Biology subjects

Migas, L. G.

Publications and source records attributed to Migas, L. G..

14 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↗

bioRxiv2026

Multimodal Molecular Mapping of the Vasculature in Human Cortex Reveals Lipid Markers of Cerebral Amyloid Angiopathy

Cerebral amyloid angiopathy (CAA) commonly co-occurs with Alzheimers disease (AD), yet the molecular changes that accompany vascular {beta}-amyloid deposition in human tissue remain incompletely defined. Herein, we use a novel imaging approach that combines matrix-assisted laser desorption/ionization imaging mass spectrometry (IMS) with immunofluorescence microscopy on the same sections of postmortem human frontal cortex to map the lipid microenvironment of leptomeningeal vasculature in cases with and without CAA. Autofluorescence-guided regions-of-interest were imaged by IMS in both negative and positive ion modes and registered to post-IMS-acquired microscopy images. Immunofluorescence microscopy using collagen IV, -smooth muscle actin (SMA), and thiazine red enabled automated segmentation of total, amyloidpositive, and amyloid-negative vasculature regions. A CAA index, the ratio of amyloid-positive area to total vasculature area in a region imaged by IMS, was used to define vasculature and classify each case into having CAA, or CAA-present and not having CAA, or CAA-absent. An interpretable machine learning approach (XGBoost models with Shapley additive explanations for interpretation) was trained on pixel-level spectra and identified lipid signatures of vascular identity shared across groups as well as class-specific marker candidates that distinguished CAA-present from CAA-absent vasculature. CAA-present vessels were enriched for gangliosides (e.g., GM1), whereas CAA-absent vessels were characterized by higher contributions from phosphatidylserines (e.g., long-chain polyunsaturated PS species). Univariate differences were inconsistent between the two groups, but multivariate models in negative mode yielded stable discriminatory features. These results define spatial lipid correlates of vascular amyloid pathology in the human brain and establish a multimodal framework for mechanistically linking lipid metabolism, vascular integrity, and CAA in AD.

neuroscience↗

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

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↗

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

Integrative Spatial Omics for Systems-Level Mapping of Pathological Niches

Spatial omics technologies are a powerful tool for mapping the relationship between cellular organization and molecular distributions in healthy and diseased tissue microenvironments. Here, we describe a novel multimodal pipeline that represents experimental and computational advances for spatiomolecular analysis of tissue samples across molecular classes. This adaptable method integrates matrix-assisted laser desorption/ionization imaging mass spectrometry spatial lipidomics, spatial transcriptomics, protein imaging via multiplexed immunofluorescence microscopy, and histopathological staining to uncover spatiomolecular profiles associated with unique cellular niches and pathological features. We demonstrate the power of this approach using two different complex human disease systems: Alzheimers disease in human brain tissue and type 2 diabetes mellitus in the human pancreas. This work establishes and demonstrates a generalizable framework for multimodal spatial integration, enabling precise mapping of molecular mechanisms that underlie complex tissue pathologies.

systems biology↗

bioRxiv2025

A Multimodal Imaging Pipeline for the Discovery of Molecular Markers of Cellular Neighborhoods

Insights into tissue microenvironments are constrained by profiling cellular and molecular components independently. To address this, we developed an integrated, multimodal workflow combining imaging mass spectrometry and highly multiplexed immunofluorescence on the same sample. Custom computational pipelines enable precise spatial co-registration, facilitating direct integration of cellular and molecular profiles. CODEX-derived cellular and neighborhood masks enable mining of IMS-derived lipid data to identify molecular markers of cell types and spatial neighborhoods in human kidney tissue. We discovered distinct carnitine distributions in cortical and medullary regions, indicating metabolic heterogeneity driven by microenvironment-specific oxygen levels and energy demands. Glomerular cell-type-specific analysis found ether-linked phosphatidylcholine and sphingomyelin, species associated with slit diaphragm integrity, localized to podocytes. Immune cell neighborhoods exhibited molecular signatures consistent with cell signaling and activation pathways in damaged tissue regions. This multi-omic framework links cellular organization with molecular signatures to unveil cellular and molecular relationships underlying tissue function and pathology.

