Advances in higher order multiplexing techniques in proteomics

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Advances in higher order multiplexing techniques in proteomics Suruchi Aggarwal, Narayan C. Talukdar, and Amit K. Yadav J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.9b00228 • Publication Date (Web): 10 May 2019 Downloaded from http://pubs.acs.org on May 10, 2019

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Journal of Proteome Research

Advances in higher order multiplexing techniques in proteomics Suruchi Aggarwal1, 2, 3, Narayan C. Talukdar2, 3, Amit K. Yadav1* 1

Drug Discovery Research Centre, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Faridabad – Gurgaon Expressway, Faridabad, Haryana, IN 121001 2 Division of Life Sciences, Institute of Advanced Study in Science and Technology, Vigyan Path, Paschim Boragaon, Garchuk, Guwahati, Assam, IN 781035 3 Department of Molecular Biology and Biotechnology, Cotton University, Panbazar, Guwahati, Assam, IN 781001 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 accuracy and coverage of quantitative proteomics along with missing values. Towards 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, enhancing coverage, 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, in order 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 Introduction Mass spectrometry has evolved greatly to become a powerful tool for charting proteome catalogs1. The proteome consists of various “proteoforms” that encompass isoforms and modforms on the proteins2. Study of the proteome using mass spectrometry helps in deeper mechanistic understanding of biological regulation, by proteins and their interconnected functions - like protein-protein interactions (PPIs), protein complexes, subcellular localization, rate of synthesis and degradation2-3. Shotgun proteomics (also called bottom up proteomics) is the most common method of studying proteome in high throughput using mass spectrometry4-5. 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 duration6. The acquired MS/MS data is searched using various computational algorithms7-12 and statistically validated using post-processing tools13-17. 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 quantitative changes in the proteome can help us in under-

standing cellular mechanisms which can aid in monitoring temporal changes to stimulation, biological stress or infection, finding new drug targets and predicting response to various drugs18-20. Numerous authors have articulated the need for better quantitative proteomics study designs to generate reproducible data across large number of samples for systems biology analysis3, 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 dynamics of the system without concerns about the loss of data and quantitation fidelity21. The bottom-up shotgun proteomics method has proven its value in generating parts list of proteome catalogs, but is suboptimal in capturing accurate quantitation with enough reliable statistical power and confidence18. Multiplexing is the capacity to quantify several samples in one single experiment. As shotgun proteomics by mass spectrometry is not fundamentally quantitative, surrogate techniques need to be employed for quantitation. Quantitation can be either label-free or label-based22. Label-free quantitation (LFQ) is a semi-quantitative method that involves peak area based or spectral counting based quantitation23-24. The details of a label-free quantitative strategy have been reviewed elsewhere and not the focus of current review25-27 as label-free 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 either at peptide precursor (MS1) or

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fragment ion (MS2 or MS3) level22, 28. Multiplexing in quantitative proteomics started with the development of ICAT29 and allowed two sample comparison. 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 2-5 different conditions are pooled together, digested and analyzed through LC-MS/MS. The known MS1 peak differences between the labels help in identification of SILAC pairs. The corresponding intensities for each channel is used for quantitation while their fragmentation spectra are used for identifying the peptide sequences31. This method has least technical variability as the different samples analyzed are mixed very early in the analytical workflow18. In isobaric chemical labeling methods like - Isobaric tags for relative and absolute quantitation (iTRAQ)32-33 or Tandem Mass Tags (TMT)34, amine-reactive tags label N-terminal and lysine residues of peptides35. The peptides are labelled 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 loss36. 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 tandem mass tag containing an amine reactive group (triazine ester) that targets the peptide N-term and ε-amino group of 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 spectrum37. For iTRAQ and TMT, it is sometimes suggested to use MS3 scans for better and accurate quantitation38-39 so as to avoid ratio compression due to co-fragmented spectra40-41. However, to avoid confusion and ambiguity, we consider these under MS2 methods only for the purpose of this review. While SILAC30, cysteine-selective dimethyl labeling (cysDML)42, amino-acid-coded mass tagging (AACT)43-44, Dimethyl37 and NeuCode45-46 labels are quantified in MS1 scan; the other labels like DiLeu37, 47, iTRAQ32-33 and TMT34 labels can be measured in 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 untargeted manner. Significant improvements in both instrumentation and labeling methods allowed ever increasing quantitation coverage. The multiplexing capacity has also taken exceptional strides in many significant biological studies28, 35. In shotgun proteomics, a number of sources of variability due to peptide digestion, separation via liquid chromatography, and ionization efficiency can lead to missing data54. Even ion selection by 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 channels55-57. This leads to a missing

