**4. Results and discussion**

This study compared the effects of metallic eluate from stainless steel slag and heat treatment as an experimental stress factor on algal status, specifically that of *Chlorella* sp. [1, 12– 14]. Here, CA medium containing 50 vol% of the metallic eluate was used for FCM analysis. To interpret correlativity among several parameters, FCM data are generally expressed as a 1D histogram and 2D scatter or contour plots. The more parameters an operator uses, the more difficult it becomes for the user to find high correlativity among parameters and to present an effective graph. The PCA method produced new comprehensive axes including several parameters, which have different inclination factors among parameters. The primary (PC1) and secondary (PC2) and tertiary (PC3) components, respectively, reflect 53.4, 34, and 12.6% of information for the data examined in this study (data not shown). **Figure 6A** and **B** presents the principal component loading of PC1 and PC2. Each loading shows that all parameters, including the algal size (FSS-H), red fluorescence intensity (Red-H), and yellow fluorescence (Yellow-H), are positively correlated with PC1 (**Figure 6A**). Particularly, correlation factors for both the algal size and the red fluorescence intensity were more strongly positive with PC1 than the yellow fluorescence intensity was. By contrast, the red fluorescence intensity and the yellow fluorescence intensity, respectively, show inverse and positive correlation with PC2 (**Figure 6B**). The 2D scatter plots using new axes show patterns with individually different vectors treatment dependently, as expressed by the score plot of PC1 versus PC2 (**Figure 6C**). The graph using new axes from PCA helps us to infer strong correlation between a particular parameter and the corresponding one. Consequently, the characteristics of both algal size and red fluorescence intensity are mainly reflected as the variation of algae on the positive PC1 axis (**Figure 6A** and **C**), whereas only yellow fluorescence mainly affected the variation of algae on the positive PC2 axis (**Figure 6B** and **C**). Results show that both the cell size (or red fluorescence intensity) and yellow fluorescence intensity of algae can be indicators that facilitate assessment of the variation for comparison of algae between control and heat treatment (**Figure 6C**), whereas both the cell size and red fluorescence can be indicators for comparison of algae between control and the metallic treatment (**Figure 6D**).

The results (**Figure 6**) from PCA analysis prompted us to produce plots of FSS or the red fluorescence for algae versus the yellow fluorescence intensity for algae (**Figure 7**). The 2D-dotted graph of the red versus yellow fluorescence intensity for control algae, for instance, showed 10<sup>2</sup> –10<sup>3</sup> on the red channel and 10<sup>1</sup> –10<sup>2</sup> on the yellow, whereas that for the heated algae showed 10<sup>1</sup> –10<sup>2</sup> on the red channel and 10<sup>1</sup> –10<sup>3</sup> on the yellow. By contrast to the heat treatment, the dot distribution of algae treated with metallic eluate closely resembled that of control, although that with the eluate shifted slightly upward relative to that of control algae [1, 12–14]. In analogy with the result (**Figure 6C**) from PCA analysis, the difference of algae between the control condition and metallic treatment is slight compared to the difference of algae between control and heat stress (**Figure 7**).

of algal signals between the control and the metallic treatment was almost identical to that of the graph of the red versus the yellow fluorescence (**Figure 7**), both distributions differed on the graph of FSS versus the red fluorescence (**Figure 8**). A distinctive population (arrow in **Figure 8**) was found from algae treated only with metal eluate but not control. Drawing on the result from algal life (cell) cycle (**Figure 4D**), detection of the distinctive population in algae treated with metal eluate indicates that the algal cell cycle proceeds smoothly under the condition with metal eluate. By contrast to algae treated with metal eluate, the cell cycle of control algae seems to reach a stable stage such as a stationary phase, resulting in the near cessation of algal proliferation or extremely low proliferation

**Figure 6.** Condition-dependent distribution of *Chlorella* obtained using PCA method. The PCA reduces multidimensional information to arbitrary one-dimensional information and produces new components such as PC1–PC3. Here, factor loading plots of each parameter for PC1 (A) and PC2 (B) are shown. A score plot of PC1 vs. PC2 (C) and that

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of PC1 vs. red fluorescence intensity of algae (D) were produced using data from different test conditions.

In addition to estimation of algal population dynamics using FCM coupled with PCA analysis, direct quantification of algae using hemocytometry was conducted as described in earlier reports [1, 12–14]. The quantification specifically examined whether algal growth dynamics implied from the result of PCA analysis (**Figure 8**) was confirmed on algae

activity.

