Modification of neuronal voltage-gated channels with fluorescent donor-acceptor pairs

Abstract

Systems and techniques are provided for making genetically engineered ion channels (ICs) with bioluminescent resonance energy transfer (BRET) complexes and using such ICs for efficient readout of neural activity and output of biological neuronal networks.SOLUTION: In one implementation, the disclosed technology includes identifying a target location in the IC for expression of a target protein including a donor tag protein and an acceptor tag protein, and modifying the genome of the neuronal cell at a portion associated with the target location in the IC. The technique further includes causing the neuronal cell to express the target protein in the IC according to the modified genome. In the first (second) state of the IC, the donor tag protein is at a first (second) distance from the acceptor tag protein that is related to the absence (presence) of energy transfer between the donor tag protein and the acceptor tag protein.SELECTED DRAWING: Figure 3A-3C

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE Embodiments of the present disclosure generally relate to detecting and mapping neural activity. [Background technology]

[0002] Biological computing utilizes living biological material to perform computations that could potentially be freed from the limitations inherent in semiconductor technology. Biological neurons are small, with much better scalability and orders of magnitude more energy efficiency than silicon-based transistor processors. In addition, biological neural networks are fault-tolerant, often able to maintain network functionality even if half of a biological neural network is destroyed. Biological neural networks possess high neuroplasticity, which enables the development of highly adaptable intelligence suitable for many different applications. [Brief explanation of the drawings]

[0003] The embodiments described herein will be more fully understood from the detailed description given below and the accompanying drawings, which should not be taken to limit the application to the particular embodiments but are for illustration and understanding only.

[0004] [Figure 1] FIG. 1 illustrates an exemplary system architecture for a biological computing platform capable of deploying bioluminescence resonance energy transfer (BRET) of genetically engineered proteins into ion channels to read out the output of a biological neuronal network, according to at least one embodiment.

[0005] [Figure 2A-2B] 2A-2B illustrate the incorporation of BRET complexes into ion channels for efficient readout of neural activity and the output of biological neuronal networks, according to at least one embodiment.

[0006] [Figure 3A-3C] 3A-3C illustrate the operation of BRET complexes formed on ion channels to efficiently read out neural activity and the output of biological neuronal networks, according to at least one embodiment.

[0007] [Figure 4] FIG. 4 is a flow diagram illustrating an exemplary method for generating genetically engineered ion channels with BRET complexes and using such ion channels to efficiently read out neural activity and the output of networks of biological neurons, according to at least one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Networks of biological computing platforms are often cultured in petri dishes, and the neuronal networks are in contact with multi-electrode arrays (MEAs). MEAs are widely used to record electrical activity from neural cells and networks. These devices are essential in neuroscience research for understanding neural transmission and brain function.

[0009] MEAs can be used to stimulate neuronal activity (action potentials) using electrical signals and to detect such activity. Additionally, neuronal activity can be stimulated and / or detected using optical signals, e.g., transmitted to and from the network using optical fibers. For example, data can be input into a neuronal network by transmitting appropriately tailored electrical signals through the MEA to stimulate ("fire") spontaneous electrical activity in neurons. Similarly, readout of the network's computational output can be performed by detecting electrical signals induced by neurons within the MEA. Alternatively, optical readout techniques can be used to reduce interference between input and output electrical signals. For example, fluorescent genetically modified potential indicator (GEVI) proteins, which change their emission wavelength in response to an applied electric field, can be used to detect neuronal action potentials. However, GEVI neuronal activity detection and readout techniques have significant drawbacks. For example, GEVI fluorescence requires illuminating neurons with external light, which can eventually (over several days) damage the neurons.

[0010] Despite its widespread application, MEA-based measurements suffer from significant limitations that hinder their effectiveness and accuracy, hindering our ability to stimulate cells. The first challenge is neuronal migration over time. As neurons move away from the electrodes, measurement reproducibility deteriorates, leading to data discrepancies. Another limitation is that MEA electrodes are typically larger than individual neuronal cells, impairing spatial resolution. This size discrepancy makes it difficult to record single-neuron activity with high precision. Additionally, while conventional fluorescent readouts of activity are a common method for measuring neuronal function, they can introduce phototoxicity, negating the long-term recording benefits offered by MEAs. The light used in these systems can damage neurons, affecting their normal function and viability over time. Crosstalk is another significant issue, as field effects between multiple electrodes can introduce erroneous readouts. When multiple electrodes simultaneously detect changes in the electric field caused by action potential (AP) polarization, it can be difficult to identify the source of the signal. This problem is further exacerbated with high-density MEAs, which typically improve spatial resolution. Crosstalk can also be a problem with adjacent or offset AC circuits, further complicating signal accuracy. Finally, when multiple spikes occur on the same electrode, sorting them by waveform similarity (clustering) becomes complicated and highly dependent on the sampling rate. The spike sorting process is computationally intensive, especially given a typical recording rate of 40 kHz, which generates 50–100 data points per spike.

[0011] Aspects and embodiments of the present disclosure address these and other challenges of existing neuronal activity detection and readout technologies by providing systems and techniques that use bioluminescence resonance energy transfer (BRET) without relying on external illumination of neurons. The proposed systems and techniques overcome the aforementioned challenges and limitations by using BRET conjugates that introduce optical control into MEA-based measurements. One major advantage of this approach includes passive optical monitoring of neuronal cells and networks, thereby eliminating the need for optical inputs that could artificially excite or activate tagged channels. Using passive optical methods such as the disclosed BRET conjugates eliminates artificial excitation or activation of cells, thereby preserving their natural activity and improving the accuracy and reliability of long-term recordings.

