Biology subjects

Otopalik, A. G.

Publications and source records attributed to Otopalik, A. G..

4 recordsLinked to original sources

bioRxiv2025

Networks of sexually dimorphic neurons that regulate social behaviors in Drosophila

Neural mechanisms underlying sexually dimorphic social behaviors remain enigmatic in most species. In Drosophila, sexually dimorphic P1/pC1x neurons have been described as a site of sensory integration that regulates mating and aggressive behaviors. We show that the male P1/pC1x population forms a highly intertwined network with male-specific mAL and aSP-a neurons that is poised to regulate male behavior. The 48 P1/pC1x cell types exhibit heterogeneous synaptic connections with a subset receiving strong input from identified sensory pathways. We also describe circuit motifs by which P1 and sexually dimorphic aIPg neurons co-regulate social behaviors. Genetic driver lines for these cell types were generated and used to discover distinct roles for P1/pC1x cell types in promoting social acoustic signaling and male-male interactions. Our results reveal unexpected diversity in the connectivity and behavioral roles of the P1/pC1x cell types and provide essential genetic tools for interrogating their neurophysiological and behavioral functions.

neuroscience↗

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↗

bioRxiv2018

Neuronal morphologies built for reliable physiology in a rhythmic motor circuit

The neurons of the crustacean stomatogastric ganglion (STG) exhibit highly-conserved firing patterns, voltage waveforms, and circuit functions despite quantifiable animal-to-animal variability in their neuronal morphologies. In recent work, we showed that one neuron type, the Gastric Mill (GM) neuron, is electrotonically compact and operates much like a single compartment, despite having thousands of branch points and a total cable length on the order of 10 mm. Here, we explore how STG neurite morphology shapes voltage signal propagation and summation in four STG neuron types. We use focal glutamate photo-uncaging in tandem with somatic intracellular recordings to examine passive electrotonic structure and voltage signal summation in the GM neuron and three additional STG neuron types: Lateral Pyloric (LP), Ventricular Dilator (VD), and Pyloric Dilator (PD) neurons. In each neuron, we measured the amplitudes and apparent reversal potentials (Erevs) of inhibitory responses evoked with focal glutamate photo-uncaging at more than 20 sites varying in their distance (100-800 m) from the somatic recording site in the presence of TTX. Apparent Erevs were relatively invariant (mean CVs = 0.04, 0.06, 0.05, and 0.08 for 5-6 GM, LP, PD, VD neurons, respectively), suggesting that all four neuron types are similarly electrotonically uniform and compact. We then characterized the directional sensitivity and arithmetic of voltage summation (with fast sequential activation of 4-6 sites) in individual STG neurites. All four neuron types showed no directional bias in voltage signal summation and linear voltage summation. We motivate these experiments with a proof-of-concept computational model that suggests the immense tapering of STG neurite diameters: from 10-20 m to sub-micron diameters at the terminal tips, may explain the uniform electrotonic structures experimentally observed and contribute to the robust nature of this central pattern-generating circuit.

neuroscience↗

bioRxiv2017

Single-Neuron Gene Expression Analysis Using the Maxwell 16 LEV System in the Neural Systems and Behavior Course

Gene expression analysis from single cells has become increasingly prominent across biological disciplines; thus, it is important to train students in these approaches. Here, we present an experimental and analysis pipeline that we developed for the Neural Systems & Behavior (NS&B) course at Marine Biological Laboratory. Our approach used the Maxwell(R) 16 LEV simplyRNA Tissue Kit and GoTaq(R) 2-Step RT-qPCR System for gene expression analysis from single neurons of the crustacean stomatogastric ganglion, a model system to study the generation of rhythmic motor patterns. We used double-stranded RNA to knockdown expression of a putative neuromodulator-activated sodium channel. We then examined the electrophysiological responses to known neuromodulators and confirmed that the response was reduced. Finally, we measured how mRNA levels of several ion channel genes changed in response. Our results provide new insights into the neural mechanisms underlying the generation and modulation of rhythmic motor patterns.

neuroscience↗