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Reviews Cite This: J. Proteome Res. 2019, 18, 2360−2369

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Advances in Higher Order Multiplexing Techniques in Proteomics Suruchi Aggarwal,†,‡,§ Narayan C. Talukdar,‡,§ and Amit K. Yadav*,† †

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Drug Discovery Research Centre, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Third Milestone, Faridabad − Gurgaon Expressway, Faridabad, Haryana 121001, India ‡ Division of Life Sciences, Institute of Advanced Study in Science and Technology, Vigyan Path, Paschim Boragaon, Garchuk, Guwahati, Assam 781035, India § Department of Molecular Biology and Biotechnology, Cotton University, Panbazar, Guwahati, Assam 781001, India ABSTRACT: Proteomics by mass spectrometry (MS) allows the large-scale identification and quantitation of the cellular proteins in a given biological context. Systems biology studies from proteomics data are largely limited by the accuracy and coverage of quantitative proteomics along with missing values. Toward this end, statistically robust biological observations are required, comprising multiple replicates, preferably with little technical variations. Multiplexed labeling techniques in proteomics allow quantitative comparisons of several biological samples or conditions. In this focused Review, we discuss an emerging technique called higher order multiplexing or enhanced multiplexing, a unique combination of traditional MS1- and MS2-based quantitative proteomics methods that allows for expanding the multiplexing capability of MS methods to save valuable instrument time, achieve statistical robustness, enhance coverage and quantitation accuracy, and reduce the run-to-run variability. We discuss the various innovative studies and experimental designs that exploit the power of this technique and its variants to provide an overview of a rapidly growing area and also to highlight the advantages and challenges that lie ahead in the widespread adoption of this technique. KEYWORDS: cPILOT, hyperplexing, iTRAQ, SILAC, TMT, quantitative proteomics, multiplexing, proteome dynamics

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INTRODUCTION Mass spectrometry (MS) has greatly evolved to become a powerful tool for charting proteome catalogs.1 The proteome consists of various “proteoforms” that encompass isoforms and mod-forms on the proteins.2 The study of the proteome using MS helps in a deeper mechanistic understanding of biological regulation by proteins and their interconnected functions, like protein−protein interactions (PPIs), protein complexes, subcellular localization, and rate of synthesis and degradation.2,3 Shotgun proteomics (also called bottom-up proteomics) is the most common method of studying proteome in highthroughput using MS.4,5 In this method, proteins are digested into peptides, and the resulting complex mixture is separated through high-performance liquid chromatography (LC) before being fed into the mass spectrometer. The mass analyzer selects “top n” high-abundance peptide ions, followed by their fragmentation in the subsequent analyzer. The mass spectrometer operates in data-dependent acquisition (DDA) mode, in which some masses already seen by analyzer are not selected for fragmentation for a short duration.6 The acquired MS/MS data are searched using various computational algorithms7−12 and are statistically validated using postprocessing tools.13−17 The identifications are used to infer proteins, and the lists are interpreted for their biological relevance. Biological questions demand not only identification but also reliable quantitation to capture the proteome dynamics. The © 2019 American Chemical Society

quantitative changes in the proteome can help us in understanding cellular mechanisms that can aid in monitoring temporal changes to stimulation, biological stress, or infection, finding new drug targets, and predicting the response to various drugs.18−20 Numerous authors have articulated the need for better quantitative proteomics study designs to generate reproducible data across a large number of samples for systems biology analysis.3,21 For a true systems biology approach, there is a need to generate deep profiling data with robust and reproducible quantitation, which can be used for modeling and understanding the dynamics of the system without concerns about the loss of data and quantitation fidelity.21 The bottom-up shotgun proteomics method has proven its value in generating a parts list of proteome catalogs, but it is suboptimal in capturing accurate quantitation with enough reliable statistical power and confidence.18 Multiplexing is the capacity to quantify several samples in one single experiment. Because shotgun proteomics by MS is not fundamentally quantitative, surrogate techniques need to be employed for quantitation. Quantitation can be either labelfree or label-based.22 Label-free quantitation (LFQ) is a semiquantitative method that involves peak-area-based or spectral-counting-based quantitation.23,24 The details of a label-free quantitative strategy have been reviewed elsewhere Received: April 9, 2019 Published: May 10, 2019 2360

DOI: 10.1021/acs.jproteome.9b00228 J. Proteome Res. 2019, 18, 2360−2369

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Journal of Proteome Research

Figure 1. Conceptual overview of experimental designs for higher order multiplexing studies. The 4-plex labels shown are only for demonstration purpose and can be extended to 8-, 10-, or 11-plex. (A) Multitagging, TMT-SILAC hyperplexing, and mPDP designs in which the cells are cultured in light (or heavy) SILAC media and later transferred to heavy (or light) media to monitor gradual changes at different time points using iTRAQ or TMT labels. (B) Hyperplexing and SILAC-iTRAQ-TAILS approach where the two conditions are labeled in differential cultures of SILAC and then treatments (or time points) are iTRAQ- (or TMT-) labeled. For N-terminomics, the isobaric labeling is carried out at protein terminals before digestion so as to negatively enrich the iTRAQ-labeled peptides by enriching and degrading the other peptides. (C) Hyperplexing-BONCAT (BONPlex) or MITNCAT approach. Here the pulsed SILAC labels and BONCAT labels (AHA tag) are added during specific time windows and subsequently labeled with isobaric labels to study newly synthesized proteins in a temporal manner. In all of the methods, the samples from both labelings were combined together before injection onto the mass spectrometer.

and are not the focus of the current Review25−27 because labelfree methods do not allow multiplexing. Label-based strategies use stable isotopes for quantitation, either metabolically in cell culture or via chemical labeling. Label-based methods are used for MS quantitation at either peptide precursor (MS1) or fragment ion (MS2 or MS3) levels.22,28 Multiplexing in quantitative proteomics started with the development of ICAT 29 and allowed two sample comparisons. Many techniques later emerged that pushed the initial limits with increasing multiplexing capability by labeling samples with

different stable isotopes of carbon, nitrogen, hydrogen, and oxygen. In metabolic labeling like stable isotope labeling by amino acids in cell culture (SILAC),30 cells grown in differentially labeled media for two to five different conditions are pooled together, digested, and analyzed through LC−MS/MS. The known MS1 peak differences between the labels help in the identification of SILAC pairs. The corresponding intensities for each channel are used for quantitation, whereas their fragmentation spectra are used for identifying the peptide 2361


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Journal of Proteome Research Table 1. Overview of Multiplexing Possible for Each Combination of MS1 and MS2 Techniquesa NeuCodeb

SILAC labels

plex

TMT (2- to 18-plex)

2-plex 6-plex 10-plex 11-plex 18-plexc 2-plex 4-plex 6-plex 8-plex 2-plex 4-plex 6-plex 10-plex 12-plex

iTRAQ (4- to 8-plex)

DiLeu (2- to 12-plex)

