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Mearhyn Petite

As seen in: PLOS ONE, BioRxiv
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Articles

VNC-Dist: A machine learning-based semi-automated pipeline for quantification of neuronal position in the C. elegans ventral nerve cord

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By Saber Saharkhiz, Wesley Chan, Mearhyn Petite, Chloe B. Kirezi
| PLOS ONE
2. Materials and methods 2.1.1. Strains. Worms were maintained at 20°C, and all strains were derived from the N2 genetic background. We used the following alleles and transgenes organized by the linkage group (LG): LGX: vang-1(tm1422) and sax-3(zy5). LGII: unc-4(zy123[unc-4::mNG]) and zySi2[unc-30p::mcherry::H2B::unc-30]. LGIII: vab-7(zy137[vab-7::mNG::3xFlag]), and vab-7(zy142[vab-7::mNG::T2A::mScarlet-I::H2B]). LGIV: prkl-1(ok3182). 2.1.2. Construction of DD, DA, DB fluorescent reporter strains.
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VNC-Dist: A machine learning-based pipeline for quantification of neuronal positioning in the ventral nerve cord of C. elegans

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By Saber Saharkhiz, Mearhyn Petite, Tony Roenspies, Theodore J Perkins
| BioRxiv
Abstract The ventral nerve cord (VNC) of newly hatched C. elegans contains 22 motoneurons organized into three distinct classes: DD, DA, and DB, that show stereotypical positioning and arrangement along its length. The VNC represents a genetically tractable model to investigate mechanisms involved in neuron sorting and positioning. However, accurately and efficiently mapping and quantifying all motoneuron positions within large datasets is a major challenge.
Open in Who Shared Issue with article?

VNC-Dist: A machine learning-based tool for quantification of neuronal positioning in the ventral nerve cord of C. elegans

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By Tony Roenspies, Theodore J Perkins, Saber Saharkhiz, Mearhyn Petite
| BioRxiv
Abstract The ventral nerve cord (VNC) of newly hatched C. elegans contains 22 motoneurons organized into three distinct classes: DD, DA, and DB, that show stereotypical positioning and arrangement along its length. The VNC represents a genetically tractable model to investigate mechanisms involved in neuron sorting and positioning. However, accurately and efficiently mapping and quantifying all motoneuron positions within large datasets is a major challenge.
Open in Who Shared Issue with article?
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