An instrumented microphysiological apparatus with integrated sensors for monitoring cellular metabolism

Abstract

An exemplary apparatus and method are disclosed that can provide a fully instrumented microphysiological system having a microchamber for the culture and monitoring of cells and biological samples. The exemplary apparatus is supported by integrated bioelectronic circuits and a graphical user interface for easy user configuration and monitoring. The system has integrated microfluidics to maintain its microphysiological environment within each well. The exemplary apparatus incorporates metabolic and environment sensors inside each well, allowing multiple wells to perform concurrent measurements in real-time.

Description

AN INSTRUMENTED MICROPHYSIOLOGICAL APPARATUS WITH INTEGRATED SENSORS FOR MONITORING CELLULAR METABOLISMStatement Regarding Federally Sponsored Research or Development

[0001] This invention was made with government support under Grant No. 0841259 and Grant No.1450032 awarded by the National Science Foundation, and Grant No. R21 HD097601 awarded by the National Institutes of Health. The government has certain rights in the invention.Related Applications

[0002] This PCT application claims priority to, and the benefit of, U.S. Provisional Patent Application No. 63 / 678,938, filed August 02, 2024, entitled “IMSIS: AN INSTRUMENTED MICROPHYSIOLOGICAL SYSTEM WITH INTEGRATED SENSORS FOR MONITORING CELLULAR METABOLIC ACTIVITIES,” and U.S. Provisional Patent Application No. 63 / 728,902, filed December 06, 2024, entitled “IMSIS: AN INSTRUMENTED MICROPHYSIOLOGICAL SYSTEM WITH INTEGRATED SENSORS FOR MONITORING CELLULAR METABOLIC ACTIVITIES,” both are incorporated by reference herein in its entirety.Background

[0003] Well plates are widely used in biological experiments, particularly in pharmaceutical sciences and cell biology research, due to their versatility in supporting a variety of fluorescent markers for high-throughput monitoring of cellular activities. However, using fluorescent markers in traditional well plates has challenges; for example, they can be potentially toxic to cells and, thus, may perturb their biological functions. It can also be difficult to monitor multiple analytes concurrently and in real-time inside each well.

[0004] There is a benefit to improving the monitoring of cells.Summary

[0005] An exemplary platform system, microchamber apparatus, and methods thereof are disclosed for a modularized, fully instrumented microchamber device for the culturing / preserving and continuous monitoring of cells and biological samples. The microchamber device (i) is fully self-contained, having ports to nutrient reservoirs that are automatically brought into the microchamber via microfluidic channels, and (ii) includes sensors and front-end electronics for the sensing circuits for measuring metabolic analytes and other analytes in real-time as a proxy for biomarkers to monitor cellular activity, including metabolic rates, on an ongoing basis. The controller of the exemplary apparatus can record sensor measurements to guide, with minimalhuman oversight or interaction, the exemplary system as a fully or semi-fully autonomous instrument and employ the sensor measurements to keep the sample in the exemplary apparatus viable or to culture the sample. The microchamber device, as a modular component, is configured to attachably and reattachably connect to a standalone breakout processing device / platform, e.g., as a complete standalone measurement system. The breakout processing device may include a self- contained graphical user interface, or interface, for user configuration and monitoring, as well as connect to a computing device for further computing and analysis. The exemplary device can be defined for multiple uses, e.g., to be used and then returned to the manufacturer for sterilization and reuse without degradation.

[0006] In having the analog front end close to the chamber (e.g., on a breakout board attached to the microchamber or physically close to the microchamber), the microchamber module comprising the sensors and the breakout board may be made disposable / reusable. Researchers and users employ a specific sensor module with a required subset of sensors to attach the microchamber module to the base station to do measurements. Once the measurements are done, the sensor module can be discarded or reused after sterilization and refurbishing. The cost for each sensor module can be minimal at a large production scale to make it disposable and financially feasible. The modular microchamber device can improve user experience in such a way that it is a completely plug-and-play system without having to deal with unnecessary details of system components.

[0007] In addition, the microchamber may be configured to provide an optimized microphysiological environment and conditions for cell growth by delivering nutrients in an autonomous manner and at an optimized laminar flow. The microchamber is formed over a plate having integrated electrodes for optimal measurement (e.g., impedance measurement and electrochemical measurements) with low parasitic loss.

[0008] The architecture is scalable to achieve a high level of throughput, and the miniaturized design, in some embodiments, ensures portability, suitable for small offices and field applications. A study was conducted that successfully developed and evaluated the monitoring in real-time of mitochondrial functions of live embryos in O2 consumption, H2O2 release as an indication of reactive oxygen species (ROS) production, and extracellular acidity rate (ECAR) before and after the introduction of external substrates. Gas-permeable membranes are typically used for Clark sensor construction for O2 sensors, which tend to be bulky. The exemplary system may employ a polymer (e.g., Nafion) to act as the gas-permeable membrane on the sensor surface to achieve miniaturization. The microchamber device can be configured with on-board sensors and DAQ circuitries to perform continuous measurement of the sample (e.g., tissue or cells). Theonboard sensors can include amperometric sensors, potentiometric sensors, enzymic sensors, impedance sensors, capacitance sensors, resistive sensors, temperature sensors, electrochemical and optical sensors, ultrasonic sensors, magnetic sensors, potentiometric sensors, and various other biosensors described or referenced herein.

[0009] The exemplary system can support microfluidics to maintain live cells / tissue viability for an extended period of time. The modularized, fully instrumented device can be employed to culture cells or to preserve tissue as an ex vivo, environmentally controlled chamber to preserve or keep cells, tissue, or other organic material alive or preserved outside of a living organism. The chamber can maintain the cell or tissue's viability and structural integrity, preventing decay to allow for further study or use.

[0010] The exemplary system (e.g., platform / instrument) can provide the electrical interface with the supporting back-end electronics in a user-friendly, secure, and quick-connect manner in having the exemplary microchamber apparatus being attachable into a zero-force insertion socket for each well of the instrument. The system and mechanical design of the interface can eliminate the complicated mechanical loading system, also for improved system operational reliability, usability, and cost. The exemplary system can be implemented with sensors that mitigate or eliminate sensor- to-sensor interference. The sensor module may include the sensor chip, the break-out board, microfluidics, and module clamps / housing that can be pre- assembled, sterilized, and stored individually for safety, reliability, and usability. The exemplary system can employ a self-contained user-interface controller that can control every aspect of the operation with a local input / output interface for the user without the need for an external computing device.

[0011] To improve the usability of the exemplary system, the controller of the exemplary system may be equipped with a software agent configured to monitor the measurements from the microchamber. Continuous and ongoing measurements of biological samples can be challenging. The requirements for the performance of the measurements (precision and accuracy) may be high for the exemplary system, as a high-quality scientific instrument, to provide sufficient fidelity in the sensing or electrical characteristics, or changes in such characteristics, of the biological samples. In addition, the high-quality measurements are also subject to the evolving conditions in the microchamber, e.g., (i) due to nutrients being introduced into the measurement environment that changes the measurement characteristics in the chamber, (i) the sample as a live specimen changing conditions in the microchamber due to natural biological processes, as well as (iii) sensor fouling due to the on-going exposure to the electrodes to biological samples and contaminants. In some embodiments, the software agent, implemented locally or in a networked / cloud infrastructure, may employ a trained Al model or algorithm to determine when measurement conditions are stable orunstable (e.g., anomaly detection) to guide (e.g., the user) on the data acquisition or to trigger data acquisitions of the samples. In addition, the software Al agent may also guide users to provide necessary compensations / corrections to the measured results due to sensor fouling or other environmental factors.

[0012] The software Al agent, trained using historical data (e.g., OCR and / or ECAR signals) collected of the system can provide objective and consistent framework for identifying features during experiments where the current phase of the experiment is likely concluded and whether users should move on to the next phase of the experiment, thereby reducing variability of experiment results and improving efficiency. A typical workflow for most instrumentation involves users making decisions about when to proceed with the next step based on visual inspection of realtime data or pre-defined, potentially rigid, timelines. Such workflows are subject to the user’s level of experience, which can introduce variability due to individual interpretation and the user’s familiarity with the instrument platform.

[0013] To improve the manufacturing and robustness of the exemplary system, the system is configured with several mechanical and system design features. In some embodiments, to facilitate the alignment and proper assembly of electronic and sensor components, the sensor elements (e.g., sensor plate) may be mechanically configured to independently and separately mount to internal components, allowing validation of the alignment to be performed prior to further assembly. The sensor elements (e.g., on a glass substrate) having multiple sensor lines have to be ensured to make contact with electrical pins of electronics (e.g., on a printed circuit board) integrated in the exemplary microchamber device, and the sensing elements themselves have to be precisely positioned in a pre-defined target location in the microchamber. Alignment of internal gaskets with the chamber components also ensures the device maintains the proper location of gaskets relative to the associated microchamber components during manufacturing / assembly. The biological specimens to be employed in the modularized, fully instrumented device are likely labor- and timeintensive to prepare, in addition to being potentially very unique and expensive to acquire (if not irreplaceable). Instrument failure or error during usage or specimen placement would be detrimental to an experiment and potentially irreversible.

[0014] The exemplary apparatus may be configured to have the microchamber operate as a pod that is encapsulated by a housing that houses the microchamber assembly and associated electronics. The encapsulation provides an additional degree of freedom to the assembly of the microchamber that allows internal components to be properly fitted or sealed to each other as well as to the internal components of the pod under compression or tension, e.g., to ensure proper sealing over an extended period of time (e.g., during storage and usage). In addition, the external housingmay have mechanical features to allow for straightforward assembly, e.g., snapped together, of the housing in a quick action while putting the internal components under compression or tension. The housing may then be reinforced with additional attachment fixtures (e.g., screws).

[0015] To improve the operation and robustness of the exemplary system, e.g., where the system may be stored or transported in unknown conditions, the exemplary system is configured with an electronic function for pins or electrode connectivity testing as part of the system calibration process, which can be initiated during the manufacturing of the exemplary microchamber apparatus or prior to or during each usage. The calibration process identifies component connection errors as well as parasitic issues at the system level to indicate the hardware and operational readiness of the system for running experiments.

[0016] To improve usability, the exemplary microchamber apparatus may be configured with viewing ports to allow specimens to be observed during their placement or seed into the microchamber, and also during their retrieval from the microchamber. The viewing may be made compatible with microscopes to allow for precision placement and validation / observation of the specimen prior to an experiment, and easy retrieval after an experiment.

[0017] In an aspect, an apparatus is disclosed comprising: a sensor plate with electrodes formed on, or proximal to, a surface thereof, wherein the sensor plate has an area that defines a first region with the electrodes and a second region with terminals for the electrodes; a chamber housing member configured to sealably couple to the sensor plate to define an internal volume for a cell culture or tissue ex- vivo chamber, the housing having a recess or channel formed therein and around the cell culture or tissue ex-vivo chamber to receive a sealing element to form the seal with the sensor plate when coupled thereto, wherein the chamber housing member includes a port assembly extending in a direction away from the sensor plate, the port assembly being in fluid connection with the internal volume; a printed circuit board configured to couple to the chamber housing member, the printed circuit board including a surface having conductive pins extending therefrom, the conductive pins having positions in correspondence to the second region of the sensor plate when the printed circuit board is operatively coupled to the sensor plate to electrically couple the terminals; and a clamp housing assembly configured to fixably couple to the chamber housing member to encapsulate the chamber housing member; and a retaining member (e.g., plastic backing element) fixably coupled to the sensor plate, to maintain an alignment between the electrodes and the conductive pins when the sensor plate is being pushed towards the chamber housing member.

[0018] In some embodiments, the clamp housing assembly includes (i) a first clamp housing member that mounts the chamber housing member and (ii) a second clamp housing member havingan attaching member (e.g., hook, clip-on fin) to attach the first clamp housing member via an insertion operation.

[0019] In some embodiments, the apparatus described herein further comprises: a compression member (e.g., coil spring), disposed in the clamp housing assembly, configured to urge the sensor plate towards the chamber housing member, to maintain compression of the sealing element between the sensor plate and the cell culture or tissue ex- vivo chamber (e.g., to prevent leakage of cells / samples from the cell culture or tissue ex-vivo chamber).

[0020] In some embodiments, the retaining member is formed of a transparent material.

[0021] In some embodiments, the apparatus described herein further comprises: one or more foam layers operatively coupled to the retaining member, wherein the one or more foam layers are disposed in a contact space between the compression member and the sensor plate, such that the compression member pushes, via the one or more foam layers, the sensor plate without physically touching the sensor plate.

[0022] In some embodiments, the attaching member is a hook or a clip-on fin.

[0023] In some embodiments, the apparatus described herein further comprises: a viewing port, formed on a surface of the clamp housing assembly, configured to optically expose the cell culture or tissue ex-vivo chamber to the outside of the apparatus, wherein the viewing port is aligned with one or more viewing holes on the one or more foam layers, the compression member, and clamps.

[0024] In some embodiments, the apparatus described herein further comprises: a microchamber lid configured to seal the port assembly from the fluid connection with the internal volume when being rotated.

[0025] In some embodiments, the microchamber lid has a set of stirring tips configured to stir the internal volume when the microchamber lid is being rotated.

[0026] In some embodiments, the electrodes of the sensor plate form (i) potentiometric sensors (e.g., to measure potential of hydrogen (pH) and extracellular acidity rate (ECAR) in the cell culture or tissue ex-vivo chamber) and (ii) amperometric sensors (e.g., to measure oxygen consumption rate (OCR), and reactive oxygen species (ORS) or other species that are electrochemically active, of living cells and / or biological samples in the cell culture or tissue ex- vivo chamber).

[0027] In some embodiments, one or more electrodes, in the electrodes of the sensor plate, are configured to be transparent potentiometric sensors, and wherein the one or more electrodes are located at the center / middle of the sensor plate, or proximal thereto.

[0028] In some embodiments, the electrodes of the sensor plate include at least one of an amperometric sensor, potentiometric sensor, enzymic sensor, impedance sensor, capacitance sensor, resistive sensor, temperature sensor, electrochemical and optical sensor, ultrasonic sensor, magnetic sensor, and potentiometric sensor.

[0029] In another aspect, a method is disclosed comprising: providing an apparatus comprising: a sensor plate with electrodes formed on, or proximal to, a surface thereof, wherein the sensor plate has an area that defines a first region with the electrodes and a second region with terminals for the electrodes; a chamber housing member configured to sealably couple to the sensor plate to define an internal volume for a cell culture or tissue ex-vivo chamber, the housing having a recess or channel formed therein and around the cell culture or tissue ex-vivo chamber to receive a sealing element to form the seal with the sensor plate when coupled thereto, wherein the chamber housing member includes a port assembly extending in a direction away from the sensor plate, the port assembly being in fluid connection with the internal volume; and a printed circuit board configured to couple to the chamber housing member, the printed circuit board including a surface having conductive pins extending therefrom, the conductive pins having positions in correspondence to the second region of the sensor plate when the printed circuit board is operatively coupled to the sensor plate to electrically couple the terminals; and a clamp housing assembly configured to fixably couple to the chamber housing member to encapsulate the chamber housing member; and mounting the sensor plate to the chamber housing member via an alignment member to define the internal volume for the cell culture or tissue ex-vivo chamber to form an internal assembly, wherein the mounting maintain an alignment between the electrodes and the conductive pins; and assembling the internal assembly via a clamp housing assembly configured to fixably couple to the chamber housing member to encapsulate the chamber housing member.

[0030] In some embodiments, the clamp housing assembly includes (i) a first clamp housing member that mounts the chamber housing member and (ii) a second clamp housing member having an attaching member (e.g., hook, clip-on fin) to attach the first clamp housing member via an insertion operation.

[0031] In some embodiments, the method described herein further comprises: inspecting alignment between the electrodes and the conductive pins, and the location of gaskets relative to the associated microchamber components, before the internal assembly is assembled in the clamp housing assembly.

[0032] In some embodiments, the method described herein further comprises: placing one or more foam layers in a contact space between the compression member and the sensor plate so thecompression member pushes, via the one or more foam layers, the sensor plate without physically touching the sensor plate.

[0033] In some embodiments, the attaching member is a hook or a clip-on fin.

[0034] In some embodiments, the method described herein further comprises: aligning the clamp housing assembly, the sensor plate, and the chamber housing member to form a viewing port, the viewing port being configured to optically expose the cell culture or tissue ex vivo chamber to the outside of the clamp housing assembly.

[0035] In some embodiments, the apparatus includes a microchamber lid.

[0036] In some embodiments, the microchamber lid has a set of stirring tips configured to stir the internal volume when the microchamber lid is being rotated.

[0037] In another aspect, a system is disclosed comprising: an instrument comprising a main control board configured to operatively connect with an apparatus of any one of the abovediscussed apparatus claims.

