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Automated Pixel-Wise Recalibration for Improving Mass Accuracy and Peak Assignment in MALDI Mass Spectrometry Imaging
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DOI：10.1021/jasms.6c00113.png)
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Abstract
En 中文
Mass misalignment remains a major limitation in axial MALDI-TOF mass spectrometry imaging (MSI), leading to degraded mass accuracy, peak broadening, and reduced reliability of spatial molecular interpretation. Here, we present a fully automated pixel-level postacquisition recalibration framework that improves mass alignment while preserving single-pixel spectral fidelity. The method employs a “one-pixel, one-model” strategy using high-confidence endogenous calibration reference features, combined with dynamic peak matching and RANSAC (random sample consensus)-based robust regression, to correct both systematic and spatially heterogeneous mass drift. Applied to a mouse brain MSI data set (N = 33,903 pixels), the approach reduced the median absolute mass error from 28.1 to 5.6 ppm and restored spectral compactness in averaged spectra without introducing detectable peak-shape distortion. The improved mass alignment enabled the use of narrower extraction windows, resulting in enhanced spatial contrast and more reliable visualization of both high- and low-abundance lipid species. Notably, the method facilitated the resolution of overlapping spectral features and revealed biologically distinct lipid distributions that were obscured in uncalibrated data. These gains translated into improved consistency with reference-assisted annotation under stringent mass tolerances and enhanced interpretability in unsupervised multivariate analyses by reducing instrument-driven variance. The robustness and transferability of the framework were further demonstrated across independent data sets acquired under different matrix systems (DHB and NEDC), ion modes, and biological models (mouse brain and zebrafish), including data affected by substantial instrumental instability. In all cases, recalibration consistently reduced mass-error dispersion, improved spectral quality, and enhanced spatial signal extraction under fixed narrow-window conditions. Application of the strategy to different MALDI-MSI platforms also demonstrated its broad applicability and effectiveness in reducing mass errors. Overall, this pixel-level recalibration strategy provides a practical and broadly applicable postprocessing solution for improving mass accuracy, spectral fidelity, and spatial interpretability in MALDI-TOF MSI, thereby enabling more reliable molecular annotation and spatial analyses on widely accessible instrumentation.
Keywords:
MALDI mass spectrometry imaging
mass drift
pixel-level recalibration
mass accuracy
peak assignment
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