Metabolic flux analysis using ¹³C peptide label measurements

An ensemble approach to the structure-function problem in microbial communities

The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.

Tracing metabolic flux through time and space with isotope labeling experiments

Metabolism is dynamic and must function in context-specific ways to adjust to changes in the surrounding cellular and ecological environment. When isotopic tracers are used, metabolite flow (i.e. metabolic flux) can be quantified through biochemical networks to assess metabolic pathway operation. The cellular activities considered across multiple tissues and organs result in the observed phenotype and can be analyzed to discover emergent, whole-system properties of biology and elucidate misconceptions about network operation. However, temporal and spatial challenges remain significant hurdles and require novel approaches and creative solutions. We survey current investigations in higher plant and animal systems focused on dynamic isotope labeling experiments, spatially resolved measurement strategies, and observations from re-analysis of our own studies that suggest prospects for future work. Related discoveries will be necessary to push the frontier of our understanding of metabolism to suggest novel solutions to cure disease and feed a growing future world population.

Concurrent isotope-assisted metabolic flux analysis and transcriptome profiling reveal responses of poplar cells to altered nitrogen and carbon supply

Reduced nitrogen is indispensable to plants. However, its limited availability in soil combined with the energetic and environmental impacts of nitrogen fertilizers motivates research into molecular mechanisms toward improving plant nitrogen use efficiency (NUE). We performed a systems-level investigation of this problem by employing multiple 'omics methodologies on cell suspensions of hybrid poplar (Populus tremula × Populus alba). Acclimation and growth of the cell suspensions in four nutrient regimes ranging from abundant to deficient supplies of carbon and nitrogen revealed that cell growth under low-nitrogen levels was associated with substantially higher NUE. To investigate the underlying metabolic and molecular mechanisms, we concurrently performed steady-state ¹³ C metabolic flux analysis with multiple isotope labels and transcriptomic profiling with cDNA microarrays. The ¹³ C flux analysis revealed that the absolute flux through the oxidative pentose phosphate pathway (oxPPP) was substantially lower (~threefold) under low-nitrogen conditions. Additionally, the flux partitioning ratio between the tricarboxylic acid cycle and anaplerotic pathways varied from 84%:16% under abundant carbon and nitrogen to 55%:45% under deficient carbon and nitrogen. Gene expression data, together with the flux results, suggested a plastidic localization of the oxPPP as well as transcriptional regulation of certain metabolic branchpoints, including those between glycolysis and the oxPPP. The transcriptome data also indicated that NUE-improving mechanisms may involve a redirection of excess carbon to aromatic metabolic pathways and extensive downregulation of potentially redundant genes (in these heterotrophic cells) that encode photosynthetic and light-harvesting proteins, suggesting the recruitment of these proteins as nitrogen sinks in nitrogen-abundant conditions.

Cell-Type Specific Metabolic Flux Analysis: A Challenge for Metabolic Phenotyping and a Potential Solution in Plants

Stable isotope labelling experiments are used routinely in metabolic flux analysis (MFA) to determine the metabolic phenotype of cells and tissues. A complication arises in multicellular systems because single cell measurements of transcriptomes, proteomes and metabolomes in multicellular organisms suggest that the metabolic phenotype will differ between cell types. In silico analysis of simulated metabolite isotopomer datasets shows that cellular heterogeneity confounds conventional MFA because labelling data averaged over multiple cell types does not necessarily yield averaged flux values. A potential solution to this problem—the use of cell-type specific reporter proteins as a source of cell-type specific labelling data—is proposed and the practicality of implementing this strategy in the roots of Arabidopsis thaliana seedlings is explored. A protocol for the immunopurification of ectopically expressed green fluorescent protein (GFP) from Arabidopsis thaliana seedlings using a GFP-binding nanobody is developed, and through GC-MS analysis of protein hydrolysates it is established that constitutively expressed GFP reports accurately on the labelling of total protein in root tissues. It is also demonstrated that the constitutive expression of GFP does not perturb metabolism. The principal obstacle to the implementation of the method in tissues with cell-type specific GFP expression is the sensitivity of the GC-MS system.

