Michele Perni1, Pavan K Challa1, Julius B Kirkegaard2, Ryan Limbocker1, Mandy Koopman3, Maarten C Hardenberg3, Pietro Sormanni1, Thomas Müller1, Kadi L Saar1, Lianne W Y Roode1, Johnny Habchi1, Giulia Vecchi1, Nilumi Fernando1, Samuel Casford1, Ellen A A Nollen3, Michele Vendruscolo4, Christopher M Dobson5, Tuomas P J Knowles6. 1. Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. 2. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, CB3 0WA, UK. 3. University of Groningen, University Medical Center Groningen, European Research Institute for the Biology of Aging, 9713 AV, Groningen, The Netherlands. 4. Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. Electronic address: mv245@cam.ac.uk. 5. Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. Electronic address: cmd44@cam.ac.uk. 6. Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. Electronic address: tpjk2@cam.ac.uk.
Abstract
BACKGROUND: The nematode worm C. elegans is a model organism widely used for studies of genetics and of human disease. The health and fitness of the worms can be quantified in different ways, such as by measuring their bending frequency, speed or lifespan. Manual assays, however, are time consuming and limited in their scope providing a strong motivation for automation. NEW METHOD: We describe the development and application of an advanced machine vision system for characterising the behaviour of C. elegans, the Wide Field-of-View Nematode Tracking Platform (WF-NTP), which enables massively parallel data acquisition and automated multi-parameter behavioural profiling of thousands of worms simultaneously. RESULTS: We screened more than a million worms from several established models of neurodegenerative disorders and characterised the effects of potential therapeutic molecules for Alzheimer's and Parkinson's diseases. By using very large numbers of animals we show that the sensitivity and reproducibility of behavioural assays is very greatly increased. The results reveal the ability of this platform to detect even subtle phenotypes. COMPARISON WITH EXISTING METHODS: The WF-NTP method has substantially greater capacity compared to current automated platforms that typically either focus on characterising single worms at high resolution or tracking the properties of populations of less than 50 animals. CONCLUSIONS: The WF-NTP extends significantly the power of existing automated platforms by combining enhanced optical imaging techniques with an advanced software platform. We anticipate that this approach will further extend the scope and utility of C. elegans as a model organism.
BACKGROUND: The nematode worm C. elegans is a model organism widely used for studies of genetics and of human disease. The health and fitness of the worms can be quantified in different ways, such as by measuring their bending frequency, speed or lifespan. Manual assays, however, are time consuming and limited in their scope providing a strong motivation for automation. NEW METHOD: We describe the development and application of an advanced machine vision system for characterising the behaviour of C. elegans, the Wide Field-of-View Nematode Tracking Platform (WF-NTP), which enables massively parallel data acquisition and automated multi-parameter behavioural profiling of thousands of worms simultaneously. RESULTS: We screened more than a million worms from several established models of neurodegenerative disorders and characterised the effects of potential therapeutic molecules for Alzheimer's and Parkinson's diseases. By using very large numbers of animals we show that the sensitivity and reproducibility of behavioural assays is very greatly increased. The results reveal the ability of this platform to detect even subtle phenotypes. COMPARISON WITH EXISTING METHODS: The WF-NTP method has substantially greater capacity compared to current automated platforms that typically either focus on characterising single worms at high resolution or tracking the properties of populations of less than 50 animals. CONCLUSIONS: The WF-NTP extends significantly the power of existing automated platforms by combining enhanced optical imaging techniques with an advanced software platform. We anticipate that this approach will further extend the scope and utility of C. elegans as a model organism.
Authors: Mandy Koopman; Quentin Peter; Renée I Seinstra; Michele Perni; Michele Vendruscolo; Christopher M Dobson; Tuomas P J Knowles; Ellen A A Nollen Journal: Nat Protoc Date: 2020-05-20 Impact factor: 13.491
Authors: Romain F Laine; Tessa Sinnige; Kai Yu Ma; Amanda J Haack; Chetan Poudel; Peter Gaida; Nathan Curry; Michele Perni; Ellen A A Nollen; Christopher M Dobson; Michele Vendruscolo; Gabriele S Kaminski Schierle; Clemens F Kaminski Journal: ACS Chem Biol Date: 2019-06-27 Impact factor: 4.634
Authors: Tessa Sinnige; Prashanth Ciryam; Samuel Casford; Christopher M Dobson; Mario de Bono; Michele Vendruscolo Journal: PLoS One Date: 2019-05-31 Impact factor: 3.240
Authors: Priyanka Joshi; Michele Perni; Ryan Limbocker; Benedetta Mannini; Sam Casford; Sean Chia; Johnny Habchi; Johnathan Labbadia; Christopher M Dobson; Michele Vendruscolo Journal: Commun Biol Date: 2021-07-07
Authors: Ryan Limbocker; Sean Chia; Francesco S Ruggeri; Michele Perni; Roberta Cascella; Gabriella T Heller; Georg Meisl; Benedetta Mannini; Johnny Habchi; Thomas C T Michaels; Pavan K Challa; Minkoo Ahn; Samuel T Casford; Nilumi Fernando; Catherine K Xu; Nina D Kloss; Samuel I A Cohen; Janet R Kumita; Cristina Cecchi; Michael Zasloff; Sara Linse; Tuomas P J Knowles; Fabrizio Chiti; Michele Vendruscolo; Christopher M Dobson Journal: Nat Commun Date: 2019-01-15 Impact factor: 14.919