Cytometry Part A

Measurable morphological features of single circulating tumor cells in selected solid tumors—A pilot study

Abstract Liquid biopsies developed into a range of sensitive technologies aiming to analyze for example, circulating tumor cells (CTCs) in peripheral blood, which significantly deepens understanding of the metastatic process. Nevertheless, examination of CTCs is mostly limited to their enumeration and usually only 2–3 markers‐based phenotyping, not offering yet sufficient insight into their biology. In contrast, quantitative analysis of their morphological details might extend our knowledge about dissemination and even improve CTC isolation or label‐free identification methods dependent on their physical features such as size, and deformability. Current study was conducted to describe CTCs' and their size, shape, presence of protrusions, and micronuclei across various types of cancers (lung, n  = 29; ovarian, n  = 24, breast, n  = 54; and prostate, n  = 33). Epithelial (pan‐keratins), mesenchymal (vimentin), and two exclusion markers were used to identify CTCs and classify them into four epithelial and epithelial‐mesenchymal transition‐related phenotypes using standardized and throughput method, imaging flow cytometry. The morphological characteristics of CTCs, including their nuclei, such as circularity, the maximum, and minimum diagonal values were determined using an open‐source software QuPath. On average, detected CTCs ( n  = 1156) were larger, and more irregular in shape compared to leukocytes/endothelial cells ( n  = 400). Epithelial and mesenchymal CTCs had the largest (median = 18.2 μm) and the smallest diameter (median = 10.4 μm), respectively. In terms of cancer‐specific variations, the largest CTCs were identified in lung cancer, whereas the smallest—in prostate and breast cancers. Epithelial CTCs and those negative for both epithelial and mesenchymal markers exhibited the highest degree of elongation, whereas mesenchymal CTCs were the most irregular in shape. Protrusions and micronuclei were observed extremely rarely within CTCs of breast and prostate cancer (0.6%–0.8% of CTCs). Micronuclei were observed only in epithelial and epithelial‐mesenchymal CTCs. This study underscores the significant variability in the morphological features of CTCs in relation to their phenotypic classification or even the particular organ of origin, potentially influencing for example, size‐dependent CTC isolation methods. It demonstrates for the first time the morphological measurements of CTCs undergoing epithelial‐mesenchymal transition, and some specific morphological details (i.e., protrusions, micronuclei) within CTCs in general.

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

AbstractWe 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

Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning‐based label‐free light scattering with multi‐modal static cytometry

AbstractCervical 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.

Single‐detector multiplex imaging flow cytometry for cancer cell classification with deep learning

AbstractImaging 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.

Light scattering pattern specific convolutional network static cytometry for label‐free classification of cervical cells

AbstractCervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS‐net) based on deep learning algorithm and integrate it into a 2D light scattering static cytometry for automatic, label‐free analysis of single cervical cells. An accuracy rate of 95.46% for the classification of normal cervical cells and cancerous ones (mixed C‐33A and CaSki cells) is obtained. When applied for the subtyping of label‐free cervical cell lines, we obtain an accuracy rate of 93.31% with our LSPS‐net cytometric technique. Furthermore, the three‐way classification of the above different types of cells has an overall accuracy rate of 90.90%, and comparisons with other feature descriptors and classification algorithms show the superiority of deep learning for automatic feature extraction. The LSPS‐net static cytometry may potentially be used for cervical cancer early screening, which is rapid, automatic and label‐free.