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Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi‐color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron‐scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U‐net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single‐detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single‐cell analysis with deep learning in various biomedical fields. [ABSTRACT FROM AUTHOR]

Recent development of imaging flow cytometry (IFC) has enabled the measurements of single cells with high throughput, where fluorescent labels provide specificity for cellular diagnosis. The fluorescent labels may disturb the cell functions, and the requirements for high‐throughput measurements limit the cell image quality. Here, we develop the high‐content video flow cytometry (VFC) that measures unlabeled single cells with a rate of approximately 1000 cells per minute. For the obtained big data, the frame of interest (FOI) is automatically prepared by a digital cell filtering technique with machine learning. Cervical carcinoma cell lines (Caski, HeLa and C33‐A cells) are differentiated with an accuracy of 91.5%, 90.5%, and 90.5% by deep learning in a three‐way classification, respectively. The high‐content VFC not only provides high‐quality images of single cells with high throughput and rewinding, but also performs automatic digital cell filtering and label‐free cell classification that may have clinical applications. [ABSTRACT FROM AUTHOR]

Cervical cancer is a high‐risk disease that threatens women's health globally. In this study, we developed the multi‐modal static cytometry that adopted different features to classify the typical human cervical epithelial cells (H8) and cervical cancer cells (HeLa). With the light‐sheet static cytometry, we obtain brightfield (BF) images, fluorescence (FL) images and two‐dimensional (2D) light scattering (LS) patterns of single cervical cells. Three feature extraction methods are used to extract multi‐modal features based on different data characteristics. Analysis and classification of morphological and textural features demonstrate the potential of intracellular mitochondria in cervical cancer cell classification. The deep learning method is used to automatically extract deep features of label‐free LS patterns, and an accuracy of 76.16% for the classification of the above two kinds of cervical cells is obtained, which is higher than the other two single modes (BF and FL). Our multi‐modal static cytometry uses a variety of feature extraction and analysis methods to provide the mitochondria as promising internal biomarkers for cervical cancer diagnosis, and to show the promise of label‐free, automatic classification of early cervical cancer with deep learning‐based 2D light scattering. [ABSTRACT FROM AUTHOR]