Optimisation strategies for acquiring and analysing data-independent acquisition on linear ion traps for single-cell proteomics experiments
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Eclipse
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Erwin Marten Schoof Brian C. Searle
PROVIDER: MSV000090688 | MassIVE | Thu Nov 10 04:15:00 GMT 2022
SECONDARY ACCESSION(S): PXD038086
REPOSITORIES: MassIVE
Similar Datasets
Project description:Here we revisited the use of linear ion traps mass analyzers in DIA methods to evaluate their potential to boost peptide and protein identifications in low input and single-cell proteomics applications
2021-09-09 | PXD025766 | Pride
Project description:Quantitative analysis of discovery-based proteomic workflows now relies on high-throughput large-scale methods for identification and quantitation of proteins and post-translational modifications. Advancements in label-free quantitative techniques, using either data-dependent or data-independent mass spectrometric acquisitions, have coincided with improved instrumentation featuring greater precision, increased mass accuracy, and faster scan speeds. We recently reported on a new quantitative method called MS1 Filtering (Schilling et al. (2012) Mol. Cell. Proteomics 11, 202-214) for processing data-independent MS1 ion intensity chromatograms from peptide analytes using the Skyline software platform. In contrast, data-independent acquisitions from MS2 scans, or SWATH, can quantify all fragment ion intensities when reference spectra are available. As each SWATH acquisition cycle typically contains an MS1 scan, these two independent label-free quantitative approaches can be acquired in a single experiment. Here, we have expanded the capability of Skyline to extract both MS1 and MS2 ion intensity chromatograms from a single SWATH data-independent acquisition in an Integrated Dual Scan Analysis approach. The performance of both MS1 and MS2 data was examined in simple and complex samples using standard concentration curves. Cases of interferences in MS1 and MS2 ion intensity data were assessed, as were the differentiation and quantitation of phosphopeptide isomers in MS2 scan data. In addition, we demonstrated an approach for optimization of SWATH m/z window sizes to reduce interferences using MS1 scans as a guide. Finally, a correlation analysis was performed on both MS1 and MS2 ion intensity data obtained from SWATH acquisitions on a complex mixture using a linear model that automatically removes signals containing interferences. This work demonstrates the practical advantages of properly acquiring and processing MS1 precursor data in addition to MS2 fragment ion intensity data in a data-independent acquisition (SWATH), and provides an approach to simultaneously obtain independent measurements of relative peptide abundance from a single experiment.
| S-EPMC4563724 | biostudies-literature
Project description:Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.
| S-EPMC7338082 | biostudies-literature
Project description:The application of human bone in forensic proteomics is an expanding novel purpose in the aim of successfully quantifying biological estimations used in medico-legal investigations with greater accuracy. In this project, different extraction protocols were tested on n=30 human bone samples, including S-Trap technology with two different lysis buffer, and ZipTips. Additionally, a comparison was made between data-dependent acquisition (DDA) and data-independent acquisition (DIA) mode.
Results showed less missing data using S-Traps instead of the more routine reverse-phase media tips (ZipTip). The type of lysis buffer when using S-Traps does not impact largely the analysis conducted in forensic proteomic workflows. Lastly, it was found that when open-source software is used for data processing for both DDA and DIA modes, DIA has the upper advantage in terms of acquiring a larger number of proteins due to its greater sensitivity to those with a lower abundance.
2024-01-10 | MSV000093803 | MassIVE
Project description:Data-dependent precursor ion selection is widely used in shotgun proteomics to profile the protein components of complex samples. Although very popular, this bottom-up method presents major drawbacks in terms of detectable dynamic range. Recently, we demonstrated the superior performance of a data-independent method we termed precursor acquisition independent from ion count (PAcIFIC). Here, we report a faster, accurate, multiplexed, and quantitative PAcIFIC method. Our results show that the time needed to perform such analysis can be decreased by 33% to 66% using modern ion trap instruments and that high mass accuracy can be applied to such a strategy. Quantification capability is demonstrated on protein standards and a whole bacterial cell lysate using isobaric tagging. Finally, we confirm in yeast the dynamic range capabilities of such a method where proteins down to less than 50 copies per cell can be monitored without sample prefractionation.
| S-EPMC3217585 | biostudies-literature
Project description:Mass spectrometry with data-independent acquisition (DIA) is a promising method to improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory by systematically measuring all peptide precursors in a biological sample. However, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms (SNPs) and alternative site localizations in phosphoproteomics data. We report Specter (https://github.com/rpeckner-broad/Specter), an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly through comparison to a spectral library, thus circumventing the problems associated with typical fragment-correlation-based approaches. We validate the sensitivity of Specter and its performance relative to that of other methods, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.
