Devices, systems, and methods for stem cell analysis

Abstract

A system and method for stem cell analysis and a device including a reader head having a plurality of probes are disclosed, each of which probes comprises a shank having a plurality of recording sites and a plate having a plurality of wells, each well of which is configured to receive at least one stem cell, and the reader head is configured to automatically probe at least one stem cell, to characterize at least one stem cell, or to stimulate an electrically active cell by using the plurality of recording sites of the plurality of probes.

Description

TECHNICAL FIELD

[0001] Various embodiments of the present disclosure generally relate to devices, systems, and methods for stem cell probing and for analyzing data from stem cell probing. In particular, certain embodiments of the present disclosure relate to devices, systems and methods for measuring stem cell electrical activity by using a stem cell recording device. BACKGROUND ART

[0002] Stem cells are three-dimensional branched cell structures cultured from induced pluripotent stem cells (iPSCs), and can be used as a model of specific organs such as the brain or heart. Stem cells are utilized, for example, to develop and discover drugs or other therapies and / or medical technologies. The background description provided herein is for the purpose of generally presenting the context of the present disclosure. Unless otherwise indicated herein, the subject matter described in this section is not prior art to the claims in this application and is not admitted to be prior art or a suggestion of prior art by inclusion in this section. SUMMARY OF THE INVENTION

[0003] According to some aspects of the present disclosure, devices, systems and methods for stem cell probing and for analysis of probing are disclosed. In one aspect, a device for stem cell analysis is disclosed. The device may include a reader head having a plurality of probes, each of the plurality of probes including a shank having a plurality of recording sites and a plate having a plurality of wells, each well of the plurality of wells being configured to receive at least one stem cell, and the reader head is configured to automatically probe the at least one stem cell, measure a property of the at least one stem cell, or stimulate electrically active cells by using the plurality of recording sites of the plurality of probes.

[0004] In another embodiment, a device for stem cell analysis is disclosed. The device may include a plurality of probes, each of which includes a shank having a plurality of recording sites, a camera, and a plate having a plurality of wells, each well of the plurality of wells configured to receive at least one stem cell, the plate includes at least one reference label, the camera is configured to interpret at least one reference label, and the plurality of probes are aligned with one of the wells based on the interpretation of at least one reference label.

[0005] In another embodiment, a system for stem cell analysis is disclosed. The system may include a stem cell probing device having at least one probe comprising a shank having a plurality of recording sites; and a plate having a plurality of wells, each well of the plurality of wells configured to receive at least one stem cell; and a control / analysis unit configured to receive data from the plurality of recording sites and to automatically position at least one probe based on the data.

[0006] Please understand that both the general description above and the detailed description below are illustrative and for illustrative purposes only, and therefore do not limit the embodiments disclosed as claimed.

[0007] The accompanying drawings, incorporated herein and constituting part thereof, illustrate various exemplary embodiments and, together with this specification, serve to illustrate the principles of the disclosed embodiments. [Brief explanation of the drawing]

[0008] [Figure 1] A diagram illustrating an exemplary stem cell recording system according to one or more embodiments. [Figure 2] A diagram illustrating an exemplary probe according to one or more embodiments. [Figure 3A]A diagram illustrating further exemplary probes according to one or more embodiments. [Figure 3B] A diagram illustrating the exemplary probe of Figure 3A in an exemplary configuration according to one or more embodiments. [Figure 4A] A diagram illustrating an exemplary plate according to one or more embodiments. [Figure 4B] A diagram illustrating a cross-section of the exemplary well of Figure 4A according to one or more embodiments. [Figure 5] A diagram illustrating an exemplary environment for classifying stem cells according to one or more embodiments. [Figure 6] A diagram illustrating an exemplary method for classifying stem cells according to one or more embodiments. [Figure 7] An illustrative schematic diagram for classifying stem cells according to one or more embodiments. [Figure 8] Exemplary machine learning training flowcharts according to several embodiments of the present disclosure. [Figure 9] A simplified functional block diagram of a computer according to one or more embodiments. [Modes for carrying out the invention]

[0009] This disclosure can be understood by referring to the following description and accompanying drawings, in which similar elements are referenced by the same reference numerals. The technical terms used below may be interpreted in their broadest reasonable manner, even if used in connection with the detailed descriptions of some specific examples of this disclosure. In fact, some terms may be further emphasized below; however, any technical term intended to be interpreted in any restricted manner will be explicitly and specifically defined in this “Modes for Carrying Out the Invention” section. Both the summary provided above and the detailed descriptions below are for illustrative and explanatory purposes only and do not impose any restrictions on any particular feature as claimed.

[0010] In this disclosure, the term “based on” means “at least partially based on.” The singular indefinite and definite articles include multiple references unless the context otherwise specifies. The term “example” is used to mean “one example” rather than “ideal.” The term “includes” and other variations thereof are intended to cover non-exclusive inclusion, for example, that a process, method, or product containing a list of elements does not necessarily contain only those elements, but may include other elements that are not explicitly enumerated or that are specific to such a process, method, article, or apparatus. The term “or” is used disjunctively, such that “at least one of A or B” includes (A), (B), (A and A), (A and B), etc. Relative terms such as “substantially,” “approximately,” “about,” and “generally” are used to indicate a possible variation of ±10% of a stated or understood number.

[0011] While terms such as "first," "second," and "third" are used herein in some instances to describe various elements, it will be understood that these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, without departing from the scope of the various described embodiments, a first element may be named a second element, and similarly, a second element may be named a first element. Both the first and second elements are contacts, but they are not the same contact.

[0012] As used herein, the term "if" is optionally interpreted, depending on the context, to mean "when," "upon," "in accordance with determining," or "in accordance with detecting." Similarly, the phrase "if it is determined that" or "[statement or event] is detected" is optionally interpreted, depending on the context, to mean "when it is determined," "in accordance with determining," "[statement condition or event] is detected," or "[statement condition or event] is detected."

[0013] The systems, devices, and methods disclosed herein are described in detail through several examples and with reference to the accompanying drawings. The examples discussed herein are merely illustrative and are provided to assist in describing the apparatus, devices, systems, and methods described herein. None of the features or components shown in the accompanying drawings or discussed below should be taken as mandatory for any particular implementation of any of these devices, systems, or methods unless specifically designated as mandatory.

[0014] Furthermore, with respect to any method described, whether or not the method is described in relation to a flowchart, it should be understood that "unless otherwise specified or required by context, no explicit or implicit ordering of steps performed in the execution of the method does not imply that those steps must not be performed in the order presented, but rather they may be performed in a different order or in parallel."

[0015] As used herein, the term “exemplary” is used to mean “an example” rather than “ideal.” Furthermore, the indefinite articles ("a" and "an") herein do not indicate a limitation of quantity, but rather indicate the existence of one or more of the referenced items.

