DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

Huffman, Gray; Specht, Harrison; Chen, Albert T.; Slavov, Nikolai

doi:10.1101/512152

Cited by 11 publications

(19 citation statements)

References 60 publications

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“…The quantitative comparisons between LF-DIA and plexDIA throughout this article are for intersected sets of proteins so that the results would not be influenced by proteins analyzed only by one method and not the other. For examples, compared distributions were for the same set of proteins to avoid "survival biases" 49 . | plexDIA analysis proteins present only in one samples but missing from another.…”

Section: Discussionmentioning

confidence: 99%

Increasing the throughput of sensitive proteomics by plexDIA

Derks

Leduc

Huffman

et al. 2021

Preprint

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Current mass-spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. We aimed to increase the throughput of high-sensitivity proteomics while achieving high proteome coverage and quantitative accuracy. We developed a general experimental and computational framework, plexDIA, for simultaneously multiplexing the analysis of both peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. Specifically, plexDIA using 3-plex nonisobaric mass tags enables quantifying 3-fold more protein ratios among nanogram-level samples. Using 1 hour active gradients and first-generation Q Exactive, plexDIA quantified about 8,000 proteins in each sample of labeled 3-plex sets. Furthermore, plexDIA increases the consistency of protein quantification, resulting in over 2-fold reduction of missing data across samples. We applied plexDIA to quantify proteome dynamics during the cell division cycle in cells isolated based on their DNA content. The high sensitivity and accuracy of plexDIA detected many classical cell cycle proteins and discovered new ones. These results establish a general framework for increasing the throughput of highly sensitive and quantitative protein analysis.

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Section: Discussionmentioning

confidence: 99%

Increasing the throughput of sensitive proteomics by plexDIA

Derks

Leduc

Huffman

et al. 2021

Preprint

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“…Data analysis followed a previously reported approach, Data-driven Optimization of MS (DO-MS), for evaluating and optimizing MS experiments. 12 Specifically, Figures 2 and 4 were generated by plotting variables reported by MaxQuant. The Pearson correlation values displayed in Figure 5a were computed from the subset of peptides observed in all samples.…”

Section: Data Analysis and Visualizationmentioning

confidence: 99%

“…11 This growing use of the concept motivated us to benchmark its benefits and limitations in controlled experiments and to extend the previously suggested approaches for optimizing ultrasensitive MS experiments. 12 We explored the role of isobaric carriers in (i) facilitating the detection of precursor ions in MS1 survey scans and (ii) facilitating sequence identification by providing peptide fragment ions to MS2 spectra. These benefits must be balanced with possible adverse effects on quantification.…”

Section: Introductionmentioning

confidence: 99%

“…4,17 Thus, experimental designs should take into account the costs and benefits of each analytical strategy and choose whether to emphasize throughput or quantitative accuracy. This optimization has already been discussed, 4,12,17 but the trade-offs of varying the number of cells in the isobaric carriers remain incompletely characterized. Here, we sought to empirically characterize these trade-offs and use the data to provide recommendations for experimental designs.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Accuracy and Depth of Protein Quantification in Experiments Using Isobaric Carriers

2020

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The isobaric carrier approach, which combines small isobarically labeled samples with a larger isobarically labeled carrier sample, finds diverse applications in ultrasensitive mass spectrometry analysis of very small samples, such as single cells. To enhance the growing use of isobaric carriers, we characterized the trade-offs of using isobaric carriers in controlled experiments with complex human proteomes. The data indicate that isobaric carriers directly enhance peptide sequence identification without simultaneously increasing the number of protein copies sampled from small samples. The results also indicate strategies for optimizing the amount of isobaric carrier and analytical parameters, such as ion accumulation time, for different priorities such as improved quantification or an increased number of identified proteins. Balancing these trade-offs enables adapting isobaric carrier experiments to different applications, such as quantifying proteins from limited biopsies or organoids, building single-cell atlases, or modeling protein networks in single cells. In all cases, the reliability of protein quantification should be estimated and incorporated in all subsequent analyses. We expect that these guidelines will aid in explicit incorporation of the characterized trade-offs in experimental designs and transparent error propagation in data analysis.

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“…Here, we demonstrate sample preparation by nPOP as part of the SCoPE2 protocol 36,40 . Specifically, we replaced Minimal ProteOmic sample Preparation (mPOP) module 35 with nPOP and used all other modules of the SCoPE2 workflow, including an isobaric carrier 42 , Data-Driven Optimization of Mass Spectrometry (DO-MS) 43 , Data-driven Alignment of Retention Times for IDentification (DART-ID) 44 , and the SCoPE2 data analysis pipeline 40,45,46 .…”

Section: Single-cell Protein Analysis With Npopmentioning

confidence: 99%

Exploring functional protein covariation across single cells using nPOP

Leduc

Huffman

Slavov

2021

Preprint

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Many biological functions, such as the cell division cycle, are intrinsically single-cell processes regulated in part by protein synthesis and degradation. Investigating such processes has motivated the development of single-cell mass spectrometry (MS) proteomics. To further advance single-cell MS proteomics, we developed a method for automated nano-ProteOmic sample Preparation (nPOP). nPOP uses piezo acoustic dispensing to isolate individual cells in 300 picoliter volumes and performs all subsequent preparation steps in small droplets on a hydrophobic slide. This allows massively parallel sample preparation, including lysing, digesting, and labeling individual cells in volumes below 20 nl. Single-cell protein analysis using nPOP classified cells by cell type and by cell cycle phase. Furthermore, the data allowed us to quantify the covariation between cell cycle protein markers and thousands of proteins. Based on this covariation, we identify cell cycle associated proteins and functions that are shared across cell types and those that differ between cell types.

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DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

Cited by 11 publications

References 60 publications

Increasing the throughput of sensitive proteomics by plexDIA

Increasing the throughput of sensitive proteomics by plexDIA

Optimizing Accuracy and Depth of Protein Quantification in Experiments Using Isobaric Carriers

Exploring functional protein covariation across single cells using nPOP

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