Characterizing metabolic stress-induced phenotypes of Synechocystis PCC6803 with Raman spectroscopy

Bacterial strain and culture conditions

Synechocystis sp. PCC6803 (ATCC^®27184™) were used to create axenic kanamycin resistant mutants for the comparative growth experiments (Heidorn et al., 2011; Tanniche, Collakova & Senger, 2019, unpublished data). This strain was used with kanamycin (15 µg/mL) to help prevent contamination. Cells were cultivated in the BG-11 medium (Rippka et al., 1978) (adjusted to pH 7.0) supplemented with 5 mM glucose. Three different stress conditions were analyzed: (i) NaCl (50, 100, and 150 mM), (ii) acetate (7.5, 15, and 30 mM) and (iii) MgSO₄ (0, 15.6, 31.2, and 62.5 mM; representing 0%, 25%, 50% and 100% of BG-11). Cultures grown in BG-11 were used as controls, and cells harvested in mid-exponential phase were used to initiate stress experiments. Each culture type condition experiment was performed in biological triplicate. Cultures were grown in 50 mL volume in 125 mL flasks, incubated at 25 °C under continuous light (20 µE), agitated at 140 rpm, and ambient CO₂. Cells were harvested at mid-exponential phase and washed twice with ice-cold purified water. 2 µL of cells were kept for Raman scans and the remaining pellet was freeze-dried and prepared for analyses by UPLC and GC-FID.

Raman spectroscopy

To prepare samples for Raman analysis, 2 µL of washed cells were dried at room temperature on aluminum foil (three samples per biological replicate). Dried cells were analyzed using a PeakSeeker PRO-785 Raman microscope (Agiltron; Woburn, MA) with 10X objective. Measurements were carried out using the following settings: laser excitation of 785 nm (30 mW) for 5 seconds with spectral resolution of 13 cm⁻¹. Twenty individual scans were taken per sample by focusing on different areas of the dried cells. Raman data were collected using RSIQ™  software.

Computational methods

Acquired Raman spectra were processed and analyzed using the Rametrix™ LITE Toolbox (Fisher et al., 2018) and Rametrix™ PRO Toolbox (Senger & Robertson, 2020) for MATLAB^®. MATLAB^® R1018A with the Statistics and Machine Learning Toolbox was used for all calculations. Data analysis with Rametrix™ LITE Toolbox (Fisher et al., 2018) consisted of averaging the 20 spectra replicates and then baselining with the Goldindec algorithm (Liu et al., 2015) (baseline polynomial order = 3, estimated peak ratio = 0.5, and smoothing window size = 5) and vector normalizing over the biological range (400 cm⁻¹–1,800 cm⁻¹). PCA was performed, and outlier spectra were identified and excluded from analysis. Next, DAPC was applied, and several DAPC models were constructed by varying the number of principal components (PCs) used to build each one. DAPC models enabled the separation of Raman spectra according to a preset experimental factor. Here, the culture medium composition and environmental stimuli (acetate, NaCl, and MgSO₄) concentrations were used. The Rametrix™ PRO Toolbox (Senger & Robertson, 2020) was used to apply leave-one-out analysis to DAPC clustering. This analysis determines the ability of DAPC models to predict correctly the classification when presented with an unknown Raman spectrum. The performance of DAPC models are quantitated by prediction accuracy (percent of unknown spectra classified correctly), sensitivity (true positive percentage), and specificity (true negative percentage).