[0012] In some embodiments, a BRET complex comprising a target protein in combination with a donor tag protein (also referred to herein as donor tag or simply donor for brevity) and an acceptor tag protein (acceptor tag or acceptor) can be genetically engineered to be attached to a sodium channel protein chain, e.g., by altering the genome of a neuron such that gene expression of the sodium channel replaces the protein at the target location with the BRET complex. The donor tag can comprise one or more luciferase molecules, and the acceptor tag can comprise one or more fluorophore molecules. The sodium channel protein chain responds to changes in the potential across the neuronal membrane during the activation-inactivation cycle of the neuron by changing the shape of the sodium channel, which changes the distance between the donor tag and the acceptor tag. A change, e.g., a decrease, in the distance between the donor tag and the acceptor tag can result in resonance energy transfer, causing the fluorophore acceptor to emit light, which can be detected by a readout photodetector. A donor-acceptor pair positioned close to each other during the activation phase (e.g., when the membrane potential exceeds -55 mV, opening the channel's activation gate) can be used to detect neuronal activation. Similarly, a donor-acceptor pair positioned close enough to each other during the inactivation phase (e.g., when the membrane potential reaches +30 mV, closing the inactivation gate) can be used to detect neuronal inactivation. In some embodiments, luciferase (or nanoluciferase, etc.) molecules can emit light in the presence of a specific substrate, such as coelenterazine (or furimazine, in the case of nanoluciferase). The luciferase donor oxidizes the substrate, which then donates one or more electrons to the luciferase donor molecule, increasing the energy of the luciferase donor. This additional energy is transferred to the fluorophore acceptor and emitted as light. Because the substrate (e.g., furimazine or coelenterazine) is consumed in the oxidation process, new substrate can be added to the Petri dish while luminescence (non-irradiation luminescence) imaging / readout is in progress.

[0013] Advantages of the disclosed technology include (but are not limited to) readout and / or visualization of neural activity without exposing neurons to light (bleaching). This facilitates sustained and long-term use of neural networks for biological computation and observation and experimentation using in vitro neurons. In addition, the disclosed technology may ensure faster detection / readout times than existing GEVI technologies. Furthermore, because neuronal networks (whether naturally occurring or cultured in a Petri dish) may contain different neurons with different types of ion channels (e.g., sodium Na+ channels, potassium K+ channels, calcium Ca+ channels, etc.) or different isoforms of the same type of channel (e.g., sodium NaV1.1 channels and NaV1.2 channels, etc.), different BRET complexes can be used with different ion channels / isoforms, with each ion channel / isoform being independently detected using a different BRET complex.

[0014] Some embodiments are discussed with respect to the use of neurons on an MEA that have a modified BRET complex containing a target protein in combination with a donor tag protein and an acceptor tag protein within the neuronal cells. However, it should be understood that such modified neurons containing such modified BRET complexes can also be used for other purposes, such as studying neural activity in living organisms. For example, animal neurons can be modified to contain the modified BRET complex, and the neural activity of such animals can be studied by monitoring the luminescence of the neurons.

[0015] 1 illustrates an exemplary system architecture for a biological computing platform 100 that can deploy bioluminescence resonance energy transfer (BRET) of genetically engineered proteins within ion channels to read out the output of a network of biological neurons, according to at least one embodiment. It should be understood that FIG. 1 illustrates one possible application of the disclosed technology, and that various other applications are possible, including (but not limited to) providing instrumentation and techniques for the study of the structure, functionality, operation, etc. of individual neurons and neuronal networks.

[0016] Biological computing platform 100 can be used to perform in vitro training of biological neurons and, among other things, implement real-time synthetic biological intelligence (SBI). Biological computing platform 100 can be a biological computing cloud platform that provides network access to biological neural networks (e.g., exposes biological neural network resources through the cloud). In one embodiment, biological computing platform 100 externalizes the biological neuron (e.g., cortical neuron) network and provides an interface between the biological neural network and a virtual environment running on a computing device. Thus, biological computing platform 100 creates an efferent (e.g., visual or other input) / efferent (e.g., motor or other output) loop between the biological neural network and the virtual environment.

[0017] 1, the biological computing platform 100 can include one or more MEAs 105 connected to one or more server computing devices 110 and / or client computing devices 125 via a network 120. The network 120 can be a local area network, a wide area network, a private network (e.g., an intranet), a public network (e.g., the Internet), or a combination thereof. The connection between the MEA 105 and the server computing device 110 can include a wired connection, a wireless connection, or a combination thereof. Alternatively, the MEA 105 can be directly connected to the server computing device 110 (e.g., via a wired or wireless connection).

[0018] The server computing device 110 and / or the client computing device 125 may include physical machines and / or virtual machines hosted by physical machines. The physical machines may be rack-mounted servers, desktop computers, or other computing devices. In one embodiment, the server computing device 110 may include virtual machines managed and provided by a cloud provider system. Each virtual machine provided by a cloud service provider may be hosted on a physical machine configured as part of a cloud. Such physical machines are often located in a data center. The cloud provider system and cloud may be provided as an infrastructure-as-a-service (IaaS) layer. One example of such a cloud is Amazon®'s Elastic Compute Cloud (EC2®).

[0019] The server computing device 110 may host the MEA interface 150 and one or more virtual environments 155. The MEA interface 150 and the virtual environments 155 may be hosted on the same server computing device 110 or may be hosted on separate server computing devices that may be connected via the network 120.

