In vitro or ex vivo method for determining the effect of a biological sample on a biological model using large-scale neural activity

Abstract

The present invention relates to an in vitro or ex vivo method for determining the effect of a biological sample on a biological interface, the method comprising, in particular, the implementation of a bioreceptor comprising a multi-compartment microfluidic device incorporating a relevant cell co-culture to which the sample is applied. The response of the neural network to said sample, in particular a change in the cell / neural network, is recorded and subsequently analysed. A differential diagnosis is subsequently carried out by comparing the network markers for true positives and the test samples.

Description

[0001] DESCRIPTION

[0002] TITLE: In vitro or ex vivo method for determining the effect of a biological sample on a biological model using large-scale neuronal activity.

[0003] FIELD OF THE INVENTION

[0004] The invention relates to the field of diagnosis, in particular that of diagnostic bioreceptors, associated with microfluidic technology as biosensors. Thus, the invention relates to a method for determining the effect of a biological sample on a biological model, to a bioreceptor implemented in such a method as well as to the uses of said bioreceptor.

[0005] STATE OF THE ART

[0006] Central and / or peripheral nervous system pathologies affect more than 700 million people worldwide, more than 10 million in Europe and tens of thousands in France. These pathologies are considered to be the most complex in terms of etiology, progression and treatment. These neurocognitive disorders (e.g. Alzheimer's disease, Parkinson's disease, head injuries, strokes, or amyotrophic lateral sclerosis) lead to cognitive, functional and / or behavioral deficits in a subject, i.e. an alteration of abilities related to language, social interactions, memory, logical reasoning or autonomy. These pathologies, also called neurological or nervous disorders, therefore constitute a major public health problem.

[0007] Currently, the diagnosis of these conditions is mainly made after the appearance and observation of the first symptoms. Following this initial clinical diagnosis, measurements of biological fluids, such as blood or cerebrospinal fluid (CSF), supplemented by imaging tests are carried out. For other neuronal disorders, such as head trauma (or concussions), the diagnosis is based on the establishment of a Glasgow Coma Scale score established by clinical observations and symptoms.

[0008] In recent years, the presence of new biomarkers has been highlighted for the diagnosis of these neurological or nervous disorders. For example, microRNAs present in biological fluids such as CSF or blood can be used to diagnose concussions using very traditional biological assay techniques such as polymerase chain reaction (PCR) or enzyme-linked immunosorbent assay (ELISA).

[0009] Current treatments are part of a support and secondary and tertiary prevention approach aimed at preserving quality of life, preventing complications and behavioral crises by anticipating advanced stages of diseases. In addition, the search for new treatments is made difficult by the complexity of establishing a reliable differential diagnosis. Indeed, for several different conditions such as neurodegenerative diseases, there may be identical or similar clinical symptoms due to the involvement of the same dysfunctional proteins in these disorders.

[0010] In order to ensure effective and early treatment of the disease, it is therefore essential to establish a reliable and early diagnosis of such a neurological or nervous condition and thus be able to improve the identification of appropriate treatment.

[0011] However, to date, there is no sensitive, rapid and reproducible test allowing the early and deterministic diagnosis of a cognitive, functional and / or behavioral deficit associated with a neurological and / or nervous condition.

[0012] At the same time, methods for measuring the activity of an agent, such as a drug, on neurological activity generally rely on behavioral alterations in living animals or use tissue biosensors. However, this type of behavioral testing on animal models is expensive, time-consuming, difficult to quantify, and difficult to reproduce.

[0013] The use of tissue-based biosensors overcomes some of the limitations imposed by behavioral tests and provides an outcome that is easier to quantify. However, such biosensors have limited sensitivity and cannot detect subtle alterations in cognitive function. Tissue-based biosensors capable of detecting agents that alter or otherwise modify neuronal function typically consist of cultured neurons held on an electrode array that records passive properties of the cell membrane, such as input impedance, or spontaneous action potential activity. Due to low sensitivity, these types of biosensors are primarily implemented for the determination of acute cell death due to exposure to high concentrations of toxic agents (e.g., excitotoxicity induced by high concentrations of glutamate in the synaptic cleft).Furthermore, most biosensors only provide short-term data. In general, conventional solutions have the disadvantages that: / ) they do not allow or only allow limited detection of the presence of unsuspected or new agents that could be the cause or indicate the presence of a neurological and / or nervous disorder; / / ) they mainly detect the effect of fast-acting agents, whereas agents that require several hours or days to produce their effect cannot generally be detected with known methods; and / / / ) they do not allow distinction of affected neuronal populations.

[0014] It is clear from the above that there is a clear need to develop new solutions that enable rapid, effective, early and reliable differential diagnosis of neurological and / or nervous disorders.

[0015] DESCRIPTION OF THE INVENTION

[0016] The inventors have developed, unexpectedly and surprisingly, a method for determining the effect of a biological sample on a biological model involving a neural network. This method is implemented by means of an innovative biosensor comprising in particular a bioreceptor comprising, advantageously in the form of, a multi-compartmental microfluidic device. This method, including the bioreceptor for determining the effect of a biological sample on a biological model and its uses, are characterized throughout the present description.

[0017] An object of the present invention is to obtain data on the state of the neural network exposed to a biological sample and then to analyze this data in order to quickly and inexpensively establish a differential diagnosis of a neurological and / or nervous condition in a subject with an improved specificity / sensitivity ratio. Ultimately, the aim is to propose a treatment adapted to the subject aimed at preserving quality of life, preventing complications and anticipating the progression of the pathology, in particular towards advanced stages of the pathologies.

[0018] Thus, the present invention relates to an in vitro or ex vivo method for determining the effect of a biological sample on a biological model, comprising the following steps of: a.Providing a bioreceptor comprising, advantageously in the form of, a multi-compartment microfluidic device comprising: i) at least a first compartment and a second compartment; ii) at least one means forming a biological interface to enable communication by neuronal connection between the first and second compartments; iii) the culture of at least one type of cell or explant per compartment, the first compartment comprising at least the culture of neurons in the form of a neural network and the second compartment comprising at least the culture of neurons in the form of a neural network and / or the culture of non-neuronal cells or the culture of an explant; iv) at least one device allowing the recording of the functional activity of the neurons on a plurality of measurement points arranged in a spatially distributed manner in the first compartment; b.Bringing the neurons and / or non-neuronal cells or the explant in culture in the second compartment into direct or indirect contact with said biological sample; c. Recording the functional activity of the neurons in culture in the first compartment at the plurality of measurement points over a measurement duration following the contacting according to step b); d. Converting the recording of the functional activity of the neurons in culture in the first compartment into functional activity data; e. Analyzing the functional activity data obtained in step d); f. Determining at least one parameter characteristic of the state of the neural network in the first compartment from the functional activity data; g.Carry out a comparison between the at least one parameter characteristic of the state of the neural network and a reference value of at least one parameter characteristic of the state of the neural network in order to determine the effect of said biological sample.

[0019] In the rest of the presentation and for reasons of simplification:

[0020] - the term “neuron” is used equivalently to “neuronal cells”;

[0021] - the term “neurological and / or nervous disorder” is used in an equivalent manner to “pathology of the central and / or peripheral nervous system” or “neurocognitive disorder” or “cognitive, functional and / or behavioral deficit”; and

[0022] - the term “affection” is used in an equivalent manner to “pathology”, “affected” or even “lesion”.

[0023] In the context of the present invention, by "biological model" is meant the culture of cells, non-human, human derived from pluripotent stem cells or primary human, alone or in co-culture, neuronal or non-neuronal isolated from their natural biological environment, in a multi-compartmental microfluidic architecture in which the compartments are connected directly or indirectly. In other words, it is a question of cultivating cells in an artificial environment with a minimum of alteration of the natural conditions or in vivo conditions.

[0024] In the context of the present invention, the term "biological interface" means a system comprising a contact junction between cell populations included in the device of the invention allowing communication between the cells by exchanging information via electrical and / or chemical communication. For the purposes of the invention, it may be a culture of neuronal cells, non-neuronal cells or even a tissue explant.

[0025] In the context of the present invention, the term "bioreceptor" denotes a set of molecules and / or cells allowing the selective recognition of a molecule (or analyte). For example, enzymes, cells, aptamers, nanoparticles or even antibodies may be mentioned. For the purposes of the invention, the bioreceptor is a culture of neurons, for example human neurons derived from pluripotent stem cells, included in a multi-compartmental microfluidic device modeling a network architecture that may be characteristic of a neurological and / or nervous pathology. This bioreceptor is associated with a transducer that will convert the association of the analyte and the bioreceptor into a measurable signal.According to the invention, this combination of a bioreceptor and a transducer is defined as a biosensor whose advantage is the establishment of a rapid and reliable diagnosis, in particular thanks to the presence of a biological interface within these microfluidic architectures which makes the bioreceptor more physiological, that is to say that it is an innovative experimental model whose physical, biochemical and biological organization, operation and reactions make it possible to transpose the results obtained to humans. In the context of the present invention, by "biosensor" is therefore meant a device comprising at least one bioreceptor as previously defined and a means of converting the functional activity of neurons into functional activity data as defined subsequently.

[0026] In the context of the present invention, by "functional activity of neurons" is meant the emission and propagation of a nervous message in the form of electrical signals and / or secretions of neurotransmitters.

