Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics

Distler, Ute; Kuharev, Jörg; Navarro, Pedro J.; Levin, Yishai; Schild, Hansjörg; Tenzer, Stefan

doi:10.1038/nmeth.2767

Cited by 420 publications

(443 citation statements)

References 27 publications

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“…This quantification method was later implemented on other platforms (Grossmann et al, 2010). The method has since been applied to assess protein abundances in a variety of systems, including Escherichia coli (Silva et al, 2006a), budding yeast (Saccharomyces cerevisiae ;Carroll et al, 2011), human cell lines (Distler et al, 2014), and plants (Blackburn et al, 2010;Helm et al, 2014). To assess its accuracy in our experiments, we analyzed the commercial protein standards Universal Protein Standard1 (UPS1; 48 proteins at equimolar abundances) and UPS2 (48 proteins at six different concentrations).…”

Section: Assessment Of the Top-3 Quantification Accuracymentioning

confidence: 99%

Label-Free Protein Quantification for Plant Golgi Protein Localization and Abundance

et al. 2014

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The proteomic composition of the Arabidopsis (Arabidopsis thaliana) Golgi apparatus is currently reasonably well documented; however, little is known about the relative abundances between different proteins within this compartment. Accurate quantitative information of Golgi resident proteins is of great importance: it facilitates a better understanding of the biochemical processes that take place within this organelle, especially those of different polysaccharide synthesis pathways. Golgi resident proteins are challenging to quantify because the abundance of this organelle is relatively low within the cell. In this study, an organelle fractionation approach targeting the Golgi apparatus was combined with a label-free quantitative mass spectrometry (dataindependent acquisition method using ion mobility separation known as LC-IMS-MS E [or HDMS E ]) to simultaneously localize proteins to the Golgi apparatus and assess their relative quantity. In total, 102 Golgi-localized proteins were quantified. These data show that organelle fractionation in conjunction with label-free quantitative mass spectrometry is a powerful and relatively simple tool to access protein organelle localization and their relative abundances. The findings presented open a unique view on the organization of the plant Golgi apparatus, leading toward unique hypotheses centered on the biochemical processes of this organelle.

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Section: Assessment Of the Top-3 Quantification Accuracymentioning

confidence: 99%

Label-Free Protein Quantification for Plant Golgi Protein Localization and Abundance

et al. 2014

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“…Analyses were performed in the ion mobility‐enhanced data‐independent acquisition mode with drift time‐specific collision energies as described in detail by Distler et al. (2014, 2016). Continuum LC‐MS data were processed for signal detection, peak picking, and isotope and charge state deconvolution using Waters ProteinLynx Global Server (PLGS) version 3.0.2 (Li et al., 2009).…”

Section: Methodsmentioning

confidence: 99%

“…The freely available software ISOQuant (http://www.isoquant.net) was used for postidentification analysis including retention time alignment, exact mass and retention time (EMRT) and ion mobility clustering, data normalization, isoform/homology filtering, and calculation of absolute in‐sample amounts for each detected protein according to the TOP3 quantification approach (Distler et al., 2014, 2016; Kuharev, Navarro, Distler, Jahn & Tenzer, 2015). Only peptides with a minimum length of seven amino acids, which were identified with scores above or equal to 5.5 in at least two runs, were considered.…”

Section: Methodsmentioning

confidence: 99%

SUMO1‐conjugation is altered during normal aging but not by increased amyloid burden

et al. 2018

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SummaryA proper equilibrium of post‐translational protein modifications is essential for normal cell physiology, and alteration in these processes is key in neurodegenerative disorders such as Alzheimer's disease. Recently, for instance, alteration in protein SUMOylation has been linked to amyloid pathology. In this work, we aimed to elucidate the role of protein SUMOylation during aging and increased amyloid burden in vivo using a His6‐HA‐SUMO1 knock‐in mouse in the 5XFAD model of Alzheimer's disease. Interestingly, we did not observe any alteration in the levels of SUMO1‐conjugation related to Alzheimer's disease. SUMO1 conjugates remained localized to neuronal nuclei upon increased amyloid burden and during aging and were not detected in amyloid plaques. Surprisingly however, we observed age‐related alterations in global levels of SUMO1 conjugation and at the level of individual substrates using quantitative proteomic analysis. The identified SUMO1 candidate substrates are dominantly nuclear proteins, mainly involved in RNA processing. Our findings open novel directions of research for studying a functional link between SUMOylation and its role in guarding nuclear functions during aging.

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“…MS/MS spectra, despite HDMS E and UDMS E developments [13]. DIA is therefore a rather mature and diverse approach for analyzing proteomes than a new MS acquisition method, as it has been said and even advertised in our field.…”

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confidence: 99%

DIA is not a new mass spectrometry acquisition method

2017

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Contrary to what is being said by several colleagues (and even advertised), data-independent analysis/acquisition (DIA) is not a new mass spectrometry acquisition method. Here we draw a timeline of events showing that DIA has been around since the early 2000s. Keywords: Data-dependent analysis / Data-independent analysisIt is commonly heard in conference talks or read in a few papers [1] that data-independent analysis/acquisition (DIA) is a new way of analyzing proteomes in a shotgun fashion. That is not quite accurate. Back in 2004, Venable et al. proposed a modified method for MS/MS "based on the sequential isolation and fragmentation of precursor windows (of 10 m/z)", which was referred as "not data dependent" [2]. About a year later, Silva et al., while looking for a precise relative quantification method, proposed the quantitation of peptides based on MS acquisition without any previous isolation [3]. Here, they proposed to alternate the collision energy in low and high energy during MS acquisition. These data are then collected in two channels: one containing the signals of intact peptides and the other the chimeric fragmented peptides [4]. This is the so called MS E method.Both methods, described more than 10 years ago, are not dependent on MS data for fragmentation. Thus, they are DIA. DIA, data-independent analysis/acquisition; SWATH, sequential window acquisition of all theoretical mass spectra MS1 and sequential MS2 windowed acquisitions [5]. At the time, windows of 20 m/z overlapping by 1 m/z. More recently, the concept of SWATH (sequential window acquisition of all theoretical mass spectra) reverberated the use of DIA [6]. Gillet et al., while describing SWATH, actually cited Venable's paper from 2004, supporting the idea that DIA was already something existent. Not only that, but a simple PubMed search can show hundreds of papers using MS E in the past decade, in all kinds of different questions. The main differences in these methods rely on the data processing (spectral library vs. deconvolution) and the use or not of a selection window in the quadrupole. It is true though that SWATH has awaken DIA as a possibility for any mass spectrometer, in times where DDA started reaching its limits in proteomics, which is the focus on the identification of high-abundant proteins in a limited dynamic range. Another contributing factor was the significant developments around MS-based targeted proteomics using SRM and, more recently, PRM [7]. Empathy for DIA became stronger, and new developments on algorithms to deal with DIA data such as and Skyline DIA is now evolving even more. A few months ago, a new strategy proposed multiplexing narrowband and wideband DIA acquisitions in a single analytical workflow [12]. One could risk saying that this is a combination of SWATH and MS E . This multimode acquisition (MMA) strategy would avoid DIA drawbacks on sensitivity and chimeric C

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Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics

Cited by 420 publications

References 27 publications

Label-Free Protein Quantification for Plant Golgi Protein Localization and Abundance

Label-Free Protein Quantification for Plant Golgi Protein Localization and Abundance

SUMO1‐conjugation is altered during normal aging but not by increased amyloid burden

DIA is not a new mass spectrometry acquisition method

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