Analysis of variance (ANOVA), pairwise comparisons using Tukey’s honest significant difference (HSD), and regression statistical analyses required the representation of the Rametrix™  LITE outputs (spectra intensity values, PCA, and DAPC) as a single numerical value. For example, the hundreds of intensity values at each Raman shift (400–1,800 cm⁻¹) had to be reduced to a single numerical value for each spectrum. This is also true as data-rich spectra were reduced from hundreds of intensity values to tens of PCs by PCA and canonical values by DAPC. To calculate this single numerical value for statistical comparisons between spectra, the distance formula was applied to determine the similarity between two spectra, where one was a reference. When using PCA data, the Total Principal Component Distance (TPD) was calculated according to Eq. (1), where the first five PCs (representing more than 95% of the dataset variance) were used. (1)${\displaystyle TPD=\sum _{i=1}^5\sqrt{{\left(P_{x,i}-P_{reference,i}\right)}^2}.}$ Here, P_x,i is the ith PC of spectrum x and P_reference,i represents the ith PC of the reference. The calculation was repeated for all x spectra in the analysis. Different spectra were chosen as the reference in each of the four sub-studies presented in this research. These are identified for each in Appendix S1 in the Raw Distances and Mean Distances sheets. More details about TPD and its value in statistical calculations have been published (Senger et al., 2019). Similarly, the distance calculation was applied to each spectrum to calculate the Total Spectral Distance (TSD), based on normalized intensity values across all Raman shifts, and the Total Canonical Distance (TCD), based on the first five canonicals in the DAPC model. Reduction of spectral data into a single value allowed statistical analysis including analysis of variance (ANOVA), pairwise comparisons using Tukey’s honest significant difference (HSD), and regression. These analyses were performed to assess whether significant differences exist among the separated groups of spectra (ANOVA), to determine which group differences are statistically significant (pairwise comparisons), and establish a linear fit among data.

Biomass measurements

Fatty acid content was determined by gas chromatography-flame ionization detection (GC-FID) after direct hydrolysis and conversion to fatty acid methyl esters (FAME). Lyophilized cells were incubated with methanolic HCl and 10 µg heptadecanoic acid (C17:0) as internal standard at 75 °C for 2 h to form FAME. GC-FID was done on an Agilent 7890A series GC equipped with FID. For fatty acid identification purposes, the same GC was coupled to a 5,975C series single quadrupole mass spectrometer (MS) and fatty acids were identified by comparing the spectra with the corresponding spectra in the NIST library (Agilent Technologies, Santa Clara, CA).

Protein hydrolysis was carried out in a custom-made teflon hydrolysis chamber (see photo in Appendix S2) under vacuum at 110 °C in the presence of 6N HCl. 50 mM norvaline was used as an internal standard. Amino acid levels were determined after derivatization with AccQ-Tag^TM reagent using a Waters Acquity^TMH-class ultra-performance liquid chromatography (UPLC) system equipped with a fluorescent detector (Waters Corporation, Mildford, MA).

Public access

The Rametrix™ LITE Toolbox is shared through GitHub with license agreement (https://github.com/SengerLab/RametrixLITEToolbox), and the Rametrix™ PRO Toolbox is also accessible through GitHub with license agreement (https://github.com/SengerLab/RametrixPROToolbox). Raw Raman spectra (in *.SPC file format) and biological measurement raw data are shared in the Supplemental Information 3.

Raman spectroscopy of Synechocystis grown in the presence of acetate and salts

Raman spectroscopy was used to identify phenotypic changes in Synechocystis induced by the presence of: (i) acetate, (ii) NaCl, and (iii) MgSO₄. Cells were scanned with Raman microscopy and the resulting spectra were analyzed using the Rametrix™  LITE (Fisher et al., 2018) and PRO (Senger & Robertson, 2020) Toolboxes. The obtained spectra were averaged, truncated (400–1,800 cm⁻¹), baselined, and vector normalized prior to further processing using PCA and DAPC in the Rametrix™  LITE. Rametrix™  PRO was used to determine the ability to predict a particular metabolic phenotype given a Raman spectrum of cells grown under unknown growth conditions. Next, statistical analyses (i.e., ANOVA and pairwise comparisons) were performed on TCD and TPD results. TPD and TSD produced similar results, allowing TSD to be omitted in the analysis. Finally, specific Raman bands assigned to certain metabolites (i.e., amino acids and fatty acids) were compared to standardized analytical methods (i.e., UPLC, GC-FID). The remainder of the results section is divided according to analysis type: (i) Rametrix™  analyses and (ii) correlation of individual Raman bands with well-established analytical approaches.

