标题： 癌细胞的定量相位显微镜空间特征 显示英文标题

标题： Quantitative phase microscopy spatial signatures of cancer cells

摘要： 我们通过使用细胞的无标签定量相位图像中发现的空间形态和纹理信息，实现了对活体健康细胞和癌细胞的流式分类。 我们将健康细胞与原发性肿瘤细胞以及原发性肿瘤细胞与转移性癌细胞进行比较，其中肿瘤活检样本和正常组织均来自同一个体。 为了模拟通过流式细胞术分析液体活检，细胞在未附着于基底时被成像。 我们使用了低相干离轴干涉相位显微镜设置，该设置允许单次曝光采集模式，因此适用于动态细胞流动过程中的定量成像。 采集后，提取了细胞的光程延迟图，并用于计算15个参数，这些参数来源于细胞的三维形态和纹理。 在每组分析数十个细胞后，我们发现大多数计算参数在组间差异具有高度统计显著性，所有统计显著参数的趋势相同。 此外，一种专门设计的机器学习算法，基于从相位图中提取的特征，以81%-93%的灵敏度和81%-99%的特异性正确分类细胞类型（健康/癌/转移性）。 本文提出的用于液体活检的定量相位成像方法可以作为在成像流式细胞仪中对新鲜分离的活癌细胞进行分阶段分析的先进技术的基础。 显示英文摘要

摘要： We present cytometric classification of live healthy and cancer cells by using the spatial morphological and textural information found in the label-free quantitative phase images of the cells. We compare both healthy cells to primary tumor cell and primary tumor cells to metastatic cancer cells, where tumor biopsies and normal tissues were isolated from the same individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells were imaged while unattached to the substrate. We used low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow. After acquisition, the optical path delay maps of the cells were extracted, and used to calculate 15 parameters derived from cellular 3-D morphology and texture. Upon analyzing tens of cells in each group, we found high statistical significance in the difference between the groups in most of the parameters calculated, with the same trends for all statistically significant parameters. Furthermore, a specially designed machine learning algorithm, implemented on the phase map extracted features, classified the correct cell type (healthy/cancer/metastatic) with 81%-93% sensitivity and 81%-99% specificity. The quantitative phase imaging approach for liquid biopsies presented in this paper could be the basis for advanced techniques of staging freshly isolated live cancer cells in imaging flow cytometers.