[0027] In the context of the present invention, by "a means of converting the functional activity of neurons into functional activity data" we mean a transducer. Transducers can be grouped into 3 main categories: Optical (e.g. optical fiber), electrochemical (e.g. amperometric, potentiometric, impedimetric or conductimetric), and mass-based (e.g. piezoelectric and magnetoelastic).

[0028] In the context of the present invention, by "node", "node link" or "vertices / edge" we mean a neuron, an assembly of cells or population of neurons connected in a continuous manner and exhibiting functional activity. Note that the brain comprises approximately 100 billion neurons and 1 neuron can establish up to 10,000 connections. In the context of the present invention, by "module" we mean several nodes connected together forming groups or "clusters".

[0029] In the context of the present invention, by "action potentials" or "spikes" we mean the appearance of nerve impulses, namely the succession of a transient and local depolarization of the plasma membrane followed by a repolarization of the internal membrane (possibly followed by a hyperpolarization for non-myelinated cells).

[0030] In the context of the present invention, "burst(s)" means several action potentials in a given time. A burst is generally defined by a succession of at least four action potentials in a defined time, for example, for 100 ms. A burst generally contains between 10 and 100,000 spikes for such a duration.

[0031] In the context of the present invention, "network burst(s)" means groups of action potentials forming a network, i.e. following a defined synchrony of appearance. In other words, network bursts are detected by the synchronous action potentials of several electrodes.

[0032] Preferably, the subject of the present invention is an in vitro or ex vivo method for determining the effect of a biological sample on a biological model as defined above, having the following technical characteristics, taken alone or in combination:

[0033] - the at least one parameter characteristic of the state of the neural network in the first compartment, being selected from the group consisting of: a number of action potential(s), an inter-action potential interval or “ISI”, a coefficient of variation of inter-action potential interval(s), a number of active electrodes, a mean normalized action potential rate, a number of bursts, a number of electrode(s) having captured one or more bursts, a mean duration of bursts, a mean of action potentials in a burst, a mean of inter-action potential intervals (ISI) in a burst, an inter-burst interval or “IBI”, a frequency of bursts, a percentage of bursts, a number of network bursts, a frequency of network bursts, a duration of network bursts, a mean of action potentials in network bursts, a mean of inter-action potential intervals (ISI) in network bursts,a number of electrodes participating in forming network bursts, a percentage of bursts in network bursts, a coefficient of variation of inter-burst intervals (IBI) in network bursts, an area under the cross-correlation curve, and a synchrony index,

[0034] - the at least one parameter characteristic of the state of the neural network in the first compartment being selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; iii) an average of the action potentials per second; iv) a network connectivity index or “Small World Index”; v) a z-score; vi) a participation coefficient; and vii) a centrality index of a node,

[0035] - the biological interface comprises, advantageously consists of at least one of the elements selected from the group consisting of fluidic microchannels; PDMS microchannels; a porous membrane, the porosity of which is, advantageously, between 10 nm and 40 pm and the pore density of which is, advantageously, between 10 and 1.10 9 pores per cm 2 , advantageously between 1.10 5 and 1.10 9 pores per cm 2 ; a porous capillary membrane, advantageously it is a membrane on which at least one organoid is cultivated (i.e. a three-dimensional multicellular structure which reproduces in vitro the microanatomy of an organ), made of polycarbonate, polyester, polyethylene terephthalate and / or polytetrafluoroethylene; a gel; a hydrogel and their mixtures;

[0036] - the construction of a graph representing the neural network in step e), which is obtained by implementing graph theory; advantageously the nodes of the graph correspond to the measurement points of the functional activity and the connections between nodes correspond to the correlations of the axonal communications;

[0037] - step f) comprises the determination of two parameters, advantageously three parameters, four parameters, preferably five parameters, six parameters, or even seven parameters, characteristic of the state of the neural network in the first compartment from the functional activity data, said parameters being selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; iii) an average of the action potentials per second; iv) a network connectivity index or “Small World Index”; v) a z-score; vi) a participation coefficient; and vii) a centrality index of a node;

[0038] - the network connectivity index is the ratio between the connection coefficient and the average of the minimum inter-node lengths;

[0039] - the average of the action potentials per second must have a value greater than or equal to, advantageously strictly greater than, 0.5, preferably 1 or 1.5 or even 2, to allow the analysis of at least one parameter being selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; iv) a network connectivity index or “Small World Index”; v) a z-score; vi) a participation coefficient; and vii) a centrality index of a node;

[0040] - the connection coefficient has a value greater than or equal to 0, preferably 0.5, advantageously between 0 and 1;

[0041] - the average of the minimum inter-node lengths has a value greater than or equal to 1, preferably 1.5 or 2;

[0042] - the network connectivity index has a value greater than or equal to 0, preferably 0.5, advantageously between 0 and 1;

[0043] - the z-score or “z-score” has a value greater than or equal to 0, preferably 5, advantageously between 0 and 10;

[0044] - the participation coefficient or “Participation Coefficient” has a value greater than or equal to 0, preferably 0.5, advantageously between 0.5 and 1;

[0045] - the centrality index of a node has a value greater than or equal to 0, preferably 5, advantageously between 6 and 10;

[0046] - the device allowing the recording of the functional activity of the neurons on a plurality of measurement points arranged in a spatially distributed manner in the first compartment according to step a.iv), preferably arranged in a spatially distributed manner in the first compartment and at least one additional compartment, advantageously arranged in a spatially distributed manner in all the compartments, is a device allowing recording in indirect contact with the cells in culture, selected from the group consisting of:

[0047] - a device for recording activity by planar or non-planar microelectrode networks, semi-solid electrodes, by amperometry or voltammetry;

[0048] - a fluorescence imaging recording device, such as calcium imaging or transmembrane ion flux imaging;

[0049] - a device for recording intracellular, extracellular or patch-clamp electrophysiological activity in whole-cell, attached-cell, inside-out or outside-out configuration;

[0050] - step d) of carrying out a conversion of the recording of the functional activity of the neurons in culture in the first compartment into functional activity data is carried out via a means of converting the functional activity of neurons into functional activity data is an algorithmic system for converting electrical and / or electrophysiological data into binary data; - the duration of measurement of the recording of the functional activity of the neurons in culture in the first compartment according to step c) is between 300 ms and 20 min, advantageously between 1 min and 15 min, preferably between 5 min and 12 min;

[0051] - the multi-compartment microfluidic device further comprises a third compartment and at least one means forming a biological interface to enable communication by neural connection between the first and third compartments and / or at least one means forming a biological interface to enable communication by neural connection between the second and third compartments; advantageously a fourth compartment and at least one means forming a biological interface to enable communication by neural connection between the first and fourth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the second and fourth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the third and fourth compartments;preferably a fifth compartment and at least one means forming a biological interface to enable communication by neural connection between the first and fifth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the second and fifth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the third and fifth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the fourth and fifth compartments;

[0052] - the contacting according to step b) is indirect in that the biological sample is applied to the biological interface of the second compartment and / or to at least one of the biological interfaces of the third compartment and / or to at least one of the biological interfaces of the fourth compartment and / or to at least one of the biological interfaces of the fifth compartment;

[0053] - each of the compartments included in the device of the invention comprises the culture of one, two or even three types of neuronal and / or non-neuronal cells

[0054] - neurons are selected from the group consisting of glutamatergic, GABAergic, serotonergic, cholinergic, dopaminergic, adrenergic, noradrenergic, sensory neurons and motor neurons;

[0055] - non-neuronal cells are selected from the group consisting of glial cells (including microglia / macrophages and macroglia (i.e. astrocytes, oligodendrocytes, Schwann cells, and ependymocytes)), epithelial, connective, thyroid, fat, blood, immune, bone, cartilage, gastric, pancreatic, liver, intestinal, pulmonary, endothelial, muscle, vascular, cardiac, mesenchymal, retinal pigment epithelium cells, and retinal cells;

[0056] - the explant is a tissue of cerebral, epithelial, ocular, thyroid, fatty, vascular, bone, cartilaginous, gastric, pancreatic, hepatic, intestinal, pulmonary, endothelial, muscular, retinal, cardiac and placental origin; and / or

[0057] - the biological sample is selected from the group consisting of blood, saliva, urine, tears, sweat, sputum, mucus, pus, lymph, cerebrospinal fluid, nasopharyngeal secretions, oropharyngeal secretions, synovium, pleural fluid, peritoneal fluid, pericardial fluid, aqueous humor, amniotic fluid and plasma; and / or

[0058] - the biological sample may be an “agent” or “test agent”, i.e. a compound having properties that modulate the functional activity of neurons or an agent for which it is not known whether it has properties that modulate the functional activity of neurons, in which case the method of the invention makes it possible to identify and / or characterize possible properties of said agent, or even to identify a “threshold concentration”. In other words, in particular when the test agent is a drug, this is the concentration of the agent under a minimal treatment regime (i.e. for a pharmacological composition, under the lowest therapeutic dosage generally prescribed for animals or humans).In this embodiment of the invention, the comparison with a reference value carried out in step g) consists of comparing at least one parameter characteristic of the state of the neural network of the invention consisting of a value obtained before application of the agent or test agent and / or a value obtained after application of the agent or test agent, said value being earlier in time to ensure monitoring of the effect of said agent or test agent.

