scholarly journals Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
G. Guo ◽  
M. Papanicolaou ◽  
N. J. Demarais ◽  
Z. Wang ◽  
K. L. Schey ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Spatial Distribution ◽  
High Resolution ◽  
Mass Spectrometry Imaging ◽  
Direct Detection ◽  
High Mass ◽  
Imaging Data ◽  
Distribution Maps ◽  
Automated Annotation ◽  
Peptide Mass

AbstractSpatial proteomics has the potential to significantly advance our understanding of biology, physiology and medicine. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a powerful tool in the spatial proteomics field, enabling direct detection and registration of protein abundance and distribution across tissues. MALDI-MSI preserves spatial distribution and histology allowing unbiased analysis of complex, heterogeneous tissues. However, MALDI-MSI faces the challenge of simultaneous peptide quantification and identification. To overcome this, we develop and validate HIT-MAP (High-resolution Informatics Toolbox in MALDI-MSI Proteomics), an open-source bioinformatics workflow using peptide mass fingerprint analysis and a dual scoring system to computationally assign peptide and protein annotations to high mass resolution MSI datasets and generate customisable spatial distribution maps. HIT-MAP will be a valuable resource for the spatial proteomics community for analysing newly generated and retrospective datasets, enabling robust peptide and protein annotation and visualisation in a wide array of normal and disease contexts.

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>

Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE

2021 ◽  
Vol 93 (40) ◽  
pp. 13421-13425
Author(s):  
Dušan Veličković ◽  
Tamara Bečejac ◽  
Sergii Mamedov ◽  
Kumar Sharma ◽  
Namasivayam Ambalavanan ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Data Sets ◽  
Imaging Data ◽  
Automated Annotation

scholarly journals Enhancing the Throughput of FT Mass Spectrometry Imaging Using Compressed Sensing and Subspace Modeling

2021 ◽  
Author(s):  
Yuxuan Richard Xie ◽  
Daniel C. Castro ◽  
Stanislav S. Rubakhin ◽  
Jonathan V. Sweedler ◽  
Fan Lam
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Compressed Sensing ◽  
Mass Spectrometry Imaging ◽  
Chemical Space ◽  
Chemical Compositions ◽  
Ion Cyclotron Resonance ◽  
Imaging Data ◽  
Subspace Modeling ◽  
Ft Icr

Mass spectrometry imaging (MSI) allows for untargeted mapping of the chemical compositions of tissues with attomole detection limits. MSI using Fourier transform-based mass spectrometers, such as FT-ion cyclotron resonance (FT-ICR), grants the ability to examine the chemical space with unmatched mass resolution and mass accuracy. However, direct imaging of large tissue samples on FT-ICR is restrictively slow. In this work, we present an approach that combines the subspace modeling of ICR temporal signals with compressed sensing to accelerate high-resolution FT-ICR MSI. A joint subspace and sparsity constrained reconstruction enables the creation of high-resolution imaging data from the sparsely sampled and short-time acquired transients. Simulation studies and experimental implementation of the proposed acquisition in investigation of brain tissues demonstrate a factor of 10 enhancement in throughput of FT-ICR MSI, without the need for instrumental or hardware modifications.

scholarly journals Potential use of multivariate curve resolution for the analysis of mass spectrometry images

The Analyst ◽  
2015 ◽  
Vol 140 (3) ◽  
pp. 837-846 ◽  
Author(s):  
Joaquim Jaumot ◽  
Romà Tauler
Keyword(s):  
Mass Spectrometry ◽  
Spatial Distribution ◽  
Mass Spectrometry Imaging ◽  
Multivariate Curve Resolution ◽  
Imaging Data ◽  
Compound Identification ◽  
Curve Resolution ◽  
Potential Use

The application of MCR-ALS to mass spectrometry imaging data provides spatial distribution and MS spectra of components, allowing compound identification.

scholarly journals Clusterwise Peak Detection and Filtering Based on Spatial Distribution To Efficiently Mine Mass Spectrometry Imaging Data

2019 ◽  
Vol 91 (18) ◽  
pp. 11888-11896 ◽  
Author(s):  
Jonatan O. Eriksson ◽  
Melinda Rezeli ◽  
Max Hefner ◽  
Gyorgy Marko-Varga ◽  
Peter Horvatovich
Keyword(s):  
Mass Spectrometry ◽  
Spatial Distribution ◽  
Mass Spectrometry Imaging ◽  
Peak Detection ◽  
Imaging Data

scholarly journals Unveiling the spatial distribution of aflatoxin B1 and plant defense metabolites in maize using AP‐SMALDI mass spectrometry imaging

2021 ◽  
Author(s):  
Laura Righetti ◽  
Dhaka Ram Bhandari ◽  
Enrico Rolli ◽  
Sara Tortorella ◽  
Renato Bruni ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Spatial Distribution ◽  
Plant Defense ◽  
Aflatoxin B1 ◽  
Mass Spectrometry Imaging

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.

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