Method for analyzing nerve cells, device for analyzing nerve cells, and computer program

Abstract

In a first embodiment of the present invention, provided is a method of analyzing neurons that comprises: a cell region identification step for identifying a region of a neurite of a neuron or a region of a cell body thereof on the basis of time series images obtained by imaging the neuron in times series; an agglutination detection step for imaging, in time series, a first fluorescent protein with which a specific protein expressed within the neuron has been tagged, and detecting, on the basis of the luminance of the first fluorescent protein included in the time series images, whether there is an agglutination of the specific protein that has agglutinated in the region of the neurite or the region of the cell body identified in the cell region identification step; and an analysis step for categorizing the neuron among a plurality of groups on the basis of a detection result of the agglutination detection step and analyzing a viability state of neurons for each of the groups.

Description

[Technical field]

[0001] The present invention relates to a method for analyzing nerve cells, an apparatus for analyzing nerve cells, and a computer program. [Background technology]

[0002] Fluorescence observation of cells is now possible by introducing genes that code for fluorescent proteins into living cells. Fluorescent proteins such as Green Fluorescent Protein (GFP) and Red Fluorescent Protein (RFP) are used. These fluorescent proteins are used as markers to indicate the location of genes and proteins within cells.

[0003] Patent Document 1 describes a method for synchronizing cell cycles by introducing a gene encoding a fluorescent protein into multiple cells and using the fluorescence emitted from the cells as an indicator. There is a demand for new methods for analyzing cells using fluorescent proteins. [Patent Document 1] JP 2009-072155 A General Disclosure

[0004] In a first aspect of the present invention, there is provided a method for analyzing nerve cells, the method for analyzing nerve cells comprising: The method for analyzing nerve cells may further include an image acquisition step of acquiring the time-series images. The method for analyzing nerve cells may further include a cell region identification step of identifying a region of a neurite or a region of a cell body of the nerve cell based on the above-mentioned. The method for analyzing nerve cells may further include a step of imaging a first fluorescent protein tagged with a specific protein expressed in the nerve cell in a time series manner, Based on the time series images The presence or absence of the specific protein aggregates in the neurite region or the cell body region identified in the cell region identification step is determined by measuring the brightness of the first fluorescent protein included in the time-series images. Based on The method may include a step of detecting aggregates. The nerve cell analysis method may include a correspondence step of matching the cell body region identified in the cell region identification step with the neurite region. The method for analyzing nerve cells includes classifying the nerve cells into a plurality of groups based on the detection result of the aggregate detection step. vinegar R classification The method may include a process. The specific protein is a protein that exhibits aggregation properties within the nerve cell, and the multiple groups may be classified based on whether or not the region of the cell body can be matched to the region of the neurite, and the presence or absence of aggregates of the specific protein that have aggregated in the region of the cell body.

[0005] In a second aspect of the present invention, there is provided a method for analyzing nerve cells, the method comprising the steps of: The method may further comprise the step of analyzing a survival state of the nerve cells for each of the groups. The survival state of the nerve cells may include at least one of a survival time, a survival rate, and life or death of the nerve cells.

[0006] In a third aspect of the present invention, The analyzing step may include displaying a relationship between the plurality of groups and a survival state of the plurality of groups for each of the groups.

[0007] In a fourth aspect of the present invention, The analysis step may classify cells into (i) a group of cells in which the cell body and the neurites are associated with each other and the aggregates are present in the region of the cell body, (ii) a group of cells in which the cell body and the neurites are not associated with each other and the aggregates are present in the region of the cell body, (iii) a group of cells in which the cell body and the neurites are associated with each other and the aggregates are not present in the cell body, and (iv) a group of cells in which the cell body and the neurites are not associated with each other and the aggregates are not present in the region of the cell body.

[0008] In a fifth aspect of the present invention, The step of associating may include determining the correspondence based on a distance between the cell body region and the neurite region.

[0009] In a sixth aspect of the present invention, The step of associating may include associating the neurite with a region of the cell body in an extension direction of a tangent to an end point of the neurite.

[0010] In a seventh aspect of the present invention, The time-series images may include at least one of phase contrast image data, fluorescence image data including the brightness of the first fluorescent protein, and data including a video image file.

[0011] In an eighth aspect of the present invention, the cell region identification step comprises: The image acquisition step may include capturing images of a first fluorescent protein tagged to a specific protein expressed in the neuron in a time series, and identifying a region in the time series images where the brightness of the first fluorescent protein exceeds a first brightness threshold as a region of a neurite of the neuron.

[0012] In a ninth aspect of the present invention, The cell region identification step may identify, among regions in the time series images where the brightness of the first fluorescent protein exceeds the first brightness threshold, regions that exceed a predetermined size threshold as regions of the neurite or cell body of the neuron, and further may track the identified identical neurite and the identified identical cell body in time series, respectively, to calculate a survival time related to the survival state.

[0013] In a tenth aspect of the present invention, Aggregate Detection The step of determining a region in the time series image where the brightness of the first fluorescent protein exceeds a second brightness threshold different from the first brightness threshold as a cell body of the neuron. There are aggregates inside As an area Inspection You may put it out.

[0014] In an eleventh aspect of the present invention, The cell region identification step may further identify the region of the cell body or neurite of the neuron based on the time series images obtained by capturing a second fluorescent protein different from the first fluorescent protein expressed in the neuron in a time series. The aggregate detection step may detect whether the aggregate in the identified neurite is present inside the axon or inside the dendrite based on the first fluorescent protein and the second fluorescent protein. The analysis step may classify the aggregate in the neurite into a cell group that is present on the side close to the cell body and a cell group that is present on the side close to the end of the neurite based on the detection result of the aggregate detection step, and analyze the survival state of the neuron for each cell group.

[0015] In a twelfth aspect of the present invention, there is provided an apparatus for analyzing nerve cells, the apparatus comprising: The imaging unit may be configured to acquire time-series images of nerve cells in a time series manner. The apparatus may further comprise an identification unit that identifies a neurite region or a cell body region of the nerve cell based on the time-series images.The apparatus may further comprise a detection unit that detects the presence or absence of aggregates of the specific protein that have aggregated in the neurite region or the cell body region of the nerve cell identified by the identification unit based on the time-series images obtained by capturing in time series images of a first fluorescent protein tagged with a specific protein expressed in the nerve cell, using the brightness of the first fluorescent protein included in the time-series images as a reference. The imaging device may further include a correspondence section that associates the region of the cell body identified by the identification section with the region of the neurite. The nerve cells are classified into a plurality of groups based on the detection result of the detection unit. Classification The device may include a section. The specific protein is a protein that exhibits aggregation properties within the nerve cell, and the multiple groups may be classified based on whether or not a correspondence can be established between the region of the cell body and the region of the neurite, and on the presence or absence of aggregates of the specific protein that have aggregated in the region of the cell body.

[0016] In a thirteenth aspect of the present invention, The neural network may further include an analysis unit configured to analyze a survival state of the neural cells for each group. The survival state of the neural cells may include at least one of a survival time, a survival rate, and life or death of the neural cells.

[0017] In a fourteenth aspect of the present invention, The analysis unit may display a relationship between the plurality of groups and a survival state of the plurality of groups for each of the groups.

[0018] In a fifteenth aspect of the present invention, The analysis unit may classify the cells into (i) a cell group in which the cell body and the neurites are associated with each other and the aggregates are present in the region of the cell body, (ii) a cell group in which the cell body and the neurites are not associated with each other and the aggregates are present in the region of the cell body, (iii) a cell group in which the cell body and the neurites are associated with each other and the aggregates are not present in the cell body, and (iv) a cell group in which the cell body and the neurites are not associated with each other and the aggregates are not present in the region of the cell body.

[0019] In a sixteenth aspect of the present invention, The matching unit may include at least one of matching based on the distance between the cell body region and the neurite region, or matching the neurite with the cell body region in the extension direction of a tangent at an end point of the neurite.

[0020] In a seventeenth aspect of the present invention, the identification unit comprises: The imaging unit may capture images of a first fluorescent protein tagged to a specific protein expressed in the nerve cell in a time series, and a region in the time series images where the brightness of the first fluorescent protein exceeds a first brightness threshold may be identified as a region of a neurite of the nerve cell.

[0021] In an eighteenth aspect of the present invention, The identification unit may identify regions in the time series images where the brightness of the first fluorescent protein exceeds the first brightness threshold, that exceed a predetermined size threshold, as regions of the neurite or cell body of the neuron, and may further track the identified identical neurite and the identified identical cell body in time series, respectively, to calculate a survival time related to the survival state.

[0022] In a nineteenth aspect of the present invention, detectionThe unit classifies a region of the cell body of the neuron in which the brightness of the first fluorescent protein in the time-series images exceeds a second brightness threshold different from the first brightness threshold. There are aggregates inside As an area Inspection You can put it out.

[0023] In a twentieth aspect of the present invention, there is provided a computer program, which may include a computer readable storage medium having instructions thereon, which, when executed by a processor or programmable circuit, may cause the processor or programmable circuit to perform operations including a method of analysing the neuronal cells.

[0024] The above summary of the invention does not list all of the necessary features of the present invention. Also, subcombinations of these features may also be inventions. [Brief description of the drawings]

[0025] [Figure 1] 1 shows an example of the device configuration of a nerve cell analysis device in this embodiment. [Diagram 2] An example of a specific configuration of a nerve cell analysis device in this embodiment will be described. [Diagram 3] 1 shows an example of the flow of a nerve cell analysis method according to this embodiment. [Figure 4] 11 shows an example of a flow of imaging nerve cells in S200 in this embodiment. [Figure 5A] 11 shows an example of a flow for detecting a cell body region in S210 in this embodiment. [Figure 5B] An example of a flow for extracting a region (cellular region) exceeding a brightness threshold in S212 in this embodiment will be described. [Figure 5C] An example of the contraction and expansion processing of S213 in this embodiment will be described. [Figure 5D] 11 shows an example of a flow for extracting an area exceeding a size threshold in S214 in this embodiment. [Figure 5E]1 shows an example of a screen displayed on the output unit of the nerve cell analysis device in the flow of detecting the region of the cell body in S210 in this embodiment. [Figure 6A] 13 shows an example of a flow for tracking the cell body of S230 in this embodiment. [Figure 6B] An example of tracking an area between frames before and after S231 in this embodiment will be described. [Figure 7A] 11 shows an example of a flow for detecting a neurite region in S250 in this embodiment. [Figure 7B] 11 shows an example of a flow for detecting a neurite region in S250 in this embodiment. [Figure 7C] 1 shows an example of a screen displayed on the output unit of the nerve cell analysis device in the flow of detecting the neurite region in S250 in this embodiment. [Figure 8A] 13 shows an example of a flow of tracking neurites in S270 in this embodiment. [Figure 8B] 13 shows an example of a flow for tracking an area between frames before and after S271 in this embodiment. [Figure 8C] 13 is an example of a flow for recognizing multiple neurite regions that are closer than the distance threshold in S273 as one neurite region in this embodiment. [Figure 9] 13 shows an example of a screen displayed on the output unit of the neuronal analysis device in the flow of associating neurites with cell bodies in S290 in this embodiment. [Figure 10A] 1 shows an example of a screen displayed on the output unit of the neuronal analysis device in the flow of analyzing the neuronal survival time in S310 in this embodiment. [Figure 10B] 1 shows an example of a graph of intra-neurite aggregate survival time displayed on the output unit of the neuronal analysis device in the flow of analyzing neuronal survival time in S310 in this embodiment. [Figure 10C] 1 shows an example of a cell survival rate graph displayed on the output unit of the neuronal analysis device in the flow of analyzing the neuronal survival time in S310 in this embodiment. [Figure 11] 11 shows the flow of steps S280 and S281 of the first modified example. [Figure 12] 2 illustrates an example of a hardware configuration of a computer. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. In addition, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0027] FIG. 1 shows an example of the device configuration of a nerve cell analysis device in this embodiment. The nerve cell analysis device 1 according to the present invention analyzes the relationship between the presence or absence of aggregates in the neurite and / or cell body region of the nerve cell and the state of the nerve cell, such as the life or death, survival time, and survival rate of the nerve cell. Specifically, the nerve cells may be classified into a plurality of cell groups (for example, may be classified into four cell groups as described later) based on the result of the intracellular aggregate detection process and the result of the intraneurite aggregate detection process, and the state of the nerve cell may be analyzed for each cell group. The nerve cell analysis device 1 may be placed in an environment in which nerve cells are cultured. For example, the nerve cell analysis device 1 may be provided in a CO2 incubator in which the temperature, humidity, and carbon dioxide gas concentration are adjusted. The nerve cell analysis device 1 includes an imaging device 10, an output unit 160, a calculation device 170, and an input unit 180.