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value problem when one or more channels are absent. This also reduces the accuracy of quantitation40. 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 limited capacity of multiplexing21. 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 hyperplexing59. There are differences in the labeling techniques used in these studies. For example, cPIcoupled with LOT uses dimethyl/diacetyl37 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 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 single mass spectrometry run, providing more robust and reproducible data with lesser variability63. 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 that increases the ion abundance and thus helps in detection of otherwise low abundant ions64. It also enhances MS2 ion signals for better peptide sequencing and avoids sequencing the same peptide from multiple samples repeatedly, thereby saving valuable mass spectrometer time65. The need to quantitate multiple analytes across large number of samples to achieve experimental robustness at much lower cost, generate high-quality data, get higher statistical rigor and saving 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 times 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-saving 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 proteomics59, 66. There have been many types of higher order multiplexing experiments applied to diverse kinds of ingenious experimental designs as depicted in Fig. 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 follow-


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ing text, we attempt to chart the chronology of such experiments, highlighting the important developments, studies and their experimental designs from a bioanalytical mass spectrometry point of view.



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Figure 1. The figure represents 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) represents the 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) represents a hyperplexing and SILAC-iTRAQ-TAILS approach where the two conditions are labeled in differential cultures of SILAC and then treatment (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) represents the hyperplexing-BONCAT (BONPlex) or MITNCAT ap-


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proach. 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 the methods, the samples from both labeling were combined together before injection on to the mass spectrometer.



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Table 1. Overview of multiplexing possible for each combination of MS1 and MS2 techniques. Note that not all such combinations have been demonstrated yet (already published ones are shown in bold red). Users can pick a strategy based on the multiplexing desired and availability of labels. Labels TMT (2 to 18-plex)

iTRAQ (4 to 8-plex)

DiLeu (2 to 12-plex)

SILAC

NeuCode**

Dimethyl

Diacetyl

Plex

2-plex

3-plex

5-plex

2-plex

4-plex

6-plex

2-plex

2-plex

2-plex

4

6

10

4

8

12

4

4

6-plex

12

1859

30

12

24

36

12

1258

10-plex

2063-65

30

50

20

40

60

20

20

11-plex

22

33

55

22

44

66

22

22

18-plex*

36

5466

90

36

72

108

36

36

2-plex

467

6

10

4

8

12

4

4

4-plex

868

12

20

8

16

24

8

858

6-plex

12

1860

30

12

24

36

12

12

8-plex

16

24

40

16

32

48

16

16

2-plex

4

6

10

4

8

12

4

4

4-plex

8

12

20

8

16

24

8

8

6-plex

12

18

30

12

24

36

12

12

10-plex

20

30

50

20

40

60

20

20

72

2469

24

12-plex

24

36

60

24

48

* Customized Light-TMT, Medium-TMT and Heavy-TMT (each 6-plex) labels used for targeted study by Everley et al 2013. ** Higher order multiplexing has not been demonstrated yet with NeuCode labels.

Figure 2. The 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), while the squares depict the isobaric MS2 labels (iTRAQ/TMT/Dileu).


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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 al68. 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 (Fig. 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 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 SILAC approach method in protein pilot dictionary. The authors devised their own analysis strategy for quantitation of the two mixed labels for studying the degradation rate from time 0 to 8 h, they also calculated the effect of using SILAC only approach instead of SILACiTRAQ multitagging approach to study the dynamics. They observed that if SILAC only strategy was used, the rate constants measured were much higher than the true value and suggested that SILAC-iTRAQ combined multiplexing method provided a more accurate and robust method for calculating rate constants for protein turn over. Welle et al63 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 next section). In early 2018, Zecha et al65 also established the power of multitagging approach to study proteoform dynamics in HeLa cells. Here, the aim of the authors was to quantify differences in synthesis and degradation rates of proteoforms of certain proteins to highlight the network level changes introduced by the changing protein concentrations. 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) (Fig. 1B)58. They exemplified the use of acetyl groups for generating mass difference in MS1 and coupled it with 4-plex iTRAQ and 6-plex TMT for achieving 8-plex and 12-plex multiplexing capability 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 with the 8plex variant as well. Subsequently, there have been many ex-

periments using this technology for higher order multiplexing applied to diverse biological problems42, 62, 71-73. Building upon the success of the cPILOT approach , Frost et al69 recently extended the above approach to 24-plexing by using dimethyl tags for MS1 and DiLeu tags for MS2. Since both the tags are chemical tagging approaches, this approach can be used directly 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 as dimethyl labeling can be easily used chemically post sample collection. While the authors have acknowledged that the sensitivity of dimethyl tags is ~20% lesser than the SILAC labeling due to losing out on the hydrophobic peptides after dimethyl treatment69, 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 36plex using triplex SILAC with 12-plex DiLeu tags or to 72plex using 6-plex NeuCode with 12plex DiLeu tags (Table 1). Hyperplexing (2012) Another breakthrough in higher order multiplexing was announced by Dephoure et al59, 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 mass spectrometry. 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 (Fig. 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 labeling biological conditions (control and treatments) followed by different N-terminal TMT tags (also on internal lysine residues) for labeling temporal dimension (post treatment) 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 robust statistical analysis of shotgun proteomics data to monitor small changes that were statistically significant. The data was searched twice, once with TMT as fixed modification on lysine and second search as SILAC only search with no TMT. Sequest12 and Vista75 were used for analysis with custom filtering of data. Later, the group also published 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 heavy66. SILAC-iTRAQ TAILS (2015) In 2015, Schlage et al67 combined SILAC and iTRAQ labels with TAILS method to study matrix metalloproteinases. 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 secretome of cells like keratinocytes and fibroblasts. The study intended



