In vitro or ex vivo methods for determining the effects of biological samples on biological models 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, said method comprising in particular the use of a bioreceptor comprising a multicompartment microfluidic device incorporating cells of a relevant co-culture to which the sample is applied. The response of the neuronal network to said sample, in particular the changes in the cells / neuronal network, is recorded and subsequently analyzed. A differential diagnosis is then performed by comparing the network indicators to the true positives with the test samples.

Description

[Technical field]

[0001] The present invention relates to the field of diagnostics, in particular to the field of diagnostic bioreceptors associated with microfluidics techniques as biosensors. The present invention thus relates to a method for determining the influence of a biological sample on a biological model, to a bioreceptor realized in such a method, as well as to the use of such a bioreceptor. [Background technology]

[0002] Pathologies of the central and / or peripheral nervous system affect more than 700 million people worldwide, more than 10 million in Europe, and tens of thousands in France. These pathologies are considered the most complex in terms of etiology, prognosis, but also treatment. These neurocognitive disorders (e.g. Alzheimer's disease, Parkinson's disease, cranial trauma, cerebrovascular accidents, or amyotrophic lateral sclerosis) lead to cognitive, functional, and / or behavioral deficits in the patient, i.e. altered abilities in language, social interaction, memory, logical reasoning, or autonomy. These pathologies, so-called neurological and / or neurological disorders, therefore represent a major public health problem.

[0003] Currently, the diagnosis of these conditions is made essentially after the onset and observation of the first symptoms. This initial clinical diagnosis is followed by examination of body fluids, specifically blood or cerebrospinal fluid (CSF), and is supplemented by imaging studies. For other neurological disorders, such as cranial trauma (or concussion), the diagnosis is based on the establishment of a Glasgow score, which is established by clinical findings and symptoms.

[0004] In recent years, the existence of new biomarkers has been demonstrated that allow the diagnosis of these neurological and / or neurological disorders: for example, microRNAs present in body fluids such as cerebrospinal fluid (LCS) or blood, allow the diagnosis of concussion by very traditional techniques of bioassays, such as polymerase chain amplification (i.e. polymerase chain reaction, or PCR) or enzyme immunoassays on solid supports (i.e. enzyme-linked immunosorbent assay, or ELISA).

[0005] Current treatment is carried out in terms of secondary and tertiary, adjunctive and preventive approaches aimed at preserving quality of life and preventing complications and behavioral episodes by predicting the progression stage of the disease. In addition, the search for new therapeutic approaches is made difficult by the complexity of establishing a reliable differential diagnosis. In fact, several different disorders, such as neurodegenerative diseases, may present with the same or similar clinical symptoms due to the involvement of the same dysfunctional proteins in these disorders.

[0006] Therefore, it is essential to establish a reliable and early diagnosis of such neurological disorders or diseases to ensure efficient and early management of the disease, thereby enhancing the identification of appropriate treatments.

[0007] However, to date there is no sensitive, rapid and reproducible test capable of early and definitively diagnosing cognitive, functional and / or behavioral disorders associated with neurological diseases and / or neurological disorders.

[0008] At the same time, methods for measuring the activity of agents, such as drugs, on neurological activity are generally based on behavioral changes in living animals or use tissue biosensors, but these types of behavioral tests in animal models are costly, time-consuming, difficult to quantify, and rarely reproducible.

[0009] The use of tissue-based biosensors overcomes some of the limitations imposed by behavioral tests and can provide more easily quantifiable results. However, such biosensors have limited sensitivity and cannot detect subtle changes in cognitive function. Tissue biosensors capable of detecting agents that alter or otherwise modify neuronal function usually consist of cultured neurons held on a network of electrodes that record passive properties of the cell membrane, such as input impedance or spontaneous action potentials. Due to their low sensitivity, these types of biosensors are essentially used to determine acute cell death due to exposure to high concentrations of toxic substances (e.g., excitotoxicity caused by high concentrations of glutamate in the synaptic cleft). In addition, most biosensors only provide short-term data.

[0010] In general, conventional solutions have drawbacks.

[0011] i) Inability to detect, or barely detectable presence of, undetected or new drugs that may cause or indicate the presence of neurological disorders and / or disease.

[0012] ii) drugs that act quickly are generally detected, but drugs that take hours or days to be effective are generally not detected by known methods; and iii) the inability to distinguish affected neuronal populations;

[0013] From the above, it is clear that there is a need to develop new solutions that allow for fast, efficient, early and reliable differential diagnosis of neurological and / or neurological disorders. Summary of the Invention

[0014] The inventors have unexpectedly and surprisingly been able to develop a method for determining the effect of a biological sample on a biological model with a neural network, which is realized by an innovative biosensor, in particular with a bioreceptor comprising a multi-compartment microfluidic device, preferably with a bioreceptor in the form of a multi-compartment microfluidic device, and which includes a bioreceptor and its use for determining the effect of a biological sample on a biological model, which method is described throughout this description.

[0015] The aim of the present invention is to obtain data on the state of neural networks exposed to a biological sample and to analyze these data to establish a rapid and cost-effective differential diagnosis of neurological and / or neurological disorders in a subject with improved specificity / sensitivity ratios, in short, to provide the subject with an appropriate treatment with the aim of maintaining the quality of life, preventing complications and predicting the progression of the pathology, in particular the stage of the progression of the pathology.

[0016] Thus, the present invention provides an in vitro or ex vivo method for determining the effect of a biological sample on a biological model, comprising: a. Providing a bioreceptor comprising a multi-compartment microfluidic device, preferably in the form of a multi-compartment microfluidic device, said microfluidic device comprising: A first compartment and a second compartment, The first compartment comprises at least a neuronal culture in the form of a neuronal network, and the second compartment comprises at least a neuronal culture in the form of a neuronal network and / or a non-neuronal cell culture or an explant culture, with one compartment having at least one type of cell or explant culture, a first compartment and a second compartment; at least one means for forming a biological interface that allows communication between the first compartment and the second compartment by a neural connection; at least one device enabling recording of functional activity of said neurons over a plurality of measurement points spatially distributed within said first compartment; Equipped with Providing a bioreceptor; b. contacting said biological sample directly or indirectly with a neuronal and / or non-neuronal cell culture or explant culture in culture in said second compartment; c. after the contacting step of step b, performing a recording of the functional activity of the neurons in the culture in the first compartment for the plurality of measurement points over a measurement period; d. converting the recordings 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 characteristic parameter of a state of a neural network in the first compartment from said functional activity data; g. determining the effect of the biological sample by comparing the at least one characteristic parameter of the state of the neural network with a reference value of the at least one characteristic parameter of the state of the neural network; The present invention relates to a method comprising the steps of:

[0017] For the sake of simplicity in the remainder of this disclosure:

[0018] The term "neuron" is used synonymously with "nerve cell."

[0019] The term "neurological and / or neurological disorder" is used synonymously with "disorder of the central and / or peripheral nervous system" or "neurocognitive disorder" or "cognitive, functional and / or behavioral disorder."

[0020] The term "disease" is used synonymously with "lesion," "disorder," or "lesion."

[0021] In the context of the present invention, a "living organism model" refers to the culture of human or non-human cells, derived from human pluripotent stem cells or primary cells, isolated from their natural living environment, alone or in co-culture with neuronal or non-neuronal cells, in a multi-compartment microfluidic architecture, where the compartments are directly or indirectly connected. In other words, the cultivation of cells in an artificial environment, where the changes from the natural or in vivo conditions are minimized, is utilized here.

[0022] In the context of the present invention, a "biological interface" should be understood to refer to a system comprising contact junctions between cell populations contained in the device of the present invention that allows intercellular communication by exchange of information through electrical and / or chemical communication, which in the present invention may be a culture of neural cells, non-neural cells, or tissue explants.

[0023] In the context of the present invention, a "bioreceptor" refers to a set of molecules and / or cells that allow selective recognition of a molecule (or analyte). For example, enzymes, cells, aptamers, nanoparticles or antibodies. For the purposes of the present invention, a bioreceptor is a culture of neurons, for example human neurons derived from pluripotent stem cells, contained in a multicompartment microfluidic device that models a network structure that may be characteristic of neurological and / or neurological disorders. This bioreceptor is associated with a transducer that converts the association of the analyte with the bioreceptor into a measurable signal. According to the present invention, this combination of bioreceptor and transducer is defined as a biosensor that has the advantage of establishing a rapid and reliable diagnosis, especially thanks to the presence of a biological interface within these microfluidic architectures that make the bioreceptor more physiological, i.e. it is an innovative experimental model that allows the application of the results obtained by physical, biochemical and biological organization, functions and reactions to humans. Thus, in the context of the present invention, a "biosensor" refers to a device comprising at least one bioreceptor as defined above and a means for converting the functional activity of neurons into functional activity data as defined below.

[0024] In the context of the present invention, "functional activity of a neuron" means the release and propagation of neural messages in the form of electrical signals and / or secretion of neurotransmitters.

[0025] In the context of the present invention, "means for converting the functional activity of neurons into functional activity data" refers to transducers. Transducers can be divided into three main categories: optical (e.g., fiber optics), electrochemical (e.g., amperometric, potentiometric, impedance-measuring, or conductometric), and mass-based (e.g., piezoelectric and magnetoelastic).

