Scaling up Complex Organoid Models: Interview with Maryna Panamarova, Wellcome Sanger Institute

Welcome to this interview for Oxford Global.

Today I'm joined by Maryna Panamarova, who is a 3D cellular modelling Specialist at the Wellcome Sanger Institute.

Maryna will be presenting at Cell 2025, her presentation Scaling up Complex Organoid Models, Challenges and Solutions, where she will discuss a case study that used vascularized hair bearing skin organoids supplemented with macrophages.

We're looking forward to hearing more about that.

But first, Maryna, thank you very much for joining me today.

Hi Tom, and thank you so much for inviting me.

Could you start by telling us a bit about your career journey from your academic path to your current role as a Cellular Modelling Specialist at the Sanger Institute?

Of course, my interest in stem cell research began during my undergrad studies when I had a chance to intern at the Centre for Regenerative Medicine at the University of Edinburgh.

It was a particularly exciting time in the field as induced pluripotent stem cells have just been described by Shinya Yamanaka's lab and novel differentiation protocols were just being set up.

I felt incredibly privileged to work alongside inspiring scientists and mentors at such an early stage in my career.

Building on the lab experience I gained through internships, I went on to do my PhD at the University of Cambridge, the Gurdon Institute.

There I studied mechanisms that govern early cell state decisions during embryonic development.

Following this, I moved to King's College London as Post Doctoral Fellow, investigating the pathological mechanisms of muscular dystrophy and developing cellular models of the disease for drug screening.

And then in 2020 I joined the Wellcome Singer Institute where I now lead the technical development of strategies to streamline and scale the production of diverse organoid models.

My work focuses on process optimization, automation and sample tracking to ensure reproducibility across the organoids that we produce, as well as training colleagues to embed these practises across the team.

Fantastic, thank you.

And what drew you specifically to working on complex cellular organoid models and what excites you about this area in particular?

In my research career, I have previously used animal models to study how the human body works in health and disease.

And while these models are helpful to understanding processes in the whole body, they often fall short when it comes to mimicking what actually happens in human cells and tissues, and this makes them unreliable for studying certain human specific diseases.

And then traditional lab grown cells in a flat 2 dimensional layers also have big limitations because they can't come to the complexity of human tissues.

And that's why new 3D organoid models are so exciting.

They're grown from patient derived human cells and they reflect better the way cells exist in the body.

Even more promising is the trend towards creating complex organoid models that include several different cell types.

So this allows researchers to study how cells interact with each other, just like in real human tissue.

And by carefully tweaking these interactions with tools like CRISPR or adding perfusion systems that mimic the blood flow, scientists can uncover the biological processes behind diseases and identify more reliable drug targets, ultimately paving the way for better treatments.

That's fascinating. Thank you.

So just to switch gears to your presentation, so I think that we mentioned at the top, your case study used vascularized hair bearing skin organoids supplemented with macrophages.

Could you describe a bit more about this model and why it's a useful example of exploring scale up challenges?

The hair-bearing skin organoid that I'm going to be talking about in my presentations are generated by guiding IPSC clusters through a stepwise differentiation that induces the simultaneous formation of both ectoderm and mesenchyme.

And over the course of several months these organoids develop all of the structures that exist in human skin.

They keratinised epidermis, derma, melanocytes, adipocytes, even hair follicles innervated by neurons as well as vessel like epithelial structures.

And when you supplement this organic with tissue resident like macrophages, the single cell sequencing profile of this organoid very closely of examples that of prenatal skin.

And while this model offers an unprecedented window into human skin development, it comes with several hurdles for scaling.

The long culture period with very frequent media changes, difficulties of imaging of these organoids at later stages of organoid development because they just become so dense. And also, the inherent variability between organoids.

So in my talk at the Cell UK 2025 conference, I will discuss automation and streamlining strategies that we have implemented to enable the generation of this model at a higher scale.

That's great.

And what are some of the key hurdles that you faced when trying to scale up these complex organoid systems from small scale experiments to more robust and reproducible models?

So one of the biggest challenges we face with organoid models, many different kinds of organoid models, is batch to batch variability.

Even subtle differences during differentiation of cell culture can shift outcomes in pretty significant ways.

For example, small changes in the log numbers of cytokines, growth factors and other media components can strongly influence the cell state of certain cell types within an organoid.

And when you're working with many different kinds of patient samples, you need to be sure that the differences that you are seeing are due to the patient profile rather than media composition.

