on 06-13-201801:27 AM - edited on 10-15-202106:09 AM by Closed Account
David M. Horn,1 Tara L. Schroeder,1 Ioanna Ntai,1 Richard D. Leduc,2 Ryan T. Fellers, 2 Joseph B. Greer, 2 and Neil L. Kelleher 2 ASMS 2018 Purpose: Here we demonstrate the use of the ProSightPD™ nodes for the Thermo Scientific™ Proteome Discoverer™ software framework to analyze complex top down proteomics data. We also demonstrate how sliding window deconvolution results from Thermo Scientific™ Biopharma Finder™ can be used to determine differentially expressed proteoforms.
Methods: For this work, Study 1 data from Ntai et al1 were used for the analysis. Both the “High/High” and “Low/High” GELFrEE fractions were analyzed using ProSightPD Base and ProSightPD High Mass, respectively. The High/High data were analyzed using a five-step search to maximize the number of identifications, while the Low/High data were analyzed using a three-step search. PrSMs were filtered by a minimum C score of 3. Sliding window deconvolution in the Biopharma Finder software was used to process the High/High data using Xtract deconvolution. The quantitative results were integrated with the filtered human PrSMs from ProSightPD via a Perl script. Normalization and p-value calculations were produced using InfernoRDN and Microsoft® Excel.
Results: The High/High runs identified 2,374 proteoforms total with 735 producing a C-score of 3 or better. The Low/High runs identified 254 proteoforms with 38 proteoforms having a C-score of 3 or better. Many of the proteoforms that were identified had masses greater than 40 kDa. Integration of the sliding window deconvolution results with the High/High data produced over 400 proteoforms that differ significantly between the WHIM2 and WHIM16 samples.
1 Thermo Fisher Scientific, San Jose, CA
2 Northwestern University, Evanston, IL