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Multiparameter Flow Cytometry Notes

One of the most powerful application of flow cytometry is for evaluate drug and vaccine efficacy. Multiparameter flow cytometry relies on the use of fluid systems, fluorescent proteins, and optical systems to detect and collect fluorophore signals. When the technology first became available multiparameter meant 2 colors, today it can mean up to 32 colors. The availability of dyes and antibodies along with hardware and software improvements, make possible simultaneous analysis of a wide range of parameters. Researchers can probe a single cell for multiple markers, define the composition of a cell population or evaluate protein expression levels. However, the complexity of of  multiplexed flow cytometry studies creates challenges to generating reproducible and publishable data. Below are design, execution and analysis best practices to keep in mind when planning flow cytometry experiments.

Sample processing:

One of its advantages is that cytometry can be used to generate a lot of data per cell basis. Researchers can probe a heterogeneous cell population derived from almost any solid tissue or body fluid. Regardless of the source, the cell processing protocol must be optimized to yield homogenous, single-cell suspension of viable cells. The protocol must therefore be optimized for pH, temperature and other conditions that will impact cell viability and/or produce autofluorescence. And, it must also yield sufficient numbers of quality cells for analysis. Sample quality directly correlates to data quality. As in every scientific experiments, technical skill and expertise is the only guarantee for experimental success.

Panel design:

Flow cytometry can be applied to almost any type of study if a fluorescent probe/marker is available for it. However, it requires the correct combination of fluorophores and antibody conjugates titrated to match the expression level of the marker(s) of interest. Titration is a simple but important control for minimizing nonspecific binding and optimizing signal detection to ensuring reducibility.  Each panel must also include appropriate controls specific to each experiment. Controls are needed to exclude signals from unwanted cell population as well as to differentiate signals from dead, damaged or dying cells. While it may be possible to use up to 32 colors, more is not always better. With increasing number of fluorochromes there is some loss of sensitivity due to background and spillover.  It is therefore best to design panels with the minimum number of markers needed to address specific research questions. It is also important to optimize the instrument to capture weak signals but exclude background noise. Instrument manufacturers will provide performance test certification. However, quality control beyond machine performance is the best practice for producing highest quality data.

Data acquisition & analysis:

With simultaneous evaluation of several parameters, large volumes of data can be generated from a limited quantity of samples and fast. However, to be informative the data must capture relevant events and in sufficient numbers. This requires gating strategies that effectively exclude coincidental and background noise. The large and complex data files flow cytometry yields can be both challenging and time consuming to analyze. Making sense of the seemingly random colored dots requires expert analysis, standardized statistical analysis methods and accesses to very expensive statistical software.

Flow cytometry has evolved a great deal since its inception. It has become  a powerful and versatile research tool.  Reproducibility is the key to scientific discovery. Continued evaluation and optimization of flow cytometry best practices is key to generating publishable results.

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Fig. 1 Survival after challenge with INFV H1N1 A/Pert/261/2009 (Tamiflu-resistant strain). Inoculum 1xLD90=1.0E+05 PFU/mouse
Survival after challenge with INFV H1N1 A/Pert/261/2009 (Tamiflu-resistant strain) 1.0E+05 PFU/mouse
Survival and weight change in BALB/c mice challenged with INFV A/ Texas/36/91 (H1N1) and treated with antiviral Osletamivir Phosphate (Tamiflu)
Lung viral load and Survival (30 % weight loss cut-off) in BALB/c mice challenged with INFV H3N2 A/HK/1/68.

Alpha (UK) – B. 1.1.7 / 501Y.V1

amino acid mutations: del69–70 HV, del144 Y, N501Y, A570D, D614G, P681H, T761I, S982A, D1118H

Beta (South Africa) – B.1.351

amino acid mutations: K417N, E484K, N501Y, D614G, A701V

Gamma (Brazil) – P.1

amino acid mutations: L18F, T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, D614G, H655Y, T1027I

Epsilon (Ca, USA) B.1.427

amino acid mutations: L452R, D614G

SARS-CoV-2 Parental Strain Wild Type (Wuhan)
SARS-CoV-2 D614G Variant

amino acid mutations: D614G

Epsilon (Ca, USA) B.1.429

amino acid mutations: S13I, W152C, L452R, D614G

SARS-CoV-2 Delta Variant

amino acid mutations: L452R, E484Q