A Neural Network Based Automated IFT-20 Sensory Neuron Classifier for Caenorhabditis elegans

TL;DR

This paper introduces a neural network classifier that automatically identifies sensory neurons in C. elegans using single-color fluorescent images, achieving over 91% accuracy without genetic modifications.

Contribution

A novel neural network-based method for neuron identification in C. elegans that relies solely on single-color images, avoiding genetic markers.

Findings

Achieved 91.61% accuracy in neuron classification

Developed an iterative, landmark-based identification process

Eliminated the need for genetic modifications in neuron identification

Abstract

Determining neuronal identity in imaging data is an essential task in neuroscience, facilitating the comparison of neural activity across organisms. Cross-organism comparison, in turn, enables a wide variety of research including whole-brain analysis of functional networks and linking the activity of specific neurons to behavior or environmental stimuli. The recent development of three-dimensional, pan-neuronal imaging with single-cell resolution within Caenorhabditis elegans has brought neuron identification, tracking, and activity monitoring all within reach. The nematode C. elegans is often used as a model organism to study neuronal activity due to factors such as its transparency and well-understood nervous system. The principal barrier to high-accuracy neuron identification is that in adult C. elegans, the position of neuronal cell bodies is not stereotyped. Existing approaches to address this issue use genetically encoded markers as an additional identifying feature. For example, the NeuroPAL strain uses multicolored fluorescent reporters. However, this approach has limited use due to the negative effects of excessive genetic modification. In this study, I propose an alternative neuronal identification technique using only single-color fluorescent images. I designed a novel neural network based classifier that automatically labels sensory neurons using an iterative, landmark-based neuron identification process inspired by the manual annotation procedures that humans employ. This design labels sensory neurons in C. elegans with 91.61% accuracy.