Spatial Omics: Enabling New Insights into Cell Architecture and Function

Spatial omics techniques allow researchers to map biomolecules within intact tissue samples at high resolution, providing information about the spatial organization and localization of biomolecules. Various techniques have been developed that enable spatially resolving the presence and quantities of different types of biomolecules like RNA, proteins or metabolites within intact tissues. These techniques overcome limitations of traditional bulk analysis methods that average signals from whole tissues, cells or regions and lose spatial information.

Spatial Transcriptomics

One of the earliest and widely used spatial omics techniques is spatial transcriptomics. This technique enables researchers to map the distribution and localization of mRNA transcripts within intact tissue samples. In spatial transcriptomics, tissue sections are placed on slides coated with an array of oligonucleotide probes with spatial barcodes. Cellular RNAs from the tissue sample are then hybridized to the spatial barcodes. The cellular location of each RNA molecule can be determined based on the spatial barcode it hybridizes to. After sequencing, the Spatial Omics expression profiles of thousands of genes can be reconstructed. This provides insights into regions with distinct gene expression patterns within tissues and cell type-specific gene expression signatures.

Multiple companies now offer commercial solutions for spatial transcriptomics. The technique has been applied to profile a wide variety of human and animal tissues like brain, lungs, kidney, liver and more. It has enhanced understanding of cell type heterogeneity, disease pathology and altered gene expression patterns associated with various diseases. The technique also enables discovery of novel cell types or subpopulations based on distinct gene expression profiles. Researchers are now coupling spatial transcriptomics with other omic layers like proteomics or epigenomics to generate multi-omics spatial maps of tissues.

Spatial Proteomics

Spatial proteomics enables mapping of protein expression and localization within tissues. Major techniques in this category include mass spectrometry imaging (MSI) and multiplexed ion beam imaging (MIBI). In MSI, tissue sections are analyzed using mass spectrometers to detect protein and peptide signatures directly from the tissue based on their mass-to-charge ratio without requiring antibodies or probes. The detection signals are localized to generate spatially resolved protein maps. MIBI utilizes secondary ion mass spectrometry to detect protein-tagged antibodies within intact tissue sections with subcellular resolution. Both MSI and MIBI enable mapping up to hundreds of proteins simultaneously from intact tissues at subcellular resolution.

Spatial proteomics has provided novel insights into protein localization patterns in the brain, implications of altered proteome organization in cancer and more. However, the techniques still have limited throughput and resolution compared to transcriptomics. Efforts are ongoing to develop high-throughput imaging mass cytometry approaches amenable to studying clinical samples and animal models at higher resolution and proteomic depth. Coupling spatial proteomics data with transcriptomics or other modalities also holds promise for generating multi-omic spatial maps of proteins, RNA and other biomolecules within tissues.

Spatial Metabolomics

Spatial metabolomics enables mapping small molecule metabolites within intact tissues. Major techniques in this category include metabolite imaging mass spectrometry (MIMS), fluorescence imaging and Raman imaging. In MIMS, mass spectrometry is used to detect spatial distribution of metabolites directly from tissue sections. While initial applications focused on localization of lipids, ongoing developments aim to increase detection of polar metabolites. Fluorescence imaging utilizes metabolite-specific fluorescent tags and probes for mapping distribution of targeted classes of metabolites like antioxidants and reactive species within tissues. Raman imaging detects intrinsic vibrational fingerprints of metabolites to build spatially resolved chemical maps of tissues.

Early applications of spatial metabolomics included profiling altered metabolite signatures in various cancers and localized changes associated with ischemia injury. Current efforts aim to characterize spatial metabolome organization within normal and diseased brains, analyze metabolic microenvironments in tumors and understand implications of subcellular metabolite compartmentalization. Integration of spatial metabolomics with other omics modalities may help establish relationships between metabolite gradients, proteomes and transcriptomes to generate multi-layer spatial maps elucidating functional organization within healthy and diseased tissues.

Spatial omics techniques have transformed our understanding of complex biomolecular architectures within tissues by enabling high-resolution spatial mapping of biomolecules. Integration of multiple spatial omics modalities holds promise for generating multi-dimensional spatial maps elucidating complex interplay between genomes, transcriptomes, proteomes and metabolomes orchestrating tissue architecture and function. Ongoing advancement of techniques, analytical approaches and integration with other data types is likely to uncover novel biology and insights into disease mechanisms with spatial context in the future. Spatial omics also represents a powerful approach towards developing spatially resolved maps of human tissues as part of large-scale reference atlases characterizing normal human anatomy.

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