2-plex 4 12 2063−65 22 36 467 868 12 16 4 8 12 20 24

dimethyl

diacetyl

3-plex

5-plex

2-plex

4-plex

6-plex

2-plex

2-plex

6 1859 30 33 5466 6 12 1860 24 6 12 18 30 36

10 30 50 55 90 10 20 30 40 10 20 30 50 60

4 12 20 22 36 4 8 12 16 4 8 12 20 24

8 24 40 44 72 8 16 24 32 8 16 24 40 48

12 36 60 66 108 12 24 36 48 12 24 36 60 72

4 12 20 22 36 4 8 12 16 4 8 12 20 2469

4 1258 20 22 36 4 858 12 16 4 8 12 20 24

a

Note that not all such combinations have been demonstrated yet (already published ones are shown in bold). Users can pick a strategy based on the multiplexing desired and the availability of labels. bHigher order multiplexing has not yet been demonstrated with NeuCode labels. c Customized light-TMT, medium-TMT, and heavy-TMT (each 6-plex) labels used for the targeted study by Everley et al. 2013.66

sequences.31 This method has the least technical variability because the different samples analyzed are mixed very early in the analytical workflow.18 In isobaric chemical labeling methods, like isobaric tags for relative and absolute quantitation (iTRAQ)32,33 or tandem mass tags (TMTs),34 amine-reactive tags label N-terminal and lysine residues of peptides.35 The peptides are labeled after digestion and mixed before further analysis in LC−MS. The isobaric tags include a mass variable reporter group seen in MS2 fragmentation spectra and a balancer group, which is lost during fragmentation as a neutral loss.36 The reporter and balancer group combine together to form isobaric labels seen as one peak in MS1, but the reporter peak intensities are used to quantitate 2−11 samples in MS2. N,N-Dimethyl leucines (DiLeu) are a novel low-cost alternative to TMT, containing an amine reactive group (triazine ester) that targets the peptide N-term and ε-amino group of the lysine side chain, a balancer, and a reporter group. Each label shows an m/z shift of 145.1 Da. Intense a1 reporter ions are observed at reporter m/z of 115.1, 116.1, 117.1, and 118.1 in MS2 spectrum.37 For iTRAQ and TMT, it is sometimes suggested to use MS3 scans for better and more accurate quantitation38,39 so as to avoid ratio compression due to cofragmented spectra.40,41 However, to avoid confusion and ambiguity, we consider these under MS2 methods only for the purpose of this Review. SILAC,30 cysteine-selective dimethyl labeling (cysDML),42 amino-acid-coded mass tagging (AACT),43,44 dimethyl,37 and NeuCode45,46 labels are quantified in MS1 scan, while the other labels like DiLeu,37,47 iTRAQ,32,33 and TMT34 labels can be measured in the MS2 scan. These topics have been covered in some excellent reviews recently22,28,35,48−53 and will not be covered in depth here. With the help of labeling techniques, quantitative changes in the proteome can be captured for thousands of proteins in an untargeted manner. Significant improvements in both instrumentation and labeling methods have allowed ever increasing quantitation coverage. The multiplexing capacity has also taken exceptional strides in many significant biological studies.28,35 In shotgun proteomics, a number of sources of variability due to peptide digestion, separation via LC, and ionization efficiency can lead to missing data.54 Even ion selection by the

DDA method has an inherent stochastic nature that causes variability in multiple runs of the same sample. This limitation also accentuates the issue in labeling-based quantitation methods. Metabolic or isobaric labels, when used as a single quantitative technique, suffer from orphan peptides in one or more channels.55−57 This leads to a missing value problem when one or more channels are absent. This also reduces the accuracy of quantitation.40 The labeling techniques are being used to avoid multiple runs of case-control studies, thus reducing run-to-run variations, but this does not completely solve the problem due to the limited capacity of multiplexing.21

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HIGHER ORDER MULTIPLEXING Higher order multiplexing is defined as the unique combination of the MS1- and MS2-based quantitative techniques to achieve multiplexing by multiplying the channels used in each dimension. This broadly encompasses the techniques followed in a number of studies that are based on this idea, like the cPILOT58 and hyperplexing.59 There are differences in the labeling techniques used in these studies. For example, cPILOT uses dimethyl/diacetyl37 coupled to DiLeu/iTRAQ/ TMT tags. In other studies, the SILAC labels have been paired with either TMT59 or iTRAQ60,61 labels. Although some authors have also used the terms like enhanced multiplexing28,58,62 to refer to the multiplexing of 12-plex or higher, we believe the term is arbitrary and therefore define the term to exclusively reflect the combination of MS1 and MS2 labels for the purpose of this Review. The central idea revolves around the use of two types of mass encoding, allowing different biological samples to be mixed together. It provides better, in-depth proteome quantitation across several samples in a single MS run, providing more robust and reproducible data with less variability.63 The higher order multiplexing helps increase the MS1 peptide signal by pooling the respective isobaric precursors (six TMT isobaric labels adding to a combined SILAC light/medium/heavy peak, for example). The missing value problem is alleviated to some extent by mixing labeled isobaric samples, which increases the ion abundance and thus helps in the detection of otherwise low-abundance ions.64 It also enhances MS2 ion signals for better peptide sequencing 2362


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Welle et al.63 extended the approach to study the proteome dynamics in human dermal fibroblasts using the combination of SILAC-TMT for 12-plex (2 × 6-plex) and 20-plex (2 × 10plex) to demonstrate the kinetic changes in the protein synthesis and degradation rates between quiescent and dividing cells (discussed in detail in the next section). In early 2018, Zecha et al.65 also established the power of the multitagging approach to study proteoform dynamics in HeLa cells. Here the aim of the authors was to quantify differences in the synthesis and degradation rates of proteoforms of certain proteins to highlight the network-level changes introduced by the changing protein concentrations.

and avoids sequencing the same peptide from multiple samples repeatedly, thereby saving valuable mass-spectrometer time.65 The need to quantitate multiple analytes across a large number of samples to achieve experimental robustness at a much lower cost, generate high-quality data, get higher statistical rigor, and save mass-spectrometer run time makes higher order multiplexing a tantalizing proposition. The value of multiplexing is going to increase with proteomics being applied to a variety of biological and clinical questions in the time to come. Although there is an initial operational cost in performing such experiments, eventually it helps to save funds in the long run and provides greater data quality per unit dollar spent. Apart from the indirect cost-savings and reduction in the magnitude of missing values, higher order multiplexing also reduces the issue of run-to-run variability and brings proper statistical rigor along with better accuracy to quantitative proteomics.59,66 There have been many types of higher order multiplexing experiments applied to diverse kinds of ingenious experimental designs, as depicted in Figure 1. We have compiled a list of higher order multiplexing, both theoretically possible (by multiplying MS1 and MS2 labeling channels) as well as those already demonstrated in publications, in Table 1. In the following text, we attempt to chart the chronology of such experiments, highlighting the important developments, studies, and their experimental designs from a bioanalytical MS point of view.

