Enhanced bioprocess control to advance the manufacture of mesenchymal stromal cell-derived extracellular vesicles in stirred-tank bioreactors.

Extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) act as signaling mediators of cellular responses. However, despite representing a promising alternative to cell-based therapies, clinical translation of EVs is currently limited by their lack of scalability and standardized bioprocessing. Herein, we integrated scalable downstream processing protocols with standardized expansion of large numbers of viable cells in stirred-tank bioreactors to improve EV production. Higher EV yields were linked to EV isolation by tangential flow filtration followed by size exclusion chromatography, rendering 5 times higher number of EVs comparatively to density gradient ultracentrifugation protocols. Additionally, when compared to static culture, EV manufacture in bioreactors resulted in 2.2 higher yields. Highlighting the role of operating under optimal cell culture conditions to maximize the number of EVs secreted per cell, MSCs cultured at lower glucose concentration favored EV secretion. While offline measurements of metabolites concentration can be performed, in this work, Raman spectroscopy was also applied to continuously track glucose levels in stirred-tank bioreactors, contributing to streamline the selection of optimal EV collection timepoints. Importantly, MSC-derived EVs retained their quality attributes and were able to stimulate angiogenesis in vitro, therefore highlighting their promising therapeutic potential.

Impact of dual-baculovirus infection on the Sf9 insect cell transcriptome during rAAV production using single-cell RNA-seq.

The insect cell-baculovirus expression vector system (IC-BEVS) has shown to be a powerful platform to produce complex biopharmaceutical products, such as recombinant proteins and virus-like particles. More recently, IC-BEVS has also been used as an alternative to produce recombinant adeno-associated virus (rAAV). However, little is known about the variability of insect cell populations and the potential effect of heterogeneity (e.g., stochastic infection process and differences in infection kinetics) on product titer and/or quality. In this study, transcriptomics analysis of Sf9 insect cells during the production of rAAV of serotype 2 (rAAV2) using a low multiplicity of infection, dual-baculovirus system was performed via single-cell RNA-sequencing (scRNA-seq). Before infection, the principal source of variability in Sf9 insect cells was associated with the cell cycle. Over the course of infection, an increase in transcriptional heterogeneity was detected, which was linked to the expression of baculovirus genes as well as to differences in rAAV transgenes (rep, cap and gfp) expression. Noteworthy, at 24 h post-infection, only 29.4% of cells enclosed all three necessary rAAV transgenes to produce packed rAAV2 particles, indicating limitations of the dual-baculovirus system. In addition, the trajectory analysis herein performed highlighted that biological processes such as protein folding, metabolic processes, translation, and stress response have been significantly altered upon infection. Overall, this work reports the first application of scRNA-seq to the IC-BEVS and highlights significant variations in individual cells within the population, providing insight into the rational cell and process engineering toward improved rAAV2 production in IC-BEVS.

Dissecting insect cell heterogeneity during influenza VLP production using single-cell transcriptomics.

The insect cell-baculovirus expression vector system (IC-BEVS) has been widely used to produce recombinant protein at high titers, including complex virus-like particles (VPLs). However, cell-to-cell variability upon infection is yet one of the least understood phenomena in virology, and little is known about its impact on production of therapeutic proteins. This study aimed at dissecting insect cell population heterogeneity during production of influenza VLPs in IC-BEVS using single-cell RNA-seq (scRNA-seq). High Five cell population was shown to be heterogeneous even before infection, with cell cycle being one of the factors contributing for this variation. In addition, infected insect cells were clustered according to the timing and level of baculovirus genes expression, with each cluster reporting similar influenza VLPs transgenes (i.e., hemagglutinin and M1) transcript counts. Trajectory analysis enabled to track infection progression throughout pseudotime. Specific pathways such as translation machinery, protein folding, sorting and degradation, endocytosis and energy metabolism were identified as being those which vary the most during insect cell infection and production of Influenza VLPs. Overall, this study lays the ground for the application of scRNA-seq in IC-BEVS processes to isolate relevant biological mechanisms during recombinant protein expression towards its further optimization.

Transcriptome analysis of Sf9 insect cells during production of recombinant Adeno-associated virus.

