Figure 3. High-throughput targeted analysis of HLA peptidomic data by SWATH-MS.
(A) SWATH-MS coordinates of two HLA class I allele-specific assay libraries (HLA-A02 and -B07) were combined to extract SWATH data generated from the HLA peptidome of JYEBV+ cells. Sixteen summed transition groups are shown here for simplicity. (B, C) Visualization of two extracted SWATH transition groups corresponding to the self-HLA-A02 peptide, KILPTLEAV and the non-self HLA-A02 EBV peptide, YVLDHLIVV. (D) Reproducibility of intensity measurements for technical replicates. (E) Dynamic range of transition group intensities following targeted analysis of SWATH-MS HLA peptidomic data generated from various cell types expressing different combinations of HLA alleles. SWATH/DIA data were acquired in four independent international laboratories.
DOI: http://dx.doi.org/10.7554/eLife.07661.020
Figure 3—source data 1. OpenSWATH analysis.
elife07661s007.xlsx (7.3MB, xlsx)
DOI: 10.7554/eLife.07661.021
Figure 3—figure supplement 1. OpenSWATH analysis of HLA peptidomic data.
(A) HLA class I peptides isolated from JY cells were acquired in SWATH/DIA mode using windows of 10 Da (blue) or 25 Da (red) width each. The graph shows the proportion of peptides that were confidently extracted (FDR < 0.01) using OpenSWATH from a merged (A02+B07) or unmerged (A02 or B07) HLA allele-specific assay library. (B) pyProphet statistical analysis from a JY HLA class I peptide extract. The histogram plots show the distribution of decoy and target transition groups according to their discriminant score (dscore) determined by the pyProphet software. (C) HLA class I peptides were isolated form various cell types and analyzed by SWATH-MS using windows of 25 Da width each. The histogram shows the number of HLA peptides that were confidently extracted (FDR < 0.01) using OpenSWATH from different HLA allele-specific assay library.
Figure 3—figure supplement 2. OpenSWATH analysis and PyProphet statistics of HLA peptidomic data acquired at ETH Zurich, Switzerland.
HLA-A02 and HLA-B07 peptides were isolated from JY cells. Graphs showing ROC, d_score performance and d-score distributions were generated automatically using the iPortal workflow.
Figure 3—figure supplement 3. OpenSWATH analysis and PyProphet statistics of HLA peptidomic data acquired at ETH Zurich, Switzerland.
HLA-A03, -A26, -B51 and -B57 peptides were isolated from PBMCs. Graphs showing ROC, d_score performance and d-score distributions were generated automatically using the iPortal workflow.
Figure 3—figure supplement 4. OpenSWATH analysis and PyProphet statistics of HLA peptidomic data acquired at University of Oxford, UK.
HLA-A03, -B07 and -B35 peptides were isolated from Jurkat cells. Graphs showing ROC, d_score performance and d-score distributions were generated automatically using the iPortal workflow.
Figure 3—figure supplement 5. OpenSWATH analysis and PyProphet statistics of HLA peptidomic data acquired at Monash University, Australia.
HLA-B27 peptides were isolated from C1R cells. Graphs showing ROC, d_score performance and d-score distributions were generated automatically using the iPortal workflow.
Figure 3—figure supplement 6. OpenSWATH analysis and PyProphet statistics of HLA peptidomic data acquired at Centro National de Biotechnologia, Madrid, Spain.
HLA-B40 peptides were isolated from C1R cells. Graphs showing ROC, d_score performance and d-score distributions were generated automatically using the iPortal workflow.
Figure 3—figure supplement 7. Visualization and analysis of SWATH-MS HLA peptidomic data in Skyline.
Skyline is a free open-source software for targeted data analysis of various types of peptidomic data. It specifically facilitates manual and automated analysis of SWATH data and other data-independently acquired MS data using assay libraries. The software itself can be downloaded from the website: http://skyline.maccosslab.org. (A) Skyline-daily or Skyline v2.6 was used to import HLA peptide SWATH assay libraries, and to import, extract, and visualize SWATH HLA peptidomic data. (B) The ‘Advanced Peak Picking Models’ was used to work with decoy transition groups and for large-scale, automated SWATH data analysis. For more information, see Schubert et al. (2015b).