Neuronal atlas of the dorsal horn defines its architecture and links sensory input to transcriptional cell types.

Four years ago, the labs of Patrik Ernfors and Sten Linnarsson from the Karolinska Institute published findings on a classification of sensory afferent neurons based on their gene expression (Usoskin et al., 2014; also see related PRF comment by Hoon here). At the time this work was ground breaking, providing a radically different view of the specificity of peripheral somatosensory neurons. The results from this work advanced the concept that sensory neurons are dedicated for the detection of select types (modalities) of stimuli. They also provided a wonderful resource to help identify molecular markers that can be used to investigate subsets of neurons. The possibilities for new discoveries based on the transcriptomes of single cells (sc) are still being explored and, significantly, there have been several excellent follow-up reports that have substantially expanded the characterization of sub-populations of somatosensory neurons (Li et al., 2016; Nguyen et al., 2017). Importantly, the latter paper (Nguyen et al., 2017) points to some of the shortcomings of the single cell sequencing technique and with the interpretation of scRNA data. In addition, because the follow-up studies sequenced neurons at far greater depth (more sequencing and more neurons sequenced), they provide more information about the precise divisions of subclasses of nerves and potential molecular markers that might be useful to dissect types of primary afferents.

Now, the Ernfors and Linnarsson groups report scRNA sequencing data for neurons in the dorsal horn of the spinal cord (Häring et al., 2018). Also, using a different approach (sequencing RNA from nuclei), the group of Ariel Levine recently reported a classification of spinal cord neurons (Sathyamurthy et al., 2018), and there was another smaller analysis of a subclass of spinal cord neurons (Chamessian et al., 2018). The dust is still settling on these papers, but if the first DRG classification is anything to go on, these latest papers will likely have effects on the way the spinal cord is studied in the future. The data do clearly show that there are very big differences in the genes expressed by inhibitory and excitatory neurons and that neurons can be further subdivided based on gene expression, but not a lot new can be gleaned about the function of these different subsets of neurons. Therefore, unlike the results from the DRG sequencing, what can be immediately inferred from these data is less obvious. Nevertheless, the timing of these studies is opportune, because in the last 5-10 years it has become more apparent how incredibly important the spinal cord is as a control center for information flow and processing. What had been largely lacking in the spinal cord was a full set of appropriate molecular tools to fully dissect how spinal circuits are connected and to test potential mechanisms for sensory processing. Unfortunately, the RNAseq data from the different spinal cord surveys (Häring et al., 2018; Sathyamurthy et al., 2018) cannot be easily compared because of the differing methodologies employed to prepare RNA from cells. Also, although all abundant cell types were probably sequenced and classified, it is less clear if classes of neurons with lower overall abundance in the spinal cord were distinguished. Further sequencing should be able to address this potential concern. However, even as it stands, the new resources should catalyze work that will eventually provide answers for how the spinal cord performs computations.

What’s next? An intriguing possibility for using information from the new spinal sequencing, which was highlighted by the papers of Ernfors and Levine (Häring et al., 2018; Sathyamurthy et al., 2018), is to pair it with the detection of neurons activated by particular stimuli (with the immediate early gene cfos). The cfos gene has long been used to mark neurons that have been recently excited because of its rapid increase in expression and its short half-life. With use of cfos labeling in conjunction with in situ hybridization of markers that define specific types of neurons, these two studies (Häring et al., 2018; Sathyamurthy et al., 2018) suggest that circuits recruited by specific stimuli can be identified. The latter type of exciting experiments may indeed illuminate new pathways that underlie spinal cord processing. Lastly, in the future, it will be interesting to investigate if human DRG and spinal cord neurons are similar to those in the mouse and to examine the spinal cord for changes in gene expression after injury and in chronic pain states.