The insect cell-baculovirus expression vector system (IC-BEVS) has emerged as an alternative time- and cost-efficient production platform for recombinant Adeno-associated virus (AAV) for gene therapy. However, a better understanding of the underlying biological mechanisms of IC-BEVS is fundamental to further optimize this expression system toward increased product titer and quality. Here, gene expression of Sf9 insect cells producing recombinant AAV through a dual baculovirus expression system, with low multiplicity of infection (MOI), was profiled by RNA-seq. An 8-fold increase in reads mapping to either baculovirus or AAV transgene sequences was observed between 24 and 48 h post-infection (hpi), confirming a take-over of the host cell transcriptome by the baculovirus. A total of 336 and 4784 genes were identified as differentially expressed at 24 hpi (vs non-infected cells) and at 48 hpi (vs. infected cells at 24 hpi), respectively, including dronc, birc5/iap5, and prp1. Functional annotation found biological processes such as cell cycle, cell growth, protein folding, and cellular amino acid metabolic processes enriched along infection. This work uncovers transcriptional changes in Sf9 in response to baculovirus infection, which provide new insights into cell and/or metabolic engineering targets that can be leveraged for rational bioprocess engineering of IC-BEVS for AAV production.

Gene Expression Analysis of Adapted Insect Cells during Influenza VLP Production Using RNA-Sequencing.

Adaptive laboratory evolution has been used to improve production of influenza hemagglutinin (HA)-displaying virus-like particles (VLPs) in insect cells. However, little is known about the underlying biological mechanisms promoting higher HA-VLP expression in such adapted cell lines. In this article, we present a study of gene expression patterns associated with high-producer insect High Five cells adapted to neutral pH, in comparison to non-adapted cells, during expression of influenza HA-VLPs. RNA-seq shows a decrease in the amount of reads mapping to host cell genomes along infection, and an increase in those mapping to baculovirus and transgenes. A total of 1742 host cell genes were found differentially expressed between adapted and non-adapted cells throughout infection, 474 of those being either up- or down-regulated at both time points evaluated (12 and 24 h post-infection). Interestingly, while host cell genes were found up- and down-regulated in an approximately 1:1 ratio, all differentially expressed baculovirus genes were found to be down-regulated in infected adapted cells. Pathway analysis of differentially expressed genes revealed enrichment of ribosome biosynthesis and carbohydrate, amino acid, and lipid metabolism. In addition, oxidative phosphorylation and protein folding, sorting and degradation pathways were also found to be overrepresented. These findings contribute to our knowledge of biological mechanisms of insect cells during baculovirus-mediated transient expression and will assist the identification of potential engineering targets to increase recombinant protein production in the future.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer.

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

Expression of Extracellular Vesicle PIWI-Interacting RNAs Throughout hiPSC-Cardiomyocyte Differentiation.

Extracellular Vesicles (EV) play a critical role in the regulation of regenerative processes in wounded tissues by mediating cell-to-cell communication. Multiple RNA species have been identified in EV, although their function still lacks understanding. We previously characterized the miRNA content of EV secreted over hiPSC-cardiomyocyte differentiation and found a distinct miRNA expression in hiPSC-EV driving its in vitro bioactivity. In this work, we investigated the piRNA profiles of EV derived from key stages of the hiPSC-CM differentiation and maturation, i.e., from hiPSC (hiPSC-EV), cardiac progenitors (CPC-EV), immature (CMi-EV), and mature (CMm-EV) cardiomyocytes, demonstrating that EV-piRNA expression differs greatly from the miRNA profiles we previously identified. Only four piRNA were significantly deregulated in EV, one in hiPSC-EV, and three in CPC-EV, as determined by differential expression analysis on small RNA-seq data. Our results provide a valuable source of information for further studies aiming at defining the role of piRNA in the bioactivity and therapeutic potential of EV.

Multi attribute method implementation using a High Resolution Mass Spectrometry platform: From sample preparation to batch analysis.

