# Seeding hESCs to achieve optimal colony clonality

## TL;DR

This paper investigates how to optimize the seeding of human embryonic stem cells to ensure clonally homogeneous colonies, using experimental data and a new stochastic model to predict colony growth dynamics.

## Contribution

The study introduces a multi-population stochastic exponential model that captures colony growth heterogeneity and predicts optimal seeding conditions for homogeneous hESC colonies.

## Key findings

- Colony populations are multi-modal with different founding cell numbers.
- Cell-cell interactions influence colony growth and merging.
- Model predicts timescales for colony merging and size-based founding cell estimation.

## Abstract

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, cross-contamination and merger of colonies should be avoided. This motivates us to experimentally study and quantitatively model the growth of hESC colonies. The colony population is unexpectedly found to be multi-modal. We associate these sub-populations with different numbers of founding cells, and predict their occurrence by considering the role of cell-cell interactions and cell behaviour on randomly seeded cells. We develop a multi-population stochastic exponential model for the colony population which captures our experimental observations, and apply this to calculate the timescales for colony merges and over which colony size no longer predicts the number of founding cells. These results can be used to achieve the best outcome for homogeneous colony growth from different cell seeding densities.