Thermo Fisher Scientific Introduces Peptide Calibration Reagent to Optimize Liquid Chromatography Performance

"Thermo Fisher Scientific, the world leader in serving science, today announced the availability of the Thermo Scientific Pierce Peptide Retention Time Calibration Mixture for the prediction of peptide retention times on reversed-phase high-performance liquid chromatography (HPLC) columns. 
The convenient, ready-to-use Pierce® Peptide Retention Time Calibration Mixture contains 15 synthetic, heavy peptides mixed at an equimolar ratio to elute across the chromatographic gradient. It can be used with Thermo Scientific Pinpoint Software to predict peptide retention time from sequence alone, using hydrophobicity factors, or to predict peptide retention time between instrument platforms. 

The Pierce Peptide Retention Time Calibration Mixture streamlines the transition from qualitative protein discovery results to the development of targeted mass spectrometry (MS) assays on Thermo Scientific Triple Quadrupole, Orbitrap and Exactive Instruments and all other mass spectrometers. It also saves time in peptide purification by increasing the prediction efficiency of peptide retention profiles. The mixture is useful in evaluating different reversed-phase column and gradient options, monitoring for autosampler and HPLC column performance characteristics and normalizing results between experiments and over time."

SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists

Motivation: Alignment of multiple liquid chromatography/mass spectrometry (LC/MS) experiments is a necessity today, which arises from the need for biological and technical repeats. Due to limits in sampling frequency and poor reproducibility of retention times, current LC systems suffer from missing observations and non-linear distortions of the retention times across runs. Existing approaches for peak correspondence estimation focus almost exclusively on solving the pairwise alignment problem, yielding straightforward but suboptimal results formultiple alignment problems.

Results: We propose SIMA, a novel automated procedure for alignment of peak lists from multiple LC/MS runs. SIMA combines hierarchical pairwise correspondence estimation withsimultaneous alignment and global retention time correction. It employs a tailored multidimensional kernel function and a procedure based on maximum likelihood estimation to find the retention time distortion function that best fits the observed data. SIMA does not require a dedicated reference spectrum, is robust with regard to outliers, needs only two intuitive parameters and naturally incorporates incomplete correspondence information. In a comparison with seven alternative methods on four different datasets, we show that SIMA yields competitive and superior performance on real-world data.

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Network-Based Pipeline for Analyzing MS Data: An Application toward Liver Cancer

Current limitations in proteome analysis by high-throughput mass spectrometry (MS) approaches have sometimes led to incomplete (or inconclusive) data sets being published or unpublished. In this work, we used an iTRAQ reference data on hepatocellular carcinoma (HCC) to design a two-stage functional analysis pipeline to widen and improve the proteome coverage and, subsequently, to unveil the molecular changes that occur during HCC progression in human tumorous tissue. The first involved functional cluster analysis by incorporating an expansion step on a cleaned integrated network. The second used an in-house developed pathway database where recovery of shared neighbors was followed by pathway enrichment analysis. In the original MS data set, over 500 proteins were detected from the tumors of 12 male patients, but in this paper we reported an additional 1000 proteins after application of our bioinformatics pipeline. Through an integrative effort of network cleaning, community finding methods, and network analysis, we also uncovered several biologically interesting clusters implicated in HCC. We established that HCC transition from a moderate to poor stage involved densely connected clusters that comprised of PCNA, XRCC5, XRCC6, PARP1, PRKDC, and WRN. From our pathway enrichment analyses, it appeared that the HCC moderate stage, unlike the poor stage, is enriched in proteins involved in immune responses, thus suggesting the acquisition of immuno-evasion. Our strategy illustrates how an original oncoproteome could be expanded to one of a larger dynamic range where current technology limitations prevent/limit comprehensive proteome characterization.

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Uniquant: an alternative to MaxQuant

UNiquant, a Program for Quantitative Proteomics Analysis Using Stable Isotope Labeling

Abstract

Stable isotope labeling (SIL) methods coupled with nanoscale liquid chromatography and high resolution tandem mass spectrometry are increasingly useful for elucidation of the proteome-wide differences between multiple biological samples. Development of more effective programs for the sensitive identification of peptide pairs and accurate measurement of the relative peptide/protein abundance are essential for quantitative proteomic analysis. We developed and evaluated the performance of a new program, termed UNiquant, for analyzing quantitative proteomics data using stable isotope labeling. UNiquant was compared with two other programs, MaxQuant and Mascot Distiller, using SILAC-labeled complex proteome mixtures having either known or unknown heavy/light ratios. For the SILAC-labeled Jeko-1 cell proteome digests with known heavy/light ratios (H/L = 1:1, 1:5, and 1:10), UNiquant quantified a similar number of peptide pairs as MaxQuant for the H/L = 1:1 and 1:5 mixtures. In addition, UNiquant quantified significantly more peptides than MaxQuant and Mascot Distiller in the H/L = 1:10 mixtures. UNiquant accurately measured relative peptide/protein abundance without the need for postmeasurement normalization of peptide ratios, which is required by the other programs.

Keywords (keywords): 

Quantitative proteomics; Stable isotope labeling; LC-MS/MS; Software Development

peptide retention time

Is peptide RT charge specific?
"Not in the sense that ESI-charge is influencing the retention time but
in the sense that longer peptide tend to elute later in the
chromatogram and also are more likely to have higher charge states,
there is a slight correlation between charge state and retention time.
however it would work as prediction tool if you consider predicting
the charge state of a peptide, which is actually not that difficult to
do." by Hannes.

BMC Genomics. 2010 Dec 1;11 Suppl 3:S8.

ICPD-a new peak detection algorithm for LC/MS.

Zhang J, Haskins W.

Department of Electrical Engineering, University of Texas at San Antonio, Texas, USA. michelle.zhang@utsa.edu

Abstract

BACKGROUND: The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery.

RESULTS: In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection.

CONCLUSIONS: The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods.