Biology subjects

Demichev, V.

Publications and source records attributed to Demichev, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Reverse engineering Lewy bodies suggests alpha-synuclein pathology as condensatopathy

Understanding how soluble monomeric alpha-synuclein transitions into pathological alpha-synuclein (Lewy bodies) has remained a central challenge in neurodegeneration. Existing models, largely based on treating cells and animals with pre-formed alpha-synuclein fibrils cannot account for the emergence of the first inclusion without pre-existing seeds. Here, we combine in vitro reconstitutions, computational modeling and a minimal cellular system to show that phase separation of alpha-synuclein with an interacting scaffold can recapitulate Lewy body-like structures. An unbiased proteomics screen, live-cell imaging, correlative light-electron microscopy, and soft X-ray tomography show that Lewy body-like structures are associated with redistribution of actin and membrane-bound organelles, with mitochondria accumulating at their periphery in an actin-dependent manner. Structural analyses further indicate that alpha-synuclein condensates provide local environments in which the insoluble beta-sheets can form. Together, these findings support a seed-independent trajectory from soluble alpha-synuclein through condensation and progressive immobilization to Lewy body-like assemblies and, subsequently, insoluble beta-sheet fibrils. This framework offers a new mechanistic route for understanding the disease progression and promises to guide the design of innovative therapeutic interventions.

neuroscience↗

bioRxiv2026

SILAC-Site: A streamlined workflow for determining phosphopeptide stoichiometries

Mass spectrometry-based proteomics enables in-depth investigation of protein phosphorylation, quantifying tens of thousands of phosphosites per sample following phosphopeptide enrichment. However, critical information on phosphosite occupancy (stoichiometry) is typically lost during the phospho-enrichment process. Here, we introduce SILAC-Site, a SILAC-based fractionation-free and chemical labelling-free workflow for direct phosphosite stoichiometry evaluation using stable isotope labeling and phosphatase treatment. By acquiring treated or untreated peptides together with their heavy-labelled dephosphorylated counterparts within the same LC-MS runs, this approach provides internally controlled stoichiometry estimates compatible with high-throughput data-independent acquisition proteomics. Applying SILAC-Site to S. cerevisiae, we show that the majority of phosphopeptides identified only after enrichment possess low stoichiometries, and that inferred stoichiometry strongly correlates with the direct detection of phosphopeptides in samples without enrichment. Based on these findings, we propose the analysis of samples without enrichment as a simple complementary addition to a typical phosphoproteomics workflow, facilitating recovery of phosphorylation stoichiometry information.

biochemistry↗

bioRxiv2025

Large-scale quantitative benchmark reveals accuracy limits of proteomics

Quantitative proteomics has become a streamlined method for high-throughput interrogation of biological samples, with a wide range of applications in both basic and clinical research. We present a benchmark data set and methodology for evaluating the accuracy of proteomics in a high-throughput setting. We build upon previous works by incorporating the variation of sample amounts and the background proteome into the experiment design. This approach allows us to comprehensively map the factors that determine the accuracy of proteomic measurements, revealing significant systematic errors missed by previous works. We envision these data to assist in future method optimisation as well as provide a comprehensive benchmark for data analysis software algorithms.

biochemistry↗

bioRxiv2025

Pho-Tip: one-pot dephosphorylation for rapid and sensitive analysis of DIA phosphoproteomics data

Recent advances in instrumentation and data processing have transformed data-independent acquisition (DIA) proteomics into a reliable technology for quantitative profiling of post-translational modifications. However, analysis of DIA phosphoproteomics data is challenging due to the large search space, wherein all combinations of phosphosites on a peptide need to be considered. Current approaches therefore face significant hurdles in detecting low-abundant phosphorylated peptides, in particular when working with low sample amounts. Here we introduce Pho-Tip, a lossless one-pot dephosphorylation strategy. We show that Pho-Tip enables comprehensive mapping of phosphorylated peptide sequences, facilitating streamlined creation of experiment-focused in silico predicted spectral libraries and thus rapid and sensitive analysis of DIA phosphoproteomics experiments.

biochemistry↗

bioRxiv2025

pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics

The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the SDRF format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

bioinformatics↗

bioRxiv2025

Quantitative Substrate Kinetics Screening: One-Pot LC/MS Based Approach to Map Protease Specificity

