Dr Miguel A Tam, and Dr Kenta Yamamoto, BioLegend, examine the evolution, trends and best practices for smooth and efficient workflows for flow cytometry.
Flow cytometry is a well-established analytical technique that has been supporting reliable diagnostics and groundbreaking research for decades. Take, for example, its use for CD4 T cell counting to facilitate HIV monitoring. As we gain deeper understanding of normal biological processes as well as diseases, researchers and clinicians are increasingly seeking to analyse as many cellular parameters as possible to unravel their complexity. Accordingly, advances in flow cytometry technology – both in instrumentation and reagent offerings – are enabling researchers to decipher these questions, leading to new discoveries and diagnostic tools.
Overview, evolution, trends
Flow cytometry is a technique used to rapidly analyse single cells in order to characterise their physical, chemical and biological properties.
From its inception, flow cytometry relied on the measurement of scattered light. A major breakthrough in the technology was the development of biologically active fluorescent reagents that could be detected by the instruments. The main principle involves a well-defined light source such as that produced by lasers that excites fluorescent tags bound to the cells or particles present in a sample. As the cells flow through the instrument, the bound reagents are excited and they emit light in the form of fluorescence which is a signal that can then be quantified using optimised detection systems.
Over the last 50 years, both the variety of fluorescent reporters and the number of different signals that an instrument can detect have been expanding significantly. Simultaneously, the capacity to conjugate fluorescent molecules to proteins to better characterise biological systems has evolved accordingly, enabling the proliferation of highly specific tools to interrogate cells. A key component of the technology are monoclonal antibodies that can be conjugated to fluorescent molecules to specifically bind targets of interest, including cellular components and other targets. Thus, the technology has evolved from single-colour analysis to two-colours, then progressively increasing to eight, 16, and ultimately 20 to 30 colours with the most advanced traditional instruments1.
The limitation here comes from the need of fluorophores that can be spectrally separated and confidently detected by the instruments. Traditionally, instruments have relied on detectors or filters that collect a small fraction of the signal emitted by the excited fluorophores, typically a few nanometers wide (of the signal wavelength) covering the maximum intensity or peak of the signal. Expanding to more than 30 of such filters is impractical and costly, so an alternative solution was needed. The need to expand beyond 30 colours drove the evolution of flow cytometry from this polychromatic or traditional approach to what is now known as spectral flow cytometry.
Spectral flow cytometry is based on the principle of collecting or measuring the entire emission spectrum of the fluorescent dyes used as reporters, not just the maximum emission. As such, all that is needed to distinguish two different dyes is that they have a sufficiently different spectral signature. While initial proof-of- concept spectral flow cytometers have existed for more than a decade, there has been significant technological advancements over the last several years1,2. These new spectral flow cytometers allow for simultaneous analysis of a significant number of dyes with higher degree of spectral similarity that largely exceeds capable plexity on currently available traditional flow cytometers, thus increasing the depth and width by which biological systems can be characterised using flow cytometry.
As the evolution towards more parameters continues to unfold, companies that provide flow cytometry reagents have also evolved to provide an extensive portfolio of conjugated antibodies and fluorescent dyes to characterise biological processes. With thousands of flow reagents in the market, supporting both traditional and spectral cytometry, the variety of flow reagents is supported by companies that have established two pillars in their research and product development pipeline: antibody development and fluorescent dye conjugation. A good antibody portfolio provides antibodies against important immune regulatory proteins, key markers for phenotyping, tumour control, neurodegeneration, stem cell-relevant proteins and more, covering multiple organisms, including human, mouse, rat and non-human primates.
Fluorescent molecules that are conjugated to antibodies can be grouped according to their chemical structure into small organic dyes, protein-based dyes, and polymer or multimer dyes. Best antibody development practices are summarised in Fig. 1. Reagents produced under ISO certification provide an extra level of traceability to support internal documentation and quality assurance.
Another trend is the integration of additional parameters to help characterise samples of interest even further. This includes the simultaneous capture of images as well as the adoption of automation to increase high throughput capabilities in both number of cells analysed per sample, and total number of samples analysed per unit of time.
Overall, the evolution of flow cytometry has provided new tools and ways to support cutting edge research and reliable diagnostics. Specifically, by adding more parameters to an experiment, researchers can analyse more cell populations in a shorter time and at the same time they can characterise cells more deeply. The total number of cells analysed has also increased, providing less variability by processing more samples in the same experiment or assay. However, despite impressive advances in modern flow cytometry, many bottlenecks remain that require constant research and investment.
