scholarly journals The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging

Metabolites ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 752
Author(s):  
Juliana Pereira Lopes Gonçalves ◽  
Christine Bollwein ◽  
Anna Melissa Schlitter ◽  
Benedikt Martin ◽  
Bruno Märkl ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Classification Accuracy ◽  
Mass Spectrometry Imaging ◽  
Ductal Adenocarcinoma ◽  
Accurate Information ◽  
Learning Approaches ◽  
Kidney Tumors ◽  
Diagnosis And Prognosis ◽  
Tumor Tissues ◽  
The Impact

Knowing the precise location of analytes in the tissue has the potential to provide information about the organs’ function and predict its behavior. It is especially powerful when used in diagnosis and prognosis prediction of pathologies, such as cancer. Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums. To gain accurate information about the tissue, there is a need to build robust classification models. We have investigated the impact of histological annotation on the classification accuracy of different tumor tissues. Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is over 20% in accuracy. On the other hand, in more homogeneous samples, such as kidney tumors, histological annotations have a slenderer impact on the classification accuracy.

scholarly journals Satellite-like Gold Nanocomposites for Targeted Mass Spectrometry Imaging of Tumor Tissues

2017 ◽  
Vol 1 (2) ◽  
pp. 141-153 ◽  
Author(s):  
Yu-Ting Tseng ◽  
Scott G. Harroun ◽  
Chien-Wei Wu ◽  
Ju-Yi Mao ◽  
Huan-Tsung Chang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Tumor Tissues ◽  
Gold Nanocomposites

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 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 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>

High-grade sarcoma diagnosis and prognosis: Biomarker discovery by mass spectrometry imaging

PROTEOMICS ◽  
2016 ◽  
Vol 16 (11-12) ◽  
pp. 1802-1813 ◽  
Author(s):  
Sha Lou ◽  
Benjamin Balluff ◽  
Marieke A. de Graaff ◽  
Arjen H.G. Cleven ◽  
Inge Briaire - de Bruijn ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Biomarker Discovery ◽  
High Grade ◽  
Diagnosis And Prognosis ◽  
High Grade Sarcoma

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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