Optimizing peptide matrices for identifying T-cell antigens

Abstract

Mapping T-cell epitopes for a pathogen or vaccine requires a complex method for screening hundreds to thousands of peptides with a limited amount of donor sample. We describe an optimized deconvolution process by which peptides are pooled in a matrix format to minimize the number of tests required to identify peptide epitopes. Four peptide pool matrices were constructed to deconvolute the HIV-specific T-cell response in three HIV-infected individuals. ELISpot assays were used to map peptide antigens. Many HIV peptides were mapped in all three individuals. However, there were several challenges and limitations associated with the deconvolution process. Peptides that induced low-frequency responses or were masked by peptide competition within a given pool were not identified, because they did not meet the threshold criteria for a positive response. Also, amino acid sequence variation limited the ability of this method to map autologous HIV peptides. Alternative analysis strategies and revisions to the original matrix optimizations are presented that address ways to increase peptide identification. This optimized deconvolution method allows for efficient mapping of T-cell peptide epitopes. It is rapid, powerful, efficient, and unrestricted by HLA type.

Figure 1

Setting an appropriate threshold involves balancing the number of tests with sensitivity. For three donors, the top panel of graphs shows the number of peptides to be tested in the second round vs. threshold values. The number of peptides requiring testing decreases exponentially as threshold increases. The corresponding graphs below indicate the percent of positive peptides identified vs. threshold. As threshold increases, positive peptides are missed.

Figure 2

Reproducibility of peptide identification. Mapping experiments were performed multiple times using PBMC from donor 128 and 113. The total number of times a given positive peptide was tested in matrix format depended on how many matrices contained the peptide and how many times the matrices were tested. For example, the same peptide sequence contained in two separate matrices, each tested three times, will have been tested six times (three times for each of the two matrices). On the X-axis is plotted the number of times a given positive peptide was identified divided by the total number of times it was tested (# matrices + # replicates). The Y-axis shows the frequency of the peptide-specific T cell response when the peptide was tested individually.