Evaluation of GC/MS-Based 13C-Positional Approaches for TMS Derivatives of Organic and Amino Acids and Application to Plant 13C-Labeled Experiments

Analysis of plant metabolite 13C-enrichments with gas-chromatography mass spectrometry (GC/MS) has gained interest recently. By combining multiple fragments of a trimethylsilyl (TMS) derivative, 13C-positional enrichments can be calculated. However, this new approach may suffer from analytical biases depending on the fragments selected for calculation leading to significant errors in the final results. The goal of this study was to provide a framework for the validation of 13C-positional approaches and their application to plants based on some key metabolites (glycine, serine, glutamate, proline, α-alanine and malate). For this purpose, we used tailor-made 13C-PT standards, harboring known carbon isotopologue distributions and 13C-positional enrichments, to evaluate the reliability of GC-MS measurements and positional calculations. Overall, we showed that some mass fragments of proline_2TMS, glutamate_3TMS, malate_3TMS and α-alanine_2TMS had important biases for 13C measurements resulting in significant errors in the computational estimation of 13C-positional enrichments. Nevertheless, we validated a GC/MS-based 13C-positional approach for the following atomic positions: (i) C1 and C2 of glycine_3TMS, (ii) C1, C2 and C3 of serine_3TMS, and (iii) C1 of malate_3TMS and glutamate_3TMS. We successfully applied this approach to plant 13C-labeled experiments for investigating key metabolic fluxes of plant primary metabolism (photorespiration, tricarboxylic acid cycle and phosphoenolpyruvate carboxylase activity).


Introduction
Plant growth and seed yield establishment are supported at the molecular level by the activity of central metabolic pathways [1]. These pathways act together to sustain carbon and nutrient uptake, their assimilation and use and ultimately their remobilization from source-to-sink tissues [1][2][3]. Hence, it is essential to develop methods and tools to monitor the activity of these pathways. For this purpose, labeling experiments with stable isotopes ( 13 C and 15 N) was applied to plants and provided valuable information at different levels of integration [4]. Indeed, analysis of 15 N-enrichments at the organ level after a pulse-chase experiment allowed the separate evaluation of the contribution of nitrogen uptake and remobilization processes to seed filling [5]. Conversely, labeling experiments with 13 C or 15 N metabolic probes followed by analysis of 13 C or 15 N enrichments at the metabolite level allowed the evaluation of protein turnover and carbon allocations to major metabolites, thereby reflecting the regulation of the activity of central metabolic pathways [6][7][8][9][10][11][12][13][14]. However, there may be more than one pathway contributing to the biosynthesis of some important metabolites [4,15]. Thus, access to positional information of metabolite [36]. Thus, these standards could be used to assess the accuracy of calculations from 13 C-positional GC-MS approaches. Here, we took advantage of 13 C-PT standard samples to evaluate such calculation accuracy for some important metabolites of plant metabolism (glycine, serine, glutamate, proline, α-alanine and malate). First, we selected multiple mass fragments from a same metabolite (TMS derivative) and assessed their CID patterns. Next, we used the mean 13 C-enrichment of these fragments to calculate some 13 C-positional enrichments. Finally, we applied the validated calculations to plant 13 C-labeled experiments and reconnected some calculated 13 C-positional enrichments to known metabolic processes regulated by light and dark conditions (photorespiration and PEPc/TCA cycle).

Positional Identification of Carbon Backbones from Mass Fragments of TMS Derivatives
For Glutamate_3TMS and Malate_3TMS, the identity of carbons contained in TMS derivative mass fragments was based on previously published studies that used metabolites labeled in specific positions [29,31]. For Proline_2TMS, we separately injected a nonlabeled proline standard and a commercial U-13 C-Proline standard ( 13 C isotopic purity of 99%, Cambridge Isotope Laboratories (Eurisotop, Saint-Aubin, France)). For other TMS derivatives, we retrieved mass tables from GC-MS spectra of the Golm Metabolome Database corresponding to the injection of either 13 C-reference substances or 13 C-analytes produced with a determined 13 C isotopic signature [37,38]. For each TMS derivative and each fragment analyzed, the mass isotopic cluster was used to calculate the area distribution for each m/z expressed as a percentage of the total area for the mass isotopic cluster (relative intensity from 0 to 100%).

