Just like compensation in conventional flow cytometry, the quality and fidelity of spectral flow cytometry data greatly depends on the use of appropriate controls for the calculation of an accurate spectral unmixing matrix.
Although the same basic rules for control optimization apply to both conventional and spectral flow cytometry, the increased multiplexing capability of spectral flow cytometry may lead to increased requirement for precious sample and higher chance to run into unmixing errors. Rather than trying to salvage data by manually “fixing” spectral unmixing, the most appropriate approach is to identify the source of unmixing errors and optimize assay-specific controls accordingly. In this webinar, we will comprehensively review all the main factors that may contribute to spectral unmixing errors.
We will also describe the control optimization process that was used to correctly unmix data from a 38-color panel and to successfully sort six cell populations in real time without any unmixing adjustments.
What you will learn:
- Impact of spectral unmixing errors on data interpretation
- Pros and cons of using cells or beads as unmixing controls
- How to identify spectral unmixing errors
- Main sources of spectral unmixing errors (gating, cell number, brightness, treatment, signature and autofluorescence mismatch)
- Tips and tricks to prevent spectral unmixing errors
- Examples of mis-diagnosed spectral unmixing errors
Speakers
Mirko Corselli
Associate Director, Market Development
BD Biosciences
Dr. Corselli has over 20 years of experience in flow cytometry. He received his Ph.D. in clinical and experimental hematology and oncology from the University of Genova. At BD Biosciences, he’s led the development and dissemination of content on high parameter flow cytometry, image sorting and single-cell multiomic analysis in immunology and immuno-oncology.
