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Gas Chromatography–Mass Spectrometry (GC–MS)

Gas Chromatography–Mass Spectrometry (GC–MS)

Gas chromatography–mass spectrometry (GC–MS) is the combination of analytical techniques of gas chromatography and mass spectrometry in a single instrument for analyzing a mixture of compounds. The gas chromatograph separates the compounds in the mixture, and the mass spectrometer analyzes each compound separately to determine the molecular masses and molecular structures.
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall....

Mass Spectrometry: Complex Analysis

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...

Mass Spectrometers

Mass Spectrometers

This lesson details the instrumentation of a mass spectrometer—a physical instrument to perform mass spectrometry on analyte molecules and record the characteristic mass spectra. This is achieved via three chief functions:

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Related Experiment Video

Updated: May 17, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

SagMSI: A graph convolutional network framework for precise spatial segmentation in mass spectrometry imaging.

Mudassir Shah¹, Linlin Wang¹, Lei Guo²

¹Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China.

Analytica Chimica Acta

|May 15, 2025

Summary

This summary is machine-generated.

SagMSI, a novel graph convolution network (GCN) approach, enhances spatial segmentation for Mass Spectrometry Imaging (MSI) data. It accurately delineates tissue structures and sub-organs, outperforming existing methods for spatial metabolomics.

Keywords:

Deep neural network Dimensionality reduction Graph convolutional network learning Mass spectrometry imaging Spatial segmentation

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Last Updated: May 17, 2025

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Biomedical imaging
Spatial metabolomics
Computational biology

Background:

Mass Spectrometry Imaging (MSI) is vital for spatial metabolomics, enabling label-free analysis of metabolite distribution in tissues.
The complexity of MSI data, including large size, high dimensionality, and spectral nonlinearity, presents significant challenges for accurate spatial segmentation.
Existing deep learning methods, like CNNs, often fail to capture the full structural information inherent in MSI data.

Purpose of the Study:

To develop an advanced unsupervised segmentation strategy for Mass Spectrometry Imaging (MSI) data.
To improve the capture of comprehensive structural information in MSI data beyond traditional deep learning methods.
To enable flexible, effective, and precise spatial segmentation for enhanced biochemical interpretation of MSI data.

Main Methods:

Proposed SagMSI, an unsupervised graph convolution network (GCN)-based segmentation strategy.
Integrated spatial-aware graph construction with a GCN module within a deep neural network.
Applied SagMSI to simulated and experimental MSI datasets, comparing against t-SNE + k-means, Cardinal, and CNN-based methods.

Main Results:

SagMSI demonstrated superior performance in segmenting complex tissues compared to existing methods.
The approach effectively revealed detailed sub-structures and delineated distinct sub-organ boundaries with minimal noise.
Quantitative evaluations using silhouette coefficient and adjusted rand index confirmed SagMSI's accuracy.

Conclusions:

MSI data is effectively modeled using graph structures to integrate biomolecular profiles and spatial adjacency.
The GCN framework generates robust pixel representations by learning contextual information, enabling precise MSI segmentation.
SagMSI offers high flexibility, noise robustness, and applicability for exploring complex tissue structures and identifying tissue-specific marker ions.