Vol.:(0123456789)1 3 Bioprocess and Biosystems Engineering (2021) 44:2567–2578 https://doi.org/10.1007/s00449-021-02628-1 RESEARCH PAPER Compartment‑specific metabolome labeling enables the identification of subcellular fluxes that may serve as promising metabolic engineering targets in CHO cells Andy Wiranata Wijaya1  · Andreas Ulmer1 · Lara Hundsdorfer1 · Natascha Verhagen1 · Attila Teleki1 · Ralf Takors1 Received: 27 April 2021 / Accepted: 27 August 2021 / Published online: 30 September 2021 © The Author(s) 2021 Abstract 13C labeling data are used to calculate quantitative intracellular flux patterns reflecting in vivo conditions. Given that approaches for compartment-specific metabolomics exist, the benefits they offer compared to conventional non-compart- mented 13C flux studies remain to be determined. Using compartment-specific labeling information of IgG1-producing Chi- nese hamster ovary cells, this study investigated differences of flux patterns exploiting and ignoring metabolic labeling data of cytosol and mitochondria. Although cellular analysis provided good estimates for the majority of intracellular fluxes, half of the mitochondrial transporters, and NADH and ATP balances, severe differences were found for some reactions. Accurate flux estimations of almost all iso-enzymes heavily depended on the sub-cellular labeling information. Furthermore, key dis- crepancies were found for the mitochondrial carriers vAGC1 (Aspartate/Glutamate antiporter), vDIC (Malate/H+ symporter), and vOGC (α-ketoglutarate/malate antiporter). Special emphasis is given to the flux of cytosolic malic enzyme (vME): it could not be estimated without the compartment-specific malate labeling information. Interesting enough, cytosolic malic enzyme is an important metabolic engineering target for improving cell-specific IgG1 productivity. Hence, compartment-specific 13C labeling analysis serves as prerequisite for related metabolic engineering studies. Keywords Compartment-specific · Metabolomics · 13C Metabolic flux analysis · Chinese hamster ovary cells · Eukaryotes · Multi-compartments Abbreviations 13C MFA 13C metabolic flux analysis CHO Chinese hamster ovary VCD Viable cell density PPP Pentose phosphate pathway CAC Citric acid cycle MID Mass isotopomer distribution MPC1/2 Mitochondrial pyruvate carrier ME Malic enzyme Symbols ci pmol L−1 Concentration of metabolite i cx cell L−1 Viable cell density dof [-] Degree of freedom E [-] Expected MID measurement data fi [-] Cytosolic fraction of metabolite i I [-] Isotopomer distribution vector IMM [-] Isotopomer mapping matrices MID [-] Mass isotopomer distribution n [-] Number of measurement data O [-] Observed MID simulation p pmol cell−1  h−1 Vector containing estimated fluxes using mass-isotopomers data p [-] Number of fitted parameter Qi pmol L−1  h−1 Feed-rate of metabolite i qi pmol cell−1  h−1 Cell-specific rate of exo- metabolite i qm pmol cell−1  h−1 Vector containing measured extracellular rates S [-] Stoichiometric matrix of biochemical network M [-] Measurement information matrix Andy Wiranata Wijaya and Andreas Ulmer were equally contributed for this work (first co-authorship). * Ralf Takors ralf.takors@ibvt.uni-stuttgart.de 1 Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany http://orcid.org/0000-0001-9388-7357 http://crossmark.crossref.org/dialog/?doi=10.1007/s00449-021-02628-1&domain=pdf 2568 Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 vj pmol cell−1  h−1 Intracellular flux of biochemi- cal reaction j v pmol cell−1  h−1 Vector containing intracellular metabolic fluxes Greek symbols α [-] Statistical confidence interval β [-] Reversibility constant σ [-] Measurement standard deviation of MID Θ [-] Parameter Indices ex [-] Compartment indication feed [-] Feed in [-] Compartment indication i [-] Compound/metabolite i j [-] Biochemical reactions j meas [-] Indication for measurement vector net [-] Indication for net fluxes X [-] Cells/biomass Introduction 13C metabolic flux analysis (13C MFA) is a key tool for quantitative analysis in systems metabolic engineering. First, applications dealt with prokaryotic cells [1] but the technique was also applied for eukaryotes, such as yeast [2, 3], fungi [4], mammalian [5–8], and plant [9] cells. Among others, prokaryotes and eukaryotes differ in cellular com- partmentation, which is particularly important when using 13C MFA. In eukaryotes, compartmentation is essential since each cellular compartment fulfils different functions [10]. Even multi-compartment isozymes exist that serve different purposes. For example, Chinese hamster ovary (CHO) cells comprise cytosolic and mitochondrial malic enzymes (MEs) with different NAD+ and NADP+ regeneration capacities, thereby fulfilling diverse catabolic and anabolic needs [8]. Metabolic compartmentation must be considered when performing 13C MFA [10]. There are two levels of complex- ity; on the one hand, subcellular metabolic models should be used to enable proper in silico predictions. On the other hand, in vivo compartment-specific metabolome data should be available to allow data-driven studies. Nicolae et al. and Pfizenmaier & Takors provided evidence for the importance of subcellular stoichiometric models for estimating fluxes in CHO cells [11, 12]. Regarding the latter, Matuszczyk et al. [13] applied compartment-specific metabolomics in CHO outlining that cytosolic ATP pools are considerably larger than their mitochondrial counterparts. Later, Junghans et al. [8] continued investigating