Analysis of Carbon Isotopologue Distributions (CID) from 13 C-PT Standards and Plant 13 C-Labeled Experiments
We retrieved processed GC-MS spectra from two distinct GC-MS-based experiments. The first experiment consisted of the injection of tailor-made isotopic standards ( 13 C-PT samples) [26]. These standards harbored a fully controlled carbon isotopologue distribution following a binomial pattern [36]. Consequently, all fragments produced from the TMS derivatives of these standards had a fractional mean 13 C enrichment of 0.5 and their CIDs were predicted according to the number of carbons present within each mass fragment. The amino acids were analyzed with the 13 C-PT protein hydrolysates (4 biological replicates), while malate was analyzed with the 13 C-PT full metabolite extract (4 biological replicates). The second experiment consisted of the injection of 48 polar metabolite extracts from incorporation kinetics of U-13 C-pyruvate into Brassica napus (B. napus) leaf discs in either light or dark conditions for up to 6 h (4 biological replicates per condition and per timepoint) [26]. The previous processing of the spectra for all samples comprised the adjustment of the retention time with a mixture of alkanes and the peak identification with respect to authentic standards derivatized and by reference to GC-EI-MS mass spectra of TMS derivatives in the US National Institute of Standards and Technology database and in the Golm metabolome database [38].
Then, the spectra were processed again with a modified method based on the new fragments presented in Table 1. Raw areas were subjected to correction for the contribution of naturally occurring isotopes of all elements except the carbon arising from the metabolite backbone using the IsoCor v2.1.4 package of Python (https://github.com/MetaSys-LISBP/ IsoCor, accessed on 20 February 2023) in low-resolution mode. For samples arising from U-13 C-pyruvate incorporations, an additional correction was performed to account for the contribution of naturally occurring isotopes of the carbon arising from the metabolite backbone and the isotopic purity of the tracer U-13 C-pyruvate (99%). Practically, this correction led to a value of 0% if labeling was at natural 13 C abundance (1.1%) and a value of 100% if labeling was at the maximal possible 13 C enrichment. Samples of 13 C-PT were not concerned by this additional correction since they already held fully controlled 12 C/ 13 C patterns at all positions. A corrective method dedicated to IsoCor was specifically created for the TMS-derivative fragments described in this study (Table 1). The IsoCor files and a GC-MS example file are available in the following INRAE dataverse, and all information will be updated indefinitely [39]. The method is publicly available and can also be run directly from GC-MS source files using a Galaxy Workflow URL available at the previously mentioned INRAE dataverse. For this purpose, each raw GC-MS dataset should contain a column "Name" for the fragments considered and a column "Area". The name of each fragment must be written exactly as specified in the GC-MS example file from the previously mentioned INRAE dataverse. Fractional CIDs and fractional mean 13 C enrichments were directly obtained from IsoCor outputs (isotopologue_fraction and mean_enrichment). For 13 C-PT samples, CIDs were rescaled to Pascal's Triangle coefficients by multiplying with 2 n , where n is the number of carbon atoms [36]. 2.3. Calculations of 13 C-Positional Enrichments 13 C-positional enrichments were calculated by combining mean 13 C enrichments of several mass fragments that shared part of the carbon backbone from the same TMS derivative. A workflow has been created to calculate these 13 C-positional enrichments directly from the outputs of our 13 C-processing method with a URL and explanations available at the dedicated INRAE dataverse [39]. Briefly, by considering a metabolite X with 3 carbons, the mean 13 C enrichment (M 13 C) of the mass fragment containing the C1-C2-C3 carbons (M 13 C (X) C1C2C3) can be written as a function of positional mean 13 C enrichments (using fractional values here): Considering another fragment containing the C2-C3 carbons, the mean 13 C enrichment (M 13 C(X) C2-C3 ) is calculated by: Then, the mean 13 C enrichment for the C1 carbon (M 13 C(X) C1 ) can be calculated by: Following the same rationale, we calculated some 13 C-positional enrichments based on the mass fragments detailed in Table 1. For alanine_2TMS, we used the following equations by combining the C2C3 fragment at either m/z 116 or 190 with C1 and C1C2 fragments: For glutamate_3TMS, we used the following equation by combining the C2C3C4C5 fragment at either m/z 156 or 246 with the C1C2C3C4C5 fragment: For glycine_3TMS, we used the following equation by combining the C2 fragment at either m/z 174, 100 or 86 with the C1C2 fragment: For proline_2TMS, we used the following equation by combining the C2C3C4C5 fragment at either m/z 142 or 216 with the C1C2C3C4C5 fragment: For serine_3TMS, we used the following equations by combining the C1C2 and C2C3 (m/z 204) fragments with either the C2 fragment (Method A; Equation (8) The C3 13 C-positional enrichment of malate_3TMS was also calculated by combining the C2, C4 and C2C3C4 fragments:

Data Management, Visualization and Statistical Analysis
Raw data were retreated and visualized using custom R scripts based on the packages tidyr, dplyr and ggplot2 [40][41][42]. For 13 C-PT standards, the normal distribution of data was assumed based on the observed normal distribution for measurement errors (CID accuracy) [26,35]. Thus, comparison of measured/calculated values with theorical (expected) values was based on the 95% confidence intervals calculated with a Student's t-test table: (mean − 1.96 * (SD/ √ (number of samples); mean + 1.96 * (SD/ √ (number of samples)). For plant 13 C-labeled experiments, the data did not follow a normal distribution based on a Shapiro-Wilk test for each group of mean values (1 metabolite/1 fragment/1 time point/1 condition). Thus, statistical comparisons of two groups of mean values was achieved with a Wilcoxon-Mann-Whitney test (p-value < 0.05, two sided) using built-in functions from Rstudio v2022.07.2 build 576 [43].

