Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning.
Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning.
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DOI：
10.1021/acsnano.2c10159
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发表时间：
2023-01-10
期刊：
影响因子：
17.1
通讯作者：
Robb, Nicole C.
中科院分区：
文献类型：
--
作者：
Shiaelis, Nicolas;Tometzki, Alexander;Peto, Leon;McMahon, Andrew;Hepp, Christof;Bickerton, Erica;Favard, Cyril;Muriaux, Delphine;Andersson, Monique;Oakley, Sarah;Vaughan, Ali;Matthews, Philippa C.;Stoesser, Nicole;Crook, Derrick W.;Kapanidis, Achillefs N.;Robb, Nicole C.
近几十年来，以2019冠状病毒病大流行为代表的病毒暴发的频率和规模日益增加，迫切需要快速和敏感的诊断方法。在这里，我们提出了一种病毒检测和鉴定的方法，该方法使用卷积神经网络来区分不同病毒的荧光标记完整颗粒的显微镜图像。我们的检测在不到5分钟的时间内实现标记、成像和病毒鉴定，不需要任何裂解、纯化或扩增步骤。经过训练的神经网络能够将SARS-CoV-2与阴性临床样本以及流感和季节性人类冠状病毒等其他常见呼吸道病原体区分开来。我们还能够区分密切相关的流感毒株以及SARS-CoV-2变体。通过软件更新，可以很容易地将其他和新的病原体纳入测试，从而提供了在未来传染病爆发或大流行中快速利用该技术的潜力。因此，单粒子成像与深度学习相结合，为传统的病毒诊断和基因组测序方法提供了一个有希望的替代方案，并具有潜在的重大影响。
The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of fluorescently labeled intact particles of different viruses. Our assay achieves labeling, imaging, and virus identification in less than 5 min and does not require any lysis, purification, or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses. We were also able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Additional and novel pathogens can easily be incorporated into the test through software updates, offering the potential to rapidly utilize the technology in future infectious disease outbreaks or pandemics. Single-particle imaging combined with deep learning therefore offers a promising alternative to traditional viral diagnostic and genomic sequencing methods and has the potential for significant impact.
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影响因子：
7.7
作者：
Laine, Romain F.;Goodfellow, Gemma;Kaminski, Clemens F.
通讯作者：
Kaminski, Clemens F.
影响因子：
4.6
作者：
Lyonnais S;Hénaut M;Neyret A;Merida P;Cazevieille C;Gros N;Chable-Bessia C;Muriaux D
通讯作者：
Muriaux D
影响因子：
5.7
作者：
Huang, Wei E.;Lim, Boon;Cui, Zhanfeng
通讯作者：
Cui, Zhanfeng
影响因子：
3.1
作者：
Sadasiv EC;Yeh TT;Chang PW
通讯作者：
Chang PW
DOI：
10.1093/cid/ciaa1382
发表时间：
2021-11-02
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
--
作者：
Arnaout R;Lee RA;Lee GR;Callahan C;Cheng A;Yen CF;Smith KP;Arora R;Kirby JE
通讯作者：
Kirby JE