[0038] In some embodiments, the instrument further includes a plurality of intermediate data acquisition board, each intermediate data acquisition board configured to couple to a respective apparatus and to the main control board, the intermediate data acquisition board having power regulation and instrumentation circuit for each respective apparatus (e.g., to minimize common mode noise and provide enhanced SNR).

[0039] In some embodiments, the system includes a processor; and a memory having instructions stored thereon, wherein execution of the instructions causes the processor to: receive measurement data; determine, via a trained Al model executing (locally or globally), an estimate of measurement stability; and output the estimate, wherein the estimate is used to generate a notification to a user that a measurement and conditions in the microchamber are stable.

[0040] In some embodiments, the estimate includes a stability indicator of baseline measurements.

[0041] In some embodiments, the estimate includes a stability indicator of basal measurements.

[0042] In some embodiments, the estimate includes a stability indicator of therapeutic measurements.

[0043] In an aspect, a method is disclosed comprising: receiving measurement data (e.g., from a system or apparatus of any one of above claims); determining, via a trained Al model executing (locally or globally), an estimate of measurement stability; and outputting the estimate, wherein the estimate is used to generate a notification to a user that a measurement and conditions in the microchamber are stable.

[0044] In some embodiments, , the trained Al model was trained, in part using historical measurement data acquired from the system or apparatus.

[0045] In some embodiments, the output of the trained Al model is used to indicate stability for baseline measurement when no specimen is provided in the microchamber, basal measurement when a cell / tissue specimen is provided in the microchamber, and therapeutic measurement when a stimulus or condition under study is provided or invoked in the microchamber.Brief Description of the Drawings

[0046] Figs. 1A - IE each shows a modularized, fully instrumented microchamber device having a well plate for the culturing and monitoring of cells and biological samples, in accordance with an illustrative embodiment.

[0047] Figs. 2A and 2B show example components and system integration for the microchamber device of Figs. 1 A and 1 B in accordance with an illustrative embodiment.

[0048] Fig. 3 shows an example training process for an artificial intelligence (Al) model, and the runtime operation of the Al model in the exemplary apparatus, in accordance with an illustrative embodiment.

[0049] Figs. 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B, 7C, and 7D show example microchamber designs for the microchamber device of Figs. 1 A and IB in accordance with an illustrative embodiment. Specifically, Figs. 4A - 4B show an example design of a microchamber having one or more Euer lock seals as a first embodiment. Figs. 5A - 5B show an example design of a microchamber having one or more Luer lock seals and microfluidic support (e.g., inlet port, outlet port) as a second embodiment. Figs. 6A - 6B show an example design of a microchamber having cell culture or tissue ex- vivo chambers aligned along a vertical axis of the microchamber as a third embodiment. Figs. 7A - 7B show an example design of a microchamber having separate integrated channels configured to prevent cross-talk between enzymatic sensors as a fourth embodiment. Figs. 7C - 7D show an example design of a cell culture or tissue ex- vivo chamber having a port (e.g., inlet, outlet) and integrated channels to support microfluidics as a fifth embodiment.

[0050] Figs. 8A - 8G show an example integrated sensor plate-microchamber (i.e., microsensor chip-microchamber) module, known as a sensor pod, of the exemplary apparatus, and associated components of the module, including two clamps that enclose a microsensor chip, a microchamber, a breakout printer circuit board (PCB), pogo pins, connectors, an o-ring, a coil spring, a microchamber lid, and foam layers therebetween.

[0051] Fig. 9A shows an example circuit for the controller circuit board, in accordance with an illustrative embodiment.

[0052] Figs. 9B - 9C each shows an example biosensor chip / sensor plate, in accordance with an illustrative embodiment.

[0053] Figs. 9D - 9E each shows a pod embodiment of the exemplary system, in accordance with an illustrative embodiment.

[0054] Figs. 10A - 10D show an example graphical user interface of the exemplary apparatus, in accordance with an illustrative embodiment.

[0055] Figs. 11 A - 11C show measurement results, by the exemplary apparatus, for tissue responses, using various cancer drugs (e.g., Stauroporine, CCNU, 2deoxydglucose).

[0056] Figs. 12A - 12B show the training results using a multi -objective negative loglikelihood - continuous ranked probability score (NLL-CRPS) metric to measure the performance of the trained AI / ML model of the fabricated apparatus.Detailed Description

[0057] Some references, which may include various patents, patent applications, and publications, are cited in a reference list and discussed in the disclosure provided herein. The citation and / or discussion of such references is provided merely to clarify the description of the disclosed technology and is not an admission that any such reference is “prior art” to any aspects of the disclosed technology described herein. In terms of notation, “[n]” corresponds to the nth reference in the list. For example, [1] refers to the first reference in the list. All references cited and discussed in this specification are incorporated herein by reference in their entirety and to the same extent as if each reference was individually incorporated by reference.

[0058] Example Apparatus

[0059] Figs. 1A - ID each show a modularized, fully instrumented microchamber device 100 that has a well plate for the culturing and monitoring of cells and biological samples in accordance with an illustrative embodiment. In the example shown in Figs. 1A - ID, the exemplary apparatus 100 includes a sensor plate 102, a microchamber 108, a printed circuit board 116, a modular / re-attachable microchamber housing 109 (shown as 109a, 109b, 109c), a controller circuit board 126, and a system plant 140. The sensor plate 102 (e.g., multisensor chip) has an area that defines a first region with the electrodes 104 and a second region with terminals 106 for the electrodes. The electrodes 104 are formed on, or proximal to, the surface of the sensor plate 102. Fig. IB shows an enhanced instrumented printed circuit board 116 with, e.g., self-calibration and self-test circuitry 119. Figs. 1C and ID show the fully instrumented microchamber device 100 operable with an Al software agent controller environment. Fig. IE shows the driver 122 (shown asthe driving circuitry) of Fig. 1 A implemented as part of the controller circuit board 126. In having the driver 122 implemented separately and external to the

[0060] The microchamber 108 is configured to sealably couple to the sensor plate 102 to define an internal volume for a cell culture or tissue ex vivo chamber 1 10. The microchamber 108 has a recess or channel formed therein and around the cell culture or tissue ex vivo chamber 110 to receive a sealing element to form the seal with the sensor plate 102 when coupled thereto. The microchamber 108 also includes a port assembly 134 extending in a direction away from the sensor plate and being in fluid connections (e.g., 136a, 136b) with the internal volume of the cell culture or tissue ex vivo chamber 110. The microchamber has a set of channels (e.g., for one or more inlets and outlets) that couple ports of the port assembly to the internal volume of the cell culture or tissue ex vivo chamber 110.

[0061] The electrodes 104 of the sensor plate 102 form integrated sensors to detect and measure metabolic activities of living cells from the cell culture or tissue ex vivo chamber 110, then generate sensing data 112 based on the measurements collected from the cell culture or tissue ex vivo chamber 110. The electrode 104 then transmits, via the terminals 106, the sensing data as an analog signal 114 to the conductive pins 118 of the printed circuit board 116.

[0062] The printed circuit board 116 (i.e., self-calibration circuitry), coupled with the sensor plate 102 and the microchamber 108, comprises a surface having conductive pins 118 extending therefrom. The conductive pins 118 have positions in correspondence to the second region of the sensor plate 102 when the printed circuit board 116 is operatively coupled to the sensor plate 102 to electrically couple the terminals 106.

[0063] The conductive pins 118 receives, via the terminals 106, the analog signal 114 from the electrodes 104 and transmits the analog signal 114 to a driver 122 via a serial transmission 120a (also referred to as 120b - 120d). The driver 122, coupled with the conductive pins 118, drives the printed circuit board to transmit, via the connector 124, the analog signal 114 to the controller circuit board 126 in a serial transmission 120c.

[0064] The physical assembly of the microchamber device 100 may be configured to improve the manufacturability and reduce / avoid failure of the microchamber device 100 during use. High-quality equipment and validation processes ensure that unique specimens that are labor- and time-intensive are not lost or have to be redone due to equipment failure. In some embodiments, the sensor plate 102 may be fixably attached to structures in the microchamber device 100 via a retaining member (e.g., plastic backing component) to allow independent alignment and assembly of the sensor plate 102 to the microchamber device 100. The retaining member may be, in part or whole, of a transparent material, so when the sensor glass chip is attached, during the assemblyprocess, it allows for straightforward visual inspection of alignment during assembly. The manufacturing / assembly process may be employed using automated machine vision methods in manufacturing and assembly lines.

[0065] Microchamber may be designed or manufactured for multiple reuse. In some embodiments, the reuse is by the user (where the device is designed to be disassembled by the user after usage, sterilized, and reassembled). In other embodiments, the reuse is by the manufacturer (where the device is designed to be disassembled by the manufacturer, sterilized, and reassembled. In an example, a user may purchase and deploy a “new” sterilized microchamber from a sealed pouch. Once done using it, the user can choose to discard it (disposable) or mail it back to the manufacturer for a credit for sterilization. The manufacturer can disassemble the device, sterilize the component (e.g., replacing certain components, e.g., the gasket components, foam), and reassemble to then send back to the user a sealed pouch for next use at a reduced price. The shipping and storage pouch may be labeled for biological / hazardous material. The manufacturer may pre-sterilize the components in a bath / heat, etc. prior to disassembly.

[0066] In some embodiments, the internal components of the microchamber device 100 may be put under tension (e.g., to put the physical casing and internal structure and external housing component in tension against seals to put the seals of the system in compression). In some embodiments, compression forms and / or springs may be incorporated into the internal structure of the microchamber device 100.

[0067] In some embodiments, one side of the housing components of the microchamber device 100 may be configured with clamps to allow for components to be snapped into position while maintaining alignment with one another (as well as to improve assembly time).

[0068] In some embodiments, the microchamber device 100 may be configured with viewing ports to allow for visual inspection of a sample while in the microchamber device 100 or during placement of the sample into the chamber of the microchamber device 100. The microchamber device 100 may be configured with both viewing and lighting ports to allow the microchamber of the microchamber device 100 to be viewed / inspected with a microscope.

[0069] Self-calibration / self-testing operation. Fig. IB also shows a printed circuit board with self-contained circuitries, e.g., self-calibration / self-testing circuitry 119 to allow selfcalibrations and self-testing to assess the quality of electrical connections within the microchamber housing. In some embodiments, the self-calibration / self-testing circuitry 119 is configured to maintain high quality of signal integrity for signal 114, by detecting, via conductive pins 118 and connector 124, any misconnections that may cause open-circuit or shorts and any high-resistance connections that require user adjustments.

[0070] In some embodiments, the self-calibration / self-testing circuitry 119 is configured to send a test signal into the conductive pins 118 and sense, for example, the connectivity of the connected load, to verify the proper electrical connection between the driving circuitry 122 and the conductive pins 118. The self-calibration / self-testing circuitry 119 ensures that the numerous electrical connections of the printed circuit board 116 are properly established.

[0071] While the self-calibration / self-testing circuitry 119 can interrogate the conductive pins 118 without external input, it would do so when connected to the controller circuit board 126, which provides power to the printed circuit board.

[0072] The self-calibration / self-testing circuitry 119 is configured to be initiated via manual operation as well as at the beginning of an analysis operation to ensure that the conductive pins 118 are properly connected to the driving and / or sensing electronics. The self-calibration / self-testing circuitry 119 can address manufacturing, assembly, transportation, and storage issues, e.g., mitigate issues associated with dust in the components, oxidation issues, and / or physical misalignment. High-quality equipment and validation processes ensure that unique specimens that are labor- and time-intensive are not lost or have to be redone due to equipment failure.

[0073] The controller circuit board 126, coupled with the connector 124 and under the control of the processor 130, receives the analog signal 1 14 and converts the analog signal 114 to a digital signal using an analog-to-digital converter 128. A user interface 132, under the control of the processor 130, displays information (e.g., sensing data, notifications) and records user interactions. The controller circuit board 126 transmits the user interactions, processed by the processor 130, to the connector 124 via a serial transmission 120c. The driver 122 (i) receives, via the connector 124, the serial transmission 120c and (ii) controls the sensor plate 102 and system plant 140 via serial communications 133a and 133b, based on user interactions.

[0074] The controller circuit board 126 can be connected to multiple modular / re-attachable microchamber housings (e.g., 109a - 109c) and control the microchamber assembly housed by each modular / re-attachable microchamber housing independently. Additionally, the controller circuit board 126 can separately control every component (e.g., sensor plate, microchamber, system plant) of a microchamber assembly. The controller circuit board 126 may include additional self-testing and self-calibration circuits that can improve the accuracy and reduce the impact of component variability. In addition to self-testing, the self-testing and self-calibration circuits may be configured to detect deviations of amplifier gains and voltage shifts, and provide self-adjustments either in hardware or in software to correct any errors these deviations may cause.

[0075] The entire system can perform self-testing and self-calibration using a set of calibration boards in places where the microchamber assemblies would be plugged in. Thecalibration process can be initiated and controlled, in some embodiments, by the users from the user interface.

[0076] The port assembly 134 is coupled with the system plant 140 to lead a laminar flow of nutrient 138 to the cell culture or tissue ex vivo chamber 110 via fluid connections (e.g., 136a, 136b). The system plant 140 comprises a nutrient reservoir 142 and a pump 144, wherein the nutrient reservoir 142 supplies, with the help of the pump 144, the laminar flow of nutrients 134 to the port assembly 134.

[0077] The nutrient reservoir 142 and pump 144 are configured for precise control of fluid delivery and removal (e.g., adjustable pumps and valves configured to operate on feedback from integrated sensors formed by, or in part by, the electrodes to control fluid flow rates to the internal volume of the cell culture or tissue ex vivo chamber 110.

[0078] A modular / re-attachable microchamber housing 109a (i.e., external housing) encapsulates the sensor plate 102, the microchamber 108, and the printed circuit board 116 to form a microchamber assembly. The exploded view 146 of a microchamber assembly shows a complete integration of the sensor plate, printed control board, microchamber, and external housing assembly.

[0079] Fully instrumented and self-contained Al system. The fully instrumented microchamber device is configured as a self-contained, fully instrumented system with processing and analysis automation to allow for samples to be grown, measured, and analyzed with minimum or no human interaction. The system has onboard computing hardware for data acquisition and can be operatively coupled to a separate computing system for analysis. The onboard computing hardware may be coupled to an embedded or computer display to provide the status of the cell / tissue growth, measurements, analysis, or a combination thereof.

[0080] The separate computing system may be equipped with Al or machine learning tools and software for analysis of the measurement and / or drive control of the cell / tissue growth. Fig. 1C shows a host computer 150, with the trained Al model 152 operating thereon, locally communicating with the controller circuit board 126. Specifically, the trained Al model 152, through an Al agent 154, can receive, directly from the controller circuit board 126, information (e.g., sensing data, notifications) about cells and biological samples cultured in the microchamber 108. The Al agent 154 can then generate indicators (e.g., bar charts, data graphs, etc.) that help the user / research (i) determine whether a whole baseline cell measurement / monitoring process, or a phase thereof, finishes and (ii) prepare for subsequent steps (e.g., continuing cell culturing, removing cells and samples, reconfiguring culture chamber 110, etc.). The Al agent 154 can then transmit the generated indicators back to the controller circuit board 126, and the user / researcher can view, via the user interface 132, the generated indicators to make corresponding decisions.

[0081] Fig. ID shows the same host computer 150 remotely communicating, via the network 154, with the controller circuit board 126. Specifically, the trained Al model 152, through the Al agent 154 can (i) receive, via the network 154, information (e.g., sensing data, notifications) about cells and biological samples from the controller circuit board 126, and (ii) transmit, via the network 154, back to the controller circuit board 126, the generated indicators that helps the user / research decide subsequent steps of a cell measurement / monitoring process.

[0082] In some embodiments, the trained Al model 152 is a predictive model (e.g., a convolutional neural network, a random forest, etc.) that was trained, using datasets acquired from existing cell measurement / monitoring experiments, to predict, via generation of indicators, an outcome (e.g., finish, fail, etc.) of a cell measurement / monitoring process based on conditions of cells and biological samples in microchamber 108.

[0083] The software Al agent 154, via the trained Al model 152, can provide users with suggestions as to when the current phase of an experiment (baseline, basal, therapeutics, etc.) has concluded satisfactorily based on data collected so far and when the next phase of the experiment can be started. Basal measurement may include determination / measurement of basal metabolic rate as the amount of energy the body expends at baseline condition (without stimuli) to maintain basic bodily functions (breathing, circulation, etc.). The baseline measurement may be performed when tissue / cells are not yet placed in the microchamber. Basal measurement may be triggered once the cell / tissue is placed in the microchamber and measurement and microchamber conditions have reached equilibrium (stable measurements). Therapeutic measurement is initiated when a stimulus, e.g., therapeutic, is being introduced into the microchamber.