Results
A recent study previously reported the use of some fragments for a 13 C-positional GC-MS approach without a reliable evaluation of measurement accuracy [29]. Here, we evaluated this accuracy for both compounds (malate and glutamate) but also for some key metabolites of plant central metabolism (glycine, serine, proline and α-alanine). These last metabolites had promising fragmentation patterns for 13 C-positional approaches (Table 1). Besides this, glycine and serine could also be used to follow the metabolic pathways involved in serine biosynthesis (glycolysis versus photorespiration), while α-alanine could be used as a proxy for pyruvate (same carbon backbone). Finally, proline could also be used as a proxy to evaluate PEPc contribution to glutamate biosynthesis.

Selection of Mass Fragments
For malate and glutamate, we took advantage of previously identified fragments for malate_3TMS and glutamate_3TMS derivatives [29,31], summarized in Table 1. These fragments allowed for the calculation of all 13 C-positional enrichments for malate and C1 13 C-enrichment for glutamate (reflecting potentially PEPc contribution). For others, we performed injections of 13 C-reference substances or analyzed GC-MS spectra of the Golm Metabolome Database corresponding to the injection of either 13 C-reference substances or 13 C analytes produced with a controlled 13 C pattern [37,38]. The molecular losses and chemical formulas of mass fragments were then determined according to common rules of mass fragmentations for amino acids [32] (Table 1). Overall, the identity of the carbons contained in each mass fragment was confirmed by mass shifts with 13 C-labeled molecules ( Figure 1). For alanine_2TMS, we obtained mass fragments containing C1, C2-C3 and C1-C2 carbons allowing us to perform multiple calculations to get access to 13 C enrichments at C2 and C3 positions ( Figure 1A). For glycine_3TMS, there were several mass fragments containing the C2 carbon, thus allowing us to select the most accurate fragments for calculations of 13 C enrichment at C1 position ( Figure 1B). For serine_3TMS, we obtained fragments containing C2, C1-C2, C2-C3 and C1-C2-C3 carbons, thus allowing us to calculate 13 C enrichments at C1 and C3 positions with two possible combinations of mass fragments (based on C2 or on C1-C2-C3 fragments) ( Figure 1C). Finally, there were three mass fragments for proline (two C2-C3-C4-C5 and one C1-C2-C3-C4-C5) for calculating 13 C enrichment at the C1 position and identifying a potential PEPc contribution. (based on C2 or on C1-C2-C3 fragments) ( Figure 1C). Finally, there were three mass fragments for proline (two C2-C3-C4-C5 and one C1-C2-C3-C4-C5) for calculating 13 C enrichment at the C1 position and identifying a potential PEPc contribution.

