Spatial transcriptomics is a scientific method that allows researchers to measure gene activity within cells and tissues while keeping track of their exact location. It quantifies messenger RNA (mRNA) molecules directly within their original tissue environment. This preservation creates a detailed map of gene expression across a tissue sample. The technology combines visual information about tissue structure with molecular data on gene activity, offering a comprehensive view of biological processes.
The Need for Spatial Context in Biology
Understanding biological processes requires knowing not only which genes are active, but also precisely where that activity occurs within a tissue. Traditional methods for studying gene activity, such as bulk RNA sequencing, involve grinding up an entire tissue sample, which mixes all the cells together and averages their gene expression. This process loses the crucial spatial information, making it impossible to tell which specific cells or regions were responsible for certain gene activities.
Similarly, single-cell RNA sequencing (scRNA-seq) has revolutionized the ability to study gene expression at the level of individual cells, revealing the unique “fingerprint” of each cell type. However, to perform scRNA-seq, cells must first be separated from their original tissue, which also sacrifices their spatial arrangement. This dissociation prevents researchers from observing how cells interact with their neighbors or how their location influences their function within a tissue.
Just as understanding a city requires knowing its neighborhoods, understanding biology requires knowing where gene activity occurs. This spatial context is fundamental for understanding how cells organize, communicate, and function in their native environment, crucial for both normal biology and disease development.
How Spatial Transcriptomics Uncovers Cellular Secrets
Spatial transcriptomics works by combining tissue imaging with advanced gene sequencing techniques to link gene expression to specific locations. The process typically begins with preparing a thin section of tissue, which is then placed onto a specialized glass slide. This slide is covered with a dense grid of tiny spots, each containing unique DNA “barcodes” that act as spatial tags.
The tissue section is then processed to allow its mRNA molecules to bind to these barcoded probes on the slide. Each mRNA molecule captured at a particular spot receives the unique spatial barcode for that location. This step ensures that the gene activity from different areas of the tissue is tagged with its precise origin.
Next, the captured mRNA, now labeled with spatial barcodes, is converted into complementary DNA (cDNA) and then collected from the slide. These cDNA molecules are then prepared for high-throughput sequencing, a process that reads the genetic code of millions of molecules simultaneously. During the computational analysis, the spatial barcodes are used to map each sequenced gene back to its original position on the tissue section.
Finally, the gene expression data is overlaid onto a high-resolution microscope image of the tissue. This creates a visually rich map that shows which genes are active and exactly where they are expressed within the tissue’s intricate structure. Different platforms employ variations of this core principle, such as in situ capturing (ISC) or in situ sequencing (ISS).
Transformative Applications Across Life Sciences
Spatial transcriptomics has broad applications across various fields. In cancer research, it provides a deeper understanding of the tumor microenvironment, including cancer cells, immune cells, and other supporting cells. Researchers use this technology to map tumor heterogeneity, identifying different cancer cell subpopulations and their spatial distribution within a tumor. This aids in pinpointing specific areas like the tumor interface or tertiary lymphoid structures, which are important for understanding tumor progression and response to therapies.
In neuroscience, spatial transcriptomics is being used to create detailed maps of brain regions and to investigate neurological disorders. It allows scientists to explore how gene expression varies across different areas of the brain, identifying specific cell types and their organization. This has led to insights into brain development, such as mapping cortical cell types in the human brain during prenatal and postnatal stages. It also helps in understanding diseases like Alzheimer’s and Parkinson’s by revealing the spatial patterns of gene expression changes associated with disease progression, for example, identifying activated microglia near amyloid beta plaques in Alzheimer’s disease.
Developmental biology also benefits from spatial transcriptomics, as it enables the tracking of gene expression changes during embryogenesis and organ formation. This technology helps reconstruct complex developmental pathways, such as embryonic development, tissue regeneration, and stem cell dynamics. By creating tissue reference maps, researchers can characterize and map the spatial organization of cell types as they differentiate and form organs, aiding understanding of developmental disorders and regenerative medicine.
In immunology, spatial transcriptomics helps map the distribution and interactions of immune cells within tissues. It is useful for studying immune responses in various diseases, including infectious diseases and autoimmune conditions. By analyzing the spatial organization of gene expression, researchers gain insights into how immune cells respond to infections, such as in studies of lung tissue from COVID-19 patients, or how they contribute to autoimmune diseases like rheumatoid arthritis.