Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans

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.

Biomechanical Profiling of Caenorhabditis elegans Motility

“…The biomechanical features of the C. elegans swim gait, such as the propulsion force, mechanical power, and bending modulus, can be estimated by incorporating kinematics data into an elasto-hydrodynamic model Sznitman R et al, 2010). We restricted our analysis to the swim gait because biomechanical analysis of the crawling gait is complicated by the presence of lubrication forces, surface tension, and surface rheology.…”

“…The wave efficiency (U/c) and the shape parameter (l/L) (Gray and Lissman 1964), which are used to monitor the nematode's motility gait, were derived from the kinematics features. Finally, we incorporated the kinematics data into an elasto-hydrodynamic model Sznitman R et al, 2010) to estimate the propulsion forces, mechanical Figure 1 Pipeline for analysis of C. elegans biomechanics. (A) Image series are acquired on a standard transmitted light stereo microscope at $15 frames per second.…”

A multi-animal tracker for studying complex behaviors

“…Machine learning approaches had been previously applied to tracking single worms (for example, see [38, 39]). Our approach, however, is slightly different and is particular suitable for tracking multiple animals in long-temporal experiments in which noise accumulates over time.…”

Modeling Fluid Flow Induced by C. elegans Swimming at Low Reynolds Number

“…The past decade has evinced significant research into the locomotion of microorganisms, in particular that of the nematode Caenorhabditis elegans both theoretical and experimental [16,15,17,2,3,11,14]. However, while there have been a number of experimental investigations into the induced fluid movement of bacterial flagella or carpets [10,7], artificial helices [18], and nematodes [16], to the authors' knowledge, there has been little theoretical investigations into the induced fluid flows, particularly at the intermediate scale of C. elegans, a 1mm long, unsegmented round worm.…”