Successfully producing large quantities of biotherapeutics, including monoclonal antibodies and recombinant protein products, depends on robust and reliable cell line development (CLD). Issues that arise during this foundational step such as genetic instability or unwanted post-translational modification can result in costly failures that may not be detected until late in the process, after a considerable investment of time and resources.
Another common issue emerges due to trade-offs between productivity and stability in cell line selection. As a rule, scientists typically prioritize high-producing clones during development, but selected clones may not be able to maintain their performance under large-scale manufacturing conditions due to factors such as stress from bioreactors. Leveraging automated screening and predictive capabilities at various stages in the CLD workflow can mitigate the risk that these issues will become significant enough to derail new biologics.
Automation is not likely to replace humans completely, but it can move the needle on some of the challenges posed by manual workflows. Several companies hoping to support biologics development and manufacturing activities have released new products aimed not only at reducing waste and cutting costs but also analyzing data to make real time decisions about productivity and stability.
Tool manufacturers have also focused on enabling the use of gene editing techniques and next-generation sequencing (NGS) to improve clone generation and characterization on a large scale. In practice, automation makes it possible to edit, isolate, and screen thousands of clones for desired genetic changes made with CRISPR-Cas9 and transposase-based systems, ensuring that the best clones are selected for downstream development. Scientists also have automated tools for rapidly assessing the genetic stability of clones as well as for checking integration sites and off-target effects.
To explore the burgeoning role of automation in CLD beyond liquid handling and highlight the ways that automated workflows are outperforming manual workflows, GEN interviewed Michael Lutz, PhD, CEO of iotaSciences, which offers platforms for handling single cells based on its proprietary GRID technology. Its list of products includes the Single-Cell Cloning Platform XT, which delivers 96 verified monoclonal cultures in a 96-well plate, ready for NGS characterization and any further downstream application. The platform features iotaSciences’ Single-Cell Automated Identification (SCAI) system, which is designed for identifying and verifying single cells from images.
GEN also interviewed Adam Causer, PhD, global product manager for the Solentim portfolio at Advanced Instruments. The company’s portfolio includes the Solentim® Ecosystem, which uses high-resolution whole-well imaging and AI-driven analysis to provide insights into cell morphology and monoclonality. This system adapts seamlessly to a wide array of cell types, including CHO, HEK, iPSCs, MSCs, MSC-like cell lines, hybridomas, and Sf9 cells. The company also offers STUDIUS™, an integrated data management platform that consolidates data across a typical CLD workflow.
Their responses have been edited both for brevity and clarity.
CEO, iotaSciences
GEN: Is automation making it easier for gene editing techniques (including CRISPR- and transposase-based techniques) to be used for clone generation? Also is it easier to use NGS technologies to characterize clones?
Lutz: For sure, automation is facilitating any gene editing techniques for clone generation. According to a recent [internal] survey about CRISPR-Cas9, single-cell cloning poses the biggest challenge of such workflow. Automation helps to overcome this by performing all tedious liquid handling steps, while reliably assuring monoclonality and high cloning efficiency.
GEN: Is automation improving cell screening by allowing finer distinctions to be made? For example, does automation make it easier to use AI/machine learning to evaluate images?
Lutz: Both automation and AI/machine learning tools are of great help to evaluate images and ensure rapid single-cell verification. [iotaSciences’] SCAI system allows such process in only a few minutes. Further automated reporting and documentation tools provide insights into production efficiency.
Global Product Manager
Advanced Instruments
Causer: Automation has fundamentally transformed cell screening in CLD by incorporating sophisticated imaging technologies and artificial intelligence, [allowing] for the high-throughput analysis of cellular images [and] enabling the detection of subtle phenotypic differences for selecting the most promising clones. AI-driven systems are particularly adept at reducing human error and bias in image interpretation, which enhances the consistency and reliability of the screening outcomes.
Automated platforms have also proven capable of delivering high-quality, high-producing cell lines while significantly reducing timelines and costs and minimizing the variability often seen in manual processes. Additionally, automation optimizes resource utilization and reduces reagent waste, supporting more sustainable bioprocessing practices.
GEN: To what extent is automation working end-to-end to improve CLD? Are there gaps between operations or CLD phases that must still be accomplished manually and what are the prospects for these gaps to be closed?
Lutz: From our perspective, automation provides an end-to-end solution with enhanced outcomes in terms of reliable and high quality results significantly faster compared to manual processes. Once monoclonal cultures have been identified and documented there remains a manual gap towards the next operational steps. However, equipment and processes should be put in place to overcome any related issues or bottlenecks.
Causer: Automation has enabled significant advancements in CLD by bridging previously isolated stages of the typical workflow. From single-cell seeding, to productivity analysis and clone selection, modern systems accelerate these processes. However, integrating data seamlessly across the entire workflow remains a challenge for many laboratories. Data silos—created by separate systems for seeding, imaging, and productivity analysis—require manual data consolidation, which can introduce errors and delays.
Fully integrated systems eliminate the need for manual data transfer and enable real-time monitoring and predictive analytics. Importantly, integration also simplifies regulatory compliance by generating comprehensive, audit-ready datasets across the entire development process.
GEN: Is automation making it easier to adapt CLD to different modalities? Besides proteins, possibilities include vectors for vaccines, gene therapies, or therapeutic cells.
Lutz: It depends on the CLD platform. Vectors and therapeutic cells should be easier to adapt with the appropriate underlying automation technology given the [relationship between] robustness of cells and overall viability.
Causer: One of the most remarkable advantages of automation in CLD is its adaptability to diverse therapeutic modalities. For example, CHO cells for protein therapeutics demand robust growth monitoring and productivity analysis, while iPSCs and MSCs used in cell therapies require gentle handling and precise environmental control to maintain viability and potency. The flexibility of automation not only supports these varying needs but also ensures consistent performance across modalities, providing reliable data for regulatory submissions and facilitating scalability from research to production.
