Accelerate and improve biologics screening by flow cytometry
Discover how to screen higher-quality biologics in less time
3x
screening throughput
25x
faster data preparation
AI-native data
for in silico modeling
Automated processes
Connect all data sources and targets seamlessly, boosting speed and data quality
Intuitive dashboards
Visualize and annotate large data sets with ease, facilitating downstream use
Ready for AI
Improve the binding affinity and specificity of protein therapeutics with AI/ML
Labor-intensive processes
Manual handling, gating, and QC of flow cytometry data is slow and error prone
Lack of context
Raw flow cytometry data, without metadata, is difficult to search and analyze
Dead-end datasets
Data architecture is unfit for AI/ML, limiting the speed and accuracy of cytometry analysis
Explore resources
Learn how to transform your scientific data into AI-based outcomes.
Unlock the full value of your flow cytometry data
Replatform
Collect and centralize data from flow cytometers and send to downstream targets
Engineer
Contextualize and harmonize the data for search and analytics/AI
Analytics
Explore cytometry data with visualization/analytics tools for rapid QC and insights
AI
Use the data to build in silico models and predict how biologics will behave
Streamlined flow cytometry for antibody screening
A leading biopharma company looked to increase the throughput of its antibody screening method by streamlining the scientific data workflow. Learn how TetraScience helped accelerate and improve lead identification.