Information processing device, information processing system, information processing method, and computer program

Abstract

This information processing device comprises an index value calculation unit that calculates a specific index value indicating the local density of a cell population.

Description

Information processing device, information processing system, information processing method, and computer program

[0001] The technology disclosed herein relates to information processing for assessing the state of a cell population.

[0002] For example, cell culture is performed for the development of pharmaceuticals and regenerative medicine. Cells may lose their proliferation and gene expression phenotype if they proliferate excessively. Therefore, a technique for monitoring cell confluency during cell culture is known (see, for example, Patent Document 1).

[0003] Special Publication No. 2022-508215

[0004] In the conventional cell confluency monitoring technology described above, the cell confluency is monitored over the entire culture vessel. However, simply monitoring the cell confluency over the entire culture vessel poses the problem that it is not possible to properly evaluate the state of the cell population, and for example, it is not possible to adequately control cell proliferation. This problem is not limited to cell culture, but is a common problem when evaluating the state of a cell population.

[0005] This specification discloses a technique that can solve the above-mentioned problems.

[0006] The technology disclosed in this specification can be realized, for example, in the following forms.

[0007] (1) The information processing device disclosed in this specification includes an index value calculation unit that calculates a specific index value that represents the local density of a cell population. This information processing device can appropriately evaluate the local density of a cell population using the specific index value, and can accurately predict, for example, a cell proliferation rate.

[0008] (2) In the information processing device, the specific index value may be an index value for evaluating a risk of intercellular contact inhibition. With this configuration, the specific index value can be used to appropriately evaluate the risk of intercellular contact inhibition, and for example, to accurately predict a cell proliferation rate.

[0009] (3) In the information processing device, the specific index value may be a neighborhood score indicating the number of other cell units located near each cell unit, which is a single cell or a mass of an aggregated cell population that constitutes the cell population. With this configuration, the neighborhood score can be calculated as a specific index value that appropriately represents the local density of the cell population.

[0010] (4) In the information processing device, the index value calculation unit may include a neighborhood region setting unit that sets a neighborhood region for each cell unit that constitutes the cell population, and a region division unit that divides the neighborhood region into a plurality of small regions, and calculates the neighborhood score that indicates the number of the small regions in which other cell units are located. With this configuration, it is possible to calculate a neighborhood score that appropriately represents the local density of the cell population while avoiding complex processing.

[0011] (5) In the information processing device, the neighborhood region setting unit may set the neighborhood region so that the area of ​​the neighborhood region increases as the area of ​​the cell unit increases. With this configuration, a neighborhood region of an appropriate size according to the area of ​​the cell unit can be set, and as a result, a neighborhood score that appropriately represents the local density of the cell population can be calculated.

[0012] (6) In the information processing device, the neighborhood region setting unit may set a region of a predetermined shape with a minimum area that encompasses the cell unit, and set the region obtained by expanding the region in the circumferential direction by a predetermined dimension as the neighborhood region. This configuration makes it possible to set a neighborhood region of an appropriate size according to the area of ​​the cell unit while avoiding complex processing.

[0013] (7) In the information processing device, the neighboring region setting unit may be configured to identify the contour of each cell unit and set the neighboring region as a region obtained by extending the contour outward by a predetermined dimension. This configuration makes it possible to set a neighboring region of an appropriate size according to the area of ​​each cell unit while avoiding complex processing.

[0014] (8) In the information processing device, the specific index value may be a closed region change characteristic expressed by the appearance and disappearance of closed regions surrounded by a plurality of cell inclusion regions, the cell inclusion regions being regions of a predetermined shape centered on each cell unit, which is a single cell or a mass of an aggregated cell population that constitutes the cell population, when the cell inclusion regions are gradually expanded. With this configuration, the closed region change characteristic can be calculated as a specific index value that appropriately represents the local density of the cell population.

[0015] (9) In the information processing device, the specific index value may be a cell region change characteristic expressed by a change in the number or area ratio of cell regions, which are regions occupied by individual cell units that constitute the cell population or clumps of the cell population in an aggregated state, when a plurality of the cell regions are connected to each other by gradually expanding the cell regions. With this configuration, the cell region change characteristic can be calculated as a specific index value that appropriately represents the local density of the cell population.

[0016] (10) The information processing device may further include a specific processing execution unit that executes specific information processing using the specific index value, and a result output unit that outputs a result of the specific information processing. With this configuration, the specific information processing can be executed with high accuracy using the specific index value that represents the local density of the cell population, and the processing result can be output.

[0017] (11) In the information processing device, the specific information processing may include a process of classifying, based on the specific index value, morphological information representing the morphology of each cell unit that is a single cell or a mass of an aggregated cell population that constitutes the cell population. With this configuration, the morphological information representing the morphology of each cell unit that constitutes the cell population can be classified according to local density.

