Cell2location is a computational tool designed to precisely map the spatial distribution of different cell types within a tissue. It helps bridge the gap between detailed information about individual cells and their original locations within the complex environment of biological tissues. This tool essentially functions like a cellular GPS.
The Need for Spatial Understanding
Traditional methods for studying cells, such as single-cell RNA sequencing (scRNA-seq), have revolutionized our understanding of cell types by providing detailed gene expression profiles for thousands of individual cells. However, a significant limitation of scRNA-seq is that it requires cells to be dissociated from their tissue, which results in the loss of their original spatial context.
The recognition of this limitation led to the development of spatial transcriptomics technologies. These advanced techniques capture gene expression while preserving the spatial organization of cells within a tissue section.
While spatial transcriptomics can show which genes are active at specific locations, it often captures mRNA from multiple cells within a single measurement spot, making it challenging to identify the exact cell types and their proportions at each precise location. Therefore, a computational solution was needed to accurately deconvolve these mixed signals and assign specific cell types to their spatial coordinates.
Deciphering Tissue Composition
Cell2location addresses this challenge by combining the strengths of two distinct data types: single-cell RNA sequencing and spatial transcriptomics. First, it uses scRNA-seq data to generate “reference cell type signatures,” which are essentially unique gene expression profiles for each identified cell type or subpopulation.
This initial step often involves clustering single-cell data to identify distinct cell groups and then calculating the average gene expression for each cluster. After establishing these reference signatures, cell2location takes spatial transcriptomics data as input.
This data provides mRNA counts for specific genes at various defined locations across a tissue section. The model then employs a Bayesian statistical framework, specifically using Negative Binomial regression, to decompose the mRNA counts observed at each spatial location.
This decomposition estimates the contribution of each reference cell type to the gene expression observed at that particular spot. The result is a detailed map that quantifies the relative and absolute abundance of each cell type at every spatial location within the tissue. The model also accounts for technical variations, such as differences in measurement sensitivity across technologies, ensuring robust and accurate spatial mapping of cell types.
Unlocking Biological Insights
The ability of cell2location to precisely map cell types within their native tissue environment provides valuable insights across various biological fields. In disease research, this spatial understanding is useful for dissecting complex conditions like cancer and neurodegenerative disorders. For instance, in cancer, knowing the exact location and composition of immune cells within a tumor microenvironment can help researchers understand disease progression, identify potential therapeutic targets, and predict responses to treatments.
Cell2location contributes to developmental biology by showing how tissues and organs form and mature. By mapping cell types during different developmental stages, scientists can observe how cells differentiate, migrate, and organize to create complex structures.
This allows for a deeper understanding of the processes that govern healthy development and how disruptions in spatial organization can lead to developmental disorders. For example, it has been used to discover fine regional astrocyte subtypes in the mouse brain and map rare pre-germinal center B cell populations in human lymph nodes.
This spatial mapping capability also aids in deciphering complex tissue functions, such as those in the human lung or reproductive system. Researchers can identify co-located groups of cells that might be interacting, leading to the discovery of new cellular interactions and functional units within tissues. The comprehensive cellular maps generated by cell2location serve as a foundation for further investigations into cellular communication, tissue remodeling, and the mechanisms underlying both health and disease.