Table S1: Fatty acid composition (% total lipids) of the oil extracted from Thraustochytrium

Transcription

1 Supplementary Information Table S: Fatty acid composition (% total lipids) of the oil extracted from Thraustochytrium sp. AH-2 (PRA-296 TM ) by GC-FAME technique according to the protocol and conditions described in Ref. 8. Fatty acids Common Name Amount a (% total lipids) C4: C5: C6: C7: C8: C22: myristic acid pentadecylic acid palmitic acid margaric acid stearic acid behenic acid 4.9 ± ± ±..2 ±.5.94 ±.3.42 ±.5 Sum SFAs 42.6 ±.3 C6: C8:n-9 C8: C22:n-9 C24:n-9 palmitoleic acid oleic acid methyl trans-vaccenate erucic acid nervonic acid 2.88 ±.9.52 ± ± ±..7 ±.3 Sum MUFAs 6.5 ±.44 C8:3n-3 C2:4n-3 C2:5n-3 C22:5n-3 C22:6n-3 C8:2n-6 C2:3n-6 C2:4n-6 C22:5n-6 -linolenic acid (ALA) eicosatetraenoic acid (ETA) eicosapentaenoic acid (EPA) docosapentaenoic acid (DPA) docosahexaenoic acid (DHA) linoleic acid (LA) dihomo- -linolenic acid (DGLA) arachidonic acid (AA) docosapentaenoic acid (osbond acid). ±..26 ± ±.9.63 ± ± ±.. ±.. ± ±.42 Sum PUFAs 5.29 ±.45 a values are mean ± standard deviation obtained in triplicates.

2 Figure S2: Average EMSC-corrected (a) absorbance and (b) 2 nd derivative spectra of dehydrated formalin-fixed (blue) and hydrated live (red) thraustochytrium cells (PRA-296 TM ) acquired using FPA-FTIR and synchrotron FTIR microspectroscopic techniques, respectively. 2

3 Predicted Y Predicted Y Predicted Y Figure S3: Complementary PLS-DA results to that presented in Fig. 4 including linear regression models trained by replicate II spectral set (left) and their corresponding prediction results of replicate I samples used as an independent validation set (right). Note that the numbers of the cell samples included in replicate I and II sets are 8 and 79, respectively. Predicted vs. Reference 2 AMCQC AMCQ2C AMCQD AMCQ8A Thraustochytrids (a) Yeast AMCQC (b) Yeast AMCQ2C (c) Yeast AMCQD -2 Samples 3

4 Predicted Y Predicted Y 2 Predicted vs. Reference AMCQC AMCQ2C AMCQD AMCQ8A Thraustochytrids (d) Yeast AMCQ8A (e) Thraustochytrids - -2 Samples 4

5 Table S4: Complementary SIMCA classification results at 95% significance limit obtained based on the cross-validation approach using replicate II spectral data as a training set and spectra in replicate I set as independent validation (test) samples, with the same parameters used in Table 2. Note that the numbers of the cell samples included in replicate I and II sets are 8 and 79, respectively. Samples Class membership 5% Yeasts Rhodotorula sp. AMCQC AMCQ2C AMCQD AMCQ8A Thraustochytrids C-R_2 * C-R_3 * C-R_5 * C-R_8 * C-R_9 * C-R_ * C-R_2 * C-R_3 * C-R_7 * C-R_2 * C-R_2 * 2C-R_2 * 2C-R_4 * * 2C-R_6 * 2C-R_7 * 2C-R_8 * 2C-R_ * 2C-R_2 * 2C-R_4 * 2C-R_5 * 2C-R_8 * 2C-R_9 * 2C-R_2 * D-R_ * D-R_2 * D-R_4 * D-R_7 * D-R_8 * D-R_ * D-R_3 * D-R_4 * D-R_7 * D-R_8 * D-R_9 * D-R_2 * D-R_2 * 5