[0026] In the context of the present invention, a "node", "node-link" or "vertex / edge" refers to a neuron, collection of cells, or population of neurons that are continuously connected and have functional activity. Note that the brain contains approximately 100 billion neurons, and a single neuron can establish up to 10,000 connections.

[0027] In the context of the present invention, a "module" refers to multiple nodes connected together to form a group or "cluster."

[0028] In the context of the present invention, an "action potential" or "spike" refers to the appearance of a nerve impulse, specifically, a sequence of temporary and localized depolarization of the cell membrane followed by repolarization of the inner membrane (possibly followed by hyperpolarization of unmyelinated cells).

[0029] In the context of the present invention, a "burst" refers to several action potentials within a given period. A burst is generally defined by a succession of at least four action potentials within a defined period, e.g., 100 ms. A burst typically contains in the range of 10-100,000 spikes for such a period.

[0030] In the context of the present invention, a "network burst" refers to a group of action potentials that form a network, i.e. that follow a defined synchronization of occurrence. In other words, a network burst is detected by synchronized action potentials of several electrodes.

[0031] Preferably, the present invention relates to 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 features, alone or in combination:

[0032] The at least one characteristic parameter of the state of the neural network in the first compartment may be a number of action potential(s), an inter-action potential interval or "ISI", a coefficient of variation of the inter-action potential interval(s), a number of probed electrodes, a normalized mean occurrence rate of action potentials (un taux moyen de potentiel d'actions normalise), number of bursts, number of electrode(s) capturing one or more burst(s), average duration of a burst, average action potentials in a burst, average inter-action potential interval (ISI) in a burst, inter-burst interval or "IBI", frequency of the burst, percentage of the bursts, number of network bursts, frequency of network bursts, duration of a network burst, average action potentials in a network burst, average inter-action potential interval (ISI) in a network burst, number of electrodes participating in forming a network burst, percentage of bursts in a network burst, coefficient of variation of inter-burst interval (IBI) in a network burst, surface area under the cross-correlation curve, and synchronization index.

[0033] The at least one characteristic parameter of the state of the neural network in the first compartment is i) connection coefficient, ii) the average of the minimum internode length; iii) average action potentials per second; iv) Network connectivity index, i.e., the “Small World Index”; v) the z score, or “z-score”; vi) the coefficient of participation, i.e. the "participation coefficient", and vii) Node centrality index is selected from the group consisting of:

[0034] The biological interface may be a fluidic microchannel, a PDMS microchannel, with a porosity preferably in the range of 10 nm-40 μm and a pore density preferably in the range of 10-1·10 9 pieces / 1cm 2 is in the range of 1·10 5-1·10 9 pieces / 1cm 2 The organoid comprises, and preferably consists of, at least one element selected from the group consisting of a porous membrane having pores in the range of 0.1 to 1.0 mm, a porous capillary membrane, preferably consisting of a membrane made of polycarbonate, polyester, polyethylene terephthalate, and / or polytetrafluoroethylene, on which at least one organoid is cultured (i.e., a three-dimensional multicellular structure that reproduces the microanatomy of an organ in vitro), a gel, a hydrogel, and a mixture thereof.

[0035] Step e. Construction of a graph representing the neural network, which is obtained using graph theory, preferably with the nodes of the graph corresponding to measurement points of functional activity and the connections between the nodes corresponding to correlations of axonal communication.

[0036] said step f comprises determining from said functional activity data two, preferably three, preferably four, even five, particularly six and more particularly seven parameters of said state of said neural network in said first compartment; The characteristic parameters are: i) connection coefficient, ii) the average of the minimum internode length; iii) average action potentials per second; iv) Network connectivity index, i.e., the “Small World Index”; v) the z score, or “z-score”; vi) the coefficient of participation, i.e. the "participation coefficient", and vii) Node centrality index is selected from the group consisting of:

[0037] The network connectivity index is the ratio of the connectivity coefficient to the average of the minimum internode length.

[0038] i) connection coefficient, ii) the average of the minimum internode length; iii) average action potentials per second; iv) Network connectivity index, i.e., the “Small World Index”; v) the z score, or “z-score”; vi) the coefficient of participation, i.e. the "participation coefficient", and vii) Node centrality index In order to allow the analysis of at least one parameter selected from the group consisting of: the average of action potentials per second must have a value equal to or greater than 0.5, suitably strictly greater than 0.5, preferably 1 or 1.5, and even more preferably 2.

[0039] The connection coefficient has a value greater than or equal to 0, preferably 0.5, and preferably in the range 0-1.

[0040] The average inter-node minimum length has a value that is 1 or more, preferably 1.5 or 2 or more.

[0041] The network connectivity index has a value that is greater than or equal to 0, preferably greater than or equal to 0.5, and suitably in the range 0-1.

[0042] A z score, or "z score," has a value that is 0 or greater, preferably a value that is 5 or greater, and suitably a value in the range 0-10.

[0043] The coefficient of participation, or "participation coefficient", has a value that is greater than or equal to 0, preferably greater than or equal to 0.5, and suitably in the range 0.5-1.

[0044] The centrality index of a node has a value that is greater than or equal to 0, preferably greater than or equal to 5, and preferably in the range of 6-10.

[0045] The device allowing to record the functional activity of the neuronal network according to step a. over a plurality of spatially distributed measurement points in the first compartment, preferably in the first compartment and at least one further compartment, suitably in all compartments, is a device allowing indirect contact recording with cultured cells, the device comprising: a device for recording activity by amperometric or voltammetric methods using an array of planar or non-planar semi-solid microelectrodes; A fluorescence imaging recording device including calcium imaging or transmembrane ion flow imaging; Devices for recording intercellular, extracellular, or patch clamp electrophysiological activity in whole-cell, attached-cell, inside-out, or outside-out configurations. is selected from the group consisting of:

[0046] Step d. converting the recordings of the functional activity of the neurons in culture in the first compartment into functional activity data is performed by a means for converting the recordings of the functional activity of the neurons into functional activity data, which means is preferably an algorithmic system for converting electrical and / or electrophysiological data into binary data.

[0047] The measurement period of the recording of the functional activity of the neurons in culture in the first compartment according to step c is in the range of 300 ms-20 min, advantageously in the range of 1 min-15 min, preferably in the range of 5 min-12 min.

[0048] The multi-compartment microfluidic device further comprises a third compartment and at least one means for forming a biological interface enabling communication by neural connections between the first and third compartments and / or at least one means for forming a biological interface enabling communication by neural connections between the second and third compartments. Preferably, the device further comprises a fourth compartment and at least one means for forming a biological interface enabling communication by neural connections between the first and fourth compartments and / or at least one means for forming a biological interface enabling communication by neural connections between the second and fourth compartments and / or at least one means for forming a biological interface enabling communication by neural connections between the third and fourth compartments. The device preferably comprises a fifth compartment and at least one means for forming a biological interface enabling communication via a neural connection between the first compartment and the fourth compartment and / or at least one means for forming a biological interface enabling communication via a neural connection between the second compartment and the fifth compartment and / or at least one means for forming a biological interface enabling communication via a neural connection between the third compartment and the fourth compartment and / or at least one means for forming a biological interface enabling communication via a neural connection between the fourth compartment and the fifth compartment.

[0049] The contacting 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.

[0050] Each of the compartments contained in the device of the invention contains cultures of one, two or three types of neuronal and / or non-neuronal cells.

[0051] The neurons are selected from the group consisting of glutamatergic, GABAergic, serotonergic, cholinergic, dopaminergic, adrenergic, noradrenergic, sensory neurons and motor neurons.

[0052] The non-neuron is selected from the group consisting of glial cells (including microglia / macrophages and macroglia (i.e. astrocytes, oligodendrocytes, Schwann cells, and ependymal cells)), epithelial cells, connective cells, thyroid cells, adipocytes, blood cells, immune cells, bone cells, chondrocytes, gastric cells, pancreatic cells, liver cells, intestinal cells, lung cells, endothelial cells, muscle cells, vascular cells, cardiac cells, mesenchymal cells, retinal pigment epithelial cells, and retinal cells.

[0053] The explants are tissues derived from brain, epithelium, eye, thyroid, adipose, vascular, bone, cartilage, stomach, pancreas, liver, intestine, lung, endothelium, muscle, retina, heart and placenta.

[0054] 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, pericardial fluid, aqueous humor, amniotic fluid, and plasma.

[0055] The biological sample may be a "drug" or a "test agent", i.e. a compound with properties modulating the functional activity of neurons, or a drug that may or may not have properties modulating the functional activity of neurons. In this case, the method of the invention makes it possible to identify and / or characterize the possible properties of said drug, and even to identify a "threshold concentration". In other words, in particular if the test agent is a pharmaceutical product, it is the concentration of the drug under a minimal treatment regime (i.e. for pharmacological compositions, generally prescribed for animals or humans to have a minimal therapeutic dose). In this embodiment of the invention, the comparison with the reference value carried out in step g is made by comparing at least one characteristic parameter of the state of the neuronal network of the invention, consisting of a value obtained before application of the drug or test agent, with a value obtained after application of the drug or test agent, which value is earlier in time to ensure monitoring of the effect of the drug or test agent.

[0056] Thus, in the context of the present invention:

[0057] By "number of action potentials(s)" is meant the number of action potentials detected during recording (detection parameters defined prior to recording).

