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

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.