A further complication is the widespread reliance of on Matrigel, which you know as many of you know, is derived from mouse tumours.

And it also varies in composition between lots, and this adds yet another layer of unpredictability.

Transitioning to animal free scaffolds offers a promising way to reduce this risk.

And finally, organoid culture itself is very labour intensive and as experimental scale grows, managing such complex workflows for culture data generation, data tracking, quickly becomes unsustainable and prone to error.

So this is where automation can make a transformative difference within the throughput while maintaining the quality and reliability of the models.

Fantastic, thank you.

What kinds of solutions or strategies have you and your team developed that might be applicable to other researchers working on different organoid or complex cell systems?

One of the most effective ways to overcome the challenges of scaling organoid models is by building in robust, quantitative QC checkpoints at critical stages of organoid generation.

These checks need to be tailored to each particular model and the intended outcome.

But what truly matters is that these QC checks should provide an unbiased and measurable readout of the progress.

In our work we rely heavily on live imaging to monitor organoid development in real time and ensure that the differentiation is on track.

Also routine organoid handling, often stretches the limit of our standard liquid handling system and to bridge that gap, at Sanger.

We're fortunate to have a team that designs media printed custom tools tailored to a specific application that we can request.

This is an approach that has given us the flexibility and precision where off-the-shelf solutions fell short.

That's really interesting.

So our industry partners in biopharma are already engaging with these models at scale or are we still in a kind of early adoption phase right now?

My perspective, industry partners in biopharma are already engaged in adopting complex models for preclinical research.

The main challenge of course lies in developing platforms that are robust enough for drug discovery and capable of generating reliable target hit data.

That's that where challenges exist, so do opportunities.

And in recent years, I've seen technology companies in particular in the sector respond with released liquid handling, imaging and data analysis solutions that were specifically designed to support complex cellular model workflows.

And equally, I think there is a momentum behind the legislative movement driving that shift.

The FDA Modernization Act of 2022 along with FDA's glowing endorsement of so-called now the novel approaches and methodologies supports alternatives aimed at reducing and replacing animal testing requirements.

So I'm hopeful that with both technological innovation and regulatory support being in alignment, this will provide even bigger incentive for biopharma to go full in on the complex cellular models.

And then with that in mind, looking ahead, are there any particular advances, they could be, technical, computational or collaborative that you think would be most critical in taking organoid models to the next level?

At the end of the day, organoid models are tools, and their value lies in the insight they can provide into bigger questions about human physiology and disease. And that's why the very first step is always to define the question you want to answer and then choose the model that is truly fit for purpose.

And when it comes to taking organoid models to the next level, I strongly believe in a systems level approach, one that brings together a deep understanding of the tissue composition first of all, and also the computational models that can predict the cellular interaction, as well as tissue engineering strategies that can help to recreate this complexity in vitro.

And you know, I truly believe that better organoid models cannot be built in isolation and they require better understanding of the human tissue as well as more powerful computational tools to interpret the data.

Absolutely.

Thank you.

How do you see collaborations between academic institutions like the Sanger Institute and industry partners also evolving around these technologies?

At Sanger, where we deliver cutting edge signs at scale, we often encounter technical challenges for which no readymade solution exists and where in-house technologies simply aren't sufficient.

That's where we collaborate with industry technology providers and it's really this type of collaboration has really become invaluable.

By testing their equipment, systems and solutions, we can assess whether their solutions address our gaps.

And in some cases, we even beta-test new technologies as they emerge.

And during my time at Sanger, we've had several successful partnership about this kind, and I look forward to seeing hopefully many more in future.

Thank you so much.

One final question, what's the next big question you're excited to tackle in your own research?

And one of the biggest challenges in organoid research is analysing huge number of microscope images and videos that we collect to monitor organoid development.

These videos, images hold valuable clues about how organoid grow and respond to various conditions, but often the volume of this data creates a big bottleneck in this data interpretation.

Deep learning tools like convolution neural networks can speed this up by detecting stuff or changes we might miss by eye.

So I'm at the stage where I'm very excited to learn and test these models on our data sets, specifically the blood vessel growth data set that we have recently acquired and hopefully make our organoid studies faster and more.

Thank you so much, Maryna.

And if you are interested in scaling up organoid models or 3D cellular modelling or anything else cell, anything else we've talked about today, then please do come along to Cell 2025 where Maryna will be there with her presentation and there'll be many more.

But before we go, thank you so much for joining me today and thanks for your time.

Thank you, Tom.