Spatial OMICS: Mapping the Molecular Landscape Within its Native Tissue Context - Healthcare-netizens/arpita-kamat GitHub Wiki
The "-omics" revolution has provided an unprecedented wealth of information about the molecular constituents of biological systems, encompassing genomics, transcriptomics, proteomics, and metabolomics. However, traditional bulk "-omics" analyses often homogenize tissue samples, losing the critical spatial context of individual cells and molecules within their native microenvironment. Spatial OMICS represents a groundbreaking convergence of spatial biology and "-omics" technologies, enabling the high-resolution mapping and quantification of various molecular layers (DNA, RNA, proteins, metabolites) directly within intact tissue sections. This powerful approach is unveiling a new dimension of biological understanding, revealing how molecular features are spatially organized and interconnected to drive cellular identity, function, and interactions in health and disease.
At its core, spatial OMICS aims to integrate comprehensive molecular profiling with precise spatial coordinates within a tissue sample. This is achieved through a diverse and rapidly evolving toolkit of methodologies that can be broadly categorized into two main strategies: in situ "-omics" and spatially resolved capture followed by sequencing or mass spectrometry.
In situ "-omics" techniques perform molecular analysis directly within the tissue section while preserving spatial information. These methods often involve iterative rounds of imaging, hybridization, ligation, or antibody staining, coupled with sophisticated image analysis to identify and quantify multiple analytes (e.g., RNA transcripts, proteins) at subcellular resolution. Examples include Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH), Spatial Transcriptomics and Cytometry Sequencing (STARmap), and Cyclic Immunofluorescence (CyCIF). These techniques excel at high spatial resolution and the ability to simultaneously profile a large number of targets within individual cells in their spatial context.
Spatially resolved capture followed by sequencing or mass spectrometry involves capturing molecular analytes from specific locations within a tissue section and then analyzing them using downstream "-omics" platforms like next-generation sequencing (NGS) or mass spectrometry. This often involves spatially barcoded arrays, microdissection techniques, or specialized tissue processing methods to link molecular information to its spatial origin. Examples include Spatial Transcriptomics (ST), Visium Spatial Gene Expression, Nanostring GeoMx Digital Spatial Profiler (for RNA and protein), and MALDI-based spatial metabolomics and lipidomics. These techniques can often profile a broader range of molecules across larger tissue areas, although the spatial resolution may vary depending on the method.
The integration of spatial information with multi-omic data provides a transformative advantage over traditional bulk "-omics". Tissues are complex ecosystems composed of diverse cell types with distinct molecular profiles and intricate spatial relationships. Bulk analysis averages these signals, obscuring crucial information about cellular heterogeneity and the spatial organization of molecular processes. Spatial OMICS allows researchers to dissect this complexity, revealing how gene expression patterns, protein localization, metabolic activities, and even genomic variations are spatially orchestrated within tissues. This spatial context is paramount for understanding fundamental biological processes like tissue development, organogenesis, immune responses, neurocircuitry, and the pathogenesis of diseases such as cancer, neurodegenerative disorders, and infectious diseases.
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