[0020] The MEA 105 (also known as a microelectrode array) is a device containing multiple plates or shanks through which neural signals are acquired and / or transmitted. In an embodiment, an HD-MEA is used. The plates or shanks are typically arranged in a grid or other array and function as a neural interface connecting the neurons 135 to electronic circuitry. The MEA 105 includes a recording chamber 140 containing a number of biological neurons 135 and / or a solution or other medium (e.g., saline solution). These biological neurons 135 may be cultured neurons (e.g., cultured from stem cells) and / or extracted neurons (e.g., extracted from mouse or rat brains). The biological neurons 135 may be derived from a general cell line or from a cell line with a specific trait to be tested. For example, the biological neurons 135 may be cultured from stem cells of an individual with a specific genotype, a specific individual for whom testing is being performed, or an individual with a specific medical condition. In one embodiment, the neurons 135 include cortical cells derived from an embryonic rodent source. In one embodiment, neurons 135 comprise cortical cells derived from a human induced pluripotent stem cell (hiPSC) source. In some embodiments of the present disclosure, neurons 135 may comprise a BRET complex incorporating a donor-acceptor (DA) pair.

[0021] Neurons can be grown or harvested from numerous sources via multiple methods. Most in-depth in vitro electrophysiological studies of neural cells have been performed on primary neurons. This process involves isolating cortical cells from the dissected cortex of (typically) rodent embryos. These cells are then grown in nutrient-rich medium and can be maintained for several months. These cultures develop complex morphologies with numerous dendritic and axonal connections, resulting in functional biological neural networks (BNNs). In some embodiments, such cultures are developed from embryos (e.g., mouse embryos). The properties of monolayer, slice, or organotypic cultures can be investigated using appropriate electrophysiological techniques. The development of spontaneous activity from these cultures has been well documented. These developmental stages have also been modeled and found to exhibit emergent connectivity and firing rates indicative of fundamental criticality.

[0022] In one embodiment, the protocol works by inhibiting the dual SMAD signaling pathway. SMADs comprise a family of structurally similar proteins that are key signaling factors for the transforming growth factor beta (TGF-β) superfamily of receptors, which are important in regulating cell development and growth. This abbreviation refers to homology with Caenorhabditis elegans SMA ("small" worm-like phenotype) and the Drosophila MAD family ("maternal to decapentaplegic") genes. SMAD inhibition has been found to promote differentiation into the anterior neuroectodermal lineage.

[0023] In embodiments, neuronal cultures (e.g., long-term cortical and / or other types of neurons) derived from hiPSCs and / or other sources, along with appropriate biomarkers indicating that the cells are not only neural but, more specifically, cortical, are implemented to form networks comparable to in vivo neuronal networks within an organism or in vitro networks found in primary neuronal cell cultures. In addition to avoiding the ethical issues associated with harvesting embryonic rodents, hiPSC-derived cells, in embodiments, have been demonstrated to survive for over six months while remaining active and can be expanded exponentially, resulting in a relatively low cost per cell, even in large-scale culture. This allows for the growth and maintenance of functional neuronal "wetware" for computation.

[0024] Historically, neural cultures studied have been two-dimensional, sparse neural cultures (e.g., containing thousands of neurons). Sparse neural networks are scattered on a 2D grid so that cells do not overlap each other. Such cell arrangements have been used because they allow for the study of individual cells and facilitate research. However, in some embodiments, much denser neuronal arrangements than those used in the past are used. A dense arrangement of neurons (e.g., with hundreds of thousands to millions of neurons) allows neurons to overlap each other and arrange themselves not only on a two-dimensional grid, but also to form a three-dimensional arrangement in which multiple neurons are stacked vertically. A dense arrangement of neurons allows neurons to form spontaneous three-dimensional (3D) structures, such as neurospheres, effectively improving the intelligence of biological neural networks incorporating neurons 135. In one embodiment, a dense arrangement of neurons 135 includes at least 10,000 cells per square millimeter, at least 20,000 cells per square millimeter, or at least 50,000 cells per square millimeter. The high density arrangement of neurons allows for the development of computational assemblies of neurons 135 in embodiments.

[0025] When biological neurons are excited, they generate ionic currents through their membranes, causing voltage changes between the inside and outside of the cell. During recording, electrodes on the MEA convert the voltage changes from the environment, carried by ions, into an electrical current carried by electrons (electron current). During stimulation, the electrodes convert the electronic current into an ionic current through the MEA. This triggers voltage-gated ion channels on the membrane of excitable neurons, depolarizing the neurons and triggering action potentials.

[0026] The magnitude and shape of the recorded signal may depend on the properties of the medium (e.g., solution) in which the neuron or group of neurons is located (e.g., the medium's electrical conductivity, capacitance, and uniformity), the nature of the contact between the neuron and the electrode (e.g., the contact area and adhesion), the properties of the electrode (e.g., its shape, impedance, and noise), the analog signal processing (e.g., the system's gain, bandwidth, and behavior outside the cutoff frequency), and the data sampling characteristics (e.g., sampling rate and digital signal processing). For recordings of a single neuron partially covering a planar electrode, the voltage at the contact pad is approximately equal to the voltage in the neuron-electrode overlap region multiplied by the ratio of the surface area of ​​the overlap region to the total area of ​​the electrode. Another means of predicting neuron-electrode behavior is to model the system using geometry-based finite element analysis, avoiding the limitations of oversimplifying the system in a lumped-circuit diagram.

[0027] In some embodiments, blinding is used to enhance the ability to distinguish between detection of electrical signals generated by neurons and detection of electrical signals generated on command by the MEA 105. Blinding prevents electrode stimulation triggered on command from the MEA interface 150 and / or virtual environment 155 from interfering with detection of electrical signals generated by neurons 135. One or more blinding schemes may be used.