[0058] "Inter-action potential interval" or "ISI" means the average duration between detected action potentials.

[0059] "Coefficient of variation of inter-action potential interval(s)" means the standard deviation of the ISIs divided by the mean of the ISIs. This parameter reflects the regularity of the action potentials and their distribution.

[0060] "Number of active electrodes" refers to the number of electrodes showing activity equal to or greater than the minimum mean rate of occurrence of action potentials defined prior to recording.

[0061] "Normalized mean occurrence rate of action potentials" is the number of action potentials divided by the recording time (mean). This parameter is usually defined in Hz or spikes / sec (number of action potentials / sec). This parameter is determined only from functional activity data measured on the active electrodes defined prior to recording.

[0062] "Number of bursts" refers to the number of bursts in the recording per electrode or the total (sum of bursts from all electrodes).

[0063] "Number of electrodes capturing bursts" refers to the total number of electrodes measuring the number of bursts / min. The number of bursts / min can be defined prior to recording. For example, it can be at least 5 bursts / min.

[0064] "Mean burst duration" means the average time between the first and last action potentials that define each measured burst.

[0065] "Mean action potentials in a burst" or "mean spikes in a burst" refers to the average number of action potentials in a measured burst.

[0066] "Mean inter-action potential interval (ISI) in a burst" means the mean inter-action potential interval defined in a burst.

[0067] "Interburst interval" or "IBI" means the average duration between different ones of the recorded bursts.

[0068] "Burst frequency" is the total number of bursts divided by the duration of the recording. This parameter is usually defined in Hertz or bursts per minute.

[0069] "Percentage of burst" is the ratio of the number of action potentials in a burst to the total number of action potentials recorded.

[0070] "Number of network bursts" means the total number of network bursts identified in the record.

[0071] "Network burst frequency" is the total number of network bursts divided by the duration of the recording. This parameter is usually defined in Hertz.

[0072] "Network burst duration" means the average time between two action potentials in a network burst.

[0073] "Average action potentials in network bursts" refers to the average of action potentials detected within all recorded network bursts.

[0074] "Mean inter-action potential interval (ISI) in network bursts" means the average of the inter-action potential intervals defined in all recorded network bursts.

[0075] "Number of electrodes participating in forming a network burst" refers to the average number of active electrodes in a network burst.

[0076] "Percentage of bursts in a network burst" means the ratio of the number of action potentials in a network burst to the total number of action potentials recorded.

[0077] The "coefficient of variation of the interburst interval (IBI) in network bursts" is the standard deviation of the IBI divided by the mean of the IBI.

[0078] "Area under the cross-correlation curve" means the area under the cross-correlation curve between electrodes. This parameter may be determined according to the method defined in "Neural Spike Train Synchronization Indices: Definitions, Interpretations, and Applications" by Halliday, Rosenber, Breeze & Conway, 2006.

[0079] The "synchrony index" is a unitless measure of synchrony that ranges between 0 and 1. This parameter can be determined according to the publication "A comparison of binless spike train measures (Paiva et al., 2010)".

[0080] "Connectivity coefficient" or "clustering coefficient" refers to the probability that two nodes are connected to each other knowing that they have a common neighbor node.

[0081] "Average minimum internode length" means the average length between two connected nodes.

[0082] "Average action potentials per second" refers to the average number of nerve impulses per second, i.e., the sequence of transient and localized depolarization of the cell membrane followed by repolarization of the intracellular membrane (and possibly hyperpolarization for unmyelinated cells).

[0083] "Network connectivity index" or "small-world index" refers to the structural (physical, i.e., synapse and axon) and physiological (functional / symmetric and effective / causal) connections between two or more nodes, which is a reflection of the robustness of a neural network. Preferably, the network connectivity index is determined by the ratio of the connectivity coefficient to the average of the minimum internode length.

[0084] The z-score, or "z-score", is a measure that allows one to characterize how node connectivity is distributed within a module, i.e. it is a measure for characterizing the intramodule.

[0085] The coefficient of participation, or "coefficient of participation", means a measure that makes it possible to characterize how the connectivity of nodes is distributed between or within several modules.

[0086] "Node centrality index" means a value proportional to the number of times this node is passed through when randomly traversing a graph representing a neural network according to the invention by randomly choosing one of the connections starting from this node.

[0087] These characteristic parameters, which represent the state of the neuronal network in the first compartment determined from the recorded functional activity data, can be used alone or in combination and are grouped into four categories:

[0088] 1. Analysis of spikes (action potentials) This category includes the following parameters: number of action potential(s), mean action potential(s) per second, inter-action potential interval or "ISI", coefficient of variation of inter-action potential interval(s), number of probed electrodes, and mean occurrence rate of normalized action potential(s).

[0089] 2. Burst Analysis This category includes the following parameters: number of bursts, number of electrode(s) that captured one or more burst(s), average burst duration, average action potential during a burst, average inter-action potential interval (ISI) during a burst, inter-burst interval or "IBI", frequency of said bursts, and percentage of said bursts.

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

[0091] 4. Network Connectivity This last category applies to any kind of network, including neural networks. It includes the following parameters, some of which are determined using a connectivity algorithm called cross-correlation (see Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons, Deblasi et al., 2019): the area under the cross-correlation curve, the synchronization index, the connectivity coefficient, the average minimum internode length, the network connectivity index or "small-world index", the coefficient of participation or "participation coefficient", and the node centrality index.

[0092] In particular, the apparatus enabling recording of the functional activity of neurons at a plurality of measurement points spatially distributed in the first compartment according to step a comprises electrodes in direct or indirect contact with the neurons, said device enabling recording of differences in the polarization of neurons and allowing neurons to communicate with each other by action potentials characterized by a potential difference of more than 1 μV.

[0093] The means for converting functional activity into functional activity data according to step a of the present invention is an algorithmic system that converts electrical and / or electrophysiological data into binary data. Specifically, this is - converting the electrophysiological data by a signal converter included in the recording device, said converter being capable of being coupled to a signal amplifier; Binarizing the electrophysiological activity of neurons, either individually or collectively, for each node by establishing a detection threshold; - creating a time-space matrix of the functional activity of the nodes based on the data obtained in the previous step, the so-called matrix data; - creating a map representing the cross-temporal and weighted time-spatial correlation of the activity of each node connected to other nodes in order to provide a set of quantitative data (or matrix data) that proves to be characteristic for each biological model according to the invention in a pathological representation configuration. In particular, this involves generating a "functional activity signature" that may be compared to a "reference library of functional activity signatures", "previously recorded functional activity signatures of the same network", or "collection of previously recorded functional activities of the same network", thereby allowing the establishment of relative differences between the recorded signature and the reference signature.

[0094] In the context of the present invention, a "functional activity signature" is to be understood as an activity profile, i.e. possible changes in the functional activity (i.e. electrical activity) of neurons in a culture in the form of a network, in the first compartment of a device according to the present invention.

[0095] In the context of the present invention, a "library of functional activity signatures" is to be understood as a collection of different activity signatures for multiple biological samples (e.g., two or more, suitably more than 10, preferably more than 100, more than 1,000, or more than 10,000, or more than 1,000,000 biological samples) that are distinguishable from one another.

[0096] In the context of the present invention, the comparison with reference values ​​carried out in step g) comprises comparing at least one characteristic parameter of the state of the neural network of the invention, consisting of a value obtained from a previously created sample (seconds, minutes, hours, days, months and / or years), with at least one characteristic parameter 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 with a parameter consisting of a value obtained from a reference library or a reference collection. In particular: when carrying out an in vitro or ex vivo method for determining the effect of a biological sample on a biological model in order to ensure monitoring of the state of the subject (i.e. the outcome of the remission or exacerbation of a pathology, in particular a neurological and / or neurological disorder, the appearance of a pathology, in particular a neurological and / or neurological disorder, or the effect of a treatment by administration of a drug / test agent), monitoring of the value of at least one characteristic parameter of the state of the neural network over time is carried out using evolution conditions (in particular using standard deviations, using derivatives, rising or falling thresholds preferably comprised in the range of 30 seconds - 60 minutes, preferably 1 - 50 minutes, 2 - 40 minutes, 3 - 30 minutes, even 4 - 20 minutes, preferably 5 - 10 minutes over the analysis period), when comparing at least one characteristic parameter of the state of the neuronal network with a library of existing functional activities, all of the recorded parameters i)-vii) should be taken into account, which in particular makes it possible to obtain a more reliable differential diagnosis, i.e. to distinguish between different possible diagnoses that may present with the same or similar clinical symptoms, since the same malfunctioning protein is involved in these disorders. In this case, the comparison step according to the invention comprises, preferably consists of, an absolute comparison with other subjects, so-called "true positives" and / or "true negatives", forming a group or "cluster" of reference subjects / data, or The comparison with a reference value carried out in step f) may comprise an absolute comparison with a library and monitoring the condition of the subject, as described above.

[0097] 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, preferably consists of, an absolute comparison with respect to a library of functional activities.

[0098] For example, in the case of establishing a differential diagnosis between Alzheimer's disease and Parkinson's disease, step g) of the method of the invention comprises, preferably consists of, an absolute comparison with respect to a library of functional activities. Then, preferably, monitoring of the values ​​of the network connectivity index (parameter iv) over time is performed using evolution conditions (rising or falling thresholds over the average period with standard deviation, using derivatives) to monitor the progression of the disorder.

