Toward a more accurate 3D atlas of C. elegans neurons.

ABSTRACT:

Background

Determining cell identity in volumetric images of tagged neuronal nuclei is an ongoing challenge in contemporary neuroscience. Frequently, cell identity is determined by aligning and matching tags to an "atlas" of labeled neuronal positions and other identifying characteristics. Previous analyses of such C. elegans datasets have been hampered by the limited accuracy of such atlases, especially for neurons present in the ventral nerve cord, and also by time-consuming manual elements of the alignment process.

Results

We present a novel automated alignment method for sparse and incomplete point clouds of the sort resulting from typical C. elegans fluorescence microscopy datasets. This method involves a tunable learning parameter and a kernel that enforces biologicall

SUBMITTER: Skuhersky M 

PROVIDER: S-EPMC9145532 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature