1
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Zhou R, Yu Y, Li C. Revealing neural dynamical structure of C. elegans with deep learning. iScience 2024; 27:109759. [PMID: 38711456 PMCID: PMC11070340 DOI: 10.1016/j.isci.2024.109759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 01/27/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Caenorhabditis elegans serves as a common model for investigating neural dynamics and functions of biological neural networks. Data-driven approaches have been employed in reconstructing neural dynamics. However, challenges remain regarding the curse of high-dimensionality and stochasticity in realistic systems. In this study, we develop a deep neural network (DNN) approach to reconstruct the neural dynamics of C. elegans and study neural mechanisms for locomotion. Our model identifies two limit cycles in the neural activity space: one underpins basic pirouette behavior, essential for navigation, and the other introduces extra Ω turns. The combination of two limit cycles elucidates predominant locomotion patterns in neural imaging data. The corresponding energy landscape explains the switching strategies between two limit cycles, quantitatively, and provides testable predictions on neural functions and circuit roles. Our work provides a general approach to study neural dynamics by combining imaging data and stochastic modeling.
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Affiliation(s)
- Ruisong Zhou
- School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China
| | - Yuguo Yu
- Research Institute of Intelligent and Complex Systems, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
| | - Chunhe Li
- School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China
- Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
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2
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Nicoletti M, Chiodo L, Loppini A, Liu Q, Folli V, Ruocco G, Filippi S. Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families. PLoS One 2024; 19:e0298105. [PMID: 38551921 PMCID: PMC10980225 DOI: 10.1371/journal.pone.0298105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 01/13/2024] [Indexed: 04/01/2024] Open
Abstract
The nematode Caenorhabditis elegans is a widely used model organism for neuroscience. Although its nervous system has been fully reconstructed, the physiological bases of single-neuron functioning are still poorly explored. Recently, many efforts have been dedicated to measuring signals from C. elegans neurons, revealing a rich repertoire of dynamics, including bistable responses, graded responses, and action potentials. Still, biophysical models able to reproduce such a broad range of electrical responses lack. Realistic electrophysiological descriptions started to be developed only recently, merging gene expression data with electrophysiological recordings, but with a large variety of cells yet to be modeled. In this work, we contribute to filling this gap by providing biophysically accurate models of six classes of C. elegans neurons, the AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. We test our models by comparing computational and experimental time series and simulate knockout neurons, to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. Our models represent a step forward toward the modeling of C. elegans neuronal networks and virtual experiments on the nematode nervous system.
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Affiliation(s)
- Martina Nicoletti
- Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
- Center for Life Nano- & Neuro-Science (CLN2S@Sapienza), Istituto Italiano di Tecnologia, Rome, Italy
| | - Letizia Chiodo
- Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Alessandro Loppini
- Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Qiang Liu
- Department of Neuroscience, City University of Hong Kong, Hong Kong, China
| | - Viola Folli
- Center for Life Nano- & Neuro-Science (CLN2S@Sapienza), Istituto Italiano di Tecnologia, Rome, Italy
- D-tails s.r.l., Rome, Italy
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science (CLN2S@Sapienza), Istituto Italiano di Tecnologia, Rome, Italy
| | - Simonetta Filippi
- Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
- Istituto Nazionale di Ottica del Consiglio Nazionale delle Ricerche (CNR-INO), Florence, Italy
- ICRANet—International Center for Relativistic Astrophysics Network, Pescara, Italy
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3
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Lanza E, Lucente V, Nicoletti M, Schwartz S, Cavallo IF, Caprini D, Connor CW, Saifuddin MFA, Miller JM, L’Etoile ND, Folli V. See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons. PLoS One 2024; 19:e0300628. [PMID: 38517838 PMCID: PMC10959381 DOI: 10.1371/journal.pone.0300628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/29/2024] [Indexed: 03/24/2024] Open
Abstract
In the emerging field of whole-brain imaging at single-cell resolution, which represents one of the new frontiers to investigate the link between brain activity and behavior, the nematode Caenorhabditis elegans offers one of the most characterized models for systems neuroscience. Whole-brain recordings consist of 3D time series of volumes that need to be processed to obtain neuronal traces. Current solutions for this task are either computationally demanding or limited to specific acquisition setups. Here, we propose See Elegans, a direct programming algorithm that combines different techniques for automatic neuron segmentation and tracking without the need for the RFP channel, and we compare it with other available algorithms. While outperforming them in most cases, our solution offers a novel method to guide the identification of a subset of head neurons based on position and activity. The built-in interface allows the user to follow and manually curate each of the processing steps. See Elegans is thus a simple-to-use interface aimed at speeding up the post-processing of volumetric calcium imaging recordings while maintaining a high level of accuracy and low computational demands. (Contact: enrico.lanza@iit.it).