[0099] For example, in the case of determining the presence or absence of a head trauma, step g) of the method of the present invention preferably comprises monitoring the progression of the disorder by monitoring the value of the network connectivity index (parameter iv) over time using evolution conditions (rising or falling thresholds over an average period with standard deviation, using derivatives).

[0100] For example, the means for converting functional activity into functional activity data is an algorithmic system that converts electrical and / or electrophysiological data into binary data.

[0101] According to the invention, the method includes converting the signals of functional activity recorded by the MEA2100-Headstage system (Multichannel systems, Reutlingen, Germany) into digital signals by an analog-digital converter, possibly coupled to an amplifier or set of amplifiers, said converter being directly integrated in the recording device. This binary signal of the data stream is read by the MEA2100-256-Systems software (Multichannel systems, Reutlingen, Germany) provided in the device. Alternatively, the method includes converting the signals of functional activity recorded by the M768tMEA-16 (Axion Biosystems, Atlanta, GA, USA) system into digital signals by an analog-digital converter, possibly coupled to an amplifier or set of amplifiers, said converter being directly integrated in the recording device. This binary signal of the data stream is read by the Axis Navigator (Axion Biosystems, Atlanta, GA, USA) software provided in the device.

[0102] For example, the means of converting the digital activity of a node into a binary signal of the node is conventionally ensured by thresholding algorithms known to those skilled in the art, which involve applying an appropriate filter to analyze the background noise of the signal and to apply a binarization threshold to the entire signal.

[0103] The method of the invention allows to apply a biological sample from a subject (e.g. a sample of cerebrospinal fluid, blood, saliva mucus or a test agent) to a bioreceptor, preferably in the form of a multicompartment microfluidic device integrating relevant cell co-cultures (of neurons and / or non-neurons and / or explant cells). Preferably, the sample is applied indirectly to the neuronal culture, i.e. application is not made to the neuronal culture but to at least one of the relevant co-cultures, and then acts through a biological interface connected to the neuronal culture, for example by axons and / or synapses extending into the microchannels of the multicompartment microfluidic device of the invention. The response of the neuronal network after application of said biological sample, i.e. possible modifications of the functional activity of the neuronal network, is then recorded and analyzed.

[0104] In particular, said modification of the functional activity of a neuronal network can be achieved by any means known to the person skilled in the art, in particular by Altered functional communication, i.e. a change in the effectiveness of intercellular communication in which the ability of one or more neurons (groups of neurons) to activate target neurons to which they are synaptically or non-synaptically connected is increased, decreased or destroyed; alteration of the network of axonal and / or dendritic connections, for example by destruction of said axons and / or dendrites, Alterations in one or more cell types that impair neuronal communication, e.g., glial cell impairment only within the node, and / or Modification of the action potential, i.e. an increase or decrease in the amplitude, frequency, duration, "threshold" potential that allows membrane depolarization, and / or periodicity of the action potential; This can be done by:

[0105] These modifications modify at least one parameter selected from the group consisting of i) connectivity coefficient, ii) average internode minimum length, iii) average action potentials per second, iv) network connectivity index, v) z score, vi) participation coefficient, and vii) node centrality index. As a result, a functional activity signature derived from the correlation of said parameters is obtained, which indicates whether it is characteristic of a pathological condition. Finally, the diagnosis is performed by comparing the functional activity signature thus obtained with so-called "true positives", i.e. functional activity signatures whose network index is true positive.

[0106] By carrying out the method of the present invention in differential diagnosis, the functional analysis of the neural network makes it possible to obtain results in less than 72 hours, suitably less than 48 hours, preferably less than 24 hours, or less than 12 hours, 6 hours, 3 hours, 2 hours, 1 hour, or less than 30 minutes, 20 minutes, 15 minutes, or less than 10 minutes, or even instantly.

[0107] It will be apparent from the above that the innovation of the present invention therefore lies in using the network indices as diagnostic indices, in identifying the relevant explant configuration (i.e. neuronal or non-neuronal or tissue explants) to establish a given diagnosis, and in quantifying the network indices for each diagnosis type so that the diagnosis can be proven.

[0108] One of the advantages of the method of the invention is that it allows to establish a reliable differential diagnosis, i.e. the diagnosis makes it possible to distinguish a neurological and / or neurological disease from another pathology presenting with similar or similar symptoms, or from a pathology that has few or no noticeable symptoms during initial auscultation.

[0109] Another advantage of the method of the present invention is that it guarantees a rapid differential diagnosis. Moreover, the method of the present invention can improve the specificity / sensitivity ratio. In fact, the method of the present invention has a better detection resolution compared to the amplification limit of conventional molecular coupling (e.g., ELISA) or PCR, and specificity is guaranteed because it uses neurons and at least one associated related co-culture, thereby making it possible to model specific cellular and molecular structures for diagnosing a given pathology.

[0110] Furthermore, the method of the present invention is inexpensive since it does not require the use of specific equipment, as in the case of ELISA, immunospecific markers, PCR, or other biochemical assay techniques.

[0111] Finally, the method of the invention requires the use of very small amounts of fluid, preferably in the microliter range, given the implementation in microfluidic devices, and therefore is less restrictive and, depending on the method of sampling intervention, may be less invasive.

[0112] The present invention also relates to a bioreceptor and, optionally, a biosensor comprising said bioreceptor for determining the effect of a biological sample on a biological model. In particular, the present invention comprises a bioreceptor that can be implemented in the method of the present invention as described above.

[0113] The present invention therefore relates to a bioreceptor and optionally a biosensor comprising said bioreceptor, for determining the effect of a biological sample on a biological model, preferably in the form of a multicompartment microfluidic device, said multicompartment microfluidic device comprising: a first compartment and a second compartment, the first compartment comprising at least a neuronal culture in the form of a neuronal network, and the second compartment to which the biological sample is applied comprises at least a neuronal culture in the form of a neuronal network and / or a non-neuronal cell culture or an explant culture, one compartment comprising at least one type of cell or explant culture; At least one means for forming a biological interface enabling communication by a neural connection between the first compartment and the second compartment (FIG. 1); At least one device enabling recording of functional activity of said neurons over a plurality of measurement points spatially distributed within said first compartment, said device being optionally coupled to a means for converting said functional activity into data; It consists of:

[0114] Preferably, the present invention relates to a bioreceptor and optionally a biosensor comprising said bioreceptor for determining the effect of a biological sample on a biological model as defined above, having, alone or in combination, the following technical features:

[0115] The biological interface is a fluidic microchannel, a PDMS microchannel, with a porosity preferably in the range of 10 nm-40 μm and a pore density preferably in the range of 10-1·10 9 pieces / 1cm 2 and preferably in the range of 1·10 5 -1·10 9 pieces / 1cm 2 The organoid comprises, and preferably consists of, at least one element selected from the group consisting of a porous membrane having pores in the range of 0.1 to 1.0 mm, a porous capillary membrane, preferably consisting of a membrane made of polycarbonate, polyester, polyethylene terephthalate, and / or polytetrafluoroethylene, on which at least one organoid is cultured (i.e., a three-dimensional multicellular structure that reproduces the microanatomy of an organ in vitro), a gel, a hydrogel, and a mixture thereof.

[0116] means for converting the recording of functional activity of neurons in the culture of the first compartment into functional activity data.

[0117] Analyzing means configured to determine from the functional activity data at least one characteristic parameter of a state of the neuronal network in the first compartment, said at least one parameter being: i) connection coefficient, ii) the average of the minimum internode length; iii) average action potentials per second; iv) Network connectivity index, i.e., the “Small World Index”; v) the z score, or “z-score”; vi) the coefficient of participation, i.e. the "participation coefficient", and vii) Node centrality index is selected from the group consisting of:

[0118] and analyzing means configured to determine from the functional activity data at least one characteristic parameter of the state of the neuronal network in the first compartment, said at least one parameter being selected from the group consisting of number of action potential(s), inter-action potential interval or "ISI", coefficient of variation of inter-action potential interval(s), number of probed electrodes, average rate of occurrence of normalized action potentials, number of bursts, number of electrode(s) capturing one or more burst(s), average duration of bursts, average action potential during a burst, inter-action potential interval during a burst (ISI), average duration of a burst, average of action potentials during ... The parameters are selected from the group consisting of: average of I), interburst interval or "IBI", burst frequency, percentage of bursts, number of network bursts, frequency of network bursts, duration of network bursts, average of action potentials in network bursts, average inter-action potential interval (ISI) in network bursts, number of electrodes participating in forming a network burst, percentage of bursts in a network burst, coefficient of variation of interburst interval (IBI) in network bursts, surface area under the cross-correlation curve, and synchronization index.

[0119] means for performing a comparison of said at least one characteristic parameter of a state of said neural network with a reference value of said at least one characteristic parameter of a state of said neural network in order to determine an effect of said biological sample.

