Precise, fast and comprehensive analysis of intact glycopeptides and modified glycans with pGlyco3

Great advances have been made in mass spectrometric data interpretation for intact glycopeptide analysis. However, accurate identification of intact glycopeptides and modified saccharide units at the site-specific level and with fast speed remains challenging. Here, we present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A glycan ion-indexing algorithm developed for glycan-first search makes pGlyco3 5–40 times faster than other glycoproteomic search engines without decreasing accuracy or sensitivity. By combining electron-based dissociation spectra, pGlyco3 integrates a dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization. Our evaluation shows that the site-specific glycan localization probabilities estimated by pGlycoSite are suitable to localize site-specific glycans. With pGlyco3, we confidently identified N-glycopeptides and O-mannose glycopeptides that were extensively modified by ammonia adducts in yeast samples. The freely available pGlyco3 is an accurate and flexible tool that can be used to identify glycopeptides and modified saccharide units.

pSiteExplorer: Visualization of the mass spectrometric data underlying phospho-site quantification

Mass spectrometry based phospho-proteomics is a widely used approach to assess protein phosphorylation. Intensities of phospho-peptide ions are obtained by integrating the MS signal over their chromatographic peaks. How individual peptide measurements mapping to the same phospho-site are combined for the quantification of the given site is, however, in most cases hidden from researchers conducting, reviewing, and reading these studies. I here describe pSiteExplorer, an R script that visualizes the peak intensities associated with phospho-sites in MaxQuant output tables. Barplots of MS intensities originating from phospho-peptides with distinct amino acid sequences due to missed cleavages, different numbers of phosphates and from all off-line chromatographic fractions and charge states are displayed. This tool will help gaining a deeper insight into phospho-site quantifications by contrasting individual and summed phospho-peptide intensities with the site-level values derived by MaxQuant. This will support the validation of quantification results, for example, for the selection of candidates for follow-up studies.

MO435MULTIMODAL IMAGING FOR MOLECULAR TISSUE ANALYSIS

Background and Aims MALDI mass spectrometric imaging (MALDI MSI) is a powerful histologic tool for the analysis of biomolecules in tissue samples. MALDI MSI measurements result in a high sensitivity and accuracy of spatial distribution of biomolecules in tissue samples. For more detailed analysis of MALDI MSI data and correlation between the molecular and microscopic levels, a combination of MALDI MSI data and histological staining is essential. By combining MALDI MSI data and histological data, much more information are obtained than by analyzing both methods individually. Therefore, MALDI MSI datasets and histological staining were fused to a 3D model presenting a biomolecule distribution of the whole organ and provides more information than a single tissue section. We have developed, established and validated an algorithm for an automatic registration of MALDI data with different histological image data for cross-process evaluation of multimodal datasets to create 3D models. This multimodal imaging approach simplifies and improves molecular analyses of tissue samples in clinical research and diagnosis. Method The datasets for fusion and creation of a 3D model consist of mass spectrometric data, histological and immunohistochemical staining methods. Histological tissue sections of a whole mouse kidney were prepared. For MALDI MSI data, organ sections were analyzed by using a Rapiflex mass-spectrometer. Results A mathematical registration was used to achieve a perfect superposition of the individual histological sections of mass spectrometric data. It is feasible to combine mass spectrometric data, histological and immunohistochemical datasets in high numbers and reconstruct the measured mouse kidney. By using different imaging methods, a variety of information about tissue structure as well as tissue changes and protein distributions can be obtained. The fusion of the data also offers a virtual incision of the organ from arbitrary angle and level. The algorithms are adapted to take the data fusion automatically offering a high-throughput approach for clinical diagnostics and the possibility to involved artificial intelligence in its interpretation in research. Conclusion A successful fusion of MALDI MSI data and different histological and immunohistochemical staining datasets of a whole organ is performed.

P0659MULTIMODAL IMAGING OF MALDI MSI DATA

Background and Aims MALDI mass spectrometric imaging (MALDI MSI) is a powerful histologic tool for the analysis of biomolecules in tissue samples. MALDI MSI measurements results in a high sensitivity and accuracy of spatial distribution of biomolecules in tissue samples The resolution information of MALDI MSI is in the range of 5-10 µm in the spatial distribution and has the ability to identify proteins, peptides, lipids and small biomolecules directly in tissue samples in one analytical step..For a more detailed analysis of MALDI MSI data and a correlation between the molecular and microscopic level, a combination of MALDI MSI data and histological staining is essential. By combining MALDI MSI data and histological data, much more information are obtained than from a single analysis of both methods. Therefore, MALDI MSI data sets and histological staining were fused to a 3D model presenting a biomolecule distribution of the whole organ and provide more information than a single tissue section. We developed, established and validate an algorithm for an automatic registration of MALDI data with different histological image data for the cross-process evaluation of multimodal data sets for creating 3D models. This multimodal image approach simplifies and improves molecular analyses of tissue samples clinical research and diagnosis. Method The data sets for the fusion and creating of a 3D model consist of mass spectrometric data as well as histological and Immunohistochemical staining methods. Histological tissue sections of a whole mice kidney were prepared. For MALDI MSI data the organ sections were coated and incubated with a trypsin solution were performed by using a sprayer for MALDI imaging. As matrix, α-cyano-4-hydroxycinnamic acid was used. MALDI MSI was performed using the Rapiflex. For histological staining the hematoxylin-eosin and Gomori staining were chosen. For Immunohistochemical double staining and immunofluorescence, were used for the detection of Collagen type I, smooth muscle actin and the cell nuclei. Results By using a mathematical registration, a perfect superposition of the individual histological sections mass spectrometric data was achieved. It is possible to combine mass spectrometric data, histological and Immunohistochemical data sets in a high number and to reconstruct the measured mice kidney. By using different imaging methods, a variety of information about tissue structure as well as tissue changes and protein distribution can be obtained. The fusion of the data also offers a virtual incision of the organ from any angle and level. The algorithms are adapted to take the data fusion automatically offering a high-throughput approach for clinical diagnostics and the possibility to involved artificial intelligence in its interpretation in research. Conclusion There is a successful fusion of MALDI MSI data and different histological and Immunohistochemical staining data sets of a whole organ

Phenotype Prediction using a Tensor Representation and Deep Learning from Data Independent Acquisition Mass Spectrometry

A novel approach for phenotype prediction is developed for mass spectrometric data. First, the data-independent acquisition (DIA) mass spectrometric data is converted into a novel file format called “DIA tensor” (DIAT) which contains all the peptide precursors and fragments information and can be used for convenient DIA visualization. The DIAT format is fed directly into a deep neural network to predict phenotypes without the need to identify peptides or proteins. We applied this strategy to a collection of 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in classifying malignant from benign samples. We further applied refined model to 492 samples of thyroid nodules to predict thyroid cancer; and achieved a predictive accuracy of 91.7% in an independent cohort of 216 test samples. In conclusion, DIA tensor enables facile 2D visualization of DIA proteomics data as well as being a new approach for phenotype prediction directly from DIA-MS data.

Characterization of Eight Novel Spiroleptosphols from Fusarium avenaceum

Chemical analyses of Fusarium avenaceum grown on banana medium resulted in eight novel spiroleptosphols, T1, T2 and U–Z (1–8). The structures were elucidated by a combination of high-resolution mass spectrometric data and 1- and 2-D NMR experiments. The relative stereochemistry was assigned by 1H coupling and NOESY/ROESY experiments. Absolute stereochemistry established for 7 by vibrational circular dichroism was found analogous to that of the putative polyketide spiroleptosphol from Leptosphaeria doliolum.