Metabolic profiling reveals that time related physiological changes in mammalian cell perfusion cultures are bioreactor scale independent

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

Metabolomic Analysis Reveals That the Mechanism of Astaxanthin Improves the Osteogenic Differentiation Potential in Bone Marrow Mesenchymal Stem Cells

At present, little research has been done on the metabolic phenotype of the differentiation of mesenchymal stem cells (MSCs) into osteoblasts. In this study, the effect of astaxanthin on improving osteogenic differentiation potential of mesenchymal stem cells was studied by metabolomics. Results showed that L-methionine, L-tyrosine, and 2-hydroxycinnamic acid were upregulated in MSCs treated with astaxanthin, while L-lysine, L-pipecolic acid, L-histidine, L-arginine, D-fructose, and L-aspartic acid were downregulated in samples treated with astaxanthin. In addition, astaxanthin exhibited a significant dose-dependent relationship with these markers. Metabolic pathway enrichment analysis revealed that AST mainly regulated phenylalanine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; and pantothenate and CoA biosynthesis during the process of osteogenic differentiation of MSCs. Furthermore, the staining results showed that astaxanthin could actively promote the osteogenic differentiation of mesenchymal stem cells. These findings clearly indicate that astaxanthin plays an important role in inducing osteogenic differentiation of mesenchymal stem cells. In addition, the changed metabolites can be used to monitor the differentiation process.

Metabolomic profiling of CHO fed-batch growth phases at 10, 100, and 1,000 L

Established bioprocess monitoring is based on quick and reliable methods, including cell count and viability measurement, extracellular metabolite measurement, and the measurement of physicochemical qualities of the cultivation medium. These methods are sufficient for monitoring of process performance, but rarely give insight into the actual physiological states of the cell culture. However, understanding of the latter is essential for optimization of bioprocess development. Our study used LC-MS metabolomics as a tool for additional resolution of bioprocess monitoring and was designed at three bioreactors scales (10 L, 100 L, and 1,000 L) to gain insight into the basal metabolic states of the Chinese hamster ovary (CHO) cell culture during fed-batch. Metabolites characteristics of the four growth stages (early and late exponential phase, stationary phase, and the phase of decline) were identified by multivariate analysis. Enriched metabolic pathways were then established for each growth phase using the CHO metabolic network model. Biomass generation and nucleotide synthesis were enriched in early exponential phase, followed by increased protein production and imbalanced glutathione metabolism in late exponential phase. Glycolysis became downregulated in stationary phase and amino-acid metabolism increased. Phase of culture decline resulted in rise of oxidized glutathione and fatty acid concentrations. Intracellular metabolic profiles of the CHO fed-batch culture were also shown to be consistent with scale and thus demonstrate metabolomic profiling as an informative method to gain physiological insight into the cell culture states during bioprocess regardless of scale.

Transcriptome and proteome analysis of steady-state in a perfusion CHO cell culture process

Long-term continuous protein production can be reached by perfusion operation. Through the continuous removal of waste metabolites and supply of nutrients, steady-state (SS) conditions are achieved after a certain transient period, where the conditions inside the reactor are not only uniform in space but also constant in time. Such stable conditions may have beneficial influences on the reduction of product heterogeneities. In this study, we investigated the impact of perfusion cultivation on the intracellular physiological state of a CHO cell line producing a monoclonal antibody (mAb) by global transcriptomics and proteomics. Despite stable viable cell density was maintained right from the beginning of the cultivation time, productivity decrease, and a transition phase for metabolites and product quality was observed before reaching SS conditions. These were traced back to three sources of transient behaviors being hydrodynamic flow rates, intracellular dynamics of gene expression as well as metabolism and cell line instability, superimposing each other. However, 99.4% of all transcripts and proteins reached SS during the first week or were at SS from the beginning. These results demonstrate that the stable extracellular conditions of perfusion lead to SS also of the cellular level.

