Automated image analysis reduces user-to-user variability in flow cytometry gating strategies

Differences in how users gate populations within experiments are major sources of variability in flow cytometry data analysis. Incorporating automated image analysis can substantially reduce user bias. Users are given access to an expansive array of image-derived label-free parameters that can help with assessment of sample quality, optimization of gating strategies, and discovery of morphological features that are not resolved with light scatter or fluorescence parameters. The result is a streamlined workflow that can enable new flow cytometry applications and improved accuracy for data-driven cell analysis. This webinar will demonstrate how automated image analysis combined with flow cytometry can help: identify singlets versus doublets and aggregates, differentiate live versus dead cells, and help reduce user-to-user variability in gating strategies to enable improved data accuracy.

 

Learning Objectives