Accepted: 14 December 2022

Received: 3 September 2022	Revised: 13 December 2022
DOI: 10.1002/pmic.202200116
TECHNICAL BRIEF
An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis

Marybeth Creskey1	Leyuan Li2,3
Janice Mayne2,3	Krystal Walker2,3
Xu Zhang1,2	Daniel Figeys2,3
Zhibin Ning2,3 Emily EF Fekete1
Anna Ampaw4	Robert Ben4

Correspondence
Daniel Figeys, School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada. Email: dfigeys@uottawa.ca
Xu Zhang, Regulatory Research Division, Centre for Oncology, Radiopharmaceuticals and Research, Biologic and Radiopharmaceutical Drugs Directorate, Health Products and Food Branch, Health Canada, Ottawa, ON K1A 0K9, Canada.
Email: xu.zhang@hc-sc.gc.ca
Funding information
Government of Canada through Health Canada, Genome Canada, Grant/Award Numbers: OGI-156, OGI-149; Natural Sciences and Engineering Research Council of Canada, Grant/Award Numbers: NSERC, 210034; Ontario Ministry of Economic Development and Innovation, Grant/Award Numbers: ORF-DIG-14405, 13440

1Regulatory Research Division, Centre for Oncology, Radiopharmaceuticals and Research, Biologic and Radiopharmaceutical Drugs Directorate, Health Products and Food Branch, Health Canada, Ottawa, Canada
2School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Canada
3Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
4Department of Chemistry, Faculty of Science, University of Ottawa, Ottawa, Canada
Abstract
Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in –omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the additional reagent cost. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50μg dry TMT per channel was used to label 6–12 μg peptides, yielding high TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for robust quantitative proteomics and metaproteomics in high throughput applications.
KEYWORDS
high throughput, metaproteomics, microbiome, proteomics, robust, TMT workflow
Abbreviations: ACN, acetonitrile; BL, baseline; IRI, ice recrystallization inhibitor; KES, kestose; LC-MS, liquid chromatography coupled with mass spectrometer; PCA, principal component analysis; TMT, tandem mass tag.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2023 The Authors. Proteomics published by Wiley-VCH GmbH
Proteomics 2023, 23:2200116	www.proteomics-journal.com 1of6
https://doi.org/10.1002/pmic.202200116

FIGURE 1 Workflow of dry tandem mass tag (TMT) plate- or tube strip-based TMT labeling for high throughput quantitative proteomic and metaproteomic sample analyses

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1		MAIN
High-resolution mass spectrometry (MS)-based proteomic and metaproteomic technologies allow for deep insights into protein- and proteoform-level information of many types of samples, including cells, tissues, and microbiomes. Along with other –omic approaches, proteomics has been widely applied for both clinical and preclinical studies [1–3], enabling extensive molecular profiling and biomarker discovery for diseases. Proteomics and metaproteomics have also been applied in in vitro or ex vivo assays [4], allowing for proteomicsbased high throughput drug screenings. For the study of microbiome, metaproteomics enables large-scale analysis of proteins in microbial communities [5, 6] and cannot only estimate the functional activities, but also assess the microbiome biomass and analyze community structures [4, 7]. These advantages make proteomics and metaproteomics suitable for screening drugs that target host–microbiome interactions. The latter are implicated in various diseases ranging from gastrointestinal, metabolic diseases to neurological disorders, and cancers [8–10].
The development of multiplexing approaches, such as isobaric tandem mass tags (TMTs), dramatically reduces the MS time required per sample, which vastly improves the throughput of proteomic analyses. An in-depth discussion of the technologies and advantages of multiplexing proteomics has been reviewed in several recent publications should there be further interest in more details [11, 12]. One disadvantage of TMT-based proteomics is that it increases the reagent cost and introduces additional sample preparation steps. In a typical TMT exper-

