**1. Introduction**

Flow cytometry (FCM) can provide cell optical information from microbes to model animal and plant cells. Over the last several decades, FCM with those fundamental characteristics has

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served as a powerful and invaluable tool in fields such as cell biology, microbiology, protein engineering, and health care [1]. Actually, FCM has functions to conduct several procedures such as cell counting, biomarker detection, cell cycle analysis, and cell sorting. Clear patterning graphs from FCM data can elucidate correlation among several parameters. Recent FCM systems enable a user to analyze up to a dozen multiparameters including scattered light parameters in a single assay [2]. In fact, multiparametric detection realizes high-throughput measurement and cost-performance and is also time-saving of experiments in life science. For instance, ten combined experiments must be conducted when one examines five parameters of interest using several designed FCM experiments with three-color fluorophores (designated as three-color FCM). By contrast, when using a designed FCM experiment with fivecolor fluorophores (five-color FCM), correlation between the five parameters can be examined from only one experiment, in principle. Generally, the number of combined experiments is calculable using Pascal's triangle (**Figure 1**).

However, the number of available colors used in each experiment is restricted in conjunction with both numbers of excitation lasers and corresponding emission filters used in an instrument. **Figure 2** portrays excitation and emission spectra of representative fluorophores, as examined using online software (SpectraViewer; thermo Fisher Scientific Inc.). When only a single blue laser operating at 488 nm is used for multicolor FCM, the emission spectra of fluorophores shown in **Figure 2** resemble those in **Figure 3**. Several areas of overlapping of

**Figure 1.** Correlation between the number of target parameters and that of colors used in an experiment, as shown by the Pascal's triangle. Ten combined experiments must be conducted to measure five parameters of interest using twocolor FCMs for case 1 of **Figure 1**, although a system using four-color FCMs requires only five combined experiments to measure them. It is noteworthy that an experiment using just one-color FMC cannot examine any correlation between target parameters except for scattered light parameters. Multicolor FCM (more than two-color FCM) must be used to find correlation between parameters.

**Figure 2.** Fluorescence properties of representative fluorophores examined using online software (SpectraViewer; thermo Fisher Scientific Inc.). Dotted lines and solid lines respectively show the excitation spectrum and emission spectrum of each fluorophore. A vertical blue line signifying 488 nm as an example is included in each graph.

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Efficient Interpretation of Multiparametric Data Using Principal Component Analysis as… http://dx.doi.org/10.5772/intechopen.71460 83

served as a powerful and invaluable tool in fields such as cell biology, microbiology, protein engineering, and health care [1]. Actually, FCM has functions to conduct several procedures such as cell counting, biomarker detection, cell cycle analysis, and cell sorting. Clear patterning graphs from FCM data can elucidate correlation among several parameters. Recent FCM systems enable a user to analyze up to a dozen multiparameters including scattered light parameters in a single assay [2]. In fact, multiparametric detection realizes high-throughput measurement and cost-performance and is also time-saving of experiments in life science. For instance, ten combined experiments must be conducted when one examines five parameters of interest using several designed FCM experiments with three-color fluorophores (designated as three-color FCM). By contrast, when using a designed FCM experiment with fivecolor fluorophores (five-color FCM), correlation between the five parameters can be examined from only one experiment, in principle. Generally, the number of combined experiments is

However, the number of available colors used in each experiment is restricted in conjunction with both numbers of excitation lasers and corresponding emission filters used in an instrument. **Figure 2** portrays excitation and emission spectra of representative fluorophores, as examined using online software (SpectraViewer; thermo Fisher Scientific Inc.). When only a single blue laser operating at 488 nm is used for multicolor FCM, the emission spectra of fluorophores shown in **Figure 2** resemble those in **Figure 3**. Several areas of overlapping of

**Figure 1.** Correlation between the number of target parameters and that of colors used in an experiment, as shown by the Pascal's triangle. Ten combined experiments must be conducted to measure five parameters of interest using twocolor FCMs for case 1 of **Figure 1**, although a system using four-color FCMs requires only five combined experiments to measure them. It is noteworthy that an experiment using just one-color FMC cannot examine any correlation between target parameters except for scattered light parameters. Multicolor FCM (more than two-color FCM) must be used to

calculable using Pascal's triangle (**Figure 1**).

82 Multidimensional Flow Cytometry Techniques for Novel Highly Informative Assays

find correlation between parameters.

**Figure 2.** Fluorescence properties of representative fluorophores examined using online software (SpectraViewer; thermo Fisher Scientific Inc.). Dotted lines and solid lines respectively show the excitation spectrum and emission spectrum of each fluorophore. A vertical blue line signifying 488 nm as an example is included in each graph.

**2. FCM analysis of microalgae**

which requires food nutrients for microbes.

for microalgal studies using FCM [9].

