BackgroundCrustaceans have been studied extensively as model systems for nervous system function from single neuron properties to behavior. However, lack of molecular sequence information and tools have slowed the adoption of these physiological systems as molecular model systems. In this study, we sequenced and performed de novo assembly for the nervous system transcriptomes of two decapod crustaceans: the Jonah crab (Cancer borealis) and the American lobster (Homarus americanus).ResultsForty-two thousand, seven hundred sixty-six and sixty thousand, two hundred seventy-three contigs were assembled from C. borealis and H. americanus respectively, representing 9,489 and 11,061 unique coding sequences. From these transcripts, genes associated with neural function were identified and manually curated to produce a characterization of multiple gene families important for nervous system function. This included genes for 34 distinct ion channel types, 17 biogenic amine and 5 GABA receptors, 28 major transmitter receptor subtypes including glutamate and acetylcholine receptors, and 6 gap junction proteins – the Innexins.ConclusionWith this resource, crustacean model systems are better poised for incorporation of modern genomic and molecular biology technologies to further enhance the interrogation of fundamentals of nervous system function.
Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.
The medicinal leech is one of the most venerated model systems for the study of fundamental nervous system principles, ranging from single-cell excitability to complex sensorimotor integration. Yet, molecular analyses have yet to be extensively applied to complement the rich history of electrophysiological study that this animal has received. Here, we generated the first de novo transcriptome assembly from the entire central nervous system of Hirudo verbana, with the goal of providing a molecular resource, as well as to lay the foundation for a comprehensive discovery of genes fundamentally important for neural function. Our assembly generated 107,704 contigs from over 900 million raw reads. Of these 107,704 contigs, 39,047 (36%) were annotated using NCBI’s validated RefSeq sequence database. From this annotated central nervous system transcriptome, we began the process of curating genes related to nervous system function by identifying and characterizing 126 unique ion channel, receptor, transporter, and enzyme contigs. Additionally, we generated sequence counts to estimate the relative abundance of each identified ion channel and receptor contig in the transcriptome through Kallisto mapping. This transcriptome will serve as a valuable community resource for studies investigating the molecular underpinnings of neural function in leech and provide a reference for comparative analyses.
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