Speeding up flow cytometry in clinical development

Flow cytometry is one of many analytical techniques used in clinical development to help discover new biomarkers and aid in development of new therapies. This article highlights how automated gating can expedite data processing and management, leading to improved productivity and insights in flow cytometry analysis for clinical research.

Introduction

Flow cytometry is a very versatile method, and its field of application in both research and clinical settings is extremely wide (oncology, hematology, transplantation, autoimmunity, tumour immunology, chemotherapy, etc.) [1], making it useful not only in the discovery of new biomarkers but also in clinical validation and routine implementation.

Different commercial flow cytometry methods have been used in research and clinical labs since the 1970s and can help laboratories to detect, identify and count specific cell types. Within a few minutes, thousands of cells can be counted and analysed, providing a very detailed picture of the cellular composition of any tissue or body fluid. Because flow cytometry measures dozens of cell parameters, the data generated can be vast and require significant computational power to manage and store.

It also requires specific expertise to interpret this data correctly, and there is a lack of standardization in assay and instrument set-up. Although significant progress has been made in dealing with the output of highly automated workflows, true data integration platforms are still needed. Here, we look at how automated gating can expedite data processing and management, leading to improved insights in flow cytometry analysis for clinical research.

Value of flow cytometry in clinical research

It is widely recognized that data exploitation is a crucial strategy to increase the output of research laboratories. Powerful analytical tools are required to access and exploit the masses of data produced from flow cytometry in clinical research. Unlocking this data enables researchers to better understand the underlying mechanisms of disease, working to ease the bottlenecks currently existing in the pipeline.

There is an opportunity for research organizations to address the gap between innovative research and actionable insights that could improve the translation of research into biomarkers and therapies. A key but often overlooked requirement for achieving this is identifying the relevant data out of the vast quantity of information generated. Only after this can the appropriate conclusions be drawn in the context of the original research question, using the right analytical methods and models.

Although some clinical researchers are beginning to implement data initiatives, there is a long road ahead before the real potential of analytical data is realized.

Automating flow cytometry

Flow cytometry data analysis is built upon the principle of gating, which is necessary for the visualization of correlations in multiparameter data. Manual gating is the traditional approach used by many labs but is time-consuming and can only be completed accurately by users with sufficient experience of the technique and knowledge of the biological processes at play. It is resource intensive and an impractical method for completing high volumes of flow cytometry data analysis [2]. A key weakness in manual processes is the subjectivity in interpreting results, with user bias playing a part in the conclusions drawn from data.

Automated gating is an established computational approach and offers a different approach to the manual method. Automated flow cytometry workflows outperform manual gated techniques by reducing the level of human effort and variability in subjective data analysis, while still achieving a high degree of accuracy. As well as drastically reducing analysis time, automated gating addresses the challenge of subjectivity in manual methods, and could even lead to the discovery of novel, biologically relevant populations that had not previously been considered.

For research organizations that have the capacity to implement changes within their laboratory, switching to an automated process in flow cytometry has vast potential. The technology has established itself as one of the main tools to support the clinical monitoring of diseases since it allows the examination of the expression of specific cell markers [3], but it is vital that such labs are able to keep up with automation innovation to gain the most out of the data.

Although there are many software solutions that enable manual and automated gating for researchers, there is a lack of tools available optimized for late research and clinical development. There is therefore a need for a solution that enables the use of automated machine learning to perform gating on data derived from clinical samples, but also reproduces analyses from raw files and facilitates demonstrable reproducibility. The use of one common platform that aggregates, structures and digitalizes workflows across an organization can unlock the potential of that data, providing easy access and opportunities for greater and more productive crossdepartment collaborations.

Regulatory filing with unbiased analysis

There is also a need to develop robust flow cytometric methods to ensure compliance with appropriate regulatory guidelines. Although regulated method validation is not mandatory for assays developed to support early biomarker discovery, method qualification is advisable to ensure consistent and reproducible data [4].