iment, 800 μg TMT10 reagent per sample (corresponding to ∼120 US dollars) was recommended to label 25–100 μg peptides. Zecha et al. reported that the TMT-to-peptide ratio could be reduced to one while maintaining sufficient labeling efficiency [13]. Additionally, the TMT labeling reaction volume can be decreased to 25 μl with 100 μg TMT per sample, which greatly reduced the cost of TMT reagents to ∼15 US dollars per sample. In this conventional TMT labeling workflow, 100 μg of TMT reagents in 5 μl acetonitrile (ACN) were mixed with 20 μl peptide samples [13]. Extra caution is needed for accurate transfer of 5 μl ACN solution given its nature of high volatility, which otherwise may result in poor inter-sample consistency and impact the robustness of the quantitation. Therefore, the conventional TMT labeling workflow is challenging for applications with large sample numbers, such as high throughput drug screening.
Here, we established a streamlined TMT labeling procedure using 96-well plates or 12-strip tubes pre-loaded with dry TMT reagents. Briefly, TMT reagents were aliquoted into low-binding 96-well plates or 12-strip tubes with as little as 50 μg reagents (in 50 μl ACN) in each well or tube (Figure 1). The plates or strip tubes were then dried with a vacuum concentrator and stored at –80◦C prior to peptide labeling. For peptide labeling, the protein digest was prepared or re-suspended in100 mM TEAB containing 20%–30% v/v ACN and directly added to the dry TMT plate or tube for incubation at room temperature for 1–2 h (Figure 1). While even smaller amount of TMT per channel is possible, this study only tested 50 and 100 μg TMT per channel that require 15 and 25μl of reaction volume, respectively. We selected TMT11plex to fit into a row (12 wells) of a 96-well plate or a 12-tube strip. The

FIGURE 2 Comparison of labeling efficiency between convenient dry TMT and standard wet TMT labeling workflows. Identified PSMs (upper), TMT labeling efficiency (middle), and over labeling rate (lower) were shown for microbiome samples with 1 h MS run (A–C), microbiome samples with 2 h MS run (D–F), and Caco-2 cell samples (G–I), respectively. Each bar in the plots represents one replicate in the group. For group names, W and D refer to wet and dry, 100 or 50 refers to the μg of TMT reagents, and P20 or P12 to the μg of peptides. MS, mass spectrometry; PSM, peptide-spectrum match; TMT, tandem mass tag

individually labeled samples were then quenched using a final concentration of 0.4% hydroxylamine for 15 min, pooled row-wise and desalted for MS analysis (Figure 1).
To evaluate whether the use of dry TMT reagents achieves sufficient TMT labeling efficiency, we compared the dry TMT workflow with the conventional labeling workflow (wet TMT) in two different sample types (microbiome and intestinal Caco-2 cells) with two different instrument platforms (Orbitrap Exploris 480 and Fusion Lumos). For this evaluation, equal amount of the 11 channels of TMT11 reagents were combined to generate a TMT11 mix, which was used to label either microbiome or Caco-2 cell digests.
For microbiome samples, two different amounts of peptide inputs were tested with 25 μl reaction volume for 100 μg TMT groups and 15 μl reaction volume for 50 μg TMT groups, which resulted in a total of 6 groups (W100P10, W100P20, D100P10, D100P20, D50P12, and D50P6; where W and D refer to wet and dry, 100 or 50 refers to the μg of TMT reagents, and P20 or P12 to the μg of peptides; Figure 2A). These labeled peptides were analyzed on an Exploris 480 mass spectrometer coupled with an UltiMate 3000 RSLCnano liquid chromatography system. An average of 7307 ± 260 peptide-spectrum matches (PSMs) per 500 ng sample over a 60-min MS run, corresponding to 7206 ± 235 gut microbial peptides, were identified using MetaLab (version 2.1) [14] with the integrated human gut microbial gene catalog as a database [15]. There was no obvious difference of protein identifications between groups (Figure 2A). TMT full labeling efficiency of ∼99% was obtained for all tested microbiome samples (98.7%–99.6%) and we did not observe obvious difference between wet and dry TMT groups with both high and low peptide inputs (Figure 2B). A TMT labeling efficiency of >95% is considered acceptable for accurate quantifications [16, 17], and therefore the established dry TMT workflow maintained efficient TMT labeling efficiency. In this study, we further increased the depth of measurement with 120 min MS run, which did not change the findings (Figure 2D,E).

