Isolation and Culture of Larval Cells from C. elegans

Cell culture is an essential tool to study cell function. In C. elegans the ability to isolate and culture cells has been limited to embryonically derived cells. However, cells or blastomeres isolated from mixed stage embryos terminally differentiate within 24 hours of culture, thus precluding post-embryonic stage cell culture. We have developed an efficient and technically simple method for large-scale isolation and primary culture of larval-stage cells. We have optimized the treatment to maximize cell number and minimize cell death for each of the four larval stages. We obtained up to 7.8×104 cells per microliter of packed larvae, and up to 97% of adherent cells isolated by this method were viable for at least 16 hours. Cultured larval cells showed stage-specific increases in both cell size and multinuclearity and expressed lineage- and cell type-specific reporters. The majority (81%) of larval cells isolated by our method were muscle cells that exhibited stage-specific phenotypes. L1 muscle cells developed 1 to 2 wide cytoplasmic processes, while L4 muscle cells developed 4 to 14 processes of various thicknesses. L4 muscle cells developed bands of myosin heavy chain A thick filaments at the cell center and spontaneously contracted ex vivo. Neurons constituted less than 10% of the isolated cells and the majority of neurons developed one or more long, microtubule-rich protrusions that terminated in actin-rich growth cones. In addition to cells such as muscle and neuron that are high abundance in vivo, we were also able to isolate M-lineage cells that constitute less than 0.2% of cells in vivo. Our novel method of cell isolation extends C. elegans cell culture to larval developmental stages, and allows use of the wealth of cell culture tools, such as cell sorting, electrophysiology, co-culture, and high-resolution imaging of subcellular dynamics, in investigation of post-embryonic development and physiology

Cell-specific microarray profiling experiments reveal a comprehensive picture of gene expression in the C. elegans nervous system

A novel strategy for profiling Caenorhabditis elegans cells identifies transcripts highly enriched in either the embryonic or larval C. elegans nervous system, including 19 conserved transcripts of unknown function that are also expressed in the mammalian brain

Comparative Developmental Expression Profiling of Two C. elegans Isolates

Gene expression is known to change during development and to vary among genetically diverse strains. Previous studies of temporal patterns of gene expression during C. elegans development were incomplete, and little is known about how these patterns change as a function of genetic background. We used microarrays that comprehensively cover known and predicted worm genes to compare the landscape of genetic variation over developmental time between two isolates of C. elegans. We show that most genes vary in expression during development from egg to young adult, many genes vary in expression between the two isolates, and a subset of these genes exhibit isolate-specific changes during some developmental stages. This subset is strongly enriched for genes with roles in innate immunity. We identify several novel motifs that appear to play a role in regulating gene expression during development, and we propose functional annotations for many previously unannotated genes. These results improve our understanding of gene expression and function during worm development and lay the foundation for linkage studies of the genetic basis of developmental variation in gene expression in this important model organism

Global Prediction of Tissue-Specific Gene Expression and Context-Dependent Gene Networks in Caenorhabditis elegans

Tissue-specific gene expression plays a fundamental role in metazoan biology and is an important aspect of many complex diseases. Nevertheless, an organism-wide map of tissue-specific expression remains elusive due to difficulty in obtaining these data experimentally. Here, we leveraged existing whole-animal Caenorhabditis elegans microarray data representing diverse conditions and developmental stages to generate accurate predictions of tissue-specific gene expression and experimentally validated these predictions. These patterns of tissue-specific expression are more accurate than existing high-throughput experimental studies for nearly all tissues; they also complement existing experiments by addressing tissue-specific expression present at particular developmental stages and in small tissues. We used these predictions to address several experimentally challenging questions, including the identification of tissue-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues. We also investigate the role of tissue context in gene function through tissue-specific functional interaction networks. To our knowledge, this is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data

Coordinated regulation of cholinergic motor neuron traits through a conserved terminal selector gene

Cholinergic motor neurons are defined by the co-expression of a battery of genes which encode proteins that act sequentially to synthesize, package and degrade acetylcholine and reuptake its breakdown product, choline. How expression of these critical motor neuron identity determinants is controlled and coordinated is not understood. We show here that in the nematode Caenorhabditis elegans all members of the cholinergic gene battery, as well as many other markers of terminal motor neuron fate, are co-regulated by a shared cis-regulatory signature and a common trans-acting factor, the phylogenetically conserved COE (Collier/Olf/EBF)-type transcription factor UNC-3. UNC-3 initiates and maintains expression of cholinergic fate markers and is sufficient to induce cholinergic fate in other neuron types. UNC-3 furthermore operates in negative feedforward loops to induce the expression of transcription factors that repress individual, UNC-3-induced terminal fate markers resulting in diversification of motor neuron differentiation programs in specific motor neuron subtypes. A chordate ortholog of UNC-3, Ciona intestinalis COE, is also both required and sufficient for inducing a cholinergic fate. Thus, UNC-3 is a terminal selector for cholinergic motor neuron differentiation whose function is conserved across phylogeny

Stochastic blockmodeling of the modules and core of the caenorhabditis elegans connectome

Recently, there has been much interest in the community structure or mesoscale organization of complex networks. This structure is characterised either as a set of sparsely inter-connected modules or as a highly connected core with a sparsely connected periphery. However, it is often difficult to disambiguate these two types of mesoscale structure or, indeed, to summarise the full network in terms of the relationships between its mesoscale constituents. Here, we estimate a community structure with a stochastic blockmodel approach, the Erdős-Rényi Mixture Model, and compare it to the much more widely used deterministic methods, such as the Louvain and Spectral algorithms. We used the Caenorhabditis elegans (C. elegans) nervous system (connectome) as a model system in which biological knowledge about each node or neuron can be used to validate the functional relevance of the communities obtained. The deterministic algorithms derived communities with 4–5 modules, defined by sparse inter-connectivity between all modules. In contrast, the stochastic Erdős-Rényi Mixture Model estimated a community with 9 blocks or groups which comprised a similar set of modules but also included a clearly defined core, made of 2 small groups. We show that the “core-in-modules” decomposition of the worm brain network, estimated by the Erdős-Rényi Mixture Model, is more compatible with prior biological knowledge about the C. elegans nervous system than the purely modular decomposition defined deterministically. We also show that the blockmodel can be used both to generate stochastic realisations (simulations) of the biological connectome, and to compress network into a small number of super-nodes and their connectivity. We expect that the Erdős-Rényi Mixture Model may be useful for investigating the complex community structures in other (nervous) systems