How to Adopt New Technology with Stem Cell-Based Models

Scientists seek out the latest technology for a number of reasons. For example, rapid progress in stem cell-based modeling, including in technologies such as organoids and organ-on-a-chip (OoC), has expanded the ability to accurately replicate human biology, and has opened new opportunities for researchers to address fundamental and applied biological problems. Additionally, regulatory milestones such as the FDA Modernization Act 2.0 underscore the growing acceptance of these more complex models in drug discovery, prompting scientists to seek out these clinically predictive tools. From excitement to execution, here we evaluate how we can strike a balance adopting new technology for organoid research within practical limits.

While some industries, like the academic and technology-development sector, are quick to embrace the abilities of advanced in vitro modeling, progress in the technology is not matched by advances in training and education —keeping certain industries in the early stages of adoption. As the technology continues to evolve, the growing number of options available can make the model selection process daunting, leading scientists to take comfort in established methods.

To prop up confidence in technology adoption, there are strategies that can help you find the right one for you and your research.

Define Your Models of Interest

You may already have an idea of which models to incorporate into your research, but to ensure a more seamless integration into your workflow, a thorough evaluation can be helpful. Potential models can be evaluated for their performance using Foundational Data and Context of Use (COU) assays.¹ By defining your models of interest along these two paradigms, you can quickly narrow down the list of options and find a predictive model with the biology you want to study.

• Foundational Data: Gathering foundational data is crucial for understanding the fundamental biology within your model of interest. This involves a thorough characterization of how cells behave in and across different model types under varying conditions, such as mechanical stretch, perfusion, presence of chemical signals or other cell types, and more. By evaluating these experimental inputs, you can understand the phenotypic or genotypic response of the cells under different culture models. Model types can include organoid, air-liquid interface culture, or microfluidic chips, each with different experimental inputs that can affect the cell biology within the model. As a result, because cell biology can vary, some models may retain specific functional characteristics that make them better suited for certain applications. With foundational data, you can quickly identify what can and cannot be ascertained using the model; it establishes competencies and limits of insight—allowing you to choose the appropriate system for your fit-for-purpose application.
• Context of Use Assays: The purpose of COU assays is to quantify a model’s ability to predict known clinical outcomes. It provides empirical evidence that a particular assay is useful when used with the model in question. This can be done by passing a library of compounds with known clinical outcomes through the COU assay, and evaluating the model’s ability to predict human response. By comparing the model’s predictions against established clinical data, researchers are able to gauge the accuracy and relevance of the model in their workflow. Ultimately, successful COU assays not only enhance the credibility of the model, but also facilitate informed decision-making in the drug development process.

3 Questions to Ask Yourself: Balance the Excitement of New Technology with Practical Implementation

Together with biological relevance, practical considerations are emerging as a significant determinant of technology adoption. Oftentimes, researchers want to jump to the most complex system, but this might not be necessary for the level of question being asked. It is important to balance enthusiasm for new technology with the practical realities of implementation.

For example, an academic researcher who is studying the interactions between human airway epithelial and immune cells during RSV infection might benefit from a complex model that demonstrates the cell-cell interactions critical in understanding the mechanisms underlying disease onset and progression. On the other hand, pharmaceutical researchers may prioritize throughput and compatibility with existing workflows. In this case, simpler models, while not including all cell types to study cell-cell interactions, allow for high-volume drug screening to capture donor-specific drug responses or test multiple treatments simultaneously.

As you define your models of interest, here are a few questions to consider as you go through the model selection process:

• Does the model of interest require new lab equipment or can existing resources be used? Assess your current lab capabilities, and consider the compatibility of your existing workflows. For example, some OoC systems use pumps or gravity-driven perfusion flows to drive cell interactions. If your lab has a compatible setup already, or your existing infrastructure could be adapted to integrate the OoC seamlessly, then a new workflow may be worth investing in.
• Can your desired readout be achieved with simpler models? First, consider what is necessary in the model to reach your desired readout, and build your experiment from there. By starting with a more straightforward approach, you can gain valuable insights that can be expanded upon as your research questions evolve. Additionally, simpler models often require fewer resources, making them more budget-friendly.
• Do you have the right personnel or is training required? Engage with colleagues and experts who have experience with the models you are considering. They may know some of the challenges that you would face when implementing the technology and can provide you some guidance and training.

Navigating the rapidly evolving landscape of new technology can be a complex process, but with pragmatic exploration, you can begin to understand the strengths and limitations of your models of interest. By following these tips and strategies, you can find yourself better equipped to make informed decisions in finding the best model for your research.