Quantitative Proteomics by Mass Spectrometry

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It was found that the deuterium effect is inconsequential when the labels are in close proximity to hydrophilic groups. This finding opened the door for a new isobaric mass tag: the deuterium isobaric amine reactive tag DiART. This causes more frequent fragmentation of the bond that releases the reporter ion thus allowing for improved sensitivity Figure 3B. Chen et al.

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Isobaric mass tagging continues to be an innovative method for relative quantitation in proteomics. Experiments rely on collision induced dissociation CID , which produces spectra that simultaneously provide quantitative and identification information for each peptide in an experiment. Combining CID with higher-energy collisional dissociation HCD on Orbitrap instruments is gaining popularity as it significantly improves the accuracy of peptide quantitation. This comes with the consequence of longer scan times that limit the number of peptides detected [12].

Continual improvements in reagent costs and protocol reproducibility will lead to an increased prevalence of this technique in both the literature and in the clinic. The isobaric region consists of a reporter region that produces fragment ions with mass to charge ratios of — Th and a complementary balancing region.

Reproduced from [11] with permission. Similar to labeling with isobaric tags, chemical derivatization occurs at the peptide level. The main difference from isobaric tagging is that quantitation is achieved using the MS spectra, rather than the MS 2 spectra. Chemical derivatization of peptides involves adding a label containing a stable isotope in a predictable manner.

The most prevalent derivatization strategy in the literature is dimethyl labeling, a technique that is faster and significantly cheaper than isobaric tagging. Mixing light- and heavy-labeled samples reveals a mass discrimination of 4 and 8 Da for arginine- and lysine-terminating peptides, respectively. This method is particularly well suited to applications that are not feasible with metabolic labeling, such as in the case of human tissue samples [13].

Mesenchymal stem cells MSCs show therapeutic potential for repairing tissues and treating diseases; however, historically there has been difficulty identifying the protein pathways responsible for the pluripotency of these cells. In a study by She et al. The technique was effective despite the high sequence homology and large concentration range of protein analytes in their study [14]. Dimethyl labeling has also been used to study the post-translational modification PTM ubiquitination. Tryptic digestion of ubiquitinated proteins yields isopeptides that include a trademark diglycine branch.

Despite the uniqueness of this occurrence, false-positive results are frequently observed. It was proposed that the additional N-terminus possessed by these distinctive peptides can be used for additional methylations to enhance their detection in complex samples [15]. Dimethyl labeling also enhances a 1 and b 2 signal peaks upon CID, which allowed for the improved sequencing of diglycine-branched isopeptides. As shown by Wang et al. Stable-isotope labels may also be incorporated into peptides enzymatically through the use of trypsin and 18 O-labeled water. The proteolytic mechanism of trypsin includes the transfer of two oxygen atoms to the C-terminus of the generated peptides.

This method was recently used to identify proteins associated with biofilm formation and gingivitis [17]. Through a comparison of the infectious and non-infectious strains of gingivitis-causing bacteria, a family of C-terminal domain proteins was identified to increase upon biofilm formation thus providing an opportunity to target these proteins.


An issue with chemical or enzymatic quantitation methods is that differentially labeled peptides with identical sequences appear as distinct peaks in each spectrum requiring additional analysis time. Yang et al. Following this, peptides were labeled with heavy or light versions of formaldehyde to counteract the additional mass introduced by the oxygen label.

This produced isobaric peptides that could be accurately quantified by their MS 2 spectra with increased sensitivity [18].

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To overcome these challenges, Pan et al. Peptides that had been previously digested by trypsin in aqueous solution could be modified with the addition of a heavy-isotope labeled amino acid [19]. This innovative new method could be coupled with SILAC to allow greater multiplexing and increase the prominence of enzymatic labeling. Figure 4: QIRT quantitation by isobaric terminal labeling labeling strategy. Two protein samples are isobarically tagged by combining enzymatic and chemical labeling techniques.

Arginine R side chains remain unaffected. Once the peptide samples are combined, peptides of identical sequence are represented by one MS peak. In this example, orange peptides are more abundant in Mixture A and green peptides are more abundant in Mixture B. These relative abundances are reflected in the MS2 spectra where heavier peak in each fragment ion pair is representative of the peptide that originated from Mixture B. Reproduced from [18] with permission.

Label-free quantitation is faster and cheaper than isotopic labeling strategies and offers exceptional suitability for high-throughput global proteome analysis.

