**Photosynthetic Pigments Changes of Three Phenotypes of Picocyanobacteria** *Synechococcus* **sp. under Di**ff**erent Light and Temperature Conditions**

#### **Sylwia Sliwi ´ ´ nska-Wilczewska 1,\*, Zofia Konarzewska 1, Kinga Wi´sniewska <sup>2</sup> and Marta Konik <sup>3</sup>**


Received: 16 August 2020; Accepted: 1 September 2020; Published: 3 September 2020

**Abstract:** It is estimated that the genus *Synechococcus* is responsible for about 17% of net primary production in the Global Ocean. Blooms of these organisms are observed in tropical, subtropical and even temperate zones, and they have been recorded recently even beyond the polar circle. The long-term scenarios forecast a growing expansion of *Synechococcus* sp. and its area of dominance. This is, among others, due to their high physiological plasticity in relation to changing environmental conditions. Three phenotypes of the genus *Synechococcus* sp. (Type 1, Type 2, and Type 3a) were tested in controlled laboratory conditions in order to identify their response to various irradiance (10, 55, 100 and 145 μmol photons m−<sup>2</sup> s<sup>−</sup>1) and temperature (15, 22.5 and 30 ◦C) conditions. The highest total pigment content per cell was recorded at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> at all temperature variants with the clear dominance of phycobilins among all the pigments. In almost every variant the highest growth rate was recorded for the Type 1. The lowest growth rates were observed, in general, for the Type 3a. However, it was recognized to be less temperature sensitive in comparison to the other two types and rather light-driven with the highest plasticity and adaptation potential. The highest amounts of carotenoids were produced by Type 2 which also showed signs of the cell stress even around 55 μmol photons m−<sup>2</sup> s−<sup>1</sup> at 15 ◦C and 22.5 ◦C. This may imply that the Type 2 is the most susceptible to higher irradiances. Picocyanobacteria *Synechococcus* sp. require less light intensity to achieve the maximum rate of photosynthesis than larger algae. They also tolerate a wide range of temperatures which combined together make them gain a powerful competitive advantage. Our results will provide key information for the ecohydrodynamical model development. Thus, this work would be an important link in forecasting future changes in the occurrence of these organisms in the context of global warming.

**Keywords:** abiotic stressors; environmental stress; growth; light intensity; photosynthetic pigments; picocyanobacteria; plant physiology

#### **1. Introduction**

The discovery of autotrophic picoplankton in the late 1970s [1,2] has contributed to numerous scientific studies on these organisms and demonstrated their significant role as a missing link in the carbon cycle and a major producer in oceanic waters [3]. Many researchers proved that picoplankton also plays an important role in more productive waters, often exceeding the abundance and biomass of other phytoplankton species [4]. The genus *Synechococcus* is a polyphyletic group of picoplanktonic cyanobacteria that constitutes one of the major contributors to oceanic primary production [5,6] and is a key worldwide distributed component of marine planktonic communities [7]. It is estimated that for about 17% of net primary production in the Global Ocean is responsible solely the genus *Synechococcus* [8]. Blooms of these organisms are observed in tropical, subtropical and even temperate zones [9]. The present global warming causes temperature rise which was recognized as a main cause of the massive shift of species northwards [10]. Furthermore, *Synechococcus* has been recorded far beyond the polar circle, e.g., dragged with a strong Atlantic inflow in 2014, as far as 82.5◦ N [11]. In the future ocean scenarios, a growing expansion of *Synechococcus* sp. and its area of dominance is forecasted [8,12]. A significant increase in the frequency of their blooms has already been detected [9]. This is, among others, due to their high physiological plasticity in relation to changing environmental conditions [13]. Organisms from the genus *Synechococcus* are represented by three phenotypes that complement each other and fill tightly the ecological niche due to varying photosynthetic pigment profiles and high chromatic adaptation potential.

The photosynthetic pigment observed in cells of picoplanktonic cyanobacteria is chlorophyll *a* (Chl *a*), carotenoid (Car) pigments, and phycobiliproteins (Phyco) [14]. Chl *a* is the most important pigment because it controls photosynthesis and this transformation of the absorbed energy from sunlight into chemical compounds determines the biomass growth rates [14]. The most dominant Car pigment is zeaxanthin (Zea), representing 40% to 80%. The presence of cell-specific Zea content in *Synechococcus* sp. and high Zea/Chl *a* ratios may be regarded as a diagnostic feature [15]. Besides Zea, β-carotene (β-Car) is also present among Car pigments [16]. Car pigments play an important photoprotective role against damage to the photosystem [17]. Furthermore, cells of picocyanobacteria contain accessory phycobilin pigments instead of the additional chlorophylls that are common among other phytoplankton organisms. There are three types of Phyco containing: phycoerythrin (PE), phycocyanin (PC), and allophycocyanin (APC), which absorb green, yellow-orange, and red light, respectively [18]. In cyanobacterial cells, Phyco are organized into aggregates consisting of many subunits called phycobilisomes, which are connected in regular rows to the surface of thylakoid membranes. The main component of the core complex is APC while PE is located in the peripheral parts of these formations [19]. Phyco absorb light in the 500−650 nm range and provide additional energy to photosynthetic centers. The transfer process is highly efficient and reaches 80−90% of the energy absorbed by phycobilin pigments. Their role is vital, especially in case of any light shortages to maintain high photosynthesis rate which guarantees cyanobacteria competitive advantage in low-light conditions. The red PE absorbs the blue-green light that penetrates the deepest into the water column. It enables conducting photosynthesis even at the bottom of the euphotic zone. The deeper live an organism, the more PE it contains and the higher is the PE to Chl *a* pigment ratio. In the cells of cyanobacteria living in the upper layers of the ocean the dominant pigments are the blue PC and APC [19].

The distinction between the three main identified phenotypes of the genus *Synechococcus* is based on the phycobilin pigments composition [20,21]. Six et al. [22] in their research presented a classification that divides marine *Synechococcus* to Type 1, Type 2, and Type 3. Organisms with the dominance of PC were classified as Type 1. Type 2 incorporates phenotypes with a dominance of PE, more specifically PEI, while Type 3 consists of organisms in which PC, as well as PEI and PEII, dominates in phycobilisomes. Furthermore, Six et al., [22] divided Type 3 into four subcategories from a to d, according to the increasing phycoerythrobilin (PEB) and phycocyanobilin (PCB) ratios. Organisms with high levels of PE are found mainly in oligotrophic oceans, while green (PC-rich) phenotypes prefer turbid freshwater [23,24]. In general, picocyanobacteria prefer lower irradiance intensity to reach the maximum rate of photosynthesis than larger algae [25]. Furthermore, studies have shown that the reduction of radiation intensity does not change the efficiency of carbon incorporation during photosynthesis, as is the case with larger plant organisms that exceed 3 μm. Marine *Synechococcus* sp. is able to saturate photosynthesis and growth rates at very low radiation [26]. Under culture conditions, some strains of picoplankton have shown the ability to survive and grow again after periods of total

darkness [27,28]. Platt et al. [29] observed photosynthetic picoplankton at a depth of 1000 m in the depths of the eastern Pacific Ocean and Cai et al. [30] confirmed the presence of small populations of *Synechococcus* sp. in the Chesapeake Bay during winter months. Furthermore, Ernst [31] isolated *Synechocystis* sp. (Maple BO 8402) from the Lake Constance with a different type of pigmentation than any described so far. This strain contained Phyco similar to the PC, characterized by very strong red fluorescence occurring after stimulation of the cells with wavelengths of 600 nm but also with wavelengths of 436 and 546 nm [32]. Most cyanobacteria, especially those living all year round in coastal ocean waters, contain PE [23,33,34].

The main aim of this study was to determine the acclimatization capacity of three Baltic phenotypes of *Synechococcus* sp.: Type 1, Type 2, Type 3a. Furthermore, the study focused on the effect of irradiance, temperature, and their mutual interactions on the content and proportions of cell-specific photosynthetic pigments of the examined cyanobacterial phenotypes. The cell-specific Chl *a* and Car content was determined by the HPLC method, whereas the content of Phyco was determined by the spectrophotometric method. The detailed characterization of the quantitative and qualitative composition of pigments is important to determine the level of acclimatization of the examined phenotypes of cyanobacteria to specific environmental conditions. The knowledge of biology and especially the physiology of these organisms by capturing their reactions to various environmental factors is important for forecasting the possible expansion of these organisms.

#### **2. Results**

#### *2.1. The Cell Concentration and the Growth Rate of Three Synechococcus sp. Phenotypes under Di*ff*erent Culture Conditions*

In this study, the concentration of picocyanobacterial cells as well as the growth rate under different irradiance and temperature conditions were determined for the three studied phenotypes of *Synechococcus* sp. (Type 1, Type 2, and Type 3a). In general, factorial tests showed that both irradiance and temperature significantly affected the number of cells of three *Synechococcus* sp. phenotypes (ANOVA, *p* < 0.001, *p* < 0.01, *p* < 0.01, for Type 1, Type 2, and Type 3a, respectively; Table S1). Moreover, ANOVA results indicated that for each picocyanobacteria phenotype the effect of temperature on the culture concentration was higher than the influence of irradiance and the interaction of both factors (Table S1). The highest picocyanobacterial cell numbers (59.5 <sup>×</sup> 107 and 60.2 <sup>×</sup> <sup>10</sup><sup>7</sup> cell mL<sup>−</sup>1) was noted for *Synechococcus* sp. Type 1 at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 55 μmol photons m−<sup>2</sup> s−1, respectively and 30 ◦C (Figure 1Aa), and it was about 4-fold higher that the minimum values observed in 15 ◦C and <sup>145</sup> <sup>μ</sup>mol photons m−<sup>2</sup> <sup>s</sup>−<sup>1</sup> (15.2 <sup>×</sup> <sup>10</sup><sup>7</sup> cell mL<sup>−</sup>1). For *Synechococcus* sp. Type 2 (Figure 1Ba) and Type 3a (Figure 1Ca) the maximum cell concentration were recorded at the temperature of 22.5 ◦C and 30 ◦C, respectively. Moreover, the highest picocyanobacterial cell numbers for Type 2 was found at irradiance 55 <sup>μ</sup>mol photons m−<sup>2</sup> s−<sup>1</sup> (49.4 <sup>×</sup> 10<sup>7</sup> cell mL−1), whereas for Type 3a at <sup>10</sup> <sup>μ</sup>mol photons m−<sup>2</sup> <sup>s</sup>−<sup>1</sup> (25.8 <sup>×</sup> <sup>10</sup><sup>7</sup> cell mL<sup>−</sup>1). For both phenotypes, similar to Type 1, the minimum number of cells were obtained at 15 ◦C and 145 <sup>μ</sup>mol photons m−<sup>2</sup> <sup>s</sup>−<sup>1</sup> (about 9.7 <sup>×</sup> 107 and 6.5 <sup>×</sup> 107 cell mL<sup>−</sup>1, respectively).

It was found that analyzed phenotypes of *Synechococcus* sp. showed different growth rates (μ) under different temperature and light conditions. For *Synechococcus* sp. Type 1, Type 2, and Type 3a the highest growth rate was recorded at the highest temperature (30 ◦C). Moreover, the highest growth rate for Type 1 (Figure 1Ab) and Type 2 (Figure 1Bb) was noted at 55 μmol photons m−<sup>2</sup> s−<sup>1</sup> (0.457, 0.443, respectively) whereas for Type 3a at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> (0.396; Figure 1Cb). On the other hand, for Type 1, Type 2, and Type 3a, the shortest growth rate (0.359, 0.327, 0.298, respectively) was obtained at 15 ◦C and 145 μmol photons m−<sup>2</sup> s<sup>−</sup>1.

**Figure 1.** Changes in the number of cells (N <sup>×</sup> 10<sup>7</sup> mL<sup>−</sup>1; **<sup>a</sup>**) and the growth rate (μ; **<sup>b</sup>**) obtained after 14 days of experiment for three phenotypes of *Synechococcus* sp.: Type 1 (**A**), Type 2 (**B**), Type 3a (**C**) under different irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1) and temperature (◦C) conditions.

#### *2.2. The Total Pigments Content for Three Phenotypes of the Genus Synechococcus*

The acclimation mechanisms of three *Synechococcus* sp. phenotypes was characterized by the concentration of changes in composition and proportion of photosynthetic pigments i.e., chlorophyll *a* (Chl *a*), zeaxanthin (Zea), β-carotene (β-Car), phycoerythrin (PE), phycocyanin (PC), and allophycocyanin (APC) under different light (μmol photons m−<sup>2</sup> s−1) and temperature ( ◦C) conditions. In this work, the composition and proportions of Chl *a* and Car pigments (Zea and β-Car) of three *Synechococcus* sp. phenotypes were determined by HPLC method, while the content of phycobilins (Phyco) were determined by spectrophotometric method.