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cPILOT: COMBINED PRECURSOR ISOTOPIC LABELING AND ISOBARIC TAGGING (2012) In 2012, Robinson and Evans devised another technique of higher order multiplexing known as combined precursor isotopic labeling and isobaric tagging (cPILOT) (Figure 1B).58 They exemplified the use of acetyl groups for generating the mass difference in MS1 and coupled it to 4-plex iTRAQ and 6-plex TMT for achieving 8-plex and 12-plex multiplexing capabilities, respectively. They applied this technique to identify and quantify 3-nitrotyrosine (3NT) modifications in mouse spleen tissues. Although the authors used the method with 4-plex iTRAQ, they argued that it can be easily coupled to the 8-plex variant as well. Subsequently, there have been many experiments using this technology for higher order multiplexing applied to diverse biological problems.42,62,71−73 Building upon the success of the cPILOT approach, in 2018, Frost et al.69 extended the above approach to 24-plexing by using dimethyl tags for MS1 and DiLeu tags for MS2. Because both of the tags are chemical tagging approaches, this approach can be directly used on biological samples, eliminating the need for metabolic labeling in cell cultures. This allows the approach to be used even for patient samples (tissue, plasma, serum, urine, saliva, etc.) in clinical studies, drug discovery or drug testing strategies because dimethyl labeling can be easily used chemically post-sample collection. While the authors have acknowledged that the sensitivity of dimethyl tags is ∼20% less than that of the SILAC labeling due to losing out on the hydrophobic peptides after dimethyl treatment,69,74 the possibility for labeling samples without involving cell culture has powerful applications for biological studies. In addition, with the enhanced multiplexing of replicates, the efficiency issue can largely be attenuated. The strategy can also be extended to 36-plex using triplex SILAC with 12-plex DiLeu tags or to 72-plex using 6-plex NeuCode with 12plex DiLeu tags (Table 1).

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MULTITAGGING: THE FIRST STEPS (2010) The first study that demonstrated the combination of MS1 and MS2 to achieve higher order multiplexing was conducted by Jayapal et al.68 By an innovative approach of combining two types of quantitative labeling techniques, the authors opened up a new avenue for discovery experiments, allowing the ease of access to identification and robust quantitative measurements across multiple samples (Figure 1A). The authors monitored the degradation rates of proteins in the bacteria transitioning from exponential to stationary growth phase by applying 8-plex quantitation. The authors combined the duplex SILAC-based label-chase method70 with 4-plex iTRAQ to monitor temporal proteome dynamics and degradation rates of proteins from Streptomyces coelicolor and referred to the technique as the dual labeling strategy. The S. coelicolor M145 spores were cultured and maintained in 13C615N4-labeled arginine media. This labeled the cultures with heavy arginine. The cultures were subsequently transferred to 12C614N4 arginine culture, and temporal samples were collected at four time points. The collected samples at each time point were then labeled with one of the 4-plex iTRAQ labels. Since iTRAQ labels the lysine and peptide N-terminal residues, the data could be searched by changing the SILAC approach method in the protein pilot dictionary. The authors devised their own analysis strategy for the quantitation of the two mixed labels for studying the degradation rate from time 0 to 8 h; they also calculated the effect of using a SILAC-only approach instead of the SILAC-iTRAQ multitagging approach to study the dynamics. They observed that if a SILAC-only strategy was used, then the rate constants measured were much higher than the true value and suggested that the SILACiTRAQ combined multiplexing method provided a more accurate and robust method for calculating rate constants for protein turnover.

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HYPERPLEXING (2012)

Another breakthrough in higher order multiplexing was announced by Dephoure et al.,59 who demonstrated hyperplexing as a technique to challenge the statistical and reproducibility issues in large-scale data in a single experiment. This was the first example of a higher order multiplexing technique employing 18-plex to be used in MS. By mixing triplex SILAC labels with 6-plex TMT labels, the proteins from 18 yeast samples could be quantitatively measured in a single experiment with several replicates59 (Figure 1B). The authors were able to study the temporal dynamics of yeast proteome upon rapamycin stimulation. The cells were first metabolically labeled using heavier lysine and arginine (SILAC labels) for 2363


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Journal of Proteome Research labeling biological conditions (control and treatments), followed by different N-terminal TMT tags (also on internal lysine residues) for labeling temporal dimension (posttreatment) to measure proteomes in 18-plex in single experiment, surpassing all previous multiplexing throughput achieved. The coverage and quantitation achieved in the study with multiple replicates was also unparalleled by any previous study. This design allowed the robust statistical analysis of shotgun proteomics data to monitor small changes that were statistically significant. The data were searched twice, once with TMT as a fixed modification on lysine, followed by a second SILAC-only search with no TMT. Sequest12 and Vista75 were used for the analysis with custom filtering of data. Later, the group also published a 54-plex66 method and discussed the possibility of achieving 90-plex in a single run exploiting the elemental mass defects in isotopologs of the labels by customizing three forms of TMT tagslight, medium, and heavy.66

labeled proteins, the yields of N-terminome are higher and much easier to read.77

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TMT-SILAC HYPERPLEXING (2016) In an extension to the previously discussed multitagging approach, Welle et al.63 enhanced the multiplexing using TMT and SILAC at different time intervals to study the kinetics of protein clearance and synthesis in human fibroblasts cells.63 This methodology combines the hyperplexing method from Dephoure et al. along with the synchronous precursor selection (SPS) technique to reduce ion interferences and improve TMT quantitation39 and analysis of protein dynamics. The aim of the study was to monitor the kinetics of the protein turnover rate under varying cellular and environmental conditions. To achieve the said goal, human dermal fibroblasts were grown to confluence. This led to the cell division arrest in the fibroblasts with contact inhibition. This was done to make sure that the protein turnover rate measured after this step was not influenced by any previous cellular division. After this step, the cells were transferred to a medium containing heavy arginine (13C6) and lysine (13C6). As the cells kept nourishing, the protein pool kept increasing in SILAC-labeled peptides as compared with nonlabeled peptides. There were 10 SILAC cultures that were mixed with TMT tags at 10 time points, from 0 to 336 h. In another sets of cultures, the fibroblasts cells were SILAC-labeled, followed by TMT labels for four time points to study the degradation and synthesis kinetics between dividing and quiescent cells. The authors used both SILAC and TMT labeling on lysine residues and thus were unable to search both SILAC and TMT labels in a single database search. They conducted two searches, one with unlabeled SILAC and TMT 10-plex78 and another with a new modification with the combined mass of both TMT and SILAC on lysine. Because the aim of the study was to measure the kinetics of protein synthesis and degradation, a kinetic model was developed to interpret the different quantitation values. The authors claimed that they could reduce the time and cost of the study. The MS runs for 10 samples in a single shot took only 3 h of machine time with nearly no missing time points. They also suggested that a similar experiment without multiplexing with dynamic SILAC would have taken more than 24 h of machine time, and all proteins were unlikely to be quantified at all of the time points.63 The authors observed that these benefits of using a multiplexing technology came at the cost of some quantitative precision and proteome coverage. Distinguishing between the 10-plex labels of TMT required a high resolution for peak separation as few reporters are very close in their masses, it led to a high scan time on MS and a smaller number of MS2 scans for sequencing.78 In this experiment, MS2 scans aid in sequence identification and MS3 scans aid in accurate quantitation. If the number of MS2 ions decreases, then the protein coverage suffers despite having more MS3 ions. The authors also observed that there was higher experimental noise in MS3 scans as compared with MS1, leading to somewhat reduced accuracy and precision. Another problem faced by the authors was that of coisolation and fragmentation of interfering ions.63 This is a problem associated with mainly the particular isobaric labeling rather than with multiplexing.38,55 The authors suggested that the technology should be used cautiously and in places where the experimental cost and time is more precious than the protein coverage.

