System and method for analyzing compartementalized cell or tissue cultures

Abstract

Methods for analyzing cell or tissue cultures are described. In an embodiment, the method includes: receiving cellular or tissue data, wherein: the cell or tissue cultures are organized by organizational structures in the device; and the cellular or tissue data is correlated with at least one of: a physiological component of the cell or tissue cultures, and a position of the cell or tissue cultures in the device; identifying at least one region of interest in the cellular or tissue data corresponding to at least one of: the physiology of the cell or tissue cultures in the device and at least one of the organizational structures in the device; and processing the region of interest in the cellular or tissue data to measure at least one parameter relating to the cell or tissue cultures. Corresponding systems and computer-readable media are also described.

Description

SYSTEM AND METHOD FOR ANALYZING COM PARTEM ENTALIZED CELL OR TISSUE CULTURESTECHNICAL FIELD

[0001] The technical field generally relates to cell and tissue culture analysis. More particularly, the technical field relates to techniques for analyzing cells and tissues organized in 2D and 3D cultured in vitro according to a compartmentalized configuration.BACKGROUND

[0002] Models that can predict human or animal responses to various physical, chemical, and biological stimuli, and that are easily scalable to test the effects of single or multiple stimuli combined are desirable. Especially human in vitro models that can predict the effects of biological and chemical compounds in vivo are in large demand.

[0003] In vitro models can be used to refine, reduce, and replace animal testing, for instance in the pharmaceutical, chemical, vaccine, and cosmetic industries. In vitro models can offer scalability and are generally easier and more ethical to use than animals, offering opportunities for performing various experiments directly on biological materials derived from humans. These experiments can be performed for instance in relation to toxicity testing, efficacy testing, drug screening, drug repurposing, disease modelling, etc. In vitro models can advance research and development with applications for medical countermeasures for chemical, biological, radiological and nuclear agents that threaten the civilian population.

[0004] However, sorting, tracking, and classifying cells as well as processing images of cells and tissues cultured in vitro can be difficult, inaccurate, time consuming, subjective and computationally intensive due to the large variability of cellular morphology, multiple stages of cellular development, and random 2D and 3D organization of cells on each culture plate. In addition, evaluating the response of cells and tissues to stimuli over time remains a major challenge as imaging, tracking, sorting, and classifying cells organized in 2D and 3D structures can also be very labor intense and inaccurate due to growth and migration of cells within the culture over time, random organization of cells in the 2D and 3D structures, and challenges in imaging multiple planes (e.g., X, Y, and Z planes) of 2Dand 3D structures with reasonable magnification and focus for detailed imaging analysis. Accordingly, there remain significant challenges with respect to the analysis of cells and tissues cultured using existing in vitro models.SUMMARY

[0005] In the following disclosure, systems, methods and computer-readable media are provided to acquire and analyze data from cells and tissues precisely organized in defined compartments with known X, Y, and / or Z coordinates. The precise positioning of cells and tissues within well-defined microstructures restricts cell growth and migration within predetermined compartments enabling: 1) Faster microscope set up and automated data acquisition through imaging as the X, Y, and / or Z position of the cells is well known within the culture. 2) Standardization, as all images share the same coordinates. If data is acquired through sensors, compartmentalization also increases standardization as cells and tissues are confined or enriched on a precise compartment creating a microenvironment. 3) A tissue / cell type can be assigned to one compartment and another tissue or cell type to another compartment, and it can be easily identified by the X, Y and / or Z position what image or sensor signal belongs to what tissue. 4) The compartmentalization inhibits cells and tissues cultures to grow and / or migrate to other compartments, confining specific tissues and cells to their respective compartments. 5) The distance between compartments can also be controlled to promote or avoid contact and or migration between cells and tissues in different compartments. 6) The X, Y, and / or Z positioning of the cells significantly accelerates data acquisition through imaging and sensors, accelerating cellular and tissue tracking, sorting, classifying and monitoring over time. For instance, neuronal cells can be added to one compartment, connected by a channel to another compartment containing muscle cells. By acquiring data through images or sensors from the neuronal compartment, information on the neuronal response to stimuli can be obtained. By acquiring data from the muscle compartment, information on the muscular response to stimuli can be obtained. By acquiring data from the connecting channel, information can be obtained about the neuromuscular communications when the neuronal, the muscle, or both compartments are stimulated. 7) By controlling the Z (e.g., the height) of the compartments data acquisition through imaging and sensors becomes faster and automated. For example, a ceiling in a neuronal channel can limit 3D cell and tissue cultures (e.g., organoids, neurospheres, etc.) from moving inthe Z axis (up and down) inside the culture, thus the 3D cell and tissue cultures are kept at substantially the same height across assay windows. This can allow obtaining comparable images across assay windows by imaging each assay windows at a predetermined depth of focus, instead of having to adjust the depth of focus when imaging each individual assay window. 8) Precise and standard data acquisition facilitates data annotation, analysis, automation of data analysis and use of advanced analytics for implementation of artificial intelligence.

[0006] According to an aspect, a method is provided for analyzing cell or tissue cultures cultured in a compartmentalized cell culturing device. The method includes: receiving cellular or tissue data associated with the cell or tissue cultures, wherein: the cell or tissue cultures are organized by organizational structures in the device comprising at least first and second compartments in fluid communication via at least one channel; and the cellular or tissue data is correlated with at least one of: a physiological component of the cell or tissue cultures, and a position of the cell or tissue cultures in the device; identifying at least one region of interest in the cellular or tissue data corresponding to at least one of: the physiology of the cell or tissue cultures in the device and at least one of the organizational structures in the device; and processing the region of interest in the cellular or tissue data to measure at least one parameter relating to the cell or tissue cultures.

[0007] According to an aspect, a computing system for analyzing cell or tissue cultures cultured in a compartmentalized cell culturing device is provided. The system includes: at least one processor; and memory having executable instructions stored thereon which, when executed by the at least one processor, cause the computing system to: receive cellular or tissue data associated with the cell or tissue cultures, wherein: the cell or tissue cultures are organized by organizational structures in the device comprising at least first and second compartments in fluid communication via at least one channel; and the cellular or tissue data is correlated with at least one of: a physiology of the cell or tissue cultures, and a position of the cell or tissue cultures in the device; identify at least one region of interest in the cellular or tissue data corresponding to at least one of: the physiology of the cell or tissue cultures in the device, and at least one of the organizational structures in the device; and process the region of interest in the cellular or tissue data to measure at least one parameter relating to the cell or tissue cultures.

[0008] According to an aspect, a non-transitory computer-readable medium is provided. The computer-readable medium has instructions stored thereon which, when executed by at least one processor of a computing system, cause the computing system to: receive cellular or tissue data associated with cell or tissue cultures cultured in a compartmentalized cell culturing device, wherein: the cell or tissue cultures are organized by organizational structures in the device comprising at least first and second compartments in fluid communication via at least one channel; and the cellular or tissue data is correlated with at least one of: a physiology of the cell or tissue cultures and a position of the cell or tissue cultures in the device; identify at least one region of interest in the cellular or tissue data corresponding to at least one of: the physiology of the cell or tissue cultures in the device and to at least one of the organizational structures; and process the region of interest in the cellular or tissue data to measure at least one parameter relating to the cell or tissue cultures.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The attached figures illustrate various features, aspects and implementations of the technology described herein.

[0010] Fig. 1 is an exploded perspective view showing an implementation of a multi-well device that includes a multi-well grid layer, a microfluidic layer and a base layer, in accordance with an implementation.

[0011] Figs 2A-2C are, respectively, a top perspective view (Fig. 2A), a top view (Fig. 2B) and a side view (Fig. 2C) of the microfluidic layer of Fig. 1.

[0012] Fig. 3 is an enlarged top perspective view of a microfluidic unit of the microfluidic layer of Fig. 1 , and a further enlarged perspective view of the microchannels of the microfluidic unit.

[0013] Figs. 4A-4C are, respectively, a top perspective view (Fig. 4A), a top view (Fig. 4B), and a side view (Fig. 4G) of the base layer of Fig. 1.

[0014] Fig. 5 is an enlarged perspective view of the microfluidic unit of Fig. 3A, showing a flow in different compartments (white arrows).

[0015] Fig. 6A is a top perspective view of an optional transparent lid and Fig. 6B is top perspective view of the lid deposited over the multi-well device of Fig. 1 .

[0016] Fig. 6C is a top view of the multi-well device of Fig. 1.

[0017] Fig. 6D is a bottom view of the multi-well device of Fig. 1.

[0018] Figs. 7A-7D illustrate an implementation of a multi-well device using a 384-well plate, with Fig. 7A showing a multi-well grid layer comprising 24 microfluidic units, Fig. 7B showing an enlarged view of one of the 24 microfluidic units, Fig. 7C showing an enlarged view of an assay window composing the microfluidic unit, and Fig. 7D showing a picture of the assay window showing neurons in culture.

[0019] Fig. 8A is a top view of a microfluidic layer that includes a microfluidic unit, in accordance with another implementation.

[0020] Fig. 8B is a bottom view of the microfluidic layer of Fig. 8A.

[0021] Fig. 8C is bottom view of the microfluidic layer of Fig. 8A, shown in proximity of a base layer.

[0022] Fig. 8D is a top view of the microfluidic layer and base layer of Fig. 8D.

[0023] Fig. 9 is an exploded perspective view of a multi-well device as described herein, with an electrode layer shown underneath a microfluidic layer.

[0024] Fig. 10 is a top view of a microfluidic layer that includes 96 microfluidic units.

[0025] Fig. 11 is a top view of an electrode layer that include 96 electrode grids.

[0026] Fig. 12 is a top view of a microfluidic layer that includes 24 microfluidic units, the microfluidic layer being layered with an electrode layer that includes a corresponding 24 electrode grids, with an enlarged portion showing a microfluidic unit that includes microchannels and electrodes aligned along the microchannels.

[0027] Fig. 13 is a portion of a microfluidic layer and an electrode layer, the electrode layer being superposed to the microfluidic layer and including a group of electrodes, each electrode including a tip.

[0028] Fig. 14 is a portion of a microfluidic layer and an electrode layer, the electrode layer being superposed to the microfluidic layer and including a group of electrodes, each electrode including a tip.

[0029] Fig. 15 is a prior art picture showing cultured neuronal cells randomly distributed with respect to the electrodes of an electrode layer.

[0030] Fig. 16 is a picture of axons growing according to an organized architecture in microchannels, and electrodes being aligned along the axons.

[0031] Fig. 17 is an exploded perspective view showing an implementation of a multiwell device that includes a multi-well grid layer, a base layer and a cell culture layer, in accordance with another implementation.

[0032] Fig. 18 is a top view of a cell culture layer in combination with the multi-well grid layer and the base layer of Fig. 17.

[0033] Fig. 18A is an enlarged top view of a portion of the cell culture layer and the multiwell grid layer of Fig. 18, the portion including a NMJ preparation unit that includes two neuronal cells inlets.

[0034] Fig. 18B is an enlarged top view of another portion of the cell culture layer and the multi-well grid layer of Fig. 18, the portion including a NMJ preparation unit that includes a single neuronal cells inlet.

[0035] Fig. 19 is a bottom view of a cell culture device that includes a cell culture layer having a neuronal cells inlet, a first muscle cells reservoir, a second muscle cells reservoir, a neuronal cells channel having an abutment wall, and a neuromuscular junction chamber that includes two pillars.

[0036] Fig. 20 is a front view of the cell culture device of Fig. 19.

[0037] Fig. 21 A is a bottom perspective view of the cell culture device of Fig. 19.

[0038] Fig. 21 B is an enlarged view of a portion of the cell culture device of Fig. 21 A, showing a portion of the neuronal cells inlet, a portion of the first muscle cells reservoirand a portion of the second muscle cells reservoir, the neuromuscular junction chamber, the neuronal cells channel and the two pillars.

[0039] Fig. 22A is a front perspective view of the cell culture device of Fig. 19 and of a cell culture dish.

[0040] Fig. 22B is a front perspective view of the cell culture device and the cell culture dish shown in Fig. 22A, with the cell culture device being shown deposited onto a cell culture layer receiving surface of the cell culture dish.

[0041] Fig. 23 is an enlarged bottom view of a portion of a cell culture device, showing a portion of a neuronal cells inlet, a portion of a first muscle cells reservoir, a portion of a second muscle cells reservoir, a neuromuscular junction chamber, a neuronal cells channel, a neurosphere and muscle cells.