To conduct a precise comparison of algae of control and metallic treatments, the plot of FSS versus red fluorescence for algae was produced (**Figure 8**). Although the dot distribution

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**4. Results and discussion**

90 Multidimensional Flow Cytometry Techniques for Novel Highly Informative Assays

treatment (**Figure 6D**).

and heat stress (**Figure 7**).

on the red channel and 10<sup>1</sup>

on the red channel and 10<sup>1</sup>

10<sup>2</sup> –10<sup>3</sup>

10<sup>1</sup> –10<sup>2</sup>

This study compared the effects of metallic eluate from stainless steel slag and heat treatment as an experimental stress factor on algal status, specifically that of *Chlorella* sp. [1, 12– 14]. Here, CA medium containing 50 vol% of the metallic eluate was used for FCM analysis. To interpret correlativity among several parameters, FCM data are generally expressed as a 1D histogram and 2D scatter or contour plots. The more parameters an operator uses, the more difficult it becomes for the user to find high correlativity among parameters and to present an effective graph. The PCA method produced new comprehensive axes including several parameters, which have different inclination factors among parameters. The primary (PC1) and secondary (PC2) and tertiary (PC3) components, respectively, reflect 53.4, 34, and 12.6% of information for the data examined in this study (data not shown). **Figure 6A** and **B** presents the principal component loading of PC1 and PC2. Each loading shows that all parameters, including the algal size (FSS-H), red fluorescence intensity (Red-H), and yellow fluorescence (Yellow-H), are positively correlated with PC1 (**Figure 6A**). Particularly, correlation factors for both the algal size and the red fluorescence intensity were more strongly positive with PC1 than the yellow fluorescence intensity was. By contrast, the red fluorescence intensity and the yellow fluorescence intensity, respectively, show inverse and positive correlation with PC2 (**Figure 6B**). The 2D scatter plots using new axes show patterns with individually different vectors treatment dependently, as expressed by the score plot of PC1 versus PC2 (**Figure 6C**). The graph using new axes from PCA helps us to infer strong correlation between a particular parameter and the corresponding one. Consequently, the characteristics of both algal size and red fluorescence intensity are mainly reflected as the variation of algae on the positive PC1 axis (**Figure 6A** and **C**), whereas only yellow fluorescence mainly affected the variation of algae on the positive PC2 axis (**Figure 6B** and **C**). Results show that both the cell size (or red fluorescence intensity) and yellow fluorescence intensity of algae can be indicators that facilitate assessment of the variation for comparison of algae between control and heat treatment (**Figure 6C**), whereas both the cell size and red fluorescence can be indicators for comparison of algae between control and the metallic

The results (**Figure 6**) from PCA analysis prompted us to produce plots of FSS or the red fluorescence for algae versus the yellow fluorescence intensity for algae (**Figure 7**). The 2D-dotted graph of the red versus yellow fluorescence intensity for control algae, for instance, showed

distribution of algae treated with metallic eluate closely resembled that of control, although that with the eluate shifted slightly upward relative to that of control algae [1, 12–14]. In analogy with the result (**Figure 6C**) from PCA analysis, the difference of algae between the control condition and metallic treatment is slight compared to the difference of algae between control

To conduct a precise comparison of algae of control and metallic treatments, the plot of FSS versus red fluorescence for algae was produced (**Figure 8**). Although the dot distribution

on the yellow, whereas that for the heated algae showed

on the yellow. By contrast to the heat treatment, the dot

–10<sup>2</sup>

–10<sup>3</sup>

**Figure 6.** Condition-dependent distribution of *Chlorella* obtained using PCA method. The PCA reduces multidimensional information to arbitrary one-dimensional information and produces new components such as PC1–PC3. Here, factor loading plots of each parameter for PC1 (A) and PC2 (B) are shown. A score plot of PC1 vs. PC2 (C) and that of PC1 vs. red fluorescence intensity of algae (D) were produced using data from different test conditions.

of algal signals between the control and the metallic treatment was almost identical to that of the graph of the red versus the yellow fluorescence (**Figure 7**), both distributions differed on the graph of FSS versus the red fluorescence (**Figure 8**). A distinctive population (arrow in **Figure 8**) was found from algae treated only with metal eluate but not control. Drawing on the result from algal life (cell) cycle (**Figure 4D**), detection of the distinctive population in algae treated with metal eluate indicates that the algal cell cycle proceeds smoothly under the condition with metal eluate. By contrast to algae treated with metal eluate, the cell cycle of control algae seems to reach a stable stage such as a stationary phase, resulting in the near cessation of algal proliferation or extremely low proliferation activity.

