It will soon be commonplace to localize gene expression in tissues.
Space has been a formidable, if not final, frontier in gene expression. But that frontier is eroding as methods developers put transcripts onto various tissue maps. The variety and creativity of these approaches makes this a fascinating area to watch.
Spatial gene expression is critical for understanding cell identity and function in the tissue context. The popularity of model organism expression atlases and the Allen Institute for Brain Science's mouse and human brain atlases attest to the power of spatial gene expression. However, existing atlases were largely created using reporter genes or in situ hybridization—low throughput methods that make it painstaking to construct references and that limit the ability to assess multiple samples.
A bevy of recent tools offer greater flexibility and scale; highly multiplexed fluorescence in situ hybridization, in situ sequencing of imaged sections or three-dimensional tissues, and algorithmic methods that project gene expression onto limited existing spatial information, among others, offer very different solutions.
There is good reason to expect the innovation to continue. Many methods are currently being optimized, and each has unique strengths and limitations with respect to ease, speed, spatial resolution, quantitative accuracy, and the number of genes than can be profiled. Recent initiatives, including the Human BioMolecular Atlas Program of the US National Institutes of Health and the Human Cell Atlas, a major international undertaking, have strong technological components and define spatial mapping as an explicit goal.
How to integrate gene expression data into a spatial coordinate system, and how to visualize and compare these kinds of data sets are difficult open questions for the computational community, which has had little data to work with so far. We anticipate that improvements in data generation and analysis will bring spatial transcriptomics into wider practice and will be transformative for biology.
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Nawy, T. Spatial transcriptomics. Nat Methods 15, 30 (2018). https://doi.org/10.1038/nmeth.4542
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