Kovacevic I, Bokinsky A, Liu H, McCreedy E, McAuliffe M, Mohler W, Shroff H, Wu Y, Christensen RP, Marquina-Solis J, Kumar A, Winter PW, Guo M, Colon-Ramos DA, Bao Z, Tashakkori N, Santella A
[
Elife,
2015]
The nematode Caenorhabditis elegans possesses a simple embryonic nervous system comprising 222 neurons, a number small enough that the growth of each cell could be followed to provide a systems-level view of development. However, studies of single cell development have largely been conducted in fixed or pre-twitching live embryos, because of technical difficulties associated with embryo movement in late embryogenesis. We present open source untwisting and annotation software which allows the investigation of neurodevelopmental events in post-twitching embryos, and apply them to track the 3D positions of seam cells, neurons, and neurites in multiple elongating embryos. The detailed positional information we obtained enabled us to develop a composite model showing movement of these cells and neurites in an "average" worm embryo. The untwisting and cell tracking capability we demonstrate provides a foundation on which to catalog C. elegans neurodevelopment, allowing interrogation of developmental events in previously inaccessible periods of embryogenesis.
[
J Vis Exp,
2013]
C. elegans is a powerful model system, in which genetic and molecular techniques are easily applicable. Until recently though, techniques that require direct access to cells and isolation of specific cell types, could not be applied in C. elegans. This limitation was due to the fact that tissues are confined within a pressurized cuticle which is not easily digested by treatment with enzymes and/or detergents. Based on early pioneer work by Laird Bloom, Christensen and colleagues developed a robust method for culturing C. elegans embryonic cells in large scale. Eggs are isolated from gravid adults by treatment with bleach/NaOH and subsequently treated with chitinase to remove the eggshells. Embryonic cells are then dissociated by manual pipetting and plated onto substrate-covered glass in serum-enriched media. Within 24 hr of isolation cells begin to differentiate by changing morphology and by expressing cell specific markers. C. elegans cells cultured using this method survive for up 2 weeks in vitro and have been used for electrophysiological, immunochemical, and imaging analyses as well as they have been sorted and used for microarray profiling.
[
PLoS One,
2022]
The nematode Caenorhabditis elegans (C. elegans) is a model organism used frequently in developmental biology and neurobiology [White, (1986), Sulston, (1983), Chisholm, (2016) and Rapti, (2020)]. The C. elegans embryo can be used for cell tracking studies to understand how cell movement drives the development of specific embryonic tissues. Analyses in late-stage development are complicated by bouts of rapid twitching motions which invalidate traditional cell tracking approaches. However, the embryo possesses a small set of cells which may be identified, thereby defining the coiled embryo's posture [Christensen, 2015]. The posture serves as a frame of reference, facilitating cell tracking even in the presence of twitching. Posture identification is nevertheless challenging due to the complete repositioning of the embryo between sampled images. Current approaches to posture identification rely on time-consuming manual efforts by trained users which limits the efficiency of subsequent cell tracking. Here, we cast posture identification as a point-set matching task in which coordinates of seam cell nuclei are identified to jointly recover the posture. Most point-set matching methods comprise coherent point transformations that use low order objective functions [Zhou, (2016) and Zhang, (2019)]. Hypergraphs, an extension of traditional graphs, allow more intricate modeling of relationships between objects, yet existing hypergraphical point-set matching methods are limited to heuristic algorithms which do not easily scale to handle higher degree hypergraphs [Duchenne, (2010), Chertok, (2010) and Lee, (2011)]. Our algorithm, Exact Hypergraph Matching (EHGM), adapts the classical branch-and-bound paradigm to dynamically identify a globally optimal correspondence between point-sets under an arbitrarily intricate hypergraphical model. EHGM with hypergraphical models inspired by C. elegans embryo shape identified posture more accurately (56%) than established point-set matching methods (27%), correctly identifying twice as many sampled postures as a leading graphical approach. Posterior region seeding empowered EHGM to correctly identify 78% of postures while reducing runtime, demonstrating the efficacy of the method on a cutting-edge problem in developmental biology.