scholarly journals Adaptive pixel mass recalibration for mass spectrometry imaging based on locally endogenous biological signals.

2020 ◽  
Author(s):  
Raphaël La Rocca ◽  
Christopher Kune ◽  
Mathieu Tiquet ◽  
Lachlan Stuart ◽  
Gauthier Eppe ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Biological Samples ◽  
Mass Spectrometry Imaging ◽  
A Priori ◽  
Mass Shift ◽  
High Mass ◽  
Whole Body ◽  
False Positive Detection ◽  
Data Adaptive ◽  
The Impact

<p>Mass spectrometry imaging (MSI) is a powerful and convenient method for revealing the spatial chemical composition of different biological samples. Molecular annotation of the detected signals is only possible if a high mass accuracy is maintained over the entire image and the m/z range. However, the heterogeneous molecular composition of biological samples could lead to small fluctuations in the detected m/z-values, called mass shift. The use of internal calibration is known to offer the best solution to avoid, or at least to reduce, mass shifts. Their “a priori” selection for a global MSI acquisition is prone to false positive detection and therefore to poor recalibration. To fill this gap, this work describes an algorithm that recalibrates each spectrum individually by estimating its mass shift with the help of a list of pixel specific internal calibrating ions, automatically generated in a data-adaptive manner (https://github.com/LaRoccaRaphael/MSI_recalibration). Through a practical example, we applied the methodology to a zebrafish whole body section acquired at high mass resolution to demonstrate the impact of mass shift on data analysis and the capability of our algorithm to recalibrate MSI data. In addition, we illustrate the broad applicability of the method by recalibrating 31 different public MSI datasets from METASPACE from various samples and types of MSI and show that our recalibration significantly increases the numbers of METASPACE annotations (gaining from 20 up to 400 additional annotations), particularly the high-confidence annotations with a low false discovery rate.<br></p>

scholarly journals Adaptive pixel mass recalibration for mass spectrometry imaging based on locally endogenous biological signals.

2020 ◽  
Author(s):  
Raphaël La Rocca ◽  
Christopher Kune ◽  
Mathieu Tiquet ◽  
Lachlan Stuart ◽  
Gauthier Eppe ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Biological Samples ◽  
Mass Spectrometry Imaging ◽  
A Priori ◽  
Mass Shift ◽  
High Mass ◽  
Whole Body ◽  
False Positive Detection ◽  
Data Adaptive ◽  
The Impact

<p>Mass spectrometry imaging (MSI) is a powerful and convenient method for revealing the spatial chemical composition of different biological samples. Molecular annotation of the detected signals is only possible if a high mass accuracy is maintained over the entire image and the m/z range. However, the heterogeneous molecular composition of biological samples could lead to small fluctuations in the detected m/z-values, called mass shift. The use of internal calibration is known to offer the best solution to avoid, or at least to reduce, mass shifts. Their “a priori” selection for a global MSI acquisition is prone to false positive detection and therefore to poor recalibration. To fill this gap, this work describes an algorithm that recalibrates each spectrum individually by estimating its mass shift with the help of a list of pixel specific internal calibrating ions, automatically generated in a data-adaptive manner (https://github.com/LaRoccaRaphael/MSI_recalibration). Through a practical example, we applied the methodology to a zebrafish whole body section acquired at high mass resolution to demonstrate the impact of mass shift on data analysis and the capability of our algorithm to recalibrate MSI data. In addition, we illustrate the broad applicability of the method by recalibrating 31 different public MSI datasets from METASPACE from various samples and types of MSI and show that our recalibration significantly increases the numbers of METASPACE annotations (gaining from 20 up to 400 additional annotations), particularly the high-confidence annotations with a low false discovery rate.<br></p>

scholarly journals Using Biological Signals for Mass Recalibration of Mass Spectrometry Imaging Data

2020 ◽  
Author(s):  
Raphaël La Rocca ◽  
Christopher Kune ◽  
Mathieu Tiquet ◽  
Lachlan Stuart ◽  
Theodore Alexandrov ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Biological Samples ◽  
Mass Spectrometry Imaging ◽  
Mass Shift ◽  
High Mass ◽  
Whole Body ◽  
Imaging Data ◽  
Data Adaptive ◽  
The Impact ◽  
Mass Shifts

