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首页> 外文期刊>Environmental Science & Technology >Morphological Indicator for Directed Evolution of Euglena gracilis with a High Heavy Metal Removal Efficiency
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Morphological Indicator for Directed Evolution of Euglena gracilis with a High Heavy Metal Removal Efficiency

机译:euglena Gracilis定向演化的形态学指标,具有高重金属去除效率

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摘要

In the past few decades, microaigae-based bio-remediation methods for treating heavy metal (HM)-polIuted wastewater have attracted much attention by virtue of their environment friendliness, cost efficiency, and sustainability. However, their HM removal efficiency is far from practical use. Directed evolution is expected to be effective for developing microalgae with a much higher HM removal efficiency, but there is no non-invasive or label-free indicator to identify them. Here, we present an intelligent cellular morphological indicator for identifying the HM removal efficiency of Euglena gracilis in a non-invasive and label-free manner. Specifically, we show a strong monotonic correlation (Spearman's ρ = -0.82, P = 2.1 × 10~(-5)) between a morphological meta-feature recognized via our machine learning algorithms and the Cu~(2+) removal efficiency of 19 E. gracilis clones. Our findings firmly suggest that the morphology of E. gracilis cells can serve as an effective HM removal efficiency indicator and hence have great potential, when combined with a high-throughput image-activated cell sorter, for directed-evolution-based development of E. gracilis with an extremely high HM removal efficiency for practical wastewater treatment worldwide.
机译:在过去的几十年中,凭借其环境友好,成本效率和可持续性,凭借其环境友好,成本效率和可持续性,对治疗重金属(HM)的生物修复方法具有很大的关注。然而,他们的HM去除效率远远不可使用。指导的演化预计将有效地开发微藻以更高的HM去除效率,但没有无侵入性或无标签的指标来识别它们。在这里,我们提出了一种智能细胞形态学指示剂,用于以非侵入性和无标记的方式鉴定Euglena Gracilis的HM去除效率。具体而言,我们展示了通过我们的机器学习算法识别的形态元特征与19个E. Gracilis克隆。我们的研究结果牢固地表明,E. Gracilis细胞的形态可以用作有效的HM去除效率指示器,并且当与高通量图像激活的细胞分类器结合时,具有很大的潜力,用于E.的定向演化的发展。 Gracilis具有极高的HM清除效率,适用于全球实际废水处理。

著录项

  • 来源
    《Environmental Science & Technology》 |2021年第12期|7880-7889|共10页
  • 作者单位

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan;

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan;

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan;

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan;

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Institute of Technological Sciences Wuhan University Wuhan Hubei 430072 China;

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Kanagawa Institute of Industrial Science and Technology Ebina Kanagawa 243-0435 Japan PRESTO Japan Science and Technology Agency Kawaguchi Saitama 332-0012 Japan;

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan;

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan;

    Graduate School of Science Technology and Innovation Kobe University Hyogo Kobe 657-8501 Japan Engineering Biology Research Center Kobe University Hyogo Kobe 657-8501 Japan;

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Kanagawa Institute of Industrial Science and Technology Ebina Kanagawa 243-0435 Japan;

    Department of Chemistry The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Institute of Technological Sciences Wuhan University Wuhan Hubei 430072 China Department of Bioengineering University of California Los Angeles California 90095 United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    microalgae; wastewater treatment; directed evolution; machine learning; single-cell analysis; imaging flow cytometry;

    机译:微藻;废水处理;定向演变;机器学习;单细胞分析;成像流量细胞计量;

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