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1. OptoPi: An open source flexible platform for the analysis of small animal behaviour

Author

Xavier Cano-Ferrer, Ruairí J.V. Roberts, Alice S. French, Joost de Folter, Hui Gong, Luke Nightingale, Amy Strange, Albane Imbert, and Lucia L. Prieto-Godino

Subjects
Behaviour, Open hardware, 3D-printing, Laser cutting, Optogenetics, Light stimulation, Science (General), Q1-390
Abstract

Behaviour is the ultimate output of neural circuit computations, and therefore its analysis is a cornerstone of neuroscience research. However, every animal and experimental paradigm requires different illumination conditions to capture and, in some cases, manipulate specific behavioural features. This means that researchers often develop, from scratch, their own solutions and experimental set-ups. Here, we present OptoPi, an open source, affordable (∼ £600), behavioural arena with accompanying multi-animal tracking software. The system features highly customisable and reproducible visible and infrared illumination and allows for optogenetic stimulation. OptoPi acquires images using a Raspberry Pi camera, features motorised LED-based illumination, Arduino control, as well as irradiance monitoring to fine-tune illumination conditions with real time feedback. Our open-source software (BioImageProcessing) can be used to simultaneously track multiple unmarked animals both in on-line and off-line modes. We demonstrate the functionality of OptoPi by recording and tracking under different illumination conditions the spontaneous behaviour of larval zebrafish as well as adult Drosophila flies and their first instar larvae, an experimental animal that due to its small size and transparency has classically been hard to track. Further, we showcase OptoPi’s optogenetic capabilities through a series of experiments using transgenic Drosophila larvae.

Published
2023
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2. SARS-CoV-2 detection by a clinical diagnostic RT-LAMP assay [version 2; peer review: 2 approved]

Author

Michael D. Buck, Enzo Z. Poirier, Ana Cardoso, Bruno Frederico, Johnathan Canton, Sam Barrell, Rupert Beale, Richard Byrne, Simon Caidan, Margaret Crawford, Laura Cubitt, Sonia Gandhi, Robert Goldstone, Paul R. Grant, Kiran Gulati, Steve Hindmarsh, Michael Howell, Michael Hubank, Rachael Instrell, Ming Jiang, George Kassiotis, Wei-Ting Lu, James I. MacRae, Iana Martini, Davin Miller, David Moore, Eleni Nastouli, Jerome Nicod, Luke Nightingale, Jessica Olsen, Amin Oomatia, Nicola O'Reilly, Anett Rideg, Ok-Ryul Song, Amy Strange, Charles Swanton, Samra Turajlic, Mary Wu, Caetano Reis e Sousa, and The Crick COVID-19 Consortium

Subjects
Medicine, Science
Abstract

The ongoing pandemic of SARS-CoV-2 calls for rapid and cost-effective methods to accurately identify infected individuals. The vast majority of patient samples is assessed for viral RNA presence by RT-qPCR. Our biomedical research institute, in collaboration between partner hospitals and an accredited clinical diagnostic laboratory, established a diagnostic testing pipeline that has reported on more than 252,000 RT-qPCR results since its commencement at the beginning of April 2020. However, due to ongoing demand and competition for critical resources, alternative testing strategies were sought. In this work, we present a clinically-validated procedure for high-throughput SARS-CoV-2 detection by RT-LAMP that is robust, reliable, repeatable, specific, and inexpensive.

Published
2021
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3. Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation.

Author

Daniel Franco-Barranco, Zudi Lin, Won-Dong Jang, Xueying Wang, Qijia Shen, Wenjie Yin, Yutian Fan, Mingxing Li, Chang Chen, Zhiwei Xiong, Rui Xin, Hao Liu, Huai Chen, Zhili Li, Jie Zhao 0020, Xuejin Chen, Constantin Pape, Ryan Conrad, Luke Nightingale, Joost de Folter, Martin L. Jones, Yanling Liu, Dorsa Ziaei, Stephan Huschauer, Ignacio Arganda-Carreras, Hanspeter Pfister, and Donglai Wei 0001

Published
2023
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4. Current Progress and Challenges in Large-scale3D Mitochondria Instance Segmentation

Author

Donglai Wei, Hanspeter Pfister, Ignacio Arganda-Carreras, Stephan Huschauer, Dorsa Ziaei, Yanling Liu, Martin Jones, Luke Nightingale, Jozefus De Folter, Ryan Conrad, Constantin Pape, Xuejin Chen, Jie Zhao, Zhili Li, Huai Chen, Hao Liu, Rui Xin, Zhiwei Xiong, Chang Chen, Mingxing Li, Yutian Fan, Wenjie Yin, Qijia Shen, Xueying Wang, Won-Dong Jang, Zudi Lin, and Daniel Franco-Barranco

Abstract

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. Additionally, we present a retrospective evaluation of the scoring system performed using TIMISE, our novel open-source evaluation toolbox, which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results.