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SILAC-iTRAQ TAILS (2015) In 2015, Schlage et al.67 combined SILAC and iTRAQ labels with the TAILS method to study matrix metalloproteinases (MMPs). This approach called SILAC-ITRAQ-TAILS (for iTRAQ-based terminal amine isotopic labeling of substrates) is a degradomics workflow to quantify N-terminal peptides from the secretome of cells like keratinocytes and fibroblasts. The study intended to identify and quantify MMPs from mouse embryonic fibroblasts (MEFs). SILAC was used to label arginine with heavier mass (13C615N4) in MEF cells, and iTRAQ-TAILS was added to intact proteins before protein digestion on lysine and N-term of the protein, aiding in the enrichment of N-terminome.76 Since different amino acids were labeled for MS1 and MS2 labeling (arginine for SILAC and N-term and lysines for iTRAQ-tails), the authors searched the data twice. The search was first conducted with SILAC as a fixed modification and iTRAQ as the variable and then with iTRAQ as a fixed modification and SILAC as the variable. The simplistic design ensured that the data analysis did not become a bottleneck. Identification becomes a challenge in such experiments because none of the currently available algorithms allows searching with fixed and variable modifications on the same amino acid. By employing the dual search strategy, the ingenious design of the study surpasses the need to introduce new modifications to search the mixed labels. In addition, because the labeling of iTRAQ-TAILS is achieved before the enrichment step, the quantification of multiple peptides is possible for a protein with the help of SILAC labels. The authors have used only two tags from the iTRAQ labels, making it effectively a 4-plex design (2 × 2 for SILAC and iTRAQ), but it can be easily extended by using a full range of iTRAQ labels to maximize the multiplexing benefits from this design. Using this multiplexed design for the study of Nterminome can aid in the identification of neo-N-termini as compared with natural N-termini. Since the authors have labeled only arginine in SILAC and lysine in iTRAQ, the multiplexing is achieved without complicating the search or quantitation. Because this technique enriches and removes nonlabeled peptides as compared with iTRAQ-labeled Ntermini, this negative enrichment of the N-terminome allows protein quantitation through multiple internal tryptic peptides with SILAC labels. As the sample complexity is also reduced along with multiple peptides sequenced for the terminal2364


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Figure 2. Chronology of higher order multiplexing studies is shown with the year and highest level of multiplexing achieved by combining the MS1 and MS2 labels. Representative techniques are shown only once with subsequent improvements in bullet points. The diamonds depict the MS1 metabolic (SILAC) or chemical labels (dimethyl/diacetyl), whereas the squares depict the isobaric MS2 labels (iTRAQ/TMT/Dileu).

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HYPERPLEXING WITH BONCAT (2016)

MITNCAT: MULTIPLEX ISOBARIC TAGGING/NONCANONICAL AMINO ACID TAGGING (2018) Another extension of the hyperplexing and BONCAT method was shown by Rothenberg et al.81 in 2018, using 10-plex TMT instead of iTRAQ to measure protein synthesis rates with temporal resolution after stimulation. The labeling by BONCAT79 and pSILAC70 enabled the enrichment of newly translated proteins, and TMT labeling helped in multiplexed quantitation (Figure 1C). Monitoring the epidermal growth factor (EGF) stimulation of HeLa cells, the authors measured protein synthesis rates with great temporal resolution. The multiplexing allowed greater temporal resolution (within the first 15 min to a few hours) for thousands of proteins. Among other previously enumerated benefits, pSILAC was also useful in both BONPlex60 and MITNCAT studies for retaining only the important peptides against the nonspecific background proteins, a major challenge with newly synthesized proteins. The authors metabolically labeled Arg with 13C615N4 and Lys with 13C615N2 heavy labels and measured the temporal response from 15 to 120 min in MCF10A cells. The experiment achieved 10-plex using only heavy SILAC to study protein synthesis. It could have been expanded to 20plex if both SILAC channels were used. The data were searched with combined masses of MS1 and MS2 labels used in the study. The statistical analysis was manually carried out as per the experimental design.

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Kumar et al., devised a multiplexing technique by combining the power of three techniques, pulsed metabolic labeling (pSILAC70), biorthogonal noncanonical amino acid tagging (BONCAT79), and the isobaric labeling (iTRAQ), to identify and quantify strain-specific temporal dynamic changes in proteins that were newly synthesized and secreted after Mycobacterium tuberculosis (Mtb) infection (Figure 1C). The technique was later named BONPlex. The study attempted to monitor newly translated secreted proteins as the THP-1 cell immune response to infection by different Mtb strains (avirulent, virulent, and hypervirulent clinical strains). In this study, THP-1 human macrophages were infected with two laboratory strains (H37Ra, avirulent; H37Rv, virulent) and two clinical strains (BND 433, virulent; JAL2287, virulent) of Mycobacterium tuberculosis in two parallel triplex SILAC experiment sets, each with a control and two infections. SILAC and azidohomoalanine (AHA) labeling in infected cells was started 6 h after the infection. The secretome after digestion was labeled with 6-plex iTRAQ labels to study pSILAC and AHA (or BONCAT) incorporation in specific time windows after infection (not throughout). The experiment consisted of two sets with experimental replicates in linear and nonlinear chromatographic separation modes. Each run allowed 18-plex multiplexing, saving considerable machine time. This study is the only one using each combination of SILAC and iTRAQ labels as a separate biological sample (infection strain or time point). The authors have circumvented the issue of complex data by searching a combined mass of iTRAQ and various SILAC labels (light, medium, or heavy) and using their own in-house developed tools for the quantitation and integration of multiplexed data.60 The multiplexed design allowed the identification and quantification of a very low-abundance, newly synthesized secretome,80 which without multiplexing would have been lost due to lower ion signals being neglected by the mass analyzer. The combined isobaric labels increase the signal and rescue these spectra for deeper depth and coverage of the proteome.

