Discriminating between single cells and aggregates of cells, known as doublets, is a critical step in flow cytometry (FACS) data analysis. Doublets can arise when two or more cells pass through the laser beam simultaneously, leading to inaccurate measurements of fluorescence intensity and cell size. These artifacts can skew data and misrepresent the true cell population distribution. For instance, a doublet may appear to have twice the DNA content of a single cell, potentially leading to misidentification during cell cycle analysis.
Accurate cell analysis hinges on the elimination of these artificial events. Excluding doublets ensures data integrity, thereby improving the reliability and reproducibility of downstream analyses and conclusions. Historically, doublet discrimination techniques were rudimentary, but advances in flow cytometer technology and data analysis software have allowed for more sophisticated and reliable methods of doublet exclusion, crucial for high-quality data in research and clinical settings. This leads to more accurate quantification of cell populations, more reliable detection of rare events, and a greater confidence in the biological insights derived from flow cytometry experiments.