[0059] Thus, within the framework of the present invention, by:

[0060] - “number of action potential(s)” means the number of action potentials detected during the recording (detection parameters defined before the recording);

[0061] - “inter-action potential interval or “ISI”” refers to the average duration between detected action potentials;

[0062] - "coefficient of variation of inter-action potential interval(s)" refers to the standard deviation of the ISI divided by the mean of the ISI. This parameter concerns the regularity of the action potentials and accounts for their distribution;

[0063] - "number of active electrodes" means the number of electrodes that have activity greater than or equal to a minimum average action potential rate, defined before recording, - "a normalized average action potential rate" means the number of action potentials divided by the recording time (average). This parameter is generally defined in Hz or spike / s (action potential / second). This parameter is determined solely from the functional activity data measured on the active electrodes, defined before recording;

[0064] - “number of bursts” means the number of bursts in a recording per electrode or in total (sum of bursts from all electrodes),

[0065] - "number of electrodes that have captured bursts" means the total number of electrodes that have measured a number of bursts / minute. This number of bursts / minute can be defined before recording. It can be, for example, a minimum of 5 bursts / minute;

[0066] - “average burst duration” means the average time between the first and last action potential defining each measured burst;

[0067] - “average of action potentials in a burst” or “average of spikes in a burst” means the average number of action potentials in the measured bursts;

[0068] - “average of the inter-action potential intervals (ISI) in a burst” means the average of the inter-action potential intervals defined in a burst;

[0069] - “inter-burst interval” or “IBI” refers to the average duration between the different bursts recorded;

[0070] - “burst frequency” refers to the total number of bursts divided by the recording time. This parameter is generally defined in hertz or burst / min;

[0071] - “burst percentage” refers to the ratio between the number of action potentials in a burst and the total number of action potentials recorded;

[0072] - “number of network bursts” means the total number of network bursts identified during recording;

[0073] - "network burst frequency" refers to the total number of network bursts divided by the recording time. This parameter is generally defined in hertz;

[0074] - “network burst duration” refers to the average time between two action potentials in a network burst;

[0075] - “average of action potentials in network bursts” means the average of the action potentials detected within all the recorded network bursts;

[0076] - “mean inter-action potential intervals (ISI) in network bursts” means the mean of the inter-action potential intervals defined in all recorded network bursts;

[0077] - “number of electrodes participating in forming network bursts”, we designate the average number of active electrodes in a network burst;

[0078] - "burst percentage in network bursts" refers to the ratio between the number of action potentials in a network burst and the total number of action potentials recorded; - "inter-burst interval variation coefficient (I Bl) in network bursts" refers to the standard deviation of the IBI divided by the average of the IBI;

[0079] - “area under the cross-correlation curve” refers to the area under the cross-correlation curve between electrodes. This parameter can be determined according to the method defined by Halliday, Rosenber, Breeze & Conway 2006, in their publication “Neural Spike Train Synchronization Indices: Definitions, Interpretations, and Applications”;

[0080] - “Synchrony index” means a measure of synchrony (without unit) between 0 and 1. This parameter can be determined in accordance with the publication “A comparison of binless spike train measures” (Paiva et al, 2010);

[0081] - “connection coefficient” or “clustering coefficient” refers to the probability that two nodes are connected knowing that they have a neighbor in common;

[0082] - “average of the minimum inter-node lengths” means the average of the lengths between two connected nodes; and

[0083] - by "average action potentials per second" we mean the average number of nerve impulses per second, namely the succession of a transient and local depolarization of the plasma membrane followed by a repolarization of the internal membrane (possibly followed by a hyperpolarization for non-myelinated cells);

[0084] - by "network connectivity index" or "Small World Index"; we mean a structural connection (physical connections, i.e. synapses and axons) and physiological connections (functional connectivity / symmetrical relationship and effective connectivity / causal relationship) between two or more nodes, which is a reflection of the robustness of the neural network. Preferably, the network connectivity index is determined by the ratio between the connection coefficient and the average of the minimum inter-node lengths;

[0085] - by z-score or “z-score” we mean a measure allowing us to characterize the way in which the connectivity of the nodes is distributed in the modules, that is to say to characterize intra-modules;

[0086] - by participation coefficient or “Participation Coefficient” we mean a measure allowing to characterize the way in which the connectivity of the nodes is distributed between several modules or inter-module; and

[0087] - by "centrality index of a node" we mean a value proportional to the number of passages through this node during a random traversal of the graph, representing the neural network according to the invention, by randomly borrowing one of the connections starting from a node.

[0088] These parameters characteristic of the state of the neural network in the first compartment, determined from the recorded functional activity data, can be used alone or in combination with each other. They are divided into the following four categories: 1 / analysis of spikes (action potentials). This category includes the following parameters: number of action potential(s), average action potentials per second, interpotential action interval or "ISI", coefficient of variation of inter-action potential interval(s), number of active electrodes, average normalized action potential rate;

[0089] 2 / burst analysis. This category includes the following parameters: number of bursts, number of electrode(s) having captured one or more bursts, average burst duration, average action potentials in a burst, average inter-action potential intervals (ISI) in a burst, inter-burst interval or “IBI”, burst frequency and percentage of bursts.

[0090] 3 / Network Burst Analysis. This category includes the following parameters: number of network bursts, frequency of network bursts, duration of network bursts, average action potentials in network bursts, average inter-action potential intervals (ISI) in network bursts, number of electrodes participating in forming network bursts, percentage of bursts in network bursts, coefficient of variation of inter-burst intervals (IBI) in network bursts.

[0091] 4 / network connectivity. This last category applies to all types of networks, including neural networks. It includes the following parameters, some of which are determined using connectivity algorithms called CrossCorrelation (see “Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons” Deblasi et al, 2019): area under the cross-correlation curve, synchrony index, connection coefficient, average minimum inter-node length, network connectivity index or “Small World Index”, participation coefficient or “Participation Coefficient”, and centrality index of a node.

[0092] In particular, the device allowing the recording of the functional activity of the neurons on a plurality of measurement points arranged in a spatially distributed manner in the first compartment according to step a.iv) comprises electrodes in direct or indirect contact with the neurons. This device makes it possible to record the difference in polarization of the neurons allowing them to communicate with each other by action potentials characterized by a potential difference greater than 1 pV.

[0093] The means for converting functional activity into functional activity data according to step a.iv) of the invention is an algorithmic system for converting electrical and / or electrophysiological data into binary data.In particular, this involves: i) converting electrophysiological data by means of a signal transducer provided with the recording equipment, said transducer being able to be coupled with a signal amplifier; ii) establishing a detection threshold for binarizing the electrophysiological activity of the neurons, individually or collectively by node; iii) creating a spatio-temporal matrix of functional activity of the nodes from the data called matrix data obtained in the previous step; and iv) creating a map representing the weighted cross-spatio-temporal correlation of the activity of each node connected to other nodes in order to provide a set of quantitative data (or matrix data), which prove to be characteristic for each biological model according to the invention, in pathological representation configuration.In particular, it is a matter of generating a “functional activity signature” which will be compared to a “reference library of functional activity signatures” or to a “functional activity signature of the same previously recorded network” or to a “collection of functional activities of the same previously recorded network”, thus making it possible to establish the relative differences between the recorded signatures and the reference signatures.

[0094] In the context of the present invention, by "functional activity signature" is meant an activity profile, that is to say a possible alteration of the functional activity (i.e. electrical activity) of the neurons in culture in the form of a network in the first compartment of the device according to the invention.

[0095] In the context of the present invention, by "functional activity signature library" is meant a collection of activity signatures for a multiplicity of different biological samples (for example 2 or more, advantageously more than 10, preferably more than 100, more than 1,000, or even more than 10,000 or even more than 1,000,000 biological samples) which can be identified from each other.

[0096] In the context of the invention, the comparison with a reference value carried out in step g) consists of comparing at least one parameter characteristic of the state of the neural network of the invention consisting of a value obtained from a sample taken previously (in terms of seconds, minutes, hours, days, months and / or years) with at least one parameter characteristic of the state of the neural network obtained after application of the sample whose effect is to be determined by the method of the invention; and / or consisting of a value from a reference library or reference collection. In particular,

[0097] - in the case of implementing the in vitro or ex vivo method for determining the effect of a biological sample on a biological model to ensure the monitoring of the state of a subject (i.e. monitoring the upward or downward evolution of a pathology, in particular a neurological and / or nervous condition, the appearance of a pathology, in particular a neurological and / or nervous condition or the effect of a treatment by administration of an agent / test agent), then monitoring over time of the values ​​of at least one parameter characteristic of the state of the neural network with an evolution criterion (upward or downward threshold over an analysis period advantageously between 30 sec and 60 min, advantageously between 1 and 50 min, between 2 and 40 min, between 3 and 30 min, or even between 4 and 20 min, preferably between 5 and 10 min, in particular with standard deviation, use of the derivative) is performed;

[0098] - in the case of a comparison of at least one parameter characteristic of the state of the neural network with an existing functional activity library, it is necessary to consider all the parameters i) to vii) recorded, which in particular makes it possible to obtain a more reliable differential diagnosis, i.e. to be discriminating between the different possible diagnoses for which there may be identical or similar clinical symptoms due to the involvement in these disorders of the same dysfunctional proteins. In this case, the comparison step according to the invention comprises, preferably consists of, an absolute comparison with other so-called “true positive” and / or “true negative” subjects forming groupings, or “clusters”, of reference subjects / data; or

[0099] - the comparison with a reference value carried out in step f) may also consist of an absolute comparison with a library and monitoring of the state of a subject, as described previously.

[0100] For example, in the case of determining the presence or absence of a SARS-CoV-2, or COVID-19, infection, step f) of the method of the invention comprises, advantageously consists of, an absolute comparison with respect to a functional activity library.