Published
2023
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5. Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Author

Zooniverse Volunteer Community, Helen Spiers, Roger Hutchings, Anne Weston, Chris Lintott, Harry Songhurst, Luke Nightingale, Joost de Folter, Martin L. Jones, Lucy M. Collinson, Amy Strange, Christopher J. Peddie, Steve Hindmarsh, and Community, Zooniverse Volunteer

Subjects
Volunteers, business.industry, Nuclear Envelope, Deep learning, Image processing, Cell Biology, Gold standard (test), Biology, Biochemistry, Bottleneck, Domain (software engineering), Cell biology, Microscopy, Electron, Deep Learning, Structural Biology, Genetics, Humans, Computer vision, Microscopist, Segmentation, Artificial intelligence, business, Molecular Biology, Envelope (motion), HeLa Cells
Abstract

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realizing the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope (NE) of HeLa cells imaged with serial blockface scanning electron microscopy. We present our approach for aggregating multiple volunteer annotations to generate a high-quality consensus segmentation and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the NE, which we share here, in addition to our archived benchmark data.

Published
2021
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6. Automatic instance segmentation of mitochondria in electron microscopy data

Author

Spiers H, de Folter J, Luke Nightingale, Martin L. Jones, Lucy M. Collinson, and Amy Strange

Subjects
law, Computer science, business.industry, Segmentation, Pattern recognition, Artificial intelligence, Electron microscope, business, Public repository, law.invention
Abstract

We present a new method for rapid, automated, large-scale 3D mitochondria instance segmentation, developed in response to the ISBI 2021 MitoEM Challenge. In brief, we trained separate machine learning algorithms to predict (1) mitochondria areas and (2) mitochondria boundaries in image volumes acquired from both rat and human cortex with multi-beam scanning electron microscopy. The predictions from these algorithms were combined in a multi-step post-processing procedure, that resulted in high semantic and instance segmentation performance. All code is provided via a public repository.

Published
2021
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7. Clinical outcomes of COVID-19 in long-term care facilities for people with epilepsy