[0028] In some embodiments, MEA interface 150 and / or integrated circuit 145 may know when electrodes are stimulated and / or which electrodes are stimulated. The electric fields generated by electrode stimulation may be much larger than the electric fields generated by neurons 135. Thus, in one embodiment, the detected electrical signals are applied to a filter that may filter electric fields / signals above a threshold size (e.g., detected by more than a threshold number of electrodes), which are caused by active stimulation of electrodes by integrated circuit 145 and / or MEA interface 150. Such filtering may be performed, for example, by integrated circuit 145 and / or server computing device 110. However, smaller electric fields generated by neurons 135 may only be detected by a small number of electrodes and therefore may not be filtered. Additionally, signals may be filtered based on voltage. For example, the electric signals generated by electrodes 130 may have a much larger voltage than the electric signals generated by neurons 135. For example, the electrical signals generated by electrodes 130 may have voltages on the order of thousandths of a volt, while the electrical signals generated by neurons 135 may have voltages on the order of millionths of a volt. Thus, the electrical signals may additionally or alternatively be filtered based on voltage.

[0029] In one embodiment, the approximate timing at which electrical signals are output to the electrodes and / or optical signals are output through the optics is known. Due to unpredictable delays in command transmission, knowledge of the timing may not be perfect. Therefore, blinding may be performed by ignoring electrical and / or optical signals output at or around the time that the electrical and / or optical signals are output to the electrodes 130 and / or the optics. In one embodiment, an internal counter of commands is maintained (e.g., by the server computing device 110 and / or the integrated circuit 145). Each time the internal counter increments, this may indicate that a new electrical and / or optical signal is to be output to one or more electrodes. Thus, in one embodiment, when the internal counter increments, the electrical and / or optical signals are ignored for a set amount of time.

[0030] In some embodiments, multiple blinding techniques may be combined.

[0031] In one embodiment, a blinding method (e.g., consensus blinding) based on blinding all signals when more than 15 simultaneous large (>75 mV) spikes are detected is implemented, blocking stimuli delivered by the system from being registered as cellular activity. In some embodiments, a new blinding method called "command count blinding" is implemented. This method blinds all motor activity readouts when a command to generate any form of stimulus is sent. During testing, this was found to be significantly more robust than previously used consensus blinding and to be capable of handling increased density and variability of sensory stimuli.

[0032] The MEA 105 can be used to conduct electrophysiological experiments on dissociated cell cultures (e.g., cultures of biological neurons). In dissociated neuronal cultures, neurons spontaneously form biological neural networks. This phenomenon can be enhanced by using very high-density neural cultures, as described above. The MEA 105 can include an electrode array 130 and a recording chamber 140 containing a live culture of biological neurons 135 in a nutrient-rich solution that keeps the biological neurons alive. The electrode array 130 can be a planar array (e.g., a two-dimensional (2D) grid) or a three-dimensional (3D) array (e.g., a 3D matrix). The electrode array 130 can be used to perform measurements in 2D (or 3D) coordinates with high spatial and temporal resolution and excellent signal quality. In addition, the electrode array 130 can also be used to apply electrical impulses in 2D or 3D coordinates.

[0033] In addition to recording changes in electrical activity resulting from action potentials, the MEA 105 has the potential to stimulate cells over a range of voltages. The delivery of external electrical stimuli is relatively non-invasive to the cells and effectively induces action potentials or responses in a manner comparable to internal electrical stimulation. Using appropriate coding schemes, external electrical stimuli can convey a range of information. Different coding schemes are discussed in more detail below. In this way, there is the ability not only to "read" information from neural cultures, but also to "write" data to them.

[0034] One or more of the MEAs 105 may be active MEAs that include an integrated circuit 145 (or multiple integrated circuits), such as a CMOS circuit. The integrated circuit 145 may include processing logic (e.g., a general-purpose or special-purpose processor), a network adapter, a digital-to-analog converter (DAC), an analog-to-digital converter (ADC), and / or other components. The network adapter may be a wired network adapter (e.g., an Ethernet network adapter) or a wireless network adapter (e.g., a Wi-Fi network adapter) that allows the MEA 105 to connect to the network 120. In one embodiment, the integrated circuit 145 includes a processing unit, which may be a general-purpose processor, a microcontroller, a digital signal processor (DSP), a programmable logic controller (PLC), a microprocessor, or a programmable logic device, such as a field programmable gate array (FPGA) or complex programmable logic device (CPLD). In one embodiment, the integrated circuit 145 includes memory, which may be non-volatile memory (e.g., RAM) and / or volatile memory (e.g., ROM, flash memory, etc.). In one embodiment, integrated circuit 145 is a system on a chip (SoC) that includes a processing unit, memory, a network adapter, a DAC, and / or an ADC.

[0035] In one embodiment, one or more MEAs 105 are passive MEAs connected to one or more integrated circuits 145 via one or more leads and / or a printed circuit board (PCB).

[0036] In one embodiment, one or more of the MEAs 105 further includes a light source capable of delivering light pulses to specific 2D coordinates within the 2D grid. The light source may include a light-emitting element (e.g., a light-emitting diode (LED), a light bulb, a laser, etc.) capable of emitting light having one or more specific wavelengths. Thus, optogenetics can be used to manipulate neural activity. Additionally, lasers of specific wavelengths can be used to precisely target specific neurons. Responses to the light stimulation can then be measured using electrodes within the MEA 105. Unlike electrical stimulation, light stimulation manipulates specific cells (e.g., neurons) that may express targeted opsin proteins, making it possible to investigate the role of neuronal subpopulations in neural circuits. In some embodiments, immunofluorescence of specifically modified calcium, which is cleaved and activated upon entry into neurons, can also be paired with a camera to image neuronal activation.

