Quantitative analysis of tumour spheroid structure
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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
eLife Sciences Publications Ltd
2021-11-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/73020 |
| _version_ | 1797998473182707712 |
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| author | Alexander P Browning Jesse A Sharp Ryan J Murphy Gency Gunasingh Brodie Lawson Kevin Burrage Nikolas K Haass Matthew Simpson |
| author_facet | Alexander P Browning Jesse A Sharp Ryan J Murphy Gency Gunasingh Brodie Lawson Kevin Burrage Nikolas K Haass Matthew Simpson |
| author_sort | Alexander P Browning |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T10:49:16Z |
| format | Article |
| id | doaj.art-d7012f092c5e49fb9eff737de77d254c |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-11T10:49:16Z |
| publishDate | 2021-11-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-d7012f092c5e49fb9eff737de77d254c2022-12-22T04:28:58ZengeLife Sciences Publications LtdeLife2050-084X2021-11-011010.7554/eLife.73020Quantitative analysis of tumour spheroid structureAlexander P Browning0https://orcid.org/0000-0002-8753-1538Jesse A Sharp1https://orcid.org/0000-0002-2865-4853Ryan J Murphy2https://orcid.org/0000-0002-9844-6734Gency Gunasingh3Brodie Lawson4https://orcid.org/0000-0003-1317-5988Kevin Burrage5https://orcid.org/0000-0002-8111-1137Nikolas K Haass6https://orcid.org/0000-0002-3928-5360Matthew Simpson7https://orcid.org/0000-0001-6254-313XSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Melbourne, AustraliaSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Melbourne, AustraliaSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, AustraliaThe University of Queensland Diamantina Institute, The University of Queensland, Brisbane, AustraliaSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Melbourne, AustraliaSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Melbourne, Australia; Department of Computer Science, University of Oxford, Oxford, United KingdomThe University of Queensland Diamantina Institute, The University of Queensland, Brisbane, AustraliaSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, AustraliaTumour 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.https://elifesciences.org/articles/73020tumour spheroidinferencediffusionsteady-stateuncertainty quantificationFUCCI |
| spellingShingle | Alexander P Browning Jesse A Sharp Ryan J Murphy Gency Gunasingh Brodie Lawson Kevin Burrage Nikolas K Haass Matthew Simpson Quantitative analysis of tumour spheroid structure eLife tumour spheroid inference diffusion steady-state uncertainty quantification FUCCI |
| title | Quantitative analysis of tumour spheroid structure |
| title_full | Quantitative analysis of tumour spheroid structure |
| title_fullStr | Quantitative analysis of tumour spheroid structure |
| title_full_unstemmed | Quantitative analysis of tumour spheroid structure |
| title_short | Quantitative analysis of tumour spheroid structure |
| title_sort | quantitative analysis of tumour spheroid structure |
| topic | tumour spheroid inference diffusion steady-state uncertainty quantification FUCCI |
| url | https://elifesciences.org/articles/73020 |
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