Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Molecular & Cellular Proteomics, Год журнала: 2024, Номер 23(2), С. 100712 - 100712

Опубликована: Янв. 4, 2024

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate and reproducible quantitative proteomics. This review provides comprehensive overview of recent advances in both the experimental computational methods DIA proteomics, from data schemes to analysis strategies software tools. are categorized based on design precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, parallel accumulation-serial fragmentation (PASEF)-enhanced methods. For analysis, major classified into spectrum reconstruction, sequence-based search, library-based de novo sequencing sequencing-independent approaches. A wide array tools implementing these reviewed, with details their overall workflows scoring approaches at different steps. The generation optimization spectral libraries, which critical resources also discussed. Publicly available benchmark datasets covering global proteomics phosphoproteomics summarized facilitate performance evaluation various workflows. Continued synergistic developments versatile components expected further enhance power DIA-based

Язык: Английский

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Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Май 9, 2024

Abstract Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, normalization, missing value imputation (MVI), and differential analysis. The plethora options each step makes it challenging to identify optimal workflows that maximize the identification proteins. To their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques top-ranked workflows, uncover high-performing rules demonstrate optimality has conserved properties. Via machine learning, confirm are indeed predictable, with average cross-validation F1 scores Matthew’s correlation coefficients surpassing 0.84. We introduce ensemble inference integrate results from individual top-performing for expanding proteome coverage resolve inconsistencies. Ensemble provides gains pAUC (up 4.61%) G-mean 11.14%) facilitates effective aggregation information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, spectral counts. However, further development evaluation needed establish acceptable frameworks conducting multiple workflows.

Язык: Английский

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AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июнь 2, 2024

Abstract Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and application areas. The scale of data generated on novel instrumentation acquisition strategies pose a challenge bioinformatic analysis. Search engines need make optimal use the for biological discoveries while remaining statistically rigorous, transparent performant. Here we present alphaDIA, modular open-source search framework independent (DIA) proteomics. We developed feature-free identification algorithm particularly suited detecting patterns in produced by sensitive time-of-flight instruments. It naturally adapts novel, eTicient scan modes that are not yet accessible previous algorithms. Rigorous benchmarking demonstrates competitive quantification performance. While supporting empirical spectral libraries, propose new strategy named end-to-end transfer learning using fully predicted libraries. This entails continuously optimizing deep neural network predicting machine experiment specific properties, enabling generic DIA analysis any post-translational modification (PTM). AlphaDIA provides high performance running locally or cloud, community.

Язык: Английский

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A Framework for Quality Control in Quantitative Proteomics

Journal of Proteome Research, Год журнала: 2024, Номер unknown

Опубликована: Сен. 9, 2024

A thorough evaluation of the quality, reproducibility, and variability bottom-up proteomics data is necessary at every stage a workflow, from planning to analysis. We share vignettes applying adaptable quality control (QC) measures assess sample preparation, system function, quantitative System suitability samples are repeatedly measured longitudinally with targeted methods, we examples where they used on three instrument platforms identify severe failures track function over months years. Internal QCs incorporated protein peptide levels allow our team preparation issues differentiate sample-specific issues. External QC prepared alongside experimental verify consistency potential results during batch correction normalization before assessing biological phenotypes. combine these controls rapid analysis (Skyline), longitudinal metrics (AutoQC), server-based deposition (PanoramaWeb). propose that this integrated approach useful starting point for groups facilitate assessment ensure valuable time collect best possible. Data available Panorama Public ProteomeXchange under identifier PXD051318.

Язык: Английский

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A framework for quality control in quantitative proteomics

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Апрель 13, 2024

A thorough evaluation of the quality, reproducibility, and variability bottom-up proteomics data is necessary at every stage a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures assess sample preparation, system function, quantitative System suitability samples are repeatedly measured longitudinally with targeted methods, we examples where they used on three instrument platforms identify severe failures track function over months years. Internal QCs incorporated protein peptide-level allow our team preparation issues differentiate sample-specific issues. External QC prepared alongside experimental verify consistency potential results during batch correction normalization before assessing biological phenotypes. combine these controls rapid analysis (Skyline), longitudinal metrics (AutoQC), server-based deposition (PanoramaWeb). propose that this integrated approach useful starting point for groups facilitate assessment ensure valuable time collect best possible. Data available Panorama Public ProteomeXchange under identifier PXD051318.