Several factors complicate the validation of flow cytometric methods, including the complexity of the data output and interpretation of results. Additional attention should be given to capturing metadata for validating data, ensuring comparability between experiments, and therefore strengthening the position of flow cytometry as a robust and repeatable method. Furthermore, in clinical development, there is a need for adherence to regulations such as FDA 21 CFR Part 111 and relevant Good Practice guidelines.

Where researchers need data to support a regulatory filing, guided/ semi-automated analysis is key because it is 100% reproducible. However, rich data underpinning the information needed for filing and unbiased analysis can help uncover new insights by finding novel populations or clustering non-intuitive populations together, for instance

Unbiased analysis tools allow complex multi-dimensional data to be simplified, unified, processed, and visualized so that it can be more easily explored and compared. It can be very useful in exploring data without any prior assumptions to uncover novel insights. It is a complementary technique to semi-automated approaches and when they are interoperable, it enables comparisons.

Only appropriate data analytics can unlock insights and facilitate decision making for an individual company’s data; for example, when using public proteomics and transcriptomics data, or when sharing gated cytometry data between researchers working across different platforms.

Case example: COVID-19 immunophenotyping

In March 2020, King’s College London (KCL) launched the COVID-IP (COVID–ImmunoPhenotype) project, in collaboration with Guy’s and St Thomas’ NHS Foundation Trust, the Francis Crick Institute in London, and the European Bioinformatics Institute (EMBL-EBI) in Cambridge, UK, to better understand the immunophenotype of patients infected with SARS-CoV-2 (the coronavirus responsible for COVID-19) [5].

Immunophenotyping using flow cytometry has become the method of choice in identifying and sorting cells within complex populations and is used to identify cells based on the types of markers or antigens present on the cell’s surface, nucleus, or cytoplasm. This technique helps identify the lineage of cells using antibodies that detect markers or antigens on the cells.

Immunophenotypes vary greatly between individuals, giving strong clues as to what mechanisms the human immune system must employ to protect us from COVID-19, and indicating ways in which it can go wrong, worsening rather than improving the patient’s condition. The COVID-IP project performed immunophenotyping on blood samples from more than 120 COVID-19 patients, consisting of eight complementary panels per patient for a comprehensive overview of the immune response. These generated thousands of datasets, requiring significant manpower to analyse the results which increases the risk of inconsistency and interoperator variability.

Immunophenotypes vary greatly between individuals, giving strong clues as to what mechanisms the human immune system must employ to protect us from COVID-19, and indicating ways in which it can go wrong, worsening rather than improving the patient’s condition. The COVID-IP project performed immunophenotyping on blood samples from more than 120 COVID-19 patients, consisting of eight complementary panels per patient for a comprehensive overview of the immune response. These generated thousands of datasets, requiring significant manpower to analyse the results which increases the risk of inconsistency and interoperator variability.

Compared with the KCL manual pipeline, automated processing has provided a strong correlation (Pearson’s correlation coefficient, r = 0.93) (Fig. 1a) and reduced variation for each gating step (Fig. 1b). The fast-processing time has reduced the number of full-time equivalent staff from more than 10 over eight weeks using manual gating, to 1.5 over two weeks using automated gating.

Unlocking insights

The data generated by clinical research laboratories holds enormous opportunity for the development of life-changing therapies but cannot be leveraged without appropriate data analytics to unlock insights and facilitate decision making. Automation provides laboratories with the tools to find valuable insights from their data that is transparent, accurate and highly reproducible.

Researchers can rapidly explore large data assets to drive development decisions, and use the time saved on laborious data processing for higher value-added tasks. Importantly, newer platforms enable collaboration between both computational and non-computational researchers in cytometry and will motivate reproducible analysis by helping users and reviewers validate computational and manual analyses and analysis pipelines for clinical research. Now, researchers have the tools to generate information with higher transparency, reproducibility and quality to expedite the research and discovery of diseases.


The author

Satnam Surae PhD
Aigenpulse, Milton, Abingdon, Oxfordshire, UK

E-mail: info@aigenpulse.com

References

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