A new technological approach in diagnostic pathology: mass spectrometry imaging-based metabolomics for biomarker detection in urachal cancer

AbstractUrachal adenocarcinomas (UrC) are rare but aggressive. Despite being of profound therapeutic relevance, UrC cannot be differentiated by histomorphology alone from other adenocarcinomas of differential diagnostic importance. As no reliable tissue-based diagnostic biomarkers are available, we aimed to detect such by integrating mass-spectrometry imaging-based metabolomics and digital pathology, thus allowing for a multimodal approach on the basis of spatial information. To achieve this, a cohort of UrC (n = 19) and colorectal adenocarcinomas (CRC, n = 27) as the differential diagnosis of highest therapeutic relevance was created, tissue micro-arrays (TMAs) were constructed, and pathological data was recorded. Hematoxylin and eosin (H&E) stained tissue sections were scanned and annotated, enabling an automized discrimination of tumor and non-tumor areas after training of an adequate algorithm. Spectral information within tumor regions, obtained via matrix-assisted laser desorption/ionization (MALDI)-Orbitrap-mass spectrometry imaging (MSI), were subsequently extracted in an automated workflow. On this basis, metabolic differences between UrC and CRC were revealed using machine learning algorithms. As a result, the study demonstrated the feasibility of MALDI-MSI for the evaluation of FFPE tissue in UrC and CRC with the potential to combine spatial metabolomics data with annotated histopathological data from digitalized H&E slides. The detected Area under the curve (AUC) of 0.94 in general and 0.77 for the analyte taurine alone (diagnostic accuracy for taurine: 74%) makes the technology a promising tool in this differential diagnostic dilemma situation. Although the data has to be considered as a proof-of-concept study, it presents a new adoption of this technology that has not been used in this scenario in which reliable diagnostic biomarkers (such as immunohistochemical markers) are currently not available.

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Combined Immunohistochemistry after Mass Spectrometry Imaging for Superior Spatial Information

PROTEOMICS - CLINICAL APPLICATIONS ◽

10.1002/prca.201800035 ◽

2018 ◽

Vol 13 (1) ◽

pp. 1800035 ◽

Cited By ~ 11

Author(s):

Katharina Kriegsmann ◽

Rémi Longuespée ◽

Michael Hundemer ◽

Christiane Zgorzelski ◽

Rita Casadonte ◽

...

Keyword(s):

Mass Spectrometry ◽

Mass Spectrometry Imaging ◽

Spatial Information

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MALDI mass spectrometry imaging of fresh and processed food: constituents, ingredients, contaminants and additives

10.1101/2021.12.23.473956 ◽

2021 ◽

Author(s):

Julia Kokesch-Himmelreich ◽

Oliver Wittek ◽

Alan M. Race ◽

Sophie Rakete ◽

Claus Schlicht ◽

...

Keyword(s):

Mass Spectrometry ◽

Mass Spectrometry Imaging ◽

Spatial Information ◽

Beta Carotene ◽

Relevant Information ◽

Maldi Mass Spectrometry ◽

Analysis Tool ◽

Processed Food ◽

Ms Imaging ◽

Wide Range

Mass Spectrometry imaging (MS imaging) provides spatial information for a wide range of compound classes in different sample matrices. We used MS imaging to investigate the distribution of components in fresh and processed food, including meat, dairy and bakery products. The MS imaging workflow was optimized to cater to the specific properties and challenges of the individual samples. We successfully detected highly nonpolar and polar constituents such as beta-carotene and anthocyanins, respectively. For the first time, the distribution of a contaminant and a food additive was visualized in processed food. We detected acrylamide in German gingerbread and investigated the penetration of the preservative natamycin into cheese. For this purpose, a new data analysis tool was developed to study the penetration of analytes from uneven surfaces. Our results show that MS imaging has great potential in food analysis to provide relevant information about components' distributions, particularly those underlying official regulations.

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Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging

10.1101/440057 ◽

2018 ◽

Author(s):

Paolo Inglese ◽

Gonçalo Correia ◽

Pamela Pruski ◽

Robert C Glen ◽

Zoltan Takats

Keyword(s):

Mass Spectrometry ◽

Mass Spectra ◽

Mass Spectrometry Imaging ◽

Spatial Information ◽

Desorption Electrospray Ionization ◽

Molecular Characteristics ◽

Ionization Mass ◽

Batch Effects ◽

Tissue Specimens

AbstractSupervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images, and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models.Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to co-expression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.

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

10.1101/2020.09.25.285619 ◽

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.