Author

Simona Balestrini, Matthias J. Koepp, Sonia Gandhi, Hannah M. Rickman, Gee Yen Shin, Catherine F. Houlihan, Jonny Anders-Cannon, Katri Silvennoinen, Fenglai Xiao, Sara Zagaglia, Kirsty Hudgell, Mariusz Ziomek, Paul Haimes, Adam Sampson, Annie Parker, J. Helen Cross, Rosemarie Pardington, Eleni Nastouli, Charles Swanton, Josemir W. Sander, Sanjay M. Sisodiya, Jim Aitken, Zoe Allen, Rachel Ambler, Karen Ambrose, Emma Ashton, Alida Avola, Samutheswari Balakrishnan, Caitlin Barns-Jenkins, Genevieve Barr, Sam Barrell, Souradeep Basu, Rupert Beale, Clare Beesley, Nisha Bhardwaj, Shahnaz Bibi, Ganka Bineva-Todd, Dhruva Biswas, Michael J. Blackman, Dominique Bonnet, Faye Bowker, Malgorzata Broncel, Claire Brooks, Michael D. Buck, Andrew Buckton, Timothy Budd, Alana Burrell, Louise Busby, Claudio Bussi, Simon Butterworth, Matthew Byott, Fiona Byrne, Richard Byrne, Simon Caidan, Joanna Campbell, Johnathan Canton, Ana Cardoso, Nick Carter, Luiz Carvalho, Raffaella Carzaniga, Natalie Chandler, Qu Chen, Peter Cherepanov, Laura Churchward, Graham Clark, Bobbi Clayton, Clementina Cobolli Gigli, Zena Collins, Sally Cottrell, Margaret Crawford, Laura Cubitt, Tom Cullup, Heledd Davies, Patrick Davis, Dara Davison, Vicky Dearing, Solene Debaisieux, Monica Diaz-Romero, Alison Dibbs, Jessica Diring, Paul C. Driscoll, Annalisa D'Avola, Christopher Earl, Amelia Edwards, Chris Ekin, Dimitrios Evangelopoulos, Rupert Faraway, Antony Fearns, Aaron Ferron, Efthymios Fidanis, Dan Fitz, James Fleming, Daniel Frampton, Bruno Frederico, Alessandra Gaiba, Anthony Gait, Steve Gamblin, Kathleen Gärtner, Liam Gaul, Helen M. Golding, Jacki Goldman, Robert Goldstone, Belen Gomez Dominguez, Hui Gong, Paul R. Grant, Maria Greco, Mariana Grobler, Anabel Guedan, Maximiliano G. Gutierrez, Fiona Hackett, Ross Hall, Steinar Halldorsson, Suzanne Harris, Sugera Hashim, Emine Hatipoglu, Lyn Healy, Judith Heaney, Susanne Herbst, Graeme Hewitt, Theresa Higgins, Steve Hindmarsh, Rajnika Hirani, Joshua Hope, Elizabeth Horton, Beth Hoskins, Michael Howell, Louise Howitt, Jacqueline Hoyle, Mint R. Htun, Michael Hubank, Hector Huerga Encabo, Deborah Hughes, Jane Hughes, Almaz Huseynova, Ming-Shih Hwang, Rachael Instrell, Deborah Jackson, Mariam Jamal-Hanjani, Lucy Jenkins, Ming Jiang, Mark Johnson, Leigh Jones, Nnennaya Kanu, George Kassiotis, Gavin Kelly, Louise Kiely, Anastacio King Spert Teixeira, Stuart Kirk, Svend Kjaer, Ellen Knuepfer, Nikita Komarov, Paul Kotzampaltiris, Konstantinos Kousis, Tammy Krylova, Ania Kucharska, Robyn Labrum, Catherine Lambe, Michelle Lappin, Stacey-Ann Lee, Andrew Levett, Lisa Levett, Marcel Levi, Hon Wing Liu, Sam Loughlin, Wei-Ting Lu, James I. MacRae, Akshay Madoo, Julie A. Marczak, Mimmi Martensson, Thomas Martinez, Bishara Marzook, John Matthews, Joachim M. Matz, Samuel McCall, Laura E. McCoy, Fiona McKay, Edel C. McNamara, Carlos M. Minutti, Gita Mistry, Miriam Molina-Arcas, Beatriz Montaner, Kylie Montgomery, Catherine Moore, David Moore, Anastasia Moraiti, Lucia Moreira-Teixeira, Joyita Mukherjee, Cristina Naceur-Lombardelli, Aileen Nelson, Jerome Nicod, Luke Nightingale, Stephanie Nofal, Paul Nurse, Savita Nutan, Caroline Oedekoven, Anne O'Garra, Jean D. O'Leary, Jessica Olsen, Olga O'Neill, Nicola O'Reilly, Paula Ordonez Suarez, Neil Osborne, Amar Pabari, Aleksandra Pajak, Venizelos Papayannopoulos, Stavroula M Paraskevopoulou, Namita Patel, Yogen Patel, Oana Paun, Nigel Peat, Laura Peces-Barba Castano, Ana Perez Caballero, Jimena Perez-Lloret, Magali S. Perrault, Abigail Perrin, Roy Poh, Enzo Z. Poirier, James M. Polke, Marc Pollitt, Lucia Prieto-Godino, Alize Proust, Clinda Puvirajasinghe, Christophe Queval, Vijaya Ramachandran, Abhinay Ramaprasad, Peter Ratcliffe, Laura Reed, Caetano Reis e Sousa, Kayleigh Richardson, Sophie Ridewood, Fiona Roberts, Rowenna Roberts, Angela Rodgers, Pablo Romero Clavijo, Annachiara Rosa, Alice Rossi, Chloe Roustan, Andrew Rowan, Erik Sahai, Aaron Sait, Katarzyna Sala, Emilie Sanchez, Theo Sanderson, Pierre Santucci, Fatima Sardar, Adam Sateriale, Jill A. Saunders, Chelsea Sawyer, Anja Schlott, Edina Schweighoffer, Sandra Segura-Bayona, Rajvee Shah Punatar, Maryam Shahmanesh, Joe Shaw, Mariana Silva Dos Santos, Margaux Silvestre, Matthew Singer, Daniel M. Snell, Ok-Ryul Song, Moira J. Spyer, Louisa Steel, Amy Strange, Adrienne E. Sullivan, Michele S.Y. Tan, Zoe H. Tautz-Davis, Effie Taylor, Gunes Taylor, Harriet B. Taylor, Alison Taylor-Beadling, Fernanda Teixeira Subtil, Berta Terré Torras, Patrick Toolan-Kerr, Francesca Torelli, Tea Toteva, Moritz Treeck, Hadija Trojer, Ming-Han C. Tsai, James M.A. Turner, Melanie Turner, Jernej Ule, Rachel Ulferts, Sharon P. Vanloo, Selvaraju Veeriah, Subramanian Venkatesan, Karen Vousden, Andreas Wack, Claire Walder, Philip A. Walker, Yiran Wang, Sophia Ward, Catharina Wenman, Luke Williams, Matthew J. Williams, Wai Keong Wong, Joshua Wright, Mary Wu, Lauren Wynne, Zheng Xiang, Melvyn Yap, Julian A. Zagalak, Davide Zecchin, Rachel Zillwood, Santhakumari Carthiyaniamma, Jane DeTisi, Julie Dick, Andrea Hill, Karin Kipper, Birinder Kullar, Sarah Norris, Fergus Rugg-Gunn, Rebecca Salvatierra, Gabriel Shaya, Astrid Sloan, Priyanka Singh, James Varley, Ben Whatley, and Academic Medical Center