Both light and temperature significantly affected the cell-specific Chl *a* content of *Synechococcus* sp. Type 1, Type 2, and Type 3a (ANOVA, *p* < 0.001, for all) and Phyco content (ANOVA, *p* < 0.001, *p* < 0.001, and *p* < 0.001, for Type 1, Type 2, and Type 3a, respectively). Moreover, these factors significantly affected the cell-specific Car content of *Synechococcus* sp. phenotypes (ANOVA, *p* < 0.001, *p* < 0.001, *p* < 0.001 for Type 1, Type 2, and Type 3a, respectively; Table S2). Generally, ANOVA results indicated that the effect of irradiance on the Chl *a* and Phyco concentration for picocyanobacteria phenotypes was higher than the influence of temperature and the interaction of the two factors (Table S2). In contrast, the cell-specific Car content of *Synechococcus* sp. Type 1, Type 2, and Type 3a was more affected by temperature and the interaction of the two factors than by irradiance (Table S2).

The maximum cell-specific concentration of Chl *<sup>a</sup>* (about 8.11 pg·cell<sup>−</sup>1) was noted for *Synechococcus* sp. Type 3a at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> light intensity and 15 ◦C, and it was about 5.5 times higher than the minimum at 145 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 30 ◦C (Figure 2Ca). For *Synechococcus* sp. Type 1 and Type 2 the maximum cell-specific Chl *<sup>a</sup>* concentrations (4.51 pg·cell−<sup>1</sup> and 4.82 pg·cell<sup>−</sup>1, respectively) were recorded at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C for Type 1 and 30 ◦C for Type 2. On the other hand, the minimum values for these phenotypes were obtained at 145 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 30 ◦C (0.68 pg·cell−<sup>1</sup> and 0.67 pg·cell<sup>−</sup>1, respectively; Figure 2Aa−Ba).

On the basis of the results obtained in this study, it was found that the analyzed phenotypes were characterized by a similar maximum cell-specific Car content. It was also shown that cell-specific Car content was the lowest among all analyzed photosynthetic pigments. The total Car content for *Synechococcus* sp. Type 1, Type 2, and Type 3a constituted approximately 7%, 11%, and 12% of the sum of Chl *a* and Phyco, respectively. It was also found that for Type 2 (Figure 2Bb) and Type 3a (Figure 2Cb) the maximum cell-specific Car content (2.01 pg·cell−<sup>1</sup> and 2.25 pg·cell<sup>−</sup>1, respectively) were recorded at 190 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 30 ◦C. By contrast, the minimum values of cell-specific Car content

were obtained at 100 <sup>μ</sup>mol photons m−<sup>2</sup> s−<sup>1</sup> and 22.5 ◦C (1.20 pg·cell<sup>−</sup>1, for Type 2 and 0.60 pg·cell<sup>−</sup>1, for Type 3a). On the other hand, for *Synechococcus* sp. Type 1, the reported maximum value of cell-specific Car content (1.74 pg·cell−1) at 100 <sup>μ</sup>mol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C was approximately 4-fold higher compared to the recorded minimum values at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 30 ◦C (Figure 2Ab).

It was noted that the total Phyco pigments were always greater than cell-specific Chl *a* and Car content of the three examined *Synechococcus* sp. phenotypes. The study found that the total Phyco content for *Synechococcus* sp. Type 1, Type 2, and Type 3a constituted about 80%, 75%, and 65% of the sum of Chl *a* and Car, respectively. The highest cell-specific Phyco content was measured in *Synechococcus* sp. Type 2 (45.90 pg·cell<sup>−</sup>1) at 10 <sup>μ</sup>mol photons m−<sup>2</sup> <sup>s</sup>−<sup>1</sup> and 30 ◦C (Figure 2Bc) while the minimum values of these pigments was noted at 55 <sup>μ</sup>mol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C (2.70 pg·cell−1). The greatest decrease in the cell-specific Phyco content was noted for *Synechococcus* sp. Type 1 (Figure 2Ac), which under minimal conditions (100 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C) was about 30 times lower than the recorded under maximum values at 10 <sup>μ</sup>mol photons m−<sup>2</sup> <sup>s</sup>−<sup>1</sup> and 30 ◦C (33.56 pg·cell<sup>−</sup>1). In turn, *Synechococcus* sp. Type 3a showed the highest resistance to light and temperature, and its decrease in the cell-specific Phyco content under minimal conditions (145 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C) was about 12.7 times lower (2.25 pg·cell<sup>−</sup>1) than the recorded under maximum values (10 <sup>μ</sup>mol photons m−<sup>2</sup> <sup>s</sup>−<sup>1</sup> and 22.5 ◦C; Figure 2Cc).

**Figure 2.** Changes in content (pg·cell<sup>−</sup>1) of Chl *a* (**a**), sum of total Car (**b**), and sum of total Phyco (**c**) obtained after 14 days of experiment for three phenotypes of *Synechococcus* sp.: Type 1 (**A**), Type 2 (**B**), Type 3a (**C**) under different irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1) and temperature (◦C) conditions.

#### *2.3. E*ff*ect of Irradiance and Temperature on Phycocyanin, Phycoerythrin, and Allophycocyanin Content*

The presence of phycoerythrin (PE), phycocyanin (PC), and allophycocyanin (APC) was demonstrated for all picocyanobacterial phenotypes by spectrophotometric analysis. It was found that irradiance and temperature as well as their interaction significantly affected the cell-specific PE content of *Synechococcus* sp. (ANOVA, *p* < 0.001, for Type 1, Type 2, and Type 3a), PC content (ANOVA, *p* < 0.001, fot Type 1, *p* < 0.001, for Type 2, and *p* < 0.001, for Type 3a) and APC content (ANOVA, *p* < 0.001, *p* < 0.01, and *p* < 0.05, for Type 1, Type 2, and Type 3a, respectively; Table S3). ANOVA indicated that for most of *Synechococcus* sp. phenotypes, the effect of irradiance on PE was higher than the effect of temperature. In contrast, the PC and APC content of analyzed phenotypes was more affected by temperature than by irradiance and by the interaction of both factors (Table S3).

In all the phenotypes, the cell-specific (pg·cell−1) PE, PC, and APC pigment contents were environmentally driven (Figure 3). The cell-specific PE content increased with decrease of irradiance and increase of the temperature, reaching the highest values at the intensity of 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and temperature 22.5 ◦C (21.16 pg·cell−<sup>1</sup> for Type 3a; Figure 3Ca) and 30 ◦C (8.59 pg·cell−<sup>1</sup> for Type 1 and 40.35 pg·cell−<sup>1</sup> for Type 2; Figure 3Aa,Ba). Under these conditions, the PE in the cells of the tested picocyanobacteria increased approximately 20.0-fold, 19.7-fold, and 13.6-fold, for Type 1, Type 2, and Type 3a, respectively, compared with the observed minimum values at 100–145 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C.

**Figure 3.** Changes in content (pg·cell<sup>−</sup>1) of PE (**a**), PC (**b**), and APC (**c**) obtained after 14 days of experiment for three phenotypes of *Synechococcus* sp.: Type 1 (**A**), Type 2 (**B**), Type 3a (**C**) under different irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1) and temperature (◦C) conditions.

On the basis of the conducted analyzes, it was found that the conditions under which the *Synechococcus* sp. Type 1 and Type 2 achieved the highest concentrations of the cell-specific PC were the low light intensity of 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and a high temperature of 30 ◦C. On the other hand, for Type 3a the maximal value of this pigment was noted at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C. The highest concentration value of PC pigments under optimal conditions was observed for *Synechococcus* Type 1 (20.95 pg·cell<sup>−</sup>1; Figure 3Ab), and the lowest for *Synechococcus* Type 2 (4.64 pg·cell<sup>−</sup>1; Figure 3Bb). The greatest decrease in cell-specific PC (about 64-fold) was noted for *Synechococcus* Type 1. However, the least susceptible to analyzed factors was *Synechococcus* Type 3a, with a 10-fold decrease in PC pigments (Figure 3Cb).

The highest cell-specific APC content (4.34 pg·cell<sup>−</sup>1) was recorded for *Synechococcus* sp. Type 1 in the 55 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 30 ◦C (Figure 3Ac). For these light and temperature conditions, over 18-fold increase was observed in relation to the lowest recorded values at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup>

and 15 ◦C. For *Synechococcus* sp. Type 2 and Type 3a the maximum cell-specific APC concentrations (1.09 pg·cell−<sup>1</sup> and 1.98 pg·cell−1, respectively) were recorded at 55−100 <sup>μ</sup>mol photons m−<sup>2</sup> s−<sup>1</sup> and 22.5−30 ◦C. On the other hand, the minimum values for these phenotypes were obtained at 145 μmol photons m−<sup>2</sup> <sup>s</sup>−<sup>1</sup> and 15 ◦C (0.28 pg·cell−<sup>1</sup> for Type 2 and 0.44 pg·cell<sup>−</sup>1, for Type 3a; Figure 3Bc,Cc).

#### *2.4. E*ff*ect of Irradiance and Temperature on Zeaxanthin and* β*-carotene*

On the basis of the results, the effect of irradiance and temperature on changes in individual Car pigments in the cells of the picocyanobacterial phenotypes was determined. In all the *Synechococcus* sp. phenotypes, the cell-specific (pg·cell<sup>−</sup>1) pigment contents were environmentally driven (Figure 4). In the most of three tested phenotypes, the cell-specific concentrations of Zea (ANOVA, *p* < 0.001, *p* < 0.001, *p* < 0.001 for Type 1, Type 2, and Type 3a, respectively) and β-Car (ANOVA, *p* < 0.001, *p* < 0.01, *p* > 0.05 for Type 1, Type 2, and Type 3a, respectively) were affected by irradiance and temperature (Table S4). ANOVA indicated that in Type 1 and Type 3a, the effect of temperature on Zea was higher than the effect of irradiance. In contrast, the Zea content of Type 2 was more affected by irradiance than by temperature and by the interaction of both factors. It was also noted that for all tested phenotypes, effect of irradiance on β-Car was not statistically significant (Table S4).

**Figure 4.** Changes in content (pg·cell<sup>−</sup>1) of Zea (**a**) and <sup>β</sup>-Car (**b**) obtained after 14 days of experiment for three phenotypes of *Synechococcus* sp.: Type 1 (**A**), Type 2 (**B**), Type 3a (**C**) under different irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1) and temperature (◦C) conditions.

The highest Zea content for *Synechococcus* sp. Type 2 and Type 3a (1.85 pg·cell−<sup>1</sup> and 2.11 pg·cell<sup>−</sup>1, respectively) was noted at 100 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 30 ◦C while the lowest value of this pigment were 1.02 pg·cell−<sup>1</sup> for Type 2 and 0.53 pg·cell−<sup>1</sup> for Type 3a at 55 <sup>μ</sup>mol photons m−<sup>2</sup> s−<sup>1</sup> and 22.5 ◦C (Figure 4Ba,Ca). Moreover, the highest value of Zea content for Type 1 was found at irradiance 55 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C (1.68 pg cell−1) while the minimum Zea content was obtained at 30 ◦C and 10 <sup>μ</sup>mol photons m−<sup>2</sup> s−<sup>1</sup> (0.37 pg·cell<sup>−</sup>1; Figure 4Aa). The highest values of <sup>β</sup>-Car in Type 2 and Type 3a were noted at 55 <sup>μ</sup>mol photons m−<sup>2</sup> <sup>s</sup>−<sup>1</sup> and 15 ◦C and 30 ◦C (0.32 pg·cell−<sup>1</sup> and 0.40 pg·cell<sup>−</sup>1, respectively; Figure 4Bb,Cb). In turn, the lowest content of <sup>β</sup>-Car being found in Type 1 (0.12 pg·cell<sup>−</sup>1) at 145 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C (Figure 4Ab).

#### *2.5. E*ff*ect of Irradiance and Temperature on Pigments Ratios*

Light and temperature as well as their interaction were found to significantly affect the Zea/Chl *a* ratio only in *Synechococcus* sp. Type 2 (ANOVA, *p* < 0.001) and the effect of light was higher than the effect of temperature and the interaction of both factors (Table S5). On the other hand, irradiance and temperature as well as their interaction significantly affected the β-Car/Chl *a* ratio in three *Synechococcus* sp. phenotypes (ANOVA, *p* < 0.001, *p* < 0.01, and *p* < 0.001, for Type 1, Type 2, and Type 3, respectively). ANOVA indicated that in Type 1 and Type 2, the effect of light on β-Car/Chl *a* ratio was higher than the effect of temperature. In contrast, the β-Car/Chl *a* ratio of Type 3a was more affected by temperature than by irradiance and by the interaction of both factors (Table S5). The highest values of Zea/Chl *a* ratio in *Synechococcus* sp. Type 2, at the 145 μmol photons m−<sup>2</sup> s−<sup>1</sup> and the temperature of 30 ◦C (2.3; Figure 5Ba) was about 11 times higher than the lowest values observed at the light intensity of 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 30 ◦C. In turn, the lowest value of Zea/Chl *a* ratio was noted in Type 3a under the same light and temperature conditions (0.8; Figure 5Ca). Besides, the highest β-Car/Chl *a* ratio was also observed for *Synechococcus* sp. Type 2, which at the irradiance of 145 μmol photons m−<sup>2</sup> s<sup>−</sup>1, and the temperature of 15 ◦C was 0.19 (Figure 5Bb). On the other hand, the lowest pigments ratio was recorded for *Synechococcus* sp. Type 3a, which under the same light and temperature conditions was 0.14 (Figure 5Cb).

