Spatial transcriptomics and its added value
Biology has been associated with microscopy as its primary tool for several hundred years. Since the second half of the twentieth century biochemical and subsequently molecular biology methods truly revolutionized biology and arguably made it a science of the 21st century. One of such groundbreaking methods is RNA sequencing with its further evolution to single cell RNA sequencing.
However, those next generation methods inherently lack one of the key elements which traditional microscopy brings: spatial information. Only in recent years have we seen the rise of a technology which combines both layers of information. Spatial transcriptomics allows for the identification of hundreds or even thousands of genes, including their spatial location.
Spatial transcriptomics methods
In brief, the identification of genes using spatial transcriptomics can be achieved via 2 groups of technologies: image-based, sometimes referred to as in situ, and array-based (Figure 1).
Figure 1. Classification of spatial transcriptomics methods. Image from Heumos et al. (2023)
Regarding their spatial resolution, spatial transcriptomics methods can also be split into methods which allow single cell resolution and methods with lower spatial resolution - around 10 micrometers or more.
It's worth noting, however, that spatial transcriptomics methods with single cell and subcellular resolution can’t detect the same number of genes as classical single cell RNA-seq methods. For now, the detection is limited by hundreds or few thousands of selected genes. In contrast, spatial methods lacking single-cell resolution are usually capable of detecting the entire transcriptome in an unbiased manner.
Nevertheless, with the assistance of newly emerging bioinformatics analysis tools all types of spatial transcriptomics methods can offer benefits as described below.
The added value of spatial transcriptomics
Analysis of cell-cell interactions in tissues. For instance, spatial transcriptomics methods have enabled discovery of increased macrophage-fibroblast interaction in COVID-19 patients or fibroblast-tumor interaction in lung cancer patients.
Identification of cell clusters that take into account both gene expression and spatial location. This approach allows study of cell types in their native tissue locations - analysis of so-called cellular neighborhoods or tissue microenvironments.
Detection of spatially variable genes. This type of analysis enables study of differential gene expression with spatial patterns.
To overcome currently existing limitations of spatial transcriptomics and to fully exploit its benefits, researchers often complement spatial technologies with scRNA-seq studies or combine several methods with different resolutions and throughputs. Analysis of individual data sets or their combinations can pose a challenge due to either the lack of methods tailored for spatial omics or their novelty, along with the absence of benchmarks for these analysis methods (Figure 2).
Figure 2. Example of subcellular spatial omics data and analysis. Image from Park et al. (2022)
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- Heumos et al. Nat Rev Genet (2023). https://doi.org/10.1038/s41576-023-00586-w
- Rendeiro et al. Nature (2021). https://doi.org/10.1038/s41586-021-03475-6
- Tang et al. Brief Bioinform (2023). https://doi.org/10.1093/bib/bbac563
- Schürch et al. Cell (2020). https://doi.org/10.1016/j.cell.2020.07.005
- Park et al. Genome Biol (2022). https://doi.org/10.1186/s13059-022-02824-6