Best practices to breakthrough bottlenecks
While flow cytometry technology is rapidly evolving to enable higher parameter cell analyses, the core fundamentals related to practicing good cytometry technique remains relatively unchanged. Especially as researchers pursue experiments and multicolour panels with ever higher degrees of complexity, it is now even more important to get the basics right from the start – establishing a solid experimental foundation with proper controls and following good experimental practices. Here are some of the common pain-points encountered during a flow cytometry experiment, and tips to help resolve these issues:
Strategic multicolour panel construction
When designing a multicolour panel experiment, having a well-thought-out panel before lifting a pipette will significantly improve the chances of obtaining the highest quality data. As a general guide, particularly when using traditional flow cytometry, balancing expected protein/biomarker target abundance andfluorophore brightness is the main principle behind multicolour panel construction. A fluorophore’s brightness is determined by the Staining Index (SI)3, which factors in the mean signal intensity of the positive population, relative to the background staining on the negative population.
Brighter fluorophores with a low background typically yield higher SI value for a given conjugate/marker detection, allowing better separation between positive and negative cell populations. Lower abundance cell and biomarkers with low frequency of expression benefit from bright fluorophores, whereas markers with high expression levels can be identified using dimmer fluorophores in a multicolour panel.
Overlooking these technical parameters may result in lower assay sensitivity, lack of identification of relevant populations, or misclassification of cell types. This is especially the case when using fluorophores with significant spectral overlap, discussed in more detail in the following section. Peer reviewed publications are a good resource to understand expected biological expression levels. Representative data for antibodies of interest from trusted manufacturers can also provide valuable information regarding fluorophore brightness and antigen expression.
Careful selection of antibody-fluorophore combination will also minimise spectral overlap and spillover. Try to avoid analysing co-expressed markers on thesame cell types with fluorophores that exhibit high degree of spectral overlap, as well as those being directly compared in a bivariate plot.
Having pre-planned gating and analysis strategies is essential to avoid compensation issues and ensure unbiased data analysis. Comparing excitation/emission properties of different fluorophores on spectra viewers available across different commercial vendor websites can help in choosing the best fluorophore combination for the markers of interest.
Spectral overlap, unmixing, and compensation
When planning a new flow cytometry experiment, the term compensation may be intimidating to those attempting to run their first trial. Applying compensation for flow cytometer detectors – a mathematical correction factor to remove fluorescence signal coming from a different fluorochrome than the one the detector is primarily intended to capture – is a necessary step typically required for multicolour flow cytometry experiments.
The need for compensation is largely a consequence of the configuration of conventional flow cytometer detectors, as well as the physical properties of the fluorophores (Fig. 2). Detectors equipped on cytometers (typically photomultiplier tubes, or avalanche photodiodes, PMTs and APDs, respectively) usually capture emitted fluorescent signals of specific wavelengths that are segmented via passage through a series of dichroic mirrors and filters. However, as multicolour flow cytometry panels typically involve use of multiple fluorochromes with some degree of spectral overlap due to their emission wavelength distribution, compensation needs to be applied per each detector. Compensation ensures that the signal being detected is as specific as it can be for a given fluorescent parameter by reducing signal coming from all others.
Analogous to the application of compensation for spectral flow cytometer instruments is spectral unmixing. Here, single colour controls are also used to generate a spectral fingerprint across all excitation sources and detectors for each fluorophore being used in the experiment. The spectral fingerprint is then used by an unmixing algorithm to identify the signal from each fluorophore separately.
The ideal end result after successfully applying either compensation or spectral unmixing in the respective instrument is the same – the signal being identified is as specific as it can be for each individual fluorochrome.
Setting up proper single-colour controls is critical to obtaining good compensation and spectral unmixing matrices. Typically, unstained controls that are used with single-colour controls should contain a heterogeneous mixture of particles or cells that produce both, a positive and a negative signal for a single fluorophore. Ideally but not necessarily, the signal intensity of the positive population should be at least as bright to that of the biological sample of interest, and spectrally identical. For instance, commercially available compensation beads would typically give a positive signal that is higher than what’s observed in standard biological samples. As the name suggests, compensation beads are a good source for generating single- colour controls, but the difference in the positive fluorescence signal needs to be considered when adjusting PMT voltage/gains values, which will in turn affect ultimate compensation values.