Oxidized alginate hydrogels with the GHK peptide enhance cord blood mesenchymal stem cell osteogenesis: A paradigm for metabolomics-based evaluation of biomaterial design

Oxidized alginate hydrogels are appealing alternatives to natural alginate due to their favourable biodegradability profiles and capacity to self-crosslink with amine containing molecules facilitating functionalization with extracellular matrix cues, which enable modulation of stem cell fate, achieve highly viable 3-D cultures, and promote cell growth. Stem cell metabolism is at the core of cellular fate (proliferation, differentiation, death) and metabolomics provides global metabolic signatures representative of cellular status, being able to accurately identify the quality of stem cell differentiation. Herein, umbilical cord blood mesenchymal stem cells (UCB MSCs) were encapsulated in novel oxidized alginate hydrogels functionalized with the glycine-histidine-lysine (GHK) peptide and differentiated towards the osteoblastic lineage. The ADA-GHK hydrogels significantly improved osteogenic differentiation compared to gelatin-containing control hydrogels, as demonstrated by gene expression, alkaline phosphatase activity and bone extracellular matrix deposition. Metabolomics revealed the high degree of metabolic heterogeneity in the gelatin-containing control hydrogels, captured the enhanced osteogenic differentiation in the ADA-GHK hydrogels, confirmed the similar metabolism between differentiated cells and primary osteoblasts, and elucidated the metabolic mechanism responsible for the function of GHK. Our results suggest a novel paradigm for metabolomics-guided biomaterial design and robust stem cell bioprocessing. STATEMENT OF SIGNIFICANCE: Producing high quality engineered bone grafts is important for the treatment of critical sized bone defects. Robust and sensitive techniques are required for quality assessment of tissue-engineered constructs, which result to the selection of optimal biomaterials for bone graft development. Herein, we present a new use of metabolomics signatures in guiding the development of novel oxidised alginate-based hydrogels with umbilical cord blood mesenchymal stem cells and the glycine-histidine-lysine peptide, demonstrating that GHK induces stem cell osteogenic differentiation. Metabolomics signatures captured the enhanced osteogenesis in GHK hydrogels, confirmed the metabolic similarity between differentiated cells and primary osteoblasts, and elucidated the metabolic mechanism responsible for the function of GHK. In conclusion, our results suggest a new paradigm of metabolomics-driven design of biomaterials.

Comparison of human isogeneic Wharton's jelly MSCs and iPSC-derived MSCs reveals differentiation-dependent metabolic responses to IFNG stimulation

Variability among donors, non-standardized methods for isolation, and characterization contribute to mesenchymal stem/stromal cell (MSC) heterogeneity. Induced pluripotent stem cell (iPSCs)-derived MSCs would circumvent many of current issues and enable large-scale production of standardized cellular therapy. To explore differences between native MSCs (nMSCs) and iPSC-derived MSCs (iMSCs), we developed isogeneic lines from Wharton’s jelly (WJ) from the umbilical cords of two donors (#12 and #13) under xeno-free conditions. Next, we reprogrammed them into iPSCs (iPSC12 and iPSC13) and subsequently differentiated them back into iMSCs (iMSC12 and iMSC13) using two different protocols, which we named ARG and TEX. We assessed their differentiation capability, transcriptome, immunomodulatory potential, and interferon-γ (IFNG)-induced changes in metabolome. Our data demonstrated that although both differentiation protocols yield iMSCs similar to their parental nMSCs, there are substantial differences. The ARG protocol resulted in iMSCs with a strong immunomodulatory potential and lower plasticity and proliferation rate, whereas the TEX protocol raised iMSCs with a higher proliferation rate, better differentiation potential, though weak immunomodulatory response. Our data suggest that, following a careful selection and screening of donors, nMSCs from umbilical’s cord WJ can be easily reprogrammed into iPSCs, providing an unlimited source of material for differentiation into iMSCs. However, the differentiation protocol should be chosen depending on their clinical use.

