Utilizing the Whole Spectrum for Flow Cytometry

Though spectral flow cytometry hands users a novel way of measuring based on fluorescence, to make it work they need to exert the same effort as they do for traditional forms of this technology. Sample preparation remains relevant to good data generation.

Spectral flow cytometers are configured to detect the full spectrum emitted by fluorescently tagged suspension cells.¹ Therefore, an even higher number of parameters can be measured simultaneously compared to traditional flow cytometry, enabling more comprehensive immunophenotypic analyses of complex biological mixtures.

By separating overlapping spectral signals emitted by all fluorophores involved in an assay, spectral flow cytometry improves sensitivity and specificity. Sophisticated data analysis tools (incl. the deployment of artificial intelligence) are being developed for extracting meaningful insights from the large datasets generated from this recent technological innovation.

Over the past years it has been utilized in immunology, oncology and autoimmune research and drug development, including the characterization, manufacturing and monitoring of CAR-T cells.
Since these fluorescence measurements can be made increasingly quantitative, flow cytometry reemerges as one of the main go-to instruments for discovering and translating biomarkers connected to novel modalities.² Evidently, drug developer's willingness to invest in these additional efforts significantly reduces the later risk of clinical trial failures. Standardization of workflows associated with flow cytometry is advanced by a NIST consortium to collaboratively focus on solutions that offer better value propositions to end users based on their varying needs.³

The technology's information richness comes from its data output, which is potentially impacted by many well-known factors. These introduce variability at every stage from sample handling to instrument and assay setup prior to post-acquisition data analysis.

Concerns regarding carryover and spillover effects

Regardless of the type of flow cytometry employed the essence of this technique relies on its ability to flawlessly separate. While spectral flow cytometry offers significant advantages, effects of carryover and spillover on data quality remain potential challenges. These are particularly pertinent for translationally sensitive applications like CAR-T cell monitoring. Carryover refers to the persistence of fluorescent signals from one sample to the next, while spillover occurs when the emission spectrum of one fluorophore overlaps with the detection channels of another. Scientists are addressing these issues through various strategies, such as:

• Advanced compensation algorithms: Sophisticated compensation algorithms are being developed to accurately correct for spillover effects, improving the overall accuracy of data analysis.
• Optimized experimental design: Careful experimental planning and execution, including appropriate sample preparation and staining protocols, helps minimizing carryover and spillover.
• High-performance instrumentation: The use of high-performance instruments with advanced optics and detectors can reduce the impact of carryover and spillover.
• Data quality control measures: Implementing rigorous data quality control measures, such as regular instrument calibration and maintenance, help ensuring accurate and reliable results.

Focusing on PE antibody titration

Phycoerythrin (PE) is a widely used fluorochrome in flow cytometry due to its bright fluorescence signal. However, its broad emission spectrum causes spectral spillover into adjacent channels, complicating multicolor analyses. Titrating PE-conjugated antibodies is a crucial for mitigating these issues in order to:

• Determine optimal Signal-to-Noise Ratios: Determining the appropriate antibody concentration ensures strong specific signals while minimizing background noise.
• Reduce Spectral Spillover: Proper titration limits excessive fluorescence intensity, thereby decreasing spillover into neighboring channels.
• Support Cost-Effectiveness: Using the minimal effective antibody amount conserves reagents and reduces experimental costs.

Zhang et al. recently applied the precision of the Curiox Laminar Wash™ technology for conducting a comprehensive titration of 266 antibodies, including those conjugated with phycoerythrin (PE), to optimize their usage in single cell proteogenomic analyses of human bone marrow cells.⁴ Optimal antibody concentration was determined by evaluating the staining index, which measures the separation between positive and negative populations. The concentration that provided the highest staining index, indicating clear distinction with minimal background, was selected as optimal.

This meticulous titration ensured that each antibody, including those conjugated with PE, was used at a concentration with maximized signal clarity while minimizing spectral spillover and carryover effects, thereby enhancing the accuracy of subsequent single-cell analyses. This process involved preparing serial dilutions of the PE antibodies, staining target cell populations, and analyzing the signal-to-noise ratio at each concentration to identify the most efficient antibody amount that provided a high specific signal with reduced background noise.