Validation of GC/MS Based 13 C-Positional Approaches
Next, we evaluated the accuracy of CID measurements associated with the selected mass fragments by using 13 C-PT standards. These standards harbored a binomial distribution for their carbon isotopologues for each fragment of each TMS derivative. For ala-nine_2TMS, both C2-C3 fragments showed a good accuracy, while C1 and C1-C2 fragments had some minor biases of measurements ( Figure 2A). However, their statistical evaluation questioned the possibility of performing 13 C-positional enrichment

Validation of GC/MS Based 13 C-Positional Approaches
Next, we evaluated the accuracy of CID measurements associated with the selected mass fragments by using 13 C-PT standards. These standards harbored a binomial distribution for their carbon isotopologues for each fragment of each TMS derivative. For alanine_2TMS, both C2-C3 fragments showed a good accuracy, while C1 and C1-C2 fragments had some minor biases of measurements ( Figure 2A). However, their statistical evaluation questioned the possibility of performing 13 C-positional enrichment calculations for alanine. Regarding glutamate_3TMS, only the C2-C3-C4-C5_246 fragment (with M0 at m/z 246) had a good accuracy, while other fragments showed also minor biases of measurements with significant statistical differences ( Figure 2B). Interestingly, all fragments for glycine_3TMS and serine_3TMS showed very good accuracies, except for the C1-C2-C3 fragment of serine_3TMS ( Figure 2C,E). Nevertheless, it was still possible to calculate 13 C enrichments at C1 and C3 positions by combining C2, C1-C2 and C2-C3 fragments. Mass fragments of proline_2TMS had the highest biases of measurements, except for the C2-C3-C4-C5_142 fragment (M0 at m/z 142) ( Figure 2D). Finally, C2, C2-C3 and C4 mass fragments of malate_3TMS showed important biases of measurements compared to other fragments ( Figure 2F). These results seriously questioned the 13 C-positional enrichment calculations previously reported for C3 and C4 positions [29,34].
Then, we evaluated the accuracy of 13 C-positional enrichment calculations (equations described in the material and methods). Indeed, 13 C-PT standards had equal proportions of each possible isotopologue and each possible 13 C-isotopomer leading to a positional mean 13 C enrichment of 50% for each carbon of each metabolite [36]. For alanine_2TMS, we calculated 13 C-enrichments for C1, C2 and C3 positions but also for the C1-C2-C3 molecule based on the C2-C3 fragment with M0 at either m/z 116 or 190 ( Figure 3A). Overall, we showed that 13 C-positional enrichment calculations for C2 and C3 positions were prone to large and statistically significant errors (calculated values around 0.75 and 0.20, respectively, compared to the expected value of 0.5). This result confirmed that the combination of small errors of measurements for C1 and C1-C2 fragments of alanine_2TMS resulted in larger errors after calculations. Thus, it is crucial to evaluate the measurement accuracy of mass fragments prior to application of 13 C-positional GC-MS approaches to biological investigations. Besides this, the calculated fragment C1-C2-C3 for alanine_2TMS had a very good accuracy. For glutamate_3TMS, we only calculated 13 C-enrichments for the C1 position based on the mass fragments available. While the direct measurement of 13 C-enrichment for the C1 position was highly inaccurate (M0 at m/z 117), the combination of C1-C2-C3-C4-C5 and C2-C3-C4-C5_246 fragments surprisingly provided reasonable 13 C-positonal enrichment values close to the expected 0.5 ( Figure 3B). Thus, a combination of several fragments can represent a good alternative when some direct ions are inaccurate for MS measurements. For glycine_3TMS and serine_3TMS, the different combinations of accurate mass fragments yielded accurate 13 C-positional enrichments for all calculations considered ( Figure 3C,E). Nevertheless, for serine_3TMS, the combination "A" using C1-C2, C2-C3 and C2 fragments provided 13 C-positional enrichments with smaller variability compared to combination "B" using C1-C2-C3, C1-C2 and C2-C3 fragments (see the C2 position, Figure 3E). For proline_2TMS, 13 C-enrichment calculations at the C1 position using C1-C2-C3-C4-C5 and C2-C3-C4-C5_142 fragments were relatively accurate (mean value close to 0.5) but were subjected to an important variability (standard deviation close to ±0.4), thus preventing any further consideration for the study ( Figure 3D). Finally, for malate_3TMS, the combination of C1-C2-C3-C4 fragments (M0 at m/z 245 or 335) with the C2-C3-C4 fragment allowed for the accurate calculation of 13 C enrichment at the C1 position ( Figure 3F). However, the fragment at m/z 245 showed a better resolution than the fragment at m/z 335, i.e., a small standard deviation after calculations. Conversely, the inaccuracy of mean 13 C-enrichment measurements for C2, C2-C3 and C4 fragments led to statistically significant biases for calculations of 13 C enrichments at C3 and C4 positions. Thus, only the C1 position was accurately determined for malate_3TMS. Overall, the results validated the use of some fragments and specific combinations to calculate accurate 13 Cpositional enrichments for the following atomic positions: (i) C1 and C2 of glycine_3TMS, (ii) C1, C2 and C3 of serine_3TMS, and (iii) C1 of malate_3TMS and glutamate_3TMS.
