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. 2023 Sep 1;34(10):2087–2092. doi: 10.1021/jasms.3c00095

Automated Sample Preparation Workflow for Tandem Mass Tag-Based Proteomics

Dong-Gi Mun †, Neha S Joshi †,‡, Rohit Budhraja †, Gunveen S Sachdeva †, Taewook Kang †, Firdous A Bhat †, Husheng Ding †, Benjamin J Madden §, Jun Zhong †, Akhilesh Pandey †,‡,∥,*
PMCID: PMC10557128  PMID: 37657774

Abstract

graphic file with name js3c00095_0004.jpg

Although tandem mass tag (TMT)-based isobaric labeling has become a powerful approach for multiplexed protein quantitation, automating the workflow for this technique has not been easy to achieve for widespread adoption. This is because preparation of TMT-labeled peptide samples involves multiple steps ranging from protein extraction, denaturation, reduction, and alkylation to tryptic digestion, desalting, labeling, and cleanup, all of which require a high level of proficiency. The variability resulting from multiple processing steps is inherently problematic, especially with large-scale clinical studies that involve hundreds of samples where reproducibility is critical for quantitation. Here, we sought to compare the performance of a recently introduced platform, AccelerOme, for an automated proteomic workflow employing TMT labeling with the manual processing of samples. Cell pellets were prepared and subjected to a 16-plex experiment using an automated platform and a conventional manual protocol. Single-shot liquid chromatography with tandem mass spectrometry analysis revealed a higher number of proteins and peptides identified using the automated platform. Efficiency of tryptic digestion, alkylation, and TMT labeling were similar in both manual and automated processes. In addition, comparison of quantitation accuracy and precision showed similar performance in an automated workflow compared to manual sample preparation by an expert. Overall, we demonstrated that the automated platform performs at a level similar to a manual process performed by an expert for TMT-based proteomics. We anticipate that this automated workflow will increasingly replace manual pipelines and has the potential to be applied to large-scale TMT-based studies, providing robust results and high sample throughput.

Introduction

Mass spectrometry-based proteomics has proven to be a powerful technique for clinical research, providing not only in-depth protein identification but also robust quantification. Analyzing a large number of samples has been facilitated by multiplexing approaches employing isobaric labeling such as isobaric tags for relative and absolute quantification (iTRAQ)1 and tandem mass tags (TMTs),2 which enable global quantitation of thousands of proteins across multiple samples. TMT is currently one of the most frequently used multiplexing approaches for quantitative proteomics, given its capacity to analyze up to 18 samples.3 However, generating TMT-labeled peptide samples involves multiple steps of protein extraction, denaturation, reduction, alkylation, tryptic digestion, desalting, labeling, pooling, and cleanup for mass spectometry analysis. A high level of proficiency in experimental methods is required for such manual sample preparation. Importantly, different levels of operator experience might affect the outcome of the experiment. Thus, preparing samples for quantitative proteomics manually can lead to poor reproducibility and variation, especially when handling hundreds of clinical samples from a large series.

A label-free workflow, which is an alternative to the TMT-based approach, involves fewer processing steps than labeling experiments. The less complicated nature of sample preparation in label-free workflows has made it easier to develop automation platforms for deployment.4 Various commercial automation platforms have been implemented in clinical and research laboratories. For example, AssayMap Bravo (Agilent Technologies) has been applied to process protein digestion and to enrich peptides of interest.5−7 Other platforms such as Biomek Lab Workstations (Beckman Coulter) and LH-1808 liquid handler (AMTK) have been used to process serum or plasma samples in a high-throughput manner.8,9 Most recently, an automated TMT 16-plex workflow for processing plasma samples, which do not require cell lysis steps, has been introduced using Hamilton Vantage liquid handler.10 In addition, several platforms including OT-2 (Opentrons),11 PreON (PreOmics), and AssayMap Bravo have developed methods that can perform TMT labeling. Although these platforms are equipped with liquid dispensers to minimize some of the laborious steps in sample preparation, the entire process is still not fully automated for TMT labeling experiments, which require manual interaction between different automated steps. In addition, one critical step of measuring the peptide amount is not available as a feature on any platform. A recent introduction is the launch of an automated sample preparation platform, AccelerOme (Thermo Scientific), which supports automatic sample processing for both label-free and TMT labeling experiments using prebuilt methods and reagents kits.

