Miguel Tam, Director of Strategic Marketing at Revvity’s BioLegend, explores the future directions and applications of flow cytometry in drug discovery, highlighting the opportunities it presents and the challenges that lie ahead 

Flow cytometry has long been a cornerstone in biomedical research and clinical diagnostics, offering unparalleled capabilities to analyse multiple parameters of single cells in a high-throughput manner. Its utility in drug discovery and development has steadily expanded, and as the landscape of therapeutic innovation evolves, so too does the potential of flow cytometry.

Emerging applications in drug discovery 

Some of the most exciting and challenging areas in future drug discovery are immunotherapy and biomarker discovery and their corresponding impact in precision medicine. Modern flow cytometry has helped advance these areas in recent years and will continue to do so as the technology itself develops and integrates further with the drug discovery process.  

Immunotherapy development 

The rise of immunotherapies, such as immune checkpoint inhibitors and CAR-T cells, underscores the importance of immune profiling. Flow cytometry facilitates the characterization of immune cell populations, helping researchers optimise therapeutic strategies and monitor immune-related adverse events. The prime example of immune checkpoint molecules, programmed cell death protein 1 (PD-1), was discovered by Dr Tasuku Honjo in 19921. In 2000, Dr Honjo’s lab was able to detect the molecule and study PD-1’s mechanism of action via flow cytometry as suitable reagents were developed and became available2. Since then, anti-PD-1 treatment has become one of the most used immunotherapies, benefiting thousands of patients for several types of cancer.   

In addition to analysis of cell-bound proteins, the development of bead-based, soluble molecule detection assays based on flow cytometry opened new avenues of cell physiology profiling, providing insights into metabolic or disease biomarkers, cellular signaling, immune regulatory mechanisms, and growth factor biology. These advances enabled researchers to gain a deeper understanding of how immune cells interact with their environment, ultimately improving the design of next-generation immunotherapies. Future assays that increase the number of molecules detected simultaneously, or even integration with other metabolic assays will further increase flow cytometry’s value in immunotherapy development. 

Precision medicine and biomarker discovery 

Traditional medical treatments are designed for most patients, which may not be the best solution for each person. Precision medicine, or “personalised medicine” is a modern and innovative approach to design treatments that account for a patient’s genetic profile, environment, lifestyle, and other individual factors. Flow cytometry is already playing a pivotal role in the era of precision medicine with its ability to characterize cell heterogeneity, combining decades of robust performance with new, advanced capabilities. An area where this technology can make a difference is in the identification of novel biomarkers associated with therapeutic responses or resistance. 

Moreover, advancements in high-dimensional flow cytometry, such as the newest spectral flow cytometers and the associated conjugated antibodies, allow for the simultaneous analysis of dozens of parameters, providing a more comprehensive understanding of complex biological systems. This capability is critical for uncovering proteins and/or physiological cell states that can stratify patient populations, leading to personalised therapeutic strategies. For example, the identification of unique immune cell phenotypes in autoimmune diseases has the potential to guide the development of targeted therapies tailored to individual patient profiles3.

Automation, data analysis, and artificial intelligence

In addition to emerging research and therapeutic areas, the technology itself is experiencing several areas of growth and improvement. Modern flow cytometers can make optical measurements of 50 or more parameters per cell, at tens of thousands of cells per second with over five orders of magnitude of dynamic range. Although flow cytometry is used in most drug discovery stages, it has traditionally been limited to low-throughput applications in drug discovery due to constraints in sample processing. However, advancements in sampling technologies have significantly enhanced its capacity for high-throughput screening, enabling the analysis of tens of thousands of compounds per day. Flow cytometers capable of acquiring samples in 96-, 384-, and even 1536-well plate formats, position the technology very well in the drug development workflow. 

Similar to sample acquisition, data analysis is vital within the ecosystem of flow cytometry tools. An important area of development for flow cytometry is data analysis, as the number of samples increases with high-throughput solutions, the number of parameters increases with spectral cytometry, and multimodal approaches are developed to interrogate samples and develop targeted therapies. To this end, integration of artificial intelligence (AI) within the flow cytometry space has the potential to revolutionise how we perform flow cytometry and interpret results. Machine learning algorithms could design better workflows, as well as help identify patterns and rare cell populations. Particularly in data analysis and interpretation, AI can help reduce human error, increase throughput, and help optimise quality control processes, among other potential improvements, going beyond research and discovery to include drug development and clinical applications4. 

