Mass spectrometry has long been a cornerstone in proteomics research, primarily used for qualitative analysis to identify proteins and post-translational modifications. However, as scientists sought to understand the dynamic changes in protein expression across cells, tissues, and organisms, they began developing quantitative proteomics methods. These techniques allow for a more comprehensive view of biological systems, enabling researchers to track how proteins change under different conditions, such as disease or environmental stress.
In the early days of protein research, the focus was on individual proteins or complexes, but with advances in technology, the field of proteomics has expanded significantly. Ten years ago, analyzing hundreds of proteins was considered advanced, but today, platforms like Shanghai Creation can analyze thousands. This level of depth enables researchers to study global protein dynamics, complementing other omics fields such as genomics, transcriptomics, and metabolomics. Together, these approaches offer a more holistic understanding of biological processes and their responses to various stimuli.
While DNA chips are widely used to monitor gene expression changes after disease onset, they don’t always reflect protein-level differences. To better compare protein levels between samples, scientists often use two-dimensional electrophoresis combined with mass spectrometry. However, this method is labor-intensive, has low throughput, and suffers from poor reproducibility. Hence, there’s a growing need for more efficient and accurate quantification techniques.
Quantitative proteomics is typically divided into relative and absolute quantification. Relative methods, such as SILAC, ICAT, and ICPL, compare protein abundance between samples. Absolute quantification involves spiking unlabeled samples with known concentrations of isotopically labeled peptides. While absolute quantification is more precise, it requires expensive reagents and extensive assay development. As a result, relative methods like SILAC remain more commonly used.
SILAC, or Stable Isotope Labeling by Amino Acids in Cell Culture, was developed by Professor Matthias Mann at the University of Southern Denmark. The technique involves culturing two cell groups in media containing either normal or stable isotope-labeled amino acids. After several generations, the labeled amino acids replace natural ones in the proteins, creating a detectable molecular weight difference. When mixed and analyzed by mass spectrometry, the ratio of light to heavy peptides reflects protein abundance.
One of SILAC's key advantages is its high labeling efficiency—over 90% after 6–8 passages—and minimal bias due to the labeling occurring before sample processing. It also allows for low sample input and high sensitivity, making it ideal for detecting subtle changes in protein expression. However, limitations exist, such as issues with certain cell lines that convert labeled arginine into proline, and challenges in culturing sensitive or difficult-to-grow cells.
Classic SILAC experiments involve labeling multiple samples and comparing them directly in mass spectrometry. Double-SILAC compares two samples, while triple-SILAC allows for three. In vivo applications, such as in mice, have also been successful, with studies showing no adverse effects from isotopic labeling. Similar approaches have been applied to nematodes, enabling researchers to study protein dynamics during development and stress responses.
To address the complexity of tissue samples, the Super-SILAC approach was introduced. By mixing multiple SILAC-labeled cell lines as internal standards, researchers can achieve greater accuracy when analyzing heterogeneous tissues like tumors. This method enhances the reliability of quantitative comparisons and supports cancer biology research.
For those conducting SILAC experiments, tools like Thermo Fisher Scientific’s SILAC media and isotopically labeled amino acids are essential. These products support a wide range of cell types, including stem cells, and help ensure accurate labeling. The experimental process involves culturing cells in labeled media, lysing them, and analyzing the resulting proteome using mass spectrometry. With careful planning and execution, SILAC remains a powerful tool for exploring protein dynamics in both basic and translational research.
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