Proteomics can provide insight into complex biological systems by analyzing expression, function, modification and interaction. Label-free quantification is a method to determine the relative content of protein in two or more biological samples, but different from other labeled methods, it does not use stable isotopes for protein chemical binding and labeling. Label-free quantitative proteomics provides a powerful tool for the isolation and identification of thousands of proteins from complex biological samples.
In label-free quantification proteomics, two quantification strategies are generally used: (i) measurement of chromatographic elution peak area or (ii) spectrum counting. The measurement of peak area involves calculating and comparing the average intensity of the peak area of all peptides from each protein in the biological sample. In contrast, spectrum counting is based on the number of mass spectrum (MS)/MS spectra generated by the protein. The more abundant the protein in a biological sample, the more peptides will be selected for fragmentation. Both techniques can be used to quantify differences between proteins, especially for low-abundance proteins.
In the proteomic analysis of exosome samples, label-free is the most commonly used technique. This is because the samples to be tested often involve exosomes from different sources. If they are derived from different cell lines or fluids, the protein spectrum will have relatively large differences. Therefore, in addition to the quantitative differences of common proteins, the differential protein analysis with and without is also involved. In this case, label-free is recommended.
First of all, in a label-free experiment, there is no limit to the number of samples, each sample is analyzed separately. For large-scale analysis of a large number of samples, label-free proteomics becomes the preferred strategy because of its flexible research design and convenient for multi-group comparison. In addition, due to multiplexing, the samples are not diluted, making it possible to detect specific low abundance proteins under specific conditions. When bias is avoided in research design and reference samples are added to monitor the performance of the whole experiment, label-free proteomics can provide a favorable strategy for the discovery of biomarkers.
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