Automating the Discovery of Optimal Conditions for Stem Cell Differentiation into Neurons

Details

Pluripotent stem cells (PSCs) are defined by their capacity for self-renewal and differentiation, making them an attractive platform for modelling human tissue formation in vitro. However, generating scalable, uniform differentiated cell types from PSCs remains challenging because of the limited control we have over the cellular environment. While growth factors can direct differentiation, surprisingly little is known about how the dynamics of their application shape cell fate.

This project builds directly on our recent work exploring how the application dynamics of agonists activating the all-trans-retinoic acid (atRA) and WNT signalling pathways influence Ntera-2 differentiation. In that work, atRA and CHIR99021 were applied either as pulses or through sustained replenishment at frequencies ranging from 1 to 48 hours, with differentiation tracked using fluorescent labelling of surface markers for loss of pluripotency (TRA-1-60) and pro-neuronal commitment (A2B5), alongside morphological changes.

To make these complex experiments possible, we developed an open-source liquid-handling and imaging platform that requires minimal human input and operates at very low cost. The platform can test over 60 conditions in a single experiment and collect data from more than 10 million cells — throughput that would be unfeasible manually. Using it, we showed that atRA dynamics did not significantly alter A2B5 expression, whereas CHIR dynamics had a pronounced effect: pulsed CHIR produced higher A2B5 expression than sustained application, with shorter pulses (≤12 hr) generating circular cells with glial-like morphology and longer pulses (≥18 hr) generating elongated neuron-like cells.

These findings demonstrate that not only ligand identity but also the timing and frequency of signalling determine cell fate. The successful applicant will continue and extend this line of research — using and further developing our automated platform to systematically map, model, and optimise the signalling conditions that steer PSCs toward defined neuronal outcomes, and to apply the same framework to other developmental pathways. What the project offers

You will work at the interface of stem cell biology, automation, and quantitative image analysis, gaining hands-on experience with open-source robotics, high-throughput live-cell imaging, and data-driven optimisation of differentiation protocols.

Candidate profile

We welcome applicants with a background in cell/molecular biology, neuroscience, bioengineering, or a related quantitative discipline. An interest in laboratory automation, coding (e.g. Python), or image analysis is an advantage but not essential — enthusiasm for building and troubleshooting experimental systems matters most.

Funding Notes

This is a self-funded opportunity. Applicants are responsible for securing their own tuition fees and living costs (e.g. through personal funds, scholarships, or sponsorship). Guidance on potential funding schemes can be discussed with the supervisor.