Biomedical subjects

Vadim Demichev

Publications and source records attributed to Vadim Demichev.

3 recordsLinked to original sources

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2026

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, FragPipe, 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 Sample and Data Relationship Format 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.

Proteomics↗

PubMed2025

SynchroSep-MS: Parallel LC Separations for Multiplexed Proteomics.

Achieving high throughput remains a challenge in MS-based proteomics for large-scale applications. We introduce SynchroSep-MS, a novel method for parallelized, label-free proteome analysis that leverages the rapid acquisition speed of modern mass spectrometers. This approach employs multiple liquid chromatography columns, each with an independent sample, simultaneously introduced into a single mass spectrometer inlet. A precisely controlled retention time offset between sample injections creates distinct elution profiles, facilitating unambiguous analyte assignment. We modified the DIA-NN workflow to effectively process these unique parallelized data, accounting for retention time offsets. Using a dual-column setup with mouse brain peptides, SynchroSep-MS detected approximately 16,700 unique protein groups, nearly doubling the peptide information obtained from a conventional single proteome analysis. The method demonstrated excellent precision and reproducibility (median protein %RSDs less than 4%) and high quantitative linearity (median R2 greater than 0.96) with minimal matrix interference. SynchroSep-MS represents a new paradigm for data collection and the first example of label-free multiplexed proteome analysis via parallel LC separations, offering a direct strategy to accelerate throughput for demanding applications such as large-scale clinical cohorts and single-cell analyses without compromising peak capacity or causing ionization suppression.

Proteomics↗