to calculate 13 C enrichments at C1 and C3 positions by combining C2, C1-C2 and C2-C3 fragments. Mass fragments of proline_2TMS had the highest biases of measurements, except for the C2-C3-C4-C5_142 fragment (M0 at m/z 142) ( Figure 2D). Finally, C2, C2-C3 and C4 mass fragments of malate_3TMS showed important biases of measurements compared to other fragments ( Figure 2F). These results seriously questioned the 13 C-positional enrichment calculations previously reported for C3 and C4 positions [29,34].  Values represent the mean ± SD for four biological replicates. Statistical differences between predicted and measured CID for each isotopologue of each fragment are denoted with an asterisk (*) and were established by considering the 95% confidence intervals of the measured CID. The complete dataset is available in Table S1. proline_2TMS, and (F) malate_3TMS. Black bars, expected mean 13 C-enrichments; blue bars, measured mean 13 C-enrichments; red bars, calculated mean 13 C-enrichments. Values represent the mean ± SD for four biological replicates. Statistical differences between predicted and measured/calculated mean 13 C enrichments for each isotopologue of each fragment are denoted with an asterisk (*) and were established by considering the 95% confidence intervals of the measured/calculated values. For convenience, fragments containing similar carbons were denoted by adding the m/z at M0 (see blue bars "C2_174", "C2_100" and "C2_186" for glycine_3TMS, for example). When multiple fragments could be used to calculate the same 13 C-positional enrichment, the original fragment used for calculation was denoted by adding its m/z at M0 (see red bars "C1_174", "C1_100" and "C1_186" for glycine_3TMS, for example). The complete dataset in available in Table S1. (E) proline_2TMS, and (F) malate_3TMS. Black bars, expected mean 13 C-enrichments; blue bars, measured mean 13 C-enrichments; red bars, calculated mean 13 C-enrichments. Values represent the mean ± SD for four biological replicates. Statistical differences between predicted and measured/calculated mean 13 C enrichments for each isotopologue of each fragment are denoted with an asterisk (*) and were established by considering the 95% confidence intervals of the measured/calculated values. For convenience, fragments containing similar carbons were denoted by adding the m/z at M0 (see blue bars "C2_174", "C2_100" and "C2_186" for glycine_3TMS, for example). When multiple fragments could be used to calculate the same 13 C-positional enrichment, the original fragment used for calculation was denoted by adding its m/z at M0 (see red bars "C1_174", "C1_100" and "C1_186" for glycine_3TMS, for example). The complete dataset in available in Table S1.

Application to Plant 13 C-Labeled Experiments
Next, we used our validated 13 C-positional approach to investigate key metabolic steps in plants by using incorporation kinetics of 13 C-metabolic probes. For this purpose, we reanalyzed GC-MS spectra of a recent experiment, which followed the fate of U-13 Cpyruvate into B. napus leaf discs during light or dark conditions [26]. Indeed, the light/dark regulation of photorespiration and PEPc activities introduced some differences of mean 13 C-enrichments and isotopologues fractions into glycine, serine, malate and glutamate, but they were not investigated at the atomic position level.
First, we focused our analysis on mass fragments of glycine_3TMS and serine_3TMS. Previous results confirmed that decarboxylations from the TCA cycle were partly reassimilated by photosynthesis leading to 13 C enrichments for glycine and serine under light conditions only (photorespiration).
Here, we observed a statistically significant increase of mean 13 Cenrichments for the three fragments containing the C2 (M0 at m/z 174, 100 and 86) in light conditions compared to dark conditions ( Figure 4A). There were no major differences for the C1-C2 fragment in both conditions, but it had a large biological variability in plant samples (mean value of 0.2 ± 0.1). Therefore, it was not possible to use a 13 C-positional approach for glycine_3TMS. However, C1-C2, C2 and C2-C3_204 fragments of serine_3TMS showed statistically significant increases in 13 C enrichment in light conditions compared to dark conditions with a relatively small biological variability ( Figure 4B). Using the combination "A", we calculated the 13 C enrichment for C1 and C3 positions. Overall, the calculated 13 C positional enrichments were below 0, and there were no major statistical differences between light and dark conditions except for the C2 position ( Figure 4C). Thus, our 13 Cpositional approach formerly showed that serine was only labeled at the C2 position in light conditions during the experiment. Based on the properties of the glycine decarboxylase (GDC) and serine hydroxymethyl aminotransferase (SHMT), serine was produced from the combination of non-labeled glycine and 13 C-2-glycine ( 13 C at C2 position) ( Figure 4D). Thus, we could also deduce that the C1 position of glycine_2TMS was not 13 C labeled during the experiment.