Rametrix™ analyses

Acetate-induced phenotypes

Raman spectra of cultures growing in three concentrations of acetate (7.5 mM, 15 mM and 30 mM) and the control (BG-11 without acetate) were analyzed by Raman spectroscopy and the Rametrix™  LITE and Rametrix™  PRO Toolboxes. PCA, DAPC, and PC contributions results are shown in Fig. 1. PCA of Raman spectra (Fig. 1A) showed some separation of clusters in the two first PCs, which contained more than 68% of the dataset variance. This clustering is more noticeable with the DAPC model (Fig. 1B) constructed with 12 PCs (representing more than 99% of the dataset variance). Although four marked clusters were revealed, based on the concentration of acetate in the BG-11 medium, only three major groups were observed. These clusters suggest that phenotypic differences could be generated as a result of acetate addition to BG-11 medium and that cells grown in the presence of 7.5 mM and 15 mM could have similar phenotypic responses. Next, Raman shift contributions for both PCA and DAPC were studied to point to molecular differences between clusters of Raman spectra. In this particular case, it provided information about the molecular differences between cultures grown in the presence and absence of acetate. It does not provide information about accumulation or depletion of individual biomolecules (e.g., carotenoids), but it does suggest the associated band intensities change significantly. Raman shift contributions between groups in PCA are shown in Fig. 1C and the contributions associated with DAPC model are represented in Fig. S1. Raman band assignments were selected based on published libraries (De Gelder et al., 2007; Movasaghi, Rehman & Rehman, 2007; Zhu et al., 2011). The full list of assignments is given in Appendix S1, and highlights are given in Table 1.

Table 1:

Highlighted Raman shift contributions to PCA and DAPC models.

Study	Model	Biomolecules and Raman bands (cm−1)
Acetate-induced	PCA	Glycerol (630 cm−1), amino acid (573 cm−1) protein (1,156 cm−1), and carotenoids (1,156, 1,518 cm−1)
Acetate-induced	DAPC	Proteins (524, 618, 1,005, 1,155 cm−1), carotenoids (1,155, 1,528 cm−1), amino acids (532, 638, 715, 920, 988, 1,005, 1,196 1,327, 1,495, 1,550, 1,558, 1,615 cm−1), phosphatidylinositol (415 cm−1), polysaccharides (477 cm−1), DNA/RNA (746, 1,510 cm−1), and lipids (1,086 cm−1)
NaCl-induced	PCA	Glycerol (630 cm−1); proteins, amides and lipids (1,260 cm−1); carotenoids (1,157, 1,520 cm−1); and amino acids (515, 573, 669, 684, 1,188 cm−1)
NaCl-induced	DAPC	Sterols (430 cm−1), phosphatidylinositol (594 cm−1), DNA/RNA (746 cm−1), DNA (1,090, 1,329 cm−1), amino acids (730, 854, 1,040, 1,495 cm−1), carotenoids (1,157, 1,517 cm−1), nucleic acids (650, 1,200, 1,329 cm−1), and lipids (1,255 cm−1)
MgSO4-induced	PCA	Sterols (429 cm−1), glycerol (630 cm−1), carotenoids and carotene (1,157, 1,520 cm−1), amino acids (573, 684, 1,188 cm−1), lipids (1,380 cm−1), and phospholipids (1,330 cm−1)
MgSO4-induced	DAPC	Carotenoids (1,157, 1,520 cm−1) and amino acids (850, 992 cm−1) in addition to phosphatidylinositol (412 cm−1) and saccharides (850, 868, 1,456 cm−1)

