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Vallejo Ramirez PP, Zammit J, Vanderpoorten O, Riche F, Blé FX, Zhou XH, Spiridon B, Valentine C, Spasov SE, Oluwasanya PW, Goodfellow G, Fantham MJ, Siddiqui O, Alimagham F, Robbins M, Stretton A, Simatos D, Hadeler O, Rees EJ, Ströhl F, Laine RF, Kaminski CF. OptiJ: Open-source optical projection tomography of large organ samples. Sci Rep 2019; 9:15693. [PMID: 31666606 PMCID: PMC6821862 DOI: 10.1038/s41598-019-52065-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 10/09/2019] [Indexed: 12/20/2022] Open
Abstract
The three-dimensional imaging of mesoscopic samples with Optical Projection Tomography (OPT) has become a powerful tool for biomedical phenotyping studies. OPT uses visible light to visualize the 3D morphology of large transparent samples. To enable a wider application of OPT, we present OptiJ, a low-cost, fully open-source OPT system capable of imaging large transparent specimens up to 13 mm tall and 8 mm deep with 50 µm resolution. OptiJ is based on off-the-shelf, easy-to-assemble optical components and an ImageJ plugin library for OPT data reconstruction. The software includes novel correction routines for uneven illumination and sample jitter in addition to CPU/GPU accelerated reconstruction for large datasets. We demonstrate the use of OptiJ to image and reconstruct cleared lung lobes from adult mice. We provide a detailed set of instructions to set up and use the OptiJ framework. Our hardware and software design are modular and easy to implement, allowing for further open microscopy developments for imaging large organ samples.
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Affiliation(s)
- Pedro P Vallejo Ramirez
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Joseph Zammit
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Oliver Vanderpoorten
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Fergus Riche
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Francois-Xavier Blé
- Clinical Discovery Unit, Early Clinical Development, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | - Xiao-Hong Zhou
- Bioscience, Respiratory, Inflammation and Autoimmunity, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Bogdan Spiridon
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | | | - Simeon E Spasov
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | | | - Gemma Goodfellow
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Marcus J Fantham
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Omid Siddiqui
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Farah Alimagham
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Miranda Robbins
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Andrew Stretton
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Dimitrios Simatos
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Oliver Hadeler
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Eric J Rees
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Florian Ströhl
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037, Tromsø, Norway
| | - Romain F Laine
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Medical Research Council Laboratory for Molecular Cell Biology (LMCB), University College London, Gower Street, London, WC1E 6BT, UK
| | - Clemens F Kaminski
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
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Laine RF, Goodfellow G, Young LJ, Travers J, Carroll D, Dibben O, Bright H, Kaminski CF. Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure. eLife 2018; 7:40183. [PMID: 30543181 PMCID: PMC6331195 DOI: 10.7554/elife.40183] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 12/10/2018] [Indexed: 02/02/2023] Open
Abstract
Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.
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Affiliation(s)
- Romain F Laine
- Department of Chemical Engineering and BiotechnologyUniversity of CambridgeCambridgeUnited Kingdom
| | - Gemma Goodfellow
- Department of Chemical Engineering and BiotechnologyUniversity of CambridgeCambridgeUnited Kingdom
| | - Laurence J Young
- Department of Chemical Engineering and BiotechnologyUniversity of CambridgeCambridgeUnited Kingdom
| | | | | | | | | | - Clemens F Kaminski
- Department of Chemical Engineering and BiotechnologyUniversity of CambridgeCambridgeUnited Kingdom
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