Skuhersky, MichaelWu, TailinYemini, EviatarNejatbakhsh, AminBoyden, EdwardTegmark, Max2022-12-092022-12-092022-05-28Skuhersky M, Wu T, Yemini E, Nejatbakhsh A, Boyden E, Tegmark M. Toward a more accurate 3D atlas of C. elegans neurons. BMC Bioinformatics. 2022 May 28;23(1):195. doi: 10.1186/s12859-022-04738-3. PMID: 35643434; PMCID: PMC9145532.1471-210510.1186/s12859-022-04738-335643434https://hdl.handle.net/20.500.14038/51429Background: 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 biologically realistic deformation. We also present a pipeline for creating alignment atlases from datasets of the recently developed NeuroPAL transgene. In combination, these advances allow us to label neurons in volumetric images with confidence much higher than previous methods. Conclusions: We release, to the best of our knowledge, the most complete full-body C. elegans 3D positional neuron atlas, incorporating positional variability derived from at least 7 animals per neuron, for the purposes of cell-type identity prediction for myriad applications (e.g., imaging neuronal activity, gene expression, and cell-fate).en© 2022. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Caenorhabditis elegansCell atlasNeuron identificationPoint-cloud alignmentJournal ArticleBMC bioinformatics