Mazzochette

Introduction:

In the Microsystems Lab, we are studying the sense of touch, specifically how our bodies undergo mechanotransduction, the process in which mechanical signals such as forces and displacements are converted into electrochemical signals that our nervous system can understand. The human body is incredibly complex, so we are first tackling the touch sensation problem in a popular neuroscience model organism, the Caenorhabditis elegans (C. elegans).

C. elegans is a small nematode, about 1 mm in length and about 50 um wide. It consists of about 1000 somatic cells and exactly 302 neurons. It was the first animal to have its genome fully mapped, as well as its complete neural network. Finally, it is a convenient sample in a biology lab because it grows to become a full adult animal in only four days. Because of all these traits, the animal lends itself easily to genetic mutation, allowing us to modulate different aspects of the worm’s composition for study.

We know that the worm has exactly six neurons that respond to gentle touch sensation. The morphology of these neurons is consistent across the species, with three of the cell bodies located near the animal’s tail and the other three cell bodies located about 50% of the length of the body from the head. Each of the neurons has a long process that runs from the cell body towards the head. <citation – wormbase> There exists strong evidence that points to specific ion channels in the processes of the neurons that open and close as strain is applied to the neuron. <citation – o hagan> As those channels open and close, ions flow in and out of the cell, creating a change in electric potential.

One method we are using to characterize the sense of touch is to quantify the behavioral response of the C. elegans to gentle touch. To do this, we probe the body of the worm with nano- and micro-scale forces and score whether or not the worm responds. We’ve built micro-scale cantlievers with an embedded strain gauge to apply calibrated forces and integrated them into a clamping system. <citation – sj park force clamp system paper> We apply forces to a freely moving worm and observe the response through a video taken through a stereoscope. A behavioral response is scored positively when the worm’s trajectory changes immediately after a force application.

Currently, this system can apply forces with high precision, but the low spatial and temporal precision of targeting and analyzing behavioral response limits the data throughput and quality. Targeting of the worm is done semi-manually and the behavioral response is scored visually frame by frame, as indicated in the Figure below.

I am currently building a second generation of the system to enable automated tracking of the worm in real time. In this project I seek to design a more robust version of the hardware system as well as investigate methods for thresholding the images, the first step in the image processing. Acquiring images and thresholding are the first two steps in the process of tracking the worm. It is imperative that they must be robust in order to accurately find the worm skeleton and target later in the processing pipeline.

Prior to starting this project, I already had a working prototype for acquiring a data set of video and images, as well as a basis code to skeletonize the worm. After capturing the images of the worm, I have written code based on the algorithm developed in <C. FY paper> to skeletonize the worm and find specified target. The basis for my code development is a library of C++ functions called OpenCV, an open source computer vision platform that employs many novel algorithms in computer vision as well as a set of functions, classes and structures for computation with images.

Methods:

Results:

Conclusions:

References:

Appendix: