Spatial Biology
The Spatial Biology Lab at the Sahlgrenska academy/GU is a collaborative research hub focused on spatially resolved molecular profiling of tissues. This effort brings together key expertise in pathology, histology, microscopy, mass spectrometry based biochemial imaging, in situ and ex situ next generation sequencing and data anlysis for integrated spatial biology experiments.
We integrate comprehensive competence within spatial biology, from optimized sample collection, tissue preparation, histological annotation, multiomic data acquistion and data analysis. The lab provides key competence for integrating advanced spatial technologies covering all steps in a spatial biology workflow. This is key for detailed exploration of tissue architecture and molecular distributions to advance scientific discovery across diverse biomedical fields.
Spatial Biology Technologies
We established advanced technologies for spatial biology and in situ molecular analysis. This coveres whole genome wide spatial trascriptomics along with spatial proteomics using the GeoMx platform. Further, our lab has key expertise in spatial lipid and metabolite analysis using chemical imaging, specifiaclly mass spectrometry imaging (MSI). Here, we have to platforms including a Bruker Rapiflex TOF/TOF and a Waters Synapt ion mobility TOF equipped with a DESI source. We furher integrate those spatial omics tools with established techniques such as fluorescent and brightfield microscopy for immunohistochemistry, RNAscope.
Histology – Molecular Pathology
Microscopy and histopathology offer critical visualization tools for examining tissue and cellular structures. Utilizing brightfield and fluorescence imaging, these techniques enable detailed observation of morphological and molecular features, supporting research across various biological disciplines. Our histology core provides expertise in tissue fixation, embedding, microtome sectioning, and a wide range of staining protocols, enabling reliable preservation and visualization of tissue morphology and molecular targets. These processes are optimized to maintain tissue integrity and compatibility with downstream omics technologies.
Spatial Transcriptomics and Proteomics
Spatial transcriptomics connects tissue morphology with multiplexed RNA expression data, preserving spatial architecture. This technology allows detailed molecular profiling within defined tissue regions, providing insights into cellular heterogeneity and tissue microenvironments.
Spatial Metabolomics – Mass Spectrometry Imaging
Mass spectrometry imaging (MSI) delivers label-free spatial maps detailing the chemical composition of tissue sections, covering biomolecules such as lipids, metabolites, peptides, and proteins. Complementary MSI platforms are optimized for diverse molecular classes and spatial resolutions, offering in-depth molecular insights into tissue heterogeneity and physiological status.
Data Analysis
Advanced data analysis plays a vital role in spatially-resolved molecular research, enabling meaningful interpretation of complex imaging mass spectrometry datasets. Our approach integrates established software, innovative correlative modelling techniques, and versatile open-source tools to provide robust and adaptable analysis tailored to the specific needs of spatial lipidomics, biomarker discovery, and metabolic studies.
Software Platforms
We utilize commercial tools such as SCiLS Lab for efficient visualization, tissue segmentation, and statistical analysis of mass spectrometry imaging data. These established tools ensure reproducible exploration of lipid and metabolite distributions across complex tissue samples.
Correlative Imaging and OnPLS Modeling
A core feature of our analysis pipeline is a correlative chemical imaging strategy using OnPLS (Orthogonal Projections to Latent Structures) modelling (Ref). This novel multiblock approach enables the alignment and joint analysis of MSI and hyperspectral microscopy datasets, uncovering spatial relationships between molecular features with high precision. By integrating spatial chemometrics, OnPLS modelling provides enhanced sensitivity and interpretability, supporting the discovery of subtle biochemical patterns.
Open-Source Pipelines
For isotope-labelled lipidomics, we offer custom open-source scripts that quantify isotopic enrichment at the pixel level within MSI datasets (Github). These tools are indispensable for metabolic tracing studies and deliver analytical flexibility beyond conventional platforms.
Groups
Jörg Hanrieder
Professor, Department of Neuroscience and Physiology, University of Gothenburg
The Hanrieder Group develops advanced mass spectrometry–based molecular imaging techniques to investigate chemical changes in brain tissue linked to Alzheimer’s disease. Their research focuses on the spatial and temporal dynamics of beta-amyloid protein deposits and their interactions with lipid metabolism. By studying these molecular interactions, the group aims to uncover mechanisms of neurodegeneration and identify potential drug targets and biomarkers for neurodegenerative diseases.
Contact
Martin Johansson
Professor and Senior Physician, Department of Biomedicine, University of Gothenburg; Group Leader at Sahlgrenska Center for Cancer Research
Martin Johansson’s research focuses on the pathology and metabolism of renal cell carcinoma, with particular attention to clear cell and papillary renal cell subtypes. His group investigates tumor biology, metabolic alterations, including pseudohypoxia, and renal regeneration mechanisms. The goal is to elucidate tumor heterogeneity and identify new therapeutic targets to improve treatment strategies for kidney cancer.
Anders Ståhlberg
Professor, Department of Laboratory Medicine, University of Gothenburg
Anders Ståhlberg’s group applies single-cell and ultrasensitive molecular approaches, including liquid biopsies and advanced spatial analyses, to investigate sarcomas and other cancers. Their work utilizes 3D culture systems and spatial genomics to reveal tumor heterogeneity, microenvironmental influences, and molecular disease mechanisms, supporting early diagnosis and tailored patient monitoring. This integrative spatial biology research enables the identification of clinically relevant biomarkers and targets for personalized cancer therapy.