[0028] The imaging device 10 is a device that captures images of nerve cells to generate images of the nerve cells. The imaging device 10 captures observation images of the nerve cells multiple times in time series to generate multiple images. The imaging device 10 may include an optical fiber 7, a transmitted illumination unit 40, an excitation light source 70, a microscope unit 150, and a camera 300.

[0029] The optical fiber 7 is a component that introduces the light emitted from the excitation light source 70 into the microscope unit 150 .

[0030] The transmitted illumination unit 40 is a light source unit used when observing nerve cells using phase contrast. The transmitted illumination unit 40 includes a field lens 44, a polarizing mirror 45, and a transmitted illumination light source 47. The transmitted illumination unit 40 irradiates nerve cells with light that penetrates the nerve cells.

[0031] The excitation light source 70 is a light source used for fluorescent observation of nerve cells. The excitation light source 70 irradiates light to be reflected by nerve cells via the optical fiber 7 and the microscope unit 150.

[0032] The microscope unit 150 is a device for magnifying and observing nerve cells using a microscope. The nerve cells are not particularly limited, and may be established cell lines such as N1E-115 cells (mouse neuroblastoma) and PC-12 cells (rat pheochromocytoma), primary cultured cells collected from brain nerve tissues of mice or rats, or nerve cells differentiated from embryonic stem cells (ES cells) or induced pluripotent stem cells (iPS cells). For example, the nerve cells may be nerve cells differentiated from iPS cells derived from a patient with a neurodegenerative disease. The microscope unit 150 may include a stage 23, an objective lens unit 27, a fluorescent filter unit 34, an imaging lens unit 38, a collector lens 41, a field lens 411, and a polarizing mirror 452.

[0033] A chamber 100 is placed on the stage 23. The chamber 100 has a transparent culture vessel 20. The culture vessel 20 is filled with a culture medium, and nerve cells are cultured in the culture medium. In order to observe a fluorescent image, the nerve cells may be labeled with one or more types of fluorescent substances (e.g., fluorescent proteins). A part a of the bottom surface of the chamber 100 and a part b of the top surface of the chamber 100 may each be transparent. The top surface of the chamber 100 may be open or may be covered with a transparent lid. A culture environment suitable for observing nerve cells can be provided by having the top surface or bottom surface of the chamber 100 transparent, or having the top surface of the chamber 100 open or covered with a transparent lid.

[0034] A humidifier (not shown) may be connected to the chamber 100 via a silicon tube. By connecting the humidifier, the calculation device 170 described later can control the humidity and carbon dioxide gas concentration inside the chamber 100 to values ​​close to those suitable for culturing nerve cells. In addition, the chamber 100 may be provided with a temperature control device (not shown) such as a heater or a cooler. For example, the temperature control device may be an electric heater or a Peltier cooler. By providing the temperature control device in the chamber 100, the temperature inside the chamber 100 can be controlled to a value close to that suitable for culturing nerve cells. Furthermore, a carbon dioxide gas cylinder may be connected to the chamber 100. By connecting the carbon dioxide gas cylinder, the pH of the medium in which the nerve cells are cultured can be controlled to a value close to that suitable for culturing nerve cells. The humidity, carbon dioxide gas concentration, and temperature inside the chamber 100 are measured by sensors (not shown). The measurement results are sent to the calculation device 170.

[0035] 1 (a direction perpendicular to the paper surface). By moving the objective lens unit 27 in the x-axis direction, the objective lens arranged in the optical path of the imaging device 10 can be switched. This makes it possible to switch the type and magnification of the objective lens.

[0036] The fluorescence filter section 34 includes a plurality of types of filter blocks in the x-axis direction in Fig. 1. By moving the fluorescence filter section 34 in the x-axis direction, the filter blocks arranged in the optical path of the imaging device 10 are switched.

[0037] The camera 300 has an imaging sensor (not shown). The camera 300 may be a cooled camera. A cooled camera is a camera that can suppress noise caused by heat by cooling the imaging sensor. The imaging sensor may be a CMOS image sensor (Complementary Metal Oxide Semiconductor) or a CCD image sensor (Charge Coupled Device). When focusing the imaging by the camera 300, the x and y coordinates of the stage 23 and the z coordinate of the objective lens unit 27 may be controlled by the computing device 170. The camera 300 may be housed in a housing different from that of the imaging device 10.

[0038] The output unit 160 outputs the processing result of the arithmetic device 170. For example, the output unit 160 may output a survival rate curve of nerve cells, which is the analysis result. For example, the output unit 160 may be a monitor connected to the arithmetic device 170. The output unit 160 may be referred to as a display unit.

[0039] The arithmetic device 170 is connected to the imaging device 10 and controls the imaging device 10. Specifically, the arithmetic device 170 switches the combination of the type of objective lens of the objective lens unit 27 and / or the type of filter block of the fluorescence filter unit 34 arranged in the optical path of the imaging device 10. For example, phase contrast observation and fluorescence observation differ in both the type of filter block arranged in the optical path and the type of objective lens. Furthermore, the two types of fluorescence observation differ only in the type of filter block arranged in the optical path. Furthermore, the light sources (transillumination unit 40 and excitation light source 70) used in phase contrast observation and fluorescence observation are also different. For this reason, the inside of the arithmetic device 170 (for example, the imaging device control unit 171 described later) may switch one or more of the filter block, the objective lens, and the light source depending on whether phase contrast observation or fluorescence observation is performed.

[0040] When performing phase contrast observation, the arithmetic device 170 turns on the light source for transmitted illumination 47 and turns off the light source for excitation 70 to enable the optical path of the transmitted illumination unit 40. When performing phase contrast observation, the light emitted from the light source for transmitted illumination 47 illuminates the observation position c of the culture vessel 20 via the field lens 44, the polarizing mirror 45, and the upper part b of the chamber 100. The light transmitted through the observation position c reaches the light receiving surface of the camera 300 via the bottom surface of the culture vessel 20, the bottom part a of the chamber 100, the objective lens unit 27, the fluorescent filter unit 34, and the imaging lens unit 38. At this time, a phase contrast image at the observation position c of the culture vessel 20 is formed on the camera 300. The camera 300 captures the phase contrast image and generates an image. Data of the generated image may be recorded inside the arithmetic device 170 (for example, the recording unit 190 described later) and / or output to the output unit 160.

[0041] When performing fluorescence observation, the arithmetic device 170 turns on the excitation light source 70 and turns off the transmitted illumination light source 47 to enable the optical path of the excitation light source 70. When performing fluorescence observation, the light emitted from the excitation light source 70 illuminates the observation position c of the culture vessel 20 via the optical fiber 7, the collector lens 41, the field lens 411, the polarizing mirror 452, the fluorescence filter unit 34, the objective lens unit 27, the bottom a of the chamber 100, and the bottom surface of the culture vessel 20. When the nerve cells cultured in the culture vessel 20 are fluorescently labeled, the fluorescent substance of the nerve cells at the observation position c is excited and emits fluorescence. The fluorescence emitted from the nerve cells reaches the light receiving surface of the camera 300 via the bottom surface of the culture vessel 20, the bottom a of the chamber 100, the objective lens unit 27, the fluorescence filter unit 34, and the imaging lens unit 38. At this time, a fluorescent image at the observation position c of the culture vessel 20 is formed on the camera 300. The camera 300 captures a fluorescent image and generates an image. Data of the generated image may be recorded inside the computing device 170 (for example, in the recording unit 190 described below) and / or output to the output unit 160.

[0042] In addition to controlling the imaging device 10, the computing device 170 receives multiple images of nerve cells captured by the imaging device 10 from the imaging device 10, and analyzes the relationship between the presence or absence of aggregates in the neurite and / or cell body region of the nerve cells and the state of the nerve cells, such as life or death, survival time, and survival rate, based on the multiple images. The configuration of the computing device 170 will be described later.

[0043] The input unit 180 inputs instructions and data from the observer to the arithmetic device 170. For example, the input unit 180 may input instructions from the observer regarding conditions for imaging nerve cells and conditions for culturing nerve cells (temperature, humidity, carbon dioxide concentration, etc.) to the arithmetic device 170. The input unit 180 may also input instructions from the observer regarding a fluorescence brightness threshold and conditions for analyzing nerve cells to the arithmetic device 170. For example, the input unit 180 may be a keyboard or a mouse connected to the arithmetic device 170.

[0044] 2 shows an example of a specific configuration of the arithmetic device 170 in this embodiment. The arithmetic device 170 includes an imaging device control unit 171, a recording unit 190, a discrimination unit 350, a detection unit 400, a correspondence unit 550, and an analysis unit 600.

[0045] The imaging device control unit 171 controls the imaging device 10. For example, the imaging device control unit 171 controls the objective lens unit 27, the fluorescent filter unit 34, the transillumination unit 40, the excitation light source 70, the camera 300, and the like described in FIG. 1. As an example, when the imaging conditions of the nerve cells are input to the input unit 180 by the observer, the imaging device control unit 171 performs necessary adjustments among switching the type of the objective lens of the objective lens unit 27 in the microscope unit 150, switching between the transillumination light source 47 and the excitation light source 70, switching the type of the fluorescent filter of the fluorescent filter unit 34, the position of the stage 23, and the height of the objective lens of the objective lens unit 27, according to the input imaging conditions. After the imaging device control unit 171 performs necessary adjustments, the camera 300 images the nerve cells and generates an image. The camera 300 sends data of the generated image to the imaging device control unit 171. In addition, data of the generated image may be recorded in the recording unit 190 and / or output to the output unit 160.

[0046] The recording unit 190 stores commands and / or data necessary for the operation of the nerve cell analysis device 1. For example, the recording unit 190 may be, but is not limited to, a memory, an internal hard disk drive, or an external recording medium. The arithmetic device 170 has a central processing unit (CPU), and the CPU executes a computer program recorded in the recording unit 190 to realize the imaging device control unit 171 and the like.

[0047] The identification unit 350 identifies the neurite region and / or the cell body region of the neuron in the image captured by the imaging device 10. A method for identifying the neurite region and / or the cell body region of the neuron by the identification unit 350 will be described later in detail.

[0048] The detection unit 400 detects the presence or absence of protein aggregates in the neurite region and / or cell body region identified by the identification unit 350. A method for the detection unit 400 to detect the presence or absence of protein aggregates in the neurite region and / or cell body region will be described later in detail. Note that the detection unit 400 does not necessarily have to be provided. In this case, the identification unit 350 detects the presence or absence of protein aggregates in the neurite region and / or cell body region.

[0049] It is known that there are two types of neurites in nerve cells: axons and dendrites. An axon is a long, thin protruding structure extending from the cell body of a nerve cell, and is responsible for outputting signals in the nerve cell. A dendrite is a protruding structure with multiple branches extending from the cell body of a nerve cell, and is responsible for inputting signals in the nerve cell. In this specification, axons and dendrites are collectively referred to as neurites. In addition, in the cell body of a nerve cell, intracellular organelles such as nuclei and ribosomes exist, and axons and dendrites meet.

[0050] The associating unit 550 identifies the association between the cell body and neurites that constitute the nerve cell. A method for identifying the association between the cell body and neurites of the nerve cell will be described in detail later.

[0051] The analysis unit 600 analyzes the relationship between the result of the determination of the presence or absence of aggregates by the detection unit 400 and the state of the neuron. For example, the state of the neuron may be life or death of the neuron. For example, the analysis unit 600 may analyze the relationship between the presence or absence of aggregates in the neurites and / or cell body of the neuron and life or death of the neuron. For example, the analysis unit 600 may analyze the presence or absence of aggregates in the neurites and / or cell body of the neuron and at least one of the survival time of the neuron, the survival pattern of aggregates in the neuron, the survival rate of the neuron, and the average survival time of the neuron. The analysis unit 600 may record the result of the analysis in the recording unit 190 and / or output the result to the output unit 160.

[0052] Fig. 3 is an example of a flow of a nerve cell analysis method in this embodiment. The state of a nerve cell in this embodiment can be analyzed by performing the processes of S200 to S310 in Fig. 3. For convenience of explanation, the processes of S200 to S310 will be explained in order, but at least some of these processes may be executed in parallel, or each step may be interchanged without departing from the spirit of the present invention.

[0053] First, in S200, the imaging device 10 images nerve cells and generates a plurality of images. (The step S200 is sometimes referred to as the "image acquisition step.") In S200, imaging the neurons to generate a plurality of images includes steps S201 to S206, as shown in FIG.

