Relationship between Sample Loading Amount and Peptide Identification and Its Effects on Quantitative Proteomics

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• 作者：Kehui Liu^† ; ; Jiyang Zhang^† ; ^‡ ; ; Jinglan Wang^† ; ; Liyan Zhao^† ; ; Xu Peng^† ; ; Wei Jia^† ; ; Wanta
• 刊名：Analytical Chemistry
• 出版年：2009
• 出版时间：February 15, 2009
• 年：2009
• 卷：81
• 期：4
• 页码：1307-1314
• 全文大小：469K
• 年卷期：v.81,no.4(February 15, 2009)
• ISSN：1520-6882

文摘

The relationship between sample loading amount and peptide identification is crucial for the optimization of proteomics experiments, but few studies have addressed this matter. Herein, we present a systematic study using a replicate run strategy to probe the inherent influence of both peptide physicochemical properties and matrix effects on the relationship between peptide identification and sample loading amounts, as well as its applications in protein quantification. Ten replicate runs for a series of laddered loading amounts (ranging between 0.0110 μg) of total digested proteins from Saccharomyces cerevisiae were performed with nanoscale liquid chromatography coupled with linear ion trap/Fourier transform ion cyclotron resonance (nanoLC-LTQ-FT) to obtain a nearly saturated peptide identification. This permitted us to differentiate the linear correlativity of peptide identification by the commonly used peptide quantitative index, the area of constructed ion chromatograms (XIC) (SA, from MS and tandem MS data) in the given experiments. The absolute loading amount of a given complex sample affected the final qualitative identification result; thus, optimization of the sample loading amount before every proteomics study was essential. Peptide physicochemical properties had little effect on the linear correlativity between SA-based peptide quantification and loading amount. The matrix effects, rather than the static physicochemical properties of individual peptides, affect peptide measurability. We also quantified the target protein by selecting peptides with good parallel linear correlativity based upon SA as signature peptides and revised the data by multiplying by the reciprocal of the slope coefficient. We found that this optimized the linear protein abundance relativity at every amount range and thus extended the linear dynamic range of label-free quantification. This empirical rule for linear peptide selection (ERLPS) can be adopted to correct comparison results in proteolytic peptide-based quantitative proteomics, such as accurate mass tag (AMT) and targeted quantitative proteomics, as well as in tag-labeled comparative proteomics.