Light scattering from normal and cervical cancer cells

CT- and MRI-Aided Fluorescence Tomography Reconstructions for Biodistribution Analysis

OBJECTIVES

Optical fluorescence imaging can track the biodistribution of fluorophore-labeled drugs, nanoparticles, and antibodies longitudinally. In hybrid computed tomography-fluorescence tomography (CT-FLT), CT provides the anatomical information to generate scattering and absorption maps supporting a 3-dimensional reconstruction from the raw optical data. However, given the CT's limited soft tissue contrast, fluorescence reconstruction and quantification can be inaccurate and not sufficiently detailed. Magnetic resonance imaging (MRI) can overcome these limitations and extend the options for tissue characterization. Thus, we aimed to establish a hybrid CT-MRI-FLT approach for whole-body imaging and compared it with CT-FLT.

MATERIALS AND METHODS

The MRI-based hybrid imaging approaches were established first by scanning a water and coconut oil-filled phantom, second by quantifying Cy7 concentrations of inserts in dead mice, and finally by analyzing the biodistribution of AF750-labeled immunoglobulins (IgG, IgA) in living SKH1 mice. Magnetic resonance imaging, acquired with a fat-water-separated mDixon sequence, CT, and FLT were co-registered using markers in the mouse holder frame filled with white petrolatum, which was solid, stable, and visible in both modalities.

RESULTS

Computed tomography-MRI fusion was confirmed by comparing the segmentation agreement using Dice scores. Phantom segmentations showed good agreement, after correction for gradient linearity distortion and chemical shift. Organ segmentations in dead and living mice revealed adequate agreement for fusion. Marking the mouse holder frame and the successful CT-MRI fusion enabled MRI-FLT as well as CT-MRI-FLT reconstructions. Fluorescence tomography reconstructions supported by CT, MRI, or CT-MRI were comparable in dead mice with 60 pmol fluorescence inserts at different locations. Although standard CT-FLT reconstruction only considered general values for soft tissue, skin, lung, fat, and bone scattering, MRI's more versatile soft tissue contrast enabled the additional consideration of liver, kidneys, and brain. However, this did not change FLT reconstructions and quantifications significantly, whereas for extending scattering maps, it was important to accurately segment the organs and the entire mouse body. The various FLT reconstructions also provided comparable results for the in vivo biodistribution analyses with fluorescent immunoglobulins. However, MRI additionally enabled the visualization of gallbladder, thyroid, and brain. Furthermore, segmentations of liver, spleen, and kidney were more reliable due to better-defined contours than in CT. Therefore, the improved segmentations enabled better assignment of fluorescence signals and more differentiated conclusions with MRI-FLT.

CONCLUSIONS

Whole-body CT-MRI-FLT was implemented as a novel trimodal imaging approach, which allowed to more accurately assign fluorescence signals, thereby significantly improving pharmacokinetic analyses.

Predictions of cervical cancer identification by photonic method combined with machine learning

Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors-to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing an optical sensor and a prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing four algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes, Convolutional Neural Networks) and assessment of their performance for classification of tissue as healthy or sick. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.

Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes

Measurement of nuclear-to-cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label-free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes and orientations to calculate cross-polarized diffraction image (p-DI) pairs divided into three nuclear size groups of OCM_S , OCM_O and OCM_L based on three prostate cell structures. Binary classifications were conducted among the three groups with image parameters extracted by the algorithm of gray-level co-occurrence matrix. The averaged accuracy of support vector machine (SVM) classifier on test dataset of p-DI was found to be 98.8% and 97.5% respectively for binary classifications of OCM_S vs OCM_O and OCM_O vs OCM_L for the prostate cancer cell structure. The values remain about the same at 98.9% and 97.8% for the smaller prostate normal cell structures. The robust performance of SVM over clustering classifiers suggests that the high-order correlations of diffraction patterns are potentially useful for label-free detection of single cells with large N:C ratios.

