Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium

Abstract

Standardization of immunophenotyping requires careful attention to reagents, sample handling, instrument setup, and data analysis, and is essential for successful cross-study and cross-center comparison of data. Experts developed five standardized, eight-color panels for identification of major immune cell subsets in peripheral blood. These were produced as pre-configured, lyophilized, reagents in 96-well plates. We present the results of a coordinated analysis of samples across nine laboratories using these panels with standardized operating procedures (SOPs). Manual gating was performed by each site and by a central site. Automated gating algorithms were developed and tested by the FlowCAP consortium. Centralized manual gating can reduce cross-center variability, and we sought to determine whether automated methods could streamline and standardize the analysis. Within-site variability was low in all experiments, but cross-site variability was lower when central analysis was performed in comparison with site-specific analysis. It was also lower for clearly defined cell subsets than those based on dim markers and for rare populations. Automated gating was able to match the performance of central manual analysis for all tested panels, exhibiting little to no bias and comparable variability. Standardized staining, data collection, and automated gating can increase power, reduce variability, and streamline analysis for immunophenotyping.

Figure 1. Individual and central manual analysis of B-cell, T-reg, T-cell subsets.

(A) CV’s between sites are shown for each subset from the lyophilized cell (Cyto-trol) experiments. Centralized gating decreases the coefficient of variability for nearly all cell populations (Memory IgD+ cells in the B-cell panel are an exception) across all staining panels. Site-specific gating strategies for the DC/Mono/NK panel were non-comparable (no CVs shown). (B) Comparison of inter-site CVs for cryopreserved and lyophilized cells. CVs for cryopreserved cells are generally larger than for lyophilized cells (with the exception of IgD+ cell populations in the B-cell panel). For the lyophilized cell protocol, 68 files were analyzed and for the cryopreserved cell protocol, 60 files were analyzed for each panel.

Figure 2. Example of inter- and intra-site variability from experiment 1 (lyophilized cells).

(A) Examples of T-cell panel gating from two sites. (2 files analyzed) (B) Two replicates of the T cell panel from one site. (2 files analyzed).

Figure 3. Center, biological and residual variability per population and gating method for the B-cell panel.

For most cell populations, the center and sample variability were comparable across automated and manual gating methods. The IgD marker is poorly resolved as evidenced by the higher variability in automated analysis. Y-axis is the standard deviation of the center, sample and residual components estimated from the random effects model. (n = 63 files).

Figure 4. Estimated cell proportions from each population and gating method in the B-cell panel.

Estimated proportions and 95% confidence intervals are shown for each sample, gating method, and cell population in the B-cell panel. There is little bias in automated gating compared to central manual gating, with the exception of small differences in automated gating for rare populations based on poorly resolved markers such as Memory IgD+. Data for other panels is shown in the Supplementary Material. (n = 63 files).

Figure 5. Power analysis comparing site-specific and central gating for the B-cell panel.

Assuming 80% power and a 5% significance level, the expected minimum detectable effect size is shown for each cell population as a function of increasing sample size. The change in sensitivity is shown for site-specific gating (dotted line), central gating in the absence (dashed line) of and in the presence (solid line) of center-to-center variability. Site-specific vs. standardized central gating has a larger impact on the sensitivity of the assay than center-to-center technical variability. Data for other panels is shown in the supplementary material.