Blood, Sweat, and Standardization in the Laboratory

How to conquer flow cytometric variability and improve reproducibility of studies

The relentless march of scientific progress hinges on reliable data. In preclinical research, flow cytometry has proven to be a customizable tool that answers pharmacodynamic and biomarker-driven questions and dissects the complexities of cellular populations with laser-like precision. By analyzing light scatter and fluorescence, researchers can identify and characterize specific cell types, a crucial step in understanding disease processes and developing new drugs. Additionally, flow cytometrists have come together and agreedon basic best practices pertaining to instrumentation and assay validation. However, a shadow lurks within this realm: the specter of data variability. Volatility can be introduced at any point whether it be the conditions under which specimens are collected and transported to the laboratory or the way samples are processed and analyzed [1].

The Quest for Reproducibility: A Scientific Imperative

The foundation of scientific discovery rests upon reproducibility - the ability to consistently generate similar results when an experiment is repeated under identical conditions. Sadly, the scientific landscape is marred by a "reproducibility crisis" [2]. Studies have revealed alarmingly high rates of failed replications, casting doubt on the validity of existing research and hindering progress. This lack of consistency has a ripple effect, delaying the translation of basic science into real-world applications, ultimately impacting patient care.

The Flow Cytometry Conundrum: Why Data Can Be Fickle

Flow cytometry, while undeniably valuable, is not immune to variability. Several factors can contribute to inconsistent results, including instrument calibration, operator technique, and most importantly, sample preparation. This becomes particularly critical when working with whole blood, a complex biological matrix containing a diverse range of cell types and interfering substances like red blood cells (RBCs). Optimizing sample preparationprotocols is paramount to ensuring robust and reproducible flow cytometric data.

Whole Blood Analysis: Navigating the Roadblocks

The use of whole blood in flow cytometry offers undeniable advantages. It provides a more complete picture of the immune system, reflecting the in vivo cellular composition. However, the presence of RBCs creates a major hurdle. These abundant hemoglobin-containing erythrocytes can obscure the signals from the leukocytes (white blood cells) of interest, leading to underestimation and skewed results. Here is where the discussion gets interesting: Laminar Wash and C-Free technology emerge as potential solutions for conquering variability in whole blood analysis.

Delving into the Data: A Tale of Two Technologies

Let us take a closer look at the data presented for Laminar Wash AUTO1000 and C-Free PlutoLT, both designed to enhance flow cytometry reproducibility in whole blood analysis.

Figure 1.Laminar Wash™ AUTO1000 small volume TBNKM immunostaining results. Initial volumes of 5 mL, 10 mL, 25 mL, and 50 mL of apparently healthy donor patient derived blood were processed using EasySep RBC Depletion Reagent (STEMCELL Technologies) and the compact Laminar Wash (LW) system (Curiox Biosystems) in technical replicates (n=2). Samples were acquired on a ThermoFisher Attune NxT flow cytometry system at a rate of 100 mL/min.

The Laminar Wash AUTO1000 data demonstrate consistency across a range of whole blood volumes (5 mL, 12.5 mL, 25 mL, 50 mL), as demonstrated in Figure 1. The table highlights the frequency percentages of key immune cell populations like CD45+ (total leukocytes), CD3+ (T cells), CD8+ (cytotoxic T cells), and CD19+ (B cells). Notably, the coefficient of variation (CV) for precision performance is a mere 14.24%, indicating excellent reproducibility. This suggests that Laminar Wash effectively removes RBCs while preserving the integrity of leukocyte populations, leading to reliable data regardless of blood volume used.

Figure 2. Next-Generation whole blood (WB) sample preparation for flow cytometry analysis. Representative dot plots of one donor WB (technical replicates, n=4) processed with conventional centrifuge method or C-Free technology are shown. Lower non-CD45+ events in PlutoLT-washed samples indicates better wash efficiency compared to the conventional centrifuge method. Population resolution and frequencies are consistent between both methods. Samples were stained with basic immunophenotyping panel (Viability stain eF780, CD45 (HI30) eF506, CD3 (OKT3) SB600, CD4 (OKT4) PerCP-Cy5.5, CD8 (SK1) PE-Dazzle 594, CD19 (HIB19) FITC, CD16+CD56 (B73.1+HCD56) PE, CD11b (ICRF44) BV421, CD11c (Bu15) PE-Cy7) and acquired at a rate of 100 µL/min on a ThermoFisher Attune NxT flow cytometry system.

The C-Free PlutoLT data reinforce the importance of proper sample preparation (Figure 2). C-Free technology appears to remove debris more efficiently compared to conventional centrifuge methods, as evidenced by a higher percentage of CD45+ cells within live population. Additionally, stain indices and frequencies of various immune cell subsets remain consistent across different time points (0h and 48h post-collection). These findings suggest that C-Free facilitates reliable cell analysis over time, which can be crucial for studying cellular responses to stimulation or drug treatment.

The Takeaway: Optimizing Sample Preparation for Robust Data and Reduced Costs

The data presented for both Laminar Wash and C-Free technologies showcase the benefits of implementing optimized sample preparation methods. Consistent and reproducible data translate into several advantages for preclinical research:

• Reduced Time and Resource Waste: Eliminating the need to repeat experiments due to unreliable data saves valuable time and resources.
• Enhanced Confidence in Results: Reproducible data fosters greater confidence in the findings, leading to more robust scientific conclusions.
• Improved Patient Outcomes: Reliable preclinical data ultimately contributes to the development of more effective therapies and better patient outcomes.

Beyond Laminar Wash and C-Free, a range of optimization strategies exist for flow cytometry using whole blood. These include utilizing standardized protocols, meticulously maintaining instruments, and employing rigorous quality control measures. Importantly, researchers and institutions should consider collaborating and sharing best practices to collectively address the challenges of flow cytometric variability. Additionally, the inclusion of biomarkers has been found to statistically increase the overall likelihood of success for clinical trials [3]. Flow cytometry can contribute to the development of relevant biomarkers early on.

As research delves deeper into complex biological processes, the need for robust and reliable data becomes ever more critical. By embracing flow cytometry optimization techniques, we can navigate the path to a future where "blood, sweat and standardization" become the hallmarks of scientific progress, ultimately delivering the promise of improved patient care.

References:

1. Sędek Ł, Montero-Flores J, van der Sluijs A, et al. Impact of pre-analytical and analytical variables associated sample preparation on flow cytometric stainings obtained with EuroFlow panels, Cancers, 2022, 14(3):473. doi: 10.3390/cancers14030473
2. Kahn RA, Virk H, Laflamme C, et al., Science Forum: Antibody characterization is critical to enhance reproducibility in biomedical research, eLife, 2024,13:e100211. https://doi.org/10.7554/eLife.100211
3. Wong CH, Siah KW, Lo AW, Estimation of clinical trial success rates and related parameters, Biostatistics, 2019, 20:273-286. doi: 10.1093/biostatistics/kxx069

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