<p>Mass spectrometry imaging (MSI) is a powerful and convenient method to reveal the spatial chemical composition of different biological samples. The molecular annotation of the detected signals is only possible when high mass accuracy is maintained across the entire image and the <i>m/z</i> range. However, the heterogeneous molecular composition of biological samples could result in fluctuations in the detected <i>m/z</i>-values, called mass shift. Mass shifts impact the interpretability of the detected signals by decreasing the number of annotations and by affecting the spatial consistency and accuracy of ion images. The use of internal calibration is known to offer the best solution to avoid, or at least to reduce, mass shifts. The selection of internal calibrating signals for a global MSI acquisition is not trivial, prone to false positive detection of calibrating signals and therefore to poor recalibration. To fill this gap, this work describes an algorithm that recalibrates each spectrum individually by estimating its mass shift with the help of a list of internal calibrating ions generated automatically in a data-adaptive manner. The method exploits RANSAC (<i>Random Sample Consensus</i>) algorithm, to select, in a robust manner, the experimental signal corresponding to internal calibrating signals by filtering out calibration points with infrequent mass errors and by using the remaining points to estimate a linear model of the mass shifts. We applied the method to a zebrafish whole body section acquired at high mass resolution to demonstrate the impact of mass shift on data analysis and the capacity of our algorithm to recalibrate MSI data. We illustrate the broad applicability of the method by recalibrating 31 different public MSI datasets from METASPACE from various samples and types of MSI and show that our recalibration significantly increases the numbers of METASPACE annotations, especially the high-confident annotations at a low false discovery rate.</p>

scholarly journals Exemplifying the Screening Power of Mass Spectrometry Imaging over Label-Based Technologies for Simultaneous Monitoring of Drug and Metabolite Distributions in Tissue Sections

2015 ◽  
Vol 21 (2) ◽  
pp. 187-193 ◽  
Author(s):  
Richard J. A. Goodwin ◽  
Anna Nilsson ◽  
C. Logan Mackay ◽  
John G. Swales ◽  
Maria K. Johansson ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Cyclic Peptide ◽  
Analysis Data ◽  
Resolving Power ◽  
High Mass ◽  
Whole Body ◽  
Ion Cyclotron Resonance ◽  
Label Free ◽  
Tissue Sections

Mass spectrometry imaging (MSI) provides pharmaceutical researchers with a suite of technologies to screen and assess compound distributions and relative abundances directly from tissue sections and offer insight into drug discovery–applicable queries such as blood-brain barrier access, tumor penetration/retention, and compound toxicity related to drug retention in specific organs/cell types. Label-free MSI offers advantages over label-based assays, such as quantitative whole-body autoradiography (QWBA), in the ability to simultaneously differentiate and monitor both drug and drug metabolites. Such discrimination is not possible by label-based assays if a drug metabolite still contains the radiolabel. Here, we present data exemplifying the advantages of MSI analysis. Data of the distribution of AZD2820, a therapeutic cyclic peptide, are related to corresponding QWBA data. Distribution of AZD2820 and two metabolites is achieved by MSI, which [14C]AZD2820 QWBA fails to differentiate. Furthermore, the high mass-resolving power of Fourier transform ion cyclotron resonance MS is used to separate closely associated ions.

scholarly journals Contrast optimization of mass spectrometry imaging (MSI) data visualization by threshold intensity quantization (TrIQ)

2021 ◽  
Vol 7 ◽  
pp. e585
Author(s):  
Ignacio Rosas-Román ◽  
Robert Winkler
Keyword(s):  
Mass Spectrometry ◽  
Data Visualization ◽  
Mass Spectrometry Imaging ◽  
Dynamic Range ◽  
Extreme Values ◽  
Ambient Ionization ◽  
Data Sets ◽  
Threshold Intensity ◽  
High Background ◽  
The Impact

Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient discovery of ‘regions of interest’ (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values (‘outliers’) and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.

scholarly journals Contrast Optimization of Mass Spectrometry Imaging (MSI) Data Visualization by Threshold Intensity Quantization (TrIQ)

2020 ◽  
Author(s):  
Robert Winkler ◽  
Ignacio Rosas-Román
Keyword(s):  
Mass Spectrometry ◽  
Data Visualization ◽  
Mass Spectrometry Imaging ◽  
Dynamic Range ◽  
Extreme Values ◽  
Ambient Ionization ◽  
Data Sets ◽  
Threshold Intensity ◽  
High Background ◽  
The Impact

<div>Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential</div><div>mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient</div><div>discovery of ‘regions of interest’ (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values (‘outliers’) and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.</div><div><br></div>

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