[0084] The suggestions may come from an Al model trained using historical data. The agent 154 can (i) monitor the progress of the current experiment, (ii) compare the process with the predicted trajectory, and (iii) make real-time assessments of whether the current phase of an experiment is completed. The agent 154 can collect real-time measurement data for ongoing experiments in a format needed for future training purposes. The agent 154 may be implemented via a run-time control loop that calls a function subroutine to read real-time measurements from memory configured to be updated with measurements from the controller circuit board 126; the subroutine updates the GUI and saves the data in a pre-defined data store. The agent 154 may call, via a function subroutine, the trained Al model to generate estimates of measurement stability or instability to trigger an alert / notification to the user. The agent may have a main function that allows different subroutines to be invoked, e.g., to transfer a file, operate with a host computer, display measurement results, display / plot data, among other functions described herein. The Al agent 154 is configured to operate on a set of higher- level heuristics while using the trained Almodel (i.e., data inquiries from the agent to the trained model) as an analysis engine to manage in a more situational environment specific to the current ongoing experiment.

[0085] The software Al agent 154 can provide a more adaptive and responsive experimental workflow for the exemplary system. By continuously comparing the Al model's predictions with the actual real-time measurements, the Al agent 154 can dynamically assess the congruence between the expected and observed data trajectories of OCR and ECAR. When a predefined level of agreement is sustained over a specific period, it signifies a high level of confidence that the experiment is evolving as anticipated, providing a robust trigger for initiating the next procedural step. This real-time feedback loop allows for more precise control over the experimental timeline and for significant improvement in efficiency and user friendliness of the system.

[0086] The trained Al model 152 can enhance the adaptability and responsiveness of an experiment workflow for the users. By continuously comparing the Al model’s predictions with real-time measurements (e.g., OCR, ECAR), the Al agent 154 can dynamically evaluate how closely the observed data aligns with expectations. When this alignment remains consistent over a defined period (e.g., outputs / measurements are stable), the Al agent 154 can trigger (i) the experiment to advance to the next procedural step or (ii) notify users (e.g., using indicators) that the experiment can advance to the next step. When the Al model’s prediction differs (e.g., by a predefined range) from real-time measurements, the Al agent 154 can notify the users that the measurements are not stable for the experiment to advance to the next step. This real-time feedback loop, by the trained Al model 152, provides precise control over the experimental timeline and improvement in efficiency and user friendliness of the exemplary apparatus.

[0087] The study of cellular metabolism plays a pivotal role in understanding fundamental biological processes and developing therapeutic interventions for a wide range of diseases. Realtime monitoring of metabolic activity provides invaluable insights into cellular responses to various external stimuli. Integrated miniature metabolic sensors and the supporting hardware and software implemented in the software Al agent 154 can provide an integrated approach to understanding at the cellular level the real-time mitochondrial activities and bioenergetics. Revealing cellular bioenergetics information may require careful interpretation of dynamic sensor data streams from the sensors and determination of timing for subsequent procedural steps, such as stability of baseline, basal, and therapeutic steps. The sensor data points collected by the platform may include raw current and voltage readings from the sensor. The trained Al model 152 or software agent 154 can convert the raw data into rate of change information for the sensor readings; the rate of change information may then be converted to metabolic readings such as the oxygen consumption rate(OCR) and extracellular acidification rate (ECAR). Typically, a task is considered concluded whenthe rate of change in sensor readings becomes zero (or close to zero) in the case of baseline measurements, or stays constant in the case of basal and other phases of measurements. Due to the noisiness of the data and the potential choppiness of the trajectory, determining whether the current measurement data have met the criteria for stability, and therefore, concluding the current phase of the experiment, can be challenging. Currently, these determinations often rely on subjective human assessment based on noisy sensor data displayed in the result window in the GUI. Correctly making the procedural decisions can reduce the variability of experiment results and improve efficiency. For inexperienced users, making procedural decisions can be a challenge. The software Al agent can enhance precision and efficiency for experiments on the exemplary system. The software Al agent 154, in some embodiments, is configured to operate in parallel with the real-time metabolic measurement software, employing a predictive model trained on historical experimental data collected by the platform. By continuously comparing predicted metabolic trajectories with live data during an experiment, the agent 154 can provide objective, data-driven guidance on the conclusion of the experiment’s current phase and the time for initiating the next procedural step. The procedural steps may include determining when baseline measurements (or any perturbation to the microchamber) have stabilized, e.g., when cells / tissues are loaded into the microchamber, when a therapeutic or external stimulus can be added to the microchamber, when nutrients are added to the microchamber, etc. The software Al agent can improve the efficiency, reliability, and reproducibility of the exemplary system and ensure that a user, upon initial use of the system, is able to properly use the device in the context of an experiment and live specimens.

[0088] Common Mode Noise Reduction Integrated Electronics. Fig. IE shows a configuration of the electronics to operate with low common-mode noise. In Fig. IE, the driver 122 (i.e., driving circuitry) is implemented as part of the controller circuit board 126, which can be configured with more extensive differential signaling to reduce the impact of common-mode noise in the exemplary apparatus. Multiple circuit boards (e.g., see Fig. 9D) may make up the controller circuit board 126. The driver, 122, may be positioned next to (or very close to) the sensor pod, making the parasitic very small, and making the differential signaling happen much earlier in the signal chain. In having the driver 122 located on the external controller circuit board 126, the active components of the data acquisition circuitries (e.g., amplifiers, amplifiers, filters) are laid out in a region of the electric circuit with a larger ground plane to better reject common mode noise introduced or coupled into the system from external sources. The layout can reduce stray capacitances, avoid ground loop issues, or mitigate interferences from external electromagnetic sources or the environment. Fig. 9D shows an example implementation of the electronics with improved common-mode noise reduction circuitries.

[0089] Example Component and System Integration

[0090] Fig. 2A shows exploded views of a multi-sensor chip 102 (i.e., sensor plate) (shown as 102’), a microchamber assembly, and a controller circuit box 212 connected to microchamber assemblies 100a - lOOf.

[0091] Subpanel (a) shows a close-up image of the multi-sensor chip 102 with a three- electrode configuration, previously discussed in (Cheng et al., 2022a).

[0092] Subpanel (b) shows an exploded view of a microchamber assembly including a biosensor chip 102’ (i.e., sensor plate), a chamber 108 attached to the biosensor chip 102’, breakout boards 1 16 / 116’ (i.e., printed circuit board) with spring pins 106 / 106’ (i.e., terminals) for connection to the biochip 102’, a 202 gasket to seal the chamber, and several metal screws to securely hold the assembly together and ensure electrical connectivity.

[0093] Subpanel (c) shows an example sectional view 204 of the assembly and the gasketchip interface.

[0094] Fig. 2B shows a main controller box 212, e.g., as described in relation to Figs. 1 A - IE, connected to the microchamber assemblies 100a - lOOf, where the controller circuit box houses the controller circuit board 126. In one embodiment, each microchamber assembly is implemented as a sensor pod (see Fig. 8 A) connected to the controller circuit box 212 through a magnet-based connector 124. In another embodiment, each microchamber assembly (e.g., sensor pod) and the controller circuit box 212 can share a viewing port / hole (e.g., in a center of the bottom housing of the assembly 100a - lOOf) to allow light from the microscope to reach the center of the microchamber (see 108, Figs. 1A - IE), to facilitate visual inspection of cells / tissue in the microchamber.

[0095] Example Artificial Intelligence (Al) and Machine Learning (ML) Model

[0096] AI / ML Model Training Process. Fig. 3 shows an example training process and workflow 300a, 300b for the Al model (see 152, Figs. 1C - ID) (e.g., LSTM) for an Al-based software agent 154 in the exemplary apparatus. As shown in the example of Fig. 3, the training process 300a includes training data preparation 302, data splitting and sequencing 304, training configuration 306, model training 308, and model performance evaluation 310. Table 2 shows descriptions of each step, 302 - 310, of the training process 300a.Table 2

[0097] After being trained (in 300a), the Al agent (e.g., 154 see Figs. 1C and ID) working with the trained Al model (e.g., 152), in the operation 300b, can provide users with suggestions as to when the current phase of an experiment (baseline 316, basal 18, therapeutics 320, etc.) has concluded satisfactorily based on data collected so far and when the next phase of the experiment can be started. Specifically, in the operation 300b, the Al agent (e.g., 154) can first collect (i) data of the samples / cells when they are loaded (312) into the exemplary system 100 and (ii) systemconfiguration during setup (314) for the exemplary apparatus. In runtime, the Al agent (e.g., 154) is configured to track the experiment’s progress by monitoring baseline measurements 316, basal measurements 318 (and results 322 thereof), and therapeutic measurements 320 (and results 324 thereof), from all the sensors (see 104, Figs. 1A - IE).

[0098] In the example, Al agent (e.g., 154), working with the trained Al model (e.g., 152) can then (i) provide estimated baseline, basal, and therapeutic measurements in the next step of the experiment, based on the collected data and monitored progress, and (ii) assess a congruence between the estimated measurements and the real-time measurements. When a predefined level of agreement is sustained over a period, the Al agent (e.g., 154) can conclude that the experiment is progressing as anticipated and provides a trigger for initiating the next step of the experiment (i.e., current step is complete). Otherwise, the Al agent (e.g., 154) can notify the users that measurement conditions are not yet stabilized for the measurement to be recorded or for the experiment to advance to the next step. To this end, the Al agent (e.g., 154) or algorithm, working with the trained Al model (e.g., 152), can guide (e.g., the user) on the data acquisition or trigger data acquisitions of the samples.

[0099] AI / ML Model. In one implementation, the AI / ML model may be based on a Long Short-Term Memory Network configured to run on remote computing hardware and in real-time or near real-time. The Al software agent (e.g., 154) may store all the historical data acquired by the platform (see 100, Figs. 1 A - IE) , with a certain degree of long-range temporal dependencies, as time-series data, e.g., with a 60-second interval. Other time intervals may be used. Long Short- Term Memory Network (LSTM) may be used as its configuration can handle time-series data with non-linear patterns and predict or estimate trends, based on long-term dependencies in the data. Other types of Al or ML models described or referenced herein may be alternatively used.

[0100] LSTM is a type of Recurrent Neural Network (RNN) configured to leam long-range dependencies in sequential data. LTSM uses a memory cell and gating mechanisms (e.g., input, forget, output gates) to control the flow of information over time, allowing LTSM to selectively remember or forget information. LSTM can leam complex, non-linear relationships in the data, which is suited for the datasets in the exemplary apparatus. LSTM is configured to address the vanishing gradient problem in current RNNs, facilitating their capture of long-range temporal dependencies. As a result, LSTM can process time series with different durations without requiring strict fixed lengths (though batching often involves padding).

[0101] LSTM can also learn relevant features from the training data, reducing the need for manual feature engineering, although the input training datasets may still need labeling for clarity. A downside of an LSTM model is that it requires more data and computational resources fortraining than an autoregressive integrated moving average (ARIMA) model. The complexity of training may be increased using hyperparameters of the network that need careful tuning (e.g., number of layers, number of units per layer, learning rate, etc.). The LSTM model is also prone to overfitting, especially with limited data, requiring regularization techniques (e.g., dropout).

[0102] Machine Learning. In addition to the machine learning features described above, the exemplary apparatus can be implemented using one or more artificial intelligence and machine learning operations. The term “artificial intelligence” can include any technique that enables one or more computing devices or computing systems (i.e., a machine) to mimic human intelligence. Artificial intelligence (Al) includes but is not limited to knowledge bases, machine learning, representation learning, and deep learning. The term “machine learning” is defined herein to be a subset of Al that enables a machine to acquire knowledge by extracting patterns from raw data. Machine learning techniques include, but are not limited to, logistic regression, support vector machines (SVMs), decision trees, Naive Bayes classifiers, and artificial neural networks. The term “representation learning” is defined herein to be a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, or classification from raw data. Representation learning techniques include, but are not limited to, autoencoders and embeddings. The term “deep learning” is defined herein to be a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, classification, etc., using layers of processing. Deep learning techniques include, but are not limited to, artificial neural networks or multilayer perceptron (MLP).

[0103] An artificial neural network (ANN) is a computing system including a plurality of interconnected neurons (e.g., also referred to as “nodes”). This disclosure contemplates that the nodes can be implemented using a computing device (e.g., a processing unit and memory as described herein). The nodes can be arranged in a plurality of layers, such as an input layer, an output layer, and optionally one or more hidden layers with different activation functions. An ANN having hidden layers can be referred to as a deep neural network or multilayer perceptron (MLP). Each node is connected to one or more other nodes in the ANN. For example, each layer is made of a plurality of nodes, where each node is connected to all nodes in the previous layer. The nodes in a given layer are not interconnected with one another, i.e., the nodes in a given layer function independently of one another. As used herein, nodes in the input layer receive data from outside of the ANN, nodes in the hidden layer(s) modify the data between the input and output layers, and nodes in the output layer provide the results. Each node is configured to receive an input, implement an activation function (e.g., binary step, linear, sigmoid, tanh, or rectified linear unit (ReLU) function), and provide an output in accordance with the activation function. Additionally, each nodeis associated with a respective weight. ANNs are trained with a dataset to maximize or minimize an objective function. In some implementations, the objective function is a cost function, which is a measure of the ANN’S performance (e.g., error such as LI or L2 loss) during training, and the training algorithm tunes the node weights and / or bias to minimize the cost function. This disclosure contemplates that any algorithm that finds the maximum or minimum of the objective function can be used for training the ANN. Training algorithms for ANNs include, but are not limited to, backpropagation. It should be understood that an artificial neural network is provided only as an example machine learning model. This disclosure contemplates that the machine learning model can be any supervised learning model, semi-supervised learning model, or unsupervised learning model. Optionally, the machine learning model is a deep learning model. Machine learning models are known in the art and are therefore not described in further detail herein.

[0104] A convolutional neural network (CNN) is a type of deep neural network that has been applied, for example, to image analysis applications. Unlike traditional neural networks, each layer in a CNN has a plurality of nodes arranged in three dimensions (width, height, depth). CNNs can include different types of layers, e.g., convolutional, pooling, and fully-connected (also referred to herein as “dense”) layers. A convolutional layer includes a set of filters and performs the bulk of the computations. A pooling layer is optionally inserted between convolutional layers to reduce the computational power and / or control overfitting (e.g., by downsampling). A fully-connected layer includes neurons, where each neuron is connected to all of the neurons in the previous layer. The layers are stacked similarly to traditional neural networks. GCNNs are CNNs that have been adapted to work on structured datasets such as graphs.

[0105] Other Supervised Learning Models. A logistic regression (LR) classifier is a supervised classification model that uses the logistic function to predict the probability of a target, which can be used for classification. LR classifiers are trained with a data set (also referred to herein as a “dataset”) to maximize or minimize an objective function, for example, a measure of the LR classifier’s performance (e.g., an error such as LI or L2 loss), during training. This disclosure contemplates that any algorithm that finds the minimum of the cost function can be used. LR classifiers are known in the art and are therefore not described in further detail herein.

[0106] A Naive Bayes’ (NB) classifier is a supervised classification model that is based on Bayes’ Theorem, which assumes independence among features (i.e., the presence of one feature in a class is unrelated to the presence of any other features). NB classifiers are trained with a data set by computing the conditional probability distribution of each feature given a label and applying Bayes’ Theorem to compute the conditional probability distribution of a label given an observation. NB classifiers are known in the art and are therefore not described in further detail herein.

[0107] A k-NN classifier is an unsupervised classification model that classifies new data points based on similarity measures (e.g., distance functions). The k-NN classifiers are trained with a data set (also referred to herein as a “dataset”) to maximize or minimize a measure of the k-NN classifier’s performance during training. This disclosure contemplates any algorithm that finds the maximum or minimum. The k-NN classifiers are known in the art and are therefore not described in further detail herein.

[0108] A majority voting ensemble is a meta-classifier that combines a plurality of machine learning classifiers for classification via majority voting. In other words, the majority voting ensemble’s final prediction (e.g., class label) is the one predicted most frequently by the member classification models. The majority voting ensembles are known in the art and are therefore not described in further detail herein.

[0109] Example Microchamber Design

[0110] Figs. 4A - 4B show an example design of a microchamber having one or more Luer lock seals as a first embodiment. Figs. 5A - 5B show an example design of a microchamber having one or more Luer lock seals and microfluidic support (e.g., inlet port, outlet port) as a second embodiment. Figs. 6A - 6B show an example design of a microchamber having cell culture or tissue ex vivo chambers aligned along a vertical axis of the microchamber as a third embodiment. Figs. 7A - 7B show an example design of a microchamber having separate integrated channels configured to prevent cross-talk between enzymatic sensors as a fourth embodiment. Figs. 7C - 7D show an example design of a cell culture or tissue ex vivo chamber having a port (e.g., inlet, outlet) and integrated channels to support microfluidics as a fifth embodiment.

