Bioinformatics Meets Proteomics — Bridging the Gap Between Mass Spectrometry Data Analysis and Cell Biology

Abstract

Proteomics research programs typically comprise the identification of protein content of any given cell, their isoforms, splice variants, post-translational modifications, interacting partners and higher-order complexes under different conditions. These studies present significant analytical challenges owing to the high proteome complexity and the low abundance of the corresponding proteins, which often requires highly sensitive and resolving techniques. Mass spectrometry plays an important role in proteomics and has become an indispensable tool for molecular and cellular biology. However, the analysis of mass spectrometry data can be a daunting task in view of the complexity of the information to decipher, the accuracy and dynamic range of quantitative analysis, the availability of appropriate bioinformatics software and the overwhelming size of data files. The past ten years have witnessed significant technological advances in mass spectrometry-based proteomics and synergy with bioinformatics is vital to fulfill the expectations of biological discovery programs. We present here the technological capabilities of mass spectrometry and bioinformatics for mining the cellular proteome in the context of discovery programs aimed at trace-level protein identification and expression from microgram amounts of protein extracts from human tissues.

Advances in proteomics data analysis and display using an accurate mass and time tag approach

“…Several other laboratories have recently begun to adopt and/or adapt the AMT tag approach (Geromanos, et al, 2004, Johnson, et al, 2004, Kearney & Thibault, 2003, Radulovic, et al, 2004. In addition, Waters Corporation has recently implemented a process referred to as Ion Mapping that uses the accurate mass from a TOF mass spectrometer in conjunction with an accurate elution time to uniquely identity peaks, or features, within mass spectra.…”

A label‐free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen

“…Comparison to SILAC: spectral counting and spectral TIC were then compared with SILAC using the same experimental conditions whereby two pools of HeLa cells were grown with either light ( 12 C 6 ) or heavy ( 13 C 6 ) lysine and 2008,8,[994][995][996][997][998][999] Cell Biology 997 arginine amino acids. Heavy cell lysate was passed over pTyr column while light lysate was passed over the Tyr column.…”

“…Label-free approaches to quantitative proteomics have gained prominence in recent years since no additional chemistry or sample preparation steps are required. These include biostatistical profiling [8,9] in clinical proteomics whereby peptide ions that show intensity changes over many samples are recorded and represent potential protein biomarkers, methods for comparing the ratios of integrated peak areas/intensities of phosphorylated to nonphosphorylated peptide ions between two samples [10,11], a strategy to assess the stoichiometry of phosphorylation sites [12], and the popular method of quantifying protein levels from different samples based on the number of MS/MS spectra that identify the protein of interest (spectral counting) [13][14][15][16][17].We demonstrate a method to survey relative protein quantity between samples that is an extension to the spectral counting technique whereby the average of the TIC for all of the MS/MS spectra that identify a protein is used as a quantitative measure. Each spectral count gets a unique abundance value rather than equivalent values of "1" as with spectral counting.…”

“…The spectral TIC ratios for these proteins were all approximately 1:1, suggesting nonspecific binding. The difference in the ratios is likely due to a dynamic range limit of the spectral counting method similar to the situation for the BSA digest example above.Comparison to SILAC: spectral counting and spectral TIC were then compared with SILAC using the same experimental conditions whereby two pools of HeLa cells were grown with either light ( 12 C 6 ) or heavy ( 13 C 6 ) lysine and 2008,8,[994][995][996][997][998][999] Cell Biology 997 arginine amino acids. Heavy cell lysate was passed over pTyr column while light lysate was passed over the Tyr column.…”

“…These include biostatistical profiling [8,9] in clinical proteomics whereby peptide ions that show intensity changes over many samples are recorded and represent potential protein biomarkers, methods for comparing the ratios of integrated peak areas/intensities of phosphorylated to nonphosphorylated peptide ions between two samples [10,11], a strategy to assess the stoichiometry of phosphorylation sites [12], and the popular method of quantifying protein levels from different samples based on the number of MS/MS spectra that identify the protein of interest (spectral counting) [13][14][15][16][17].…”