Molecular development of muscle spindle and Golgi tendon organ sensory afferents revealed by single proprioceptor transcriptome analysis
Oliver, K. M.; Florez-Paz, D. M.; Badea, T. C.; Mentis, G. Z.; Menon, V.; de Nooij, J. C.
Show abstract
Anatomical and physiological analyses have long revealed differences between proprioceptive groups Ia, II, and Ib sensory neurons, yet the molecular correlates of these three muscle afferent subtypes remain unknown. We performed single cell RNA sequencing of genetically identified adult proprioceptors and, using unbiased bioinformatics approaches, detected five molecularly distinct neuronal clusters. Validation of cluster-specific transcripts in dorsal root ganglia (DRG) and skeletal muscle provides evidence these clusters correspond to functionally distinct muscle spindle (MS) or Golgi tendon organ (GTO) afferent proprioceptors. Remarkably, while we uncovered just one type of GTO afferents, four of the five clusters represent MS afferents, thus demonstrating a previously unappreciated diversity among these muscle proprioceptors. In vitro electrophysiological recordings reveal just two broadly distinct proprioceptor types, and suggest that the refinement of functional subtype diversity may occur along multiple axes of maturation. Lineage analysis between proprioceptor transcriptomes at different developmental stages show little or no correlation for transcripts that define adult MS or GTO afferents, supporting the idea that proprioceptor subtype identity emerges late in development. Together, our data provide the first comprehensive molecular signature for groups Ia and II MS afferents and group Ib GTO afferents, and offer new strategies for genetic interrogation of the role of these individual proprioceptor subtypes in regulating voluntary motor behavior.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.