Rerouting of carbon flux in a glycogen mutant of cyanobacteria assessed via isotopically non-stationary 13 C metabolic flux analysis

Cyanobacteria, which constitute a quantitatively dominant phylum, have attracted attention in biofuel applications due to favorable physiological characteristics, high photosynthetic efficiency and amenability to genetic manipulations. However, quantitative aspects of cyanobacterial metabolism have received limited attention. In the present study, we have performed isotopically non-stationary ¹³ C metabolic flux analysis (INST-¹³ C-MFA) to analyze rerouting of carbon in a glycogen synthase deficient mutant strain (glgA-I glgA-II) of the model cyanobacterium Synechococcus sp. PCC 7002. During balanced photoautotrophic growth, 10-20% of the fixed carbon is stored in the form of glycogen via a pathway that is conserved across the cyanobacterial phylum. Our results show that deletion of glycogen synthase gene orchestrates cascading effects on carbon distribution in various parts of the metabolic network. Carbon that was originally destined to be incorporated into glycogen gets partially diverted toward alternate storage molecules such as glucosylglycerol and sucrose. The rest is partitioned within the metabolic network, primarily via glycolysis and tricarboxylic acid cycle. A lowered flux toward carbohydrate synthesis and an altered distribution at the glucose-1-phosphate node indicate flexibility in the network. Further, reversibility of glycogen biosynthesis reactions points toward the presence of futile cycles. Similar redistribution of carbon was also predicted by Flux Balance Analysis. The results are significant to metabolic engineering efforts with cyanobacteria where fixed carbon needs to be re-routed to products of interest. Biotechnol. Bioeng. 2017;114: 2298-2308. © 2017 Wiley Periodicals, Inc.

Plant lipidomics at the crossroads: From technology to biology driven science

The identification and quantification of lipids from plant tissues have become commonplace and many researchers now incorporate lipidomics approaches into their experimental studies. Plant lipidomics research continues to involve technological developments such as those in mass spectrometry imaging, but in large part, lipidomics approaches have matured to the point of being accessible to the novice. Here we review some important considerations for those planning to apply plant lipidomics to their biological questions, and offer suggestions for appropriate tools and practices. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

Isotopically Nonstationary Metabolic Flux Analysis (INST-MFA) of Photosynthesis and Photorespiration in Plants

Photorespiration is a central component of photosynthesis; however to better understand its role it should be viewed in the context of an integrated metabolic network rather than a series of individual reactions that operate independently. Isotopically nonstationary ¹³C metabolic flux analysis (INST-MFA), which is based on transient labeling studies at metabolic steady state, offers a comprehensive platform to quantify plant central metabolism. In this chapter, we describe the application of INST-MFA to investigate metabolism in leaves. Leaves are an autotrophic tissue, assimilating CO₂ over a diurnal period implying that the metabolic steady state is limited to less than 12 h and thus requiring an INST-MFA approach. This strategy results in a comprehensive unified description of photorespiration, Calvin cycle, sucrose and starch synthesis, tricarboxylic acid (TCA) cycle, and amino acid biosynthetic fluxes. We present protocols of the experimental aspects for labeling studies: transient ¹³CO₂ labeling of leaf tissue, sample quenching and extraction, mass spectrometry (MS) analysis of isotopic labeling data, measurement of sucrose and amino acids in vascular exudates, and provide details on the computational flux estimation using INST-MFA.

Deciphering cyanobacterial phenotypes for fast photoautotrophic growth via isotopically nonstationary metabolic flux analysis

BACKGROUND

Synechococcus elongatus UTEX 2973 is the fastest growing cyanobacterium characterized to date. Its genome was found to be 99.8% identical to S. elongatus 7942 yet it grows twice as fast. Current genome-to-phenome mapping is still poorly performed for non-model organisms. Even for species with identical genomes, cell phenotypes can be strikingly different. To understand Synechococcus 2973's fast-growth phenotype and its metabolic features advantageous to photo-biorefineries, ¹³C isotopically nonstationary metabolic flux analysis (INST-MFA), biomass compositional analysis, gene knockouts, and metabolite profiling were performed on both strains under various growth conditions.

RESULTS

The Synechococcus 2973 flux maps show substantial carbon flow through the Calvin cycle, glycolysis, photorespiration and pyruvate kinase, but minimal flux through the malic enzyme and oxidative pentose phosphate pathways under high light/CO₂ conditions. During fast growth, its pool sizes of key metabolites in central pathways were lower than suboptimal growth. Synechococcus 2973 demonstrated similar flux ratios to Synechococcus 7942 (under fast growth conditions), but exhibited greater carbon assimilation, higher NADPH concentrations, higher energy charge (relative ATP ratio over ADP and AMP), less accumulation of glycogen, and potentially metabolite channeling. Furthermore, Synechococcus 2973 has very limited flux through the TCA pathway with small pool sizes of acetyl-CoA/TCA intermediates under all growth conditions.

