SpheroidAnalyseR—an online platform for analyzing data from 3D spheroids or organoids grown in 96-well plates
Spheroids and organoids are increasingly popular three-dimensional (3D) cell culture models. Spheroid models are more physiologically relevant to a tumor compared to two-dimensional (2D) cultures and organoids are a simplified version of an organ with similar composition. Spheroids are often only formed from a single cell type which does not represent the situation in vivo. However, despite this, both spheroids and organoids can be used in cell migration studies, disease modelling and drug discovery. A drawback of these models is, however, the lack of appropriate analytical tools for high throughput imaging and analysis over a time course. To address this, we have developed an R Shiny app called SpheroidAnalyseR: a simple, fast, effective open-source app that allows the analysis of spheroid or organoid size data generated in a 96-well format. SpheroidAnalyseR processes and analyzes datasets of image measurements that can be obtained via a bespoke software, described herein, that automates spheroid imaging and quantification using the Nikon A1R Confocal Laser Scanning Microscope. However, templates are provided to enable users to input spheroid image measurements obtained by user-preferred methods. SpheroidAnalyseR facilitates outlier identification and removal followed by graphical visualization of spheroid measurements across multiple predefined parameters such as time, cell-type and treatment(s). Spheroid imaging and analysis can, thus, be reduced from hours to minutes, removing the requirement for substantial manual data manipulation in a spreadsheet application. The combination of spheroid generation in 96-well ultra-low attachment microplates, imaging using our bespoke software, and analysis using SpheroidAnalyseR toolkit allows high throughput, longitudinal quantification of 3D spheroid growth whilst minimizing user input and significantly improving the efficiency and reproducibility of data analysis. Our bespoke imaging software is available from https://github.com/GliomaGenomics. SpheroidAnalyseR is available at https://spheroidanalyser.leeds.ac.uk, and the source code found at https://github.com/GliomaGenomics.
1. Ferreira LP, Gaspar VM, Mano JF. Design of spherically structured 3D in vitro tumor models -Advances and prospects. Acta Biomater. 2018 Jul;75:11–34. https://doi.org/10.1016/j.actbio.2018.05.034 PMID:29803007
2. Mehta G, Hsiao AY, Ingram M, Luker GD, Takayama S. Opportunities and challenges for use of tumor spheroids as models to test drug delivery and efficacy. J Control Release. 2012 Dec;164(2):192–204. https://doi.org/10.1016/j.jconrel.2012.04.045 PMID:22613880
3. Antoni D, Burckel H, Josset E, Noel G. Three-dimensional cell culture: a breakthrough in vivo. Int J Mol Sci. 2015;16(3):5517-27. Epub 2015/03/15. https://doi.org/10.3390/ijms16035517 PMID: 25768338
4. de Souza N. Organoids. Nature Methods. 2018;15(1):23-. https://doi.org/10.1038/nmeth.4576
5. Zanoni M, Cortesi M, Zamagni A, Arienti C, Pignatta S, Tesei A. Modeling neoplastic disease with spheroids and organoids. J Hematol Oncol. 2020;13(1):97. Epub 2020/07/18. https://doi.org/10.1186/s13045-020-00931-0 PMID: 32677979
6. Nunes AS, Barros AS, Costa EC, Moreira AF, Correia IJ. 3D tumor spheroids as in vitro models to mimic in vivo human solid tumors resistance to therapeutic drugs. Biotechnol Bioeng. 2019 Jan;116(1):206–26. https://doi.org/10.1002/bit.26845 PMID:30367820
7. Mehta G, Hsiao AY, Ingram M, Luker GD, Takayama S. Opportunities and challenges for use of tumor spheroids as models to test drug delivery and efficacy. J Control Release. 2012;164(2):192-204. Epub 2012/05/23. https://doi.org/10.1016/j.jconrel.2012.04.045 PMID: 22613880
8. Katt ME, Placone AL, Wong AD, Xu ZS, Searson PC. In Vitro Tumor Models: Advantages, Disadvantages, Variables, and Selecting the Right Platform. Front Bioeng Biotechnol. 2016;4:12. Epub 2016/02/24. https://doi.org/10.3389/fbioe.2016.00012 PMID: 26904541
9. Pant D, Narayanan SP, Vijay N, Shukla S. Hypoxia-induced changes in intragenic DNA methylation correlate with alternative splicing in breast cancer. J Biosci. 2020;45(1):3. https://doi.org/10.1007/s12038-019-9977-0 PMID:31965981
10. Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R. Statistical practice in high-throughput screening data analysis. Nat Biotechnol. 2006 Feb;24(2):167–75. https://doi.org/10.1038/nbt1186 PMID:16465162
11. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9(7):671-5. Epub 2012/08/30. https://doi.org/10.1038/nmeth.2089 PMID: 22930834
12. Chen W, Wong C, Vosburgh E, Levine AJ, Foran DJ, Xu EY. High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately. J Vis Exp. 2014;(89). Epub 2014/07/22. https://doi.org/10.3791/51639 PMID: 25046278