**2. FCM analysis of microalgae**

**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,

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 emis-

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

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.

the emission intensity of each fluorophore was calculated from each excitation spectrum in **Figure 2**.

84 Multidimensional Flow Cytometry Techniques for Novel Highly Informative Assays

sion efficiency of each fluorophore.

of the data.

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, which requires food nutrients for microbes.

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 for microalgal studies using FCM [9].

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**.

*Chlorella* (initial density of 1.0 × 104

Cr<sup>2</sup> O3

steel slag [1, 12, 14].

number of algae (1.0 × 104

(**Figure 4C**) [1, 11, 12, 14].

described in reports of earlier studies [1, 12, 14].

**FeO SiO2 CaO Al2**

20–22]. In brief, slag used for this study mainly contains SiO<sup>2</sup>

because it is generally difficult to distinguish FeO and Cr<sup>2</sup>

cells/ml adjusted using hemocytometry) grown in CA

Efficient Interpretation of Multiparametric Data Using Principal Component Analysis as…

, CaO, Al<sup>2</sup>

O3

cells/ml), and ultrapure water. Therefore, nutrient amounts of CA

O3

http://dx.doi.org/10.5772/intechopen.71460

**O3 ZnO NiO CuO**

formed from Fe and Cr in a

, MgO, MnO, and

O3

87

medium as a control condition for 1 week under an LD cycle and algae treated with metal eluate for 1 week as a test condition were prepared, respectively, as described in earlier reports [1, 12, 14]. Moreover, algae treated with heat for 5 min at 100°C were prepared. Here, the test conditions were reference standards subjected, respectively, to metallic eluate from steelmaking by-products and heat stress. A detailed description of the metal eluate reveals that the metal eluate was made from stainless steel slag (**Table 1**) subjected to a leaching test based on JIS K0058-1: 2005 (method for chemicals in slags Part 1: Leaching test) [12, 14, 20–23]. **Table 1** presents compositions of stainless steel slag particles used for this study [12, 14,

[12, 14]. Here, all Fe and Cr compounds are described, respectively, as FeO or Cr<sup>2</sup>

suspended metal solution at the occasion of elemental analysis after alkali fusion of stainless

After elution from slag at pH 6 adjusted with HCl, the solution was filtrated with a 0.45 μm pore filter to eliminate slag particles. Then the solution was used for bioassay with *Chlorella* as a test solution including trace metals. **Table 2**, which shows components of the metal eluate used for this study, includes environmental quality standards for soil pollution, marine pollution, and water pollution, along with other standards for eluent and drinking water for reference. In this study, CA medium containing eluates was first made from 25 vol% of the concentrated CA medium, which had four times that amounts of respective chemicals for making CA medium, and 75 vol% of mixture of arbitrary amounts of eluate, a definite

medium containing eluates were the same as those of CA medium alone, but the concentrations of chemicals derived from eluate differed from those of CA medium without eluate as

To characterize each algal sample using FCM, this study used a cell analyzer (Muse™; Merck Millipore Corp., Hayward, CA) with a green laser operating at 532 nm as an excitation light source, a photodiode for detection of FSS, and two fluorescence filters of a 680/30 nm band pass (BP) filter suitable for chlorophyll fluorescence (red fluorescence) and a 576/28 nm BP filter suitable for chlorophyll degradation (yellow fluorescence)

This study was undertaken to evaluate the correlativity between algal properties and the test condition. To evaluate the correlativity among multiple properties of algae and each stress factor, PCA of multivariate analysis was used for this study using software for multivariate analysis (Institute of Statistical Analyses, Inc.). A dimensional reduction technique, PCA, reduces multi-dimensional information to arbitrary one-dimensional information, which is a

**O3 MgO MnO Cr2**

Slag A 0.74 44.1 33 5.39 7.68 4.09 3.29 0.01 0.06 0.024

**Table 1.** Chemical compositions of steel slag used for this study (mass%) referred from the literature [1, 12–14].

**Figure 4.** Fluorescence characteristics of algae and microphotographs of *Chlorella*-like alga experimentally isolated from ciliate *Paramecium bursaria*. A bright field image of *Chlorella*-like alga (A) and the corresponding fluorescence image derived from chlorophyll (B) are shown. Panel C presents fluorescence characteristics of *Chlorella*-like alga obtained using fluorescence spectroscopy. Emission spectra of algae are shown with (dotted line, heated algae) and without heat treatment (solid line, control algae). Yellow (dotted arrow) and pink (solid arrow) areas, respectively, represent detection ranges of yellow and red fluorescence channels for FCM used for this study (see *Research methods*). Panels A-D were referred and partly modified from the literature [1, 13, 15, 18].