mitochondrial and cytosolic met- abolic patterns under different cultivation conditions. They found that pool sizes differed between cytosol and mitochon- dria for all conditions. Given that subcellular metabolomics are very laborious [8, 13] the question arises what differences may occur if 13C flux analysis is based on whole-cell metabolomics instead of compartment-specific measurements. In other words, whether the additional lab-efforts justify the information gain of subcellular studies. Alternative approaches such as superimposing the pat- terns of two independent 13C experiments using labeled glucose and labeled glutamine also aim to decipher subcel- lular flux distributions [6]. However, they rely on glutamine synthase deficient cells whereas the suggested subcellular metabolomics approach may be universally applicable. Given that labeling dynamics in metabolite pools expressed by the 13C labeling turn-over (τ13C) are a key information for quantifying fluxes, influencing factors may be considered. Two factors, pool size of metabolite i and net labeling flux j through this pool exist [14]. Either factor may change when a system’s analysis shifts from simplifying single to realistic multi-compartment analysis. Differences in τ13C may occur originating from individual pool sizes and fluxes inside the compartments. In theory, the same metabo- lite in different compartment might present a different labe- ling dynamic providing that the metabolite turn-over time is different. Thus, resulting on a different labeling dynamics (τ13C). Exploiting the unique subcellular labeling dataset of Junghans et al. [8] this study investigated whether subcel- lular labeling information is crucial to obtain the correct compartment-specific flux patterns. Flux distributions con- sidering and ignoring subcellular metabolite labeling were performed using CHO as the showcase. This study investi- gated whether significant differences exist using whole-cell and compartment-specific metabolic information. Materials and methods This study was based on published metabolome and 13C iso- topologue data [8]. In particular, the 13C dataset covering the first 24 h was used to focus on the exponential growth phase. Cell culture and experimental set‑up The CHO DP-12 cell line (ATCC ® CRL-1445TM) was cul- tivated in a suspension with TC-42 medium (Xell AG, Biele- feld, Germany) supplemented with 42 mM d-glucose, 6 mM ʟ-glutamine, and 200 mM methotrexate. Precultures were cultivated in pre-sterilized disposable shake flasks (Corning Inc., NY, USA) with culture volume ranging from 125 mL to 1 L at an initial viable cell density (VCD) of 0.4 × 106 cells/ mL in a humidified shaking incubator (Infors HT Minitron, 2569Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 Infors GmbH, Einsbach, Germany) at 37 °C, 150 rpm, and 5% saturated CO2. Bioreactor cultivations were performed in a two-fold parallel CellFerm Pro bioreactor system (DASGIP, Eppen- dorf, Germany) equipped with pitched blade impellers and a process control system. Bioreactor cultivations were started with a VCD of about 0.4 × 106 cells/mL, temperature was set to 37 °C and agitation to 150 rpm. In addition, the dis- solved oxygen content was controlled using an amperomet- ric electrode (Mettler-Toledo Inc., Columbus, OH, USA) at 40%. The pH was measured with a conventional pH probe (Mettler-Toledo Inc., Columbus, OH, USA) and maintained at 7.1 using 1 M Na2CO3 or CO2 gassing. Carbon labe- ling experiments were performed in the same setup using [U-13C6]-d-glucose as a carbon tracer with an average iso- topic ratio of 25% [U-12C6]- and 75% [U-13C6]-d-glucose. Experiments were performed as biological duplicates. In addition to carbon labeling experiments, bioreactor culti- vations with [U-12C6]-d-glucose were performed using the same conditions for metabolome profiling. Extracellular and intracellular analytics VCD was monitored with a 12 h interval with Cedex XS, an offline cell counting system (Innovatis AG, Bielefeld, Germany). Extracellular d-glucose and ʟ-lactate were moni- tored offline with LaboTRACE, an amperometric biosensor system (Trace Analytics GmbH, Braunschweig, Germany). Extracellular antibody (IgG1) concentrations were meas- ured using ELISA as reported previously [15]. Extracellular amino acid concentrations were quantified with reversed- phase chromatography (Agilent 1200 Series, Agilent Tech- nologies, Waldbronn, Germany) [8]. Sampling for metabolomics was performed using differ- ential fast filtration [8, 13]. Then, processed samples were analyzed regarding metabolome quantification using an Agilent 1200 HPLC system coupled with an Agilent 6410B (Agilent Technologies, Waldbronn, Germany) triple quad- rupole mass spectrometer equipped with an electrospray ion source. Analytical sample preparation and methodology were conducted as reported previously [8, 16]. 