[0037] In some embodiments, neurons 135 can be electrically stimulated via electrodes 130 of MEA 105, while neural activity of neurons 135 can be optically detected using, for example, photodetector 160. For example, photodetector 160 can detect light emitted by fluorophore molecules of a donor-acceptor (DA) pair inserted into neurons 135 (e.g., using genetic engineering). Photodetector 160 can be (or include) a complementary metal-oxide semiconductor (CMOS) image sensor, a charge-coupled device (CCD), a hybrid CMOS-CCD image sensor, a photomultiplier tube (e.g., an array of photocathode-based pixels), a photodiode, a phototransistor, a 2D imaging camera, or any other suitable photodetector. Photodetector 160 can detect visible light, infrared (IR) light, ultraviolet (UV) light, and / or waves in any other portion of the electromagnetic spectrum. Transmission of light emitted from neuron 135 to photodetector 160 can be accomplished through air, one or more optical fibers, waveguides, etc., or using any combination thereof. The range of light detected by photodetector 160 can correspond to (or include) light emitted from DA pairs engineered into neuron 135. The optical signal detected by photodetector 160 is converted into an electrical signal that can be transmitted to one or more integrated circuits 145, MEA interface 150 of server computing device 110, and / or other computing devices for any appropriate processing, such as signal filtering, noise removal, decoding, and reformatting into a suitable format understandable by software and / or a human operator. Mechanisms for optically detecting neural activity are discussed in more detail below.

[0038] 2A-2B illustrate the incorporation of BRET conjugates into ion channels for efficient readout of neural activity and the output of biological neuronal networks, according to at least one embodiment. The term "ion channel," as used throughout this disclosure, refers to sodium (Na+) channels, potassium (K+) channels, and / or any other transmembrane channel capable of transporting ions. FIG. 2A shows a planar topology of a protein chain 200 forming an ion channel (IC) of a neuronal cell. For simplicity, FIG. 2A illustrates a sodium channel formed by protein chain 200, but similar BRET incorporation techniques can be used with other ICs. Protein chain 200 is embedded in a cell membrane 202. Protein chain 200 forming the IC includes four domains 210, 220, 230, and 240. Each of domains 210-240 can include six transmembrane segments, e.g., segments 221-226 in domain 220. The fourth segment 224 has a positive amino acid that, when stimulated by a transmembrane potential (also referred to herein simply as voltage), moves toward the outside (upper side in FIG. 2A ) of the cell membrane 202, opening the IC to the flow of ions (e.g., Na+ ions, K+ ions, etc.) into the neuronal cell. The different segments are connected by short loops 227. The fourth and fifth segments of each unit are connected by longer P loops, such as P loop 228 of domain 220. The different domains are connected by interdomain loops 229. An inactivation gate 250 is located between the third domain 230 and the fourth domain 240.

[0039] Figure 2B shows the protein chain 200 of Figure 2A coiled to form an ion channel for voltage-controlled transmembrane transport of ions. Domains 210-240 coil around a central pore 260, which functions as an ion conduit. P loops (e.g., P loop 228) line the inner surface of the pore 260 and act as activation gates for the IC. Before an action potential occurs, the IC is in an inactivated state, the pore 260 is closed to ions, and the plasma membrane 202 is at a resting voltage, e.g., approximately -70 mV in some neurons (the sign indicates that the intracellular side of the plasma membrane 202 has a lower potential than the extracellular side). At the initiation of an action potential, the membrane voltage begins to rise, and the fourth segment of each domain (e.g., segment 224 of domain 220) moves upward. When the membrane voltage reaches approximately -55 mV, the P loop (e.g., P loop 228 of domain 220), acting as an activation gate, opens the pore 260 to the flow of ions from the extracellular side to the intracellular side of the neuron. The flow of ions raises the membrane voltage to approximately +30 mV, at which point the inactivation gate 250 closes, the IC becomes inactive, and a refractory period begins, during which the membrane voltage gradually decreases (e.g., as a result of the outward flow of potassium ions, K+, through additional potassium channels) back to the resting potential corresponding to the down phase of the action potential.

[0040] 2A , to monitor different phases of an action potential, a BRET complex 270 comprising a donor-acceptor (DA) pair can be attached to protein chain 200 at a location where the chain changes shape in response to changes in membrane potential. In some embodiments, BRET complex 270 can be attached to P-loop 228. In some embodiments, BRET complex 270 can be attached to one of short loops 227, to interdomain loop 229 connecting different domains of protein chain 200, to the portion of the protein chain supporting inactivation gate 250, and / or to some other portion of protein chain 200.

[0041] Insertion of the BRET complex 270 into the ion channel can be achieved using various genetic engineering techniques. In some embodiments, the techniques can include the clustered regularly interspaced short palindromic repeats (CRISPR) system, which uses a guide RNA and the Cas9 enzyme. The guide RNA is designed to find and bind to a specific sequence in cellular DNA. The guide RNA and synthetic sequence can be added by introducing plasmid DNA, for example, using lipofection or electroporation. The guide RNA has RNA bases complementary to those of the target DNA sequence in the genome, ensuring that the guide RNA binds only to the target sequence. The Cas9 enzyme binds to the guide RNA and cleaves both strands of DNA at the target sequence. The cell then detects the DNA damage and attempts to repair the damage, introducing a targeted mutation at that point.

[0042] Another method of inserting the BRET complex into a host cell is via a viral vector. In such an embodiment, a plasmid containing the BRET complex sequence, along with a viral coat and / or reverse transcriptase sequence (itself inserted into the plasmid), is presented and introduced into a packaging cell line, after which viral particles are released, which can then be purified and later introduced into neuronal cell cultures to deliver the BRET complex. The virus produced will depend on the packaging sequence selected and may include, but is not limited to, third generation lentivirus (LV) or replication-deficient adeno-associated virus (AAV).

[0043] 3A-3C illustrate the operation of a BRET complex formed within an ion channel for efficient readout of neural activity and the output of a biological neuronal network, according to at least one embodiment. As shown in FIG. 3A, BRET complex 300 can include a targeting (T) protein 302 that can be inserted into IC protein chain 200. In some embodiments, donor protein 302 can be inserted into a targeting portion of IC protein chain 200 that changes its geometry (shape) in response to changes in neuronal action potentials. Such a targeting portion can include short loop 227, P loop 228, and interdomain loop 229 in some embodiments. In some embodiments, the targeting portion can include one or more segments within any one of the domains of the IC (e.g., segments 221-226 within domain 220).

