4.8 Article

Culture-Free Detection of Crop Pathogens at the Single-Cell Level by Micro-Raman Spectroscopy

期刊

ADVANCED SCIENCE
卷 4, 期 11, 页码 -

出版社

WILEY
DOI: 10.1002/advs.201700127

关键词

crop pathogens; culture-free detection; micro-Raman spectroscopy; single cells

资金

  1. Natural Science Foundation of China [31401705, 31200063]
  2. Natural Science Foundation of Hainan Province [317010]
  3. State Key Laboratory of Marine Resource Utilization in South China Sea [2016005]
  4. Project of Innovation & Development of Marine Economy
  5. Foundation of Hainan University [KYQD1561]

向作者/读者索取更多资源

The rapid and sensitive identification of invasive plant pathogens has important applications in biotechnology, plant quarantine, and food security. Current methods are far too time-consuming and need a pre-enrichment period ranging from hours to days. Here, a micro-Raman spectroscopy-based bioassay for culture-free pathogen quarantine inspection at the single cell level within 40 min is presented. The application of this approach can readily and specifically detect plant pathogens Burkholderia gladioli pv. alliicola and Erwinia chrysanthemi that are closely related pathogenically. Furthermore, the single-bacterium detection was able to discriminate them from a reference Raman spectral library including multiple quarantine-relevant pathogens with broad host ranges and an array of pathogenic variants. To show the usefulness of this assay, Burkholderia gladioli pv. alliicola and Erwinia chrysanthemi are detected at single-bacterium level in plant tissue lesions without pre-enrichment. The results are confirmed by the plate-counting method and a genetic molecular approach, which display comparable recognition ratios to the Raman spectroscopy-based bioassay. The results represent a critical step toward the use of micro-Raman spectroscopy in rapid and culture-free discrimination of quarantine relevant plant pathogens.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据