[0016] As used herein, a "machine learning model" generally encompasses instructions, data, or a model that is configured to receive input, and applies one or more of weighting, biasing, classification, or analysis with respect to the input to generate an output. The output may include, for example, classification of the input, analysis based on the input, a design, process, prediction, or recommendation related to the input, or any other suitable type of output. Machine learning models are generally trained by using training data (e.g., experimental data or samples supplied to the model as input data) to establish, tune, or modify one or more aspects of the model (e.g., weightings, biases, criteria, etc. for forming classifications or clusters). Some aspects of the machine learning model may operate linearly or in parallel on the input via a network (e.g., a neural network) or via any suitable configuration.

[0017] Execution of the machine learning model may include deployment of one or more machine learning techniques such as linear regression, logistic regression, random forest, gradient boosted machine (GBM), deep learning, or deep neural networks. Supervised or unsupervised training may be employed. For example, supervised learning may include providing training data and labels corresponding to the training data (e.g., as ground truth). Unsupervised approaches may include clustering, classification, and the like. K-means clustering or K-nearest neighbor algorithms, which may be supervised or unsupervised, may also be used. Combinations of K-nearest neighbor algorithms and unsupervised clustering techniques may also be used. Any suitable type of training (e.g., stochastic, gradient boosted, random seeded, recursive, epoch or batch-based, etc.) may be used.

[0018] Stem cells are three-dimensional branched cell structures cultured from induced pluripotent stem cells (iPSCs), and can be used in vitro to model key features of target organs. Stem cells provide an in vitro preclinical model that combines scalability and the physiological relevance of in vitro systems. Stem cells possess the human genome; that is, stem cells recapitulate physiologically relevant circuits, making them particularly valuable for modeling human-specific genomic features. This genetic fidelity enhances the applicability of research findings to human diseases. As three-dimensional structures, stem cells derive utility from their complex three-dimensional cellular architecture that mimics the heterogeneity and layered organization of organs such as the human brain.

[0019] Unlike animal models, stem cells are highly scalable, enabling large-scale drug screening that would not be feasible with more labor-intensive methods. Furthermore, physiological data from stem cells is far more translatable than that derived from current preclinical models such as conventional behavioral tests in animals, which often fail to predict human responses. Additionally, stem cells can possess alleles such as the APOE4 allele (implicated in Alzheimer's disease but not naturally expressed in rodents) that are not naturally expressed in animals.

[0020] One example is brain stem cells, which can preserve critical aspects of the neural network found in the intact brain, and can be used to elucidate the circuit organization of the brain, model disease processes or pathologies, conduct toxicity tests, or determine the effects of drug candidates on those pathologies. It is desirable to electrically probe the interior of brain stem cells in a manner that does not disrupt delicate cell connections but measures single-unit action potentials (e.g., the instantaneous cell-scale electrical activity of individual nerve cells) and field activity (e.g., the population-scale electrical activity of groups of nerve cells within the stem cell). The systems, devices, and methods discussed herein can reliably measure physiological activity across entire stem cells in a continuous, automated, and scalable manner at both the single-cell scale and the population scale (e.g., measurements can be efficiently performed on many stem cells in a short period of time).

[0021] One current method for probing brain stem cell circuit configurations involves the use of a planar microelectrode array (MEA), in which lithographically determined conductive microelectrodes are assembled on a plane (typically at the bottom of a well into which a tissue sample, such as a stem cell or thin section, is introduced). The MEA electrodes are connected to a circuit that amplifies any signals present on the electrodes so that certain parts of the stem cells or other tissue in contact with the MEA can be detected by the MEA (if electrically active).

[0022] This technique causes planar MEA electrodes to measure signals in extremely close proximity to the electrode (i.e., on the outer surface of the stem cell). Since stem cells do not normally make close contact with the MEA, considerable effort and time are spent culturing the stem cells onto the surface to achieve closer contact and to ensure that the resulting signal level is robust enough for measurement, in order to record from intact stem cells. In practice, the formation of good electrical contact is generally hindered by the irregular stem cell shape and sealing with non-neuronal extracellular matrix or Matrigel: the latter is commonly used as a scaffold to promote stem cell growth. Even when this process is successful, a large portion of the interior of the stem cell is not sampled.

[0023] Rather than recording from the surface of an intact structure, stem cells are often dissociated or sliced, and as a result, 2D cell layers may adhere to the MEA surface. However, the main advantage of brain stem cells is that they are a more realistic model with complexes. 3D cell constructions are thinned out. Neural circuits or microcircuits are likely to contain interconnected neurons of diverse cell types at numerous depths within brain stem cells. Conventional MEA systems, by design, cannot sample these circuits, and therefore, it is unlikely that they will generate data from neural circuits that have predictive power to explain underlying disease states or drug efficacy.

[0024] Attempts to develop MEAs with three-dimensional topography to overcome the shortcomings of strictly planar MEAs have not been very successful. One challenge was developing a process to produce electrodes with a sufficiently high aspect ratio to effectively penetrate the tissue so that the measurement electrodes are in close proximity to the target nerve cells. It also proved difficult to incorporate multiple measurement electrodes onto a single electrode "spike": that is, each penetration portion of the MEA produced only a single measurement result rather than sampling along the entire trajectory of the "spike," thus making it impossible to implement (e.g., measure) a complete three-dimensional sample of stem cells. Several fabrication methods have generally proven to be expensive and difficult to scale. This application also involves cumbersome and possibly destructive techniques to ensure good electrical contact by applying pressure to the electrode array against the stem cells.

[0025] Another conventional method used to record within brain stem cells is patch clamping. This method involves targeting and recording several signals from a single nerve cell simultaneously. Due to the lack of organization (the current dilution of active nerve cells within brain stem cells in in vitro or two-dimensional cultures), this low-throughput sampling can be extremely difficult to identify active nerve cells. Even if patch clamping is successful, this method cannot simultaneously record from multiple cells that make up neural circuits (which may be important for understanding mechanisms related to drug efficacy and toxicity).

[0026] There is a need for improved systems, methods, and devices for improved electrical readout from stem cells, including brain organisms. Embodiments disclosed herein address the challenges referenced above by recording from within intact stem cells by capturing critical elements of neural networks spanning various regions with spatiotemporal resolution to resolve single-cell spike activity. This ability becomes increasingly beneficial as stem cells become larger and more heterogeneous, making deep probing critical for precise circuit-level analysis. The disclosed embodiments can perform long-term recordings of numerous estimated single-unit and local field potentials.

[0027] The disclosed system utilizes high-density probes to penetrate tissue for rapid and reliable data acquisition from deep within stem cells. In contrast to conventional MEA systems, the probes facilitate rapid and precise recording.

