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A Multidimensional Mass Spectrometry-Based Workflow for De Novo Structural Elucidation of Oligosaccharides from Polysaccharides.

ABSTRACT: Carbohydrates play essential roles in a variety of biological processes that are dictated by their structures. However, characterization of carbohydrate structures remains extremely difficult and generally unsolved. In this work, a de novo mass spectrometry-based workflow was developed to isolate and structurally elucidate oligosaccharides to provide sequence, monosaccharide compositions, and glycosidic linkage positions. The approach employs liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based methods in a 3-dimensional concept: one high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-QTOF MS) analysis for oligosaccharide sequencing and two ultra high performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-QqQ MS) analyses on fractionated oligosaccharides to determine their monosaccharides and linkages compositions. The workflow was validated by applying the procedure to maltooligosaccharide standards. The approach was then used to determine the structures of oligosaccharides derived from polysaccharide standards and whole food products. The integrated LC-MS workflow will reveal the in-depth structures of oligosaccharides.

SUBMITTER: Castillo JJ

PROVIDER: S-EPMC8344699 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundImaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples. However, automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows. The characterization of molecular properties within cellular and histological features is done via time-consuming, non-objective, and irreproducible definitions of regions of interest, which are often accompanied by a loss of spatial resolution due to mass spectra averaging.MethodsWe developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACiAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). SPACiAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution. To demonstrate the method, matrix-assisted laser desorption-ionization (MALDI) Fourier-transform ion cyclotron resonance (FTICR) imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens. The SPACiAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features to identify metabolic profiles. Spatial correlation networks were generated to analyze metabolic heterogeneity associated with cellular features.ResultsTo demonstrate the new method, the SPACiAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that are not distinguishable via morphology alone. The semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, we demonstrated intra- and intertumoral metabolic heterogeneity within HER2/neu-positive and -negative gastric tumor cells.ConclusionsWe developed the SPACiAL workflow to provide IHC-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity. The multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types. The SPACiAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice that is required for diagnostics, biomarker discovery, or therapy response prediction.

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A Multidimensional Mass Spectrometry-Based Workflow for De Novo Structural Elucidation of Oligosaccharides from Polysaccharides.

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