Just like a treasure map leads explorers to hidden riches, knowing the precise location of gene expression within cells and tissues can guide researchers to uncover many biological mysteries. With spatial transcriptomics technologies, researchers are charting detailed gene expression and cellular organization landscapes across various tissues, revealing crucial insights into health and disease.
Download this poster from Drug Discovery News to learn about different spatial transcriptomics technologies and how they enable a deep understanding of cellular heterogeneity, disease mechanisms, and cell-cell interactions.
Just like a treasure map leads explorers to hidden riches, knowing the precise location of gene expression within cells and tissues can guide researchers to uncover many biological mysteries. In the past, scientists could only collect fragmented pieces of this map through traditional RNA sequencing, lacking the vital spatial context. Now, with spatial transcriptomics technologies, researchers are charting detailed landscapes of gene expression and cellular organization across various tissues, unlocking a treasure trove of knowledge about health and disease.
MICRODISSECTION
Using an infrared laser, scientists cut out specific regions from the tissue section under the microscope while ensuring that the surrounding tissue remains unaffected. They then extract RNA from these isolated cells and analyze their gene expression using techniques such as high throughput RNA sequencing (1).
SPATIAL MICROARRAYS
This technique involves mounting the tissue section onto a microarray coated with thousands to millions of spatially barcoded oligonucleotide probes. These probes capture RNA molecules from the tissue onto distinct spots on the array. Scientists then sequence the RNA and map the gene expression patterns back to their original locations in the tissue (2,3).
CELLULAR HETEROGENEITY AND TISSUE ARCHITECTURE
Researchers can pinpoint cell types and their distribution within a tissue, shedding light on how cells organize themselves within a tissue microenvironment (5).
CELL-CELL INTERACTIONS
By analyzing the expression of signaling molecules, receptors, and cell adhesion proteins across different cell populations, researchers can elucidate the molecular cues mediating cell-cell communication during physiological processes and disease pathogenesis (6).
DISEASE MECHANISMS
Spatial transcriptomics helps researchers identify satially localized gene expression changes associated with disease progression, heterogeneity, and response to therapy, uncovering critical molecules and pathways driving pathology (3).
BIOMARKER DISCOVERY
Correlating spatial gene expression profiles with clinical data may reveal novel biomarkers for disease diagnosis, prognosis, and therapeutic response prediction(3).
IN SITU HYBRIDIZATION
Scientists design fluorescently labeled oligonucleotide probes that target specific RNA sequences of interest and apply them to the tissue section, where they bind to their target RNA. Using fluorescence microscopy, they visualize the bound probes, mapping gene expression patterns across different cell types and tissue regions (3).
IN SITU SEQUENCING
After reverse transcribing RNA molecules in the tissue into complementary DNA (cDNA) templates, scientists hybridize targeted probes to specific cDNA sequences and amplify and sequence them in situ. During sequencing, they use fluorescently labeled nucleotides that bind to specific DNA bases, each emitting a unique color. Analyzing fluorescence signals enables researchers to determine RNA sequences and their spatial locations within the tissue (4).
REFERENCES
1. Emmert-Buck, M. R. et al. Laser capture microdissection. Science 274, 998–1001 (1996).
2. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
3. Wang, Y. et al. Spatial transcriptomics: technologies, applications and experimental considerations. Genomics 115, 110671 (2023).
4. Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat Methods 10, 857–860 (2013).
5. Liu, S-Q. et al. Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer. Journal of Hematology & Oncology 15, 19 (2022).
6. Wang, X., Almet, A. A. & Nie, Q. The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics. Seminars in Cancer Biology 95, 42–51 (2023).