Untargeted GC-MS Metabolomics

Untargeted metabolomics refers to the high-throughput analysis of the metabolic state of a biological system (e.g., tissue, biological fluid, cell culture) based on the concentration profile of all measurable free low molecular weight metabolites. Gas chromatography-mass spectrometry (GC-MS), being a highly sensitive and high-throughput analytical platform, has been proven a useful tool for untargeted studies of primary metabolism in a variety of applications. As an omic analysis, GC-MS metabolomics is a multistep procedure; thus, standardization of an untargeted GC-MS metabolomics protocol requires the integrated optimization of pre-analytical, analytical, and computational steps. The main difference of GC-MS metabolomics compared to other metabolomics analytical platforms, including liquid chromatography-MS, is the need for the derivatization of the metabolite extracts into volatile and thermally stable derivatives, the latter being quantified in the metabolic profiles. This analytical step requires special care in the optimization of the untargeted GC-MS metabolomics experimental protocol. Moreover, both the derivatization of the original sample and the compound fragmentation that takes place in GC-MS impose specialized GC-MS metabolomic data identification, quantification, normalization and filtering methods. In this chapter, we describe the integrated protocol of untargeted GC-MS metabolomics with both the analytical and computational steps, focusing on the GC-MS specific parts, and provide details on any sample depending differences.

In vitro modeling of experimental succinic semialdehyde dehydrogenase deficiency (SSADHD) using brain-derived neural stem cells

We explored the utility of neural stem cells (NSCs) as an in vitro model for evaluating preclinical therapeutics in succinic semialdehyde dehydrogenase-deficient (SSADHD) mice. NSCs were obtained from aldh5a1+/+ and aldh5a1-/- mice (aldh5a1 = aldehyde dehydrogenase 5a1 = SSADH). Multiple parameters were evaluated including: (1) production of GHB (γ-hydroxybutyrate), the biochemical hallmark of SSADHD; (2) rescue from cell death with the dual mTOR (mechanistic target of rapamycin) inhibitor, XL-765, an agent previously shown to rescue aldh5a1-/- mice from premature lethality; (3) mitochondrial number, total reactive oxygen species, and mitochondrial superoxide production, all previously documented as abnormal in aldh5a1-/- mice; (4) total ATP levels and ATP consumption; and (5) selected gene expression profiles associated with epilepsy, a prominent feature in both experimental and human SSADHD. Patterns of dysfunction were observed in all of these parameters and mirrored earlier findings in aldh5a1-/- mice. Patterns of dysregulated gene expression between hypothalamus and NSCs centered on ion channels, GABAergic receptors, and inflammation, suggesting novel pathomechanisms as well as a developmental ontogeny for gene expression potentially associated with the murine epileptic phenotype. The NSC model of SSADHD will be valuable in providing a first-tier screen for centrally-acting therapeutics and prioritizing therapeutic concepts of preclinical animal studies applicable to SSADHD.

Metabolomics Analysis of the Osteogenic Differentiation of Umbilical Cord Blood Mesenchymal Stem Cells Reveals Differential Sensitivity to Osteogenic Agents

Mesenchymal stem cells (MSCs) of fetal origin, such as umbilical cord blood MSCs (UCB MSCs), have emerged as a promising cell source for musculoskeletal tissue regeneration because of their higher proliferation potential, lack of donor site morbidity, and their off-the-shelf potential. MSCs differentiated toward the osteogenic lineage exhibit a specific metabolic phenotype characterized by reliance to oxidative phosphorylation for energy production and reduced glycolytic rates. Currently, limited information exists on the metabolic transitions at different stages of the osteogenic process after osteoinduction with different agents. Herein, the osteoinduction efficiency of BMP-2 and dexamethasone on UCB MSCs was assessed using gas chromatography-mass spectrometry (GC-MS) metabolomics analysis, revealing metabolic discrepancies at 7, 14, and 21 days of induction. Whereas both agents when administered individually were able to induce collagen I, osteocalcin, and osteonectin expression, BMP-2 was less effective than dexamethasone in promoting alkaline phosphatase expression. The metabolomics analysis revealed that each agent induced distinct metabolic alterations, including changes in amino acid pools, glutaminolysis, one-carbon metabolism, glycolysis, and tricarboxylic acid cycle. Importantly, we showed that in vitro-differentiated UCB MSCs acquire a metabolic physiology similar to primary osteoblasts when induced with dexamethasone but not with BMP-2, highlighting the fact that metabolomics analysis is sensitive enough to reveal potential differences in the osteogenic efficiency and can be used as a quality control assay for evaluating the osteogenic process.