[0042] Fig. 24 is an exploded perspective view of a cell culture layer in combination with a cell culture plate that includes a multi-grid layer and a base layer, and an electrode layer provided underneath the cell culture layer.

[0043] Fig. 25 is a flowchart illustrating a method for analyzing compartmentalized cell cultures, according to an embodiment.

[0044] Figs. 26A and 26B are images of cells cultured in an embodiment of a compartmentalized cell culture device, the images being captured using a plate reader with fluorescence detection at two separate points in time.

[0045] Figs. 27A and 27B are images of cells cultured in another embodiment of a compartmentalized cell culture device, the images being respectively captured using a plate reader via phase contrast microscopy and plate reader with fluorescence detection.

[0046] Fig. 28A illustrates an arrangement of electrodes in an embodiment of a compartmentalized cell culture device. Fig. 28B illustrates electrical response of axons and / or axonal branches as measured by the electrodes.

[0047] Figs. 29 and 29A-29D illustrate an image of compartmentalized cultured cells covering an area representative of three compartments, according to an embodiment.

[0048] Figs. 30A and 30B respectively illustrate subregions of images of cultured cells exhibiting high and low cell counts in a first compartment.

[0049] Figs. 31 A and 31 B respectively illustrate subregions of images of cultured cells exhibiting low and high nucleus aggregation in a first compartment.

[0050] Figs. 32A and 32B respectively illustrate subregions of images of cultured cells exhibiting low and high fiber breadth / thickness in microchannels.

[0051] Figs. 33A and 33B respectively illustrate subregions of images of cultured cells exhibiting long and short axonal length in microchannels and a second compartment.

[0052] Figs. 34A and 34B respectively illustrate subregions of images of cultured cells exhibiting low and high number of branching microchannels and second compartment.

[0053] Figs. 35A and 35B respectively illustrate subregions of images of cultured cells exhibiting low and high number of branching junctions in second compartment.

[0054] Figs. 36A and 36B respectively illustrate subregions of images of cultured cells exhibiting low and high neuron straightness in second compartment.

[0055] Fig. 37 illustrates fields within a region of interest of an image of cultured cells that can be processed to measure axonal thickness.

[0056] Fig. 38 illustrates fields within regions of interest of an image of cultured cells that can be processed to measure axonal density.

[0057] Figs. 39A and 39B illustrates fields within a region of interest of an image of cultured cells that can be processed to measure tubulin breakdown.

[0058] Fig. 40 illustrates fields within a region of interest of an image of cultured cells that can be processed to measure connections.

[0059] Fig. 41 is a block diagram of a system for analyzing compartmentalized cell cultures, according to an embodiment.

[0060] Fig. 42A is a fluorescence image of a neuronal cell culture cultured using a standard cell culture plate (Petri dish). Fig, 42B is a fluorescence image of a neuronal cellculture cultured using a compartmentalized cell culture device comprising organizational structures that promote neuronal cell organization in vitro that is similar to in vivo, with the neuronal cell bodies (soma) confined to one compartment while axons are extended through channels to a third compartment.

[0061] Fig. 43A is a fluorescence image of neuronal cells cultured using a compartmentalized cell culture device comprising organizational structures and stained with a calcium indicator, enabling monitoring of Calcium activity at the individual cell level. Fig. 43B is a graph illustrating monitored single cell activity over time.

[0062] Fig. 44A is a frame of a video of compartmentalized cultured neuromuscular cells captured using calcium imaging, in accordance with an embodiment. Fig. 44B is a graphical representation of observed dose-response relationship of the muscle and neuron cells of Fig. 44A to glutamate stimulation.

[0063] Fig. 45A is a frame of a video of compartmentalized cultured neuromuscular cells captured using brightfield imaging, in accordance with an embodiment. Fig. 45B is a graphical representation of movement detected in muscle cells of Fig. 45A in response to stimulation of neuron cells.

[0064] Fig. 46A is a frame of a video of compartmentalized cultured neuromuscular cells comprising beads and captured using brightfield imaging in accordance with an embodiment. Fig. 46B is a graphical representation of movement detected in individual beads in the muscle cells of Fig. 46A in response to stimulation of neuron cellsDETAILED DESCRIPTION

[0065] Techniques described herein relate to systems, devices, and methods for analyzing compartmentalized cell cultures, and particularly compartmentalized cell cultures that allow cells to be grown according to an organization defined by organizational structures within the compartmentalized cell culturing device,

[0066] It will be appreciated that positional descriptions such as “above”, “below”, “left”, “right”, “inwardly”, “outwardly”, “distal”, “proximal” and the like used in the present description should, unless otherwise indicated, be taken in the context of the figures and should not be considered limiting. The term “outwardly” is intended to refer to a featurethat extends toward an exterior side of a reference axis. The term “inwardly” is intended to refer to a feature that extends toward an interior side of a reference axis. The term “distal” is meant to refer to a feature of the system that is provided closest to a patternreceiving surface of a cell culture well, when the patterning layer holder is placed in an upright position. The term “proximal” is meant to refer to a feature of the system that is provided farthest to a pattern-receiving surface of a cell culture well, when the patterning layer holder is placed in an upright position.

[0067] It will be appreciated that, for simplicity and clarity of illustration, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements or steps. In addition, numerous specific details are set forth in order to provide a thorough understanding of the exemplary embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein may be practised without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the embodiments described herein. Furthermore, this description is not to be considered as limiting the scope of the embodiments described herein in any way but rather as merely describing the implementation of the various embodiments described herein.

[0068] One or more systems described herein may be implemented at least in part in computer program(s) executed on processing device(s), each comprising at least one processor, a data storage system (including volatile and / or non-volatile memory and / or storage elements), and optionally at least one input and / or output electronic device. “Processing devices” encompass computers, servers and / or specialized electronic devices which receive, process and / or transmit data. As an example, “processing devices” can include processing means, such as microcontrollers, microprocessors, and / or CPUs, or be implemented on FPGAs. For example, and without limitation, a processing device may be a programmable logic unit, a mainframe computer, a server, a personal computer, a cloud-based program or system, a laptop, a personal data assistant, a cellular telephone, a smartphone, a wearable device, a tablet, a video game console or a portable video game device.

[0069] Each program is preferably implemented in a high-level programming and / or scripting language, for instance an imperative e.g., procedural or object-oriented, or a declarative e.g., functional or logic, language, to communicate with a computer system. However, a program can be implemented in assembly or machine language if desired. In any case, the language may be a compiled or an interpreted language. Each such computer program is preferably stored on a storage media or a device readable by a general or special purpose programmable computer for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. In some embodiments, the system may be embedded within an operating system running on the programmable computer.

[0070] Furthermore, the system, processes and methods of the described embodiments are capable of being distributed in a computer program product comprising a computer readable medium that bears computer-usable instructions for one or more processors. The computer-usable instructions may also be in various forms including compiled and non-compiled code.

[0071] The processor(s) are used in combination with storage medium, also referred to as “memory” or “storage means”. Storage medium can store instructions, algorithms, rules and / or trading data to be processed. Storage medium encompasses volatile or non- volatile / persistent memory, such as registers, cache, RAM, flash memory, ROM, diskettes, compact disks, tapes, chips, as examples only. The type of memory is of course chosen according to the desired use, whether it should retain instructions, or temporarily store, retain or update data. Steps of the proposed method are implemented as software instructions and algorithms, stored in computer memory and executed by processors.

[0072] Various implementations of devices enabling compartmentalized cell culture will now be described.Description of examples of devices enabling compartmentalized cell cultureIn vitro models with neurons

[0073] Figs. 1 to 24 show implementations of cell culture devices for culturing cells according to a compartmentalized configuration. In this type of compartmentalized configuration, cells of a given cell type, or certain portions of cells (for instance cell bodiesof neurons versus axons) can be cultured in a dedicated compartment of a device that includes multiple compartments. These implementations are described in more detail in US Application No. US 2016 / 0186112 and PCT Application No. PCT / CA2021051226, which are incorporated herein by reference in their entirety. A brief summary of the features illustrated in Figs. 1 to 16 is provided below.

[0074] The cell culture device for culturing cells according to a compartmentalized configuration shown in Figs. 1 to 7 and 9 to 16 is exemplified as a multi-well device 1. The multi-well device 1 includes a multi-well grid layer 10 comprising a plurality of wells 12, a microfluidic layer 20 comprising a plurality of microfluidic units 28 with microchannels 65, and a base layer 30 configured for being positioned underneath the microfluidic layer 20 and configured for being detachably connected to, or assembled with, the microfluidic layer 20 and / or to the multi-well grid layer 10.

[0075] The plurality of wells 12 of the multi-well grid layer 10 are a plurality of bottomless wells. The multi-well grid layer 10 includes a rectangular frame 14 surrounding a central portion comprising the plurality of wells 12. In the illustrated implementations, 384 well are defined in the central portion. The multi-well grid layer 10 can include any number of wells, for instance 6, 12, 24, 48, 96, 384 or 1536 wells. In some implementations, the multi-well grid layer 10 can be a commercially available bottomless multi-well plate comprising 6, 12, 24, 48, 96, 384 or 1536 wells, and the microfluidic layer 20 can be configured to comprise inlets, outlets and a central assay window that are vertically aligned with at least some of the bottomless wells 12 of the multi-grid layer 10. In some implementations, the multi-well grid layer 10 can be a commercially available bottomless multi-well plate which complies with American National Standards Institute of the Society for Laboratory Automation and Screening (ANSI / SLAS) microplate standards.

[0076] The base layer 30 is configured for being positioned underneath the microfluidic layer 20, and can be configured for being detachably connected, or assembled, to the microfluidic layer 20 and / or to the multi-well grid layer 10. Fig. 1 illustrates an example in which the base layer 30 is positioned underneath the microfluidic layer 20, which is itself positioned underneath the multi-well grid layer 10. Alternatively, in other implementations, the microfluidic layer 20 can be integral to (e.g., moulded with) the base layer 30.

[0077] As shown Figs. 5 and 6C to 6D, once the multi-well grid 10, the microfluidic layer 20 and the base layer 30 are superposed to each other, the multi-well grid layer 10, the microfluidic layer 20, and the base layer 30 form microchannel networks 24 into which a fluid can flow from at least one well 12 of the multi-well grid layer 10 to at least another well 12 (via inlets / outlets 67a, 67b, 67c, 67d, the compartments 62, 64 and also the microchannels 65 of the microfluidic units 28 of the microfluidic layer 20). Therefore, liquids, particles and cells can advantageously flow in the microfluidic unit 28 using passive flow, with the microchannels 65 being positioned substantially transversally to the direction of the fluidic flow from the inlet to the outlet.

[0078] As shown in Figs. 2A to 2C, the microfluidic layer 20 includes an upper surface 25, a bottom surface 26 and a plurality of microfluidic units 28. For instance, in Fig. 2, the microfluidic layer 20 comprises 24 (6 x 4) microfluidic units 28.

[0079] In the implementation shown in Fig. 3, each of the microfluidic unit 28 includes a central main chamber 60, which in turn comprises a first compartment 62 and a second compartment 64 that each extend substantially longitudinally. In the illustrated implementation, the first and second compartments 62, 64 are substantially parallel, and separated by a plurality of microfluidic channels 65 extending between the first and second compartments 62, 64. The microfluidic channels 65 are configured to provide a fluidic communication between the first and second compartments 62, 64. In some implementations, the microchannels 65 are dimensioned to enable neuronal growth between the first and second compartment 62, 64, with neuronal cell bodies remaining in one compartment, while axons extent along microchannels 65 to the other compartment.

[0080] In the implementation shown in Fig. 3, the microfluidic unit 28 includes a first inlet (67a or 67b) and a first outlet (67a or 67b) disposed diagonally at opposite ends of the first compartment 62 of the central main chamber 60. In this implementation, the microfluidic unit 28 also includes two diagonally extending microfluidic arms 66a, 66b extending diagonally in opposite directions from the first compartment 62 of the central main chamber 60. The microfluidic arms 66a, 66b provide a fluidic communication of the first inlet (67a or 67b) and the first outlet (67a or 67b) with the first compartment 62 of the central main chamber 60.