13C metabolic flux analysis Isotopic non-stationary 13C MFA was performed in MAT- LAB 2018a (The MathWorks, Inc., MA, USA). Before performing 13C MFA, measured 13C labeling distributions were corrected for natural stable isotope abundances [17]. Parameter optimization was conducted using MATLAB least square optimization fmincon function in combination with GlobalSearch and MultiStart algorithm in a multi-core computing machine [18]. The first derivative of each isoto- pomer balance was solved using MATLAB Ordinary Differ- ential Equations ode15s solver. The study used the metabolic and carbon-atom transition model in the previous study [8]. Details of the model are indicated in Table S1 (Supplemen- tary Material S1) and are displayed in Fig. 1. Metabolite balancing The two-compartment CHO-cell model comprises the stoi- chiometric matrix S (Supplementary Material S1, Table S1) consisting of m metabolites and n reactions (m × n). The fol- lowing cell-specific rates [pmol cell−1  h−1] were defined: q for cellular uptake and secretion rates, k as inter-compart- ment transport, and v as compartment-specific reaction. The balance of metabolite i participating in reaction j localized externally, in cytosol, or in mitochondria was described by Eqs. 1 and 2: where ci denotes the concentration of metabolite i [mol L−1], cx denotes VCD [cell L−1], t denotes time [h], and Qi,feed denotes the feed-rate of metabolite i [pmol L−1  h−1]. The process model describing the batch cultivation is given in Eq. 1 and allows the estimation of q for metabolite i by time-series analysis of extracellular concentrations ci. Therefore, the metabolic steady-state was defined as mir- rored in the constraint dc,intracellular dt = 0 , which is a prerequisite for 13C flux analysis. Both stationary and non-stationary labeling patterns were analyzed, originating from the meta- bolic steady-state condition. Metabolic flux analysis MFA was performed using the metabolic network S con- sidering the following constraints: (i) pool sizes of cyto- solic and mitochondrial metabolites were in a steady-state and (ii) the entire system was (over)-determined because of the ample 13C labeling information. Fluxes were estimated according to: where M is the measurement matrix containing the stoi- chiometric coefficients of qmeas (measured rates [pmol cell−1  h−1]) and p contains the estimated fluxes using mass- isotopomer data [pmol cell−1  h−1]). (1) dCi,ex dt = Qi,feed + qicX , (2) dci,in dt = ( −qi − ki + n∑ j=1 vj ) ⋅ cx = 0, (3)v = ( S M )−1( 0[ qmeas p ] ) , 2570 Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 Fig. 1 Metabolic model of CHO cells used in this study (modified figure from Junghans et al. [8]). Arrow coloring indicates the locali- zation of biochemical reactions as follows: black encodes single compartment; red encodes multi-compartments; and blue encodes inter-compartment transporters. In addition, multi-compartment metabolites are indicated in red (color figure online) 2571Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 Isotopomer balancing and bidirectional reactions Isotopomer balancing was applied to mathematically describe the incorporation of 13C tracers into intracellular metabolite carbon skeletons [19, 20]. Isotopomer balances for intracellular metabolites are according to Eq. 4: where the isotopomer transition from reactant k to product m is described by IMMk→m. Furthermore, Eq. 5 was used to describe labeling dilution by extracellular pools (ʟ-lactate, ʟ-glutamate, ʟ-aspartate, and ʟ-alanine): Exchange fluxes were defined for each reversible bio- chemical reaction [21, 22] according to Eq. 6: Parameter estimation and uncertainty Parameter (flux) estimation was achieved by fitting the simu- lated mass isotopomer distribution (MID) to the measured in vivo MID as presented in Eq. 7: Cytosolic and mitochondrial MIDs were defined for sub- cellular studies. Non-compartmented analysis considered that no subcellular measurements were available. Instead, only entire cell labeling patterns should exist. Consequently, compartment-specific information was merged again, apply- ing Eq. 8: (4) d � �i�� � dt = N� j=1 ⎡ ⎢⎢⎣ 𝛼 ⎛ ⎜⎜⎝ 0 ⊗ k = 1 � n� m=1 ���k→m � �k ⎞ ⎟⎟⎠ rj + (1 − 𝛼) � virj�i �⎤⎥⎥⎦ with 𝛼 = � 1, ifvij > 0 0, else , (5) d ( �i,ex ) dt = 1 ci,ex [ cX ( ⇀ qi,ex ⋅ �i,in − ↼ qi,ex ⋅ �i,ex ) − dci,ex dt �i,ex ] with ⇀ qi,ex = �i ⋅ q net i,ex ↼ qi,ex = ⇀ qi,ex − qnet i,ex . (6) ⇀ vj = �j ⋅ v net j ↼ vj = ⇀ vj − vnet j . (7)min f (Φ) = ∑( MIDsim i −MID exp i �i )2 . where f denotes the molar fraction of metabolite i in the cytosol. During simulations, f was treated as an optimization parameter for those metabolites presented in both compart- ments; pyruvate, citrate, α-ketoglutarate, malate, alanine, aspartate, asparagine, and glutamine. Accordingly, f serves as an alternate indicator for the importance of considering compartments properly. Furthermore, flux estimation was achieved by fitting the measured non-compartment metabo- lome data with calculated MID using Eq. 9: A χ2 statistical test was used to assess goodness of fit as described in Eq. 10: Parameter uncertainty is essential to evaluate the flux dif- ferences including versus excluding compartment-specific data. Conventional parameter uncertainty estimates make use of the local calculation of the Jacobian matrix as a lin- earized proxy for variance. However, this approach only shows poor performance if a complex and non-linear set of equations should be analyzed, as it is the case in this 13C MFA study. Thus, confidence intervals of each parameter (fluxes) were estimated using the Chi-squared (χ2) statistics, which works best for non-linear equations as demonstrated by Antoniewicz et al. [23]. The method relies on the assump- tion that the minimized variance-weighted sum of squared residuals is χ2 distributed. Thus, the residual difference evaluating the global optimum and fixing one parameter is χ2 distributed with one degree of freedom. Statistical analysis The significant differences between the two analyses were assessed using Welch’s t-test for unequal variances [24]. Results Prior to the 13C MFA studies, a metabolic network model was formulated (Supplementary Material S1). First the structural identifiability and calculability of the network was assessed applying well established methodologies (Supple- mentary Material S4). Next, the identifiability of distinct (8)MIDcomb i = MID cyt i ⋅ f +MIDmit i ⋅ (1 − f ), (9)min f (Φ) = ∑( MIDcomb i −MID exp i �i )2 . (10) �2 = ∑ ( xsim − xexp )2 �2 dof = (n − p) �2 ≤ �2 (1−�),dof . 