Intracellular CHO Cell Metabolite Profiling Reveals Steady-State Dependent Metabolic Fingerprints in Perfusion Culture

Perfusion cell culture processes allow the steady-state culture of mammalian cells at high viable cell density, which is beneficial for overall product yields and homogeneity of product quality in the manufacturing of therapeutic proteins. In this study, the extent of metabolic steady state and the change of the metabolite profile between different steady states of an industrial Chinese hamster ovary (CHO) cell line producing a monoclonal antibody (mAb) was investigated in stirred tank perfusion bioreactors. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) of daily cell extracts revealed more than a hundred peaks, among which 76 metabolites were identified by tandem MS (MS/MS) and high resolution Fourier transform ion cyclotron resonance (FT-ICR) MS. Nucleotide ratios (Uridine (U)-ratio, nucleotide triphosphate (NTP)-ratio and energy charge (EC)) and multivariate analysis of all features indicated a consistent metabolite profile for a stable culture performed at 40 × 10⁶ cells/mL over 26 days of culture. Conversely, the reactor was operated continuously so as to reach three distinct steady states one after the other at 20, 60, and 40 × 10⁶ cells/mL. In each case, a stable metabolite profile was achieved after an initial transient phase of approximately three days at constant cell density when varying between these set points. Clear clustering according to cell density was observed by principal component analysis, indicating steady-state dependent metabolite profiles. In particular, varying levels of nucleotides, nucleotide sugar, and lipid precursors explained most of the variance between the different cell density set points. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:879-890, 2017.

Human embryonic and induced pluripotent stem cells maintain phenotype but alter their metabolism after exposure to ROCK inhibitor

Human pluripotent stem cells (hPSCs) are adhesion-dependent cells that require cultivation in colonies to maintain growth and pluripotency. Robust differentiation protocols necessitate single cell cultures that are achieved by use of ROCK (Rho kinase) inhibitors. ROCK inhibition enables maintenance of stem cell phenotype; its effects on metabolism are unknown. hPSCs were exposed to 10 μM ROCK inhibitor for varying exposure times. Pluripotency (TRA-1-81, SSEA3, OCT4, NANOG, SOX2) remained unaffected, until after prolonged exposure (96 hrs). Gas chromatography–mass spectrometry metabolomics analysis identified differences between ROCK-treated and untreated cells as early as 12 hrs. Exposure for 48 hours resulted in reduction in glycolysis, glutaminolysis, the citric acid (TCA) cycle as well as the amino acids pools, suggesting the adaptation of the cells to the new culture conditions, which was also reflected by the expression of the metabolic regulators, mTORC1 and tp53 and correlated with cellular proliferation status. While gene expression and protein levels did not reveal any changes in the physiology of the cells, metabolomics revealed the fluctuating state of the metabolism. The above highlight the usefulness of metabolomics in providing accurate and sensitive information on cellular physiological status, which could lead to the development of robust and optimal stem cell bioprocesses.

