Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks.

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producer clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling.

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

From observational to actionable: rethinking omics in biologics production.

As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.

Microfluidic chip-based single-cell cloning to accelerate biologic production timelines.

Cell line development (CLD) represents a critical, yet time-consuming, step in the biomanufacturing process as significant resources are devoted to the scale-up and screening of several hundreds to thousands of single-cell clones. Typically, transfected pools are fully recovered from selection and characterized for growth, productivity, and product quality to identify the best pools suitable for single-cell cloning (SCC) using limiting dilution or fluorescence-activated cell sorting (FACS). Here we report the application of the Berkeley Lights Beacon Instrument (BLI) in an early SCC process to accelerate the CLD timeline. Transfected pools were single-cell cloned when viabilities reached greater than 85% or during selection when viabilities were less than 30%. Clones isolated from these accelerated processes exhibited comparable growth, productivity, and product quality to those derived from a standard CLD process and fit into an existing manufacturing platform. With these approaches, up to a 30% reduction in the overall CLD timeline was achieved. Furthermore, early process-derived clones demonstrated equivalent long-term stability compared with standard process-derived clones over 50 population doubling levels (PDLs). Taken together, the data supported early SCC on the BLI as an attractive approach to reducing the standard CLD timeline while still identifying clones with acceptable manufacturability.

Assuring Clonality on the Beacon Digital Cell Line Development Platform.

During biomanufacturing cell lines development, the generation and screening for single-cell derived subclones using methods that enable assurance of clonal derivation can be resource- and time-intensive. High-throughput miniaturization, automation, and analytic strategies are often employed to reduce such bottlenecks. The Beacon platform from Berkeley Lights offers a strategy to eliminate these limitations through culturing, manipulating, and characterizing cells on custom nanofluidic chips via software-controlled operations. However, explicit demonstration of this technology to provide high assurance of a single cell progenitor has not been reported. Here, a methodology that utilizes the Beacon instrument to ensure high levels of clonality is described. It is demonstrated that the Beacon platform can efficiently generate production cell lines with a superior clonality data package, detailed tracking, and minimal resources. A stringent in-process quality control strategy is established to enable rapid verification of clonal origin, and the workflow is validated using representative Chinese hamster ovary-derived cell lines stably expressing either green or red fluorescence protein. Under these conditions, a >99% assurance of clonal origin is achieved, which is comparable to existing imaging-coupled fluorescence-activated cell sorting seeding methods.

In Situ Gene Therapy via AAV-CRISPR-Cas9-Mediated Targeted Gene Regulation.

Development of efficacious in vivo delivery platforms for CRISPR-Cas9-based epigenome engineering will be critical to enable the ability to target human diseases without permanent modification of the genome. Toward this, we utilized split-Cas9 systems to develop a modular adeno-associated viral (AAV) vector platform for CRISPR-Cas9 delivery to enable the full spectrum of targeted in situ gene regulation functionalities, demonstrating robust transcriptional repression (up to 80%) and activation (up to 6-fold) of target genes in cell culture and mice. We also applied our platform for targeted in vivo gene-repression-mediated gene therapy for retinitis pigmentosa. Specifically, we engineered targeted repression of Nrl, a master regulator of rod photoreceptor determination, and demonstrated Nrl knockdown mediates in situ reprogramming of rod cells into cone-like cells that are resistant to retinitis pigmentosa-specific mutations, with concomitant prevention of secondary cone loss. Furthermore, we benchmarked our results from Nrl knockdown with those from in vivo Nrl knockout via gene editing. Taken together, our AAV-CRISPR-Cas9 platform for in vivo epigenome engineering enables a robust approach to target disease in a genomically scarless and potentially reversible manner.