2572 Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 2573Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 fluxes was checked by simulating intracellular 13C labeling patterns assuming pool sizes measured by Junghans et al. [8]. Results presented in the Supplementary Material S4 indicate the good identifiability of intracellular fluxes which motivated us to continue the study by analyzing real labeling patterns and flux distributions. In the study by Junghans et al. [8] CHO-DP12 cells were cultivated in a bioreactor to investigate three dis- tinct growth scenarios; (I) exponential growth with no (carbon and nitrogen) limitation; (II) moderate growth with ʟ-glutamine depletion and ʟ-asparagine saturation; and (III) stationary phase with severe nitrogen limitation. However, the current study regarding the impact of subcel- lular 13C data only covers the exponential growth phase during the first 24 h. This period is typically investigated in vitro because labeling and cultivation conditions can be controlled easily, giving accurate results regarding flux distributions and cell-specific productivities [5, 7]. Fur- thermore, additional cultivation study data investigating the same cell line and process conditions was used for broadening the data set of subcellular versus cellular 13C metabolomics for flux analysis (see Supplementary Mate- rial S6). The summary of all estimated intracellular fluxes is provided in Supplementary Material S2. Cell growth and carbon labeling studies During the exponential growth phase, cells grew with 0.025 ± 0.001  h−1. Carbon and nitrogen sources were con- stantly consumed, and metabolic byproducts were steadily released with constant specific rates (Supplementary Mate- rial S1, Table S2). d-Glucose was consumed as a major car- bon source while ʟ-glutamine and ʟ-asparagine served as primary nitrogen sources. In addition, the Warburg effect [25] was observed, showing a glucose-to-lactate ratio of 0.93 mold-glucose/moll-lactate. 13C carbon labeling was introduced by the addition of 75% [U-13C6]-d-glucose after 2.5 days, revealing no phenotypic changes, i.e., no alterations of cel- lular metabolism. 13C metabolic flux analysis using compartment‑specific metabolome data 13C MFA was performed using compartment-specific metabolome data reflecting subcellular pools of cytosol and mitochondria together with isotopomer profiles of the said compartments. Flux estimations were performed at least 100 times with randomized input values and rational boundary values for each parameter (Supplementary Material S2). Finally, the chi-square tests achieved 228.87, which served the statistical constraint of 232.92 on a 95% significance level. Glycolysis and PPP High glycolytic (0.112 ± 0.017 pmol cell−1  h−1 of hexoki- nase) and extremely low PPP fluxes (0.008 ± 0.001 pmol cell−1  h−1 of G6P dehydrogenase) were found. The lat- ter accounted for 6.68% of the d-glucose consumed. These observations are in agreement with the findings of Ahn & Antoniewicz [5], who performed 13C MFA in adherent CHO-K1 cells. In addition, approximately 15% (0.016 ± 0.002 pmol cell−1  h−1) of intracellular G6P was continuously in exchange with endogenous glycogen. In vivo mitochondrial shuttle Glycolytic carbon fueled into mitochondria via two transport mechanisms; 77% entered via the mitochondrial pyruvate carrier (MPC1/2) and 23% via a putative l-alanine trans- porter. MPC1/2 showed the highest mitochondrial transport activities while other transporters exchanged compounds for different purposes; (i) mitochondrial citrate carrier (citrate/ malate antiporter; 0.049 ± 0.002 pmol cell−1  h−1) served as a citrate exporter to provide cytosolic acetyl-CoA for the de novo lipid biosynthesis pathway; (ii) the malate-aspartate shuttle comprising 2-oxoglutarate carrier (α-ketoglutarate/ mal antiporter) and aspartate-glutamate carrier (aspartate/ glutamate antiporter), which is often described as an indi- rect NADH shuttle because imported malate is oxidized to oxaloacetate, releasing NADH, fulfilled a different function; malate was net exported from mitochondria to fuel cytosolic ME. Cytosolic malic enzyme and NADPH production NADPH is a key electron donator for anabolic pathways and is essential for monoclonal antibody biosynthesis. Ahn & Antoniewicz, Templeton et al. [5, 7] suggested MEs as key NADPH producers in CHO cells. This hypothesis was further confirmed via compartment-specific flux analysis by Junghans et al. [8]. Cytosolic ME (MEcyt) was identified as Fig. 2 A Intracellular flux distribution estimated using compartment- specific (left) and non-compartmented data (right); B fluxes of bio- chemical reactions involving single-compartment metabolites; C fluxes of biochemical reactions involving multi-compartment metab- olites; and D mitochondrial carrier fluxes estimated with compart- ment-specific and non-compartmented data (* indicates significance p < 0.05) ◂ 2574 Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 the primary provider serving NADPH needs. Compartment- specific 13C MFA estimated that about 86% of the NADPH requirement was fulfilled by MEcyt (0.09 ± 0.01  pmol cell−1  h−1). 