DOI: 10.7717/peerj.8535/table-1

Next, TCD, TPD and TSD (both raw and mean values) were calculated using PCA and DAPC data and used in ANOVA and pairwise comparisons. Full results are given in the Appendix S1. In general, as the phenotype deviates from the reference (represented here by phenotypes of cells grown on BG-11 medium), the TCD (or the TPD) mean value should become larger. Similar phenotypes should have comparable values. In the acetate-induced phenotypes, TCD and TPD mean values increased proportionally to the increase of acetate concentration. Based on TCD data (using 12 PCs in DAPC model) ANOVA results indicated that differences between the groups are significant (p < 0.001). With TPD data, ANOVA returned a significant p-value (p < 0.001). This result indicates that even though no clear clustering was observed in PCA (Fig. 1A), there are statistically significant differences between the groups. Pairwise comparisons identified all phenotypes (groups) to be significantly different based on the TCD data (p < 0.001). However, using TPD data, 7.5 mM and 15 mM induced phenotypes were found not significantly different (p = 0.997). The small difference in mean TPD and the near clustering observed in Fig. 1B are consistent with this result. Next, regression (Appendix S1) was computed between acetate concentrations (0 mM, 7.5 mM, 15 mM, 30 mM) and the TCD and TPD mean values, respectively. Results show a linear relationship among the data with a coefficient of determination (R²) of 0.97 and 0.84 for TPD and TCD, respectively.

Rametrix™ PRO was used to apply leave-one-out analysis to the DAPC clustering in Fig. 1B. The purpose of this analysis is to determine the ability of the DAPC model to predict precisely the classification (i.e., 0 mM, 7.5 mM, 15 mM, 30 mM of acetate) when presented with a Raman spectrum of Synechocystis cells grown in an unknown condition. Rametrix™ PRO results are also given in Appendix S1, and more details of Rametrix™ PRO calculations are given in Senger & Robertson (2020). Here, four classification options, 0 mM, 7.5 mM, 15 mM and 30 mM, were assessed. With four different classifications, the random chance specificity was calculated as 25% and the random chance specificity as 75% for this dataset. Therefore, model performance sensitivity and specificity must be higher than these values if the model is truly able to predict Synechocystis phenotypes based on Raman spectra. Highlighted Rametrix™ PRO results are given in Table 2, and all sensitivity and specificity values exceeded random chance values, with specificity and sensitivity values ranging between 56–100% and 81–100%, respectively.

Table 2:

Highlighted Rametrix™ PRO results.

Study	Classification predicted	Sensitivity	Specificity
All Studies	Random chance**	25%	75%
Acetate-induced	0 mM	100%	89%
Acetate-induced	7.5 mM	67%	81%
Acetate-induced	15 mM	56%	100%
Acetate-induced	30 mM	56%	96%
NaCl-induced	0 mM	100%	100%
NaCl-induced	50 mM	33%	96%
NaCl-induced	100 mM	44%	100%
NaCl-induced	150 mM	44%	89%
MgSO4-induced	62.5 mM	100%	100%
MgSO4-induced	31.2 mM	89%	96%
MgSO4-induced	15.6 mM	100%	89%
MgSO4-induced	0 mM	56%	78%
All phenotypes	BG-11 control	100%	100%
All phenotypes	Acetate-induced	56%	100%
All phenotypes	NaCl-induced	71%	95%
All phenotypes	MgSO4-induced	93%	100%

DOI: 10.7717/peerj.8535/table-2

Notes:

*All DAPC models were built with 12 PCs representing 99% of the dataset variance.

**The random chance sensitivity and specificity values were calculated statistically, not by RametrixTM PRO. They were the same for all studies here.

NaCl-induced metabolic phenotypes

In order to determine the effect of NaCl addition to BG-11 medium on Synechocystis metabolic phenotypes, cells were grown in BG-11 at three salt concentrations (50 mM, 100 mM, and 150 mM) and compared to a control (cells grown in BG-11 alone with no NaCl added). PCA, DAPC and PC contribution results generated by the Rametrix™ LITE are represented in Fig. 2. PCA of Raman spectra (Fig. 2A), again, showed no apparent clustering in the first two PCs, which comprised about 69% of the dataset variance. However, the application of DAPC (Fig. 2B) demonstrated four clusters representative of the three different concentrations of NaCl and the control. These results were obtained using 12 PCs (representing over 99% of the dataset variance). Based on the presence of NaCl, two major groups were noticed (Fig. 2B), suggesting significant cellular phenotype differences could occur in response to as little as 50 mM NaCl in BG-11 medium. Next, Raman shift contributions to the PCA and DAPC results were examined. Raman shift contributions between the groups in PCA are shown in Fig. 2C, and those related to DAPC contributions are represented in Fig. S2. Specific lists of Raman band contributions and molecular assignments (for both PCA and DAPC) are given in Appendix S1, and highlights are given in Table 1.