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mPDP: MULTIPLEXED PROTEOME DYNAMICS STUDIES (2018) In 2018, the TMT-SILAC Hyperplexing approach was modified by Savitski et al.64 to study protein dynamics with a multiplexed approach, mPDP or “multiplexed proteome dynamics prof iling”. The authors coupled multiple labeling techniques to enhance the sensitivity and quantitation depth of the protein turnover rate and established the technique with three different experiments. In one experiment, two sets of SILAC, one changing from light to heavy and another from heavy to light, were used to dynamically label the nascent and mature proteins, respectively. The dynamic temporal changes tagged by the SILAC labels were then encoded by TMT 102365


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experiment feasible.59,60,63−66,68 Table 1 summarizes the multiplexing capacity (both demonstrated and proposed) that can allow versatile experimental designs. While many advantages have been highlighted so far, no technique is without some pitfalls. The operational cost may be a potential barrier to entry for laboratories on a tight budget. This technique also comes with the inherent problem of isotopic interferences in using isobaric labels (iTRAQ and TMT). Ratio compression is a common problem due to ion interferences and simultaneous fragmentation of coeluting peptides.41 As a result, not all labels may be accurately quantified.40 While MS3 and multinotch MS3 with SPS are believed to remove such biases from TMT reporter tags,84 this causes reduced proteome coverage.38,39 Acquiring highly multiplexed spectra may also increase the overall experimental noise, although no study has directly measured the spectrum quality from such multiplexed studies.63 The technique also needs newer computational analysis and advanced bioinformatics expertise to search, process, analyze, and visualize the data in the context of the biological question asked. This is a challenge for nonexperts with no computational or MS skills. This Review thus also attempts to draw the attention of computational researchers to focus on this growing area and develop better tools for analysis and interpretation.

plex to study treatment-dependent effects on nascent and mature proteins. THP-1 cells were grown in normal arginine and lysine medium, later changed to a medium containing heavy arginine and lysine. These cells were treated with either vehicle or different bromodomain and extra-terminal domain (BET) inhibitors JQ1-Az, JQ1-VHL, or PROTAC at different concentrations and were TMT-labeled for five time points. Another set of SILAC cultures was grown in heavy medium of labeled arginine and lysine residues and then transferred to the unlabeled medium. The five treatments labeled with TMT were labeled with the remaining five TMT tags. The two sets with five TMT tags each were mixed together and run in a single shot of MS. A similar experimental design was followed for Jurkat cells and MCF7 cells to investigate estrogen receptor 1 (ESR1) agonists. As the study focused on the synthesis and degradation rates by differentially labeling nascent and mature proteins with light and heavy SILAC, the total SILAC signal in light and heavy channels remained constant and equal after mixing. This allowed deeper proteome coverage and quantitative profiling because the mixing improved SILAC pair intensities and thus reduced the problem of missing values in the replicates.

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PROS AND CONS OF HIGHER ORDER MULTIPLEXING As discussed in previous sections, several groups have innovatively applied variants of the higher order multiplexing techniques. A timeline of studies is depicted in Figure 2 to demonstrate the evolution of the rapidly growing field. We have discussed the advantages of methods that resonate well across the different study designs. From studying protein dynamics using the multitagging approaches68 to studying protein synthesis rates with MITNCAT,81 this Review has endeavored to highlight the studies facilitated by this technique in less than a decade since inception. We have also discussed through examples the variants of this technique and its applications for studying biologically important phenomena as well as for demonstrating the technical prowess of what MS can achieve. We sincerely hope that the readers pick a few cues for planning the next interesting multiplexed experiment from this discourse. With the help of improved multiplexing capacity, 72-plex (with currently available technology, 6-plex NeuCode with 12-plex Dileu), 90-plex66 (5-plex SILAC), and 108-plex (6-plex NeuCode) with customized (light, medium, and heavy) TMT-labeled isobaric tags are also possible. Singleshot analysis of such complex data can save a great amount of precious researcher and instrument time. This will enable the researchers to plan ingenious studies instead of focusing on monitoring and managing long and drab technical runs and will bring the focus back to interesting science. There are clear advantages of using the higher order multiplexing approach. First, the cost of accuracy is very low. Increased throughput without validation usually comes at the cost of accuracy in quantitation, replete with missed opportunities and false-positives,82 issues that higher order multiplexing can mitigate. Second, the mixing of the labels can be cost-efficient because it generates statistically robust and higher value data, which indirectly considerably cuts down the cost of the experiment and the manpower time. Third, the batch ef fect can be minimized83 while analyzing a large number of samples. The mitigation of run-to-run variations and reduced missing values makes the analysis of cohorts, clinical trials, testing different drugs, and testing drug leads in a single

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CONCLUSIONS

In this Review, we described the chronology of the experimental designs (Figure 2) and biological use cases for the applications of higher order multiplexing techniques, which can aid the reader in the better design of proteomics studies and facilitate interesting biological problems. The journey started with multitagging, cPILOT, hyperplexing, and BONPlex are examples to demonstrate the need for more robust designs for proteomics researchers to discover the mechanistic understandings of the cellular processes reproducibly. The studies described in this Review represent the use of technology to surpass the multiplexing limits of proteomics and its wider applications on a global scale. Such multiplexing studies will usher in the systems biology era driven by proteomics, with applications in drug discovery and other related biological fields. The benefits of multiplexed analysis are immense, despite some data analysis challenges. We hope to enthuse the readers enough to start investing in such experiments where data makes more statistical sense and is informationally richer and superior in quality. This Review also intends to draw the attention of bioinformatics researchers to devise better methods, tools, and analytical pipelines for handling such complex data. The community may benefit from more discussions about the applications of the approaches and the formulation of standardized guidelines for planning, executing, and analyzing such experiments. With tighter budgets, it will be useful to devote funds to studies or experimental designs that are likely to be far more reproducible and accurate than to conduct multiple experiments that cannot provide conclusive answers. These developments are very exciting from both biological and analytical viewpoints, and systems-level temporal dynamics can now be regularly studied using MS proteomics. The era of systems biology from proteomics data has arrived. 2366


