The turnover rates of proteins are an important proteome parameter that is specific to organisms, tissues, and cellular states, and show promise as a new source of disease markers. Measurement of protein turnover rates may be achieved in intact animal models by stable isotope labeling using various isotope labels including heavy lysine, valine, or water, followed by mass spectrometry analysis of multiple time points. To enable large-scale data analysis, there is an unmet need for software tools which can automate the extraction of relative isotope abundance information from mass spectra and which is broadly applicable to multiple labeling strategies.
We describe RIAna.py (Relative Isotope Abundance Analyzer), an open-source software tool in Python designed to facilitate analysis for multiple isotope labels under a unified workflow. RIAna accepts as inputs mzid or tab-delimited files from database search results, and the corresponding mzML raw data through the Pymzml package. RIAna extracts the intensities of centroided MS1 peaks for each isotopomer for each qualifying peptide within user-defined retention time and mass precision ranges. It then returns the integrated areas-under-curve of the isotopomer chromatograms using the trapezoid method in SciPy. We applied RIAna toward a large dataset on inbred C57BL/6 mice labeled with H2O or C6-lysine (see companion poster by RB and DH). High-resolution mass spectrometry data were acquired on an Orbitrap instrument then searched against UniProt. Peptide mass isotopomers were then integrated over 0.5 min retention time windows at isotopomers 0,1,2,3,4,5 for H2O labeling, and 0,6,12 for C6-lysine labeling. Kinetic curve-fitting was performed using a custom R script with quasi-Newtonian optimization. Our analysis shows that RIAna provides isotopomer intensities and kinetic rate constants comparable to manual analysis but in a fraction of time required, and is applicable to different labeling methods. RIAna is freely available under MIT license on our GitHub repository.