Biology subjects

Stahl, P. L.

Publications and source records attributed to Stahl, P. L..

6 recordsLinked to original sources

bioRxiv2025

Spatially resolved integrative analysis of transcriptomic and metabolomic changes in tissue injury studies

Recent developments in spatially resolved -omics have enabled studies linking gene expression and metabolite levels to tissue morphology, offering new insights into biological pathways. By capturing multiple modalities on matched tissue sections, one can better probe how different biological entities interact in a spatially coordinated manner. However, such cross-modality integration presents experimental and computational challenges. To align multimodal datasets into a shared coordinate system and facilitate enhanced integration and analysis, we propose MAGPIE (Multi-modal Alignment of Genes and Peaks for Integrated Exploration), a framework for co-registering spatially resolved transcriptomics, metabolomics, and tissue morphology from the same or consecutive sections. We illustrate the generalisability and scalability of MAGPIE on spatial multi-omics data from multiple tissues, combining Visium with both MALDI and DESI mass spectrometry imaging. MAGPIE was also applied to newly generated multimodal datasets created using specialised experimental sampling strategy to characterise the metabolic and transcriptomic landscape in an in vivo model of drug-induced pulmonary fibrosis, to showcase the linking of small-molecule co-detection with endogenous responses in lung tissue. MAGPIE highlights the refined resolution and increased interpretability of spatial multimodal analyses in studying tissue injury, particularly in pharmacological contexts, and offers a modular, accessible computational workflow for data integration.

bioinformatics↗

bioRxiv2024

Hidden network preserved in Slide-tags data allows reference-free spatial reconstruction

Spatial transcriptomics technologies aim to spatially map gene expression in tissues and typically use oligonucleotide array surfaces that have undergone spatial indexing. These arrays are used to capture nucleic acids diffusing from adjacently placed tissues, allowing subsequent sequencing to reveal both gene and position. Slide-tags is a recently developed method by Russell et al. that inverts this principle. Instead of capturing molecules released from the tissue, probes are detached from a pre-decoded bead array and diffused into tissues, tagging nuclei with spatial barcodes. We reanalyzed this data and discovered a latent, spatially informative cell-bead network formed incidentally from barcode diffusion and the biophysical properties of the tissue. This allows us to treat Slide-tags as a new network-based imaging-by-sequencing approach. By optimizing spatial constraints encoded in the cell-bead network structure, we could achieve unassisted tissue reconstruction, a fundamental shift from classical spatial technologies based on pre-indexed arrays.

bioinformatics↗

bioRxiv2023

Translational mapping of spatially resolved transcriptomes in human and mouse pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis and limited treatment options. Efforts to identify effective treatments are thwarted by limited understanding of IPF pathogenesis and poor translatability of available preclinical models. To address these limitations, we generated spatially resolved transcriptome maps of human IPF and bleomycin-induced mouse lung fibrosis. We uncovered distinct fibrotic niches in the IPF lung, characterized by aberrant alveolar epithelial cells in a microenvironment dominated by TGF{beta} signaling alongside factors such as p53 and ApoE. We also identified a clear divergence between the arrested alveolar regeneration in the IPF fibrotic niches, and the active tissue repair in the acutely fibrotic mouse lung. Our study offers in-depth insights into the IPF transcriptional landscape and proposes alveolar regeneration as a promising therapeutic strategy for IPF.

molecular biology↗

bioRxiv2022

Chromatin accessibility profiling in tissue sections by spatial ATAC

Current methods for epigenomic profiling are limited in the ability to obtain genome wide information with spatial resolution. Here we introduce spatial ATAC, a method that integrates transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture to perform spatially resolved epigenomics. We show that spatial ATAC enables the discovery of the regulatory programs underlying spatial gene expression during mouse organogenesis, lineage differentiation and in human pathological samples.

genomics↗

bioRxiv2020

Three-dimensional spatial transcriptomics uncovers cell type dynamics in the rheumatoid arthritis synovium

The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics to study local cellular interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-seq data coupled to quantitative and cell type-specific chemokine-driven dynamics at and around organized structures of infiltrating leukocyte cells in the synovium.

genomics↗

bioRxiv2018

Conbase: a software for discovery of clonal somatic mutations in single cells through read phasing

Here we report the development of Conbase, a software application for the identification of somatic mutations in single cell DNA sequencing data with high rates of allelic dropout and at low read depth. Conbase leverages data from multiple samples in a dataset and utilizes read phasing to call somatic single nucleotide variants and to accurately predict genotypes in whole genome amplified single cells in somatic variant loci. We demonstrate the accuracy of Conbase on simulated datasets, in vitro expanded fibroblasts and clonally in vivo expanded lymphocyte populations isolated directly from a healthy human donor.

bioinformatics↗