DAPC model and PCA were used to compute TCD, TPD, TSD, and their mean values. Multi-way ANOVA and pairwise comparisons were used to assess these parameters, and full results are given in Appendix S1. As mentioned previously, the TCD and TPD mean values increase with the increased difference from the reference phenotype. Using the TPD mean values, as the concentration of NaCl increased, the distance also increased. However, a small difference was observed between the 50 mM and the 100 mM TPD means, suggesting comparable phenotypes. The TCD mean values and the clustering in the Fig. 2B confirm this result. ANOVA revealed that molecular phenotypes induced at different NaCl concentrations were significantly different (p < 0.001) for TCD and TPD data. Pairwise comparison allowed the identification of significantly different phenotypes. Based on the TPD data, the phenotypic responses associated with 100 mM NaCl were not significantly different from the BG-11 medium (p = 0.165) and 50 mM (p = 0.091). Pairwise comparisons also showed that the phenotypes observed at 150 mM NaCl were not statistically different from the phenotypes at 50 mM (p = 0.04). On the other hand, based on the TCD data, the phenotypes resulting from 50 mM, 100 mM, and 150 mM NaCl treatments were all statistically different from the BG-11 medium control (all p-values < 0.001), but they were not different from one another (all p-values >0.027). Finally, Regression (Appendix S1) was applied between NaCl concentrations and the TCD and TPD mean values. A coefficient of determination (R²) of 0.64 and 0.51 were obtained for TPD and TCD, respectively.

Rametrix™ PRO was used to apply leave-one-out analysis to DAPC in Fig. 2B. Similar to the previous analysis, four classification options were included: 0 mM, 50 mM, 100 mM, and 150 mM NaCl. Each of these conditions was selected as the positive condition to generate accuracy, sensitivity, and specificity results. As mentioned previously, sensitivity and specificity values should exceed those of the random chance values to confirm the model has value in identifying Synechocystis phenotypes. Highlighted results for a DAPC model built with 12 PCs (representing more than 99% of dataset variability) are given in Table 2. Results for additional DAPC models are given in Appendix S1. The best-performing model (Table 2) returned sensitivity and specificity values that exceeded the random chance values. In addition, 0 mM classification showed the highest sensitivity and specificity rates (100%). This suggests that NaCl-induced phenotypes may occur below 50 mM and remain somewhat similar through 150 mM. The BG-11 control phenotypes were significantly distinguishable from the ones observed for the other three conditions, as shown in Fig. 2B.

MgSO₄-induced phenotypes

Synechocystis cells growing under different levels of MgSO₄ present in BG-11 medium were analyzed using Raman spectroscopy. PCA, DAPC and PCA contributions generated by the Rametrix™ LITE Toolbox are presented in Fig. 3. Again, no clustering was observed with PCA of Raman spectra (Fig. 3A) in the first two PCs (representing over 75% of the dataset variance). Application of DAPC using 12 PCs (representative of more than 99% of the dataset variance) revealed four distinguishable clusters (Fig. 3B), based on the concentration of MgSO₄ in BG-11 medium. These clusters formed two separate groups representing cells grown under limiting concentrations of MgSO₄ and BG-11 medium, confirming that limiting MgSO₄ in the medium is linked with altering cellular phenotypes. Analysis of Raman shift contributions to the PCA and DAPC groups was also performed. Full results are given in Appendix S1, and highlights are given in Table 1. DAPC contributions are also identified in Fig. S3.

