Biology subjects

Parnas, M.

Publications and source records attributed to Parnas, M..

10 recordsLinked to original sources

bioRxiv2025

Noninvasive detection of bacterial biofilms using an insect olfactory brain-based gas sensor

Bacteria emit volatile organic compounds (VOCs) that can be targeted for disease detection. Biological olfactory systems have keen senses of smell, can detect VOCs at low concentrations, and are naturally adapted to classifying mixtures of VOCs as odors. Here, we employed locust (Schistocerca americana) olfactory neural circuitry to differentiate biofilm and planktonic cultures of Pseudomonas aeruginosa and Staphylococcus aureus using their odors. In vivo extracellular neural recordings were taken from the second-order olfactory processing center (antennal lobe) of locusts. The VOCs from biofilm cultures evoked distinct spiking responses compared to the planktonic cultures for both bacterial species. By analyzing the population neuronal responses, we classified individual bacterial biofilm vs. planktonic odors with up to 96% accuracy. The neural responses were highly discriminatory within the first couple of seconds of odor presentation and our analysis was conducted on less than five seconds of data, highlighting the potential of our biological sensor for real-time biofilm detection.

neuroscience↗

bioRxiv2025

An insect brain-based bioelectronic neural sensor for the systemic detection and precise classification of endometriosis

Endometriosis is a chronic inflammatory disease with limited screening options and a recognized diagnostic delay. To investigate detection potentialities, a bioelectronic sensor is realized to detect endometriotic vs endometrial models via their emitted volatile organic compounds (VOCs) by leveraging an insect olfactory system combined with computational analytical techniques for classification. Our analyses of cell culture headspace-evoked neural responses show that our sensor can distinguish multiple cell lines by their scent (i.e., emitted VOC mixture). By combining neural responses across experiments, we obtained high-dimensional population neural response templates that were used to classify unknown samples with a high accuracy. We obtained an accuracy of 89% in differentiating 4 cell lines at two growth timepoints (24 hr. and 72 hr.) and obtained an accuracy of 88% in classifying epithelial co-cultures of endometriotic and endometrial cell lines cultured at 0%, 25%, 50%, 75%, and 100% in ascending/descending ratios. Our results support the hypothesis that endometriosis is detectable via the metabolic differences found in the emitted gas mixtures or scent from various cell lines and demonstrates the effectiveness of our sensor in distinguishing the subtle changes in endometriotic vs endometrial models pertaining to durations in growth and co-cultures.

bioengineering↗

bioRxiv2023

Employing a honeybee olfactory neural circuit as a novel gas sensor for the detection of human lung cancer biomarkers

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, for the first time, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers and their mixtures at concentration ranges relevant to human breath, parts-per-billion to parts-per-trillion. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we could classify individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create synthetic lung cancer vs. synthetic healthy breath, honeybee population neuronal responses were also able to classify those complex breath mixtures successfully (100% success rate with a leave-one-trial-out method). Finally, we used separate training and testing datasets containing responses to the synthetic lung cancer and healthy breath mixtures. We identified a simple metric, the peak response of the neuronal ensemble, with the ability to distinguish synthetic lung cancer breath from the healthy breath with 86.7% success rate. This study provides proof-of-concept results that a powerful biological gas sensor, the honeybee olfactory system, can be used to detect human lung cancer biomarkers and their complex mixtures at biological concentrations.

bioengineering↗

bioRxiv2022

Lateral Axonal Modulation is Required for Stimulus-Specific Olfactory Conditioning in Drosophila

