SPATIAL TRANSCRIPTOMICS

Spatial information is critical for understanding gene expression patterns within tissue and how individual cells interact with their surrounding environment.

In recent years, single-cell technology has gained favor over traditional bulk RNA approaches. Bulk RNA sequencing masks heterogeneity within a sample, making it challenging to characterize complex tissue such as tumors. Single-cell RNA sequencing allows for identifying rare or unique cell populations that are otherwise difficult or impossible to isolate.

However, today’s single-cell technologies require dissociation of tissue and subsequent loss of spatial information. Spatial transcriptomics allows you to acquire gene expression data at single-cell or near-single-cell resolution without losing spatial information.

Spatial transcriptomics methods are now being used to identify biomarkers and elucidate the mechanisms underlying disease. Spatial transcriptomics datasets can be integrated with other single-cell experiments to characterize complex tissues better.

When performing a spatial transcriptomics experiment, we recommend running a single-cell or single-nuclei RNA sequencing study in parallel. This is typically done using serial sections of the same tissue block. This provides several advantages.

Better identification of cell types. In a Visium experiment, each spot may capture up to 20 cells, and the spot RNA may be thought of as a “bulk RNAseq” population representing a small mixture of heterogeneous cells. Deconvoluting the spot transcriptome into separate cell types is typically done using a reference set of publicly available single-cell RNAseq data for cell types believed to be in the tissue of interest. However, the quality and validity of cell-type assignments are most significant when the single cell or single nuclei RNAseq data are obtained from the same Visium tissue sample. For example, Visium + single cell/single nuclei profiling of serial sections can capture rare or novel cell types that may be present in your tissue but missing from reference datasets.

Better detection of genes. Single-cell RNA-seq, by definition, captures individual cells and at greater sequencing depth per cell than is possible with spatial transcriptomes. Integration of single-cell and spatial transcriptomes boosts average read, UMI, and gene counts per cell, providing more in-depth information from your spatial experiments.