Язык: Английский

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Combining Data Independent Acquisition with Spike-in SILAC (DIA-SiS) Improves Proteome Coverage and Quantification

Molecular & Cellular Proteomics, Год журнала: 2024, Номер unknown, С. 100839 - 100839

Опубликована: Сен. 1, 2024

Язык: Английский

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Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Янв. 26, 2025

Abstract Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary data across diverse liquid chromatography−mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct comprehensive evaluation proteome multiple LC-MS platforms. To systematically analyze assess quality large-scale data, develop control (QC) system named MSCohort, which extracted 81 metrics individual experiment whole cohort evaluation. Additionally, present standard operating procedure (SOP) high-throughput analysis based on MSCohort QC system. Our study involves 20 reveals that, when combined with unified SOP, generated by data-independent acquisition (DIA) workflow in urine samples exhibit high robustness, sensitivity, reproducibility Furthermore, apply SOP to hybrid benchmarking clinical colorectal cancer (CRC) including 527 experiments. Across three different platforms, analyses report quantitative consistent disease patterns. This work lays groundwork studies spanning paving way precision medicine research.

Язык: Английский

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Comparative Proteomics of Salinity Stress Responses in Fish and Aquatic Invertebrates

PROTEOMICS, Год журнала: 2025, Номер unknown

Опубликована: Фев. 9, 2025

Fluctuating salinity is symptomatic of climate change challenging aquatic species. The melting polar ice, rising sea levels, coastal surface and groundwater salinization, increased evaporation in arid habitats alter worldwide. Moreover, the frequency intensity extreme weather events such as rainstorms floods increase, causing rapid shifts brackish habitat salinity. Such alterations disrupt homeostasis ultimately diminish fitness, organisms by interfering with metabolism, reproduction, immunity, other critical aspects physiology. Proteins are central to these physiological mechanisms. They represent molecular building blocks phenotypes that govern organismal responses environmental challenges. Environmental cues regulate proteins a concerted fashion, necessitating holistic analyses proteomes for comprehending stress responses. Proteomics approaches reveal causes population declines enable bioindication geared toward timely interventions prevent local extinctions. effects on have been performed since mid-1990s, propelled invention two-dimensional protein gels, soft ionization techniques mass spectrometry (MS), nano-liquid chromatography 1970s 1980s. This review summarizes current knowledge regulation from organisms, including key methodological advances over past decades.

Язык: Английский

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Cloud-Enabled Scalable Analysis of Large Proteomics Cohorts

Journal of Proteome Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Rapid advances in depth and throughput of untargeted mass-spectrometry-based proteomic technologies enable large-scale cohort proteogenomic analyses. As such, the data infrastructure search engines required to process must also scale. This challenge is amplified that rely on library-free match between runs (MBR) search, which enhanced depth-per-sample completeness. However, date, no MBR-based could scale cohorts thousands or more individuals. Here, we present a strategy deploy distributed cloud environment without source code modification, thereby enhancing resource scalability throughput. Additionally, an algorithm, Scalable MBR, replicates MBR procedure popular DIA-NN software for samples. We demonstrate can MS raw files few hours compared days original results are almost indistinguishable those native MBR. additionally show empirical spectra generated by better approximates semiempirical alternatives such as ID-RT-IM preserving user choice use libraries large analysis. The method has been tested over 15,000 injections available Proteograph Analysis Suite.

Язык: Английский

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Tree-based quantification infers proteoform regulation in bottom-up proteomics data

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

Abstract Quantitative readout is essential in proteomics, yet current bioinformatics methods lack a framework to handle the inherent multi-level nature of data (fragments, MS1 isotopes, charge states, modifications, peptides and genes). We present AlphaQuant, which introduces tree-based quantification . This approach organizes quantitative into hierarchical tree across levels. It allows differential analyses at fragment level, recovering up 50-fold more regulated proteins compared state-of-the-art approach. Using gradient boosting on features, we address largely unsolved challenge scoring accuracy, as opposed precision. Our method clusters with similar behavior, providing new protein grouping problem enabling identification proteoforms directly from bottom-up data. Combined deep learning classification, infer phosphopeptides proteome alone, validating our findings EGFR stimulation then describe proteoform diversity mouse tissues, revealing distinct patterns post translational modifications alternative splicing.

Язык: Английский

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