Cell lines have become an underlying necessity for biomedical research success, simplifying the investigation of more complex biological systems. Optimizing cell lines takes significant effort to achieve confident results. Guided by genomic research and biological insights along with emerging technologies that are advancing cell engineering and improving culture environments, cell-line optimization techniques are reaching new heights. The increased understanding of cell behavior has allowed the propagation of more cell types, while maintaining stability and consistency within an experiment.
Progress in cell biology has consistently opened the doors to an expansion of cell-based applications. Whether enhancing the cell culture environment, examining functional genomics, introducing automation, or creating more relevant models, cell-line optimization continues to evolve. This evolution in turn pushes research, discovery, and development further into novel and unexplored corners of biology.
A better understanding of cells and their environment
Ensuring the healthiest environment for a cell type tends to be a big area of focus in research laboratories and across the pharmaceutical industry. For instance, with cells designed for the large-scale production of monoclonal antibodies (mAbs) for therapeutic purposes, significant effort goes into developing and optimizing cell environments to maximize growth and metabolism. This is especially relevant with the emergence of biosimilars, where new yet similar protocols must be developed. “Optimizing for the best product takes a well-designed experimental approach,” explains Robert E. Newman Jr., Ph.D., senior director of ATCC Cell Systems. “Balancing feed, media, and bioreactor conditions to achieve high yield and acceptable protein quality at lower cost can be a challenge.”
Standardized cell lines such as CHO cells are commonly used for bioproduction, where set protocols with optimal conditions are a good starting point. But what about other areas of research and development? Newman advises that a solid grasp of cell biology can go a long way in setting up optimal conditions for any cell line. Different cell types have so much genetic diversity and varying environmental preferences that we must still rely on information on a cell line–by–cell line basis. In support of this, recent advances in commercially available individual media formulations and more stable supplements seeded from the latest in cell biology research can provide more applicable environments for growing cells as well as offer more cost-effective solutions.
A big step forward has been in our knowledge of cell-cycle mechanisms, modulators of cell health, and related genetics. Newman adds, “Genes that drive cell culture performance such as cell-cycle regulators, metabolic genes, genes involved in protein secretion, and those related to protein structure and function, can now be manipulated using a new wave of gene-engineering approaches. This genetic optimization combined with the ability to control the external environment using bioreactors, media, feeds, supplements, and extracellular matrices allows for the creation of ideal models for basic research, drug development, bioproduction, and cell-based therapies.”
Applying functional genomics and enhancing genomic stability
More recent approaches to bettering cell-line growth involve genetic modification. As Newman mentions, expanding knowledge of genes related to cell growth and metabolism, signaling and interactions, or even cell robustness can be applied to develop enhanced cell lines for targeted applications. 
Philippe Collin, head of cell-line engineering at Horizon, comments, “In order to advance our understanding of each gene’s relevance in a process, functional genomics creates a platform to interrogate gene function without bias, identifying key elements and features that impact cell biology, whether it be for cell health or related to a specific pathway. Functional genomics is accelerating the identification of these unknown factors to help with cell growth and production.”
Image: Functional genomics is accelerating the identification of a myriad of factors to help with cell growth and production. Image courtesy of Horizon.
Many cell lines commonly used as models for various diseases are known to be very unstable, changing over time into what could be characterized as a completely different type.
Once relevant or associated factors have been identified, cells can be engineered to create more robust and useful versions of themselves. One challenge, Collin cautions, is maintaining genomic stability. Many cell lines commonly used as models for various diseases are known to be very unstable, changing over time into what could be characterized as a completely different type. Thus, modifying cell lines requires constant monitoring to ensure that the inserted genetic modification remains indefinitely. Collin offers that in order to account for inherent genomic stability in modified cell lines, multiple clones that are generated can be frozen and later used if the current clone becomes too variable.
Using CRISPR for gene editing and other modifications undoubtedly makes cell-line enhancement that much easier. Even so, new adjustments to the method, including the use of synthetic reagents such as guide RNA, Cas9 protein, and Cas9 mRNA, can alleviate ongoing issues with inserting toxic DNA into cells. Systematically approaching optimization by applying genetic modifications and adjusting cell environments with relevant amounts and types of growth factors, media, and conditioned media can create the optimal workhorse for any experiment.
Embracing automation and the potential of artificial intelligence
Frank F. Craig, Ph.D., CEO of Sphere Fluidics, a company that focuses on the development of single-cell analysis systems (such as Cyto-Mine®), sees the establishment of novel automated systems that process, monitor, and analyze cells for improved cell-line productivity and stability as an exciting progression in optimizing cell lines for both research and biopharmaceutical applications. “Automated, cost-effective technologies can simplify bioengineering processes, allowing more focus on quality science instead of operational activities,” says Craig.
“By employing automation in early cell-line development, new test systems can be developed that identify high-expression cell lines and include productivity assessments such as single-cell analysis and imaging using a single desktop device. Automating the entire process integrates previously disparate processes to achieve a productive and scalable system for a company to find and rapidly progress their unique cell line,” explains Craig.
Image: The Cyto-Mine® system can be used to identify unstable clones early so that they can be eliminated from the analysis or inform you of genetic drift in a cell population. Image courtesy of Sphere Fluidics.
Moreover, introducing the potential of artificial intelligence (AI) to assess correlations between different devices and cell data can ultimately be used to generate the optimal cell line. The power of AI is already being used to create models that design multiple optimal techniques and then apply predictive analysis to develop the best approach to engineering the most useful and robust cell line. While current data is being developed using more standard cell lines, data on any cell type could be input to expand the modeling approach to develop cell lines specific to an application.
3D modeling and the possibilities of co-culture
Novel cell models offer additional opportunities for better cell-line optimization. Consider 3D cultures that more closely mimic circumstances in vivo (it seems obvious that cancer cells don’t grow in a monolayer in the body as they do in a flask). The model also allows for healthier cell growth, encourages cell-cell interaction, and improves signaling. “Provided with the right matrix, various cell types can be set up for optimal growth,” says Lubna Hussain, senior product manager at Lonza.
However, similar challenges are inherent to 3D models as to any cell line. Each type requires specific optimization. This comes into play especially when co-culturing cells. Co-culturing immune cells with a cell line of interest in immuno-oncology applications, for example, or when growing endothelial cells with cancer cell lines for angiogenesis assays, presents two variable growing preferences in one environment. Hussain suggests optimizing for the more difficult to grow cell type and monitoring the combined environment to make sure the second type can adapt. “Doing a publication search on an application of interest can also help set up a co-culture environment for success, applying prior knowledge instead of starting from scratch.”
Since the 3D market is expanding and still in early phases, there are limited established methodologies to follow. Method development is still in the trial-and-error phase where new testing approaches can lead to unexpected positive outcomes and new possible optimizations. Using these models to mimic in vivo environments upfront can improve preparation and optimize discovery stages, providing a solid baseline for further clinical studies.
These techniques and new technologies allow a shift in focus from process development and operations to furthering research with better discovery and clinical outcomes. Optimizing cell lines helps to ensure cell-line authentication and more reliable experimental reproducibility, putting greater confidence in research and test results both from the start and in downstream applications.