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Related Concept Videos

MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
Mass Analyzers: Common Types01:19

Mass Analyzers: Common Types

The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...

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Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research.

Siwei Bi1, Manjiangcuo Wang2, Qianlun Pu2

  • 1Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.

Analytical Chemistry
|December 16, 2023
PubMed
Summary
This summary is machine-generated.

Mass spectrometry imaging (MSI) analysis is challenging due to complex data. Multi-MSIProcessor (MMP) software offers a solution for spatial metabolomics, enabling efficient biomarker discovery from diverse biosamples.

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Area of Science:

  • Biomedical research
  • Analytical chemistry
  • Metabolomics

Background:

  • Mass spectrometry imaging (MSI) is a powerful tool for molecular visualization in biomedical research.
  • Analyzing complex, high-dimensional MSI data for spatially resolved metabolomics and biomarker discovery presents significant challenges.
  • Current methods struggle with mining bioinformatics-related peaks from batch MSI datasets.

Purpose of the Study:

  • To introduce Multi-MSIProcessor (MMP), a novel software for processing and analyzing mass spectrometry imaging data.
  • To provide a comprehensive platform for spatial metabolomics, integrating data handling, visualization, and biomarker discovery.
  • To address the challenges of analyzing batch MSI data and enable complete spatially resolved metabolomics.

Main Methods:

  • Development of Multi-MSIProcessor (MMP) software with integrated functions for data read-in, visualization, processing, and biomarker discovery.
  • Support for multiple data input formats including AFADESI-MSI, mzXML, and imzmL, ensuring compatibility with various MSI platforms (e.g., MALDI/SIMS-MSI).
  • Application of MMP to analyze multiple clinical biosamples with complex heterogeneity, demonstrating its capabilities in data quality assessment and differential peak analysis.

Main Results:

  • MMP enables deep mining of batch MSI data with flexible adaptability and open-source code for personalized analysis.
  • Demonstrated rapid establishment of complete MSI analysis workflows for heterogeneous clinical biosamples.
  • Successfully screened and annotated differential MS peaks, identified abnormal metabolic pathways, and assessed batch sample data quality.

Conclusions:

  • MMP provides a novel, adaptable platform for spatial metabolomics analysis of multiple samples.
  • The software facilitates comprehensive MSI data analysis, meeting diverse research requirements.
  • MMP enhances biomarker discovery and understanding of biological functions through spatially resolved metabolomics.