13C Metabolic flux analysis using non‑compartmented metabolome data An additional 13C MFA was performed to investigate the importance of distinct sub-cellular information to eluci- date proper in vivo subcellular flux patterns. Analyzing the merged data (Eq. 6) via 13C MFA yielded a Chi-squared value of 140.12 on the 95% confidence level, which was accepted as a good fit (with 154.30 as the χ2 statistical threshold on 95% confidence interval). This study was performed using the same model con- sisting of 42 intracellular biochemical reactions. Figure 2A provides the comparison of intracellular flux distributions estimated with (left) and without (right) sub-cellular infor- mation (Fig. 2A). The related single-compartment key fluxes and iso-enzymatic rates are depicted as bar plots in Fig. 2B, C. Notably, the term ‘iso enzymes’ encodes fluxes connect- ing the same substrates and products but localized in differ- ent compartments. Biochemical reactions localized in a single compartment Figure 2B, C left shows fluxes of biochemical reactions that exist in one compartment (cytosol or mitochondria) only. Most of them revealed similar results irrespective of whether compartment-specific information was used (black) or not (gray). Figure 2B demonstrates the case the metabolome pools and the respective fluxes were the same for both stud- ies, yielding a similar τ13C. This is also true for citrate syn- thase vCS, although identifiability was poor. Similar results were observed for cytosolic-based reactions: pyruvate car- boxylase (vpc) and PEP carboxykinase (vpepck) (Fig. 2C). These single-compartment reactions possessed the particu- larity of utilizing the same metabolites but in different com- partments (Fig. 1). In this particular case, no statistically sound difference between vpc and vpepck was found, most likely because compartment-specific OAA values lacked. Iso‑enzymatic reactions localized in different compartments Special emphasis is laid on the so-called iso-enzymatic reac- tions of Fig. 2C right that catalyze similar conversions in different compartments. The fluxes of malate dehydroge- nase (vmdh), ME (vme), aspartate amino-transferases (vast), and alanine amino-transferases (valt) are localized in cytosol and mitochondria, respectively. Of the eight iso-enzymes analyzed, seven conversion rates were significantly different. The only exception is the mitochondrial malate dehydroge- nase (vmdh,mit) which revealed statistical similarity although fluxes even reversed. On contrary, the cytosolic malate dehy- drogenase (vmdh,cyt) also disclosed flux reversion but with a sound statistical identifiability. Non-compartmented data were not able to properly reflect real fluxes of the amino-transferases (vast), namely alanine amino-transferases (valt) and aspartate amino transferases (vast). The analysis of whole-cell data resulted in flux over- estimation compared to compartment-specific analysis. Notably, the substrate aspartate occurred in cytosol and mitochondria and is a key player of the aspartate-malate shuttle. Moreover, alanine was involved in the co-transport of glycolytic carbon into mitochondria with the MPC1/2. In this case, proper localization and labeling information of the compound is key to estimate fluxes correctly. In addition, severe bias was observed for fluxes of both malic enzymes (vme) as displayed in Fig. 2C right. By trend, 13C flux estimations using non-compartmented data identi- fied significantly lower (about 30%) cytosolic vme,cyt than the non-compartmented data. Regarding mitochondria, the opposite was found. The finding for vme using non-compart- mented data is consistent with the observations of Ahn & Antoniewicz, Templeton et al. [5, 7] who also performed 13C MFA with cellular data. Importantly, cytosolic ME activ- ity via vme,cyt was identified as a key supplier for NADPH needed for IgG production in CHO cells (Junghans et al. [8]). Mitochondrial metabolite carriers Comparing shuttle activities using sub-cellular and cel- lular labeling information reveals significant differences for half of the inter-compartment transporters, namely the aspartate/glutamate antiporter (vAGC1), malate carrier (vDIC), α-ketoglutarate/malate antiporter (vOGC), and the putative alanine carrier (vmAla) (Fig. 2D). Similar to the identification of aspartate amino-transferases, the proper identification of vAGC1 depends on the labeling turnover τ13C of Asp in both compartments. Missing compartment-specific measurements lead to the different shuttle fluxes, which are also reflected in the biased flux vast. The same scenario also holds true for the putative alanine carrier (vmAla) and the corresponding reac- tions (alanine amino-transferases; valt). Shuttle estimations regarding vDIC and vOGC using non-compartment-specific data contradict flux calculations using compartment-specific information estimation. The sub-cellular labeling informa- tion of malate is essential to get accurate flux estimates. Interestingly, the flux estimation of putative asparagine car- rier (vmAsn) was not biased by the use of whole-cell labeling data only. This may reflect that vmAsn heavily depends on the 2575Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 measured ʟ-asparagine uptake rate (qAsn) irrespective of the existence of additional subcellular information. Estimated cytosol–mitochondrial fraction (f factor) Using Eq. 8, f factors were estimated for each metabolite and compared with the measurements