In addition to estimation of algal population dynamics using FCM coupled with PCA analysis, direct quantification of algae using hemocytometry was conducted as described in earlier reports [1, 12–14]. The quantification specifically examined whether algal growth dynamics implied from the result of PCA analysis (**Figure 8**) was confirmed on algae

**Figure 7.** Distribution of *Chlorella* obtained using FCM on a graph of the red fluorescence intensity vs. the yellow fluorescence intensity, modified from the literature [1, 12–14]. The heat stress and the metallic treatment samples, respectively, derive from algae treated with heat and algae treated with a metallic solution containing concentrations of eluate of 50 vol%.

**Figure 8.** Distribution of *Chlorella* obtained using FCM on a graph of algal size vs. red fluorescence intensity. The heat stress and the metallic treatment samples, respectively, derive from algae treated with heat and those treated with a metallic solution containing concentrations of eluate of 50 vol%. The arrow indicates the distinctive population detected

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**Figure 9.** Effect of metallic eluate used for this study on algal growth modified from the literature [1, 12, 14]. The dotted

only from those treated with metallic eluate.

line shows the proliferation ratio of control algae.

treated with metallic eluate. **Figure 9** shows the relation between the *Chlorella* proliferation ratio and the concentrations of the metallic eluate from steel slag in the test solution. As described in the explanation of research methods, all nutrient amounts derived from the CA medium, other than elements derived from slag eluate, were constant with each experiment condition. Results show that the number of algae increased according to the concentration of eluate up to 30 vol% (**Figure 9**). The algal numbers under more than 30 vol% of eluate (up to 70 vol%) were almost constant [12–14]. Reportedly, the addition of metallic eluate used for this study increases the concentration of aquatic CO<sup>2</sup> related to photosynthesis of algae [12–14]. The increased aquatic CO<sup>2</sup> , which is found to be related to the presence of Ca2+ in eluate, might improve the rates of photosynthesis and algal proliferation [12–14].

It is noteworthy that approaches using PCA method (mainly **Figure 8**) have already exposed the effects of metallic eluate on algal growth without the proliferation test of algae treated with metallic eluate. Actually, 2–4 cells of autospore (St. 2) and algae after division (St. 3) other than algae at the growth stage (St. 1) were detected from control, whereas all types of algae at each stage (Sts. 1–3) were done from algae treated with metallic eluate (**Figure 10**). Efficient Interpretation of Multiparametric Data Using Principal Component Analysis as… http://dx.doi.org/10.5772/intechopen.71460 93

**Figure 8.** Distribution of *Chlorella* obtained using FCM on a graph of algal size vs. red fluorescence intensity. The heat stress and the metallic treatment samples, respectively, derive from algae treated with heat and those treated with a metallic solution containing concentrations of eluate of 50 vol%. The arrow indicates the distinctive population detected only from those treated with metallic eluate.

treated with metallic eluate. **Figure 9** shows the relation between the *Chlorella* proliferation ratio and the concentrations of the metallic eluate from steel slag in the test solution. As described in the explanation of research methods, all nutrient amounts derived from the CA medium, other than elements derived from slag eluate, were constant with each experiment condition. Results show that the number of algae increased according to the concentration of eluate up to 30 vol% (**Figure 9**). The algal numbers under more than 30 vol% of eluate (up to 70 vol%) were almost constant [12–14]. Reportedly, the addition

**Figure 7.** Distribution of *Chlorella* obtained using FCM on a graph of the red fluorescence intensity vs. the yellow fluorescence intensity, modified from the literature [1, 12–14]. The heat stress and the metallic treatment samples, respectively, derive from algae treated with heat and algae treated with a metallic solution containing concentrations of eluate of 50 vol%.

the presence of Ca2+ in eluate, might improve the rates of photosynthesis and algal prolif-

It is noteworthy that approaches using PCA method (mainly **Figure 8**) have already exposed the effects of metallic eluate on algal growth without the proliferation test of algae treated with metallic eluate. Actually, 2–4 cells of autospore (St. 2) and algae after division (St. 3) other than algae at the growth stage (St. 1) were detected from control, whereas all types of algae at each stage (Sts. 1–3) were done from algae treated with metallic eluate (**Figure 10**).

related to

, which is found to be related to

of metallic eluate used for this study increases the concentration of aquatic CO<sup>2</sup>

photosynthesis of algae [12–14]. The increased aquatic CO<sup>2</sup>

92 Multidimensional Flow Cytometry Techniques for Novel Highly Informative Assays

eration [12–14].