[0044] In some embodiments, the IC protein chain 200 can be engineered using a target protein 302 to replace one or more proteins in the IC protein chain 200. For example, the genome of a neuron can be engineered so that gene expression in the neuron results in an IC protein chain 200 having a target protein 302 at a target location within the chain. The target location can be identified using techniques disclosed in more detail below. The target protein 302 can be expressed in a tagged state. More specifically, the target protein 302 can be tagged with a donor tag protein 306 that can harvest energy, e.g., valence electron chemical energy, from a substrate molecule 310, as schematically shown by the solid arrow in FIG. 3A. Additionally, the target protein 302 can be tagged with an acceptor tag protein 308, which can be a fluorophore molecule that can emit light when induced to an excited state (e.g., by the donor tag protein 306). 3A, in the first (dark) state of BRET complex 300, donor tag protein 306 and acceptor tag protein 308 can be located at a distance L1 that exceeds the effective energy transfer distance. In some embodiments, L1 > 10 nm, such that donor tag protein 306 cannot donate energy to acceptor tag protein 308. In some embodiments, distance L1 can be different from 10 nm, e.g., shorter or longer than 10 nm, and can depend on the specific properties of the donor / acceptor and their relative orientations.

[0045] In some embodiments, the donor tag protein 306 may comprise a luciferase molecule, a nanoluciferase molecule, etc. The substrate 310 may comprise coelenterazine, furimazine, and / or similar molecules. The acceptor tag protein 308 may comprise a green fluorescent protein (GFP), a yellow fluorescent protein (YFP), a Venus protein, etc.

[0046] As shown in FIG. 3B, changes in the cell membrane potential can change the shape of the IC protein chain, bringing the donor tag 306 and the acceptor tag 308 closer together. Accordingly, in the second (light) state of the BRET complex 300, the donor tag 306 and the acceptor tag 308 can be positioned at a distance L2 that is less than the effective energy transfer distance between the molecules. In some embodiments, L2 < 10 nm. As a result, the donor tag 306 can donate energy to the acceptor tag 308, for example, via resonant non-radiative (Förster) energy transfer, as shown schematically by the arrow in FIG. 3B. As shown in FIG. 3C, the excited acceptor tag 308 emits light 312, which can be detected by the photodetector 160, indicating that a particular stage of IC activity is occurring.

[0047] The type / stage of activity detected using BRET complex 300 depends on the placement of BRET complex 300 within IC protein chain 200. In particular, placing BRET complex 300 at a target location where distance decreases (L1 → L2) upon activation of the IC (e.g., inside a loop whose curvature increases, as shown in Figures 3A-3C) creates a light indicator of an activation stage of the IC. Similarly, placing BRET complex 300 at a target location where distance decreases with deactivation of the IC creates a light indicator of an inactivation stage of the IC. In some embodiments, the selection of the target location of BRET complex 300 can be performed to minimize potential disruption of IC protein chain function.

[0048] In some embodiments, the BRET complex 300 can be inserted into the sodium voltage-gated channel NaV, which is naturally expressed in mature (human) neurons and is essential for their function. For example, sodium voltage-gated channels can include the NaV1.1 isoform, which is associated with the function of inhibitory neurons, and the NaV1.2 isoform, which is expressed in excitatory neurons. Both of these channels are abundantly expressed in certain mature neurons and are required for physiological function. The NaV1.1 isoform is transcribed and translated from the human gene SCN1A, and the NaV1.2 isoform is transcribed and translated from the human gene SCN2A.

[0049] Identification of target regions of sodium (and other ion) channels for insertion of BRET complexes can be informed by existing X-ray crystallography studies of NaV isoforms and studies of the conformational changes that occur during activation and inactivation of these channels. To ensure that the BRET complex is inserted into functionally unimportant regions of the sodium channel and avoid disrupting the channel's normal function, target regions for insertion of the BRET complex can be selected from portions of the protein identified as unstructured regions in X-ray crystallography data. While such unstructured regions are not necessarily functionally unimportant, insertion of the BRET complex into such regions is less disruptive than altering more structured regions. Meanwhile, the positioning, movement, and deformation (or other conformational changes) of unstructured regions are expected to correlate with the conformational changes of adjacent, functionally important structured regions, effectively capturing the physiochemical properties of these structured regions.

[0050] In one non-limiting example, a target region of a sodium channel can comprise approximately 10-20 amino acid residues. The R group of each amino acid residue within these sequences has a wide range of physiochemical properties, encompassing subregions that can be dominated by the following residues: nonpolar / hydrophobic residues, polar / hydrophilic residues, positively charged residues, and / or negatively charged residues. In some embodiments, the target region of the channel can be selected based on, for example, the particular physiochemical profile of the fluorophore in combination with the specific tags selected for attachment to the donor and acceptor proteins, allowing for the selection of fluorophores (e.g., GFP, YFP, Venus protein, etc.).

[0051] In some embodiments, the BRET complex may have an amino acid sequence (linker) attached to each side of the D-A pair. The presence of the linker may allow the inserted BRET D complex to physiochemically adapt to the target region of the ion channel, provide additional flexibility to the BRET complex, and / or optimize the physical distance between the donor and acceptor tag proteins, allowing BRET operation (e.g., energy transfer from luciferase to the fluorophore and light emission by the fluorophore) to be correlated with ion channel activity.

