With a constant increase in demand for new drugs, pharmaceutical companies face pressure to continuously expand their research and development (R&D) efforts. Flow cytometry is a diverse and crucial technique in pharmaceutical research, used to investigate disease aetiology and alterations in immune responses, as well as for quantitative studies. Flow cytometry has the potential to be used in every stage of drug discovery and development and so making it more efficient could have major consequences for big pharma.
There are several different steps during the flow cytometry data life cycle which include: data acquisition, processing, sub-population selection, results integration, data analytics and insight generation. Flow cytometry data analysis is built upon the principle of gating, which is necessary for the visualisation of correlations in multiparameter data. During the processing step, gating is traditionally completed manually, which is time and resource-intensive and subject to possible inconsistency. Automated gating is a relatively new computational approach for scientists to use but offers a differentiated approach to the manual method.
Manual vs automated gating
Manual gating is a standard approach used by many laboratories There has been some reluctance from some laboratories to move towards computational approaches – they are hesitant to change a process which has been used consistently over the years, despite it often providing variable results.
Automated gating offers a range of benefits in comparison to manual gating, including: accelerating data processing, speeding up the overall flow cytometry process and ultimately helping pharmaceutical companies to create effective drugs for patients. Advantages include:
- Reducing hands on processing time
Reducing the hands-on time from hours to minutes for every analysis whilst retaining control. Through using supervised machine learning algorithms to apply gates individually for each file, automated methods can mimic human gating strategies. However, manual gating takes longer, with an average FCS file taking 15-30 minutes to create. This means that large projects are particularly time consuming when completed manually.
- Ensuring quality control
Quality control is critical when it comes to flow cytometry data analysis in highly regulated environments. With manual gating, quality control for large and extended studies is impractical. However, assessment of data quality for individual files and small experiments is certainly manageable. In contrast, automatic gating allows for automated filtering of individual files for low quality data, in addition to the identification and normalisation of batch effects across larger datasets.
- Improving reproducibility
As well as quality, data reproducibility is a vital aspect of pharma research that can be compromised by manual gating in flow cytometry. Automated gating has a very high reproducibility rate – unwanted variation is removed; consistency is increased dramatically during every step, from raw data to results. Manual gating in comparison can often lead to poor reproducibility between scientists due to its subjective nature.
- Accelerating data collaboration
Automated gating facilitates a collaborative approach to data sharing, where data, pipelines, and current work can be shared within and outside of groups and organisations, with varying levels of permissions for data access. Manual gating is inefficient because the sharing of multiple versions of data files can result in frustration, inefficiency and compromised data security.
- Data throughput
Data throughput can benefit from automatic gating, as data can be uploaded in bulk and gating pipelines are run in parallel, rather than gating each file manually, which is highly laborious.
Automated gating strategy
Our CytoML Suite addresses the need for a solution that allows gated and analysed data to be exported from one platform and imported to another, to reproduce analyses from raw files and facilitate demonstrable reproducibility. It does this by leveraging usable machine learning to empower cytometry processing and analytics. As well as automating processing, the platform enables the reuse of processed cytometry data, integrating population counts identified by manual gating to increase the value of the data and enable cross-project analysis. CytoML automates every stage of the flow cytometry data lifecycle, from data acquisition to insight generation. Furthermore, automated gating with CytoML is more robust to variation than manual gating. The huge benefits that automatic gating provides enable CytoML to accelerate a company’s R&D efforts, through a simple processing switch.
For more information about how your company could benefit from our automated data solution, download our whitepaper.