In this study, we sought to test the performance of an automated TMT labeling workflow on the AccelerOme platform. Equal amounts of labeled peptides from manual and automated workflows were analyzed, and the results of protein identification and quantitation were compared. In addition, efficiency of cysteine alkylation, TMT labeling, and protein digestion were compared. Overall, we observed a similar performance of the automated platform for TMT-labeling experiments compared with manual processing by an expert. We anticipate that the automated TMT workflow will increasingly replace manual processing, particularly for large-scale TMT-based studies, offering more robust results and higher sample throughput.

Results and Discussion

Assessing the Performance of an Automated Sample Preparation Platform

The AccelerOme platform consists of several modules that include a thermomixer and a UV flow cell, which provide end-to-end automated sample processing for both label-free and TMT-based labeling experiments. A maximum of 36 samples per run can be processed in the label-free workflow, and 32 samples can be processed for TMT 16-plex experiments. The premade reagents are provided as kits, which contain lysis buffer, universal nuclease, reagents for reduction and alkylation, trypsin/Lys-C protease mix, peptide cleanup cartridges, and TMT reagents. The platform allows for experiments using a preoptimized and standardized workflow for both label-free and labeling approaches.

In this study, we chose to test the performance of AccelerOme using a TMT 16-plex labeling workflow. To this end, we performed TMT 16-plex labeling experiments using the same sample, prepared either manually or using the automated sample preparation approach (Figure 1A). The same number of Jurkat cells was distributed and processed manually by an expert with highly trained skills in sample preparation and a novice with little experience in proteomics. Each individual aliquoted ∼100 000 cells into separate tubes and processed them to generate TMT-labeled peptides following the same procedures as described in the Experimental Methods section. In parallel, the same samples were processed using a built-in TMT 16-plex labeling workflow embedded into the AccelerOme platform. Three replicates were performed for each of the manual and automated experiments. Concentration of TMT-labeled peptides was estimated by colormetric assay for manually prepared samples and by an in-line UV spectrophotometer incorporated into the AccelerOme platform. The same amount of unfractionated TMT-labeled peptide was analyzed using an Orbitrap Tribrid Eclipse coupled to a 40 cm analytical column in a single shot.

Figure 1.

Figure 1

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Comparison of identifications from manual and automated TMT experiments. (A) Overall workflow for manual and automated TMT-labeled sample preparation. (B) Identified peptides and proteins from manual and automated sample preparation. (C) Venn diagrams showing the overlap of the identified proteins across three replicates (R1, R2, and R3) in manual and automated approaches.

Manual processing resulted in an average of 14 127 (novice; manual #1) and 21 433 (expert; manual #2) peptides corresponding to 2366 and 3204 proteins, respectively, from the three replicates. Experiments from the automated approach resulted in an average of 27 186 peptides corresponding to 3953 proteins (Figure 1B). Automated workflow resulted in 23% additional protein identifications compared to the result of manual experiment #2. Notably, we observed a large variation across the three replicates of the results from manual experiment #1. This is likely because of the experience level of the novice, which shows the benefit of the automated sample preparation approach allowing reproducible sample preparation. We further examined the overlap of proteins across the three replicates. We observed that ∼47%, 68%, and 66% of proteins were identified in all three runs from manual experiment #1, manual experiment #2, and the automated experiment, respectively (Figure 1C). The repetitive protein identifications of manual #2 and automated workflow show typical range of overlap among technical replicates acquired in data-dependent acquisition mode.12 Overall, these results indicate that the automated workflow does not cause any deleterious effects, while providing possible additional gains in protein identifications.

We started with a relatively low number of cells (100 000 cells) corresponding to ∼10 μg of extracted protein. A manual process starting with a low number of cells might be more sensitive to sample losses, which mainly originate from surface exposure and multiple liquid-transfer steps.13 Additional protein identifications obtained through the automated workflow might be an advantage of the automated platform through minimization of such losses. Although the suggested minimum loading amount is 10 μg in the built-in method, the automated workflow could benefit processing of material from a single or low number of cells including laser capture microdissection of frozen or formalin-fixed paraffin-embedded samples with TMT labeling after further optimization on the AccelerOme platform.14

Comparing Efficiency of Trypsin Digestion, Alkylation, and TMT Labeling

We next compared the efficiency of alkylation of cysteines, tryptic digestion, and labeling with TMT tags in manual and automated sample preparation workflows. Thus, we analyzed mass spectrometry data against a protein database by considering cysteine alkylation and TMT tags on peptide N-termini and lysines as variable modifications. The maximum number of missed cleavages was set to three for evaluating the digestion efficiency.

