Missing Value Monitoring Enhances the Robustness in Proteomics Quantitation

In global proteomic analysis, it
is estimated that proteins span
from millions to less than 100 copies per cell. The challenge of protein
quantitation by classic shotgun proteomic techniques relies on the
presence of missing values in peptides belonging to low-abundance
proteins that lowers intraruns reproducibility affecting postdata
statistical analysis. Here, we present a new analytical workflow MvM
(missing value monitoring) able to recover quantitation of missing
values generated by shotgun analysis. In particular, we used confident
data-dependent acquisition (DDA) quantitation only for proteins measured
in all the runs, while we filled the missing values with data-independent
acquisition analysis using the library previously generated in DDA.
We analyzed cell cycle regulated proteins, as they are low abundance
proteins with highly dynamic expression levels. Indeed, we found that
cell cycle related proteins are the major components of the missing
values-rich proteome. Using the MvM workflow, we doubled the number
of robustly quantified cell cycle related proteins, and we reduced
the number of missing values achieving robust quantitation for proteins
over ∼50 molecules per cell. MvM allows lower quantification
variance among replicates for low abundance proteins with respect
to DDA analysis, which demonstrates the potential of this novel workflow
to measure low abundance, dynamically regulated proteins.