This approach ensured that the PE antibodies provided a bright signal with minimal bleed into adjacent channels. This is crucial for high-dimensional flow cytometry analyses where spectral overlap can compromise data quality.

Data graph summarizing staining indices of PE antibody conjugates bound by bone marrow cells. Using a total of 131 antibodies, CD34+ progenitor cells and CD34- mature lineage cells were combined to accurately represent cell populations. For each titration (1:25, 1:50, 1:100, 1:200, and 1:400), 50,000 cells were stained and processed on Curiox's Laminar Wash HT2000 System. After staining, cells were analyzed on a five-laser Cytek Aurora full-spectrum flow cytometer. The graph highlights the optimal PE antibody concentrations of 1:100 for CD4, CD45RA, and CD162, as these yielded the highest stain indices, indicating robust specific signals and efficient antibody use.⁴

Data graph summarizing staining indices of PE antibody conjugates bound by bone marrow cells. Using a total of 131 antibodies, CD34+ progenitor cells and CD34- mature lineage cells were combined to accurately represent cell populations. For each titration (1:25, 1:50, 1:100, 1:200, and 1:400), 50,000 cells were stained and processed on Curiox's Laminar Wash HT2000 System.

After staining, cells were analyzed on a five-laser Cytek Aurora full-spectrum flow cytometer. The graph highlights the optimal PE antibody concentrations of 1:100 for CD4, CD45RA, and CD162, as these yielded the highest stain indices, indicating robust specific signals and efficient antibody use.⁴

The optimal concentration identified from this titration study was then incorporated into the Infinity Flow platform- an advanced experimental and computational workflow that leverages machine learning to impute hundreds of cell surface proteins on millions of cells. This methodology allows for deep proteomic profiling at the single-cell level, facilitating the exploration of complex cell states. By comparing co-normalized data from Infinity Flow and CITE-seq, the study effectively isolated transitional cell states that were previously challenging to define.

The connection between washing precision and spectral overlap

Inadequate washing between incubation steps can lead to non-specific binding and increased background fluorescence, complicating data interpretation. Replacing the centrifuge with the constant laminar flow of the Curiox system streamlines mouse and human digested tissue sample processing, yielding superior cell retention and viability, improved detection of rare immune cell populations, and cleaner, more reproducible washes prior to flow cytometry.

Working with smaller antibody volumes is possible with this setup, allowing for reagent cost reductions. While studies directly linking poor washing precision to spectral spillover and carryover are limited, several publications discuss related issues:

1. "An Introduction to Spectral Overlap and Compensation Protocols in Flow Cytometry" explains how spectral overlap occurs when fluorophores have overlapping emission spectra, leading to spill over into adjacent detectors. The importance of proper compensation to correct for this spillover is emphasized.
2. "Antibodies 101: Flow Compensation" discusses the necessity of compensation in flow cytometry to correct for spillover from one fluorescent channel to another, which is crucial when multiple antibodies with similar emission spectra are used.
3. "Understanding the Trumpet Effect: How to Design Aurora Panels Around Spreading Error" addresses the phenomenon of spreading error, also known as the "trumpet effect," which occurs due to spillover spreading error after compensation, which is applied in conventional flow cytometry, or after spectral unmixing has been applied. This resource provides insights into designing panels to minimize this effect.

Proper staining and compensation protocols are essential in minimizing these issues. Indeed, ensuring thorough wash steps during immunophenotyping can help reducing staining artifacts from non-specific antibody binding and background fluorescence, thereby consistently improving data quality.

References:
1. McCausland M., Lin Y. D., Nevers T. et al. With great power comes great responsibility: high-dimensional spectral flow cytometry to support clinical trials. Bioanalysis 2021, 13(21), 1597-1616. https://doi.org/10.4155/bio-2021-0201
2. Ullas S, Sinclair C. Applications of flow cytometry in drug discovery and translational research. Int J Mol Sci. 2024, 25(7):3851. https://doi.org/10.3390/ijms25073851
3. Wang L., Maragh S., Kwee E. et al. Measurement solutions and standards for advanced therapy. Mol Ther 2024, 32(1):101219. https://doi.org/10.1016/j.omtm.2024.101219
4. Zhang X., Song B., Carlino M.J. et al. An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors. Nat Immunol 2024, 25:703-715. https://doi.org/10.1038/s41590-024-01782-4

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