Exploiting the multiplexing capabilities of tandem mass tags for high-throughput estimation of cellular protein abundances by mass spectrometry

Ahrné, Erik; Martínez-Segura, Amalia; Syed, Afzal Pasha; Viña-Vilaseca, Arnau; Gruber, Andreas J.; Marguerat, Samuel; Schmidt, Alexander

doi:10.1016/j.ymeth.2015.04.032

Cited by 7 publications

(7 citation statements)

References 45 publications

(56 reference statements)

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“…While high-quality data and coverage of the global proteome can be achieved with significantly less material, phosphoproteome coverage is negatively impacted or requires specialized methods to recover. 18 , 19 This creates a key challenge relating to variability in the amount of protein available per patient, due to external variables in the study that impact sample size availability. In these cases, the investigators need to decide whether to exclude sample-limited patients, to reduce the protein loading per patient for the study, or to include that individual patient at reduced protein loading.…”

Section: Introductionmentioning

confidence: 99%

“…The most widely utilized workflow for clinical proteomics studies seeking to achieve deep quantitative proteomic and phosphoproteomic measurements employs a tandem mass tag (TMT) isobaric labeling approach and employs an enrichment step to increase phosphopeptide specificity. − In settings where the deepest achievable coverage of the phosphoproteome is a priority, it is recommended to use on the order of 300–400 μg of peptides in each TMT channel. While high-quality data and coverage of the global proteome can be achieved with significantly less material, phosphoproteome coverage is negatively impacted or requires specialized methods to recover. , This creates a key challenge relating to variability in the amount of protein available per patient, due to external variables in the study that impact sample size availability. In these cases, the investigators need to decide whether to exclude sample-limited patients, to reduce the protein loading per patient for the study, or to include that individual patient at reduced protein loading.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality

Sanford

Hansen

Gritsenko

et al. 2021

J. Am. Soc. Mass Spectrom.

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Global and phosphoproteome profiling has demonstrated great utility for the analysis of clinical specimens. One barrier to the broad clinical application of proteomic profiling is the large amount of biological material required, particularly for phosphoproteomics—currently on the order of 25 mg wet tissue weight. For hematopoietic cancers such as acute myeloid leukemia (AML), the sample requirement is ≥10 million peripheral blood mononuclear cells (PBMCs). Across large study cohorts, this requirement will exceed what is obtainable for many individual patients/time points. For this reason, we were interested in the impact of differential peptide loading across multiplex channels on proteomic data quality. To achieve this, we tested a range of channel loading amounts (approximately the material obtainable from 5E5, 1E6, 2.5E6, 5E6, and 1E7 AML patient cells) to assess proteome coverage, quantification precision, and peptide/phosphopeptide detection in experiments utilizing isobaric tandem mass tag (TMT) labeling. As expected, fewer missing values were observed in TMT channels with higher peptide loading amounts compared to lower loadings. Moreover, channels with a lower loading have greater quantitative variability than channels with higher loadings. A statistical analysis showed that decreased loading amounts result in an increase in the type I error rate. We then examined the impact of differential loading on the detection of known differences between distinct AML cell lines. Similar patterns of increased data missingness and higher quantitative variability were observed as loading was decreased resulting in fewer statistical differences; however, we found good agreement in features identified as differential, demonstrating the value of this approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality

Sanford

Hansen

Gritsenko

et al. 2021

J. Am. Soc. Mass Spectrom.

View full text Add to dashboard Cite

show abstract

“…In settings where the deepest achievable coverage of the phosphoproteome is a priority, it is recommended to use on the order of 400 mg of peptides in each TMT channel. While high quality data and coverage of the global proteome can be achieved with signi cantly less material, phosphoproteome coverage is negatively impacted or requires specialized methods to recover (18,19). This creates a key challenge relating to variability in the amount of protein available per patient, due to external variables in the study that impact sample size availability.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of differential peptide loading on tandem mass tag-based proteomic and phosphoproteomic data quality