Biofluidic Random Laser Cytometer for Biophysical Phenotyping of Cell Suspensions

Phenotypic profiling of single floating cells in liquid biopsies is the key to the era of precision medicine. A random laser in biofluids is a promising tool for the label-free characterization of the biophysical properties as a result of the high brightness and sharp peaks of the lasing spectra, yet previous reports were limited to the random laser in solid tissues with dense scattering. In this report, a random laser cytometer is demonstrated in an optofluidic device filled with gain medium and human breast normal/cancerous cells. The multiple lightscattering event induced by the microscale human cells promotes random lasing and influences the lasing properties in term of laser modes, spectral wavelengths, and lasing thresholds. A sensing strategy based on analyzing the lasing properties is developed to determine both the whole cell and the subcellular biophysical properties, and the malignant alterations of the cell suspensions are successfully detected. Our results provide a new approach to designing a label-free biophysical cytometer based on optofluidic random laser devices, which is advantageous for further research in the field of random laser bioapplication.

Light scattering methods for tissue diagnosis

Light scattering has become a common biomedical research tool, enabling diagnostic sensitivity to myriad tissue alterations associated with disease. Light-tissue interactions are particularly attractive for diagnostics due to the variety of contrast mechanisms that can be used, including spectral, angle-resolved, and Fourier-domain detection. Photonic diagnostic tools offer further benefit in that they are non-ionizing, non-invasive, and give real-time feedback. In this review, we summarize recent innovations in light scattering technologies, with a focus on clinical achievements over the previous ten years.

Development and evaluation of realistic optical cell models for rapid and label-free cell assay by diffraction imaging

Methods for rapid and label-free cell assay are highly desired in life science. Single-shot diffraction imaging presents strong potentials to achieve this goal as evidenced by past experimental results using methods such as polarization diffraction imaging flow cytometry. We present here a platform of methods toward solving these problems and results of optical cell model (OCM) evaluations by calculations and analysis of cross-polarized diffraction image (p-DI) pairs. Four types of realistic OCMs have been developed with two prostate cell structures and adjustable refractive index (RI) parameters to investigate the effects of cell morphology and index distribution on calculated p-DI pairs. Image patterns have been characterized by a gray-level co-occurrence matrix (GLCM) algorithm and four GLCM parameters and linear depolarization ratio δ_L have been selected to compare calculated against measured data of prostate cells. Our results show that the irregular shapes of and heterogeneity in RI distributions for organelles play significant roles in the spatial distribution of scattered light by cells in comparison to the average RI values and their differences among the organelles. Discrepancies in GLCM and δ_L parameters between calculated and measured p-DI data provide useful insight for understanding light scattering by single cells and improving OCM.

Clinical lymphocytes construction for light scattering inversion study: a three-dimensional morphology constructed method from defective confocal images

Constructing models of cells' realistic internal and external morphology is vital for correlation between light scattering and morphology of the scattering structure. The image stack obtained from fluorescent confocal microscopy is at present used to construct the cell's three-dimensional (3-D) morphology. However, due to the poor labeling quality and unavoidable optical noise present in the image stacks, 3-D morphologies are difficult to construct and are an impediment to the statistical analyses of cell structures. We propose a method called the "area and shape constraint method (ASCM)" for constructing 3-D morphology. Blurred 3-D morphologies constructed by common methods from image stacks considered as defective and which are commonly discarded are well restored by the ASCM. Seventy-four clinical blood samples and a series of standard fluorescent spheres are selected to evaluate the validity and precision of our proposed ASCM. Both the qualitative and quantitative results obtained by ASCM indicate the good performance of the method in constructing the cell's 3-D morphology.

Angular-dependent light scattering from cancer cells in different phases of the cell cycle

Cancer cells in different phases of the cell cycle result in significant differences in light scattering properties. In order to harvest cancer cells in particular phases of the cell cycle, we cultured cancer cells through the process of synchronization. Flow cytometric analysis was applied to check the results of cell synchronization and prepare for light scattering measurements. Angular-dependent light scattering measurements of cancer cells arrested in the G1, S, and G2 phases have been performed. Based on integral calculations for scattering intensities from 5° to 10° and from 110° to 150°, conclusions have been reached. Clearly, the sizes of the cancer cells in different phases of the cell cycle dominated the forward scatter. Accompanying the increase of cell size with the progression of the cell cycle, the forward scattering intensity also increased. Meanwhile, the DNA content of cancer cells in every phase of the cell cycle is responsible for light scattering at large scatter angles. The higher the DNA content of cancer cells was, the greater the positive effect on the high-scattering intensity. As expected, understanding the relationships between the light scattering from cancer cells and cell cycles will aid in the development of cancer diagnoses. Also, it may assist in the guidance of antineoplastic drugs clinically.