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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.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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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 is an ionization technique, 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...
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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...
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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
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Updated: Jan 14, 2026

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Aird-MSI: A High Compression Rate and Decompression Speed Format for Mass Spectrometry Imaging Data.

Shuochao Li1, Hongping Sheng2, Pengyuan Du1

  • 1Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province 250000, China.

Journal of Proteome Research
|October 17, 2025
PubMed
Summary
This summary is machine-generated.

A new Aird compression format significantly reduces file sizes and speeds up analysis for spatial metabolomics data. This advancement enhances cloud-based analysis and real-time visualization for mass spectrometry imaging.

Keywords:
Airddata compressionmass spectrometry imagingspatial metabolomics

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Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Analytical Chemistry

Background:

  • Mass spectrometry imaging is crucial for spatial metabolomics.
  • The imzML format presents challenges in data storage, transmission, and computational efficiency due to large file sizes and high parsing overhead.
  • These limitations hinder cloud-based analysis and real-time visualization.

Purpose of the Study:

  • To introduce an enhanced Aird compression format optimized for spatial metabolomics.
  • To address the limitations of the imzML format in terms of storage, transmission, and computational efficiency.
  • To improve data handling for cloud-based analysis and real-time visualization.

Main Methods:

  • Developed a dynamic combinatorial compression algorithm for integer-based encoding of m/z and intensity data.
  • Implemented a coordinate-separation storage strategy for rapid spatial indexing.
  • Validated the Aird format on 47 public spatial metabolomics datasets.

Main Results:

  • Aird achieved a 70% reduction in storage footprint compared to imzML (mean compression ratio: 30.89%).
  • Near-lossless data precision was maintained (F1-score = 99.75% at 0.1 ppm m/z tolerance).
  • Loading speeds were accelerated by 13-fold in MZmine for high-precision datasets.

Conclusions:

  • The Aird format overcomes critical bottlenecks in spatial metabolomics by improving storage efficiency, computational speed, and analytical precision.
  • It reduces I/O latency for large datasets, enabling robust infrastructure for translational applications.
  • Aird supports applications like disease biomarker discovery and pharmacokinetic imaging with near-native feature detection accuracy.