Quantitative analysis of tumour spheroid structure
Abstract
Tumour spheroids are common in vitro experimental models of avascular tumour growth. Compared with traditional two-dimensional culture, tumour spheroids more closely mimic the avascular tumour microenvironment where spatial differences in nutrient availability strongly influence growth. We show that spheroids initiated using significantly different numbers of cells grow to similar limiting sizes, suggesting that avascular tumours have a limiting structure; in agreement with untested predictions of classical mathematical models of tumour spheroids. We develop a novel mathematical and statistical framework to study the structure of tumour spheroids seeded from cells transduced with fluorescent cell cycle indicators, enabling us to discriminate between arrested and cycling cells and identify an arrested region. Our analysis shows that transient spheroid structure is independent of initial spheroid size, and the limiting structure can be independent of seeding density. Standard experimental protocols compare spheroid size as a function of time; however, our analysis suggests that comparing spheroid structure as a function of overall size produces results that are relatively insensitive to variability in spheroid size. Our experimental observations are made using two melanoma cell lines, but our modelling framework applies across a wide range of spheroid culture conditions and cell lines.
Data availability
Code, data, and interactive figures are available as a Julia module on GitHub (https://github.com/ap-browning/Spheroids). Code used to process the experimental images is available on Zenodo (https://doi.org/10.5281/zenodo.5121093).
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Quantitative analysis of tumour spheroid structurePublicly available at Github (https://github.com).
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Image processing algorithm to identify structure of tumour spheroids with cell cycle labellingPublicly available at Zenodo (https://zenodo.org/).
Article and author information
Author details
Funding
Australian Research Council (DP200100177)
- Nikolas K Haass
- Matthew Simpson
ARC Centre of Excellence for Mathematical and Statistical Frontiers (CE140100049)
- Alexander P Browning
- Jesse A Sharp
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Browning et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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