Abstract
Mass spectrometry-based spectral count has been a common choice of label-free proteome quantification due to the simplicity for the sample preparation and data generation. The discriminatory nature of spectral count in the MS data-dependent acquisition, however, inherently introduces the spectral count variation for low-abundance proteins in multiplicative LC-MS/MS analysis, which hampers sensitive proteome quantification. As many low-abundance proteins play important roles in cellular processes, deducing low-abundance proteins in a quantitatively reliable manner greatly expands the depth of biological insights. Here, we implemented the Moment Adjusted Imputation error model in the spectral count refinement as a post PLGEM-STN for improving sensitivity for quantitation of low-abundance proteins by reducing spectral count variability. The statistical framework, automated spectral count refinement by integrating the two statistical tools, was tested with LC-MS/MS datasets of MDA-MB468 breast cancer cells grown under normal and glucose deprivation conditions. We identified about 30% more quantifiable proteins that were found to be low-abundance proteins, which were initially filtered out by the PLGEM-STN analysis. This newly developed statistical framework provides a reliable abundance measurement of low-abundance proteins in the spectral count-based label-free proteome quantification and enabled us to detect low-abundance proteins that could be functionally important in cellular processes.
Highlights
(SpI)[12] and QSpec[13] methods
This combined statistical approach was validated by MDA-MB468 breast cancer (BC) cells grown under high glucose (HG) and glucose deprivation (GD) conditions
After in-gel digestion of proteins, LC-MS/MS analysis of extracted peptides followed by protein sequence database searching, we identified a total of 2,525 proteins (at least two unique peptides with false discovery rate (FDR)≤0.1%) (Supplementary Table S1)
Summary
(SpI)[12] and QSpec[13] methods. The statistical tools designed for gene expression microarray have been used for the analysis of label-free MS proteomics[14], the significance analysis of microarrays (SAM)[15] and the normalized spectral abundance factor coupled with Power Law Global Error Model-Signal To Noise (PLGEM-STN) statistics[16] are two examples. We developed the low-abundance protein-centric refinement to quantify them for better sensitivity by implementing the Moment Adjusted Imputation (MAI) error model. The MAI model in conjunction with PLGEM-STN tool reduces the variation of SC between replicate analyses, enhancing the validity of p-values for the low-abundance proteins. This combined statistical approach was validated by MDA-MB468 breast cancer (BC) cells grown under high glucose (HG) and glucose deprivation (GD) conditions. The SC refinement via MAI method results in additional identification of DEPs with better sensitivity and is generally applicable for the in-depth proteome analysis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