[0028] In some embodiments, a trained machine learning model may be used to identify effective biomarkers based on at least neural circuit behavior and stem cell-specific features. Identifying such biomarkers can enable rapid evaluation of new drug candidates. Some embodiments described herein that employ a trained machine learning model may utilize classification methods that have shown that "differences in electrophysiological features are more evident between stem cell classes (e.g., derived from various stem cell maturation protocols) than within any single class." Thus, the use of a trained machine learning model can significantly increase the predictive power of the model despite the inherent variability of the input data.

[0029] Figure 1 depicts a stem cell analysis system 100 that may be configured to perform high-throughput recording of at least one stem cell 107 (e.g., automated recording of at least one stem cell 107). As depicted in Figures 1-3B, the stem cell analysis system 100 may include a stem cell analysis device 105 which may include a leader head 110, a plate 115, an alignment assembly 117 (e.g., an electric stage 120 and / or robotic arm 127 which may be configured to automate alignment), a camera 130, and a control / analysis unit 180 (e.g., capital equipment such as a computer). In some examples, the control / analysis unit 180 may be local to the stem cell analysis device 105. Alternatively, the control / analysis unit 180 may be remote to the stem cell analysis device 105. For example, the stem cell analysis device 105 may communicate with the control / analysis unit 180 via a wired or wired (e.g., cloud-based) channel. The control / analysis unit 180 may have any of the features described below with respect to Figure 9.

[0030] The reader head 110 may be electrically coupled to a control / analysis unit 180 or other computing device to receive data from the reader head 110 and, in some examples, to send commands to the reader head 110. The reader head 110 includes one or more needle-like structures (hereinafter referred to as "probes") 125 which can be configured to penetrate at least one stem cell 107 (e.g., brain stem cells) and measure electrical activity with minimal damage to the stem cell. Exemplary embodiments of the probes 125 are shown in Figures 2 and 3. Figure 2 shows an exemplary probe 205 which can be used as probe 125. Figure 3A shows an exemplary probe 305 which can be used additionally or alternatively as probe 125.

[0031] As shown in Figure 2, the probe 205 may include a proximal end 222. The proximal end 222 may include a bonding pad (not depicted and having any features known in the art) for making an electrical connection between the probe 205 and the reader head 110. The probe 205 may also include a tip 225. The tip 225 may include a shank 207. The shank 207 may be any preferred length, such as 6 mm, 7 mm, 8 mm, 9 mm, 10 mm, or 11 mm. The shank 207 may be manufactured to a predetermined length based on stem cell categorization. For example, the shank 207 may be manufactured to be 9 mm if intended for use with brain stem cells. The tip 225 may include a micron-scale conductive recording site 220 (e.g., an electrode) which may be configured to measure the electrical activity of nerve cells and / or other electrical activity within the stem cells 107. For example, the recording site 220 may measure and transmit data relating to the voltage or other aspects of the electrical signals of the stem cells 107. In some examples, the recording site 220 may be an alternative or additional stimulating site configured to stimulate (e.g., deliver electrical energy to) the stem cells 107. For example, site 220 may stimulate electrically active cells of the stem cells 107. The shank 207 may comprise silicon or other suitable material. The shank 207 may comprise a pointed tip 209.

[0032] As shown in Figures 3A and 3B, in some embodiments, the probe 305 may include multiple tip portions 325 on multiple shanks 307. Each of the shanks 307 may have a tip portion 325. The shanks 307 and tip portions 325 may have any of the characteristics of the shanks 207 and tip portions 225 described above. Although not labeled in Figure 3A, the tip portion 225 may include a recording site 220 (described above). Each shank 307 may include a sharp tip 310 having any of the characteristics of the sharp tip 209. The shanks 307 may optionally be parallel to each other. In at least some examples, the shanks 307 may be arranged in a single column such that their faces penetrate all of the shanks 307. Alternatively, the shanks 307 may be arranged in other patterns (e.g., multiple rows and columns). The proximal ends of the shanks 307 may be coupled to a single proximal end 322 having any of the characteristics of the proximal end 222. Since each shank 307 may contain an array of micron-scale conductive recording sites 220, having multiple shanks 307 on a single probe 305 structure may span a two-dimensional recording surface having strategically positioned shanks 307 and / or micron-scale conductive recording sites 220 for recording from a piece of cellular tissue.

[0033] In one embodiment, all or part of the probes 125, 205, 305 (e.g., shank 207) may consist of a lithographically patterned silicon substrate together with micron-scale conductive recording sites 220 (e.g., on the tips 225, 325 of each shank 207, 307). The recording sites 220 may be interconnected and enable measurement of cellular activity along the length of each shank 207, 307. The probes 125, 205, 305 may be manufactured using any suitable semiconductor fabrication technique. The micron-scale conductive recording sites 220 may be spaced on the shanks 207, 307 in any suitable arrangement. In some embodiments, the micron-scale conductive recording sites 220 may be spaced based on intended stem cell categorization. The shape of the probes 125, 205, 305 and / or the micron-scale conductive recording sites 220 may be optimized for the corresponding type of stem cell in the biological tissue. For example, the recording site 220 may be positioned such that, when probes 125, 205, and 305 are inserted into the stem cells 107, they have a high-density recording site less than approximately 150-300 micrometers below the surface of the stem cells 107, since brain stem cells have the highest density of relevant structures within that region. In some embodiments, probes 125, 205, and 305 may include a lumen 250 (shown in Figure 2) which can be configured to allow the injection of a drug or other compound into the stem cells 107.

[0034] Furthermore, a complete three-dimensional array 350 of electrodes can be generated by stacking two or more probes 305 on top of each other (e.g., in parallel, orthogonally, etc.), as depicted in Figure 3B. When the array 350 of probes 305 penetrates at least one stem cell 107, the entire volume of stem cell tissue for electrical activity on a regular 3D grid can be sampled, which can provide superior functional mapping compared to conventional methods of surface (cortical electroencephalography (ECoG)) or scalp (electroencephalography (EEG)) recording. The individual probes 305 forming the 3D array 350 may differ in design so that the combined 3D arrangement of all recording sites 220 covers (e.g., optimally covers) the anatomical arrangement of the cells within a particular type of stem cell.

[0035] A dedicated culture dish (e.g., plate 115) may be used for the purpose of immobilizing (e.g., fixing) at least one stem cell 107 for entry by the reader head 110. Plate 115 may have the same or similar standard dimensions as a microwell droplet plate so that standard laboratory instruments for sample preparation, dispensing, and analysis can process the plate before and after analysis by the subject system. In some embodiments, small wells on plate 115 may help capture and immobilize stem cells 107 for analysis. Plate 115 may include features such as a gas or fluid pump, microchannel, or integrated heater to maintain the stem cell-containing buffer and reagents in appropriate condition and keep the stem cells viable during measurement. Plate 115 may be made from a transparent material such as glass, a silicone-based polymer, and / or similar material, which may be configured to allow optical imaging and / or modulation using light sources of various wavelengths. Plate 115 may have a unique serial number that can be used to track a specific experimental setting consisting of markings / markers such as reference labels (e.g., fluorescent tags) for guiding the alignment of the reader head 110, and / or manufacturing history and / or software.