[0120] The multi-compartment microfluidic device further comprises a third compartment and at least one means for forming a biological interface enabling communication by neural connections between the first compartment and the third compartment (1, 3) and / or at least one means for forming a biological interface enabling communication by neural connections between the second compartment and the third compartment (Figure 2), preferably the device further comprises a fourth compartment and at least one means for forming a biological interface enabling communication by neural connections between the first compartment and the fourth compartment and / or at least one means for forming a biological interface enabling communication by neural connections between the second compartment and the fourth compartment and / or at least one means for forming a biological interface enabling communication by neural connections between the third compartment and and at least one means for forming a biological interface enabling communication via a neural connection between the first compartment and the fourth compartment (Figure 3), the device preferably comprising a fifth compartment and at least one means for forming a biological interface enabling communication via a neural connection between the first compartment and the fourth compartment and / or at least one means for forming a biological interface enabling communication via a neural connection between the second compartment and the fifth compartment and / or at least one means for forming a biological interface enabling communication via a neural connection between the third compartment and the fourth compartment and / or at least one means for forming a biological interface enabling communication via a neural connection between the fourth compartment and the fifth compartment (Figure 4).

[0121] It may be included in a portable or non-portable kit for the purpose of determining the effect of the biological sample on a biological model, ultimately to establish a differential diagnosis of a neurological and / or neurological disorder.

[0122] The present invention also relates to the use of the aforementioned bioreceptors and biosensors comprising said bioreceptors in an in vitro or ex vivo method for the differential diagnosis of neurological and / or neurological disorders, preferably selected from the group consisting of Alzheimer's disease, Parkinson's disease, head trauma, cerebrovascular disorders, thrombotic or embolic occlusion or ischemia, transient ischemic attacks, neurological forms of SARS-CoV-2 infection, neurotoxicity due to organophosphorus compounds, etc., analgesia, neuroinflammatory diseases such as multiple sclerosis, otoneuritis, myelitis, lupus, Crohn's disease, hearing impairment due to damage to the auditory nerve, amyotrophic lateral sclerosis, retinal neuropathy such as diabetes-induced, epilepsy, psoriasis, herpes, meningoencephalitis, isolated lymphocytic meningitis, Guillain-Barre syndrome or mononuclear polyradiculoneuropathy, peripheral neuropathy, and myelopathy.

[0123] The present invention also relates to the use of said bioreceptors and biosensors comprising said bioreceptors in in vitro or ex vivo methods for monitoring the preventive and / or therapeutic treatment of neurological disorders and / or neurological diseases, preferably gene therapy, cell therapy, axonal regeneration therapy, treatment by administration of one or more therapeutic and / or prophylactic and / or anesthetic agents.

[0124] The present invention also relates to the use of the aforementioned bioreceptors and biosensors comprising said bioreceptors in a method for rapid and sensitive screening of drugs or test agents as potential pharmaceuticals. Indeed, the device of the present invention allows to identify the possible therapeutic effect of a test agent and to determine the physiologically relevant concentration (e.g. the amount present in a specific tissue under a defined posology) of this drug identified as a pharmaceutical. Indirect application of the test agent to the neuronal culture in the first compartment generates a recognizable or characteristic signature of functional activity, which allows to rapidly and efficiently determine the therapeutic benefit of the drug. The present invention is described in a non-limiting manner by the following embodiments with reference to the attached drawings.

[0125] It is believed that, in light of the specification and embodiments, one of ordinary skill in the art will be able to make and use the methods and bioreceptors claimed without further explanation.

[0126] The present invention also relates to an in vitro or ex vivo method for determining the effect of a biological sample on a biological model, comprising: a. providing a bioreceptor comprising a multi-compartment microfluidic device (10), said microfluidic device (10) comprising: a first compartment (1) and a second compartment (2), the first compartment (1) comprising at least a neuronal culture in the form of a neuronal network, and the second compartment (2) to which the biological sample is applied comprises at least a neuronal culture in the form of a neuronal network and / or a non-neuronal cell culture or an explant culture, one compartment comprising at least one type of cell or explant culture; at least one means for forming a biological interface (21) enabling communication by a neural connection between said first compartment and said second compartment; at least one device (40) enabling recording of functional activity of said neurons over a plurality of measurement points spatially distributed within said first compartment, said device being optionally coupled to means (50) for converting said functional activity into data; Equipped with Providing a bioreceptor; b. contacting said biological sample directly or indirectly with neurons in culture in said first compartment or with a neuronal and / or non-neuronal cell culture or explant culture in culture in said second compartment; c. after the step of contacting the neurons in culture in the first compartment (1) with the biological sample of step b, performing a recording of the functional activity of the neurons in culture in the first compartment (1) for the plurality of measurement points over a measurement period; d. converting the recordings 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 to generate graphs representative of the neural network; f. determining from said functional activity data at least one characteristic parameter of a state of a neural network in said first compartment (1), The at least one characteristic parameter is i) connection coefficient, ii) the average of the minimum internode length; iii) average action potentials per second; iv) Network connectivity index, i.e., the “Small World Index”; v) the z score, or “z-score”; vi) the coefficient of participation, i.e. the "participation coefficient", and vii) Node centrality index and g. determining the effect of the biological sample by comparing the at least one characteristic parameter of the state of the neural network with a reference value of the at least one characteristic parameter of the state of the neural network; The method includes: [Brief description of the drawings]

[0127] [Figure 1] FIG. 1 is a diagram depicting a bioreceptor of the present invention in the form of a multicompartment microfluidic device with two compartments. [Diagram 2]FIG. 2 is a diagram depicting a bioreceptor of the invention in the form of a multicompartment microfluidic device with three compartments. [Diagram 3] FIG. 3 is a diagram depicting a bioreceptor of the invention in the form of a multicompartment microfluidic device with four compartments. [Figure 4] FIG. 4 is a diagram depicting a bioreceptor of the invention in the form of a multicompartment microfluidic device with five compartments. [Diagram 5] FIG. 5 is a diagram representing the different elements required to carry out the method of the invention. [Figure 6] FIG. 6 is a diagram illustrating the steps performed of the method according to the invention. [Figure 7] FIG. 7 represents the steps performed of the method according to the invention and the data obtained at each of these steps for healthy subjects and for subjects suffering from a neurological and / or neurological disorder. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0128] As mentioned above, the present invention relates in a first embodiment, represented in Figures 1 to 5, to a bioreceptor comprising a multicompartment microfluidic device comprising, in the first embodiment, a first compartment 1 and a second compartment 2, each of which comprises, in the first embodiment, a culture of at least one type of cell or explant and at least one means for forming a biological interface 21 allowing communication by neural connection between the first compartment 1 and the second compartment 2.

[0129] The bioreceptor of the invention also comprises at least one device 40, making it possible to record the functional activity of neurons over a plurality of measurement points spatially distributed in the first compartment. This device may be coupled to means 50 for converting this functional activity into functional activity data, means 60 for analyzing these functional activity data, configured to determine from the functional activity data at least one characteristic parameter of the state of the neuronal network in the first compartment, and means 70 for carrying out a comparison of the at least one characteristic parameter of the state of the neuronal network with a reference value of the at least one characteristic parameter of the state of the neuronal network in order to determine the effect of the biological sample.

[0130] The at least one parameter is i) connection coefficient, ii) the average of the minimum internode length; iii) average action potentials per second; iv) Network connectivity index, i.e., the “Small World Index”; v) the z score, or “z-score”; vi) the coefficient of participation, i.e. the "participation coefficient", and vii) Node centrality index may be selected from the group consisting of:

[0131] The at least one parameter may be selected from the group consisting of number of action potential(s), inter-action potential interval or "ISI", coefficient of variation of inter-action potential interval(s), number of probing electrodes, average rate of occurrence of normalized action potentials, number of bursts, number of electrode(s) capturing one or more burst(s), average duration of bursts, average action potentials in a burst, average inter-action potential interval (ISI) in a burst, inter-burst interval or "IBI", frequency of bursts, percentage of bursts, number of network bursts, frequency of network bursts, duration of network bursts, average action potentials in a network burst, average inter-action potential interval (ISI) in a network burst, number of electrodes participating in forming a network burst, percentage of bursts in a network burst, coefficient of variation of inter-burst interval (IBI) in a network burst, surface area under the cross-correlation curve, and synchronization index.

[0132] When two or more parameters are used to characterize the state of the neuronal network in the first compartment from the functional activity data, these may consist of a combination of parameters belonging to the same group or a combination of parameters each taken from one of the two aforementioned groups.

[0133] In a second embodiment represented by FIG. 2, the bioreceptor of the invention further comprises a third compartment 3, at least one means 31 for enabling communication by a neural connection between the first compartment 1 and the third compartment 3 by forming a biological interface, and / or at least one means 32 for enabling communication by a neural connection between the second compartment 2 and the third compartment 3 by forming a biological interface.

[0134] In a third embodiment represented by FIG. 3, the bioreceptor of the invention further comprises a fourth compartment 4, at least one means 41 enabling communication via a neural connection between the first compartment 1 and the fourth compartment 4 by forming a biological interface, and / or at least one means 42 enabling communication via a neural connection between the second compartment 2 and the fourth compartment 4 by forming a biological interface, and / or at least one means 43 enabling communication via a neural connection between the third compartment 3 and the fourth compartment 4 by forming a biological interface.