TSD, TCD and TPD values were calculated based on spectra, DAPC, and PCA results. These values were analyzed further by ANOVA and pairwise comparisons. All results are available in the Appendix S1. In general, TCD and TPD increased with the decrease of MgSO₄ concentration, with the 15.6 mM classification mean values being closer to the reference when using the TPD means, compared to TCD mean values. Next, ANOVA and pairwise comparison tests were performed. It was found that differences between the groups (in both DAPC and PCA) are statistically significant (p < 0.001). Pairwise comparisons allowed the identification of significantly different phenotypes. With both TPD and TCD data, all phenotypes were different from those observed when cells were grown in the original BG-11 medium containing 62.5 mM MgSO₄. These results confirm that even a 50% reduction in MgSO₄ from the original BG-11 medium gives rise to various phenotypes. Results also show that phenotypes associated with the reduced MgSO₄ content (31 mM) are not significantly different from 0 mM (p = 0.096) and phenotypes with even more reduced content (15.6 mM) (p = 0.032), when using TPD data. Similar results were found when using TCD values. Regression analysis (Appendix S1) between MgSO₄ concentrations and both TCD and TPD mean values was performed. Results show linear relationships between the data for both cases with a correlation coefficient (R) of 0.81 and 0.76 for TPD and TCD, respectively.

For Rametrix™ PRO leave-one-out analysis on the DAPC model (Fig. 3B), four classification options were included for the four tested concentrations of MgSO₄. Each one of these conditions was treated as a positive condition to generate accuracy, sensitivity, and specificity results. Again, results for several DAPC models are given in Appendix S1, and results for the DAPC model generated with 12 PCs (the best performer) are given in Table 2. Results confirmed the BG-11 cluster (62.5 mM MgSO₄) (100% sensitivity and specificity) is significantly different from the other clusters. Again, all sensitivity and specificity values exceeded random chance, meaning the DAPC model was capable of applying these classifications for an unknown Raman spectrum of Synechocystis with accuracy greater than random chance.

Classification of all phenotypes

All Raman scans performed in this study were compiled in a single dataset and included the growth conditions in the presence of the following environmental stimuli: (i) acetate, (ii) NaCl, (iii) MgSO₄ and (iv) BG-11 medium (set as the control). Data analysis included the generation of PCA and DAPC models and validation with the leave-one-out analysis using Rametrix™ LITE and Rametrix™ PRO Toolboxes, respectively. PCA results are given in Fig. 4A and show some clustering, based on the two first two PCs which covered over 59% of the dataset variance. A DAPC model was constructed based on 15 PCs (representing over 99% of the dataset variance) and indicates four distinct clusters based on the presence or absence of environmental stimuli in the BG-11 medium (Fig. 4B). These clusters could be the result of potential differences in the cellular phenotypes obtained with every growth condition. Most importantly, a major segregation was observed for cells grown under MgSO₄, clearly separating from the other conditions. Raman shift contributions to PCA (Fig. 4C) and DAPC groups (Fig. S4) are listed in the Appendix S1.

The analysis of TCD and TPD, using the BG-11 medium as a reference, allowed the classification of phenotypes. Both TCD and TPD mean values indicated the distance from the control (indicating more different phenotypes): (i) acetate-, (ii) NaCl-, and (iii) limited MgSO₄-induced phenotypes. It appears that MgSO₄ limitation generated the most significant changes in the cellular phenotypic responses (also shown in Fig. 4B). In addition, multi-way ANOVA indicated statistically significant differences between the groups representing each phenotype (p < 0.001). Using TPD data, pairwise comparisons indicated that all groups are significantly different from each other, only acetate (over all concentrations together) was not statistically different from BG-11 (p = 0.021). Based on TCD data (using 15 PCs), all phenotypes are significantly different (p < 0.001).

Next, Rametrix™ PRO was applied to determine if the phenotypes generated by the DAPC model could be predicted from Raman spectra (Appendix S1). Results are highlighted in Table 2. Again, all results exceeded the random chance values, indicating ability by the DAPC model to identify environmental stimuli exposure with accuracy much better than random chance.

