Abstract
The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors depends on the spatial organization of their cells. In the past decade high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To assess the ability and potential of spatial gene expression technologies to drive biological discovery, we present a curated database of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field such as usage of experimental techniques, species, tissues studied and computational approaches used. Our analysis places current methods in historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Figure 1 revised to include ligation mediated SNV detection and single cell cDNA amplification; Figure 3 revised to reduce confusion and correct misunderstandings per Long Cai's request; corresponding parts of the Supplement were updated reflecting the paper in a more detailed manner.
