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bioRxiv · 10.64898/2025.11.30.691393

IsoSpace: Chemistry-Informed Dimensionality Reduction and Automated Isotope Candidate Detection in Imaging Mass Spectrometry

Meenakshi, M.·Migas, L. G.·Djambazova, K. V.·Dufresne, M.·Spraggins, J. M.·Van de Plas, R.
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Abstract

Imaging mass spectrometry (IMS) experiments simultaneously map thousands of ion species throughout tissue. Its high-dimensionality makes human interpretation difficult and dimensionality reduction (DR) methods are common to facilitate exploration. Traditional DR methods, such as principal component analysis (PCA), are general approaches, designed to work across application domains. They are usually unaware of, and unable to exploit correlations specific to a particular measurement type. Compression-focused, such blind methods often deliver physically impossible latent patterns or make feature combinations that confuse rather than aid human understanding. We introduce a novel chemistry-informed DR method, IsoSpace, with built-in awareness of mass spectrometry-relevant patterns. Although generalizable, IsoSpace substantiates chemistry-informed as sensitive to potential isotopic relation-ships. Like traditional DR methods, IsoSpace groups mass-over-charge (m/z)-features to deliver a low-dimensional representation of IMS data. Unlike traditional methods, IsoSpace ensures that retrieved latent patterns constitute potential isotopic families. IsoSpaces decomposition facilitates IMS interpretation at the molecular species rather than ion species level, implicitly automating isotope candidate detection. Most de-isotoping techniques ignore spatial relationships or rely on molecular class assumptions, making them less suitable for molecularly diverse tissue environments. IsoSpace avoids such assumptions, integrating spatial and spectral cues to empirically detect potential isotopic peaks. IsoSpace uses non-negative matrix factorization and an m/z -pattern matrix to uncover isotopic-like sequences, evaluating them by intra-pattern correlation. In a mouse pup example with 879 m/z -peaks, IsoSpace identified 71 potential isotopic patterns, substantially reducing data complexity while preserving chemical un-derstanding. IsoSpace o!ers chemical-interpretation-permissive DR with unsupervised isotope candidate detection for heterogeneous samples. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/691393v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@5e8977org.highwire.dtl.DTLVardef@93150eorg.highwire.dtl.DTLVardef@4b7aa4org.highwire.dtl.DTLVardef@160aa43_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 0:C_FLOATNO Graphical abstract for IsoSpace, chemistry-informed dimensionality reduction and au-tomated isotope candidate detection in imaging mass spectrometry. C_FIG

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Meenakshi, M., Migas, L. G., Djambazova, K. V., Dufresne, M., Spraggins, J. M., Van de Plas, R.. 2025-12-02. IsoSpace: Chemistry-Informed Dimensionality Reduction and Automated Isotope Candidate Detection in Imaging Mass Spectrometry. https://doi.org/10.64898/2025.11.30.691393

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