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Segmentation of precursor mass range using "tiling" approach increases peptide identifications for MS1-based label-free quantification.

ABSTRACT: Label-free quantification is a powerful tool for the measurement of protein abundances by mass spectrometric methods. To maximize quantifiable identifications, MS(1)-based methods must balance the collection of survey scans and fragmentation spectra while maintaining reproducible extracted ion chromatograms (XIC). Here we present a method which increases the depth of proteome coverage over replicate data-dependent experiments without the requirement of additional instrument time or sample prefractionation. Sampling depth is increased by restricting precursor selection to a fraction of the full MS(1) mass range for each replicate; collectively, the m/z segments of all replicates encompass the full MS(1) range. Although selection windows are narrowed, full MS(1) spectra are obtained throughout the method, enabling the collection of full mass range MS(1) chromatograms such that label-free quantitation can be performed for any peptide in any experiment. We term this approach "binning" or "tiling" depending on the type of m/z window utilized. By combining the data obtained from each segment, we find that this approach increases the number of quantifiable yeast peptides and proteins by 31% and 52%, respectively, when compared to normal data-dependent experiments performed in replicate.

SUBMITTER: Vincent CE

PROVIDER: S-EPMC3607285 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Segmentation of precursor mass range using "tiling" approach increases peptide identifications for MS1-based label-free quantification.

Vincent Catherine E CE Potts Gregory K GK Ulbrich Arne A Westphall Michael S MS Atwood James A JA Coon Joshua J JJ Weatherly D Brent DB

Analytical chemistry 20130212 5

Label-free quantification is a powerful tool for the measurement of protein abundances by mass spectrometric methods. To maximize quantifiable identifications, MS(1)-based methods must balance the collection of survey scans and fragmentation spectra while maintaining reproducible extracted ion chromatograms (XIC). Here we present a method which increases the depth of proteome coverage over replicate data-dependent experiments without the requirement of additional instrument time or sample prefra ...[more]

PMID: 23350991

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Project description:For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.

Dataset Information

Segmentation of precursor mass range using "tiling" approach increases peptide identifications for MS1-based label-free quantification.

Publications

Segmentation of precursor mass range using "tiling" approach increases peptide identifications for MS1-based label-free quantification.

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