| S-EPMC5924490 | biostudies-literature
Project description:Advances in proteomics and mass spectrometry have enabled the study of limited cell populations, such as single-cell proteomics, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive nominal resolution measurements, these instruments are effectively limited to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. We demonstrate a workflow using a newly released, hybrid quadrupole-LIT instrument for developing targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Gas-phase fraction-based DIA enables rapid target library generation in the same background chemical matrix as each quantitative injection. Using a new software tool embedded within EncyclopDIA for scheduling parallel reaction monitoring assays, we show consistent quantification across three orders of magnitude of input material. Using this approach, we demonstrate measuring peptide quantitative linearity down to 25x dilution in a background of only a 1 ng proteome without requiring stable isotope labeled standards. At 1 ng total protein on column, we found clear consistency between immune cell populations measured using flow cytometry and immune markers measured using LIT-based proteomics. We believe hybrid quadrupole-LIT instruments represent an economic solution to democratizing mass spectrometry in a wide variety of laboratory settings.
2024-06-19 | PXD052847 | panorama
Project description:Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40-85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature.
| S-EPMC4563719 | biostudies-literature
Project description:BackgroundStochastic fluctuations in molecular numbers have been in many cases shown to be crucial for the understanding of biochemical systems. However, the systematic study of these fluctuations is severely hindered by the high computational demand of stochastic simulation algorithms. This is particularly problematic when, as is often the case, some or many model parameters are not well known. Here, we propose a solution to this problem, namely a combination of the linear noise approximation with optimisation methods. The linear noise approximation is used to efficiently estimate the covariances of particle numbers in the system. Combining it with optimisation methods in a closed-loop to find extrema of covariances within a possibly high-dimensional parameter space allows us to answer various questions. Examples are, what is the lowest amplitude of stochastic fluctuations possible within given parameter ranges? Or, which specific changes of parameter values lead to the increase of the correlation between certain chemical species? Unlike stochastic simulation methods, this has no requirement for small numbers of molecules and thus can be applied to cases where stochastic simulation is prohibitive.ResultsWe implemented our strategy in the software COPASI and show its applicability on two different models of mitogen-activated kinases (MAPK) signalling -- one generic model of extracellular signal-regulated kinases (ERK) and one model of signalling via p38 MAPK. Using our method we were able to quickly find local maxima of covariances between particle numbers in the ERK model depending on the activities of phospho-MKKK and its corresponding phosphatase. With the p38 MAPK model our method was able to efficiently find conditions under which the coefficient of variation of the output of the signalling system, namely the particle number of Hsp27, could be minimised. We also investigated correlations between the two parallel signalling branches (MKK3 and MKK6) in this model.ConclusionsOur strategy is a practical method for the efficient investigation of fluctuations in biochemical models even when some or many of the model parameters have not yet been fully characterised.
| S-EPMC3814289 | biostudies-literature
Project description:Although single cell RNA-seq has had a tremendous impact on biological research, a corresponding technology for unbiased mass spectrometric analysis of single cells has only recently become available. Significant technological breakthroughs including miniaturized sample handling have enabled proteome profiling of single cells. Further, a trapped ion mobility spectrometry (TIMS) in combination with parallel accumulation-serial fragmentation operated with data-dependant acquisition mode has allowed improved proteome coverage from low-input samples. It has been demonstrated that modulating ion flux in TIMS affects overall performance of proteome profiling. However, the effect of TIMS settings for analyzing low-input samples has been less investigated. Thus, we sought to optimize conditions of TIMS in regards of ion accumulation/ramp times and ion mobility range for low-input samples. We observed that ion accumulation times of 180 ms and monitoring a narrower ion mobility range from 0.7 to 1.3 Vs cm-2 resulted in a substantial gain in the depth of proteome coverage and in detecting proteins with low abundance. We applied these optimized conditions for proteome profiling of sorted human primary T cells, which yielded an average of 365, 804, 1,116, and 1,651 proteins from single, five, ten, and forty T cells, respectively. Notably, we demonstrated that the depth of proteome coverage from low number of cells was sufficient to delineate several essential metabolic pathways and T cell receptor signaling pathway. Finally, we showed the feasibility of detecting post-translational modifications including phosphorylation and acetylation from single cells. We believe that these parameters could be applied to label-free analysis of single cells obtained from clinically relevant samples.
2024-08-02 | PXD039066 | Pride