**Figure 5.** Changes in Zea/Chl *a* ratio (**a**) and β-Car/Chl *a* ratio (**b**) obtained after 14 days of experiment for three phenotypes of *Synechococcus* sp.: Type 1 (**A**), Type 2 (**B**), Type 3a (**C**) under different irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1) and temperature (◦C) conditions.

Since Phyco pigments participate in the transfer of excitation energy to Chl *a* in photosystems, the analysis of changes in these pigments in relation to Chl *a* and Car was also performed (Table S6). It was found that irradiance and temperature as well as their interaction significantly affected the Phyco/Chl *a* ratio in *Synechococcus* sp. Type 1, Type 2, and Type 3 (ANOVA, *p* < 0.001, *p* < 0.001, and *p* < 0.01, respectively) and Phyco/Car ratio (ANOVA, *p* < 0.001, *p* < 0.001, and *p* < 0.001 for Type 1, Type 2, and Type 3a, respectively). ANOVA indicated that in Type 1 and Type 2, the effect of temperature on Phyco/Chl *a* ratio was higher than the effect of irradiance and the interaction of both factors. In turn, the Phyco/Chl *a* ratio of Type 3a was more affected by irradiance than by temperature. For Phyco/Car ratio the effect of temperature for three analyzed phenotypes was higher than the effect of irradiance and the interaction of both factors (Table S7).

The highest Phyco/Chl *a* ratio and Phyco/Car ratio were observed for *Synechococcus* sp. Type 1, which at the light intensity of 55 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and the temperature of 30 ◦C was 16.5 and 62.5, respectively. Moreover, the highest values of these pigment ratio in Type 1 was about 33 times and 125 times, respectively higher than the lowest values observed at the light intensity of 100 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C. Conversely, for *Synechococcus* sp. Type 3a the lowest values of Phyco/Chl *a* ratio as well as Phyco/Car ratio were found at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 22.5 ◦C (5.0 and 21.1, respectively) and were about 7 and 21 times higher, respectively, than the minimums obtained at PAR 100 μmol photons m−<sup>2</sup> s−<sup>1</sup> and 15 ◦C (Table S6).

#### **3. Discussion**

#### *3.1. Occurrence and Abundance of Picocyanobacteria under Changing Irradiance and Temperature Conditions*

Changes in the number of cells of photoautotrophic organisms inhabiting surface waters are the result of the interaction of several physical and chemical environmental factors [35]. Light and temperature play a key role in the occurrence of autotrophic picoplankton [32] and are the main factors causing the appearance of cyanobacteria both at depths and in coastal waters [36,37]. Additionally, light and temperature may be more important abiotic factors influencing the occurrence of picocyanobacteria than the availability of nutrients [36]. In spring, the number of autotrophic picoplankton cells begin to increase which is triggered by the temperature increase due to more intensive insolation of the surface water layers. Their growth reaches its maximum values during summer [36]. Gławdel et al. [38] showed that in the coastal waters of the southern Baltic Sea during the summer period, the autotrophic picoplankton, composed mainly of cyanobacteria in the total biomass exceeded even bacterioplankton. Three phenotypes of picocyanobacteria of the genus *Synechococcus* (Type 1, Type 2, and Type 3a) were isolated from the southern Baltic Sea. This area is characterized by large changes of environmental conditions. Autotrophic organisms living in such a variable ecosystem show the ability to quickly adapt which is essential for their survival. In this work, the influence of temperature and PAR irradiance on the autecology of the investigated phenotypes of *Synechococcus*: Type 1, Type 2, and Type 3a were demonstrated.

It was found that the increasing intensity of light had a negative effect on the cell concentration of the three studied phenotypes of *Synechococcus* sp. The number of picocyanobacteria cells increased as the PAR irradiance decreased, reaching the maximum value in the range of 10–55 μmol photons m−<sup>2</sup> s−<sup>1</sup> and the minimum value at 145 μmol photons m−<sup>2</sup> s−1. Besides, it was shown that *Synechococcus* sp. Type 2 was the most susceptible to high light intensity. Its number of cells was more than 5-fold lower in high light compared to low light. On the other hand, the cell number decreased about 4-fold in the high light compared to low light for both *Synechococcus* sp. Type 1 and Type 3a. Literature data also indicated that picocyanobacteria of the genus *Synechococcus* in natural aquatic communities are adapted to low light and show maximum growth in the deeper layers of the euphotic zone [26,29,33,39,40]. The high abundance of autotrophic picoplankton was recorded even at a depth of 90 m [33]. This may indicate the ability of these organisms to survive seasonal changes and their fall into the aphotic zone. Besides, it is considered that *Synechococcus* sp. found in natural surface water layers may show photoinhibition of growth under high light [29,39,41] as well as the low rate of photosynthesis in the surface layer compared to greater depths [33,39]. On the other hand, Sliwi ´nska-Wilczewska et al. ´ [13] showed that the number of cells of green and brown phenotypes of *Synechococcus* sp. increased with the increase in light and was the highest in 280 μmol photons m−<sup>2</sup> s<sup>−</sup>1. Furthermore, Kana and Glibert [42,43] showed that *Synechococcus* sp. could occur and grow in the irradiance reaching even 2000 μmol photons m−<sup>2</sup> s−1. These studies confirmed that *Synechococcus* sp. can grow in maximally coastal waters due to their adaptation to high light intensities. Thus, picocyanobacteria of the genus *Synechococcus* can occur both at the near-surface layers and deeper waters. Furthermore, the ability of *Synechococcus* to grow in low light intensities and their low photoinhibition in exposure to high irradiance could give picocyanobacteria an advantage in changeable light-limited waters.

Temperature is also a very important factor controlling picocyanobacteria abundance in aquatic ecosystems [7,8,37]. Based on the conducted experiments, the influence of increasing temperature on the number of cells of the studied *Synechococcus* sp. phenotypes was found. The most favorable temperature conditions for the growth of *Synechococcus* sp. Type 1 and Type 2 were at 30 ◦C, while the highest number of cells for Type 3a was recorded at 22.5 ◦C. The most susceptible to high temperature was *Synechococcus* sp. Type 2. Its abundance was more than 5 times higher at 30 ◦C compared to the abundance recorded at 15 ◦C. On the other hand, for both *Synechococcus* sp. Type 1 and Type 3a, the increase in cell numbers along with the increase in temperature was about 4 times greater than that recorded at the lowest temperature. In laboratory studies, Jodłowska and Sliwi ´nska [ ´ 44] also found

that increasing temperatures from 15 ◦C to 30 ◦C increased picocyanobacterial abundances. Similar observations were made by Sliwi ´nska-Wilczewska et al. [ ´ 13] who showed that with an increase in temperature from 10 ◦C to 25 ◦C, the number of cells of the green, red and brown *Synechococcus* sp. phenotype. was increased. Picocyanobacteria prefer high temperature for growth and their temperature optimum is higher than for eukaryotic phytoplankton organisms [37]. Furthermore, Paerl and Huisman [45] explained that the global temperature rise would stabilize or even inhibit the eukaryotic phytoplankton while the number of cyanobacteria would increase. Many cyanobacteria species demonstrate the highest increase in growth at 30−35 ◦C [46]. Noaman et al. [47] also demonstrated that the optimum temperature for growth of *Synechococcus leopoliensis* was 35 ◦C. An increase in temperature causes an increase in the number of picocyanobacteria cells, and their maximum occurrence was in the summer period when the water temperature is the highest [48]. This relationship is also apparent for the entire autotrophic picoplankton [49] and was confirmed by numerous studies [36,50,51]. Regarding climate change, picocyanobacteria of the genus *Synechococcus* achieves maximal growth rates at high temperatures and thus can be promoted by future global warming [7,8].

This study also showed that the analyzed phenotypes of *Synechococcus* sp.: Type 1, Type 2, and Type 3a has different growth rates. The highest growth rate was recorded for *Synechococcus* sp. Type 1. It was related to the smallest size obtained by these picocyanobacteria [44]. On the other hand, the lowest growth rate was observed for *Synechococcus* sp. Type 3a. Additionally, it was shown that this phenotype reached the largest cell size in cultures [44]. The research conducted by Stal et al. [52] on PE-rich and PC-rich phenotypes of *Synechococcus* also showed differences in the rate of cell growth depending on their size and picocyanobacteria with a larger cell size grew slower. Small cell size of *Synechococcus* Type 1 resulting in faster nutrient uptake allows picocyanobacteria to compete effectively with larger phytoplankton organisms in surface waters. On the other hand, increasing the cell volume of *Synechococcus* Type 3a may result in better light absorption at greater depths.

#### *3.2. Changes in Pigments Content and Pigment Ratios under Di*ff*erent Irradiance and Temperature Conditions*

Cyanobacteria living in coastal waters are often exposed to changes in light and temperature conditions. These factors influence the content of cyanobacterial photosynthetic pigments in aquatic ecosystems [53–57]. The factorial experiments performed in this study showed a negative effect of the increasing intensity of light on the cell-specific Chl *a* content for the three examined phenotypes of picocyanobacteria, obtaining the highest content at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and the lowest for 145 μmol photons m−<sup>2</sup> s<sup>−</sup>1. The conducted factorial experiments also showed a statistically significant influence of temperature on the cell-specific Chl *a* content for the examined phenotypes. The highest concentration of this pigment was observed at 30 ◦C for *Synechococcus* sp. Type 2 and at 15 ◦C for *Synechococcus* sp. Type 1 and Type 3a. The greatest decrease in the cell-specific Chl *a* content was noted for *Synechococcus* sp. Type 2, which under minimal conditions was about 7 times lower than the recorded under maximum values. On the other hand, *Synechococcus* sp. Type 3a showed the highest resistance to high values of irradiance, and its decrease in the content of Chl *a* in cells under minimal conditions was about 5.5 times higher than the recorded maximum values. Kana and Glibert [43] also showed that the concentration of this pigment was the highest for *Synechococcus* cells adapted to low light. On the other hand, the greatest decrease in Chl *a* content was recorded in the light greater than 700 μmol photons m−<sup>2</sup> s−<sup>1</sup> [42]. High content of Chl *a* in low light may indicate that picocyanobacteria of the genus *Synechococcus* may occur in highly shaded waters [52] and even under conditions of extreme radiation deficiency [58].

High light intensity is an unfavorable environmental factor for many photoautotrophic organisms [59]. However, cyanobacteria living in an environment with a high light intensity developed a defense strategy consisting of special pigmentation of the cells [39,60,61]. Convergence between the accumulation of Car pigments under the influence of high light intensity allows them to be assigned a protective role. The highest content of Zea and β-Car was recorded for *Synechococcus* sp. Type 3a. Zea is an accessory pigment at low light intensities but becomes dominant for cells growing under higher ones [16]. Our research showed that for the examined cyanobacteria cells the amount of Zea was much higher than that of β-Car. The study found that the Zea content for *Synechococcus* sp. Type 1, Type 2, and Type 3a was 93%, 89%, and 87% of the sum of Car pigments, respectively. Guillard et al. [62] observed that Zea may constitute as much as 50−81% of Car pigments for cyanobacteria of the genus *Synechococcus*. The high cell-specific Zea content in the *Synechococcus* sp. is related to the existence of these organisms in surface sea waters and places of exposure to high levels of solar radiation [62,63]. The cell-specific Car content of the tested picocyanobacteria phenotypes changed significantly in response to irradiance increase, which suggests that these organisms reorganize their pigments in order to protect against the unfavorable environmental conditions.

