**5. Flow cytometry**

Flow cytometry is a technique that started as an immunological technique at the beginning; however, currently, it represents a tool to perform fast and multiparametric analyses in molecular biology, microbiology, virology, toxicology, cancer biology, and infectious diseases that can affect any organism [1].

The equipment needed to perform flow cytometry is a cytometer. This is a machine capable of analyzing cells or particles mixed in a liquid solution that makes them pass one by one into tubes with a unique system of fluids. The positioning of cells in a line allows the exposure of every single cell to a laser light, which interrogates each cell individually. Then, the interpretation is performed by a computer that analyzes the light as numeric and graphical data in a standardized format (\*.fcs), which can later be read and analyzed by any flow cytometry software [1, 2].

For the flow cytometry data analysis, the first step involves standardizing the studied cell population. Then, the cohort points for the negative and positive phenotypic screening molecules should be identified in the selected cell population. To reach a better identification of the phenotypic molecules, it is necessary to use a fluorescent positive control that can be a sample of cells from the same population with the maximal expression of the molecule; also, a negative control without a fluorescent signal should be considered (**Figure 3**) [2, 4].

To assess cell damage, the measurement of several indicators is available. In this context, cell viability is used as an indicator of cytotoxicity and involves the use of kits with contrast fluorescent colors (red and green). The viable cells will be the ones that have no damage at all, and they will be detected with a green color (495–515 nm);

#### **Figure 3.**

*Flow cytometry methodology. 1) Labelling of cells or particles with fluorescent molecules. 2) Cell mixture leaves the nozzle in droplets, laser beam strikes each cell or particle by the FSC detector, which identifies cell size, and the SCC detector, which identifies fluorescence/granularity/complexity. 3) Conversion of luminescent signals into numerical and graphical data to select the cell population according to its size and complexity. 4) Detection of fluorescent markers in cells by a pseudocolor quadrant density plot: Negative cells without fluorescence (-/-). Positive cells to fluorescent marker 1(+/-). Positive cells to fluorescent marker 2 (-/+). Positive cells to both fluorescent markers (+/+).*

whereas cells with severe damage are discriminated by red brilliant (495–615 nm); the positive cells to both of the parameters, are in a process of early death, but still viable [64]. To be more specific in the cell death state, it is possible to define the apoptosis level using an annexin V/propidium iodide (PI) kit, which discriminates live cells by the absence to both fluorescent dyes; whereas the positive cells for only annexin V are in early apoptosis, while the positive cells for only PI are in necrosis; and the positive cells for both annexin V and PI dyes, are in frank or late apoptosis [65, 66].

#### **5.1 Detection of MNi and other abnormalities by flow cytometry**

Cytotoxicity and genotoxicity can be evaluated by flow cytometry. The initial approach to estimate genotoxicity by MNi detection is possible by ethidium monoazide bromide (EMA) staining to label the chromatin of necrotic and mid/late-stage apoptotic cells. In addition, stripping of cytoplasmic membranes and incubation with the pan-nucleic acid dye SYTOX Green plus RNase to provide a suspension of free nuclei also allows detection of MNi [67, 68].

Some authors have used cytometric techniques to quantify MNi in normochromatic and polychromatic erythrocytes, leading to a significant reduction of the counting time by 100 orders of magnitude and also reducing the number of experimental animals needed to perform the studies with the *in vivo* peripheral blood erythrocyte technique [67–69]. Flow cytometry is also used for counting MNi in bone marrowderived erythrocytes and peripheral blood erythrocytes through *in vivo* experiments. Still, the most relevant advantage has been the adaptation of three approaches: flow cytometry, image recognition, and machine learning to detect both MNi and other nuclear abnormalities (NBUDs, NPBs) as well as necrotic and apoptotic cells, which opens a new perspective in the CBMN assay with lymphocytes [70–73].