of Junghans et al. [8] (Table 1). As indicated, all estimated cytosolic fractions (f) were poorly identified with pyruvate showing the smallest difference of 8.59% only. On average, 59.71% difference was found compared to the real labeling fraction. Notably, the best estimates of pyruvate and asparagine also enabled improved flux values for the corresponding biochemical reactions, e.g. vMPC1/2, vpdh for pyruvate, and vasns, vmAsn for asparagine. Cellular NADH and NADPH production Table 2 shows a comparison of NADH and NADPH pro- duction via compartment-specific analysis and neglection of sub-cellular data. Neglecting sub-cellular data, NADPH production is underestimated by approximately 25%. This reflects the 30% underestimation of cytosolic vME when cellular and not subcellular data are used. In the case of NADH and ATP, the utilization of different datasets disclosed only minor differences. NADH and ATP fluxes were overestimated by 9% and 14% for non-compartmented data, respectively. Challenging the key statements by an additional data set To investigate whether or not the observed flux characteris- tics may be specific for the data sets used, additional data of cultivations with the same cell line, cultivation conditions, and analytical tools was used. Figure S6-1:S6-3 (Supple- mentary Material S6) outlines that very similar key mes- sages are obtained analyzing the new data set: Glycolytic fluxes are fairly similar irrespective whether subcellular or cellular 13C metabolomics is used. On contrary, fluxes for cytosolic malate dehydrogenase and malic enzyme differ sta- tistically significant depending on the granularity of meta- bolic labeling resolution. The same holds true for shuttle activities such as DIC, GC1, and OGC which is in agreement with the results derived from the other data sets. Discussion This study challenges the information gain when perform- ing 13C MFA with compartment-specific metabolome data compared to exploiting cellular labeling information not distinguishing between cytosol and mitochondria. Figure 2 outlines the complexity of the interactions. A group of fluxes (vpgi, vGAPdh vG6Pdh, and vphdgh) located in a single compartment (here: cytosol) disclose equal values irrespective of the analytical approach selected. Interest- ingly, this also holds true for vcs, located in mitochondria, primarily due to poor flux identifiability. Furthermore, vpepck and vpc revealed such high flux variances that no distinc- tion could be found whether cellular or subcellular 13C data were used. Apparently, both reactions depend on cytosolic (OAAcyt) and mitochondrial oxaloacetate (OAAmit). They act at the interphase of the two compartments and rely on proper sub-cellular measurement information (τ13C) for cor- rect identification. Distinct OAA measurements were not available in the current study due to challenging analytical access to the compound. Accordingly, flux estimations might be biased by the quality of OAA pool estimations. In addition, some other fluxes should be interpreted with great care, too. This holds particularly true for mitochondrial malate dehydrogenase (vmdh,mit) and the pyruvate carrier vMPC1. Both disclose large error bars rendering a discrimi- nation between cellular versus subcellular approaches hardly possible (Fig. 2C, D). Flux imprecisions reflect the lack of reliable CO2 evolution rates ( qCO2 ) and CO2 labeling profiles. The whole-cell (cellular) flux estimation failed to esti- mate the mitochondrial and cytosolic fluxes of the amino- transferases valt and vast. This may reflect that those fluxes Table 2 Comparison of NADH, ATP, and NADPH net production rates in compartment-specific analysis and whole-cell analysis (values presented in pmol cell−1  h−1) NADH ATP NADPH Compartment-specific 0.55692 0.22752 0.10577 Non-compartmented 0.60815 0.25914 0.07924 Table 1 Complete list of estimated and measured cytosolic fractions of subcellular metabolites used for 13C MFA Metabolites Cytosolic fraction (f) Estimated Measurement (Junghans et al. [8]) % difference (meas- urement as the refer- ence value) Mal 0.100 0.829 87.9 Pyr 0.910 0.838 8.59 aKG 0.100 0.714 85.99 Cit 0.995 0.489 103.48 Glu 0.373 0.827 54.90 Ala 0.100 0.840 88.1 Asn 0.717 0.805 10.48 Asp 0.500 0.809 38.20 2576 Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 heavily depend on the compartment-specific labeling infor- mation of alanine and aspartate. Not providing this infor- mation by using whole-cell labeling data leads to the large discrepancies given in Fig. 2C. Almost all mitochondrial carrier fluxes were poorly esti- mated when using non-compartmented data. Inaccurate esti- mations of vAGC1 and vmAla are also reflected by the results of vast and valt. In addition, the poor estimation of the malate carriers vDIC and vOGC depended on vme. In general, fluxes of transporters and bioreactions heavily relied on the labeling dynamics measured in the related metabolites. Regarding vMPC1, the reduced shuttle activity based on non-compart- mented data reflects the missing malate exported into cyto- sol (Fig. 2D). To check whether the additional use of labeled glu- tamine [6] might have achieved similar subcellular flux resolutions as the compartment-specific analysis, simula- tions were performed using [U-13C5]-ʟ-glutamine (Sup- plementary Material S3). Interestingly, without informa- tion about compartment-specific metabolomics, cytosolic 13C signals obtained from simulations are pretty similar to those of the whole-cell. This is mainly due