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In the evaluations using human Caco-2 cells, consistent and similar TMT labeling efficiencies for both 50 and 100 μg TMT groups were obtained (>99% for all samples; Figure 2G,H). We also

FIGURE 3 PCA score plot of ex vivo cultured metaproteomes with or without kestose and IRIs. The first two principal components (PC1 and PC2) were displayed. Fresh, fresh microbiome; Ctrl, frozen microbiome without IRI; IRIa 1–5 and IRIb1–5, frozen microbiome with different concentrations of IRIa or IRIb with 1 as the highest and 5 as the lowest concentrations. BL, baseline; IRI, ice recrystallization inhibitor; KES, kestose; PBS, phosphate buffered saline

performed separate database search with TMT labels on non-lysine amino acid residues (i.e., histidine, serine, threonine, and tyrosine) as variable modifications, which showed no obvious difference between groups in both microbiome and Caco-2 (Figure 2C,F,I). An average TMT labeling on non-lysine residues of <5% was obtained for all tested samples, which was at a similar level to those in a previous study [13].
We then evaluated the labeling efficiency using individual TMT reagent. A labeling of 22 aliquots of a Caco-2 lysate using two TMT strips (Strip 1 and Strip 2) was performed and resulting mixtures were analyzed using liquid chromatography coupled with mass spectrometer (LC-MS) with both single-shot (90 min) and fractionated (90 min of eight fractions) approaches. As expected, >99% labeling efficiency was achieved for unfractionated single-shot sample analysis and slightly lower TMT labeling rate was observed for fractionated samples (98.4% and 98.7% for Strip 1 and Strip 2, respectively) (Figure S1). MaxQuant search with a human proteome database was performed [18], which quantified 2093 and 4690 protein groups, corresponding to 10,101 and 37,747 peptide sequences, for single-shot and fractionated samples, respectively. The calculated protein abundance ratios to that in reference channel (channel 1) were consistently distributed and close to one, the theoretical value (i.e., log2-transformed ratio of 0 in the plot), for both fractionated and single-shot samples (Figure S2). The high labeling efficiency and accurate calculated protein abundance ratios suggest the dry TMT labeling workflow has high quantification accuracy.

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Next, we applied this TMT labeling workflow in a high throughput study using an ex vivo microbiome assay to assess the effects of ice recrystallization inhibitors (IRIs) and prebiotic kestose (KES) on the cultured microbiome. Briefly, fresh stool collected from a healthy adult volunteer was processed as previously described [19], and stored at –80◦C for 2 weeks with or without two different IRIs, that is, β-4- bromophenyl-D-glucose (IRIa) and N-2-fluorophenyl-D-gluconamide (IRIb) [20, 21]. Both fresh and the frozen microbiotas were cultured in vitro with and without 5 mg/ml KES, a prebiotic with known effects on modulating the microbiota [22], using a previous established Rapid Assay of Individual Microbiome (RapidAIM) [4]. In total, 97 microbiome samples were generated for multiplexed metaproteomic analysis using the dry TMT workflow. We randomized the 97 samples into two dry TMT 96-well plates (Plate1 row A-H and Plate2 row A-B, labeled as P1A-H and P2A-B, respectively) with the channel 1 of each row as reference (pooled sample) (the remaining three wells and an additional three rows, P2C-E, were added with pooled samples as well; Figure S3). Metaproteomic analysis of the resulting 13 mixtures identified 28,605 microbial peptides and 10,656 protein groups. Similar to the findings using Caco-2, the calculated protein abundance ratios were consistently distributed and close to one for the pooled sample experiments (P2C, P2D, and P2E) (Figure S4). On the contrary, the protein abundance ratio distributions of the IRI study samples (P1A to P1H and P2A to P2B) showed much higher variations (Figure S4), suggesting marked changes of protein expressions in response to either prebiotic treatments or storage conditions of the frozen microbiotas.