In this study, the factorial experiments carried out showed a negative effect of irradiance on the cell-specific PE, PC, and APC as well as the total sum of Phyco pigments content for the three studied phenotypes of the genus *Synechococcus*. Moreover, it was shown that the cell-specific content of these pigments increased with increasing temperature for Type 1 and Type 2. In turn, for Type 3a, a negative effect of increasing temperature on Phyco content was noted. On the basis of the conducted analyzes, it was found that the conditions under which the examined phenotypes of picocyanobacteria achieved the highest concentrations of the total sum of cell-specific Phyco content were at low light intensity of 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> and high temperatures ranging between 22.5 and 30 ◦C. The greatest decrease in Phyco pigments (about 30-fold) in cyanobacteria cells under the influence of increasing light intensity was noted for *Synechococcus* Type 1. However, the least susceptible to high irradiance was *Synechococcus* Type 3a, with a 13-fold decrease in Phyco pigments. Among all Phyco pigments present in picocyanobacteria cells, the highest content of PE was observed for *Synechococcus* Type 2, whereas for *Synechococcus* Type 1 PC was the dominant pigment. A study by Kana and Glibert [42,43] also showed that the concentration of PE and PC were dependent on the intensity of light. The concentration of PC is related to the number of phycobilisomes [42]. The greatest increase of PC in cells was observed in low light, suggesting a change in phycobilisome numbers in growth-limiting light [42]. Cyanobacteria of the genus *Synechococcus*, depending on the light, can change their number and size of phycobilisomes and this may be associated with acclimatization to different light levels [42]. Photoaclimatization is visible when there is a reduction in photosynthetic pigments with increasing irradiance [64–66]. Hence, it may be concluded that the studied *Synechococcus* sp. phenotypes have a high ability to photoacclimatize to changing environmental conditions.

Based on conducted experiments, the highest Zea/Chl *a* ratio and β-Car/Chl *a* ratio was noted for *Synechococcus* sp. Type 2. On the other hand, the lowest ratios of the discussed pigments were recorded for *Synechococcus* sp. Type 3a. Tang and Vincent [67] showed that the content of Car and Chl *a* increases with increasing temperature. However, carotenoids grow more slowly with temperature, therefore the Car/Chl *a* ratio decreases with temperature [67]. Most cyanobacteria show photoinhibition at low temperatures [68], and an increase in the Car/Chl *a* ratio at low temperature may result in an increase in photoprotective pigments such as carotenoids [69,70]. Studies have shown that a high Car/Chl *a* ratio is characteristic for surface water populations [16]. In addition, Paerl et al. [71] and Paerl [72] suggested that a high Car/Chl *a* ratio has a dual role in cells as it maintains high photosynthetic light absorption capacity and protects cells from photooxidation which may explain why the deeper-living PE-rich *Synechococcus* sp. Type 2 had the highest Zea/Chl *a* ratio and β-Car/Chl *a* ratio of all studied phenotypes. This study also showed an increase in the Phyco/Chl *a* ratio and Phyco/Car ratio in the cells of the investigated cyanobacterial phenotypes with a decrease of irradiance and an increase of temperature. It is related to the advantage of Phyco pigments over Chl *a* and Car pigments for the tested picocyanobacteria phenotypes at low light intensity. Furthermore, a change in color from green, red and brown at low irradiances to bright yellow at high light levels was also observed for three phenotypes of cyanobacteria of the genus *Synechococcus* (Type 1, Type 2, and type 3a, respectively). A clear difference in the color of picocyanobacteria was associated with a change of the proportions between the pigments. At low light intensity, picocyanobacteria phenotypes showed the maximum content of Phyco and Chl *a* pigments. At the highest irradiance, the share of the Car pigments,

mainly Zea, increased significantly in picocyanobacterial cells. Similar tendencies were observed by Kana and Glibert [16,42] for picocyanobacteria of the genus *Synechococcus*. Picocyanobacteria can acclimate to different light intensities by changing the content of pigments, especially Phyco and Chl *a* [73–75]. In this work, we observed the effect of light intensity and temperature on the cell-specific pigment content of all studied picocyanobacterial phenotypes. The concentration of Phyco and Chl *a* was the highest for picocyanobacteria cells acclimated to low light and decreased with increasing irradiance. Inverse relationships were noted for the cell-specific Car content. The high content of Phyco pigments and Chl *a* observed in our work indicated that the tested picocyanobacteria phenotypes are well adapted to low light conditions and high temperatures. Besides, the highest differences in the Phyco/Chl *a* ratio and Phyco/Car ratio were observed in *Synechococcus* sp. Type 1, which may confirm that this phenotype showed the best photoaclimatization abilities of all analyzed organisms. Because this PC-rich phenotype occurs in more productive waters [18,34,76], this observation may be important in the era of climate change and the associated mass occurrence of *Synechococcus* sp. in many places around the world [8,9]. It should be emphasized that Flombaum et al. [8] predicted that the number of *Synechococcus* sp. cells would increase by 14% at the end of the 21st century.

#### **4. Materials and Methods**

#### *4.1. Culture Conditions*

Three different phenotypes of picocyanobacteria from the genus *Synechococcus* were examined: BA-120 (Type 2), BA-124 (Type 1), and BA-132 (Type 3a). The strains were isolated from the coastal zone of the Gulf of Gdansk (the southern Baltic Sea) and maintained as unialgal cultures in the Culture Collection of Baltic Algae (CCBA) at the Institute of Oceanography, University of Gda ´nsk, Poland. Cyanobacteria were cultured on the BG-11 mineral medium [77], which was prepared with water from the Baltic Sea (salinity 8), which was filtered using 0.45 μm filters (Macherey-Nagel MN GF-5, Dueren, Germany) and autoclaved.

The cultures of cyanobacteria were acclimatized to the new conditions corresponding to the incubation conditions of the proper culture. After a week, the culture, which was in the logarithmic growth phase, was used to establish the proper, experimental culture. After the acclimatization time, proper cultures with known initial cell numbers were prepared. For this purpose, a specific volume of inoculum was taken from the actively growing acclimatization culture and added to the sterile media. The optimal number of the initial proper culture was set at 10<sup>7</sup> cells in 1 mL of the medium. The inoculum selected in this way allowed for a constant increase in the number of cyanobacterial cells without inhibiting logarithmic population growth. The incubation of cultures lasted 14 days. After that time, for three phenotypes of cyanobacteria of the genus *Synechococcus* the cell concentration, the growth rate and photosynthetic pigments were determined. Each variant of the experiment was conducted in three repetitions and the results of the experiments were presented as an average of three measurements.

The cultures of the examined cyanobacterial strains were carried out in thermostat under the following temperature conditions (◦C): 15, 22.5, and 30. The effect of PAR irradiance was tested in photoperiod (16 h of light and 8 h of darkness) at the following values (μmol photons m−<sup>2</sup> s<sup>−</sup>1): 10, 55, 100, and 145. 36 W Philips fluorescent lamps (Philips Lighting, Amsterdam, The Netherlands) were used as light sources and two additional 120 W halogen lamps by OSRAM (Osram Licht AG, Berlin, Germany) were used for the highest irradiance (145 μmol photons m−<sup>2</sup> s−1). Measurements of PAR irradiance were made with Li-Cor (Lincoln, NE, USA), model LI-189 with cosine collector.

It is worth mentioning here that a change in the color of the cultures of three phenotypes of picocyanobacteria *Synechococcus* sp. under different light was observed. The phenotypes were shown to be dark green, red and brown at low irradiance (for Type 1, Type 2, and type 3a, respectively), while in the high light their color turned to bright yellow. It was also shown that the examined phenotypes showed differences in PAR absorption spectra when exposed to low and high light (Figure 6).

**Figure 6.** Left-side panel—photographs of the picocyanobacterial phenotypes in 100 mL glass Erlenmeyer flasks: Type 1 (**A**), Type 2 (**B**), and type 3a (**C**), obtained from low (left) and high (right) light; right-side panel—Absorbance spectra measured in the PAR range determined for the picocyanobacterial phenotypes at an optical density (OD750) = 0.1, obtained from low light (LL) and high light (HL).

#### *4.2. Calculation of Cell Density and Growth Rates*

Cell density was calculated using linear regression models based on cell concentration (N mL<sup>−</sup>1) and optical density (OD) at 750 nm [44]. Calculation of the cell number was conducted using the procedure described by Guillard and Sieracki [78], with a light microscope (Nikon Eclipse 80i, Nikon, Tokyo, Japan) and the Bürker counting chamber. To determine the growth rate of cyanobacteria, cell counts were conducted in cultures at two-day intervals from inoculation to the 14th day of culture. Based on these data the parameters characterizing the growth of cyanobacterial cells in the logarithmic phase: growth rate coefficient and cell doubling time were determined [78].

#### *4.3. Determination of the Chlorophyll and Carotenoids Content*

The concentration of photosynthetic pigments of analyzed picocyanobacteria was measured by the HPLC method. After 14 days of incubation, 40 mL of culture was filtered using 0.45 μm filters (Macherey-Nagel MN GF-5) to separate the picocyanobacteria cells from the medium. Chl *a* and Car were extracted from the picocyanobacteria cells with 90% acetone (V = 5 mL) and sonicated for 2 min. Then, the test-tube with the extract was held in the dark for 2 h at −80 ◦C. After 2 h, the pigment extract was centrifuged at 10,000 rpm for 5 min to remove filter particles (Sigma 2-16P, Osterode am Harz, Germany).

Chromatographic analyses were carried out using HPLC equipment of Waters company (Waters Chromatography Europe BV, Etten-Leur, The Netherlands) equipped with: Spectro Vision FD-300 fluorescence detector, Waters 486 absorption detector, Pharmacia autosampler LKB 2157, Waters Millennium Chromatography software. Measurements of pigment absorption were taken at 440 nm. Pigment separation was carried out according to a method proposed by Llewellyn and Mantour [79], with modifications [80] at room temperature on Vydac 201TP (C18) column 250 mm long. As an eluent A; 0.5 M ammonium acetate/methanol (20/80) was used and as eluent B; acetone/methanol (20/80) was used. Before injection of pigments extract (40 μL) the column was conditioned using an isocratic flow of eluents (40% A and 60% B) for 15 min. The analysis was performed at a flow rate of 1.0 mL min<sup>−</sup>1. Chl *a*, Zea, and β-Car standards were used for the qualitative and quantitative determination of pigments (The International Agency for 14C Determination, VKI, Hørsholm, Denmark). The pigments present in the cells of cyanobacteria strains of the genus *Synechococcus* were identified based on retention times and absorbance spectrum, which were compared with the standards. Calibration curves were plotted for each standard used to quantify assimilation pigments.

#### *4.4. Determination of the Phycobiliproteins Content*

The 40 mL of the test material was filtered through a 0.45 μm filter (Macherey-Nagel MN GF-5) and stored in −80 ◦C. Reagent for phycobiliprotein extraction contained 0.25 M Trizma Base, 10 mM binary EDTA and 2 mg mL−<sup>1</sup> lysozyme. A pH of 5.5 was obtained by acidifying with concentrated HCl. The filters were homogenized in 5 mL of reagent, sonicated for 5 min and incubated first in the dark at 37 ◦C for about 2 h, then at 1.5 ◦C for about 20 h. After this time the pigment extract was centrifuged in experimental flasks for 10 min, at 10,000 rpm. Absorption measurements in 1 cm glass cuvettes on Beckman spectrophotometer (Indianapolis, IN, USA), model DU 530, at wavelengths (nm): 565, 620, 650 and 750, were conducted. The pigment contents: PE, PC, and APC were calculated based on Bennett and Bogorad [81] and Bryant et al. [82].

#### *4.5. Statistical Analyses*

To test the influence of a single factor as well as an interplay of factors on studied parameters the two-way ANOVA was used. Moreover, to determine the significance of treatment levels a post hoc test (Tukey's HSD) was conducted. The impact of every environmental agent, as well as an interplay of factors on studied parameters, were measured using the method of orthogonal polynomial tables as described by Fisher and Yates [83]. Furthermore, to describe the connection of the factors and studied parameters regression equations were generated. Data are described as the mean ± standard deviation (SD). Levels of significance were \* *p* < 0.05, \*\* *p* < 0.01, and \*\*\* *p* < 0.001. The statistical analyses were executed using the Statistica® 13.1 software (StatSoft Polska, Cracow, Poland).

