Modern solutions for connecting instruments to insights: Flow cytometry

Flow cytometry is a core technique in modern pharmaceutical development pipelines. It functions as a flexible, high-throughput assay due to its capacity to process large quantities of cells in relatively short acquisition times, while simultaneously monitoring multiple cellular parameters. Flow cytometry is routinely used to assess cell death in cytotoxicity assays and observe changes in expression of one or more targets in response to stimuli. These measurements provide critical data for understanding drug safety, efficacy, and mechanism-of-action within early pipeline studies. Additionally, flow cytometry allows for the classification of heterogeneous cell populations in immunotyping assays, supporting the identification and characterization of immune cell subsets. The technique can be extended to other stages of cell and gene therapy R&D pipelines, including quality control assessments of genetically modified cells to ensure product consistency and safety. Despite its power, efficiency, and technological advancements, the modality is not without its challenges. Inefficiencies in experiment planning and execution, data management, and analysis highlight the need for ongoing innovation within and optimization of end-to-end workflows.

Experimental setup: Manual, fragmented, and time-consuming

The complexity of flow cytometry begins long before data acquisition. Scientists often spend hours manually preparing plate-based assays, curating conditions for 96 or 384 wells by hand. Each well may require unique reagent combinations and concentrations, and without standardized plate maps, researchers must rely on their own conventions for format, terminology, and reagent naming. This lack of consistency not only slows down experimental setup but also complicates collaboration and reproducibility across teams and projects.

Capturing essential reagent information—such as lot numbers, expiration dates, and supplier details—is another persistent challenge. These details are typically recorded manually, if at all, and the process is so tedious that we often see it skipped. The result is incomplete metadata, which can hinder troubleshooting and compromise data integrity. Experimental design is further complicated by the need to juggle multiple disconnected systems for sample management, instrument scheduling, and documentation. The absence of standardized metadata capture and enrichment means that critical contextual information is often lost, making it difficult to reconstruct experiments or share insights with colleagues.

Data analysis: Bottlenecks, variability, and limited flexibility

Once samples are acquired, scientists face a new set of hurdles in data analysis. Most rely on proprietary software to interpret results, manually adjusting gates for each sample to distinguish cell populations. This process is highly subjective and can vary significantly between users, introducing variability and making it difficult to compare results across experiments or operators.

Troubleshooting analysis is particularly challenging for less experienced users. When data does not look as expected, it can be difficult to pinpoint whether the issue stems from incorrect compensation, unexpected biological responses, or missing fluorophores. The lack of standardized troubleshooting workflows means that valuable time is often spent diagnosing issues that could be avoided with better documentation or automated checks.

To expedite routine analyses, scientists frequently build custom templates. While these templates can streamline workflows, they come at the cost of flexibility—experiments need to closely match the template structure, limiting the ability to modify markers or assay parameters. Capturing the rationale behind gating decisions or analysis choices is rarely systematic, and the lineage of analytical steps is often confined to proprietary software, making it difficult to audit or reproduce results outside these platforms.

Data interoperability: Fragmented files and tracking difficulties

Another challenge lies in managing the broad array of data and analysis files generated by flow cytometry. Data is saved in multiple formats—FCS, WSP, CSV, and others—some of which are proprietary and not easily accessible outside specific software environments. Files are often stored across a patchwork of locations, from instrument workstations to shared drives and individual laptops, with no standardized naming conventions to aid in tracking or retrieval.

This fragmentation makes it difficult to maintain a clear record of data provenance, track changes, or ensure that the correct version is used for downstream analysis. Retrieving files for reuse, quality control, or retrospective analysis is often a manual and error-prone process, limiting the ability to leverage historical data for new research or regulatory submissions. The lack of interoperability between systems and formats further compounds these challenges, creating barriers to efficient data management and collaboration.

To overcome these hurdles, there is a strong need for a solution that can automate and streamline the scientific workflow. Platforms that integrate experimental design, file capture, and data standardization/contextualization workflows can build a broad foundation for accelerating experiments and producing high-quality results. Solutions are open, modular, and cloud-native, leveraging industry standards to enhance flexibility, interoperability, and long-term value. The guiding principles we use to build these solutions for clients are detailed below and summarized in figure 1 for reference.