Katerina V. Djambazova

Molecular imaging by imaging mass spectrometry (IMS) has become a key modality for spatial proteomics, lipidomics, glycomics, and metabolomics. It maps hundreds to thousands of molecular species concurrently throughout tissue without prior labeling. However, reporting thousands of ion images makes IMS measurements very high-dimensional, complicating interpretation. Furthermore, IMS data contain implicit chemical relationships. For example, the same molecular species can be reported by several separately-measured ion species, each an isotopic variant or isotopologue of that molecule. While conventional dimensionality reduction methods such as principal component analysis can address the dimensionality challenge, they typically do not preserve chemical relationships ( e . g ., isotopologue grouping), making biological interpretation harder. As advanced, higher-dimensional measurement types such as ion mobility IMS (IM-IMS) expand into spatial omics, addressing interpretability in a chemically informed way becomes pressing. Therefore, we present IsoMobil, a dimensionality-reduction framework for IM-IMS data that empirically detects potential isotopologues. Besides reducing dataset complexity, it facilitates interpretation at the (biologically relevant) molecular-species level rather than ion-species level. The algorithm finds spatially coherent ion species, filters them based on isotope-induced mass-to-charge ( m/z ) distances and mobility-bin consistency (isotopologues have near-identical collisional cross-sections). This yields a compact representation where isotopologue-candidate families, rather than individual ion-species, form latent dimensions. In a synthetic benchmark, IsoMobil outperformed (F1=1.0) spatial-only and m/z -based methods (F1≈0.67). In a human colon case study, IsoMobil found 77 isotopologue-candidate groups (COSH-P-quality≥0.85) among 6344 lipid ion species. By automating isotopologue discovery, IsoMobil lifts biological interpretation of exploratory, untargeted spatial omics by IM-IMS to the molecular-species level.

White matter hyperintensities (WMHs) are a common feature of Alzheimer’s disease and are associated with cognitive decline, yet their molecular composition and spatial heterogeneity remain incompletely defined. Here, we identify distinct lipid signatures in human AD WMHs compared to matched normal-appearing white matter (NAWM) from the same donors. Using an integrated multimodal approach combining magnetic resonance imaging, matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS), histological staining, and complementary liquid chromatography–tandem mass spectrometry, we resolve spatially localized lipid alterations within tissue sections while preserving spatial context. This approach reveals region-specific heterogeneity in WMH lipid composition across anterior and posterior brain regions that may be obscured by bulk lipidomics alone. Machine learning–based analysis using Shapley additive explanations (SHAP) identified lipid features that contribute to WMH classification, with sulfatide, hexosylceramide, and phosphatidylinositol species emerging as key discriminators. In anterior brain regions, WMHs were associated with differential abundance and depletion of specific sulfatide and hexosylceramide species (SHexCer 44:2;3O, SHexCer 42:2;3O, HexCer 41:1;3O, SHexCer 42:2;2O), whereas posterior WMHs were characterized by reduced phosphatidylinositol species (PI 36:1), demonstrating that AD-associated white matter pathology is governed by region-specific, heterogeneous lipid remodeling rather than uniform global degradation.

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. IsoSpace’s 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. Figure 0: Graphical abstract for IsoSpace, chemistry-informed dimensionality reduction and au-tomated isotope candidate detection in imaging mass spectrometry.

Multiplexed molecular imaging, such as imaging mass spectrometry, provides spatially-contextualized insights that are transforming biomedical research by advancing our understanding of tissue organization and disease mechanisms. However, the spatial-statistical properties of molecular imaging data are often underutilized, and scalable computational tools to analyze them are lacking. Here, we demonstrate how local and global spatial autocorrelation (SAC) metrics can be used to quantify spatial dependence and spatial heterogeneity, properties that violate the common assumption of independent and identically distributed measurements. We develop mathematically rigorous methods for SAC-based exploratory analysis. Furthermore, we introduce a novel spatial feature extractor, the Moran quadrant map, and develop two advanced workflows based on it: Moran-Felsenszwalb segmentation for tissue domain segmentation and Moran-HOG clustering for colocalization-based image analysis. Finally, our open-source Moran Imaging toolbox ( https://github.com/vandeplaslab/Moran_Imaging ) provides scalable Python implementations, including a novel parallelized spatial lag algorithm, unlocking SAC-based analyses and biological insights for large-scale imaging.

