Label-free and noninvasive optical detection of the distribution of nanometer-size mitochondria in single cells

Imaging the intracellular refractive index distribution (IRID) for dynamic label-free living colon cancer cells via circularly depolarization decay model (CDDM)

Label-free detection of intracellular substances for living cancer cells remains a significant hurdle in cancer pathogenesis research. Although the sensitivity of light polarization to intracellular substances has been validated, current studies are predominantly focused on tissue lesions, thus label-free detection of substances within individual living cancer cells is still a challenge. The main difficulty is to find specific detection methods along with corresponding characteristic parameters. With refractive index as an endogenous marker of substances, this study proposes a detection method of intracellular refractive index distribution (IRID) for label-free living colon cancer (LoVo) cells. Utilizing the circular depolarization decay model (CDDM) to calculate the degree of circular polarization (DOCP) modulated by the cell allows for the derivation of the IRID on the focal plane. Experiments on LoVo cells demonstrated the refractive index of single cell can be accurately and precisely measured, with precision of 10-3 refractive index units (RIU). Additionally, chromatin content during the interphases (G1, S, G2) of cell cycle was recorded at 56.5%, 64.4%, and 71.5%, respectively. A significantly finer IRID can be obtained compared to the phase measurement method. This method is promising in providing a dynamic label-free intracellular substances detection method in cancer pathogenesis studies.

Multi-wavelength multi-direction laser light scattering for cell characterization using machine learning-based methods

Cell identification and analysis play a crucial role in many biology- and health-related applications. The internal and surface structures of a cell are complex and many of the features are sub-micron in scale. Well-resolved images of these features cannot be obtained using optical microscopy. Previous studies have reported that the single-cell angular laser-light scattering patterns (ALSP) can be used for label-free cell identification and analysis. The ALSP can be affected by cell properties and the wavelength of the probing laser. Two cell properties, cell surface roughness and the number of mitochondria, are investigated in this study. The effects of probing laser wavelengths (blue, green, and red) and the directions of scattered light collection (forward, side, and backward) are studied to determine the optimum conditions for distinguishing the two cell properties. Machine learning (ML) analysis has been applied to ALSP obtained from numerical simulations. The results of ML analysis show that the backward scattering is the best direction for characterizing the surface roughness, while the forward scattering is the best direction for differentiating the number of mitochondria. The laser light having red or green wavelength is found to perform better than that having the blue wavelength in differentiating the surface roughness and the number of mitochondria. This study provides important insights into the effects of probing laser wavelength on gaining information about cells from their ALSP.

Integration of light scattering with machine learning for label free cell detection

Light scattering has been used for label-free cell detection. The angular light scattering patterns from the cells are unique to them based on the cell size, nucleus size, number of mitochondria, and cell surface roughness. The patterns collected from the cells can then be classified based on different image characteristics. We have also developed a machine learning (ML) method to classify these cell light scattering patterns. As a case study we have used this light scattering technique integrated with the machine learning to analyze staurosporine-treated SH-SY5Y neuroblastoma cells and compare them to non-treated control cells. Experimental results show that the ML technique can provide a classification accuracy (treated versus non-treated) of over 90%. The predicted percentage of the treated cells in a mixed solution is within 5% of the reference (ground-truth) value and the technique has the potential to be a viable method for real-time detection and diagnosis.

Assessment of internal refractive index profile of stochastically inhomogeneous nuclear models via analysis of two-dimensional optical scattering patterns

SIGNIFICANCE

Optical scattering signals obtained from tissue constituents contain a wealth of structural information. Conventional intensity features, however, are mostly dictated by the overall morphology and mean refractive index of these constituents, making it very difficult to exclusively sense internal refractive index fluctuations.

AIM

We perform a systematic analysis to elucidate how changes in internal refractive index profile of cell nuclei can best be detected via optical scattering.

APPROACH

We construct stochastically inhomogeneous nuclear models and numerically simulate their azimuth-resolved scattering patterns. We then process these two-dimensional patterns with the goal of identifying features that directly point to subnuclear structure.

RESULTS

Azimuth-dependent intensity variations over the side scattering range provide significant insights into subnuclear refractive index profile. A particular feature we refer to as contrast ratio is observed to be highly sensitive to the length scale and extent of refractive index fluctuations; further, this feature is not susceptible to changes in the overall size and mean refractive index of nuclei, thereby allowing for selective tracking of subnuclear structure that can be linked to chromatin distribution.

CONCLUSIONS

Our analysis will potentially pave the way for scattering-based assessment of chromatin reorganization that is considered to be a key hallmark of precancer progression.

Application of Mie theory for enhanced size determination of microparticles using optical particle counters

The approach to particle sizing with optical particle counters is often simple interpolation of calibration data. A method is presented that uses the results of Mie-theory-based simulations to describe the signal between calibration points, thus reducing the number of necessary calibration points or increasing the sizing accuracy significantly. Through the use of Mie theory, particles with a refractive index differing from the calibration particles can be measured without an individual calibration. The method can be used with custom research setups or commercially available optical particle counters with various detector designs. If needed, the method can be applied to particle counters for which only the light wavelength used is known. The method is tested using a commercially available optical particle counter with a polystyrene microsphere calibration, measuring polystyrene microspheres as well as THP-1 cells, Chinese hamster ovary cells, and yeast cells. Without material specific calibration, simple interpolation results in about half the actual particle sizes for these biological samples, whereas the presented method yields accurate results.

