A team has developed a new tool, called cell2location, that can resolve fine-grained cell types in spatial transcriptomic data.
Spatial transcriptomics
The cellular architecture of tissues underpins cell-cell communication, organ function and pathology. Over the past decade, the emergence of spatial transcriptomics technologies has provided key opportunities to map resident cell types and cellular signalling in situ. However, defining spatial transcriptomics workflows that allow for comprehensive mapping of resident cell types in tissue is still a challenge. This is due to the large amount of cellular diversity across organs. Nonetheless, these cell types and their subtle transcriptional differences have the potential to yield large discoveries. Another explanation for this challenge is due to the diversity of spatial tissue architecture. For example, discrete anatomical regions within the brain with distinct cell types. In order to delve further into these cellular landscapes, it is necessary to develop methods that are sufficiently sensitive to resolve these fine-grained differences in cell types across diverse tissues.
Approaches that couple single-cell and spatial transcriptomics can provide a scalable approach to address these challenges. Unfortunately, it previously hasn’t been possible to combine single-cell RNA-Seq (scRNA-seq) data with spatial information at the necessary scale. Current methods do not resolve fine-grained cell types in complex tissues, which is important to make full use of spatial transcriptomic data.
Cell2location – a new tool
In a recent paper, published in Nature Biotechnology, researchers presented a new tool, known as cell2location, which aims to address these existing challenges. Cell2location is a Bayesian model that is designed to resolve fine-grained cell types in spatial transcriptomic data as well as create cellular maps of diverse tissues. It can handle complex experimental settings, enabling the joint analysis of multiple scRNA-seq and spatial transcriptomic datasets. In addition, compared to existing tools cell2location accounts for the technical sources of variation and borrows statistical strength across locations.
The team applied this tool to several types of human and mouse tissue in order to provide three-dimensional data on the cell types that were present and their locations. Cell2location was able to provide a more detailed analysis of astrocytes in the mouse brain. It was also able to detect subtle differences in the genes that the cells expressed (down to as few as ten different genes). In the human lymph node, the team spatially mapped a rare pre-germinal center B cell population. Meanwhile, in the human gut, they were also to resolve fine immune cell populations in lymphoid follicles.
Altogether, this data demonstrates the richness of data that can be provided by cell2location, which opens up new doors for the use of single-cell sequencing in pathology.
Image credit: canva
