How Does Ratio Compression Arise in TMT-Based Quantitative Proteomics and Why Is It Limited to Relative Quantification?
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Absence of absolute quantification references: TMT workflows typically do not incorporate isotopically labeled internal standards or external calibration curves, both of which are essential for determining absolute protein abundances.
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Variability in MS response: Ionization efficiencies and instrument response factors vary among peptides and proteins, precluding direct correlation between signal intensity and absolute concentration. As a result, TMT-based measurements are constrained to comparative analyses across conditions rather than providing standalone absolute quantification.
Mechanism and Impact of Ratio Compression
In tandem mass tag (TMT)-based quantitative proteomics, ratio compression represents a well-recognized analytical artifact. It primarily arises from the co-isolation and co-fragmentation of multiple peptide ions during MS/MS analysis, where reporter ion signals of similar m/z values are convoluted due to overlapping contributions from different peptides. This interference causes a blending of reporter intensities across samples, leading to an underestimation of true abundance differences.
As a result, fold changes between samples can be artificially compressed, particularly when large expression differences exist. In extreme cases, ratios may converge toward unity, masking biologically significant alterations and leading to inaccurate quantification of protein expression levels.
To mitigate this artifact, several correction strategies have been developed, including computational algorithms for ratio compression adjustment and normalization approaches utilizing spiked-in standards or reference channels, which help to partially restore the quantitative accuracy.
Why TMT-Based Quantification Is Restricted to Relative, Not Absolute, Measurements
TMT operates through isotopic labeling of peptides, enabling simultaneous analysis of multiple samples via comparison of reporter ion intensities. However, the approach is inherently designed for relative rather than absolute quantification.
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