Phenotypic Analysis of Microalgae Populations Using Label-Free Imaging Flow Cytometry and Deep Learning
Phenotypic Analysis of Microalgae Populations Using Label-Free Imaging Flow Cytometry and Deep Learning
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DOI：
10.1021/acsphotonics.1c00220
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发表时间：
2021-03-10
期刊：
影响因子：
7
通讯作者：
Ozcan, Aydogan
中科院分区：
文献类型：
--
作者：
Isil, Cagatay;de Haan, Kevin;Ozcan, Aydogan
温度、营养物质和污染物等环境因素影响微藻种群的生长速度和物理特性。藻类在海洋生态系统中起着至关重要的作用，对藻类的监测对观察生态系统的状态具有重要意义。然而，使用传统的光学显微镜分析这些微藻种群是耗时的，并且需要专家识别和计数藻类细胞，这反过来又大大限制了每次实验中可以测量的样品的体积。在这项工作中，我们使用高通量和现场便携式成像流式细胞仪，使用图像处理和深度学习对海洋微藻种群进行自动无标签表型分析。成像流式细胞仪通过捕获和重建以100 mL/h流速连续流动的液体的无透镜彩色全息图，提供液体样品中所含微藻的色强和相位图像。我们从这些全息图像中提取样品中每个藻类细胞的空间和光谱特征，并使用卷积神经网络进行藻类自动识别。研究人员分析了这些特征，以及样本中藻类的组成和生长速度，以了解不同藻类种群之间的相互作用以及毒素暴露的影响。作为概念验证，我们通过分析不同浓度的铜对微藻单一培养和混合培养的影响来证明该系统的有效性。
Environmental factors such as temperature, nutrients, and pollutants affect the growth rates and physical characteristics of microalgae populations. As algae play a vital role in marine ecosystems, the monitoring of algae is important to observe the state of an ecosystem. However, analyzing these microalgae populations using conventional light microscopy is time-consuming and requires experts to both identify and count the algal cells, which in turn considerably limits the volume of the samples that can be measured in each experiment. In this work we use a high-throughput and field-portable imaging flow cytometer to perform automated label-free phenotypic analysis of marine microalgae populations using image processing and deep learning. The imaging flow cytometer provides color intensity and phase images of microalgae contained in a liquid sample by capturing and reconstructing the lens-free color holograms of the continuously flowing liquid at a flow rate of 100 mL/h. We extracted the spatial and spectral features of each algal cell in a sample from these holographic images and performed automated algae identification using convolutional neural networks. These features, alongside the composition and growth rate of the algae within the samples, were analyzed to understand the interactions between different algae populations as well as the effects of toxin exposure. As proof of concept, we demonstrated the effectiveness of the system by analyzing the impact of various concentrations of copper on microalgae monocultures and mixtures.