[0101] For example, in the case of establishing a differential diagnosis of Alzheimer's disease and Parkinson's disease, step g) of the method of the invention comprises, advantageously consists of, an absolute comparison with a functional activity library. Then, and with the aim of monitoring the evolution of the condition, monitoring over time of the values ​​of the network connectivity index (parameter iv)) with an evolution criterion (threshold upward or downward over a mean period of time with standard deviation, use of the derivative) is advantageously carried out.

[0102] For example, in the case of determining the presence or absence of a head injury, step g) of the method of the invention comprises, advantageously consists of, monitoring the evolution of the condition by monitoring over time the values ​​of the network connectivity index (parameter iv)) with an evolution criterion (threshold upward or downward over an average period of time with standard deviation, use of the derivative). For example, the means for converting functional activity into functional activity data is an algorithmic system for converting electrical and / or electrophysiological data into binary data.

[0103] According to the invention, it may involve the conversion of the functional activity signal recorded by the MEA2100-Headstage system (Multichannel systems, Reutlingen, Germany) into a digital signal by an analog-to-digital converter, possibly coupled to an amplifier or set of amplifiers, said converter being directly integrated into the recording equipment. This binary data stream signal is read by the MEA2100-256-Systems software (Multichannel systems, Reutlingen, Germany), supplied with the equipment. Alternatively, it may also involve the conversion of the functional activity signal recorded by the M768tMEA-16 system (Axion Biosystems, Atlanta, GA, USA) into a digital signal by an analog-to-digital converter, possibly coupled to an amplifier or set of amplifiers, said converter being directly integrated into the recording equipment.This binary data stream signal is read by the Axis Navigator software (Axion Biosystems, Atlanta, GA, USA) supplied with the equipment.

[0104] For example, the means of converting the digital activity of a node into a binary signal of a node is provided by a thresholding algorithm, conventionally known to those skilled in the art, which consists of analyzing the background noise of the signal and applying suitable filters in order to apply a binarization threshold to the entire signal.

[0105] The method of the invention makes it possible to apply a biological sample originating from a subject (e.g., a sample of cerebrospinal fluid, blood, saliva mucus or a test agent) into a bioreceptor comprising, advantageously in the form of, a multi-compartmental microfluidic device integrating a relevant cell co-culture (of neuronal and / or non-neuronal cells and / or explant). Preferably, the sample is applied to the neuron culture indirectly, i.e., the application is not made to the neuron culture but to at least one of the associated co-cultures which then acts via the biological interface connected to the neuron culture by axons and / or synapses extending, for example, into the microchannels of the multi-compartmental microfluidic device of the invention.The response of the neural network, namely a possible modification of the functional activity of the neural network, following the application of said biological sample is then recorded and analyzed.

[0106] In particular, said modification of the functional activity of the neural network can be achieved by any means known to those skilled in the art, in particular by:

[0107] - altered functional communication, i.e., changes in the efficiency of cell-to-cell communication in which the ability of one or more neurons to activate target neurons to which they are synaptically or non-synaptically connected is either increased, decreased, or destroyed;

[0108] - a modification of the axonal and / or dendritic connection networks, for example by destruction of said axons and / or dendrites;

[0109] - a modification of one or more cell types affecting neuronal communication, for example disruption of glial cells only within a node, and / or

[0110] - a modification of action potentials, i.e. an increase or decrease in the amplitude, frequency, duration, “threshold” potential allowing membrane depolarization and / or the rhythm of action potentials.

[0111] These alterations then result in a modification of at least one parameter selected from the group consisting of i) a connection coefficient; ii) an average of the minimum inter-node lengths; iii) an average of the action potentials per second; iv) a network connectivity index; v) a z-score; vi) a participation coefficient; and vii) a centrality index of a node. This results in obtaining a functional activity signature, resulting from the correlation of said parameters, which is characteristic of a pathological state or not. Ultimately, the diagnosis is made by comparing the functional activity signature thus obtained to a so-called "true positive" functional activity signature, i.e. for which the network markers are true positives.

[0112] The result of implementing the method of the invention in a differential diagnosis makes it possible to obtain a result in less than 72 hours, advantageously in less than 48 hours, preferably in less than 24 hours, or even in less than 12 hours, 6 hours, 3 hours, 2 hours, 1 hour, or in less than 30 minutes, 20 minutes, 15 minutes or even less than 10 minutes or even immediately by functional analysis of the neural network.

[0113] It is clear from the above that the innovation of the invention therefore lies in the use of network markers as diagnostic markers, in the identification of a relevant neuron-cell-explant architecture (i.e. neuronal or non-neuronal cells or tissue explant) for establishing a given diagnosis and in the quantification of network markers by type of diagnosis making it possible to certify the diagnosis.

[0114] One of the advantages of the method of the invention is that it makes it possible to establish a reliable differential diagnosis, that is to say a diagnosis making it possible to differentiate a neurological and / or nervous pathology from another which presents close or similar symptoms, or even a pathology which presents few or no observable symptoms during a primary clinical auscultation.

[0115] Another advantage of the method of the invention is that it ensures a rapid differential diagnosis. In addition, the method of the invention makes it possible to improve the specificity / sensitivity ratio. Indeed, the method of the invention has a better detection resolution compared to a coupling of conventional molecules (eg ELISA) or the amplification limits of PCR; ensures specificity due to the use of neurons and at least one associated relevant co-culture making it possible to model a specific cellular and molecular architecture to diagnose a given pathology.

[0116] In addition, the method of the invention is inexpensive since it does not require the use of specific machines such as ELISA assays, immunospecific labeling, PCR or other biochemical assay techniques.

[0117] Finally, the method of the invention is not very restrictive and, depending on the sampling intervention methods, can be minimally invasive, since it requires the use of a very small volume of fluid, preferably of the order of a microliter, taking into account the implementation of a microfluidic device.

[0118] The invention also relates to a bioreceptor, as well as a biosensor comprising said bioreceptor where appropriate, for determining the effect of a biological sample on a biological model. In particular, it is a bioreceptor capable of being implemented in the method of the invention as described above.

[0119] The invention therefore relates to a bioreceptor, as well as a biosensor comprising said bioreceptor where appropriate, for determining the effect of a biological sample on a biological model, comprising, advantageously in the form of, a multi-compartmentalized device comprising: i) at least a first compartment and a second compartment; ii) at least one means forming a biological interface to allow communication by neuronal connection between the first and second compartments (Figure 1); iii) the culture of at least one type of cells or an explant per compartment, the first compartment comprising at least the culture of neurons in the form of a neural network and the second compartment to which said biological sample can be applied, comprising at least the culture of neurons in the form of a neural network and / or the culture of non-neuronal cells or the culture of an explant;iv) at least one device allowing the recording of the functional activity of the neurons on a plurality of measurement points arranged in a spatially distributed manner in the first compartment, the device being capable of being combined with a means for converting the functional activity into data. Preferably, the subject of the present invention is a bioreceptor, as well as a biosensor comprising said bioreceptor where appropriate, for determining the effect of a biological sample on a biological model as described above having the following technical characteristics, taken alone or in combination:;

[0120] - the biological interface comprises, advantageously consists of at least one of the elements selected from the group consisting of fluidic microchannels; PDMS microchannels; a porous membrane, the porosity of which is, advantageously, between 10 nm and 40 pm and the pore density of which is, advantageously, between 10 and 1.109 pores per cm2, advantageously between 1.105 and 1.109 pores per cm2; a porous capillary membrane, advantageously this is a membrane on which at least one organoid (i.e. a three-dimensional multicellular structure which reproduces in vitro the microanatomy of an organ) is cultivated, made of polycarbonate, polyester, polyethylene terephthalate and / or polytetrafluoroethylene; a gel; a hydrogel and mixtures thereof;

[0121] - it further comprises a means for converting the recording of the functional activity of the neurons in culture in the first compartment into functional activity data;

[0122] - it further comprises an analysis means arranged so as to determine at least one parameter characteristic of the state of the neural network in the first compartment from the functional activity data, the at least one parameter being selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; iii) an average of the action potentials per second; and iv) a network connectivity index or “Small World Index”; v) a z-score; vi) a participation coefficient; vii) a centrality index of a node;

[0123] - it further comprises an analysis means arranged to determine at least one parameter characteristic of the state of the neural network in the first compartment from the functional activity data, the at least one parameter being selected from the group consisting of: a number of action potential(s), an inter-action potential interval or “ISI”, a coefficient of variation of inter-action potential interval(s), a number of active electrodes, a mean normalized action potential rate, a number of bursts, a number of electrode(s) having captured one or more bursts, a mean duration of the bursts, an average of action potentials in a burst, an average of the interpotential action intervals (ISI) in a burst, an inter-burst interval or “IBI”, a frequency of the bursts, a percentage of the bursts, a number of network bursts, a frequency of network bursts, a duration of network bursts,an average of action potentials in network bursts, an average of inter-action potential intervals (ISI) in network bursts, a number of electrodes participating in forming network bursts, a percentage of bursts in network bursts, a coefficient of variation of inter-burst intervals (IBI) in network bursts, an area under the cross-correlation curve, and a synchrony index;

[0124] - it further comprises a means for carrying out a comparison between the at least one parameter characteristic of the state of the neural network and a reference value of at least one parameter characteristic of the state of the neural network in order to determine the effect of a biological sample;

[0125] - the multi-compartment microfluidic device further comprises a third compartment and at least one means forming a biological interface to enable communication by neural connection between the first and third compartments (1, 3) and / or at least one means forming a biological interface to enable communication by neural connection between the second and third compartments (Figure 2); advantageously a fourth compartment and at least one means forming a biological interface to enable communication by neural connection between the first and fourth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the second and fourth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the third and fourth compartments (Figure 3);preferably a fifth compartment and at least one means forming a biological interface to enable communication by neural connection between the first and fifth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the second and fifth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the third and fifth compartments and / or at least one means forming a biological interface to enable communication by neural connection between the fourth and fifth compartments (Figure 4);

[0126] - it can be included in a portable or non-portable kit, aimed at determining the effect of a biological sample on a biological model in order to, ultimately, establish a differential diagnosis of a neurological and / or nervous condition.

