Studying retinal diseases is challenging due to the complex nature of the eye. Retinal organoids resemble the eye in structure and function and are an excellent way of recapitulating complex biology in-vitro. Organoids have strongly impacted basic research, drug discovery, disease modeling, and personalized medicine. The size of the global organoid market was valued at $517 million in 2021 and could grow to $1.2 billion by 2031. Organoid use has grown by >200% in the pharmaceutical industry, academia, and hospitals with 4,000 publications on retinal organoids since 2022 and 13,000 on brain organoids since 2022. The next frontier for organoid research is developing high-throughput methods, requiring increasing numbers of organoids while reducing organoid variability and labor-intensive manual handling. For retinal organoids, the yield and quality can be mixed and only those that possess specific biology can be used. Currently this selection process is manual and can take days or weeks. An automated sorting system that can image, classify, and sort high from low-quality organoids would significantly increase the speed, capacity, and efficiency of organoid culture while decreasing costs. Currently there is no system in existence capable of sorting large retinal organoids (Ø > 0.5mm) quickly, with low variability, and for large quantities of organoids. In collaboration with IOB, CSEM has developed a custom-built robotic sorter, the OrganEYEzer, to automate the imaging, classification, and sorting of organoids, significantly reducing the variability of organoids and the time required.
Mature, high-quality retinal organoids contain retinal tissue on their outer surface and are surrounded by hair-like structures. Without these features, the organoids physiological relevance. Therefore, sorting of organoids is crucial for drug screening and development. Unfortunately, organoid selection is subjective to each person, is therefore biased, and the results can drift over time. The OrganEYEzer scans and images all organoids in a petri dish and a deep-learning algorithm distinguishes and classifies high-quality organoids from incompletely developed ones. Critically, these algorithms are not as susceptible to selection drift and can be trained based on the inputs of numerous experts, reducing bias.
Additionally, organoid experiments require manual handling and sorting of organoids. This involves imaging each organoid under the microscope, picking it up, and gently transferring it to a well-plate without damaging it. After the identification of high-quality organoids, a pipetting arm carefully picks them up and gently places them into a well plate. The OrganEYEzer reduces the time by approximately 50% compared to manual sorting over a full day. It is also not limited to sorting retinal organoids and can also be used for brain organoids.
The OrganEYEzer features an open, compact, and lightweight design that is compatible with sterile cell culture biosafety cabinets. We currently have a working prototype of the OrganEYEzer fabricated using off-the-shelf components and we estimate a COGS for a commercialized system at ~$25,000. Finally, we have received increasing interest in the OrganEYEzer technology from multinational pharmaceutical companies, start-ups, academia, liquid-handling companies, and microscopy companies, demonstrating the growth and potential of organoids in biotechnology.