First, we assessed the completeness of protein digestion by comparing the number of missed cleavages at internal lysine or arginine residues. Peptides with no missed cleavage sites were 95%, 93%, and 95% for the three replicates in manual experiment #1 and 93%, 93%, and 93% for the three replicates in manual experiment #2. In the automated approach, 95%, 96%, and 96% of peptides were identified with no missed cleavage sites (Figure 2A). In addition, we compared the distribution of the peptide length (Figure 2B). Again, similar trends were observed in both manual and automated approaches for sample preparation. We used the same trypsin/Lys-C mixture to perform protein digestion in both approaches although overnight digestion was performed in manual experiments and a much shorter time for digestion (∼1 h) in the automated approach. Notably, digestion in shorter time using the automated sample preparation platform resulted in similar performance of digestion efficiency.

Figure 2.

Figure 2

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Evaluation of the efficiency of reactions. (A) Distribution of missed cleavages in identified peptides. (B) Distribution of the length of identified peptides (number of amino acids as indicated). (C) Efficiency of cysteine alkylation in manual and automated workflows. (D) Efficiency of TMT labeling in manual and automated workflows.

Next, we evaluated the efficiency of cysteine alkylation in manual and automated workflows by considering cysteine alkylation as a variable modification. Most of the cysteine residues were alkylated in both manual and automated approaches with >99% alkylation efficiency, confirming lack of any unanticipated results from the automated approach (Figure 2C). Notably, we observed less occurrence of side reactions during alkylation, such as methionine carbamidomethylation, in the automated approach (Figure S1A). However, a systematic investigation of overalkylation or in other side reactions needs to be performed.

To evaluate the completeness of the labeling of TMT tags to free primary amines, we considered TMT modifications on peptide N-termini and lysine as variable modifications. Peptides modified with TMT reagents at either peptide N-termini or lysine were counted to calculate the TMT labeling efficiency. While the two manual experiments resulted in ∼96% TMT labeling efficiency, nearly 100% efficiency was achieved in all three replicates processed using the automated approach (Figure 2D). This again indicates the benefit of the automated approach for reproducible TMT labeling. It has been reported that side reactions of TMT can occur at other residues such as serine, threonine, tyrosine, and histidine.15 To assess the occurrence of TMT overlabeling, we performed a protein database search considering TMT labeling on serine, threonine, tyrosine, and histidine as variable modifications and TMT labeling on lysine and peptide N-terminus as static modifications. We observed a slightly higher proportion of TMT-labeled tyrosine in the automated workflow as compared to manual processing (Figure S1B). Further systematic investigations are necessary to reduce such side reactions on the AccelerOme platform.

Comparison of TMT-Based Quantitation

We next evaluated the performance of quantitation of the automated workflow compared to manual processing. The accuracy of quantitation was determined by calculating ratios between different channels for each protein, using the intensity of the 126 channel as a denominator. As the same number of cells was labeled in each channel, the expected ratio is 1. The measured ratios of the first and second replicates from manual experiment #1 showed a large variation, while the measured ratios in the third run agreed with the expected ratio (Figure 3A). The result from manual no. 2 showed smaller variation in most channels of each replicate (Figure 3B). The calculated ratios of the automated workflow were also in agreement with the expected ratio (Figure 3C). Overall, the results indicated that similar performance in quantitative accuracy was achieved in the automated workflow compared with manual experiments processed by the expert but not a novice.

Figure 3.

Figure 3

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Comparison of accuracy and precision of TMT-based quantitation. (A–C) Box plots of measured ratios of protein abundance from each channel of manual experiment #1 (A), manual experiment #2 (B), and automated experiment (C). Ratios were calculated using the abundance of the 126 channel as a denominator. The expected value is represented by a red dashed line. (D–F) Distribution of percent coefficient of variation (CV) of abundance across 16 channels at protein level from manual experiment #1 (D), manual experiment #2 (E), and automated experiment (F).

To compare the quantitative precision, we calculated the coefficient of variations of reporter ion intensities across 16 channels at the protein level. The median CVs were 13.9%, 13.9%, and 5.8% for each run from manual experiment #1 and 9.3%, 5.4%, and 6.9% for each run from manual experiment #2 (Figure 3D,E). Automated processing resulted in median CVs of 7.3%, 7.6%, and 6.6% from the three runs (Figure 3F). This again indicates that the precision of the TMT-based quantitation of automated proteomic workflow is comparable to results of manual processing by an expert.

