Periodic Reporting for period 1 - ECtomics (Dissecting spinal cord ependymal cell heterogeneity by single-cell transcriptomics)
Reporting period: 2017-04-01 to 2019-03-31
A close look at the central canal is enough to see that ECs are heterogeneous. Based on their shape, ECs are classified in three subtypes: tanycytes, which typically enwrap blood vessels; radial ECs, which resemble embryonic stem/progenitor cells of the nervous system and locate exclusively to the dorsal and ventral poles of the central canal; and the most abundant cuboidal ECs. ECs also vary at the molecular level, with specific proteins detected only in subsets of ECs. Due to limitations of traditional methods, requiring many cells as input or based on only a handful of cherry-picked molecular markers, it remained unclear whether this heterogeneity reflects functional differences between ECs or different maturation states; and the precise identity of the spinal cord stem cell is still elusive.
Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies make it possible for the first time to sequence thousands of RNA transcripts within a cell. The transcriptome of a cell can then be interpreted as its proof of identity and inform about its function.
The overall objective of this project is the comprehensive characterisation of spinal cord ECs using scRNA-seq, and to map computationally-defined ependymal subtypes back onto the tissue. This research is a necessary step for investigating the function and potential of different spinal cord EC subtypes and may inspire new therapeutic strategies to, in the future, promote spinal cord repair.
To prepare libraries of the cells and carry out scRNA-seq, we teamed up with Dr. Andy May from the Chan Zuckerberg Biohub (San Francisco, USA). We followed the Smart-seq2 protocol to prepare the cDNA libraries and sequenced them on the NovaSeq 6000 Sequencing System (Illumina) using 2x100bp paired-end reads. This returned 280 GB of raw data, which were transferred back to Dundee for computational analysis.
One of the main objectives of my project was to identify EC subtypes based on their transcriptional profiles. Initially, this is a computational challenge. After preprocessing and filtering out low-quality cells, the resulting dataset contains 995 high-quality cell transcriptomes. In preliminary analyses, I explored a number of approaches for normalisation, dimensionality reduction, manifold learning, clustering and differential expression analysis, to get a deep understanding of the computational tools and the dataset. I set on the broadly-used R package Seurat. Clustering of the cells revealed transcriptionally distinct clusters sharing known EC genes and others not associated with ECs before, some of which I could validate in the Allen Spinal Cord Atlas. To identify marker genes specific of each cluster, I carried out differential gene expression analysis between them and Gene Ontology enrichment analysis to find what set them apart and infer the biology and likely functions of each cluster. This part of the analysis is still ongoing, but we expect to publish our findings in summer of 2020.
An important aim of my project was to validate the computationally-defined EC subtypes experimentally, in intact tissue samples. For that, I used a combination of RNAscope and antibody staining in the same sample to detect multiple RNA transcripts and proteins respectively. Using the set of marker genes identified by differential expression analysis, I confirmed that the proportions of ependymal subtypes are accurately represented in our single-cell dataset. More importantly, with these experiments we are placing EC subtypes in the tissue context and investigating a potential correlation between molecular features and cell shape. Additionally, I am using these spatial methods to investigate whether the proportions of EC subtypes change in the spinal cord of younger and older mice. This data is helping us to establish, for example, whether the EC subtypes that we have defined are stable subtypes or transitional states.
I presented preliminary results of the project in international scientific conferences throughout the action, and the whole dataset will be made freely available for the research community as soon as we submit the paper describing our findings for publication.
Our resource is of tremendous importance for basic biologists comparing model systems to human biology and lays the groundwork for a great range of possibilities – from investigating the functions of EC subtypes by genetically labelling and manipulating specific ECs in vivo, to rigorously comparing ECs at different developmental stages and between non-regenerative and regenerative species. For example, an intriguing question is: does the specialisation of ECs into more precise functions in ""higher"" vertebrates curtail their regenerative capacity?
Moreover, because cells are the basic functional units, understanding EC biology at this new level of resolution is critical in realising the potential of endogenous stem cells in the mammalian spinal cord; and in devising strategies to harness their potential to, in the future, promote spinal cord repair in humans."