Yuanming Feng

Modeling of a Type II Photofrin‐mediated Photodynamic Therapy Process in a Heterogeneous Tissue Phantom

Abstract We present a quantitative framework to model a Type II photodynamic therapy (PDT) process in the time domain in which a set of rate equations are solved to describe molecular reactions. Calculation of steady-state light distributions using a Monte Carlo method in a heterogeneous tissue phantom model demonstrates that the photon density differs significantly in a superficial tumor of only 3 mm thickness. The time dependences of the photosensitizer, oxygen and intracellular unoxidized receptor concentrations were obtained and monotonic decreases in the concentrations of the ground-state photosensitizer and receptor were observed. By defining respective decay times, we quantitatively studied the effects of photon density, drug dose and oxygen concentration on photobleaching and cytotoxicity of a photofrin-mediated PDT process. Comparison of the dependences of the receptor decay time on photon density and drug dose at different concentrations of oxygen clearly shows an oxygen threshold under which the receptor concentration remains constant or PDT exhibits no cytotoxicity. Furthermore, the dependence of the photosensitizer and receptor decay times on the drug dose and photon density suggests the possibility of PDT improvement by maximizing cytotoxicity in a tumor with optimized light and drug doses. We also discuss the utility of this model toward the understanding of clinical PDT treatment of chest wall recurrence of breast carcinoma.

Label-free classification of cultured cells through diffraction imaging

Automated classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. We have investigated this possibility experimentally and numerically using a diffraction imaging approach. A fast image analysis software based on the gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images. The results of GLCM analysis and subsequent classification demonstrate the potential for rapid classification among six types of cultured cells. Combined with numerical results we show that the method of diffraction imaging flow cytometry has the capacity as a platform for high-throughput and label-free classification of biological cells.

Polarization Imaging and Classification of Jurkat T and Ramos B Cells Using a Flow Cytometer

Label‐free and rapid classification of cells can have awide range of applications in biology. We report a robust method of polarization diffraction imaging flow cytometry (p‐DIFC) for achieving this goal. Coherently scattered light signals are acquired from single cells excited by a polarized laser beam in the form of two cross‐polarized diffraction images. Image texture and intensity parameters are extracted with a gray level co‐occurrence matrix (GLCM) algorithm to obtain an optimized set of feature parameters as the morphological “fingerprints” for automated cell classification. We selected the Jurkat T cells and Ramos B cells to test the p‐DIFC methods capacity for cell classification. After detailed statistical analysis, we found that the optimized feature vectors yield accuracies of classification between the Jurkat and Ramos ranging from 97.8% to 100% among different cell data sets. Confocal imaging and three‐dimensional reconstruction were applied to gain insights on the ability of p‐DIFC method for classifying the two cell lines of highly similar morphology. Based on these results we conclude that the p‐DIFC method has the capacity to discriminate cells of high similarity in their morphology with “fingerprints” features extracted from the diffraction images, which may be attributed to subtle but statistically significant differences in the nucleus‐to‐cell volume ratio in the case of Jurkat and Ramos cells.

Analysis of cellular objects through diffraction images acquired by flow cytometry.

It was found that the diffraction images acquired along the side scattering directions with objects in a cell sample contain pattern variations at both the global and local scales. We show here that the global pattern variation is associated with the categorical size and morphological heterogeneity of the imaged objects. An automated image processing method has been developed to separate the acquired diffraction images into three types of global patterns. Combined with previously developed method for quantifying local texture pattern variations, the new method allows fully automated analysis of diffraction images for rapid and label-free classification of cells according to their 3D morphology.

Analysis of diffraction imaging in non-conjugate configurations

Diffraction imaging of scattered light allows extraction of information on scatterers morphology. We present a method for accurate simulation of diffraction imaging of single particles by combining rigorous light scattering model with ray-tracing software. The new method has been validated by comparison to measured images of single microspheres. Dependence of fringe patterns on translation of an objective based imager to off-focus positions has been analyzed to clearly understand diffraction imaging with multiple optical elements. The calculated and measured results establish unambiguously that diffraction imaging should be pursued in non-conjugate configurations to ensure accurate sampling of coherent light distribution from the scatterer.

Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model

The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dices similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

Modeling of Oxygen Transport and Cell Killing in Type-II Photodynamic Therapy

Photodynamic therapy (PDT) provides an effective option for treatment of tumors and other diseases in superficial tissues and attracts attention for in vitro study with cells. In this study, we present a significantly improved model of in vitro cell killing through Type‐II PDT for simulation of the molecular interactions and cell killing in time domain in the presence of oxygen transport within a spherical cell. The self‐consistency of the approach is examined by determination of conditions for obtaining positive definitive solutions of molecular concentrations. Decay constants of photosensitizers and unoxidized receptors are extracted as the key indices of molecular kinetics with different oxygen diffusion constants and permeability at the cell membrane. By coupling the molecular kinetics to cell killing, we develop a modeling method of PDT cytotoxicity caused by singlet oxygen and obtain the cell survival ratio as a function of light fluence or initial photosensitizer concentration with different photon density or irradiance of incident light and other parameters of oxygen transport. The results show that the present model of Type‐II PDT yields a powerful tool to quantitate various events underlying PDT at the molecular and cellular levels and to interpret experimental results of in vitro cell studies.

Pattern recognition and classification of two cancer cell lines by diffraction imaging at multiple pixel distances

Abstract Rapid and label-free imaging methods for accurate cell classification are highly desired for biology and clinical research. To improve consistency of classification performance, we have developed an approach of pattern analysis by gray level co-occurrence matrix (GLCM) algorithm to extract textural features at multiple pixel distances from cross-polarized diffraction image (p-DI) pairs, which were acquired with a method of polarization diffraction imaging flow cytometry using one time-delay-integration camera for significantly reduced blurring. Support vector machine (SVM) based classification was performed to discriminate HL-60 from MCF-7 cells using the GLCM features and consistency of optimized SVM classifiers was evaluated on three test data sets. It has been shown that the classification accuracy of the best performing SVM classifiers at or above 98.0% can be achieved among all four data sets for each of the three incident beam polarizations. These results suggest that the p-DI pair data provide a new platform for rapid and label-free classification of single cells with high and consistent accuracy.

Acquisition of cross-polarized diffraction images and study of blurring effect by one time-delay-integration camera.

Blurred diffraction images acquired from flowing particles affect the measurement of fringe patterns and subsequent analysis. An imaging unit with one time-delay-integration (TDI) camera has been developed to acquire two cross-polarized diffraction images. It was shown that selected elements of Mueller matrix of single scatters can be imaged with pixel matching precision in this configuration. With the TDI camera, the effect of blurring on imaging of scattered light propagating along the side directions was found to be much more significant for biological cells than microspheres. Despite blurring, classification of MCF-7 and K562 cells is feasible since the effect has similar influence on extracted image parameters. Furthermore, image blurring can be useful for analysis of the correlations among texture parameters for characterization of diffraction images from single cells. The results demonstrate that with one TDI camera the polarization diffraction imaging flow cytometry can be significantly improved and angular distribution of selected Mueller matrix elements can be accurately measured for rapid and morphology-based assay of particles and cells without fluorescent labeling.

Prediction and Analysis of Post-Translational Pyruvoyl Residue Modification Sites from Internal Serines in Proteins

Most of pyruvoyl-dependent proteins observed in prokaryotes and eukaryotes are critical regulatory enzymes, which are primary targets of inhibitors for anti-cancer and anti-parasitic therapy. These proteins undergo an autocatalytic, intramolecular self-cleavage reaction in which a covalently bound pyruvoyl group is generated on a conserved serine residue. Traditional detections of the modified serine sites are performed by experimental approaches, which are often labor-intensive and time-consuming. In this study, we initiated in an attempt for the computational predictions of such serine sites with Feature Selection based on a Random Forest. Since only a small number of experimentally verified pyruvoyl-modified proteins are collected in the protein database at its current version, we only used a small dataset in this study. After removing proteins with sequence identities >60%, a non-redundant dataset was generated and was used, which contained only 46 proteins, with one pyruvoyl serine site for each protein. Several types of features were considered in our method including PSSM conservation scores, disorders, secondary structures, solvent accessibilities, amino acid factors and amino acid occurrence frequencies. As a result, a pretty good performance was achieved in our dataset. The best 100.00% accuracy and 1.0000 MCC value were obtained from the training dataset, and 93.75% accuracy and 0.8441 MCC value from the testing dataset. The optimal feature set contained 9 features. Analysis of the optimal feature set indicated the important roles of some specific features in determining the pyruvoyl-group-serine sites, which were consistent with several results of earlier experimental studies. These selected features may shed some light on the in-depth understanding of the mechanism of the post-translational self-maturation process, providing guidelines for experimental validation. Future work should be made as more pyruvoyl-modified proteins are found and the method should be evaluated on larger datasets. At last, the predicting software can be downloaded from http://www.nkbiox.com/sub/pyrupred/index.html.