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>

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>

Automatic Registration of Mass Spectrometry Imaging Data Sets to the Allen Brain Atlas

2014 ◽  
Vol 86 (8) ◽  
pp. 3947-3954 ◽  
Author(s):  
Walid M. Abdelmoula ◽  
Ricardo J. Carreira ◽  
Reinald Shyti ◽  
Benjamin Balluff ◽  
René J. M. van Zeijl ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Brain Atlas ◽  
Data Sets ◽  
Automatic Registration ◽  
Imaging Data ◽  
Allen Brain Atlas ◽  
Allen Brain

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 Accessible and reproducible mass spectrometry imaging data analysis in Galaxy

2019 ◽  
Author(s):  
Melanie Christine Föll ◽  
Lennart Moritz ◽  
Thomas Wollmann ◽  
Maren Nicole Stillger ◽  
Niklas Vockert ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Mass Spectrometry Imaging ◽  
Ease Of Use ◽  
Reproducible Research ◽  
Data Sets ◽  
Imaging Data ◽  
Available N ◽  
The Galaxy ◽  
Analysis Platform

AbstractBackgroundMass spectrometry imaging is increasingly used in biological and translational research as it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired data sets are large and complex and often analyzed with proprietary software or in-house scripts, which hinder reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many MSI researchers.FindingsWe have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Further, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research.ConclusionThe Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access together with high levels of reproducibility and transparency.

scholarly journals Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning

2020 ◽  
Author(s):  
Wanqiu Zhang ◽  
Marc Claesen ◽  
Thomas Moerman ◽  
M. Reid Groseclose ◽  
Etienne Waelkens ◽  
...  
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Deep Learning ◽  
Mass Spectrometry Imaging ◽  
Spatial Information ◽  
Image Clustering ◽  
Imaging Data ◽  
The Neural Network ◽  
Clustering Quality ◽  
The Impact

AbstractComputational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction.The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, are used to cluster ion images based on spatial expressions.We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters.Additionally, we introduce the Relative Isotope Ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes.The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.

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>

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