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.

Physical characterization of hematopoietic stem cells using multidirectional label-free light scatterings

An experimental setup capable of measuring simultaneous 2D scattered light angular distribution from two directions to study cell morphology without the use of bio-labels was developed. Experiments with hematopoietic stem cells (CD34⁺ cells) show good agreement with detailed numerical simulations of light scattering. Numerical simulations and computer models of cells are used to identify physical features of cells with the largest scattering cross sections. This allows for determination of size, geometry of the nucleus and distribution of mitochondria in hematopoietic stem cells by means of our label-free method.

Relation between clinical mature and immature lymphocyte cells in human peripheral blood and their spatial label free scattering patterns

A single living cell's light scattering pattern (LSP) in the horizontal plane, which has been denoted as the cell's "2D fingerprint," may provide a powerful label-free detection tool in clinical applications. We have recently studied the LSP in spatial scattering planes, denoted as the cell's "3D fingerprint," for mature and immature lymphocyte cells in human peripheral blood. The effects of membrane size, morphology, and the existence of the nucleus on the spatial LSP are discussed. In order to distinguish clinical label-free mature and immature lymphocytes, the special features of the spatial LSP are studied by statistical method in both the spatial and frequency domains. Spatial LSP provides rich information on the cell's morphology and contents, which can distinguish mature from immature lymphocyte cells and hence ultimately it may be a useful label-free technique for clinical leukemia diagnosis.

Scattering pulse of label free fine structure cells to determine the size scale of scattering structures

Scattering pulse is sensitive to the morphology and components of each single label-free cell. The most direct detection result, label free cell's scattering pulse is studied in this paper as a novel trait to recognize large malignant cells from small normal cells. A set of intrinsic scattering pulse calculation method is figured out, which combines both hydraulic focusing theory and small particle's scattering principle. Based on the scattering detection angle ranges of widely used flow cytometry, the scattering pulses formed by cell scattering energy in forward scattering angle 2°-5° and side scattering angle 80°-110° are discussed. Combining the analysis of cell's illuminating light energy, the peak, area, and full width at half maximum (FWHM) of label free cells' scattering pulses for fine structure cells with diameter 1-20 μm are studied to extract the interrelations of scattering pulse's features and cell's morphology. The theoretical and experimental results show that cell's diameter and FWHM of its scattering pulse agree with approximate linear distribution; the peak and area of scattering pulse do not always increase with cell's diameter becoming larger, but when cell's diameter is less than about 16 μm the monotone increasing relation of scattering pulse peak or area with cell's diameter can be obtained. This relationship between the features of scattering pulse and cell's size is potentially a useful but very simple criterion to distinguishing malignant and normal cells by their sizes and morphologies in label free cells clinical examinations.

Realistic optical cell modeling and diffraction imaging simulation for study of optical and morphological parameters of nucleus

Coherent light scattering presents complex spatial patterns that depend on morphological and molecular features of biological cells. We present a numerical approach to establish realistic optical cell models for generating virtual cells and accurate simulation of diffraction images that are comparable to measured data of prostate cells. With a contourlet transform algorithm, it has been shown that the simulated images and extracted parameters can be used to distinguish virtual cells of different nuclear volumes and refractive indices against the orientation variation. These results demonstrate significance of the new approach for development of rapid cell assay methods through diffraction imaging.

Pattern recognition cytometry for label-free cell classification by 2D light scattering measurements

We develop a pattern recognition cytometric technique for label-free cell classification. Two dimensional (2D) light scattering patterns from single cells and cell aggregates are obtained with a static cytometer. Good performance of the cytometric setup is verified by comparing yeast cell experimental results with theoretical simulations. Adaptive boosting (AdaBoost) method (a machine learning algorithm) is adopted for the analysis of the 2D light scattering patterns. It is shown that aggregates of three yeast cells can be well differentiated from aggregates of four yeast cells by this pattern recognition cytometric technique. We demonstrate that the pattern recognition cytometry can perform label-free classification of normal cervical cells and HeLa cells with a high accuracy rate.

2D light scattering static cytometry for label-free single cell analysis with submicron resolution

Conventional optical cytometric techniques usually measure fluorescence or scattering signals at fixed angles from flowing cells in a liquid stream. Here we develop a novel cytometer that employs a scanning optical fiber to illuminate single static cells on a glass slide, which requires neither microfluidic fabrication nor flow control. This static cytometric technique measures two dimensional (2D) light scattering patterns via a small numerical aperture (0.25) microscope objective for label-free single cell analysis. Good agreement is obtained between the yeast cell experimental and Mie theory simulated patterns. It is demonstrated that the static cytometer with a microscope objective of a low resolution around 1.30 μm has the potential to perform high resolution analysis on yeast cells with distributed sizes. The capability of the static cytometer for size determination with submicron resolution is validated via measurements on standard microspheres with mean diameters of 3.87 and 4.19 μm. Our 2D light scattering static cytometric technique may provide an easy-to-use, label-free, and flow-free method for single cell diagnostics.

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 method's 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.