
pmid: 21328538
AbstractIn analytical sciences, there is a general need for quality control to assess whether a product or a process meets defined requirements. Especially in proteomics, which implies analysis of ten thousands of analytes within a complex mixture, quality control to validate LC‐MS performance and method setup is inevitable to achieve day‐to‐day‐, inter‐system‐, as well as inter‐user reproducibility. Thus, results deriving from LC‐MS analyses can be benchmarked and the need for system maintenance can be revealed. In particular with the advent of label‐free quantification of peptides and proteins, which above all depends on highly stable and reproducible LC separations, HPLC performance has to be appropriately monitored throughout the entire analytical procedure to assure quality and validity of the obtained data. Oftentimes, proteolytic digests of standard proteins are used in this context; however, this approach implies some limitations, such as inadequate batch‐to‐batch reproducibility, limited (if any) dynamic range and compositional inflexibility. Here, we present an alternative strategy of nano‐LC‐MS/MS quality control based on a mixture of synthetic peptides covering the entire LC‐gradient as well as a dynamic range of more than two orders of magnitude. Thus, (i) reproducibility of LC separation, (ii) MS performance (including limit of detection, identification and quantification), as well as (iii) overall nano‐LC‐MS system performance and reproducibility can be routinely monitored even in highly complex samples.
Blood Platelets, Proteomics, Quality Control, Saccharomyces cerevisiae Proteins, Blood Proteins, Reference Standards, Mitochondrial Proteins, Isotopes, Tandem Mass Spectrometry, Data Interpretation, Statistical, Humans, Nanotechnology, Amino Acid Sequence, Peptides, Chromatography, Liquid
Blood Platelets, Proteomics, Quality Control, Saccharomyces cerevisiae Proteins, Blood Proteins, Reference Standards, Mitochondrial Proteins, Isotopes, Tandem Mass Spectrometry, Data Interpretation, Statistical, Humans, Nanotechnology, Amino Acid Sequence, Peptides, Chromatography, Liquid
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