Label Free Quant

Proteomics Services

Label Free Quant

There are diverse in-silico solutions to estimate protein abundance from label-free samples.

MS-based quantitation (”quant”) can be either absolute or relative. While absolute quant almost always includes a label-free component, relative quantitation can be performed in a labelled or label-free manner.

Two main problems make Label-Free Quant (LFQ) difficult:

1) Different peptides may have unpredictable detectability (sometimes called “flyability”). Hence, in MS different peptides can be non-comparable entities.

2) A given peptide may not have been identified in all samples compared. In addition, its flyability may vary between runs. Some software – such as MaxQuant – can correct for this issue.

To compare multiple samples, labelled quantitation is always preferable when feasible. because it allows parallel handling and thus reduces deviation, and because the data is more complete for individual peptides. However, labelling is restricted to relative changes and can only be converted into absolute abundance values if the absolute copy numbers of the references are known.

For LFQ, the two main questions are which peptide feature(s) to use for quantitation (in the past, number of spectrum, now more often extracted ion chromatogram intensity), and how to summarise individual peptide information at protein level.

Depending on experiment, DC Biosciences results tables may contain LFQ columns based on the following methods:

– iBAQ (MaxQuant): a commonly used averaging method which is a mean-equivalent, but where the denominator is the number of observable tryptic peptides.

– MaxLFQ (MaxQuant): a complex normalisation method based on globally minimalizing inter-sample differences for each observed peptide.

– Top3: average of the intensities of a protein’s 3 (or-less) most intense peptides; based on the “best-flyer” hypothesis, which postulates that highest intensity peptides have the same detectability across the proteome.

Because of the lower precision of LFQ methods, they may benefit more from replicates than labelled-methods.

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