Profiling convoluted single-dimension proton NMR spectra: a Plackett-Burman approach for assessing quantification error of metabolites in complex mixtures with application to cell culture

Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring

High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.

Drivers of human gut microbial community assembly: coadaptation, determinism and stochasticity

Microbial community assembly is a complex process shaped by multiple factors, including habitat filtering, species assortment and stochasticity. Understanding the relative importance of these drivers would enable scientists to design strategies initiating a desired reassembly for e.g., remediating low diversity ecosystems. Here, we aimed to examine if a human fecal-derived defined microbial community cultured in bioreactors assembled deterministically or stochastically, by completing replicate experiments under two growth medium conditions characteristic of either high fiber or high protein diets. Then, we recreated this defined microbial community by matching different strains of the same species sourced from distinct human donors, in order to elucidate whether coadaptation of strains within a host influenced community dynamics. Each defined microbial ecosystem was evaluated for composition using marker gene sequencing, and for behavior using ¹H-NMR-based metabonomics. We found that stochasticity had the largest influence on the species structure when substrate concentrations varied, whereas habitat filtering greatly impacted the metabonomic output. Evidence of coadaptation was elucidated from comparisons of the two communities; we found that the artificial community tended to exclude saccharolytic Firmicutes species and was enriched for metabolic intermediates, such as Stickland fermentation products, suggesting overall that polysaccharide utilization by Firmicutes is dependent on cooperation.

13C Labeling of Nematode Worms to Improve Metabolome Coverage by Heteronuclear Nuclear Magnetic Resonance Experiments

Nuclear magnetic resonance (NMR) spectroscopy is widely used as a metabolomics tool, and 1D spectroscopy is overwhelmingly the commonest approach. The use of 2D spectroscopy could offer significant advantages in terms of increased spectral dispersion of peaks, but has a number of disadvantages—in particular, heteronuclear 2D spectroscopy is often much less sensitive than 1D NMR. One factor contributing to this low sensitivity in ¹³C/¹H heteronuclear NMR is the low natural abundance of the ¹³C stable isotope; as a consequence, where it is possible to label biological material with ¹³C, there is a potential enhancement of sensitivity of up to around 90fold. However, there are some problems that can reduce the advantages otherwise gained—in particular, the fine structure arising from ¹³C/¹³C coupling, which is essentially non-existent at natural abundance, can reduce the possible sensitivity gain and increase the chances of peak overlap. Here, we examined the use of two different heteronuclear single quantum coherence (HSQC) pulse sequences for the analysis of fully ¹³C-labeled tissue extracts from Caenorhabditis elegans nematodes. The constant time ct-HSQC had improved peak shape, and consequent better peak detection of metabolites from a labeled extract; matching this against reference spectra from the HMDB gave a match to about 300 records (although fewer actual metabolites, as some of these represent false positive matches). This approach gives a rapid and automated initial metabolome assignment, forming an ideal basis for further manual curation.

Quantitative Analysis of Central Energy Metabolism in Cell Culture Samples

Nuclear magnetic resonance (NMR) is one of the key analytical platforms used in the analysis of intracellular and extracellular metabolites. Despite the technological advances that allow for the production of high-quality data, the sampling procedures of cultured cells are less well standardized. Different cell lines and culture media composition require adjustments of the protocols to result meaningful quantitative information. Here we provide the workflow for obtaining quantitative metabolic data from adherent mammalian cells using NMR spectroscopy. The robustness of NMR allows for the implementation of the here described protocol to other cell types with only minor adjustments.

Quantitative NMR analysis of intra- and extracellular metabolism of mammalian cells: A tutorial

Metabolomics analysis of body fluids as well as cells is depended on many factors. While several well-accepted standard operating procedures for the analysis of body fluids are available, the NMR based quantitative analysis of cellular metabolites is less well standardized. Experimental designs depend on the cell type, the quenching protocol and the applied post-acquisition workflow. Here, we provide a tutorial for the quantitative description of the metabolic phenotype of mammalian cells using NMR spectroscopy. We discuss all key steps of the process, starting from the selection of the appropriate culture medium, quenching techniques to arrest metabolism in a reproducible manner, the extraction of the intracellular components and the profiling of the culture medium. NMR data acquisition and methods for both qualitative and quantitative analysis are also provided. The suggested methods cover experiments for adherent cells and cells in suspension. We ultimately describe the application of the discussed workflow to a thyroid cancer cell line. Although this tutorial focuses on mammalian cells, the given guidelines and procedures may be adjusted for the analysis of other cell types.