Effective and stimulus-specific learning is essential for animals survival. Two major mechanisms are known to aid stimulus-specificity of associative learning. One is accurate stimulus-specific representations in neurons. The second is limited effective temporal window for the reinforcing signals to induce neuromodulation only after sensory stimuli. However, these mechanisms are often imperfect in preventing unspecific associations; different sensory stimuli can be represented by overlapping populations of neurons, and more importantly the reinforcing signals alone can induce neuromodulation even without coincident sensory-evoked neuronal activity. Here, we report a crucial neuromodulatory mechanism that counteracts both limitations and is thereby essential for stimulus specificity of learning. In Drosophila, olfactory signals are sparsely represented by cholinergic Kenyon cells (KCs), which receive dopaminergic reinforcing input. We find that KCs have numerous axo-axonic connections mediated by the muscarinic type-B receptor (mAChR-B). By using functional imaging and optogenetic approaches, we show that these axo-axonic connections suppress both odor-evoked calcium responses and dopamine-evoked cAMP signals in neighboring KCs. Strikingly, behavior experiments demonstrate that mAChR-B knockdown in KCs impairs olfactory learning by inducing undesired changes to the valence of an odor that was not associated with the reinforcer. Thus, this local neuromodulation acts in concert with sparse sensory representations and global dopaminergic modulation to achieve effective and accurate memory formation. HighlightsO_LILateral KC axo-axonic connections are mediated by muscarinic type-B receptor C_LIO_LIKC connections suppress odor-evoked calcium responses and dopamine-evoked cAMP C_LIO_LIknockdown of the muscarinic type-B receptor impairs olfactory learning C_LIO_LIImpaired learning is due to changes to the valence of the unconditioned odor C_LI

neuroscience↗

bioRxiv2022

Harnessing insect olfactory neural circuits for noninvasive detection of human cancer

There is overwhelming evidence that metabolic processes are altered in cancer cells and these changes are manifested in the volatile organic compound (VOC) composition of exhaled breath. Here, we take a novel approach of an insect olfactory neural circuit-based VOC sensor for cancer detection. We combined an in vivo antennae-attached insect brain with an electrophysiology platform and employed biological neural computation rules of antennal lobe circuitry for data analysis to achieve our goals. Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line by analyzing individual cell culture VOC composition-evoked olfactory neural responses in the insect antennal lobe. By evaluating cancer vs. non-cancer VOC-evoked population neural responses, we show that olfactory neurons response-based classification of oral cancer is sensitive and reliable. Moreover, this brain-based cancer detection approach is very fast (detection time ~ 250 ms). We also demonstrate that this cancer detection technique is effective across changing chemical environments mimicking natural conditions. Our brain-based cancer detection system comprises a novel VOC sensing methodology that will spur the development of more forward engineering technologies for noninvasive detection of cancer.

bioengineering↗

bioRxiv2021

Molecular Mechanisms of Neurotransmitter Release Control Distinct Features of Sensory Coding Reliability

To survive, animals must recognize reoccurring stimuli. A key requirement for repeated identification of stimuli is reliable representation by the neural code on each encounter. Synaptic transmission underlies neural codes propagation between brain regions. A hallmark of chemical synapses is their plasticity, which enables signal transfer to be modified in an activity-dependent manner. Despite many decades of intense research on synapses, it remains unclear how the plastic features of synaptic transmission can maintain reliable neural coding. By studying the olfactory system of Drosophila melanogaster, we aimed to obtain a deeper mechanistic understanding of how synaptic function shapes neural coding reliability in the live, behaving animal. We show that the properties of the active zone (AZ), the presynaptic site of neurotransmitter release, are critical for generating a reliable neural code. Reducing neurotransmitter release probability specifically at AZs of olfactory sensory neurons disrupted both neural coding and behavioral reliability. Strikingly, these defects were rescued within a day by target-specific synaptic plasticity, whereby a homeostatic increase in the number of AZs compensated the drop in release probability. These findings demonstrate an important role for synaptic plasticity in maintaining neural coding reliability and are of pathophysiological interest by uncovering an elegant mechanism through which the neural circuitry can counterbalance perturbations.