Subjects
Male, Pediatrics, CCC, Crick COVID Consortium, SWGC, Sir William Gowers Centre, Comorbidity, Residential Facilities, Cohort Studies, Behavioral Neuroscience, Epilepsy, 0302 clinical medicine, Case fatality rate, Infection control, 030212 general & internal medicine, Care Models, COVID-19, coronavirus disease 2019, Aged, 80 and over, Surveillance, Middle Aged, 3. Good health, Treatment Outcome, Neurology, UCLH, University College London Hospitals NHS Foundation Trust, PEG, percutaneous endoscopic gastrostomy, STE, St Elizabeth’s Centre, Female, medicine.symptom, PPE, personal protective equipment, Cohort study, Adult, medicine.medical_specialty, Isolation (health care), Clinical Neurology, Asymptomatic, Article, Young Adult, 03 medical and health sciences, medicine, Humans, YE, Young Epilepsy, Aged, SARS-CoV-2, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Infection Control, business.industry, SARS-CoV-2, Prevention, Vulnerable people, COVID-19, TM, The Meath, medicine.disease, Long-Term Care, CCE, Chalfont Centre for Epilepsy, United Kingdom, Long-term care, Neurology (clinical), business, 030217 neurology & neurosurgery, Contact tracing
Abstract

Highlights • We found a high asymptomatic rate in vulnerable people with epilepsy. • Enhanced surveillance allows to quickly contain outbreaks. • We report a low rate of COVID-19 morbidity and mortality in a long-term care facility. • Preventative measures allow reducing resident-to-resident and -to-caregiver transmission. • Children and young adults appear to have lower infection rates., In this cohort study, we aim to compare outcomes from coronavirus disease 2019 (COVID-19) in people with severe epilepsy and other co-morbidities living in long-term care facilities which all implemented early preventative measures, but different levels of surveillance. During 25-week observation period (16 March–6 September 2020), we included 404 residents (118 children), and 1643 caregivers. We compare strategies for infection prevention, control, and containment, and related outcomes, across four UK long-term care facilities. Strategies included early on-site enhancement of preventative and infection control measures, early identification and isolation of symptomatic cases, contact tracing, mass surveillance of asymptomatic cases and contacts. We measured infection rate among vulnerable people living in the facilities and their caregivers, with asymptomatic and symptomatic cases, including fatality rate. We report 38 individuals (17 residents) who tested severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive, with outbreaks amongst residents in two facilities. At Chalfont Centre for Epilepsy (CCE), 10/98 residents tested positive: two symptomatic (one died), eight asymptomatic on weekly enhanced surveillance; 2/275 caregivers tested positive: one symptomatic, one asymptomatic. At St Elizabeth’s (STE), 7/146 residents tested positive: four symptomatic (one died), one positive during hospital admission for symptoms unrelated to COVID-19, two asymptomatic on one-off testing of all 146 residents; 106/601 symptomatic caregivers were tested, 13 positive. In addition, during two cycles of systematically testing all asymptomatic carers, four tested positive. At The Meath (TM), 8/80 residents were symptomatic but none tested; 26/250 caregivers were tested, two positive. At Young Epilepsy (YE), 8/80 children were tested, all negative; 22/517 caregivers were tested, one positive. Infection outbreaks in long-term care facilities for vulnerable people with epilepsy can be quickly contained, but only if asymptomatic individuals are identified through enhanced surveillance at resident and caregiver level. We observed a low rate of morbidity and mortality, which confirmed that preventative measures with isolation of suspected and confirmed COVID-19 residents can reduce resident-to-resident and resident-to-caregiver transmission. Children and young adults appear to have lower infection rates. Even in people with epilepsy and multiple co-morbidities, we observed a high percentage of asymptomatic people suggesting that epilepsy-related factors (anti-seizure medications and seizures) do not necessarily lead to poor outcomes.

Published
2021

8. SARS-CoV-2 detection by a clinical diagnostic RT-LAMP assay

Author

Jerome Nicod, James I. MacRae, Amin Oomatia, Laura Cubitt, Iana Martini, Simon Caidan, Kiran Gulati, Sonia Gandhi, Luke Nightingale, Michael D. Buck, Enzo Z. Poirier, Wei-Ting Lu, Margaret Crawford, Nicola O’Reilly, Amy Strange, Michael Howell, Steve Hindmarsh, Charles Swanton, Jessica Olsen, Samra Turajlic, George Kassiotis, Paul Grant, Rupert Beale, Caetano Reis e Sousa, Mary Wu, Ming Jiang, Robert L. Goldstone, Sam Barrell, Eleni Nastouli, Rachael Instrell, Bruno Frederico, Davin Miller, Anett Rideg, Ana Cardoso, Ok-Ryul Song, Johnathan Canton, Mike Hubank, David Moore, and Richard D. Byrne

