title: Improving label-free quantitative proteomics strategies by distributing shared peptides and stabilizing variance.
abstract: In a previous study, we demonstrated that spectral counts-based label-free proteomic quantitation could be improved by distributing peptides shared between multiple proteins. Here, we compare four quantitative proteomic approaches, namely, the normalized spectral abundance factor (NSAF), the normalized area abundance factor (NAAF), normalized parent ion intensity abundance factor (NIAF), and the normalized fragment ion intensity abundance factor (NFAF). We demonstrate that label-free proteomic quantitation methods based on chromatographic peak area (NAAF), parent ion intensity in MS1 (NIAF), and fragment ion intensity (NFAF) are also improved when shared peptides are distributed on the basis of peptides unique to each isoform. To stabilize the variance inherent to label-free proteomic quantitation data sets, we use cyclic-locally weighted scatter plot smoothing (LOWESS) and linear regression normalization (LRN). Again, all four methods are improved when cyclic-LOWESS and LRN are applied to reduce variation. Finally, we demonstrate that absolute quantitative values may be derived from label-free parameters such as spectral counts, chromatographic peak area, and ion intensity when using spiked-in proteins of known amounts to generate standard curves.
authors.affiliation: †Stowers Institute for Medical Research, 1000 E. 50th Street, Kansas City, Missouri 64110, United States.
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: †Stowers Institute for Medical Research, 1000 E. 50th Street, Kansas City, Missouri 64110, United States.
authors.forename: Zhihui
authors.initials: Z
authors.lastname: Wen
authors.affiliation: †Stowers Institute for Medical Research, 1000 E. 50th Street, Kansas City, Missouri 64110, United States.|∥Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, Kansas 66160, United States.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Washburn
authors.affiliation: †Stowers Institute for Medical Research, 1000 E. 50th Street, Kansas City, Missouri 64110, United States.
authors.forename: Laurence
authors.initials: L
authors.lastname: Florens
mesh_terms: D010455:Peptides / Q000032:analysis*; D040901:Proteomics / Q000379:methods*
chemical_list: D010455:Peptides