Neurons convert synaptic or sensory inputs into cellular outputs. It is not well understood how a single neuron senses, processes multiple stimuli, and generates distinct neuronal outcomes. Here, we describe the mechanism by which the C. elegans PVD neurons sense two mechanical stimuli: external touch and proprioceptive body movement. These two stimuli are detected by distinct mechanosensitive DEG/ENaC/ASIC channels, which trigger distinct cellular outputs linked to mechanonociception and proprioception. Mechanonociception depends on DEGT-1 and activates PVD's downstream command interneurons through its axon, while proprioception depends on DEL-1, UNC-8, and MEC-10 to induce local dendritic Ca 2+ increase and dendritic release of a neuropeptide NLP-12. NLP-12 directly modulates neuromuscular junction activity through the cholecystokinin receptor homolog on motor axons, setting muscle tone and movement vigor. Thus, the same neuron simultaneously uses both its axon and dendrites as output apparatus to drive distinct sensorimotor outcomes.

Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in C. elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.

Summary Mechanotransduction channels have been proposed as force sensors in various physiological processes, such as hearing and touch. In particular, TMC1 has been shown to constitute the pore of hair cell mechanotransduction channels, but little is known about how force is sensed by TMC channels. Here, we identify UNC-44/ankyrin as an essential component of the TMC-1 mechanotransduction channel complex in the sensory cilia of Caenorhabditis elegans mechanoreceptor neurons. Ankyrin binds indirectly to TMC-1 via evolutionarily conserved CIB proteins, which are required for TMC-1-mediated mechanosensation in C. elegans OLQ neurons and body wall muscles. Mechanosensory activity conferred by ectopically expressed TMCs in mechanoinsensitive neurons depends on both ankyrin and CIB proteins, indicating that the ankyrin-CIB subcomplex is required for TMC mechanosensitivity. Our work indicates that ankyrin is a long-sought intracellular tether that transmits force to TMC mechanotransduction channels.

New designs of microfluidic devices can facilitate recording of C. elegans larvae neuronal responses to precise mechanical stimuli, which reveal new understanding of development of mechanosensory neurons and circuits.

No abstract