Multiple quantification strategies for mass spectrometry-based proteomics are available, and they all come with different advantages and disadvantages. To put it simple: there is no one-size-fits-all. The ideal quantitative proteomics approach would enable reproducible, comprehensive, sensitive and unbiased analysis that provides accurate and precise quantitative data with a high dynamic range and within reasonable analysis time at low cost.
However, such a single technique does not exist. All available quantitative techniques have their strengths and weaknesses. Thus, choosing the most appropriate quantification strategy depends on several factors:
- the biological research question that determines the experimental design,
- the number and complexity of the samples as well as the total protein amount available per sample, and
- the LC-MS instrumentation and expertise available in the MS-lab.
From an analytical perspective, the difference between the various quantitative approaches is mainly the stage at which the samples are combined. Each step of the workflow where samples are processed separately is a potential cause for errors, and may introduce variability. Thus, combining samples as early as possible has huge advantages.
LABEL-FREE vs STABLE ISOTOPE LABELING
The main benefit of metabolic labeling (SILAC) is the possibility to combine differentially labeled samples at the earliest possible stage during sample preparation. This eliminates non-systematic errors that can potentially occur at any step in the workflow. Thus, if metabolic labeling is possible it should always be considered first, especially when many sample handling steps are carried out (e.g. subcellular fractionation). Chemical labeling strategies are preferred for tissue samples, body fluids, primary cell culture or organisms that are not auxotroph for amino acids used in SILAC. However, the number of samples that can be multiplexed is limited. Thus, label-free is ultimately usually the method of choice for experiments that necessitate the analysis of multiple samples. The label-free quantification workflow is straightforward and theoretically allows for an unlimited number of samples to be compared.
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