Lien vers Pubmed [PMID] – 27500930
PLoS ONE 2016;11(8):e0159961
Flow cytometry, initially developed to analyze surface protein expression in hematopoietic cells, has increased in analytical complexity and is now widely used to identify cells from different tissues and organisms. As a consequence, data analysis became increasingly difficult due the need of large multi-parametric compensation matrices and to the eventual auto-fluorescence frequently found in cell suspensions obtained from solid organs. In contrast with conventional flow cytometry that detects the emission peak of fluorochromes, spectral flow cytometry distinguishes the shapes of emission spectra along a large range of continuous wave lengths. The data is analyzed with an algorithm that replaces compensation matrices and treats auto-fluorescence as an independent parameter. Thus, spectral flow cytometry should be capable to discriminate fluorochromes with similar emission peaks and provide multi-parametric analysis without compensation requirements. Here we show that spectral flow cytometry achieves a 21-parametric (19 fluorescent probes) characterization and deals with auto-fluorescent cells, providing high resolution of specifically fluorescence-labeled populations. Our results showed that spectral flow cytometry has advantages in the analysis of cell populations of tissues difficult to characterize in conventional flow cytometry, such as heart and intestine. Spectral flow cytometry thus combines the multi-parametric analytical capacity of the highest performing conventional flow cytometry without the requirement for compensation and enabling auto-fluorescence management.