Unravelling the molecular heterogeneity within the spinal cord stem cell niche by single-cell transcriptomics
Funded in: 2017, 2018, 2019
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Problem: Ependymal cells (ECs) are key players in response to spinal cord injury, yet we know surprisingly little about them.
Target: Detailed characterisation of ECs in the mouse and human spinal cord, cell by cell/at single-cell resolution.
Goal: A better understanding of spinal cord ECs: cell types and cell states, functions, potential for therapeutic intervention.
Ependymal cells (ECs) are the cells lining the spinal cord central canal. In healthy settings, they are mostly quiet (do not divide) but contribute to the physiological functioning of the tissue. After spinal cord injury, however, ECs quickly multiply and mobilize and are thought to generate specialised cells, which may prevent further tissue damage, but can also hinder functional repair. Indeed, when cultured in a dish, ECs are the only spinal cord cells that can give rise to many of the specialised cell types that make up the spinal cord (neurons, astrocytes and oligodendrocytes).
These findings led to the belief that ECs are the spinal cord stem cells. But not all ECs seem to be stem cells and the precise identity of the spinal cord stem cell remains elusive.
ECs have been historically defined based on their location (surrounding the canal) and morphology (for instance, cell shape). Molecularly, ECs have been studied as a whole (as a cell population) or using a handful of marker genes. These studies showed that ECs are a disparate cell population, but lacked the tools/power to resolve differences between ECs. Because different EC subtypes may have different roles, including stem cells, it is important to characterise them more precisely.
Here, we will take advantage of cutting-edge technology to capture the genes expressed in individual cells. A cell’s identity can be inferred by the genes that the cell expresses (messenger RNA or mRNA). We will use single-cell RNA-sequencing to “read” thousands of mRNA molecules inside individual ECs from the mouse spinal cord. These rich data sets will allow us to characterise spinal cord EC subtypes and cell states, and inform us about their likely function and relationships. The comprehensive characterization of mouse EC will provide us with new, defining marker genes to map the computationally-defined EC subtypes in the mouse and human spinal cord using high-resolution 3D microscopy.
Our work will be a tremendous resource for the research community. We aim for the data we generate to allow alignment of mouse models with the human context. Our work may make it possible to selectively label and manipulate EC subtypes, to study how they behave in healthy and injured spinal cord and to discover the mechanisms that drive cell type and/or cell state transitions.
Dissecting EC heterogeneity will be critical for realising the potential of endogenous spinal cord stem cells and for developing novel therapeutic strategies to promote spinal cord repair.