![]() Cells with low fluorescence signal are enriched in smaller cells, mostly in G0/G1 phase, while the cells with high fluorescence signal are larger and in G2/M phase. We demonstrated that the variability in the distribution of both background and specific fluorescence signal is related to the cell cycle state of the measured cell population. Here, we investigated the association between cell cycle status, cell size, and the variability of fluorescence intensity distribution as measured by flow cytometry. On the other hand, it must be noted as an obstacle and a potential pitfall of fluorescence-based techniques 12, 24. ![]() This interesting phenomenon, together with the technological advancements, opened a large field of investigation and application of autofluorescence in biological research 14, 20, 21 and biomedical diagnosis 22, 23. These molecules are excited by and emit over a broad range of wavelengths and often overlap the spectra of commonly used fluorescent probes 19. A number of endogenous fluorophores have been described, including aromatic amino acids, cytokeratines, collagen and elastin, NAD(P)H, flavins, fatty acids, vitamin A derivatives, porphyrins and lipofuscin, and these can be exploited as intrinsic biomarkers 18. Background fluorescence is influenced by cellular phenotype 13, 14, metabolic state 15, 16, and proliferation rate 17. The background, native fluorescence is a normal characteristic of every particle, cell and tissue. Two major technical limitations of flow cytometry are background fluorescence, sometimes referred to as autofluorescence, and spreading error, which can contribute to the incorrectly identified heterogeneity within cell populations 12. Besides scRNA-seq and mass cytometry, current state-of-the-art fluorescence-based flow cytometry allows measurement of 40+ colours simultaneously 9, 10, and represents a re-emerging technology for large scale single-cell analysis 11, with deeper understanding the cell cycle and cell volume effects in polychromatic flow cytometry data now becoming more than necessary. Several strategies were developed to remove cell cycle effects from scRNA-seq (for review see 8) and mass cytometry data 6. Unbiased cell clustering may therefore be obtained by correcting for cell cycle effects 7, 8. Exploration of data obtained with single-cell RNA sequencing (scRNA-seq) revealed that the cell cycle and cell volume can act as sources of bias, introducing within-cell-type phenotypic and functional heterogeneity 4, 5, 6, 7. Therefore we advise using caution and additional experimental validation when comparing populations defined by fractions at both ends of fluorescence signal distribution to avoid biases caused by the effect of cell cycle and cell size.Ĭell cycle is an essential biological process that significantly contributes to the transcriptional heterogeneity in cell differentiation 1, cell death 2, and carcinogenesis 3. We show that variability in the distribution of background and specific fluorescence signals is related to the cell cycle state of the selected population, with the 10% low fluorescence signal fraction enriched mainly in cells in their G0/G1 cell cycle phase, and the 10% high fraction containing cells mostly in the G2/M phase. Here, we describe the association between cell cycle status and cell size, and the variability in the distribution of fluorescence intensity as determined with flow cytometry, at population scale. Cell cycle, together with changes in the cell size, are two of the factors that alter the functional properties of analyzed cells and thus affect the interpretation of obtained results. Most of the single cell samples prepared from in vitrocultures or clinical specimens contain a variable cell cycle component. The distribution of fluorescence signals measured with flow cytometry can be influenced by several factors, including qualitative and quantitative properties of the used fluorochromes, optical properties of the detection system, as well as the variability within the analyzed cell population itself.
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