Beckman Coulter Life Sciences has introduced an automated solution for the gruelling and time-consuming process of manual gating flow cytometry data, with no programming skills required. This novel feature is part of the new Cytobank v10 platform upgrade, which is available now.
Inconsistencies among operators have been identified as one of the top contributors to variability in flow cytometry data analysis. Cytobank Automatic gating can address this issue and reduce variability compared to manual gating. Also, depending on the complexity of the gating strategy, Cytobank Automatic gating can take up to 75% less time to complete compared with manual gating of the full dataset for population identification.
“When customers talk we listen, and we’ve heard a lot about the reproducibility crisis in biomedical research, along with the struggles that come with inconsistencies from manual gating,” said Nicole Weit, senior technical product manager for Biodiscovery Flow Software. “Manually gating files is tedious and time consuming, especially for large and complex panels or datasets with many files. Laboratory staff can end up adjusting gates for hours or sometimes even days. We’ve been on a relentless mission to stop the madness and provide a solution to these challenges, so that customers can level up their flow cytometry data analysis without having to be programmers.”
Cytobank Automatic gating allows lab staff to set up their own gating strategy on a set of files, train a model and then reproducibly analyse more samples using the trained model. The trained model can then easily be shared with colleagues around the globe. This two-step workflow saves users hands-on time, reduces variability, and empowers collaboration across institutions. When new data needs to be analysed, users simply start a new automatic gating inference run with the trained model.
The Cytobank platform is a cloud-based analysis solution for cytometry data and provides powerful visual data management and controlled data sharing. By providing access to machine learning-assisted analysis without the need for coding knowledge or programming skills, the platform helps researchers discover new insights from high-dimensional data with the use of unbiased approaches that reduce reproducibility issues, eliminating operator-dependent bias.