[0036] Figure 4A depicts a top view of an exemplary plate (e.g., plate 115), and Figure 4B depicts a cross-sectional view of an exemplary well (e.g., well 405). The cross-sectional view in Figure 4B passes through line A-A' in Figure 4A. Plate 115 may have any number of wells 405. Each well 405 may be insulated from one another (e.g., to prevent any interwell fluid flow from occurring to prevent contamination). Each well 405 may contain various media components and / or compounds for investigation (e.g., drugs). Each well 405 may have multiple stem cell traps 410. Each stem cell trap 410 may have a substantially "V"-shaped structure / shape that can be configured to immobilize stem cells of varying size and shape.

[0037] The plate 115 may be transparent to allow optical imaging from the bottom. The plate 115 may have an integrated heater (e.g., heater 425) and a temperature sensor 426 to control the medium temperature. The plate 115 may have an electrical ground 420 that is in electrical contact with the solution inside any well 405. The plate 115 may include reference marks 415, which may be any pattern on the plate 115 having fixed coordinates with respect to the wells 405, and which may be recognizable by an image processing unit (not shown) that helps align the reader head 110 to a desired position.

[0038] As shown in Figure 1, an alignment assembly 117, such as a motorized stage 120 and / or a robotic arm 127, may be used to bring the leader head 110 into a precise orientation and position and to sequentially penetrate each stem cell 107 on the plate 115. Positional tolerances may be close to or less than 5-10 micrometers in each of the Cartesian and / or spherical coordinate axes. In some embodiments, the alignment assembly 117 (e.g., motorized stage 120 and / or robotic arm 127) may operate automatically. The motorized stage 120 and / or robotic arm 127 may be configured to move not only in the XYZ degrees of freedom (e.g., along three directions perpendicular to each other) but also in angular degrees of freedom around these axes. It should be noted that either the plate 115 or the leader head 110 (e.g., plate 115 on the motorized stage 120 or leader head 110 on the robotic arm 127) may be moved while the other is fixed and held in place to initiate precise insertion. In some embodiments, the position and / or orientation of both the plate 115 and the leader head 110 can be controlled.

[0039] A high-resolution camera 130 with a magnifying optical component may be used to image each plate 115 prior to or during analysis. Alternatively, a microscope may be used instead of or in addition to the camera 130. By using image processing techniques, the primary camera may determine the center of mass of each stem cell 107 on the plate 115 as viewed from above, and a set of coordinates for their targets (e.g., stem cells 107) may be stored. This information may be used to drive the reader head 110 to the correct position for insertion into each stem cell 107 (e.g., sequentially). A secondary camera (not depicted) with a line of sight perpendicular or nearly perpendicular to the insertion trajectory of probes 125, 205, 305 may be used to measure the insertion angle and distance from the probe end to the stem cell surface. Additionally or alternatively, camera 130 may include one or more cameras (or microscopes) configured to receive and / or interpret fluorescence signals from reference labels 415 on the plate 115. In some examples, both the reader head 110 and the plate 115 may include a reference marker 415, which may be a fluorescent tag or a simple optical feature.

[0040] Electronics and software may be used to control mechanical mechanisms and for reading and analyzing probe 125, 205, and 305 data. The analytical software may include algorithms for characterizing stem cell network activity using electrophysiological ("e-phys") biomarkers. e-phys biomarkers are data products resulting from detailed analysis of field and single-unit spike activity. Examples include, but are not limited to, gamma, alpha, or sharp-wave ripple (or other frequency band) power, coherence between two regions, and spike frequency. e-phys biomarkers may be useful in high-throughput screening of drug candidates. Further details of such analytical software are described below.

[0041] In some embodiments, electrical readouts of probes 125, 205, and 305 may be used by the control / analysis unit 180 during insertion into stem cells 107 to determine or select the depth (e.g., Z-direction) to which probes 205, 305 should be inserted (e.g., in a closed-loop configuration). A recording site 220 may be in electrical communication with the control / analysis unit 180, and the control / analysis unit 180 may use data measured by the recording site 220 to automatically position the reader head 110 and / or probes 125, 205, and 305 on the reader head 110. For example, impedance measurements or neural signal processing may distinguish between different states (e.g., probes 125, 205, and 305 in air; probes 125, 205, and 305 in conductive solution; probes 125, 205, and 305 in stem cells 107 without neighboring active neurons; probes 125, 205, and 305 in stem cells 107 with neighboring active neurons, etc.).

[0042] During use, probes 125, 205, and 305 can be inserted into stem cells 107. Upon entry of stem cells 107, the root mean square voltage (V) measured by probes 125, 205, and 305 is measured. rms An increase in ) is possible. For example, V rms The number of V values ​​at the recording site 220 may increase by approximately twofold upon entry of stem cells 107 (which may still be within a suitable range for spike detection). In some examples, the control / analysis unit 180 may analyze measurements from all or a subset of the recording site 220 (e.g., a tip subset of the recording site 220) once inserted, in order to provide closed-loop control over the positioning of probes 125, 205, 305 and to interrupt the movement of probes 125, 205, 305 (e.g., in the Z direction) before probes 125, 205, 305 are inserted too deeply. For example, the movement of probes 125, 205, 305 may interrupt the recording site 220 if the number of V values ​​is below the threshold. rms Measurement may be interrupted or stopped. In some cases, a given stem cell 107 may be considered successfully probed if at least 25% of the recording sites 220 record spikes from the stem cell 107.

[0043] Figure 5 depicts an exemplary environment 500 for classifying stem cells according to one or more embodiments. For example, environment 500 may be used to classify stem cells as being of a specific type. In some embodiments, environment 500 may classify stem cells as dorsal forebrain organoids (dFOs), ventral forebrain organoids (vFOs), or vFOs with differentiated oligodendrocytes (vFO OLs). In other embodiments, environment 500 may be used to classify stem cells as being affected by or not affected by a disease such as Alzheimer's disease (AD). Environment 500 may also be used to efficiently screen for possible treatments (e.g., drugs). This efficiency and throughput are necessarily made possible by both the stem cell analysis device 105 and the stem cell analysis module 510. For the device, robotic control with the speed and precision to move from stem cell to stem cell, and critically, robotic control targeting each stem cell with precise depth to maximize the number of neurons extracted based on closed-loop control, is critical. Analytical efficiency is critically driven by a trained machine learning model515 that dramatically reduces the minimum required recording period before the model's predictive power is compromised. The high-throughput capabilities of the technology described herein (combined with full system integration and automation) represent a significant advance beyond current technology. The process described herein can significantly reduce the time and resources required to screen large drug libraries. Unlike conventional systems that require extensive preparation and manual intervention, the technology described herein streamlines data acquisition and ensures consistent, high-quality results. The technology described herein enhances efficiency and reliability and minimizes human error and variability, which is critically important for accelerating the drug development process.

