Virus detection and identification in minutes using single-particle imaging and deep learning

Shiaelis, Nicolas; Tometzki, Alexander; Peto, Leon; McMahon, Andrew; Hepp, Christof; Bickerton, Erica; Favard, Cyril; Muriaux, Delphine; Andersson, Monique; Oakley, Sarah; Vaughan, Alison; Matthews, Philippa C.; Stoesser, Nicole; Crook, Derrick W.; Kapanidis, Achillefs N.; Robb, Nicole C.

doi:10.1101/2020.10.13.20212035

Cited by 22 publications

(23 citation statements)

References 30 publications

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“…This innovative technology does not require lysis, purification, or amplification process and yields results in 5 min. The technique involves taking direct throat swabs of infected persons and rapid labeling of the virus particles in the sample with short fluorescent DNA strands; the nanoimaging system and machine learning software rapidly detects the virus [ 35 ].…”

Section: State-of-the-art Of Nanomaterials As Anti-sars-cov-2mentioning

confidence: 99%

Nanotechnology as a Shield against COVID-19: Current Advancement and Limitations

Rai

Bonde

Yadav

et al. 2021

Viruses

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The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global health problem that the WHO declared a pandemic. COVID-19 has resulted in a worldwide lockdown and threatened to topple the global economy. The mortality of COVID-19 is comparatively low compared with previous SARS outbreaks, but the rate of spread of the disease and its morbidity is alarming. This virus can be transmitted human-to-human through droplets and close contact, and people of all ages are susceptible to this virus. With the advancements in nanotechnology, their remarkable properties, including their ability to amplify signal, can be used for the development of nanobiosensors and nanoimaging techniques that can be used for early-stage detection along with other diagnostic tools. Nano-based protection equipment and disinfecting agents can provide much-needed protection against SARS-CoV-2. Moreover, nanoparticles can serve as a carrier for antigens or as an adjuvant, thereby making way for the development of a new generation of vaccines. The present review elaborates the role of nanotechnology-based tactics used for the detection, diagnosis, protection, and treatment of COVID-19 caused by the SARS-CoV-2 virus.

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Section: State-of-the-art Of Nanomaterials As Anti-sars-cov-2mentioning

confidence: 99%

Nanotechnology as a Shield against COVID-19: Current Advancement and Limitations

Rai

Bonde

Yadav

et al. 2021

Viruses

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“…Their system achieves 98.75% accuracy in classifying a dataset of 553 coronavirus sequences ranging from 1260 to 31,029 base pairs. Shiaelis et al [120] deploy Computer Vision in single-particle fluorescence microscopy imaging to detect SARS-CoV-2. We refer readers to our section on Computer Vision for a description of how Computer Vision problems commonly use Deep Learning.…”

Section: Precision Diagnosticsmentioning

confidence: 99%

Deep Learning applications for COVID-19

2021

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This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.

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“…To increase the sensitivity of the test used for diagnosis of viruses such as SARS-CoV-2, Shiaelis et al (2020) developed a fast diagnosis system (5 min) using labeling, immobilization and single-particle imaging in TIRF of individual viruses, connected to a machine-learning analysis. The system was tested using three influenza strains and one human coronavirus (hCoV), viruses that cannot be distinguished in fluorescence microscopes due to their similar size and shape.…”

Section: Antivirals Study By Super-resolution Microscopymentioning

confidence: 99%

“…The system was tested using three influenza strains and one human coronavirus (hCoV), viruses that cannot be distinguished in fluorescence microscopes due to their similar size and shape. The system achieved high accuracy values (95%), and identified and classified the virus strains presented in a sample without purification or amplification ( Shiaelis et al, 2020 ).…”

Section: Antivirals Study By Super-resolution Microscopymentioning

confidence: 99%

Towards a Quantitative Single Particle Characterization by Super Resolution Microscopy: From Virus Structures to Antivirals Design

Arista-Romero

Pujals

Albertazzi

2021

Front. Bioeng. Biotechnol.

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In the last year the COVID19 pandemic clearly illustrated the potential threat that viruses pose to our society. The characterization of viral structures and the identification of key proteins involved in each step of the cycle of infection are crucial to develop treatments. However, the small size of viruses, invisible under conventional fluorescence microscopy, make it difficult to study the organization of protein clusters within the viral particle. The applications of super-resolution microscopy have skyrocketed in the last years, converting this group into one of the leading techniques to characterize viruses and study the viral infection in cells, breaking the diffraction limit by achieving resolutions up to 10 nm using conventional probes such as fluorescent dyes and proteins. There are several super-resolution methods available and the selection of the right one it is crucial to study in detail all the steps involved in the viral infection, quantifying and creating models of infection for relevant viruses such as HIV-1, Influenza, herpesvirus or SARS-CoV-1. Here we review the use of super-resolution microscopy (SRM) to study all steps involved in the viral infection and antiviral design. In light of the threat of new viruses, these studies could inspire future assays to unveil the viral mechanism of emerging viruses and further develop successful antivirals against them.

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Virus detection and identification in minutes using single-particle imaging and deep learning

Cited by 22 publications

References 30 publications

Nanotechnology as a Shield against COVID-19: Current Advancement and Limitations

Nanotechnology as a Shield against COVID-19: Current Advancement and Limitations

Deep Learning applications for COVID-19

Towards a Quantitative Single Particle Characterization by Super Resolution Microscopy: From Virus Structures to Antivirals Design

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