This article focuses on how contamination of single-stained controls affects the quality of unmixed outputs, and discusses tools in Resolve that help you easily identify and remove contaminants from controls to maximize the quality of unmixed outputs.

Unmixing spectral cytometry data is a complex process that combines both wet- and dry-lab workflows. Errors introduced in either part can propagate and compromise downstream analysis. Here we describe Ozette Resolve Spectral Unmixing, a solution that improves unmixing through quality evaluation of controls and adaptive, event-level unmixing, with guidance on optimizing results using appropriate unstained controls.

Scientific complexity can obscure simple errors, with potentially severe consequences. Flow cytometry, a powerful yet intricate tool, exemplifies how errors in data generation and analysis can propagate unnoticed, affecting critical outcomes in drug development and patient care. Ozette addresses these challenges by integrating validated computational methods, quality monitoring, and transparent data analysis, ensuring reliable insights that accelerate clinical research and improve decision-making.

Ozette recognizes the challenges of producing high-quality cytometry data, which stem from technical complexities and the need for diverse expertise. That is why we have launched the Ozette Blog, a platform to share our insights, expertise, and innovative solutions for generating and analyzing cytometry data at scale.