Quality control of biopharmaceuticals such as monoclonal antibodies (mAbs) has been evolving and becoming more challenging as the requirements of the regulatory agencies increase due to the demanding complexity of products under evaluation. Mass Spectrometry (MS)-based methods such as the multi-attribute method (MAM) are being explored to achieve a deeper understanding of the attributes critical for the safety, efficacy, and quality of these products. MAM uses high mass accuracy/high-resolution MS data that enables the direct and simultaneous monitoring of relevant product quality attributes (PQAs, in particular, chemical modifications) in a single workflow, replacing several orthogonal methods, reducing time and costs associated with these assays. Here we describe a MAM implementation process using a QTOF high resolution platform. Method implementation was accomplished using NIST (National Institute for Standards and Technology) mAb reference material and an in-process mAb sample. PQAs as glycosylation profiles, methionine oxidation, tryptophan dioxidation, asparagine deamidation, pyro-Glu at N-terminal and glycation were monitored. Focusing on applications that require batch analysis and high-throughput, sample preparation and LC-MS parameters troubleshooting are discussed. This MAM workflow was successfully explored as reference analytical tool for comprehensive characterization of a downstream processing (DSP) polishing platform and for a comparability study following technology transfer between different laboratories.

Application of LDH assay for therapeutic efficacy evaluation of ex vivo tumor models.

The current standard preclinical oncology models are not able to fully recapitulate therapeutic targets and clinically relevant disease biology, evidenced by the 90% attrition rate of new therapies in clinical trials. Three-dimensional (3D) culture systems have the potential to enhance the relevance of preclinical models. However, the limitations of currently available cellular assays to accurately evaluate therapeutic efficacy in these models are hindering their widespread adoption. We assessed the compatibility of the lactate dehydrogenase (LDH) assay in 3D spheroid cultures against other commercially available readout methods. We developed a standardized protocol to apply the LDH assay to ex vivo cultures, considering the impact of culture growth dynamics. We show that accounting for growth rates and background release levels of LDH are sufficient to make the LDH assay a suitable methodology for longitudinal monitoring and endpoint assessment of therapeutic efficacy in both cell line-derived xenografts (xenospheres) and patient-derived explant cultures. This method has the added value of being non-destructive and not dependent on reagent penetration or manipulation of the parent material. The establishment of reliable readout methods for complex 3D culture systems will further the utility of these tumor models in preclinical and co-clinical drug development studies.

Online monitoring of hiPSC expansion and hepatic differentiation in 3D culture by dielectric spectroscopy.

Hepatocyte-like cells derived from human-induced pluripotent stem cells (hiPSC-HLC) are expected to have important applications in drug screening and regenerative medicine. However, hiPSC-HLC are difficult to produce on a large-scale to obtain relevant numbers for such applications. The aim of this study was to implement a novel integrated strategy for scalable production of hiPSC-HLC and demonstrate the applicability of dielectric spectroscopy to monitor hiPSC expansion/differentiation processes. We cultured hiPSC as three-dimensional (3D) aggregates in stirred-tank bioreactors (STB) operated in perfusion with an in situ capacitance probe. Dissolved oxygen concentration and dilution rate were controlled along the process and after 5 days of cell expansion, the hepatic differentiation was integrated in sequential steps for 28 days. The hiPSC were able to grow as 3D aggregates and the expression of hepatic markers and albumin production after differentiation confirmed that hepatocyte differentiation improved when compared to 2D culture. These hiPSC-HLC exhibited functional characteristics of hepatocytes including glycogen storage and drug metabolization capacity. Our results also show a good correlation between the cell permittivity measured online and the aggregate biovolume measured by standard offline methods, demonstrating for the first time the potential of dielectric spectroscopy to monitor hiPSC expansion and differentiation in STB.

A computational diffusion model to study antibody transport within reconstructed tumor microenvironments.

BACKGROUND: Antibodies revolutionized cancer treatment over the past decades. Despite their successfully application, there are still challenges to overcome to improve efficacy, such as the heterogeneous distribution of antibodies within tumors. Tumor microenvironment features, such as the distribution of tumor and other cell types and the composition of the extracellular matrix may work together to hinder antibodies from reaching the target tumor cells. To understand these interactions, we propose a framework combining in vitro and in silico models. We took advantage of in vitro cancer models previously developed by our group, consisting of tumor cells and fibroblasts co-cultured in 3D within alginate capsules, for reconstruction of tumor microenvironment features. RESULTS: In this work, an experimental-computational framework of antibody transport within alginate capsules was established, assuming a purely diffusive transport, combined with an exponential saturation effect that mimics the saturation of binding sites on the cell surface. Our tumor microenvironment in vitro models were challenged with a fluorescent antibody and its transport recorded using light sheet fluorescence microscopy. Diffusion and saturation parameters of the computational model were adjusted to reproduce the experimental antibody distribution, with root mean square error under 5%. This computational framework is flexible and can simulate different random distributions of tumor microenvironment elements (fibroblasts, cancer cells and collagen fibers) within the capsule. The random distribution algorithm can be tuned to follow the general patterns observed in the experimental models. CONCLUSIONS: We present a computational and microscopy framework to track and simulate antibody transport within the tumor microenvironment that complements the previously established in vitro models platform. This framework paves the way to the development of a valuable tool to study the influence of different components of the tumor microenvironment on antibody transport.