CONCLUSIONS

This study employed flux analysis to investigate phenotypic heterogeneity among two cyanobacterial strains with near-identical genome background. The flux/metabolite profiling, biomass composition analysis, and genetic modification results elucidate a highly effective metabolic topology for CO₂ assimilatory and biosynthesis in Synechococcus 2973. Comparisons across multiple Synechococcus strains indicate faster metabolism is also driven by proportional increases in both photosynthesis and key central pathway fluxes. Moreover, the flux distribution in Synechococcus 2973 supports the use of its strong sugar phosphate pathways for optimal bio-productions. The integrated methodologies in this study can be applied for characterizing non-model microbial metabolism.

Unsupervised Identification of Isotope-Labeled Peptides

In vivo isotopic labeling coupled with high-resolution proteomics is used to investigate primary metabolism in techniques such as stable isotope probing (protein-SIP) and peptide-based metabolic flux analysis (PMFA). Isotopic enrichment of carbon substrates and intracellular metabolism determine the distribution of isotopes within amino acids. The resulting amino acid mass distributions (AMDs) are convoluted into peptide mass distributions (PMDs) during protein synthesis. With no a priori knowledge on metabolic fluxes, the PMDs are therefore unknown. This complicates labeled peptide identification because prior knowledge on PMDs is used in all available peptide identification software. An automated framework for the identification and quantification of PMDs for nonuniformly labeled samples is therefore lacking. To unlock the potential of peptide labeling experiments for high-throughput flux analysis and other complex labeling experiments, an unsupervised peptide identification and quantification method was developed that uses discrete deconvolution of mass distributions of identified peptides to inform on the mass distributions of otherwise unidentifiable peptides. Uniformly (13)C-labeled Escherichia coli protein was used to test the developed feature reconstruction and deconvolution algorithms. The peptide identification was validated by comparing MS(2)-identified peptides to peptides identified from PMDs using unlabeled E. coli protein. Nonuniformly labeled Glycine max protein was used to demonstrate the technology on a representative sample suitable for flux analysis. Overall, automatic peptide identification and quantification were comparable or superior to manual extraction, enabling proteomics-based technology for high-throughput flux analysis studies.

Assessing compartmentalized flux in lipid metabolism with isotopes

Metabolism in plants takes place across multiple cell types and within distinct organelles. The distributions equate to spatial heterogeneity; though the limited means to experimentally assess metabolism frequently involve homogenizing tissues and mixing metabolites from different locations. Most current isotope investigations of metabolism therefore lack the ability to resolve spatially distinct events. Recognition of this limitation has resulted in inspired efforts to advance metabolic flux analysis and isotopic labeling techniques. Though a number of these efforts have been applied to studies in central metabolism; recent advances in instrumentation and techniques present an untapped opportunity to make similar progress in lipid metabolism where the use of stable isotopes has been more limited. These efforts will benefit from sophisticated radiolabeling reports that continue to enrich our knowledge on lipid biosynthetic pathways and provide some direction for stable isotope experimental design and extension of MFA. Evidence for this assertion is presented through the review of several elegant stable isotope studies and by taking stock of what has been learned from radioisotope investigations when spatial aspects of metabolism were considered. The studies emphasize that glycerolipid production occurs across several locations with assembly of lipids in the ER or plastid, fatty acid biosynthesis occurring in the plastid, and the generation of acetyl-CoA and glycerol-3-phosphate taking place at multiple sites. Considering metabolism in this context underscores the cellular and subcellular organization that is important to enhanced production of glycerolipids in plants. An attempt is made to unify salient features from a number of reports into a diagrammatic model of lipid metabolism and propose where stable isotope labeling experiments and further flux analysis may help address questions in the field. This article is part of a Special Issue entitled: Plant Lipid Biology edited by Kent D. Chapman and Ivo Feussner.

Quantifying plant phenotypes with isotopic labeling & metabolic flux analysis

Analyses of metabolic flux using stable isotopes in plants have traditionally been restricted to tissues with presumed homogeneous cell populations and long metabolic steady states such as developing seeds, cell suspensions, or cultured roots and root tips. It is now possible to describe these and other metabolically more dynamic tissues such as leaves in greater detail using novel methods in mass spectrometry, isotope labeling strategies, and transient labeling-based flux analyses. Such studies are necessary for a systems level description of plant function that more closely represents biological reality, and provides insights into the genes that will need to be modified as natural resources become ever more limited and environments change.

