How to Properly Analyze Organoids: From Molecules to Electrical Signals at a Glance [Reading Science]

Offering Solutions for Reproducibility and Standardization Through AI-Based Integrated Analysis

Organoids have now evolved beyond simple experimental models to become next-generation biological platforms that precisely replicate human organ development, disease mechanisms, and drug responses. While the use of organoids is rapidly expanding in new drug development and precision medicine, the challenge of how accurately and consistently they can be analyzed remains unresolved.

Professor Park Sungjun from the Department of Advanced Convergence and the College of Medicine at Seoul National University, along with Professors Cho Seungwoo and Jin Yunhee from Yonsei University, have published a comprehensive review that systematically organizes next-generation analysis platforms for measuring the molecular, electrical, mechanical, and optical properties of organoids from multiple perspectives and integrating these analyses using AI-based methods. This study is significant in that it provides an overview of organoid analysis technologies and offers solutions to long-standing issues of comparability and reproducibility that have been repeatedly raised in research settings.

Next-Generation Organoid Molecular, Electrical, Mechanical, and Optical Property Analysis Platform. Provided by Research Team

Plenty of Technology, But No Standards

As organoid research has proliferated, analysis technologies have also rapidly advanced. Various tools have been developed, including omics-based molecular analysis, single-cell and spatial transcriptomic analysis, electrophysiological measurements, high-resolution optical imaging, and mechanical property analysis. However, discrepancies between technologies and a lack of standardization continue to hinder research. Even with the same organoid, differences in culture conditions, maturation stages, and analysis methods can lead to varying results, making it difficult to compare or reproduce research outcomes.

The research team diagnosed this issue as "an abundance of technologies but a lack of a common language and interpretative standards." The focus of this study was to systematically classify existing analysis technologies and organize which research questions each technology is best suited to address, thereby helping researchers select analysis strategies that fit their objectives. In particular, whole genome sequencing (WGS) and whole exome sequencing (WES) for genome stability assessment, spatial omics that provide both cellular diversity and positional information, and electrophysiological analyses that can record the functions of neural and cardiac organoids over the long term were presented as essential tools for understanding organoid complexity.

The Next Step: Nondestructive Analysis and AI Integration

The research team identified nondestructive analysis and AI-based multi-data integration as the next steps in organoid analysis. While traditional fixation and sectioning-based analyses provide detailed information, they are limited in their ability to track organoid growth, differentiation, and functional changes over time. In contrast, live imaging, label-free analysis, and electrical and optical sensing technologies based on flexible electronics enable continuous observation of organoids over time without causing damage.

When combined with AI-based computational analysis, it becomes possible to interpret diverse types of information-such as images, electrical signals, and omics data-within a unified context. The researchers emphasized, however, that as the use of AI expands, it is essential to ensure high-quality data, standardized analysis workflows, and the biological interpretability of algorithms. Reducing batch-to-batch heterogeneity due to culture processes and environmental conditions, as well as establishing robust data management systems and computing infrastructure capable of handling large-scale data, were also highlighted as key challenges.

Professor Park Sungjun stated, "In the future, the key challenges in organoid analysis will be establishing long-term monitoring systems for genome stability, commercializing high-efficiency spatial omics technologies, advancing nondestructive high-resolution imaging technologies, and ensuring the transparency and reliability of AI prediction models. In particular, while AI is becoming increasingly important, the quality of data and the biological interpretability of predictions must be ensured to provide clinically grounded predictions."

This study clearly indicates that the direction of organoid analysis lies not in the competition to advance individual technologies, but in organically integrating multiple modalities. This is expected to serve as an important benchmark as organoid research expands into clinical and industrial applications, such as evaluating the responsiveness and toxicity of drug candidates and establishing patient-specific treatment strategies.

The results of this study were published in the internationally renowned journal Nature Reviews Bioengineering. The title of the paper is "Organoid analytical toolkits."