Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning

ABSTRACT: Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1,710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

ORGANISM(S): Homo sapiens (human) Sorghum bicolor Mus musculus (mouse) Populus trichocarpa (poplar tree)

SUBMITTER:

PROVIDER: S-BIAD1283 | biostudies-other |

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation.

Project description:Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 14 metabolites were uniquely identified by NMR, and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism, leading to fatty-acid and complex-lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques: the improved detection and annotation of metabolites.

| S-EPMC6668615 | biostudies-literature

Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging.

Project description:Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.

| S-EPMC8582378 | biostudies-literature

Unsupervised machine learning using an imaging mass spectrometry dataset automatically reassembles grey and white matter.

Project description:Current histological and anatomical analysis techniques, including fluorescence in situ hybridisation, immunohistochemistry, immunofluorescence, immunoelectron microscopy and fluorescent fusion protein, have revealed great distribution diversity of mRNA and proteins in the brain. However, the distributional pattern of small biomolecules, such as lipids, remains unclear. To this end, we have developed and optimised imaging mass spectrometry (IMS), a combined technique incorporating mass spectrometry and microscopy, which is capable of comprehensively visualising biomolecule distribution. We demonstrated the differential distribution of phospholipids throughout the cell body and axon of neuronal cells using IMS analysis. In this study, we used solarix XR, a high mass resolution and highly sensitive MALDI-FT-ICR-MS capable of detecting higher number of molecules than conventional MALDI-TOF-MS instruments, to create a molecular distribution dataset. We examined the diversity of biomolecule distribution in rat brains using IMS and hypothesised that unsupervised machine learning reconstructs brain structures such as the grey and white matters. We have demonstrated that principal component analysis (PCA) can reassemble the grey and white matters without assigning brain anatomical regions. Hierarchical clustering allowed us to classify the 10 groups of observed molecules according to their distributions. Furthermore, the group of molecules specifically localised in the cerebellar cortex was estimated to be composed of phospholipids.

| S-EPMC6744563 | biostudies-literature

Integration of Mass Spectrometry Imaging and Machine Learning Visualizes Region-Specific Age-Induced and Drug-Target Metabolic Perturbations in the Brain.

Project description:Detailed metabolic imaging of specific brain regions in early aging may expose pathophysiological mechanisms and indicate effective neuropharmacological targets in the onset of cognitive decline. Comprehensive imaging of brain aging and drug-target effects is restricted using conventional methodology. We simultaneously visualized multiple metabolic alterations induced by normal aging in specific regions of mouse brains by integrating Fourier-transform ion cyclotron resonance mass spectrometry imaging and combined supervised and unsupervised machine learning models. We examined the interplay between aging and the response to tacrine-induced acetylcholinesterase inhibition, a well-characterized therapeutic treatment against dementia. The dipeptide carnosine (β-alanyl-l-histidine) and the vitamin α-tocopherol were significantly elevated by aging in different brain regions. l-Carnitine and acetylcholine metabolism were found to be major pathways affected by aging and tacrine administration in a brain region-specific manner, indicating altered mitochondrial function and neurotransmission. The highly interconnected hippocampus and retrosplenial cortex displayed different age-induced alterations in lipids and acylcarnitines, reflecting diverse region-specific metabolic effects. The subregional differences observed in the hippocampal formation of several lipid metabolites demonstrate the unique potential of the technique compared to standard mass spectrometry approaches. An age-induced increase of endogenous antioxidants, such as α-tocopherol, in the hippocampus was detected, suggesting an augmentation of neuroprotective mechanisms in early aging. Our comprehensive imaging approach visualized heterogeneous age-induced metabolic perturbations in mitochondrial function, neurotransmission, and lipid signaling, not always attenuated by acetylcholinesterase inhibition.

| S-EPMC8291481 | biostudies-literature

Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging.

