Fig. 1: Immunopeptidomics workflow using Thunder-DDA-PASEF. | Nature Communications

Fig. 1: Immunopeptidomics workflow using Thunder-DDA-PASEF.

From: Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model

Fig. 1

a Sample preparation: 100 or 500 million cells of diverse cell lines were harvested, then lysed by sonication in 1% CHAPS in PBS buffer (m/v). Alternatively, 4 mL of unfractionated plasma were processed. b HLA-ligand peptide enrichment: was performed by immunoaffinity using the W6/32 anti-HLA-A, B, C antibody coupled to CNBr-activated agarose beads; after overnight incubation and several washes, peptides were eluted with 0.2% trifluoro-acetic acid (v/v), ultrafiltered on molecular weight cutoff filters (MWCO, 10 kDa cutoff) and desalted in HLB plates (Waters Corp.). c NanoLC-MS: analysis was performed using a nanoElute coupled to timsTOF-Pro-2 in DDA-PASEF18 with different parameters to optimize the MS acquisition. d Data analysis: Database search was performed in PEAKS XPro using unspecific cleavage. After training a MS2PIP28 timsTOF fragmentation prediction model, peptide identification was rescored using MS2Rescore (MS2R, v3.0.0b4)16,29. Data analysis was performed in R and predicted MHC-binding affinity was evaluated using NetMHCpan-4.130 and GibbsCluster-2.064 through MhcVizPipe (v0.7.9)31.

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