[0052] Table 1 lists some examples of sodium channels and donor / acceptor tags that can be used in BRET pairs to efficiently read out neural activity and the output of biological neuronal networks (PDF stands for Protein Data Bank, OMIM stands for Online Mendelian Inheritance in Man). TIFF2026032556000002.tif63170

[0053] In some embodiments, to identify target sites within an ion channel, the structure of a particular ion channel of interest (e.g., NaV1.1, NaV1.2, etc.), including both structured and unstructured regions, with and without an inserted BRET complex can be predicted using the AlphaFold protein structure database (or a similar database). An appropriate visualization and analysis system, such as the PyMOL molecular visualization tool, can be used to assess the extent to which the insertion of a BRET complex alters the structure of the functional region of the channel. Based on the information provided by the visualization and analysis system, target region selection can then be performed by balancing the following factors: (1) minimizing disruption of the normal function of the ion channel by the BRET complex, and (2) ensuring that the detectable emission pattern correlates with one or more states of the ion channel, such as an open, closed, or inactive state. In some embodiments, a particular BRET complex may be capable of detecting only one of these states, e.g., an open or closed state. In some embodiments, a particular BRET complex can be used to detect multiple such states.

[0054] Some examples of target sites for NaV1.1 and NaV1.2 sodium channels have been identified as follows: 1.SCN1A F1529_N1530insYFP Q1563_K1564insnLuc; 2.SCN1A V804_N805insnLuc; 3.SCN1A S383_D384insVenus T1424_G1425insnLuc; 4.SCN1A F1529_N1530insYFP T2022_A2023insnLuc2022; 5.SCN1A S383_D384insVenus N1528_F1529insnLuc; 6.SCN1A S383_D384insVenus N1790_P1791insnLuc; 7.SCN2A C768_P769insGFP M856_D857insLuc; 8.SCN2A C768_P769insGFP S2014_P2015insnLuc.

[0055] FIG. 4 is a flow diagram illustrating an exemplary method 400 for generating genetically engineered ion channels having a BRET complex and using such ion channels to efficiently readout neural activity and the output of a biological neuronal network, according to at least one embodiment. For ease of explanation, method 400 is shown and described as a series of operations. However, operations according to this disclosure may occur in various orders and / or simultaneously, and with other operations not shown and described herein. Furthermore, not all illustrated operations are necessarily performed to implement a method according to the disclosed subject matter. For example, in some embodiments, the operations of blocks 410-430, associated with generating neuronal cells having genetically engineered ion channels, may be performed independently of the operations of blocks 440-460, associated with detecting electrical activity of the neuronal cells using the genetically engineered ion channels.

[0056] At block 410, method 400 may include identifying a target location within an ion channel (IC) at which to express a target protein (mutated protein) comprising a donor tag protein and an acceptor tag protein. In some embodiments, the IC may include a sodium channel, a potassium channel, a calcium channel, etc. In some embodiments, the operation of block 410 may include identifying one or more unstructured regions in an x-ray image of the IC. The operation of block 410 may further include selecting a target location within the one or more unstructured regions, taking into account at least some disruption of IC function caused by replacement of native proteins of the IC with the target protein.

[0057] At block 420, method 400 may include modifying the genome of at least some neuronal cells associated with the target location within the IC. At block 430, method 400 may continue by causing the neuronal cells to express a target protein in the IC according to the modified genome. In some embodiments, the target protein may include a donor tag protein and an acceptor tag protein. In some embodiments, the donor tag protein may include a luciferase protein, and the acceptor tag protein may include GFP, YFP, Venus protein, etc.

[0058] In some embodiments, in a first state of IC, the donor tag protein is at a first distance from the acceptor tag protein. The first distance can be associated with the absence of non-radiative energy transfer (NRET) between the donor tag protein and the acceptor tag protein. In a second state of IC, the donor tag protein is at a second distance from the acceptor tag protein. The second distance can be associated with the presence of NRET between the donor tag protein and the acceptor tag protein. For example, the first distance can be greater than 10 nm and the second distance can be less than 10 nm.

[0059] In some embodiments, the first state of the IC is associated with activation of the IC (or inactivation of the IC), and the second state of the IC is associated with inactivation of the IC (or activation of the IC). In some embodiments, the first state of the IC is associated with an open IC (closed IC), and the second state of the IC is associated with a closed IC (open IC). In some embodiments, the transition from the first state of the IC to the second state of the IC can be responsive to a conformational change in the IC. The conformational change in the IC can be associated with a change in the electrical potential of the membrane of the neuronal cell.

[0060] In some embodiments, in the second state of the IC, the acceptor tag protein emits light in response to NRET from the donor tag protein. In some embodiments, the energy transferred in NRET can be generated in an oxidation reaction between the donor tag protein and a substrate molecule.

[0061] Once the neuronal cells with the above altered properties have been expanded, the neuronal cells can be used for one or more purposes.

[0062] In one embodiment, at block 440, method 400 can include contacting the neuronal cell with a substrate compound. In some embodiments, the substrate compound can include furimazine, coelenterazine, or the like. In one embodiment, at block 450, method 400 can include detecting light emitted from the acceptor tag protein in response to the IC transitioning from a first state to a second state. In some embodiments, the transition of the IC from the first state to the second state can be triggered by natural (e.g., spontaneous) electrical activity of the neuronal cell or by an external electrical stimulus to the neuronal cell.

[0063] In one embodiment, at block 460, the method 400 may include using the detected light to determine the results of a calculation performed by a neural network including neuronal cells.

[0064] Some portions of the detailed descriptions are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps require physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0065] It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless otherwise stated, as will be apparent from the following description, it will be understood that throughout this specification, descriptions utilizing terms such as "receive," "convert," and "transmit" may refer to operations and processes of a computer system or similar electronic computing device that manipulate and convert data represented as physical (electronic) quantities in the computer system's registers and memory into other data also represented as physical quantities in the computer system's memory or registers, or other information storage, transmission, or display device.

