Spatially resolved transcriptomics for studying development and disease – is it going mainstream?

February's Hot Topic

Single cell transcriptomics has revolutionized biology but it has one major disadvantage – tissues are dissolved into an amorphous ‘soup’ and all spatial information is lost. This caveat is addressed by a new and exciting suite of techniques called spatial transcriptomics, which comprise methods for linking transcriptomics data to the original location within the tissue.

Named Nature Method of the Year 2020, spatial transcriptomics brings together imaging and high throughput sequencing to create maps of gene expression within the studied tissue.1 Until recently, researches faced something of a Heisenbergian dilemma: by using RNA sequencing it was possible to obtain in-depth information about gene expression profiles at tissue level or, more recently, at a single cell resolution, however the spatial information was inherently lost. On the other hand, powerful imaging techniques informed us about the spatial context at a high resolution, but with a limited possibility of molecular characterization. Spatial transcriptomics enables us to have the best of both worlds and to “uncover the Where for every What”.2

There are a number of different approaches for performing spatial transcriptomics, however not all of them have a single cell resolution or provide information on the whole transcriptome.3 For example, in situ capturing technologies4 such as GeoMx Digital Spatial Profiler by Nanostring and Visium by 10x Genomics (workflow illustrated below) achieve resolution at a single cell level and 1-10 cells, respectively. Technology-specific limitations, including spatial resolution, gene coverage, sensitivity, and technical biases, can be solved using various bioinformatics approaches.5 By adding another layer of information, such as proteome data, the resulting spatial multiomics becomes an even more holistic approach to gain a clearer view of cell heterogeneity and disease complexity. The ability to combine spatial information and –omics data for studying a disease such as atherosclerosis, where plaques develop only in certain areas of arteries, offers exciting possibilities for elucidating the mechanisms underlying the focal nature of the disease. We look forward to seeing what spatial transcriptomics brings to the field of cardiovascular research!

Workflow diagram for Visium Spatial Gene Expression (10x Genomics). Fresh frozen or formalin-fixed paraffin-embedded tissue is sectioned, placed onto a library preparation slide, then fixed, stained either with H&E or immunofluorescence (IF) and imaged, followed by spatial barcoding and library construction. The libraries are then sequenced and data visualized. (Image provided by 10x Genomics)


1 Marx, V. Method of the Year: spatially resolved transcriptomics. Nat Methods 18, 9-14, doi:10.1038/s41592-020-01033-y (2021).

2 Spatially resolved biology: Novel insights about your tissue. Visualized., <> (2022).

3 What is spatial transcriptomics?, <> (2021).

4 Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78-82, doi:10.1126/science.aaf2403 (2016).

5 Dries, R. et al. Advances in spatial transcriptomic data analysis. Genome Res 31, 1706-1718, doi:10.1101/gr.275224.121 (2021).