Deconvolution Software to Quantify TMTProC Data
Keywords: Proteomics, Software
Johnson, A., Stadlmeier, M., Wühr, M. Feb. 3, 2021. “TMTProC Module” GitHub Repository.
Multiplexed proteomics is a powerful tool to assay cell states in health and disease, but accurate quantification of relative protein changes is impaired by interference from co-isolated peptides. Interference can be reduced by using MS3-based quantification, but this reduces sensitivity and requires specialized instrumentation. An alternative approach is quantification by complementary ions, the balancer group-peptide conjugates, which allows accurate and precise multiplexed quantification at the MS2 level and is compatible with most proteomics instruments. However, complementary ions of the popular TMT-tag form inefficiently and multiplexing is limited to five channels. Here, we evaluate and optimize complementary ion quantification for the recently released TMTpro-tag, which increases complementary ion plexing capacity to eight channels (TMTproC). Furthermore, the beneficial fragmentation properties of TMTpro increase sensitivity for TMTproC, resulting in ∼65% more proteins quantified compared to TMTpro-MS3 and ∼18% more when compared to real-time-search TMTpro-MS3 (RTS-SPS-MS3). TMTproC quantification is more accurate than TMTpro-MS2 and even superior to RTS-SPS-MS3. We provide the software for quantifying TMTproC data as an executable that is compatible with the MaxQuant analysis pipeline. Thus, TMTproC advances multiplexed proteomics data quality and widens access to accurate multiplexed proteomics beyond laboratories with MS3-capable instrumentation. The deconvolution software used to analyze the TMTproC data sets has been packaged into a stand-alone version that is compatible with the MaxQuant analysis pipeline and made available on Github (https://github.com/wuhrlab/TMTproC). The only inputs required of users are a Thermo Scientific .raw file and the output of the MaxQuant search. Complementary peak assignment and extraction of signals to Fourier transform noise (S/N) are done through a Python script. The complementary ion deconvolution algorithm and other data processing are performed by an executable file that requires the use of a free version of Matlab Runtime. We have also made the source code for these Matlab scripts available in the same Github directory.