Subjects
0301 basic medicine, 2019-20 coronavirus outbreak, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 030106 microbiology, Medicine (miscellaneous), General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Pandemic, Medicine, Medical physics, Viral rna, Diagnostic laboratory, Intensive care medicine, License, RT-LAMP, business.industry, SARS-CoV-2, Diagnostic test, Creative commons, Articles, Method Article, 3. Good health, 030104 developmental biology, business, clinical diagnostic
Abstract

The ongoing pandemic of SARS-CoV-2 calls for rapid and cost-effective methods to accurately identify infected individuals. The vast majority of patient samples is assessed for viral RNA presence by RT-qPCR. Our biomedical research institute, in collaboration between partner hospitals and an accredited clinical diagnostic laboratory, established a diagnostic testing pipeline that has reported on more than 252,000 RT-qPCR results since its commencement at the beginning of April 2020. However, due to ongoing demand and competition for critical resources, alternative testing strategies were sought. In this work, we present a clinically-validated procedure for high-throughput SARSCoV-2 detection by RT-LAMP in 25 minutes that is robust, reliable, repeatable, sensitive, specific, and inexpensive © 2021. Buck MD et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Published
2021

9. Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Author

Martin L. Jones, Lucy M. Collinson, Steve Hindmarsh, Joost de Folter, Roger Hutchings, Helen Spiers, Chris Lintott, Harry Songhurst, Amy Strange, Anne Weston, Luke Nightingale, and Christopher J. Peddie

Subjects
Computer science, business.industry, Deep learning, Volume (computing), Gold standard (test), law.invention, Domain (software engineering), law, Segmentation, Microscopist, Computer vision, Artificial intelligence, Electron microscope, business, Envelope (motion)
Abstract

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

Published
2020
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10. Standard operating procedures for SARS-CoV-2 detection by a clinical diagnostic RT-LAMP assay

Author

Kiran Gulati, Amy Strange, Laura Cubitt, Ana Cardoso, Charles Swanton, Jerome Nicod, Mike Hubank, Margaret Crawford, David Moore, James I. MacRae, Enzo Z. Poirier, Amin Oomatia, Simon Caidan, Eleni Nastouli, Robert J. Goldstone, Richard D. Byrne, Sam Barrell, Philip A. Walker, Wei-Ting Lu, Michael Howell, Caetano Reis e Sousa, Ming Jiang, Michael D. Buck, Steve Hindmarsh, Sonia Gandhi, Jessica Olsen, Davin Miller, Mary Wu, Anett Rideg, Steve Gamblin, Ok-Ryul Song, Rachael Instrell, George Kassiotis, Johnathan Canton, Paul Grant, Bruno Frederico, Nicola O’Reilly, Rupert Beale, Iana Martini, Luke Nightingale, and Samra Turajlic

Subjects
medicine.medical_specialty, business.industry, Operating procedures, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Medicine, Diagnostic test, Medical physics, Viral rna, Diagnostic laboratory, business, Standard operating procedure
Abstract

The ongoing pandemic of SARS-CoV-2 calls for rapid and cost-effective methods to accurately identify infected individuals. The vast majority of patient samples is assessed for viral RNA presence by RT-qPCR. Our biomedical research institute, in collaboration between partner hospitals and an accredited clinical diagnostic laboratory, established a diagnostic testing pipeline that has reported on more than 40,000 RT-qPCR results since its commencement at the beginning of April 2020. However, due to ongoing demand and competition for critical resources, alternative testing strategies were sought. In this work, we present a clinically-validated standard operating procedure (SOP) for high-throughput SARS-CoV-2 detection by RT-LAMP in 25 minutes that is robust, reliable, repeatable, sensitive, specific, and inexpensive.

Published
2020
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11. Pandemic peak SARS-CoV-2 infection and seroconversion rates in London frontline health-care workers

