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

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Gerard Baquer ◽  
Lluc Sementé ◽  
María García-Altares ◽  
Young Jin Lee ◽  
Pierre Chaurand ◽  
...  
Author(s):  
Gerard Baquer ◽  
LLuc Sementé ◽  
María García-Altares ◽  
Young Jin Lee ◽  
Pierre Chaurand ◽  
...  

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.


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 ◽  
...  

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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