High-Throughput, Label-Free, Single-Cell, Microalgal Lipid Screening by Machine-Learning- Equipped Optofluidic Time-Stretch Quantitative Phase Microscopy
High-Throughput, Label-Free, Single-Cell, Microalgal Lipid Screening by Machine-Learning- Equipped Optofluidic Time-Stretch Quantitative Phase Microscopy
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DOI：
10.1002/cyto.a.23084
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发表时间：
2017-05-01
期刊：
影响因子：
3.7
通讯作者：
Goda, Keisuke
中科院分区：
文献类型：
--
作者：
Guo, Baoshan;Lei, Cheng;Goda, Keisuke
为了解决全球能源危机，需要开发可靠、可持续和经济的石油替代燃料。其中一种替代品是微藻生物燃料，由于微藻通过光合作用吸收大气中的CO2，预计微藻生物燃料将在减少全球变暖的有害影响方面发挥关键作用。不幸的是，传统的分析方法仅提供群体平均脂质量，并且不能以非侵入性和无干扰的方式以单细胞分辨率表征微藻细胞的多样化群体。在这里，高通量的无标记单细胞筛选的脂质生产微藻细胞与光流体时间拉伸定量相显微镜被证明。特别是，Euglena gracilis，一种有吸引力的微藻物种，产生蜡酯（精制后适用于生物柴油和航空燃料），在脂滴内进行了研究。光流体时间拉伸定量相位显微镜是基于流体动力聚焦微流体芯片，光学时间拉伸定量相位显微镜和配备机器学习的数字图像处理器的集成。因此，它以10，000个细胞/秒的高通量提供每个单细胞的不透明度和相位图，从而实现准确的细胞分类，而无需荧光染色。具体而言，数据集用于表征E.在两种不同的培养条件下（氮充足和氮缺乏）对股薄肌细胞进行了分类，错误率仅为2.15%。该方法有望成为基于微藻的生物燃料生产的有效分析工具。(C)2017年国际细胞计数促进学会
The development of reliable, sustainable, and economical sources of alternative fuels to petroleum is required to tackle the global energy crisis. One such alternative is microalgal biofuel, which is expected to play a key role in reducing the detrimental effects of global warming as microalgae absorb atmospheric CO2 via photosynthesis. Unfortunately, conventional analytical methods only provide population-averaged lipid amounts and fail to characterize a diverse population of microalgal cells with single-cell resolution in a non-invasive and interference-free manner. Here high-throughput label-free single-cell screening of lipid-producing microalgal cells with optofluidic time-stretch quantitative phase microscopy was demonstrated. In particular, Euglena gracilis, an attractive microalgal species that produces wax esters (suitable for biodiesel and aviation fuel after refinement), within lipid droplets was investigated. The optofluidic time-stretch quantitative phase microscope is based on an integration of a hydrodynamic-focusing microfluidic chip, an optical time-stretch quantitative phase microscope, and a digital image processor equipped with machine learning. As a result, it provides both the opacity and phase maps of every single cell at a high throughput of 10,000 cells/s, enabling accurate cell classification without the need for fluorescent staining. Specifically, the dataset was used to characterize heterogeneous populations of E. gracilis cells under two different culture conditions (nitrogen-sufficient and nitrogen-deficient) and achieve the cell classification with an error rate of only 2.15%. The method holds promise as an effective analytical tool for microalgae-based biofuel production. (C) 2017 International Society for Advancement of Cytometry