[0054] FIG. 4 shows S200 in the flow. First, in S201, the culture vessel 20 in which nerve cells are cultured is placed in the chamber 100. The culture vessel 20 may be placed in the chamber 100 automatically by a robot (not shown) having a multi-joint arm provided in the imaging device 10 upon receiving an instruction from the imaging device control unit 171, or may be placed manually by an observer. Next, the imaging device control unit 171 sets the observation method to low-magnification phase-contrast observation, and the imaging device 10 images the nerve cells cultured in the culture vessel 20 upon instruction from the imaging device control unit 171. The low-magnification phase-contrast observation may be performed by turning on the transmitted illumination light source 47, turning off the excitation light source 70, and moving the objective lens unit 27 in the x-axis direction to place a low-magnification objective lens in the optical path of the imaging device 10. As a result, the imaging device 10 acquires a bird's-eye view image, which is an image of a relatively wide area.

[0055] Alternatively, instead of low-magnification observation, the imaging device control unit 171 may move the observation position c of the culture vessel 20 within the xy plane while the imaging device 10 acquires a plurality of images (tile images) observed with phase contrast, and combines these images into one image (composite image) to acquire a bird's-eye view image. The imaging device 10 may store data of the acquired bird's-eye view image in the recording unit 190 and / or output the data to the output unit 160. After acquiring the bird's-eye view image, the imaging device control unit 171 advances the process to S202.

[0056] Next, in S202, the imaging device control unit 171 judges whether or not an input regarding the conditions for time lapse imaging has been received from the observer via the input unit 180. The conditions for time lapse imaging may be determined by the observer based on the bird's-eye view image output to the output unit 160. The conditions for time lapse imaging may be one or more of an interval, a number of rounds, an observation position, and an observation method (phase contrast observation or fluorescence observation), and may further include, but are not limited to, conditions regarding the culture of nerve cells during imaging (e.g., composition of the culture medium, temperature, carbon dioxide concentration, humidity, etc.). For example, when there are three types of observation methods (e.g., phase contrast observation, green fluorescence observation, and red fluorescence observation), the imaging device control unit 171 may continuously acquire three types of images while appropriately switching between the illumination, the filter block, and the objective lens. When an input regarding the conditions for time lapse imaging has been received, the imaging device control unit 171 may proceed to processing at S203. If no input regarding the conditions for time lapse imaging is received, the imaging device control unit 171 may return the process to S202 again and request the observer to input the conditions for time lapse imaging via the output unit 160, and wait until the input is received.

[0057] Next, in S203, the imaging device control unit 171 may generate a recipe including the input conditions for time lapse imaging. The imaging device control unit 171 may record the generated recipe in the recording unit 190 and / or output the recipe to the output unit 160. After generating the recipe, the imaging device control unit 171 advances the process to S204.

[0058] Next, in S204, the imaging device control unit 171 determines whether or not an instruction to start time lapse imaging has been received from the observer via the input unit 180. If an instruction to start time lapse imaging has been received, the imaging device control unit 171 may proceed to processing at S205. If an instruction to start time lapse imaging has not been received, the imaging device control unit 171 may return the processing to S204 again, request the observer via the output unit 160 to instruct the observer to start time lapse imaging, and wait until the instruction is received. Alternatively, if an instruction to start time lapse imaging has not been received, the imaging device control unit 171 may return the processing to S204 again, request the observer via the output unit 160 to input conditions for time lapse imaging different from the conditions input previously, and wait until the input is received.

[0059] Next, in S205, the imaging device control unit 171 instructs the camera 300 to start time lapse imaging of the nerve cells. The time lapse imaging is performed according to the recipe generated in S203. The imaging device control unit 171 adjusts the position of the stage 23 and / or the objective lens of the objective lens unit 27 so that the observation position c of the culture vessel 20 coincides with the observation position described in the recipe. Next, the camera 300 images the nerve cells and acquires the image, completing the first round of imaging.

[0060] Next, the imaging device control unit 171 instructs the camera 300 to wait for the interval described in the recipe from the start time of the first round of imaging, and then to perform imaging of the second round in the same manner as the first round of imaging, according to the generated recipe. In this manner, the camera 300 repeats the above imaging until the number of rounds described in the recipe is reached.

[0061] Next, in S206, if the time lapse imaging has reached the number of rounds specified in the recipe, the imaging device control unit 171 instructs the camera 300 to end imaging. If the time lapse imaging has not reached the number of rounds specified in the recipe, the imaging device control unit 171 repeats the above imaging by the camera 300 until the specified number of rounds is reached.

[0062] During time lapse imaging, the imaging device control unit 171 accumulates data of images captured by the imaging device 10 in an implementation progress file. The imaging device control unit 171 records the implementation progress file in the recording unit 190. Furthermore, when an instruction to check the implementation progress is input from the observer via the input unit 180 during or after time lapse imaging, the imaging device control unit 171 may output an implementation progress check screen to the output unit 160 by referring to the contents of the implementation progress file at the time of input.

[0063] Furthermore, the imaging device control unit 171 may generate a video file of time-series images in which images from each round of time-lapse imaging are linked in chronological order for each observation method. For example, the imaging device control unit 171 may generate a video file of phase-contrast images in which a plurality of phase-contrast images are linked in chronological order, and / or generate a video file of fluorescence images in which a plurality of fluorescence images are linked in chronological order. In addition to / instead of this, the imaging device control unit 171 may synthesize phase-contrast images and fluorescence images for each frame, and generate a video file of a synthesized image in which each synthesized frame is linked in chronological order. The imaging device control unit 171 may generate a video file of a synthesized image in which the synthesized frames are linked in chronological order by storing data of time-series phase-contrast images (phase-contrast time-series images) captured by the imaging device 10, data of time-series fluorescence images (fluorescence time-series images), and the like. Data , and / or the generated video file Data containing may be displayed on the output unit 160 and / or recorded in the recording unit 190. After the time lapse imaging is completed, the imaging device control unit 171 advances the process to S210 and / or S250. Note that some of the steps from S201 to S206 may be omitted without departing from the spirit of the present invention.

[0064] Here, the fluorescence emitted from the nerve cell may be derived from a first fluorescent protein that has been introduced into the nerve cell beforehand. The first fluorescent protein may be a tagged (fluorescently tagged) protein expressed in the nerve cell (neurite and / or cell body). The protein expressed in the nerve cell may be a protein that exhibits aggregation in the nerve cell.

[0065] The protein exhibiting aggregation in nerve cells may be, but is not limited to, α-synuclein protein, tau protein, prion protein, TDP-43 protein, huntingtin protein, etc. Here, the protein exhibiting aggregation in nerve cells may be referred to as a "specific protein".

[0066] Alpha-synuclein is a 140 amino acid protein that is localized at the axon terminal of nerve cells. Although the physiological function of alpha-synuclein is unknown, alpha-synuclein aggregates have been found in nerve cells of patients with Parkinson's disease and dementia with Lewy bodies, suggesting that the pathology may be caused by the aggregation of alpha-synuclein.

[0067] Tau protein is a protein that is localized in the axons of neurons and contributes to the stability of microtubules. Hyperphosphorylated tau protein aggregates have been found in neurons of patients with Alzheimer's disease, suggesting that the pathology may be caused by tau protein aggregation.

[0068] Prion protein is a ubiquitous protein, not only in nervous system tissues, but also in other tissues, but its physiological function remains unknown. Prion protein aggregates have been found in the central nervous system of patients with Creutzfeldt-Jakob disease, suggesting that the pathology may be caused by prion protein aggregation.

[0069] TDP-43 protein is a ubiquitous nuclear protein that is involved in transcription and splicing, not just in the nervous system. TDP-43 protein aggregates have been found in the central nervous system of patients with amyotrophic lateral sclerosis, suggesting that the pathology may be caused by TDP-43 protein aggregation.

[0070] Huntingtin protein is a 3145 amino acid protein that is present not only in nervous system tissues but also in general tissues. Although the physiological function of huntingtin protein is unknown, huntingtin protein aggregates with abnormal polyglutamine additions have been found in the striatal neurons of patients with Huntington's disease, suggesting that the pathology may be caused by huntingtin protein aggregation.

[0071] As mentioned above, proteins that exhibit aggregability in nerve cells are targets for treating diseases. In the present invention, a first fluorescent protein, which is a fluorescently tagged protein, is expressed in nerve cells, and the relationship between the presence or absence of aggregates and the state of the nerve cells (e.g., life or death, survival time, survival rate, etc. of the nerve cells) can be analyzed. Here, the state of the nerve cells is sometimes referred to as the "survival state of the nerve cells."

[0072] The fluorescent tag may be, but is not limited to, GFP, RFP, etc. The first fluorescent protein may be introduced into the nerve cell by introducing a plasmid vector or a viral vector (e.g., adenovirus, etc.) encoding the first fluorescent protein into the nerve cell.

[0073] Next, in S210, the identification unit 350 performs the following determination based on the multiple acquired fluorescence time-series images: Prescribed A region exceeding the brightness threshold and the size threshold is extracted as a cell body region. Prescribed The step of extracting the region exceeding the brightness threshold and the size threshold as the region of the cell body includes steps S211 to S215 as shown in FIG. 5A.

[0074] 5A shows S210 in the flow. First, in S211, the imaging device control unit 171 transmits data of a plurality of images captured by the imaging device control unit 171 to the identification unit 350, whereby the identification unit 350 acquires data of the plurality of images. Here, the plurality of images may be fluorescent time-series images captured by time-lapse imaging. After the identification unit 350 acquires the plurality of images, the identification unit 350 advances the process to S212.

[0075] Next, in S212, the discrimination unit 350 binarizes the image using a fluorescence brightness threshold and extracts a region exceeding the brightness threshold. The brightness threshold may be specified by the observer. The discrimination unit 350 may receive input related to the brightness threshold from the observer via the input unit 180. Alternatively, the discrimination unit 350 may specify the brightness threshold based on a plurality of acquired images.

[0076] For example, in FIG. 5B, image 211a is an example of an image captured by the imaging device 10. The brightness of the fluorescence is indicated by shades of gray. At this time, the discrimination unit 350 extracts only areas having a brightness equal to or greater than the brightness threshold. Specifically, if the brightness of a pixel in the image exceeds the brightness threshold, a predetermined brightness (e.g., brightness 1) may be given to the pixel, and if the brightness of the pixel is equal to or less than the brightness threshold, a brightness of 0 may be given to the pixel. In this way, a binarized image such as image 212a can be obtained. After the discrimination unit 350 extracts the area in the image that exceeds the brightness threshold, the discrimination unit 350 advances the process to S213.

[0077] Next, in S213, the classification unit 350 performs an erosion process (erosion) and an expansion process (dilation) on the binarized image. The erosion process and the expansion process may be an opening process in which the erosion process and the expansion process are performed the same number of times. Specifically, the classification unit 350 first performs an erosion process on the binarized image. Next, the classification unit 350 performs an expansion process on the image that has been subjected to the erosion process the same number of times as the erosion process. The erosion and expansion processes may be performed by the method described in JP 2018-198605 A. By performing the erosion process and the expansion process, it is possible to remove small noise and separate a portion where two regions are connected.

[0078] 5C, the classification unit 350 performs erosion and expansion processing on the binarized image 212a to obtain an image from which small noise has been removed, such as image 213a. After the classification unit 350 performs erosion and expansion processing, the classification unit 350 advances the process to S214. Note that the step of S213 may be omitted, in which case the classification unit 350 may advance directly from S212 to S214.

[0079] Next, in S214, the discrimination unit 350 extracts a region exceeding the size threshold from the image that has been subjected to the contraction and expansion processing as a region of the cell body of a neuron. The size threshold may be represented by the vertical and / or horizontal length of a rectangle circumscribing the region in the image. The size threshold may be represented by the longer or shorter of the vertical and horizontal lengths of the rectangle circumscribing the region in the image. The size threshold may be represented by the size of the area of ​​the rectangle circumscribing the region in the image. The size threshold may be specified by the observer. In this case, the discrimination unit 350 may receive input regarding the size threshold from the observer via the input unit 180. Alternatively, the size threshold may be specified by the discrimination unit 350 based on a plurality of images acquired. In addition to the circumscribing rectangle, the size threshold may be specified by the length or area of ​​any circumscribing polygon or circumscribing circle.

[0080] For example, in FIG. 5D, the identification unit 350 extracts areas from the image 213a that has been subjected to contraction and expansion processing, the vertical and horizontal lengths of which are equal to or greater than the specified lengths, thereby enabling the extraction of only areas that exceed a size threshold, as in image 214a.

