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New data-independent acquisition (DIA) modes coupled to chromatographic separations are opening new perspectives in the processing of massive mass spectrometric (MS) data using chemometric methods. In this work, the application of the regions of interest multivariate curve resolution (ROIMCR) method is shown for the simultaneous analysis of MS1 and MS2 DIA raw data obtained by liquid chromatography coupled to quadrupole-time-of-flight MS analysis. The ROIMCR method proposed in this work relies on the intrinsic bilinear structure of the MS1 and MS2 experimental data which allows us for the fast direct resolution of the elution and spectral profiles of all sample constituents giving measurable MS signals, without needing any further data pretreatment such as peak matching, alignment, or modeling. Compound annotation and identification can be achieved directly by the comparison of the ROIMCR-resolved MS1 and MS2 spectra with those from standards or from mass spectral libraries. ROIMCR elution profiles of the resolved components can be used to build calibration curves for the prediction of their concentrations in complex unknown samples. The application of the proposed procedure is shown for the analysis of mixtures of per- and polyfluoroalkyl substances in standard mixtures, spiked hen eggs, and gull egg samples, where these compounds tend to accumulate.
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http://dx.doi.org/10.1021/acs.analchem.2c05704 | DOI Listing |
Nat Commun
August 2025
Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
Metabolite annotation in untargeted metabolomics remains challenging due to the vast structural diversity of metabolites. Network-based approaches have emerged as powerful strategies, particularly for annotating metabolites lacking chemical standards. Here, we develop a two-layer interactive networking topology that integrates data-driven and knowledge-driven networks to enhance metabolite annotation.
View Article and Find Full Text PDFAnal Chem
August 2025
Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California 95064, United States.
Mass spectrometry imaging (MSI) is a powerful tool for monitoring the spatial distributions of microbial metabolites directly from culture. MSI can identify secretion and retention patterns for microbial metabolites, allowing for the assessment of chemical communication within complex microbial communities. Microbial imaging via matrix-assisted laser desorption/ionization (MALDI) MSI remains challenging due to high sample complexity and heterogeneity associated with the required sample preparation, making annotation of molecules by MS alone challenging.
View Article and Find Full Text PDFCell Genom
August 2025
Department of Computational Biomedicine, Smidt Heart Institute, Board of Governors Innovation Center, Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA. Electronic address:
Over the past 2 to 3 years, mass-spectrometry-based single-cell proteomics (SCP) has experienced transformative improvements in microfluidic and robotic sample preparation, innovative MS1- and MS2-based multiplexing strategies, and specialized hardware (e.g., timsTOF Ultra 2, Astral), which have dramatically boosted sensitivity, throughput, and proteome coverage from picogram-level protein inputs.
View Article and Find Full Text PDFJ Proteome Res
September 2025
Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, United States.
The effective separation of complex peptide mixtures is a cornerstone of mass spectrometry-based proteomics analysis as it enhances the accuracy and depth of proteomic analyses. Here, we compare data sets collected of whole-cell tryptic peptides, which were fractionated by either conventional flame-pulled, C18 packed-bed microcapillary columns or a microfabricated pillar array column (μPAC). Sixteen samples that included four different yeast strains (Δ, Δ, Δ, and wildtype) were analyzed in quadruplicate using data-independent acquisition.
View Article and Find Full Text PDFAnal Chim Acta
October 2025
School of Chemistry, Dalian University of Technology, Dalian, 116024, China; State Key Laboratory of Medical Proteomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China. Electronic addre
Background: Carbonyl compounds (CCs) are extensively present in biological and food samples with broad concentration range and high reactivity. Despite advances in chemical isotope labelling assistant liquid chromatography-mass spectrometry (CIL-LC-MS) to address poor ionization efficiency and weak chromatographic retention, challenges remain in defining potential CCs by report ion and further accurately annotating labelled products due to the lack of experimental spectra. Improved strategies are needed to enhance the annotation of CCs.
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