[0127] The invention also relates to the use of the bioreceptor as described above, as well as a biosensor comprising said bioreceptor, in an in vitro or ex vivo method for diagnosing a neurological or nervous condition, advantageously selected from the group consisting of Alzheimer's disease; Parkinson's disease; head trauma; stroke by thrombotic or embolic occlusion or by ischemia; transient ischemic attack; neuronal form of a SARS-CoV-2 infection; neuronal intoxication, for example with organophosphorus compounds; analgesia; neuroinflammatory disease, such as multiple sclerosis, otic neuritis, myelitis, Lupus, Crohn's disease; deafness due to damage to the auditory nerve; amyotrophic lateral sclerosis; retinal neuropathy, for example induced by diabetes; epilepsy, psoriasis, herpes; meningoencephalitis; isolated lymphocytic meningitis;Guillain-Barré polyradiculoneuritis or mononeuritis; peripheral neuropathy and myelopathy.;

[0128] The invention also relates to the use of the bioreceptor as described above, as well as a biosensor comprising said bioreceptor, in an in vitro or ex vivo method for monitoring a preventive and / or curative treatment of a neurological or nervous condition, advantageously a treatment by gene therapy, cell therapy, axonal regrowth therapy, administration of one or more curative and / or preventive and / or anesthetic agents.

[0129] The invention also relates to the use of the bioreceptor as described above, as well as a biosensor comprising said bioreceptor, in a method for rapid and sensitive screening of agents or test agents as potential drugs. Indeed, the device of the invention makes it possible to identify the possible therapeutic effect of a test agent as well as to evaluate the physiologically relevant concentration (for example the quantity present in a particular tissue under a prescribed dosage regimen) of this agent identified as a drug. The indirect application of the test agent to a culture of neurons in the first compartment produces a recognizable or characteristic functional activity signature making it possible to evaluate the therapeutic interest of an agent quickly and efficiently. The present invention is illustrated in a non-limiting manner by the exemplary embodiments which follow with the support of the appended figures.

[0130] It is considered that without further details, the person skilled in the art will be able, in view of the description and the examples of embodiment, to implement and use the claimed method and bioreceptor.

[0131] The invention also relates to an in vitro or ex vivo method for determining the effect of a biological sample on a biological model, comprising the following steps of: a. Providing a bioreceptor comprising a multi-compartmentalized microfluidic device (10) comprising: i) at least a first compartment (1) and a second compartment (2); ii) at least one means forming a biological interface (21) to allow communication by neuronal connection between the first and second compartments iii) culturing at least one type of cells or explant per compartment, the first compartment (1) comprising at least the culture of neurons in the form of a neural network and the second compartment (2), onto which said biological sample can be applied, comprising at least the culture of neurons in the form of a neural network and / or the culture of non-neuronal cells or the culture of an explant;iv) at least one device (40) for recording the functional activity of the neurons at a plurality of measurement points arranged in a spatially distributed manner in the first compartment, the device being capable of being combined with a means (50) for converting the functional activity into data; b. Bringing the neurons in culture in the first compartment or the neurons and / or non-neuronal cells or the explant in culture in the second compartment into direct or indirect contact with a biological sample; c. Recording the functional activity of the neurons in culture in the first compartment (1) at the plurality of measurement points over a measurement duration following the contacting of the neurons in culture in the first compartment (1) with the biological sample according to step b);d. Carrying out a conversion of the recording of the functional activity of the neurons in culture in the first compartment (1) into functional activity data; e. Analyzing the functional activity data obtained in step d) and constructing a graph representing the neural network; f. Determining at least one parameter characteristic of the state of the neural network in the first compartment (1) from the functional activity data, the at least one parameter being selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; iii) an average of the action potentials per second; iv) a network connectivity index or “Small World Index”; v) a z-score or “z-score”; vi) a participation coefficient or “Participation Coefficient”; and vii) a centrality index of a node;g. Carry out a comparison between the at least one characteristic parameter of the state of the neural network and a reference value of at least one characteristic parameter of the state of the neural network in order to determine the effect of a biological sample.;

[0132] Figures [Fig. 1]: Diagram representing the bioreceptor of the invention in the form of a microfluidic device comprising 2 compartments.

[0133] [Fig. 2]: Diagram representing the bioreceptor of the invention in the form of a microfluidic device comprising 3 compartments.

[0134] [Fig. 3]: Diagram representing the bioreceptor of the invention in the form of a microfluidic device comprising 4 compartments.

[0135] [Fig. 4]: Diagram representing the bioreceptor of the invention in the form of a microfluidic device comprising 5 compartments.

[0136] [Fig. 5]: Diagram representing the different elements necessary for implementing the method of the invention.

[0137] [Fig. 6]: Diagram representing the steps involved in implementing the method according to the invention.

[0138] [Fig. 7]: Representation of the steps implemented by the method according to the invention and the data obtained for each of these steps for a healthy subject and a subject suffering from a neurological and / or nervous condition.

[0139] DETAILED DESCRIPTION OF THE INVENTION

[0140] As already mentioned previously, the invention relates, in a first embodiment as represented by Figures 1 and 5, to a bioreceptor comprising a multi-compartment microfluidic device comprising, in a first embodiment, a first compartment 1 and a second compartment 2, each comprising the culture of at least one type of cells or explant; and at least one means forming a biological interface 21 to allow communication by neuronal connection between the first and second compartments 1, 2.

[0141] The bioreceptor of the invention also comprises at least one device 40 allowing the recording of the functional activity of the neurons on a plurality of measurement points arranged in a spatially distributed manner in the first compartment, the device being capable of being combined with a means 50 for converting the functional activity into functional activity data, a means 60 for analyzing these functional activity data arranged so as to determine at least one characteristic parameter of the state of the neural network in the first compartment from the functional activity data, and a means 70 for carrying out a comparison between the at least one characteristic parameter of the state of the neural network and a reference value of at least one characteristic parameter of the state of the neural network in order to determine the effect of a biological sample.

[0142] Said at least one parameter can be selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; iii) an average of the action potentials per second; and iv) a network connectivity index or “Small World Index”; v) a z-score; vi) a participation coefficient; vii) a centrality index of a node.

[0143] Said at least one parameter can also be selected from the group consisting of: a number of action potential(s), an inter-action potential interval or “ISI”, a coefficient of variation of inter-action potential interval(s), a number of active electrodes, a mean rate of normalized action potential, a number of bursts, a number of electrode(s) having captured one or more bursts, a mean duration of bursts, a mean of action potentials in a burst, a mean of inter-action potential intervals (ISI) in a burst, an inter-burst interval or “IBI”, a frequency of bursts, a percentage of bursts, a number of network bursts, a frequency of network bursts, a duration of network bursts, a mean of action potentials in network bursts, a mean of inter-action potential intervals (ISI) in network bursts, a number of electrodes participating in forming network bursts,a burst percentage in network bursts, a coefficient of variation of inter-burst intervals (IBI) in network bursts, an area under the cross-correlation curve, and a synchrony index.

[0144] When two or more parameters are used to characterize the state of the neural network in the first compartment from functional activity data, it can be a combination of parameters belonging to the same group or a combination of parameters each from one of the two groups previously mentioned.

[0145] In a second embodiment as represented by Figure 2, the bioreceptor of the invention further comprises a third compartment 3; at least one means 31 forming a biological interface to allow communication by neural connection between the first and third compartments 1, 3 and / or at least one means 32 forming a biological interface to allow communication by neural connection between the second and third compartments 2, 3.In a third embodiment as represented by Figure 3, the bioreceptor of the invention further comprises a fourth compartment 4; at least one means 41 forming a biological interface to allow communication by neural connection between the first and fourth compartments 1, 4 and / or at least one means 42 forming a biological interface to allow communication by neural connection between the second and fourth compartments 2, 4 and / or at least one means 43 forming a biological interface to allow communication by neural connection between the third and fourth compartments 3, 4.

[0146] In a fourth embodiment as shown in Figure 4, the bioreceptor of the invention further comprises a fifth compartment 5; at least one means 51 forming a biological interface to allow communication by neural connection between the first and fifth compartments 1, 5 and / or at least one means 52 forming a biological interface to allow communication by neural connection between the second and fifth compartments 2, 5 and / or at least one means 53 forming a biological interface to allow communication by neural connection between the third and fifth compartments 3, 5 and / or at least one means 54 forming a biological interface to allow communication by neural connection between the fourth and fifth compartments 4, 5.