[0081] The size threshold may include not only a lower limit but also an upper limit. By setting the size threshold to an upper limit and a lower limit, the discrimination unit 350 can eliminate cell bodies that are too large or too small, and extract only areas within a specific size range as cell bodies. For example, if it is desired to remove an area where two or more neuronal bodies overlap from the analysis target because it is not suitable for analysis, it is possible to remove the area where the two neuronal bodies overlap and become large in size by setting an upper limit for the size threshold. After the discrimination unit 350 extracts the area that exceeds the size threshold, the discrimination unit 350 advances the process to S215.

[0082] If the brightness of the fluorescence tends to increase over time, the brightness threshold and / or size threshold of the image captured at an earlier time may be set to a value smaller than the brightness threshold and / or size threshold of the image captured at a later time, allowing areas with lower brightness and / or size to be included in the analysis.

[0083] Next, in S215, the classification unit 350 may store the image in which the region exceeding the size threshold has been extracted in the recording unit 190 and / or output the image to the output unit 160. The classification unit 350 may also output the image in which the region exceeding the size threshold has been extracted to the association unit 550 and the analysis unit 600.

[0084] 5E is an example of a screen that the identification unit 350 displays on the output unit 160 to allow the observer to specify the brightness threshold and / or the size threshold. In S212 and / or S214, the identification unit 350 may simultaneously read out the captured image files or video files in parallel, generate image signals for displaying the images on the output unit 160, and send the image signals to the output unit 160 in the order in which they were generated, thereby displaying the images in the display area 103. This series of processes performed by the identification unit 350, including reading out the image files, generating the image signals, and sending out the image signals, is also referred to as "playing the video file."

[0085] The playback control section 105 is a GUI (Graphic User Interface) image used by the viewer to input instructions related to the playback of the video file. The playback control section 105 may include a timeline 50, a stop button 52, a fast-rewind button 53, a play button 54, and a fast-forward button 55.

[0086] When the play button 54 is selected by the observer via the input unit 180, playback of the video file begins, and the video may begin to be displayed in the display area 103. When the stop button 52 is selected by the observer, playback of the video file stops. The playback point in the video file is reflected on a timeline 50. The left end of the timeline 50 indicates the beginning of the video file, i.e., the start time of the time lapse imaging, and the right end of the timeline 50 indicates the end of the video file, i.e., the end time of the time lapse imaging. If the time lapse imaging has not ended, the right end of the timeline indicates the current time.

[0087] A slider bar 60 is placed on the timeline 50. The slider bar 60 may indicate the playback position in the video file in real time. The left-right position of the slider bar 60 may be freely movable by the observer. When the left-right position of the slider bar 60 is moved by the observer, the playback position in the video file changes.

[0088] An appropriate brightness threshold and / or size threshold may be specified by the observer via the input unit 180. When the brightness threshold and / or size threshold are specified by the observer, the display area 104 displays the specified brightness threshold and / or size threshold. Next, the identification unit 350 may perform a series of processes from S212 to S215 based on the specified brightness threshold and / or size threshold, and display the processed image in the display area 103. From the processed image displayed in the display area 103, a new brightness threshold and / or size threshold may be further specified by the observer. The brightness threshold and / or size threshold may be specified by the observer any number of times until a desired image is obtained.

[0089] In the above example, the region of the cell body is identified based on the fluorescence derived from the first fluorescent protein. However, in addition to / instead of the fluorescence, the region of the cell body may also be identified based on an image obtained by phase contrast observation.

[0090] After the classification unit 350 outputs the image in which the area exceeding the size threshold has been extracted to the association unit 550 and the analysis unit 600, the classification unit 350 advances the process to S230.

[0091] Next, in S230, the identification unit 350 tracks the cell body. In S230, the step of tracking the cell body includes steps S231 to S236 as shown in FIG 6A.

[0092] 6A shows S230 in the flow. First, in S231, the classification unit 350 tracks areas between previous and subsequent frames for a plurality of time-series images.

[0093] 6B is an example of tracking a region between previous and next frames of acquired time-series images. In imaging of nerve cells, an image captured in a round at time T is image 231a, and an image captured in a round at time T+1 immediately after time T is image 231b. In image 231a, regions exceeding a brightness threshold (sometimes referred to as "cell regions") are region 2311, region 2312, and region 2313, and in image 231b, cell regions are region 2314, region 2315, and region 2316. At this time, the identification unit 350 may set a search range around a region in image 231b that corresponds to region 2311 in image 231a, and search for a region (i.e., a cell region) that corresponds to region 2311 in image 231b. Here, the search region may be a quadrangle (e.g., a rectangle) circumscribing the cell region, with the vertical and horizontal lengths each being slightly larger from the end points (e.g., 5%, 10%, or 20% of the vertical and horizontal lengths). In addition to a quadrangle, any polygon or circle may be defined as the search region.

[0094] For example, the identification unit 350 may recognize a cell region 2314 in the image 231b that is closer than a distance threshold preset as a search region as a cell region corresponding to the region 2311 in the image 231a. In other words, the identification unit 350 may recognize the region 2311 and the region 2314 as the same cell body and perform tracking. Similarly, the identification unit 350 may search for and track the region 2312 and the region 2313 in the image 231a to see if there is a cell region within the search range set in the image 231b.

[0095] For example, when there are multiple cell regions in the search range of image 231b, the classification unit 350 may recognize the cell region closest to the search source region in image 231a as the region of the corresponding cell body and track it with the search source region in image 231a. After the classification unit 350 tracks the regions between the previous and next frames for all or some of the frames, the classification unit 350 advances the process to S232.

[0096] Next, in S232, the identification unit 350 tracks the cell region when the cell body is not detected in the intermediate frames among the acquired time series frames. In the example of FIG. 6B, an example of tracking between two consecutive frames (e.g., time T and time T+1) is shown, but the frames before and after tracking are not limited to two consecutive frames, and tracking may be performed between frames separated by a predetermined time. The identification unit 350 executes the processes of S231 and S232 for all rounds. For example, the identification unit 350 repeatedly executes the above tracking from the first round (i.e., time T is 1) to the last round (i.e., time T is N, which is the number of rounds to be imaged).

[0097] In imaging of nerve cells, there may be cases where a cell region corresponding to a cell region detected in a round at time T does not exist in a round at time T+1. In this case, the identification unit 350 may further set a search region around the cell region detected in the round at time T in the image of the round at time T+2 immediately after that. In other words, a cell region that is closer than a distance threshold preset as a search region in an image captured in the round at time T+2 immediately after time T+1 may be recognized as the same cell body and tracked. If there are multiple cell regions in the search region, the one closest to the search source region may be recognized as the same cell body and tracked. After the identification unit 350 tracks the cell region when a cell body is not detected in the intermediate frame, the identification unit 350 proceeds to processing S2320. The result of tracking each cell body in S232 is also referred to as a "tracking result" below. In the above example, a cell area detected in a round at time T and a cell area in an image captured in a round at time T+2 are tracked. However, the tracking is not limited to the round at time T+2. For example, a cell area in an image captured in a round at time T+3 or time T+4 may be tracked with a cell area detected in the round at time T.

[0098] Next, in S2320, the discrimination unit 350 detects the time when tracking of the cell body is started and the time when tracking is ended. For example, the discrimination unit 350 may output the time when tracking of each cell body is started (also referred to as the "tracking start time") and the time when tracking is ended (also referred to as the "tracking end time") for each tracking result of the cell body to the analysis unit 600, store them in the recording unit 190, and / or output them to the output unit 160. Here, the time when tracking of the cell body is started may be the time of a frame in which the cell body first emits fluorescence with a brightness equal to or greater than a threshold in the captured image, and the time when tracking is ended may be the time of a frame in which the brightness of the cell body in the captured image becomes less than the brightness threshold. The tracking end time may be determined by the discrimination unit 350 to be the timing when cell death of the neuron occurs. After the discrimination unit 350 detects the start and end times of tracking of the cell body, the discrimination unit 350 advances the process to S233.

[0099] Next, in S233, the detection unit 400 deletes the tracking results of cell bodies that are found to have moved a distance equal to or greater than the threshold as a result of tracking performed by the identification unit 350 in S232. The detection unit 400 may determine whether or not a cell body has moved a distance equal to or greater than the threshold between frames separated by a predetermined time. This process enables the detection unit 400 to exclude cell bodies that have moved a distance greater than the expected distance as having failed to be tracked.

[0100] For example, the detection unit 400 may use the center of gravity, center, or end of the cell region as a reference for the movement distance of the cell region. For example, the detection unit 400 may place the cell region on an xy coordinate plane, and calculate the average x of the coordinate values ​​of all pixels included in the cell region in each of the x-axis direction and the y-axis direction. mean and y mean Calculate the point (x mean ,y mean For example, the detection unit 400 may define the center of gravity as the x-axis average value of the minimum and maximum values ​​of the cell region on the x-y coordinate plane. ave Similarly, calculate y, which is the average value of the minimum and maximum values ​​of the y-axis of the cell region. ave Calculate the point (x ave ,yave For example, the detection unit 400 may place the cellular region on an xy coordinate plane, and define the points on the periphery of the cellular region that are located at the ends in a predetermined direction (positive or negative direction) on the x-axis or y-axis as the ends.

[0101] For example, if the movement distance of the cell region between two consecutive frames (for example, time T and time T+1) is more than twice the size threshold used in S214, the detection unit 400 may determine that the tracking of the cell body included in the cell region has failed, and may delete the tracking result of the cell body including the two frames. In the above example, the movement distance is exemplified as twice the size threshold, but it may be three times or 0.5 times the size threshold, and is not limited thereto. This causes the cell region to be excluded from the analysis target of the analysis unit 600 described later. If the nerve cell to be observed is a cell with poor mobility, artifacts can be excluded by performing the step of S233.

[0102] For example, in the screen of FIG. 5E, the detection unit 400 may display the display area 104 for inputting a movement distance threshold into the output unit 160, and may request the observer to input the movement distance threshold. An appropriate movement distance threshold may be specified by the observer via the input unit 180. The detection unit 400 may delete the tracking results of cell bodies that are found to have moved a distance equal to or greater than the threshold, based on the specified movement distance threshold. After the detection unit 400 deletes the tracking results of cell bodies having a movement distance equal to or greater than the threshold, the detection unit 400 advances the process to S234.

[0103] Next, in S234, the detection unit 400 deletes the tracking results whose survival time is equal to or less than the threshold. The detection unit 400 deletes the tracking results of the cell body identification unit whose survival time is found to be equal to or less than the threshold as a result of tracking in S232. Here, the survival time may be the time between the time when tracking of the cell body starts and the time when tracking ends.

[0104] For example, in the screen of FIG. 5E, the detection unit 400 may display the display area 104 for inputting a survival time threshold in the output unit 160, and request the observer to input the survival time threshold. An appropriate survival time threshold may be specified by the observer via the input unit 180. The detection unit 400 may delete tracking results of cell bodies having survival times equal to or less than the threshold based on the specified survival time threshold. After the detection unit 400 deletes tracking results of cell bodies having survival times equal to or less than the threshold, the detection unit 400 advances the process to S235.

[0105] Next, in S235, the detection unit 400 assigns IDs to the tracking results of the cell bodies in the image acquired in S215 and outputs them. The detection unit 400 assigns unique IDs (cell body IDs) to the tracking results of the cell bodies tracked between the acquired time-series frames. That is, tracking results assigned the same cell body ID represent the same cell body. For example, in FIG. 6B, the detection unit 400 may assign the same cell body ID to the area 2311 and the area 2314, assuming that they have the same cell body. The detection unit 400 outputs the cell body ID together with the image to the association unit 550. The detection unit 400 may also store the cell body ID in the recording unit 190 and / or output it to the output unit 160. After the detection unit 400 assigns IDs to the tracking results of the cell bodies and outputs them, the detection unit 400 advances the process to S236.

[0106] In S236, the detection unit 400 judges that the region extracted in steps S231 to S235 is a region of the cell body. Next, the detection unit 400 may perform a step of using a brightness threshold different from the brightness threshold used in step S212 to judge and extract a region exceeding the brightness threshold as a region where aggregates exist, that is, a region where aggregates exist inside the cell body (sometimes referred to as an "intracellular aggregate region"). Specifically, the brightness threshold used in S236 to extract the intracellular aggregate region may be a larger value than the brightness threshold used in S212 to extract the cell body region. The step of S236 performed by the detection unit 400 described above may be called a second aggregate detection step (step), that is, a step of detecting aggregates present inside the cell body.

[0107] By performing the above steps S211 to S236, the detection unit 400 may determine the region of the cell body and the region of the aggregate within the cell body. (These determinations by the detection unit 400 are sometimes referred to as a "cell region identification step" and an "aggregate detection step", respectively.) .

[0108] In the above example, the detection section 400 extracts the cellular region exceeding the brightness threshold and the size threshold, but the detection section 400 may extract the region using only the brightness threshold.

