Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans

Raphael Sznitman,Paulo E Arratia,Josué Sznitman,Manaswi Gupta,Gregory D Hager,Vladimir Brezina

doi:10.1371/journal.pone.0011631

Raphael Sznitman, Paulo E Arratia + Show 4 more

Open Access

https://doi.org/10.1371/journal.pone.0011631

Copy DOI

Abstract

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

Highlights

Since its introduction in the laboratory over thirty years ago [1], the nematode Caenorhabditis elegans has become a ubiquitous model organism to study fundamental questions in biology [2]
Motility quantification has been based on crawling assays [11,12,13,14], where C. elegans is observed to crawl on a substrate
To compare the Multi-Environment Model Estimation (MEME) framework against such systems, we evaluate our algorithm quantitatively for a series of image sequences obtained for various C. elegans locomotive environments

Summary

Introduction

Since its introduction in the laboratory over thirty years ago [1], the nematode Caenorhabditis elegans has become a ubiquitous model organism to study fundamental questions in biology [2]. In the quest to understand the relationships between genes and behavior, this small, approximately 1 mm long roundworm offers a number of advantages for laboratory applications These include a short life cycle, the availability of many mutants to explore gene functions, knowledge of its complete cell lineage [7,8], simplicity of the nervous system and its wiring [9], and a fully sequenced genome [10]. Motility quantification has been based on crawling assays [11,12,13,14], where C. elegans is observed to crawl on a substrate (e.g. agar plate) With the widespread availability of microfabrication techniques, nematode motility assays are increasingly conducted in microfluidic environments [20,21,22,23] With the growing variations in environments used for nematode behavioral assays, users are in need of reliable image analysis tools capable of extracting quantitative data across a wide spectrum of experimental mediums

Methods

Results

Conclusion