[0147] As represented by Figures 6 and 7 (including sub-figures 7A, 7B', 7C', 7A, 7B and 7C), the implementation of the in vitro or ex vivo method for determining the effect of a biological sample on a biological model according to the invention makes it possible to obtain a recording of the functional activity of the neurons (Figures 7A' and 7A) in culture in the first compartment 1, that is to say, a recording of the extracellular activity of the neurons which result in peaks called "spikes" which, when a network of neurons is synchronized (or in a non-pathological condition), appear periodically. Thus, a strong activity of the network of neurons in culture in the first compartment 1 is observed by the presence of bursts of peaks called "bursts". On the contrary, other types of neurons, such as GABAergic neurons, do not exhibit synchronized functional activity (data not shown).In this case, the possible disruption of functional activity will not result in a desynchronization of the “spikes” and / or a reduction or absence of the “bursts”, but not a modification of the parameters characteristic of the state of the neural network according to the invention.

[0148] This recording of the functional activity of the neurons in the form of a cultured network in the first compartment is subjected to a conversion means 50 making it possible to obtain functional activity data, in particular binary data (Figures 7B' and 7B). These functional activity data are in turn subjected to an analysis means 60 arranged to determine at least one parameter characteristic of the state of the neural network in the first compartment from the functional activity data, the at least one parameter being selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; iii) an average of the action potentials per second; and iv) a network connectivity index or "Small World Index"; v) a z-score or "z-score"; vi) a participation coefficient or "Participation Coefficient"; vii) a centrality index of a node.

[0149] Ultimately, these data / parameters characteristic of the state of the neural network are correlated and can be represented in the form of a cross-correlation graph (Figures 7C' and 7C).

[0150] The bioreceptor comprises a means 70 for carrying out a comparison between the at least one parameter characteristic of the state of the neural network and a reference value of at least one parameter characteristic of the state of the neural network in order to determine the effect of the application of a biological sample on a biological model and, consequently, to determine the presence or absence of neurological and / or nervous damage in a subject, or even to identify new therapeutic solutions (new molecules or even new dosages) from the screening of test agents.

[0151] In particular, when the average of the action potentials per second has a value greater than 0.5 then it is possible to analyze at least one parameter being selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; and iv) a network connectivity index or "Small World Index"; v) a z-score; vi) a participation coefficient; vii) a centrality index of a node;

[0152] In particular, the analysis comprises, advantageously consists of, determining the value of the ratio of the connectivity coefficient to the average of the minimum inter-node lengths, ultimately making it possible to determine the connectivity index of the network which will then be the parameter implemented in the comparison step.

[0153] EXAMPLES OF EMBODIMENT The present invention will be further illustrated in relation to a bioreceptor comprising a multi-compartmental microfluidic device 10 comprising 2 compartments (examples 1 to 3) or 5 compartments (example 4). However, these examples are in no way limiting of the invention.

[0154] Example 1: Diagnosis of Alzheimer's disease

[0155] 1. Background

[0156] More than 10 million people in Europe suffer from neurodegenerative diseases such as Alzheimer's disease (AD), and this figure is expected to double in less than 20 years. There is no cure for these disorders, and the search for new treatments is hampered by the complexity of establishing a reliable differential diagnosis. The first lesions of AD appear in the hippocampus, an area of ​​the brain involved in memory processes (recording, retrieving, and organizing memories) and in the management of emotions, before gradually spreading to external areas following the connections established between the different brain regions. The hippocampus is a structure composed of glial cells (astrocytes or microglial cells) and neurons, the vast majority of which are glutamatergic and GABAergic neurons.

[0157] 2. Materials and methods

[0158] The bioreceptor of the invention is in the form of a multi-compartment device in which a first compartment comprises the culture of human glutamatergic neurons derived from stem cells and a second compartment comprises the culture of human GABAergic neurons derived from stem cells.

[0159] Gluamatergic neurons are stored in nitrogen at a temperature of approximately -200°C. A gradual thawing step is carried out according to conventional techniques widely known to those skilled in the art. The thawed cells are then taken up in approximately 10 mL of a dedicated culture medium at 37°C. The aliquot is centrifuged and then treated using an automatic counter or with a Malassez cell in order to determine the concentration of glutamatergic neurons and the dilutions to be carried out. The aliquot is then diluted and seeded in the first compartment of a device according to the invention previously treated according to conventional techniques widely known to those skilled in the art to facilitate the adhesion of glutamatergic neurons to the substrate. GABAergic neurons are cultured in the second compartment of said device, according to the same protocol as that detailed above.

[0160] The functional activity of the cultured glutamatergic neurons was recorded using planar microelectrode arrays (MEAs) 256MEA100 / 30iR-ITO-w / o (Multichannel systems, Reutlingen, Germany) consisting of 30 μm diameter electrodes spaced 100 μm apart. This recording was performed for 10 min using the MEA2100-256-Systems software (Multichannel systems, Reutlingen, Germany). The microelectrode array technology allows recording the functional and spontaneous extracellular activity of neurons as a marker of network connectivity.

[0161] For functional recording, two conditions were tested:

[0162] - “test” condition: sample of cerebrospinal fluid from a subject likely to have AD; and

[0163] - “reference” condition: cerebrospinal fluid sample from a so-called “true negative” subject, serving as a reference value.

[0164] In particular, regarding the "reference" condition, it should be noted that this is a sample obtained from a healthy subject with a negative result in the conventional AD test. In other words, it is a sample reporting a so-called "true negative" diagnosis.

[0165] 3. Results

[0166] The results are shown in Figures 7A', 7B', 7C', 7A, 7B and 7C.

[0167] During electrophysiological recording, cultured glutamatergic neurons showed functional activity represented by spikes (Figures 7 A' and 7A). These spikes are detected in time and space by the MEA2100-256-Systems software algorithm which then assigns a value, identified by each of the points on the spikes.

[0168] From these values ​​identified by the algorithm, a matrix (raster plot) is generated to visualize the activity of each active electrode as a function of time (Figures 7B' and 7B). This graph makes it possible to evaluate the synchronicity of a neural network, namely the ability of neurons to have activity at the same time. It is clear from these graphs that the glutamatergic neural network is synchronized in the "reference" condition (Figure 7B') and desynchronized in the "test" condition (Figure 7B).

[0169] Furthermore, the optimal network structure is defined by quantitative parameters obtained by a cross-correlation algorithm and allows to estimate the quality of network connectivity. In this case, the average of action potentials per second has a value greater than 0.5; which allows the determination of the value of the ratio of the connectivity coefficient to the average of the minimum inter-node lengths, and, ultimately, the determination of the value of the network connectivity index.

[0170] The processing and analysis, as described above, of the obtained data makes it possible to represent a neural network, defined by nodes and interactions between them (Figure 7C' and 7C). Cross-correlation is an algorithm representing the degree of the neural network and makes it possible to estimate the state function of the network.

[0171] Network analysis shows that the neural network in the “test” condition (Figure 7C) has fewer connections than the neural network in the “reference” condition (Figure 7C').

[0172] 4. Conclusion

[0173] It emerges from the implementation of the method of the invention that the sample of the “test” condition comes from a subject presenting a neurological and / or nervous condition.

[0174] Comparing the representation of the neural network in the “test” condition (Figure 7C) with a library of functional activity signatures allows for a rapid and reliable diagnosis of Alzheimer's disease.

[0175] Then, a follow-up of the state of a subject (i.e. follow the upward or downward evolution of AD) is carried out by a follow-up over time of the values ​​of the network connectivity index (parameter iv)) with an evolution criterion (threshold upward or downward over a mean period of time with standard deviation, use of the derivative) compared to the reference value corresponding to the value obtained at the time of diagnosis of the disease, i.e. the value obtained as described previously).

[0176] Example 2: Diagnosis of a head injury occurring during a rugby match

[0177] 1. Background

[0178] In some high-risk sports, such as rugby, players are at high risk of head trauma (or concussion). It can be defined as a short-term dysfunction of brain functions in the absence of macro- or microscopic lesions. The frequency of concussions has been increasing over the last 15 years. The incidence in rugby is observed between 4.1 and 7.9 / 1000 player-hours during a match. Currently, diagnosis is based on the establishment of a Glasgow Coma Scale score established by clinical observations and symptoms. Concussion can result from a blow other than to the head, the player may have very weak or even unnoticed clinical signs and symptoms, without loss of consciousness, which can make diagnosis more difficult and slower.The time between diagnosis and prognosis of the player can be very long and immobilize the player for several days, even weeks and sometimes have harmful, serious and long-term consequences.

[0179] 2. Materials and methods

[0180] The bioreceptor of the invention is in the form of a multi-compartment device in which a first compartment comprises the culture of human sensory neurons derived from stem cells and a second compartment comprises the culture of cells from the oral mucosa. See the detailed protocol in point 2 of example 1.

[0181] For functional recording, two conditions were tested:

[0182] - “test” condition: saliva sample obtained from a subject who received a shock during a rugby match; and

[0183] - “reference” condition: saliva sample obtained from a healthy subject and showing a negative result in the conventional test for detecting head trauma. In other words, this is a sample reporting a so-called “true negative” diagnosis.

[0184] 3. Results

[0185] Data are obtained according to the protocol detailed in point 3 of example 1.

[0186] 4. Conclusion

[0187] It emerges from the implementation of the method of the invention that the sample of the “test” condition comes from a subject presenting a neurological and / or nervous condition.

[0188] Comparing the representation of the neural network in “test” condition with a library of functional activity signatures makes it possible to establish a rapid and reliable diagnosis of the presence of a head trauma, ensuring rapid and effective treatment of the subject.