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Affiliation(s)
- Enrico Lanza
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
| | - Valeria Lucente
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
- D-tails s.r.l., Rome, Italy
| | - Martina Nicoletti
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
- Department of Engineering, Campus Bio-Medico University, Rome, Italy
| | - Silvia Schwartz
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
| | - Ilaria F. Cavallo
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
- D-tails s.r.l., Rome, Italy
| | - Davide Caprini
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
| | - Christopher W. Connor
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Mashel Fatema A. Saifuddin
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Julia M. Miller
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Noelle D. L’Etoile
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Viola Folli
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
- D-tails s.r.l., Rome, Italy
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4
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Gosti G, Milanetti E, Folli V, de Pasquale F, Leonetti M, Corbetta M, Ruocco G, Della Penna S. A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG. Neural Netw 2024; 170:72-93. [PMID: 37977091 DOI: 10.1016/j.neunet.2023.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.
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Affiliation(s)
- Giorgio Gosti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; Istituto di Scienze del Patrimonio Culturale, Sede di Roma, Consiglio Nazionale delle Ricerche, CNR-ISPC, Via Salaria km, 34900 Rome, Italy.
| | - Edoardo Milanetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Viola Folli
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Francesco de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, 64100 Piano D'Accio, Teramo, Italy.
| | - Marco Leonetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Via Belzoni, 160, 35121, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Via Orus, 2/B, 35129, Padova, Italy; Veneto Institute of Molecular Medicine (VIMM), Via Orus, 2, 35129, Padova, Italy.
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Via Luigi Polacchi, 11, 66100 Chieti, Italy.
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5
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Barbulescu R, Mestre G, Oliveira AL, Silveira LM. Learning the dynamics of realistic models of C. elegans nervous system with recurrent neural networks. Sci Rep 2023; 13:467. [PMID: 36627317 PMCID: PMC9832137 DOI: 10.1038/s41598-022-25421-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 11/29/2022] [Indexed: 01/12/2023] Open
Abstract
Given the inherent complexity of the human nervous system, insight into the dynamics of brain activity can be gained from studying smaller and simpler organisms. While some of the potential target organisms are simple enough that their behavioural and structural biology might be well-known and understood, others might still lead to computationally intractable models that require extensive resources to simulate. Since such organisms are frequently only acting as proxies to further our understanding of underlying phenomena or functionality, often one is not interested in the detailed evolution of every single neuron in the system. Instead, it is sufficient to observe the subset of neurons that capture the effect that the profound nonlinearities of the neuronal system have in response to different stimuli. In this paper, we consider the well-known nematode Caenorhabditis elegans and seek to investigate the possibility of generating lower complexity models that capture the system's dynamics with low error using only measured or simulated input-output information. Such models are often termed black-box models. We show how the nervous system of C. elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, we target the use of state-of-the-art recurrent neural network architectures such as Long Short-Term Memory and Gated Recurrent Units and compare these architectures in terms of their properties and their accuracy (Root Mean Square Error), as well as the complexity of the resulting models. We show that Gated Recurrent Unit models with a hidden layer size of 4 are able to accurately reproduce the system response to very different stimuli. We furthermore explore the relative importance of their inputs as well as scalability to more scenarios.