Proteases play critical roles in many biological processes and diseases. Proteases tend to have limited and overlapping specificities, which hinders our understanding of their roles in physiology. We designed a naive protease substrate library with the aim of fully utilizing modern mass spectrometry proteomics tools to quantitatively map protease substrate specificity. A library of approximately 200,000 peptides was efficiently produced in E. coli with sufficient library diversity to identify cleavage efficiency of thousands of substrates for most proteases in a single assay. Each experiment results in ample substrate kinetics data to build a positional specificity matrix that quantitatively maps protease specificity. We apply this method to two different proteases, GluC commonly used as a proteomics tool, and Fibroblast Activation Protein Alpha that is overexpressed in activated fibroblasts of epithelial cancers and fibrotic diseases. This research quantitatively maps substrate specificity for GluC and FAP in a single assay and can be applied to map substrate specificity for most proteases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=122 HEIGHT=200 SRC="FIGDIR/small/680748v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1496d11org.highwire.dtl.DTLVardef@1979fc1org.highwire.dtl.DTLVardef@17967e8org.highwire.dtl.DTLVardef@194b741_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Data-independent immunopeptidomics discovery of low-abundant bacterial epitopes

Mass spectrometry-based immunopeptidomics is a powerful approach to uncover peptides presented by human leukocyte antigen (HLA) molecules that can guide vaccine design and immunotherapies. While data-dependent acquisition (DDA) has been the standard for navigating through the complexity associated with non-enzymatic immunopeptide database searches, data-independent acquisition (DIA) is increasingly adopted in immunopeptidomics research. In this work, we compare diaPASEF to conventional ddaPASEF in terms of global immunopeptidome profiling and bacterial epitope discovery of the model intracellular pathogen Listeria monocytogenes. We show that DIA spectrum-centric workflows that search pseudo-MS/MS spectra complement DDA analysis by uncovering additional human and bacterial immunopeptides. Furthermore, we leveraged DIA-NN for generating and searching proteome-wide predicted HLA class I peptide spectral libraries, scoring approximately 150 million immunopeptide peptide precursors. This approach outperformed other spectrum-based methods in identification of MHC class I peptides and recovered low-abundant peptide precursors missed by other methods. Taken together, our results demonstrate how both DIA spectrum- and peptide-centric immunopeptidomics analysis are promising strategies to identify low-abundant immunopeptides.

molecular biology↗

bioRxiv2025

Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput

Proteomic experiments, particularly those addressing dynamic proteome properties, time series, or genetic diversity, require the analysis of large sample numbers. Despite significant advancements in proteomic technologies in recent years, further improvements are needed to accelerate measurement and enhance proteome coverage and quantitative performance. Previously, we demonstrated that incorporating a scanning MS2 dimension into data-independent acquisition methods (Scanning SWATH, or more generally scanning DIA) but also ion trapping, improves analytical depth and quantitative performance, especially in proteomic methods using fast chromatography. Here, we evaluate the scanning DIA approach combined with ion trapping via the Zeno trap in a method termed ZT Scan DIA, using a prototype Zeno trap Q-TOF instrument (SCIEX). Applying this method to established proteome standards across various analytical setups, enabling intermediate to high sample throughput, we observed a 30-40% increase in identified precursors. This enhancement extended to overall protein identification and precise quantification. Furthermore, ZT Scan DIA effectively eliminated quantitative bias, as demonstrated by its ability to deconvolute proteomes in multi-species mixtures. We propose that ZT Scan DIA can be used for a broad range of applications in proteomics, particularly in studies requiring high quantitative precision with low sample input and high-throughput workflows. Significance StatementThe advent of faster DIA proteomics methods paved the way for investigating increasingly large sample series, including patient cohorts and strain collections containing thousands of samples. Yet, recent improvements in DIA methods still entailed compromises between analytical sensitivity and selectivity. The presented combination of a scanning quadrupole with fast ion trapping in a Zeno Trap, coined ZT Scan DIA increases quantitative precision and accuracy in fast proteomics experiments. These features of ZT Scan DIA may benefit applications that deal with low input samples and high-throughput proteomic workflows in biomedical cohort studies and systems biology.

systems biology↗

bioRxiv2024

Unbiased mapping of cereblon neosubstrate landscape by high-throughput proteomics

Molecular glue degraders (MGDs) are small molecules that harness the ubiquitin-proteasome system to induce degradation of target proteins, including those lacking conventional druggable pockets. Given the challenges in their rational design, MGD discovery predominantly relies on screening-based approaches, such as cell viability assays. However, one potential limitation of such screening methods is the risk of overlooking non-essential neosubstrates of potential therapeutic value. To address this concern, we present a high-throughput proteome-wide MGD screening platform utilizing label-free, data-independent acquisition mass spectrometry (DIA-MS) for integrated proteomics and ubiquitinomics analysis. Processing a diverse set of 100 CRBN-ligands across two cancer cell lines reveals a broad array of neosubstrates, including 50 novel candidates validated by MS-based ubiquitinomics. These findings considerably expand the current landscape of CRBN-mediated neosubstrates. Comprehensive hit validation and structure-degradation relationship analyses guided by global proteomics, identifies highly selective and potent phenyl glutarimide-based degraders of novel neosubstrates, including KDM4B, G3BP2 and VCL, none of which contain the classical CRBN degron motif. This study demonstrates that comprehensive, high-throughput proteomic screening offers new opportunities in MGD drug discovery.