[0044] The environment 500 may include one or more aspects that can communicate with each other on the network 525. In some embodiments, a user 505 may interact with the stem cell analysis device 105 to achieve stem cell analysis. As depicted in Figure 5, the stem cell analysis device 105 may interact with at least one of the following: a stem cell analysis module 510 (which may be housed on or separately from the control / analysis unit 180), a trained machine learning model 515, or data storage 520.

[0045] The stem cell analysis system 100, including the stem cell analysis device 105, may be configured to perform high-throughput recording of at least one stem cell 107, as discussed in detail above. Embodiments of the stem cell analysis device 105 (or control / analysis unit 180) may be configured to acquire data from one or more other embodiments of the environment 500 (e.g., a reader head 110 or camera 130 (e.g., via one or more inputs from a user 505), a stem cell module 510, a trained machine learning model 515, data storage 520, etc.). Embodiments of the stem cell analysis device 105 or control / analysis unit 180 may be configured to transmit data to one or more embodiments of the environment 500 (e.g., a reader head 110, camera 130, stem cell analysis module 510, a trained machine learning model 515, data storage 520, etc.).

[0046] As discussed above, the stem cell analysis device 105 may include a reader head 110 and a camera 130. The reader head 110 may be configured to measure the electrical activity of stem cells (e.g., electrical spikes, local field potential ("LFP"), etc.) as will be discussed in more detail herein. The reader head 110 may be configured to transmit data to one or more other aspects of the environment 500 (e.g., a stem cell analysis module 510, a trained machine learning model 515, data storage 520). In some non-limiting examples, the stem cell analysis module may be housed within a control / analysis unit 180 (Figure 1).

[0047] The stem cell analysis module 510 may be configured to analyze at least one stem cell 107 according to one or more embodiments. In some embodiments, the stem cell analysis module 510 may be configured to analyze at least one stem cell 107 via a trained machine learning model 515, as will be discussed in more detail below. For example, the stem cell analysis module 510 may be configured to classify at least one stem cell 107 via a trained machine learning model 515 (e.g., by analyzing the output of the trained machine learning model 515).

[0048] A trained machine learning model 515 may be configured to classify at least one stem cell 107 according to one or more embodiments. In some embodiments, a first type of trained machine learning model 515 may be configured to classify at least one stem cell 107 based on spike data. Neurons fire action potentials (spikes) throughout the neural network. These spikes may be measured within the stem cell 107 by probes 125, 205, and 305 as discussed above. Before being utilized by the machine learning model 515, electrical recordings from probes 125, 205, and 305 may be processed in any preferred manner. For example, the electrical recordings may be band-filtered, spikes may be extracted, and a metric may be calculated for each identified spike. In some examples, the metric may be calculated per spike to bypass the subjective nature of spike selection, increase sample size, and reduce the likelihood of overfitting (instead of clustering spikes by cell).

[0049] In some embodiments, systems or devices other than the trained machine learning model 515 may be used to generate or train a machine learning model. For example, such a system may include instructions for generating a machine learning model, training data and ground truth, or instructions for training a machine learning model. The resulting trained machine learning model may then be provided to the trained machine learning model 515.

[0050] Generally, a machine learning model includes a set of variables (e.g., nodes, neurons, filters, etc.) that are tuned to various values ​​(e.g., weighted or biased) through the application of training data. In supervised learning, for example, if ground truth is known about the provided training data, training can proceed by feeding samples of the training data into the model (e.g., randomly) along with variables set at their initial values, based on Gaussian noise, a pre-trained model, etc. This output can be compared to ground truth to determine errors, and then backpropagated through the model to adjust the values ​​of the variables.

[0051] Training can be performed in any preferred manner (e.g., in batches) and may include any preferred training methodology (e.g., probabilistic or non-probabilistic gradient descent, gradient boosting, random forest, etc.). In some embodiments, portions of the training data may be reserved during training or used to validate the trained machine learning model, for example, the output of the trained model may be compared with the ground truth of that portion of the training data to evaluate the accuracy of the trained model. Training of the machine learning model may be configured to allow the machine learning model to learn the relationships between the training data and the ground truth data, and as a result, the trained machine learning model may be configured to determine output illegal activity warnings in response to input user marker data based on the learned relationships.

[0052] Each type of stem cell (e.g., dFO, vFO, and vFO OL) may have a diverse distribution of measured spike characteristics. These spike characteristics may include, for example, amplitude, inter-spike interval, and trough-to-peak time. In other words, the probability of observing a specific value of a particular spike characteristic can vary across stem cell types. These diverse characteristics can be used to train a machine learning model 515, and for the machine learning model 515 to classify a particular type of stem cell.

[0053] Figure 7 depicts an exemplary schematic diagram 700 for training and testing a machine learning model 515 according to one or more embodiments. For example, bootstrapping and aggregation (Bagging) techniques may be used to train an ensemble of machine learning models 515 (e.g., an artificial neural network (ANN) model). In step 702, recordings 705a, 705b, 705c, 705d, 705e, and 705f may be collected when recording sessions (e.g., from probes 125, 205, and 305). These recording sessions may be bootstrapped. Recordings 705a, 705b, and 705c may be from a first class of stem cells 107, and recordings 705d, 705e, and 705f may be from a second class of stem cells 107. Some of the recording sessions from step 702 may be allocated to validate / test the machine learning model 515. For example, as depicted in Figure 7, records 705c and 705d may be allocated for model testing.

[0054] In step 710, spike samples may be synthetically generated to augment the data from step 702. A synthetic spike sample may be generated for each stem cell 107. For example, some spikes from various recording sessions in step 702 for stem cells 107 of the same class may be used to synthetically generate new spikes for that class. For example, as shown in Figure 7, 1000 spikes may be generated for each class. Each class of stem cells may generate an equal amount of synthetic samples. A first portion of the synthetic samples generated in step 710 may be used for training, and a second portion of the synthetic samples generated in step 710 may be used to test / validate the machine learning model.

[0055] The data recorded in step 210 and the data synthesized in step 710 may include values ​​of various properties that can distinguish the classes of stem cells 107 from one another. For example, as discussed above, such properties may include, for example, amplitude, inter-spike interval, and trough-to-peak time.

[0056] In step 712, model 515 can be trained / validated / tested using test data 715 (e.g., data from steps 702 and 710). For example, multiple ANN models (e.g., 10 models M-1 to M-10) as shown in Figure 7 may be trained separately. For example, multiple ANN models may be trained until the error is minimized. The number of models shown in Figure 7 is merely illustrative, and an alternative number of machine learning models 515 may be available.