#### **5. Conclusions**

In this work, we found that the three analyzed phenotypes of the genus *Synechococcus* have diverse irradiance and temperature preferences. This, coupled with their high photoacclimation capabilities give them powerful tools to win the competition for the marine resources and provide them opportunity to dominate the area, at least as long as sufficient nutrient amounts are available. In almost all conditions the highest rate of growth was recorded for the *Synechococcus* sp. Type 1 which is the most competitive type. It prefers warmer waters −22.5 ◦C and above, but it produces the least nominal amounts of Car which is a probable cause of equalisation of the growth rates between the Type 1 and Type 2 at the highest irradiances and at the mentioned temperatures over 22 ◦C. The lowest growth rates were observed for the Type 3a for all variants. However, Type 3a was recognized to be less temperature sensitive and rather light-driven. Moreover, at low light and low temperature the highest pigment content was observed within the cells Type 3a which may suggest higher tolerance for colder waters such as tested here 15 ◦C or even below. The highest total pigment content per cell was recorded at 10 μmol photons m−<sup>2</sup> s−<sup>1</sup> at all temperature variants with the clear dominance of phycobilins among all the pigments. The high pigment content observed in picocyanobacteria cells proves that they may adapt and live in the deeper layers of the euphotic zone. The highest amounts of carotenoids were produced by Type 2. This may imply lower tolerance of this type to higher irradiance. Our results

showed that the best photoaclimation abilities of all analyzed *Synechococcus* sp. types is Type 1 with the highest differences in the Phyco/Chl *a* and Phyco/Car ratios. One of our striking observations is a significant difference between the physiological responses of different *Synechococcus* sp. phenotypes to changeable environmental conditions. Thus, this work would be an important link in forecasting future changes in the occurrence of these organisms in the context of global warming.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4409/9/9/2030/s1, Table S1: Two-way factorial ANOVA of cells concentration measured in Synechococcus sp. Type 1, Type 2, and Type 3a growing at different temperatures (◦C) and irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1): df—degrees of freedom; F—Fisher's F-test statistic; Mss—mean sum of squares; Ss—sum of squares. Levels of significance were: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001, Table S2: Two-way factorial ANOVA of cell-specific Chl *a*, Phyco, and Car content measured in *Synechococcus* sp. Type 1, Type 2, and Type 3a growing at different temperatures ( ◦C) and irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1): df—degrees of freedom; F—Fisher's F-test statistic; Mss—mean sum of squares; Ss—sum of squares. Levels of significance were: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001, Table S3: Two-way factorial ANOVA of cell-specific PE, PC, and APC content measured in *Synechococcus* sp. Type 1, Type 2, and Type 3a growing at different temperatures (◦C) and irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1): df—degrees of freedom; F—Fisher's F-test statistic; Mss—mean sum of squares; Ss—sum of squares. Levels of significance were: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001, Table S4: Two-way factorial ANOVA of cell-specific Zea and β-Car content measured in *Synechococcus* sp. Type 1, Type 2, and Type 3a growing at different temperatures (◦C) and irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1): df—degrees of freedom; F—Fisher's F-test statistic; Mss—mean sum of squares; Ss—sum of squares. Levels of significance were: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001. Table S5: Two-way factorial ANOVA of Zea/Chl *a* ratio and β-Car/Chl *a* ratio measured in *Synechococcus* sp. Type 1, Type 2, and Type 3a growing at different temperatures (◦C) and irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1): df—degrees of freedom; F—Fisher's F-test statistic; Mss—mean sum of squares; Ss—sum of squares. Levels of significance were: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001, Table S6: The Phyco/Chl *a* ratios and Phyco/Car ratios obtained after 14 days of experiment for three phenotypes of *Synechococcus* sp.: Type 1 (A), Type 2 (B), Type 3a (C) under different temperature (◦C) and light (μmol photons m−<sup>2</sup> s<sup>−</sup>1) conditions, Table S7: Two-way factorial ANOVA of Phyco/Chl *a* ratio and Phyco/Car ratio measured in *Synechococcus* sp. Type 1, Type 2, and Type 3a growing at different temperatures (◦C) and irradiance (μmol photons m−<sup>2</sup> s<sup>−</sup>1): df—degrees of freedom; F—Fisher's F-test statistic; Mss—mean sum of squares; Ss—sum of squares. Levels of significance were: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001.

**Author Contributions:** Conceptualization, S.S.-W., Z.K., K.W. and M.K.; methodology, S. ´ S.-W., Z.K., K.W. and ´ M.K.; formal analysis, S.S.-W., Z.K., K.W. and M.K.; investigation, S. ´ S.-W., Z.K., K.W. and M.K.; data curation, ´ S.S.-W., Z.K., K.W. and M.K.; writing—original draft preparation, S. ´ S.-W., Z.K., K.W. and M.K.; supervision, S. ´ S.-W. ´ All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by BMN grant number 539-O140-B416-20 and NCN PRELUDIUM 17 (UMO-2019/33/N/ST10/00585).

**Acknowledgments:** The authors would like to thank the Editor and anonymous Reviewers for their valuable comments and suggestions to improve the quality of the paper.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had a role in the design of the study; in the collection, analyses, and interpretation of data, and in the decision to publish the results.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Review* **Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research**

**Elvin S. Allakhverdiev 1,2, Venera V. Khabatova 3, Bekzhan D. Kossalbayev 4,5, Elena V. Zadneprovskaya 3, Oleg V. Rodnenkov 1, Tamila V. Martynyuk 1, Georgy V. Maksimov 2,6, Saleh Alwasel 7, Tatsuya Tomo <sup>8</sup> and Suleyman I. Allakhverdiev 3,7,9,\***


**Abstract:** Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects.

**Keywords:** carotenoids; lipid droplets; microalgae; Raman spectroscopy; Surface-enhanced Raman Spectroscopy

**Citation:** Allakhverdiev, E.S.; Khabatova, V.V.; Kossalbayev, B.D.; Zadneprovskaya, E.V.; Rodnenkov, O.V.; Martynyuk, T.V.; Maksimov, G.V.; Alwasel, S.; Tomo, T.; Allakhverdiev, S.I. Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research. *Cells* **2022**, *11*, 386. https:// doi.org/10.3390/cells11030386

Academic Editor: Alexander E. Kalyuzhny

Received: 13 December 2021 Accepted: 22 January 2022 Published: 24 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

In recent decades, Raman spectroscopy (RS) has been used in several studies on animal cells [1–3]. The method is popular among biophysicists and life science researchers. RS allows for the study of living cells in their natural conditions without any damage [4,5]. Nowadays, we can see a significant increase in the use of RS in plants, and especially in algae research.

RS is a well-known approach used in many of biomedical studies. Since biomolecules are involved, the main obstacle to the use of such methods in life science research is the low signal of Raman scattering. There are a number of modifications of RS that allow Raman scattering to be improved. There are existing approaches to detect Raman signals not only on the surface of human skin, but also inside the vasculature and various organs of patients.

In this review, we have attempted to cover the list of modifications of RS applied to biological and medical research; moreover, algal research, and especially to understand more detailed mechanisms related to the biosynthesis and transport of lipid droplets/fatty acids and carotenoids. It is important to note that algal research might represent an interest in terms of carotenoids production and further application of carotenoids for medical treatment of human diseases.

We have also covered studies on human cells [1,2] and microalgae [6,7] that we thought would be useful to introduce to the reader, especially in view of future studies of the use of algae. We have tried to cover the variety of algal species that have been used in different studies with the application of RS and its modifications. Figure 1 provides a schematic of the different research areas in which RS can be used. Biotechnological, biomedical, photosynthetic, and analytical research applications of RS are the main interest of this particular review.

**Figure 1.** Application of RS in different research.

#### **2. The Principles of the Method of Raman Spectroscopy**

Figure 2 shows the energy transition of Rayleigh and Raman scattering. The former (Rayleigh) is based on the principle that the frequency of the absorbed and scattered photon does not change—elastic scattering. In the latter (Raman), on the other hand, there is a shift in frequency of the scattered photon (change in energy or change in wavelength)—inelastic scattering. It is also essential to note that Raman scattering is divided into Stokes and anti-Stokes shifts. The Stokes shift is more likely because it is associated with the shift of the binding maxima to the longer wavelengths (the energy and frequency of the scattered photon are correspondingly lower than those of the absorbed photon—see Figure 2). The anti-Stokes shift, on the other hand, is less likely. It results from the shift of the binding maxima to the shorter wavelengths (energy and frequency of the scattered photon are correspondingly higher than those of the absorbed photon—see Figure 2).

**Figure 2.** Energy transition of Rayleigh and Raman scattering.

In order to increase the sensitivity and resolution capability of the method, scientists use the approach based on the surface plasmon resonance effect. The electrons on the surface of the metal (silver and/or gold nanoparticles) oscillate. At a certain point, the frequency of the photon resonates with the frequency of the surface electrons. The surface plasmons substantially enhance the local electric field of the incident light (on the molecules near the surface's vicinity of the metal nanoparticles) [8]. In recent decades, this effect has been used to modify the method of RS to apply it in biological and medical research.

#### **3. Raman Spectroscopy and Its Modifications: Advantages and Use**

There are many variants of Raman spectroscopy, all of which use the phenomenon of Raman scattering in different ways (Figure 3). The choice of which variant to use for a particular measurement depends on inherent factors, such as the complexity of the sample and/or the concentrations of the target analyses.

The popularity of the RS method among biophysicists around the world is explained by a list of advantages of the method. RS is a non-invasive, rapid, and sensitive method for in vitro investigations [10–12]. Usually we face an obstacle—the biomolecules are present in the cell in very low concentrations, so the Raman scattering/signal is very low. To enhance the Raman scattering signal, the modifications of the method can be used (see Table 1), such as surface-enhanced Raman spectroscopy (SERS), coherent anti-Stokes Raman scattering (CARS), surface-enhanced Raman Spectroscopy (SERCS) [13], and micro-Raman spectroscopy [14].

**Figure 3.** "Family tree" of the RS. The relatively simple RS is the root of the complex surfaceenhanced, resonance-enhanced time—and spatially-resolved techniques. Abbreviations: SERS, Surface-enhanced Raman Spectroscopy; CARS, coherent anti-Stokes Raman spectroscopy; RRS, resonance-enhanced Raman scattering; SORS, spatially offset Raman spectroscopy. Modified from Buckley and Ryder [9].

**Table 1.** The list of modifications of RS with details of the objects and molecules of interest with reference numbers of the papers used in the article.


As mentioned earlier, there is a special mechanism of Raman scattering (plasmon resonance) enhancement for modifications such as SERS. Moreover, each modification of the method can solve a specific task/objective.

The following sections describe the most commonly used types of RS.

#### *3.1. Surface-Enhanced Raman Spectroscopy (SERS)*

There are several clinical trials in progress with SERS. Sample types include blood [25,26], saliva [27], and tears. As reported in articles from two studies, SERS had a susceptibility of 80.7% and 84.1% in detecting squamous cell carcinoma of the oral cavity by analysing blood [28]. Therefore, the tests show that SERS biofluid is suitable as a sample and relies on metal nanoparticles for signal amplification. The status of clinical trials is important for the understanding and future prospect of SERS and Raman spectroscopy [29].

#### *3.2. Coherent Anti-Stokes Raman Scattering (CARS)*

Conventional Raman spectroscopy uses only a CW laser to generate spectra, while CARS and SRS use two pulsed lasers with different wavelengths to enable nonlinear optical motions. CARS microscopy can form an optical contrast of endogenous chemical structures, which is popular in various fields of biomedicine as it can provide a high-resolution image. For example, CARS microscopy has been used to visualise tissue structures, skin [30], lung, kidney, and retina [31]. Consequently, CARS has been able to obtain micron-level images of brain slices, which has worked well in cancer diagnosis [32]. Concrete prostatectomy is considered the most popular method in civilised countries for curing members of the stronger sex with clinically localised prostate cancer. In this surgery, the entire prostate is removed, but the urinary ball is reunited with the urethra [33].

#### *3.3. Resonance Raman Spectroscopy (RRS)*

One of the drawbacks of Raman spectroscopy is the low signal intensity. This drawback can be corrected with RRS. By matching the wavelength of laser excitation to the electrical absorption maximum of a particular chemical, the Raman signal of certain bands is enhanced. The study was used for a multifaceted study of haemoglobin, and the release of cytochrome-c from mitochondria during apoptosis was also studied. Okada et al. [34] as well as other scientists have used RRS to perform unlabelled studies of molecular dynamics in apoptotic cells. Observation of mitochondrial membrane stained with the dye JC-1 using RRS confirmed that the observed release of cytochrome was due to apoptosis.

#### *3.4. Spatially Offset Raman Spectroscopy (SORS)*

Although SORS technology may not be approved in any way for uniform diagnosis of patients in our time, there are significant prospects for the eventual use of this technique in the clinic. Recent advances in medicine have shown SORS can be used in blood testing, such as assessing the quality of erythrocytes during blood transfusion in patients. Vardaki et al. [35] have shown that SORS is able to profile changes in oxygenation when stored for 6 weeks. It is well known that in blood transfusions, the chemical composition of blood units changes differently from time to time. For this reason, a few units over the years are by no means determinative of the relationship between erythrocytes properties. Feng et al. [36] used SORS to measure subcortical bone and biochemical changes with increasing depth in intact mouse bone.

#### **4. Application of Raman Spectroscopy in Biomedical Research**

Due to the non-invasive, fast, and highly sensitive advantages of RS and its modifications, there is considerable demand for its use in biomedical research, such as in studying the structure and conformation of molecules of interest and investigating the mechanisms of the drug action [10,11]. Nowadays, RS is used in vivo and ex vivo to solve various biomedical issues, such as early cancer detection, monitoring the effects of different drugs on the skin, determining the composition of atherosclerotic plaques, and rapid identification of pathogenic microorganisms. Detailed information about the RS application can be found in Figure 4.