[0066] Embodiments of the present disclosure also relate to apparatus for performing the operations described herein. This apparatus may be specially configured for the foregoing purposes and / or may comprise a general-purpose computer system selectively programmed by a computer program stored on the computer system. Such computer program may be stored on a computer-readable storage medium such as, but not limited to, a floppy disk, an optical disk, a CD-ROM, any type of disk including a magneto-optical disk, a read-only memory (ROM), a random access memory (RAM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium, an optical storage medium, a flash memory device, any other type of machine-accessible storage medium, or any type of medium suitable for storing electronic instructions, each of which is connected to a computer system bus.

[0067] It is to be understood that the above description is intended to be illustrative, and not limiting. Many other embodiments will be apparent to those skilled in the art upon reading and understanding the above description. While the present disclosure has been described with reference to certain exemplary embodiments, it will be understood that the disclosure is not limited to the described embodiments, but can be practiced with modification and alteration within the spirit and scope of the appended claims. Accordingly, the specification and drawings should be interpreted in an illustrative, and not a restrictive, sense. The scope of the present disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. 1. A method for genetically modifying an ion channel in a neuronal cell, comprising: Identifying a target location within an ion channel (IC) for a target protein pair comprising a donor tag protein and an acceptor tag protein; and modifying the genome of the neuronal cell such that the neuronal cell expresses a modified IC comprising the target protein pair at a target location; Including, when expressed by a neuronal cell, the target protein pair exhibits a first state or a second state; In a first state, the donor tag protein is at a first distance from the acceptor tag protein, the first distance being associated with the absence of non-radiative energy transfer (NRET) between the donor tag protein and the acceptor tag protein; The method wherein in the second state, the donor tag protein is a second distance from the acceptor tag protein, the second distance being related to the presence of NRET between the donor tag protein and the acceptor tag protein.

2. The method of claim 1 , wherein the donor tag protein comprises a luciferase protein.

3. The acceptor tag protein is Green fluorescent protein (GFP), Yellow fluorescent protein (YFP), or Venus protein The method of claim 1 , comprising at least one of:

4. The IC, sodium channels, potassium channels, or calcium channels The method of claim 1 , comprising at least one of:

5. The method of claim 1 , wherein the first distance is greater than 10 nm and the second distance is less than 10 nm.

6. 2. The method of claim 1, wherein the first state of the IC is associated with at least one of activation of the IC or deactivation of the IC, and the second state of the IC is associated with another one of activation of the IC or deactivation of the IC.

7. 2. The method of claim 1, wherein the first state of the IC is associated with at least one of an open IC or a closed IC, and the second state of the IC is associated with another of an open IC or a closed IC.

8. 10. The method of claim 1, wherein the transition from the first state of the IC to the second state of the IC is responsive to a conformational change of the IC.

9. The method of claim 8, wherein the conformational change of the IC is associated with a change in the membrane potential of the neuronal cell.

10. 10. The method of claim 1, wherein in the second state of the IC, the acceptor tag protein emits light in response to NRET from the donor tag protein.

11. The method of claim 1, wherein the energy transferred in NRET is generated in an oxidation reaction between a donor tag protein and a substrate molecule.

12. The step of identifying a target location within the IC comprises: identifying one or more unstructured regions in an X-ray image of the IC; and selecting target positions within one or more unstructured regions with consideration given to at least some disruption of IC function caused by replacement of native proteins of the IC with the target protein; The method of claim 1 , comprising:

13. 1. A method for detecting electrical activity of neuronal cells, comprising: contacting a neuronal cell with a substrate compound, wherein the neuronal cell comprises an ion channel (IC) genetically modified with a mutant protein comprising a donor tag protein and an acceptor tag protein; and detecting light emitted from the acceptor tag protein in response to a transition of the IC from a first state to a second state; In a first state, the donor tag protein is at a first distance from the acceptor tag protein, the first distance being associated with the absence of non-radiative energy transfer (NRET) between the donor tag protein and the acceptor tag protein; The method, wherein in the second state, the donor tag protein is at a second distance from the acceptor tag protein, the second distance being related to the presence of NRET between the donor tag protein and the acceptor tag protein, and the energy transferred in the NRET is generated in an oxidation reaction between the donor tag protein and one or more molecules of a substrate compound.

14. using the detected light to determine the results of a computation performed by a neural network including neuron cells. The method of claim 13 further comprising:

15. The transition of the IC from the first state to the second state is the natural spontaneous electrical activity of neuronal cells, or External electrical stimulation of neuronal cells The method of claim 13 , wherein the at least one of

16. A neural network comprising neuronal cells, the neuronal cells comprising an ion channel (IC) genetically modified with a mutant protein comprising a donor tag protein and an acceptor tag protein; and a photodetector that detects light emitted from the acceptor-tagged protein in response to a transition of the IC from the first state to the second state; A system comprising: In a first state, the donor tag protein is at a first distance from the acceptor tag protein, the first distance being associated with the absence of non-radiative energy transfer (NRET) between the donor tag protein and the acceptor tag protein; In the second state, the donor tag protein is at a second distance from the acceptor tag protein, the second distance being related to the presence of NRET between the donor tag protein and the acceptor tag protein.

17. The donor tag protein comprises a luciferase protein, and the acceptor tag protein comprises Green fluorescent protein (GFP), Yellow fluorescent protein (YFP), or Venus protein The system of claim 16, comprising at least one of:

18. 17. The system of claim 16, wherein the first state of the IC is associated with at least one of activation of the IC or deactivation of the IC, and the second state of the IC is associated with another one of activation of the IC or deactivation of the IC.

19. 17. The system of claim 16, wherein the first state of the IC is associated with at least one of an open IC or a closed IC, and the second state of the IC is associated with another of an open IC or a closed IC.

20. a processing unit that uses the detected light to determine the results of the calculations performed by the neural network The system of claim 16 further comprising:

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