to identify and quantify matrix metalloproteinases (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 enrichment of N-terminome76. Since different amino acids were labeled for MS1 and MS2 labeling (arginine for SILAC and nterm and lysines for iTRAQ-tails) the authors searched the data twice. First the search was conducted with SILAC as fixed modification and iTRAQ as variable, and then with iTRAQ as fixed modification and SILAC as variable. The simplistic design ensured that the data analysis did not become a bottleneck. Identification becomes a challenge in such experiments as 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 of introducing new modifications for searching the mixed labels. In addition, since the labeling of iTRAQ-TAILS is achieved before the enrichment step, 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 & iTRAQ) but it can be easily extended by using full range of iTRAQ labels to maximize the multiplexing benefits from this design. Using this multiplexed design for the study of N-terminome can aid in identifications of neo-N-termini as compared to natural Ntermini. Since the authors have labeled only arginine in SILAC and lysine in iTRAQ, the multiplexing is achieved without complicating the search or quantitation. As this technique enriches and removes non-labeled peptides as compared to iTRAQ labeled N-termini, this negative enrichment of Nterminome 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 terminal labeled proteins, the yields of N-terminome are higher and much easier to read77. TMT-SILAC Hyperplexing (2016) In an extension to the previously discussed multitagging approach, Welle et al63 enhanced the multiplexing using TMT and SILAC at different time intervals to study the kinetics of protein clearance and synthesis in human fibroblasts cells63. This methodology combines the hyperplexing method from Dephoure et al along with 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 protein turnover rate in varying cellular and environmental conditions. In order 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 the protein turnover rate measures after this step is not influenced by any previous cellular division. After this step, the cells were transferred to a media containing heavy arginine (13C6) and lysine (13C6). As the cells kept nourishing, the protein pool keeps increasing in SILAC labeled peptides as compared to non-labeled peptides. There were 10 SILAC cultures that were mixed with TMT tags at ten time points, from- 0 to 336 hours. In another sets of cultures, the fibroblasts cells were SILAC labeled followed by TMT labels for four time points to study

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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 combined mass of both TMT and SILAC on lysine. Since 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 hours 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 hours of machine time and all proteins were unlikely to be quantified at all the time points63. The authors observed that these benefits of using a multiplexing technology came at the cost of some quantitative precision and proteome coverage. As distinguishing between the 10-plex labels of TMT required high resolution for peak separation since few reporters are very close in their masses, it led to high scan time on MS and lesser number of MS2 scans for sequencing78. In this experiment, MS2 scans aid in sequence identification and MS3 scans aid in accurate quantitation. If the number of MS2 ions decreases, the protein coverage suffers despite having more MS3 ions. The authors also observed that there was higher experimental noise in MS3 scans as compared to MS1, leading to somewhat reduced accuracy and precision. Another problem faced by the authors was of co-isolation and fragmentation of interfering ions63. This is a problem associated mainly with the particular isobaric labeling rather than with multiplexing38, 55. The authors suggested that the technology should be used cautiously and in places where the experimental cost and time is more precious than protein coverage. Hyperplexing with BONCAT (2016) Kumar et al60, devised a multiplexing technique by combining the power of three techniques - pulsed metabolic labeling (pSILAC70), biorthogonal non-canonical 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 (Fig. 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, THP1 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 hours 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 non-linear chromatographic separation modes. Each run allowed 18-plex multiplexing saving considerable machine time. This study is