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

Tracking the metabolic pulse of plant lipid production with isotopic labeling and flux analyses: Past, present and future

Metabolism is comprised of networks of chemical transformations, organized into integrated biochemical pathways that are the basis of cellular operation, and function to sustain life. Metabolism, and thus life, is not static. The rate of metabolites transitioning through biochemical pathways (i.e., flux) determines cellular phenotypes, and is constantly changing in response to genetic or environmental perturbations. Each change evokes a response in metabolic pathway flow, and the quantification of fluxes under varied conditions helps to elucidate major and minor routes, and regulatory aspects of metabolism. To measure fluxes requires experimental methods that assess the movements and transformations of metabolites without creating artifacts. Isotopic labeling fills this role and is a long-standing experimental approach to identify pathways and quantify their metabolic relevance in different tissues or under different conditions. The application of labeling techniques to plant science is however far from reaching it potential. In light of advances in genetics and molecular biology that provide a means to alter metabolism, and given recent improvements in instrumentation, computational tools and available isotopes, the use of isotopic labeling to probe metabolism is becoming more and more powerful. We review the principal analytical methods for isotopic labeling with a focus on seminal studies of pathways and fluxes in lipid metabolism and carbon partitioning through central metabolism. Central carbon metabolic steps are directly linked to lipid production by serving to generate the precursors for fatty acid biosynthesis and lipid assembly. Additionally some of the ideas for labeling techniques that may be most applicable for lipid metabolism in the future were originally developed to investigate other aspects of central metabolism. We conclude by describing recent advances that will play an important future role in quantifying flux and metabolic operation in plant tissues.

How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics

Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to 'measure' the in vivo fluxes within an organism that are not directly observable. In recent years, (13)C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific 'travelers' who are striving to resolve research questions with the currently available (13)C fluxomics toolset.

Using photosystem I as a reporter protein for ¹³C analysis in a coculture containing cyanobacterium and a heterotrophic bacterium

(13)C metabolism analysis of a microbial community is often hindered by the time-consuming and complicated separation procedure for a single species. However, a "reporter protein," produced uniquely by one cell type, retains (13)C fingerprint information in microbial consortia. This study describes the use of photosystem I (PSI), a multi-subunit protein complex universally found in oxygenic phototrophs, as a reliable reporter protein to probe microalgal metabolism (i.e., cyanobacterium Synechocystis sp. PCC 6803) in a mixed culture with heterotrophic bacteria (i.e., Escherichia coli). We demonstrate that efficient purification of PSI and subsequent (13)C-based amino acid analyses may decipher photomixotrophic metabolism of Synechocystis 6803 in the coculture. This study also indicates that a supplement of NaHCO3 at high concentration could significantly improve the robustness of cyanobacterial growth against bacterial contamination.

Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation

Improving plant productivity is an important aim for metabolic engineering. There are few comprehensive methods that quantitatively describe leaf metabolism, although such information would be valuable for increasing photosynthetic capacity, enhancing biomass production, and rerouting carbon flux toward desirable end products. Isotopically nonstationary metabolic flux analysis (INST-MFA) has been previously applied to map carbon fluxes in photoautotrophic bacteria, which involves model-based regression of transient (13)C-labeling patterns of intracellular metabolites. However, experimental and computational difficulties have hindered its application to terrestrial plant systems. We performed in vivo isotopic labeling of Arabidopsis thaliana rosettes with (13)CO2 and estimated fluxes throughout leaf photosynthetic metabolism by INST-MFA. Plants grown at 200 µmol m(-2)s(-1) light were compared with plants acclimated for 9 d at an irradiance of 500 µmol⋅m(-2)⋅s(-1). Approximately 1,400 independent mass isotopomer measurements obtained from analysis of 37 metabolite fragment ions were regressed to estimate 136 total fluxes (54 free fluxes) under each condition. The results provide a comprehensive description of changes in carbon partitioning and overall photosynthetic flux after long-term developmental acclimation of leaves to high light. Despite a doubling in the carboxylation rate, the photorespiratory flux increased from 17 to 28% of net CO2 assimilation with high-light acclimation (Vc/Vo: 3.5:1 vs. 2.3:1, respectively). This study highlights the potential of (13)C INST-MFA to describe emergent flux phenotypes that respond to environmental conditions or plant physiology and cannot be obtained by other complementary approaches.

(13)C metabolic flux analysis of recombinant expression hosts

Identifying host cell metabolic phenotypes that promote high recombinant protein titer is a major goal of the biotech industry. (13)C metabolic flux analysis (MFA) provides a rigorous approach to quantify these metabolic phenotypes by applying isotope tracers to map the flow of carbon through intracellular metabolic pathways. Recent advances in tracer theory and measurements are enabling more information to be extracted from (13)C labeling experiments. Sustained development of publicly available software tools and standardization of experimental workflows is simultaneously encouraging increased adoption of (13)C MFA within the biotech research community. A number of recent (13)C MFA studies have identified increased citric acid cycle and pentose phosphate pathway fluxes as consistent markers of high recombinant protein expression, both in mammalian and microbial hosts. Further work is needed to determine whether redirecting flux into these pathways can effectively enhance protein titers while maintaining acceptable glycan profiles.