[0081] In some implementations, the microfluidic unit 28 further includes a second inlet (67c or 67d) and a second outlet (67c or 67d) disposed diagonally at opposite ends of the second compartment 64 of the central main chamber 60. The inlets and outlets described above can enable addition and removal of liquids, particles, and cells in and from the microfluidic unit, respectively. In such implementations, the microfluidic unit 28 further includes two diagonally extending microfluidic arms (66c, 66d) extending diagonally in opposite directions from the second compartment 64 of the central main chamber 60. The microfluidic arms 66c, 66d provide a fluidic communication of the second inlet (67c or 67d) and the second outlet (67c or 67d) with the second compartment 64 of the central main chamber 64. Although the second inlet (67c or 67d) and second outlet (67c or 67d) and connecting arms (66c, 66d) may be present, any the second inlet (67c or 67d) and second outlet (67c or 67d) and connecting arms (66c, 66d) can be omitted, and the second compartment 64 may be present without these.

[0082] In the implementation shown in Fig. 3, the inlets and outlets 67a, 67b, 67c, 67d are shown as a bore extending from the upper surface 25 to the lower surface 26 of the microfluidic layer 20, with the rest of the components extending from the bottom surface 26 of the microfluidic layer 20 toward the upper surface 25 of the microfluidic layer 20. The inlets and outlets 67a, 67b, 67c, 67d are also configured to be vertically aligned with corresponding wells of the multi-well grid layer 10.

[0083] As mentioned hereinbefore and in the implementation shown in Fig. 4, the base layer 30 can be configured for being positioned underneath the microfluidic layer 20 and can be configured for being detachably connected, or assembled, to the microfluidic layer 20 and / or to the multi-well grid layer 10. In the implementation shown in Fig. 4, the base layer 30 includes a transparent layer 35, or translucent layer, having an upper surface 37, the transparent layer 35 being surrounded by a rectangular frame 32 comprising a plurality of hooks 34 that can be used for detachably connecting the base layer 30 to the multi-well grid layer 10. In some implementations, the transparent layer 35 is a transparent sheet that is bounded to the rectangular frame 32. In the illustrated implementation, the transparent layer 35 is integral to the rectangular frame 32.

[0084] Once the multi-well device is assembled, the bottom surface 26 of the microfluidic layer 20 can be considered as being in close contact with the upper surface 37 of thetransparent layer 35. As shown in Fig. 5, the upper surface 37 of the transparent layer 35 provides a “floor” or a “bottom” to the inlet(s), outlet(s), central chamber, arms, and microchannels 65 of the microfluidic layer 20, such that fluid and suspended cells can flow in a substantially leak-tight manner from the inlet (67a or 67b) the to the outlet (67a or 67b) of the microfluidic layer 20, via the microfluidic arms (66a, 66b) and first compartment 62 of the central main chamber 60. Fluid and suspended cells can also flow in a substantially leak-tight manner from the inlet (67c or 67d) to the outlet (67c or 67d) via the microfluidic arms (66c, 66d) and second compartment 64 central main chamber 60. Fluid and suspended cells can also flow in a substantially leak-tight manner between the first compartment 62 and second compartment 64 via the microchannels 65. The inlets and outlets (67a, 67b, 67c, 67d) can be configured to be vertically aligned with corresponding wells 12 in the multi-well grid layer 10. Therefore, once assembled, the multi-well grid layer 10, the microfluidic layer 20, the base layer 30 form microchannel networks 24 into which a fluid and cells can flow in a substantially leak-tight manner from at least one well 12 to at least another well 12, such as shown in Fig. 5. In the illustrated implementation, each microchannel network 24 includes the combination of two wells 12, a microfluidic unit 28 and the upper surface 37 of the transparent layer 35.

[0085] In the illustrated implementation, the rectangular frame 32 of the multi-well grid layer 10 includes a plurality of hooks 34 configured to slide and snap fit into corresponding slots provided in the hollow frame 14, the hooks 34 being adapted for detachably connecting the base layer 30 to the multi-well grid layer 10.

[0086] As shown in Figs. 6A and 6B, the multi-well device can also include a lid 40. In some implementations, the lid 40 can be transparent and can be surrounded by a rectangular frame 42. The lid 40 can be dimensioned to fit over the multi-well grid layer 10, such that the rectangular frame 42 of the lid 40 can at least partially surround the rectangular frame 14 of the multi-well grid layer 10.

[0087] Figs. 6C to 6D show the top and bottom portion of an assembled multi-well device 10 that includes a lid. As shown in Fig. 6C from the top, one can visualize the wells 12 (i.e., 384 wells in the illustrated implementation) of the multi-well grid layer 10, and inlets / outlets 67 of a microfluidic layer 20. As shown in Fig. 6D, from the bottom, one canvisualize the microchannel networks 24 (a total of 24 in the illustrated implementation) through the transparent layer 35 of the base layer 30.

[0088] Figs. 7A and 7B illustrate an example of a configuration of a multi-well device that includes a 384-well plate as the multi-well grid layer 10. In this configuration, 24 microfluidic units (dark square) are disposed underneath the multi-well grid layer 10, / .e., underneath the 384-well plate (Fig. 7A). In this configuration, each microfluidic unit 28 includes two pairs of inlets-outlet (67a-67b and 67c-67d), and one central compartment 60 occupying a total space corresponding to nine 9 wells ( / .e., 3 x 3 arrangement) of the 384-well plate. In this 3 x 3 arrangement, the centre well defines a central assay window 70, while the inlets and outlets align with four opposite corner wells (Fig. 7B). A zoom of the central assay window 70 shows microchannels 65 (Fig. 7C), the microchannels 65 enabling a precise organization of cultured cells. Fig. 7D shows an example of rat cortical neurons labelled with beta-tubulin antibody where the neuronal cell bodies 71 stay in the first compartment 62, while the axons 73 extend within the microchannels 65 to the second compartment 64.

[0089] Thus, each microfluidic unit 28 enables the cultures of cells according to a compartmentalized configuration, with neuronal cells bodies remaining in a given compartment (first compartment 62) with the axons extending in another compartment, in this case the microchannels 65 and the second compartment 64. Furthermore, the axons are cultured in an organized fashion, by being maintained elongated in the microchannels instead of being randomly distributed.

[0090] It is to be understood that although the microfluidic unit 28 has been described as being part of a microfluidic layer 20 that includes a plurality of microfluidic units 28, the microfluidic unit 28 can also be present as a single microfluidic unit on a microfluidic layer, such as shown in Figs. 8A-8D. The microfluidic unit 28 of Figs. 8A-8D thus includes corresponding features as those shown in Fig. 5. Accordingly, the cell culture device can include a microfluidic layer 20 with a single microfluidic unit 28.

[0091] Figs. 9 to 12 illustrate an implementation of an electrode layer 120 that can be used in cooperation with a microfluidic layer 20, a multi-well grid layer 10 and a base layer 30 as described herein. In Figs. 9 and 12, the microfluidic layer 20 includes 24 microfluidic units 28, and the electrodes 122 of the electrode layer 120 are distributed to achieve apredetermined organized architecture of the microchannels relative to the electrodes. In Figs. 10 and 11 , the microfluidic layer 20 includes 96 microfluidic units 28, and the electrode layer 120 shown in Fig. 19 includes a corresponding number of electrode grids 126 to achieve a predetermined organized architecture of the microchannels relative to the electrodes. As used herein, the term “grid” refers to a grouping of electrodes associated with a microfluidic unit 28. The electrode layer 120 can be placed onto the upper surface of the base layer 30 and underneath the microfluidic layer 20. In other words, the electrode layer 120 can be sandwiched between the upper surface of the base layer 30 and the microfluidic layer 20.

[0092] Figs. 13 and 14 illustrate examples of a microfluidic layer 20 combined with an electrode layer 120 that includes a group of electrodes 122, each electrode including a tip 124 serving as a measuring point. The microfluidic layer 20 includes microchannels 65 that can be configured to enable neuronal growth, with axons extending within the microchannels 65, and cells bodies remaining in adjacent compartments, for example. In Figs. 13 and 14, the respective tip 124 of several of the electrodes 122 of the electrode layer 120 is shown as being aligned with a given microchannel of the microchannels 65. In the context of the present description, the term “aligned” is intended to mean that there is controlled organization of the electrodes with respect to the microchannels with the goal of promoting tissue growth and differentiation always in contact with the electrodes. Without the microfluidic compartmentalizing layer, cells could grow anywhere in space and eventually never contact the electrodes. Moreover, in each assay, the number of cells in contact with electrodes would be variable, leading to high assay variability and low reproducibility. For instance, when axons are present in the microchannels, such controlled organization of the electrodes at strategic positions with respect to the microchannels can enable the electrodes to interact with an organized architecture of the axons, such as axonal fibers aligned in the same direction, towards the axonal tip or towards soma. The organized architecture promotes directional axonal growth in the microchannels instead of a variable number of axons, randomly distributed axons, growing in multiple directions and not necessarily in contact with electrodes.

[0093] Fig. 15 is a prior art figure that illustrates cultured neuronal cells randomly distributed with respect to the electrodes of an electrode layer. This random distribution of the cultured neuronal cells as depicted in prior art Fig. 15 can result in a multitude ofindiscernible signals from cells that may or may not be in electrical communication with the electrodes. For instance, if the electrodes illustrated in prior art Fig. 15 were used to stimulate the randomly distributed axons, a random number of axons would end up being stimulated given that a random number of axons are in electrical communication with the electrodes. Similarly, if the electrodes illustrated in prior art Fig. 15 were used to detect a response from the randomly distributed axons, a random number of axons can end up having a response detected given that a random number of axons are in electrical communication with the electrodes.

[0094] In neuronal cultures lacking an organized architecture, such as shown in prior art Fig. 15, any part of the neuron can end up being in contact with electrodes, such as cell bodies, axons, and / or dendrites. In such neuronal cultures lacking an organized architecture, the electrodes are used to detect or stimulate electrical responses from the whole cell culture and as a result, the readings that are obtained include a mixture of signals arising from an undetermined number of cells or cell parts that are in contact with the electrodes.

[0095] In contrast, a predetermined organized architecture of microchannels relative to electrodes can enable specific cell components of the neuronal cells to be placed in electrical communication with electrodes. For instance, Fig. 16 illustrates that the axons are placed in contact with electrodes tips #1, #2, #4, #5, #7, #8, #10 and #11. The predetermined organized architecture of microchannels relative to electrodes can enable stimulating and sensing signals from precise cells or cell parts, it is possible to control which cells and which cell parts are in contact with the electrodes. The predetermined organized architecture of microchannels relative to electrodes also promotes higher well- to-well reproducibility because every well can have the same or similar architecture of microchannels, and it is thus possible to determine the direction of the signal path from cell to cell and how compounds can impact the signal path.

[0096] Fig. 16 illustrates neuronal cells that are grown in a microfluidic unit 28, with axons extending within the microchannels 65 of the microfluidic unit 28. Fig. 16 illustrates axons growing in the vicinity of the electrodes according to an organized architecture, so that the presence of the axons in the vicinity of the electrodes can be predictable orcontrolled. The tips 124 of the electrodes that are superposed to, or placed underneath, the microfluidic layer 20 have been numbered 1-12 in Fig. 16 for ease of reference.

[0097] Fig. 16 further illustrates that following alignment of the electrode layer 124 with the microchannels of the microfluidic unit, a substantially uniform distribution of the tips of the electrodes is obtained over the microfluidic channels, and thus over the axons. In the implementation illustrated in Fig. 16, each electrode tip 124 of the 12 electrode tips is provided at a substantially constant distance from each other longitudinally and laterally, / .e., along the y axis and the x axis respectively. Still in the implementation illustrated in Fig. 16, the tip of each of the electrodes is directly adjacent or intersects a series of three microfluidic channels into which axons are growing. For instance, tip #1 in the top left corner of Fig. 16 is adjacent to a first microfluidic channel on the left-hand side, intersects a second microfluidic channel, and is adjacent to a third microfluidic channel on the righthand side. Tip #2 is longitudinally spaced-apart from tip #1 , and is also adjacent to the first microfluidic channel on the left-hand side, intersects the second microfluidic channel, and is adjacent to the third microfluidic channel on the right-hand side, and so on for tip #3. Tip #4 is laterally spaced-apart from tip #1 , and is adjacent to a fourth microfluidic channel on the left-hand side, intersects a fifth microfluidic channel, and is adjacent to a sixth microfluidic channel on the right-hand side, and so on. As mentioned above, this controlled distribution of the tips of the electrodes with respect to the microchannels of the microfluidic unit can facilitate controlling the location of stimulation of the axons present in the microchannels as well as the detection of signals from the axons present in the microchannels. In other words, the microfluidic layer and more particularly the microchannels of the microfluidic layer can facilitate positioning the axons at a given location such that the axons can be stimulated at this given location by the electrodes and the signal can be measured from the axons at this given location using the electrodes. In the implementation shown in Fig. 16, each series of three electrodes, / .e., tip #1 , tip #2 and tip #3, tip #4, tip #5 and tip #6, tip #7, tip #8 and tip #9 and tip #10, tip #11 and tip #12, connects a first population of neurons present in the upper portion of the figure with the second population of neurons present in the below portion of the figure, the expressions “upper portion” and “below portion” being used to facilitate reference to portions of the figure. In this example, the first population of neurons were seeded in a top inlet (67a or 67b) and a bottom inlet (67c or 67d) of a microfluidic unit 28 as described herein. It is to be understood that cells can also be seeded in only one of the top and bottom inlets.In vitro models of neuromuscular junctions

[0098] Figs. 17 to 24 show alternative implementations of cell culture devices for culturing cells according to a compartmentalized configuration. Similarly, to the description above of the cell culture devices in reference to Figs. 1 to 16, in these alternative implementations of compartmentalized configuration, cells of a given cell type, or certain portions of cells (for instance cell bodies of neurons versus axons) can be cultured in a dedicated compartment of a device that includes multiple compartments. These implementations are described in more detail in PCT Application No. PCT / CA2023 / 050340, which is incorporated herein by reference in its entirety. A brief summary of the features illustrated in Figs. 17 to 24 is provided below.

