Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics

Frontiers in Microbiology, Год журнала: 2023, Номер 14

Опубликована: Окт. 16, 2023

Introduction Metaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and functional activity of microbial communities, their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification quantification in metaproteomics. However, DDA-MS exhibits well-known limitations terms depth, sensitivity, reproducibility. Consequently, methodological improvements are required to better characterize protein landscape microbiomes interactions with host. Methods We present an optimized proteomic workflow utilizes information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS comprehensive metaproteomic studies complex fecal samples mice. Results discussion show implementing PASEF using DDA scheme (DDA-PASEF) increased up 5 times reached higher accuracy reproducibility compared previously published classical data-independent (DIA) methods. Furthermore, we demonstrate combination DIA, PASEF, neuronal-network-based data analysis, was superior DDA-PASEF all mentioned parameters. Importantly, DIA-PASEF expanded dynamic range towards low-abundant proteins it doubled unknown or uncharacterized functions. Compared previous studies, resulted 4 more units 16 less injected peptides shorter chromatography gradients. Moreover, 131 additional pathways distributed across even uniquely identified taxa were profiled as revealed peptide-centric taxonomic-functional analysis. tested our validated preclinical mouse model neuropathic pain assess longitudinal changes host-gut microbiome associated - unexplored topic uncovered significant enrichment two bacterial classes upon pain, and, addition, upregulation metabolic activities linked chronic well various hitherto ones. pain-associated dynamics proteome complexes implicated crosstalk between immune system gut microbiome. In conclusion, presented here provides stepping stone deeper understanding ecosystems breadth biomedical biotechnological fields.

Язык: Английский

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Narrow-window DIA: Ultra-fast quantitative analysis of comprehensive proteomes with high sequencing depth

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Июнь 6, 2023

Abstract Mass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here, we present an ultra-fast scanning data-independent acquisition (DIA) strategy consisting on 2-Th precursor isolation windows, dissolving the differences between data-dependent independent methods. This is achieved by pairing Quadrupole Orbitrap mass spectrometer with asymmetric track lossless (Astral) analyzer that provides >200 Hz MS/MS speed, high resolving power sensitivity, as well low ppm-mass accuracy. Narrow-window DIA enables profiling of up 100 full yeast per day, or ∼10,000 human proteins half-an-hour. Moreover, multi-shot fractionated samples allows coverage ∼3h, showing comparable depth next-generation RNA sequencing 10x higher throughput compared current state-of-the-art MS. High quantitative precision accuracy demonstrated peptide 3-species proteome mixture, quantifying 14,000+ single run Teaser Accurate precise label-free quantification using narrow-window

Язык: Английский

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What’s new in single-cell proteomics

Current Opinion in Biotechnology, Год журнала: 2024, Номер 86, С. 103077 - 103077

Опубликована: Фев. 14, 2024

Язык: Английский

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Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry–Based Proteomics

Molecular & Cellular Proteomics, Год журнала: 2024, Номер 23(8), С. 100800 - 100800

Опубликована: Июнь 15, 2024

Язык: Английский

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CURTAIN—A unique web-based tool for exploration and sharing of MS-based proteomics data

Proceedings of the National Academy of Sciences, Год журнала: 2024, Номер 121(7)

Опубликована: Фев. 7, 2024

To facilitate analysis and sharing of mass spectrometry (MS)-based proteomics data, we created online tools called CURTAIN (https://curtain.proteo.info) CURTAIN-PTM (https://curtainptm.proteo.info) with an accompanying series video tutorials (https://www.youtube.com/@CURTAIN-me6hl). These are designed to enable non-MS experts interactively peruse volcano plots deconvolute primary experimental data so that replicates can be visualized in bar charts or violin exported publication-ready format. They also allow assessment overall quality by correlation matrix profile plot analysis. After making a selection protein "hits", the user analyze known domain structure, AlphaFold predicted reported interactors, relative expression as well disease links. permits all identified PTM sites on protein(s) interest selected databases. links Kinase Library predict upstream kinases may phosphorylate interest. We provide examples utility analyzing how targeted degradation PPM1H Rab phosphatase counteracts Parkinson's LRRK2 kinase impacts cellular levels phosphorylation sites. reanalyzed ubiquitylation dataset, characterizing PINK1-Parkin pathway activation neurons, revealing not highlighted previously. free use open source, enabling researchers share maximize impact their data. advocate MS published format containing shareable weblink, thereby allowing readers better exploit

Язык: Английский

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An accessible workflow for high-sensitivity proteomics using parallel accumulation–serial fragmentation (PASEF)

Nature Protocols, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

Язык: Английский

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Proteomic approaches advancing targeted protein degradation

Trends in Pharmacological Sciences, Год журнала: 2023, Номер 44(11), С. 786 - 801

Опубликована: Сен. 29, 2023

Targeted protein degradation (TPD) is an emerging modality for research and therapeutics. Most TPD approaches harness cellular ubiquitin-dependent proteolytic pathways. Proteolysis-targeting chimeras (PROTACs) molecular glue (MG) degraders (MGDs) represent the most advanced approaches, with some already used in clinical settings. Despite these advances, still faces many challenges, pertaining to both development of effective, selective, tissue-penetrant understanding their mode action. In this review, we focus on progress made addressing challenges. particular, discuss utility application recent proteomic as indispensable tools enable insights into degrader development, including target engagement, selectivity, efficacy, safety,

Язык: Английский

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A critical evaluation of ultrasensitive single-cell proteomics strategies

Analytical and Bioanalytical Chemistry, Год журнала: 2024, Номер 416(9), С. 2359 - 2369

Опубликована: Фев. 15, 2024

Язык: Английский

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The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics?

Journal of Proteome Research, Год журнала: 2024, Номер 23(6), С. 1871 - 1882

Опубликована: Май 7, 2024

The coevolution of liquid chromatography (LC) with mass spectrometry (MS) has shaped contemporary proteomics. LC hyphenated to MS now enables quantification more than 10,000 proteins in a single injection, number that likely represents most specific human cells or tissues. Separations by ion mobility (IMS) have recently emerged complement and further improve the depth Given theoretical advantages speed robustness IMS comparison LC, we envision ongoing improvements paired may eventually make obsolete, especially when combined targeted simplified analyses, such as rapid clinical proteomics analysis defined biomarker panels. In this perspective, describe need for faster might drive transition, current state direct infusion proteomics, discuss some technical challenges must be overcome fully complete transition entirely gas phase

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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