Biofuel production: an odyssey from metabolic engineering to fermentation scale-up

Metabolic engineering has developed microbial cell factories that can convert renewable carbon sources into biofuels. Current molecular biology tools can efficiently alter enzyme levels to redirect carbon fluxes toward biofuel production, but low product yield and titer in large bioreactors prevent the fulfillment of cheap biofuels. There are three major roadblocks preventing economical biofuel production. First, carbon fluxes from the substrate dissipate into a complex metabolic network. Besides the desired product, microbial hosts direct carbon flux to synthesize biomass, overflow metabolites, and heterologous enzymes. Second, microbial hosts need to oxidize a large portion of the substrate to generate both ATP and NAD(P)H to power biofuel synthesis. High cell maintenance, triggered by the metabolic burdens from genetic modifications, can significantly affect the ATP supply. Thereby, fermentation of advanced biofuels (such as biodiesel and hydrocarbons) often requires aerobic respiration to resolve the ATP shortage. Third, mass transfer limitations in large bioreactors create heterogeneous growth conditions and micro-environmental fluctuations (such as suboptimal O₂ level and pH) that induce metabolic stresses and genetic instability. To overcome these limitations, fermentation engineering should merge with systems metabolic engineering. Modern fermentation engineers need to adopt new metabolic flux analysis tools that integrate kinetics, hydrodynamics, and ¹³C-proteomics, to reveal the dynamic physiologies of the microbial host under large bioreactor conditions. Based on metabolic analyses, fermentation engineers may employ rational pathway modifications, synthetic biology circuits, and bioreactor control algorithms to optimize large-scale biofuel production.

Analysis of isotopic labeling in peptide fragments by tandem mass spectrometry

Phenotype in multicellular organisms is the consequence of dynamic metabolic events that occur in a spatially dependent fashion. This spatial and temporal complexity presents challenges for investigating metabolism; creating a need for improved methods that effectively probe biochemical events such as amino acid biosynthesis. Isotopic labeling can provide a temporal-spatial recording of metabolic events through, for example, the description of enriched amino acids in the protein pool. Proteins are therefore an important readout of metabolism and can be assessed with modern mass spectrometers. We compared the measurement of isotopic labeling in MS2 spectra obtained from tandem mass spectrometry under either higher energy collision dissociation (HCD) or collision induced dissociation (CID) at varied energy levels. Developing soybean embryos cultured with or without 13C-labeled substrates, and Escherichia coli MG1655 enriched by feeding 7% uniformly labeled glucose served as a source of biological material for protein evaluation. CID with low energies resulted in a disproportionate amount of heavier isotopologues remaining in the precursor isotopic distribution. HCD resulted in fewer quantifiable products; however deviation from predicted distributions were small relative to the CID-based comparisons. Fragment ions have the potential to provide information on the labeling of amino acids in peptides, but our results indicate that without further development the use of this readout in quantitative methods such as metabolic flux analysis is limited.

Quantification of peptide m/z distributions from 13C-labeled cultures with high-resolution mass spectrometry

Isotopic labeling studies of primary metabolism frequently utilize GC/MS to quantify (13)C in protein-hydrolyzed amino acids. During processing some amino acids are degraded, which reduces the size of the measurement set. The advent of high-resolution mass spectrometers provides a tool to assess molecular masses of peptides with great precision and accuracy and computationally infer information about labeling in amino acids. Amino acids that are isotopically labeled during metabolism result in labeled peptides that contain spatial and temporal information that is associated with the biosynthetic origin of the protein. The quantification of isotopic labeling in peptides can therefore provide an assessment of amino acid metabolism that is specific to subcellular, cellular, or temporal conditions. A high-resolution orbital trap was used to quantify isotope labeling in peptides that were obtained from unlabeled and isotopically labeled soybean embryos and Escherichia coli cultures. Standard deviations were determined by estimating the multinomial variance associated with each element of the m/z distribution. Using the estimated variance, quantification of the m/z distribution across multiple scans was achieved by a nonlinear fitting approach. Observed m/z distributions of uniformly labeled E. coli peptides indicated no significant differences between observed and simulated m/z distributions. Alternatively, amino acid m/z distributions obtained from GC/MS were convolved to simulate peptide m/z distributions but resulted in distinct profiles due to the production of protein prior to isotopic labeling. The results indicate that peptide mass isotopologue measurements faithfully represent mass distributions, are suitable for quantification of isotope-labeling-based studies, and provide additional information over existing methods.