Then, we focused our analysis on mass fragments of malate_3TMS and glutamate_3TMS. Previous analysis of the considered metabolic network showed that the C1 position of malate_3TMS and glutamate_3TMS could be 13 C labeled through the activities of PEPc and TCA cycle enzymes ( Figure 5A). Thus, the evaluation of 13 C enrichment at the C1 position for these metabolites reflected the light-dependent regulation of both pathways [44,45]. Here, the additional mass fragments analyzed for malate_3TMS had good resolutions (small standard deviations), but there were no formal differences between light and dark conditions ( Figure 5B). However, the C2-C3 fragment had a strong bias at natural 13 C abundance, with a mean 13 C enrichment starting at 0.36 at the "0" time point. Therefore, this fragment could not be considered for any isotopic study. Overall, the calculated 13 C enrichment for the C1 position was relatively similar when using the two fragments (m/z 335 and 245) and was not significantly different between light and dark conditions ( Figure 5C). Thus, the decrease of TCA cycle activity in light may have been compensated by an increase of PEPc activity leading to a similar 13 C enrichment at the C1 position in both conditions. Regarding gluta-mate_3TMS, we also obtained good resolutions (small standard deviations) for the additional mass fragments analyzed, except for the C1 fragment, which may reflect its poor accuracy previously identified with 13 C-PT standards ( Figure 5D). Nevertheless, measurements at natural 13 C abundance for the C2-C3-C4-C5_156 fragment was slightly biased in plant samples (around 0.06 at the "0" time point) and could explain the large bias identified after calculations ( Figure 5E). Besides this, there were no statisticaldifferences between light and dark conditions for the mass fragments, although a trend could be observed in the C1-C2-C3-C4-C5 fragment (higher values in dark conditions than in light conditions) ( Figure 5D). Calculations of the 13 C-positional enrichment for the C1 position of glutamate_3TMS were only validated when using the C2-C3-C4-C5_246 fragment in plant samples ( Figure 5E). Interestingly, this calculation successfully identified a statistically significant increase in 13 C enrichment at the C1 position in dark conditions compared to light conditions after 4 h of labeling. However, this 13 C-positional enrichment was not formerly modified during light conditions (along the kinetic), hence questioning the importance of PEPc and the cyclic mode of the TCA cycle in contributing to glutamate biosynthesis in the light. Therefore, this interesting result may essentially reflect a higher contribution of the cyclic mode of the TCA cycle to glutamate biosynthesis in the dark. calculation successfully identified a statistically significant increase in 13 C enrichment at the C1 position in dark conditions compared to light conditions after 4 h of labeling. However, this 13 C-positional enrichment was not formerly modified during light conditions (along the kinetic), hence questioning the importance of PEPc and the cyclic mode of the TCA cycle in contributing to glutamate biosynthesis in the light. Therefore, this interesting result may essentially reflect a higher contribution of the cyclic mode of the TCA cycle to glutamate biosynthesis in the dark.    Table S2.

Identification of Mass Fragment-Specific Analytical Biases and Consequences for GC/MS-Based 13 C-Positional Approaches
In this study, we evaluated the accuracy of additional mass fragments for some TMS derivatives using 13 C-PT standards and their use for 13 C-positonal approaches. Overall, we showed that some mass fragments of proline_2TMS, glutamate_3TMS, malate_3TMS and α-alanine_2TMS had important biases for the measurement of mean 13 C enrichments.
For proline_2TMS, we observed a large and significant inaccuracy for CID measurements with the C2-C3-C4-C5 fragment (M0 at m/z 216). Analysis of MS spectra confirmed the presence of a contaminant ion at m/z 218 in both standard mixtures and plant samples visible at a natural 13 C abundance ( Figure S1A). Interestingly, the C2-C3-C4-C5 fragment with M0 at m/z 142 was already reported as weakly inaccurate due to a contamination at m/z 146 by the co-elution of isoleucine [26,46]. Since the EI-induced fragmentation of isoleucine_2TMS also produced a massive ion at m/z 218, the contamination was due to co-elution issues. Nevertheless, the fragment at m/z 142 accurately calculated 13 C enrichment at the C1 position for proline_2TMS ( Figure 3B). Thus, this last fragment should be preferentially selected for 13 C-labeling studies compared to the fragment at m/z 216.
