Deep Learning in Label-free Cell Classification.
Deep Learning in Label-free Cell Classification.
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DOI：
10.1038/srep21471
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发表时间：
2016-03-15
影响因子：
4.6
通讯作者：
Jalali B
中科院分区：
文献类型：
--
作者：
Chen CL;Mahjoubfar A;Tai LC;Blaby IK;Huang A;Niazi KR;Jalali B
无标记细胞分析对于个性化基因组学、癌症诊断和药物开发至关重要，因为它避免了染色试剂对细胞活力和细胞信号传导的不利影响。然而，目前可用的无标记细胞测定大多仅依赖于单一特征，并且缺乏足够的区分。此外，由于其低通量，通过这些测定法分析的样品量有限。在这里，我们将特征提取和深度学习与光子时间拉伸实现的高通量定量成像相结合，在无标记细胞分类中实现了创纪录的高准确性。我们的系统捕获定量光学相位和强度图像，并提取单个细胞的多个生物物理特征。这些生物物理测量形成超维特征空间，在该超维特征空间中进行监督学习以用于细胞分类。我们比较了各种学习算法，包括人工神经网络，支持向量机，逻辑回归和一种新型的深度学习管道，它采用了接收器操作特性的全局优化。作为我们的系统的增强的灵敏度和特异性的验证，我们显示了针对结肠癌细胞的白色血液T细胞的分类，以及用于生物燃料生产的脂质积累藻类菌株。该系统为数据驱动的表型诊断和更好地理解细胞中的异质基因表达开辟了新的途径。
Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.