[0057] Models M-1 to M-10 can be tested by using data allocated for testing and not used during training. Using the test data 715 as input, each of models M-1 to M-10 can predict the classification of the test data 715. Each of models M-1 to M-10 can output an output score 725 in the range of -1 to +1, where -1 indicates a 100% confidence level in the first class, +1 indicates a 100% confidence level in the second class, and 0 indicates pure chance. The output score 725 can be weighted. For example, score 725 can be expressed by equation S w =e c|s| It can be weighted by, where S w S is a weighted score that prefers a high confidence output score of 725, and c is a scaling constant. w This can be averaged to determine the final prediction of 107 stem cells per class, or 730.

[0058] After testing and validation are complete, the machine learning model 515 may operate in a similar manner to that described above for processing the test data 715 to predict a particular type of stem cell 107 in the production phase. For example, multiple machine learning models 515 may each output a prediction score, which may be weighted, and then these weighted scores may be averaged to generate a classification of stem cells 107.

[0059] In some embodiments, in addition to the machine learning model 515 described above (the first type of machine learning model 515), a second type of trained machine learning model 515 may be configured to classify at least one stem cell. The first type of machine learning model 515 may (in some examples) be based solely on spikes measured by the recording site 220. The second type of machine learning model 515 may also be at least partially based on local field potentials (LFPs). Events measured by probes 125, 205, and 305 may be synchronized across various shanks 207, 307 (e.g., by large separation distances between shanks 207, 307). In particular, these synchronized events may be measured within the vFO. These synchronized events may not only modulate the spike rate but may also exhibit LFPs and may have similarities to extracorporeal phenomena (such as sharp-wave ripples (SW-Rs)). SW-Rs may be important for brain functions such as memory consolidation; that is, if inhibited in animal models, animals may exhibit pathological conditions.

[0060] A second type of machine learning model 515 may be trained to use LFP to classify stem cells 107 (e.g., vFO) based on the presence or absence of a state such as AD. The machine learning model 515 may be trained by using data of stem cells 107 (e.g., vFO stem cells) that are known to have a state to be classified. The training data may include LFP and spike data (such as vibration frequency, field amplitude, synchronization force, inter-event interval, phase relationship between spikes and the field, or other properties). For example, spike data may be measured during detected synchronization events. For example, data from large-amplitude slow deflections may be used. These deflections may be analogous to sharp waves from the CA3 region of the hippocampus (driving ripples in the CAI region of the hippocampus).

[0061] By combining analyses of two types of machine learning models 515 (a first type of machine learning model 515 that analyzes spike data and a second type of machine learning model 515 that analyzes LFP), not only the accuracy in classifying stem cells 107 but also the sensitivity in detecting subtle circuit-level changes related to disease models or drug efficacy can be enhanced. Bayesian model averaging (BMA) can be used to combine the results from the two types of machine learning models 515. The first type of machine learning model 515 (based on spike data) can be treated as priori information in the BMA method. Such a method can enable consideration of the results of the second type of machine learning model 515 (using LFP data) while maintaining the predictive power of the first type of machine learning model 515 (using spike data).

[0062] For example, during training, the first type of machine learning model 515 can be trained using spike data as described above. The second type of machine learning model 515 can be trained using LFP data from the same dataset. The accuracy of the first type of machine learning model 515 can be labeled as the prior probability (PA). The first type of machine learning model 515 (spike model) can be used as prior information for a new LFP model. The posterior probability of a combinatorial model can be calculated using the following Bayesian rule: P(A|B) = P(B|A) × P(A) / P(B), where P(A|B) is the posterior probability.

[0063] During the production phase, the final prediction of the model ensemble (one or more models of type 1 515 and one or more models of type 2 515) may be a weighted average between the first type machine learning models 515 and the second type machine learning models 515, calculated by the predictions from the first type machine learning models 515 scaled by prior probabilities and the predictions from the second type machine learning models 515 scaled by posterior probabilities. This BMA method can provide an adaptive framework, where the model based on LFP data is simply weighted more heavily if the second type machine learning models 515 significantly improve prediction accuracy. The weighting of the machine learning model types 515 can be adjusted over time based on a continuous evaluation of the accuracy of the machine learning model types 515.

[0064] The data storage 520 may acquire data from one or more aspects of the organ analysis system 100 (e.g., a stem cell analysis device 105 (e.g., a reader head 110 or camera 130), a stem cell analysis module 510 (e.g., a trained machine learning model 515), etc.). The data storage 520 may transmit data to one or more aspects of the organ analysis system 100 (e.g., a stem cell analysis device 105 (e.g., a reader head 110 or camera 130) or a stem cell analysis module 510 (e.g., a trained machine learning model 515), etc.).

[0065] One or more of the components in Figure 5 may communicate with each other or with other systems (e.g., across the entire network 525). In some embodiments, the network 525 may connect one or more components of the environment 500 via wired connections (e.g., a USB connection between the stem cell analysis system 100 and the stem cell analysis module 510). In some embodiments, the network 525 may connect one or more aspects of the environment 500 via electronic network connections (e.g., wide area network (WAN), local area network (LAN), personal area network (PAN), content delivery network (CDN), etc.). In some embodiments, the electronic network connection includes the Internet, and information and data provided between various systems occur online. "Online" may mean connecting to or accessing source data or information from a remote location away from other devices or networks connected to the Internet. Alternatively, "online" may mean connecting to or accessing an electronic network (wired or wireless) via a mobile communication network or device. The Internet is a worldwide system of computer networks (a network or group of networks in which a party in one computer or other device connected to the network can obtain information from any other computer and communicate with a party in any other computer or device). The most widely used part of the Internet is the World Wide Web (often abbreviated as "WWW" or "Web"). "Website pages," "portals," etc., typically include locations, data storage, etc., that may contain data hosted or operated by a computer system to be accessible online, and configured to allow programs such as web browsers to perform operations (such as sending, receiving, or generating a visual display or interactive interface for manipulating data). In any case, connections within Environment 500 can be networks, wired, any other suitable connection, or any combination thereof.

[0066] Although depicted as separate components in Figure 5, it should be understood that components or parts of components within the environment 500 may, in some embodiments, be integrated with or incorporated into one or more other components. For example, the stem cell analysis module 510 may be integrated into the stem cell analysis device 105. In some embodiments, the operation or manner of one or more of the components discussed above may be distributed among one or more other components. Any preferred arrangement or integration of various systems and devices of the environment 500 may be used.

[0067] Figure 6 illustrates an exemplary method 600 for classifying stem cells according to one or more embodiments. In step 605, spike data may be received (e.g., via a stem cell analysis module 510). For example, spike data may be measured (e.g., via a reader head 110) and transmitted (e.g., to a stem cell analysis module 510).

