Abstract
Analysis of primary animal and human tissues is key in biological and biomedical research. Comparative proteomics analysis of primary biological material would benefit from uncomplicated experimental work flows capable of evaluating an unlimited number of samples. In this report we describe the application of label-free proteomics to the quantitative analysis of five mouse core proteomes. We developed a computer program and normalization procedures that allow exploitation of the quantitative data inherent in LC-MS/MS experiments for relative and absolute quantification of proteins in complex mixtures. Important features of this approach include (i) its ability to compare an unlimited number of samples, (ii) its applicability to primary tissues and cultured cells, (iii) its straightforward work flow without chemical reaction steps, and (iv) its usefulness not only for relative quantification but also for estimation of absolute protein abundance. We applied this approach to quantitatively characterize the most abundant proteins in murine brain, heart, kidney, liver, and lung. We matched 8,800 MS/MS peptide spectra to 1,500 proteins and generated 44,000 independent data points to profile the approximately 1,000 most abundant proteins in mouse tissues. This dataset provides a quantitative profile of the fundamental proteome of a mouse, identifies the major similarities and differences between organ-specific proteomes, and serves as a paradigm of how label-free quantitative MS can be used to characterize the phenotype of mammalian primary tissues at the molecular level.
Highlights
Analysis of primary animal and human tissues is key in biological and biomedical research
We report on the creation of a computer program and on the development of standardization procedures for downstream data processing that can be used to automate the quantitative analysis of label-free LC-MS/MS data such that this approach can be used for large scale comparative analysis of any protein mixture, including those in mammalian primary tissues
Numerous independent studies have shown that label-free approaches that use the inherent quantitative information in LC-MS/MS data are suitable for quantitative proteomics [17,18,19, 25]
Summary
Analysis of primary animal and human tissues is key in biological and biomedical research. We developed a computer program and normalization procedures that allow exploitation of the quantitative data inherent in LC-MS/MS experiments for relative and absolute quantification of proteins in complex mixtures Important features of this approach include (i) its ability to compare an unlimited number of samples, (ii) its applicability to primary tissues and cultured cells, (iii) its straightforward work flow without chemical reaction steps, and (iv) its usefulness for relative quantification and for estimation of absolute protein abundance. Powerful in other contexts, strategies that rely on chemical labeling with compounds enriched in isotopes (e.g. isobaric tags for relative and absolute quantification (iTRAQ) and ICAT approaches [12, 13]), heavy water [14], or fluorescence labels (e.g. the twodimensional DIGE approach [15]) are not ideal for the analysis of gene-targeted mice (or for clinical studies)
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