Multiparametric flow cytometry (FCM) realizes high-throughput measurement, but multiparametric data make it difficult to interpret the complicated information. To present clear patterning graphs from FCM data, one must grasp the essence of the data. This study estimated the usefulness of principal component analysis (PCA), which reduces multi-dimensional information to arbitrary one-dimensional information. Recently, microalgae have attracted the attention of pharmaceutical, cosmetic, and food companies. Taking alga Chlorella as an example, this chapter presents the usefulness of PCA for the evaluation of algal quality using FCM. To evaluate the algal status effectively, Chlorella (control), heated algae, and metallic-treatment algae were prepared and quantified using FCM. FCM data were subjected to PCA analysis. To interpret correlativity among parameters, FCM data are generally expressed as histograms and scatter or contour plots. An operator using multiple parameters has difficulty finding high correlativity among parameters and presenting an effective graph. The PCA method produced new comprehensive axes with different inclination factors among parameters. Scatter plots using new axes showed patterns treatment dependently with different vectors. Results show that the PCA method can extract information of test objects from data and that it can contribute to effective interpretation of cell characteristics, even if data include multiparameters from FCM.
- flow cytometry
- multivariable analysis
- cell status
- cell cycle
- trace metal elements
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 served as a powerful and invaluable tool in fields such as cell biology, microbiology, protein engineering, and health care . 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 . 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 five-color 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 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
2. FCM analysis of microalgae
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  and biofuels . 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 . 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
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, 10, 11, 12, 13, 14, 15]. Figure 4A–C portrays
3. Research methods
This study investigated the algal status such as viability using FCM after treatment of algae with the test condition. For this study, the author used
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
|Origin of slag||Eluate of stainless steel slag||Environmental quality standards|
|Soil pollution||Marine pollution||Water pollutant||Effluent standard||Drinking water standard|
|Total As||ND1 (RDL2: 0.001)||0.01||0.1||0.01||0.1||0.01|
|Total B||0.16||1||14||104, 2305||1|
|Total Be||ND (RDL: 0.0005)||2.5|
|Total Cd||ND (RDL: 0.0001)||0.01||0.1||0.01||0.036||0.003|
|Chromium (VI)||ND (RDL: 0.005)||0.05||0.5||0.05||0.5||0.05|
|Total Pb||ND (RDL: 0.0005)||0.01||0.1||0.01||0.1||0.01|
|Hg||ND (RDL: 0.0001)||0.0005||0.005||0.0005||0.005||0.0005|
|Total V||ND (RDL: 0.001)||1.5|
|Total Zn||0.099||2||0.037, 0.028, 0.019||2||1|
|F−||ND (RDL: 0.1)||0.8||15||0.84||84, 155||0.8|
|Total P||ND (RDL: 0.1)||0.005–111
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) (Figure 4C) [1, 11, 12, 14].
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 dataset from a new axis produced by PCA . According to results of the correlation matrix analysis for the data, the author calculated the contribution rate of each component, the factor loading of each parameter, and the score plot of each component. Here, each factor loading (PC1-3) generally indicates correlation factors between each parameter and each component (Figure 5). The statistical results obtained using PCA were interpreted to evaluate the algal status between control and test conditions.
After treatment of algae with and without eluate, the algae were quantified using hemocytometry. Here, CA medium containing several concentrations (0–70 vol%) of the metallic eluate was used for the experiment using hemocytometry. The algal proliferation ratio (average ± standard error) was expressed as a proportion of the number of algae treated with eluate to that of control without eluate [1, 12, 14].
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
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 102–103 on the red channel and 101–102 on the yellow, whereas that for the heated algae showed 101–102 on the red channel and 101–103 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, 13, 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).
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 distributionof 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, 13, 14]. The quantification specifically examined whether algal growth dynamics implied from the result of PCA analysis (Figure 8) was confirmed on algae treated with metallic eluate. Figure 9 shows the relation between the
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). 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.
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.
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).
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