Imaging flow cytometry (IFC) has become an established tool for cell analysis across diverse biomedical fields. However, the performance of IFC is severely limited by the fundamental trade-off among multi-color, flow speed and exposure time. Here we develop multiplex imaging flow cytometry (mIFC) that overcomes this trade-off by utilizing unique single-source single-detector technology for sensitive detection of ovarian cancer cells with the content-aware image restoration method. Our mIFC achieves efficient, non-interfering 4-channel excitation and 3-channel emission based on a metal halide lamp. The spatial wavelength division multiplexing technology with a knife-edge right-angle prism is the key optical design to simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow on a single detector. A U-net variant deep learning network based on a 3-layer encoder-decoder structure is employed to perform content-aware image restoration on captured multiplex ovarian cell images. The blurred multiplex images are converted into enhanced images, which helps to balance the trade-off between flow speed and exposure time. Our multiplex imaging flow cytometry (mIFC) with content-aware image restoration deep learning method enables automatic, high-quality detection of ovarian cancer cells and has the potential broad applications in biomedical fields.
Small extracellular vesicles (sEVs) are nanoscale bioparticles released from various cells and have important applications in clinical and basic science. In nanoscale particle tracking, the tracking trajectory length is important for the accurate sizing of nanoparticles (NP). Here, a light scattering imaging system uses a 786.4 nm laser source to collect the side scatter of individual nanoscale particles with a 10X objective lens and a CMOS camera is introduced. Supervised sliding window analysis is tested for optimized NP trajectory segmentation, followed by a machine learning algorithm that classifies Brownian motion and non-Brownian diffusions based on tracked trajectory features. Supervised sliding window analysis allows the differentiation of non-Browian diffusions with a high accuracy of 93.8% and precise sizing of standard polystyrene NPs. Imaging and size measurements of 120 nm NPs, 65 nm NPs, and plasma-derived sEVs show that optimizing the trajectory length combined with purifying the non-Brownian diffusion improves the sizing accuracy. Nanoscale EVs are expected to be reliable biomarkers for many diseases, especially those associated with cancer, where reliable and accurate size estimation methods based on light scattering imaging have potential applications.
Small extracellular vesicles (sEVs), which are nanoparticles around 100 nm, have been widely studied in recent years in many interesting areas, such as cancer detection and drug delivery. Bulk analysis of extracellular vesicles provides average information about the EV population. However, single EV characterization enables a profound understanding of the biophysical properties of EV subpopulations, establishing an insightful view of the EVs function and composition. It is worth to explore light scattering imaging method for the analysis of single sEVs. We introduce here the deep-learning-based light scattering imaging method for analyzing label-free sEVs (DeepEVAnalyzer), which has been applied to measure the size of single sEVs. We also report our recent development of a light scattering imaging method to address the inverse problem, which is demonstrated to differentiate the label-free sEVs from healthy mice and those injected with malignant cells. Light scattering imaging together with machine learning for sEVs analysis may have potential diagnostic and therapeutic applications.
Imaging flow cytometry (IFC) has been widely applied in biomedical research due to its numerous advantages, including multiparametric analysis, microscopic imaging and high-throughput detection. Previous research in our lab has demonstrated the effectiveness of two-dimensional light scattering (LS) and brightfield (BF) dual-modality imaging techniques for detecting and distinguishing unlabeled cells. As fluorescence (FL) imaging techniques are sensitive to specifically labeled cells, here we introduce a single-detector IFC enabling simultaneous imaging of LS signals and BF/FL signals for automatic single-cell analysis with deep learning. The special optical design with a knife-edge right angle (KERA) prism is adopted to simultaneously capture corresponding LS patterns in defocus and BF/FL patterns in focus on a single detector. The LS and BF dual-modality flow imaging results of 2 μm and 3.87 μm unlabeled microspheres can be obtained by our system, which can also simultaneously acquire LS and FL results for fluorescent microspheres of 2 μm and 4 μm in diameter. The results of these beads demonstrate excellent agreement between LS patterns and Mie scattering simulations. The obtained LS and BF dual-modality cell images of A2780 and Hey cells are analyzed using a visual geometry group 19 (VGG19) deep learning method through feature extraction and fusion to show accurate classification of ovarian cancer cell subtypes. In conclusion, our development of a single-detector imaging flow cytometer enables the simultaneous capture of two-dimensional light-scattering and fluorescence/brightfield images, where an automatic analysis with deep learning can be performed, showcasing potential wide applications in biomedicine.
Liver cancer is one of the most common digestive system malignancies with an average five-year survival rate of less than 20%, while traditional methods are often unautomated, labeling required, and limited for early liver cancer detection. Exosomes are a type of extracellular vesicles with a diameter of 40-150 nm, which play important role in disease diagnosis and treatment. It is of interest to develop a label-free optical system for the analysis of nanoscale exosomes. Here, we developed a label-free two-dimensional (2D) light scattering acquisition system for the measurements of microparticles and the exosomes derived from the normal liver cells. By adjusting the thickness of the light sheet for illumination in our system, nanoparticles down to 41 nm are detected. The visualization and accurate particle size analysis of liver cell exosomes are then performed by our 2D light scattering technology. Our method is expected to have important applications in the quantitative analysis field of cellular and extracellular structures that may find potential applications in clinics such as for early cancer diagnosis.
