Quantitative analysis of tumour spheroid structure

  1. Alexander P Browning
  2. Jesse A Sharp
  3. Ryan J Murphy
  4. Gency Gunasingh
  5. Brodie Lawson
  6. Kevin Burrage
  7. Nikolas K Haass
  8. Matthew Simpson  Is a corresponding author
  1. Queensland University of Technology, Australia
  2. University of Queensland, Australia

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).

The following data sets were generated

Article and author information

Author details

  1. Alexander P Browning

    School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  2. Jesse A Sharp

    School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  3. Ryan J Murphy

    School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  4. Gency Gunasingh

    The University of Queensland Diamantina Institute, University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  5. Brodie Lawson

    School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  6. Kevin Burrage

    School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  7. Nikolas K Haass

    4The University of Queensland Diamantina Institute, University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3928-5360
  8. Matthew Simpson

    School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
    For correspondence
    matthew.simpson@qut.edu.au
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6254-313X

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.

Reviewing Editor

  1. Jennifer Flegg, The University of Melbourne, Australia

Publication history

  1. Received: August 13, 2021
  2. Accepted: November 26, 2021
  3. Accepted Manuscript published: November 29, 2021 (version 1)
  4. Version of Record published: January 7, 2022 (version 2)

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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  1. Alexander P Browning
  2. Jesse A Sharp
  3. Ryan J Murphy
  4. Gency Gunasingh
  5. Brodie Lawson
  6. Kevin Burrage
  7. Nikolas K Haass
  8. Matthew Simpson
(2021)
Quantitative analysis of tumour spheroid structure
eLife 10:e73020.
https://doi.org/10.7554/eLife.73020
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    Sarcomas comprise approximately 1% of all human malignancies; treatment resistance is one of the major reasons for the poor prognosis of sarcomas. Accumulating evidence suggests that non-coding RNAs (ncRNAs), including miRNAs, long ncRNAs, and circular RNAs, are important molecules involved in the crosstalk between resistance to chemotherapy, targeted therapy, and radiotherapy via various pathways.

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    We searched the PubMed (MEDLINE) database for articles regarding sarcoma-associated ncRNAs from inception to August 17, 2022. Studies investigating the roles of host-derived miRNAs, long ncRNAs, and circular RNAs in sarcoma were included. Data relating to the roles of ncRNAs in therapeutic regulation and their applicability as biomarkers for predicting the therapeutic response of sarcomas were extracted. Two independent researchers assessed the quality of the studies using the Würzburg Methodological Quality Score (W-MeQS).

    Results:

    Observational studies revealed the ectopic expression of ncRNAs in sarcoma patients who had different responses to antitumor treatments. Experimental studies have confirmed crosstalk between cellular pathways pertinent to chemotherapy, targeted therapy, and radiotherapy resistance. Of the included studies, W-MeQS scores ranged from 3 to 10 (average score = 5.42). Of the 12 articles that investigated ncRNAs as biomarkers, none included a validation cohort. Selective reporting of the sensitivity, specificity, and receiver operating curves was common.

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    Although ncRNAs appear to be good candidates as biomarkers for predicting treatment response and therapeutics for sarcoma, their differential expression across tissues complicates their application. Further research regarding their potential for inhibiting or activating these regulatory molecules to reverse treatment resistance may be useful.

    Funding:

    This study’s literature retrieval was supported financially by the 345 Talent Project of Shengjing Hospital of China Medical University (M0949 to Tao Zhang).

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