Biology subjects

Djambazova, K. V.

Publications and source records attributed to Djambazova, K. V..

6 recordsLinked to original sources

bioRxiv2026

IsoMobil: Resolving Molecular Ambiguity in Mass Spectrometry-based Spatial Omics Through Ion Mobility

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{approx}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.

bioinformatics↗

bioRxiv2025

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

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

bioinformatics↗

bioRxiv2025

Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis

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.

bioinformatics↗

bioRxiv2025

Cellular and Spatial Drivers of Unresolved Injury and Functional Decline in the Human Kidney

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.

molecular biology↗

bioRxiv2024

Revealing In Situ Molecular Profiles of Glomerular Cell Types and Substructures with Integrated Imaging Mass Spectrometry and Multiplexed Immunofluorescence Microscopy

Glomeruli filter blood through the coordination of podocytes, mesangial cells, fenestrated endothelial cells, and the glomerular basement membrane. Cellular changes, such as podocyte loss, are associated with pathologies like diabetic kidney disease (DKD). However, little is known regarding the in situ molecular profiles of specific cell types and how these profiles change with disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is well-suited for untargeted tissue mapping of a wide range of molecular classes. Additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Herein, we demonstrate an integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 {micro}m pixel size) was used to determine lipid distributions within human glomeruli, revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types through new methods that enable MxIF microscopy to be performed on the same tissue section following MALDI IMS without sacrificing signal quality from either modality. A combination of machine-learning approaches was assembled, enabling cell-type segmentation and identification based on MxIF data followed by the mining of cell type or cluster-associated MALDI IMS signatures using classification models and interpretable machine learning. This allowed the automated discovery of spatially specific biomarker candidates for glomerular substructures and cell types. Overall, the work presented here establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments and provides a framework that applies to other kidney tissue features and organ systems.

biochemistry↗

bioRxiv2020

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species biomarker potential, our workflow delivers spatially localized explanations of the classification models decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies. HighlightsO_LIOur workflow automates the discovery of biomarker candidates in imaging mass spectrometry data by using state-of-the-art machine learning methodology to produce a shortlist of molecular species that are differentially expressed with regards to a user-provided biological class. C_LIO_LIA model interpretability method called Shapley additive explanations (SHAP), with observational Shapley values, enables us to quantify the local and global predictive importance of molecular species with respect to recognizing a user-provided biological class. C_LIO_LIBy providing spatially localized explanations for a classification models decision-making process, SHAP maps deliver insight into the spatial specificity of biomarker candidates and enable one to determine whether (and where) the relationship between a biomarker candidate and the class of interest is correlative or anticorrelative. C_LI

bioinformatics↗