IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs

Yu, Fengchao; Haynes, Sarah E.; Nesvizhskii, Alexey I.

doi:10.1016/j.mcpro.2021.100077

Cited by 226 publications

(190 citation statements)

References 50 publications

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“…FragPipe's IonQuant performed the best in terms of median CV (3.6% and 4.3% for the 2 and 1 minimum ion analysis, respectively). Notably, these CVs obtained by IonQuant were better than those reported by Yu et al [9] in the original IonQuant publication (6.4 and 7.3% median CVs), where FDR-controlled MBR was not available, but consistent with what was reported by the same authors following MBR implementation (3.6 and 4.0% median CVs) [14].…”

Section: Resultssupporting

confidence: 87%

“…We compare the results to those obtained with the most up to date version (v15.5) of the gold-standard software for DIA data processing, Spectronaut, and we include library-based and library-free approaches. In addition, since the samples were also run with DDA-PASEF for the library-based workflows, we will include in the comparison the MS1based LFQ from those runs, using the recently developed IonQuant software with the Match-Between-Runs (MBR) functionality [14], also integrated into FrapPipe. As an illustrative example of application with real samples, such as those that may occur in a real laboratory experiment, we apply the different data analysis workflows to diaPASEF and DDA-PASEF LC-MS runs obtained from the protein extracts of an inflammatory murine cell model, lipopolysaccharide (LPS)activated macrophages, with and without two anti-inflammatory compounds.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of TIMS-PASEF quantitative proteomics data-analysis workflows using FragPipe, DIA-NN, and Spectronaut from a user’s perspective

Fernandez-Vega

Farabegoli

Alonso-Martinez

et al. 2021

Preprint

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Data-independent acquisition (DIA) methods have gained great popularity in bottom-up quantitative proteomics, as they overcome the irreproducibility and under-sampling limitations of data-dependent acquisition (DDA). diaPASEF, recently developed for the timsTOF Pro mass spectrometers, has brought improvements to DIA, providing additional ion separation (in the ion mobility dimension) and increasing sensitivity. Several studies have benchmarked different workflows for DIA quantitative proteomics, but mostly using instruments from Sciex and Thermo, and therefore, the results are not extrapolable to diaPASEF data. In this work, using a real-life sample set like the one that can be found in any proteomics experiment, we compared the results of analyzing PASEF data with different combinations of library-based and library-free analysis, combining the tools of the FragPipe suite, DIA-NN and including MS1-level LFQ with DDA-PASEF data, and also comparing with the workflows possible in Spectronaut. We verified that library-independent workflows, not so efficient not so long ago, have greatly improved in the recent versions of the software tools, and now perform as well or even better than librarybased ones. We report here information so that the user who is going to conduct a relative quantitative proteomics study using a timsTOF Pro mass spectrometer can make an informed decision on how to acquire (diaPASEF for DIA analysis, or DDA-PASEF for MS1-level LFQ) the samples, and what can be expected depending on the data analysis tool used, among the different alternatives offered by the recently optimized tools for TIMS-PASEF data analysis.

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Section: Resultssupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Comparison of TIMS-PASEF quantitative proteomics data-analysis workflows using FragPipe, DIA-NN, and Spectronaut from a user’s perspective

Fernandez-Vega

Farabegoli

Alonso-Martinez

et al. 2021

Preprint

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“…Through fragment ion and peak indexing, MSFragger and the accompanying IonQuant achieve several-fold faster processing times, which renders semispecific and so-called open database searches practical to perform ( 57 , 80 ). The same group also implemented a variant of the matching between runs algorithm that feeds, amongst others, ion mobility data into a machine learning model to discriminate true and false matches ( 81 ). In addition to academic tools, commercial software also supports TIMS data increasingly, for example, PEAKS ( 82 ) or SpectroMine.…”

Section: Discussionmentioning

confidence: 99%

Trapped Ion Mobility Spectrometry and Parallel Accumulation–Serial Fragmentation in Proteomics

Meier

Park

Mann

2021

Molecular & Cellular Proteomics

111

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Recent advances in efficiency and ease of implementation have rekindled interest in ion mobility spectrometry, a technique that separates gas phase ions by their size and shape and that can be hybridized with conventional LC and MS. Here, we review the recent development of trapped ion mobility spectrometry (TIMS) coupled to TOF mass analysis. In particular, the parallel accumulation–serial fragmentation (PASEF) operation mode offers unique advantages in terms of sequencing speed and sensitivity. Its defining feature is that it synchronizes the release of ions from the TIMS device with the downstream selection of precursors for fragmentation in a TIMS quadrupole TOF configuration. As ions are compressed into narrow ion mobility peaks, the number of peptide fragment ion spectra obtained in data-dependent or targeted analyses can be increased by an order of magnitude without compromising sensitivity. Taking advantage of the correlation between ion mobility and mass, the PASEF principle also multiplies the efficiency of data-independent acquisition. This makes the technology well suited for rapid proteome profiling, an increasingly important attribute in clinical proteomics, as well as for ultrasensitive measurements down to single cells. The speed and accuracy of TIMS and PASEF also enable precise measurements of collisional cross section values at the scale of more than a million data points and the development of neural networks capable of predicting them based only on peptide sequences. Peptide collisional cross section values can differ for isobaric sequences or positional isomers of post-translational modifications. This additional information may be leveraged in real time to direct data acquisition or in postprocessing to increase confidence in peptide identifications. These developments make TIMS quadrupole TOF PASEF a powerful and expandable platform for proteomics and beyond.

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“…TMT and iTRAQ, and therefore compatible with use of carrier channels to boost the signal of rare or single cell channels (e.g. iBASIL [43]). The protocol requires no specialized humidified sample handling chambers or direct loading onto premade analytical nanoLC columns, such as those described in the nanoPOTS workflow [11].…”

Section: Discussionmentioning

confidence: 99%

Low cell number proteomic analysis using in-cell protease digests reveals a robust signature for cell cycle state classification

Kelly

al-Rawi

Lewis

et al. 2020

Preprint

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AbstractProteomic analysis of rare cell states is a major challenge. We report an advance to our PRoteomics of Intracellular iMMUnostained cell Subsets (PRIMMUS) workflow whereby fixed cells are directly digested by proteases in cellulo for mass spectrometry-based proteomics. This decreased the cell number requirement by two orders of magnitude to <2,000 human lymphoblasts. We quantitatively measured the proteomes of 8 interphase and 8 mitotic states, avoiding synchronization. From 8 replicate pseudo-timecourses, we identify a core set of 119 cell cycle-regulated proteins that segregated into five clusters. These clusters varied in mitotic abundance patterns and regulatory short linear sequence motifs controlling their localisation and interaction with E3 ubiquitin ligases. We identified protein signatures that allowed accurate cell cycle state classification. We use this classification to stage an unexpected cell population as similar in proteome to early G0/G1 and telophase cells. Our data indicate DNA damage responses and premature APC/C activation in these cells, consistent with a DNA damage-induced senescent state. The advanced PRIMMUS approach is readily and broadly applicable to characterise rare and abundant cell states.

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IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs

Cited by 226 publications

References 50 publications

Comparison of TIMS-PASEF quantitative proteomics data-analysis workflows using FragPipe, DIA-NN, and Spectronaut from a user’s perspective

Comparison of TIMS-PASEF quantitative proteomics data-analysis workflows using FragPipe, DIA-NN, and Spectronaut from a user’s perspective

Trapped Ion Mobility Spectrometry and Parallel Accumulation–Serial Fragmentation in Proteomics

Low cell number proteomic analysis using in-cell protease digests reveals a robust signature for cell cycle state classification

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