New Dimensions in Spatial Biology
Chairs: Sinem Saka and Harsharan Singh Bhatia
Friday, 13.12.2024, 11:00 am
Invited talk by Harsharan Singh Bhatia
Helmholtz Munich and Ludwig Maximilian University (LMU), Munich, Germany
Clear-Omics: Spatial molecular maps in optically cleared 3D intact specimens
Spatial molecular profiling of complex tissues is crucial for understanding cellular function in both physiological and pathological contexts. To achieve this, optical tissue clearing and 3D imaging provide an unbiased view of deep tissues within intact organs and organisms. However, while these techniques offer detailed morphological insights, they fall short in addressing deeper mechanistic questions. To bridge this gap, our lab has been developing novel tools that integrate whole-organ/organism optical clearing and imaging, deep learning-based image analysis, robotic tissue extraction, and ultrahigh-sensitive mass spectrometry-based proteomics and transcriptomics. This presentation will delve into the methodology and the recent biological applications of these groundbreaking technologies, exploring their potential to investigate a range of pathologies such as acute brain injuries, infections, cancer, and metabolic dysfunction.
Biosketch
Dr. Harsharan Singh Bhatia earned his Ph.D. from the Albert-Ludwig University of Freiburg, Germany, and completed postdoctoral training at University of California, Los Angeles (UCLA), USA and Ludwig Maximilian University (LMU), Germany. Currently, he leads the Spatial-Omics Team at Helmholtz Munich, where he develops cutting-edge 3D spatial-omics technologies. His pioneering research combines whole-organ/organism optical tissue clearing, light-sheet microscopy, robotics, and laser-aided tissue microdissection from intact pre-clinical and clinical specimens. Dr. Bhatia’s focus lies in acquiring mass spectrometry-based spatial proteomic and transcriptomic data from perturbed cells in the early stages of diseases, particularly acute brain injuries, cancer, and neurodegenerative diseases. His innovative work in spatial proteomics has been highlighted in Nature Biotechnology and served as a cover story in Cell.
Short talk by Peter Androvic
Department of Old Age Psychiatry and Cognitive Disorders, University Hospital Bonn, Bonn, Germany
Institute for stroke and dementia research, LMU University Hospital, Munich, Germany
Beyond Genes: Integrating Transcriptomics with Structure and Metabolism to Map Neurodegeneration
Deciphering the functional implications of cell state change in complex diseases requires integrating multiple views of cellular phenotype within native tissue context. We present spatially-resolved, multi-modal approaches to characterize cellular responses to brain aging and neurodegeneration, combining spatial transcriptomics, structural analysis, and spatial lipidomics.
Our Spatial Transcriptomics-correlated Electron Microscopy (STcEM) method links spatial gene expression of single cells with their ultrastructural morphology by integrating MERFISH and large area electron microscopy on adjacent tissue sections. Applying STcEM to a mouse model of demyelinating brain injury, we characterized and linked transcriptional and structural states of microglia and infiltrating T-cells in situ. By further integrating these datasets with single-cell RNA-Seq we uncovered correlations between genome-wide gene expression and ultrastructural features of microglia. We identified distinct microglia state within remyelinating lesions, characterized by aberrant inflammatory signature and accumulation of lipid droplets. To further probe functional implications, we have expanded our approach to include spatial lipidomics via mass spectrometry imaging (MSI). Our preliminary data reveal distinct lipid distributions associated with vulnerable brain regions and pathological microenvironments. By integrating MSI with MERFISH, we are mapping gene-metabolic networks and tracking their alterations in the aging brain and during amyloidosis progression.
Our framework provides an integrated, multi-scale view of the spatial, structural, transcriptional, and metabolic reorganization of the brain in response to pathology. By applying machine learning to these rich datasets, we next aim to develop predictive models of cellular behavior and disease progression, providing critical new insights into the mechanisms of brain aging and neurodegeneration.
Short talk by Jorge Trojanowski
European Molecular Biology Laboratory (EMBL), Tissue biology and disease modeling Unit, Barcelona, Spain
European Molecular Biology Laboratory (EMBL), Genome biology Unit, Heidelberg, Germany
Single-round Profiling by Amplification and Color Encoding (SPACE)-FISH in 3D microvasculature on-chip
Self-assembly of endothelial and supporting cells into a perfusable vessel network establishes the conduit for blood supply of tissues during development, and restores it during wound healing. Vessel formation involves multiple cell types and cell states that need to be coordinated spatially. So far, this process could only be studied in dissociated cells or for a small number of selected target genes in situ, but a direct mapping of transcriptional states to the tissue positions is missing. To understand the spatio-temporal regulation of the cellular states during vessel formation, we employ multiplexed fluorescence in situ hybridization (FISH) to detect gene transcription in a complex 3D in vitro vascularized tissue. Performing FISH in 3D tissues with cycling-based multiplexing approaches is challenging due to long staining times, which limits efficient multiplexing. To resolve these challenges, we developed Single-round Profiling by Amplification and Color Encoding (SPACE)-FISH which is based on multi-color labeling of the nascent RNA at 18 target gene loci with a single round of staining and imaging. The combinatorial color barcodes extend the multiplexing capacity compared to traditional FISH methods, hence SPACE-FISH circumvents prohibitively long staining and imaging times making it a viable approach for scalable high-plex 3D tissue imaging. Applying this method to microvasculature from human-derived primary cells grown on-chip over different time points, we are able to visualize a spatiotemporal expression map for relevant genes involved in the self-assembly process. In particular, we investigated the expression profiles of genes related to angiogenesis, cell type and cell cycle. This approach will be valuable for applications that require the scalable and inexpensive detection of tens of nascent transcripts in 3D tissues ranging from cell type detection to studies of transcriptional mechanisms such as mono- vs bi-allelic expression.