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spectrometry search algorithm. Journal of proteome research. 2004, 3 (5), 958−64. (11) Kim, S.; Gupta, N.; Pevzner, P. A. Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases. Journal of proteome research. 2008, 7 (8), 3354−63. (12) Eng, J. K.; McCormack, A. L.; Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5 (11), 976−89. (13) Keller, A.; Nesvizhskii, A. I.; Kolker, E.; Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 2002, 74 (20), 5383−92. (14) Kall, L.; Canterbury, J. D.; Weston, J.; Noble, W. S.; MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 2007, 4 (11), 923−5. (15) Aggarwal, S.; Yadav, A. K. False Discovery Rate Estimation in Proteomics. Methods Mol. Biol. 2016, 1362, 119−28. (16) Yadav, A. K.; Kumar, D.; Dash, D. Learning from decoys to improve the sensitivity and specificity of proteomics database search results. PLoS One 2012, 7 (11), No. e50651. (17) Yadav, A. K.; Kadimi, P. K.; Kumar, D.; Dash, D. ProteoStats–a library for estimating false discovery rates in proteomics pipelines. Bioinformatics 2013, 29 (21), 2799−800. (18) Bantscheff, M.; Schirle, M.; Sweetman, G.; Rick, J.; Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 2007, 389 (4), 1017−31. (19) Nakamura, T.; Oda, Y. Mass spectrometry-based quantitative proteomics. Biotechnol. Genet. Eng. Rev. 2007, 24, 147−63. (20) Yadav, A. K. Commentary: Deep Phosphoproteomic Measurements Pinpointing Drug Induced Protective Mechanisms in Neuronal Cells. Front. Physiol. 2017, 8, 174. (21) Rost, H. L.; Malmstrom, L.; Aebersold, R. Reproducible quantitative proteotype data matrices for systems biology. Mol. Biol. Cell 2015, 26 (22), 3926−31. (22) Ankney, J. A.; Muneer, A.; Chen, X. Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics. Annu. Rev. Anal. Chem. 2018, 11 (1), 49−77. (23) Schwanhausser, B.; Busse, D.; Li, N.; Dittmar, G.; Schuchhardt, J.; Wolf, J.; Chen, W.; Selbach, M. Global quantification of mammalian gene expression control. Nature 2011, 473 (7347), 337−42. (24) Wong, J. W.; Cagney, G. An overview of label-free quantitation methods in proteomics by mass spectrometry. Methods Mol. Biol. 2010, 604, 273−83. (25) Neilson, K. A.; Ali, N. A.; Muralidharan, S.; Mirzaei, M.; Mariani, M.; Assadourian, G.; Lee, A.; van Sluyter, S. C.; Haynes, P. A. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 2011, 11 (4), 535−53. (26) Souza, G. H.; Guest, P. C.; Martins-De-Souza, D. LC-MS(E), Multiplex MS/MS, Ion Mobility, and Label-Free Quantitation in Clinical Proteomics. Methods Mol. Biol. 2017, 1546, 57−73. (27) Zhu, W.; Smith, J. W.; Huang, C. M. Mass spectrometry-based label-free quantitative proteomics. J. Biomed. Biotechnol. 2010, 2010, 840518. (28) Arul, A. B.; Robinson, R. A. S. Sample Multiplexing Strategies in Quantitative Proteomics. Anal. Chem. 2019, 91, 178. (29) Yan, W.; Lee, H.; Deutsch, E. W.; Lazaro, C. A.; Tang, W.; Chen, E.; Fausto, N.; Katze, M. G.; Aebersold, R. A dataset of human liver proteins identified by protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry. Mol. Cell. Proteomics 2004, 3 (10), 1039−41. (30) Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann, M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 2002, 1 (5), 376−86. (31) Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +91 129 2876490. ORCID

Suruchi Aggarwal: 0000-0002-3921-321X Amit K. Yadav: 0000-0002-9445-8156 Author Contributions

S.A., N.C.T., and A.K.Y. all contributed to writing and editing the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS S.A. is supported by the ICMR-SRF (BIC/11/(17)/2015) grant, A.K.Y. is supported by the DBT-IYBA (D.O. no. BT/ 07/IYBA/2013), DBT-Big Data Initiative grant (BT/ PR16456/BID/7/624/2016), and THSTI grant, and N.C.T. is supported by DBT Twinning NER (102/IFD/SAN/1048/ 2016-2017 dated 14/06/2016 serial no. 6-17). We acknowledge Drs. Shilpa Jamwal, Dinesh Mahajan, and Yashwant Kumar for critically reviewing and proofreading the manuscript. We also acknowledge the unknown peer reviewers, whose constructive criticism helped in improving the manuscript.

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REFERENCES

(1) Aebersold, R.; Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 2016, 537 (7620), 347−55. (2) Aebersold, R.; Agar, J. N.; Amster, I. J.; Baker, M. S.; Bertozzi, C. R.; Boja, E. S.; Costello, C. E.; Cravatt, B. F.; Fenselau, C.; Garcia, B. A.; Ge, Y.; Gunawardena, J.; Hendrickson, R. C.; Hergenrother, P. J.; Huber, C. G.; Ivanov, A. R.; Jensen, O. N.; Jewett, M. C.; Kelleher, N. L.; Kiessling, L. L.; Krogan, N. J.; Larsen, M. R.; Loo, J. A.; Ogorzalek Loo, R. R.; Lundberg, E.; MacCoss, M. J.; Mallick, P.; Mootha, V. K.; Mrksich, M.; Muir, T. W.; Patrie, S. M.; Pesavento, J. J.; Pitteri, S. J.; Rodriguez, H.; Saghatelian, A.; Sandoval, W.; Schluter, H.; Sechi, S.; Slavoff, S. A.; Smith, L. M.; Snyder, M. P.; Thomas, P. M.; Uhlen, M.; Van Eyk, J. E.; Vidal, M.; Walt, D. R.; White, F. M.; Williams, E. R.; Wohlschlager, T.; Wysocki, V. H.; Yates, N. A.; Young, N. L.; Zhang, B. How many human proteoforms are there? Nat. Chem. Biol. 2018, 14 (3), 206−214. (3) Larance, M.; Lamond, A. I. Multidimensional proteomics for cell biology. Nature reviews. Nat. Rev. Mol. Cell Biol. 2015, 16 (5), 269− 80. (4) Aebersold, R.; Mann, M. Mass spectrometry-based proteomics. Nature 2003, 422 (6928), 198−207. (5) Lane, C. S. Mass spectrometry-based proteomics in the life sciences. Cell. Mol. Life Sci. 2005, 62 (7−8), 848−69. (6) Wu, C. C.; MacCoss, M. J. Shotgun proteomics: tools for the analysis of complex biological systems. Curr. Opin. Mol. Ther. 2002, 4 (3), 242−50. (7) Perkins, D. N.; Pappin, D. J.; Creasy, D. M.; Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20 (18), 3551−67. (8) Craig, R.; Beavis, R. C. A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid Commun. Mass Spectrom. 2003, 17 (20), 2310−6. (9) Yadav, A. K.; Kumar, D.; Dash, D. MassWiz: a novel scoring algorithm with target-decoy based analysis pipeline for tandem mass spectrometry. Journal of proteome research. 2011, 10 (5), 2154−60. (10) Geer, L. Y.; Markey, S. P.; Kowalak, J. A.; Wagner, L.; Xu, M.; Maynard, D. M.; Yang, X.; Shi, W.; Bryant, S. H. Open mass 2367


