Statistical methods for quantitative mass spectrometry proteomic experiments with labeling

Ann L Oberg,Douglas W Mahoney

doi:10.1186/1471-2105-13-s16-s7

Abstract

Mass Spectrometry utilizing labeling allows multiple specimens to be subjected to mass spectrometry simultaneously. As a result, between-experiment variability is reduced. Here we describe use of fundamental concepts of statistical experimental design in the labeling framework in order to minimize variability and avoid biases. We demonstrate how to export data in the format that is most efficient for statistical analysis. We demonstrate how to assess the need for normalization, perform normalization, and check whether it worked. We describe how to build a model explaining the observed values and test for differential protein abundance along with descriptive statistics and measures of reliability of the findings. Concepts are illustrated through the use of three case studies utilizing the iTRAQ 4-plex labeling protocol.

Highlights

In this manuscript we focus on statistical methods for quantitative mass spectrometry (MS) based proteomic experiments as they pertain to labeling protocols
If stable isotopes are used, the known mass shift resulting from extra neutrons together with known naturally occurring distributions of isotopes in the atmosphere are used during the relative quantification step
We have investigated the utility of accounting for the abundance-dependent data acquisition, and non-random missing data by incorporating a censoring mechanism into the normalization and differential abundance models [34]. isobaric tag for relative and absolute quantitation (iTRAQ)-like data with either peptide competition alone or peptide competition plus a machine threshold for inducing missing data were simulated with MS experiment effects ranging from 0.5 to 2.0 and study group differences of 0.5, 1.0, 1.5, 2.0 and 2.5, all on the log2 scale

Summary

Introduction

In this manuscript we focus on statistical methods for quantitative mass spectrometry (MS) based proteomic experiments as they pertain to labeling protocols. The advantage of the labeling protocol is that specimens can be distinguished in the resulting data by leveraging known properties of the labels. If stable isotopes are used, the known mass shift resulting from extra neutrons together with known naturally occurring distributions of isotopes in the atmosphere are used during the relative quantification step. In 16O/18O labeling, one specimen is mixed with “light” water containing oxygen in its natural isotopic state (mostly 16O) and a second specimen with “heavy” water containing mostly water molecules with the 18O isotope that has two extra neutrons. With stable isotope labeling by amino acids in cell culture (SILAC) cells may be grown in “light” or “heavy” medium [3,4] or mice may be fed chow containing carbon in either the natural ("light”) 12C state or the 13C ("heavy”)

Methods

Findings

Discussion

Conclusion