Computational Methods for Tracking, Quantitative Assessment, and Visualization of C. elegans Locomotory Behavior

Abstract

The nematode Caenorhabditis elegans provides a unique opportunity to interrogate the neural basis of behavior at single neuron resolution. In C. elegans, neural circuits that control behaviors can be formulated based on its complete neural connection map, and easily assessed by applying advanced genetic tools that allow for modulation in the activity of specific neurons. Importantly, C. elegans exhibits several elaborate behaviors that can be empirically quantified and analyzed, thus providing a means to assess the contribution of specific neural circuits to behavioral output. Particularly, locomotory behavior can be recorded and analyzed with computational and mathematical tools. Here, we describe a robust single worm-tracking system, which is based on the open-source Python programming language, and an analysis system, which implements path-related algorithms. Our tracking system was designed to accommodate worms that explore a large area with frequent turns and reversals at high speeds. As a proof of principle, we used our tracker to record the movements of wild-type animals that were freshly removed from abundant bacterial food, and determined how wild-type animals change locomotory behavior over a long period of time. Consistent with previous findings, we observed that wild-type animals show a transition from area-restricted local search to global search over time. Intriguingly, we found that wild-type animals initially exhibit short, random movements interrupted by infrequent long trajectories. This movement pattern often coincides with local/global search behavior, and visually resembles Lévy flight search, a search behavior conserved across species. Our mathematical analysis showed that while most of the animals exhibited Brownian walks, approximately 20% of the animals exhibited Lévy flights, indicating that C. elegans can use Lévy flights for efficient food search. In summary, our tracker and analysis software will help analyze the neural basis of the alteration and transition of C. elegans locomotory behavior in a food-deprived condition.

A Run-Length Encoding Approach for Path Analysis ofC. elegansSearch Behavior

“…Our proposed methodology is based on both computer vision and machine learning components to quantify and compare C. elegans locomotory behavior. The raw data consists of image frames from the videos we record with our in-house built worm tracker [ 9 ]. The path of each worm is generated from the worm body centroid location in each frame.…”

“…Two types of C. elegans worm data, N2 and tph-1 , are collected using our in-house built tracker, which records the movements of single animals at 30 frames per second [ 9 ], under three different conditions: on food (N2_f and tph-1 _f), off food (N2_nf and tph-1 _nf), and previously starved when placed on a plate without food (N2_nnf; the last condition was experimented for N2 wild-type worms only). Section 3 presents our findings based on 57 video recordings: 4 N2_f; 33 N2_nf, 2 N2_nnf; 13 tph-1 _f, and 5 tph-1 _nf.…”

“…C. elegans locomotory behavior can be recorded and analyzed using computer vision and machine learning approaches. Several worm trackers have been developed to analyze worm behavior in different scenarios such as for recording multiple worms at the same time [ 5 ], imaging of specific neurons [ 6 – 8 ], and recording of single animals that are freshly removed from bacterial food, particularly over a long period of time [ 9 ]. Video data recorded with the trackers can be further analyzed by quantifying the differences in the worm body and movement characteristics.…”

Vibration sensitivity found inCaenorhabditis elegans

“…Ten hermaphrodite worms at the young adult stage were transferred using a platinum wire worm pick from the population maintenance plates to unseeded standard 5.5 cm diameter NGM plates, which were kept at 20°C. Unseeded plates were used to limit behavioural variability over time: transitions from food searching to feeding behaviours affect behavioural responses and variability (Moy et al, 2015). For experiments, the plates were transferred to the custommade plate holder.…”

A Dynamic Survival Detection and Analysis System for Mosquito Larvae Viability in Drug Assays

“…Object detection or object tracking has been used for monitoring and analyzing the behaviors of larvae or issues. [16][17][18][19][20][21][22][23][24][25][26][27][28] Some systems are used for analyzing the locomotion behaviors of multiple Caenorhabditis elegans at the same time [16][17][18][19] and others for analyzing a specific single worm. [20][21][22][23] Other works are proposed for tracking drosophila 24 and their larvae.…”