Commercial flow cytometry methods have been used in basic research and clinical diagnostics labs since the 1970s, primarily for immunology and haematology applications, and, more recently, its benefits have been realised at various stages of pre-clinical and clinical drug discovery and development. It has become a critical technology, with a global market valued at USD 6.3 billion in 2020, with a predicted compound annual growth rate (CAGR) of 8.9% from 2021 to 2027[i].
Flow cytometry is appealing for drug discovery because it enables rapid, high-throughput target screening. Moreover, the method’s multiparameter capability produces different types of information, ranging from data that helps to elucidate mechanisms of action (for drugs and disease progression), to functional assays, giving flow cytometry an important role in the prioritisation, verification, and clinical validation of new biomarkers[ii].
In addition, as cellular therapeutics have become established, flow cytometry supports, for example, quality control of raw materials, assessing rates of cellular growth during complex in vitro culture processes, differentiation status, as well as final product characterisation, viability, and product stability. Furthermore, in cell therapy – specifically CAR T therapy – flow cytometry is important at all stages: from lymphocyte collection, through CAR T cell manufacturing, to in vivo monitoring of the infused cells and evaluation of their function in the tumour environment.
These new developments have served to increase the need for robust and validated flow cytometric data analysis. However, the clear advantages of this high throughput, multiparameter functionality are hampered by the immense output of highly complex data. Significant expertise is required to interpret this data correctly, and there is a lack of standardisation in assay and instrument set-up.
Moving away from manual data analysis
Flow cytometry data analysis is built upon the principle of gating, a key step necessary to visualise relevant correlations in multiparameter, multi-population data. However, gating is often seen as something of a ‘dark art’ that needs to be completed manually by those with specialist expertise to avoid errors and inconsistencies. In reality, with appropriate rules in place, gating simply becomes a laborious task – an ideal candidate for automated data handling processes. This is particularly relevant when we consider that, with an increasing number of markers, the resulting number of 2-D scatter plots increases exponentially. As a result, automated gating algorithms, that work by clustering similar populations together, have been developed.
Automated gating is based on the mathematical modelling of the fluorescence intensity distribution of particle populations. 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.
Not all automated approaches solve all challenges though – for example, random variation in automated clustering algorithms can lead to inconsistent results such that comparing results from automated gating with each other, as well as with traditional manual gating results, can be difficult.
Although a range of software platforms exist that enable automated gating, there is a lack of tools available that enable data sharing. There is, therefore, a need for a solution that allows gated and analysed data to be exported from one platform and imported into another, to reproduce analyses from raw files and facilitate demonstrable reproducibility.
Analysis in a GxP environment
The CytoML Suite from Aigenpulse automates end-to-end processes for large numbers of raw files, by leveraging usable machine learning to empower cytometry processing and analytics.
It can be implemented in a GxP environment and, as well as automating processing, the platform enables the reuse of processed cytometry data, integrating population counts identified by manual gating (in .csv format) 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.
Unbiased analysis in CytoML 5.2
Where researchers need data to support a regulatory filing, guided/semi-automated analysis is key because it is 100% reproducible. However, there is a depth of rich data that underpins the information needed for filing, and here, unbiased analysis (research use only) 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 visualised so that it can be more easily explored and compared.
Unbiased analysis can be very useful in exploring data without any prior assumptions, as a means to uncover novel insights. It is a complementary technique to semi-automated approaches and when they are interoperable, you enable comparisons.
Our customers already benefit from the semi-automated gating capabilities we have built into the CytoML experiment suite, and now the latest release of CytoML (v5.2) introduces new unbiased analysis features. Users can perform automated analyses in an unbiased manner for exploratory use cases, including FlowSOM and Phenograph for algorithm-based clustering, and use powerful dimensionality reduction methods such as tSNE and UMAP to visualise connected data. Further, with our batch processing tool, a range of parameters can be simultaneously explored to assist scientists in finding the best representation of their data. And once interesting clusters have been identified, these can be overlaid with marker expression and many types of meta-data to drive hypothesis testing. Finally, with the ability to back-gate events from selected clusters into two-dimensions, the new unbiased analysis features streamlines the process of assigning identities to populations from clustering outputs – a traditionally arduous task. To enable comparison and validation of approaches, results can also be compared with semi-automated gating methods. CytoML 5.2 has an easy-to-use interface and removes the need for difficult installation or programme scripting.
Only appropriate data analytics can unlock insights and facilitate decision making for an individual company’s data, when using public proteomics and transcriptomics data, or when sharing gated cytometry data between researchers working across different platforms. CytoML 5.2 takes us beyond current capabilities and continues the evolution of this key technique.
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[i] Grand view research (Feb 2021) Flow Cytometry Market Size, Share & Trends Analysis Report By Product (Instruments, Reagents & Consumables, Software, Accessories, Services), By Technology, By Application, By End-use, By Region, And Segment Forecasts, 2021 – 2027.
[ii] Millán O and Brunet M (2015) Flow Cytometry as Platform for Biomarker Discovery and Clinical Validation. In: Preedy V., Patel V. (eds) General Methods in Biomarker Research and their Applications. Biomarkers in Disease: Methods, Discoveries and Applications. Springer, Dordrecht.