High-throughput flow cytometry data normalization for clinical trials

Abstract

Flow cytometry datasets from clinical trials generate very large datasets and are usually highly standardized, focusing on endpoints that are well defined apriori. Staining variability of individual makers is not uncommon and complicates manual gating, requiring the analyst to adapt gates for each sample, which is unwieldy for large datasets. It can lead to unreliable measurements, especially if a template-gating approach is used without further correction to the gates. In this article, a computational framework is presented for normalizing the fluorescence intensity of multiple markers in specific cell populations across samples that is suitable for high-throughput processing of large clinical trial datasets. Previous approaches to normalization have been global and applied to all cells or data with debris removed. They provided no mechanism to handle specific cell subsets. This approach integrates tightly with the gating process so that normalization is performed during gating and is local to the specific cell subsets exhibiting variability. This improves peak alignment and the performance of the algorithm. The performance of this algorithm is demonstrated on two clinical trial datasets from the HIV Vaccine Trials Network (HVTN) and the Immune Tolerance Network (ITN). In the ITN data set we show that local normalization combined with template gating can account for sample-to-sample variability as effectively as manual gating. In the HVTN dataset, it is shown that local normalization mitigates false-positive vaccine response calls in an intracellular cytokine staining assay. In both datasets, local normalization performs better than global normalization. The normalization framework allows the use of template gates even in the presence of sample-to-sample staining variability, mitigates the subjectivity and bias of manual gating, and decreases the time necessary to analyze large datasets.

Keywords: BioConductor; bias; immunology; staining variability; template gating.

Figure 1

Gating scheme for an FCS file from the ICS data set. In this example, cells are stimulated with POL-1-PTEG peptide pool. Problematic gating of the Perforin channel for both CD4 and CD8 subsets is clearly visible.

Figure 2

Variability in the staining intensity of channels in the ICS and B-cell phenotyping data sets. In the ICS data, the Perforin channel is compared to CD57, before and after normalization. A random sampling of 12 FCS files showing the variability in staining on the channel A) before and B) after normalization, contrasted against the staining of which does not exhibit staining variability. The density is shown for the CD4+ cell subpopulation, right before the Perforin+ gate is applied. In the B-cell phenotyping data, the density of different channels in the C) locally normalized, and the D) raw data is shown. Variability across subjects is evident for the Red-A, Violet-C, Violet-A, and Blue-F channels, and their variability is reduced across subjects after normalization.

Figure 3

Percent of perforin positive CD4+ and CD8+ T-cells before (A,B) and after (C,D) normalization in a sample FCS file (505333.fcs) and the target reference file (504789.fcs). The reference is unchanged after normalization.

Figure 4

Comparison of global and local normalization approaches for the ICS data and B-cell phenotyping data. A) The proportion of Perforin positive CD4 and CD8-positive T-cells before and after local and global normalization. Local normalization leads to accurate gating of the CD4 and CD8 Perforin positive T-cells, while global normalization inflates the number of CD4 and CD8 Perforin positive T-cells. Variability is expected to decrease within stimulation groups and T-cell subsets for this data set upon accurate gating. B) Paired differences between manually gated and normalized, globally normalized, or template gated cell populations in the B-cell phenotyping data. Dotted gray line at zero is a guide to the eye. Differences closer to zero are better because they imply less bias. Narrower boxes imply greater precision.

Figure 5

Comparative gating of three potential false positive responders in the IFN-γ producing CD4+ T-cell subset stimulated with POL-1-PTEG. Gating of the A) normalized data and B) raw data with associated proportions of Perforin positive cells. Red dots indicate IFNg postive T-cells.