Customizable spectra signal colors

Customizable spectra signal colors

The user can customize the colors displayed for different types of signals: match (shifted or non-shifted), mismatch, metabolite match, or unassigned m/z. The colors assigned to each signal type are also kept in the report analysis.

WebMetabase & WebChembase: Fragment analysis validation for manual edited compound

WebMetabase & WebChembase: Fragment analysis validation for manual edited compound

The user can draw new metabolites or product chemical structures and validate them by comparing the virtual fragmentation of the compounds with the spectral information extracted from the peak. This functionality can also be applied to manually generated compounds during the new peak generation workflow in the Chromatogram Browser.

Compound Library: automation of Compound set definition.

Compound Library: automation of Compound set definition

An automatic compound set is defined during the SDF file import process. When chemical structures are imported into the Compound Library, the compound set is established, enabling users to perform various predictions—such as MetaSite fragmentation, and model predictions—directly on the generated compound set. This minimizes the time required to obtain results.

MALDI-2-Enabled Oversampling for the Mass Spectrometry Imaging of Metabolites at Single-Cell Resolution

MALDI-2-Enabled Oversampling for the Mass Spectrometry Imaging of Metabolites at Single-Cell Resolution

August 13, 2024

Jayden C. McKinnon, Rachelle Balez, Reuben S.E. Young, Mikayla L. Brown, Jeremy S. Lum, Liam Robinson, Mikhail E. Belov, Lezanne Ooi, Sara Tortorella, Todd W. Mitchell, Shane R. Ellis*

Abstract

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can provide valuable insights into the metabolome of complex biological systems such as organ tissues and cells. However, obtaining metabolite data at single-cell spatial resolutions presents a few technological challenges. Generally, spatial resolution is defined by the increment the sample stage moves between laser ablation spots. Stage movements less than the diameter of the focused laser beam (i.e., oversampling) can improve spatial resolution; however, such oversampling conditions result in a reduction in sensitivity. To overcome this, we combine an oversampling approach with laser postionization (MALDI-2), which allows for both higher spatial resolution and improved analyte ionization efficiencies. This approach provides significant enhancements to sensitivity for various metabolite classes (e.g., amino acids, purines, carbohydrates etc.), with mass spectral intensities from 6 to 8 μm pixel sizes (from a laser spot size of ∼13 μm) being commensurate with or higher than those obtained by conventional MALDI at 20 μm pixel sizes for many different metabolites. This technique has been used to map the distribution of metabolites throughout mouse spinal cord tissue to observe how metabolite localizations change throughout specific anatomical regions, such as those distributed to the somatosensory area of the dorsal horn, white matter, gray matter, and ventral horn. Furthermore, this method is utilized for single-cell metabolomics of human iPSC-derived astrocytes at 10 μm pixel sizes whereby many different metabolites, including nucleotides, were detected from individual cells while providing insight into cellular localizations.

© 2024 American Society for Mass Spectrometry. Published by American Chemical Society. All rights reserved.

Pyxis Unveiled: Advancing Single-Cell MALDI MSI Analysis for Deeper Molecular Insights

Pyxis Unveiled: Advancing Single-Cell MALDI MSI Analysis for Deeper Molecular Insights

72nd ASMS Conference on Mass Spectrometry. June 2024

Ismael Zamora1; Rachelle Balez2; Jayden C. McKinnon2; Reuben S.E. Young2; Liam Robinson2; Lezanne Ooi2; Giuseppe Arturi3; Giulia Sorbi3; Shane Ellis2Sara Tortorella3
1Mass Analytica, Sant Cugat del Vallès, Spain; 2Molecular Horizons, University of Wollongong, Wollongong, Australia; 3Mass Analytica, Bettona, Italy

Abstract

Introduction

Single-cell metabolomics and lipidomics using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI MSI) is emerging as an exciting tool to study metabolic alterations across heterogeneous cell populations. Single-cell analysis using MALDI MSI does however come with technical challenges, including sample preparation workflows compatible with fluorescent microscopy and downstream MS analysis, as well as dedicated data processing workflow to coregister microscopy with MSI and extract profiles from defined cells for later analysis. Here, we introduce an efficient single-cell MSI data analysis pipeline utilising the cutting-edge Pyxis software platform. The performance and versatility of the pipeline are demonstrated by single-cell lipidomics and metabolomics analysis of human-derived astrocytes, unravelling intricate molecular insights at the single-cell level.

Methods

Human astrocytes were generated using induced pluripotent stem cell-derived neurons. Live astrocyte cultures were stained with CellBrite Green and Hoechst, fixed with PFA and washed with cold ammonium acetate prior to microscopy. Following microscopy cells were coated in 2,5-DHA matrix via sublimation for lipid imaging or NEDC matrix using a HTC TM-Sprayer for metabolite imaging for MALDI and MALDI-2 analysis, respectively, using an Orbitrap Elite coupled to a Spectroglyph MALDI/ESI ion source (Spectroglyph LLC, Kennewick, WA, USA). Coregistration with microscopy, segmentation at single-cell level, extraction of spectra generated from single cells, and dedicated statistics to process single-cell spectra were performed using Pyxis (Mass Analytica, Spain). Features of interest were annotated using HMDB and LIPID MAPS databases integrated in Pyxis.

Preliminary data

Rich lipid and metabolite data were recorded from single cells using both MALDI and MALDI-2 with pixel sizes as low as 10 microns. Given the relatively large size of the astrocytes multiple pixels were recorded across single cells, shedding light into sub-cellular metabolite distributions. For example the [M-H]- of adenine was detected predominantly in the soma of the cells.
Ad hoc algorithms were developed and integrated to cope with the data analysis challenges. Rigid and non-rigid strategies to coregister microscopy, fluorescence and MS images were evaluated. An hybrid approach was employed, where rigid registration was applied initially to achieve a coarse alignment, followed by non-rigid registration to refine the alignment at a finer spatial scale. Utilising both approaches in a complementary fashion enhanced the accuracy of spatial integration of multi-modal imaging data. Bisecting k-means and spatial denoising facilitated single-cell border identification. An algorithm was designed to automatically isolate cells, apply user-defined exclusion criteria, calculate single-cell profiles, which then underwent multivariate statistical analysis.
Demonstrated applications of this analytical and data analysis workflow include: (i) single-cell analysis of human-derived astrocyte populations  and (ii) investigating the single-cell metabolic and lipidomic responses to inflammatory cytokine stimulation and how these are correlated with cytockeletal remodelling upon inflammatory activation.
By implementing all the necessary data analysis steps into a single, user-friendly software platform, we enable the comprehensive exploration of single-cell MSI data. This integrated approach not only streamlines the intricate process of biochemical data interpretation but also ensures that users can fully exploit the richness of information within their single-cell datasets with ease.

 

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