Regarding the recent establishment of a GC/MS-based 13 C-positional approach, we identified analytical biases for some fragments of glutamate and malate leading to large errors for calculations of 13 C-positional enrichments. Notably, we showed that the C1 fragment of glutamate_2TMS largely underestimated the level of 13 C-enrichment with a value of 0.25 compared to the theoretical value of 0.5 ( Figure 3D). A previous analysis by high resolution mass spectrometry confirmed the presence of a contaminant mass at m/z 117 but used a linear correlation between calculated and measured 13 C enrichments at the C1 position to correct for this bias [29]. However, this correction assumed a linear propagation of the bias and did not consider other possible biases for 13 C-positional enrichment calculations. Here, we confirmed that both C2-C3-C4-C5 fragments (M0 at m/z 156 and 246) can be combined with the C1-C2-C3-C4-C5 fragment to calculate accurate 13 C enrichments at the C1 position. However, we observed a factor of two between calculated and measured 13 C-positional enrichments compared to the previous study, which found a factor of five. Thus, the bias associated to the C1 fragment at m/z 117 may not follow a linear propagation. Therefore, 13 C enrichment at position C1 of glutamate_2TMS should always be calculated instead of directly measured with the fragment at m/z 117.
Besides this, we showed that the C2 (m/z 265), C2-C3 (m/z 189) and C4 (m/z 117) fragments of malate_3TMS contained significant biases for measurements of 13 C enrichments leading to large errors in 13 C-positional enrichment calculations at C3 and C4 positions ( Figures 2F and 3F). For the C2-C3 fragment of malate_3TMS, an exact mass GC-MS analysis identified a similar mass fragment at the same retention time arising from threitol_4TMS (m/z 189.07 versus m/z 189.11) [47]. Thus, a contamination by this compound in 13 C-PT standards and plant samples was potentially expected. However, both reference chemical standards ( 12 C) and plant samples had the same magnitude of inaccuracy for measurements of mean 13 C enrichment and CIDs at natural 13 C abundance ( Figure S1). Thus, an analytical bias during the fragmentation process seems a more plausible explanation. For the C4 fragment of malate_3TMS, its chemical structure was unequivocally identified from injections of 13 C standards labeled at specific positions [31]. A recent analysis by high resolution mass spectrometry revealed the presence of a contaminant mass at a weak intensity level (34% of m/z 117.0731 versus 66% of m/z 117.0366 (C4 fragment)) [29]. The authors proposed a chemical structure containing the C2 and C3 carbons based on mass fragmentation simulations and injections and a basic correction step based on the relative abundance of these two ions. However, this correction step did not consider: (i) the combination of multiple isotopic clusters starting at the same M0 for the correction of naturally occurring isotopes and (ii) possible biases arising from 13 C-labeled C2 and C3 positions. In this study, we measured an important bias, i.e., a mean 13 C enrichment of 0.4 compared to the expected value of 0.5. Thus, the use of the C4 fragment should only be considered for a qualitative evaluation of PEPc activity rather than an accurate measurement suitable for 13 C-metabolic flux analysis.
Overall, our analysis revealed that measurements and calculations of 13 C-positional enrichments for malate_3TMS were largely inaccurate except at the C1 position. Since some biases arise from mass contaminations, the use of high-resolution mass spectrometry will help to improve the accuracy of the GC/MS-based 13 C-positional approach [47]. The use of TBDMS derivatives instead of TMS derivatives could also constitute an interesting alternative. A GC-tandem MS fragmentation approach was applied to TBDMS derivatives and succeeded at measuring the complete isotopomer distribution of aspartate [30]. This method relied on redundant measurements of carbon atom enrichments from different combinations of precursor and product ions followed by estimations of isotopomer distribution with least-squared regressions. Interestingly, aspartate represents a good proxy to evaluate the 13 C composition of TCA cycle intermediates (oxaloacetate and malate) [30]. Since TBDMS derivatives of amino acids are more stable than TMS derivatives [26,48], this strategy seems highly promising for the field of plant science. In addition, a fragmentation map of precursor and product ions has been recently established for TBDMS derivatives of citrate and malate [31].