[0135] In a fourth embodiment represented by FIG. 4 , the bioreceptor of the invention further comprises a fifth compartment 5, at least one means 51 for enabling communication via a neural connection between the first compartment 1 and the fifth compartment 5 by forming a biological interface, and / or at least one means 52 for enabling communication via a neural connection between the second compartment 2 and the fifth compartment 5 by forming a biological interface, and / or at least one means 53 for enabling communication via a neural connection between the third compartment 3 and the fifth compartment 5 by forming a biological interface, and / or at least one means 54 for enabling communication via a neural connection between the fourth compartment 4 and the fifth compartment 5 by forming a biological interface.

[0136] As represented in Figures 6 and 7 (Figure 7 includes 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 neurons (Figures 7A' and 7A) in culture in the first compartment 1, i.e. a recording of the extracellular activity of neurons reflected by peaks, so-called "spikes", which appear periodically when the neuronal network is synchronized (i.e. in a non-pathological state). Thus, a strong activity of the neuronal network in culture in the first compartment 1 is observed by the sudden appearance of peaks, so-called "bursts". Conversely, other types of neurons, such as GABAergic neurons, do not show a synchronized functional activity (data not shown). In this case, even if a disturbance of the functional activity occurs, it will not result in desynchronization of the "spikes" and / or a reduction or disappearance of the "bursts", but it will also not result in a modification of the characteristic parameters of the state of the neuronal network according to the invention.

[0137] This recording of the functional activity of neurons in the form of a network in the culture of the first compartment is subjected to a conversion means 50 which makes it possible to obtain functional activity data, in particular binary data (Figures 7B' and 7B).

[0138] These functional activity data are then transmitted to an analyzing means 60 configured to determine from the functional activity data at least one characteristic parameter of the state of the neuronal network in the first compartment, said at least one parameter being: i) connection coefficient, ii) the average of the minimum internode length; iii) average action potentials per second; iv) Network connectivity index, i.e., the “Small World Index”; v) the z score, or “z-score”; vi) the coefficient of participation, i.e. the "participation coefficient", and vii) Node centrality index may be selected from the group consisting of:

[0139] Finally, the characteristic data / parameters of the neural network states are interrelated and can be represented in the form of a cross-correlation graph (FIGS. 7C' and 7C).

[0140] The bioreceptor comprises means 70 for performing a comparison of the characteristic parameters of the at least one neural network state with a reference value of the characteristic parameters of the at least one neural network state in order to determine the effect of the action of the biological sample on the biological model and thus determine the presence or absence of a neurological disease and / or neurological disorder in the subject or to identify new therapeutic solutions (new molecules or new drug doses) from screening of test drugs.

[0141] In particular, when the average number of action potentials per second is greater than 0.5, i) connection coefficient, ii) the average of the minimum internode length; iv) Network connectivity index, i.e., the “Small World Index”; v) the z score, or “z-score”; vi) the coefficient of participation, i.e. the "participation coefficient", and vii) Node centrality index It is possible to analyze at least one parameter selected from the group consisting of:

[0142] In particular, the analysis includes the determination of the value of the average connection coefficient ratio of the minimum length between nodes, and preferably consists of them, which finally makes it possible to determine the connection index of the network, which is the parameter for performing the comparison step.

[0143] [Embodiment] The invention will be further described with reference to a bioreceptor comprising a multicompartment microfluidic device 10 with two compartments (Examples 1-3) or five compartments (Example 4). Nevertheless, these examples are not intended to limit the invention.

[0144] Example 1: Diagnosis of Alzheimer's Disease 1. Background More than 10 million people in Europe suffer from neurodegenerative diseases such as Alzheimer's disease (AD), and this number is likely to double within 20 years. The lack of a curative treatment for these diseases and the complexity of establishing a reliable differential diagnosis make it difficult to find new treatments. The first lesions in AD appear in the hippocampus, a brain region involved in memory processes (recording, retrieving and organizing memories) and emotion management, and then spread gradually towards the outer regions, following the connections established between the different brain regions. The hippocampus is a structure composed by glial cells (astrocytes or microglial cells) and neurons, mostly glutamatergic and GABAergic neurons.

[0145] 2. Apparatus and method The bioreceptor of the present invention is in the form of a multi-compartment device, in which a first compartment contains a culture of stem cell-derived human glutamatergic neurons and a second compartment contains a culture of stem cell-derived human GABAergic neurons.

[0146] The glutamatergic neurons are stored in nitrogen at a temperature of about -200°C. Stepwise thawing steps are carried out according to conventional techniques well known to those skilled in the art. The thawed cells are then prepared in about 10 mL of a dedicated medium at 37°C. An aliquot is centrifuged and processed using an automatic counter or with a Malassez cell to determine the concentration of the glutamatergic neurons and the dilution to be carried out. The aliquot is then diluted and seeded in the first compartment of the device according to the invention, previously treated according to conventional techniques well known to those skilled in the art to facilitate the adhesion of the glutamatergic neurons to the substrate.

[0147] GABAergic neurons are cultured in the second compartment of the device following the same procedure detailed above.

[0148] The functional activity of glutamatergic neurons thus cultured is recorded by a planar microelectrode array (MEA) 256MEA100 / 30iR-ITO-w / o (Multichannel systems, Reutlingen, Germany), consisting of 30 μm diameter electrodes spaced 100 μm apart. The recordings last for 10 min thanks to the MEA2100-256-Systems software (Multichannel systems, Reutlingen, Germany). The microelectrode array technique makes it possible to record the functional and spontaneous extracellular activity of neurons as an index of network connectivity.

[0149] For functional recordings, two conditions were tested.

[0150] "Test" conditions: A sample of cerebrospinal fluid from a subject suspected of having AD (Alzheimer's disease); and · "Reference" condition: A CSF sample from a so-called "true negative" subject, which serves as a reference value.

[0151] In particular, it should be noted that the "reference" condition consists of samples taken from healthy subjects that show negative results for conventional AD tests, in other words, it consists of samples that reflect a so-called "true negative" diagnosis.

[0152] 3.Results The results are shown in Figures 7A', 7B', 7C', 7A, 7B, and 7C.

[0153] During electrophysiological recordings, glutamatergic neurons in culture exhibited functional activity represented by dots (FIG. 7A' and FIG. 7A). These peaks were detected in time and space by the software algorithm MEA2100-256-Systems and then assigned a value specified by each of the dots on the peak.

[0154] Based on the values ​​determined by the algorithm, a matrix (plot of a raster, i.e. raster plot) is generated to visualize the activity of each probe electrode as a function of time (Figures 7B' and 7B). This graph makes it possible to evaluate the synchrony of the network, i.e. the ability of neurons to have simultaneous activity. From these graphs it is clear that the glutamatergic network is synchronized in the "reference" condition (Figure 7B') and desynchronized in the "test" condition (Figure 7B).

[0155] Furthermore, the optimal structure of the network is defined by quantitative parameters obtained by the cross-correlation algorithm, which allow to estimate the quality of the network connections: in this case, the average value of action potentials per second is higher than 0.5, which allows to determine the value of the average connection coefficient ratio of the minimum internode length and, finally, the value of the network connection index.

[0156] As mentioned above, the processing and analysis of the obtained data makes it possible to reveal the neural network defined by the nodes and the interactions between them (FIGS. 7C' and 7C). Cross-correlation is an algorithm that reveals the order of the neural network and makes it possible to estimate the state function of the network.

[0157] Analysis of the networks reveals that the neural network under the "test" condition (Figure 7C) is less connected than the neural network under the "reference" condition (Figure 7C').

[0158] 4. Conclusion From the implementation of the method of the present invention it is clear that the sample under "test" conditions is obtained from a subject suffering from a neurological and / or neurological disorder.

[0159] By comparing the representation of the neuronal network under "test" conditions (Figure 7C) with a library of functional activity signatures, it becomes possible to establish a rapid and reliable diagnosis of Alzheimer's disease.

[0160] Monitoring the subject's condition (i.e. monitoring the remission or exacerbation of the symptoms of AD) is then carried out by monitoring the value of the network connectivity index (parameter iv) over time using evolution criteria (rising or falling thresholds over the average period with standard deviation, use of derivatives) with respect to the reference value corresponding to the value obtained at the time of diagnosis of the disease, i.e. the value obtained as described above.

[0161] Example 2: Diagnosis of head injuries occurring during a rugby match 1. Background In some dangerous sports, such as rugby, players are highly exposed to the risk of cranial trauma (or concussion), which can be defined as a short-term impairment of brain function in the absence of macroscopic or microscopic damage. The frequency of concussion has increased in the last 15 years. In rugby, incidences ranging from 4.1-7.9 per 1,000 person hours during a match are observed. Currently, diagnosis is based on the establishment of the Glasgow score, which is established by clinical findings and symptoms. Concussion can occur from impacts other than the head, and players may not lose consciousness and may only show very minor clinical signs and symptoms, or may not even be aware of it, which can make the diagnosis more difficult and delayed. The time from diagnosis to prognosis for players is very long, players may be immobilized for days or weeks, and sometimes have adverse, severe and long-lasting effects.

[0162] 2. Apparatus and method The bioreceptor of the invention is in the form of a multi-compartment device, the first compartment containing a culture of human sensory neurons derived from stem cells and the second compartment containing a culture of cells of the oral mucosa, see the procedure detailed in point 2 of Example 1.

[0163] For functional recordings, two conditions were tested.

