An optimised method to isotopically label pure synthetic peptides 'in-house' for absolute quantification in bottom-up proteomics.

Rationale: Heavy-labelled internal standards increasingly represent the gold standard for absolute quantitation in mass spectrometry (MS)-based bottom-up proteomics. The biggest drawbacks of using these standards are that they have high costs and lengthy lead times.

Methods: We describe an efficient, low-cost optimised method to enable 'in-house' heavy labelling of synthetic tryptic peptides for absolute quantification using tandem LC-MS/MS mass spectrometry.

Use of Nonhuman Sera as a Highly Cost-Effective Internal Standard for Quantitation of Multiple Human Proteins Using Species-Specific Tryptic Peptides: Applicability in Clinical LC-MS Analyses.

Quantitation of proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is complex, with a multiplicity of options ranging from label-free techniques to chemically and metabolically labeling proteins. Increasingly, for clinically relevant analyses, stable isotope-labeled (SIL) internal standards (ISs) represent the "gold standard" for quantitation due to their similar physiochemical properties to the analyte, wide availability, and ability to multiplex to several peptides. However, the purchase of SIL-ISs is a resource-intensive step in terms of cost and time, particularly for screening putative biomarker panels of hundreds of proteins.

The Edge Effect in High-Throughput Proteomics: A Cautionary Tale.

In order for mass spectrometry to continue to grow as a platform for high-throughput clinical and translational research, careful consideration must be given to quality control by ensuring that the assay performs reproducibly and accurately and precisely. In particular, the throughput required for large cohort clinical validation in biomarker discovery and diagnostic screening has driven the growth of multiplexed targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) assays paired with sample preparation and analysis in multiwell plates. However, large scale MS-based proteomics studies are often plagued by batch effects: sources of technical variation in the data, which can arise from a diverse array of sources such as sample preparation batches, different reagent lots, or indeed MS signal drift.