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Affiliation(s)
| | - Gonçalo Mestre
- grid.14647.300000 0001 0279 8114INESC-ID Lisboa, Rua Alves Redol 9, Lisbon, 1000-029 Portugal ,grid.9983.b0000 0001 2181 4263IST Técnico Lisboa, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001 Portugal
| | - Arlindo L. Oliveira
- grid.14647.300000 0001 0279 8114INESC-ID Lisboa, Rua Alves Redol 9, Lisbon, 1000-029 Portugal ,grid.9983.b0000 0001 2181 4263IST Técnico Lisboa, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001 Portugal
| | - Luís Miguel Silveira
- grid.14647.300000 0001 0279 8114INESC-ID Lisboa, Rua Alves Redol 9, Lisbon, 1000-029 Portugal ,grid.9983.b0000 0001 2181 4263IST Técnico Lisboa, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001 Portugal
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6
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Naudin L, Jiménez Laredo JL, Liu Q, Corson N. Systematic generation of biophysically detailed models with generalization capability for non-spiking neurons. PLoS One 2022; 17:e0268380. [PMID: 35560186 PMCID: PMC9106219 DOI: 10.1371/journal.pone.0268380] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 04/28/2022] [Indexed: 11/18/2022] Open
Abstract
Unlike spiking neurons which compress continuous inputs into digital signals for transmitting information via action potentials, non-spiking neurons modulate analog signals through graded potential responses. Such neurons have been found in a large variety of nervous tissues in both vertebrate and invertebrate species, and have been proven to play a central role in neuronal information processing. If general and vast efforts have been made for many years to model spiking neurons using conductance-based models (CBMs), very few methods have been developed for non-spiking neurons. When a CBM is built to characterize the neuron behavior, it should be endowed with generalization capabilities (i.e. the ability to predict acceptable neuronal responses to different novel stimuli not used during the model’s building). Yet, since CBMs contain a large number of parameters, they may typically suffer from a lack of such a capability. In this paper, we propose a new systematic approach based on multi-objective optimization which builds general non-spiking models with generalization capabilities. The proposed approach only requires macroscopic experimental data from which all the model parameters are simultaneously determined without compromise. Such an approach is applied on three non-spiking neurons of the nematode Caenorhabditis elegans (C. elegans), a well-known model organism in neuroscience that predominantly transmits information through non-spiking signals. These three neurons, arbitrarily labeled by convention as RIM, AIY and AFD, represent, to date, the three possible forms of non-spiking neuronal responses of C. elegans.
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Affiliation(s)
- Loïs Naudin
- Department of Applied Mathematics, Normandie University, Le Havre, Normandie, France
- * E-mail:
| | | | - Qiang Liu
- Department of Neuroscience, City University of Hong Kong, Kowloon, Hong Kong, SAR, China
| | - Nathalie Corson
- Department of Applied Mathematics, Normandie University, Le Havre, Normandie, France
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7
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Neural model generating klinotaxis behavior accompanied by a random walk based on C. elegans connectome. Sci Rep 2022; 12:3043. [PMID: 35197494 PMCID: PMC8866504 DOI: 10.1038/s41598-022-06988-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 02/09/2022] [Indexed: 11/09/2022] Open
Abstract
Klinotaxis is a strategy of chemotaxis behavior in Caenorhabditis elegans (C. elegans), and random walking is evident during its locomotion. As yet, the understanding of the neural mechanisms underlying these behaviors has remained limited. In this study, we present a connectome-based simulation model of C. elegans to concurrently realize realistic klinotaxis and random walk behaviors and explore their neural mechanisms. First, input to the model is derived from an ASE sensory neuron model in which the all-or-none depolarization characteristic of ASEL neuron is incorporated for the first time. Then, the neural network is evolved by an evolutionary algorithm; klinotaxis emerged spontaneously. We identify a plausible mechanism of klinotaxis in this model. Next, we propose the liquid synapse according to the stochastic nature of biological synapses and introduce it into the model. Adopting this, the random walk is generated autonomously by the neural network, providing a new hypothesis as to the neural mechanism underlying the random walk. Finally, simulated ablation results are fairly consistent with the biological conclusion, suggesting the similarity between our model and the biological network. Our study is a useful step forward in behavioral simulation and understanding the neural mechanisms of behaviors in C. elegans.
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Lanza E, Di Rocco M, Schwartz S, Caprini D, Milanetti E, Ferrarese G, Lonardo MT, Pannone L, Ruocco G, Martinelli S, Folli V. C. elegans-based chemosensation strategy for the early detection of cancer metabolites in urine samples. Sci Rep 2021; 11:17133. [PMID: 34429473 PMCID: PMC8385061 DOI: 10.1038/s41598-021-96613-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Chemosensory receptors play a crucial role in distinguishing the wide range of volatile/soluble molecules by binding them with high accuracy. Chemosensation is the main sensory modality in organisms lacking long-range sensory mechanisms like vision/hearing. Despite its low number of sensory neurons, the nematode Caenorhabditis elegans possesses several chemosensory receptors, allowing it to detect about as many odorants as mammals. Here, we show that C. elegans displays attraction towards urine samples of women with breast cancer, avoiding control ones. Behavioral assays on animals lacking AWC sensory neurons demonstrate the relevance of these neurons in sensing cancer odorants: calcium imaging on AWC increases the accuracy of the discrimination (97.22%). Also, chemotaxis assays on animals lacking GPCRs expressed in AWC allow to identify receptors involved in binding cancer metabolites, suggesting that an alteration of a few metabolites is sufficient for the cancer discriminating behavior of C. elegans, which may help identify a fundamental fingerprint of breast cancer.