**Figure 4.** Schematic view of biomedical RS application. Adapted from Desroches et al. [37].

#### *4.1. Disease Prediction*

Early diagnosis of diseases, such as those that are life-threatening, is essential to prevent their spread. Based on Raman light scattering, a diagnostic tool has been developed to study important molecules and events in real-time using new RS technologies with high sensitivity to biomolecular changes. In the last decades, there have been many publications showing that RS is used to define several diseases [38]. RS is used to collect biochemical information, such as biomolecules, cells, tissues, and organs, whose biomarkers are various biological fluids, such as urine, saliva, blood, and tears. Selected biomarkers are analysed and evaluated, revealing the relationship between Raman light scattering spectral indicators and clinical condition [39].

Cancer is one of the most common causes of death worldwide and RS makes it possible to diagnose undetected precancerous lesions in various organs, such as breast, skin, brain, gastrointestinal tract, heart, urinary, and reproductive tracts [40]. Hsu et al. [41] investigated that confocal Raman microscopy distinguishes intestinal tumours from adenocarcinomas and normal, healthy organs. RS provides simple and immediate tissue identification during surgery, which allows for cancerous organs to be distinguished from healthy tissue. Using RS, the mechanism of malignant transformation of breast tissue has been studied with great success [42].

The properties of blood vessels in a tumour mass of breast tissue were investigated by Kope´c and Abramczyk [43] using a combination of Raman and atomic force microscopy (AFM) imaging to determine biochemical composition. They found that individuals with breast cancer had higher concentrations of glycogen and lactic acid as well as an increase in the collagen–fibroblast network. An excellent, recent study by Winnard et al. [44] demonstrated the potential of RS in characterising organ-specific metastatic lesions at the molecular level to gain insight into metastatic progression. In this study, they used the combinatorial approach of RS and metabolomics. The stromal adjustments that occur in pre-metastatic lungs caused by breast cancer were analysed using RS. This work was performed with mouse lines in which mice were implanted with breast cancer cells with different metastatic potential. Changes in the extracellular matrix of the congested lungs, such as an increase in collagen and proteoglycan, were examined, and this was directly related to the metastatic potential of the breast cancer cells used [45].

Ryzhikova et al. [46] have shown that RS can be used effectively for the diagnosis of Alzheimer's disease (AD). The novelty of this work is that it is based on the analysis of cerebrospinal fluid (CSF), while the other research has focused on different body fluids for the detection of AD. It is important to emphasise that CSF is the most relevant body fluid for detecting AD. The group of Ryzhikova et al. [46] suggests that early detection of AD is potentially possible using RS. It is expected that the method will be repeated on a larger subject population.

Lednev group [47,48] diagnosed early AD in saliva and serum with potential biomarkers using RS in combination with machine learning. This project aimed to use Raman hyper-spectroscopy in combination with machine learning. New methods were developed to diagnose AD based on the analysis of biological material, such as saliva. The group used biological material from saliva samples from a normal person with AD and mild cognitive impairment. In the end, it turned out that Raman hyper-spectroscopic analysis of saliva could be effective for an accurate diagnostic method in the early stages of AD. It is also possible to diagnose lung cancer with high accuracy at an early stage, as shown by the studies of Shin et al. [49] using a combination of SERS spectra and deep learning diseases. The advantage of this method is that tissue can be seen in the near-infrared region of the electromagnetic spectrum, which in combination with the RS instrument as well as multivariate data analysis, has become an accurately reproducible and non-invasive method for studying tissue pathology.

Barnas et al. [50] used Fourier transform infrared (FTIR) and RS to study endometrial hyperplasia and cancer. The study was performed on tissues from three groups of patients: normal control patients, patients with atypical hyperplasia, and patients with endometrial cancer. It has been revealed that both methods are complementary in terms of tissue examination. The results of the research suggest that the peaks of FTIR and Raman spectra and the changes in the specific peaks (absence of the peak or shift of the peak) can be used to distinguish cancer and atypical hyperplasia from normal endometrial tissue. Further studies are needed to understand whether RS is indeed a practical approach to study carcinogenesis.

SERS immunoassays have labelled/indirect or unlabelled configuration. Without labels, the Raman measurement is based on the fingerprint of the bioanalyte, and the labelled ones are identified by the spectrum of the Raman label. Therefore, labels without labels are not as complex as the labels that make up the labels on metallic nanostructures. Two systems were used to detect proteins, nucleotides, and fatty acids of lipids. The changes that occurred in the bioassay were recorded and diagnosed with infectious and non-infectious diseases [51]. Table 2 shows some examples of bioanalytes or diseases that were detected using SERS.


**Table 2.** Bioanalytes/diseases detected using SERS.

In addition, RS is used to analyse the serum of patients with AD, patients with other types of dementia, and individuals from the control group. The results were analysed using multivariate statistics for differential identification of patients with AD. The study was a

confirmation of the concept; this proves that RS and artificial neural network classification were able to differentiate patients with sensitivity and specificity of more than 90%, which shows that a combination test can become a blood test that can support clinical evaluation for effective and accurate differential diagnosis of AD [62].

#### *4.2. Surgical Procedures*

In medicine, much attention is paid to optical instruments based on RS, which consist of intraoperative procedures in real-time. The benchmark for surgical guidance is histopathology, which also involves surgical removal of tissue followed by staining and examination under a microscope. This procedure takes a long time and in some cases, results in multiple biopsies, which causes a great deal of discomfort and suffering in patients [49]. Therefore, a sensory system that can provide results during surgery is needed. For example, endoscopic pain analysis before surgery, delineation of the sides of the lesion during surgery, and changes of the single biopsy using RS can contribute to the absolute removal of the affected tissue and reduce the cost of secondary assessments of the disease and surgery [63].

Motz et al. [64] have developed a small diameter Raman probe with integrated filters and a spherical lens to minimise low priority signals. In as little as one second, the probe can show the spectra of arteries and breast tissue at different stages of pathology, which is clinically useful. Jermyn et al. [65] studied cancers of multiple human organs during surgery with 97% accuracy using a trimodal optical imaging system combining Raman. Thus, the method demonstrated that molecular imaging with high sensitivity could dramatically impact such areas of surgical and non-invasive oncological procedures for tumour detection to reduce cancer risk and improve quality of life. Kircher et al. [66] investigated the ternary status of magnetic resonance imaging—photoacoustic Raman imaging of nanoparticles, which revealed brain tumour boundaries and visualised tumour margins using RS. They used Raman imaging to ensure monitoring of intraoperative tumour resection, and histologic interdependence proved that Raman imaging delineated brain tumour boundaries. This latest trimodal aspect using nanoparticles can ensure the clearest visualisation of even the resection of a brain tumour.

In addition, significant steps are being taken to integrate RS with other wide-field and spectroscopic methods to provide additional data to support RS measurements. It has been shown that cancer cells can be diagnosed in less than one second using the broadband fluorescence method together with a Raman micro-spectrometer [67]. The trimodal optical imaging system is a combination of Raman scattering, diffuse reflectance, and intrinsic fluorescence spectroscopy, in which various cancer organs were detected during surgery with 93%, 100%, and 97% accuracy [65]. Kircher et al. [66] also synthesised a nanoparticle with three-component magnetic resonance imaging—photoacoustic and Raman imaging with the aim of preoperative and intraoperative separation of the sides of the leading brain tumour; the presence of this RS was used to visualise the sides of the tumour.

Nowadays, RS is increasingly used for cancer diagnosis and monitoring. As mentioned above, the improvement of algorithms for processing Raman signals, as well as the development of new methods SERS and fibre-optic probes, may make it possible to obtain results with high sensitivity and specificity and to apply RS approaches to cancer diagnosis.

#### *4.3. Therapeutic Drug Monitoring (TDM)*

Therapeutic drug monitoring (TDM) is an important method in clinical pharmacology and clinical chemistry that aims to measure drug concentrations in human blood [54]. TDM has been used in medical practice since the 1960s and mainly focuses on drugs with narrow therapeutic targets [68–70].

TDM is more commonly referred to in clinical practice as the observation of drug concentrations in biological fluids over time [69,71]. Karine is important for drugs with a limited therapeutic index, where a low dose is prescribed when the difference in dosage may lead to serious therapeutic consequences such, as drug toxicity and side effects [72].

There is also increasing advocacy in the field of personalised medicine that will be useful for measuring the plasma concentration of a drug at individual doses. Individualised therapy planning in personalised medicine has become a great challenge for clinicians as it is very successful in improving patient care, so that each patient can reduce drug costs while receiving optimal treatment with minimal side effects.

Fei et al. [73] performed the synchronous pharmacokinetics of 6-mercaptopurine and methimazole in the HeLa cell directions using the automated micro-fluid concept in the SERS database, which is convenient for synergistic tumour targeting. Valves and gradient generators can be used to adjust each of their chambers to deliver the required amount of different active cells and drugs at specific concentrations. The aforementioned examples also show that SERS is a robust method to determine the number of entrapped substances.

Nowadays, there is a crucial problem of how to distinguish expired and non-expired drugs using a quick and non-invasive method. Current methods, such as HPLC, thin-layer chromatography, are time-consuming and complicated. In contrast, RS is a rapid and non-invasive method that can be used to examine expired and non-expired drugs. This is a significant problem for the medical world [72,74].

The combination of AFM and RS can distinguish the characteristics of the nucleus and cytoplasm in living cells. By combining both methods, a modification of RS can be developed: tip-enhanced Raman spectroscopy (TERS). Intracellular imaging with TERS has been applied to HeLa cells. It has been shown that the regions of the nucleus and cytoplasm can be effectively distinguished using this method, for which the local information within the cell was obtained. Crucially, scientists have shown that the viability of the cell membrane is very high (about 100%) after the AFM tip penetrates the cell membrane. The method has significant potential for future use in studies where it is necessary to investigate the various organelles and biomolecules within the cell [75].

#### *4.4. Determination of Metabolites*

Molecularly specific RS is well suited for profiling cellular metabolites, including neurotransmitters, amino acids, lipids, glucose, and nucleic acids, as well as in biofluids. Among all cellular metabolites, lipids are one of the most studied classes of biomolecules because they have large Raman scattering cross-sections. Lipids rich in intracellular bodies are referred to as lipid droplets, making their relationship to the physiological state of the cell increasingly apparent [76]. RS exploits the promise of detecting and imaging lipid droplets for quantification in cancer cells, such as HuH7 and colorectal cancer stem cells [77].

RS and its modifications are widely used in cell research. A variety of microalgae [6] and human [1–3] cells are studied using SERS. It has been shown that information about membrane lipids can be obtained, especially the conformation of membrane lipids and the molecular environment [1].

RS has been used to study nerve myelin during excitation or the effect of neurotransmitter on nerve fibre. It has been shown that the changes in the C–C bonds of the fatty acids can be detected as well as the changes in the conformation [78]. This knowledge will improve our understanding of the mechanisms of lipid–lipid interactions in myelin and many processes associated with various diseases, such as multiple sclerosis, trauma and AD.

It is important to add that human skin is exposed to ultraviolet and infrared radiation, which is the cause of a number of diseases and ageing of human skin. Carotenoids are considered antioxidants that can support the antioxidant status of the human epidermis [79]. RS is one of the most popular methods to study carotenoids. More knowledge about the mechanisms of the photoprotective function of carotenoids is important for biomedical research and the development of commercial products. Similarly, Gellermann et al. [80] used resonant Raman scattering spectroscopy (RRSS) as a novel, non-invasive, in vivo optical technique to measure the concentration of the carotenoid pigments, lutein and zeaxanthin, in the human retina of adolescents and adults. Using RRSS, they found an

apparent decrease in macular pigment concentration during the normal ageing process. They suggested the use of RS to measure macular carotenoids as a promising technology.

Ermakov et al. [81] reported that the RS technique has the potential for novel, rapid screening for carotenoid antioxidants in the largest populations at risk of vision loss due to age-related macular degeneration, an important precondition for blindness in the elderly in mature societies.

The method of RS is becoming increasingly popular in biological research. Valpapuram et al. [82] proposed a new technique combining optical biosensing and Raman micro spectroscopy. The particular advantage of this method is its ability to reduce the background signal and thus improve the signal-to-noise ratio. The researchers have shown that the combination of optical bio-sensing and Raman micro spectroscopy is a far more informative method than the conventional RS.

