Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans

Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C. elegans

We develop a data harmonization approach for C. elegans volumetric microscopy data, consisting of a standardized format, pre-processing techniques, and human-in-the-loop machine-learning-based analysis tools. Using this approach, we unify a diverse collection of 118 whole-brain neural activity imaging datasets from five labs, storing these and accompanying tools in an online repository WormID (wormid.org). With this repository, we train three existing automated cell-identification algorithms, CPD, StatAtlas, and CRF_ID, to enable accuracy that generalizes across labs, recovering all human-labeled neurons in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. This growing resource of data, code, apps, and tutorials enables users to (1) study neuroanatomical organization and neural activity across diverse experimental paradigms, (2) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (3) share data with the community and comply with data-sharing policies.

Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in C. elegans

We develop a data harmonization approach for C. elegans volumetric microscopy data, still or video, consisting of a standardized format, data pre-processing techniques, and a set of human-in-the-loop machine learning based analysis software tools. We unify a diverse collection of 118 whole-brain neural activity imaging datasets from 5 labs, storing these and accompanying tools in an online repository called WormID (wormid.org). We use this repository to train three existing automated cell identification algorithms to, for the first time, enable accuracy in neural identification that generalizes across labs, approaching human performance in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. To facilitate communal use of this repository, we created open-source software, code, web-based tools, and tutorials to explore and curate datasets for contribution to the scientific community. This repository provides a growing resource for experimentalists, theorists, and toolmakers to (a) study neuroanatomical organization and neural activity across diverse experimental paradigms, (b) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (c) inform models of neurobiological development and function.

Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration

Tracking body parts in behaving animals, extracting fluorescence signals from cells embedded in deforming tissue, and analyzing cell migration patterns during development all require tracking objects with partially correlated motion. As dataset sizes increase, manual tracking of objects becomes prohibitively inefficient and slow, necessitating automated and semi-automated computational tools. Unfortunately, existing methods for multiple object tracking (MOT) are either developed for specific datasets and hence do not generalize well to other datasets, or require large amounts of training data that are not readily available. This is further exacerbated when tracking fluorescent sources in moving and deforming tissues, where the lack of unique features and sparsely populated images create a challenging environment, especially for modern deep learning techniques. By leveraging technology recently developed for spatial transformer networks, we propose ZephIR, an image registration framework for semi-supervised MOT in 2D and 3D videos. ZephIR can generalize to a wide range of biological systems by incorporating adjustable parameters that encode spatial (sparsity, texture, rigidity) and temporal priors of a given data class. We demonstrate the accuracy and versatility of our approach in a variety of applications, including tracking the body parts of a behaving mouse and neurons in the brain of a freely moving C. elegans. We provide an open-source package along with a web-based graphical user interface that allows users to provide small numbers of annotations to interactively improve tracking results.

Unraveling the hierarchical structure of posture and muscle activity changes during mating of Caenorhabditis elegans

One goal of neurobiology is to explain how decision-making in neuromuscular circuits produces behaviors. However, two obstacles complicate such efforts: individual behavioral variability and the challenge of simultaneously assessing multiple neuronal activities during behavior. Here, we circumvent these obstacles by analyzing whole animal behavior from a library of Caenorhabditis elegans male mating recordings. The copulating males express the GCaMP calcium sensor in the muscles, allowing simultaneous recording of posture and muscle activities. Our library contains wild type and males with selective neuronal desensitization in serotonergic neurons, which include male-specific posterior cord motor/interneurons and sensory ray neurons that modulate mating behavior. Incorporating deep learning-enabled computer vision, we developed a software to automatically quantify posture and muscle activities. By modeling, the posture and muscle activity data are classified into stereotyped modules, with the behaviors represented by serial executions and transitions among the modules. Detailed analysis of the modules reveals previously unidentified subtypes of the male's copulatory spicule prodding behavior. We find that wild-type and serotonergic neurons-suppressed males had different usage preferences for those module subtypes, highlighting the requirement of serotonergic neurons in the coordinated function of some muscles. In the structure of the behavior, bi-module repeats coincide with most of the previously described copulation steps, suggesting a recursive "repeat until success/give up" program is used for each step during mating. On the other hand, the transition orders of the bi-module repeats reveal the sub-behavioral hierarchy males employ to locate and inseminate hermaphrodites.

Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals

Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, cells in living organisms often exhibit extensive and complex movements caused by organ deformation and whole-body motion. These movements pose a challenge in obtaining high-quality time-lapse cell images and tracking the intricate cell movements in the captured images. Recent advances in deep learning techniques provide powerful tools for detecting cells in low-quality images with densely packed cell populations, as well as estimating cell positions for cells undergoing large nonrigid movements. This chapter introduces the challenges of cell tracking in deforming organs and moving animals, outlines the solutions to these challenges, and presents a detailed protocol for data preparation, as well as for performing cell segmentation and tracking using the latest version of 3DeeCellTracker. This protocol is expected to enable researchers to gain deeper insights into organ dynamics and biological processes.