Unsupervised principal component analysis (PCA) was then performed using the quantified gut microbial protein groups, which demonstrated clear sample clustering according to either the treatments or the different inoculating microbiotas (Figure 3). Baseline (BL) samples (microbiotas prior to ex vivo culturing; plotted as squares) clustered together at the top of the PCA score plot. Marked differences were observed between the samples with and without the treatment of KES during culturing (Figure 3). These results are in agreement with previous studies showing the modulating effects of KES on in vitro cultured microbiota [22, 23]. As expected, microbiome culturing using fresh stools clustered closer to the BL samples compared to those using frozen microbiotas, indicating the detrimental effects of freezing process on the viability of microbiota. No obvious difference was observed with the addition of IRIb, while IRIa influenced the metaproteomic profiles of the cultured microbiome in particular in the KES groups (indicated as circles, Figure 3). These observations may indicate that microbiomes are capable of metabolizing IRIa (a glucose derivate) and therefore further investigations are needed to better demonstrate its suitability for use in live microbiota bio-banking.
In summary, we established a TMT labeling workflow that uses pre-aliquoted dry TMT reagents in 96 well plates or 12-tube strips, which streamlines the TMT labeling procedure and reduces the cost of experiments. Evaluations of the labeling efficiency using human gut microbiome and intestinal Caco-2 cell samples both demonstrated efficient TMT labeling rate (∼99%). An outstanding advantage of the established dry TMT labeling workflow is that it perfectly fits for high throughput applications with automated liquid handling system, including high throughput drug screening and large-scale clinical sample analysis. Additionally, dry TMT reagent plates or tube strips can be prepared in a large-batch manner, which holds the promise to greatly reduce reagent loss and enable better sample-to-sample consistency thanks to the relatively larger volumes of pipetting (TMT reagents can be diluted to ≤1 μg/μl for aliquoting). With the demonstrated high TMT labeling efficient, the established workflow provides an economic, robust, and easy-to-implement experimental workflow for multiplexed quantitative proteomics and metaproteomics.
ACKNOWLEDGMENTS
This work was supported by the Government of Canada through Health Canada, Genome Canada (OGI-156 and OGI-149), the Natural Sciences and Engineering Research Council of Canada (NSERC, 210034), and the Ontario Ministry of Economic Development and Innovation (ORF-DIG-14405 and project 13440). We also gratefully acknowledge Dr. Simon Sauvé, Dr. Daryl Smith, and Dr. Roger Tam from Health Canada for commenting on the manuscript.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
All MS proteomics data along with the database search results were deposited to the ProteomeXchange Consortium (http://www. proteomexchange.org) via the PRIDE partner repository with identifiers PXD034979 and PXD034936.
ORCID
Emily EF Fekete	https://orcid.org/0000-0002-7849-6021
Xu Zhang https://orcid.org/0000-0003-2406-9478
Daniel Figeys https://orcid.org/0000-0002-5373-7546
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SUPPORTING INFORMATION
Additional supporting information may be found online https://doi.org/10.1002/pmic.202200116 in the Supporting Information section at the end of the article.
How to cite this article: Creskey, M., Li, L., Ning, Z., Fekete, E. E. F., Mayne, J., Walker, K., Ampaw, A., Ben, R., Zhang, X., & Figeys, D. (2023). An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis. Proteomics, 23, e2200116. https://doi.org/10.1002/pmic.202200116