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Seeding hESCs to achieve optimal colony clonality

Lookup NU author(s): Laura Wadkin, Dr Sirio Orozco Fuentes, Dr Irina Neganova, Dr Sanja Bojic, Dr Alex Laude, Professor Majlinda Lako, Dr Nicholas Parker, Professor Anvar Shukurov

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2019, The Author(s).Human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) have promising clinical applications which often rely on clonally-homogeneous cell populations. To achieve this, it is important to ensure that each colony originates from a single founding cell and to avoid subsequent merging of colonies during their growth. Clonal homogeneity can be obtained with low seeding densities; however, this leads to low yield and viability. It is therefore important to quantitatively assess how seeding density affects clonality loss so that experimental protocols can be optimised to meet the required standards. Here we develop a quantitative framework for modelling the growth of hESC colonies from a given seeding density based on stochastic exponential growth. This allows us to identify the timescales for colony merges and over which colony size no longer predicts the number of founding cells. We demonstrate the success of our model by applying it to our own experiments of hESC colony growth; while this is based on a particular experimental set-up, the model can be applied more generally to other cell lines and experimental conditions to predict these important timescales.


Publication metadata

Author(s): Wadkin LE, Orozco-Fuentes S, Neganova I, Bojic S, Laude A, Lako M, Parker NG, Shukurov A

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2019

Volume: 9

Issue: 1

Online publication date: 25/10/2019

Acceptance date: 10/10/2019

Date deposited: 11/11/2019

ISSN (electronic): 2045-2322

Publisher: Nature Publishing Group

URL: https://doi.org/10.1038/s41598-019-51897-0

DOI: 10.1038/s41598-019-51897-0

PubMed id: 31653933


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