Biology subjects

Susoy, V.

Publications and source records attributed to Susoy, V..

3 recordsLinked to original sources

bioRxiv2022

PEZO-1 and TRP-4 mechanosensors are involved in mating behavior in Caenorhabditis elegans

Male mating in Caenorhabditis elegans is a complex behavior with a strong mechanosensory component. C. elegans has several characterized mechanotransducer proteins, but few have been shown to contribute to mating. Here, we investigated the roles of PEZO-1, a piezo channel, and TRP-4, a mechanotransducing TRPN channel, in male mating behavior. We show that pezo-1 is expressed in several male-specific neurons with known roles in mating. We show that, among other neurons, trp-4 is expressed in the PCA sensory neuron, which monitors relative sliding between the male and the hermaphrodite and inhibits neurons involved in vulva detection. Mutations in both genes compromise many steps of mating, including initial response to the hermaphrodite, scanning, turning, and vulva detection. We performed pan-neuronal imaging during mating between freely-moving mutant males and hermaphrodites. Both pezo-1 and trp-4 mutants showed spurious activation of the sensory neurons involved in vulva detection. In trp-4 mutants, this spurious activation might be caused by PCA failure to inhibit vulva-detecting neurons during scanning. Indeed, we show that without functional TRP-4, PCA fails to detect the relative sliding between the male and hermaphrodite. Cell-specific TRP-4 expression restores PCAs mechanosensory function. Our results demonstrate new roles for both PEZO-1 and TRP-4 mechanotransducers in C. elegans mating behavior.

neuroscience↗

bioRxiv2022

Automated neuron tracking inside moving and deforming animals using deep learning and targeted augmentation

Advances in functional brain imaging now allow sustained rapid 3D visualization of large numbers of neurons inside behaving animals. To decode circuit activity, imaged neurons must be individually segmented and tracked. This is particularly challenging when the brain itself moves and deforms inside a flexible body. The field has lacked general methods for solving this problem effectively. To address this need, we developed a method based on a convolutional neural network (CNN) Awith specific enhancements which we apply to freely moving Caenorhabditis elegans. For a traditional CNN to track neurons across images of a brain with different postures, the CNN must be trained with ground truth (GT) annotations of similar postures. When these postures are diverse, an adequate number of GT annotations can be prohibitively large to generate manually. We introduce targeted augmentation, a method to automatically synthesize reliable annotations from a few manual annotations. Our method effectively learns the internal deformations of the brain. The learned deformations are used to synthesize annotations for new postures by deforming the manual annotations of similar postures in GT images. The technique is germane to 3D images, which are generally more difficult to analyze than 2D images. The synthetic annotations, which are added to diversify training datasets, drastically reduce manual annotation and proofreading. Our method is effective both when neurons are represented as individual points or as 3D volumes. We provide a GUI that incorporates targeted augmentation in an end-to-end pipeline, from manual GT annotation of a few images to final proofreading of all images. We apply the method to simultaneously measure activity in the second-layer interneurons in C. elegans: RIA, RIB, and RIM, including the RIA neurite. We find that these neurons show rich behaviors, including switching entrainment on and off dynamically when the animal is exposed to periodic odor pulses.

neuroscience↗

bioRxiv2020

Natural sensory context drives diverse brain-wide activity during C. elegans mating

Natural goal-directed behaviors often involve complex sequences of many stimulus-triggered components. Understanding how brain circuits organize such behaviors requires mapping the interactions between an animal, its environment, and its nervous system. Here, we use continuous brain-wide neuronal imaging to study the full performance of mating by the C. elegans male. We show that as each mating unfolds in its own sequence of component behaviors, the brain operates similarly between instances of each component, but distinctly between different components. When the full sensory and behavioral context is taken into account, unique roles emerge for each neuron. Functional correlations between neurons are not fixed, but change with behavioral dynamics. From the contribution of individual neurons to circuits, our study shows how diverse brain-wide dynamics emerge from the integration of sensory perception and motor actions within their natural context.

neuroscience↗