[0099] With reference to Figs. 17, 18 and 24, the cell culture device is exemplified as a multi-well device 200 for preparing in vitro models of neuromuscular junctions (NMJ). The multi-well device 200 includes a multi-well grid layer 223, a base layer 225 and a cell culture layer 222. The cell culture layer 222 includes designated compartments that can be configured for receiving a given type of cells. In Figs. 17, 18 and 24, the cell culture layer 222 includes a plurality of NMJ preparation units 221 , each comprising a well defining an assay window 270. In Figs. 19 to 22B, the cell culture device is exemplified as a cell culture layer 222 that includes a single NMJ preparation unit 221. It is to be noted that the NMJ preparation units 221 of the cell culture layer 222 of the multi-well device 200 shown in Figs. 17, 18 and 24 and the single NMJ preparation unit 221 of the cell culture layer 222 of the cell culture device shown in Figs. 19 to 22B include corresponding features. As such and for ease of understanding, the cell culture layer 222 will initially be described with reference to Figs. 19 to 23.

[0100] Referring to Figs. 19 to 23, each cell culture layer 222 includes a neuronal cells inlet 224, a first muscle cells reservoir 226, and a second muscle cells reservoir 228. The cell culture layer 222 further includes a neuromuscular junction chamber 230 extending between the first and second muscle cells reservoirs 226, 228, and a neuronal cells channel 232 extending between the neuronal cells inlet 224 and the neuromuscular junction chamber 230. The neuromuscular junction chamber 230 is configured to be in fluid communication with the first and second muscle cells reservoirs 226, 228, and with the neuronal cells inlet 224 via the neuronal cells channel 232. It is to be understood thatthe location of the neuronal cells inlet 224 in the cell culture layer 222 relative to the first and second muscle cells reservoirs 226, 228 can vary from the implementations shown in Figs. 19 to 23.

[0101] The cell culture layer 222 can be placed onto a cell culture layer receiving surface of a cell culture dish, of a well of a cell culture plate or a microscopic slide, or any other type of receptacle that can be suitable for receiving the cell culture layer 222.

[0102] The neuronal cells inlet 224 is configured to receive an inlet fluid and neuronal cells therein. Examples of types of neuronal cells that can be used for the preparation of the in vitro model of NMJ can include mammalian neurons, such as rodent embryonic neurons, and neurons derived from induced pluripotent stem cells, such as human induced pluripotent stem cells. Either one of the first and second muscle cells reservoirs 226, 228, or both, can be configured to receive a muscle cells reservoir fluid and muscle cells. The muscle cells can be for instance muscle cells dissociated from organs constituting the muscular system, such as from the heart (cardiac cells), limb or body wall muscles (skeletal muscle cells), and visceral muscle (smooth muscle cells), and / or cells that have been differentiated from pluripotent cells, such as muscle cells derived from embryonic stem cells or induced pluripotent stem cells.

[0103] Either one of the first and second muscle cells reservoirs 226, 228, or both, or the neuromuscular junction chamber 230 itself, is (are) further configured to receive a test substance therein, such that the muscle cells present in the neuromuscular junction chamber 230 and eventually forming the NMJ are exposed to such test substance. The cell culture layer 222 can thus enable performing testing of substances, such as toxins, antibodies, chemicals, biological substances, drugs, patient-derived fluids (plasma, serum, blood, urine, etc.), parasites, bacteria, viruses and / or cells in selected compartments of the cell culture layer 222 such as the neuromuscular junction chamber 230 where the NMJ is formed.

[0104] The neuronal cells inlet 224, and the first and second muscle cells reservoirs 226, 228 can be through-holes extending across an entire thickness of the cell culture layer 222. Once the cell culture layer 222 is deposited onto a cell culture layer receiving surface of a cell culture plate, a cell culture dish or a microscope slide, the cell culture layer receiving surface can act as the back wall of the neuronal cells inlet 224 and the first andsecond muscle cells reservoirs 226, 228, such that fluids introduced in the neuronal cells inlet 224 and the first and second muscle cells reservoirs 226, 228 can remain therein. In alternative implementations, at least one of the neuronal cells inlet 224 and the first and second muscle cells reservoirs 226, 228 can include a back wall provided by the cell culture layer 222 itself, and thus not extend across the entire thickness of the cell culture layer 222.

[0105] In the implementations shown in Figs. 19 to 23, the neuromuscular junction chamber 230 includes a first pillar 240 and a second pillar 242 provided in a space-apart relationship relative to one another. The first and second pillars 240, 242 can serve as anchoring points to enable the assembly of muscle cells 264 therebetween and formation of a muscle fibers and / or muscle tissue attached thereto.

[0106] The neuronal cells channel 232 extends between the neuronal cells inlet 224 and the neuromuscular junction chamber 230 and is in fluid communication therewith. The neuronal cells channel 232 includes a first portion 244 having a first portion cross-section, and a second portion 246, provided downstream of the first portion 244 and having a second portion cross-section. In order to retain the portion of the neurosphere that contains the neuronal cell bodies within the first portion 244 of the neuronal cells channel 232, the second portion cross-section is smaller than the first portion cross-section, the size being determined to prevent entry of the portion of the neurosphere that contains neuronal cell bodies into the second portion 246, / .e., to prevent entry of the neuronal cell bodies in the second portion 246. The second portion 246 is further sized to house axons and enable the axons to extend away from the neuronal cell bodies, directing the axons toward the neuromuscular junction chamber 230 so as to reach the muscle cells cultured in the neuromuscular junction chamber 230. Fig. 23 illustrates an example of axons 262 extending from a neurosphere 260 located in the first portion 244 of the neuronal cells channel 232, the axons 262 extending into the second portion 246 of the neuronal cells channel 232 and out to the neuromuscular junction chamber 230 to reach muscle cells extending between the first pillar 240 and the second pillar 242.

[0107] Turning back to Figs. 17 and 18 and as mentioned above, the cell culture layer 222 can include a plurality of NMJ preparation units 21. The plurality of NMJ preparation units 221 can be provided for instance on a rectangular cell culture layer 222 that can beinserted onto a cell culture layer receiving surface of a cell culture plate. In some implementations, the cell culture plate can be a cell culture plate that complies with American National Standards Institute of the Society for Laboratory Automation and Screening (ANSI / SLAS) microplate standards. The cell culture plate can also be used in cooperation with a multi-well grid layer that is placeable over the cell culture layer and that has a number of wells that corresponds to the number of the NMJ preparation units of the cell culture layer. The multi-well grid layer may comprise any number of wells, for instance 6, 12, 24, 48, 96, 384, or 1536 wells. Fig. 17 illustrates an exploded view of an example of a combination of a cell culture layer 222, a multi-well grid layer 223 and a base layer 225.

[0108] In some implementations, the cell culture device can further include an electrode or a group of electrodes. The electrode, or group of electrodes, can be provided so as to be in contact, either direct or indirect, / .e., in electrical communication, with the neurons, the muscle cells and / or the neuromuscular junction formed in the neuromuscular junction chamber 230, and / or the axons, / .e., the neuronal extensions, extending within the neuronal cells channel 232. The electrode or group of electrodes can take the form of an electrode layer that can be placed underneath the cell culture layer 222, or superposed to the cell culture layer 222 to enable electrical communication with the neuronal cells and / or the muscle cells. Alternatively, the electrode or the group of electrodes can be provided in proximity of the cell culture layer 222, / .e., without necessarily be placed underneath or superposed thereto. When the electrode layer is provided in proximity of the cells, the distance between the electrode(s) of the electrode layer and the cells can be in the range of micrometers or millimeters, for instance. In yet other implementations, the electrodes can be provided so as to be inserted in the cultured material, such as the neuronal cells growing in the neuronal cell channel 232 and / or the neuronal cells and the muscle cells growing the neuromuscular junction chamber 230. In such implementations, the electrodes can thus be provided within one or more compartments of a NMJ preparation unit 221 , for instance on a top surface or a bottom surface of the one or more compartments of the NMJ preparation unit 221 , and / or on the sidewalls of one or more compartments of the NMJ preparation unit 221.

[0109] In some implementations, the electrode layer can be placed onto a cell culture layer receiving surface of a cell culture plate, a cell culture dish or a microscope slide, forinstance, with the cell culture layer 222 being placed onto the electrode layer. Fig. 24 illustrates an example of a cell culture device provided as a multi-well device 200 that includes a multi-well grid layer 222 and a base layer 225, with a cell culture layer 222 and an electrode layer 227 provided between the multi-well grid layer 222 and the base layer 225. Alternatively, the cell culture layer 222 can be received onto a cell culture layer receiving surface of a cell culture plate, a cell culture dish or a microscope slide, for instance, and the electrode layer can be placed onto the cell culture layer 222. In yet other implementations, a first electrode layer can be placed onto a cell culture layer receiving surface of a cell culture plate, a cell culture dish or a microscope slide, for instance, with the cell culture layer 222 being placed onto the electrode layer, and a second electrode layer can be placed onto the cell culture layer 222. In yet other implementations, the electrode or group of electrodes can be integrated in the cell culture layer 222. In yet other implementations, the electrodes can be provided as spikes or needles to facilitate contact with the neuronal cells growing in the neuronal cell channel 232 and / or the neuronal cells and the muscle cells growing the neuromuscular junction chamber 230.

[0110] In some implementations, the distribution of the electrodes over the surface area of the electrode layer can be such that it follows an orientation of the neuronal extensions growing in the neuronal cells channel 232, or an orientation of the muscle cells extending between the first and second pillars 240, 242, instead of the electrodes being provided randomly across the cell culture layer 222. The distribution of the electrodes can also be such that it includes a first group of electrodes following an orientation of the neuronal extensions growing in the neuronal cells channel 232, and a second group of electrodes following an orientation of the muscle cells extending between the first and second pillars 240, 242. Providing the electrodes in such a configuration can enable obtaining electrodes in an organized fashion which in turn, can enable to better target the function of the electrodes, for instance with respect to the stimulation of the neuronal cells or the muscle cells, or with respect to the detection of a signal from the neuronal cells or from the muscle cells.

[0111] In some implementations, the cell culture device can include any other types of sensors that can stimulate cells or measure responses of cells to stimulation, e.g., stimulating neuronal cells and / or muscle cells, measuring a response from the neuronal cells and / or muscle cells to stimulation, providing an output and / or receiving an input.Examples of sensors can include optical sensors or optical transducers, chemical sensors, and electrical sensors or electrical transducers, for instance. The sensors can be provided according to any of the implementations described above with respect to the electrodes.