[0164] "Test" conditions: Saliva samples taken from subjects who were impacted during a rugby match, and "Reference" condition: a saliva sample taken from a healthy subject and showing a negative result in a conventional test for detecting concussion. In other words, it consists of a sample showing a so-called "true negative" diagnosis.

[0165] 3.Results The data are obtained according to the procedure detailed in point 3 of Example 1.

[0166] 4. Conclusion From the implementation of the method of the present invention it is clear that the sample under "test" conditions is obtained from a subject suffering from a neurological and / or neurological disorder.

[0167] By comparing the representation of neural networks under "test" conditions with a library of functional activity signatures, it is possible to establish a rapid and reliable diagnosis of head traumatic lesions and ensure rapid and efficient management of subjects.

[0168] Example 3: Diagnosis of COVID-19 infection 1. Background Coronaviruses are known to cause severe acute respiratory syndromes. They were the origin of three deadly epidemics during the 21st century. SARS-CoV-2, the cause of COVID-19 infection, is the origin of the most recent epidemic with a significantly high mortality rate and significant economic losses. In Europe, the cumulative mortality rate is 34%, with variations between European countries, correlating with the emergence of SARS-CoV-2 mutant strains and the intense heterogeneity in symptoms (asymptomatic people, benign forms, individual deaths). The ability of coronaviruses to invade the central nervous system has already been described during two previous epidemics caused by SARS-CoV-1 and MERS-CoV. The described neurological disorders vary in severity, ranging from simple headaches, temporary confusion to cerebrovascular disorders and convulsions in the most severe cases. As with many other airborne viral diseases, an upper respiratory tract infection is the initial portal of entry into the body. Olfactory disorders after SARS-CoV-2 infection, including loss of smell (anosmia), remain one of the most common signs of infection even with current testing. In particular, neuroinvasion has been described to occur via the nasal route. In fact, the presence of intact viral particles has been demonstrated in the supporting cells of the olfactory mucosa, which may be the site of viral replication, thereby explaining the loss of taste and smell. The hypothesis regarding neuroinvasion is that viral replication takes place via the olfactory bulb (where olfactory information is processed) before entering the central nervous system via the cranial nerves.

[0169] 2. Apparatus and method The bioreceptor of the invention is in the form of a multi-compartment device, the first compartment of which contains a culture of human mitral cells from the olfactory bulb and the second compartment of which contains a culture of cells of the nasal mucosa, see the procedure detailed in point 2 of Example 1.

[0170] For functional recordings, two conditions were tested.

[0171] "Test" condition: a nasopharyngeal sample taken from a subject; and "Reference" condition: nasopharyngeal samples taken from healthy subjects and showing a negative result for conventional tests to detect COVID-19 infection. In other words, it consists of samples showing a so-called "true negative" diagnosis.

[0172] 3.Results The data is obtained according to the procedure detailed in point 3 of Example 1.

[0173] 4. Conclusion From the implementation of the method of the present invention it is clear that the sample under "test" conditions is obtained from a subject suffering from a neurological and / or neurological disorder.

[0174] By comparing the representation of neural networks under "test" conditions with a library of functional activity signatures, it will be possible to establish a rapid and reliable diagnosis of COVID-19 infection.

[0175] Example 4: Diagnosis of Parkinson's Disease 1. Background Parkinson's disease (PD) is the second most widespread neurodegenerative disease after Alzheimer's disease, with a prevalence of 4% in people over 80 years of age. From a clinical perspective, Parkinson's disease has motor symptoms that, in conjunction with non-motor symptoms, affect gait and body movements. Dementia is a common symptom of Parkinson's disease and corresponds to either Parkinson's disease dementia or dementia with Lewy bodies.

[0176] From a neuropathological point of view, PD is characterized by the loss of dopaminergic neurons in the substantia nigra (SN, an anatomical region belonging to the basal ganglia) and the presence of intracellular inclusions, the so-called Lewy bodies, which contain the protein α-synuclein, whose misfolded formation triggers a cascade of neurotoxicity.

[0177] The axonal projections of the SN extend towards the putamen and caudate nucleus (forming the striatum), where there are a series of connections with the globus pallidus and the subthalamic nucleus. In PD, degeneration of the nigrostriatal pathway (the pathway between the substantia nigra and the striatum) is the main cause of motor symptoms. The death of dopaminergic neurons in the SN and the main consequence of dopamine release is impaired dopamine signaling from the basal ganglia to the rest of the brain. The basal ganglia motor pathway controls movement. There are direct and indirect pathways. The direct pathway consists of five anatomical regions: the cortex, the striatum, the substantia nigra pars compacta, the thalamus, and the association of the globus pallidus internus with the substantia nigra pars reticulata.

[0178] 2. Apparatus and method The bioreceptor of the present invention is in the form of a multi-compartment device, in which the first compartment contains a culture of human glutamatergic and GABAergic cells derived from pluripotent stem cells, the second compartment contains a culture of GABAergic neurons, the third compartment contains a culture of glutamatergic neurons, the fourth compartment contains a culture of GABAergic neurons, and the fifth compartment contains a culture of dopaminergic neurons. These five compartments correspond to the five anatomical regions of the basal ganglia loop. See the procedure detailed in point 2 of Example 1.

[0179] For functional recordings, two conditions were tested.

[0180] "Test" conditions: a sample of cerebrospinal fluid from a subject suspected of having Parkinson's disease; and · "Reference" condition: A CSF sample from a so-called "true negative" subject, which serves as a reference value.

[0181] In particular, it is noted that the "reference" condition consists of samples taken from healthy subjects that show negative results to conventional Parkinson's disease tests, in other words, it consists of samples that reflect a so-called "true negative" diagnosis.

[0182] 3.Results The data is obtained according to the procedure detailed in point 3 of Example 1.

[0183] 4. Conclusion From the implementation of the method of the present invention it is clear that the sample under "test" conditions is obtained from a subject suffering from a neurological and / or neurological disorder.

[0184] By comparing the representation of the neuronal network under "test" conditions with a library of functional activity signatures, it becomes possible to establish a rapid and reliable diagnosis of Parkinson's disease.

Claims

1. 1. An in vitro or ex vivo method for determining the effect of a biological sample on a biological model, comprising: a. Providing a bioreceptor comprising a multi-compartment microfluidic device (10), said multi-compartment microfluidic device (10) comprising: A first compartment (1) and a second compartment (2), the first compartment (1) comprises at least a neuronal culture in the form of a neuronal network, and the second compartment (2) comprises at least a neuronal culture in the form of a neuronal network and / or a non-neuronal cell culture or an explant culture, a first compartment (1) and a second compartment (2); at least one means for forming a biological interface (21) that allows communication by neural connections between said first compartment (1) and said second compartment (2); at least one device (40) that allows recording of the functional activity of neurons over a plurality of spatially distributed measurement points within said first compartment; Equipped with providing a bioreceptor; b. contacting the biological sample directly or indirectly with a neuronal culture and / or a non-neuronal cell culture or explant culture in culture in the second compartment; c. after the contacting step of step b, performing a recording of the functional activity of the neurons in culture in the first compartment (1) for the plurality of measurement points over a measurement period; d. Converting the recordings 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; f) determining from said functional activity data at least one characteristic parameter of the state of the neural network in said first compartment (1); g. determining the effect of the biological sample by comparing the at least one characteristic parameter of the state of the neural network with a reference value of the at least one characteristic parameter of the state of the neural network; A method comprising:

2. The at least one characteristic parameter of the state of the neural network in the first compartment (1) is the number of action potential(s), inter-action potential interval or "ISI", coefficient of variation of inter-action potential interval(s), number of probed electrodes, mean rate of occurrence of normalized action potentials, number of bursts, number of electrodes capturing one or more burst(s), mean duration of bursts, mean action potentials during a burst, mean inter-action potential interval (ISI) during a burst, inter-burst interval or "IBI". , the frequency of the bursts, the percentage of the bursts, the number of network bursts, the frequency of network bursts, the duration of network bursts, the average of the action potentials in a network burst, the average of the inter-action potential intervals (ISIs) in a network burst, the number of electrodes participating in forming a network burst, the percentage of bursts in a network burst, the coefficient of variation of the inter-burst intervals (IBIs) in a network burst, the surface area under the cross-correlation curve, and a synchronization index. The method of claim 1.

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

4. said step f comprises determining from said functional activity data two, preferably three, preferably four, even five, particularly six, more particularly seven parameters of said state of said neural network in said first compartment (1); The characteristic parameters are: i) connectivity coefficients; ii) the average minimum inter-node length; iii) average action potentials per second; iv) Network connectivity index, i.e., the "Small World Index"; v) z score, or "z-score"; vi) the coefficient of participation, i.e., the "participation coefficient," and vii) Node centrality index selected from the group consisting of The method of claim 3.

5. The step g includes a step of comparing a threshold value, and in the step of comparing a threshold value, the mean number of action potentials per second is compared to a threshold value of 0.5 or more, preferably greater than 0.5, and / or the connection coefficient is compared with a connection coefficient threshold value greater than or equal to 0, preferably between 0 and 1; and / or the average inter-node minimum length is compared with an average inter-node minimum length threshold value greater than or equal to 1, preferably greater than or equal to 1.5; and / or the network connectivity index is compared with a network connectivity index threshold value greater than or equal to 0, preferably between 0 and 1; and / or the z score is greater than or equal to 0, suitably 5, preferably having a value between 0 and 10; and / or said participation coefficient is greater than or equal to 0, preferably equal to 0.5, preferably having a value lying between 0.5 and 1; and / or the centrality index of the node is greater than or equal to 0, preferably is 5, and preferably has a value between 6 and 10; The method of claim 3.