Building upon a foundational Human Kidney resource, we present a comprehensive multi-modal atlas that defines spatially resolved versus unresolved repair states and mechanisms in human kidney disease. Homeostatic interactions between injured kidney epithelium and its surrounding milieu determine successful repair outcomes, while pathogenic signaling promotes unresolved inflammation and fibrosis leading to chronic disease. We integrated multiple single-cell and spatial modalities across ∼700 samples from >350 patients (∼250 research biopsies), analyzing ∼1.7 million cells alongside complementary mouse multi-omic profiles spanning acute-to-chronic injury and aging (>300,000 cells) and spatial transcriptomic analysis of >150 human biopsies. This cross-species atlas delineates functional pathways and druggable targets across the nephron and defines gene regulatory networks and chromatin landscapes governing tubular, fibroblast, and immune cell transitions from injury to either recovery or failed repair states. We identified distinct cellular states associated with specific pathological features that show dynamic distributions between acute kidney injury (AKI) and chronic kidney disease (CKD), organized within unique spatial niches that reveal progression mechanisms from early injury to unresolved disease. Gene regulatory analyses prioritized key transcription factor activities (SOX4, SOX9, NFKB1, REL, KLFs) and their target networks establishing disease states and tissue microenvironments. These regulatory programs were directly linked to clinical outcomes, identifying molecular signatures of recovery and secreted biomarkers predictive of AKI-to-CKD progression, providing a key resource for therapeutic development and precision medicine approaches in kidney disease.

This is a protocol for preparing fresh-frozen kidney tissue for in situ spatial transcriptomics using a custom probe panel with the 10X Xenium Analyzer platform. Slide preparation is a multi-day process encompassing cryosectioning, probe hybridization, subsequent ligation and amplification, quenching and nuclei staining, and in situ imaging. Each phase of the workflow is extensively detailed within published 10X User Guides, which are cited throughout this version. After data is collected, slides are H&E stained for histopathological evaluation of healthy and diseased tissue, and used for integrated data registration to neighboring images and modalities. There are sample evaluation and QC measures built into the 10X protocols, which are carefully observed in addition to our own go/no-go criteria.

Tissue atlases provide foundational knowledge on the cellular organization and molecular distributions across molecular classes and spatial scales. Here, we construct a comprehensive spatiomolecular lipid atlas of the human kidney from 29 donor tissues using integrated multimodal molecular imaging. Our approach leverages high-spatial-resolution matrix-assisted laser desorption/ionization imaging mass spectrometry for untargeted lipid mapping, stained microscopy for histopathological assessment, and tissue segmentation using autofluorescence microscopy. With a combination of unsupervised, supervised, and interpretable machine learning, the atlas provides multivariate lipid profiles of specific multicellular functional tissue units (FTUs) of the nephron, including the glomerulus, proximal tubules, thick ascending limb, distal tubules, and collecting ducts. In total, the atlas consists of tens of thousands of FTUs and millions of mass spectrometry measurements. Detailed patient, clinical, and histopathologic information allowed molecular data to be mined on the basis of these features. As examples, we highlight the discovery of how lipid profiles are altered with sex and differences in body mass index.

Scope: This protocol describes the use of an in-house developed sublimation device to deposit small molecule matrices onto tissue sections for high resolution MALDI imaging mass spectrometry (IMS). Concentrations will vary based on matrix type.

This is a protocol for preparing fresh-frozen (LN2) and carboxymethylcellulose (CMC)-embedded kidney tissue for CODEX spatial proteomics (via PhenoCycler), following MALDI IMS data collection. Methods have been adapted from PhenoCycler User Guides. After CODEX data are collected, slides are PAS-stained for histopathological evaluation of healthy and diseased tissue. Data are used for spatial data registration to neighboring images and modalities.