Proteomics has emerged as a crucial tool for understanding the molecular mechanisms and signal transduction involved in human diseases and cancer by representing the expression, post-translational modification, and protein-protein interactions in specific cells, tissues, or organs. Maintaining a steady equilibrium of protein levels in the body is essential, as protein accumulation can lead to qualitative changes in organisms, potentially causing tumorigenesis or disease. Therefore, the quantification of proteins plays a significant role in investigating these mechanisms. This article provides an overview of the current popular protein quantification technologies and their applications in clinical research.

Introduction of HPLC-MS/MS-based Proteomics Technology

The advancement of high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) based proteomics technology relies on liquid chromatography-mass spectrometry equipment, sample separation and preparation technology, identification, and bioinformatics. The shotgun method is currently the most widely used large-scale protein identification strategy. In the bottom-up approach, proteins are enzymatically digested into peptides, which are then separated and simplified using chromatography or affinity separation techniques before being detected by mass spectrometry. A search engine is utilized to match the mass spectrum with the theoretical digestion spectrum in the database, leading to peptide-spectrum matches (PSMs) and complete proteome identification. On the other hand, top-down mass spectrometry (TD-MS) directly separates and detects intact proteins from samples, providing more information on preserved protein species compared to bottom-up approaches. These two main strategies support proteomics research on diseases and cancers.

Commonly-used Proteomics Quantitative Techniques and Their Clinical Application

1. Label-free quantification (LFQ)

Label-free quantification is a widely used proteome quantification strategy due to its simplicity, minimal invasiveness, and economic benefits. While researchers can only obtain relative protein quantities through LFQ, it is frequently employed in clinical studies to search for tumor biomarkers. For instance, MMP-7 was identified as a promising biomarker for diagnosing gastric adenocarcinoma patients. Urine E-cadherin was suggested as a marker for early detection of kidney injury in diabetic patients. Label-free quantification has also been applied to compare protein profiles in saliva and serum exosomes from healthy individuals and lung cancer patients, leading to the discovery of potential biomarkers for lung cancer. Additionally, LFQ has been used to identify sweat secreted proteins as potential sweat biomarkers for various diseases and tumors.

2. Stable isotope labeling with amino acids in cell culture (SILAC)

SILAC is an in vivo metabolic labeling method that offers high accuracy and reliability for both absolute and relative protein quantification. By adding stable isotopes of Lys and Arg to cell culture medium, proteins are labeled as light-type and heavy-type for quantification using mass spectrometry. SILAC has been used to quantify protein expression levels in colorectal cancers and identify post-translational modifications of proteins and amino acids in various diseases. The development of super-SILAC technology has enabled quantitative proteomics of tissues from model organisms, expanding the application of SILAC in clinical research.

3. Isobaric tags for relative and absolute quantification (iTRAQ)

iTRAQ technology, developed in 2004, allows the labeling of up to 8 samples simultaneously for qualitative and quantitative analyses with good reproducibility and high sensitivity. This technology has been successfully applied in medical research to analyze and identify differential proteins in tumor tissues, leading to the discovery of molecular therapeutic targets in myeloid leukemia and markers for hepatic injury.

4. Tandem mass tags (TMT)

TMT labeling enables the quantification of up to 16 samples simultaneously, offering better quantitative accuracy than other methods. Widely used in clinical sample research, TMT has been applied to identify prognostic biomarkers for various diseases, including liver failure and different types of cancer.

In addition to the above methods, other strategies such as difference gel electrophoresis (DIGE), Isotope-coded affinity tag (iCAT), and sequential windowed acquisition of all theoretical fragment ions (SWATH) can also be utilized for protein quantification. These techniques are valuable for studying molecular mechanisms and biomarkers in diseases and tumors.

Protein quantification technologies are essential for identifying biomarkers for human cancers, selecting drug targets, and conducting mechanism studies. These powerful tools aid in understanding disease mechanisms, tumor progression, biomarker discovery, and clinical research.