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Application of Mass Spectrometry Imaging for Visualizing Food Components

Foods ◽

10.3390/foods9050575 ◽

2020 ◽

Vol 9 (5) ◽

pp. 575 ◽

Cited By ~ 2

Author(s):

Yukihiro Yoshimura ◽

Nobuhiro Zaima

Keyword(s):

Mass Spectrometry ◽

High Performance ◽

Mass Spectrometry Imaging ◽

Spatial Information ◽

Secondary Ion Mass Spectrometry ◽

Desorption Electrospray Ionization ◽

Gas Chromatography Mass Spectrometry ◽

Food Science ◽

Related Factors ◽

Food Components

Consuming food is essential for survival, maintaining health, and triggering positive emotions like pleasure. One of the factors that drive us toward such behavior is the presence of various compounds in foods. There are many methods to analyze these molecules in foods; however, it is difficult to analyze the spatial distribution of these compounds using conventional techniques, such as mass spectrometry combined with high-performance liquid chromatography or gas chromatography. Mass spectrometry imaging (MSI) is a two-dimensional ionization technology that enables detection of compounds in tissue sections without extraction, purification, separation, or labeling. There are many methods for ionization of analytes, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization, and desorption electrospray ionization. Such MSI technologies can provide spatial information on the location of a specific analyte in food. The number of studies utilizing MSI technologies in food science has been increasing in the past decade. This review provides an overview of some of the recent applications of MSI in food science and related fields. In the future, MSI will become one of the most promising technologies for visualizing the distribution of food components and for identifying food-related factors by their molecular weights to improve quality, quality assurance, food safety, nutritional analysis, and to locate administered food factors.

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Mapping the Chemistry of Hair Strands by Mass Spectrometry Imaging—A Review

Molecules ◽

10.3390/molecules26247522 ◽

2021 ◽

Vol 26 (24) ◽

pp. 7522

Author(s):

Mai H. Philipsen ◽

Emma R. Haxen ◽

Auraya Manaprasertsak ◽

Per Malmberg ◽

Emma U. Hammarlund

Keyword(s):

Mass Spectrometry ◽

Sample Preparation ◽

Mass Spectrometry Imaging ◽

Spatial Information ◽

High Spatial Resolution ◽

Matrix Effects ◽

Forensic Sciences ◽

Chemical Information ◽

Hair Samples ◽

Hair Strand

Hair can record chemical information reflecting our living conditions, and, therefore, strands of hair have become a potent analytical target within the biological and forensic sciences. While early efforts focused on analyzing complete hair strands in bulk, high spatial resolution mass spectrometry imaging (MSI) has recently come to the forefront of chemical hair-strand analysis. MSI techniques offer a localized analysis, requiring fewer de-contamination procedures per default and making it possible to map the distribution of analytes on and within individual hair strands. Applying the techniques to hair samples has proven particularly useful in investigations quantifying the exposure to, and uptake of, toxins or drugs. Overall, MSI, combined with optimized sample preparation protocols, has improved precision and accuracy for identifying several elemental and molecular species in single strands of hair. Here, we review different sample preparation protocols and use cases with a view to make the methodology more accessible to researchers outside of the field of forensic science. We conclude that—although some challenges remain, including contamination issues and matrix effects—MSI offers unique opportunities for obtaining highly resolved spatial information of several compounds simultaneously across hair surfaces.

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Spatially aware clustering of ion images in mass spectrometry imaging data using deep learning

Analytical and Bioanalytical Chemistry ◽

10.1007/s00216-021-03179-w ◽

2021 ◽

Vol 413 (10) ◽

pp. 2803-2819

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, is 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. Graphical abstract

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An integrated mass spectrometry imaging and digital pathology workflow for objective detection of colorectal tumours by unique atomic signatures

Chemical Science ◽

10.1039/d1sc02237g ◽

2021 ◽

Author(s):

Bence Paul ◽

Kai Kysenius ◽

James B. Hilton ◽

Michael M W Jones ◽

Robert W. Hutchinson ◽

...

Keyword(s):

Mass Spectrometry ◽

Mass Spectrometry Imaging ◽

Cell Metabolism ◽

Digital Pathology ◽

Surrounding Tissue ◽

Essential Nutrients ◽

Tissue Changes ◽

Objective Detection ◽

Colorectal Tumours

Tumours are abnormal growths of cells that reproduce by redirecting essential nutrients and resources from surrounding tissue. Changes to cell metabolism that trigger the growth of tumours are reflected in...

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