Quantification of proteins using data‐independent analysis (MS^E) in simple andcomplex samples: A systematic evaluation

Abstract

A MS-based method for the quantification of proteins termed data-independent analysis (or MS(E)) has been introduced recently. Although this method has been applied to the analysis of various types of biological samples, a thorough evaluation to assess the performance of this approach has yet to be conducted. Presented here is the first systematic and comprehensive study investigating the MS(E) approach for quantitative analysis of low-, medium-, and high-complexity samples. We demonstrate that this method has a linear dynamic range spanning three orders of magnitude with a limit of quantification of 61 amol/uL in low-complexity samples and 488 amol/uL in high-complexity samples. In addition, comprehensive sequence coverage was obtained and accurate quantification achieved for expression ratios ranging from 1:1.5 to 1:6. However, underestimation of ratios was detected independent of sample type, consistent with other quantitative proteomic methods. The present study provides validation of the MS(E) approach for accurate quantitative proteomic analysis of biological samples while, at the same time, proving high sequence coverage of target proteins.

Dynamics of Hippocampal Protein Expression During Long-term Spatial Memory Formation

“…Raw data were also imported into Rosetta Elucidator System, version 3.3 (Rosetta Biosoftware, Seattle, WA). Elucidator was used for alignment of raw MS1 data in RT and m/z dimensions as described (54). Aligned features were extracted and quantitative measurements obtained by integration of three-dimensional volumes (time, m/z, intensity) of each feature as detected in the MS1 scans.…”

Label-Free Protein Quantification for Plant Golgi Protein Localization and Abundance

“…In an alternative approach, ion current recorded for a peptide ion is used as a measure of its abundance. The assumption is made that ion intensity is proportional to peptide amount in the sample analyzed, which holds true for nanoflow and microflow liquid chromatography (LC) systems (Levin et al, 2011;Christianson et al, 2013). Comparing peptide ion current between samples is, thus, widely used for relative quantification (Silva et al, 2005).…”

Technological advances for deciphering the complexity of psychiatric disorders: merging proteomics with cell biology

“…In addition quantitative capacity has been enhanced using label-free platforms such as liquid chromatography mass spectrometry in expression mode (LC-MS E ) (Levin et al, 2011), the advances of travelling wave-based ionmobility separation coupled with mass spectrometry (Bond et al, 2013), selected reaction monitoring (SRM) (Kuhn et al, 2004) and sequential window acquisition of all theoretical fragment-ion spectra (SWATH) (Hopfgartner et al, 2012), and labelling methods including isotope-coded affinity tags (ICAT) (Gygi et al, 1999), isotope tagging for relative and absolute quantitation (iTRAQ) (DeSouza et al, 2005), and stable isotope labelling by amino acids in cell culture (SILAC) (Ong et al, 2002). Multiplex immunoassay platforms have improved in accuracy and throughput via developments using dyecontaining microspheres combined with flow cytometric analysis for simultaneous identification and quantitation of analytes (Liu et al, 2005) as well as in the development of aptamer-based detection systems (Kraemer et al, 2011;Yoshida et al, 2012).…”