Author

Catherine F Houlihan, Nina Vora, Thomas Byrne, Dan Lewer, Gavin Kelly, Judith Heaney, Sonia Gandhi, Moira J Spyer, Rupert Beale, Peter Cherepanov, David Moore, Richard Gilson, Steve Gamblin, George Kassiotis, Laura E McCoy, Charles Swanton, Andrew Hayward, Eleni Nastouli, Jim Aitken, Zoe Allen, Rachel Ambler, Karen Ambrose, Emma Ashton, Alida Avola, Samutheswari Balakrishnan, Caitlin Barns-Jenkins, Genevieve Barr, Sam Barrell, Souradeep Basu, Clare Beesley, Nisha Bhardwaj, Shahnaz Bibi, Ganka Bineva-Todd, Dhruva Biswas, Michael J Blackman, Dominique Bonnet, Faye Bowker, Malgorzata Broncel, Claire Brooks, Michael D Buck, Andrew Buckton, Timothy Budd, Alana Burrell, Louise Busby, Claudio Bussi, Simon Butterworth, Fiona Byrne, Richard Byrne, Simon Caidan, Joanna Campbell, Johnathan Canton, Ana Cardoso, Nick Carter, Luiz Carvalho, Raffaella Carzaniga, Natalie Chandler, Qu Chen, Laura Churchward, Graham Clark, Bobbi Clayton, Clementina Cobolli Gigli, Zena Collins, Sally Cottrell, Margaret Crawford, Laura Cubitt, Tom Cullup, Heledd Davies, Patrick Davis, Dara Davison, Annalisa D'Avola, Vicky Dearing, Solene Debaisieux, Monica Diaz-Romero, Alison Dibbs, Jessica Diring, Paul C Driscoll, Christopher Earl, Amelia Edwards, Chris Ekin, Dimitrios Evangelopoulos, Rupert Faraway, Antony Fearns, Aaron Ferron, Efthymios Fidanis, Dan Fitz, James Fleming, Bruno Frederico, Alessandra Gaiba, Anthony Gait, Liam Gaul, Helen M Golding, Jacki Goldman, Robert Goldstone, Belen Gomez Dominguez, Hui Gong, Paul R Grant, Maria Greco, Mariana Grobler, Anabel Guedan, Maximiliano G Gutierrez, Fiona Hackett, Ross Hall, Steinar Halldorsson, Suzanne Harris, Sugera Hashim, Lyn Healy, Susanne Herbst, Graeme Hewitt, Theresa Higgins, Steve Hindmarsh, Rajnika Hirani, Joshua Hope, Elizabeth Horton, Beth Hoskins, Michael Howell, Louise Howitt, Jacqueline Hoyle, Mint R Htun, Michael Hubank, Hector Huerga Encabo, Deborah Hughes, Jane Hughes, Almaz Huseynova, Ming-Shih Hwang, Rachael Instrell, Deborah Jackson, Mariam Jamal-Hanjani, Lucy Jenkins, Ming Jiang, Mark Johnson, Leigh Jones, Nnennaya Kanu, Louise Kiely, Anastacio King Spert Teixeira, Stuart Kirk, Svend Kjaer, Ellen Knuepfer, Nikita Komarov, Paul Kotzampaltiris, Konstantinos Kousis, Tammy Krylova, Ania Kucharska, Robyn Labrum, Catherine Lambe, Michelle Lappin, Stacey-Ann Lee, Andrew Levett, Lisa Levett, Marcel Levi, Hon-Wing Liu, Sam Loughlin, Wei-Ting Lu, James I MacRae, Akshay Madoo, Julie A Marczak, Mimmi Martensson, Thomas Martinez, Bishara Marzook, John Matthews, Joachim M Matz, Samuel McCall, Fiona McKay, Edel C McNamara, Carlos M Minutti, Gita Mistry, Miriam Molina-Arcas, Beatriz Montaner, Kylie Montgomery, Catherine Moore, Anastasia Moraiti, Lucia Moreira-Teixeira, Joyita Mukherjee, Cristina Naceur-Lombardelli, Aileen Nelson, Jerome Nicod, Luke Nightingale, Stephanie Nofal, Paul Nurse, Savita Nutan, Caroline Oedekoven, Anne O'Garra, Jean D O'Leary, Jessica Olsen, Olga O'Neill, Paula Ordonez Suarez, Nicola O'Reilly, Neil Osborne, Amar Pabari, Aleksandra Pajak, Venizelos Papayannopoulos, Namita Patel, Yogen Patel, Oana Paun, Nigel Peat, Laura Peces-Barba Castano, Ana Perez Caballero, Jimena Perez-Lloret, Magali S Perrault, Abigail Perrin, Roy Poh, Enzo Z Poirier, James M Polke, Marc Pollitt, Lucia Prieto-Godino, Alize Proust, Rajvee Shah Punatar, Clinda Puvirajasinghe, Christophe Queval, Vijaya Ramachandran, Abhinay Ramaprasad, Peter Ratcliffe, Laura Reed, Caetano Reis e Sousa, Kayleigh Richardson, Sophie Ridewood, Rowenna Roberts, Angela Rodgers, Pablo Romero Clavijo, Annachiara Rosa, Alice Rossi, Chloe Roustan, Andrew Rowan, Erik Sahai, Aaron Sait, Katarzyna Sala, Theo Sanderson, Pierre Santucci, Fatima Sardar, Adam Sateriale, Jill A Saunders, Chelsea Sawyer, Anja Schlott, Edina Schweighoffer, Sandra Segura-Bayona, Joe Shaw, Gee Yen Shin, Mariana Silva Dos Santos, Margaux Silvestre, Matthew Singer, Daniel M Snell, Ok-Ryul Song, Louisa Steel, Amy Strange, Adrienne E Sullivan, Michele SY Tan, Zoe H Tautz-Davis, Effie Taylor, Gunes Taylor, Harriet B Taylor, Alison Taylor-Beadling, Fernanda Teixeira Subtil, Berta Terré Torras, Patrick Toolan-Kerr, Francesca Torelli, Tea Toteva, Moritz Treeck, Hadija Trojer, Ming-Han C Tsai, James MA Turner, Melanie Turner, Jernej Ule, Rachel Ulferts, Sharon P Vanloo, Selvaraju Veeriah, Subramanian Venkatesan, Karen Vousden, Andreas Wack, Claire Walder, Philip A Walker, Yiran Wang, Sophia Ward, Catharina Wenman, Luke Wiliams, Matthew J Williams, Wai Keong Wong, Joshua Wright, Mary Wu, Lauren Wynne, Zheng Xiang, Melvyn Yap, Julian A Zagalak, Davide Zecchin, Rachel Zillwood, Rebecca Matthews, Abigail Severn, Sajida Adam, Louise Enfield, Angela McBride, Kathleen Gärtner, Sarah Edwards, Fabiana Lorencatto, Susan Michie, Ed Manley, Maryam Shahmanesh, Hinal Lukha, Paulina Prymas, Hazel McBain, Robert Shortman, Leigh Wood, Claudia Davies, Bethany Williams, Kevin W Ng, Georgina H Cornish, Nikhil Faulkner, Andrew Riddell, Philip Hobson, Ana Agua-Doce, Kerol Bartolovic, Emma Russell, Lotte Carr, Emilie Sanchez, Daniel Frampton, Matthew Byott, Stavroula M Paraskevopoulou, Elise Crayton, Carly Meyer, Triantafylia Gkouleli, Andrea Stoltenberg, Veronica Ranieri, Tom Byrne, Fiona Roberts, and Emine Hatipoglu