There are two major categories of label-free methods: extracted ion chromatogram XIC -based quantitation, and spectral counting Figure 5. XIC-based quantitation is based on the assumption that higher peptide concentrations will generate a greater area underneath the integrated chromatographic peak from MS spectra. Meanwhile, peptide identification is still possible through MS 2 spectra. XIC-based quantitation in proteomics experiments often relies on MRM-based methods for improved selectivity and attomolar sensitivity [20].

These methods use several parent-to-fragment ion transitions for each peptide to avoid isobaric interference allowing many proteins to be monitored throughout the course of an experiment.

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Owing to the enhanced sensitivity of MRM, label-free quantitation is often used as a tool to validate preliminary quantitative proteomics experiments. MRM label-free quantitation methods demonstrate greater sensitivity, reproducibility and dynamic range than non-targeted MS methods; however, they have historically lacked the ability to quantify a large set of proteins. Devoting more scanning time to peptide identification decreases the number of data points available for MS quantitation, which limits the resolution, and hence accuracy of XIC-based quantitation.

In MS E , all peptides are fragmented regardless of abundance as the mass spectrometer cycles between high and low collision energies [20]. Peptides are identified by aligning precursor and fragment ion chromatographic elution profiles and quantitation is achieved by determining the area under the peak for the precursor ions XICs.

Historically, conducting MS 2 on several precursor ions at the same time has resulted in complex spectra that could not be interpreted. Again, mapping the chromatographic elution profiles of each fragment and parent ion elucidates which fragments arose from each parent ion for identification; the data may then be mined post hoc to create MRM transitions for precise quantitation as well as precursor or neutral loss spectra. One area of research where this technology is being applied is the study of genetically modified crops GMCs. A growing global demand for food places greater emphasis on the development and improvement of GMCs.

Consequentially, this creates a demand for accurate analyses that can verify that unintentional changes are not made to crops after genetic manipulation. An in silico digest of the soybean proteome provided a list of expected peptides and MS E successfully detected the majority of the soybean proteome with high sequence coverage [23]. Wasslen et al. The modified peptides exhibit reduced ion suppression since proton affinity is not critical for ionization and they dissociate to form intense a2 fragments.

TrEnDi increases ionization efficiency and leads to more sensitive as well as fully predictable MRM transitions, enhancing and simplifying label-free quantitative proteomics workflows [25]. It utilizes normalization factors, such as exponentially modified protein abundance index emPAI , absolute protein expression APEX and normalized spectral abundance factor NSAF , which account for the physicochemical properties and lengths of peptides with respect to their abilities to be ionized [26].

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For example, normalized spectral index SI N was developed to greatly improve the reproducibility of label-free quantitation by considering spectral counting in conjunction with peptide count and fragment ion intensity [27]. With the ongoing improvement of data analysis software, label-free quantitation will still hold advantages over label-dependent approaches and continue to be a reliable way to monitor protein dynamics in a given system.

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Label-free strategies are more commonly used for relative quantitation but can also be employed for absolute quantitation through the addition of internal standards with known response factors or via the creation of a standard curve. Figure 5: Common label-free quantitation strategies using standard addition methods. An unlabeled peptide mixture containing green and purple peptides can be relatively quantified via extracted ion chromatogram XIC -based quantitation or spectral counting.

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In this diagram, the green peptide is 2. Relative quantitation techniques compare the intensities of differentially labeled peptides in the context of complex samples. Since the concentration of peptides arising from separate complex samples is unknown, actual peptide concentrations cannot be concluded by comparing the intensities of two peaks.

Absolute quantitation techniques, however, determine peptide concentrations by spiking the sample of interest with known concentrations of one or more heavy isotopically labeled standard peptides. Spiked peptides are synthetically prepared according to the absolute quantitation AQUA method.

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Alternatively the quantification concatamer QconCAT method incorporates bacteria grown in SILAC medium that can be transformed with high expression vectors to produce artificial proteins composed of standard peptides. Absolute quantitation methods often utilize targeted MRM methods for optimal sensitivity to determine the concentrations of low abundance peptides.

Methods currently used to quantify peptides presented by major histocompatbility complex MHC molecules on the surface of leukocytes rely on monoclonal T-cell stocks and antibodies, which require expensive and laborious cell culture practices. AQUA was recently demonstrated to be a higher-throughput and less expensive alternative for quantifying peptides antigens [28].

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Once the most intense transitions for the peptide of interest were chosen, the method enabled the quantitation of over one hundred peptide epitopes.