs. The accuracy and reliability of the model were validated through comparison with actual measurement data. The ability to predict yeast cell structural parameters in a fast and accurate manner is of great significance for the study of cellular morphology and may have major implications for the development of new diagnostic and therapeutic tools.
