In recent years, spatial transcriptomics has gone from being a long-shot futuristic technology that many were sceptical of, to one of the trendiest and mostly widely attempted omics methods on the market. Having once been the handywork of a few isolated academic labs developing methodologies in-house, it is now commercially available from a wide range of competing companies, with kits, specialised pieces of equipment, and bespoke analysis pipelines at the ready.

Assuming you like the idea of seeing thousands of genes spatially resolved across your cells or tissue samples (and who wouldn’t?), and assuming you’ve got some funding at your disposal, how can you decide which method to paint your molecular picture with?

You will need to take into account the usual considerations when planning your research, as well as some methodology-specific and sample-specific requirements:

-Usual considerations: cost, equipment needed, time etc.

-Sample type (live cells / fixed cells / frozen sections / FFPE)

-Sample type (auto fluorescence?)

-Targeted or transcriptome-wide?

-Spatial resolution: anatomical features? Subcellular?

-Has it been demonstrated outside of originator’s lab?

Fortunately, the experts have already pulled together much of the method-specific requirements, which you can see summarised in the table below! Be sure to read the full paper (linked below the table), written by Michaela Asp, Joseph Bergenstråhle, and Joakim Lundeberg.

Table source: https://onlinelibrary.wiley.com/doi/epdf/10.1002/bies.201900221

With such exciting methods already available, I look forward to further development in this area, specifically:

•cBioportal/Cancertool for spatial datasets?

•Larger cohorts – spatial equivalent of TCGA?

•Automated spatial transcriptomics?

•High content spatial transcriptomics?

•Combining with spatial proteomics?

•Combining with spatial metabolomics?

•Combining with spatial epigenomics?

This is an exciting area with rapidly accelerating development, so I’m sure it won’t be long before these development become easily accessible. One preprint already seems to show improvement in several of these areas:

“Here, we advance the application of ST at scale, by presenting Spatial Multiomics (SM-Omics) as a fully automated high-throughput platform for combined and spatially resolved transcriptomics and antibody-based proteomics.”

https://www.biorxiv.org/content/10.1101/2020.10.14.338418v1.full

Are you using spatial transcriptomics yet? What is your method of choice?