[0068] In some embodiments, a first trained machine learning model may be trained by using the training data via method 620. In step 625, the first training data may be generated as discussed above. The first training data may be spike data generated from one or more types of stem cells (e.g., dFOs, vFOs, vFO_OLs). As discussed herein, the first training data may be measured and / or synthetic spike data of various types of stem cells. The measured spike data may be processed (e.g., via bandpass filtering, spike extraction, per-spike computing metrics, etc.). In step 630, the first training data may be received (e.g., via stem cell analysis module 510).

[0069] In step 635, the first machine learning model may be trained by using the first training data to generate a first trained machine learning model (for example, the first trained machine learning model 515, which may be the spike machine learning model 515). The first trained machine learning model may be trained to classify stem cells based on the spike data.

[0070] In the production environment, in step 610, the spike data may be analyzed via a first trained machine learning model (e.g., via the first trained machine learning model 515). In some embodiments, the first trained machine learning model may be trained to classify (e.g., label) at least one stem cell based on the spike data (e.g., the spike machine learning model 515) as described above. In step 615, a first output (e.g., stem cell classification) may be generated via the first trained machine learning model (e.g., via the first trained machine learning model 515) based on the spike data.

[0071] In step 640, local field potential ("LFP") data may be received (e.g., via the stem cell analysis module 510). For example, LFP data may be measured (e.g., via the reader head 110) and transmitted (e.g., to the stem cell analysis module 510).

[0072] In some embodiments, a second trained machine learning model may be trained by using training data via method 660. In step 665, the second training data may be generated as discussed above. As discussed herein, the second training data may be any of the data described above, including LFP data. In step 670, the second training data may be received (e.g., via stem cell analysis module 510). In step 675, the second machine learning model may be trained by using the second training data to generate a second trained machine learning model (e.g., second trained machine learning model 515). The second trained machine learning model may be trained to classify stem cells based on the LFP data (and LFP machine learning model 515).

[0073] In the production environment, in step 645, the LFP data may be analyzed via a second trained machine learning model (e.g., via the second trained machine learning model 515). In some embodiments, the first trained machine learning model may have been trained to classify (e.g., label) at least one stem cell based on the LFP data. In step 655, a second output may be generated via a second trained machine learning model (e.g., via the second trained machine learning model 515) based on the LFP data.

[0074] In step 680, the first and second outputs may be received (e.g., via the stem cell analysis module 510). In step 685, the first and second outputs may be analyzed (e.g., weighted and synthesized as described above by using BMA).

[0075] Methods 600, 620, and 660 can produce highly optimized hybrid classification models. This approach may enable the model to detect subtle changes in neural network activity (particularly in response to drug candidates or in identifying stem cells with specific pathologies).

[0076] Traditional single-unit electrophysiology provides the necessary baseline understanding but lacks the throughput and sensitivity to detect subtle patterns critically important for drug screening. The techniques described herein convert high-dimensional data into physiologically relevant features derived from spikes or local field potentials.

[0077] The high-throughput capabilities of the technologies described herein (combined with full system integration and automation) represent a significant advancement beyond current technologies. The processes described herein, from spike detection and feature extraction to classification, result in a substantial reduction in the time and resources required to screen large drug libraries. Unlike conventional systems that require extensive preparation and manual intervention, the technologies described herein streamline data acquisition to ensure consistently high-quality results. The technologies described herein enhance efficiency and reliability, minimizing human error and variability, which is critically important for accelerating the drug development process.

[0078] One or more implementations disclosed herein involve using a machine learning model (e.g., a pre-trained machine learning model 515), being implemented by using such a model, or being used to train such a model. A given machine learning model may be trained using the training flowchart 800 in Figure 8. The training data 812 may include one or more stage inputs 814 and known results 818 related to the machine learning model being trained. The stage inputs 814 are from any applicable source, including text, visual representations, data, values, comparison results, and stage outputs (e.g., one or more outputs from one or more steps in Figure 6-7). The known results 818 are included for machine learning models generated based on supervised or semi-supervised training. Unsupervised machine learning models are not trained using the known results 818. The known results 818 include known or desired outputs for future inputs similar to or within the same category as stage inputs 814 that do not have corresponding known outputs.

[0079] The training data 812 and the training algorithm 820 (one or more modules, for example, implemented by using a machine learning model or used to train a machine learning model) are provided to a training component 830, which applies the training data 812 to the training algorithm 820 to generate a machine learning model. In one implementation, the training component 830 is provided with a comparison result 816, which compares the previous output of the corresponding machine learning model to retrain the machine learning model by applying the previous results. The comparison result 816 is used by the training component 830 to update the corresponding machine learning model. The training algorithm 820 utilizes machine learning networks or models, including, but not limited to, deep learning networks such as transformers, deep neural networks (DNNs), conventional neural networks (CNs), fully convolutional networks (FCNs), recurrent neural networks (RCNs), probabilistic models such as Bayesian networks and graphic models, classifiers such as the K-nearest neighbor algorithm, or discriminative models such as decision forests and maximum margin methods, and models specifically discussed herein.

[0080] The machine learning models used herein are trained or used by adjusting one or more weights or one or more layers of the machine learning model. For example, during training, a given weight is adjusted (e.g., increased, decreased, or removed) based on the training data or input data. Similarly, layers are updated, added, or removed based on the training data and / or input data. The resulting output is adjusted based on the adjusted weights or layers.

[0081] Figure 9 depicts a simplified functional block diagram of a computer 900 that may be configured as a device for performing the methods disclosed herein in accordance with exemplary embodiments of the present disclosure. For example, computer 900 may be configured as a system according to exemplary embodiments of the present disclosure. In various embodiments, any of the systems herein may be computer 00 including, for example, a data communication interface 920 for packet data communication. Computer 900 may also include a central processing unit (CPU) 902 in the form of one or more processors for executing program instructions. Computer 900 may include an internal communication bus 908 and a storage unit 906 (such as ROM, HDD, SSD) capable of storing data on a computer-readable medium 922, although computer 900 may receive programming and data via network communication. Computer 900 may also have a memory 904 (such as RAM) for storing instructions 924 for performing the techniques presented herein, although the instructions 924 may be temporarily or permanently stored in other modules of computer 900 (e.g., a processor 902 or a computer-readable medium 922). Computer 900 may also include input / output ports 912 or a display 910 for connecting to input / output devices such as a keyboard, mouse, touchscreen, monitor, display, etc. Various system functions may be implemented in a distributed manner across many similar platforms to distribute the processing load. Alternatively, the system may be implemented by appropriate programming of a single computer hardware platform.

