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Related Concept Videos

Matrix-Assisted Laser Desorption Ionization (MALDI)01:08

Matrix-Assisted Laser Desorption Ionization (MALDI)

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Matrix-assisted laser desorption ionization (MALDI) is a powerful analytical technique used in mass spectrometry. It enables the identification and characterization of various biomolecules, including proteins, peptides, nucleic acids, and carbohydrates. MALDI spectrometry is widely employed in biological and medical research, as well as in fields like pharmacology and biochemistry.
The analyte of interest, a biomolecule or a mixture of biomolecules, is mixed with a suitable matrix material. The...
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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.
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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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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.
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Updated: Sep 29, 2025

Imaging of Biological Tissues by Desorption Electrospray Ionization Mass Spectrometry
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Laser desorption tissue imaging with Differential Mobility Spectrometry.

Maiju Lepomäki1, Anna Anttalainen2, Artturi Vuorinen3

  • 1Surgery, Faculty of Medicine and Health Technology, Tampere University, Kauppi Campus, Arvo Building, Arvo Ylpön katu 34, 33520 Tampere, Finland; Department of Pathology, Fimlab Laboratories, Arvo Ylpön katu 4, FI-33520 Tampere, Finland.

Experimental and Molecular Pathology
|March 26, 2022
PubMed
Summary

An automated tissue laser analysis system (iATLAS) uses Differential Mobility Spectrometry (DMS) to rapidly classify tissues. This technology shows promise for improving breast cancer imaging and streamlining pathological examinations.

Keywords:
Breast cancerDifferential Mobility Spectrometry (DMS)Field asymmetric ion mobility spectrometry (FAIMS)Tissue imagingTissue mapping

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Pathology

Background:

  • Pathological examination of breast carcinoma is labor-intensive.
  • A tissue pre-mapping method can improve pathologist efficiency and sampling accuracy.
  • Differential Mobility Spectrometry (DMS) offers rapid analysis of complex gas mixtures.

Purpose of the Study:

  • To develop and demonstrate an automated tissue laser analysis system (iATLAS) for tissue classification.
  • To evaluate iATLAS performance in distinguishing various porcine tissues and human breast carcinomas.
  • To establish a foundation for automated breast cancer imaging.

Main Methods:

  • An automated tissue laser analysis system (iATLAS) was developed, integrating a laser evaporator with a DMS gas analyzer.
  • The system was tested on porcine tissue samples and human breast carcinoma specimens.
  • Machine learning models, including Convolutional Neural Network (CNN) and Support Vector Machine (SVM), were employed for tissue classification.

Main Results:

  • Porcine tissues (muscle, adipose, bone, liver) and normal breast tissue were classified with 86% cross-validation accuracy using CNN.
  • Independent validation achieved 82% classification accuracy.
  • For human breast carcinomas, iATLAS demonstrated high accuracy (94% with macroscopic data) and sensitivity (93%).

Conclusions:

  • The iATLAS system provides a promising method for automated tissue imaging.
  • This technology has the potential to enhance breast cancer diagnosis and pathological workflows.
  • Further development can lead to more efficient and accurate tissue analysis.