Sanford

Wang

Hansen

et al. 2020

Preprint

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Background: Global and phosphoproteome profiling has demonstrated great utility for the analysis of clinical specimens. One major barrier to the broad clinical application of proteomic profiling is the large amount of biological material required, particularly for phosphoproteomics—currently on the order of 25 mg wet tissue weight, depending on tissue type. For hematopoietic cancers such as acute myeloid leukemia (AML), the sample requirement is in excess of 10 million (1E7) peripheral blood mononuclear cells (PBMCs). Throughout the course of a prospective study, this requirement will certainly exceed what is obtainable from many of the individual patients/timepoints. For this reason, we were interested in examining the impact of differential peptide loading across multiplex channels on proteomic data quality. Methods: To achieve this, we tested a range of channel loading amounts (20, 40, 100, 200, and 400 μg of tryptic peptides, or approximately the material obtainable from 5E5, 1E6, 2.5E6, 5E6, and 1E7 AML patient cells) to assess proteome coverage, quantification precision, and peptide/phosphopeptide detection in experiments utilizing isobaric tandem mass tag (TMT) labeling. Results: As expected, we found that fewer missing values are observed in TMT channels with higher peptide loading amounts compared to those with lower loading. Moreover, channels with lower loading amounts have greater quantitative variability than channels with higher loading amounts. Statistical analysis of the differences in means among the five loading groups showed that the 20 μg loading group was significantly different from the 400 μg loading group. However, no significant differences were detected among the 40, 100, 200 and 400 μg loading groups. Conclusions: These assessment data demonstrate the practical limits of loading differential quantities of peptides across channels in TMT multiplexes, and provide a basis for designing the optimal clinical proteomics study when specimen quantities are limited.

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“…Massive data are now affordably, easily and frequently generated by genomic and proteomic assays such as massively parallel sequencing and high-throughput mass spectrometry. Up to 1 trillion bases can be sequenced in one 6-day run by Illumina HiSeq 2500 ( Rhoads & Au, 2015 ) while mass spectrometry can now completely analyse a proteome and quantify protein concentrations in an entire organism ( Ahrne et al, 2015 ). Breakthroughs in genomic and proteomic data generation lead to development and emergence of several disciplines.…”

Section: Introductionmentioning

confidence: 99%

A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model

Pinthong

Muangruen

Suriyaphol

et al. 2016

PeerJ

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Development of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usable or meaningful until they are further analysed and interpreted. Due to the size of the data, a high performance computer (HPC) is required for the analysis and interpretation. However, the HPC is expensive and difficult to access. Other means were developed to allow researchers to acquire the power of HPC without a need to purchase and maintain one such as cloud computing services and grid computing system. In this study, we implemented grid computing in a computer training center environment using Berkeley Open Infrastructure for Network Computing (BOINC) as a job distributor and data manager combining all desktop computers to virtualize the HPC. Fifty desktop computers were used for setting up a grid system during the off-hours. In order to test the performance of the grid system, we adapted the Basic Local Alignment Search Tools (BLAST) to the BOINC system. Sequencing results from Illumina platform were aligned to the human genome database by BLAST on the grid system. The result and processing time were compared to those from a single desktop computer and HPC. The estimated durations of BLAST analysis for 4 million sequence reads on a desktop PC, HPC and the grid system were 568, 24 and 5 days, respectively. Thus, the grid implementation of BLAST by BOINC is an efficient alternative to the HPC for sequence alignment. The grid implementation by BOINC also helped tap unused computing resources during the off-hours and could be easily modified for other available bioinformatics software.

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Exploiting the multiplexing capabilities of tandem mass tags for high-throughput estimation of cellular protein abundances by mass spectrometry

Cited by 7 publications

References 45 publications

Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality

Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality

Evaluation of differential peptide loading on tandem mass tag-based proteomic and phosphoproteomic data quality

A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model

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