SERS has been used to spatially localise neurotransmitters on living cells and to study protein–neurotransmitter interactions [83,84]. Although it offers the best detection limits, the toxicity of metal nanoparticles in vivo limits its use [85]. Manciu et al. [86] have also demonstrated the usefulness of confocal RS for rapid detection of neurotransmitter predictions, but their studies were limited by in vitro spiked material. They propose realtime detection of serotonin, adenosine, and dopamine in vitro, but in addition, diffusion of dopamine in a heterogeneous base gel used as a surrogate for neural tissue. Raman mapping was performed using alpha 300 WITec confocal Raman system to obtain nonoverlapping spectral data of neurotransmitters. Their work demonstrates the power of Raman spectroscopy in the biological sciences and likely provides a novel mechanism for testing the adaptability and kinetics that stimulate the brain [86].

A rapid, non-invasive, label-free approach to biological studies is currently essential for scientific purposes. However, RS has some limitations—it requires longer acquisition times and it is not possible to optically slice the collected signal. This makes it difficult to use RS for tissue research alone. Therefore, Marchetti et al. [87] combined three methods: multiphoton microscopy, fluorescence lifetime imaging microscopy, and RS to perform an efficient study of tissues ex vivo. The mentioned tailored technique is a promising approach to expand the application of RS in biological research.

The use of the RS method in medical studies is becoming more common. For example, RS is used in dentistry. The short- and long-term effects of demineralization can be studied using the RS tool. The major advantage of RS is that it is non-invasive while providing a high degree of sensitivity. In the study of Marin et al. [88], quantitative information on the crystalline structure of the phosphate groups and the loss of the mineral fraction in the organic collagen matrix was discovered.

Nowadays, a precise, fast, and direct analysis tool is needed. The capillary sensor SERS, developed by the group of Arabi et al. [89], is proposed as an ultrasensitive tool and used for protein analysis. Trypsin is a protein that can be used as a biomarker (in urine) for the diagnosis of pancreatitis. The idea is that this approach can be effectively used for early diagnosis of the disease. In addition, and to test the feasibility of the tool, other biological fluids such as saliva and sweat have also been measured. The microsensors are relatively quick and inexpensive to produce.

Another application of RS in biological studies is high-throughput screening Raman spectroscopy (HTS-RS)—presented in the work of Arend et al. [90]. This application is a customised platform for single-cell analysis. In the study in which the group of Arend et al. [90] examined the different types of neutrophils, both infected and uninfected, it has been shown that this type of platform can potentially help to speed up the diagnosis of pathogens. Currently, the routine for such analyses takes 1 working day.

It has been discovered that RS can be used effectively in chronic renal failure (CRF) to differentiate patients with this disease from healthy patients. The group of Chen et al. [74] conducted a study on 47 samples from patients with CRF and 54 samples from control subjects. There is a prospect that the application used, which can be effectively utilised as a rapid diagnostic method for CRF. The plasma RS has been effectively used to study giant unilamellar vesicles (GUV)—simplified models of cellular plasma membranes [91]. The group of Collard et al. [91] has applied the modification of RS in combination with holographic optical tweezers (HOT): HOT-Raman microscopy for the study of curvature gradients on lipid order and cholesterol segregation in GUVs. The RS provides an overall estimate of cholesterol concentration for both leaflets of the bilayer. Importantly, the proposed method also allows for obtaining multiple Raman spectra from different regions of the lipid vesicle.

Raman spectroscopy is a promising high-sense diagnostic method for assessing the oxygen transport function of erythrocytes. Haemoglobin accounts for >95% of the dry weight of erythrocytes and is a suitable subject for RS to study the conformation of globin and haeme. To assess the conformational state of the active site of haemoglobin, we use special Raman spectra to study the conformation of deoxyhaemoglobin (d- Hb) and oxyhaemoglobin (o- Hb), as well as the ability to release oxygen. This approach is important for monitoring changes in the ability of haemoglobin in erythrocytes to carry oxygen and, accordingly, characterising the presence or development of hypoxia in patient tissues. RS has been successfully used to analyse the properties of haemoglobin from healthy donors and patients with various cardiovascular diseases [92,93], diabetes [94], and astronauts after a long space flight [95], as well as for the analysis of animal models of cerebral ischemia and reperfusion, haemorrhagic shock, etc. In addition, RS has been successfully used in experiments to alter the properties of erythrocytes under in vitro conditions. A promising application of RS is the study of the molecular mechanisms of the development of pulmonary hypertension. In patients with IPAH with a typical hemodynamic picture, changes in the ability of hematoporphyrin of Hb to bind O2 have been detected [96].

#### **5. Biotechnology Application of Raman Spectroscopy**

Biotechnology has become one of the most popular areas of research due to the demand for a number of molecules that are important for different types of practical applications [97]. Wang et al. provided a comprehensive and critical review of the most recent advances in the application of RS-enabled technologies, with focus on biomolecular applications in environmental and biotechnological fields [98].

Bioprospecting and mutagenesis are two important strategies that have been studied in the development of algae-based biofuels [18]. Considering these two strategies, there is a need to optimise biofuel production. The ability to rapidly characterise the accumulating algal lipids is essential for algal bioproduction. Confocal Raman microscopy can accomplish this task and it is also possible to localise lipid-rich regions within microalgal cells with high spatial resolution [18]. Among the existing methods, RS is the one that does not require additional long preparations of the research object.

It has been investigated that the chemical composition of lipid droplets can be obtained by using RS. It is claimed that the combination of CARS (coherent) and Raman microspectroscopy would allow accurate determination of the harvesting times for algae [4]. This is supposed to be one of the valuable interests in modern biotechnology.

It has been revealed that another modification of RS—single-cell Raman spectroscopy (SCRS) is applicable for gathering information about the lipid content of the cells and the degree of lipid unsaturation [22].

RS is one of the techniques that can fill some gaps in our knowledge of the synthesis and storage of biomolecules in algae [21].

#### *5.1. Application of Raman Spectroscopy in Algae Studies*

In algal research, RS has been used to analyse pigments, proteins, carbohydrates, and lipids [98–102] (Figure 5). Huang et al. [14] investigated the composition of microalgae to analyse them using confocal Raman microscopy. The scientists collected Raman spectra while using a 532 nm laser and found a strong background of fluorescence as a function of temporal behaviour. They also used RS to analyse the resolution of individual cells of microalgae. Kaczor et al. [103] published a study on the state of nutrients of single cells of

microalgae and the visualisation of astaxanthin in a cell of microalgae, visualising Raman in situ with a 1064 nm laser. Laser capture Raman spectroscopy and a combination of laser capture and micro-Raman spectroscopy were developed by researchers Wu et al. [23] in which the lipid composition of microalgae was analysed using a single cell. For example, Hosokawa et al. [104] used the confocal Raman microscopy method to quantitatively monitor lipids based on a single cell. A brief report on the prospects for algal research based on RS was given in two published review articles [105,106]. Wei et al. [106] have reviewed some of the recent advances in RS applications in areas related to microalgae. Strategies based on RS provide tremendous potential for non-invasive biochemical-composition analysis and imaging of living microalgal cells. The analysis of lipids carried out by the ratiometric method has provided a solid basis, but to improve the quality of data collection and to obtain an accurate analysis, one hundred percent adjustment of the data collection parameters is required.

**Figure 5.** A schematic of the experimental set-up of a typical Raman spectrometer and schematic showing applicability of RS to different aspects of algae.

In addition, the bioactive components of many microalgae species have been studied using this Raman method. Micro-Raman spectroscopy is the most effective method for studying biologically active additives. The most commonly used modifications are macro-Raman spectrometry, single-cell micro-Raman spectrometry, and Surface-enhanced Raman Spectroscopy (Table 3).


**Table 3.** Summary of the bands observed in the Raman spectra of microalgae and contributing bioactive compounds.

As shown in Table 1, two species of microalgae were studied using single-cell microand macro- RS methods. *Arthrospira platensis* was the highest with 1118, 1403, and 1576 cm−1. The significant changes (1118, 1403, and 1576 cm−1) are explained by considerable differences in the structure of secondary proteins, especially amide bonds (1400 cm<sup>−</sup>1) and the α-helix (1574 cm−1) [113,114]. Furthermore, Venkatesan et al. [115] indicated that the wavenumbers of *Arthrospira platensis* (1030–1120 cm−1) are responsible for the presence of antioxidant protein enzymes. *Phaeodactylum tricornutum* was found to have high wavenumbers of 1522 and 1160 cm−1. Moudˇríková et al. [116] found that 1160 cm−<sup>1</sup> corresponds to the polyphosphate-positive groups of microalgae. The authors used RS for quantification as well as for localisation of polyphosphate reserves within an algal cell. These authors improved the method by extracting polyphosphate with phenol–chloroform and by purifying the extract through ethanol precipitation. Wei et al. [106] noted that they (1518–1525 cm−1) correspond to the C–C regions of β-carotene, which are mainly present in the non-polar phase of microalgae. Brahma et al. [108] observed the strongest peaks around 1527 and 1158 cm−<sup>1</sup> in *Dunaliella tertiolecta* associated with carotenoid pigments. Huang et al. [14] studied *Chlorella sorokiniana* and *Neochloris oleoabundans*. *Chlorella sorokiniana* had strong peaks at 2800 and 3000 cm−1. Further support for the use of a broad band of wavenumbers for lipid identification is provided by several previous CARS studies that used peak positions at 2840 or 2845 cm−<sup>1</sup> for lipid identification [106]. Osterrothová et al. [20] tested the possibilities of Raman micro-spectroscopy for the determination of carotenoid pigments, both primary (lutein, β-carotene) and secondary (astaxanthin) carotenoids. The components of the xanthophyll cycle, violaxanthin (1525 cm<sup>−</sup>1) and

antheraxanthin (1523 cm−1), can also contribute to a shift in the position of the ν1 (C=C) band. Strong carotenoid signals were observed by Jehliˇcka et al. [117] in *Botrydiopsis alpina* at 1527 cm−1. In *Dunaliella parva*, the secondary carotenoid neoxanthin was found at 1525 and 1530 cm<sup>−</sup>1.

*B. brauniii* is a classic producer of liquid hydrocarbons known as botryococci, which can be used as fuel. Weiss et al. [111] have obtained Raman spectra of hydrocarbons isolated from *B. brauniicells*. By comparison with the Raman spectra of pure squalene and computer analysis, they found that the Raman bands at 1640 cm−<sup>1</sup> and 1647 cm−<sup>1</sup> were related to the stretching of the C=C bond in the botryococcus branch and the methylene C=C bond. produced by methylation, respectively.

Compared with the standard fatty acid mixture, the main SERS peaks obtained from the cells of *S. quadricauda* are at 1430, 1157, 1544, 1257, 1307, 961, and 596 cm<sup>−</sup>1, which is in good agreement with the literature data [22,112,118].

#### 5.1.1. Raman Spectroscopy Applied to Lipid

Normally, lipid droplets are assumed to be cellular structures that function only as static lipid storage depots. Recently, however, it has become apparent that lipid droplets may be multifunctional organelles [119,120]. Considering the information available so far, there is a need for deeper research into lipid droplets—their structures and functions in cells. Nowadays, there is a hypothesis that lipid droplets may be involved in the biosynthesis and transport of carotenoids in the cell [121]. It is known that the biosynthesis of the carotenoid astaxanthin is accompanied by a massive accumulation of lipids [122].

Another interesting finding is that the membrane fluidity of cyanobacteria can change when the temperature is lowered [123]. This is thought to be due to the desaturation of fatty acids in the membranes [124]. RS is an attractive alternative for lipid detection that has not yet been sufficiently exploited in microalgae. Mostly this is because RS is hampered in photosynthetic organisms by strong autofluorescence of the pigments, which obscures the characteristic Raman spectral features.

The intensities of the Raman spectral peaks correspond to the saturated and unsaturated C–C bonds in lipid molecules. This information is used to estimate the degree of unsaturation in lipid bodies/droplets [4,5,15,16,18]. It has been shown that Raman micro spectrometry can be used to study the triacylglycerols (TAG) content and accumulation [21], providing a new view of the biosynthesis of fatty acids in the microalgae.

Several authors have shown that the RM methods are suitable for biodiesel production from microalgae to determine the FAs content. Wu et al. [23] demonstrated a method for direct quantitative and in vivo lipid profiling of oil-producing microalgae using single-cell RS laser capture. This approach shows that lipids in microalgae determine the quantitative degree of unsaturation and the transition temperature. As the authors stated, the above factors can be measured on a single living cell of a microalga held in place with an optical trap while Raman data is collected. Raman is used in the study of FAs from microalgae for biofuel production and has shown analytical capabilities and quantification algorithms to be useful in many different organisms and lipidomics (Figure 6).

**Figure 6.** Raman spectra of various lipid molecules of *Botryococcus braunii* [23].

Another ability of Raman microscopy reported by Samek et al. [5] showed how the useful iodine number in lipid bodies in *Chlamydomonas* sp. CCALA can be determined from living algal cells. At the same time, the characteristic peaks in the Raman light spectra at 1656 cm−<sup>1</sup> and 1445 cm−<sup>1</sup> were used as markers for fatty acids in algae lipids, indicating the ratio of unsaturated and saturated carbon-carbon bonds [5].