[0112] In some implementations, the cell culture device can include an electrode set provided proximate to the in vitro model of NMJ. The electrode set can include at least one electrode configured to collect an electric signal associated with at least a portion or at least one compartment of the in vitro model of NMJ. The electrode set can take the form of an electrode layer as described above, or can take a different form. The electrode set can include more or more electrodes. The electrodes can enable providing electrical readouts comprising one or more of potential recordings, impedance spectroscopy, voltammetry and amperometry.Method of analyzing compartmentalized cell cultures

[0113] With reference now to Fig. 25, a method 300 for analyzing compartmentalized cell or tissue cultures will now be described according to an exemplary embodiment. Although in the following embodiment reference will be made to “cell” cultures, it is appreciated that the teachings apply to both cultures of individuals cells and cultures comprising clusters of cells defining tissues. A first step 301 of the method 300 can comprise providing a compartmentalized cell culturing device, the compartmentalized cell culturing device comprising at least first and second compartments in fluid communication via at least one channel. The compartmentalized cell culturing device can be a multi-well device, such as the multi-well devices 1 or 200 described above, although it is appreciated that other devices are possible.

[0114] A second step 303 of the method 300 can comprise culturing cells in the compartmentalized cell culturing device. As can be appreciated, culturing the cells in the compartmentalized device can allow cells to be grown according to an organization defined by organizational structures within the compartmentalized cell culturing device, such as the at least first and second compartments and the at least one channel. Such organization can allow maintaining a physical separation of cells, while allowing cells or portions thereof to be positioned or oriented in a predefined manner and / or to interact with other cells or portions of other cells in a predefined manner. By way of example, in embodiments where the compartmentalized cell culturing device corresponds to multi-welldevice 1 described above, the cultured cells can correspond to neuronal cells organized within organizational structures comprising first compartment 62, second compartment 64, and microchannels 65. More specifically, the neuronal cells can be cultured such that neuronal cell bodies 71 (e.g., soma and dendrites) are positioned in the first compartment 62, while axons 73 extend within the microchannels 65 to the second compartment 64 where they can branch out. As another example, where the compartmentalized cell culturing device corresponds to multi-well device 200 described above, the cultured cells can correspond to neuronal cells and muscle cells organized within organizational structures comprising reservoirs 224, 226, 228, neuromuscular junction chamber 230 (and pillars 240,242 contained therein), and neuronal cells channel 232. More specifically, the cells can be cultured such that muscle cells provided in reservoirs 226, 228 are grown to extend between first and second pillars 240, 242 in the neuromuscular junction chamber; and such that neuron cells provided in reservoir 224 are grown with a neurosphere comprising neuron cell bodies extending within the first portion 244 of the neuronal cells channel 232, and with axons extending into the second portion 246 of the neuronal cells channel 232 and out to the neuromuscular junction chamber 230 to reach the muscle cells extending between the pillars 240, 242. It is appreciated that other compartmentalized cell culturing devices and corresponding structures are possible.

[0115] In some embodiments, the compartmentalized cell culturing device can be used to test an affect of one or more physical, chemical, and / or biological stimuli, such as pressure, radiation, or substances such as toxins, antibodies, chemicals, biological substances, cosmetics, drugs or medications, patient-derived fluids (plasma, serum, blood, urine, etc.), parasites, bacteria, viruses, etc., on cultured cells. In such embodiments, culturing the cells can comprise providing the one or more stimuli in selected compartments of the compartmentalized cell culturing device, such as first compartment 62, second compartment 64, reservoirs 224, 226, 228 and / or neuromuscular junction chamber 230. As can be appreciated, in such embodiments the cultured cells can be generally retained or confined in a strategic position by the organizational structures (e.g., the cell will remain in the same compartment and / or generally within the same position within that compartment). Therefore, the position of a cell before and after the provision of a stimulus can be generally similar, making it easier to identify the same cell and monitor cell parameters before and after provision of a stimulus (e.g., at a time to before the stimulus is applied, and at a time ti, after the stimulus is applied) and assessan impact of the stimulus on the cell and / or on a tissue comprising the cell. In examples provided herein, the stimulus is in the form of a substance or compound applied to the cultured cells. It is appreciated, however, that the methods described herein can also be used to analyze the effect of other physical, chemical, or biological stimuli and / or combinations thereof.

[0116] In embodiments where the compartmentalized cell culturing device is a multi-well device, the cells can be cultured such that the organized cells are vertically aligned with (e.g., aligned along the Z-axis) and / or are visible via a well defining an assay window. In some embodiments, the compartmentalized cell culturing device can be configured such that the at least first and second compartments and the at least one channel are vertically aligned (e.g., aligned along the Z-axis) with the assay window. For example, in embodiments where the compartmentalized cell culturing device corresponds to multi-well device 1 described above, the first compartment 62, second compartment 64, and microchannels 65 (respectively corresponding to the at least first and second compartments and the at least one channel), and the cells extending therein, can be vertically aligned with, and at least partially visible through, assay window 70. As another example, in embodiments where the compartmentalized cell culturing device corresponds to multi-well device 200 described above, the neuromuscular junction chamber 230, first portion 244 of the neuronal cells channel 232, and second portion 246 of the neuronal cells channel 232 (respectively corresponding to the at least first and second compartments and the at least one channel), and the cells extending therein, can be vertically aligned with, and at least partially visible through, assay window 270.

[0117] Moreover, in embodiments where the compartmentalized cell culturing device is a multi-well device, culturing the cells can comprise culturing cells in each of a plurality of culturing units arranged within the multi-well device, such as a plurality of microfluidic units 28 or NMJ preparation units 221. In some embodiments, each of the plurality of culturing units can be cultured with a different combination of cells and / or substances. For example, a first culturing unit can correspond to a control culture, whereas each of the remaining culturing units of the plurality of culturing units can be provided with a different combination of cells and / or substances to compare with the control culture.

[0118] In some embodiments, the compartmentalized cell culturing device can comprise electrodes for stimulating or detecting electrical responses from the cultured cells. In such embodiments, the cells can be cultured such that the organized cells are aligned with the electrodes. In some embodiments, the compartmentalized cell culturing device can be configured such that organizational structures of the compartmentalized cell culturing device are aligned with electrodes and / or with specific groups of electrodes. In some embodiments, that the at least first and second compartments and the at least one channel can be aligned with electrodes and / or with specific groups of electrodes.

[0119] For example, in embodiments where the compartmentalized cell culturing device corresponds to multi-well device 1 described above, electrodes 122 in electrode layer can be aligned with the first compartment 62, second compartment 64, and microchannels 65. A first electrode or group of electrodes can be aligned with first compartment 62 for stimulating or detecting electrical responses of neuronal cell bodies 71 positioned within the first compartment. A second electrode or group of electrodes can be aligned with second compartment 64 for stimulating or detecting electrical responses of axons 73 and / or axonal branches extending in the second compartment. A third electrode or group of electrodes can be aligned with and / or positioned along microchannels 65 (such as individual microchannels or predefined subsets or groups of microchannels) following the orientation of axons for stimulating or detecting electrical responses of axons 73 extending along the microchannels. As can be appreciated, by aligning the organizational structures with electrodes and / or groups of electrodes, data acquired via the electrodes can be correlated with the physiology of the cultured cells and / or with a position of the cultured cells in the cell culturing device. For example, if it is known that an electrode is aligned with a first compartment (or a specific position within first compartment), it can be understood that data acquired via that electrode corresponds to cells positioned within the first compartment and / or to a cell structure or physiology expected to be contained within the first compartment (or the specific position within the first compartment).

[0120] As another example, in embodiments where the compartmentalized cell culturing device corresponds to multi-well device 200 described above, electrodes in electrode layer 227 can be aligned with the neuromuscular junction chamber 230 and the neuronal cells channel 232. A first electrode or group of electrodes can be aligned with first portion 244 of the neuronal cells channel 232 for stimulating or detecting electrical responses of theneuro cell bodies positioned within the neuronal cells channel 232. A second electrode or group of electrodes can be aligned with second portion 246 of the neuronal cells channel 232 to follow an orientation of the neuronal extensions growing in the neuronal cells channel for stimulating or detecting electrical responses of the neuronal extensions growing in the neuronal cells channel. A third electrode or group of electrodes can be aligned with neuromuscular junction chamber 230 to follow an orientation of muscle cells extending between the first and second pillars 240, 242 for stimulating or detecting electrical responses of the muscle cells. As mentioned above, data acquired via the electrodes can be correlated with a physiology of the cultured cells and / or with a position of the cultured cells in the cell culturing device. For example, if it is known that an electrode is aligned with a neuromuscular junction, it can be understood that data acquired via that electrode corresponds to data acquired from muscle tissue, or neuronal tissue, or neuromuscular junctions, or axons or any specific part of the tissue or of the cell.

[0121] Although in the examples provided above electrodes were described as being aligned with organizational structures in the cell culturing device, it is appreciated that other sensors can be provided as well, and that such sensors can also be positioned to allow a correlation between the acquired data and a physiology of the cultured cells and / or a position of the cultured cells in the cell culturing device. As an example, such sensors can include sensors that are configured to detect biological, physical, chemical, magnetic, or electrical information about cells.

[0122] In some embodiments, the step of culturing cells 303 can comprise providing markers to facilitate subsequent tracking of movement of the cultured cells or tissues. For example, the markers can comprise beads that are distributed among the cells or tissues. Different configurations of beads can be provided. For example, the beads can be spheres of approximately 3pm in size, the beads can be the same size or different sizes, the beads can be fluorescent, the beads can be colored, and / or the beads can be otherwise visually distinguishable from the cells or tissues. Although spherical beads are described, it is appreciated that other shapes and / or marker types are also possible.

[0123] As can be appreciated, the markers can be provided prior to, during, and / or after the cells or tissues are grown. In an embodiment, the beads can be injected or otherwise uniformly distributed in a gel in which the cells or tissues are to be grown. As the cellsgrow and assemble into tissues, the beads will be organized therewith. For example, where the cells being cultured are muscle cells, those cells will self-assemble into muscle fibers as they grow, and the beads will be endocyted in said fibers. In an embodiment, the beads can be injected during or after growth of the cells or tissues. As can be appreciated, the beads can be injected into different compartments of the cell culturing device as needed. For example, where the cell culturing device comprises a NMJ preparation unit 221 as described above, the beads can be injected into first and / or second muscle cell reservoirs 226, 228, to encourage the beads to be distributed in muscle fibres forming in the neuromuscular junction chamber 230. It is appreciated that the beads can be injected into other compartments such that they can be distributed among different cells or tissues as desired.

[0124] A third step 305 of the method 300 can comprise obtaining cellular and tissue data associated with the compartmentalized cultured cells. The data can be obtained to cover an area including the at least first and second compartments and the at least one channel. For example, the data can be acquired across predetermined X, Y, and / or Z coordinates of each well or assay in a multi-well device. In some embodiments, data can be acquired separately for each of the first and second compartments and the at least one channel, and / or any other structured in the cell culturing device. For example, a first set of data can be acquired for precise X, Y, and / or Z coordinates corresponding to first compartment, a second set of data can be acquired for precise X, Y, and / or Z coordinates corresponding to a second compartment, and a third set of data can be acquired for precise X, Y, and / or Z coordinates corresponding to the at least one channel. In embodiments described below, the data corresponds to imaging data acquired through imaging of the cell cultures and thus providing a visual representation of at least the cells organized within the at least first and second compartments and the at least one channel. It is appreciated, however, that data can be acquired via other means. For example, the data can be obtained using other sensing techniques, such as using electrodes and / or other sensors. The obtained data can be “raw” data, such as data acquired directly using the electrodes and / or other sensors, or processed data, such as images or visual representations generated from the “raw” data.