Subjects
Adult, 2019-20 coronavirus outbreak, Infectious Disease Transmission, Patient-to-Professional, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Health Personnel, Pneumonia, Viral, Viral transmission, Antibodies, Viral, Risk Assessment, Article, Betacoronavirus, Environmental health, Occupational Exposure, Pandemic, Health care, London, Medicine, Humans, Prospective Studies, Seroconversion, Pandemics, Cross Infection, business.industry, SARS-CoV-2, COVID-19, General Medicine, Occupational exposure, business, Coronavirus Infections
Published
2020

12. Scalable and robust SARS-CoV-2 testing in an academic center

Author

Jim, Aitken, Karen, Ambrose, Sam, Barrell, Rupert, Beale, Ganka, Bineva-Todd, Dhruva, Biswas, Richard, Byrne, Simon, Caidan, Peter, Cherepanov, Laura, Churchward, Graham, Clark, Margaret, Crawford, Laura, Cubitt, Vicky, Dearing, Christopher, Earl, Amelia, Edwards, Chris, Ekin, Efthymios, Fidanis, Alessandra, Gaiba, Steve, Gamblin, Sonia, Gandhi, Jacki, Goldman, Robert, Goldstone, Paul R, Grant, Maria, Greco, Judith, Heaney, Steve, Hindmarsh, Catherine F, Houlihan, Michael, Howell, Michael, Hubank, Deborah, Hughes, Rachael, Instrell, Deborah, Jackson, Mariam, Jamal-Hanjani, Ming, Jiang, Mark, Johnson, Leigh, Jones, Nnennaya, Kanu, George, Kassiotis, Stuart, Kirk, Svend, Kjaer, Andrew, Levett, Lisa, Levett, Marcel, Levi, Wei-Ting, Lu, James I, MacRae, John, Matthews, Laura E, McCoy, Catherine, Moore, David, Moore, Eleni, Nastouli, Jerome, Nicod, Luke, Nightingale, Jessica, Olsen, Nicola, O'Reilly, Amar, Pabari, Venizelos, Papayannopoulos, Namita, Patel, Nigel, Peat, Marc, Pollitt, Peter, Ratcliffe, Caetano, Reis E Sousa, Annachiara, Rosa, Rachel, Rosenthal, Chloe, Roustan, Andrew, Rowan, Gee Yen, Shin, Daniel M, Snell, Ok-Ryul, Song, Moira J, Spyer, Amy, Strange, Charles, Swanton, James M A, Turner, Melanie, Turner, Andreas, Wack, Philip A, Walker, Sophia, Ward, Wai Keong, Wong, Joshua, Wright, Mary, Wu, and Rachel, Zillwood

Subjects
Europe, COVID-19 Testing, Medical research, Clinical Laboratory Techniques, Reverse Transcriptase Polymerase Chain Reaction, Academies and Institutes, Medical Laboratory Science, Health care, Humans, Coronavirus Infections, Author Correction, United Kingdom
Published
2020