Reviews

Journal of Proteome Research proteome-wide protein quantification. Nat. Biotechnol. 2008, 26 (12), 1367−72. (32) Ross, P. L.; Huang, Y. N.; Marchese, J. N.; Williamson, B.; Parker, K.; Hattan, S.; Khainovski, N.; Pillai, S.; Dey, S.; Daniels, S.; Purkayastha, S.; Juhasz, P.; Martin, S.; Bartlet-Jones, M.; He, F.; Jacobson, A.; Pappin, D. J. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 2004, 3 (12), 1154−69. (33) Chong, P. K.; Gan, C. S.; Pham, T. K.; Wright, P. C. Isobaric tags for relative and absolute quantitation (iTRAQ) reproducibility: Implication of multiple injections. Journal of proteome research. 2006, 5 (5), 1232−40. (34) Thompson, A.; Schäfer, J.; Kuhn, K.; Kienle, S.; Schwarz, J.; Schmidt, G.; Neumann, T.; Hamon, C. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 2003, 75 (8), 1895−904. (35) Rauniyar, N.; Yates, J. R., 3rd Isobaric labeling-based relative quantification in shotgun proteomics. Journal of proteome research. 2014, 13 (12), 5293−309. (36) Aggarwal, S.; Yadav, A. K. Dissecting the iTRAQ Data Analysis. Methods Mol. Biol. 2016, 1362, 277−91. (37) Xiang, F.; Ye, H.; Chen, R.; Fu, Q.; Li, L. N,N-dimethyl leucines as novel isobaric tandem mass tags for quantitative proteomics and peptidomics. Anal. Chem. 2010, 82 (7), 2817−25. (38) Cousins, L. M.; Thomson, B. A. MS3 using the collision cell of a tandem mass spectrometer system. Rapid Commun. Mass Spectrom. 2002, 16 (11), 1023−34. (39) McAlister, G. C.; Nusinow, D. P.; Jedrychowski, M. P.; Wuhr, M.; Huttlin, E. L.; Erickson, B. K.; Rad, R.; Haas, W.; Gygi, S. P. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 2014, 86 (14), 7150−8. (40) Karp, N. A.; Huber, W.; Sadowski, P. G.; Charles, P. D.; Hester, S. V.; Lilley, K. S. Addressing accuracy and precision issues in iTRAQ quantitation. Mol. Cell. Proteomics 2010, 9 (9), 1885−97. (41) Savitski, M. M.; Mathieson, T.; Zinn, N.; Sweetman, G.; Doce, C.; Becher, I.; Pachl, F.; Kuster, B.; Bantscheff, M. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. Journal of proteome research. 2013, 12 (8), 3586−98. (42) Gu, L.; Evans, A. R.; Robinson, R. A. Sample multiplexing with cysteine-selective approaches: cysDML and cPILOT. J. Am. Soc. Mass Spectrom. 2015, 26 (4), 615−30. (43) Chen, X.; Sun, L.; Yu, Y.; Xue, Y.; Yang, P. Amino acid-coded tagging approaches in quantitative proteomics. Expert Rev. Proteomics 2007, 4 (1), 25−37. (44) Cagney, G.; Emili, A. De novo peptide sequencing and quantitative profiling of complex protein mixtures using mass-coded abundance tagging. Nat. Biotechnol. 2002, 20 (2), 163−70. (45) Merrill, A. E.; Hebert, A. S.; MacGilvray, M. E.; Rose, C. M.; Bailey, D. J.; Bradley, J. C.; Wood, W. W.; El Masri, M.; Westphall, M. S.; Gasch, A. P.; Coon, J. J. NeuCode labels for relative protein quantification. Mol. Cell. Proteomics 2014, 13 (9), 2503−12. (46) Hebert, A. S.; Merrill, A. E.; Bailey, D. J.; Still, A. J.; Westphall, M. S.; Strieter, E. R.; Pagliarini, D. J.; Coon, J. J. Neutron-encoded mass signatures for multiplexed proteome quantification. Nat. Methods 2013, 10 (4), 332−4. (47) Frost, D. C.; Greer, T.; Li, L. High-resolution enabled 12-plex DiLeu isobaric tags for quantitative proteomics. Anal. Chem. 2015, 87 (3), 1646−54. (48) Schulze, W. X.; Usadel, B. Quantitation in mass-spectrometrybased proteomics. Annu. Rev. Plant Biol. 2010, 61, 491−516. (49) He, D.; Lou, X. Y.; He, S. L.; Lei, Y. K.; Lv, B. V.; Wang, Z.; Zheng, Y. B.; Liu, Y. P. Isobaric tags for relative and absolute quantitation-based quantitative proteomics analysis provides novel insights into the mechanism of cross-incompatibility between tree peony and herbaceous peony. Functional plant biology: FPB 2019, 46, 417. (50) Bantscheff, M.; Kuster, B. Quantitative mass spectrometry in proteomics. Anal. Bioanal. Chem. 2012, 404 (4), 937−8.