[0125] In embodiments where the compartmentalized cell culturing device corresponds to a multi-well device, the cellular and tissue data can be acquired across each of theplurality of wells or assay windows in the multi-well device using predetermined acquisition parameters. For example, where the cellular and tissue data correspond to imaging data, each well or assay window can be imaged using the same predetermined depth of focus (e.g., instead of adjusting the focus when imaging each individual well or assay window). Similarly, where multiple images are captured in each well or assay window (e.g., to separately acquire images of different compartments or other regions of interest within a well or assay window), each of those images can be captured using a corresponding predetermined depth of focus (e.g., a first compartment in each well or assay window can be imaged using a first predetermined depth of focus, a second compartment in each well or assay window can be imaged using a second predetermined depth of focus, etc.). In some embodiments, an optimal or desired depth of focus can be determined for a first well, assay window and / or compartment thereof (e.g., using any suitable auto-focus technique to ensure that an object of interest contained therein is within focus), and the optical or desired depth of focus can correspond to the predetermined depth of focus that is used for imaging subsequent wells, assay windows and / or compartments. As can be appreciated, the compartments in the compartmentalized cell culturing device can be configured to organize cells along a depth (e.g., along the Z-axis). For example, where the cell culturing device comprises a NMJ preparation unit 221 , the neuronal cells channel 232 can be configured with a ceiling that places a limit on the range of movement of the neurosphere 260 along the Z-direction. In such a configuration, the neurospheres in each NMJ unit in the multi-well device will all be at substantially the same height. As such, images captured with the same depth of focus will be comparable since the neurospheres will be represented substantially uniformly across images (i.e. , all neurospheres will be in focus to the same extent). This facilitates automation of image acquisition, and is in contrast to where the neurospheres could be floating or at any depth, requiring each neurosphere to be individually focused during imaging to provide comparable images for analysis (otherwise, in some images the neurospheres will be in focus, while in others they may be partially out of focus, which is not suitable for comparison). It is appreciated that other organization structures can be provided to allow uniformity in the Z-direction, and / or to allow uniformity in any other parameter across wells / assays to facilitate automated acquisition of cellular and tissue data across such wells / assays

[0126] In embodiments described below, the imaging data is acquired through brightfield imaging or fluorescence imaging. However, it is appreciated that any suitable imagingtechnique or instrument can be used. For example, images of the cultured cells can be obtained using other techniques by detecting properties such as absorbance, fluorescence, luminescence, etc. Obtaining images of the cultured cells can include preliminary steps of dying or fluorescently tagging the cultured cell samples to enable or facilitate detecting such properties. In some embodiments, the images can be obtained to detect changes in cultured cells over time. For example, a first image of the compartmentalized cells can be obtained at a first point in time, and a second image (or series of images) can be obtained at a second point in time (or at sequential points in time). In some embodiments, a series of images can be captured in sequence, for example to record a video of the cultured cells over a given period. In some embodiments, images can be obtained of a multi-well device comprising a plurality of culturing units. In such embodiments, images can be obtained of a plurality of culturing units, and / or of a plurality of wells each defining an assay window associated with each of the plurality of culturing units. In some embodiments, assay windows of a multi-well device can be imaged separately. In some embodiments, the entirety of the multi-well device can be imaged, and subregions of the image corresponding to individual assay windows and / or to individual compartments can be isolated or extracted for analysis. In some embodiments, the obtained images can be representative of electrical response detected via electrodes comprised by the compartmentalized cell culturing device. In some embodiments, images of the compartmentalized cultured cells captured as described above can be received for subsequent processing by a computing device.

[0127] By way of example, and with reference to Figs. 26A and 26B, images 351 , 353 of compartmentalized cultured cells are shown. In the illustrated examples, cells were cultured using multi-well device 1 described above, and the images 351 , 353 were captured using a plate reader with fluorescence detection. The images 351 , 353 are framed in alignment with a well defining assay window 70, and cover an area representative of cells organized within first compartment 62, second compartment 64, and microchannels 65. Fig. 26A corresponds to an image 351 of organized cells captured at a first point in time and is representative of organized neuronal cells having healthy connections. Fig. 26B corresponds to an image 353 of the organized cells captured at a second point in time after the cells were treated with a toxic drug, and is representative of neuronal cells having destroyed connections. As can be appreciated, the imaging data can be correlated with a physiology of the cultured cells and / or with a position of thecultured cells in the cell culturing device. In particular, due to the framing of the images, it can be known that a first area of the image (e.g., a first region within first specified X, Y and / or Z coordinates) corresponds to cells and / or cell substructures within the first compartment 62, that a second area of the image (e.g., a second region within second specified X, Y, and / or Z coordinates) corresponds to cells and / or cell substructures within the second compartment 64, and that a third area of the image (e.g., a third region within third specified X, Y, and / or Z coordinates) corresponds to cells and / or cell substructures within the microchannels 65. It can also be known that a first section of the image corresponds neuronal cell bodies 71 (e.g., soma and dendrites), that a second section of the image corresponds to axons, and that a third section of the image corresponds to axons and axonal branches.

[0128] As another example, and with reference to Figs. 27A and 27B, images 355, 357 of compartmentalized cultured cells are shown. In the illustrated examples, cells were cultured using multi-well device 200 described above. Image 355 was captured using a plate reader via phase contrast microscopy, whereas image 357 was captured using a plate reader with fluorescence detection. Images 355, 357 are framed in alignment with a well defining assay window 270, and cover an area representative of cells organized within the neuromuscular junction chamber 230, first portion 244 of the neuronal cells channel 232, and second portion 246 of the neuronal cells channel 232. Images 355, 357 correspond to muscle cells extending between first and second pillars 240, 242 in the neuromuscular junction chamber 230, neuron cell bodies extending within the first portion 244 of the neuronal cells channel 232, and axons extending into the second portion 246 of the neuronal cells channel 232 and out to the neuromuscular junction chamber 230 to reach the muscle cells.

[0129] As yet another example, and with reference to Figs. 28A and 28B, image 359 of compartmentalized cultured cells is shown. In the illustrated example, cells were cultured using multi-well device 1 comprising an arrangement of electrodes as illustrated in Fig. 28A. The arrangement of electrodes includes a first electrode group 122a aligned with first compartment 62, a second electrode group 122b aligned with second compartment 64, and a third electrode group 122c aligned with, and extending along, microchannels 65. In the present embodiment, raw cellular and tissue data was obtained from measurements acquired using electrodes over an extended period of time. The raw data wassubsequently processed to generate image 359, which corresponds to a heatmap indicating cellular activity in response to electrical stimuli over time. Image 359 is framed in alignment with assay window 70, and covers an area representative of cells organized within first compartment 62, second compartment 64, and microchannels 65. More specifically, image 359 corresponds to a heat-map indicating an electrical response over time of neuronal bodies 71 within the first compartment 62 as measured by first electrode group 122a, an electrical response over time of axons 73 and / or axonal branches within the second compartment 64 as measured by second electrode group 122b, and an electrical response over time of axons 73 extending along the microchannels 65 as measured by third electrode group 122c, in response to an electrical stimulus provided by one or more electrodes of the electrode groups 122a, 122b, 122c. Although a single image 359 is shown, it is appreciated that a series of images or a video can be obtained using data acquired via electrodes over a period of time, for example to illustrate and characterize transmission of an electrical signal, for example along a path from neuron bodes 71 in first compartment 62, to axons 73 extending in microchannels 65, and to axons 73 and branches in second compartment 64. Moreover, although image 359 is shown, it is appreciated that subsequent processing can be carried out on the raw data that was used to generate the image 359.

[0130] A fourth step 307 of the method 300 can comprise processing the cellular and tissue data to measure or analyze parameters of the cultured cells, such as cell characteristics (including cell morphology, cell count, etc.), cell behaviour, morphometrical parameters, morphodynamic parameters, among others. In the present embodiment, processing the cellular and tissue data comprises processing images acquired via imaging. It is appreciated, however, that in other embodiments, raw data acquired via sensors, for example, can be processed in a similar manner. As such, in the following embodiments, the expressions “image” or “images” can be understood as being replaceable more generally by “data”.

[0131] In an embodiment, processing the images can include processing an image of at least a portion of an assay window of a cell culturing unit that comprises cells cultured in and organized by organizational structures. The image of the assay window can be processed to analyze a plurality of parameters of cultured cells contained therein. In an embodiment, processing the images can include processing a plurality of images, witheach of the plurality of images covering at least a portion of an assay window of one of a plurality of cell culturing units in a multi-well device. Each of the plurality of images can be processed to analyze a plurality of parameters of cultured cells associated with each of the plurality of cell culturing units.

[0132] In an embodiment, processing an image can include a sub-step 307a of identifying at least one region of interest within the image, and processing the region of interest to analyze at least one parameter. In some embodiments, at least a plurality of parameters can be analyzed in the region of interest. The region of interest can, for example, correspond to a region containing organizational structures within the compartmentalized cell culturing device and / or a region of cultured cells organized by the organizational structures (e.g., such as a compartment within the compartmentalized cell culturing). In an embodiment, processing an image can include identifying at least a first and a second region of interest within the image, processing the first region of interest to analyze a first parameter or a first set of parameters, and processing the second region of interest to analyze a second parameter or a second set of parameters that is different from the first parameter or first set of parameters. The first and second regions of interest can, for example, correspond to first and second regions containing organizational structures within the compartmentalized cell culturing device and / or first and second regions of cultured cells organized by the organizational structures. In some embodiments, the first and second regions of interest can me mutually exclusive, while in some embodiments the first and second regions can overlap. In some embodiments, processing an image can include processing the first region of interest to analyze a first parameter or a first set of parameters, and processing both the first and second regions of interest to analyze a second parameter or a second set of parameters that is different from the first parameter or first set of parameters.

[0133] In an embodiment, processing a region of interest, such as a first region or second region of interest, can include a sub-step 307b of identifying a plurality of fields within the region of interest, and processing the plurality of fields to analyze at least one parameter. In an embodiment, processing an image can include identifying a first set of fields within the region of interest and processing the first set of fields to analyze a first parameter or first set of parameters, identifying a second set of fields within the region ofinterest, and processing the second set of fields to analyze a second parameter or second set of parameters.

[0134] As can be appreciated, any suitable processing technique can be applied to analyze different parameters of the cultured cells. Such processing techniques can be facilitated and can provide results that more closely resemble parameters of in vivo cells due to the physical organization of the cells from which the images or other data is acquired. Moreover, additional processing techniques can be enabled due to the physical organizational of the cells which would not otherwise be possible without the organization.

[0135] For example, as shown in Fig. 42A, in prior art culturing devices, neuronal cultures form entangled, irreproducible networks. It can therefore be very challenging to distinguish individual cells and their corresponding substructures in a culture. It is also difficult to make comparisons across cultures, since each culture will form its own unique network with a randomized organizational pattern. Moreover, since the cells are cultured in a fluidic environment, the cells can be free to move around significantly over time. Therefore, it can be difficult to make comparisons between data of the same culture acquired at two different points in time, as the organization of the cells can change, and it can be difficult to identify and correlate the position of a given cell or sub-structure between two images.

[0136] Cells cultured in devices comprising organizational structures as described above can enable reproducible and precisely organized structures that greatly facilitate data acquisition, analysis, and processing and that can enable new processing techniques that are not possible with randomly organized structures. For example, as shown in Fig. 42B, neuronal cells cultured in a device such as multi-well device 1 described above enables reproducible neuronal networks that are organized in a manner closely resembling those in vivo. Neuronal cell substructures are specifically organized and are retained in specified strategic positions. Accordingly, each compartment can be analyzed separately in order to evaluate particular parameters or characteristics relating to a particular substructure. Moreover, since the location of specific substructures are known, parameters that involve directionality between substructures can be evaluated. Every culture follows a similar organization, and as such separate cultures can be easily compared to one another due to substructures being positioned in similar locations across cultures. In embodimentswhere neuronal cells are organized along vertical channels, each culture can thus effectively be read as a unique “barcode”. Finally, even though the cells are cultured in a fluidic environment, the organizational structures can reduce the mobility of cells (e.g., the cells are essentially confined or partially confined in their compartments and are generally held in place). For example, cell bodies or soma can be confined in the first compartment 62 while axons can extend from first compartment 62 through the channels 65 towards second compartment 64. As such, it can be much easier to make comparisons between data of the same cultured acquired at different points in time, since the organization of the cells remains generally the same and it can be easier to correlate the position of a given cell or substructure between two or more image. By way of example, Fig. 43A shows motor neurons cultured in multi-well device 1 for 21 days and stained with a calcium indicator. Activity was measured for 1 minute. Signals from individual cells were extracted in the first compartment 62 with an automated, high throughput pipeline (colored areas in Fig. 43A). Due to the organizational of the cells, the position and activity of individual cells could be tracked over time, for example before and after a treatment. Activity of the cells (circles in Fig. 43A) could thus be visualized and compared as shown in Fig. 43B.

[0137] In more detail now, the processing techniques can be specially configured to process regions of interest and / or fields within regions of interest to analyze different parameters. For example, in some embodiments, an entire image of an assay window can be provided as input, and the processing technique can be configured to analyze only specified regions and / or fields within the input image. In some embodiments, only portions of an image of an assay window (e.g., portions corresponding to the region of interest and / or fields of interest) can be provided as input, and the processing technique can be configured to analyze the provided portions. In some embodiments, portions of an image of an assay window corresponding to a region of interest and / or fields of interest can be extracted or cropped from the image for processing. In some embodiment, the image can be masked to isolate portions corresponding to the region of interest and / or fields of interest for processing. In some embodiments, an artificial intelligence model can be used to mask the image. For example, the artificial intelligence model can be trained using supervised training techniques to recognize regions of interest and / or fields of interest in an input image, and to generate a binary mask applicable to the input image to isolate a specified region of interest and / or field of interest for processing. The artificial intelligence model can, for example, be trained using training data comprising images ofcompartmentalized cultured cells labelled according to a region and / or field comprised therein. The artificial intelligence model can be configured to identify / classify a region contained in an input image, and generate a binary mask to isolate said region.

[0138] By way of example, and with reference to Fig. 29, image 361 of compartmentalized cultured cells are shown. In the illustrated example, cells were cultured using multi-well device 1 described above. Image 361 covers an area representative of cells organized within first compartment 62, second compartment 64, and microchannels 65. Regions of interest are identified in image 361 , and the regions of interest are processed to analyze parameters including cell number, nucleus aggregate, fiber breadth / thickness, axonal length, number of branching, number of branching junctions, neurite straightness, and total axonal material.

[0139] As shown in Fig. 29A, a first region of interest can correspond to a subregion 36T of image 361 corresponding to first compartment 62. The subregion 36T can be identified using different techniques. In an embodiment, the subregion can be identified based on a known position within image 361 (e.g., corresponding to an upper portion of image 361 having predetermined dimensions). In an embodiment, the subregion can be identified using a machine learning model trained to identify a subregion corresponding to first compartment 62. The subregion 36T can be isolated for processing, for example by cropping image 361 , applying a binary mask to image 361 , or any other technique suitable for extracting subregion 36T from image 361. The first region of interest contains the soma and dendrites of the neuronal cells and can thus be processed using suitable techniques to analyze a plurality of parameters relating to the soma and dendrites, including a number of cells, and nucleus aggregate. In some embodiments, analyzing the parameters can including a quantifying analysis, for example providing a count of the number of cells and quantifying the area of nucleus aggregate (e.g., in pm2). In some embodiments, analyzing the parameters can include a qualificative analysis, for example applying an artificial intelligence model trained to label images corresponding to first compartment, for example as “high”, “low”, or “nominal” cell count and / or nucleus aggregate. In some embodiment, the qualificative analysis can be comparative, for example to provide an indication that a first sample (e.g., corresponding to a first assay in a multi-well plate) has a higher or lower cell count of nucleus aggregate compared to one or more second samples (e.g., corresponding to one or more second assays in the multi-well plate). Exemplarysubregions 36T of images of cultured cells exhibiting high and low cell counts in first compartment 62 are shown in Figs. 30A and 30B, respectively. Low cell counts can be indicative of cell death. Exemplary subregions 36T of images of cultured cells exhibiting low and high nucleus aggregation in first compartment 62 are shown in Figs. 31 A and 31 B, respectively. High nucleus aggregation can be indicative of cell clumping.

[0140] As shown in Fig. 29B, a second region of interest can correspond to subregion 361” of image 361 corresponding to microchannels 65. The subregion 361” can be identified and isolated for processing using different techniques as described above. The second region of interest contains axons of the neuronal cells and can thus be processed using suitable techniques to analyze at least one parameter relating to axons, such as fiber breadth or thickness. The parameters can be analyzed using quantitative (e.g., measured in pm) and qualitative techniques as described above. Exemplary subregions 361” of cultured cells exhibiting low and high fiber breadth / thickness in microchannels are shown in Figs. 32A and 32B, respectively. Fiber breadth / thickness can be indicative of axonal bundling.

[0141] As shown in Fig. 29C, a third region of interest can correspond to subregion 36T” of image 361 corresponding to second compartment 64. In the illustrated example, both the second and third region of interest (e.g., corresponding to both microchannels 65 and second compartment 64) are processed to analyze a plurality of parameters. Accordingly, subregions 361” and 36T” can be identified and isolated for processing using different techniques as described above. In some embodiments, subregions 361” and 36T” can be identified and isolated separately, while in other embodiments a subregion spanning both subregions 361” and 36T” can be identified and isolated. The second and third regions of interest contain both axons and axonal branches and can thus be processed using suitable techniques to analyze a plurality of parameters relating to the axons and axonal branches, including an axonal length, number of branching, number of branching per cell, and total axonal material. The parameters can be analyzed using quantitative and qualitative techniques as described above. As an example, using quantitative techniques, the axonal length and total axonal material can be measured in pm, and the number of branches can be counted. Exemplary subregions 361” and 36T” of images of cultured cells exhibiting long and short axonal length are shown in Figs. 33A and 33B, respectively. Axonal length can be an indication for growth inhibition or stimulation. Exemplarysubregions 361” and 36T” of images of cultured cells exhibiting low and high number of branching are shown in Figs. 34A and 34B, respectively. Number of branching can be an indication for growth regeneration or growth rate.

[0142] The third region of interest corresponding to subregion 36T” of image 361 is also shown in Fig. 29D. The subregion 361 can be identified and isolated for processing using different techniques as described above. The third region of interest contains axonal branches and can thus be processed to analyze parameters relating axonal branches, such as number of branching junctions, and neurite straightness. The parameters can be analyzed using quantitative and qualitative techniques as described above. Exemplary subregions 36T” of images of cultured cells exhibiting low and high number of branching junctions in second compartment 64 are shown in Figs. 35A and 35B, respectively. The number of branching junctions can be an indication for axonal connections. Exemplary subregions 36T” of images of cultured cells exhibiting low and high neuron straightness in second compartment 64 are shown in Figs. 36A and 36B, respectively. Neurite straightness can be an indication for neuronal dynamics.

[0143] In an embodiment, one or more parameters can be analyzed by processing one or more fields in a region of interest. By way of example, and with reference to Fig. 37, an exemplary method for measuring axonal thickness is shown according to an embodiment. In the illustrated example, the second region of interest corresponding to subregion 361” of image 361 is shown. Axonal thickness is measured by analyzing a plurality of fields 375, each corresponding to a microchannel of the plurality of microchannels 65. In an embodiment, measuring the axonal thickness can comprise, for each of the plurality of fields 375, identifying at least one axon or bundle of axons contained within the field, and measuring a thickness of the identified axon or bundle of axons contained within said field. In some embodiments, the axonal thickness can be measured at a plurality of predefined areas within each field, for example at a top 379a, middle 379b, and bottom 379c area of field 375 corresponding to channel. As can be appreciated, any suitable axonal measurement technique can be used. In some embodiments, the axonal measurement technique can be adapted to only operate on the specified fields 375. In some embodiments, the specified fields 375 can be extracted (e.g., via croppy or masking) to profile only the specified fields 375 as input to the axonal measurement technique.

[0144] As another example, and with reference to Fig. 38, an exemplary method for measuring axonal density of a target molecule (e.g., tubulin) is shown according to an embodiment. In the illustrated embodiment, the second and third regions of interest corresponding to subregions 361” and 36T” of image 361 are shown. Axonal density is measured by analyzing a plurality of fields, including a plurality of fields 375 each corresponding to a microchannel of the plurality of microchannels 65, and field 377 corresponding to a region of second compartment 64 corresponding to axons after the microchannels. In an embodiment, measuring the axonal density comprises, for each of the plurality of fields 375, 377, identifying axons and measuring a total area covered by the axons. As can be appreciated, any suitable axonal density measuring technique can be used, and the technique can be adapted to operate on the specified fields 375, 377.

[0145] As another example, and with reference to Figs. 39A and 39B, an exemplary method for measuring tubulin breakdown is shown according to an embodiment. In the illustrated embodiment, the second and third regions of interest corresponding to subregions 361” and 361”’ of image 361 are shown. Tubulin breakdown is measured by analyzing a plurality of fields, including a plurality of fields 375 each corresponding to a microchannel of the plurality of microchannels 65, and field 377 corresponding to a region of second compartment 64 corresponding to axons after the microchannels. In an embodiment, as shown in Fig. 39A, measuring tubulin breakdown can comprise, for each of the plurality of fields 375, 377, identifying degenerated axons and measuring a total area covered by the degenerated axons. These can be calculated by identifying dots (as indicated by the yellow arrows), counting a number of dots per axon, and calculating a total area of dots per axon. Such measurements can assist in revealing cytoskeleton degradation. In an embodiment, as shown in Fig. 39B, measuring tubulin breakdown can comprise, for each of the plurality of fields 375, 377, identifying intact axons and measuring a total area covered by intact axons (as indicated by the blue arrows). In some embodiments, tubulin breakdown over time can be measured, for example by measuring tubulin breakdown in an image of an assay taken at a first point in time, measuring tubulin breakdown in an image of the assay taken at a second point in time, and comparing the results of the measurements.

[0146] As yet another example, and with reference to Fig. 40, an exemplary method for measuring connections is shown according to an embodiment. In the illustrated example,the second region of interest corresponding to subregion 361” of image 361 is shown. Connections measured by analyzing a plurality of fields 381 , each corresponding to a space between adjacent microchannels of the plurality of microchannels 65. In an embodiment, measuring the connections can comprise, for each of the plurality of fields 381 , measuring fluorescence density between microchannels. In some embodiments, connections can be measured by counting a number of horizontal lines between the axons within each of the plurality of fields 381.

[0147] Although examples of analyzing particular parameters by processing one or more regions of interest and / or fields were provided, it is appreciated that similar techniques can be applied to analyze other parameters. For example, in embodiments where the cultured cells comprise neuronal cells, similar techniques can be applied to analyze further parameters characterizing axonal integrity, axonal degradation, axonal transport, etc. Moreover, although examples were provided of analyzing neuronal cells cultured in a multi-well plate such a multi-well device 1 , it is appreciated that similar techniques can apply to analyzing other cells cultured in other compartmentalized cell culturing device. As an example, in embodiments where the cells correspond neuronal cells and muscle cells cultured in multi-well device 200, parameters such as muscle fiber length, thickness, and contractibility can be analyzed by processing a subregion of an image corresponding to junction chamber 230, and a parameter such as a distance between the neurosphere and muscle cells can be measured by processing a subregions of an image corresponding to junction chamber 230 and neuronal cells channel 232 (and / or second portion 246 thereof). Of course, other parameters can be analyzed in a similar fashion. If, for instance, the neurons are stimulated in one compartment, the result of this stimuli can be analysed individually in the different compartments.

[0148] In some embodiments, the parameters being analyzed can correspond to movement of cells or tissues and / or other physiological responses to stimuli of the cells or tissues. Analysis of such parameters can include measuring movement of at least one structure or shape detected in the cells or tissues within the one or more regions of interest. By way of example, and with reference to Fig. 44A, an image 501 of compartmentalized cultured cells is shown. In the illustrated example, neuromuscular cells were cultured using the multi-well device 200 described above. A video (i.e. a series or sequence of images) was captured using calcium imaging to record muscle response to neurospherestimulation, and image 501 is a representative frame of said video. More specifically, the neuromuscular cultures were stained with Fluo-8 to highlight the presence of calcium that is released when the muscle tissues contract in response to a stimulus. The fold change of the calcium signal can be measured in the video to quantify the physiological response of the cells or tissues over time. In the present example, two regions of interest were identified to extract and analyze calcium signals from different cells simultaneously. A first region of interest 503 corresponds to a subregion of the image 501 corresponding to the neuronal cell channel 232 (i.e. corresponding to a first compartment within the compartmentalized cell culturing device), and a second region of interest 505 corresponds to a subregion of image 501 corresponding to the neuromuscular junction chamber 230 (i.e. corresponding to a second compartment within the compartmentalized cell culturing device). The first region of interest 503 is sized, shaped, and positioned to allow extracting and analyzing the calcium signal from the neurosphere 260 positioned in the neuronal cell channel 232. Similarly, the second region of interest 505 is sized, shaped, and positioned to allow extracting and analyzing the calcium signal from the muscle cells 264 positioned in the neuromuscular junction chamber 230. In the present embodiment, the first region of interest 503 is circular to accommodate the shape of the neurosphere 260, whereas the second region of interest 505 is rectangular to accommodate the shape of the muscle cells / fibers 264. It is appreciated that other shapes, sizes, and / or configurations of the regions of interest are possible to accommodate and isolate different cells and / or structures or compartments as needed. Fig. 44B is a graphical representation of the observed dose-response relationship to glutamate stimulation. As can be appreciated, identifying and analyzing the first and second regions of interest 503, 505 allows to measure the movement and characterize the response of the muscle cells simultaneously and separately from the neuronal cells.