An automated RNA-Seq analysis pipeline to identify and visualize differentially expressed genes and pathways in CHO cells

Recent advances in RNA-Seq based comparative transcriptomics have opened up a unique opportunity to understand the mechanisms of different phenotypes in bioprocessing-related cell lines including Chinese hamster ovary (CHO) cells. However, simple and powerful tools are needed to translate large data sets into biologically relevant information that can be leveraged for genetic engineering and cell culture medium and process development. While tools exist to perform specific tasks associated with transcriptomics analysis, integrated end to end solutions that span the entire spectrum of raw data processing to visualization of gene expression changes on canonical pathways are rare. Additionally, these are not automated and require substantial user intervention. To address this gap, we have developed an automated RNA-Seq analysis pipeline in R which leverages the latest public domain statistical advances in transcriptomics data analysis. This pipeline reads RNA-Seq gene count data, identifies differentially expressed genes and differentially expressed pathways, and provides multiple intuitive visualizations as outputs. By using two publicly available CHO RNA-Seq datasets, we have demonstrated the utility of this pipeline. Subsequently, this pipeline was used to demonstrate transcriptomic similarity between laboratory- and pilot-scale bioreactors, helping make a case for the suitability of the lab-scale bioreactor as a scaled-down model. Automated end to end RNA-Seq data analysis approaches such as the one presented in this study will shorten the time required from acquiring sequencing data to biological interpretation of the results and can help accelerate the adoption of RNA-Seq analysis and thus mechanism-driven approaches for cell line and bioprocess optimization.

High-throughput nucleoside phosphate monitoring in mammalian cell fed-batch cultivation using quantitative matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Current methods for monitoring multiple intracellular metabolite levels in parallel are limited in sample throughput capabilities and analyte selectivity. This article presents a novel high-throughput method based on matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (TOF-MS) for monitoring intracellular metabolite levels in fed-batch processes. The MALDI-TOF-MS method presented here is based on a new microarray sample target and allows the detection of nucleoside phosphates and various other metabolites using stable isotope labeled internal standards. With short sample preparation steps and thus high sample throughput capabilities, the method is suitable for monitoring mammalian cell cultures, such as antibody producing hybridoma cell lines in industrial environments. The method is capable of reducing the runtime of standard LC-UV methods to approximately 1 min per sample (including 10 technical replicates). Its performance is exemplarily demonstrated in an 8-day monitoring experiment of independently controlled fed-batches, containing an antibody producing mouse hybridoma cell culture. The monitoring profiles clearly confirmed differences between cultivation conditions. Hypothermia and hyperosmolarity were studied in four bioreactors, where hypothermia was found to have a positive effect on the longevity of the cell culture, whereas hyperosmolarity lead to an arrest of cell proliferation. The results are in good agreement with HPLC-UV cross validation experiments. Subsequent principal component analysis (PCA) clearly separates the different bioreactor conditions based on the measured mass spectral profiles. This method is not limited to any cell line and can be applied as a process analytical tool in biotechnological processes.

Role of oxidative stress in Sclerotial differentiation and aflatoxin B1 biosynthesis in Aspergillus flavus

We show here that oxidative stress is involved in both sclerotial differentiation (SD) and aflatoxin B1 biosynthesis in Aspergillus flavus. Specifically, we observed that (i) oxidative stress regulates SD, as implied by its inhibition by antioxidant modulators of reactive oxygen species and thiol redox state, and that (ii) aflatoxin B1 biosynthesis and SD are comodulated by oxidative stress. However, aflatoxin B1 biosynthesis is inhibited by lower stress levels compared to SD, as shown by comparison to undifferentiated A. flavus. These same oxidative stress levels also characterize a mutant A. flavus strain, lacking the global regulatory gene veA. This mutant is unable to produce sclerotia and aflatoxin B1. (iii) Further, we show that hydrogen peroxide is the main modulator of A. flavus SD, as shown by its inhibition by both an irreversible inhibitor of catalase activity and a mimetic of superoxide dismutase activity. On the other hand, aflatoxin B1 biosynthesis is controlled by a wider array of oxidative stress factors, such as lipid hydroperoxide, superoxide, and hydroxyl and thiyl radicals.