A GC/MS-Based 13 C-Positional Approach Suitable for Investigating the Metabolic Fluxes Associated to Photorespiration, TCA Cycle and PEPc
Using 13 C-PT standards, we validated a GC/MS-based 13 C-positional approach for the following atomic positions: (i) C1 and C2 of glycine_3TMS, (ii) C1, C2 and C3 of serine_3TMS, and (iii) C1 of malate_3TMS and glutamate_3TMS. We showed that this approach could be successfully applied to plant 13 C-labeled experiments for investigating key metabolic fluxes of plant primary metabolism. Photorespiration is a major flux involved in the recycling of by-products from RuBiSCO oxygenase activity [49,50]. Analysis of Arabidopsis mutant lines confirmed that the GDC/SHMT enzymes catalyzed the ratelimiting step of the photorespiratory flux by converting two molecules of glycine into one molecule of serine, CO 2 and NH 3 [51]. Since photorespiration is associated to carbon losses, crop improvement can require the accurate measurement of this metabolic flux with a 13 C-labeling strategy [52]. Previous analysis showed that NMR was best suited for obtaining positional information into serine compared to LC-MS [53]. Here, we showed that GC-EI-MS can be highly accurate by combining multiple mass fragments. Since NMR analysis requires large amounts of material compared to MS analysis, this methodological improvement certainly represents a decisive advantage for 13 C-metabolic flux analysis.
Regarding TCA cycle and its PEPc anaplerotic pathway, the positional approach provided additional information compared to previous results obtained only with isotopologues of malate and glutamate in B. napus. Indeed, a 13 C-metabolic flux analysis with pathway-specific isotopologues showed that PEPc-dependent reassimilation of TCA cycle-derived CO 2 had a small contribution to citrate biosynthesis in the light compared to TCA cycle [26]. These specific isotopologues included M1, M2 and M3 from gluta-mate_2TMS_C2C5 and malate_2TMS_C2C4 fragments and suggested the occurrence of a cyclic mode of the TCA cycle in the light. Here, we did not identify an increase of 13 C enrichment at the C1 position for glutamate in the light conditions. Thus, the isotopologues produced by the TCA cycle in the light should not contain this labeling pattern ( Figure 5A). However, such a scenario is practically impossible because (i) PEPc activity fixes 13 C labeling at the C4 position of malate first, prior to a reversible fumarase step producing the same 13 C-labeling at the C1 position of malate; and (ii) the contribution of PEPc to citrate biosynthesis has been formerly identified in the same experiment. Besides this, a recent analysis of U-13 C-pyruvate incorporation in Arabidopsis leaves in the light provided contrasted results for 13 C enrichment at the C1 position of glutamate [29]. The authors identified an increase in 13 C enrichment for this C1 position and for the fragment m/z 246 containing the C2-C3-C4-C5 positions. However, these authors used the m/z 117 of glutamate_3TMS for the C1 positional analysis, i.e., a fragment that is prone to large biases ( Figures 2B and 3B). Therefore, the lack of 13 C enrichment at the C1 position of glutamate in our experiment (light conditions) could be due to a weak accuracy at small 13 C enrichments. Indeed, we observed significant differences for 13 C enrichment at the C1 position of glutamate_2TMS between light and dark conditions that had an obvious biological explanation. The TCA cycle has a higher turnover in dark conditions compared to light conditions [45], and a second turn of the TCA cycle can produce 13 C labeling at the C1 position of glutamate [26]. Thus, this positional approach seems to be well suited for investigating metabolic fluxes but only to compare light and dark conditions. Besides this, our analysis of 13 C enrichment at the C1 position of malate did not reveal differences between light and dark conditions in B. napus leaves ( Figure 5B,C). Since PEPc activity is inhibited in the dark compared to light conditions, our results suggested that the decrease in PEPc activity in the dark was perhaps compensated by higher TCA cycle activity. In this situation, a similar 13 C enrichment at the C1 position of malate was expected for both light and dark conditions.

Conclusions
The accuracy of GC/MS-based 13 C-positional approaches must be evaluated prior to further deployment for scientific investigations. For this purpose, tailor-made 13 C-PT standards are a valuable resource for evaluating biases in CID measurements and in 13 Cpositional enrichment calculations. Overall, we showed that some mass fragments of proline_2TMS, glutamate_3TMS, malate_3TMS and α-alanine_2TMS had important biases for measurements of mean 13 C enrichments. These biases were further transmitted to 13 C-positional enrichments during calculations leading to larger biases. Nevertheless, we validated a GC/MS-based 13 C-positional approach for the following atomic positions: (i) C1 and C2 of glycine_3TMS, (ii) C1, C2 and C3 of serine_3TMS, and (iii) C1 of malate_3TMS and glutamate_3TMS. We successfully applied this approach to plant 13 C-labeling experiments and obtained complementary information regarding the light/dark regulation of the photorespiratory production of serine and the biosynthesis of malate and glutamate through the TCA cycle and PEPc activities.