387996 Automated Mechanical Stimulus Microfluidic Platform for Inidividually Cultured Nematodes
The greatest risk factor for cognitive decline in older adults is aging itself; understanding the genetic basis of cognitive decline during aging is critical. The nematode Caenorhadbitis elegans is an excellent model organism for the study of age-dependent cognitive decline because it has short lifespan, simple nervous system, and powerful genetic manipulation tools. In addition, aged animal is associated with pronounced aging phenotypes, such as dynamic features of neuronal loss like in humans. However, many aspects of how normal aging affects the neural basis of cognition remain unknown. This is because lifespan experiments for large population are highly labor-intense and fluorescently labeled neuronal activities are faint, and obtaining large number of high-content data poses many experimental limitations. There are several platforms to produce animals’ survival curves, but none combines live and dead assay with imaging of neuronal responses. In contrast, microfluidic imaging systems have been used to give chemical stimuli to single worms while monitoring their behavior and neuronal function. However, long-term tracking of individual worms for live and dead assay is difficult because measuring neuronal activity are typically not be obtainable during standard lifespan experiment. Furthermore, there are no well-designed mechanical stimulus platforms because it is hard to generate well-controlled mechanical stimulus in comparison to chemical stimulus.
We developed a scalable high-throughput mechanical stimulatory microfluidic platform for performing large scale C. elegans lifespan experiments and measuring neuronal activity from mechanical stimulus. Our platform can provide precisely controlled food and temperature conditions, with tracking of individual animals for survival data, and highly temporal controlled mechanical stimulus. Rather than comparing variability between age cohorts, studies on individual differences in the trajectory of aging effects can help understand underlying mechanism of cognitive decline. We also coupled this platform with image-analysis methods to automatically measure the neuronal responses from mechanical stimulus. The data from this system could support a statistically rigorous analysis of aging and neuronal responses to mechanical stimulus.