In addition to the numerous but unappreciated roles of phytoplankton, including microalgae, in aquatic ecosystems to support yields of fish and shellfish, several microalgae have also attracted attention from several pharmaceutical and vitamin supplement developers, along with food companies [3, 4]. Biotechnologies are sometimes classified into colors based on their respective research areas: red biotechnologies are related to medicine and medical processes. White ones are associated with industrial processes including production of chemicals [3] and biofuels [5]. Gray ones are directly related to the environment. Green ones are connected to agricultural processes including environmentally friendly solutions as alternatives to traditional processes [3, 4, 6, 7]. Blue technologies are related to marine and aquatic processes. Finally, black ones are used to develop bioterrorism. Microalgal applications have the potential to be related to most of those biotechnologies. Autotrophic algal biorefineries, for instance, can present great advantages over conventional refineries that manufacture materials using fossil fuels and over conventional microbial biorefineries that use fermentation,

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The industrial application of algae demands the selection of useful algal species, the evaluation of algal features, and the assessment of their qualities in culture [4]. The algal quality demanded is particularly important because microalgal metabolisms are strongly affected by even trace levels in the concentration of various organic and inorganic pollutants such as heavy metals [1, 8]. When assessing algal quality in culture and using those algae in industrial application, analyzing their life (cell) cycle is a crucially important technique. Cell cycle analysis using FCM is a standard procedure in versatile application of FCM. Considering the cell size of microalgae, unicellular algae such as *Chlorella* sp. are convenient model organisms

Algae have chlorophyll as an endogenous fluorescent biomolecule (**Figure 4A** and **B**). FCM in analogy with spectrofluorometry can pick up the chlorophyll fluorescence of algae and can evaluate some properties including chlorophyll and scattered light signals of an individual alga [9–15]. **Figure 4A**–**C** portrays *Chlorella*-like alga and its fluorescence properties. The wavelength of the maximal fluorescence near 680 nm is from algal chlorophyll (solid curve in **Figure 4C**). Algae are sensitive to heat treatment (dotted curve in **Figure 4C**) [11–14] because the thermal stress damages the thylakoid membrane, which is related to structural and functional changes of the photosystem (PS) II and PS I, thereby interrupting the Calvin cycle [16, 17]. Inducing heat stress in algae reduces chlorophyll fluorescence (dotted curve in **Figure 4C**) and increases yellow fluorescence derived from chlorophyll degradation [11]. Consequently, red fluorescence can indicate vigorous algae, whereas yellow fluorescence indicates stressed and dying algae [11–14]. **Figure 4D** takes a dotted graph from FCM data using a *Chlorella*-like alga (SA-1 strain) to present an example. Both the cell size detected as forward scatter signals (FSS) and chlorophyll contents of algae as red fluorescence channel are correlated strongly with the algal cell cycle [9, 10, 15, 18]. Here, algae are categorized into three populations (Stages 1–3) as described in reports of previous studies [9, 10, 15, 18]: Stage (St.) 1, "growth" stage; St. 2, "maturation" stage; and St. 3, "division and autospore liberation" stage in **Figure 4D**.

**Figure 3.** Fluorescence properties of representative fluorophores excited using a blue laser operating at 488 nm as an example. The graph reflects differences in the excitation intensity of each fluorophore excited using a blue laser. Here, the emission intensity of each fluorophore was calculated from each excitation spectrum in **Figure 2**.

emission spectra occur because the spectra of some fluorophores are flared at the bottom. Along with overlapping of emissions, differences of excitation efficiency might present simultaneous difficulties for multicolor FCM analysis (**Figure 3**). Using a flow cytometer detecting two colors to five colors per single laser, even when using a more high-end instrument than that described above, one must commonly discuss and interpret correlation between multiparameters based on several combined results. Just to be sure, all fluorophores excited by an arbitrary single laser does not necessarily work together because of differences in the emission efficiency of each fluorophore.

In contrast to the benefits of multiparametric FCM, multiparametric data make it difficult to get rid of extraneous data and reach an interpretation of the complicated information. Although one can make multi-dimensional graphs digitally, it is not easy to reach an accurate and clear conclusion from any multi-dimensional graph. To present clear patterning graphs from complicated FCM data, an analyst must be able to grasp the essence of the data.

To extract the essence of FCM data, this study applied principal component analysis (PCA) for multivariate analysis to the complicated FCM data and estimated the usefulness of the PCA method. Recently, some microalgae have already generated a lot of attention from pharmaceutical developers, cosmetic manufacturers, and food companies. The industrial application of algae demands the assessment of their qualities in culture. Taking green alga *Chlorella* sp. as an example and as a convenient organism for FCM, this study presents the usability of PCA method for the assessment of algal quality using FCM.