molecular biology↗

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↗

bioRxiv2024

msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis

Multimodal imaging by matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI MSI) and immunofluorescence microscopy holds great potential for understanding pathological mechanisms by mapping molecular signatures from the tissue microenvironment to specific cell populations. However, existing open-source software solutions for analysis of MALDI MSI data are incomplete, require programming skills and contain laborious manual steps, hindering broadly applicable, reproducible, and high-throughput analysis to generate impactful biological discoveries across interdisciplinary research fields. Here we present msiFlow, an accessible open-source, platform-independent and vendor-neutral software for end-to-end, high-throughput, transparent and reproducible analysis of multimodal imaging data. msiFlow integrates all necessary steps from import and pre-processing of raw MALDI MSI data to visual analysis output, as well as registration, along with state-of-the-art and newly developed algorithms, into automated workflows. Using msiFlow, we unravel the molecular heterogeneity of leukocytes in infected tissues by spatial regulation of ether-linked phospholipids containing arachidonic acid. We anticipate that msiFlow will facilitate the broad applicability of MSI in the emerging field of multimodal imaging to uncover context-dependent cellular regulations in disease states.

bioinformatics↗

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↗

bioRxiv2022

A multiscale atlas of the molecular and cellular architecture of the human kidney

Tissue atlases provide foundational knowledge on the cellular organization and molecular distributions across molecular classes and spatial scales. Here, we construct a comprehensive spatio-molecular 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 (MALDI) imaging mass spectrometry (IMS) for untargeted lipid mapping, stained microscopy for histopathological assessment, and tissue segmentation using autofluorescence microscopy. With a combination of unsupervised, supervised, and interpretive 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 based on these features. As examples, we highlight the discovery of how lipid profiles are altered with sex and differences in body mass index.

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↗

bioRxiv2017

A Careful Consideration of the Influence of Structure, Partial charges and Basis Sets on Collision Cross Sections of Monosaccharides when Comparing Values from DFT Calculated Conformers to those Obtained Experimentally

Molecular modelling is routinely employed to assign 3D structures to collision cross sections (CCSs) derived from ion mobility mass spectrometry experiments (IM-MS). The assignment of model structures to the experimental CCSs remains an ambiguous task, where one of several methods may be used to obtain a CCS from a given set of coordinates. The most reliable of the commonly used techniques, the Trajectory Method, starts with atomic coordinates which can be accompanied by partial atomic charges, obtained using ab initio methods. Here, we use lithiated - and {beta}-glucose ions as exemplar molecules to detect the effect conformational modification and changes to the partial charge distribution have on computed collision cross sections. Six popular charge schemes (Mulliken, APT, CHelpG, MK, HLY and NPA) were examined in combination with three functionals (Hartree-Fock, B3LYP and M05) and five basis sets (STO-3G, 3-21G, 6-31G, 6-31+G and 6-31G*) on twenty unique structures. Our findings indicate that molecular conformation makes a significant contribution to fluctuations of partial charges in Electrostatic Potential (ESP) and Mulliken charge scheme; Partial charges derived using Natural Population Analysis (NPA) and ESP methods are largely independent of functional and basis set selection; and both selection of the charge scheme and functional/basis set combination play a large role in the resultant CCS, often causing few percent fluctuations in the computed values.

biochemistry↗

bioRxiv2017

ORIGAMI: A Software Suite for Activated Ion Mobility Mass Spectrometry (aIM-MS) Applied To Multimeric Protein Assemblies

We present here a software suite (ORIGAMI) that facilitates the rapid acquisition and analysis of ion mobility data following collisional activation. ORIGAMI was developed for use on Waters Synapt instruments where data acquisition is achieved by interfacing WREnS (Waters Research Enabled Software) and MassLynx. Two components are presented, the first is ORIGAMIMS which enables activation of ions by sequential increase of collision voltages prior to ion mobility analysis. We demonstrate the use of ORIGAMI on the tetrameric assemblies formed by the proteins concanavalin A (103 kDa) and alcohol dehydrogenase (143 kDa). Activation is performed in the trap collision cell of the Synapt TriWave assembly, where the collision voltage can be ramped from 0-200 V. All of the acquired data is recorded in a single file which simplifies data acquisition. This substantially decreases the time needed to perform a typical activated IM-MS experiment on a single protein charge state from approx. 2 hours to ~25 minutes. Following data acquisition the data is analysed in the second component, ORIGAMIANALYSE, which allows the user to visualise the effect of activation on the mobility of the parent ion, as well as on any produced fragment ion. The user can export the data in the form of heat maps, waterfall or wire plots. In addition, tools implemented in ORIGAMI enable easy data extraction from single or multiple MassLynx .raw files, in-depth interrogation of large datasets, statistical analysis and figure creation capabilities. We demonstrate the use of ORIGAMI on concanavalin A and alcohol dehydrogenase acquired using the traditional protocols and the ORIGAMIMS method.\n\nHighlights- New methodology for faster acquisition and data analysis following activation of ions separated by ion mobility mass spectrometry\n- Software package capable of simultaneous analysis of multiple MassLynx .raw files\n- Visualization of the change in mobility of parent and fragment ions following activation\n- Easy extraction, data processing and extensive plotting tools

bioinformatics↗