Spatial Genomics: Mapping the Genome Within its Native Tissue Context - Healthcare-netizens/arpita-kamat GitHub Wiki
Spatial Genomics: Mapping the Genome Within its Native Tissue Context The advent of next-generation sequencing (NGS) technologies has revolutionized our understanding of the genome, providing unprecedented insights into gene expression, regulation, and variation. However, traditional genomic analyses often rely on bulk tissue samples, losing the crucial spatial information about where individual cells reside within their native microenvironment.
Spatial genomics represents a groundbreaking suite of techniques that bridges this gap, allowing researchers to analyze genomic information while simultaneously preserving the spatial organization of cells and molecules within tissues. This powerful approach is unveiling a new layer of biological complexity, revealing how genomic features are intricately linked to cellular identity, function, and interactions within their spatial context, with profound implications for understanding development, disease, and therapeutic responses.
At its core, spatial genomics aims to integrate genomic data with spatial coordinates within a tissue sample. This is achieved through a variety of innovative methodologies that can be broadly categorized into in situ sequencing-based approaches and spatial transcriptomics techniques (which can often be extended to DNA or epigenomic analyses).
In situ sequencing involves directly sequencing nucleic acids within intact tissue sections, allowing for the identification of genetic variants or gene expression profiles of individual cells or localized regions while maintaining their spatial context. These techniques often rely on cyclic fluorescence in situ hybridization (FISH) or in situ reverse transcription followed by ligation and sequencing.
Spatial transcriptomics, on the other hand, typically involves capturing RNA molecules released from a tissue section onto spatially barcoded arrays. Each barcode is linked to a specific location on the array, allowing researchers to later deconvolute the sequencing data and map the gene expression profiles back to their original spatial coordinates within the tissue. Subsequent advancements have enabled higher spatial resolution, even down to the single-cell level, by employing microfluidics, droplet-based technologies, or specialized capture surfaces. These techniques can be adapted to analyze not only mRNA but also DNA for copy number variations or epigenetic modifications like DNA methylation.
The ability to spatially resolve genomic information provides a crucial advantage over bulk sequencing. Tissues are inherently heterogeneous, composed of diverse cell types with distinct genomic profiles and functional roles. Bulk sequencing averages the genomic signals across all cells, obscuring the unique characteristics of individual cell populations and their spatial relationships. Spatial genomics, in contrast, allows researchers to dissect this cellular heterogeneity within its native context, revealing how gene expression patterns, genetic alterations, or epigenetic landscapes vary across different cell types and their spatial organization within a tissue.
This spatial perspective is critical for understanding complex biological processes that are inherently spatially organized, such as tissue development, organogenesis, immune responses, and the progression of diseases like cancer.
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