How does the brain integrate information from individual neurons? One efficient way to investigate systematic neuroscience is to record the whole brain down to singular neuron level. Caenorhabditis elegans, a 1 mm long, transparent nematode species, is ideally suited as a starting point. Every C. elegans hermaphrodite has a fixed set of 302 neurons. All neuron connections have been fully characterized by electron microscopy. Despite its small and simple nervous system, C. elegans exhibits a wide range of behaviors ranging from foraging, sleep to sexual activity.
Recently, Yemini et al. genetically engineered a C. elegans strain where each neuron can be uniquely identified by its color code. This greatly facilitates comparison of neural recordings with literature as well as underlying connectomics. However, it is a daunting task to record the whole nervous system at cellular resolution of a freely moving worm. The imaging system needs to achieve high 3D imaging speed (10+ volumes per second) to avoid motion blur while also maintaining single cell resolution and reasonable field of view.Over the past decade, light sheet microscopy has emerged as a promising technique with great spatial resolution and reduced phototoxicity. Swept, confocally-aligned, planar excitation (SCAPE) microscopy, a single objective light sheet modality developed by Hillman lab, has the advantage of an open top geometry and fast 3D imaging speed.
In this proposal, I detail my work towards imaging and tracking the whole C. elegans nervous system at cellular resolution using SCAPE and the NeuroPAL strain.
The first chapter introduces fundamental concepts that link the microscopy field with the C. elegans community. The second chapter involves building a new SCAPE system that incorporates new optical components and a high-speed intensified camera. The goal is to construct a workhorse system capable of capturing real-time volumetric recordings with improved resolution. The improvements stem from an improved optical design as well as careful selection of magnification and scan parameters
While the new imaging system is capable of capturing high-speed volumetric images of freely moving NeuroPAL worms with single-cell resolution, there is no suitable neuron tracking algorithm to robustly extract neural activities from the data. Indeed, the density of the neurons as well as the vigorous movement of the worm is unprecedented. Chapter 3 and 4 constitute two parts of a broader neuron tracking algorithm. In Chapter 3, I introduce an iterative neural network based algorithm for unsupervised 3D image registration. In Chapter 4, a Gaussian Mixture Model based algorithm is proposed that simulates the raw data as the mixture of 3D Gaussian functions.
Chapter 5 is the finale where I integrate of all proposed imaging and tracking methods in recording neural activity from the whole nervous system in freely-behaving NeuroPAL worms. Three applications are demonstrated, which spans from whole nervous system recording to investigation of class-dependent ventral nerve cord motor neurons during locomotion.
In Chapter 6, I report progress towards building the next-generation SCAPE with higher resolution/collection efficiency. A custom-designed zero working distance objective is demonstrated, which uses off-the-shelf objective with novel refractive-index-matched material to achieve high collection numerical aperture without sacrificing field of view (FOV).
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/2qng-e677 |
| Date | January 2024 |
| Creators | Yan, Wenwei |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
Page generated in 0.0019 seconds