Biology subjects

Loth, M.

Publications and source records attributed to Loth, M..

6 recordsLinked to original sources

bioRxiv2026

Enabling subcellular DESI-MSI for broad adoption: Acquisition, analysis, and application

Subcellular mass spectrometry imaging (MSI) has required specialized instrumentation and approaches. Additionally, data analysis has required expensive proprietary software, or code-based open-source tools. These barriers render spatial metabolomics less accessible than peer spatial omics techniques (transcriptomics, proteomics). Here we demonstrate subcellular MSI on standard commercial hardware, analyzed using code-free, open-source software. An inexpensive, fully reversible modification of a stock DESI source takes the platform past its typical 5-10 m pixels to routine 2x2 m pixels, and 1x1 m in proof-of-principle experiments. At a tissue-glass boundary imaged at 2x2 m, tissue-specific ions showed no measurable spillover onto the adjacent glass within the {+/-}4.4 m uncertainty of the edge position. To make these data interpretable, we extend MSI.EAGLE, an open-source, vendor-agnostic application with two new methods: i) Spatial UMAP preserves tissue architecture alongside subcellular detail, where conventional clustering breaks apart at high resolution; ii) Histology Fit performs landmark-free co-registration, and recovers synthetic misalignments of up to 8 m to within one pixel of a reference registration. Together, these enable previously high-burden spatial metabolomics experiments. In mouse brain tissue, we demonstrate genuine subcellular signal by resolving mitochondrial cardiolipin into perinuclear puncta, quantifying cytoplasmic enrichment across 6,073 cells. Cell identities are assigned to MSI pixels using histology-predicted transcriptomics, linking cell provenance to metabolism in 2D space directly. Finally, we recover cell-type-specific metabolomes consistent with the transcriptomic identities. This workflow allows cellular and subcellular spatial metabolomics linked to cell provenance, with only stock MSI hardware, open-source GUI-based software, and common histology techniques, putting it within reach of many researchers and laboratories.

molecular biology↗

bioRxiv2026

Toward Computationally Complete Spatial Omics

Multimodal spatial omics has transformed biology by mapping molecular complexity within intact tissues, yet current technologies remain limited in the number of modalities measured simultaneously and often produce lower-quality data than single-modality assays. We present COSIE, a computational framework that generates high-resolution, multilayered molecular landscapes across tissue sections, individuals, and platforms. COSIE integrates histology, epigenome, transcriptome, proteome, and metabolome into a unified representation. Applied to 12 datasets spanning 10 spatial technologies, eight modalities, and nine tissue types, ranging from thousands of spots to millions of cells, COSIE outperforms existing methods. It resolves tissue structures, enhances noisy measurements, predicts unmeasured modalities, and captures dynamic processes. In human tumors, COSIE identifies invasive subregions linked to clinical outcomes and predicts spatial gene expression in TCGA samples using only histology images. By transforming fragmented data into comprehensive spatial maps, COSIE advances computationally complete spatial omics and the creation of digital tissue twins for biomedicine.

genomics↗

bioRxiv2025

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while retaining the crucial spatial context within tissues. However, existing ST platforms suffer from high costs, long turnaround times, low resolution, limited gene coverage, and small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that predicts super-resolution gene expression and automatically annotates cellular-level tissue architecture for large-sized tissues that exceed the capture areas of standard ST platforms. The accuracy of iSCALE were validated by comprehensive evaluations, involving benchmarking experiments, immunohistochemistry staining, and manual annotation by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion associated cellular characteristics that were undetectable by conventional ST experiments. Our results demonstrate iSCALEs utility in analyzing large-sized tissues with automatic and unbiased tissue annotation, inferring cell type composition, and pinpointing regions of interest for features not discernible through human visual assessment.

genomics↗

bioRxiv2022

Inflammatory Signaling and Fibroblast-Cancer Cell Interactions Transfer from a Harmonized Human Single-cell RNA Sequencing Atlas of Pancreatic Ductal Adenocarcinoma to Organoid Co-Culture

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a heterogeneous tumor microenvironment (TME) that is enriched with cancer associated fibroblasts (CAFs)1. Cell-cell interactions involving these CAFs promote an immunosuppressive phenotype with altered inflammatory gene expression. While single-cell transcriptomics provides a tool to dissect the complex intercellular pathways that regulate cancer-associated inflammation in human tumors, complementary experimental systems for mechanistic validation remain limited. This study integrated single-cell data from human tumors and novel organoid co-cultures to study the PDAC TME. We derived a comprehensive atlas of PDAC gene expression from six published human single-cell RNA sequencing (scRNA-seq) datasets2-7 to characterize intercellular signaling pathways between epithelial tumor cells and CAFs that regulate the inflammatory TME. Analysis of the epithelial cell compartment identified global gene expression pathways that modulate inflammatory signaling and are correlated with CAF composition. We then generated patient-derived organoid-CAF co-cultures to serve as a biological model of the cellular interactions learned from human tissue in the atlas. Transfer learning analysis to additional scRNA-seq data of this co-culture system and mechanistic experiments confirmed the epithelial response to fibroblast signaling. This bidirectional approach of complementary computational and in vitro applications provides a framework for future studies identifying important mechanisms of intercellular interactions in PDAC.

cancer biology↗

bioRxiv2022

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. Still, inferring biological processes requires additional post hoc statistics and annotation for interpretation of features learned from software packages developed for NMF implementation. Here, we aim to introduce a suite of computational tools that implement NMF and provide methods for accurate, clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations, and open questions in the field is followed by three vignettes for the Bayesian NMF algorithm CoGAPS (Coordinated Gene Activity across Pattern Subsets). Each vignette will demonstrate NMF analysis to quantify cell state transitions in public domain single-cell RNA-sequencing (scRNA-seq) data of malignant epithelial cells in 25 pancreatic ductal adenocarcinoma (PDAC) tumors and 11 control samples. The first uses PyCoGAPS, our new Python interface for CoGAPS that we developed to enhance runtime of Bayesian NMF for large datasets. The second vignette steps through the same analysis using our R CoGAPS interface, and the third introduces two new cloud-based, plug-and-play options for running CoGAPS using GenePattern Notebook and Docker. By providing Python support, cloud-based computing options, and relevant example workflows, we facilitate user-friendly interpretation and implementation of NMF for single-cell analyses.

systems biology↗

bioRxiv2022

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Recent advances in spatial transcriptomics (ST) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor and its microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as occurs in tumor-immune interactions. We present SpaceMarkers, a novel bioinformatics algorithm to infer molecular changes from cell-cell interaction from latent space analysis of ST data. We apply this approach to infer molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

bioinformatics↗