Identifying model error in metabolic flux analysis - a generalized least squares approach

BACKGROUND

The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit.

RESULTS

In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range).

CONCLUSIONS

The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.

Impact of Dissolved Oxygen during UV-Irradiation on the Chemical Composition and Function of CHO Cell Culture Media

Ultraviolet (UV) irradiation is advantageous as a sterilization technique in the biopharmaceutical industry since it is capable of targeting non-enveloped viruses that are typically challenging to destroy, as well as smaller viruses that can be difficult to remove via conventional separation techniques. In this work, we investigated the influence of oxygen in the media during UV irradiation and characterized the effect on chemical composition using NMR and LC-MS, as well as the ability of the irradiated media to support cell culture. Chemically defined Chinese hamster ovary cell growth media was irradiated at high fluences in a continuous-flow UV reactor. UV-irradiation caused the depletion of pyridoxamine, pyridoxine, pyruvate, riboflavin, tryptophan, and tyrosine; and accumulation of acetate, formate, kynurenine, lumichrome, and sarcosine. Pyridoxamine was the only compound to undergo complete degradation within the fluences considered; complete depletion of pyridoxamine was observed at 200 mJ/cm2. Although in both oxygen- and nitrogen-saturated media, the cell culture performance was affected at fluences above 200 mJ/cm2, there was less of an impact on cell culture performance in the nitrogen-saturated media. Based on these results, minimization of oxygen in cell culture media prior to UV treatment is recommended to minimize the negative impact on sensitive media.

Tuning a MAb glycan profile in cell culture: Supplementing N-acetylglucosamine to favour G0 glycans without compromising productivity and cell growth

Glycosylation is a critical quality attribute of many therapeutic proteins, particularly monoclonal antibodies (MAbs). Nucleotide-sugar precursors supplemented to growth medium to affect the substrate supply chain of glycosylation has yielded promising but varied results for affecting glycosylation. Glucosamine (GlcN), a precursor for N-acetylglucosamine (GlcNAc), is a major component of mammalian glycans. The supplementation of GlcN to CHO cells stably-expressing a chimeric heavy-chain monoclonal antibody, EG2-hFc, reduces the complexity of glycans to favour G0 glycoforms, while also negatively impacting cell growth. Although several researchers have examined the supplementation of glucosamine, no clear explanation of its impact on cell growth has been forthcoming. In this work, the glucosamine metabolism is examined. We identified the acetylation of GlcN to produce GlcNAc to be the most likely cause for the negative impact on growth due to the depletion of intracellular acetyl-CoA pools in the cytosol. By supplementing GlcNAc in lieu of GlcN to CHO cells producing EG2-hFc, we achieve the same shift in glycan complexity with marginal impacts on the cell growth and protein production.

Optimization of Experimental Parameters to Explore Small-Ligand/Aptamer Interactions through Use of (1) H NMR Spectroscopy and Molecular Modeling

Aptamers constitute an emerging class of molecules designed and selected to recognize any given target that ranges from small compounds to large biomolecules, and even cells. However, the underlying physicochemical principles that govern the ligand-binding process still have to be clarified. A major issue when dealing with short oligonucleotides is their intrinsic flexibility that renders their active conformation highly sensitive to experimental conditions. To overcome this problem and determine the best experimental parameters, an approach based on the design-of-experiments methodology has been developed. Here, the focus is on DNA aptamers that possess high specificity and affinity for small molecules, L-tyrosinamide, and adenosine monophosphate. Factors such as buffer, pH value, ionic strength, Mg(2+) -ion concentration, and ligand/aptamer ratio have been considered to find the optimal experimental conditions. It was then possible to gain new insight into the conformational features of the two ligands by using ligand-observed NMR spectroscopic techniques and molecular mechanics.

A correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation

Background

The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct systematic error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed.

Results

Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and systematic errors. To detect systematic errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify systematic errors as small as 2.5 % under a wide range of conditions.

Conclusion

Both the simulation framework and error correction method represent examples of time-course analysis that can be applied to further developments in ¹H-NMR methodology and the more general application of quantitative metabolomics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0197-4) contains supplementary material, which is available to authorized users.