to the relatively low information gain with respect to the key mitochondrial metabolites malate and aspartate. Compartment-specific labeling information and turnover of the latter are decisive to resolve activities of mitochondrial transporters. In general, most of the flux estimations using either non-compartmented or compartmented data led to similar values. Even global cell qualifications, such as rates of total ATP formation and NADH production, were similar. However, two main findings should be considered: 1. Often, cellular analysis achieved similar flux estimations as subcellular studies by fitting measured cytosolic labe- ling fractions for the sake of estimating pool sizes prop- erly (Table 1). In other words, flux optimization algo- rithms adapted cytosolic and mitochondrial pool sizes to complement missing labeling information. However, the simulated pool size readouts were strongly misleading. 2. Among the fluxes with the largest discrepancies is the cytosolic ME vme. Remarkably, this flux was found to be a promising metabolic engineering target to maximize the formation of heterologous proteins by improved NADPH supply [8]. Accordingly, exact estimation is a prerequisite for proper strain engineering. Figure 3 illustrates that even the result of non-compartment data analysis still fits to the subcellular kinetics published in Junghans et al. [8]. Whether or not experimentalists may have identified this enzyme as a metabolic engineering target remains open and is a matter of qualitative discus- sion rather than quantitative target identification [8]. To date, the compartment-specific analytical approach has shown its suitability for multiple metabolomic studies Fig. 3 Cell-specific produc- tion of monoclonal antibodies in CHO cells (modified from Junghans et al. [8]) 2577Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 investigating CHO cells under in vivo-like conditions [8, 15, 24–30]. The latter is enabled by fast and standardized metabolism inactivation. Furthermore, data quality essen- tially relies on the quantitative access to internal standards, such as G6P/F6P (in cytosolic space) and cis-aconitate (in mitochondrion) to correct for mitochondrial leakage. In general, fast metabolic inactivation, standardized sample processing and use of internal standards are prerequisites for any compartment-specific metabolomics approach that might be used in future applications. Conclusions Investigating the need for using subcellular 13C labeling data, the study revealed that non-compartmented data enabled to identify most fluxes involving single compart- ment metabolites. Besides, half of the mitochondrial shut- tle fluxes and global properties, such as ATP and NADH formation, were fairly well estimated without requiring further subcellular labeling information. However, there is a number of sensitive fluxes that could only be identified properly if compartment-specific pool information was used. Among those were mitochondrial shuttles that rely on alanine, aspartate and malate. Furthermore, key meta- bolic engineering targets, such as the cytosolic ME flux for NADPH formation, were severely underestimated using (total) cellular data. This may disguise their role as prom- ising metabolic engineering target if non-compartmented pool analysis is performed, only. The finding underlines the necessity to apply subcellular data for flux estimation, not only to quantify cytosolic/mitochondrial shuttle activi- ties but also to identify metabolic engineering targets and obtain valid values for real pool sizes. Supplementary Information The online version contains supplemen- tary material available at https:// doi. org/ 10. 1007/ s00449- 021- 02628-1. Acknowledgements This work was partially funded by the Bun- desministerium für Bildung und Forschung (BMBF, Funding Number 031L0077A and 031B0596B). Funding Open Access funding enabled and organized by Projekt DEAL. Declarations Conflict of interest The authors declare no financial or commercial conflict of interest. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References 1. Wiechert W (2001) 13C metabolic flux analysis. Metab Eng 3(3):195–206 2. Frick O, Wittmann C (2005) Characterization of the metabolic shift between oxidative and fermentative growth in Saccharomy- ces cerevisiae by comparative 13C flux analysis. Microb Cell Fact 4(1):1–16 3. van Winden WA, van Dam JC, Ras C, Kleijn RJ, Vinke JL, van Gulik WM, Heijnen JJ (2005) Metabolic-flux analysis of Saccha- romyces cerevisiae CEN. PK113–7D based on mass isotopomer measurements of 13C-labeled primary metabolites. FEMS Yeast Res 5(6–7):559–568 4. Zhao Z, Kuijvenhoven K, Ras C, van Gulik WM, Heijnen JJ, Ver- heijen PJ, van Winden WA (2008) Isotopic non-stationary 13C gluconate tracer method for accurate determination of the pentose phosphate pathway split-ratio in Penicillium chrysogenum. Metab Eng 10(3–4):178–186 5. Ahn WS, Antoniewicz MR (2011) Metabolic flux analysis of CHO cells at growth and non-growth phases using isotopic tracers and mass spectrometry. Metab Eng 13(5):598–609 6. Ahn WS, Antoniewicz MR (2013) Parallel labeling experi- ments with [1,2–13C] glucose and [U-13C] glutamine provide new insights into CHO cell metabolism. Metab Eng 15:34–47 7. Templeton N, Dean J, Reddy P, Young JD (2013) Peak antibody production is associated with increased oxidative metabolism in an industrially relevant fed-batch CHO cell culture. Biotechnol Bioeng 110(7):2013–2024 8. Junghans L, Teleki A, Wijaya AW, Becker M, Schweikert M, Takors R (2019) From nutritional wealth to autophagy: in vivo metabolic dynamics in the cytosol, mitochondrion and shuttles of IgG producing CHO cells. Metab Eng 54:145–159 9. Allen DK, Shachar-Hill Y, Ohlrogge JB (2007) Compartment- specific labeling information in 13C metabolic flux analysis of plants. Phytochemistry 68(16–18):2197–2210 10. Ahn WS, Antoniewicz MR (2012) Towards dynamic metabolic flux analysis in CHO cell cultures. Biotechnol J 7(1):61–74. https:// doi. org/ 10. 