6. Step g comprises comparing at least one characteristic parameter of the states of the neural network defined in step f with a reference library of functional activity signatures. The method of claim 1.

7. Step g comprises monitoring the value of said characteristic parameter iv using an upward or downward evolution condition over an analysis period; The method of claim 3.

8. In step f, at least one characteristic parameter of the state of the neural network is calculated. i) connectivity coefficients; ii) the average minimum inter-node length; iii) average action potentials per second; iv) Network connectivity index, i.e., the "Small World Index"; v) z score, or "z-score"; vi) the coefficient of participation, i.e., the "participation coefficient," and vii) Node centrality index and determining a selected from the group consisting of: The method of claim 3.

9. Determining at least one characteristic parameter of the state of the neural network comprises: determining said network connectivity index comprising a ratio of said connectivity coefficient and a minimum inter-node length, The method of claim 8.

10. the device (40) allowing recording of the functional activity of the neuronal network over a plurality of measurement points spatially distributed in the first compartment (1) according to step a) is a device allowing indirect contact recording using cultured cells, The device comprises: a device that records activity amperometrically or voltammetrically using an array of planar or non-planar semi-solid microelectrodes; a fluorescence imaging recording device including calcium imaging or transmembrane ion flow imaging; Devices for recording intercellular, extracellular, or patch clamp electrophysiological activity in whole-cell, attached-cell, inside-out, or outside-out configurations. selected from the group consisting of The method of claim 1.

11. step d) of converting the recordings of the functional activity of the neurons in culture in the first compartment (1) into functional activity data is performed by means (50) of converting the recordings of the functional activity of the neurons into functional activity data, said converting means (50) being preferably an algorithmic system for converting electrical and / or electrophysiological data into binary data; The method of claim 1.

12. the measurement period of the recording of the functional activity of the neurons in culture in the first compartment (1) according to step c) is in the range of 300 ms to 20 min, preferably in the range of 1 min to 15 min, preferably in the range of 5 min to 12 min; The method of claim 1.

13. The multi-compartment microfluidic device (10) comprises: a third compartment (3) comprising at least a neuronal culture in the form of a neuronal network and / or a non-neuronal cell culture; at least one means (31) for forming a biological interface that allows communication by neural connections between the first compartment (1) and the third compartment (3) and / or at least one means (32) for forming a biological interface that allows communication by neural connections between the second compartment (2) and the third compartment (3); Further provided with The method of claim 1.

14. The multi-compartment microfluidic device (10) comprises: a fourth compartment (4) comprising at least a neuronal culture in the form of a neuronal network and / or a non-neuronal cell culture; at least one means (41) for forming a biological interface that allows communication by neural connections between the first compartment (1) and the fourth compartment (4) and / or at least one means (42) for forming a biological interface that allows communication by neural connections between the second compartment (2) and the fourth compartment (4) and / or at least one means (43) for forming a biological interface that allows communication by neural connections between the third compartment (3) and the fourth compartment (4); Further provided with The method of claim 13.

15. The multi-compartment microfluidic device (10) comprises: a fifth compartment (5) comprising at least a neuronal culture in the form of a neuronal network and / or a non-neuronal cell culture; at least one means (51) for forming a biological interface that allows communication by neural connections between the first compartment (1) and the fourth compartment (5) and / or at least one means (52) for forming a biological interface that allows communication by neural connections between the second compartment (2) and the fifth compartment (5) and / or at least one means (53) for forming a biological interface that allows communication by neural connections between the third compartment (3) and the fourth compartment (5) and / or at least one means (54) for forming a biological interface that allows communication by neural connections between the fourth compartment (4) and the fifth compartment (5); Further provided with 15. The method of claim 14.

16. In the contacting step b, the biological sample is at the biological interface (21) of the second compartment, and / or at least one of the biological interfaces (31, 32) of the third compartment, and / or at least one of the biological interfaces (41, 42, 43) of the fourth compartment, and / or At least one of the biological interfaces (51, 52, 53, 54) of the fifth compartment, At the point of application, the biological sample is indirectly contacted. The method of claim 1.

17. the neurons are selected from the group consisting of glutamatergic, GABAergic, serotonergic, cholinergic, dopaminergic, adrenergic, noradrenergic, sensory neurons and motor neurons; and / or the non-neuron is selected from the group consisting of glial cells, epithelial cells, connective cells, thyroid cells, adipocytes, blood cells, immune cells, bone cells, chondrocytes, gastric cells, pancreatic cells, liver cells, intestinal cells, lung cells, endothelial cells, muscle cells, vascular cells, cardiac cells, mesenchymal cells, retinal pigment epithelial cells, and retinal cells; The explants are tissues derived from brain, epithelium, eye, thyroid, adipose, blood vessels, bone, cartilage, stomach, pancreas, liver, intestine, lung, endothelium, muscle, retina, heart and placenta. The method of claim 1.

18. 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. The method of claim 1.

19. The biological sample is a drug or a test agent. The method of claim 1.

20. A bioreceptor for determining the effect of a biological sample on a biological model, the bioreceptor comprising a multi-compartment microfluidic device (10), the multi-compartment microfluidic device (10) comprising: a first compartment (1) comprising at least a neuronal culture in the form of a neuronal network; a second compartment (2) in which the biological sample is placed, comprising at least a neuronal culture in the form of a neural network and / or a non-neuronal cell culture or an explant culture; at least one means (21) for forming a biological interface, thereby allowing communication by neural connection between said first compartment (1) and said second compartment (2); at least one device (40) that allows recording of the functional activity of said neurons over a plurality of spatially distributed measurement points in said first compartment; A bioreceptor comprising:

21. 21. A biosensor comprising the bioreceptor of claim 20, wherein the biosensor comprises: means for converting the recordings of the functional activity of the neurons in culture in the first compartment into functional activity data. Biosensor.

22. and analyzing means (60) configured to determine at least one characteristic parameter of the state of said neural network in said first compartment from said functional activity data; The at least one characteristic parameter is: i) connectivity coefficients; ii) the average minimum inter-node length; iii) average action potentials per second; iv) Network connectivity index, i.e., the "Small World Index"; v) z score, or "z-score"; vi) the coefficient of participation, i.e., the "participation coefficient," and vii) Node centrality index selected from the group consisting of The biosensor of claim 21.

23. and analyzing means (60) configured to determine at least one characteristic parameter of the state of said neural network in said first compartment from said functional activity data; the at least one characteristic parameter is selected from the group consisting of: the number of action potential(s), the inter-action potential interval or "ISI", the coefficient of variation of the inter-action potential interval(s), the number of probing electrodes, the mean rate of occurrence of normalized action potentials, the number of bursts, the number of electrode(s) capturing one or more burst(s), the mean duration of a burst, the mean action potentials in a burst, the mean inter-action potential interval (ISI) in a burst, the inter-burst interval or "IBI", the frequency of the bursts, the percentage of the bursts, the number of network bursts, the frequency of network bursts, the duration of network bursts, the mean action potentials in a network burst, the mean inter-action potential interval (ISI) in a network burst, the number of electrodes participating in forming a network burst, the percentage of bursts in a network burst, the coefficient of variation of the inter-burst interval (IBI) in a network burst, the surface area under the cross-correlation curve, and a synchronization index; The biosensor of claim 21.

24. and means (70) for performing a comparison of said at least one characteristic parameter of the state of said neural network with a reference value of said at least one characteristic parameter of the state of said neural network in order to determine the effect of said biological sample.

24. The biosensor of claim 22 or 23.

25. 22. Use of the bioreceptor according to claim 20 or the biosensor according to claim 21 in an in vitro or ex vivo method for diagnosing a neurological disease and / or a neurological disorder, preferably selected from the group consisting of Alzheimer's disease, Parkinson's disease, head trauma, cerebrovascular accidents, thrombotic or embolic occlusion or ischemia, transient ischemic attack, neurological forms of SARS-CoV-2 infection (COVID-19), neurotoxicity due to organophosphorus compounds or the like, analgesia, neuroinflammatory diseases such as multiple sclerosis, otoneuritis, myelitis, lupus, Crohn's disease, hearing impairment due to damage to the auditory nerve, amyotrophic lateral sclerosis, retinal neuropathy such as diabetes-induced, epilepsy, psoriasis, herpes, meningoencephalitis, isolated lymphocytic meningitis, Guillain-Barré syndrome or mononuclear polyradiculoneuropathy, peripheral neuropathy, and myelopathy.

26. Use of a bioreceptor according to claim 20 or a biosensor according to claim 21 in an in vitro or ex vivo method for monitoring the preventive and / or therapeutic treatment of neurological disorders and / or neurological diseases, preferably gene therapy, cell therapy, axonal regeneration therapy, treatment by administration of one or more therapeutic and / or prophylactic and / or anesthetic agents.

27. 22. Use of a bioreceptor according to claim 20 or a biosensor according to claim 21 in an in vitro or ex vivo method for identifying and / or characterizing the therapeutic properties of a drug and / or threshold drug concentrations.

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