[0111] First embodiment - Microchamber with Luer lock seal. Figs. 4A - 4B show an example design of a microchamber having one or more Luer lock seals. In Fig. 4A, subpanel (a), the microchamber comprises a cell culture or tissue ex vivo chamber 402 (e.g., tube) and holes for connectors (e.g., 404a - 404c) for secured and fixed connection to a breakdown board (e.g., controller circuit board). In subpanel (b), the microchamber includes a Luer lock seal 406a (e.g., gasket) inserted into a groove 408 at the bottom of the cell culture or tissue ex vivo chamber 402 to prevent fluid leakage.

[0112] In Fig. 4B, subpanel (a), the cell culture or tissue ex vivo chamber 402 has a microarchitecture 410 configured to maintain a controlled environment for living cells. In subpanel (b), the microchamber includes the seal 406a inserted into the groove 408 and the seal 406b placed inside the cell culture or tissue ex vivo chamber 402.

[0113] Second embodiment -Microchamber design with microfluidic support and Luer lock seal. Figs. 5A - 5B show an example design of a microchamber having one or more Luer lock seals and microfluidic support (e.g., inlet port, outlet port).

[0114] In Fig. 5A, subpanel (a), the microchamber comprises cell culture or tissue ex vivo chambers (e.g., 502a, 502b), holes for connectors (e.g., 503a - 503c) for secured and fixed connection to a break-out board (e.g., controller circuit board), an inlet port 504 placed inside the cell culture or tissue ex vivo chamber 502a, and an outlet port 506 placed inside the cell culture or tissue ex vivo chamber 502b. In subpanel (b), the microchamber includes a Luer lock seal 508 (e.g., gasket) positioned at the bottom of the cell culture or tissue ex vivo chamber 502a to prevent fluid leakage.

[0115] In Fig. 5B, the microchamber has a channel 512 that couples the inlet port 504 with the outlet port 506. The cell culture or tissue ex vivo chamber 502a has a microarchitecture 510a, and the cell culture or tissue ex vivo chamber 502b has a microarchitecture 510b, wherein each microarchitecture is configured to maintain a controlled environment for living cells. Additionally, the seal 508 is positioned at the bottom of the cell culture or tissue ex vivo chamber 502a to prevent fluid leakage.

[0116] Third embodiment - Vertical Microchamber Design with microfluidic support and Luer lock seal. Figs. 6A - 6B show an example design of a microchamber having cell culture or tissue ex vivo chambers aligned along a vertical axis of the microchamber. In Fig. 6A, the microchamber comprises cell culture or tissue ex vivo chambers (e.g., 602a, 602b, 602c), an inlet port 604 placed inside the cell culture or tissue ex vivo chamber 602a, and an outlet port 606 placed inside the cell culture or tissue ex vivo chamber 602b. The cell culture or tissue ex vivo chamber 602a and 602b are aligned along a vertical axis of the microchamber, so the inlet port 604 and the outlet port 606 are also aligned along the vertical axis of the microchamber. The cell culture or tissue ex vivo chamber 602c, positioned at one end of the vertical alignment of the cell culture chambers 602a and 602b, has a port 608 (e.g., inlet, outlet) and a port 610 (e.g., inlet, outlet).

[0117] In Fig. 6B, behind the cell culture or tissue ex vivo chambers 602a and 602b, a channel 612a couples the inlet port 604 with the port 608, and a channel 612b couples the outlet port 606 with the port 610.

[0118] Fourth embodiment - Microchamber design with enzymatic support sensors. Figs. 7A - 7B show an example design of a microchamber having separate integrated channels configured to prevent cross-talk between enzymatic sensors. In other words, this design is optimized for enzymatic sensors to minimize sensor-to-sensor interference in a multi-sensor environment, especially when multiple enzymatic sensors use the same catalyst for sensing.

[0119] In Fig. 7A, the microchamber comprises separate integrated channels (e.g., 802a - 802d), and holes for connectors (e.g., 804a - 808c) for secured and fixed connection to a breakdown board (e.g., controller circuit board). An enzymatic sensor integrated into a channel cannot interfere with the measurements from other enzymatic sensors in other channels.

[0120] In Fig. 7B, the microchamber includes a Luer lock seal 806 (e.g., gasket) positioned at the bottom of the integrated channels 802a - 802d. This can prevent fluid leakage and cross-talk between integrated sensors.

[0121] Fifth embodiment - Cell culture or Tissue Ex vivo chamber design to support microfluidics. Figs. 7C - 7D show an example design of a cell culture or tissue ex vivo chamber having a port (e.g., inlet, outlet) and integrated channels to support microfluidics. As shown, the cell culture or tissue ex vivo chamber has a port 802 and two integrated channels 804 and 806. The port 802 can be an inlet port or an outlet port. Each integrated channel 804 and 806 can be used to couple the inlet and outlet ports of the cell culture or tissue ex vivo chamber, as shown with other cell culture chambers (not shown).

[0122] Example Microchamber Hardware (“Pod”)

[0123] As noted, the physical assembly of the microchamber device 100 may be configured to improve the manufacturability and reduce / avoid failure of the microchamber device 100 during use. High-quality equipment and validation processes ensure that unique specimens that are labor- and time-intensive are not lost or have to be redone due to equipment failure. In some embodiments, the sensor plate 102 may be fixably attached to structures in the microchamber device 100 via a retaining member (e.g., plastic backing component) to allow independent alignment and assembly of the sensor plate 102 to the microchamber device 100.

[0124] In some embodiments, the internal components of the microchamber device 100 may be put under tension (e.g., to put the physical casing and internal structure and external housing component in tension against seals to put the seals of the system in compression). In some embodiments, compression forms and / or springs may be incorporated into the internal structure of the microchamber device 100.

[0125] In some embodiments, one side of the housing components of the microchamber device 100 may be configured with clamps to allow for components to be snapped into position while maintaining alignment with one another (as well as to improve assembly time).

[0126] In some embodiments, the microchamber device 100 may be configured with viewing ports to allow for visual inspection of a sample while in the microchamber device 100 or during placement of the sample into the chamber of the microchamber device 100. Themicrochamber device 100 may be configured with both viewing and lighting ports to allow the microchamber of the microchamber device 100 to be viewed / inspected with a microscope.

[0127] Example Integrated Sensor Plate-Microchamber Design.

[0128] In some embodiments, the microsensor chip (also called sensor plate) can be combined with the microchamber to form an integrated microsensor chip-microchamber (i.e., integrated sensor plate-microchamber) module, called a sensor pod, of the exemplary apparatus.

[0129] Clamps / Housing. Fig. 8A, subpanels (a) - (b) show an example sensor pod having a top housing 810a (shown as “top clamp” 810a) and a bottom housing 810b (shown as “bottom clamp” 810b) that enclose a microsensor chip 102, a microchamber 108, a breakout PCB 116, pogo pins 118, zero-force insertion, or magnet-based, connectors 124, a biocompatible o-ring (see 406, Figs. 1 and 8B), a coil spring 812, a lid 814 (i.e., a sealing element) for the microchamber 108, and a set of foam layers 818. In Fig. 8 A, the exemplary apparatus is also referred to as a Fluxmera platform. Fig. 8A, subpanel (c) shows a diagram of the assembly of the sensor pod and its internal components. The diagram reflects the device of subpanels (a) and (b) and is merely for illustration of the features. It’s not drawn to scale and shows features in multiple cross-sectional areas.

[0130] As shown in Figs. 8A, subpanels (a) - (c), the top and bottom clamps / housing 810a - 810b can be assembled using a clip-on mechanism (e.g., clip-on fins 816), to make assembly processes quicker and more straightforward, and eliminate unreliable electrical connections. The assembling of the top and bottom clamps 810a - 810b can be further enhanced by screws through matching holes on each of them.

[0131] When the electrode pads (see 104, Figs. 1 and 8D) on the microsensor chip 102 are aligned with pogo pins 118 on the breakout PCB 116 during the assembly process, the mounting element 814 (not shown in subpanel (a) and (b), see Fig. 8 A, subpanel C and Fig. 8E) can be first applied to independently maintain the microsensor chip 102 in position against the microchamber 108 (and the seal 406). The foam layers 818 and spring 812 can be positioned against the sealing element 814, and then the housing 810a - 810b being pushed together to lock all the components between the housing in their position with one click. The housing portion 810b includes a flip-on male connector and housing portion 810a includes a corresponding flip-on female connector.

[0132] Coil Spring. Figs. 8A, 8B, 8C, and 8D shows the coil spring 812. In the layer-by- layer view of the sensor pod in Fig. 8B, the coil spring 812 can be incorporated into the sensor pod to enhance compression (e.g., to a factor k = 25 ounces per inch, though other spring k values can be used) between the microsensor chip 102 and the bottom of the microchamber 108, which can seal the entire microchamber 108 to prevent leakage of cells or biological samples therefrom. Fig. 8C shows an example coil spring 812 during the assembly process of the exemplary apparatus. Thecoil spring 812 (and foam 818) as internal components of the microchamber device 100 put the device 100 under tension to improve sealability and reduce leakage of fluid from the microchamber.

[0133] Plastic Backing Component. Figs. 8B and 8D show an insertion of the pogo pins 118 on the PCB 116, through the slots 820 of the microchamber 108, into the electrode pads 104 on the microsensor chip 102. The alignment of the electrode pads 104 and the pogo pins 118, through the slots 820, can be maintained during the assembly process, by (i) attaching the microsensor chip 102 to a plastic backing component (see 824, Fig. 8E) at the back of the microsensor chip 102 and (ii) attaching the plastic component to the corner screw holes 822 of the microchamber 108 to the top clamp / housing (see 810a, Fig. 8A) of the sensor pod. The plastic backing component is configured to restrict the movement / deviation of the microsensor chip 102 relative to the pogo pins 118 to maintain alignment during assembly. Fig. 8E shows an example plastic backing component 824 used for alignment. The backing component 824 facilitates the alignment and proper assembly of electronic and sensor components. The sensor elements (e.g., sensor plate) may be mechanically configured to independently and separately mount to internal components, allowing validation of the alignment to be performed prior to further assembly. Biological specimens to be employed in the modularized, fully instrumented device can be labor- and time-intensive to prepare, in addition to being potentially very unique and expensive to acquire (if not irreplaceable). The backing component 824 and associated method of assembly can substantially reduce error or failure rates of the exemplary microchamber device.

[0134] Microchamber Lid. Fig. 8F shows example lid designs for the microchamber, including lid 814a without stirring tips 830 and lid 814b with stirring tips 830. Lid 814a is configured to seal the microchamber through a thin Polydimethylsiloxane (PDMS) cover (not shown) attached to the lid. Lid 814b is configured with a set of stirring tips 830. By rotating the lid, the media (e.g., 110, Figs. 1 A - ID) inside the microchamber can be stirred, which can be crucial for applications where the exemplary apparatus is used for measuring metabolic activities of suspended cells / mitochondria in the media inside the microchamber. In those applications, periodic stirring may be necessary for maintaining uniformity of cell density and even distribution of oxygen and other nutrients in the media.

[0135] The microchamber lid 814 can support (i) microfluidics via its inlet and outlet ports to maintain live cells / tissue viability for an extended period of time, or (ii) injection of therapeutics and other substances during experiments. Therefore, the microchamber 108, through its lid 814, side wall, and the sensor plate 102, can maintain a sealed environment without any air exchange with its environment.

[0136] The microchamber lid 814 can be removed from the microchamber 108 without disturbing a cell culture or a tissue slice to allow the users to extract and / or add samples under measurement or therapeutics to the microchamber 108 during experiments.

[0137] PCB Connectors. The connectors (see 124, Figs. 1, 8 A, 8E) on the breakout PCB 116 can be magnet-based connectors (instead of DB15 connectors) to provide an easy self-aligned connection when users / researchers put the sensor pod on the exemplary apparatus. The DB15 connectors require significant force to dislodge when disconnecting the sensor pod from the exemplary apparatus, causing mechanical stress to other components on the exemplary apparatus.

[0138] Viewing Ports / Holes. The sensor pod allows light to go through the bottom of the pod by placing the sensor electrodes away from the center of the sensor pod. The viewing ports / holes in the center of the bottom clamp / housing and in the thin foam layer can be aligned with the center of the glass substrate of the microsensor chip (see Fig. 8A, subpanel (c)) to allow light from the microscope to reach the center of the microchamber, to allow visual inspection of cells / tissue in the microchamber. Users / researchers can see where they load cells or biological samples inside the microchamber for measurement, and when they retrieve the cells or biological samples from the microchamber. Fig. 8G shows a microphotograph of single embryos in the microchamber, where light from the microscope illuminates the interior of the microchamber.

[0139] Advantages. Configured with the sensor pod module having clamps / housing, coil spring, and plastic backing component, the exemplary apparatus can avoid (i) leakage of cells and biological samples from the microchamber and (ii) misalignment between the electrode pads (on the microsensor chip) and the pogo pins (on the breakout PCB). Furthermore, they make the entire assembly process more efficient and easy to automate for high volume production.

[0140] Example Controller Board as Biosensor Chip

[0141] As noted above, the microchamber device 100 can be configured with on-board sensors and DAQ circuitries to perform continuous measurement of the sample (e.g., tissue or cells). The onboard sensors can include amperometric sensors, potentiometric sensors, enzymic sensors, impedance sensors, capacitance sensors, resistive sensors, temperature sensors, electrochemical and optical sensors, ultrasonic sensors, magnetic sensors, potentiometric sensors, and various other biosensors described or referenced herein.

[0142] A controller circuit board (i.e., motherboard) may operate with amperometric sensors and potentiometric sensors housed on a glass chip for each well. Each amperometric sensor may include gold working electrodes (WE) and counter electrodes (CE), and an Ag / AgCl reference electrode (RE). The WE of the pH sensor may be made of indium tin oxide (ITO). All the REs may be controlled by individual drivers, and all the CEs are returned to individual amperometric circuits.The amperometric sensors may be used for O2 and H2O2 measurements, among others described herein.

[0143] Read Channels. The supporting electronics circuits may perform signal acquisition, conditioning, and processing of the biosensors' output signals. These circuits may include a first- stage amplifier for amperometric sensors and potentiometric sensors on the biosensor chip and a second-stage amplifier. The first-stage and / or the second-stage amplifiers may have automatic gain control to maximize signal dynamic range.

[0144] Each biosensor of each sensor pod may be coupled to an individual potentiostat circuit. The read-channel circuits may couple to an analog-to-digital converter and a serial communication module.

[0145] The first-stage amplifiers, located on the motherboard, may be transimpedance amplifiers (TLA) or instrumentation amplifiers (INA), depending on the sensing mode. The second- stage amplifier may multiplex (e.g., time-multiplex) the first-stage sensor channels and send the final amplified signal to the ADC. Multiplexing (e.g., time-multiplexing) may be controlled by outputs from the serial communication module. To reduce the impact of common-mode noise, the single-ended signals from the TIAs and the INAs may be converted to differential signals before being sent to the ADC stage. The overall gain of the read channel may be adjusted by a gain control function inside the GUI, e.g., through control pins on the serial communication module. The ADC may have 14-bit, 15-bit, 16-bit, 17-bit, 18-bit, 19-bit, 20-bit, 21-bit, 22-bit, 23-bit, 24-bit, 25-bit, 26- bit, 27 -bit, 28-bit, 29-bit, or 30-bit of resolution. The ADC Fig. 9A shows an example potentiometric circuit that may be implemented. Other designs, e.g., those referenced herein, may be employed.

[0146] Graphical User Interface (GUI). The GUI can display real-time measurement results, e.g., in analyte concentrations or in analyte consumption / production rates, among others. The GUI may store measurement results in a given interval, and users can also manually save the data as a CSV file (i.e., digitized data ) at any time during measurement for further data processing.

[0147] Example Sensor Plate

[0148] Fig. 9B shows an example biosensor chip (i.e., microsensor chip, sensor plate) (see 102, Figs. 1A - ID) for the exemplary apparatus. As shown, there are a total of 6 sets of sensor chips (e.g., sensors), 1 in the middle, 4 at the comers, and 1 at the side for PH measurement.

[0149] Each sensor chip consists of gold working electrodes (WE) (e.g., 930a - 930d), counter electrodes (CE) (e.g., 932a - 932c), and Ag / AgCl reference electrodes (RE) (e.g., 934a - 934e). The WE of the pH sensor (i.e., WE_pH) is made of indium tin oxide (ITO). All the REs arecontrolled by the same driver, and all the CEs are shared among all exemplary sensors. The WE_02 electrode 938 can be used for O2 measurements.

