Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Self Cite

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430−670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

Section: Resultsmentioning

confidence: 99%

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme

Fröhlich,

Furrer,

Schori

et al. 2024

Self Cite

“…A large ensemble of DDA runs, especially when complex peptide mixtures are prefractionated (by, e.g., SDS-PAGE or HPLC), are more likely to achieve high coverage with low FDR than DIA. Nonetheless, there are many efforts to improve the processing of DIA (e.g., ref ) and we are starting to develop a mechanism to integrate DIA data into the PeptideAtlas build process. However, we did include stable isotope labeled (multiplexed) proteome data sets, including isobaric iTRAQ and TMT, , dimethyl labeling, as well as N-terminomics data sets using TAILS or COFRADIC .…”

Section: Resultsmentioning

confidence: 99%

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

van Wijk,

Leppert,

Sun

et al. 2023

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023−10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

“…In this study, several published DIA-MS acquisition schemes were compared against a well-established DDA-MS workflow to determine an optimal method that optimizes sample throughput and peptide detection. We show that a sample-specific spectral library resulted in the highest number of peptides detected compared to a library-free approach and a pan-human library . Our group has previously shown that analysis of extracellular vesicles (EV) isolated from EPS-urine further increases the number of prostate proteins in EPS-urine .…”

Section: Introductionmentioning

confidence: 91%

“…Data-independent acquisition (DIA)-MS is an emerging tool for characterizing proteomes for its ability to increase throughput and reproducibility, − yet there is limited consensus on an ideal downstream data analysis approach for the highly convoluted data generated . To investigate the utility of DIA-MS workflows for clinical biofluid cohorts, we evaluated various commonly used DIA-MS data analysis approaches − using a large clinically heterogeneous cohort of urines from 199 patients with prostate cancer. These patients spanned the risk spectrum, from clinical ISUP grade Group 1 disease (associated with better prognosis) to clinical ISUP grade Group 5 disease (associated with worse prognosis) .…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine

Ha,

Khoo,

Ignatchenko

et al. 2024

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.