Northcutt, A. J. | bioRxiv Science

bioRxiv2026

Neuronal Gene Architecture in Cancer borealis Revealed by Long-Read Genome Assembly and Deep Transcriptomic Analysis

Understanding the underlying neuronal function in non-model organisms requires accurate resolution of gene structure and transcript diversity. Here, we present a comprehensive genome annotation tor the Jonah crab (Cancer borealis), a key experimental system in crustacean neurobiology, with a particular focus on transcriptome-supported neuronal gene architecture. By integrating long-read genome assembly with extensive transcriptomic evidence, we reconstructed gene models with high confidence, enabling detailed characterization of exon-intron organization, alternative splicing, and isotorm diversity across gene families. Functional classification revealed extensive representation of neural-associated gene classes, including ion channels and receptors, transporters, enzymes, zinc finger proteins, histones, structural proteins, and cell adhesion molecules, alongside a large set of previously uncharacterized genes. In this study we particularly focused on the neuronal and ion channel gene families known to underlie circuit-level neuronal function in C. borealis. We provide an in-depth analysis of 87 genes spanning 17 neural-related gene families and 41 neuropeptides, detailing chromosomal localization, gene length, exon-intron configuration, and transcript-supported isotorm structure. For many of these genes, transcriptomic data confirmed expression and refined coding boundaries. Comparisons with existing transcriptomic datasets demonstrate strong concordance in gene expression patterns while also revealing novel transcripts and expanded gene family members not previously annotated. Together, this genome and transcriptome-integrated annotation establishes a high-resolution framework tor studying neuronal gene organization in C. borealis. T his resource enables direct connections between gene architecture, transcript diversity, and neural function, supporting future investigations in crustacean neurogenomics, comparative genomics, and the evolution of nervous system complexity.

genomics↗

bioRxiv2019

Molecular Profiling to Infer Neuronal Cell Identity: Lessons from small ganglia of the Crab Cancer borealis

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 two forms of transcriptional profiling - RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from two small crustacean 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. Our 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 two or more cell types. Therefore, our 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.\n\nSIGNIFICANCE STATEMENTSingle cell transcriptional profiling has become a widespread tool in cell identification, particularly in the nervous system, based on the notion that genomic information determines cell identity. However, many cell type classification studies are unconstrained by other cellular attributes (e.g., morphology, physiology). Here, we systematically test how accurately transcriptional profiling can assign cell identity to well-studied anatomically- and functionally-identified neurons in two small neuronal networks. While these neurons clearly possess distinct patterns of gene expression across cell types, their expression profiles are not sufficient to unambiguously confirm their identity. We suggest that true cell identity can only be determined by combining gene expression data with other cellular attributes such as innervation pattern, morphology, or physiology.

neuroscience↗