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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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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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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass.  One common type of ionization, known as electrospray ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave...
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In inductively coupled plasma–mass spectrometry (ICP–MS), an inductively coupled plasma (ICP) torch is used as an atomizer and ionizer. Solid samples are dissolved and volatilized before being introduced into the high-temperature argon plasma, while solution samples are nebulized and passed through the high-temperature argon plasma. Plasma dissociates the analytes and ionizes their component atoms to form a mixture of positive ions and molecular species. The positive ions are then...
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Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging.

David Helminiak1, Hang Hu2, Julia Laskin2

  • 1Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI, 53233 USA.

IEEE Transactions on Computational Imaging
|May 30, 2023
PubMed
Summary

Mass Spectrometry Imaging (MSI) can be significantly accelerated using a novel Deep Learning Approach for Dynamic Sampling (DLADS). This method reduces scan times by intelligently selecting informative pixels, improving throughput by 70%.

Keywords:
Compressed SensingDeep LearningMachine LearningMass Spectroscopy ImagingSparse Sampling

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Area of Science:

  • Analytical Chemistry
  • Biotechnology
  • Computer Science

Background:

  • Mass Spectrometry Imaging (MSI) traditionally requires lengthy acquisition times for high-resolution data.
  • Many pixels in MSI scans lack informative biological or chemical data, presenting an opportunity for optimization.
  • Sparse and dynamic sampling algorithms can reduce data acquisition by focusing on relevant sample regions.

Purpose of the Study:

  • To develop and evaluate a deep learning-based dynamic sampling strategy for accelerating MSI.
  • To compare the performance of the proposed deep learning approach against existing supervised learning methods.
  • To quantify the improvements in throughput and reconstruction quality offered by the new method.

Main Methods:

  • A Deep Learning Approach for Dynamic Sampling (DLADS) was developed, employing a Convolutional Neural Network (CNN).
  • DLADS integrates molecular mass intensity distributions into a third dimension for dynamic sampling decisions.
  • The DLADS method was evaluated against Supervised Learning Approach for Dynamic Sampling with Least-Squares regression (SLADS-LS) and a Multi-Layer Perceptron (SLADS-Net).

Main Results:

  • DLADS demonstrated a simulated 70% throughput improvement for Nanospray Desorption Electrospray Ionization (nano-DESI) MSI of tissues.
  • DLADS improved regression performance by 36.7% compared to single-channel SLADS-LS.
  • DLADS achieved reconstruction quality gains of 6.0% over single-channel SLADS-LS.

Conclusions:

  • Deep learning-based dynamic sampling offers a significant acceleration for MSI acquisition.
  • DLADS provides substantial improvements in both throughput and data reconstruction quality compared to traditional and supervised learning methods.
  • This approach holds promise for reducing MSI acquisition times, making high-resolution analysis more accessible.