Correlation of individual Raman bands with well-established analytical approaches

Another method of comparing phenotypes is analysis of Raman bands assigned to functional groups of biomolecules. These signals are then correlated with results from accepted analytical methods such as GC-FID and UPLC for analyzing the levels of fatty acids and amino acids, respectively. The analysis of individual Raman band intensities was conducted based on the sets of Raman bands identified in previous research (De Gelder et al., 2007; Movasaghi, Rehman & Rehman, 2007; Zhu et al., 2011) and all band assignment are available in Appendix S2.

Acetate study

The levels of amino acids (protein-derived and free amino acids) were determined by both Raman spectroscopy and UPLC analysis (Fig. S5). The general trend is an increase in amino acids levels for the three tested concentration of acetate (7.5 mM, 15 mM, 30 mM) compared to the control with an over-production of amino acids at 15 mM acetate and comparable levels at 7.5 mM and 30 mM acetate.

Raman assigned bands for the different amino acids (De Gelder et al., 2007; Zhu et al., 2011) were correlated with the UPLC data (Fig. S6). For most amino acids, correlation coefficients (R) varying between 0.61 and 0.97 were obtained. Ala, Leu, Lys, Met, and Ser had correlations with values of R ranging from 0.43 to 0.59. Since the acidic protein hydrolysis method used to release amino acid prevents from determining Cys and Trp levels (Olcott & Fraenkel-Conrat, 1947; Hugli & Moore, 1972; Yamada, Moriya & Tsugita, 1991), these amino acids were predicted using Raman band assignment alone (Fig. S7). The same trend was observed when Cys and Trp levels were higher at 15 mM of acetate.

Similarly, fatty acid content was evaluated and then compared to Raman bands (De Gelder et al., 2007; Czamara et al., 2015). Overall, there was an increase in fatty acid levels, mainly for C16:0, C18:2 and C18:3, where, at 15 mM acetate, levels of these fatty acids were the highest (Fig. S8). However, minor changes were observed for C16:1 and C18:1. Next, Raman signals corresponding to those fatty acids were compared with the GC-FID results (Fig. S9). Correlations were observed for C16:0 (R = 0.92) and C18:2 (R = 0.82), and correlation coefficients of 0.67 and 0.68 were found for C18:1 and C18:3, respectively. C16:1 had a correlation 0.46.

NaCl study

Cells growing in different concentrations of NaCl were examined for their amino acid and fatty acid levels with UPLC and GC-FID, respectively. Raman signals corresponding to these biomolecules were used for comparison. Amino acid analysis revealed an increase in their levels proportionally to the amount of NaCl supplied to the media with a distinguishable over production of amino acids in the cells grown in the presence of 150 mM NaCl (Fig. S10). Comparison with the associated Raman bands showed correlation between the UPLC and Raman microscopy data (Fig. S11), where correlation (R) values were between 0.7–0.99, and slightly less correlation was observed for Val (R = 0.64). As stated previously, Cys and Trp are only predicted using Raman spectroscopy since they degrade during acidic hydrolysis (Olcott & Fraenkel-Conrat, 1947; Hugli & Moore, 1972; Yamada, Moriya & Tsugita, 1991). Again, based on the Raman assignments, we were able to resolve the patterns of these two amino acids, showing an increase in their levels mainly at 150 mM of NaCl (Fig. S12).

Finally, GC-FID analysis of the levels of free and lipid-derived fatty acids (Fig. S13) revealed an increase in the levels of C16:0, C18:2, and C18:3 in cells subjected to 50 mM of NaCl. In addition, no changes were observed for C16:1 and C18:1 levels. These results were compared with specific Raman bands assigned to each fatty acid. With the exception of C18:2, correlations (R) between 0.7 and 0.95 were obtained. A lower correlation was observed for the C18:2 between the Raman and GC-FID data (R = 0.28). This value improved to 0.7 when the 0 mM NaCl data point was removed from the analysis.

MgSO₄ study

The correlation of the individual Raman bands with their associated biomolecules could not be achieved for this study. MgSO₄ is the major source of both elements in the BG-11 medium and sufficient amounts of the biomass could not be obtained for cells deprived of this salt, which prevented UPLC and GC-FID analyses.