Project description:Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR ( https://github.com/CeMOS-Mannheim/moleculaR ) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.

| S-EPMC10067847 | biostudies-literature

Cancer tissue classification using supervised machine learning applied to MALDI mass spectrometry imaging

Project description:With the current clinical available technologies, not all cancers are staged accurately. Because of this a large percentage of patients are misclassified before treatment, leading to under or over treatment. Therefore, a classification system based on the molecular feature is required to deter-mine the tumour behavior and metastatic potential. Here, we have shown the diagnostic potential of MALDI MSI using supervised machine learning approach in distinguishing cancerous colorectal tissue from normal with an overall accuracy of 98%. Also, shown is the capability of the technique in predicting the presence of metastasis in endometrial cancer with an overall accuracy of 80%. The development of such a model can help in determining the optimum treatment for cancerous pa-tients, reduce morbidity and better patient outcome.

2022-02-17 | PXD025594 | Pride

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Project description:Global proteomics datasets to study ash utilization by R. toruloides. PeakDecoder is a workflow for metabolite identification and accurate profiling in multidimensional LC-IM-MS-DIA measurements. It is available at https://github.com/EMSL-Computing/PeakDecoder

2023-02-03 | MSV000091202 | MassIVE

Machine-learning-enhanced time-of-flight mass spectrometry analysis.

Project description:Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.

| S-EPMC7892357 | biostudies-literature

Nucleolin-aptamer therapy in retinoblastoma: molecular changes and mass spectrometry–based imaging

Project description:Retinoblastoma (RB) is an intraocular childhood tumor which, if left untreated, leads to blindness and mortality. Nucleolin (NCL) protein which is differentially expressed on the tumor cell surface, binds ligands and regulates carcinogenesis and angiogenesis. We found that NCL is over expressed in RB tumor tissues and cell lines compared to normal retina. We studied the effect of nucleolin-aptamer (NCL-APT) to reduce proliferation in RB tumor cells. Aptamer treatment on the RB cell lines (Y79 and WERI-Rb1) led to significant inhibition of cell proliferation. Locked nucleic acid (LNA) modified NCL-APT administered subcutaneously (s.c.) near tumor or intraperitoneally (i.p.) in Y79 xenografted nude mice resulted in 26 and 65% of tumor growth inhibition, respectively. Downregulation of inhibitor of apoptosis proteins, tumor miRNA-18a, altered serum cytokines, and serum miRNA-18a levels were observed upon NCL-APT treatment. Desorption electrospray ionization mass spectrometry (DESI MS)-based imaging of cell lines and tumor tissues revealed changes in phosphatidylcholines levels upon treatment. Thus, our study provides proof of concept illustrating NCL-APT-based targeted therapeutic strategy and use of DESI MS-based lipid imaging in monitoring therapeutic responses in RB.

2016-11-15 | GSE89818 | GEO

Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning.

Project description:Multicellular spheroids (hereinafter referred to as spheroids) are 3D biological models. The metabolomic profiles inside spheroids provide crucial information reflecting the molecular phenotypes and microenvironment of cells. To study the influence of an anticancer drug on the spatially resolved metabolites, spheroids were cultured using HCT-116 colorectal cancer cells, treated with the anticancer drug Irinotecan under a series of time- and concentration-dependent conditions. The Single-probe mass spectrometry imaging (MSI) technique was utilized to conduct the experiments. The MSI data were analyzed using advanced data analysis methods to efficiently extract metabolomic information. Multivariate curve resolution alternating least square (MCR-ALS) was used to decompose each MS image into different components with grouped species. To improve the efficiency of data analysis, both supervised (Random Forest) and unsupervised (cluster large applications (CLARA)) machine learning (ML) methods were employed to cluster MS images according to their metabolomic features. Our results indicate that anticancer drug significantly affected the abundances of a variety of metabolites in different regions of spheroids. This integrated experiment and data analysis approach can facilitate the studies of metabolites in different types of 3D tumor models and tissues and potentially benefit the drug discovery, therapeutic resistance, and other biological research fields.

| S-EPMC6573030 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data