[0109] In S250, the identification unit 350 determines, based on the acquired multiple fluorescence time-series images, Prescribed The region exceeding the brightness threshold and the size threshold is extracted as the region of the neurite. In S250, the step of extracting the region exceeding the brightness threshold and the size threshold as the region of the neurite based on the multiple images includes steps S251 to S255 as shown in FIG. 7A. Note that "extraction" may also be called "identification."

[0110] 7A shows S250 in the flow. First, in S251, the image capturing device control unit 171 transmits data of a plurality of images captured by the identification unit 350, so that the identification unit 350 acquires data of the plurality of images. Here, the plurality of images may be fluorescent time-series images captured by time-lapse imaging. Note that if S211 is performed before step S251, step S251 may be omitted. After the identification unit 350 acquires a plurality of images, the identification unit 350 advances the process to S252.

[0111] Next, in S252, the classification unit 350 binarizes the image using a fluorescence brightness threshold to extract a cellular region. The brightness threshold may be specified by the observer. The classification unit 350 may receive input related to the brightness threshold from the observer via the input unit 180. Alternatively, the classification unit 350 may specify the brightness threshold based on a plurality of images acquired. The brightness threshold of S212 processed by the classification unit 350 and the brightness threshold of S252 may be the same or different. In this case, if the brightness thresholds of S212 and S252 are the same, the step of S252 may be omitted.

[0112] For example, in FIG. 7B, the image 251a is assumed to be an image captured by the imaging device 10. The brightness of the fluorescence is indicated by shades of gray. At this time, the identification unit 350 extracts only a region (cell region) having a brightness equal to or greater than the brightness threshold. Specifically, if the brightness of a pixel in the image exceeds the brightness threshold, the pixel may be given a predetermined brightness (for example, brightness 1), and if the brightness of the pixel is equal to or less than the brightness threshold, the pixel may be given a brightness of 0. In this way, an image in which the brightness is binarized can be obtained, such as the image 252a. After the identification unit 350 extracts the cell region in the image, the identification unit 350 advances the process to S253.

[0113] Next, in S253, the identification unit 350 removes cellular regions other than neurites (e.g., cell bodies and noise) from the binarized image. For example, a first removal method is to remove cellular regions other than neurites by removing cell bodies. That is, to detect only cellular regions of neurites, cellular regions such as cell bodies may be removed by excluding cell body images (e.g., brightness threshold 60, size threshold 10 pixels) from an image binarized with a brightness threshold of 150.

[0114] As a second removal method, a method of removing cell regions other than neurites from a binarized image may be performed by the method described in Japanese Patent Application Laid-Open No. 2004-163201. Specifically, the identification unit 350 extracts the cell body region detected in S210, removes the extracted cell body region from the original binarized image, and extracts the neurite region through thinning and noise removal. Specifically, in a cell process extraction method for extracting only the neurite region from an image, a binarization process is performed on the acquired image to obtain a binarized image, and the binarized image is subjected to a wavelet transform process to obtain a compressed image (DC component) from which the cell body region is extracted. Then, the extracted cell body region is removed from the binarized image to obtain an image mainly including line elements. A thinning process may be performed on the image from which the cell body region has been removed to obtain a thinned image in which the line elements have a line width of one pixel, and then a noise removal process may be performed to obtain an image of only the neurite region.

[0115] Here, the thinning process is a process for obtaining a thinned image in which the line element has a line width of one pixel. The noise removal process is a process for removing a small area corresponding to noise. After the discrimination unit 350 removes the area other than the neurites, the discrimination unit 350 proceeds to S254. Note that the step of S253 may be omitted, in which case the discrimination unit 350 may proceed directly from S252 to S254.

[0116] Next, in S254, the discrimination unit 350 extracts a region exceeding the size threshold from the image from which the cellular region other than the neurites has been removed as the neurite region of the neuron. The size threshold may be represented by the vertical and / or horizontal length of a rectangle circumscribing the cellular region in the image. The size threshold may be represented by the longer or shorter of the vertical and horizontal lengths of the rectangle circumscribing the region in the image. The size threshold may be represented by the size of the area of ​​the rectangle circumscribing the cellular region in the image. The size threshold may be specified by the observer. In this case, the discrimination unit 350 may receive an input regarding the size threshold of the cellular region from the observer via the input unit 180. Alternatively, the size threshold may be specified by the discrimination unit 350 based on a plurality of images acquired. The size threshold of S214 processed by the discrimination unit 350 and the size threshold of S254 may be the same or different. In addition to the circumscribing rectangle, the size threshold may be specified by the length or area of ​​any circumscribing polygon or circumscribing circle.

[0117] The size threshold may include not only a lower limit but also an upper limit. By having the upper limit and the lower limit of the size threshold, the discrimination unit 350 can eliminate neurites that are too large or too small, and extract only cell regions within a specific size range as neurites. For example, if it is desired to remove a location where neurites of two nerve cells overlap from the analysis target because it is not suitable for analysis, it is possible to remove the location where the neurites of two nerve cells overlap and become large by setting an upper limit to the size threshold. After the discrimination unit 350 extracts the region that exceeds the size threshold, the discrimination unit 350 advances the process to S255.

[0118] In addition, when identifying protrusions, the identification unit 350 may set different brightness thresholds and / or size thresholds depending on the position in the image. For example, in an image captured in a round at a certain time T, threshold A may be used at a position corresponding to an area extracted in an image captured in the round at the immediately preceding time T-1, and threshold B may be used at a position not corresponding to the extracted area. Here, threshold A may be set to a value smaller than threshold B. If a neurite exists at a previous time, it is highly likely that a neurite exists at the same position at a later time. Therefore, by setting in this way, the detection criteria for only that position can be relaxed, and cell regions having lower brightness and / or size can also be included in the analysis target.

[0119] Next, in S255, the identification unit 350 may store the image in which the region exceeding the size threshold is extracted in the recording unit 190 and / or output the image to the output unit 160. Furthermore, the identification unit 350 may output the image in which the cellular region exceeding the size threshold is extracted to the association unit 550 and the analysis unit 600.

[0120] 7C is an example of a screen in which a predetermined region (sometimes called a "ROI (Region of interest) region") is specified from a captured image and enlarged and displayed in the display region 103. In S252 and / or S254, the identification unit 350 may play the acquired video file and display it in the display region 103. Furthermore, the identification unit 350 may enable the observer to select the ROI region to be displayed.

[0121] An appropriate brightness threshold and / or size threshold may be specified by the observer via the input unit 180. When the brightness threshold and / or size threshold are specified by the observer, the display area 107 displays the specified brightness threshold and / or size threshold. These thresholds may be changed by the observer inputting a numerical value into a number display box in the display area 107. Next, the identification unit 350 may perform a series of processes from S252 to S255 based on the specified brightness threshold and / or size threshold, and display the processed image in the display area 103. From the processed image displayed in the display area 103, a new brightness threshold and / or size threshold may be further specified by the observer. The brightness threshold and / or size threshold may be specified by the observer any number of times until a desired image is obtained.

[0122] In the above example, the case where the region of a neurite is identified based on the fluorescence derived from the first fluorescent protein is described. However, the region of a neurite may be identified based on an image obtained by phase contrast observation in addition to / instead of the fluorescence.

[0123] After the classification unit 350 outputs the image in which the area exceeding the size threshold has been extracted to the association unit 550 and the analysis unit 600, the classification unit 350 advances the process to S270.

[0124] Next, in S270, the identification unit 350 tracks the neurite. In S270, the step of tracking the neurite includes steps S271 to S275 as shown in FIG. 8A.

[0125] 8A shows S270 in the flow. First, in S271, the classification unit 350 tracks areas between previous and subsequent frames for a plurality of time-series images.

[0126] FIG. 8B is an example of tracking the area between the previous and next frames for each connected area in the protrusion detection result image (the image processed by performing a series of processes from S252 to S255). For example, the images used for tracking the nerve cells may be an image 271a including a cell area in the time series image captured in the round at time T, and an image 271b including a cell area in the time series image captured in the round at time T+1 immediately after time T. In the image 271a, the cell area is designated as 2711, and in the image 271b, the cell areas are designated as 2712 and 2713. At this time, the identification unit 350 may set a search range 2710a around the area 2711 in the image 271a, and set a search range 2710b corresponding to the search range 2710a in the image 271b, and search for whether or not there is a cell area in the search range 2710b. Here, the search region 2710a may be a quadrangle (e.g., a rectangle) circumscribing the cell region 2711, with the vertical and horizontal lengths each being slightly larger from the end points (e.g., 5%, 10%, or 20% of the vertical and horizontal lengths). In addition to a quadrangle, any polygon or circle may be defined as the search region.

[0127] For example, when there are multiple cell regions (e.g., regions 2712 and 2713) in search range 2710b of image 271b, the identification unit 350 may recognize region 2712, which is the region with the greatest overlap (the region with the largest overlapping area) in search range 2710b, as the same neurite and perform tracking. After the identification unit 350 has performed tracking of regions between the previous and next frames for all or some of the frames, the identification unit 350 advances the process to S272.

[0128] Next, in S272, the identification unit 350 tracks the cell region when no neurite is detected in the intermediate frames between the acquired time series frames. In the example of FIG. 8B, an example of tracking between two consecutive frames is shown, but the frames before and after tracking are not limited to two consecutive frames, and tracking may be performed between frames separated by a predetermined time. The identification unit 350 executes the processes of S271 and S272 for all rounds. For example, the identification unit 350 repeatedly executes the above tracking from the first round (i.e., T is 1) to the last round (i.e., T is the number of rounds to be imaged, N).

[0129] For example, in imaging of nerve cells, a search area may be set for a cell area of ​​an image captured in a round at time T, but the search area in an image captured in a round at time T+1 may not have a corresponding cell area. In this case, the identification unit 350 may further set a search area around the area corresponding to the search area set in the round at time T in an image captured in the round immediately after that, at time T+2, and track the cell area. In other words, even if a corresponding cell area cannot be detected in an image captured in the round at time T+1, the above search area may be set and searched in an image captured in the round immediately after that, at time T+2, and the cell area that overlaps the search source area the most (the area with the largest overlapping area) among the cell areas may be recognized as the same neurite and tracked. After the identification unit 350 tracks a cell area in which a neurite is not detected in an intermediate frame, the identification unit 350 proceeds to processing at S2720. The result of tracking each neurite in S272 is also referred to as a "tracking result" below. In the above example, a cell area detected in a round at time T and a cell area in an image captured in a round at time T+2 are tracked. However, the tracking is not limited to the round at time T+2. For example, a cell area in an image captured in a round at time T+3 or time T+4 may be tracked with a cell area detected in the round at time T.

[0130] Next, in S2720, the discrimination unit 350 detects the time when the tracking of the neurite starts and the time when the tracking ends. For example, the discrimination unit 350 may output the time when the tracking of each neurite starts and the time when the tracking ends for each neurite tracking result to the analysis unit 600, store it in the recording unit 190, and / or output it to the output unit 160. Here, the tracking start time of the neurite may be the time of a frame in which the neurite first emits fluorescence with a brightness exceeding a threshold in the captured image, and the tracking end time may be the time of a frame in which the brightness of the neurite in the captured image becomes equal to or less than the threshold. After the discrimination unit 350 detects the start and end times of the tracking of the neurite, the discrimination unit advances the process to S273.

[0131] Next, in S273, the detection unit 400 recognizes regions of a plurality of neurites that are closer than the distance threshold as a single neurite region.

[0132] FIG. 8C is an example of recognizing a plurality of neurite regions closer than the distance threshold as one neurite region. Image 273a captured in the round at time T, and image 273b captured in the round at time T+1 immediately after time T. In image 273a, the cell region is region 2731, and in image 273b, the cell regions are region 2732 and region 2733. As shown in FIG. 8C, region 2731, region 2732, and region 2733 are considered to be images of the fluorescence derived from the first fluorescent protein aggregated in the same neurite. However, due to imaging conditions, etc., in image 273b captured in the round at time T+1, the neurite portion is interrupted by region 2732 and region 2733.

[0133] In such a case, even if the region 2732 and the region 2733 are disconnected, if the distance between the region 2732 and the region 2733 (for example, the distance between the end point of the region 2732 closer to the region 2733 and the end point of the region 2733 closer to the region 2732) is closer than a predetermined distance threshold, the detection unit 400 may recognize the neurites included in these regions (the region 2732 and the region 2733) as the same neurite, and may assign the same neurite ID, which will be described later, to the tracking result of the neurite. By performing the step of S273, it is possible to more accurately identify and track the neurite. Note that the distance threshold may be an appropriate value designated by the observer via the input unit 180, or may be designated based on multiple images acquired by the identification unit 350. In addition, the distance threshold may be the same as the distance threshold of S232, or may be a different value.

