[
International Worm Meeting,
2021]
The platinum worm pick, a fixture in C. elegans laboratories for decades, has two drawbacks: (1) the high cost of platinum, a significant problem in many educational settings, and (2) the reliance on an open flame for sterilization, which presents safety hazards. To address the first drawback, we evaluated whether platinum could be replaced with an alternative metal. An ideal worm pick cools quickly after heating, withstands high temperature without degradation, can be flattened and shaped easily, and is inexpensive. With these criteria in mind, we compared 90% platinum, 10% iridium wire (PT9010) with 5 alternatives: stainless steel (SS), Nickel 200, two nickel chromium (Nichrome) alloys, and iron-chromium-aluminum (FeCrAl). To measure cooling rate we built a circuit to resistively heat wires (all 255 microm in diameter) to 800 C and measured the time it took them to cool to 25 C. We found that PT9010 and FeCrAl cooled more rapidly (6-7 s) than the other metals tested (8-9 s). To assay heat resistance we conducted a bending test after 3000 heating cycles of duration 4 s at 800 C. All materials except SS showed good heat resistance, withstanding >50 bends after 3000 heating cycles. SS exhibited poor heat resistance, breaking spontaneously after ~300 cycles. All materials could be easily flattened using standard tools. With regard to cost, all alternative materials were < 0.20 USD/m, as compared to 140 USD/m for PT9010. These results show that all metal alloys tested except for SS represent reasonable, economical alternatives for worm picks. The most promising is FeCrAl which cools as rapidly as platinum, exhibits good heat resistance, and is available at a fraction of the cost. Next, to explore an alternative to flame sterilization, we designed an electric worm pick consisting of a loop of PT9010 or FeCrAl wire attached to a handle containing a rechargeable battery and circuit board. Depressing a button causes current to flow through the loop, heating it to about 800 C within 2 s. A battery charge lasts for ~500 sterilizations. Worm researchers who tested the device reported that the wire loop could be used similar to a worm pick and that electric sterilization promoted faster work since no movements to a flame were necessary. Our device represents a convenient and safer alternative to flame-sterilized worm picks. We are using a similar loop-based worm picking technique in our automated worm picking system (see abstract by Zihao Li et al).
[
International Worm Meeting,
2021]
Brood size evaluations are routinely used in C. elegans to characterize new alleles, the number of which has exploded following the implementation of efficient CRISPR/Cas9 technology. Manual brood size measurements (number of eggs laid, hatched larvae, adult progeny) are labour intensive, error-prone, and may also be disruptive for animals. Indeed, the experimenter must take individuals outside of the incubator for the long and tedious microscopic analysis of their progeny, during which larvae move constantly and L1 are especially hard to visualize. This creates variability between independent assessments. Manipulations also inevitably cause thermal1,2, mechanical3 as well as photooxidative4 stresses to the sample. Even though such classical evaluations of brood size are somewhat imprecise, that method has been used for the past few decades because no alternative has yet been proposed. Here, we present a novel machine learning-based method that fully automates C. elegans brood size measurements. All that the experimenter will have to do is to pick 1 animal per plate into a 24-well plate, place it in the incubator, and wait that the numbers come out. Although still in development, this project has passed the proof-of-concept stage. We are thus confident that it will be able to acquire, and subsequently analyze, images of entire 15.6mm wells up to once every minute. Our system will allow to measure embryonic lethality, as well as larval lethality at each larval stage. Variations in the environmental parameters will be minimal since the device fits on an incubator shelf, while light exposure, temperature, pressure and vibrations will be recorded. Beyond these basic features, we are working on expanding our device to record other parameters. For example, the neural network used in machine learning could be trained to recognize notable phenotypes (e.g. Dpy, Unc, Rol) which could be useful for segregation analyses. We also aim to couple this system to an additional high-magnification microscopic lense, in order to characterize features (e.g. length, width, turns, and velocity) of the parent during the first days of brooding. As such, we may be able to define adult features that would be predictive of an animal's brood size. Using both macroscopic and microscopic lenses along with a controllable moving platform, we expect to characterize brood size and features of hundreds of strains, with replicas easily exceeding a sample size of 50. We estimate that the cost to build the entire system will be under 500 USD.