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Affiliation(s)
- Enrico Lanza
- grid.25786.3e0000 0004 1764 2907Istituto Italiano di Tecnologia, Center for Life Nano Science, Rome, 00161 Italy
| | - Martina Di Rocco
- grid.25786.3e0000 0004 1764 2907Istituto Italiano di Tecnologia, Center for Life Nano Science, Rome, 00161 Italy ,grid.416651.10000 0000 9120 6856Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, 00161 Italy ,grid.7841.aDepartment of Biochemical Science “A. Rossi Fanelli”, Sapienza Università di Roma, 00185 Rome, Italy
| | - Silvia Schwartz
- grid.25786.3e0000 0004 1764 2907Istituto Italiano di Tecnologia, Center for Life Nano Science, Rome, 00161 Italy
| | - Davide Caprini
- grid.25786.3e0000 0004 1764 2907Istituto Italiano di Tecnologia, Center for Life Nano Science, Rome, 00161 Italy
| | - Edoardo Milanetti
- grid.25786.3e0000 0004 1764 2907Istituto Italiano di Tecnologia, Center for Life Nano Science, Rome, 00161 Italy ,grid.7841.aDepartment of Physics, Sapienza Università di Roma, Rome, 00185 Italy
| | - Giuseppe Ferrarese
- grid.25786.3e0000 0004 1764 2907Istituto Italiano di Tecnologia, Center for Life Nano Science, Rome, 00161 Italy
| | | | - Luca Pannone
- grid.414125.70000 0001 0727 6809Genetics and Rare Diseases Research Division, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, RM 00165 Italy
| | - Giancarlo Ruocco
- grid.25786.3e0000 0004 1764 2907Istituto Italiano di Tecnologia, Center for Life Nano Science, Rome, 00161 Italy
| | - Simone Martinelli
- grid.416651.10000 0000 9120 6856Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, 00161 Italy
| | - Viola Folli
- grid.25786.3e0000 0004 1764 2907Istituto Italiano di Tecnologia, Center for Life Nano Science, Rome, 00161 Italy
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9
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Caprini D, Schwartz S, Lanza E, Milanetti E, Lucente V, Ferrarese G, Chiodo L, Nicoletti M, Folli V. A Shearless Microfluidic Device Detects a Role in Mechanosensitivity for AWC ON Neuron in Caenorhabditis elegans. Adv Biol (Weinh) 2021; 5:e2100927. [PMID: 34423577 DOI: 10.1002/adbi.202100927] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/03/2021] [Indexed: 11/08/2022]
Abstract
AWC olfactory neurons are fundamental for chemotaxis toward volatile attractants in Caenorhabditis elegans. Here, it is shown that AWCON responds not only to chemicals but also to mechanical stimuli caused by fluid flow changes in a microfluidic device. The dynamics of calcium events are correlated with the stimulus amplitude. It is further shown that the mechanosensitivity of AWCON neurons has an intrinsic nature rather than a synaptic origin, and the calcium transient response is mediated by TAX-4 cGMP-gated cation channel, suggesting the involvement of one or more "odorant" receptors in AWCON mechano-transduction. In many cases, the responses show plateau properties resembling bistable calcium dynamics where neurons can switch from one stable state to the other. To investigate the unprecedentedly observed mechanosensitivity of AWCON neurons, a novel microfluidic device is designed to minimize the fluid shear flow in the arena hosting the nematodes. Animals in this device show reduced neuronal activation of AWCON neurons. The results observed indicate that the tangential component of the mechanical stress is the main contributor to the mechanosensitivity of AWCON . Furthermore, the microfluidic platform, integrating shearless perfusion and calcium imaging, provides a novel and more controlled solution for in vivo analysis both in micro-organisms and cultured cells.
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Affiliation(s)
- Davide Caprini
- Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, Rome, 00161, Italy
| | - Silvia Schwartz
- Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, Rome, 00161, Italy
| | - Enrico Lanza
- Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, Rome, 00161, Italy
| | - Edoardo Milanetti
- Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, Rome, 00161, Italy.,Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 5, Rome, 00185, Italy
| | - Valeria Lucente
- CREST OPTICS S.p.A., Via di Torre Rossa 66, Rome, 00165, Italy
| | - Giuseppe Ferrarese
- Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, Rome, 00161, Italy.,Department of Engineering, Campus Bio-Medico University, Via Álvaro del Portillo 21, Rome, 00128, Italy
| | - Letizia Chiodo
- Department of Engineering, Campus Bio-Medico University, Via Álvaro del Portillo 21, Rome, 00128, Italy
| | - Martina Nicoletti
- Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, Rome, 00161, Italy.,Department of Engineering, Campus Bio-Medico University, Via Álvaro del Portillo 21, Rome, 00128, Italy
| | - Viola Folli
- Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, Rome, 00161, Italy
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