[0150] Fig. 9C, subpanels (a) - (b) show another example biosensor chip (i.e., microsensor chip, sensor plate) (see 102, Figs. 1 A - ID) for the exemplary apparatus. As shown, the microsensor chip can comprise a set of electrochemical sensors, including amperometric and potentiometric sensors, manufactured on a glass substrate. The potentiometric sensors 940 are configured to measure pH in the microchamber environment, whereas the amperometric sensors 942a - 942b are configured to measure oxygen (O2), hydrogen peroxide (H2O2), glucose, etc.

[0151] The microsensor chip can be configured differently for different applications. In some embodiments, the microsensor chip can comprise (i) an O2 sensor 942b to measure oxygen consumption rate (OCR), (ii) an H2O2 sensor 942a to measure reactive oxygen species (ROS), and (iii) a pH sensor 940 to measure extracellular acidity rate (ECAR). In other embodiments, the microsensor chip may include more amperometric sensors (see 942c).

[0152] In subpanels (a) - (b), reference electrodes of the microsensor chip can be gold, because silver material may damage cells and biological samples. However, in some embodiments, the reference electrodes can be silver / silver chloride (Ag / AgCl) for their potential stability. The exemplary apparatus can consider redox activation voltage shift due to the use of different inert materials for the reference electrode, and the compensation can be built into the sensor calibration operations / equations. In Fig. 9C, the material for the working electrode of the pH sensor 940 is transparent indium tin oxide (ITO).

[0153] Electrode pads 944 at the edge of the microsensor chip can be used for connecting to a breakout PCB (see 116, Figs. 1A - ID) with pogo pins (see 118, Figs. 1A - ID) connected to their respective read-channel inputs inside the exemplary apparatus.

[0154] A difference between the microsensor chip configuration in subpanels (a) and (b) is that, in subpanel (a), the middle of the microsensor chip is the transparent pH working electrode where users / researchers can load their cell sample (e.g. embryo and a cancer tissue piece) in the middle that are optically visible under microscope. This feature is crucial when users / researchers want to retrieve the sample after measurement for other purposes. For example, in vitro fertilization (IVF) applications, users / researchers can retrieve embryos for further development / treatment after reading the metabolic status of the embryos. Users / researchers can retrieve cancer cells / tissues for subsequent readings / treatments after obtaining the metabolic status of the cancer cells / tissues using the exemplary apparatus.

[0155] Example Improved Noise-Reduction Configuration with Differential Analog Bus

[0156] Fig. 9D shows an example implementation of the electronic system for the instrument (e.g., controller circuit board 126). The “pod” embodiment is configured to individual mount to electronics partitioned for a given pod device. Each analog read-channel circuits are implemented on a separate printed circuit board located proximal to, or right at, the connector, interfacing with the sensor pod (see Figs. 8A - 8G) such that the read-channel input is proximal to the working electrode (WE) (see 104, Figs. IE) of each sensor that the read-channel circuit is responsible for. This configuration (see Fig. IE) can reduce and minimize the parasitic capacitance associated with the working electrode (WE), improving response time and measurement speed of the sensor, and the electronic stability of the read-channel circuit. The main board may include a serial communication module (shown as “FTDI interface & controller”) to operatively connect with the DAQ read-channel circuits and a host computer (e.g., 150).

[0157] In Fig. 9D, to mitigate or reduce the impact of common-mode noise, the exemplary apparatus implements differential signaling between printed circuit board 950 (shown as 950a, 950b, 950c) housing the read-channel circuit comprising mixed signal data acquisition instruments for each sensor pod and the main printed circuit board (952) housing the digital circuits to interface to the computing hardware (e.g., 150). As shown in Fig. 9D, multiple read-channel circuits 950 (shown as 950a, 950b, 950c) are implemented with a set of transimpedance amplifiers 954 and signal conditioning and bus driver circuits 956 to provide communication to the main printed circuit board 952 across a differential analog bus 958. The 1ststage transimpedance amplifier 954 inside each individual read-channel circuit 950 can transmit differential signals through the bus driver 956 to a 2ndstage differential amplifier 960 having multiple stages of level shifters for signal conditioning 962 to be converted to digital signals via ADC converter 964. The extensive use of differential signaling requires (i) careful design matching at the component level and at the physical layout level, and (ii) matching of internal cabling design at the system level, to reduce commonmode noise at the output to the ADC converter to generate cleaner measurement signals.

[0158] In Fig. 9A, each read-channel circuit 950a - 950c includes a potentiostat circuit 965 for each amperometric sensor to avoid crosstalk of output signals among sensors that share the same potentiostat circuit.

[0159] In Fig. 9D, each read-channel circuit 950a - 950c includes separate power regulation circuits 966 and their associated potentiostat circuit, e.g., a separate low-dropout regulator (LDO) and a layout of the power supply network. Multiple analog read-channel circuits sharing the same power distribution network and the same set of LDOs can cause them to be more susceptible to power supply noise, which the implementation mitigates.

[0160] The read-channel circuits 950a - 950c are shown implemented with a digital controller 968 to allow circuit control signals to be sent over a digital bus. The differential analog bus allows the acquired signal to be boosted over a higher range and transmitted as a current signal to reduce the effects of external noise.

[0161] The multiple- stage buffers / drivers over a differential bus can provide finer gain control and scaling of measured signals to optimize the output swing of the analog read-channel circuit to the input to the ADC converter, improving the signal-to-noise ratio (SNR) of the entire apparatus.

[0162] Fig. 9E shows another configuration of the electronic system. In Fig. 9E, the 1ststage transimpedance amplifiers 954, the 2ndstage differential amplifier 960, the signal conditioner 962 (e.g., 962a 0 962c), and the ADC converter 964 (e.g., 964a - 964c) are shown implemented as a mixed-circuit integrated circuit that can be mounted onto each respective read-channel circuit 950a - 950c. This implementation can reduce the signal transmissions that each ADC converter 964 (e.g., 964a - 964c for 950a - 950c, respectively) receives and processes, further reducing susceptibility to common-mode noise at the input and output of the ADC converter to provide cleaner measurement signals. Additionally, each read-channel circuit 950 is configured with its own signal conditioner 962 (e.g., 962a - 962c for 950a - 950c, respectively).

[0163] Example Graphical User Interface

[0164] Signal Processing Flow and Protocols. The real-time measurement results from all sensors may be displayed on the results page on a GUI, e.g., located on the main controller or through a host computer. The software Al agent (e.g., 154), through the GUI, may inform users when the read channel establishes a stable baseline and is ready for sample injection.

[0165] During an example operation, if users do not specify a stop time for the measurement sequence on the experiment setup page, the software Al agent may automatically halt the reading / recording when a stable consumption or production rate is detected. At this point, it is up to the users to decide whether to add any therapeutics. If users choose to do so, the measurement will resume from the first type of sensor to the last in a user-defined sequence. Users have the flexibility to select or deselect a sensor signal for display.

[0166] The apparatus may collect the electrochemical signal in accordance with the analyte concentration via an analog read channel front-end. In having the analog front end so close to the chamber (e.g., on a breakout board), the microchamber module comprising the sensors and the breakout board may be made disposable or reusable while providing improved signal reading and measurements. The electrochemical signal may be digitized and sent to the software Al agent through an ADC and a serial communication chip (e.g., over SPI protocol). The software Al agentmay be programmed to execute a moving average with a window size (e.g., 2000 points or other user define-able value) for the raw data and generate a smoothed data array in order to make the evaluation process less susceptible to noise. After that, it may take the calculated value as the measured signal and takes five minutes of smoothed data to evaluate the slope, which is further converted to the consumption (or production) rate through the method previously discussed in (Obeidat et al., 2018; Y. M. Obeidat et al., 2019).

[0167] If the value of the consumption (or production) rate changes by less than a user- definable value (e.g., 0.5%) over the user-defined or system-defined period, the software agent may consider the baseline stable and ready for sample injection.

[0168] After the baseline measurement sequence, the software Al agent may proceed to the basal measurement process to measure the basal metabolic rates using the O2 sensor, H2O2 sensor, and pH sensor.

[0169] After basal measurements of all three metabolites are done, the microchambers are ready for therapeutic injection in the drug development sequence. After injecting therapeutics, a similar process is repeated to collect data on oxygen consumption, H2O2 production, and pH changes.

[0170] Figs. 10A - 10D show an example graphical user interface (GUI) of the exemplary apparatus that may operate with the software Al agent, in accordance with an illustrative embodiment. Fig. 10A shows a starting screen of the user interface, where the user can load an existing project or create a new project.

[0171] Fig. 10B shows a main configuration GUI 1000 having a subpane 1002 for the user to configure the wells (e.g., shown as wells 1 - 6 for this example) for an experiment. Each well may include a plurality of biosensors (e.g., the example showing 6 biosensors). The user can specify, via the configuration GUI, a configuration profile for each well 1006, including activation voltage applied to the reference electrode for the target analytes (e.g., 02, H2O2) (1004a, 1004b), well media volume 1004c, initial skip time 1004d, cycle skip time 1004e, sample type 1004f, and well diameter and height 1004g. Wells in pane 1006 are selectable to allow individual customization as shown in pane 1004. The configuration pane 1000 also provides an input description widget 1008 for the user to fill in details about the projects / experiment (e.g., project name, project notes, etc.).

[0172] Figs. 10C - 10D each shows an example measurement result GUI, where the user can view one or more results (e.g., plots) showing real-time measurement results from all wells (e.g., wells 1 - 6). The measurement results can be automatically saved in pre-defined and user- selectable time increments, e.g., every 100 milliseconds, 500 milliseconds, second, 2 seconds, 3seconds, 4 seconds, 5 seconds, 15 seconds, 30 seconds, 60 seconds, 2 minutes, 3 minutes, 4 minutes, 5 minutes, among others. The measurement results can be In Fig. 10D, the GUI provides a wellconfiguration dialogue box for users to customize the plots in real time.

[0173] System Calibration. Before connecting the microchamber assemblies to the exemplary apparatus, a calibration option may be executed to verify the correctness of the electronic connections. The calibrated signals may be sent to the main controller for verification. If no error is found, the platform is ready to conduct experiments. In case errors are found, the user may refer to the GUI’ s guidance for troubleshooting. The system calibration function may also be integrated into the controller board within the sensor module assembly.

[0174] The calibration board may include resistors and capacitors to form a set of Randles- equivalent circuits. The resistors were chosen to be 132 kQ, 1.5 MQ, and 1.5 MQ for the read channels 0, 1, and 2, respectively. The capacitor may be chosen to be connected in parallel with the resistor, for each read channel was 100 pF. Relays are then used to configure the system between the calibration mode and the normal operation mode.

[0175] Table 1 lists the system errors and describes the calibration process to address the system errors.Table 1

[0176] Experimental Results and Additional Examples

[0177] Studies were conducted to develop an exemplary apparatus employing integrated sensors for monitoring multiple metabolic analytes in real-time and integrated microfluidic support to maintain a microphysiological environment for the living cells. The exemplary apparatus also employs a graphical user interface for easy user configuration and monitoring.

[0178] The studies successfully developed and evaluated the monitoring in real-time of mitochondrial functions of live bovine embryos in O2 consumption, H2O2 release as an indication of ROS production, and extracellular acidity changes before and after the introduction of external substrates using a laboratory-grade prototype. The data shows that a modular instrumentationsystem can detect changes in the metabolic rate at the resolution of single embryo cells. Further analysis was then performed using a pod instrument, as described in relation to Fig. 8A was conducted for cancer tissues. The pod instrument was further developed to ruggedize the laboratory grade prototype to minimize leaks, processing time, and monitoring time.

[0179] Figs. 11 A - 11C show measurement results, acquired using the fabricated pod apparatus, for tissue responses, using various cancer drugs (e.g., Stauroporine, CCNU, 2- deoxyglucose). Both Stauroporine and CCNU are compounds that induce apoptosis, causing a reduction of cellular respiration and oxygen consumption.

[0180] 2-deoxyglucose can inhibit glycolysis and induce metabolic stress. The impact of 2- deoxyglucose on oxygen consumption depends on the initial impact of the drug on cancer cells. Therefore, the impact of 2-deoxyglucose on the cancer cells relative to their basal state is less pronounced.

[0181] AI / ML Model Evaluation Results. Fig. 12A shows the training results using a multiobjective NLL-CRPS metric to measure Al model performance. Subpanels (a) - (b) show (i) the progression of model evaluation parameters (NLL and CRPS) as training progressed under different batch sizes, and (ii) the progress of the training process and where the training may stop.Furthermore, the NLL and CRPS values indicated the model’s performance in predicting actual data not seen during training.

[0182] Fig. 12B shows another example of model performance progression during training. As shown, the multi-objectives (NLL and CRPS) progressed during training as a function of batch size and dropout rates.

[0183] Discussion

[0184] Well plates are widely used in biological experiments, particularly in pharmaceutical sciences and cell biology. Its popularity stems from its versatility to support a variety of fluorescent markers for high throughput monitoring of cellular activities. However, using fluorescent markers in traditional well plates has its own challenges; namely, they can be potentially toxic to cells and, thus, may perturb their biological functions, and it is difficult to monitor multiple analytes concurrently and in real-time inside each well.

[0185] The exemplary apparatus and method can provide a fully instrumented microphysiological system with a similar well format in which each well in the microphysiological system has a set of sensors for monitoring multiple metabolic analytes in real-time. The exemplary apparatus is supported by integrated bioelectronic circuits and a graphical user interface for easy user configuration and monitoring. The system has integrated microfluidics to maintain its microphysiological environment within each well. The exemplary apparatus currently incorporatesO2, H2O2, and pH sensors inside each well, allowing up to six wells to perform concurrent measurements in real-time. Furthermore, the architecture is scalable to achieve an even higher level of throughput. The miniaturized design ensures portability, suitable for small offices and field applications.

[0186] The exemplary apparatus was successfully used to monitor in real-time the mitochondrial functions of live bovine embryos in O2 consumption, H2O2 release as an indication of ROS production, and extracellular acidity changes before and after the introduction of external substrates.

[0187] Screening biological samples using multi-well plates is a widely used method to systematically examine and analyze biological samples under various stimuli or other relevant treatments. Available in various sizes, well plates offer users the flexibility to select the most suitable well format and throughput based on their experiment requirements. Well plates are supported by numerous assay kits to monitor cellular activities and to determine the effects of external stimuli or therapeutics on biological samples. These assay kits include fluorometric assays (Page et al., 1993), cell invasion assays (Marshall, 2011), glucose uptake colorimetric assay (Park et al., 2016), lactate colorimetric assay (Park et al., 2016), ATP luminescence-based motility assay (Restouin et al., 2009), glycolysis cell-based assay (Sanchez-Martinez et al., 2015), glycogen assay (Im et al., 2015), and NAD+ / NADH assay (Im et al., 2015). Well plates allow observations of varying treatments across multiple wells (Matrosovich et al., 2006) to evaluate morphological changes (Borenfreund and Puerner, 1985; Fry et al., 1998; Terkuile et al., 1993). Screening techniques can not only benefit the field of cell biology but also facilitate advancements in the pharmaceutical industry (Samantasinghar et al., 2023).

[0188] Monitoring cellular metabolism has become increasingly important in gaining deeper insights into the mitochondrial functions of biological samples under examination. Many techniques exist for monitoring cell metabolism. They include mass spectrometry-based imaging (Junot et al., 2014), Raman spectroscopy (Wicksted et al., 1995), and fluorescence microscopy (Ruiz-Rodado et al., 2022; Stringari et al., 2011). These techniques typically require special labels for imaging and also require specialized equipment only available in large, centralized laboratories, hence, incurring higher costs and longer turnaround time. The electrochemical techniques (Amer, 2002; Davis, 1985; Ozkan et al., 2015; Yotter and Wilson, 2004) for monitoring cellular metabolic activities often do not require labels and are easier and less expensive to perform.