[0134] In addition to / instead of the distance threshold, the detection unit 400 may use the direction of the straight line or tangent of the neurite contained in the cell region as an index to recognize neurites contained in multiple regions as the same neurite. For example, in the image 273b of FIG. 8C, if the tangent at the end point of the region 2732 close to the region 2733 and the tangent at the end point of the region 2733 close to the region 2732 have the same slope, the detection unit 400 may recognize the neurites contained in the regions 2732 and 2733 as the same neurite. After the detection unit 400 recognizes the regions of multiple neurites closer than the distance threshold as the region of one neurite, the detection unit 400 proceeds to S274.

[0135] Next, in S274, the detection unit 400 deletes the tracking result of a neurite that is found to have a survival time below the threshold as a result of tracking in S272. If the neurite is in the cell region tracked by the detection unit 400 and is a neuron having a neurite with a survival time below the threshold, the detection unit 400 deletes the tracking result of the neurite. Here, the survival time may be the time between the time when tracking of the neurite starts and the time when tracking ends.

[0136] For example, in the screen of Fig. 7C, the detection unit 400 may display the display area 107 for inputting a survival time threshold in the output unit 160, and request the observer to input the survival time threshold. An appropriate survival time threshold may be specified by the observer via the input unit 180. The detection unit 400 may delete the tracking results of neurites having a survival time equal to or less than the threshold, based on the specified survival time threshold. After the detection unit 400 deletes the tracking results of neurites having a survival time equal to or less than the threshold, the detection unit 400 advances the process to S275.

[0137] Next, in S275, the detection unit 400 assigns IDs to the neurite tracking results in the image acquired in S255 and outputs them. The detection unit 400 assigns a unique ID (neurite ID) to each tracking result of the neurite tracked between the acquired time-series frames. In other words, neurites assigned the same neurite ID represent the same neurite.

[0138] 8B, the detection unit 400 may assign the same neurite ID to regions 2711 and S2712, since they are regions having the same neurite. The detection unit 400 outputs the neurite ID together with the image to the association unit 550. The detection unit 400 may also store the neurite ID in the recording unit 190 and / or output it to the output unit 160.

[0139] By performing all or a part of the steps from S251 to S275 and performing the correspondence between the neurites and the cell bodies described later, the detection unit 400 may determine that the cell region from which the neurites are extracted is a nerve cell in which an aggregate exists inside the neurite. In this case, the detection unit 400 determines that the cell region from which the neurites are extracted is a region in which an aggregate exists. In the above example, the detection unit 400 extracts the cell region that exceeds the brightness threshold and the size threshold, but the detection unit 400 may extract the cell region using only the brightness threshold. In this way, the detection unit 400 can detect the presence or absence of an aggregate existing inside the neurite. The steps from S273 to S275 performed by the detection unit 400 may be called a first aggregate detection step (step), that is, a step of detecting an aggregate existing inside the neurite. In addition, the "first aggregate detection step" and the "second aggregate detection step" may be simply called an "aggregate detection step".

[0140] In the above example, the case where the presence or absence of aggregates present inside the cell body (intracellular aggregates) and aggregates present inside the neurite (intraneurite aggregates) is determined by setting a brightness threshold has been described, but the brightness threshold may include the average brightness (m) and / or variance (σ). For example, the detection unit 400 may calculate the average brightness (m) and variance (σ) of the cell body region and / or neurite region in each image in the time series, and determine that a region having a brightness of m+σ, m+2σ, or m+3σ or more is a region where an aggregate exists.

[0141] In the above example, the case where only the first fluorescent protein is expressed in the nerve cell as the fluorescent protein has been described. However, for example, in addition to the first fluorescent protein, a second fluorescent protein different from the first fluorescent protein may be further expressed in the nerve cell, and all or some of the steps from S200 to S275 may be performed on the second fluorescent protein in addition to the first fluorescent protein. Here, the second fluorescent protein may be a protein that exhibits aggregation in nerve cells (neurites and / or cell body) to which a fluorescent tag has been added, or may be a protein that does not exhibit aggregation in nerve cells. For convenience of observation and analysis, it is preferable that the fluorescent tag of the second fluorescent protein has a different fluorescent wavelength (color tone) from the fluorescent tag of the first fluorescent protein. For example, when the fluorescent tag of the first fluorescent protein is GFP (green), the fluorescent tag of the second fluorescent protein may be RFP (red).

[0142] For example, the second fluorescent protein may be a fluorescent tag protein itself, such as GFP or RFP. It is known that GFP or RFP itself is universally expressed in the cell body and neurites when introduced into a nerve cell. Therefore, by expressing a fluorescent tag protein itself, such as GFP or RFP, in a nerve cell as the second fluorescent protein, it is possible to easily determine that the nerve cell has died, for example, when the state in which the fluorescence intensity emitted by the fluorescent tag protein is below an intensity threshold continues for a predetermined period of time or longer.

[0143] For example, the second fluorescent protein may be a marker protein localized in the axon of a neuron or a marker protein localized in the dendrite to which a fluorescent tag has been added. "Localization" may be a concept included in "aggregation." In neuronal culture, since the neurites of many neurons overlap, it is difficult to distinguish and observe the axon and the dendrite. Therefore, in the above example, the axon and the dendrite are not distinguished and are treated as neurites. Therefore, by expressing the second fluorescent protein, which is an axon marker protein or a dendrite marker protein to which a fluorescent tag has been added, in addition to the first fluorescent protein that shows aggregation in a neuron, it is possible to clearly determine whether the intraneurite aggregate is present inside the axon or inside the dendrite. For example, synapsin 1 protein and synaptophysin protein are examples of axon marker proteins, and PSD-95 protein is examples of dendrite marker proteins. In addition to the first fluorescent protein, the following steps S290 and S310 may also be performed for the second fluorescent protein.

[0144] In S290, the association section 550 identifies the association between the neurites and the cell bodies based on the neurite IDs and cell body IDs acquired from the identification section 350 and the multiple images.

[0145] Fig. 9 is an example of a screen displayed on the output unit 160 of the neuronal analysis device in S290. The association unit 550 may output the screen shown in Fig. 9 to the output unit 160. An image numbered with a cell body ID is displayed in the display area 103a, and an image numbered with a neurite ID is displayed in the display area 103b. Here, the image displayed in the display area 103a and the image displayed in the display area 103b may be the image acquired in S206 sent from the imaging device control unit 171, or may be the image output in steps S215 and S255, respectively.

[0146] In the display area 108, several candidates for the association between the cell body ID and the neurite ID are displayed. The association unit 550 associates the cell body ID "1293" with the neurite ID "40", and determines the cell body ID "1294" with the neurite ID "103" as a candidate for the association. The association unit 550 may extract candidates for the association between the cell body ID and the neurite ID based on the distance between the cell body region and the neurite region, and may display them in the display area 108 in descending order of the probability of the association. For example, the association unit 550 may determine that the closer the distance between the cell body region and the neurite region is, the higher the probability of the association is. In addition to / instead of the distance between the cell body region and the neurite region, the association unit 550 may determine that the cell body region in the extension direction of the tangent at the end point of the neurite in the neurite region is the one that has the highest probability of the association.

[0147] An appropriate association may be selected by the observer from among the candidates for association between the cell body ID and the neurite ID. In order to select an appropriate association based on the results of viewing the displayed screen, the observer may delete inappropriate association candidates and add an association that is considered appropriate via the input unit 180 using the "Delete" button and the "Add" button in Fig. 9. The appropriate association may be performed only by the association unit 550, or may be performed only by the observer's specification.

[0148] The analysis unit 600 may classify a group of nerve cells in which a cell body ID and a neurite ID can be associated and an aggregate exists inside the cell body into a "cell group in which intracellular aggregates and intraneurite aggregates exist". The analysis unit 600 may classify a group of nerve cells in which a cell body ID is assigned but a corresponding neurite ID is not assigned and an aggregate exists inside the cell body into a "cell group in which intracellular aggregates exist but intraneurite aggregates do not exist". The analysis unit 600 may classify a group of nerve cells in which a cell body ID and a neurite ID can be associated and an aggregate does not exist inside the cell body into a "cell group in which intracellular aggregates exist but intraneurite aggregates do not exist". The analysis unit 600 may classify a group of nerve cells in which a cell body ID is assigned but a corresponding neurite ID is not assigned and an aggregate does not exist inside the cell body into a "cell group in which intracellular aggregates and intraneurite aggregates do not exist". The analysis unit 600 performs the above four classifications and performs various analyses described below.

[0149] After the association unit 550 has appropriately associated the cell body ID with the process ID, the analysis unit 600 performs the four classifications as described above, and the process proceeds to S310.

[0150] Next, in S310, the analysis unit 600 obtains the association between the cell body ID and the neurite ID from the association unit 550, and analyzes the survival time of the associated nerve cells. For example, the analysis unit 600 may analyze and calculate the survival rate and / or survival time of nerve cells having intra-neurite aggregates and nerve cells not having intra-neurite aggregates. Here, regarding the survival or death of a cell, when the brightness of the neurite or the cell body is less than a brightness threshold, the nerve cell may be defined as dead. This step S310 is sometimes referred to as the "analysis process."

[0151] For example, the analysis unit 600 may determine that a cell having a cell body classified as a "cell group having intracellular aggregates but not intraneurite aggregates" is dead when the cell body is no longer detected in the middle of the captured frames, that is, when the tracking end time is recorded in S2320. The identification unit 350 may determine that a cell having a cell body is alive when the cell body emits fluorescence with a brightness exceeding a threshold value until the last frame in the image, that is, when the tracking end time is not recorded in S2320.

[0152] For example, the ratio of live / dead neurons and the survival time may be calculated as follows. The analysis unit 600 may calculate the number of cell body IDs determined to be alive and the number of cell body IDs determined to be dead, and may divide the number of cell body IDs determined to be alive by the sum of the number of cell body IDs determined to be alive and the number of cell body IDs determined to be dead to calculate the ratio of live / dead neurons. The analysis unit 600 may also calculate the time between the earliest and latest frames in which cell bodies are continuously detected as the survival time.

[0153] For example, the analysis unit 600 may determine whether the intra-neurite aggregate is alive or dead for a cell group classified as a "cell group having intra-neurite aggregates but not having intracellular aggregates" based on whether the tracking end time was recorded in S2320 for a region of a cell body having a cell body ID corresponding to a neurite ID in which an intra-neurite aggregate is present. Alternatively, when the second fluorescent protein is expressed in nerve cells, the analysis unit 600 may determine whether the intra-neurite aggregate is alive or dead for the cell group having intra-neurite aggregates using the fluorescence brightness emitted by the second fluorescent protein as an index. As an example, when the brightness emitted by the second fluorescent protein remains below a brightness threshold until the end of imaging, the cell group having the intra-neurite aggregate may be determined to be dead.

[0154] The analysis unit 600 may make a similar judgment as for the cell group with intracellular aggregates but no intraneurite aggregates, or may make a similar judgment as for the cell group with intraneurite aggregates but no intracellular aggregates. The analysis unit 600 may determine that the cell having the cell body is dead at the time when the cell body is no longer detected in the middle of the captured frame (i.e., the end time of tracking the cell body). If the time when the cell body is no longer detected is different from the time when the neurite is no longer detected, the analysis unit 600 may determine that the earlier time is the time when the cell is dead, or may determine that the later time is the time when the cell is dead.

[0155] The analysis unit 600 may determine that, for a cell group classified as a "cell group without intracellular aggregates and intraneurite aggregates," as with other groups, a cell having a cell body is dead when the cell body is no longer detected midway through an imaged frame, that is, when the tracking end time is recorded in S2320.

[0156] For example, the analysis unit 600 may analyze and calculate the live / dead ratio and / or survival time of nerve cells having intracellular aggregates and nerve cells not having intracellular aggregates. For example, the analysis unit 600 may analyze and calculate the live / dead ratio and / or survival time of the above-mentioned "four cell groups divided by the presence or absence of intraneurite aggregates and the presence or absence of intracellular aggregates."

[0157] These analysis conditions may be set in advance in the analysis unit 600. In that case, the analysis unit 600 may output the analysis conditions to the output unit 160 so that the observer can specify the analysis conditions via the input unit 180 in the display area 108 of FIG. 9. For example, when the observer selects condition 1 in the display area 108 of FIG. 9, the analysis unit 600 may receive the input and analyze and calculate the survival times of nerve cells in which intra-neurite aggregates are present and nerve cells in which intra-neurite aggregates are not present. For example, when the observer selects condition 2 in the display area 108 of FIG. 9, the analysis unit 600 may receive the input and analyze and calculate the survival times of four cell groups divided according to the presence or absence of intra-neurite aggregates and the presence or absence of intracellular aggregates.

