Discussing Advanced Human-Relevant Models with Molecular Devices
Aaron Risinger shares his expertise on human-relevant models, particularly organoids. Due to their translatability, these models offer significant potential for innovative drug discovery, development, validation and application. Aaron will discuss ways to streamline organoid development, cultivation and analysis, including the importance of selecting the right tools and partnerships, which are essential to a robust process.
Q&A
- What trends has Molecular Devices observed in adopting more advanced cellular model systems like spheroids, organoids and microtissues?
- How can we understand the trade-offs between translatability and scalability in different cellular model systems?
- What are the main research challenges associated with using human-relevant models?
- How is Molecular Devices automating and industrializing the cell culture process to scale human-relevant model programs?
1. What trends has Molecular Devices observed in adopting more advanced cellular model systems like spheroids, organoids and microtissues?
The paradigm shift that we are all experiencing right now, as we move towards translatable human model systems, is something that Molecular Devices and Danaher have been thinking deeply about. The idea here is to leverage these human-relevant model systems earlier in the drug discovery process and complement, refine and potentially reduce the use of some of our small and large animal models. We are focused on industrializing some of these concepts and reproducing complex cell biology at scale. We've been on a journey ourselves through these past six years, as we have been primarily responsible for the endpoint analysis of these complex 2D cell culture models. Over the years, we have observed our customers and collaborators shift towards increasingly sophisticated models. They are incorporating the capability to self-assemble certain 2D cell cultures into spheroids. They are beginning to integrate more advanced cellular model systems, such as microtissues, which consist of multiple 2D cell cultures arranged into a tissue mimic. We're also seeing the advent and increased use of technologies like organs on a chip, where we have microphysiological systems designed and used to understand the mechanisms of action of various disease states.
2. How can we understand the trade-offs between translatability and scalability in different cellular model systems?
Using an Eisenhower plot, we can discuss the concept of relative translatability and scalability. There is a very good reason why we have been using immortalized cell lines in our drug discovery research. These cell lines are very well characterized, can mimic particular diseases, and are amenable to gene correction and editing, creating a scenario where we can recapitulate and identify diseases using a cell-based model. They're easy to manufacture and are reproducible. But, as people began exploring alternative techniques, including using primary cells in the early research into iPSCs, they recognized some scalability limitations in these model systems. Alternatively, when looking at patient-derived organoids, the translatability increases as these models are complex multicellular systems that mimic the tissue from which they originated, making them more physiologically relevant and better at predicting therapeutic outcomes.
Limitations of conventional cell models
3. What are the main research challenges associated with using human-relevant models?
As we increase complexity in cellular biology, it comes with its challenges. Recent surveys that Molecular Devices conducted with the scientific community highlight scaling up, scaling out and reproducibility. This comes down to having access to large numbers of the same model with many different isotypes representing diverse demographics, all while obtaining batch-to-batch consistency. So, how can we create a standardized approach to provide the scale of these new human-relevant models with the reproducibility we are used to having when working with our immortalized cell lines that are so well characterized? One way to achieve this is by providing platform technologies that help industrialize these processes, converting manual approaches into more routine ones through automation.
4. How is Molecular Devices automating and industrializing the cell culture process to scale human-relevant model programs?
We've introduced our automated cell culture system, Cellexpress.ai. It is an automated fed-batch bioreactor designed to industrialize organoid culture, especially for iPSC and PDO models requiring a matrix. It enables continuous perfusion and supports large-scale production—up to 18 million organoids per batch—all uniform in passage, size and maturity. This system boosts production capacity by 25x over manual methods and supports drug perturbation, accelerating insights from patient-derived organoid research.
The Cellexpress.ai Automated Cell Culture system integrates liquid handling and a high-capacity incubator (up to 154 plates) to automate feeding, seeding and passaging. Of course, it ties in Molecular Devices’ imaging expertise, allowing continuous monitoring of the cell culture and replacing the often qualitative decisions that come with doing cell cultures manually with quantitative, tractable and traceable data. We believe this is a key step toward industrializing human-relevant model systems.
One of Cellexpress.ai’s key innovations is its unified software environment, creating an integrated work cell that eliminates the need to learn and manage multiple instruments. It incorporates image-guided decision-making, leveraging machine learning, K² analysis and neural networks—techniques long used in high-content screening. We go further by using AI-driven analysis of both transmitted light and fluorescence images to automatically guide cell culture progression, making decisions to move the phases forward in the cell culture process.
Listen to the full webinar here to learn more about standardizing human-relevant model culture with purpose-built automation to increase reproducibility, along with advanced imaging and AI-driven image analysis for in-depth characterization.
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