Unveiling dynamic metabolic signatures in human induced pluripotent and neural stem cells.

Metabolism plays an essential role in cell fate decisions. However, the methods used for metabolic characterization and for finding potential metabolic regulators are still based on characterizing cellular metabolic steady-state which is dependent on the extracellular environment. In this work, we hypothesized that the response dynamics of intracellular metabolic pools to extracellular stimuli is controlled in a cell type-specific manner. We applied principles of process dynamics and control to human induced pluripotent stem cells (hiPSC) and human neural stem cells (hNSC) subjected to a sudden extracellular glutamine step. The fold-changes of steady-states and the transient profiles of metabolic pools revealed that dynamic responses were reproducible and cell type-specific. Importantly, many amino acids had conserved dynamics and readjusted their steady state concentration in response to the increased glutamine influx. Overall, we propose a novel methodology for systematic metabolic characterization and identification of potential metabolic regulators.

Enabling PAT in insect cell bioprocesses: In situ monitoring of recombinant adeno-associated virus production by fluorescence spectroscopy.

The process analytical technology (PAT) initiative shifted the bioprocess development mindset towards real-time monitoring and control tools to measure relevant process variables online, and acting accordingly when undesirable deviations occur. Online monitoring is especially important in lytic production systems in which released proteases and changes in cell physiology are likely to affect product quality attributes, as is the case of the insect cell-baculovirus expression vector system (IC-BEVS), a well-established system for production of viral vectors and vaccines. Here, we applied fluorescence spectroscopy as a real-time monitoring tool for recombinant adeno-associated virus (rAAV) production in the IC-BEVS. Fluorescence spectroscopy is simple, yet sensitive and informative. To overcome the strong fluorescence background of the culture medium and improve predictive ability, we combined artificial neural network models with a genetic algorithm-based approach to optimize spectra preprocessing. We obtained predictive models for rAAV titer, cell viability and cell concentration with normalized root mean squared errors of 7%, 4%, and 7%, respectively, for leave-one-batch-out cross-validation. Our approach shows fluorescence spectroscopy allows real-time determination of the best time of harvest to maintain rAAV infectivity, an important quality attribute, and detection of deviations from the golden batch profile. This methodology can be applied to other biopharmaceuticals produced in the IC-BEVS, supporting the use of fluorescence spectroscopy as a versatile PAT tool.

Hybrid metabolic flux analysis and recombinant protein prediction in Pichia pastoris X-33 cultures expressing a single-chain antibody fragment.

Despite the growing importance of the Pichia pastoris expression system as industrial workhorse, the literature is almost absent in systematic studies on how culture medium composition affects central carbon fluxes and heterologous protein expression. In this study we investigate how 26 variations of the BSM+PTM1 medium impact central carbon fluxes and protein expression in a P. pastoris X-33 strain expressing a single-chain antibody fragment. To achieve this goal, we adopted a hybrid metabolic flux analysis (MFA) methodology, which is a modification of standard MFA to predict the rate of synthesis of recombinant proteins. Hybrid MFA combines the traditional parametric estimation of central carbon fluxes with non-parametric statistical modeling of product-related quantitative or qualitative measurements as a function of central carbon fluxes. It was observed that protein yield variability was 53.6 % (relative standard deviation) among the different experiments. Protein yield is much more sensitive to medium composition than biomass growth, which is mainly determined by the carbon source availability and main salts. Hybrid MFA was able to describe accurately the protein yield with normalized RMSE of 6.3 % over 5 independent experiments. The metabolic state that promotes high protein yields is characterized by high overall metabolic rates through main central carbon pathways concomitantly with a relative shift of carbon flux from biosynthetic towards energy generating pathways.