The primary aim of this study was to test the performance of the automated platform for TMT 16-plex labeling compared to a conventional manual sample preparation workflow. Thus, we analyzed unfractionated TMT-labeled samples in a single shot using the MS2-based data-dependent acquisition mode. The major limitation of this method for analyzing TMT-labeled samples is the ratio distortion of the reporter ions due to cofragmentation.16 Further investigations will be required to evaluate the quantitative accuracy in MS3 mode.16,17

Conclusions

In this study, we tested the performance of the automated TMT labeling workflow provided by the AccelerOme platform, which performs cell lysis, reduction, alkylation, tryptic digestion, labeling, pooling, cleanup, and measurement of peptide concentration. Placing commercial reagent kits and loading samples onto the platform are the only tasks required for the TMT-labeling experiment. The amount of peptide is reported after ∼6 h of processing time (for 32 samples), which makes it ready for subsequent experiments including fractionation and mass spectrometry data acquisition. Here, we demonstrated the benefit of the automated workflow by providing additional protein identifications without compromising quantitative accuracy and precision compared to the manual workflow. Importantly, the consistent performance of the automated workflow obviates the need for training and hands-on time for manual preparation.

Experimental Methods

A detailed description of mass spectrometry data acquisition and analysis can be found in the Supporting Information. The mass spectrometry proteomics data have been deposited to the Proteome Xchange Consortium via the PRIDE partner repository18 with the data set identifier PXD040788.

Manual Sample Preparation of TMT-Labeled Peptides

Three sets of Jurkat cell pellets, each containing ∼2 million cells, were lysed using lysis solution (Pierce, 1824868). Briefly, 200 μL of lysis buffer and 1 μL of universal nuclease (Pierce, 88700) were added to the cells and pipetted down 10–15 times to lyse the cells. The samples were centrifuged at 14 000g for 5 min at 4 °C to pellet down the cell debris. For each set, 10 μL of cell lysate was then transferred to 16 tubes. The samples were reduced at 37 °C for 30 min using 10 mM of dithiothreitol followed by alkylation for 30 min in the dark at room temperature using 40 mM of iodoacetamide. Subsequently, samples were digested with trypsin/Lys-C enzyme and incubated at 37 °C overnight. The digestion reaction was quenched using 20% trifluoroacetic acid, and samples were desalted using C18 tips (Glygen, TT2C18). Peptides were collected and dried using a speed vac. All dried peptide samples were reconstituted in 30 μL of 100 mM triethyl ammonium bicarbonate (TEAB) buffer. Each sample was then labeled using TMT 16-plex as per the manufacturer’s instructions (Thermo Fisher Scientific, 90110). The labeling reaction mixture was incubated at room temperature for an hour and quenched using 5 μL of 5% hydroxylamine solution with incubation for 15 min at room temperature prior to pooling. The pooled sample was then dried and desalted. The peptide amount of the samples was estimated using the colormetric peptide assay kit (Pierce, 23275).

Automated Sample Preparation of TMT-Labeled Peptides

We carried out automated sample processing using AccelerOme for the two sets of Jurkat cell pellets (2 million cells/set) to account for two sets of TMT 16-plex in one run. For automated sample preparation, we processed samples using the AccelerOme TMTpro 16-plex MS Sample Preparation Kit as per the manufacturer’s instructions. All reagents used in this experiment were provided in the kit. Briefly, the cells were resuspended in 200 μL of lysis solution and 1 μL of universal nuclease before loading onto the AccelerOme platform. The built-in TMT 16-plex method was used to perform the automated TMT experiments. Concentration of peptides was measured using an in-line UV spectrophotometer. The amount of enzymes and TMT reagents and their reaction times are summarized in Table S1.

Acknowledgments

This work was supported in part by grants from NCI to A.P. (U01CA271410 and P30CA15083).

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jasms.3c00095.

  • Additional experimental methods. Results of considering methionine carbamidomethylation and TMT labeling on serine, threonine, tyrosine, or histidine (Figure S1). Table summarizing amount of reagent and incubation time for tryptic digestion and TMT labeling (Table S1) (PDF)

Author Contributions

⊥ These authors contributed equally to this work.

Author Contributions

D.-G.M., J.Z., and A.P. designed the experiments. N.S.J., D.-G.M., R.B., and G.S.S. performed TMT experiments. D.-G.M. and B.J.M. acquired mass spectrometry data. D.-G.M., T.K., and F.A.B. analyzed mass spectrometry data. T.K. and J.Z. provided critical inputs. D.-G.M., N.S.J., and A.P. wrote the manuscript with input from all authors.

The authors declare no competing financial interest.

Special Issue

Published as part of the Journal of the American Society for Mass Spectrometryvirtual special issue “Focus: High-Throughput in Mass Spectrometry”.

Supplementary Material

js3c00095_si_001.pdf (142.3KB, pdf)

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Associated Data

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Supplementary Materials

js3c00095_si_001.pdf (142.3KB, pdf)

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