Light scattering flow cytometry has been demonstrated for label-free particle and cell analysis. The high-throughput imaging of cells or particles in flow cytometry is fundamentally challenging as motion blur may occur for weak-light measurements. Here we perform light scattering measurements on 3.87 μm and 4.19 μm microparticles in diameters with our light scattering flow cytometer. Two-dimensional (2D) light scattering patterns are imaged by a Complementary Metal Oxide Semiconductor (CMOS) sensor. The hydrodynamic focusing effect is studied for better high-throughput measurements. Motion-blur images are obtained at 2.4 mm/s flow rate with 10 ms exposure time, and a deblurring algorithm is adopted for analysis. The experimental 2D light scattering patterns agree well with the Mie theory simulation. Moreover, the number of 3.87 μm and 4.19 μm microparticles in flow can be determined, where the error is less than 5% compared with the theoretical results.
Two-dimensional (2D) light scattering has the capability for label-free single cell analysis. Recent development of flow cytometry has demonstrated the obtaining of high-content images. Here we demonstrate a flow cytometer for the obtaining of high-content 2D light scattering patterns of single cells. In our flow cytometer, single cells are flowing in a hydrodynamic focusing unit and their 2D light scattering patterns are recorded via a long working distance objective by using a high-speed complementary metal oxide semiconductor (CMOS) sensor. Big data of the 2D light scattering patterns from two types of cervical carcinoma cell lineage cells (HeLa and C33-A) are obtained with a rate of 60 frames per second. Deep learning is adopted for the classification of these two types of cells, and a high recognition accuracy is obtained. The results show that our high-content 2D light scattering flow cytometry together with deep learning can collect label-free single-cell information at high speed and has strong analytical capabilities, which may in future be used for early diagnosis of cervical carcinoma.
Leukemia is a worldwide malignant tumor with high morbidity and mortality. Developing screening methods for leukemia cells is of great significance for clinical diagnosis. Traditional biochemical and immunohistochemical detection methods that usually require fluorescence labeling are time-consuming and labor-intensive. Here we report a deep learning based 2D light scattering cytometric technique for high-precision, automatic and label-free identification of lymphocytic leukemia cells. A deep convolutional neural network (CNN) is used for learning the biological characteristics of 2D light scattering patterns. The Inception V3 network can identify different label-free acute lymphocytic leukemia cells with a high accuracy. The results show that the deep learning based 2D light scattering microfluidic cytometry is promising for early diagnosis of leukemia, and has the advantages of label free, high efficiency and high automation.
The detection of senescent cells becomes increasing important for tumor therapy and drug screening. Here a light sheet microfluidic cytometer with a disposable hydrodynamic focusing unit is developed for two dimensional (2D) light scattering measurements of single cells. The mixed polystyrene microspheres of 3.87 and 2.0 μm in diameter are successfully differentiated by our 2D light scattering microfluidic cytometer. The application of the 2D light scattering microfluidic cytometry for the label-free analysis of senescent cells without any labeling or staining is demonstrated by measurements of H2O2-treated U87 cells. Our light sheet-based 2D light scattering microfluidic cytometer is easy to assemble with a disposable hydrodynamic unit, which may find wild applications in clinics for label-free cell classification.
We have recently developed a 2D light scattering static cytometer for cellular analysis in a label-free manner, which measures side scatter (SSC) light in the polar angular range from 79 to 101 degrees. Compared with conventional flow cytometry, our cytometric technique requires no fluorescent labeling of the cells, and static cytometry measurements can be performed without flow control. In this paper we present an improved label-free static cytometer that can obtain 2D light scattering patterns in a wider angular range. By illuminating the static microspheres on chip with a scanning optical fiber, wide-angle 2D light scattering patterns of single standard microspheres with a mean diameter of 3.87 μm are obtained. The 2D patterns of 3.87 μm microspheres contain both large-angle forward scatter (FSC) and SSC light in the polar angular range from 40 to 100 degrees, approximately. Experimental 2D patterns of 3.87 μm microspheres are in good agreement with Mie theory simulated ones. The wide-angle light scattering measurements may provide a better resolution for particle analysis as compared with the SSC measurements. Two dimensional light scattering patterns of HL-60 human acute leukemia cells are obtained by using our static cytometer. Compared with SSC 2D light scattering patterns, wide-angle 2D patterns contain richer information of the HL-60 cells. The obtaining of 2D light scattering patterns in a wide angular range could help to enhance the capabilities of our label-free static cytometry for cell analysis.
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