He et al. [19] investigated the accumulation of separation of TAG in *Coccomyxa subellipsoidea* cells in the presence of N-depletion using the broadband CARS concept. Compared to simple Raman imaging, CARS microscopy showed intrinsic advantages in detection speed and spatial resolution, but the concept of CARS imaging was limited by overlapping signals, such as two-photon-excited fluorescence.

Yan et al. reviewed several RS methods in cell sorting to understand the metabolic interactions between bacteria in natural habitat. This review shows current knowledge about the research progress of recognition and assessment of single microorganism cell. The group summarised that Raman-activated cell sorting can be suitable method for cell recognition in application [125].

Thus, Raman spectrometer is used in microalgal biotechnology to screen species for highly concentrated fatty acid mutants [18]. There are two major strategies in algal biofuels development: bioprospecting and mutagenesis. This requires precise sorting and analysis of a large number of algal isolates containing TAG; FACS and ratiometric Raman analysis are the most suitable. Isolation of new algae from field samples can be carried out by UV mutagenesis to increase lipid production. FACS can be used to sample mutant populations and strains that alter lipid production during UV mutagenesis. Central to this workflow is confocal Raman microscopy, which allows for characterisation of the lipids produced by the algae in situ and rapid extraction of lipids from the cells. Raman hyper-spectroscopy is used to localise the lipid-rich region with a low pixel density, allowing faster Raman hyper-spectroscopy imaging. Confocal Raman microscopy characterises the lipid content (Figure 7).

**Figure 7.** The schematic view in lipid characterisation of microalgae. Bioprospecting of *C. reinhardtii* is performed to generate algal samples with lipid content. The mutagens are sorted by FACS based on the fluorescence of a dye to select cells with high lipid content. The selected cells and mutants are then screened using CRM. This method allows for rapid characterisation of lipids. The spectra yield depends on the number of C=C bonds and the length of the hydrocarbon chains of the lipid molecules. This workflow enables rapid characterisation of cells for molecular traits that are important for the production of biodiesel. Modified from Sharma et al [18].

5.1.2. Application of Raman Spectroscopy on Pigment Investigation in Microalgae

Thus, considering the spectra of the pigments, they are very sensitive to the excitation energy and contribute to a large extent to the Raman spectra of many algae [108,125–128]. Chen et al. showed that when a long excitation wavelength of 488 nm is used, the strongest and most abundant peaks of chlorophyll-d coincide with the peaks of chlorophyll a and chlorophyll b [129]. β-Carotene has intense peaks at 1150 cm<sup>−</sup>1, 1520 cm−1, and 1008 cm−1, and the most important overtone peaks at 2320 cm−<sup>1</sup> and 2667 cm−<sup>1</sup> [130–133]. In addition, due to the identical chemical structure, it is expected that a large number of compounds of both chlorophyll and carotenoids will have similar spectra.

Furthermore, because of the similar chemical structure, we would expect different chlorophyll compounds to give similar spectra and different carotenoids to give similar spectra. Therefore, the major Raman peaks associated with chlorophyll d and β-carotene can be used to represent common chlorophylls and carotenoids. The standard spectra of chlorophyll d and β-carotene are shown together with the experimental spectra of algae (Figure 8).

**Figure 8.** The Raman spectrum of carotenoid [132], chlorophyll [129], and triglyceride and the mean spectra acquired for starved *C. sorokiniana* and starved *N. oleoabundans* in the wavenumber regions of 750–1750 cm−<sup>1</sup> and 2450–3150 cm<sup>−</sup>1. Modified from Shutova et al. [134].

Several researchers have shown that RS methods are also suitable for the production of carotenoids by microalgae. Carotenoids are extremely important for human health [135]. Carotenoids are popular biomolecules for biomedical applications. Carotenoids are known to play the role of photoprotection molecules in the cells of phototrophs. Secondary

carotenoids are also of interest as secondary carotenogenesis is thought to be the stress response of the cell [121].

It is also believed that carotenogenic microalgae can survive in a wide range of environmental conditions [136]. Nowadays, the analysis of carotenoids in algae is performed using high-performance liquid chromatography (HPLC). However, it is important to emphasise that RS can be used for a more detailed analysis of carotenoids in algal cells [17,20,24]. In addition, the development of RS, which will be applied to algae in the future, can be used for real-time research of algal combination in nature.

Jehliˇcka et al. [7] studied how RS can be used to identify various carotenoids as well as probable biomarkers in algae. A number of laboratory grown algae with different taxonomic groups were studied. The results showed that RS is considered an optimal tool for assessing the presence of carotenoids in a given organism. The comparison was made with the HPLC method to examine the pigments in the concentrates. In summary Raman spectroscopy can be used for the detection of carotenoids and other pigments in algae.

Osterrothová et al. [20] tested the abilities of RS to determine carotenoid pigments both basic (lutein, β-carotene) and secondary (astaxanthin) carotenoids—in different species of *Chlamydomonadales* algae. They also compared the performance of RS with a standard biological pigment analysis method, such as HPLC. They described the carotenoids of algae using a combination of resonance RS and HPLC, also creating a spectral library for different stages of the algal life cycle. A comprehensive study to find pigments in biomass can show results with HPLC. However, this method requires the extraction of pigments from the biomass, which can lead to data loss (e.g., protein/lipid interactions). Raman macro–microscopy, however, makes it possible to quickly reveal the pigments of single cells, which is another advantage, especially when the heterogeneous nature of the cells is taken into account.

To map the changes in the composition of β-carotene and AXT in different cellular morphotypes of *H. pluvialis*, Collins et al. [137] used a confocal Raman microscope at 532 nm laser excitation. Using a multivariate curve, several readable spectral components were extracted from the data describing Raman scattering and fluorescence of active *H. pluvialis* cells at different life stages. Based on the results, they were able to determine the arrangement of the different pigments in the cells at different time periods. They also concluded that β-carotene can be considered as an ancestor of AXT and a site for the synthesis of AXT. Their study shows that Raman micro-spectroscopy is an important method for studying in vivo changes stimulated by the environment in the life cycle of microalgae.

In their study, Chiu et al. [138] demonstrated for the first time that RS can be used to quantify starch in addition to lipids in algal cells. Because RS is so simple and nondestructive, it is ideal for further investigation of the starch–lipid shift mechanism.

RS provides information about the vibrations of bonds in molecules. This approach is used for the study of carotenoids. It has been shown that changes in the molecular environment (such as pH change) affect the specific bands (Figure 8) in the carotenoids' spectra [134].

This is particularly attractive for applied sciences, such as biotechnology and biomedicine.

#### **6. Raman Spectroscopy for Photosynthetic Studies**

Photosynthesis is the most basic and important process on earth. It is the natural way of synthesising carbohydrates using solar energy. Scientists from all over the world are exploring it with a number of applications, one of which is Raman spectroscopy. Findings from a number of recent studies on RS applied to photosynthetic organisms are shared below.

Mishra et al. [139] recently conducted their study on Antarctic lichens using RS. Antarctic lichens are organisms that can change their metabolism and photosynthetic activity in response to changing environmental conditions. Hydration and dehydration are the investigated triggers for the activation/deactivation of photosynthetic processes

in the lichens. It has been revealed that photosynthetic activity is activated quite rapidly, which contributes to the hypothesis that the photosynthetic apparatus and carotenoids are not synthesised de novo in the early stages of photosynthesis. Another important discovery made using RS, the bands/features of the pigment scytonemin, are present in the Raman spectra of one of the lichens studied. There is a hypothesis that this pigment plays a photoprotective role in the photobionts of algae and cyanobacteriae.

#### **7. Raman Spectroscopy for Analytical Studies**

There is an ongoing need for fast and accurate detection of melamine in dairy products. Melamine is a compound that can be toxic above a certain level when added to food. Therefore, it is important to propose an approach that allows accurate detection of the toxic compound in food products. Liu et al. [120] proposed the SERS method using silver nanoparticles (AgNPs). Not only SERS but also a colourimetric method was used for this idea. The results showed that the colourimetric method can lead to false-positives in detecting the presence of different compounds (AgNPs). The SERS method, on the other hand, can overcome this limitation [120]. Importantly, the scientists suggested using both methods in tandem to achieve accurate and rapid detection of melamine in dairy products.

Fentanyl is one of the most commonly used opioids. However, fentanyl and its analogues caused numerous fatal drug overdose incidents. The problem raised by the group of Mirsafavi et al. [140] is the need for novel analytical methods to effectively distinguish fentanyl from its precursors. The vibrational spectra of this family of analytes are quite similar, so it is difficult to solve the problem using conventional methods. The SERS method enables the distinguishing of fentanyl and its precursors. This approach would be an efficient and effective aid in the field of forensics.

#### **8. Future Perspectives**

In recent decades, RS has successfully emerged as a clinical tool for diagnostic, surgical, and pathological applications. The creation of probes in conjunction with modern methods of studying information has led to a surge in studies based on combinatorial light scattering. However, when introducing RS into clinics, there are the major difficulties described in the previous section, which should be overcome by close collaboration between clinicians, material scientists, biomedical engineers, and spectroscopists. Artificial intelligence algorithms are expected to be used to solve complex clinical issues, which will accelerate the work of RS. In addition, the probes must be resistant to disinfection for daily use. Further advances in scientific and technical research, which also guarantee a high signal-to-noise ratio with the lowest laser excitation power in a short time, are accordingly worth examining in order to use RS for intraoperative procedures. Nevertheless, introducing new technology into the clinic remains a challenge, even though recent successes and prospects represent meaningful ideas for us and inspire us to solve certain complex problems, opening the door to an appreciable goal.

In addition, Raman spectroscopy is a common tool for detecting carotenoids in various biological materials, including prokaryotic bacteria, aquatic plants, and lichens. The resonant Raman amplification of the signals enables the detection of carotenoids at low concentrations. In other cases, however, microorganisms also synthesise other pigments, and the examples studied included their composition. In this case, the combinatorial scattering ranges do not at all include a series of sudden bands corresponding to this carotene, nor was there any significant broadening of the bands. The predominant carotenoid can be seen in the spectra. However, it was not possible to use a unique excitation wavelength from a range of microorganisms.

Raman spectroscopy can be used to detect the presence of carotenoids and other pigments in cyanobacteria, microorganisms, and algae. The occurrence of colour combinations in this organism is capable of producing small or moderately significant changes in saturation and in the number of combinatorial scatter bands, which interferes with the

likely unambiguous identification of carotenoid c due to shifts in power, particularly in the position of the combinatorial scatter bands.

In recent years, RS has been widely used in research, including photosynthesis and analytical research. However, very little research has been conducted on plant photosynthesis using RS. Most research has been carried out to determine the composition of pigments in cyanobacteria.

#### **9. Conclusions**

RS and a spectrum of different modifications of the RS method are increasingly used in biological and medical research. RS is gradually becoming more popular among algae experts. In the list of studies, it has been revealed that we can successfully detect and analyse the Raman scattering signal of algae. This is important not only for biotechnology, but also for a better understanding of the mechanisms of the biomolecule synthesis and storage in algal cells. In this review, we have analysed and presented a number of existing studies in biological, medical, analytical, photosynthetic, and algal research using RS. RS is effectively and widely used for a variety of studies in animals and human research. We have attempted to highlight that a greater focus on the application of RS in algal research will be beneficial for biotechnological purposes and general knowledge of the mechanisms of the biomolecule interactions in algae under natural/environmental conditions. It is worth emphasising that RS is a very attractive and promising approach for algal research, especially because of its advantages.

**Author Contributions:** Investigation, formal analysis, and data curation: E.S.A., V.V.K., B.D.K. and E.V.Z.; drawing of main conclusions and draft preparation of the manuscript: E.S.A., V.V.K., E.V.Z., O.V.R. and T.V.M.; draft preparation of the manuscript: O.V.R., T.V.M., G.V.M., S.A., T.T. and S.I.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** S.A. would like to thank the Distinguished Scientists Fellowship Program, King Saud University, Saudi Arabia, for their support. This work was supported by JSPS KAKENHI Grant Numbers 20H05114, J21K06101 to T.T.; V.V.K., E.V.Z., S.I.A. was supported by the grant from Russian Science Foundation (No: 22-44-08001); The results of part (Sections 5 and 6) were obtained within the state assignment of Ministry of Science and Higher Education of the Russian Federation (project No. 121033000136-4). G.V.M. is supported by the Russian Science Foundation (grant No:19-79-30062) as well as the Interdisciplinary Scientific and Educational School of Moscow University "Molecular Technologies of Living Systems and Synthetic Biology".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors. The data are not public.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

