Improved Quantitative Analysis of Mass Spectrometry using Quadratic Equations

MON-2, a Golgi protein, mediates autophagy-dependent longevity in Caenorhabditis elegans

The Golgi apparatus plays a central role in trafficking cargoes such as proteins and lipids. Defects in the Golgi apparatus lead to various diseases, but its role in organismal longevity is largely unknown. Using a quantitative proteomic approach, we found that a Golgi protein, MON-2, was up-regulated in long-lived Caenorhabditis elegans mutants with mitochondrial respiration defects and was required for their longevity. Similarly, we showed that DOP1/PAD-1, which acts with MON-2 to traffic macromolecules between the Golgi and endosome, contributed to the longevity of respiration mutants. Furthermore, we demonstrated that MON-2 was required for up-regulation of autophagy, a longevity-associated recycling process, by activating the Atg8 ortholog GABARAP/LGG-1 in C. elegans. Consistently, we showed that mammalian MON2 activated GABARAPL2 through physical interaction, which increased autophagic flux in mammalian cells. Thus, the evolutionarily conserved role of MON2 in trafficking between the Golgi and endosome is an integral part of autophagy-mediated longevity.

Proteomic Interrogation in Cancer Biomarker

Biomarkers factor into the diagnosis and treatment of almost every patient with cancer. The innovation in proteomics follows improvement of mass spectrometry techniques and data processing strategy. Recently, proteomics and typical biological studies have been the answer for clinical applications. The clinical proteomics techniques are now actively adapted to protein identification in large patient cohort, biomarker development for more sensitive and specific screening based on quantitative data. And, it is important for clinical, translational researchers to be acutely aware of the issues surrounding appropriate biomarker development, in order to facilitate entry of clinically useful biomarkers into the clinic. Here, we discuss in detail include the case research for clinical proteomics. Furthermore, we give an overview on the current developments and novel findings in proteomics-based cancer biomarker research.

Quantitative Peptidomics with Five-plex Reductive Methylation labels

Quantitative peptidomics and proteomics often use chemical tags to covalently modify peptides with reagents that differ in the number of stable isotopes, allowing for quantitation of the relative peptide levels in the original sample based on the peak height of each isotopic form. Different chemical reagents have been used as tags for quantitative peptidomics and proteomics, and all have strengths and weaknesses. One of the simplest approaches uses formaldehyde and sodium cyanoborohydride to methylate amines, converting primary and secondary amines into tertiary amines. Up to five different isotopic forms can be generated, depending on the isotopic forms of formaldehyde and cyanoborohydride reagents, allowing for five-plex quantitation. However, the mass difference between each of these forms is only 1 Da per methyl group incorporated into the peptide, and for many peptides there is substantial overlap from the natural abundance of ¹³C and other isotopes. In this study, we calculated the contribution from the natural isotopes for 26 native peptides and derived equations to correct the peak intensities. These equations were applied to data from a study using human embryonic kidney HEK293T cells in which five replicates were treated with 100 nM vinblastine for 3 h and compared with five replicates of cells treated with control medium. The correction equations brought the replicates to the expected 1:1 ratios and revealed significant decreases in levels of 21 peptides upon vinblastine treatment. These equations enable accurate quantitation of small changes in peptide levels using the reductive methylation labeling approach. Graphical abstract ᅟ.

Accurate and Efficient Resolution of Overlapping Isotopic Envelopes in Protein Tandem Mass Spectra

It has long been an analytical challenge to accurately and efficiently resolve extremely dense overlapping isotopic envelopes (OIEs) in protein tandem mass spectra to confidently identify proteins. Here, we report a computationally efficient method, called OIE_CARE, to resolve OIEs by calculating the relative deviation between the ideal and observed experimental abundance. In the OIE_CARE method, the ideal experimental abundance of a particular overlapping isotopic peak (OIP) is first calculated for all the OIEs sharing this OIP. The relative deviation (RD) of the overall observed experimental abundance of this OIP relative to the summed ideal value is then calculated. The final individual abundance of the OIP for each OIE is the individual ideal experimental abundance multiplied by 1 + RD. Initial studies were performed using higher-energy collisional dissociation tandem mass spectra on myoglobin (with direct infusion) and the intact E. coli proteome (with liquid chromatographic separation). Comprehensive data at the protein and proteome levels, high confidence and good reproducibility were achieved. The resolving method reported here can, in principle, be extended to resolve any envelope-type overlapping data for which the corresponding theoretical reference values are available.