[0149] As another example, and with reference to Fig. 45A, an image 551 of compartmentalized cultured cells is shown. In this example, neuromuscular cells were cultured using the multi-well device 200 described above, and a video (i.e. a series or sequence of images or frames) was captured using brightfield imaging to record muscle response to neurosphere stimulation. Image 551 is a representative frame of said video. The physiological response of the cells or tissues over time can be quantified by measuring movement of shapes or structures detected in the video using computer vision techniques. Different computer vision techniques can be used, such as backgroundsegmentation to separate an object of interest (such as muscle cells / fibres 264 in neuromuscular junction 230) from the rest of an image, and optical flow to quantify the motion or movement of objects of interest (such as muscle fibres and / or shapes contained therein) between consecutive frames of the video. It is appreciated that other computer vision techniques and combinations thereof are possible. In some embodiments, the entirety of each video frame 551 can be analyzed using the computer vision techniques, while in other embodiments, the computer vision techniques can be limited to one or more regions of interest, such as region 505. As can be appreciated, such techniques can allow generating data quantifying the motion or movement of objects of interest as illustrated in the graph of Fig. 45B. Such data can be further analyzed to quantify different aspects of the movement, such as the overall level of movement, movement frequence, and amplitude of movement, among others.

[0150] In some embodiments, measuring movement using computer vision techniques can be improved using markers embedded in the cultured cells / tissues. By way of example, Fig. 46A illustrates an image 561 of neuromuscular cells that were cultured using the multi-well device 200 such that beads 563 are distributed therein, as described above. Video was captured using brightfield imaging to record muscle response to neurosphere stimulation, and image 561 is a representative frame of said video. As can be appreciated, the beads create stronger shadows that can serve as anchoring points or points of reference that make it easier for computer vision techniques, such as optical flow, to detect movement. In some embodiments, individual beads can be recognized and tracked across frames, allowing data quantifying the motion or movement of individual beads to be generated as illustrated in Fig. 46B. The beads can be recognized, for example, using machine learning techniques that are trained to detect circular or spherical shapes. The movement of individual beads and / or sets of beads can be further analyzed to more granularly quantify and / or characterize the movement of cells or tissues. For example, by analyzing subsets of the beads, movement of different regions of muscle tissue can be tracked / measured separately, and the movement of such regions can be associated with corresponding specific neurons and / or regions of the neurosphere that cause such movement. In some embodiments, the subset of beads that are analyzed can correspond to beads that are contained within muscle, and beads that are outside the muscle can be filtered out. Although in the present embodiment a brightfield image 561 is shown, it is appreciated that beads and / or other markers can also facilitate detection of movementusing computer vision techniques applies to other types of imagery. For example, where fluorescent imaging is used, the provided beads can be fluorescent, and the computer vision techniques can be configured to track beads by detecting the light emitted thereby.

[0151] In an embodiment, at least part of the method 300 described above can be carried out on a computing system. With reference to Fig. 41 , an exemplary system 400 for analyzing compartmentalized cell cultures is shown according to an embodiment. In the illustrated embodiment, the system 400 includes a computing system 401 having at least one processor 403 and memory 405. The computing system can, for example be a laptop computer, desktop computer, server or collection of servers, among others. The computing system 401 is in operational communication with: an imaging device (for example by being configured to have access to data and images of compartmentalized cell cultures captured by imaging device, such as data uploaded to a database via a web application); persistent storage 409 (such as local storage and / or a remote database); and user input / output devices such as a display 411 , a mouse 413 and / or keyboard 415. The memory 405 stores instructions which, when executed by the at least one processor 403 can cause the at least one processor to: obtain images of compartmentalized cell cultures from imaging device 707, process the images of the compartmentalized cell cultures to analyze a plurality of parameters, including identifying regions of interest in images and processing the regions of interest to analyze parameters, identifying fields of interest within the regions of interest and processing the fields of interest to analyze parameters, store analyzed parameters in persistent storage 409 and / or present parameters to users via display 411 for example as part of a report. The computing system 401 can solicit user input, for example, by causing a visual prompt to be shown on display 411 , and can receive user input via a user input device such as the mouse 413, keyboard 415 and / or touch- enabled display 411. It is appreciated that other suitable configurations of system 400 are also possible.

[0152] In an embodiment, a non-transitory computer-readable medium can include computer-executable instructions stored thereon which, when executed, cause the computing system 401 to carry out at least part of the method 300 as described above.

[0153] As can be appreciated, cell or tissue cultures analyzed using the systems and method described above can facilitate data annotation, analysis, machine learning,automation of data analysis, and use of advance analytics for implementation of artificial intelligence to diagnose / predict conditions or states. For example, in the compartmentalized cell culturing device, a tissue or cell type can be assigned to one compartment, with other tissues or cell types being assigned to other compartments. The different tissues or cells can easily be distinguished or identified by their position (e.g., X, Y, and / or Z coordinates) in the culturing device. The cell and tissue data acquired and analyzed from the different compartments of the culturing device can be annotated or labelled according to the tissues or cells to which they correspond, according to their position in the culturing device, and / or according to an observed condition or state based on one or more parameters of the analyzed tissues or cells. For example, where the cultured cells or tissues include neurons, data acquired from the cell culturing device can be labelled as neuron data based on the position from which the data was acquired from the cell culturing device (e.g., based on whether the data was acquired from a position corresponding to a compartment intended to contain neuron cells or portions of neuronal structures, such as neurites, axons, somas, etc.). Similarly, the neuron data can be labelled as being in a particular state or condition (e.g., healthy, subjected to a toxin, other substance, or other stimulus etc.) based on the position from which the data was acquired in a multi-well device (e.g., based on whether the data was acquired from a well corresponding to a control sample, or a well corresponding to a particular substance or other stimulus) and / or based on a certain potion or structure of the neuron that has been confined or enriched at a certain compartment. Such labelled data can allow diagnosing or predicting a condition, e.g. by providing reference data against which input data from a test sample can be compared. In some embodiment, the labelled data can be used to train machine learning or artificial intelligence models, for example to generate models that are able to more accurately classify input cell or tissue data (e.g., such as images of cells or tissues obtained in vivo from a test subject) as corresponding to healthy cells, and / or cells that are subject to one or more stimuli or are representative of one or more conditions or states.

[0154] Several alternative implementations and examples have been described and illustrated herein. The implementations of the technology described above are intended to be exemplary only. A person of ordinary skill in the art would appreciate the features of the individual implementations, and the possible combinations and variations of the components. A person of ordinary skill in the art would further appreciate that any of theimplementations could be provided in any combination with the other implementations disclosed herein. It is understood that the technology may be embodied in other specific forms without departing from the central characteristics thereof. The present implementations and examples, therefore, are to be considered in all respects as illustrative and not restrictive, and the technology is not to be limited to the details given herein. Accordingly, while the specific implementations have been illustrated and described, numerous modifications come to mind.

Claims

CLAIMS1 . A method for analyzing cell or tissue cultures cultured in a compartmentalized cell culturing device, comprising: receiving cellular or tissue data associated with the cell or tissue cultures, wherein: o the cell or tissue cultures are organized by organizational structures in the device comprising at least first and second compartments in fluid communication via at least one channel; and o the cellular or tissue data is correlated with at least one of: a physiological component of the cell or tissue cultures, and a position of the cell or tissue cultures in the device; identifying at least one region of interest in the cellular or tissue data corresponding to at least one of: the physiology of the cell or tissue cultures in the device, and at least one of the organizational structures in the device; and processing the region of interest in the cellular or tissue data to measure at least one parameter relating to the cell or tissue cultures.

2. The method according to claim 1 , wherein the cellular or tissue data is acquired through sensors that are configured to detect biological, physical, chemical, magnetic or electrical information about cells or tissues.

3. The method according to claim 2, wherein the cellular or tissue data is acquired through imaging of the cell or tissue cultures.

4. The method according to any one of claims 1 to 3, wherein the at least one parameter relating to the cell or tissue cultures comprises at least one morphometrical or morphodynamic parameter relating to the cell or tissue cultures, or subcellular components of the cell or tissue cultures, organized by at least one of the organizational structures.

5. The method according to any one of claims 1 to 4, comprising processing the at least one region of interest in the cellular or tissue data to measure a plurality of parameters relating to cell or tissue cultures.

6. The method according to any one of claims 1 to 5, comprising identifying a first region of interest in the cellular or tissue data corresponding to the cell or tissue cultures organized by a first one of the organizational structures, and processing the first region of interest to measure at least a first parameter relating to the cell or tissue cultures; and identifying a second region of interest in the cellular or tissue data corresponding to the cell or tissue cultures organized by a second one of the organizational structures, and processing the second region of interest to measure at least a second parameter relating to the cell or tissue cultures.

7. The method according to claim 6, comprising identifying further regions of interest in the cellular or tissue data corresponding to at least one of: the cell or tissue cultures organized by further ones of the organizational structures, and by combinations of the organizational structures; and processing the further regions of interest to measure further parameters relating to the cell or tissue cultures.

8. The method according to any one of claims 1 to 7, comprising identifying at least one field within the at least one region of interest, and processing the at least one field within the region of interest to measure the at least one parameter relating to the cell or tissue cultures.

9. The method according to any one of claims 1 to 8, wherein the received cellular or tissue data comprises first data acquired from the cell or tissue cultures at a first point in time, and second data acquired from the cell or tissue cultures at a second point in time, further wherein the cell or tissue cultures maintain the organization defined by the organizational structures at both the first and second points in time.

10. The method according to any one of claims 1 to 9, wherein processing the at least one region of interest comprises processing at least a portion of an image derived from the cellular or tissue data, the at least a portion of the image corresponding to the cellular or tissue data contained within the region of interest.

11. The method according to claim 10, comprising applying at least one computer vision technique to detect movement in the image.

12. The method according to claim 11 , wherein the at least one computer vision technique comprises at least one of: optical flow and background segmentation.

13. The method according to any one of claims 1 to 12, wherein processing the at least one region of interest comprises processing the cellular or tissue data contained within the at least one region of interest.

14. The method according to any one of claims 1 to 13, wherein processing the at least one region of interest comprises measuring movement of at least one structure or shape detected in the cell or tissue cultures within the at least one region of interest.

15. The method according to claim 14, wherein the cell or tissue cultures comprise markers distributed therein, and processing the at least one region of interest comprises detecting and measurement movement of the markers within the at least one region of interest.

16. The method according to claim 15, wherein the markers comprise beads.

17. The method according to any one of claims 1 to 16, wherein processing the region of interest further comprises: cleaning the cellular or tissue data and filtering out artefacts or erroneous data; conducting statistical analyses on the cleaned and filtered cellular or tissue data to measure the at least one parameter relating to the cell or tissue cultures; generating at least one visualization of the cellular or tissue data based on the measured at least one parameter; and storing the resulting statistical analyses and the at least one visualization on persistent storage.

18. The method according to any one of claims 1 to 17, comprising determining at least one condition associated with the measured at least one parameter, labelling atleast a portion of the cellular or tissue data according to the determined condition, and training a machine learning model using the labelled data to predict the at the one condition from input data.

19. A computing system for analyzing cell or tissue cultures cultured in a compartmentalized cell culturing device, comprising: at least one processor; and memory having executable instructions stored thereon which, when executed by the at least one processor, cause the computing system to: receive cellular or tissue data associated with the cell or tissue cultures, wherein: o the cell or tissue cultures are organized by organizational structures in the device comprising at least first and second compartments in fluid communication via at least one channel; and o the cellular or tissue data is correlated with at least one of: a physiology of the cell or tissue cultures, and a position of the cell or tissue cultures in the device; identify at least one region of interest in the cellular or tissue data corresponding to at least one of: the physiology of the cell or tissue cultures in the device, and at least one of the organizational structures in the device; and process the region of interest in the cellular or tissue data to measure at least one parameter relating to the cell or tissue cultures.

20. A non-transitory computer-readable medium having instructions stored thereon which, when executed by at least one processor of a computing system, cause the computing system to: receive cellular or tissue data associated with cell or tissue cultures cultured in a compartmentalized cell culturing device, wherein: o the cell or tissue cultures are organized by organizational structures in the device comprising at least first and second compartments in fluid communication via at least one channel; ando the cellular or tissue data is correlated with at least one of: a physiology of the cell or tissue cultures, and a position of the cell or tissue cultures in the device; identify at least one region of interest in the cellular or tissue data corresponding to at least one of: the physiology of the cell or tissue cultures in the device, and at least one of the organizational structures; and process the region of interest in the cellular or tissue data to measure at least one parameter relating to the cell or tissue cultures.

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