Dataset Information

Critical assessment of proteome-wide label-free absolute abundance estimation strategies

ABSTRACT: There is a great interest in reliable ways to obtain absolute protein abundances at a proteome-wide scale. To this end, label-free LC-MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance. Several variants of this quantification approach have been presented, based on either the number of spectral counts per protein or MS1 peak intensities. Equipped with several datasets representing real biological environments, containing a high number of accurately quantified reference proteins, we evaluate five popular low cost and easily implemented quantification methods (APEX, emPAI, iBAQ, Top3 and MeanInt). Our results demonstrate considerably improved abundance estimates upon implementing accurately quantified reference proteins; i.e. using spiked in SIS peptides or a standard protein mix, to generate a properly calibrated quantification model. We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements. Additionally, we demonstrate that spectral count based quantification methods are associated with higher errors than MS1 peak intensity based methods. Furthermore, we investigate the impact of mis-cleaved, modified and shared peptides as well as protein size and the number of employed reference proteins on quantification accuracy. The .raw data submitted to PRIDE correspond to replicate DDA LC-MS/MS analysis of the UPS2 mix (Universal Proteomics Standard, UPS2, Sigma-Aldrich), as well as triplicate DDA LC-MS/MS analysis of the UPS2 mix spiked into samples from the following organisms M. pneumoniae, L. interrogans and D. melanogaster. The accompanying R-script called calculatePQIs.R was used to calculate the different Quantification Indices, provided that the peptide XICs or Spectral Counts have been loaded into a data.frame R object (which can be produced by SafeQuant).

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (human) Leptospira Interrogans Mycoplasma Pneumoniae Drosophila Melanogaster (fruit Fly)

SUBMITTER: Alexander Schmidt

PROVIDER: PXD000331 | Pride | 2013-11-15

REPOSITORIES: Pride

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Critical assessment of proteome-wide label-free absolute abundance estimation strategies.

Ahrné Erik E Molzahn Lars L Glatter Timo T Schmidt Alexander A

Proteomics 20130730 17

There is a great interest in reliable ways to obtain absolute protein abundances at a proteome-wide scale. To this end, label-free LC-MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance. Several variants of this quantification approach have been presented, based on either the number of spectral counts per protein or MS1 peak intensities. Equipped with several datasets representing real biological environments, containing a high number ...[more]

PMID: 23794183

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Project description:This dataset derives from experiments testing quantitation using label-free methods. We used the Sigma-Aldrich UPS1 and UPS2 protein standard sets, containing proteins of 6-83 kD; the UPS1 set includes 49 human proteins at a single molar concentration, while the UPS2 set includes six groups of eight human proteins spanning a six orders of magnitude in concentration. In both cases, dilutions of UPS1 or UPS2 proteins were made into an E. coli extract, which allows these experiments to mimic detection of low abundance proteins in a complex protein mixture. The UPS1/E. coli and UPS2/E. coli mixtures were run on three mass spectrometers, an Orbitrap Velos Elite and two ion-trap instruments (Velos and LTQ). Proteins were quantified using MS1 peak volume (Orbitrap) or MS2 intensities and spectral counts (Orbitrap, Velos, LTQ). The analyzed results show that the ion-trap instruments perform nearly as well for label-free quantitation as does the Orbitrap. In addition, the \"Top 3\" method for quantitation—measuring only the three most abundant peptides—performed no better than the \"iBAQ\" quantitation method. Data analysis: MS1 data were analyzed using MaxQuant. MaxQuant version 1.2.2.5 software was used for protein identification and quantitation. Using the search engine Andromeda, mass spectrometry data were searched against the database containing UPS and E. coli proteins. MaxQuant reports summed intensity for each protein, as well as its iBAQ value. In the iBAQ algorithm, the intensities of the precursor peptides that map to each protein are summed together and divided by the number of theoretically observable peptides, which is considered to be all tryptic peptides between 6 and 30 amino acids in length. This operation converts a measure that is expected to be proportional to mass (intensity) into one that is proportional to molar amount (iBAQ). To determine relative molar abundances for each E. coli and human protein using MS1 data, two methods were used. In the first, we determined each proteins relative iBAQ (riBAQ), a normalized measure of molar abundance. We removed from the analysis all contaminant proteins that entered our sample-preparation workflow, for example keratins and trypsin, and then divided each remaining proteins iBAQ value by the sum of all non-contaminant iBAQ values. We also measured the average intensity of the three (or fewer, if fewer peptides were detected) peptides with the highest intensity, Top3, for each protein detected9. Peptides were chosen separately for each experiment, so the same peptides were not necessarily used across the four experiments. We generated a normalized \"top three\" abundance factor by dividing the average intensity for the three most abundant peptides of an individual protein and by the sum of all Top3 values in an experiment. Peptide intensities were summed for all charge states and variable modifications. For MS2 label-free analysis with MS2 spectra, MS2 data were searched against the database described above using SEQUEST. Setting to 1% the peptide false-discovery rate, estimated using a decoy (reversed) database, proteins were identified using the PAW pipeline. Normalized molar intensity (im) was calculated by dividing i, the summed intensity for an individual protein, by its molecular mass; the i/mr value for each protein was divided by the sum of all i/mr values. Normalized molar counts (cm) were calculated similarly, except the summed spectral counts for a protein (c) was used instead.

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