MTRAQ-based quantitative analysis combined with peptide fractionation based on cysteinyl peptide enrichment

In the present study, the fractionation scheme for cysteinyl peptide enrichment (CPE) was combined with the mass differential tags for relative and absolute quantification (mTRAQ) method to reduce sample complexity and increase proteome coverage. Cysteine residues of the proteins were first alkylated using iodoacetyl PEG2-biotin instead of other conventional alkylating agents such as iodoacetamide. After trypsin digestion, amine groups were labeled with mTRAQ, and these labeled peptides were fractionated according to the presence or absence of cysteine residues using avidin-biotin affinity chromatography. With these approaches, we were able to divide the peptides into the two fractions with more than 90% fractionation efficiency for standard protein and MCF7 cell lysate. When the fractionation strategy was applied to colorectal cancer tissue samples, we were able to obtain quantitative information that was consistent with the previous study based on mTRAQ quantification, implying that the cysteine-based fractionation method does not affect mTRAQ quantification. We expect that the mTRAQ-based quantitative analysis combined with peptide fractionation through the CPE strategy would allow for deep-down analysis of proteome samples and ultimately for increasing proteome coverage with simultaneous quantification for biomarker discovery.

Determination of monoisotopic masses of chimera spectra from high-resolution mass spectrometric data by use of isotopic peak intensity ratio modeling

RATIONALE

Chimera spectra make it challenging to identify proteins in complex mixtures by LC/MS/MS. Approximately half of the spectra collected are chimera spectra even when high-resolution tandem mass spectrometry is used. Chimera spectra are generated from the co-fragmentation of different co-elute peptides, and it is often difficult to distinguish monoisotopic precursors of these peptides from each other.

METHODS

In this paper, we propose a peak intensity ratio-based monoisotopic peak determination algorithm (PIRMD) to distinguish different monoisotopic precursors of chimera spectra. Monoisotopic peaks in non-overlapping clusters are detected by the edge features of the isotopic peak intensity ratios. For multiple overlapping clusters grouped as one cluster, monoisotopic peaks can be detected by an advanced estimation of the similarity between the estimated and the experimental isotopic distribution based on the isotopic peak intensity ratios.

RESULTS

High-resolution mass spectrometric datasets acquired from mixtures of 30 synthetic peptides and mixtures of 18 proteins were used to evaluate the efficiency and accuracy of PIRMD. The results indicate that PIRMD can recognize monoisotopic precursors from the chimera spectra containing non-overlapping and overlapping isotopic clusters. Compared to several published algorithms, PIRMD identifies approximately 2 ~ 14% more spectra and has fewer false positives.

CONCLUSIONS

The results on standard datasets and actual samples demonstrated that PIRMD could notably improve the successful identification rates of the spectra by identifying more chimera spectra, and of the identified spectra, approximately 25% are chimera spectra. This novel algorithm will help to interpret spectra produced by shotgun strategy in proteomics.

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Deconvolution of overlapping isotopic clusters improves quantification of stable isotope-labeled peptides

High-resolution mass spectrometry and the use of stable isotopes have greatly improved our ability to quantify proteomes. Typically, the relative abundance of peptides is estimated by identifying the isotopic clusters and by comparing the peak intensities of peptide pairs. However, when the mass shift between the labeled peptides is small, there can be the possibility for overlap of the isotopic clusters which will hamper quantification accuracy with a typical upwards bias for the heavier peptide. Here, we investigated the impact of the overlapping peak issue with respect to dimethyl based quantification and we confirmed there can be need for correction. In addition, we present a tool that can correct overlapping issues when they arise which is based on modeling isotopic distributions. We demonstrate that our approach leads to improved accuracy and precision of protein quantification.

High-throughput peptide quantification using mTRAQ reagent triplex

BACKGROUND

Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents.

METHODS AND RESULTS

Here we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments.

CONCLUSIONS

We showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.