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Related Concept Videos

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called collision-induced...
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Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences01:20

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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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Mass Spectrometry: Complex Analysis01:21

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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.
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Electrospray Ionization (ESI) Mass Spectrometry01:12

Electrospray Ionization (ESI) Mass Spectrometry

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Higher molecular weight biomolecules are nonvolatile compounds that may decompose before ionizing or vaporizing during mass analysis with conventional electron impact ionization methods. Accordingly, electrospray ionization (ESI) is the favored method for vaporizing and ionizing biomolecules as it circumvents rapid fragmentation and enables the recording of mass signals for the entire biomolecule.
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Mass Spectrometers01:16

Mass Spectrometers

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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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Interpolation of Imaging Mass Spectrometry Data by a Window-Based Adversarial Autoencoder Method.

Lili Xu1, Qing Zhai1, Ariful Islam1,2

  • 1Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka 431-3192, Japan.

Journal of the American Society for Mass Spectrometry
|December 17, 2024
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This study introduces a novel window-based Adversarial Autoencoder (AAE) method for reconstructing high-resolution imaging mass spectrometry (IMS) data. The new approach significantly improves spatial omics research by enhancing image quality and reducing data processing time.

Keywords:
Animal tissueGenerative Artificial IntelligenceImaging mass spectrometry (IMS)Interpolation

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Area of Science:

  • Spatial omics
  • Molecular imaging
  • Biomedical research

Background:

  • Imaging mass spectrometry (IMS) is vital for spatial omics, but large datasets pose time and instrument challenges.
  • High-resolution molecular distribution data is crucial for understanding biological systems.

Purpose of the Study:

  • To develop a high-resolution IMS data reconstruction method.
  • To improve the efficiency and quality of spatial omics data analysis.

Main Methods:

  • Developed a window-based Adversarial Autoencoder (AAE) model for IMS data reconstruction.
  • Acquired and down-sampled IMS data from mouse cerebellum and kidney tissues.
  • Trained the AAE model to generate high-resolution images from lower-resolution inputs.

Main Results:

  • The window-based AAE method outperformed Bilinear and Bicubic interpolation in image evaluation metrics.
  • The model successfully reconstructed high-resolution IMS data for mouse cerebellum and kidney tissues.
  • Achieved superior performance on validation datasets, demonstrating robust reconstruction capabilities.

Conclusions:

  • The developed window-based AAE method offers a superior approach for high-resolution IMS data reconstruction.
  • This technique is valuable for analyzing large-scale IMS data from extensive biological samples, such as animal organs.
  • The method enhances the utility of IMS in spatial omics research and biomedical applications.