Clarrisa Erica
Data from single-cell RNA sequencing can reveal the molecular variety of different cell types. Recent publications of cell type atlases for the mouse spinal cord have not yet been combined. Here, using single-cell transcriptome data, we create an atlas of spinal cell types, combining the various datasets into a single frame of reference. We present a hierarchical framework of postnatal cell type interactions, with location serving as the highest level of organisation, followed by neurotransmitter status, family, and dozens of refined populations. We map the geographical distributions of each type of neuronal cell in the adult spinal cord and validate a combinatorial marker code for each. We also demonstrate intricate lineage links between several postnatal cell types. To aid in the standardisation of cell type identification, we also create the open-source cell type classifier SeqSeek. An integrated understanding of the various types of spinal cells, their molecular arrangement, and their gene expression profiles is provided by this work.