Challenges and limitations 

While the future of flow cytometry in drug discovery is unquestionable, several challenges must be addressed to fully realise its potential. Overcoming difficulties with sample preparation, standardised quality controls, cost, analysis of complex datasets, and ultimately consensus on regulation will help flow cytometry further its contribution to the drug development process in all laboratory-based phases, from research and discovery phase to clinical trials.  

Sample preparation, quality control, data analysis and standardisation 

Flow cytometry is sensitive to variations in sample preparation, which can introduce artifacts and affect data quality. Since flow cytometry is a single cell-based assay, a limitation of this technology is that it can sometimes be challenging to obtain high-quality single-cell suspensions from solid tissues. The main parameters affected are sample yield, viability, and quality of surface molecule expression. Innovations in tissue dissociation methods and the development of microfluidic devices for single-cell processing may address these issues, ensuring more reliable and reproducible results when working with solid tissues. In addition to advancing sample preparation instrumentation and reagents, developing standardised protocols and robust quality control measures will be critical, especially as the technique is applied to complex clinical samples.  

In addition to sample processing, the high-dimensional data generated by flow cytometry can be challenging to analyse and interpret. Standardising data acquisition and analysis pipelines across laboratories is crucial for reproducibility and comparability. Efforts to develop standard operating procedures as well as broadly accepted reference standards and data analysis are underway, for example through international organisations such as the EuroFlow Consortium5 and the peer reviewed collection of Optimized Multicolor Immunofluorescence Panel (OMIP), which has been helping the flow cytometry field, particularly with optimisation and standardisation, for the last 15 years6.

Cost, accessibility, regulatory, and ethical considerations 

Advanced flow cytometry instruments are costly and require specialised staff to not just acquire the samples but also to perform daily maintenance procedures. Maintenance and repair cost can also quickly add to total experimental cost. Thus, developing more user-friendly instruments with robust performance along with affordable reagents and consumables will be critical steps in democratising access to cutting-edge flow cytometry platforms.  

Lastly, as flow cytometry moves from research to clinical applications, the regulatory aspect of the application and its relationship with the drug discovery process needs to be considered. As that relationship grows tighter, more challenges will arise. For example, establishing clear guidelines for data handling, privacy, and ethical use of patient-derived samples is necessary to ensure compliance and trust, both from regulatory bodies as well as medical staff and other personnel involved. In particular, the integration of AI and machine learning in flow cytometry also raises regulatory and ethical considerations regarding data privacy and the need for transparent algorithms. Ensuring that these technologies are used responsibly and in compliance with regulatory standards will be essential to maintain public confidence and drive the successful clinical translation of flow cytometry-based discoveries. 

Conclusion 

Flow cytometry is at the forefront of innovation in drug discovery, offering unique capabilities to dissect cellular mechanisms and accelerate therapeutic development. By addressing current challenges and leveraging emerging technologies such as spectral cytometry and novel reagents and data analysis tools, the field can unlock new opportunities in precision medicine, immunotherapy, and beyond. Collaborative efforts among researchers, clinicians, and industry stakeholders will be essential to harness the full potential of flow cytometry in shaping the future of drug discovery. 

References 

  1. Ishida Y et al. Induced expression of PD-1, a novel member of the immunoglobulin gene superfamily, upon programmed cell death. EMBO J. 1992;11:3887–3895 
  2. Freeman GJ et al. Engagement of the PD-1 immunoinhibitory receptor by a novel B7 family member leads to negative regulation of lymphocyte activation. J Exp Med. 2000 Oct 2;192(7):1027-34 
  3. Preglej et al. Advanced immunophenotyping: A powerful tool for immune profiling, drug screening, and a personalized treatment approach. Front Immunol. 2023 Mar 24:14:1096096 
  4. Ng DP et al. Recommendations for using artificial intelligence in clinical flow cytometry. Cytometry B Clin Cytom. 2024 Jul;106(4):228-238  
  5. Ullas S and Sinclair C. Applications of Flow Cytometry in Drug Discovery and Translational Research. Int J Mol Sci. 2024 Mar 29;25(7):3851 
  6. Roederer M and Tarnok A. OMIPs—Orchestrating multiplexity in polychromatic science Cytometry Part A. 77A: 811812, 2010