[0158] 10A is an example of a screen displayed on the output unit 160 in S310. The analysis unit 600 may display the results of the analysis in the display area 103. For example, when the observer selects "intra-neurite aggregate display" in the display area 109, the analysis unit 600 may display an image in the display area 103 in which intra-neurite aggregates are masked and superimposed on a time-series fluorescent image or phase-contrast image. For example, when the observer selects "intracellular aggregate display" in the display area 109, the analysis unit 600 may display an image in the display area 103 in which intracellular aggregates are masked and superimposed on a time-series fluorescent image or phase-contrast image.

[0159] Furthermore, when the observer selects "Cell Selection" in display area 109, analysis unit 600 may display an image or video of only the periphery of the selected cell, or analysis data of the selected cell, in display area 103. For example, the analysis data of a cell may be data such as the ratio of the area of ​​the aggregates in the cell body to the area of ​​the cell body, the area of ​​intra-neurite aggregates, or the time during which intra-neurite aggregates exist. Here, the area of ​​intra-neurite aggregates may be the sum of all pixels included in the area in which intra-neurite aggregates exist, and the area may be expressed in square pixels or a value converted into square micrometers.

[0160] For example, if the observer selects “Display intra-neurite aggregate survival graph” in display area 109, analysis unit 600 may graph when intra-neurite aggregates appeared and disappeared in the time series images for each neurite ID and display the graph in display area 103.

[0161] FIG. 10B is an example of an intra-neurite aggregate survival graph displayed on the output unit 160. In the intra-neurite aggregate survival graph 103a of FIG. 10B, the vertical axis indicates the neurite ID, and the horizontal axis indicates the time when the intra-neurite aggregate exists in the region of each neurite ID, expressed by the frame number of the time-lapse imaging. The straight line extending in the left-right direction of the graph indicates the time when the intra-neurite aggregate exists. Here, the left end of the straight line of the graph may be the time when the intra-neurite aggregate is not detected for the first time, and the right end of the straight line of the graph may be the time when the intra-neurite aggregate is no longer detected, or the time when the cell is determined to be dead. For example, when the observer clicks on the straight line in the graph, the analysis unit 600 may display on the output unit 160 a video image of a frame in which the intra-neurite aggregate corresponding to the neurite ID exists. Also, when the observer clicks on the straight line in the graph, the analysis unit 600 may display a graph of survival of the intracellular aggregate of the cell body ID corresponding to the neurite ID side by side. Furthermore, the analysis unit 600 may classify cell groups according to the presence or absence of intracellular aggregates, and display a graph of intra-neurite aggregate persistence for each cell group.

[0162] In addition, when the observer selects the “cell survival rate graph” in the display area 109, the analysis unit 600 may calculate the survival rate of nerve cells at each time in the time-series images, graph it, and display it in the display area 103.

[0163] FIG. 10C is an example of a cell viability graph displayed on the output unit 160 of the nerve cell analysis device 1. In the cell viability graph 103b in FIG. 10C, the horizontal axis represents time, and the vertical axis represents viability, and the viability of the cell group at the start time is converted to 100%. For example, the curve 103c may be a viability curve of a cell group in which intracellular aggregates are present but intraneurite aggregates are not present, the curve 103d may be a viability curve of a cell group in which intraneurite aggregates are present but intracellular aggregates are not present, and the curve 103e may be a viability curve of a cell group in which both intracellular aggregates and intraneurite aggregates are present. The analysis unit 600 can calculate the viability curve by counting how many cells are alive for each captured frame for each classified cell group, and dividing the number of alive cells counted in the first frame for each frame.

[0164] In FIG. 10C, the analysis unit 600 may display on the output unit 160 the coordinates of the intersections of multiple graphs, that is, the time when the survival rate of the cell group is reversed and the survival rate at that time. Also, in FIG. 10C, the analysis unit 600 may display on the output unit 160 the coordinates of the points at which the slope of the curve changes significantly at a certain time. At a certain time, the time when the slope of the slope becomes suddenly large represents the time when a large number of cells die at once. Also, the time when the slope of the slope becomes horizontal represents the time when the survival rate of the cells becomes constant. By having the analysis unit 600 display the coordinates of these points on the output unit 160, it becomes easier to perform dynamic analysis of nerve cells.

[0165] 10C shows an example of a survival curve for a cell group divided by the presence or absence of intraneurite aggregates and intracellular aggregates, but the grouping of the cell groups is not limited to this. For example, the analysis unit 600 may divide the cell groups into a cell group in which aggregates exist from the start of imaging, a cell group in which aggregates appear halfway through the imaging time, and a cell group in which aggregates did not exist during the imaging time, and display the survival curves of these cell groups on the output unit 160.

[0166] Also, for example, the analysis unit 600 may group cells into a cell group with relatively many aggregates (or many cell regions) and a cell group with relatively few aggregates (or few cell regions), and display the survival rate curves of these cell groups on the output unit 160. Also, for example, the analysis unit 600 may group cells into a cell group with aggregates on the side closer to the cell body of the neurite, and a cell group with aggregates on the side closer to the end of the neurite, and display the survival rate curves of these cell groups on the output unit 160.

[0167] In addition to the above, the analysis unit 600 may display the behavior of intraneurite aggregates as time-lapse images on the output unit 160. For example, α-synuclein protein is known to be localized at the end of an axon under physiological conditions. The analysis unit 600 can display on the output unit 160 how α-synuclein protein aggregates move from the end side to the cell body side within a neurite, when they start to move, and the like, thereby facilitating dynamic analysis of nerve cells.

[0168] In this manner, by using the neuronal cell analysis device 1 of the present invention, it is possible to easily analyze, for example, whether aggregates are formed inside the neurites and / or cell body, whether the aggregates move, and whether there is a change in the survival rate of the neuronal cells after administering various drug candidates (e.g., compounds, proteins or their mutant proteins, or small interfering RNA (siRNA) thereof, etc.) to the neuronal cells.

[0169] Next, modified examples of this embodiment will be described. Nerve cells may be analyzed by combining a plurality of the modified examples described below.

[0170] [First modified example] Fig. 11 is a modified example of determining an aggregate in a neurite region. In the present embodiment described in Fig. 8A and the like, the detection unit 400 extracts a region that exceeds a brightness threshold and a size threshold, recognizes (identifies) it as a neurite region, and further determines that the region is a region in which an aggregate exists within a neurite (sometimes referred to as an "intra-neurite aggregate region"). In the first modified example, the detection unit 400 advances the process to S280 after S275 and before advancing the process to S290.

[0171] In S280, the detection unit 400 recognizes the cell region extracted in steps S271 to S275 as a neurite region. Next, the identification unit 350 may use a brightness threshold different from the brightness threshold used in step S252, and determine and extract the region exceeding the brightness threshold as a neurite aggregate region. Specifically, the brightness threshold used in S280 to extract the neurite aggregate region may be a larger value than the brightness threshold used in S252 to extract the neurite region. After step S280, the detection unit 400 advances the process to S281.

[0172] In S281, the detection unit 400 adds information on whether or not an intra-neurite aggregate region exists to the neurite ID numbered in S275. Specifically, the detection unit 400 adds information on the existence of an intra-neurite aggregate region to the neurite ID of the neurite in which the aggregate extracted in S280 exists. The detection unit 400 may output the neurite ID to which the information on the existence of an intra-neurite aggregate region has been added to the analysis unit 600. In this way, by specifying the intra-neurite aggregate region using a brightness threshold different from the brightness threshold used in S252 to extract the neurite region, it is possible to analyze the position where the aggregate exists in the neurite region in each frame (for example, whether the aggregate is on the cell body side or the terminal side of the neurite), and therefore it is possible to perform a more detailed analysis.

[0173] [Second Modification] In this embodiment, the identification unit 350 judges a region having even greater brightness among the regions from which the cell body is extracted as a region where aggregates exist (intracellular aggregate region). In a second modified example, the identification unit 350 may judge a cell having a cell region at a predetermined ratio (e.g., the ratio of the area of ​​the cell region to the area of ​​the entire cell body) as a cell having an intracellular aggregate region. Here, the "intraneurite aggregate region" and the "intracellular aggregate region" may be simply referred to as "aggregate region".

[0174] Specifically, after the detection unit 400 completes step S235, the detection unit 400 calculates the area of ​​the cell body region and the cell region. Here, the area of ​​the entire cell body and the area of ​​the cell region may be the sum of all pixels included in the region where the cell body is extracted and the sum of all pixels included in the cell region, and the area may be expressed in square pixels or a value converted into square micrometers. Next, the detection unit 400 may calculate a value obtained by dividing the area of ​​the cell region by the area of ​​the cell body region. The detection unit 400 may determine that a cell having a ratio of the area of ​​the cell region to the area of ​​the entire cell body that is equal to or greater than a predetermined value is a cell having an intracellular aggregate region. The predetermined value may be 3%, 5%, or 10%, but is not limited thereto.

[0175] Furthermore, in the above case where a second fluorescent protein is further expressed in the nerve cell, the detection unit 400 may determine that a region in which the second fluorescent protein has a brightness equal to or greater than a threshold value is a cell body region, and calculate the area of ​​the cell body region. Next, after calculating the area of ​​the cell region as described above, a value may be calculated by dividing the area of ​​the cell region by the area of ​​the cell body region.

[0176] 12 shows an example of a hardware configuration of a computer 1900 functioning as the arithmetic device 170. The computer 1900 according to this embodiment includes a CPU peripheral unit having a CPU 2000, a RAM 2020, a graphic controller 2075, and a display device 2080, which are mutually connected by a host controller 2082, an input / output unit having a communication interface 2030, a hard disk drive 2040, and a CD-ROM drive 2060, which are connected to the host controller 2082 by an input / output controller 2084, and a legacy input / output unit having a ROM 2010, a flexible disk drive 2050, and an input / output chip 2070, which are connected to the input / output controller 2084.

[0177] The host controller 2082 connects the RAM 2020 to the CPU 2000 and the graphic controller 2075, which access the RAM 2020 at a high transfer rate. The CPU 2000 operates based on the programs stored in the ROM 2010 and the RAM 2020, and controls each part. The graphic controller 2075 acquires image data generated by the CPU 2000 or the like on a frame buffer provided in the RAM 2020, and displays the image data on the display device 2080. Alternatively, the graphic controller 2075 may include a frame buffer therein for storing image data generated by the CPU 2000 or the like. The display device 2080 can display various information (e.g., moving images, analysis results, etc.) generated inside the arithmetic device 170.

[0178] The input / output controller 2084 connects the host controller 2082 to the communication interface 2030, the hard disk drive 2040, and the CD-ROM drive 2060, which are relatively high-speed input / output devices. The communication interface 2030 communicates with other devices via a network, either wired or wirelessly. The communication interface also functions as hardware for performing communication. The hard disk drive 2040 stores programs and data used by the CPU 2000 in the computer 1900. The CD-ROM drive 2060 reads programs or data from a CD-ROM 2095 and provides them to the hard disk drive 2040 via the RAM 2020.

[0179] The input / output controller 2084 is also connected to the ROM 2010, the flexible disk drive 2050, and the input / output chip 2070, which are relatively slow input / output devices. The ROM 2010 stores a boot program executed by the computer 1900 at startup and / or a program dependent on the hardware of the computer 1900. The flexible disk drive 2050 reads programs or data from the flexible disk 2090 and provides them to the hard disk drive 2040 via the RAM 2020. The input / output chip 2070 connects the flexible disk drive 2050 to the input / output controller 2084, and also connects various input / output devices to the input / output controller 2084 via, for example, a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0180] The program provided to the hard disk drive 2040 via the RAM 2020 is stored in a recording medium such as a flexible disk 2090, a CD-ROM 2095, or an IC card and provided by the user. The program is read from the recording medium, installed in the hard disk drive 2040 in the computer 1900 via the RAM 2020, and executed by the CPU 2000.

[0181] A program installed in the computer 1900 and causing the computer 1900 to function as the arithmetic unit 170 includes an imaging device control module, a detection module, an association module, and an analysis module. These programs or modules may act on the CPU 2000 or the like to cause the computer 1900 to function as the imaging device control unit 171, the recording unit 190, the identification unit 350, the detection unit 400, the association unit 550, and the analysis unit 600, respectively.

[0182] The information processing described in these programs is read into the computer 1900, and functions as specific means in which the software and the above-mentioned various hardware resources work together, such as the imaging device control unit 171, the recording unit 190, the identification unit 350, the detection unit 400, the association unit 550, and the analysis unit 600. These specific means then realize calculation or processing of information according to the intended use of the computer 1900 in this embodiment, thereby constructing a specific calculation device 170 according to the intended use.

