Moens, R. A. R. | bioRxiv Science

bioRxiv2025

Unmixing of Imaging Mass Spectrometry Measurements Using Microscopy-Informed Constraints

Imaging mass spectrometry (IMS) provides spatially resolved molecular information of organic tissue but can be limited by pixel signals mixing contributions from adjacent biological structures, e.g. of single cells and multicellular functional tissue units (FTUs). This paper proposes computational methods to predict mass spectral profiles of biological structures on the basis of IMS data by "unmixing" pixel-level signals, leveraging microscopy-based boundary information of these structures. By modeling each biological structure as having a unique mass spectrum, we formulate a linear mixing model and solve the corresponding inverse problem that unmixes blended signals. In particular, we cover both overdetermined and underdetermined linear system scenarios and compare ordinary least squares, nonnegative least squares, and singular value thresholding to a custom algorithm, coined Tissue-informed Unmixing of Labeled regions by Inverse Problem (TULIP), specifically tailored to IMS data. Validation on a synthetic in-situ single cell dataset and demonstration on a largescale kidney FTU dataset illustrate the potential of these methods for enhanced in-situ tissue structure analysis, e.g. in cellular and tissue studies.

bioinformatics↗

bioRxiv2024

Preserving Full Spectrum Information in ImagingMass Spectrometry Data Reduction

MotivationImaging mass spectrometry (IMS) has become an important tool for molecular characterization of biological tissue. However, IMS experiments tend to yield large datasets, routinely recording over 200,000 ion intensity values per mass spectrum and more than 100,000 pixels, i.e., spectra, per dataset. Traditionally, IMS data size challenges have been addressed by feature selection or extraction, such as by peak picking and peak integration. Selective data reduction techniques such as peak picking only retain certain parts of a mass spectrum, and often these describe only medium-to-high-abundance species. Since lower-intensity peaks and, for example, near-isobar species are sometimes missed, selective methods can potentially bias downstream analysis towards a subset of species in the data rather than considering all species measured. ResultsWe present an alternative to selective data reduction of IMS data that achieves similar data size reduction while better conserving the ion intensity profiles across all recorded m/z -bins, thereby preserving full spectrum information. Our method utilizes a low-rank matrix completion model combined with a randomized sparse-format-aware algorithm to approximate IMS datasets. This representation offers reduced dimensionality and a data footprint comparable to peak picking, but also retains complete spectral profiles, enabling comprehensive analysis and compression. We demonstrate improved preservation of lower signal-to-noise-ratio signals and near-isobars, mitigation of selection bias, and reduced information loss compared to current state-of-the art data reduction methods in IMS.

bioinformatics↗