[0189] Among metabolic activities of interest, oxygen consumption rate (OCR), reactive oxidative species (ROS) production, and extracellular acidification rate (ECAR) are three major indicators that can reveal samples’ mitochondrial functions and their bioenergetics, such as ATPutilization and glycolysis rate (Divakaruni et al., 2014). Oxygen serves as the terminal electron acceptor in the electron transport chain for ATP synthesis from ADP under normal cellular conditions when sufficient oxygen is available to cells (Babcock, 1999). It is one of the crucial markers for assessing the energy metabolism of mitochondria. The lack of oxygen consumption can also reveal the other pathway of cellular ATP production through glycolysis. The glycolytic pathway often accompanies the generation of lactate as its end product (Rogatzki et al., 2015), which acidifies the microenvironment around the cell. Furthermore, it is well understood that abnormally regulated ROS is associated with numerous diseases, such as cardiovascular disease (Kornfeld et al., 2015) and neurodegenerative disorders (Wang et al., 2014). Therefore, tracking ROS along with OCR and ECAR has physiological relevance. Previous studies have demonstrated a clear correlation between OCR and hydrogen peroxide (H2O2, a prevalent form of ROS released by mitochondria) production rate (HPR), and this connection aids researchers in obtaining a deeper understanding of cellular activities and metabolism (Cheng et al., 2022a; Li Puma et al., 2020). Many applications exist for studying oxygen and hydrogen peroxide metabolic processes, including but not limited to using oxygen consumption rate as an indicator of oocyte quality for in vitro fertilization (Tejera et al., 2011), exploring the impact of obesity -induced mitochondria dysfunction (Yin et al., 2014), examining cancer cells from a metabolism standpoint (Zhao et al., 2016), and determining drug toxicity (Begriche et al., 2011). Measurements of oxygen consumption, ROS production, and cellular acidification are also proven to be useful in cancer research (Vander Heiden et al., 2009).

[0190] A commonly employed method for monitoring metabolism with well plates involves the use of fluorescent markers and measurement of fluorescence (or phosphorescence) lifetime (Bird et al., 2005; Kurokawa et al., 2015; Mik et al., 2008) or fluorescence intensity (quenching) using optical sensors (Hynes et al., 2006; Kenwood et al., 2014; Will et al., 2006). This technique also allows for the quantification of metabolic activity by tracking the changes in fluorescence signal intensity over time, providing valuable insights into the metabolic processes of the biological samples being studied (Van Der Windt et al., 2016; Yepez et al., 2018; Zdrazilova et al., 2022). For example, Seahorse XF uses fluorescence quenching to measure OCR and ECAR over time and is compatible with standard well plate format, allowing the use of multichannel pipettes to achieve high throughput. However, the light sources and optical probes inherent to Seahorse XF hinder it from being further miniaturized. It also requires a large quantity of biological samples (cells) to obtain meaningful readings. Moreover, Seahorse XF’s ability is limited to OCR and ECAR, lacking the capacity for direct detection of other metabolites. Another commercial platform, O2k-FluoRespirometer (O2K), uses electrochemical sensors to measure OCR. Although it also has theability to measure ROS using a special kit, it is not label-free and requires an even larger sample size due to its larger well volume. Additionally, it lacks the ability to measure ECAR when compared to Seahorse XF. Other research platforms using the hybrid method include the one presented by Bavli et al. (Bavli et al., 2016), where lactate production rate (LPR) was added for its metabolic flux measurement.

[0191] Label-free techniques become highly desirable when biological samples need to be retrieved after measurement for future utilization. Several research platforms using label-free electrochemical sensors to measure OCR and other metabolites were proposed (Tanumihardja et al., 2021 ; Weltin et al., 2017). They are either limited in sensitivity and dynamic range or do not include some important metabolic flux measurements, such as ROS. More importantly, these research platforms were not designed to achieve high throughput.

[0192] Based on a previous study on metabolic sensors using electrochemical techniques (Catandi et al., 2024, 2023; Cheng et al., 2022a, 2022b; Obeidat et al., 2018; Obeidat et al., 2019a, 2019b), the study presents the exemplary apparatus to allow non-destructive, label-free, and realtime measurements of OCR, ROS, and ECAR simultaneously. Different systems reported sensor performance differently in terms of dynamic range and detection range.

[0193] The study reported exemplary apparatus’ performance in both and compared them. The exemplary apparatus can provide several benefits: 1) the exemplary apparatus reports higher dynamic range and comparable sensitivity for OCR and ECAR compared to the existing systems due to its sensor design and the native sensor integration into the system to reduce the impact of system-level noise on sensor performance; 2) the exemplary apparatus is scalable of not only allowing multiple metabolic flux measurements to be performed simultaneously on the same cell(s), but also allowing multiple independent measurements to be performed on different groups of cells simultaneously to achieve high throughput; 3) the highly integrated nature of the design results in a highly compact and mobile system that can be easily placed inside a variety of commercial and custom incubators making it more versatile for a wider range of applications. The exemplary apparatus has been successfully applied to measure the metabolic activities of live bovine embryos to achieve high-resolution measurements of OCR, ROS, and ECAR during the basal phase, followed by the oligomycin-induced ATP synthase inhibition phase to reveal the cellular mitochondrial responses before and after the introduction of an external stress agent. The exemplary apparatus presented in the study holds the potential to expand its applications by evaluating the effects of therapeutics on various other biological samples, such as precision-cut tissue slices and stem cells, in small offices or field settings.

[0194] Conclusion

[0195] The construction and arrangement of the systems and methods as shown in the various implementations, are illustrative only. Although only a few implementations have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes, proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative implementations. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the implementations without departing from the scope of the present disclosure.

[0196] The present disclosure contemplates methods, systems, and program products on any machine-readable media for accomplishing various operations. The implementation of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Implementations within the scope of the present disclosure include program products, including machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a computer or other machine with a processor. By way of example, such machine- readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures, and which can be accessed by a general purpose or special purpose computer or other machine with a processor.

[0197] When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a machine, the machine properly views the connection as a machine-readable medium. Thus, any such connection is properly termed a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

[0198] Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also, two or more steps may be performed concurrently or withpartial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.

[0199] Each and every feature described herein, and each and every combination of two or more of such features, is included within the scope of the present invention, provided that the features included in such a combination are not mutually inconsistent.

[0200] Although example embodiments of the disclosed technology are explained in detail herein, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the disclosed technology be limited in its scope to the details of construction and arrangement of components set forth in the following description or illustrated in the drawings. The disclosed technology is capable of other embodiments and of being practiced or carried out in various ways.

[0201] It must also be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” or “approximately” one particular value and / or to “about” or “approximately” another particular value. When such a range is expressed, other exemplary embodiments include from the one particular value and / or to the other particular value.

[0202] By “comprising” or “containing” or “including” is meant that at least the named compound, element, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, materials, particles, method steps, even if the other such compounds, material, particles, method steps have the same function as what is named.

[0203] Unless otherwise expressly stated, it is in no way intended that any method set forth herein be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim does not actually recite an order to be followed by its steps or it is not otherwise specifically stated in the claims or descriptions that the steps are to be limited to a specific order, it is no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including: matters of logic with respect to arrangement of steps or operational flow; plain meaning derived from grammatical organization or punctuation; the number or type of embodiments described in the specification.

[0204] While the methods and systems have been described in connection with certain embodiments and specific examples, it is not intended that the scope be limited to the particularembodiments set forth, as the embodiments herein are intended in all respects to be illustrative rather than restrictive.

[0205] The following patents, applications, and publications, as listed below and throughout this document, are hereby incorporated by reference in their entirety herein.Reference List #1 :[1] Amer, M.M.B., 2002. An accurate amperometric glucose sensor-based glucometer with eliminated cross-sensitivity. J. Med. Eng. Technol. 26, 208-213. https: / / doi.org / 10.1080 / 03091900210156797[2] Armbruster, D.A., Pry, T., 2008. Limit of blank, limit of detection and limit of quantitation.Clin. Biochem. Rev. 29 Suppl 1, S49-52.[3] Asif, A., Park, S.H., Manzoor Soomro, A., Khalid, M.A.U., Salih, A.R.C., Kang, B., Ahmed, E,Kim, K.H., Choi, K.H., 2021. Microphysiological system with continuous analysis of albumin for hepatotoxicity modeling and drug screening. J. Ind. Eng. Chem. 98, 318-326. https: / / doi.org / 10.1016 / j-jiec.2021.03.035[4] Babcock, G.T., 1999. How oxygen is activated and reduced in respiration. Proc. Natl. Acad.Sci. 96, 12971-12973. https: / / doi.org / 10.1073 / pnas.96.23.12971[5] Bavli, D., Prill, S., Ezra, E., Levy, G., Cohen, M., Vinken, M., Vanfleteren, J., Jaeger, M.,Nahmias, Y, 2016. Real-time monitoring of metabolic function in liver-on-chip microdevices tracks the dynamics of mitochondrial dysfunction. Proc. Natl. Acad. Sci. 113. https: / / doi.org / 10.1073 / pnas.1522556113[6] Begriche, K., Massart, J., Robin, M.-A., Borgne-Sanchez, A., Fromenty, B., 2011. Drug- induced toxicity on mitochondria and lipid metabolism: Mechanistic diversity and deleterious consequences for the liver. J. Hepatol. 54, 773-794. https: / / doi.Org / 10.1016 / j.jhep.2010.l l.006[7] Bird, D.K., Yan, L„ Vrotsos, K.M., Eliceiri, K.W., Vaughan, E.M., Keely, P.J., White, J.G.,Ramanujam, N., 2005. Metabolic Mapping of MCF10A Human Breast Cells via Multiphoton Fluorescence Lifetime Imaging of the Coenzyme NADH. Cancer Res. 65, 8766-8773. https: / / doi.org / 10.1158 / 0008-5472.CAN-04-3922[8] Borenfreund, E., Puerner, J. A., 1985. Toxicity determined in vitro by morphological alterations and neutral red absorption. Toxicol. Lett. 24, 119-124. https: / / doi.org / 10.1016 / 0378- 4274(85)90046-3[9] Catandi, G.D., Cheng, M.-H., Chicco, A.J., Chen, T., Carnevale, E.M., 2023. L-carnitine enhances developmental potential of bovine oocytes matured under high lipidconcentrations in vitro. Anim. Reprod. Sci. 252, 107249. https: / / doi.org / 10.1016Zj.anireprosci.2023.107249

[0010] Catandi, G.D., Fresa, K.J., Cheng, M.-H., Whitcomb, L.A., Broeckling, C.D., Chen, T.W.,Chicco, A. J., Carnevale, E.M., 2024. Follicular metabolic alterations are associated with obesity in mares and can be mitigated by dietary supplementation. Sci. Rep. 14, 7571. https: / / doi.org / 10.1038 / s41598-024-58323-0

[0011] Cheng, M.-H., Chicco, A.J., Ball, D., Chen, T.W., 2022a. Analysis of mitochondrial oxygen consumption and hydrogen peroxide release from cardiac mitochondria using electrochemical multi-sensors. Sens. Actuators B Chem. 360, 131641. https: / / doi.Org / 10.1016 / j.snb.2022.131641

[0012] Cheng, M.-H., Jannaman, E.A., Trowbridge, K., Reed, L., Schoolcraft, W.B., Chicco, A.J.,Yuan, Y, Carnevale, E.M., Chen, T.W., 2022b. NOVEL MICROSENSORS REVEALED THE IMPACT OF HIGH MATERNAL BODY WEIGHT AND ADVANCED MATERNAL AGING ON INDIVIDUAL HUMAN OOCYTE METABOLIC FUNCTION. Fertil. Steril.118, el53. https: / / doi.Org / 10.1016 / j.fertnstert.2022.08.446

[0013] Davis, G., 1985. Electrochemical techniques for the development of amperometric biosensors.Biosensors 1, 161-178. https: / / doi.org / 10.1016 / 0265-928X(85)80002-X

[0014] De La Torre-Sanchez, J.F., Preis, K., Seidel, G.E., 2006. Metabolic regulation of in-vitro- produced bovine embryos. I. Effects of metabolic regulators at different glucose concentrations with embryos produced by semen from different bulls. Reprod. Fertil. Dev.18, 585. https: / / doi.org / 10.1071 / RD05063

[0015] Divakaruni, A.S., Paradyse, A., Ferrick, D.A., Murphy, A.N., Jastroch, M., 2014. Analysis andInterpretation of Microplate-Based Oxygen Consumption and pH Data, in: Methods in Enzymology. Elsevier, pp. 309-354. https: / / doi.org / 10.1016 / B978-0-12-801415-8.00016-3

[0016] Feeny, R.M., Wydallis, J.B., Chen, T, Tobet, S., Reynolds, M.M., Henry, C.S., 2015. Analysis of Nitric Oxide from Chemical Donors Using CMOS Platinum Microelectrodes. Electroanalysis 27, 1104-1109. https: / / doi.org / 10.1002 / elan.201400510

[0017] Fry, R.C., Simpson, T.L., Squires, T.J., 1998. Ultrasonically guided transvaginal oocyte recovery from calves treated with or without GnRH. Theriogenology 49, 1077-1082. https: / / doi.org / 10. 1016 / S0093-691X(98)00057-0

[0018] Gerlache, M., Senturk, Zuh., Quarin, G., Kauffmann, J., 1997. Electrochemical behavior of H2 O 2 on gold. Electroanalysis 9, 1088-1092. https: / / doi.org / 10.1002 / elan.1140091411

[0019] Houghton, F.D., Thompson, J.G., Kennedy, C.J., Leese, H.J., 1996. Oxygen consumption and energy metabolism of the early mouse embryo. Mol. Reprod. Dev. 44, 476-485. https: / / doi.org / 10. 1002 / (SICI)1098-2795(199608)44:4<476::AID-MRD7>3.0.CO;2-I

[0020] Hynes, J., Marroquin, L.D., Ogurtsov, V.I., Christiansen, K.N., Stevens, G.J., Papkovsky,D.B., Will, Y, 2006. Investigation of Drug-Induced Mitochondrial Toxicity Using Fluorescence-Based Oxygen-Sensitive Probes. Toxicol. Sci. 92, 186-200. https: / / doi.org / 10.1093 / toxsci / kfj208

[0021] Im, I., Jang, M„ Park, S.J., Lee, S.-H., Choi, J.-H., Yoo, H.-W., Kim, S„ Han, Y.-M., 2015.Mitochondrial Respiratory Defect Causes Dysfunctional Lactate Turnover via AMP- activated Protein Kinase Activation in Human-induced Pluripotent Stem Cell-derived Hepatocytes. J. Biol. Chem. 290, 29493-29505. https: / / doi.org / 10.1074 / jbc.M115.670364

[0022] Junot, C., Fenaille, F., Colsch, B., Becher, E, 2014. High resolution mass spectrometry based techniques at the crossroads of metabolic pathways. Mass Spectrom. Rev. 33, 471-500. https: / / doi.org / 10.1002 / mas.21401

[0023] Kenwood, B.M., Weaver, J.L., Bajwa, A., Poon, I.K., Byme, F.L., Murrow, B.A., Calderone,J. A., Huang, L., Divakaruni, A.S., Tomsig, J.L., Okabe, K., Lo, R.H., Cameron Coleman, G., Columbus, L., Yan, Z., Saucerman, J.J., Smith, J.S., Holmes, J.W., Lynch, K.R., Ravichandran, K.S., Uchiyama, S., Santos, W.L., Rogers, G.W., Okusa, M.D., Bayliss, D.A., Hoehn, K.L., 2014. Identification of a novel mitochondrial uncoupler that does not depolarize the plasma membrane. Mol. Metab. 3, 114-123. https: / / doi.Org / 10.1016 / j.molmet.2013. l l.005

[0024] Komfeld, O.S., Hwang, S., Disatnik, M.-H., Chen, C.-H., Qvit, N., Mochly-Rosen, D., 2015.Mitochondrial Reactive Oxygen Species at the Heart of the Matter: New Therapeutic Approaches for Cardiovascular Diseases. Circ. Res. 116, 1783-1799. https: / / doi.org / 10. 1161 / CIRCRESAHA. 116.305432

[0025] Krishtalik, L.I., 2003. pH-dependent redox potential: how to use it correctly in the activation energy analysis. Biochim. Biophys. Acta BBA - Bioenerg. 1604, 13-21. https: / / doi.org / 10.1016 / S0005-2728(03)00020-3

[0026] Kurokawa, H., Ito, H., Inoue, Mai, Tabata, K., Sato, Y, Yamagata, K., Kizaka-Kondoh, S.,Kadonosono, T., Yano, S., Inoue, Masahiro, Kamachi, T, 2015. High resolution imaging of intracellular oxygen concentration by phosphorescence lifetime. Sci. Rep. 5, 10657. https: / / doi.org / 10.1038 / srepl0657

[0027] Leese, H.J., Conaghan, J., Martin, K.L., Hardy, K., 1993. Early human embryo metabolism.BioEssays 15, 259-264. https: / / doi.org / 10.1002 / bies.950150406

[0028] Li Puma, L.C., Hedges, M., Heckman, J.M., Mathias, A.B., Engstrom, M.R., Brown, A.B.,Chicco, A. J., 2020. Experimental oxygen concentration influences rates of mitochondrial hydrogen peroxide release from cardiac and skeletal muscle preparations. Am. J. Physiol.- Regul. Integr. Comp. Physiol. 318, R972-R980. https: / / doi.org / 10.1152 / ajpregu.00227.2019

