Article

SILAC Surrogates: Rescue of Quantitative Information for Orphan Analytes in Spike-In SILAC Experiments

Analytical Chemistry (Impact Factor: 5.83). 10/2013; 85(22). DOI: 10.1021/ac4021352
Source: PubMed

ABSTRACT Super-SILAC enables the sensitive and accurate analysis of complex biological tissue and tumor samples by comparison of light peptides observed in biological samples to heavy peptides from SILAC cell culture spike-ins. However, despite the use of multiple cell lines for Super-SILAC spike-in standards, the full protein and peptide profiles of biological samples are not completely represented in these internal standards, leading to orphan analytes for which sample to standard ratios cannot be calculated. This problem is exacerbated in some biological systems, such as muscle tissue, which lack adequate cell culture lines to reflect their complex and idiosyncratic protein profiles, resulting in up to 40% of peptide analytes without heavy cognates. Furthermore, these unquantified orphan analytes may be among the most biologically interesting and significant species, since their presence is not common to cell lines cultured in vitro. Here, we report on the development of a surrogate analysis strategy to interpolate quantitative relationships between peptide species, observed across multiple biological samples, which lack representation within the spike-in standards. The precision and accuracy of this method was assessed by replicate experiments in which surrogate-derived ratios from defined mixtures of spike-in SILAC standard and tissue lysate were compared against traditional SILAC ratios for species where both light and heavy peptide cognates were observed. We demonstrate the robustness of our SILAC Surrogates strategy across a variety of murine tissues, including liver, spleen, brain and muscle. Our approach increases the quantitative coverage and precision within a biological sample by rescuing previously intractable peptide species and applying additional evidence to improve the precision of existing quantifications.

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