[0082] The programmatic aspects of this technology can typically be considered as a “product” or “manufactured item” in the form of executable code or associated data acquired on or embodied within a type of machine-readable medium. “Storage” type media include any or all of the tangible memory of a computer, processor, etc., or its associated modules (various semiconductor memories, tape drives, disk drives, etc., that can provide non-temporary storage at any given time for software programming). All or part of the software may be transmitted as appropriate over the Internet or various other communication networks. Such communications can, for example, enable the loading of software from one computer or processor to another (e.g., from a management server or host computer on a mobile communication network to a server's computer platform, or from a server to a mobile device). Therefore, other types of media that may contain software elements include optical, electrical, and electromagnetic waves used over wired and optical terrestrial communication network networks and over various wireless links, across physical interfaces between local devices. Physical elements carrying such waves, such as wired or wireless links and optical links, can also be considered media for carrying software. Unless limited to non-temporary, tangible “storage” media as used herein, terms such as computer or machine “readable media” refer to any medium involved in providing instructions to a processor for execution.

[0083] In the above description of exemplary embodiments of the Invention, it should be understood that various features of the Invention are sometimes grouped together within a single embodiment, figure, or description for the purpose of streamlining the Disclosure and to aid in understanding one or more of the various inventive aspects. However, the method of the Disclosure should not be construed to reflect an intention that the claimed invention requires more features than expressly asserted in each claim. Rather, as reflected in the following claims, the embodiments of the Invention reside within fewer features than all the features of a single, previously disclosed embodiment combined. Accordingly, the claims following the embodiments for carrying out the Invention are expressly incorporated herein into the embodiments for carrying out the Invention, and each claim is self-contained as a distinct embodiment of the Invention.

[0084] Furthermore, while some embodiments described herein include some features included in other embodiments but not others, combinations of features from various embodiments are intended to fall within the scope of the invention and form various embodiments, as will be understood by those skilled in the art. For example, in the following claims, any of the claimed embodiments may be used in any combination.

[0085] Accordingly, although several embodiments have been described, those skilled in the art will recognize that other and further modifications can be made without departing from the spirit of the invention and that all such changes and modifications that fall within the scope of the invention are intended to be claimed. For example, functionality may be added or removed from block diagrams, and some operations may be interchangeable between functional blocks. Some steps may be added to or removed from the methods described within the scope of the invention. The subject matter disclosed above should be considered illustrative but not restrictive, and the appended claims are intended to cover all such modifications, enhancements and other implementations that fall within the true spirit and scope of this disclosure. Accordingly, to the maximum extent permitted by law, the scope of this disclosure shall be determined by the broadest and most acceptable interpretation of the following claims and their equivalents, and should not be limited or restricted by the foregoing detailed description. Although various implementations of this disclosure have been described, it will be apparent to those skilled in the art that many more implementations are possible within the scope of this disclosure. Accordingly, this disclosure should not be restricted except in light of the appended claims and their equivalents.

Claims

1. A device for stem cell analysis, A leader head having multiple probes, each of which has a shank having multiple recording sites or multiple stimulation sites, A plate having multiple wells, wherein each well of the multiple wells is configured to receive at least one stem cell, comprising: A device wherein the reader head is configured to automatically probe the at least one stem cell and to measure the properties of the at least one stem cell, or to stimulate an electrically active cell using the plurality of recording sites or the plurality of stimulation sites of the plurality of probes.

2. The device according to claim 1, further comprising a robotic arm coupled to the leader head, the robotic arm configured to automatically position the leader head relative to the wells of the plurality of wells.

3. The device according to claim 1, further comprising an electric stage configured to automatically position the plate.

4. The device according to claim 1, wherein the shank has a sharp end.

5. The device according to claim 1, wherein each of the plurality of wells comprises at least one stem cell trap, and the at least one stem cell trap has a substantially "V" shape for immobilizing the stem cells of the at least one stem cell received in the stem cell trap.

6. The device according to claim 1, further comprising a camera.

7. The device according to claim 6, wherein the plate comprises at least one reference mark, the camera is configured to interpret the at least one reference mark, and the reader head is automatically aligned with the wells of the plurality of wells based on the interpretation of the at least one reference mark.

8. The device according to claim 1, wherein the plurality of recording sites or the plurality of stimulation sites are lithographically patterned on the shank.

9. The device according to claim 1, wherein the plurality of recording sites or the plurality of stimulation sites are in electrical communication with a control and analysis unit, and the control and analysis unit is configured to automatically position the plurality of probes based on data measured by the plurality of recording sites.

10. The device according to claim 9, wherein the control and analysis unit is configured to stop the advancement of the plurality of probes based on the data measured by the plurality of recording sites.

11. The device according to claim 9, wherein the control and analysis unit is configured to automatically select a depth for positioning the plurality of probes within the stem cells of at least one stem cell based on the data measured by the plurality of recording sites.

12. A device for stem cell analysis, A plurality of probes, each of which comprises a shank having a plurality of recording sites or a plurality of stimulation sites, Camera and, A plate having multiple wells, each well of which is configured to receive at least one stem cell, and the plate having at least one reference label, comprising: The camera is configured to interpret the at least one reference mark, and the plurality of probes are aligned with the wells of the plurality of wells based on the interpretation of the at least one reference mark in the device.

13. The device according to claim 12, further comprising a robotic arm coupled to a plurality of probes, the robotic arm configured to automatically position the plurality of probes relative to the plurality of wells.

14. The device according to claim 12, wherein each of the plurality of wells comprises at least one stem cell trap, and the at least one stem cell trap has a substantially "V" shape for immobilizing the stem cell of the at least one stem cell received in the stem cell trap.

15. The device according to claim 12, wherein the plurality of recording sites or the plurality of stimulation sites are in electrical communication with a control and analysis unit, and the control and analysis unit is configured to automatically position the plurality of probes based on data measured by the plurality of recording sites.

16. The device according to claim 15, wherein the control and analysis unit is configured to stop the advancement of the plurality of probes based on the data measured by the plurality of recording sites.

17. The device according to claim 15, wherein the control and analysis unit is configured to automatically select a depth for positioning the plurality of probes within the stem cells of at least one stem cell based on the data measured by the plurality of recording sites.

18. A system for stem cell analysis, It is a stem cell probing device, A probe comprising at least one shank having multiple recording sites, A stem cell probing device comprising: a plate having multiple wells, each well of which is configured to receive at least one stem cell; A system comprising: a control and analysis unit configured to receive data from the plurality of recording sites and to automatically position the at least one probe based on the data.

19. The system according to claim 18, wherein the control and analysis unit is configured to stop the advance of at least one probe based on the data from the plurality of recording sites.

20. The system according to claim 18, wherein each of the plurality of wells comprises at least one stem cell trap, the at least one stem cell trap has a substantially "V" shape for immobilizing the stem cell of the at least one stem cell received in the stem cell trap, and each of the wells comprises a V shape.

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