[0029] Marshall, J., 2011. Transwell® Invasion Assays, in: Wells, C.M., Parsons, M. (Eds.), CellMigration, Methods in Molecular Biology. Humana Press, Totowa, NJ, pp. 97-110. https: / / doi.org / 10.1007 / 978-l-61779-207-6_8

[0030] Matrosovich, M., Matrosovich, T., Garten, W., Klenk, H.-D., 2006. New low-viscosity overlay medium for viral plaque assays. Virol. J. 3, 63. https: / / doi.org / 10.1186 / 1743-422X-3-63

[0031] Mik, E.G., Johannes, T., Zuurbier, C.J., Heinen, A., Houben-Weerts, J.H.P.M., Balestra, G.M.,Stap, J., Beek, J.E, Ince, C., 2008. In Vivo Mitochondrial Oxygen Tension Measured by a Delayed Fluorescence Lifetime Technique. Biophys. J. 95, 3977-3990. https: / / doi.org / 10.1529 / biophysj.107.126094

[0032] Obeidat, Y., Catandi, G., Carnevale, E., Chicco, A.J., DeMann, A., Field, S., Chen, T, 2019. A multi-sensor system for measuring bovine embryo metabolism. Biosens. Bioelectron. 126, 615-623. https: / / doi.Org / 10.1016 / j.bios.2018.09.071

[0033] Obeidat, Y.M., Cheng, M.-H., Catandi, G., Carnevale, E., Chicco, A.J., Chen, T.W., 2019.Design of a multi-sensor platform for integrating extracellular acidification rate with multimetabolite flux measurement for small biological samples. Biosens. Bioelectron. 133, 39- 47. https : / / doi.org / 10.1016 / j .bios .2019.02.069

[0034] Obeidat, Y.M., Evans, A.J., Tedjo, W, Chicco, A.J., Carnevale, E„ Chen, T.W., 2018.Monitoring oocyte / embryo respiration using electrochemical -based oxygen sensors. Sens. Actuators B Chem. 276, 72-81. https: / / doi.Org / 10.1016 / j.snb.2018.07.157

[0035] Ozkan, S.A., Kauffmann, J.-M., Zuman, P., 2015. Electroanalysis in Biomedical andPharmaceutical Sciences: Voltammetry, Amperometry, Biosensors, Applications, Monographs in Electrochemistry. Springer Berlin Heidelberg, Berlin, Heidelberg. https: / / doi.org / 10.1007 / 978-3-662-47138-8

[0036] Page, B„ Page, M„ Noel, C., 1993. A NEW FLUOROMETRIC ASSAY FORCYTOTOXICITY MEASUREMENTS IN-VITRO. Int. J. Oncol. https: / / doi.org / 10.3892 / ijo.3-3.473

[0037] Park, H.S., Kim, J.H., Sun, B.K., Song, S.U., Suh, W, Sung, J.-H., 2016. Hypoxia induces glucose uptake and metabolism of adipose-derived stem cells. Mol. Med. Rep. 14, 4706- 4714. https: / / doi.org / 10.3892 / mmr.2016.5796

[0038] Restouin, A., Aresta, S., Prebet, T., Borg, J.-P., Badache, A., Collette, Y., 2009. A simplified,96-well-adapted, ATP luminescence-based motility assay. BioTechniques 47, 871-875. https : / / doi.org / 10.2144 / 000113250

[0039] Rogatzki, M.J., Ferguson, B.S., Goodwin, M.L., Gladden, L.B., 2015. Lactate is always the end product of glycolysis. Front. Neurosci. 9. https: / / doi.org / 10.3389 / fnins.2015.00022

[0040] Ruiz-Rodado, V., Lita, A., Larion, M., 2022. Advances in measuring cancer cell metabolism with subcellular resolution. Nat. Methods 19, 1048-1063. https: / / doi.org / 10.1038 / s41592- 022-01572-6

[0041] Samantasinghar, A., Ahmed, F., Rahim, C.S.A., Kim, K.H., Kim, S., Choi, K.H., 2023.Artificial intelligence-assisted repurposing of lubiprostone alleviates tubulointerstitial fibrosis. Transl. Res. 262, 75-88. https: / / doi.Org / 10.1016 / j.trsl.2023.07.010

[0042] Sanchez-Martinez, R., Cruz-Gil, S., De Cedron, M.G., Alvarez-Fernandez, M., Vargas, T,Molina, S., Garcia, B., Herranz, J., Moreno-Rubio, J., Reglero, G., Perez-Moreno, M., Feliu, J., Malumbres, M., De Molina, A.R., 2015. A link between lipid metabolism and epithelial-mesenchymal transition provides a target for colon cancer therapy. Oncotarget 6, 38719-38736. https : / / doi. org / 10.18632 / oncotarget .5340

[0043] Stringari, C., Cinquin, A., Cinquin, O., Digman, M.A., Donovan, P.J., Gratton, E., 2011.Phasor approach to fluorescence lifetime microscopy distinguishes different metabolic states of germ cells in a live tissue. Proc. Natl. Acad. Sci. 108, 13582-13587. https: / / doi.org / 10.1073 / pnas.1108161108

[0044] Suski, J.M., Lebiedzinska, M., Bonora, M., Pinton, P, Duszynski, J., Wieckowski, M.R.,2012. Relation Between Mitochondrial Membrane Potential and ROS Formation, in: Palmeira, C.M., Moreno, A. J. (Eds.), Mitochondrial Bioenergetics, Methods in Molecular Biology. Humana Press, Totowa, NJ, pp. 183-205. https: / / doi.org / 10.1007 / 978-l-61779- 382-0_12

[0045] Tanumihardja, E., Slaats, R.H., Van Der Meer, A.D., Passier, R., Olthuis, W., Van Den Berg,A., 2021. Measuring Both pH and O 2 with a Single On-Chip Sensor in Cultures of Human Pluripotent Stem Cell-Derived Cardiomyocytes to Track Induced Changes in Cellular Metabolism. ACS Sens. 6, 267-274. https: / / doi.org / 10.1021 / acssensors.0c02282

[0046] Tejera, A., Herrero, J., De Los Santos, M.J., Garrido, N., Ramsing, N., Meseguer, M., 2011.Oxygen consumption is a quality marker for human oocyte competence conditioned by ovarian stimulation regimens. Fertil. Steril. 96, 618-623.e2. https: / / doi.Org / 10.1016 / j.fertnstert.2011.06.059

[0047] Terkuile, F., White, N.J., Holloway, P., Pasvol, G., Krishna, S., 1993. Plasmodium falciparum:In Vitro Studies of the Pharmacodynamic Properties of Drugs Used for the Treatment of Severe Malaria. Exp. Parasitol. 76, 85-95. https: / / doi.org / 10.1006 / expr.1993.1010

[0048] Vaamonde-Garcia, C., Loureiro, J., Valcarcel-Ares, M.N., Riveiro-Naveira, R.R., Ramil-Gomez, O., Hermida-Carballo, L., Centeno, A., Meijide-Failde, R., Blanco, F.J., Lopez- Armada, M.J., 2017. The mitochondrial inhibitor oligomycin induces an inflammatory response in the rat knee joint. BMC Musculoskelet. Disord. 18, 254. https : / / doi.org / l 0. 1186 / s 12891 -017- 1621 -2

[0049] Van Der Windt, G.J.W., Chang, C., Pearce, E.L., 2016. Measuring Bioenergetics in T CellsUsing a Seahorse Extracellular Flux Analyzer. Curr. Protoc. Immunol. 113. https : / / doi.org / l 0.1002 / 0471142735.im0316bs 113

[0050] Vander Heiden, M.G., Cantley, L.C., Thompson, C.B., 2009. Understanding the WarburgEffect: The Metabolic Requirements of Cell Proliferation. Science 324, 1029-1033. https: / / doi.org / 10.1126 / science.l 160809

[0051] Wang, X., Wang, W, Li, L., Perry, G., Lee, H., Zhu, X., 2014. Oxidative stress and mitochondrial dysfunction in Alzheimer’s disease. Biochim. Biophys. Acta BBA - Mol. Basis Dis. 1842, 1240-1247. https: / / doi.Org / 10.1016 / j.bbadis.2013.10.015

[0052] Weltin, A., Hammer, S., Noor, F., Kaminski, Y, Kieninger, J., Urban, G.A., 2017. Accessing3D microtissue metabolism: Lactate and oxygen monitoring in hepatocyte spheroids. Biosens. Bioelectron. 87, 941-948. https: / / doi.Org / 10.1016 / j.bios.2016.07.094

[0053] Wicksted, J.P., Erckens, R.J., Motamedi, M., March, W.F, 1995. Raman Spectroscopy Studies of Metabolic Concentrations in Aqueous Solutions and Aqueous Humor Specimens. Opt. Publ. Group 49, 987-993.

[0054] Will, Y, Hynes, J., Ogurtsov, V.I., Papkovsky, D.B., 2006. Analysis of mitochondrial function using phosphorescent oxygen-sensitive probes. Nat. Protoc. 1, 2563-2572. https: / / doi.org / 10.1038 / nprot.2006.351

[0055] Yepez, V.A., Kremer, L.S., luso, A., Gusic, M., Kopajtich, R., Konankova, E., Nadel, A.,Wachutka, L., Prokisch, H., Gagneur, J., 2018. OCR-Stats: Robust estimation and statistical testing of mitochondrial respiration activities using Seahorse XF Analyzer. PLOS ONE 13, e0199938. https: / / doi.org / 10.1371 / joumal.pone.0199938

[0056] Yin, X., Lanza, I.R., Swain, J.M., Sarr, M.G., Nair, K.S., Jensen, M.D., 2014. AdipocyteMitochondrial Function Is Reduced in Human Obesity Independent of Fat Cell Size. J. Clin. Endocrinol. Metab. 99, E209-E216. https: / / doi.org / 10.1210 / jc.2013-3042

[0057] Yotter, R.A., Wilson, D.M., 2004. Sensor Technologies for Monitoring Metabolic Activity inSingle Cells — Part II: Nonoptical Methods and Applications. IEEE Sens. J. 4, 412-429. https: / / doi.org / 10. 1109 / JSEN.2004.830954

[0058] Zdrazilova, L., Hansikova, H., Gnaiger, E., 2022. Comparable respiratory activity in attached and suspended human fibroblasts. PLOS ONE 17, e0264496. https: / / doi.org / 10.1371 / joumal.pone.0264496

