scholarly journals rMSIannotation: a peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios

2021 ◽  
pp. 338669
Author(s):  
Lluc Sementé ◽  
Gerard Baquer ◽  
María García-Altares ◽  
Xavier Correig-Blanchar ◽  
Pere Ràfols
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Annotation Tool ◽  
Peak Annotation ◽  
Intensity Ratios

scholarly journals rMSIcleanup: An open-source tool for matrix-related peak annotation in mass spectrometry imaging and its application to silver-assisted laser desorption/ionization

2019 ◽  
Author(s):  
Gerard Baquer ◽  
LLuc Sementé ◽  
María García-Altares ◽  
Young Jin Lee ◽  
Pierre Chaurand ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Open Source ◽  
Mass Spectrometry Imaging ◽  
Laser Desorption ◽  
R Package ◽  
Visual Exploration ◽  
Laser Desorption Ionization ◽  
Analytical Technique ◽  
Desorption Ionization ◽  
Peak Annotation

AbstractMass spectrometry imaging (MSI) has become a mature, widespread analytical technique to perform non-targeted spatial metabolomics. However, the compounds used to promote desorption and ionization of the analyte during acquisition cause spectral interferences in the low mass range that hinder downstream data processing in metabolomics applications. Thus, it is advisable to annotate and remove matrix-related peaks to reduce the number of redundant and non-biologically-relevant variables in the dataset. We have developed rMSIcleanup, an open-source R package to annotate and remove matrix-related signals based on its chemical formula and the spatial distribution of its ions. To validate the annotation method, rMSIcleanup was challenged with several images acquired using silver-assisted laser desorption ionization MSI (AgLDI MSI). The algorithm was able to correctly classify m/z signals related to silver clusters. Visual exploration of the data using Principal Component Analysis (PCA) demonstrated that annotation and removal of matrix-related signals improved spectral data post-processing. The results highlight the need for including matrix-related peak annotation tools such as rMSIcleanup in MSI workflows.Resources availabilityThe R package presented in this publication is freely available under the terms of the GNU General Public License v3.0 at https://github.com/gbaquer/rMSIcleanup. The datasets used in the experiments can be accessed upon request to the corresponding author.

scholarly journals Sample preparation strategy for the detection of steroid-like compounds using MALDI mass spectrometry imaging: pulmonary distribution of budesonide as a case study

2021 ◽  
Author(s):  
Riccardo Zecchi ◽  
Pietro Franceschi ◽  
Laura Tigli ◽  
Davide Amidani ◽  
Chiara Catozzi ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Mass Spectrometry Imaging ◽  
Maldi Mass Spectrometry ◽  
Adult Rabbit ◽  
Chronic Obstructive ◽  
Ionization Mass ◽  
Obstructive Pulmonary Disease ◽  
Imaging Protocol ◽  
Girard’S Reagent

AbstractCorticosteroids as budesonide can be effective in reducing topic inflammation processes in different organs. Therapeutic use of budesonide in respiratory diseases, like asthma, chronic obstructive pulmonary disease, and allergic rhinitis is well known. However, the pulmonary distribution of budesonide is not well understood, mainly due to the difficulties in tracing the molecule in lung samples without the addition of a label. In this paper, we present a matrix-assisted laser desorption/ionization mass spectrometry imaging protocol that can be used to visualize the pulmonary distribution of budesonide administered to a surfactant-depleted adult rabbit. Considering that budesonide is not easily ionized by MALDI, we developed an on-tissue derivatization method with Girard’s reagent P followed by ferulic acid deposition as MALDI matrix. Interestingly, this sample preparation protocol results as a very effective strategy to raise the sensitivity towards not only budesonide but also other corticosteroids, allowing us to track its distribution and quantify the drug inside lung samples. Graphical abstract

scholarly journals Shotgun lipidomics and mass spectrometry imaging unveil diversity and dynamics in Gammarus fossarum lipid composition

iScience ◽  
2021 ◽  
Vol 24 (2) ◽  
pp. 102115
Author(s):  
Tingting Fu ◽  
Oskar Knittelfelder ◽  
Olivier Geffard ◽  
Yohann Clément ◽  
Eric Testet ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Lipid Composition ◽  
Mass Spectrometry Imaging ◽  
Shotgun Lipidomics ◽  
Gammarus Fossarum

scholarly journals Diagnosis of Agglomeration and Crystallinity of Active Pharmaceutical Ingredients in Over the Counter Headache Medication by Electrospray Laser Desorption Ionization Mass Spectrometry Imaging

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 610
Author(s):  
Mariann Inga Van Meter ◽  
Salah M. Khan ◽  
Brynne V. Taulbee-Cotton ◽  
Nathan H. Dimmitt ◽  
Nathan D. Hubbard ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Laser Desorption ◽  
Ionization Mass Spectrometry ◽  
Ionization Mass ◽  
Active Pharmaceutical Ingredients ◽  
Laser Desorption Ionization ◽  
Over The Counter ◽  
Pharmaceutical Ingredients ◽  
Desorption Ionization

Agglomeration of active pharmaceutical ingredients (API) in tablets can lead to decreased bioavailability in some enabling formulations. In a previous study, we determined that crystalline APIs can be detected as agglomeration in tablets formulated with amorphous acetaminophen tablets. Multiple method advancements are presented to better resolve agglomeration caused by crystallinity in standard tablets. In this study, we also evaluate three “budget” over-the-counter headache medications (subsequently labeled as brands A, B, and C) for agglomeration of the three APIs in the formulation: Acetaminophen, aspirin, and caffeine. Electrospray laser desorption ionization mass spectrometry imaging (ELDI-MSI) was used to diagnose agglomeration in the tablets by creating molecular images and observing the spatial distributions of the APIs. Brand A had virtually no agglomeration or clustering of the active ingredients. Brand B had extensive clustering of aspirin and caffeine, but acetaminophen was observed in near equal abundance across the tablet. Brand C also had extensive clustering of aspirin and caffeine, and minor clustering of acetaminophen. These results show that agglomeration with active ingredients in over-the-counter tablets can be simultaneously detected using ELDI-MS imaging.

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