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

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.

Accurate 3D morphological computational model reconstruction of suspended cells imaged through stratified media by the precise depth-varying point spread function method

Accurate three-dimensional (3D) morphological computational models of cells are important in a number of biological studies. This study proposes a precise depth-varying point spread function (PDV-PSF) method for reconstructing 3D computational models of suspended cells from two-dimensional (2D) confocal image stacks. Our approach deblurs the 2D images in horizontal plane and corrects the deformation in vertical direction to overcome the refractive index mismatch problem caused by suspended cells imaging through stratified media. Standard fluorescent polystyrene spheres and Jurkat T-lymphocytes are selected to evaluate the validity and accuracy of this PDV-PSF method. Qualitative and quantitative results demonstrate that our approach has superior performance in 3D morphological computational models reconstruction of suspended cells.

Engineered Tissue Models to Replicate Dynamic Interactions within the Hematopoietic Stem Cell Niche

Hematopoietic stem cells are the progenitors of the blood and immune system and represent the most widely used regenerative therapy. However, their rarity and limited donor base necessitate the design of ex vivo systems that support HSC expansion without the loss of long-term stem cell activity. This review describes recent advances in biomaterials systems to replicate features of the hematopoietic niche. Inspired by the native bone marrow, these instructive biomaterials provide stimuli and cues from cocultured niche-associated cells to support HSC encapsulation and expansion. Engineered systems increasingly enable study of the dynamic nature of the matrix and biomolecular environment as well as the role of cell-cell signaling (e.g., autocrine feedback vs paracrine signaling between dissimilar cells). The inherent coupling of material properties, biotransport of cell-secreted factors, and cell-mediated remodeling motivate dynamic biomaterial systems as well as characterization and modeling tools capable of evaluating a temporally evolving tissue microenvironment. Recent advances in HSC identification and tracking, model-based experimental design, and single-cell culture platforms facilitate the study of the effect of constellations of matrix, cell, and soluble factor signals on HSC fate. While inspired by the HSC niche, these tools are amenable to the broader stem cell engineering community.

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.

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.

Two-Dimensional Light Scattering Anisotropy Cytometry for Label-Free Classification of Ovarian Cancer Cells via Machine Learning

We develop a single-mode fiber-based cytometer for the obtaining of two-dimensional (2D) light scattering patterns from static single cells. Anisotropy of the 2D light scattering patterns of single cells from ovarian cancer and normal cell lines is investigated by histograms of oriented gradients (HOG) method. By analyzing the HOG descriptors with support vector machine, an accuracy rate of 92.84% is achieved for the automatic classification of these two kinds of label-free cells. The 2D light scattering anisotropy cytometry combined with machine learning may provide a label-free, automatic method for screening of ovarian cancer cells, and other types of cells. © 2019 International Society for Advancement of Cytometry.

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.

Classification and Recognition of Ovarian Cells Based on Two-Dimensional Light Scattering Technology

Ovarian cancer is a very insidious malignant tumor. In order to detect ovarian cancer cells early, the classification and recognition of ovarian cancer cells is mainly studied by two-dimensional light scattering technology. Firstly, a single-cell two-dimensional light scattering pattern acquisition platform based on single-mode optical fiber illumination is designed to collect a certain number of two-dimensional light scattering patterns of ovarian cancer cells and normal ovarian cells. Then, the HOG (Histogram of Oriented Gradient) algorithm is used to extract shaving anisotropy feature of two-dimensional light scattering pattern. The results show that the accuracy of classification and identification of ovarian cancer cells by two-dimensional light scattering technology is 90.81%, which suggests that the specificity of cancer cells and normal cells can be characterized by two-dimensional light scattering technology.

Automatic Classification of Label-Free Cells from Small Cell Lung Cancer and Poorly Differentiated Lung Adenocarcinoma with 2D Light Scattering Static Cytometry and Machine Learning

Small cell lung cancer (SCLC) needs to be classified from poorly differentiated lung adenocarcinoma (PDLAC) for appropriate treatment of lung cancer patients. Currently, the classification is achieved by experienced clinicians, radiologists and pathologists based on subjective and qualitative analysis of imaging, cytological and immunohistochemical (IHC) features. Label-free classification of lung cancer cell lines is developed here by using two-dimensional (2D) light scattering static cytometric technique. Measurements of scattered light at forward scattering (FSC) and side scattering (SSC) by using conventional cytometry show that SCLC cells are overlapped with PDLAC cells. However, our 2D light scattering static cytometer reveals remarkable differences between the 2D light scattering patterns of SCLC cell lines (H209 and H69) and PDLAC cell line (SK-LU-1). By adopting support vector machine (SVM) classifier with leave-one-out cross-validation (LOO-CV), SCLC and PDLAC cells are automatically classified with an accuracy of 99.87%. Our label-free 2D light scattering static cytometer may serve as a new, accurate, and easy-to-use method for the automatic classification of SCLC and PDLAC cells. © 2018 International Society for Advancement of Cytometry.

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.