Biology Reference
In-Depth Information
Chapter 13
Global Quantitative Proteomics Using Spectral Counting:
An Inexpensive Experimental and Bioinformatics
Workflow for Deep Proteome Coverage
Tiago S. Balbuena, Diogo Ribeiro Demartini, and Jay J. Thelen
Abstract
As the field of proteomics shifts from qualitative identification of protein “subfractions” to quantitative
comparison of proteins from complex biological samples, it is apparent that the number of approaches for
quantitation can be daunting for the result-oriented biologist. There have been many recent reviews on
quantitative proteomic approaches, discussing the strengths and limitations of each. Unfortunately, there
are few detailed methodological descriptions of any one of these quantitative approaches. Here we present
a detailed bioinformatics workflow for one of the simplest, most pervasive quantitative approach—spectral
counting. The informatics and statistical workflow detailed here includes newly available freeware, such as
SePro and PatternLab which post-process data according to false discovery rate parameters, and statisti-
cally model the data to detect differences and trends.
Key words
Computational proteomics, GeLC, Label-free proteomics, MudPIT, Quantitative
proteomics
1
Introduction
Mass spectrometry-based quantitative techniques have been extensively
used throughout the proteomics community for several years [
1
,
2
].
In discovery-driven experiments, these techniques can be generally
distinguished on the basis of the presence or the absence of a labeling
procedure. Although significant improvements on key analytical steps
and on large-scale data analysis have been achieved, protein identifi-
cation and quantification using mass spectrometry is still a challeng-
ing endeavor, especially for complex biological samples [
3
].
In the past 5 years, label-free quantitation has gained popular-
ity, being the most published approach for quantitative proteomics
[
4
]. Labeling techniques, such as iTRAQ (isobaric tag [
5
]),
isotope-coded affinity tags (ICAT [
6
]), and stable isotope labeling
Tiago S. Balbuena and Diogo Ribeiro Demartini equally contributed to this work.
