NPIF Rutherford Fellowship

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

An animal’s movement can be considered as a projection of neural network; thus, reverse-analysing the movements potentially reveal individual neural functions at network level. Many image-processing platforms have been developed to analyse animal’s movement, none of them have achieved at single-neuronal resolution level. However, this reverse-analysis approach is applicable to quantify the progressive shifts of neuronal functions over time, thus, potentially reveal the network implications of neurodegenerative disorder (e.g. identifying candidate molecules that progress or prevent Alzheimer’s disease or Parkinson’s diseases by precisely analysing mobility). Therefore, the establishment of this approach is highly desired. For this study, the model organism Caenorhabditis elegans is one of the best model systems. This is because large experimental data sets can be acquired with ease, a wide range of experimental techniques are established, and the well-defined cellular lineage and the short life span allow rigorous and expeditious experimental verification. My proposed project is to develop custom soft- and hard-ware for computational phenotyping to elucidate the roles of all 302 neurons of C. elegans at the network level. My long-term objectives are to apply this system not only to identify molecules that trigger neurodegenerative disorders, but also to identify locomotor functions of broadly expressed genes such as neuropeptides. As a proof-of-concept of reverse-analysis, I analysed the published behavioural data from worms with laser-ablated neurons with this system. Preliminary results show the micro-movements can be classified at a single 50-segmented position along worm body. The combination of this analysis at different segments can sufficiently cover 95 worm body muscle movement which are controlled by 113 motor neurons and influenced by other neurons. Thus this demonstrates the potential for characterising individual neurons involved in the movement. Additionally, this approach can be further optimised by hardware improvements. Since this new system does not require standard microscopy, it can be vastly expanded with ease to increase the amount of data obtained for high-throughput assays. Together with C. elegans’ biological features, this system allows us to understand functional shifts of individual neurons in single animal’s life-span with a high-throughput manner. Consequently, our study should provide experiment and computational methodologies to understand the single neuronal functions at network level. Furthermore, we can provide a comprehensive data of single-neuronal functions in single animal’s life-span which detect abnormal locomotion behaviours comparing between different individuals as well as at the different developmental stage (e.g. age) in the same animals.