Gene machines

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A new twist on PIFE: photoisomerisation-related fluorescence enhancement

(2023)

Authors:

Evelyn Ploetz, Benjamin Ambrose, Anders Barth, Richard Börner, Felix Erichson, Achillefs N Kapanidis, Harold D Kim, Marcia Levitus, Timothy M Lohman, Abhishek Mazumder, David S Rueda, Fabio D Steffen, Thorben Cordes, Steven W Magennis, Eitan Lerner
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Deep Antimicrobial Susceptibility Phenotyping (DASP) Training and Evaluation Dataset, and Trained Models.

University of Oxford (2023)

Authors:

Aleksander Zagajewski, Piers Turner, Conor Feehily, Nicole Stoesser, Christoffer Nellaker, Achillefs Kapanidis

Abstract:

Dataset of microscopy images of untreated and treated E.coli lab strains and clinical isolates, and machine learning models trained on them. Corresponding publications: https://doi.org/10.1101/2022.12.08.22283219 Corresponding analysis code: https://github.com/KapanidisLab/Deep-Learning-and-Single-Cell-Phenotyping-for-Rapid-Antimicrobial-Susceptibility-Testing
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Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output

(2023)

Authors:

Lennart Grabenhorst, Martina Pfeiffer, Thea Schinkel, Mirjam Kümmerlin, Jasmin Maglic, Gereon Brüggenthies, Florian Selbach, Alexander Murr, Philip Tinnefeld, Viktorija Glembockyte
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Overcoming the High Concentration Barrier in Single-Molecule Fluorescence Experiments through Adaptable Fluorogenic ssDNA Label

EUROPEAN BIOPHYSICS JOURNAL WITH BIOPHYSICS LETTERS 52:SUPPL 1 (2023) S192-S192

Authors:

Mirjam Kummerlin, Achillefs Kapanidis
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Virus detection and identification in minutes using single-particle imaging and deep learning

ACS Nano American Chemical Society (2023)

Authors:

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

Abstract:

ABSTRACT

The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current 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 single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes 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. Additionally, we were able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. 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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