[0189] Example 3: Diagnosis of COVID-19 disease

[0190] 1. Background Coronaviruses are known to cause severe acute respiratory syndromes. These have been responsible for three deadly epidemics during the 21st century. SARS-CoV-2, responsible for the disease COVID-19, is the cause of the most recent pandemic with a significantly high mortality rate and dramatic economic costs. In Europe, the cumulative mortality rate is 34% and varies from one European country to another, correlated with the emergence of new SARS-CoV-2 variants and a high heterogeneity of symptoms (asymptomatic individuals, mild forms, death of the individual). The ability of coronaviruses to invade the central nervous system had already been described during the two previous epidemics caused by SARS-CoV-1 and MERS-CoV.The neurological impairments described are of varying severity, ranging from simple headaches to temporary confusion, to strokes and seizures in the most severe forms. Like many other airborne viral diseases, upper respiratory tract infection is the first point of entry into the body. Especially since olfactory impairment, including loss of odor detection (anosmia), following SARS-CoV-2 infection remains one of the most common and predictive symptoms of infection, with tests even underway. It has been suggested that neuroinvasion occurs via the nasal route. Indeed, the presence of intact viral particles in the supporting cells of the olfactory mucosa has been highlighted, highlighting that this site could be the site of viral replication, explaining the loss of taste and smell.The hypothesis regarding neuroinvasion is that viral spread occurs via the olfactory bulb (seat of olfactory sensory information processing) before entering the central nervous system via the cranial nerves.

[0191] 2. Materials and methods

[0192] The bioreceptor of the invention is in the form of a multi-compartment device in which a first compartment comprises the culture of human mitral cells derived from the olfactory bulb and a second compartment comprises the culture of cells from the nasal mucosa. See the detailed protocol in point 2 of example 1.

[0193] For functional recording, two conditions were tested:

[0194] - “test” condition: nasopharyngeal sample obtained from a subject; and

[0195] - “reference” condition: nasopharyngeal sample obtained from a healthy subject and showing a negative result in the conventional COVID-19 disease detection test. In other words, this is a sample reporting a so-called “true negative” diagnosis.

[0196] 3. Results

[0197] Data are obtained according to the protocol detailed in point 3 of example 1. 4. Conclusion

[0198] It emerges from the implementation of the method of the invention that the sample of the “test” condition comes from a subject presenting a neurological and / or nervous condition.

[0199] Comparing the representation of the neural network in “test” condition with a library of functional activity signatures allows for a rapid and reliable diagnosis of COVID-19 disease.

[0200] Example 4: Diagnosis of Parkinson's disease

[0201] 1. Background

[0202] Parkinson's disease (PD) is the second most common neurodegenerative disease, after Alzheimer's disease, with a prevalence of 4% in people over 80 years of age. Clinically, it presents with motor symptoms, affecting gait and body movements, associated with non-motor symptoms. Dementia is a common symptom in Parkinson's disease and corresponds to either parkinsonian dementia or dementia with Lewy bodies.

[0203] From a neuropathological point of view, PD is characterized by a loss of dopaminergic neurons in the substantia nigra (SN, an anatomical region belonging to the basal ganglia) and by the presence of intracellular inclusions called Lewy bodies. It is in these Lewy bodies that the protein α-synuclein is found, which when misfolded and misshaped, leads to a cascade of neurotoxicity.

[0204] Axonal projections from the SN extend to the putamen and caudate nucleus (which form the striatum) where there is a series of connections to the globus pallidus and subthalamic nucleus. In PD, degeneration of the nigrostriatal pathway (pathway between the substantia nigra and striatum) is the primary cause of motor symptoms. A key consequence of the death of dopaminergic neurons in the SN and the decrease in dopamine is the disruption of dopaminergic signaling from the basal ganglia to the rest of the brain. The basal ganglia motor circuit controls movement. Within this circuit, there is a direct pathway and an indirect pathway. The direct pathway is composed of 5 anatomical regions: the cortex, the striatum, the substantia nigra pars compacta, the thalamus, and the association of the internal globus pallidus and the substantia nigra reticularis.

[0205] 2. Materials and methods The bioreceptor of the invention is in the form of a multi-compartmental device in which a first compartment comprises the culture of human glutamatergic and GABAergic cells derived from pluripotent stem cells, a second compartment comprises the culture of GABAergic neurons, a third compartment comprises the culture of glutamatergic neurons, a fourth compartment comprises the culture of GABAergic neurons and a fifth compartment comprises dopaminergic neurons. These five compartments correspond to the five anatomical regions of the basal ganglia loop. See the detailed protocol in point 2 of Example 1.

[0206] For functional recording, two conditions were tested:

[0207] - “test” condition: sample of cerebrospinal fluid from a subject likely to be suffering from Parkinson’s disease; and

[0208] - “reference” condition: cerebrospinal fluid sample from a so-called “true negative” subject, serving as a reference value.

[0209] In particular, regarding the "reference" condition, it should be noted that this is a sample obtained from a healthy subject with a negative result in the conventional Parkinson's disease test. In other words, it is a sample reporting a so-called "true negative" diagnosis.

[0210] 3. Results

[0211] Data are obtained according to the protocol detailed in point 3 of example 1.

[0212] 4. Conclusion

[0213] It emerges from the implementation of the method of the invention that the sample of the “test” condition comes from a subject presenting a neurological and / or nervous condition.

[0214] Comparing the representation of the neural network in “test” condition with a library of functional activity signatures allows for a rapid and reliable diagnosis of Parkinson's disease.

Claims

DEMANDS 1. An in vitro or ex vivo method for determining the effect of a biological sample on a biological model, comprising the following steps of: a. Providing a bioreceptor comprising a multi-compartment microfluidic device (10) comprising: i) at least a first compartment (1) and a second compartment (2); ii) at least one means forming a biological interface (21) to enable communication by neuronal connection between the first and second compartments; iii) the first compartment (1) comprising at least the culture of neurons in the form of a neural network and the second compartment (2) comprising at least the culture of neurons in the form of a neural network and / or the culture of non-neuronal cells or the culture of an expiant; iv) at least one device (40) enabling the recording of the functional activity of neurons at a plurality of measurement points arranged in a spatially distributed manner in the first compartment; b.a. Place the neurons and / or non-neuronal cells or the expiry in culture in the second compartment in direct or indirect contact with said biological sample; c. Record the functional activity of the neurons in culture in the first compartment (1) at the plurality of measurement points over a measurement duration following contact according to step b); d. Convert the recording of the functional activity of the neurons in culture in the first compartment (1) into functional activity data; e. Analyze the functional activity data obtained in step d); f. Determine at least one characteristic parameter of the state of the neural network in the first compartment (1) from the functional activity data; g.Perform a comparison between at least one characteristic parameter of the state of the neural network and a reference value of at least one characteristic parameter of the state of the neural network in order to determine the effect of said biological sample.

2. A method according to claim 1, characterized in that at least one characteristic parameter of the state of the neural network in the first compartment (1) is selected from the group consisting of: a number of action potentials, an action potential inter-interval or "ISI", a coefficient of variation of the action potential inter-interval, a number of active electrodes, a normalized average action potential rate, a number of bursts, a number of electrodes that have captured one or more bursts, an average burst duration, an average of action potentials in a burst, an average of action potential inter-intervals (ISI) in a burst, an inter-burst interval or "IBI", burst frequency, percentage of bursts, number of network bursts, network burst frequency, network burst duration, average action potentials in network bursts, average action potential inter-intervals (ISI) in network bursts, number of electrodes participating in forming network bursts, percentage of bursts in network bursts, coefficient of variation of inter-burst intervals (IBI) in network bursts, area under the cross-correlation curve, and synchrony index.

3. A method according to claim 1 or 2, characterized in that at least one characteristic parameter of the state of the neural network in the first compartment (1) is selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; iii) an average of the action potentials per second; iv) a network connectivity index or "Small World Index"; v) a z-score; vi) a participation coefficient; and vii) a centrality index of a node.

4. A method according to claim 3, characterized in that step f) comprises the determination of two parameters, advantageously three parameters, preferably four parameters, or even five parameters, in particular six parameters, more particularly seven parameters characteristic of the state of the neural network in the first compartment from the functional activity data, said parameters being selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum internode lengths; iii) an average of the action potentials per second; iv) a network connectivity index or "Small World Index"; v) a z-score; vi) a participation coefficient; and vii) a centrality index of a node.

5. A method according to claim 3 or 4, characterized in that step g) comprises a threshold comparison step, namely: - the average of action potentials per second is compared to a threshold of average action potentials per second whose value is greater than or equal to, advantageously strictly greater than 0.5; and / or - the connection coefficient is compared to a connection coefficient threshold whose value is greater than or equal to 0, advantageously between 0 and 1; and / or - the average of the minimum internode lengths is compared to a threshold of average minimum internode lengths whose value is greater than or equal to 1, advantageously 1.5; and / or - the network connectivity index (connectivity between nodes) is compared to a connectivity threshold between nodes whose value is greater than or equal to 0, advantageously between 0 and 1; and / or - the z-score has a value greater than or equal to 0, advantageously 5, preferably between 0 and 10; and / or - the participation coefficient or "Participation Coefficient" has a value greater than or equal to 0, advantageously 0.5 and preferably between 0.5 and 1; and / or - the centrality index of a node has a value greater than or equal to 0, advantageously 5 and preferably between 6 and 10.

