We introduce inline digital holographic microscopy (in-line DHM) as a label-free technique for detecting tumor cells in blood. The optimized DHM platform fingerprints every cell flowing through a microchannel at 10000 cells per second, based on three features – size, maximum intensity and mean intensity. To identify tumor cells in a background of blood cells, we developed robust gating criteria using machine-learning approaches. We established classifiers from the features extracted from 100000-cell training sets consisting of red blood cells, peripheral blood mononuclear cells and tumor cell lines. The optimized classifier was then applied to targeted features of a single cell in a mixed cell population to make quantitative cell-type predictions. We tested our classification system with tumor cells spiked at different levels into a background of lysed blood that contained predominantly peripheral blood mononuclear cells. Results show that our holographic screening method can readily detect as few as 10 tumor cells per mL, and can identify tumor cells at a false positive rate of at most 0.001%. This purely optical approach obviates the need for antibody labeling and allows large volumes of sample to be quickly processed. Moreover, our in-line DHM approach can be combined with existing circulation tumor cell enrichment strategies, making it a promising tool for label-free analysis of liquid-biopsy samples.

中文翻译：

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无标记，高通量全息筛查和血液中肿瘤细胞计数

我们引入在线数字全息显微镜（在线DHM）作为检测血液中肿瘤细胞的无标记技术。优化的DHM平台基于三个特征（大小，最大强度和平均强度）以每秒10000个细胞的速度对流经微通道的每个细胞进行指纹识别。为了在血细胞背景中鉴定肿瘤细胞，我们使用机器学习方法开发了鲁棒的门控标准。我们从100个提取的特征中建立了分类器000细胞训练集，由红细胞，外周血单个核细胞和肿瘤细胞系组成。然后将优化的分类器应用于混合细胞群体中单个细胞的目标特征，以进行定量的细胞类型预测。我们测试了我们的分类系统，将肿瘤细胞以不同水平掺入到裂解血中，其中主要含有外周血单个核细胞。结果表明，我们的全息筛选方法可以轻松检测到每毫升10个肿瘤细胞，并且可以以至多0.001％的假阳性率识别肿瘤细胞。这种纯粹的光学方法消除了抗体标记的需要，并允许快速处理大量样品。而且，我们的在线DHM方法可以与现有的循环肿瘤细胞富集策略结合使用，