[0183] As an example, when communication is performed between the computer 1900 and an external device, the CPU 2000 executes a communication program loaded on the RAM 2020 and instructs the communication interface 2030 to perform communication processing based on the processing contents described in the communication program. The communication interface 2030, under the control of the CPU 2000, reads outgoing data stored in a transmission buffer area or the like provided on a storage device such as the RAM 2020, the hard disk drive 2040, the flexible disk 2090, or the CD-ROM 2095, and transmits the data to the network, or writes received data received from the network to a reception buffer area or the like provided on the storage device. In this way, the communication interface 2030 may transfer the transmitted and received data to and from the storage device by a DMA (Direct Memory Access) method, or alternatively, the CPU 2000 may transfer the transmitted and received data by reading data from the source storage device or the communication interface 2030 and writing the data to the destination communication interface 2030 or the storage device.

[0184] Furthermore, the CPU 2000 reads all or a necessary portion of files or databases stored in an external storage device such as the hard disk drive 2040, the CD-ROM drive 2060 (CD-ROM 2095), or the flexible disk drive 2050 (flexible disk 2090) into the RAM 2020 by DMA transfer or the like, and performs various processes on the data in the RAM 2020. The CPU 2000 then writes the processed data back to the external storage device by DMA transfer or the like. In such processes, the RAM 2020 can be regarded as temporarily holding the contents of the external storage device, and therefore in this embodiment, the RAM 2020 and the external storage device are collectively referred to as a memory, a recording unit, a storage device, or the like.

[0185] Here, the storage device etc. stores information necessary for the information processing of the arithmetic unit 170, such as moving image data, as necessary, and supplies it to each component of the arithmetic unit 170 as necessary.

[0186] In this embodiment, various information such as various programs, data, tables, databases, etc. are stored in such a storage device and are subject to information processing. The CPU 2000 can also hold a part of the RAM 2020 in a cache memory and perform reading and writing on the cache memory. Even in this form, the cache memory performs part of the functions of the RAM 2020, so in this embodiment, the cache memory is also included in the RAM 2020, memory, and / or storage device unless otherwise specified.

[0187] Furthermore, the CPU 2000 performs various processes on the data read from the RAM 2020, including various calculations, information processing, conditional determination, information search and replacement, and the like described in this embodiment, as specified by the instruction sequence of the program, and writes the data back to the RAM 2020. For example, when performing conditional determination, the CPU 2000 determines whether various variables shown in this embodiment satisfy a condition such as greater than, smaller than, greater than or equal to, less than or equal to, compared with other variables or constants, and branches to a different instruction sequence or calls a subroutine if the condition is met (or is not met).

[0188] Furthermore, the CPU 2000 can search for information stored in a file or database in the storage device. For example, when a plurality of entries in which attribute values ​​of a first attribute and attribute values ​​of a second attribute are respectively associated with each other are stored in the storage device, the CPU 2000 can obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition by searching for an entry in which the attribute value of the first attribute matches a specified condition from among the plurality of entries stored in the storage device and reading the attribute value of the second attribute stored in that entry.

[0189] The above-described programs or modules may be stored in an external recording medium. As the recording medium, in addition to the flexible disk 2090 and the CD-ROM 2095, optical recording media such as DVDs or CDs, magneto-optical recording media such as MOs, tape media, semiconductor memories such as IC cards, etc. may be used. Also, a storage device such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet may be used as the recording medium, and the programs may be provided to the computer 1900 via the network.

[0190] In the present disclosure, the arithmetic device 170 has a configuration in which the arithmetic device 170 has a CPU 2000 as a processor, but the type of the processor is not particularly limited. For example, a GPU, ASIA, FPGA, or the like can be used as the processor as appropriate. In addition, in the present disclosure, the arithmetic device 170 has a configuration in which the arithmetic device 170 has a hard disk drive 2040 as an auxiliary storage device, but the type of the auxiliary storage device is not particularly limited. For example, instead of the hard disk drive 2040, or together with the hard disk drive 2040, another storage device such as a solid state drive may be used.

[0191] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is clear to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included in the technical scope of the present invention.

[0192] It should be noted that the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically indicated as "before," "prior to," etc., and may be realized in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is explained using "first," "next," etc. for convenience, it does not mean that it is essential to perform the process in this order. [Explanation of symbols]

[0193] 1. Neuronal analysis device 7. Optical Fiber 10. Imaging device 20 Culture vessel 23 Stage 27 Objective lens section 34 Fluorescence filter section 38 Imaging lens section 40 Transillumination section 41 Collector lens 44 Field Lens 45 Polarizing Mirror 47 Light source for transmitted illumination 50 Timeline 52 Stop button 53 Rewind button 54 Play button 55 Fast forward button 60 Slader Bar 70 Excitation Light Source 100 Chamber 103 Display area 104 Display area 105 Playback control section 107 Display area 108 Display area 109 Display area 150 Microscope Section 160 Output section 170 Arithmetic equipment 171 Imaging device control section 180 Input section 190 Recording Department 300 Camera 350 Identification Unit 400 Detector 411 Field Lens 452 Polarizing Mirror 550 Mapping section 600 Analysis Department 1900 Computer 2000 CPU 2010 ROM 2020 RAM 2030 Communication Interface 2040 Hard Disk Drive 2050 Flexible Disk Drive 2060 CD-ROM drive 2070 I / O chip 2075 Graphics Controller 2080 display device 2082 Host Controller 2084 I / O Controller 2090 Flexible Disk 2095 CD-ROM

Claims

1. an image acquisition step of acquiring time-series images of nerve cells in a time series; a cell region identification step of identifying a neurite region or a cell body region of the nerve cell based on the time-series images; an aggregate detection step of detecting the presence or absence of aggregates of the specific protein that have aggregated in the neurite region or the cell body region identified in the cell region identification step, based on the time-series images obtained by capturing a first fluorescent protein tagged with a specific protein expressed in the nerve cell in a time-series manner, on the basis of the brightness of the first fluorescent protein included in the time-series images; a correspondence process for correlating the cell body region identified in the cell region identification process with the neurite region; A classification step of classifying the nerve cells into a plurality of groups based on the detection result of the aggregate detection step; Equipped with the specific protein is a protein that exhibits aggregation in the nerve cell, The plurality of groups are The classification is based on whether or not the cell body region can be associated with the neurite region, and on the presence or absence of aggregates of the specific protein aggregated in the cell body region. Methods for analyzing neurons.

2. comprising an analysis step of analyzing the survival state of the nerve cells for each of the groups; The survival state of the neuron includes at least one of the survival time, survival rate, and life and death of the neuron. The method for analyzing nerve cells according to claim 1 .

3. The analysis step includes displaying a relationship between the plurality of groups and a survival state of the plurality of groups for each of the groups. The method for analyzing nerve cells according to claim 2.

4. The analysis step comprises: (i) a group of cells in which the cell body and the neurites are associated with each other and the aggregate is present in the region of the cell body; (ii) a group of cells in which the cell body and the neurites are not associated with each other and the aggregates are present in the region of the cell body; (iii) a group of cells in which the cell body and the neurites are associated with each other and the aggregate is not present in the cell body; (iv) a group of cells in which the cell body and the neurite are not associated with each other and the aggregate is not present in the region of the cell body; The method for analyzing nerve cells according to claim 2.

5. The step of associating determines the correspondence based on a distance between the region of the cell body and the region of the neurite. A method for analyzing nerve cells according to claim 1 or 2.

6. The step of associating the neurite with a region of the cell body in an extension direction of a tangent at an end point of the neurite is in a corresponding relationship. A method for analyzing nerve cells according to claim 1 or 2.

7. The time-series images include at least one of data of a phase contrast image, data of a fluorescence image including the brightness of the first fluorescent protein, and data including a moving image file. The method for analyzing nerve cells according to claim 3.

8. The cell region identification step includes: the image acquisition step includes capturing images of a first fluorescent protein tagged to a specific protein expressed in the neuron in a time series manner, and identifying a region in the time series images where the brightness of the first fluorescent protein exceeds a first brightness threshold as a region of a neurite of the neuron. The method for analyzing nerve cells according to claim 2.

9. The cell region identification step includes: identifying a region exceeding a predetermined size threshold among regions in which the brightness of the first fluorescent protein in the time-series images exceeds the first brightness threshold as a region of the neurite or cell body of the neuron; Furthermore, the identified neurite and the identified cell body are tracked in time series to calculate a survival time related to the survival state. The method for analyzing nerve cells according to claim 8.

10. the aggregate detection step detects a region in which the brightness of the first fluorescent protein in the time-series images exceeds a second brightness threshold different from the first brightness threshold as a region in which an aggregate exists inside the cell body of the nerve cell; The method for analyzing nerve cells according to claim 8.

11. the cell region identification step further includes identifying a region of a cell body or a neurite of the nerve cell based on the time series images obtained by capturing a second fluorescent protein, which is different from the first fluorescent protein, expressed in the nerve cell in a time series manner; The aggregate detection step detects whether the identified aggregate in the neurite is present inside an axon or inside a dendrite based on the first fluorescent protein and the second fluorescent protein; The analysis step classifies the aggregates in the neurite into a cell group present on a side close to the cell body and a cell group present on a side close to the end of the neurite based on the detection result of the aggregate detection step, and analyzes the survival state of the nerve cells for each cell group. The method for analyzing nerve cells according to claim 2.

12. an imaging unit for acquiring time-series images of nerve cells in a time series; an identification unit that identifies a region of a neurite or a region of a cell body of the nerve cell based on the time-series images; a detection unit that detects the presence or absence of aggregates of the specific protein that have aggregated in the neurite region or the cell body region of the nerve cell identified by the identification unit based on the time-series images obtained by capturing in time series a first fluorescent protein tagged with a specific protein expressed in the nerve cell, using the brightness of the first fluorescent protein included in the time-series images as a reference; a correspondence unit that associates the area of ​​the cell body identified by the identification unit with the area of ​​the neurite; A classification unit that classifies the nerve cells into a plurality of groups based on a detection result of the detection unit; Equipped with the specific protein is a protein that exhibits aggregation in the nerve cell, The plurality of groups are The classification is based on whether or not the cell body region can be associated with the neurite region, and on the presence or absence of aggregates of the specific protein aggregated in the cell body region. A device for analyzing nerve cells.

13. An analysis unit that analyzes a survival state of the nerve cells for each of the groups, The survival state of the neuron includes at least one of the survival time, survival rate, and life and death of the neuron. The nerve cell analysis device according to claim 12.

14. The analysis unit displays a relationship between the plurality of groups and a survival state of the plurality of groups for each of the groups. The nerve cell analysis device according to claim 13.

15. The analysis unit, (i) a group of cells in which the cell body and the neurites are associated with each other and the aggregate is present in the region of the cell body; (ii) a group of cells in which the cell body and the neurites are not associated with each other and the aggregates are present in the region of the cell body; (iii) a group of cells in which the cell body and the neurites are associated with each other and the aggregate is not present in the cell body; (iv) a group of cells in which the cell body and the neurite are not associated with each other and the aggregate is not present in the region of the cell body; The nerve cell analysis device according to claim 14.

16. The matching unit includes at least one of matching based on a distance between the region of the cell body and the region of the neurite, or matching the neurite with a region of the cell body in an extension direction of a tangent at an end point of the neurite. The nerve cell analysis device according to claim 12 or 13.

17. The identification unit is capturing images of a first fluorescent protein tagged with a specific protein expressed in the nerve cell in a time series manner by the imaging unit, and identifying a region in the time series images where the brightness of the first fluorescent protein exceeds a first brightness threshold as a region of a neurite of the nerve cell. The nerve cell analysis device according to claim 12 or 13.

18. The identification unit is identifying a region exceeding a predetermined size threshold among regions in which the brightness of the first fluorescent protein in the time-series images exceeds the first brightness threshold as a region of the neurite or cell body of the neuron; Furthermore, the identified neurite and the identified cell body are tracked in time series, respectively, to calculate a survival time related to a survival state of the nerve cell. The nerve cell analysis device according to claim 17.

19. the detection unit detects a region in which the luminance of the first fluorescent protein in the time-series images exceeds a second luminance threshold different from the first luminance threshold as a region in which an aggregate exists inside the cell body of the nerve cell; The nerve cell analysis device according to claim 17.

20. 1. A computer program comprising a computer readable storage medium having instructions thereon, The instructions, when executed by a processor or programmable circuit, cause the processor or programmable circuit to: Executing an operation including the nerve cell analysis method according to any one of claims 1 to 11, Computer program.

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