Principal elementary mode analysis (PEMA).

Principal component analysis (PCA) has been widely applied in fluxomics to compress data into a few latent structures in order to simplify the identification of metabolic patterns. These latent structures lack a direct biological interpretation due to the intrinsic constraints associated with a PCA model. Here we introduce a new method that significantly improves the interpretability of the principal components with a direct link to metabolic pathways. This method, called principal elementary mode analysis (PEMA), establishes a bridge between a PCA-like model, aimed at explaining the maximum variance in flux data, and the set of elementary modes (EMs) of a metabolic network. It provides an easy way to identify metabolic patterns in large fluxomics datasets in terms of the simplest pathways of the organism metabolism. The results using a real metabolic model of Escherichia coli show the ability of PEMA to identify the EMs that generated the different simulated flux distributions. Actual flux data of E. coli and Pichia pastoris cultures confirm the results observed in the simulated study, providing a biologically meaningful model to explain flux data of both organisms in terms of the EM activation. The PEMA toolbox is freely available for non-commercial purposes on http://mseg.webs.upv.es.

Analysis of culture media screening data by projection to latent pathways: The case of Pichia pastoris X-33.

Cell culture media formulations contain hundreds of individual components in water solutions which have complex interactions with metabolic pathways. The currently used statistical design methods are empirical and very limited to explore such a large design space. In a previous work we developed a computational method called projection to latent pathways (PLP), which was conceived to maximize covariance between envirome and fluxome data under the constraint of metabolic network elementary flux modes (EFM). More specifically, PLP identifies a minimal set of EFMs (i.e., pathways) with the highest possible correlation with envirome and fluxome measurements. In this paper we extend the concept for the analysis of culture media screening data to investigate how culture medium components up-regulate or down-regulate key metabolic pathways. A Pichia pastoris X-33 strain was cultivated in 26 shake flask experiments with variations in trace elements concentrations and basal medium dilution, based on the standard BSM+PTM1 medium. PLP identified 3 EFMs (growth, maintenance and by-product formation) describing 98.8% of the variance in observed fluxes. Furthermore, PLP presented an overall predictive power comparable to that of PLS regression. Our results show iron and manganese at concentrations close to the PTM1 standard inhibit overall metabolic activity, while the main salts concentration (BSM) affected mainly energy expenditures for cellular maintenance.

Design of pathway-level bioprocess monitoring and control strategies supported by metabolic networks.

In this chapter we explore the basic tools for the design of bioprocess monitoring, optimization, and control algorithms that incorporate a priori knowledge of metabolic networks. The main advantage is that this ultimately enables the targeting of intracellular control variables such as metabolic reactions or metabolic pathways directly linked with productivity and product quality. We analyze in particular design methods that target elementary modes of metabolic networks. The topics covered include the analysis of the structure of metabolic networks, computation and reduction of elementary modes, measurement methods for the envirome, envirome-guided metabolic reconstruction, and macroscopic dynamic modeling and control. These topics are illustrated with applications to a cultivation process of a recombinant Pichia pastoris X33 strain expressing a single-chain antibody fragment (scFv).

Projection to latent pathways (PLP): a constrained projection to latent variables (PLS) method for elementary flux modes discrimination.

BACKGROUND: Elementary flux modes (EFM) are unique and non-decomposable sets of metabolic reactions able to operate coherently in steady-state. A metabolic network has in general a very high number of EFM reflecting the typical functional redundancy of biological systems. However, most of these EFM are either thermodynamically unfeasible or inactive at pre-set environmental conditions. RESULTS: Here we present a new algorithm that discriminates the "active" set of EFM on the basis of dynamic envirome data. The algorithm merges together two well-known methods: projection to latent structures (PLS) and EFM analysis, and is therefore termed projection to latent pathways (PLP). PLP has two concomitant goals: (1) maximisation of correlation between EFM weighting factors and measured envirome data and (2) minimisation of redundancy by eliminating EFM with low correlation with the envirome. CONCLUSIONS: Overall, our results demonstrate that PLP slightly outperforms PLS in terms of predictive power. But more importantly, PLP is able to discriminate the subset of EFM with highest correlation with the envirome, thus providing in-depth knowledge of how the environment controls core cellular functions. This offers a significant advantage over PLS since its abstract structure cannot be associated with the underlying biological structure.