neuroscience↗

bioRxiv2020

Two Parallel Pathways Mediate Olfactory-Driven Backward Locomotion

Although animals switch to backward walking upon sensing an obstacle or danger in their path, the initiation and execution of backward locomotion is poorly understood. The discovery of Moonwalker Descending Neurons (MDNs), made Drosophila useful to study neural circuits underlying backward locomotion. MDNs were demonstrated to receive visual and mechanosensory inputs. However, whether other modalities converge onto MDNs and what are the neural circuits activating MDNs are unknown. We show that aversive but not appetitive olfactory input triggers MDN-mediated backward locomotion. We identify in each hemisphere, a single Moonwalker Subesophageal Zone neuron (MooSEZ), which triggers backward locomotion. MooSEZs act both upstream and in parallel to MDNs. Surprisingly, MooSEZs also respond mostly to aversive odor. Contrary to MDNs, blocking MooSEZs activity has little effect on odor-evoked backward locomotion. Thus, this work reveals another important modality input to MDNs in addition to a novel olfactory pathway and MDN-independent backward locomotion pathway.

neuroscience↗

bioRxiv2020

Disruption of Glial Ca2+ Oscillations at the Drosophila Blood-Brain Barrier Predisposes to Seizure-Like Behavior

Glia play key roles in regulating multiple aspects of neuronal development and function from invertebrates to humans. We recently found microdomain Ca2+ signaling in Drosophila cortex glia and astrocytes regulate extracellular K+ buffering and neurotransmitter uptake, respectively. Here we identify a role for ER store-operated Ca2+ entry (SOCE) in perineurial glia (PG), a distinct population that contributes to the blood-brain barrier (BBB). PG show a diverse range of Ca2+ oscillatory activity that varies based on their locale within the brain. Unlike cortex glia and astrocytes, PG Ca2+ oscillations do not require extracellular Ca2+ and are blocked by inhibition of SOCE or gap junctions. Disruption of these components triggers heat shock and mechanical-induced seizure-like episodes without effecting PG morphology or large molecule BBB permeability. These findings indicate SOCE-mediated Ca2+ oscillations in PG increase the susceptibility of seizure-like episodes in Drosophila, providing an additional link between glial Ca2+ signaling and neuronal activity.

neuroscience↗

bioRxiv2018

Inhibitory muscarinic acetylcholine receptors enhance aversive olfactory conditioning in adult Drosophila

Olfactory associative learning in Drosophila is mediated by synaptic plasticity between the Kenyon cells of the mushroom body and their output neurons. Both Kenyon cells and their inputs are cholinergic, yet little is known about the physiological function of muscarinic acetylcholine receptors in learning in adult flies. Here we show that aversive olfactory learning in adult flies requires type A muscarinic acetylcholine receptors (mAChR-A) specifically in the gamma subtype of Kenyon cells. Surprisingly, mAChR-A inhibits odor responses in both Kenyon cell dendrites and axons. Moreover, mAChR-A knockdown impairs the learning-associated depression of odor responses in a mushroom body output neuron. Our results suggest that mAChR-A is required at Kenyon cell presynaptic terminals to depress the synapses between Kenyon cells and their output neurons, and may suggest a role for the recently discovered axo-axonal synapses between Kenyon cells.

neuroscience↗

bioRxiv2018

PySight: plug and play photon counting for fast intravital microscopy

Imaging increasingly large neuronal populations at high rates pushed multi-photon microscopy into the photon-deprived regime. We present PySight, an add-on hardware and software solution tailored for photon-deprived imaging conditions. PySight more than triples the median amplitude of neuronal calcium transients in awake mice, and facilitates single-trial intravital voltage imaging in fruit flies. Its unique data streaming architecture allowed us to image a fruit flys olfactory response over 234 x; 600 x; 330{micro}m3 at 73 volumes per second, outperforming top-tier imaging setups while retaining over 200 times lower data rates. PySight requires no electronics expertise nor custom synchronization boards, and its open-source software is extensible to any imaging method based on single-pixel (bucket) detectors. PySight offers an optimal data acquisition scheme for ever increasing imaging volumes of turbid living tissue.

neuroscience↗