[0059] Zhao, H., Yang, L., Baddour, J., Achreja, A., Bernard, V., Moss, T, Marini, J.C., Tudawe, T.,Seviour, E.G., San Lucas, F.A., Alvarez, H., Gupta, S., Maiti, S.N., Cooper, L., Peehl, D., Ram, P.T., Maitra, A., Nagrath, D., 2016. Tumor microenvironment derived exosomes pleiotropically modulate cancer cell metabolism. eLife 5, el0250.Reference List #2:[1 '] Agilent Seahorse Extracellular Flux (XF) Pro analyzer, A Transformative Live-Cell Metabolic Assay Platform, https: / / www.agilent.com / cs / library / datasheets / public / flyer-xf-data-sheet- 5994-4264en.pdf / , 2023 (accessed 10 March 2024).[2'] Bavli, D., Prill, S., Ezra, E., Levy, G., Cohen, M., Vinken, M., Vanfleteren, J., Jaeger, M., Nahmias, Y, 2016. Real-time monitoring of metabolic function in liver-on-chip microdevices tracks the dynamics of mitochondrial dysfunction. Proc. Natl. Acad. Sci. 113.[3'] Gnaiger, E., 2024. O2k-Specifications for respirometry and comprehensive OXPHOS analysis. Tanumihardja, E., Slaats, R.H., Van Der Meer, A.D., Passier, R., Olthuis, W., Van Den Berg, A., 2021. Measuring Both pH and O 2 with a Single On-Chip Sensor in Cultures of Human Pluripotent Stem Cell-Derived Cardiomyocytes to Track Induced Changes in Cellular Metabolism. ACS Sens. 6, 267-274.[4'] Weltin, A., Hammer, S., Noor, F., Kaminski, Y, Kieninger, J., Urban, G.A., 2017. Accessing 3D microtissue metabolism: Lactate and oxygen monitoring in hepatocyte spheroids. Biosens. Bioelectron. 87, 941-948. https: / / doi.Org / 10.1016 / j.bios.2016.07.094EMBODIMENTSEmbodiment 1. An apparatus comprising: a sensor plate with electrodes formed on, or proximal to, a surface thereof, wherein the sensor plate has an area that defines a first region with the electrodes and a second region with terminals for the electrodes; a chamber housing member configured to sealably couple to the sensor plate to define an internal volume for a cell culture chamber, the housing having a recess or channel formed therein and around the cell culture chamber to receive a sealing element to form the seal with the sensor plate when coupled thereto, wherein the chamber housing member includes a port assembly extending in a direction away from the sensor plate, the port assembly being in fluid connection with the internal volume; a printed circuit board configured to couple to the chamber housing member, the printed circuit board comprising a surface having conductive pins extending therefrom, the conductive pins having positions in correspondence to the second region of the sensor plate when the printed circuit board is operatively coupled to the sensor plate to electrically couple the terminals; and a clamp housing assembly configured to fixably couple to the chamber housing member to encapsulate the chamber housing member; and a retaining member fixably coupled to the sensor plate, to maintain an alignment between the electrodes and the conductive pins when the sensor plate is being pushed towards the chamber housing member.Embodiment 2. The apparatus of embodiment 1 , wherein the clamp housing assembly comprises (i) a first clamp housing member that mounts the chamber housing member and (ii) a second clamp housing member having an attaching member to attach the first clamp housing member via an insertion operation.Embodiment 3. The apparatus of any one of embodiments 1-2, further comprising: a compression member, disposed in the clamp housing assembly, configured to urge the sensor plate towards the chamber housing member, to maintain compression of the sealing element between the sensor plate and the cell culture chamber.Embodiment 4. The apparatus of any one of embodiments 1-3, wherein the retaining member is formed of a transparent material.Embodiment 5. The apparatus of any one of embodiments 1-4, further comprising: one or more foam layers operatively coupled to the retaining member, wherein the one or more foam layers are disposed in a contact space between the compression member and the sensor plate, such that the compression member pushes, via the one or more foam layers, the sensor plate without physically touching the sensor plate.Embodiment 6. The apparatus of any one of embodiments 1-5, wherein the attaching member is a hook or a clip-on fin.Embodiment 7. The apparatus of any one of embodiments 1-6, further comprising: a viewing port, formed on a surface of the clamp housing assembly, configured to optically expose the cell culture chamber to the outside of the apparatus, wherein the viewing port is aligned with one or more viewing holes on the one or more foam layers and the compression member.Embodiment 8. The apparatus of any one of embodiments 1-7, further comprising: a microchamber lid configured to seal the port assembly, from the fluid connection with the internal volume, when being rotated.Embodiment 9. The apparatus of any one of embodiments 1-8, wherein the microchamber lid has a set of stirring tips configured to stir the internal volume, when the microchamber lid is being rotated.Embodiment 10. The apparatus of any one of embodiments 1-9, wherein the electrodes of the sensor plate form (i) potentiometric sensors and (ii) amperometric sensors.Embodiment 11. The apparatus of any one of embodiments 1-10, wherein one or more electrodes, in the electrodes of the sensor plate, are configured to be transparent potentiometric sensors, and wherein the one or more electrodes are located at the center / middle of the sensor plate, or proximal thereto.Embodiment 12. The apparatus of any one of embodiments 1-9, wherein the electrodes of the sensor plate include at least one of an amperometric sensor, potentiometric sensor, enzymic sensor, impedance sensor, capacitance sensor, resistive sensor, temperature sensor, electrochemical and optical sensor, ultrasonic sensor, magnetic sensor, and potentiometric sensor.Embodiment 13. A method comprising: providing an apparatus comprising: a sensor plate with electrodes formed on, or proximal to, a surface thereof, wherein the sensor plate has an area that defines a first region with the electrodes and a second region with terminals for the electrodes; a chamber housing member configured to sealably couple to the sensor plate to define an internal volume for a cell culture chamber, the housing having a recess or channel formed therein and around the cell culture chamber to receive a sealing element to form the seal with the sensor plate when coupled thereto, wherein the chamber housing member includes a port assembly extending in a direction away from the sensor plate, the port assembly being in fluid connection with the internal volume; and a printed circuit board configured to couple to the chamber housing member, the printed circuit board comprising a surface having conductive pins extending therefrom, the conductive pins having positions in correspondence to the second region of the sensor plate when the printed circuit board is operatively coupled to the sensor plate to electrically couple the terminals; and a clamp housing assembly configured to fixably couple to the chamber housing member to encapsulate the chamber housing member; and mounting the sensor plate to the chamber housing member via an alignment member to define the internal volume for the cell culture chamber to form an internal assembly, wherein the mounting maintain an alignment between the electrodes and the conductive pins; and assembling the internal assembly via a clamp housing assembly configured to fixably couple to the chamber housing member to encapsulate the chamber housing member.Embodiment 14. The method of embodiment 13, wherein the clamp housing assembly includes (i) a first clamp housing member that mounts the chamber housing member and (ii) a second clamp housing member having an attaching member to attach the first clamp housing member via an insertion operation.Embodiment 15. The method of any one of embodiments 1 -14, further comprising: inspecting alignment between the electrodes and the conductive pins before the internal assembly is assembled in the clamp housing assembly.Embodiment 16. The method of any one of embodiments 13-15, further comprising: placing one or more foam layers in a contact space between the compression member and the sensor plate so the compression member pushes, via the one or more foam layers, the sensor plate without physically touching the sensor plate.Embodiment 17. The method of any one of embodiments 13-16, wherein the attaching member is a hook or a clip-on fin.Embodiment 18. The method of any one of embodiments 13-17, further comprising: aligning the clamp housing assembly, the sensor plate, and the chamber housing member to form a viewing port, the viewing port being configured to optically expose the cell culture chamber to the outside of the clamp housing assembly.Embodiment 19. The method of any one of embodiments 13-18, wherein the apparatus includes a microchamber lid.Embodiment 20. The method of embodiment 19, wherein the microchamber lid has a set of stirring tips configured to stir the internal volume when the microchamber lid is being rotated.Embodiment 21. An apparatus comprising: a sensor plate with electrodes formed on, or proximal to, a surface thereof, wherein the sensor plate has an area that defines a first region with the electrodes and a second region with terminals for the electrodes; a chamber housing member configured to sealably couple to the sensor plate to define an internal volume for a cell culture chamber, the housing having a recess or channel formed therein and around the cell culture chamber to receive a sealing element to form the seal with the sensor plate when coupled thereto, wherein the chamber housing member includes a port assembly extending in a direction away from the sensor plate, the port assembly being in fluid connection with the internal volume; and a printed circuit board configured to couple to the chamber housing member, the printed circuit board comprising a surface having conductive pins extending therefrom, the conductive pins having positions in correspondence to the second region of the sensor plate when the printed circuit board is operatively coupled to the sensor plate to electrically couple the terminals.Embodiment 22. The apparatus of embodiment 21, further comprising:an external housing assembly to encapsulate the sensor plate, the chamber housing member, and the printed circuit board, wherein the external housing assembly includes a first external housing member and a second external housing member, wherein the second external housing member is fixably coupled to the first external housing to define a space for the sensor plate, the chamber housing member, and the printed circuit board.Embodiment 23. The apparatus of any one of embodiments 21-22, wherein the external housing assembly includes holes for connectors for secured and fixed connection to a break-down board.Embodiment 24. The apparatus of any one of embodiments 21-23, wherein the chamber housing member includes a port assembly extending in a direction away from the sensor plate, the printed circuit board having an open region for the port assembly to surround a portion of the port assembly when the printed circuit board is seated in the external housing assembly.Embodiment 25. The apparatus of any one of embodiments 21-24, wherein the printed circuit board has a length extending from the external housing assembly, the printed circuit board having a connector attached thereon located at said length.Embodiment 26. The apparatus of any one of embodiments 21-25, wherein at least one of the first external housing member and the second external housing member has a recess to receive the connector when the printed circuit board is seated in the external housing assembly, the recess having a wall region to contact and support the connector.Embodiment 27. The apparatus of any one of embodiments 21-26, wherein the internal volume for the cell culture chamber is configured to house and maintain living cells in a controlled environment, wherein the internal volume for a cell culture chamber is sized to facilitate laminar flow formation.Embodiment 28. The apparatus of any one of embodiments 21-27, wherein the chamber housing member has a set of channels that couple ports of the port assembly to the internal volume for the cell culture chamberEmbodiment 29. The apparatus of any one of embodiments 21-28, wherein the electrodes of the sensor plate form integrated sensors to detect and measure metabolic activities of living cells.Embodiment 30. The apparatus of any one of embodiments 21-29, wherein the electrodes of the sensor plate form (i) potentiometric sensors and (ii) amperometric sensors.Embodiment 31. The apparatus of any one of embodiments 21-30, wherein one or more electrodes, in the electrodes of the sensor plate, are configured to be transparent potentiometric sensors, and wherein the one or more electrodes are located at the center / middle of the sensor plate, or proximal thereto.Embodiment 32. The apparatus of any one of embodiments 21-31, wherein the transparent potentiometric sensors are made of indium tin oxide (ITO).Embodiment 33. The apparatus of any one of embodiments 21-32, further comprising: a retaining member operatively coupled to the sensor plate to maintain an alignment between the conductive pins and electrode pads of the sensor plate.Embodiment 34. The apparatus of any one of embodiments 21-33, further comprising: one or more foam layers operatively coupled to the retaining member; and a spring, positioned on the one or more foam layers, configured to urge the one or more foam layers toward the retaining member.Embodiment 35. The apparatus of any one of embodiments 21-34, further comprising two housing, including a top housing and a bottom housing, defining an enclosure for the sensor plate, the chamber housing member, the printed circuit board, the retaining member, the one or more foam layers, and the spring.Embodiment 36. The apparatus of any one of embodiments 21-35, further comprising: a viewing port on the bottom housing, aligned with one or more viewing holes on the one or more foam layers, configured to optically expose the cell culture chamber to the outside the enclosure.Embodiment 37. The apparatus of any one of embodiments 21-36, further comprising: a host computer, operatively coupled to the printed circuit board, configured to: receive, in a cell measurement / monitoring process, information of living cells and biological samples in the cell culture chamber; predict, via a trained Al model, outcomes of the cell measurement / monitoring process based on conditions of the living cells and biological samples in the cell culture chamber, wherein the trained Al model was trained using datasets acquired from either the historical measurement data or existing cell measurement / monitoring experiments; and output the predicted outcomes of the cell measurement / monitoring process, wherein the output is subsequently employed for determining next steps of the cell measurement / monitoring process.Embodiment 38. The apparatus of any one of embodiments 21-36, further comprising: a host computer, located on a cloud infrastructure, configured to: receive, in a cell measurement / monitoring process, information of living cells and biological samples in the cell culture chamber; predict, via a trained Al model, outcomes of the cell measurement / monitoring process based on conditions of the living cells and biological samples in the cell culture chamber, wherein the trained Al model was trained using either the historical measurement data or datasets acquired from existing cell measurement / monitoring experiments; and output the predicted outcomes of the cell measurement / monitoring process, wherein the output is subsequently employed for determining next steps of the cell measurement / monitoring process.Embodiment 39. A microchamber device configured to house and maintain living cells in a controlled environment comprising the apparatus of any one of embodiments 1-38.Embodiment 40. A system comprising a plurality of microchambers, each comprising the apparatus of any one of embodiments 1-39.Embodiment 41. The system of any one of embodiments 1-40 further comprising: external reservoirs and / or pumps, and other microfluidic support apparatus, for precise control of fluid delivery and removal configured to operate on feedback from integrated sensors formed by, or in part by, the electrodes to control fluid flow rates to the internal volume for a cell culture chamber.Embodiment 42. A method comprising: providing a first apparatus of any one of embodiments 1-41; attaching the first apparatus to an external controller; performing cell culture and monitoring of first samples using the first apparatus; detaching the first apparatus from the external controller; and providing a second apparatus of any one of embodiments 1-41; and re- attaching the second apparatus to the external controller to performing cell culture and monitoring for second samples using the second apparatus.Embodiment 43. A system comprising: a processor; and a memory having instructions stored thereon, wherein execution of the instructions causes the processor to: receive measurement data (e.g., acquired from the device of any one of embodiments 1-42);determine, via a trained Al model executing (locally or globally), an estimate of measurement stability; and output the estimate, wherein the estimate is used to generate a notification to a user that a measurement and conditions in the microchamber are stable.Embodiment 44. The system of Embodiment 43, wherein the estimate includes a stability indicator of baseline measurements.Embodiment 45. The system of Embodiment 44, wherein the estimate includes a stability indicator of basal measurements.Embodiment 46. The system of any one of Embodiments 43 - 44, wherein the estimate includes a stability indicator of therapeutic measurements.Embodiment 47. A method comprising: receiving measurement data (e.g., from a system or apparatus of any one of Embodiments 1- 46; determining, via a trained Al model executing (locally or globally), an estimate of measurement stability; and outputting the estimate, wherein the estimate is used to generate a notification to a user that a measurement and conditions in the microchamber are stable.Embodiment 48. The method of Embodiment 47, wherein the trained Al model was trained, in part using historical measurement data acquired from the system or apparatus.Embodiment 49. The method of Embodiments 47 or 48, wherein the output of the trained Al model is used to indicate stability for baseline measurement when no specimen is provided in the microchamber, basal measurement when cell / tissue specimen is provided in the microchamber, and therapeutic measurement when a stimuli or condition under study is provided or invoked in the microchamber.

Claims

CLAIMSWhat is claimed:

1. An apparatus comprising: a sensor plate with electrodes formed on, or proximal to, a surface thereof, wherein the sensor plate has an area that defines a first region with the electrodes and a second region with terminals for the electrodes; a chamber housing member configured to sealably couple to the sensor plate to define an internal volume for a cell culture or tissue ex- vivo chamber, the housing having a recess or channel formed therein and around the cell culture or tissue ex- vivo chamber to receive a sealing element to form the seal with the sensor plate when coupled thereto, wherein the chamber housing member includes a port assembly extending in a direction away from the sensor plate, the port assembly being in fluid connection with the internal volume; a printed circuit board configured to couple to the chamber housing member, the printed circuit board comprising a surface having conductive pins extending therefrom, the conductive pins having positions in correspondence to the second region of the sensor plate when the printed circuit board is operatively coupled to the sensor plate to electrically couple the terminals; and a clamp housing assembly configured to fixably couple to the chamber housing member to encapsulate the chamber housing member; and a retaining member fixably coupled to the sensor plate, to maintain an alignment between the electrodes and the conductive pins when the sensor plate is being pushed towards the chamber housing member during assembly of the sensor plate and the chamber housing member.

2. The apparatus of claim 1, wherein the clamp housing assembly comprises (i) a first clamp housing member that mounts the chamber housing member and (ii) a second clamp housing member having an attaching member to attach the first clamp housing member via an insertion operation.

3. The apparatus of any one of claims 1-2, further comprising: a compression member, disposed in the clamp housing assembly, configured to urge the sensor plate towards the chamber housing member, to maintain compression of the sealing element between the sensor plate and the cell culture or tissue ex-vivo chamber.

4. The apparatus of any one of claims 1-3, wherein the retaining member is formed of a transparent material at pre-defined positions to allow visual inspection for alignment during assembly.

5. The apparatus of any one of claims 1-4, further comprising: one or more foam layers operatively coupled to the retaining member, wherein the one or more foam layers are disposed in a contact space between the compression member and the sensor plate, such that the compression member pushes, via the one or more foam layers, the sensor plate without physically touching the sensor plate.

6. The apparatus of any one of claims 1-5, wherein the attaching member is a hook or a clip-on fin.

7. The apparatus of any one of claims 1-6, further comprising: a viewing port, formed on a surface of the clamp housing assembly, configured to optically expose the cell culture or tissue ex-vivo chamber to the outside of the apparatus, wherein the viewing port is aligned with one or more viewing holes on the one or more foam layers, the compression member, and the clamps.

8. The apparatus of any one of claims 1-7, further comprising: a microchamber lid configured to seal the port assembly, from the fluid connection with the internal volume, when being rotated (e.g., wherein the microchamber lid has a set of stirring tips configured to stir the internal volume, when the microchamber lid is being rotated).

9. The apparatus of any one of claims 1-8, wherein the microchamber includes an inlet port and an outlet port.

10. The apparatus of any one of claims 1-9, wherein the electrodes of the sensor plate form (i) potentiometric sensors and (ii) amperometric sensors.11 . The apparatus of any one of claims 1- 10, wherein one or more electrodes, in the electrodes of the sensor plate, are configured to be transparent potentiometric sensors, and wherein the one or more electrodes are located at the center / middle of the sensor plate, or proximal thereto.

12. The apparatus of any one of claims 1-9, wherein the electrodes of the sensor plate include at least one of an amperometric sensor, potentiometric sensor, enzymic sensor, impedance sensor, capacitance sensor, resistive sensor, temperature sensor, electrochemical and optical sensor, ultrasonic sensor, magnetic sensor, and potentiometric sensor.

13. A method comprising : providing an apparatus comprising: a sensor plate with electrodes formed on, or proximal to, a surface thereof, wherein the sensor plate has an area that defines a first region with the electrodes and a second region with terminals for the electrodes; a chamber housing member configured to sealably couple to the sensor plate to define an internal volume for a cell culture or tissue ex- vivo chamber, the housing having a recess or channel formed therein and around the cell culture or tissue ex-vivo chamber to receive a sealing element to form the seal with the sensor plate when coupled thereto, wherein the chamber housing member includes a port assembly having an inlet port and an output port extending in a direction away from the sensor plate, the port assembly being in fluid connection with the internal volume; and a printed circuit board configured to couple to the chamber housing member, the printed circuit board comprising a surface having conductive pins extending therefrom, the conductive pins having positions in correspondence to the second region of the sensor plate when the printed circuit board is operatively coupled to the sensor plate to electrically couple the terminals; and a clamp housing assembly configured to fixably couple to the chamber housing member to encapsulate the chamber housing member; and mounting the sensor plate to the chamber housing member via an alignment member to define the internal volume for the cell culture or tissue ex-vivo chamber to form an internal assembly, wherein the mounting maintains an alignment between the electrodes and the conductive pins; and assembling the internal assembly via a clamp housing assembly configured to fixably couple to the chamber housing member to encapsulate the chamber housing member.

14. The method of claim 13, wherein the clamp housing assembly includes (i) a first clamp housing member that mounts the chamber housing member and (ii) a second clamp housingmember having an attaching member to attach the first clamp housing member via an insertion operation.

15. The method of any one of claims 13-14, further comprising: inspecting alignment between the electrodes and the conductive pins before the internal assembly is assembled in the clamp housing assembly.

16. The method of any one of claims 13-15, further comprising: placing one or more foam layers in a contact space between the compression member and the sensor plate so the compression member pushes, via the one or more foam layers, the sensor plate without physically touching the sensor plate.

17. The method of any one of claims 13-16, wherein the attaching member is a hook or a clip-on fin.

18. The method of any one of claims 13-17, further comprising: aligning the clamp housing assembly, the sensor plate, and the chamber housing member to form a viewing port, the viewing port being configured to optically expose the cell culture or tissue ex-vivo chamber to the outside of the clamp housing assembly.

19. The method of any one of claims 13-18, wherein the apparatus includes a microchamber lid (e.g., wherein the microchamber lid has a set of stirring tips configured to stir the internal volume when the microchamber lid is being rotated).

20. The method of any one of claims 13-18, further comprising: controlling the microchamber lid using a set of stirring tips disposed on the microchamber lid.

21. A system comprising: an instrument comprising a main control board configured to operatively connect with an apparatus of any one of claims 1-12.

22. The system of claim 21, wherein the instrument further includes a plurality of intermediate data acquisition board, each intermediate data acquisition board configured to couple to a respective apparatus and to the main control board, the intermediate data acquisition board having powerregulation and instrumentation circuit for each respective apparatus (e.g., to minimize common mode noise and provide enhanced SNR).

23. The system of claim 21 or 22, comprising: a processor; and a memory having instructions stored thereon, wherein execution of the instructions causes the processor to: receive measurement data; determine, via a trained Al model executing (locally or globally), an estimate of measurement stability; and output the estimate, wherein the estimate is used to generate a notification to a user that a measurement and conditions in the microchamber are stable.

24. The system of claim 23, wherein the estimate includes a stability indicator of baseline measurements.

25. The system of claim 23 or 24, wherein the estimate includes a stability indicator of basal measurements.

26. The system of any one of claims 23 - 25, wherein the estimate includes a stability indicator of therapeutic measurements.

27. A method comprising: receiving measurement data (e.g., from a system or apparatus of any one of claims 1-12 and 21-26) determining, via a trained Al model executing (locally or globally), an estimate of measurement stability; and outputting the estimate, wherein the estimate is used to generate a notification to a user that a measurement and conditions in the microchamber are stable.

28. The method of claim 27, wherein the trained Al model was trained, in part using historical measurement data acquired from the system or apparatus.

29. The method of claim 27 or 28, wherein the output of the trained Al model is used to indicate stability for baseline measurement when no specimen is provided in the microchamber, basal measurement when cell / tissue specimen is provided in the microchamber, and therapeutic measurement when a stimuli or condition under study is provided or invoked in the microchamber.

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