2022 Annual Meeting
Genetic Engineering of Caenorhabditis Elegans for Fluorescent Microscopy in Muscle Activation-Contraction Analysis and Neuron ID.
Networks of neurons intricately coordinate with each other to carry out specific behaviors. More can be discovered about how these neurons communicate with each other to control body movements. Caenorhabditis elegans becomes a straightforward and conclusive living organism for studying neuromuscular control due to its simple nervous system (302 neurons), fully annotated genome, and transparent body for optical microscopy. The interest in studying neurons sparked two unique projects that genetically modified C.elegans with a fluorescent protein for measuring neuronal activity. Within these engineered C.elegans, the fluorescent protein and promoter DNA were assembled with HiFi assembly and introduced to the nematode through microinjection as an extrachromosomal array to form a new strain. These strains can help achieve automated neuron identification in whole-brain images and analyze the delay between muscle activation and muscle contraction for studying neuromuscular control. A supervised machine learning algorithm could be programmed to annotate neurons in large batches of whole-brain images. However, gathering the training set was challenging as it called for perfect imaging conditions and unbiased and accurate manual neuron annotations from a pool of 302 potential neurons. Therefore, to simplify the problem, four strains of C.elegans were genetically engineered to carry mcherry fluorescent labels for only 10 to 40 neurons each. Narrowing the potential neuron labels for an image allows accurate annotations to be made with ease. For the project that involves measuring the muscle activation-contraction delay, the exposure time was decreased to compromise with noise and motion blur. However, fewer photons would be captured by the fluorescent microscope, which resulted in unclear and difficult to process images. Two strains of C.elegans were genetically engineered to carry a brighter GCaMP7 fluorescent protein that responses to calcium influx during muscle activation. A brighter signal would increase the photons being captured by the microscope. This significantly increased the signal to noise ratio and helped collect a better measure of muscle activation-contraction delay. Overall, these two projects used the same genetic engineering techniques to engineer new strains of C.elegans for creating an automated neuron identification algorithm and learning about its muscle activation-contraction patterns. These data will play a key role in revealing insights into neurons' control of body movements.