Advances and applications of stable isotope labeling-based methods for proteome relative quantitation

Advances of stable isotope technology in food safety analysis and nutrient metabolism research

Food quality, safety, and the regulatory metabolism of food nutrients in cells are primary factors in determining human health. However, residues of undesirable or hazardous compounds in food products and dysregulation in the nutrient metabolism inevitably occur occasionally. For years, chromatography-mass spectrometry technology has been recognized as an essential research tool in food analysis and nutrient metabolism research, and it is more accurate and robust when coupled with stable isotopes. In this study, we summarize the applications of stable isotope technology in the quantification of contaminant residues (pesticides, veterinary drugs, mycotoxins, polycyclic aromatic hydrocarbons, and other hazardous compounds) in foods and in the nutrients (glucose, lipids, amino acids and proteins) metabolism research. The aim of this review was to serve as a reference for providing effective analysis techniques for protecting food quality and human health, and to pave the way for the broader application of stable isotope technology.

Chemical isotope labeling for quantitative proteomics

Advancements in liquid chromatography and mass spectrometry over the last decades have led to a significant development in mass spectrometry-based proteome quantification approaches. An increasingly attractive strategy is multiplex isotope labeling, which significantly improves the accuracy, precision and throughput of quantitative proteomics in the data-dependent acquisition mode. Isotope labeling-based approaches can be classified into MS1-based and MS2-based quantification. In this review, we give an overview of approaches based on chemical isotope labeling and discuss their principles, benefits, and limitations with the goal to give insights into fundamental questions and provide a useful reference for choosing a method for quantitative proteomics. As a perspective, we discuss the current possibilities and limitations of multiplex, isotope labeling approaches for the data-independent acquisition mode, which is increasing in popularity.

A1 Ions: Peptide-Specific and Intensity-Enhanced Fragment Ions for Accurate and Multiplexed Proteome Quantitation

Accurate proteome quantitation is of great significance to deeply understand various cellular and physiological processes. Since a1 ions, generated from dimethyl-labeled peptides, exhibited high formation efficiency (up to 99%) and enhanced intensities (2.34-fold by average) in tandem mass spectra, herein, we proposed an a1 ion-based proteome quantitation (APQ) method, which showed high quantitation accuracy (relative errors < 7%) and precision (median coefficients of variation ≤ 11%) even in a 20-fold dynamic range. Notably, due to the mass differences of a1 ions from peptides with different N-terminal amino acids, APQ demonstrated interference-free capacity by distinguishing target peptides from the coisolated ones. By designing an isobaric dimethyl labeling strategy, we achieved simultaneous proteome-wide measurements across up to eight samples. Using APQ to quantify the time-resolved proteomic profiles during a TGF-β-induced epithelial-mesenchymal transition, we found many differentially expressed proteins associated with fatty acid degradation, indicating that fatty acid metabolism reprogramming occurred during the process. The APQ method combines high quantitation accuracy with multiplexing capacity, which is suitable for deep mining and understanding of dynamic biological processes.

Site-Specific Conjugation Quantitation of a Cysteine-Conjugated Antibody-Drug Conjugate Using Stable Isotope Labeling Peptide Mapping LC-MS/MS Analysis

Drug-load (DL) characterization of antibody-drug conjugates (ADCs) is an important analytical task due to its designation as a critical quality attribute (CQA) affecting potency and stability. Intact and subunit liquid chromatography-mass spectrometry (LC-MS) analyses can determine global drug-to-antibody ratios (DARs) that correlate well with other orthogonal analytical methods; however, peptide mapping liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis has struggled to provide complementary site-specific quantitation of drug conjugation sites. The peptide mapping method described herein utilizes stable isotope labeling to accurately quantitate the site-specific conjugation levels of a cysteine-conjugated ADC to provide "bottom-up" DAR characterization in parallel with protein sequence and post-translational modification (PTM) characterization in one multi-attribute analytical method (MAM).

The Isotopic Ac-IP Tag Enables Multiplexed Proteome Quantification in Data-Independent Acquisition Mode

Data-independent acquisition (DIA) is an increasingly used approach for quantitative proteomics. However, most current isotope labeling strategies are not suitable for DIA as they lead to more complex MS2 spectra or severe ratio distortion. As a result, DIA suffers from a lower throughput than data-dependent acquisition (DDA) due to a lower level of multiplexing. Herein, we synthesized an isotopically labeled acetyl-isoleucine-proline (Ac-IP) tag for multiplexed quantification in DIA. Differentially labeled peptides have distinct precursor ions carrying the quantitative information but identical MS2 spectra since the isotopically labeled Ac-Ile part leaves as a neutral loss upon collision-induced dissociation, while fragmentation of the peptide backbone generates regular fragment ions for identification. The Ac-IP-labeled samples can be analyzed using general DIA liquid chromatography–mass spectrometry settings, and the data obtained can be processed with established approaches. Relative quantification requires deconvolution of the isotope envelope of the respective precursor ions. Suitability of the Ac-IP tag is demonstrated with a triplex-labeled yeast proteome spiked with bovine serum albumin that was mixed at 10:5:1 ratios, resulting in measured ratios of 9.7:5.3:1.1.