(51) Bantscheff, M.; Lemeer, S.; Savitski, M. M.; Kuster, B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal. Bioanal. Chem. 2012, 404 (4), 939− 65. (52) Li, H.; Han, J.; Pan, J.; Liu, T.; Parker, C. E.; Borchers, C. H. Current trends in quantitative proteomics - an update. J. Mass Spectrom. 2017, 52 (5), 319−341. (53) Lindemann, C.; Thomanek, N.; Hundt, F.; Lerari, T.; Meyer, H. E.; Wolters, D.; Marcus, K. Strategies in relative and absolute quantitative mass spectrometry based proteomics. Biol. Chem. 2017, 398 (5−6), 687−699. (54) Webb-Robertson, B. J.; Wiberg, H. K.; Matzke, M. M.; Brown, J. N.; Wang, J.; McDermott, J. E.; Smith, R. D.; Rodland, K. D.; Metz, T. O.; Pounds, J. G.; Waters, K. M. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometrybased label-free global proteomics. Journal of proteome research. 2015, 14 (5), 1993−2001. (55) Gan, C. S.; Chong, P. K.; Pham, T. K.; Wright, P. C. Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ). Journal of proteome research. 2007, 6 (2), 821−7. (56) Boehm, A. M.; Putz, S.; Altenhofer, D.; Sickmann, A.; Falk, M. Precise protein quantification based on peptide quantification using iTRAQ. BMC Bioinf. 2007, 8, 214. (57) Ow, S. Y.; Salim, M.; Noirel, J.; Evans, C.; Rehman, I.; Wright, P. C. iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly. Journal of proteome research. 2009, 8 (11), 5347−55. (58) Robinson, R. A.; Evans, A. R. Enhanced sample multiplexing for nitrotyrosine-modified proteins using combined precursor isotopic labeling and isobaric tagging. Anal. Chem. 2012, 84 (11), 4677−86. (59) Dephoure, N.; Gygi, S. P. Hyperplexing: a method for higherorder multiplexed quantitative proteomics provides a map of the dynamic response to rapamycin in yeast. Sci. Signaling 2012, 5 (217), No. rs2. (60) Kumar, A.; Jamwal, S.; Midha, M. K.; Hamza, B.; Aggarwal, S.; Yadav, A. K.; Rao, K. V. Dataset generated using hyperplexing and click chemistry to monitor temporal dynamics of newly synthesized macrophage secretome post infection by mycobacterial strains. Data in brief. 2016, 9, 349−54. (61) Jamwal, S.; Aggarwal, S.; Yadav, A. K.; Rao, K. V. S.; Kumar, A. Method of Hyperplexing in Mass Spectrometry to Elucidate Temporal Dynamics of Proteome. Patent WO2018042454A3, 2018. (62) King, C. D.; Dudenhoeffer, J. D.; Gu, L.; Evans, A. R.; Robinson, R. A. S. Enhanced Sample Multiplexing of Tissues Using Combined Precursor Isotopic Labeling and Isobaric Tagging (cPILOT). J. Visualized Exp. 2017, No. 123, No. e55406. (63) Welle, K. A.; Zhang, T.; Hryhorenko, J. R.; Shen, S.; Qu, J.; Ghaemmaghami, S. Time-resolved Analysis of Proteome Dynamics by Tandem Mass Tags and Stable Isotope Labeling in Cell Culture (TMT-SILAC) Hyperplexing. Mol. Cell. Proteomics 2016, 15 (12), 3551−3563. (64) Savitski, M. M.; Zinn, N.; Faelth-Savitski, M.; Poeckel, D.; Gade, S.; Becher, I.; Muelbaier, M.; Wagner, A. J.; Strohmer, K.; Werner, T.; Melchert, S.; Petretich, M.; Rutkowska, A.; Vappiani, J.; Franken, H.; Steidel, M.; Sweetman, G. M.; Gilan, O.; Lam, E. Y. N.; Dawson, M. A.; Prinjha, R. K.; Grandi, P.; Bergamini, G.; Bantscheff, M. Multiplexed Proteome Dynamics Profiling Reveals Mechanisms Controlling Protein Homeostasis. Cell 2018, 173 (1), 260−274.e25. (65) Zecha, J.; Meng, C.; Zolg, D. P.; Samaras, P.; Wilhelm, M.; Kuster, B. Peptide Level Turnover Measurements Enable the Study of Proteoform Dynamics. Mol. Cell. Proteomics 2018, 17 (5), 974−992. (66) Everley, R. A.; Kunz, R. C.; McAllister, F. E.; Gygi, S. P. Increasing throughput in targeted proteomics assays: 54-plex quantitation in a single mass spectrometry run. Anal. Chem. 2013, 85 (11), 5340−6. (67) Schlage, P.; Kockmann, T.; Kizhakkedathu, J. N.; auf dem Keller, U. Monitoring matrix metalloproteinase activity at the 2368


Reviews

Journal of Proteome Research epidermal-dermal interface by SILAC-iTRAQ-TAILS. Proteomics 2015, 15 (14), 2491−502. (68) Jayapal, K. P.; Sui, S.; Philp, R. J.; Kok, Y. J.; Yap, M. G.; Griffin, T. J.; Hu, W. S. Multitagging proteomic strategy to estimate protein turnover rates in dynamic systems. Journal of proteome research. 2010, 9 (5), 2087−97. (69) Frost, D. C.; Rust, C. J.; Robinson, R. A. S.; Li, L. Increased N, N-Dimethyl Leucine Isobaric Tag Multiplexing by a Combined Precursor Isotopic Labeling and Isobaric Tagging Approach. Anal. Chem. 2018, 90 (18), 10664−10669. (70) Schwanhausser, B.; Gossen, M.; Dittmar, G.; Selbach, M. Global analysis of cellular protein translation by pulsed SILAC. Proteomics 2009, 9 (1), 205−9. (71) Evans, A. R.; Gu, L.; Guerrero, R., Jr.; Robinson, R. A. Global cPILOT analysis of the APP/PS-1 mouse liver proteome. Proteomics: Clin. Appl. 2015, 9 (9−10), 872−84. (72) Evans, A. R.; Robinson, R. A. Global combined precursor isotopic labeling and isobaric tagging (cPILOT) approach with selective MS(3) acquisition. Proteomics 2013, 13 (22), 3267−72. (73) Gu, L.; Robinson, R. A. High-throughput endogenous measurement of S-nitrosylation in Alzheimer’s disease using oxidized cysteine-selective cPILOT. Analyst 2016, 141 (12), 3904−15. (74) Lau, H. T.; Suh, H. W.; Golkowski, M.; Ong, S. E. Comparing SILAC- and stable isotope dimethyl-labeling approaches for quantitative proteomics. Journal of proteome research. 2014, 13 (9), 4164−74. (75) Bakalarski, C. E.; Elias, J. E.; Villen, J.; Haas, W.; Gerber, S. A.; Everley, P. A.; Gygi, S. P. The impact of peptide abundance and dynamic range on stable-isotope-based quantitative proteomic analyses. Journal of proteome research. 2008, 7 (11), 4756−65. (76) Prudova, A.; auf dem Keller, U.; Butler, G. S.; Overall, C. M. Multiplex N-terminome analysis of MMP-2 and MMP-9 substrate degradomes by iTRAQ-TAILS quantitative proteomics. Mol. Cell. Proteomics 2010, 9 (5), 894−911. (77) Wang, Z.; Zhang, Y.; Hong, X.; Xu, P. N-terminomics: proteomic strategies for protein N-terminal profiling. Shengwu Gongcheng Xuebao 2016, 32 (8), 1001−1009. (78) McAlister, G. C.; Huttlin, E. L.; Haas, W.; Ting, L.; Jedrychowski, M. P.; Rogers, J. C.; Kuhn, K.; Pike, I.; Grothe, R. A.; Blethrow, J. D.; Gygi, S. P. Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal. Chem. 2012, 84 (17), 7469−78. (79) Dieterich, D. C.; Link, A. J.; Graumann, J.; Tirrell, D. A.; Schuman, E. M. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc. Natl. Acad. Sci. U. S. A. 2006, 103 (25), 9482−7. (80) Eichelbaum, K.; Winter, M.; Diaz, M. B.; Herzig, S.; Krijgsveld, J. Selective enrichment of newly synthesized proteins for quantitative secretome analysis. Nat. Biotechnol. 2012, 30 (10), 984−990. (81) Rothenberg, D. A.; Taliaferro, J. M.; Huber, S. M.; Begley, T. J.; Dedon, P. C.; White, F. M. A Proteomics Approach to Profiling the Temporal Translational Response to Stress and Growth. iScience. 2018, 9, 367−381. (82) White, F. M. The potential cost of high-throughput proteomics. Sci. Signaling 2011, 4 (160), No. pe8. (83) Gregori, J.; Villarreal, L.; Mendez, O.; Sanchez, A.; Baselga, J.; Villanueva, J. Batch effects correction improves the sensitivity of significance tests in spectral counting-based comparative discovery proteomics. J. Proteomics 2012, 75 (13), 3938−51. (84) Hogrebe, A.; von Stechow, L.; Bekker-Jensen, D. B.; Weinert, B. T.; Kelstrup, C. D.; Olsen, J. V. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat. Commun. 2018, 9 (1), 1045.

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