scholarly journals Distribution of Glycerophospholipids in the Adult Human Lens

Biomolecules ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 156 ◽  
Author(s):  
Jo Seng ◽  
Jessica Nealon ◽  
Stephen Blanksby ◽  
Todd Mitchell
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Imaging Mass Spectrometry ◽  
Human Lens ◽  
Limited Data ◽  
Outer Cortex ◽  
Adult Human ◽  
Cell Age ◽  
Human Lenses ◽  
The Relationship

In humans, the age of fibre cells differs across the ocular lens, ranging from those formed before birth in the core of the lens to those formed just prior to death in the outer cortex. The distribution of glycerophospholipids in the adult human lens should reflect this range; however, limited data currently exists to confirm this hypothesis. Accordingly, this study aimed to determine the distribution of glycerophospholipids in adult human lens using mass spectrometry imaging. To achieve this, 20-µm thick slices of two human lenses, aged 51 and 67 were analysed by matrix-assisted laser desorption ionisation imaging mass spectrometry. The data clearly indicate that intact glycerophospholipids such as phosphatidylethanolamine, phosphatidylserine, and phosphatidic acid are mainly present in the outer cortex region, corresponding to the youngest fibre cells, while lyso-phosphatidylethanolamine, likely produced by the degradation of phosphatidylethanolamine, is present in the nucleus (older fibre cells). This study adds further evidence to the relationship between fibre cell age and glycerophospholipid composition.

scholarly journals Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

2020 ◽  
Author(s):  
Leonoor E.M. Tideman ◽  
Lukasz G. Migas ◽  
Katerina V. Djambazova ◽  
Nathan Heath Patterson ◽  
Richard M. Caprioli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Molecular Species ◽  
Imaging Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Decision Making Process ◽  
Spatial Specificity ◽  
The Relationship ◽  
Insight Into ◽  
Imaging Mass Spectrometry Data

AbstractThe search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species’ biomarker potential, our workflow delivers spatially localized explanations of the classification model’s decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species’ potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies.HighlightsOur workflow automates the discovery of biomarker candidates in imaging mass spectrometry data by using state-of-the-art machine learning methodology to produce a shortlist of molecular species that are differentially expressed with regards to a user-provided biological class.A model interpretability method called Shapley additive explanations (SHAP), with observational Shapley values, enables us to quantify the local and global predictive importance of molecular species with respect to recognizing a user-provided biological class.By providing spatially localized explanations for a classification model’s decision-making process, SHAP maps deliver insight into the spatial specificity of biomarker candidates and enable one to determine whether (and where) the relationship between a biomarker candidate and the class of interest is correlative or anticorrelative.

Robust Simulation Of Imaging Mass Spectrometry Data

2021 ◽  
Author(s):  
Anastasia Sarycheva ◽  
Anton Grigoryev ◽  
Evgeny N. Nikolaev ◽  
Yury Kostyukevich
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Mass Spectrometry Imaging ◽  
Imaging Mass Spectrometry ◽  
Real Data ◽  
Sample Surface ◽  
Mass Spectrometry Data ◽  
Ion Composition ◽  
Simulation Parameters ◽  
Imaging Mass Spectrometry Data

Mass spectrometry imaging (MSI) with high resolution in mass and space is an analytical method that produces distributions of ions on a sample surface. The algorithms for preprocessing and analysis of the raw data acquired from a mass spectrometer should be evaluated. To do that, the ion composition at every point of the sample should be known. This is possible via the employment of a simulated MSI dataset. In this work, we suggest a pipeline for a robust simulation of MSI datasets that resemble real data with an option to simulate the spectra acquired from any mass spectrometry instrument through the use of the experimental MSI datasets to extract simulation parameters.

scholarly journals Visualising mouse neuroanatomy and function by metal distribution using laser ablation-inductively coupled plasma-mass spectrometry imaging

2015 ◽  
Vol 6 (10) ◽  
pp. 5383-5393 ◽  
Author(s):  
Bence Paul ◽  
Dominic J. Hare ◽  
David P. Bishop ◽  
Chad Paton ◽  
Van Tran Nguyen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Laser Ablation ◽  
Mouse Brain ◽  
Inductively Coupled Plasma ◽  
Mass Spectrometry Imaging ◽  
Imaging Mass Spectrometry ◽  
Metal Distribution ◽  
Inductively Coupled ◽  
Plasma Mass Spectrometry ◽  
And Function

Studying the neuroanatomy of the mouse brain using imaging mass spectrometry and chemometric analysis.

Surface analysis by imaging mass spectrometry

2009 ◽  
Vol 74 (7-8) ◽  
pp. 1101-1116 ◽  
Author(s):  
Veronika Vidová ◽  
Michael Volný ◽  
Karel Lemr ◽  
Vladimír Havlíček
Keyword(s):  
Mass Spectrometry ◽  
Inductively Coupled Plasma ◽  
Mass Spectrometry Imaging ◽  
Structural Information ◽  
Imaging Techniques ◽  
Imaging Mass Spectrometry ◽  
Molecular Surface ◽  
Ionization Mass Spectrometry ◽  
Ionization Mass ◽  
Desorption Ionization

A review of four MS-based techniques available for molecular surface imaging is presented. The main focus is on the commercially available mass spectrometry imaging techniques: secondary ion mass spectrometry (SIMS), matrix assisted laser desorption ionization mass spectrometry (MALDI-MS), desorption electrospray ionization mass spectrometry (DESI-MS) and laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS). A short historical perspective is presented and traditional desorption ionization techniques are also briefly described. The four techniques are compared mainly with respect to their usage for imaging of biological surfaces. MALDI is evaluated as the most successful in life sciences and the only technique usable for imaging of large biopolymers. SIMS is less common but offers superior spatial lateral resolution and DESI is considered to be an emerging alternative approach in mass spectrometry imaging. LA-ICP ionization is unbeatable in terms of limits of detection but does not provide structural information. All techniques are considered extremely useful, representing a new wave of expansion of mass spectrometry into surface science and bioanalysis. A minireview with 121 references.

Age-Related Changes in the Lateral Lipid Distribution in a Human Lens Described by Mass Spectrometry Imaging

2015 ◽  
Vol 21 (3) ◽  
pp. 297-303 ◽  
Author(s):  
Jaroslav Pol ◽  
Helena Faltyskova ◽  
Lukas Krasny ◽  
Michael Volný ◽  
Ondrej Vlacil ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Human Lens ◽  
Lipid Distribution ◽  
Age Related ◽  
Age Related Changes

Imaging phosphorylated peptide distribution in human lens by MALDI MS

The Analyst ◽  
2015 ◽  
Vol 140 (12) ◽  
pp. 4284-4290 ◽  
Author(s):  
Jing Jiao ◽  
Aizhu Miao ◽  
Ying Zhang ◽  
Qi Fan ◽  
Yi Lu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Maldi Ms ◽  
Human Lens ◽  
New Strategy ◽  
Phosphorylated Peptide

A new strategy for on-tissue dephosphorylation treatment was established for phosphorylated peptide distribution by mass spectrometry imaging.

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