1002/ biot. 20110 0052 11. Nicolae A, Wahrheit J, Bahnemann J, Zeng AP, Heinzle E (2014) Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation. BMC Syst Biol 8(1):50 12. Pfizenmaier J, Takors R (2017) Host organisms: mammalian cells. In: Industrial biotechnology, vol 2. pp 643–671 13. Matuszczyk JC, Teleki A, Pfizenmaier J, Takors R (2015) Compartment-specific metabolomics for CHO reveals that ATP pools in mitochondria are much lower than in cytosol. Biotech- nol J 10(10):1639–1650 14. Buescher JM, Antoniewicz MR, Boros LG, Burgess SC, Brunengraber H, Clish CB et al (2015) A roadmap for inter- preting 13C metabolite labeling patterns from cells. Curr Opin Biotechnol 34:189–201 https://doi.org/10.1007/s00449-021-02628-1 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1002/biot.201100052 2578 Bioprocess and Biosystems Engineering (2021) 44:2567–2578 1 3 15. Pfizenmaier J, Matuszczyk JC, Takors R (2015) Changes in intracellular ATP-content of CHO cells as response to hyperos- molality. Biotechnol Prog 31(5):1212–1216 16. Teleki A, Sánchez-Kopper A, Takors R (2015) Alkaline condi- tions in hydrophilic interaction liquid chromatography for intra- cellular metabolite quantification using tandem mass spectrom- etry. Anal Biochem 475:4–13 17. Fernandez CA, Des Rosiers C, Previs SF, David F, Brunen- graber H (1996) Correction of 13C mass isotopomer distribu- tions for natural stable isotope abundance. J Mass Spectrom 31(3):255–262 18. Agnarsson J, Sunde M, Ermilova I (2013) Parallel optimization in MATLAB. Project in Computational Science Report 19. Wiechert W, de Graaf AA (1997) Bidirectional reaction steps in metabolic networks: I. Modeling and simulation of carbon isotope labeling experiments. Biotechnol Bioeng 55(1):101–117 20. Schmidt K, Carlsen M, Nielsen J, Villadsen J (1997) Modeling isotopomer distributions in biochemical networks using isoto- pomer mapping matrices. Biotechnol Bioeng 55(6):831–840 21. Hofmann U, Maier K, Niebel A, Vacun G, Reuss M, Mauch K (2008) Identification of metabolic fluxes in hepatic cells from transient 13C-labeling experiments: Part I. Experimental obser- vations. Biotechnol Bioeng 100(2):344–354 22. Schaub J, Mauch K, Reuss M (2008) Metabolic flux analysis in Escherichia coli by integrating isotopic dynamic and isotopic sta- tionary 13C labeling data. Biotechnol Bioeng 99(5):1170–1185 23. Antoniewicz MR, Kelleher JK, Stephanopoulos G (2006) Deter- mination of confidence intervals of metabolic fluxes estimated from stable isotope measurements. Metab Eng 8(4):324–337 24. Welch BL (1947) The generalization of student’s’ problem when several different population variances are involved. Biometrika 34(1/2):28–35 25. Warburg O (1956) On the origin of cancer cells. Science 123(3191):309–314 26. Pfizenmaier J, Junghans L, Teleki A, Takors R (2016) Hyperos- motic stimulus study discloses benefits in ATP supply and reveals miRNA/mRNA targets to improve recombinant protein production of CHO cells. Biotechnol J 11(8):1037–1047 27. Becker M, Junghans L, Teleki A, Bechmann J, Takors R (2019) The less the better: how suppressed base addition boosts produc- tion of monoclonal antibodies with Chinese Hamster Ovary cells. Front Bioeng Biotechnol 7:76 28. Becker M, Junghans L, Teleki A, Bechmann J, Takors R (2019) Perfusion cultures require optimum respiratory ATP supply to maximize cell-specific and volumetric productivities. Biotechnol Bioeng 116(5):951–960 29. Verhagen N, Wijaya AW, Teleki A, Fadhlullah M, Unsöld A, Schilling M et al (2020) Comparison of ʟ-tyrosine containing dipeptides reveals maximum ATP availability for ʟ-prolyl-ʟ- tyrosine in CHO cells. Eng Life Sci 20(9–10):384–394 30. Verhagen N, Teleki A, Heinrich C, Schilling M, Unsöld A, Takors R (2020) S-adenosylmethionine and methylthioadenosine boost cellular productivities of antibody forming Chinese hamster ovary cells. Biotechnol Bioeng 117(11):3239–3247 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Compartment-specific metabolome labeling enables the identification of subcellular fluxes that may serve as promising metabolic engineering targets in CHO cells Abstract Introduction Materials and methods Cell culture and experimental set-up Extracellular and intracellular analytics 13C metabolic flux analysis Metabolite balancing Metabolic flux analysis Isotopomer balancing and bidirectional reactions Parameter estimation and uncertainty Statistical analysis Results Cell growth and carbon labeling studies 13C metabolic flux analysis using compartment-specific metabolome data Glycolysis and PPP In vivo mitochondrial shuttle Cytosolic malic enzyme and NADPH production 13C Metabolic flux analysis using non-compartmented metabolome data Biochemical reactions localized in a single compartment Iso-enzymatic reactions localized in different compartments Mitochondrial metabolite carriers Estimated cytosol–mitochondrial fraction (f factor) Cellular NADH and NADPH production Challenging the key statements by an additional data set Discussion Conclusions Acknowledgements References