PubMed Health⌕ Search

PubMed · 15749284

Stable isotope methods for high-precision proteomics.

Abstract

Stable isotope tagging methods provide a useful means of determining the relative expression level of individual proteins between samples in a mass spectrometer with high precision (coefficients of variation less than 10%). Because two or more samples tagged with different numbers of stable isotopes can be mixed before any processing steps, sample-to-sample recovery differences are eliminated. Mass spectrometry also allows post-translational modifications, splice variations and mutations (often unnoticed in immunoassays) to be detected and identified, increasing the clinical relevance of the assay and avoiding the issues of non-specific binding and cross-reactivity observed in immunoassays. Several stable isotope tagging methods are available for use in proteomics research. We discuss the advantages and disadvantages of each technique with respect to biomarker discovery, target validation, efficacy and toxicology screening and clinical diagnostic applications.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Luke V Schneider, Michael P Hall. 2005-03-01. Stable isotope methods for high-precision proteomics.. https://doi.org/10.1016/s1359-6446(05)03381-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Comprehensive identification of carboxylic acids by using bromine isotope-based chemical isotope labelling and structure-guided molecular network.

Carboxylic acids (CAs) are important contributors to the flavor quality of sauce-flavor Chinese Baijiu, yet their comprehensive analysis remains challenging due to poor ionization efficiency, weak chromatographic retention, and limited annotation capability. Herein, we developed a workflow for the high-coverage discovery and annotation of CAs in Baijiu by coupling chemical isotope labeling-liquid chromatography-mass spectrometry with a structure-guided molecular network strategy (SGMNS). A bromine-containing derivatization reagent, 1-(3-aminopropyl)-3-bromoquinolin-1-ium bromide (APBQ), was designed and synthesized to exploit the natural isotope distribution of bromine and characteristic MS/MS fragmentation behavior. Following APBQ derivatization, the target CAs showed superior chromatographic retention and favorable analytical performance. Based on isotopic peak pairing in MS1 and diagnostic fragment validation in MS2, 372 potential CA derivatives were discovered from pooled Baijiu samples and 355 of them were validated by diagnostic fragments in MS2 spectra. To address the scarcity of derivatized spectral libraries, SGMNS was employed for annotation using a background network constructed from APBQ-labeled candidates derived from the Expanded Chinese Baijiu Compound Database. The developed method was further applied to profile Baijiu samples, revealing pronounced differences in CA composition across the seven fermentation rounds. Notably, rounds 3 to 5 exhibited the largest numbers of differential CAs. This study provided an effective analytical strategy for large-scale CA profiling, offering new insight into the chemical basis of flavor formation during multi-round fermentation of sauce-flavor Baijiu.

Isotope Labeling↗

Combining Data Independent Acquisition With Spike-In SILAC (DIA-SiS) Improves Proteome Coverage and Quantification.

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

Isotope Labeling↗

Quantitative proteomics by stable isotope labeling and mass spectrometry.

The goal of quantitative proteomics is to systematically study static state or perturbation-induced changes in protein profile. Most of the recently developed mass spectrometry (MS)-based quantitative proteomic methods employ stable isotope labeling to introduce signature mass tags to peptides/proteins that can be used by a mass spectrometer to quantify each analyte and to determine the sample from which it originates. In this chapter, we discuss several methods for the introduction of mass tags to proteins and peptides for MS-based quantitative proteomic analysis, including isotope-coded affinity tags, stable isotope labeling by amino acids in cell culture, global internal standard technology, and mass-coded abundance tagging.

Isotope Labeling↗