プロテオミクスとバイオインフォマティクスのジャーナル

プロテオミクスとバイオインフォマティクスのジャーナル
オープンアクセス

ISSN: 0974-276X

概要

LC/MS Based Monitoring of Endogenous Decay Markers for Quality Assessment of Serum Specimens

Jorg Oliver Thumfart, Nada Abidi, Sonani Mindt, Victor Costina, Ralf Hofheinz, Frank Klawonn, Michael Neumaier and Peter Findeisen

Introduction: Preanalytical variations have major impact on most biological assays. Specifically MS-based multiparametric proteomics analyses of blood specimens are seriously affected by limited protein stability due to high intrinsic proteolytic activity of serum and plasma. However, the direct analysis of sample quality (DASQ) for serum specimens is not readily available. Here we propose the mass spectrometry based monitoring of peptide patterns that are ex vivo changing in a time dependent manner to alleviate these constrains.

Materials and methods: Serum specimens from healthy controls (n=3) and patients with colorectal cancer were analyzed for a set of endogenous peptides (n=62). The respective proteolytic fragments were monitored with LC/MS at different preanalytical points in time ranging from 1 h to 48 h after blood withdrawal. An algorithm was constructed with a training set of serum specimens from colorectal cancer patients (n=30). An independent test set of patients (n=20) was used for further validation.

Results: The coefficient of determination (R2) for the linear regression of true and estimated points in time was 0.89. However, the classification accuracy for specimens with a preanalytical time span below 8 h was higher when compared to older specimens (>8 h).

Conclusion: Endogenous peptides are processed in blood specimens in a time dependent manner. This ‘proteomic degradation clock’ can be used to estimate the preanalytical quality of serum specimens. This is specifically relevant prior to in depth proteomic profiling approaches or other laborious analyses in research or diagnostic applications. Accordingly, specimens with low quality can be identified and subsequently be excluded from further analyses to avoid any unwanted preanalytical bias.

 

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