A trend in recent years regarding detection systems is the use of APD instead of PMT detectors. APDs have high quantum efficiency that can be used to improve red and near infrared (NIR) optical detection. This expands the spectral range of the flow cytometer and the sensitivity of the red and NIR spectrum4 resulting in effectively more dyes or parameters added to the experiment, and potentially increased resolution of the data. However, newer generations of PMTs also promise higher sensitivity and expanded red/ infrared detection. Serving technology using either PMT or APD, reagent manufacturers also continue to develop suitable dyes and conjugates to facilitate multicolour panel design with expanded options in the read and NIR spectrum.
Antibody titration
In addition to the use of proper single-colour controls as mentioned above, titrating antibodies is a key exercise to help overcome artifacts associated with spectral overlap in a multicolour flow cytometry assay. Antibody titration involves step-wise antibody dilutions to test marker staining efficiency across multiple concentrations. This helps determine the optimal concentration that maximises the resolution of positive signal above background. An optimal concentration for a given antibody conjugate is determined by plotting the SI or Signal-to-Noise ratio (S/N, another similar fluorophore metric) vs antibody concentration. The optimal concentration is in the middle of the maximum SI or S/N range (Fig. 3). This value assumes other variables remain constant, such as staining volume and cell number5.
As titrating antibodies can be time-consuming, it is often overlooked from the list of optimisation steps to perform a successful experiment. However, this is a worthwhile effort that can save time and avoid pitfalls in the long-run, especially for larger multicolour experiments to obtain high-quality data. Using a titrated amount of antibody, useful to identify positive signal from background, helps mitigate issues related to non-specific fluorescence signals affecting other fluorophores and channels. Titration is especially important when using viability dyes (e.g. DNA-based dyes, amine-reactive, fixable dyes) as oversaturated staining can falsely lead to over-exclusion of live cells from analysis.
Future directions
Although flow cytometry is a robust technology, the diversity of systems and reagents available to researchers inevitably introduces variability related to multicolour panel use, data analysis and more. Some steps have been taken in an attempt to standardise sample and data analysis. An example of this is the publication of the Optimised Multicolour Immunofluorescence Panels (OMIP)6. Simultaneously, reagent companies and application groups are adopting methods and protocols to validate and characterise flow cytometry reagents. Several reagent suppliers are also providing excellent panel design services, producing consistent and often validated results. More standardised applications are important not only for the reproducibility of results but also for education, quality control and for better data analysis.
It is also interesting to observe the evolution of spectral cytometry applied to cell sorters and not just cell analysers to help with cell isolation and functional studies. In addition to this, other components of the technology, such as data analysis, will continue to evolve. As more data is generated with newer generations of flow cytometers and cell sorters, stronger and more reliable data analysis tools are needed.
Easier data sharing and collaboration will also be a key factor as there is a need for multisite studies, such as large clinical trials, to exchange insights and findings. Secure, cloud-based informatics solutions are becoming more prominent to facilitate communication between labs. Together, advancing technologies, more aligned approaches, and more standardised solutions should serve as a roadmap for successful design and execution of future flow cytometry studies7, leading to accelerated, stronger life science research, drug development and diagnostics.
Volume 23 – Issue 4, Fall 2022
References:
- Robinson, JP. 2022. BioTechniques. 72 (4): 159-169.
- Nolan JP and Condello D. 2013. Curr. Protoc. Cytom. 63:1.27.1-1.27.13.
- Maecker, HT et al. 2004. Cytometry A. 62(2):169-73.
- McKinnon, KM. Curr. Protoc. Immunol. 120: 5.1.1-5.1.11.
- Sharma D et al. 2012. Biotechniques. 53(1): 57–60.
- Wang W and Creusot RJ. 2021. Cytometry A. 99(9):866-874.
- Kalina T. 2020. Cytometry A. 97(2):137-147
About the authors
Miguel A Tam received his Ph.D. in Clinical Immunology from Gothenburg University, Sweden, in 2007. After initial postgraduate studies at Gothenburg University, he continued his academic career at UCSD characterising the innate immune response against pathogens. He joined BioLegend in 2012, contributing in multiple roles and is now Director of Strategic Marketing responsible for Product Management, Technical Services, and Scientific Applications.
Kenta Yamamoto received his Ph.D. in Pathobiology and Molecular Medicine from Columbia University in 2016. He joined BioLegend the same year while pursuing postdoctoral studies at UCSD to investigate cancer immunotherapy and cell therapy, and now holds a Product Management role with responsibility for the Cell Analysis portfolio, which includes flow cytometry reagents.
