What is the fundamental advantage of spatial transcriptomics over standard single-cell RNA sequencing?
ASpatial transcriptomics detects more genes per cell
BSpatial transcriptomics retains the physical location of each measurement within the tissue
CSpatial transcriptomics is cheaper per sample
DSpatial transcriptomics requires fewer cells
scRNA-seq dissociates tissue into single cells before sequencing, destroying all spatial information — which cells were neighbors, which resided in specific tissue regions, and how gene expression varied across anatomical structures. Spatial transcriptomics preserves this information, enabling analysis of tissue architecture, spatial gene expression gradients, cell-cell communication, and spatial organization of cell types. This spatial context is critical for understanding tissue function, disease pathology, and developmental patterning.
Question 2 True / False
Imaging-based spatial transcriptomics methods like MERFISH can currently measure the full transcriptome (all ~20,000 genes) at single-cell resolution in a tissue section.
TTrue
FFalse
Answer: False
Current imaging-based methods typically measure hundreds to a few thousand genes per experiment (MERFISH routinely detects ~500-1,000 genes, with newer versions reaching several thousand). While this is far more than traditional FISH, it falls short of the full transcriptome. Sequencing-based methods like Visium capture transcriptome-wide data but at lower spatial resolution (multi-cell spots). Ongoing technology development is narrowing this gap, with methods like MERFISH+ and Slide-seq V2 pushing toward both higher gene counts and finer resolution.
Question 3 Short Answer
Explain why cell-cell communication analysis is more reliable with spatial transcriptomics data than with dissociated scRNA-seq data.
Think about your answer, then reveal below.
Model answer: Cell-cell communication analysis infers signaling interactions by identifying ligand-receptor pairs where the ligand is expressed in one cell type and the receptor in another. With dissociated scRNA-seq, any two cell types in the dataset could theoretically interact, but in reality only cells that are physically proximal can communicate through short-range signals (paracrine, juxtacrine). Spatial transcriptomics reveals which cell types are actually neighbors in the tissue, restricting the analysis to biologically plausible interactions. This eliminates false-positive ligand-receptor predictions between cell types that are expressed in the same tissue but located in distant compartments.
Tools like CellChat and NicheNet have been adapted for spatial data, using the spatial coordinates to constrain interaction analysis to cells within a defined radius. This spatial constraint dramatically reduces the number of predicted interactions and increases the proportion that are biologically meaningful.