A robotic system for automated genetic manipulation and analysis ofCaenorhabditis elegans

Abstract

The nematode Caenorhabditis elegans is one of the most widely studied organisms in biology due to its small size, rapid life cycle, and manipulable genetics. Research with C. elegans depends on labor-intensive and time-consuming manual procedures, imposing a major bottleneck for many studies, especially for those involving large numbers of animals. Here, we describe a general-purpose tool, WormPicker, a robotic system capable of performing complex genetic manipulations and other tasks by imaging, phenotyping, and transferring C. elegans on standard agar media. Our system uses a motorized stage to move an imaging system and a robotic arm over an array of agar plates. Machine vision tools identify animals and assay developmental stage, morphology, sex, expression of fluorescent reporters, and other phenotypes. Based on the results of these assays, the robotic arm selectively transfers individual animals using an electrically self-sterilized wire loop, with the aid of machine vision and electrical capacitance sensing. Automated C. elegans manipulation shows reliability and throughput comparable with standard manual methods. We developed software to enable the system to autonomously carry out complex protocols. To validate the effectiveness and versatility of our methods, we used the system to perform a collection of common C. elegans procedures, including genetic crossing, genetic mapping, and genomic integration of a transgene. Our robotic system will accelerate C. elegans research and open possibilities for performing genetic and pharmacological screens that would be impractical using manual methods.

Automated multimodal imaging ofCaenorhabditis elegansbehavior in multi-well plates

“…The image analysis software also employs a Mask-Region Convolutional Neural Network (Mask-RCNN) to automatically annotate and enumerate worm embryos in each well (Figs. 1 F and G) (30,31).…”

“…Each video was sampled by 5 images that are linearly spaced across the 20 s period, with adjacent images separated by 4 sec. Following previously described methods (30), we deployed a mask-regional convolutional neural network (Mask-RCNN) to detect and identify eggs in each image. The Mask-RCNN model was trained, evaluated, and deployed by PyTorch GPU (v2.0.0 + cu1.1.8) on a system equipped with a 13th Intel(R) Core(TM) i7-13700K processor and an NVIDIA GeForce RTX 4080 GPU.…”