biochemistry↗

bioRxiv2023

CysQuant: simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry

Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel approach for simultaneous quantification of cysteine oxidation degrees and protein abundancies. CysQuant involves light/heavy iodoacetamide isotopologues for differential labeling of reduced and reversibly oxidized cysteines analyzed by data-dependent acquisition (DDA) or data-independent acquisition mass spectrometry (DIA-MS). Using plexDIA with in silico predicted spectral libraries, we quantified an average of 18% cysteine oxidation in Arabidopsis thaliana by DIA-MS, including a subset of highly oxidized cysteines forming disulfide bridges in AlphaFold2 predicted structures. Applying CysQuant to Arabidopsis seedlings exposed to excessive light, we successfully quantified the well-established increased reduction of Calvin-Benson cycle enzymes and discovered yet uncharacterized redox-sensitive disulfides in chloroplastic enzymes. Overall, CysQuant is a highly versatile tool for assessing the cysteine modification status that can be widely applied across various mass spectrometry platforms and organisms.

molecular biology↗

bioRxiv2023

QuantUMS: uncertainty minimisation enables confident quantification in proteomics

Mass spectrometry-based proteomics has been rapidly gaining traction as a powerful analytical method both in basic research and translation. While the problem of error control in peptide and protein identification has been addressed extensively, the quality of the resulting quantities remains challenging to evaluate. Here we introduce QuantUMS (Quantification using an Uncertainty Minimising Solution), a machine learning-based method which minimises errors and eliminates bias in peptide and protein quantification by integrating multiple sources of quantitative information. In combination with data-independent acquisition proteomics, QuantUMS boosts accuracy and precision of quantities, as well as reports an uncertainty metric, enabling effective filtering of data for downstream analysis. The algorithm has linear complexity with respect to the number of mass spectrometry acquisitions in the experiment and is thus scalable to infinitely large proteomic experiments. For an easy implementation in a proteomics laboratory, we integrate QuantUMS in our automated DIA-NN software suite.

bioinformatics↗

bioRxiv2023

Speedy-PASEF: Analytical flow rate chromatography and trapped ion mobility for deep high-throughput proteomics

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs and facilitate new approaches in systems biology and biomedical research. Here we propose Speedy-PASEF, a combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition and data analysis with the DIA-NN software suite, for conducting fast, high-quality proteomic experiments that require only moderate sample amounts. For instance, using a 500-l/min flow rate and a 3-minute chromatographic gradient, Speedy-PASEF quantified 5,211 proteins from 2 g of a mammalian cell-line standard at high quantitative accuracy and precision. We further used Speedy-PASEF to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-minute chromatographic gradient and alternating column regeneration on a dual pump system, for processing 398 samples per day. Speedy-PASEF delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates. Speedy-PASEF thus facilitates acquisition of high-quality proteomes in large numbers.

biochemistry↗

bioRxiv2022

One-stop analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. We have integrated MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data dependent acquisition (DDA), or both data types combined. We compared MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, and in silico library-based DIA-NN and MaxDIA. We demonstrated the fast and sensitive performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

bioinformatics↗

bioRxiv2022

Slice-PASEF: fragmenting all ions for maximum sensitivity in proteomics

Quantitative mass spectrometry (MS)-based proteomics has become a streamlined technology with a wide range of usage. Many emerging applications, such as single-cell proteomics, spatial proteomics of tissue sections and the profiling of low-abundant posttranslational modifications, require the analysis of minimal sample amounts and are thus constrained by the sensitivity of the workflow. Here, we present Slice-PASEF, a mass spectrometry technology that leverages trapped ion mobility separation of ions to attain the theoretical maximum of tandem MS sensitivity. We implement Slice-PASEF using a new module in our DIA-NN software and show that Slice-PASEF uniquely enables precise quantitative proteomics of low sample amounts. We further demonstrate its utility towards a range of applications, including single cell proteomics and degrader drug screens via ubiquitinomics.

biochemistry↗

bioRxiv2022

MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

bioinformatics↗

bioRxiv2022

OxoScan-MS: Oxonium ion scanning mass spectrometry facilitates plasma glycoproteomics in large scale

Protein glycosylation is a complex and heterogeneous post-translational modification. Specifically, the human plasma proteome is rich in glycoproteins, and as protein glycosylation is frequently dysregulated in disease, glycoproteomics is considered an underexplored resource for biomarker discovery. Here, we present OxoScan-MS, a data-independent mass spectrometric acquisition technology and data analysis software that facilitates sensitive, fast, and cost-effective glycoproteome profiling of plasma and serum samples in large cohort studies. OxoScan-MS quantifies glycosylated peptide features by exploiting a scanning quadrupole to assign precursors to oxonium ions, glycopeptide-specific fragments. OxoScan-MS reaches a high level of sensitivity and selectivity in untargeted glycopeptide profiling, such that it can be efficiently used with fast microflow chromatography without a need for experimental enrichment of glycopeptides from neat plasma. We apply OxoScan-MS to profile the plasma glycoproteomic in an inpatient cohort hospitalised due to severe COVID-19, and obtain precise quantities for 1,002 glycopeptide features. We reveal that severe COVID-19 induces differential glycosylation in disease-relevant plasma glycoproteins, including IgA, fibrinogen and alpha-1-antitrypsin. Thus, with OxoScan-MS we present a strategy for quantitatively mapping glycoproteomes that scales to hundreds and thousands of samples, and report glycoproteomic changes in severe COVID-19.

systems biology↗

bioRxiv2022

The Proteomic Landscape of Genome-Wide Genetic Perturbations

Functional genomic strategies help to address the genotype phenotype problem by annotating gene function and regulatory networks. Here, we demonstrate that combining functional genomics with proteomics uncovers general principles of protein expression, and provides new avenues to annotate protein function. We recorded precise proteomes for all non-essential gene knock-outs in Saccharomyces cerevisiae. We find that protein abundance is driven by a complex interplay of i) general biological properties, including translation rate, turnover, and copy number variations, and ii) their genetic, metabolic and physical interactions, including membership in protein complexes. We further show that combining genetic perturbation with proteomics provides complementary dimensions of functional annotation: proteomic profiling, reverse proteomic profiling, profile similarity and protein covariation analysis. Thus, our study generates a resource in which nine million protein quantities are linked to 79% of the yeast coding genome, and shows that functional proteomics reveals principles that govern protein expression. Highlights- Nine million protein quantities recorded in ~4,600 non-essential gene deletions in S. cerevisiae reveal principles of how the proteome responds to genetic perturbation - Genome-scale protein expression is determined by both functional relationships between proteins, as well as common biological responses - Broad protein expression profiles in slow-growing strains can be explained by chromosomal aneuploidies - Protein half-life and ribosome occupancy are predictable from protein abundance changes across knock-outs - Functional proteomics annotates missing gene function in four complementary dimensions

systems biology↗

bioRxiv2022

High-throughput proteomics of nanogram-scale samples with Zeno SWATH DIA

The ability to conduct high-quality proteomic experiments in high throughput has opened new avenues in clinical research, drug discovery, and systems biology. Next to an increase in quantitative precision, recent developments in high-throughput proteomics have also gained proteomic depth, to the extent that earlier gaps between classic and high-throughput experiments have significantly narrowed. Here we introduce and benchmark Zeno SWATH, a data-independent acquisition technique that employs a linear ion trap pulsing (Zeno trap pulsing) in order to increase proteomic depth and dynamic range in proteomic experiments. Combined with the high acquisition speed, these gains in sensitivity are particularly attractive for conducting high-throughput proteomics experiments with high chromatographic flow rates and fast gradients. We demonstrate that when combined with either micro-flow- or analytical-flow-rate chromatography, Zeno SWATH increases protein identification in complex samples 5- to 10-fold when compared to current SWATH acquisition methods on the same instrument. Using 20-min micro-flow chromatography, Zeno SWATH identified > 6,000 proteins from a 62.5 ng load of human cell lysate with more than 5,000 proteins consistently quantified in triplicate injections with a median CV of 6%. Using 5-min analytical-flow-rate chromatography (800 {micro}l/min), Zeno SWATH identified 4,907 proteins from a triplicate injection of 2 {micro}g of a human cell lysate; or more than 3,000 proteins from 250 ng tryptic digest. Zeno SWATH hence facilitates precise proteomic experiments with small sample amounts using a fast and robust high flow-rate chromatographic method, broadening the application space that requires precise proteomic experiments on a large scale.

biochemistry↗