Ramona Unveils Revolutionary Vireo™ for Live-Cell Imaging
On January 15, 2025, Ramona, a frontrunner in advanced imaging technologies, announced the launch of its latest innovation, the Vireo™. This cutting-edge live-cell imaging system is designed to significantly enhance and accelerate the process of drug discovery, a critical area in biomedical research. Leveraging 24 miniaturized 4k video microscopes paired with real-time AI image processing, the Vireo can analyze every well in a 96-well plate in under two minutes—far surpassing the speed of conventional systems. This remarkable throughput not only sets a new standard but also enables scientists to perform dynamic assays that yield data-driven insights essential for rapid medical advancements.
Gregor Horstmeyer, the CEO of Ramona, emphasized the importance of high-quality imaging technology in today’s research landscape. "The Vireo represents a considerable leap forward for the industry. We’ve tackled existing challenges of throughput, temporal resolution, and risks of photodamage, providing researchers with tools that maximize efficiency," he stated.
Real-World Applications Demonstrate Vireo’s Impact
The impact of the Vireo system is already visible in practical applications within research labs. For instance, Jason Stein, a PhD Associate Professor at the UNC Neuroscience Research Center, noted that his team can now image and analyze an entire 96-well plate filled with human cortical organoids in just 2 minutes. Similarly, Jieun (Esther) Park, a Postdoctoral Fellow at the Carolina Institute for Developmental Disabilities, shared that their large-scale differentiating project, transitioning over 100 induced pluripotent stem cell (iPSC) lines into brain organoids, has seen a drastic reduction in processing time from over five minutes to only 15 seconds per 96-well plate.
The Vireo employs advanced AI analysis tools such as automated segmentation and viability assessment that streamline the entire workflow from imaging to actionable insights. Case studies have already shown its ability to distinguish between the cytotoxic and cytostatic effects of chemotherapeutic compounds and analyze the growth dynamics of 3D tumor organoids. Furthermore, it quantifies morphological features across a multitude of experimental conditions, showcasing its vast potential in various research domains.
Exciting Launch and Conference Debut
The official launch of the Vireo follows a successful year-long early access program in 2024, during which leading research institutes tested its capabilities. This state-of-the-art imaging system is set to debut at the Society for Laboratory Automation and Screening (SLAS) conference in San Diego from January 25 to 29, where it has been nominated for the esteemed SLAS2025 New Product Award. Conference attendees will have the opportunity to witness live demonstrations and presentations, underscoring the unique capabilities of the Vireo system.
Researchers interested in experiencing the Vireo firsthand can attend the SLAS conference or visit the official website at
www.ramonaoptics.com/vireo for inquiries about early access. Ramona continues to revolutionize the field of microscopy, merging machine learning with innovative optical technologies to push the boundaries of what is possible in optical imaging.
About Ramona
Founded with the mission to redefine microscopy, Ramona blends state-of-the-art machine learning techniques with groundbreaking optics, creating powerful imaging solutions. Their flagship technology, the Multi-Camera Array Microscope (MCAM™), is recognized as the first gigapixel imaging engine capable of synchronously capturing cellular-level details across extensive areas, maximizing efficiency without compromising resolution. Ramona's AI-enabled tools are built for high-throughput applications in model organism research, cellular screening, and beyond, allowing scientists to gain insights swiftly and effectively.
For further details about their innovative imaging solutions and to stay updated, please visit Ramona’s website
www.ramonaoptics.com.