13. Scalable and Resilient SARS-CoV-2 testing in an Academic Centre

Author

Charles Swanton, James I. MacRae, Nnennaya Kanu, Steve Hindmarsh, Karen Ambrose, Andrew Levett, Judith Heaney, Maria Greco, Christopher Earl, Stuart Kirk, Rachel Instrell, Simon Caidan, Peter Cherepanov, Vicky Dearing, Wei-Ting Lu, Laura E. McCoy, George Kassiotis, Mary Wu, Amelia Edwards, Andrew Rowan, Michael Howell, Jessica Olsen, Venizelos Papayannopoulos, Amar Pabari, Caetano Reis e Sousa, Wai Keong Wong, Nicola O’Reilly, Marc Pollitt, Jerome Nicod, Philip A. Walker, Ming Jiang, Marg Crawford, Ganka Bineva-Todd, Alessandra Gaiba, Melanie Turner, Jacki Goldman, James M. A. Turner, Chloe Roustan, Sonia Gandhi, Catherine Moore, Nigel Peat, Rachel Rosenthal, John N. S. Matthews, Amy Strange, Chris Ekin, Marcel Levi, Richard D. Byrne, Laura Churchward, Steve Gamblin, Graham Clark, Jim Aitken, Deb Jackson, Mark Johnson, Svend Kjaer, Joshua Wright, Annachiara Rosa, Andreas Wack, Ok-Ryul Song, Laura Cubitt, Mariam Jamal-Hanjani, David Moore, Leigh Jones, Debbie Hughes, Paul Grant, Sophie Ward, Efthymios Fidanis, Namita Patel, Eleni Nastouli, Dhruva Biswas, Gee Yen Shin, Daniel M Snell, Mike Hubank, Lisa Levett, Moria Spyer, Peter J. Ratcliffe, Luke Nightingale, Sam Barrell, Rupert Beale, Robert L. Goldstone, and Catherine F Houlihan

Subjects
0303 health sciences, business.industry, Computer science, medicine.disease, Pipeline (software), Turnaround time, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Health care, Scalability, Pandemic, medicine, Web application, 030212 general & internal medicine, Medical emergency, Diagnostic laboratory, business, Bespoke, 030304 developmental biology
Abstract

The emergence of the novel coronavirus SARS-CoV-2 has led to a pandemic infecting more than two million people worldwide in less than four months, posing a major threat to healthcare systems. This is compounded by the shortage of available tests causing numerous healthcare workers to unnecessarily self-isolate. We provide a roadmap instructing how a research institute can be repurposed in the midst of this crisis, in collaboration with partner hospitals and an established diagnostic laboratory, harnessing existing expertise in virus handling, robotics, PCR, and data science to derive a rapid, high throughput diagnostic testing pipeline for detecting SARS-CoV-2 in patients with suspected COVID-19. The pipeline is used to detect SARS-CoV-2 from combined nose-throat swabs and endotracheal secretions/ bronchoalveolar lavage fluid. Notably, it relies on a series of in-house buffers for virus inactivation and the extraction of viral RNA, thereby reducing the dependency on commercial suppliers at times of global shortage. We use a commercial RT-PCR assay, from BGI, and results are reported with a bespoke online web application that integrates with the healthcare digital system. This strategy facilitates the remote reporting of thousands of samples a day with a turnaround time of under 24 hours, universally applicable to laboratories worldwide.

Published
2020
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14. Canine left ventricular function during experimental pancreatitis

Author

Phillip Kish, Walter Tambolini, Roy D. Goldfarb, Luke Nightingale, Peter Weber, Michael Lefkowitz, and Daniel J. Loegering

Subjects
medicine.medical_specialty, Mean arterial pressure, Cardiac output, Systole, Heart Ventricles, Blood Pressure, Contractility, Dogs, Afterload, Coronary Circulation, Internal medicine, Heart rate, medicine, Animals, Cardiac Output, business.industry, Myocardium, Heart, Stroke Volume, Stroke volume, Blood flow, Myocardial Contraction, Preload, Endocrinology, Pancreatitis, Acute Disease, Vascular Resistance, Surgery, business
Abstract

Left ventricular contractility following induction of experimental pancreatitis (EP) was studied. Contractility was evaluated by analyzing the left ventricular end systolic pressure-diameter relationship ( Σ ES ). Σ ES is independent of large changes in preload, afterload, and heart rate, but sensitive to changes in ventricular contractility. Following injection of 100,000 IU trypsin in 4% taurocholate into the pancreas to induce EP, seven of eight dogs survived 5 hr. These dogs exhibited an initial significant reduction in mean arterial pressure (MABP) which stabilized at 90% of control at 3–5 hr post-EP. Cardiac output (CO) dropped slowly after EP induction (from 3.08 ± 0.43 to 2.22 ± 0.22 liters/min) associated with no significant change in peripheral resistance. Stroke work and stroke volume were markedly depressed reflecting the changes in MABP and CO. No consistent changes in + dP/dt or − dP/dt were observed. The ratio of endo/epicardial blood flow was unchanged as was blood Ca 2+ levels throughout the experiment. Ventricular contractility as reflected by Σ ES tended to improve (from 49.7 to 69.6 mm Hg/mm at 4 hr following EP). Therefore, it was concluded that these animals exhibited no loss of ventricular contractility during EP.

Published
1985
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