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Development of a sensitive and reliable reverse transcription-droplet digital polymerase chain reaction (RT-ddPCR) assay for the detection of Citrus tristeza virus

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Abstract

Citrus tristeza virus (CTV) is one of the most important citrus viruses in the world. In this study, we established a reverse-transcription droplet digital polymerase chain reaction (RT-ddPCR) method for the sensitive and accurate quantification of CTV. Quantitative linearity, sensitivity and accuracy of RT-ddPCR were compared to those of reverse-transcription real time PCR (RT-qPCR) by using 10-fold serial dilutions of the CTV RNA transcripts. Both methods showed a high degree of linearity (R2 = 0.991) and quantitative correlation, although RT-ddPCR revealed 100-fold higher sensitivity than RT-qPCR. The detection results for heat-treatment citrus samples also showed that the positive detection rate of RT-ddPCR (73.2%) was higher than that of RT-qPCR (53.6%). In summary, the results indicated that RT-ddPCR may contribute to improved analytical sensitivity and accuracy for CTV detection.

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Acknowledgments

This work was partially supported by Overseas Expertise Introduction Project for Discipline Innovation (111 Center) (B18044), Chongqing Research Program of Basic Research and Frontier Technology (cstc2019jcyj-msxmX0557), Fundamental Research Funds for the Central Universities (XDJK2018AA002) and The National Key Research and Development Program of China (SQ2019YFD100010/02). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

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Correspondence to Yan Zhou.

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The research did not involve any studies with human participants or animals performed by any of the authors.

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Wang, Y., Yang, Z., Zhao, J. et al. Development of a sensitive and reliable reverse transcription-droplet digital polymerase chain reaction (RT-ddPCR) assay for the detection of Citrus tristeza virus. Eur J Plant Pathol (2020). https://doi.org/10.1007/s10658-019-01920-x

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Keywords

  • Citrus tristeza virus (CTV)
  • RT-ddPCR
  • Detection