Streamlining Flow Cytometry Analysis in the Tetra Data and AI Workspace

May 8, 2024

Advancements in flow cytometry since its emergence in the late 1960s have made cellular analysis a cornerstone of the biopharma industry. Critical research domains such as oncology and immunology depend on the flow cytometer’s ability to dissect complex cell populations and their characteristics at an unprecedented scale.

However, as the volume of flow cytometry data expands, so does the challenge of managing and analyzing it effectively. TetraScience is committed to enhancing the experience of data management and analysis, making it effective, efficient, and collaborative. TetraScience recently announced a partnership with BD (Becton, Dickinson and Company), a leading global medical technology company, to advance cloud-based single-cell analysis, aiming to streamline workflows and reduce time-to-insight. Prominent analysis software for applications including flow cytometry will be conveniently co-located with our customers’ scientific data via the Tetra Data and AI workspace. Industry leaders are eager to see the efficiency and accuracy gains from the outset.

We are excited about the addition to the TetraScience product! We look forward to exploring how it can streamline our processes and enhance the traceability of our analysis. We are eager to see the potential benefits this solution can offer.

Erim Sezer, Associate Director
Digital Transformation IT PH at Bayer

Why flow cytometry matters

Flow cytometry is a transformative technology that has profoundly impacted the biopharmaceutical industries due to its ability to provide detailed analysis of cell populations at high throughput. The research and development of advanced cell and gene therapies depend on flow cytometry to characterize and quantify specific cell types after genetic modification. By using fluorescent markers and lasers, flow cytometry can analyze the expression of cellular proteins, allowing researchers to identify and quantify populations of interest, such as stem cells or immune cells, which are crucial targets in regenerative medicine and immunotherapy.

Integrating flow cytometry analytics and data management

While flow cytometry analysis is well understood and has excellent analytical solutions, the volume of data presents several challenges. Traditional approaches to data management too often involve significant manual handling and temporary storage solutions. In many labs, scientists still carry USB sticks to shuttle data between flow cytometers and the analysis workstations.

Scientists may store analysis results separately from the raw data files, which frequently strips away the full experimental context. It is difficult to share your analysis with your peers or lab manager without copying and pasting images or tables from PowerPoint and Excel.

Working directly with flow cytometry analysis software in the Tetra Data and AI Workspace allows scientists to address these core challenges to their data analysis process:

Operational inefficiencies. Dependency on physical access to specific computers or lab setups for data analysis can lead to significant delays, especially with equipment shared among multiple users or if data needs to be re-processed.
Low collaboration efficiency. Manual data handling often leads to bottlenecks in collaborative projects, with teams physically transferring data between members. This can lead to delays and reduced responsiveness in fast-paced research environments.
Accessibility of analysis results for automation, analytics, and AI. Once the data is analyzed, the complex analysis falls prey to the same data management challenges as the raw flow cytometry data. Information that belongs together lives in shared drives, emails, presentations, and spreadsheets. The opportunity for potentially time-saving analytics applications, such as understanding the performance of a particular cell sorting protocol across many populations, is much more difficult.
Data lineage loss. When data is transferred manually between systems, the audit trail of who did what and when can become obscured, complicating compliance with regulatory standards such as FDA or EMA guidelines requiring detailed data histories.

A typical flow cytometry analysis workflow requires extensive manual data handling.

This workflow presents many pain points:

Manual transfer / transcription of data
Data maintenance and upkeep
No integration between functions
Incomplete data or poor data quality
Requires scientists to be physically in the lab
Difficult to scale

Tetra Data and AI Workspace

In a recent press release TetraScience unveiled the Tetra Data and AI Workspace, which solves scientific data challenges by seamlessly co-locating scientific data from instruments and using best-in-class analytic tools and their analysis outputs for frictionless analysis and data management in a single workspace. This integration is particularly beneficial for the following reasons:

Automated data upload and enrichment

Raw data from flow cytometry experiments (FCS files) are automatically uploaded to the Tetra Scientific Data and AI Cloud and enriched with metadata. This automation enhances data searchability and accessibility, allowing scientists to quickly locate and analyze relevant datasets. Reducing manual handling errors saves time and improves data accuracy.

Integration with ELN and LIMS

After analysis in the FCS analysis software via the Tetra Scientific Data and AI Cloud, results can be directly pushed to an ELN or LIMS. Direct integration with systems like Benchling ensures that the results and associated metadata are automatically captured once the data is analyzed within the Tetra Scientific Data and AI Cloud. This direct integration streamlines the documentation and further analysis of experimental data, maintaining high data integrity and reducing the chances of manual errors.

The Tetra Scientific Data and AI Cloud automates and contextualizes all raw and analyzed data, freeing scientists from the pains of manual data wrangling.

The Tetra Scientific Data and AI Cloud delivers value in many ways:

Secure and centralized data storage
Reduces scientific labor
Ensures data integrity
Facilitates automation
Analysis from anywhere in the world
API/SQL endpoints for integration

Enhanced collaboration

The cloud-based nature of the Tetra Data and AI Workspace allows real-time data access. It supports collaborative workflows by allowing multiple scientists to access and analyze data from anywhere. This feature is invaluable, especially in multi-site studies in the biopharmaceutical industries, where insights can be shared and developed without the usual geographical or departmental constraints.

The seamless transition from analysis to documentation facilitates a smooth workflow, which is essential for maintaining data integrity and accelerating project timelines.

Cell and gene therapy workflows

Understanding cells' phenotypic and functional characteristics post-treatment is crucial in a typical cell or gene therapy scenario. The Tetra Data and AI Workspace can facilitate this analysis by combining flow cytometry data with genetic data, enhancing the understanding of therapeutic effects at a cellular level.

Demonstration of workflow efficiency

Consider a scenario where scientists analyze human T cells' response to a treatment. Once generated, the initial flow cytometry data is automatically uploaded to the Tetra Scientific Data and AI Cloud. Scientists can then quickly access the FCS analysis software in the same place as the data, perform the necessary gating strategies, and analyze the data. The results are seamlessly integrated into the cloud and automatically pushed to an ELN like Benchling, where they are ready for further analysis and reporting.

This capability accelerates the research cycle and improves data quality through better management and accessibility, ultimately supporting the rapid development of therapies tailored to individual patient needs.

Conclusion

The integration of FCS analysis with the Tetra Data and AI Workspace puts advanced, easier, and more efficient data management in the hands of scientific researchers in the biopharmaceutical industry. By automating data handling, co-locating data with analytical applications, and improving collaboration, these tools support the rapid development of innovative therapies, particularly in complex fields like cell and gene therapy. This capability saves valuable research time, enhances data integrity, and facilitates regulatory compliance, driving the future of scientific innovation.

Are you interested in learning more about how the Data and AI Workspace can improve your flow cytometry workflow? Please contact us for a demo.