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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 quantitation and integration of multiplexed data60. The multiplexed design allowed identification and quantification of very low abundance newly synthesized secretome80, 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. MITNCAT - Multiplex isobaric tagging/non-canonical amino acid tagging (2018) Another extension of the hyperplexing and BONCAT method was shown by Rothenberg et al81 recently, using 10-plex TMT instead of iTRAQ to measure protein synthesis rates with temporal resolution after stimulation. The labeling by BONCAT79 and pSILAC70 enabled enrichment of newly translated proteins and TMT labeling helped in multiplexed quantitation (Fig. 1C). Monitoring the EGF stimulation of HeLa cells, the authors measured protein synthesis rates with great temporal resolution. The multiplexing allowed greater temporal resolution (within first 15 minute to few hours) for thousands of proteins. Among other enumerated benefits earlier, 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 13C6 15N4 and Lys with 13C6 15N2 heavy labels, and measured the temporal response from 15 min to 120 minutes in MCF10A cells. The experiment achieved 10-plex using only heavy SILAC to study protein synthesis. It could have been expanded to 20-plex if both SILAC channels were used. The data was 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. mPDP - Multiplexed proteome dynamics studies (2018) The TMT-SILAC Hyperplexing approach was recently modified by Savitski et al64 to study protein dynamics with a multiplexed approach, mPDP or “multiplexed proteome dynamics profiling”. The authors coupled multiple labeling techniques to enhance sensitivity and quantitation depth of 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 label dynamically the nascent and mature proteins respectively. The dynamic temporal changes tagged by the SILAC labels were then encoded by TMT 10-plex to study treatmentdependent effects on nascent and mature proteins. THP-1 cells were grown in normal arginine & 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 JQ1Az, 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 re-

maining 5 TMT tags. The two sets with five TMT tags each were mixed together and run in a single shot of MS. Similar experimental design was followed for Jurkat cells and MCF7 cells to investigate estrogen receptor1 (ESR1) agonists. As the study focused on synthesis and degradation rates by differentially labeling nascent and mature proteins with light and heavy SILAC, the total SILAC signal in light and heavy channel remained constant and equal after mixing. This allowed deeper proteome coverage and quantitative profiling as the mixing improved SILAC pair intensities and thus reduced the problem of missing values in the replicates. The pros & 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 Fig. 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 MITNCAT81, 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 phenomenon as well as for demonstrating the technical prowess of what mass spectrometry can achieve. We sincerely hope that the readers pick 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 (5plex SILAC) and 108-plex (6-plex NeuCode) with customized (light, medium and heavy) TMT labeled isobaric tags is also possible. Single shot analysis of such complex data can save a lot of precious researcher and machine time. This will enable the researchers to plan ingenious studies instead of focusing on monitoring and managing long and drab technical runs, and bring the focus back to interesting science. There are clear advantages of using the higher order multiplexing approach. Firstly, 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 positives82 – issues that higher order multiplexing can mitigate. Secondly, the mixing of the labels can be cost efficient as it generates statistically robust and higher value data that indirectly cuts down the cost of experiment and manpower time considerably. Thirdly, batch effect can be minimized83 while analyzing a large number of samples. The mitigation of run-to-run variations and reduced missing values makes it feasible for analysis of cohorts, clinical trials, testing different drugs and testing of drug leads in a single experiment59-60, 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. Operational cost may be a potential barrier to entry for labs on 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 co-eluting peptides41. As a



result, not all labels may be accurately quantified40. While MS3 and multi-notch MS3 with synchronous precursor selection (SPS) are believed to remove such biases from TMT reporter tags84, this causes reduced proteome coverage38-39. Acquiring highly multiplexed spectra may also increase the overall experimental noise, although no study has directly measured the spectrum quality from such multiplexed studies63. The technique also need 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 non-experts with no computational or mass spectrometry 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. Conclusion In this review, we described the chronology of the experimental designs (Fig. 2) and biological use cases for the applications of higher order multiplexing techniques, which can aid the reader in better design of proteomics studies and facilitate interesting biological problems. The journey started by multitagging, cPILOT, hyperplexing and BONPlex; are just representative 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 be benefitted with more discussions about the applications of the approaches and formulating 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 mass spectrometry proteomics. The era of systems biology from proteomics data has arrived.

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Author Contributions SA, NCT and AKY, all contributed to writing and editing the manuscript.

ACKNOWLEDGMENT SA is supported by the ICMR-SRF (BIC/11/(17)/2015) grant, AKY is supported by DBT-IYBA (D.O.No.BT/07/IYBA/2013), DBT-Big Data Initiative grant (BT/PR16456/BID/7/624/2016), and THSTI grant and NCT is supported by DBT Twinning NER (102/IFD/SAN/1048/2016-2017 dated 14/06/2016 Serial No. 617). Authors acknowledge Drs. Shilpa Jamwal, Dinesh Mahajan and Yashwant Kumar for critically reviewing and proofreading the manuscript. The authors also acknowledge the unknown peerreviewers, whose constructive criticism helped in improving the manuscript.

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AUTHOR INFORMATION Corresponding Author *Amit Kumar Yadav, Drug Discovery Research Center, Translational Health Science and Technology Institute THSTI, NCR Biotech Science Cluster, 3rd Milestone, Faridabad – Gurgaon Expressway, Faridabad, Haryana, IN 121001 Email: [email protected] Phone: +91 129 2876490


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