**Figure 9.** Effect of metallic eluate used for this study on algal growth modified from the literature [1, 12, 14]. The dotted line shows the proliferation ratio of control algae.

method can extract information of test objects from data and that it can contribute to effective interpretation of cell characteristics, even if the data include several optical parameters from

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This research was mainly supported by a Grant for Young Scientists from the Iron and Steel Institute of Japan and partly by a Grant-in-Aid for Exploratory Research from Japan Society

for the Promotion of Science (KAKENHI Grant Numbers 23658280 and 17 K05955).

Department of Chemical Science and Engineering, National Institute of Technology,

[1] Takahashi T. Quality assessment of microalgae exposed to trace metals using flow cytometry. In: Shiomi N, Waisundara VY, editors. Superfood and Functional Food – Development of Superfood and its Role in Medicine. Croatia: InTechOpen; 2017. p. 29-45

[2] Baumgarth N, Roederer M. A practical approach to multicolor flow cytometry for immu-

[3] Spolaore P, Joannis-Cassan C, Duran E, Isambert A. Commercial applications of microalgae. Journal of Bioscience and Bioengineering. 2006;**101**:87-96. DOI: 10.1263/jbb.101.87 [4] Arashida R. Characteristics of the microalgae euglena and its applications in foods and ecological fields. The Japan Society of Photosynthesis Reserach. 2012;**22**:33-38

[5] Chisti Y. Biodeisel from microalgae. Biotechnology Advances. 2007;**25**:294-306. DOI:

[6] Mallick N. Biotechnological potential of immobilized algae for wastewater N, P and metal removal: A review. Biometals. 2002;**15**:377-390. DOI: 10.1023/A:1020238520948 [7] Hameed MSA, Ebrahim OH. Biotechnological potential uses of immobilized algae.

[8] Nriagu JO, Pacyna JM. Quantitative assessment of worldwide contamination of air, water and soils by trace metals. Nature. 1988;**333**:134-139. DOI: 10.1038/333134a0

nophenotyping. Journal of Immunological Methods. 2000;**243**:77-97

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Address all correspondence to: mttaka@cc.miyakonojo-nct.ac.jp

Miyakonojo College, Miyakonojo, Miyazaki, Japan

10.1016/j.biotechadv.2007.02.001

multiparametric FCM.

**Acknowledgements**

**Author details**

Toshiyuki Takahashi

**References**

**Figure 10.** Effects of metallic eluate used for this study on algal growth. Here, each dotted graph in this figure is made from **Figure 8**.

Consequently, the cell cycle of algae treated with metallic eluate could continue to proceed smoothly even for algae after 7-day incubation when the control algae proliferation activity occurred at a low rate.

## **5. Conclusion**

Multicolor FCM systems enable us to analyze up to a dozen multiparameters in a single assay and realize high-throughput measurement in life science. Countervailing the advantages of multiparametric FCM, multiparametric data make it difficult to interpret the resultant complicated information. Although multiparametric FCM is attractive relative to single or little parametric FCM in terms of cost performance and saving time of experiments, those benefits are meaningless unless the method leads to accurate and clear conclusions from multiparametric data. To elicit clear patterning graphs from FCM data and to grasp the essence of the data, this study examined the usefulness of PCA method of multivariate analysis. Comparison of control algae with several algae treated with test conditions such as heat and metallic eluate was conducted using FCM. To ascertain differences between control and test conditions about algal properties, FCM data were subjected to PCA analysis. Consequently, results from PCA analysis imply that both the red fluorescence intensity and the yellow one of algae can be an indicator for assessment of the variation for comparison of algae between control and heat treatment (**Figure 6C**), whereas both the cell size and the red fluorescence of algae can be an indicator for comparison of algae between control and metallic treatment (**Figure 6D**). It is striking that approaches coupled with PCA analysis have already exposed the effects of metallic eluate on algal growth with no proliferation test of algae. The result reveals that the low concentrations of metallic eluate used for this study induce algae to increase for a more prolonged period than in the control condition. Results show that PCA method can extract information of test objects from data and that it can contribute to effective interpretation of cell characteristics, even if the data include several optical parameters from multiparametric FCM.