6. A method according to any one of the preceding claims, characterized in that step g) comprises a comparison of at least one characteristic parameter of the state of the neural network as defined by step f) to a reference library of functional activity signatures.

7. A method according to any one of claims 3 to 6, characterized in that step g) includes monitoring the values ​​of parameter iv) with a criterion of upward or downward evolution over an analysis period.

8. A method according to any one of claims 3 to 7, characterized in that at step f) the determination of at least one characteristic parameter of the state of the neural network being selected from the group consisting of: i) a connection coefficient; ii) an average of the minimum inter-node lengths; iii) a network connectivity index or "Small World Index"; iv) a z-score; v) a participation coefficient; and vi) a centrality index of a node; is carried out when the average of the action potentials per second is greater than 0.

5.

9. A method according to claim 8, characterized in that the determination of at least one characteristic parameter of the state of the neural network comprises, advantageously consists of determining the network connectivity index, which consists of the value of the ratio of the average connection coefficient to the minimum inter-node lengths.

10. A method according to any one of the preceding claims, characterized in that the device (40) for recording the functional activity of neurons at a plurality of measurement points arranged spatially in the first compartment (1) according to step a.iv) is a device (40) for recording in indirect contact with cultured cells, selected from the group consisting of: - a device for recording activity using planar or non-planar microelectrode arrays, semi-solid electrodes, by amperometry or voltammetry; - a fluorescence imaging recording device, such as calcium imaging or transmembrane ion flux imaging; and - a device for recording intracellular, extracellular or patch-clamp electrophysiological activity in whole cell, attached cell, inside-out or outside-out configuration.

11. A method according to any one of the preceding claims, characterized in that step d) of carrying out a conversion of the recording of the functional activity of neurons in culture in the first compartment (1) into functional activity data is carried out via a means of converting the functional activity of neurons into functional activity data, the conversion means (50) preferably being an algorithmic system for converting electrical and / or electrophysiological data into binary data.

12. A method according to any one of the preceding claims, characterized in that the measurement time of the recording of the functional activity of the neurons in culture in the first compartment according to step c) is between 300 ms and 20 min, advantageously between 1 min and 15 min, preferably between 5 min and 12 min.

13. A method according to any one of the preceding claims, characterized in that the multi-compartment microfluidic device (10) further comprises: i) a third compartment (3) comprising at least the culture of neurons in the form of a neural network and / or the culture of non-neuronal cells; and ii) at least one means (31) forming a biological interface to enable communication by neuronal connection between the first and third compartments (1, 3) and / or at least one means (32) forming a biological interface to enable communication by neuronal connection between the second and third compartments (2, 14. A method according to claim 13, characterized in that the multi-compartment microfluidic device (10) further comprises: iii) a fourth compartment (4) comprising at least the culture of neurons in the form of a neural network and / or the culture of non-neuronal cells; and iv) at least one means (41) forming a biological interface to enable communication by neuronal connection between the first and fourth compartments (1, 4) and / or at least one means (42) forming a biological interface to enable communication by neuronal connection between the second and fourth compartments (2, 4) and / or at least one means (43) forming a biological interface to enable communication by neuronal connection between the third and fourth compartments (3, 4).

15. A method according to claim 14, characterized in that the multi-compartment microfluidic device (10) further comprises: v) a fifth compartment (5) comprising at least the culture of neurons in the form of a neural network and / or the culture of non-neuronal cells; and vi) at least one means (51) forming a biological interface to enable communication by neuronal connection between the first and fifth compartments (1, 5) and / or at least one means (52) forming a biological interface to enable communication by neuronal connection between the second and fifth compartments (2, 5) and / or at least one means (53) forming a biological interface to allow communication by neuronal connection between the third and fifth compartments (3, 5) and / or at least one means (54) forming a biological interface to allow communication by neuronal connection between the fourth and fifth compartments (4, 5).

16. A method according to any one of the preceding claims, characterized in that the contacting according to step b) is indirect in that the biological sample is applied to the biological interface of the second compartment (21) and / or to at least one of the biological interfaces of the third compartment (31, 32) and / or to at least one of the biological interfaces of the fourth compartment (41, 42, 43) and / or to at least one of the biological interfaces of the fifth compartment (51, 52, 53, 54).

17. A method according to any one of the preceding claims, characterized in that: - neurons are selected from the group consisting of glutamatergic, GABAergic, serotonergic, cholinergic, dopaminergic, adrenergic, noradrenergic, sensory neurons and motor neurons; and / or - non-neuronal cells are selected from the group consisting of cells glial, epithelial, connective, thyroid, fatty, blood, immune, bone, cartilage, gastric, pancreatic, hepatic, intestinal, pulmonary, endothelial, muscular, vascular, cardiac, mesenchymal, and retinal pigment epithelium cells and retinal cells; and / or - the expiatory tissue is of cerebral, epithelial, ocular, thyroid, fatty, vascular, bone, cartilaginous, gastric, pancreatic, hepatic, intestinal, pulmonary, endothelial, muscular, retinal, cardiac and placental origin.

18. A method according to any one of the preceding claims, characterized in that the biological sample is selected from the group consisting of blood, saliva, urine, tears, sweat, sputum, mucus, pus, lymph, cerebrospinal fluid, nasopharyngeal secretions, oropharyngeal secretions, synovial fluid, pleural fluid, peritoneal fluid, pericardial fluid, aqueous humor, amniotic fluid and plasma.

19. A method according to any one of claims 1 to 17, characterized in that the biological sample is the agent or a test agent.

20. Bioreceptor for determining the effect of a biological sample on a biological model, comprising a multi-compartment microfluidic device (10) comprising: i) at least a first compartment (1) and a second compartment (2); ii) at least one means (21) forming a biological interface to enable communication by neuronal connection between the first and second compartments (1, 2); iii) the first compartment (1) comprising at least the culture of neurons in the form of a neural network and the second compartment (2) to which said biological sample can be applied, comprising at least the culture of neurons in the form of a neural network and / or the culture of non-neuronal cells or the culture of an expiant; iv) at least one device (40) enabling the recording of the functional activity of neurons on a plurality of measurement points arranged in a spatially distributed manner in the first compartment.

21. Biosensor comprising a bioreceptor according to claim 20, characterized in that it further comprises a means for converting the recording of the functional activity of neurons in culture in the first compartment into functional activity data.

22. Biosensor according to claim 21, characterized in that it further comprises an analysis means (60) arranged to determine at least one characteristic parameter of the state of the neural network in the first compartment from the activity data functional, at least one parameter being selected from the group consisting of: i) a connection coefficient; ii) an average of minimum inter-node lengths; iii) an average of action potentials per second; iv) a network connectivity index or "Small World Index"; v) a z-score; vi) a participation coefficient; and vii) a centrality index of a node.

23. Biosensor according to claim 21, characterized in that it further comprises an analysis means (60) arranged to determine at least one characteristic parameter of the state of the neural network in the first compartment from the functional activity data, the at least one parameter being selected from the group consisting of: a number of action potential(s), an action potential inter-interval or "ISI", a coefficient of variation of the action potential inter-interval(s), a number of active electrodes, a normalized average action potential rate, a number of bursts, a number of electrode(s) that have captured one or more bursts, an average duration of bursts, an average number of action potentials in a burst, an average of the action potential inter-intervals (ISI) in a burst, an inter-burst interval or "IBI", a burst frequency, a percentage of bursts, a number of network bursts, a network burst frequency,a duration of network bursts, an average of action potentials in network bursts, an average of inter-action potential intervals (ISI) in network bursts, a number of electrodes participating in forming network bursts, a percentage of bursts in network bursts, a coefficient of variation of inter-burst intervals (IBI) in network bursts, an area under the cross-correlation curve, and a synchrony index.

24. Biosensor according to claim 22 or 23, characterized in that it further comprises a means (70) for making a comparison between at least one characteristic parameter of the state of the neural network and a reference value of said at least one characteristic parameter of the state of the neural network in order to determine the effect of said biological sample.

25. Use of the bioreceptor according to claim 20 or of a biosensor according to any one of claims 21 to 24 in an in vitro or ex vivo method for diagnosing a neurological and / or nervous disorder, advantageously selected from the group consisting of Alzheimer's disease; Parkinson's disease; head injury; stroke, by thrombotic or embolic occlusion or by ischemia; transient ischemic attack; neuronal form of a SARS-CoV-2 (= covid-19) infection; Neuronal intoxication, for example with organophosphate compounds; analgesia; neuro-inflammatory disease, such as multiple sclerosis, otic neuritis, myelitis, lupus, Crohn's disease; deafness due to damage to the auditory nerve; amyotrophic lateral sclerosis; retinal neuropathy, for example induced by diabetes; epilepsy, psoriasis, herpes; meningoencephalitis; isolated lymphocytic meningitis; Guillain-Barré type polyradiculoneuritis or mononeuritis; peripheral neuropathy and myelopathy.

26. Use of the bioreceptor according to claim 20 or of a biosensor according to any one of claims 21 to 24 in an in vitro or ex vivo method of monitoring preventive and / or curative treatment of a neurological or nervous condition, advantageously treatment by gene therapy, cell therapy, axonal regrowth therapy, administration of one or more curative and / or preventive and / or anesthetic agents.

27. Use of the bioreceptor according to claim 20 or of a biosensor according to any one of claims 21 to 24 in an in vitro or ex vivo process for the identification and / or characterization of the therapeutic properties of an agent and / or a threshold concentration of an agent.

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