MSI-R

MSI-R performs adaptive pixel mass recalibration of mass spectrometry imaging (MSI) data to correct spectrum-specific mass shifts and improve molecular annotation accuracy.


Key Features:

  • Per-pixel spectral recalibration: Recalibrates each spectrum individually by estimating its specific mass shift.
  • Data-adaptive internal calibrants: Generates lists of pixel-specific internal calibrating ions automatically in a data-adaptive manner.
  • Mass-shift mitigation across image and m/z range: Addresses mass shifts arising from pixel-to-pixel ion count variability across the entire image and m/z range.
  • False-positive reduction: Uses adaptive, pixel-specific calibrants rather than global standards to minimize the risk of false positive annotations.
  • High-resolution MSI support: Applied to and compatible with high-resolution MSI datasets, including whole-body tissue sections.

Scientific Applications:

  • High-resolution zebrafish whole-body MSI: Validated on a high-resolution zebrafish whole-body section MSI dataset to mitigate mass shift impacts.
  • Public METASPACE datasets: Recalibrated 31 diverse public MSI datasets from METASPACE spanning various sample types and MSI methods.
  • Molecular annotation improvement: Increased the number of annotations in tested datasets by up to 400 additional entries and improved high-confidence annotations with a low false discovery rate.

Methodology:

Recalibration is performed by estimating spectrum-specific mass shifts using automatically generated lists of pixel-specific internal calibrating ions in a data-adaptive manner.

Topics

Details

License:
Apache-2.0
Tool Type:
command-line tool
Programming Languages:
Python
Added:
3/19/2021
Last Updated:
6/5/2025

Operations

Publications

La Rocca R, Kune C, Tiquet M, Stuart L, Eppe G, Alexandrov T, De Pauw E, Quinton L. Adaptive Pixel Mass Recalibration for Mass Spectrometry Imaging Based on Locally Endogenous Biological Signals. Analytical Chemistry. 2021;93(8):4066-4074. doi:10.1021/acs.analchem.0c05071. PMID:33583182.