6 8A-GC5-R_ * 8A-GC5-R_5 * 8A-GC5-R_7 * 8A-GC5-R_9 * 8A-GC5-R_2 * 8A-GC5-R_2 * 8A-GC5-R_25 * 8A-GC5-R_26 * 8A-GC5-R_28 * 8A-GC5-R_3 * 8A-GC5-R_32 * 8A-GC5-R_34 * 8A-GC5-R_35 * 8A-GC5-R_37 * 8A-GC5-R_38 * 8A-GC5-R_43 * 8A-GC5-R_44 * 8A-GC5-R_46 * 8A-GC5-R_49 * 8A-GC5-R_5 * PRA-R_ * PRA-R_4 * PRA-R_5 * PRA-R_7 * PRA-R_ * PRA-R_ * PRA-R_3 * PRA-R_4 * PRA-R_6 * PRA-R_7 * PRA-R_9 * PRA-R_2 * PRA-R_2 * PRA-R_24 * PRA-R_25 * PRA-R_27 * PRA-R_28 * PRA-R_3 * PRA-R_33 * PRA-R_34 * PRA-R_36 * PRA-R_39 * PRA-R_4 * PRA-R_42 * 6

7 Predicted Y (%UFAs per total lipids, Factor-4) Predicted Y (%UFAs per total lipids, Factor-2) Predicted Y (%UFAs per total lipids, Factor-) Figure S5: Optimised s and the predictions for quantitative determination of the time-course %UFAs based on crossvalidation approach using 2 nd derivative spectral input over the two spectral windows that contain biological information about the cells (i.e and cm - ) and different number of latent factors. Note that the numbers of the cell samples included in replicate I and II sets are 8 and 79, respectively. 4 latent factors 2 latent factors latent factor Cross-validation model A Cross-validation model A Cross-validation model A 6 (training set: replicate I) 7 6 (training set: replicate I) 7 6 (training set: replicate I) Slope Offset RMSE R Reference Y (%UFAs per total lipids, Factor-4) 2 Slope Offset RMSE R Reference Y (%UFAs per total lipids, Factor-2) 2 Slope Offset RMSE R Reference Y (%UFAs per total lipids, Factor-) 6 (validation set: replicate II) 7 6 (validation set: replicate II) 7 6 (validation set: replicate II) y =.7x.766 RMSEP:.928 R 2 : y =.9483x RMSEP: R 2 : y =.945x RMSEP: R 2 :

8 Predicted Y (%UFAs per total lipids, Factor-4) Predicted Y (%UFAs per total lipids, Factor-2) Predicted Y (%UFAs per total lipids, Factor-) 4 latent factors 2 latent factors latent factor Complementary cross-validation model B Complementary cross-validation model B Complementary cross-validation model B 6 (training set: replicate II) 7 6 (training set: replicate II) 7 6 (training set: replicate II) Slope Offset RMSE R Reference Y (%UFAs per total lipids, Factor-4) 2 Slope Offset RMSE R Reference Y (%UFAs per total lipids, Factor-2) 2 Slope Offset RMSE R Reference Y (%UFAs per total lipids, Factor-) 6 (validation set: replicate I) 7 6 (validation set: replicate I) 7 6 (validation set: replicate I) y =.9627x RMSEP: R 2 : y =.9248x RMSEP: R 2 : y =.879x RMSEP: R 2 :

9 Regression coefficients Figure S6: Comparison of the corresponding PLSR regression coefficients obtained from the optimised calibration models with different number of Regression coefficients latent factors as shown in Fig. S5, relative to 2 nd derivative spectra of the yeast and the thraustochytrium cells, in order to confirm that the optimised PLSR models and the predictions genuinely reflect spectral information without the contribution from the spectral noise. (a) Cross validation model A (replicate I as a calibration set) (b) Complimentary cross validation model B (replicate II as a calibration set) d 2 Abs / d(cm - ) 2 2 nd derivative spectra thraustochytrid yeast AMCQ8A d 2 Abs / d(cm - ) 2 2 nd derivative spectra thraustochytrid yeast AMCQ8A latent factor latent factor 2 latent factors 2 latent factors 4 latent factors 4 latent factors Wavenumber (cm - ) Wavenumber (cm - ) 9

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