[0018] (12) In the information processing device, the specific information processing may include a process of predicting a proliferation rate of the cell population using morphological information representing the morphology of each cell unit, which is an individual cell or a mass of an aggregated cell population that constitutes the cell population, and the specific index value. With this configuration, the proliferation rate of the cell population can be predicted with high accuracy using the specific index value that represents the local density of the cell population.

[0019] (13) In the information processing device, the cell population may be a population of adhesive animal cells. With this configuration, the specific index value can be used to appropriately evaluate the local density of the population of adhesive animal cells, and for example, to accurately predict the cell proliferation rate.

[0020] The techniques disclosed in this specification can be realized in various forms, such as an information processing device and method for evaluating the state of a cell population, a cell analysis device and method, a cell culture device and method, an information processing system, a computer program for realizing these methods, and a non-transitory recording medium on which the computer program is recorded.

[0021] Schematic diagram of a low local confluence state and a high local confluence state. Schematic diagram of a low local confluence state and a high local confluence state. Schematic diagram of a low local confluence state and a high local confluence state. Schematic diagram of a relationship between local cell confluence and cell proliferation rate. Schematic diagram of a relationship between local cell confluence and cell proliferation rate. Schematic diagram of a relationship between local cell confluence and cell proliferation rate. Schematic diagram of a relationship between total cell area and cell number and cell proliferation rate. Schematic diagram of a method for calculating a neighborhood score NS. Schematic diagram of a method for calculating a neighborhood score NS. Schematic diagram of a method for calculating a neighborhood score NS. Schematic diagram of a method for calculating a neighborhood score NS. Schematic diagram of a method for setting a neighborhood region R1. Schematic diagram of a relationship between the neighborhood score NS and cell proliferation rate. Schematic diagram of a relationship between morphological information of cells Ce and the neighborhood score NS. Schematic diagram of a relationship between morphological information of cells Ce and the neighborhood score NS. 1 is an explanatory diagram showing an example in which NS is applied to a large-scale dataset; 2 is an explanatory diagram showing an example in which neighborhood score NS is applied to a large-scale dataset; 3 is an explanatory diagram showing the configuration of an information processing device 100 for calculating neighborhood score NS; 4 is a flowchart showing score calculation processing; 5 is an explanatory diagram showing a method for calculating closed region change characteristics BD; 6 is an explanatory diagram showing an example of a duration diagram PD; 7 is an explanatory diagram showing the relationship between the local density of a cell population and the duration diagram PD; 8 is an explanatory diagram showing a landscape function created based on the duration diagram PD; 9 is an explanatory diagram showing reference arrangement patterns P01 and P02;

[0022] (Regarding local confluency) Figure 1 is an explanatory diagram conceptually illustrating the local confluency of a cell population. Figure 1 shows two states of a cell population cultured in a culture vessel. Each cell Ce constituting the cell population is, for example, an animal cell that proliferates while adhering to the bottom surface of the culture vessel.

[0023] For the two states shown in Figure 1, the total area occupied by cells Ce per unit area (total cell area (confluency)) is approximately the same. That is, the overall confluency of the cell population in the two states is approximately the same. However, the local confluency of the cell population in the two states differs. That is, the state shown on the left side of Figure 1 (hereinafter referred to as the "unrestricted state" or "low local confluency state") is a natural state in which cells are cultured without physical space constraints. In this state, the distance between a certain cell Ce(0) and another nearby cell Ce(N) is relatively large, so the space around the cell Ce(0) is not restricted by the other cells Ce(N), and the local confluency is relatively low. On the other hand, the state shown on the right side of Figure 1 (hereinafter referred to as the "restricted state" or "high local confluency state") is a state in which cells are cultured with physical space constraints. In this state, the distance between a cell Ce(0) and another nearby cell Ce(N) is relatively small, so the space around the cell Ce(0) is restricted by the other cells Ce(N), resulting in a relatively high local density.

[0024] A high local confluence state (restricted state) can be achieved, for example, by the heterogeneous seeding method. Figure 2 is an explanatory diagram showing a schematic of the heterogeneous seeding method. In the heterogeneous seeding method, for example, PDMS (polydimethylsiloxane) is placed in the peripheral region of a culture vessel SC, and PS (polystyrene) is placed in the central region. A cell population is then placed in a cloning ring CR placed in the central region and allowed to stand for a certain period of time. The cloning ring CR is then removed from the culture vessel SC. This creates a high local confluence state for the cell population.

[0025] 3 and 4 are explanatory diagrams that schematically show a low local confluency state (non-limiting state) and a high local confluency state (limiting state). In each of FIGS. 3 and 4, the cell densities (1000, 2000, or 3000 cells / cm) in the entire culture vessel are plotted for the non-limiting and limiting states. ２3 and 4 , the states of the cell populations are shown schematically. As shown in FIGS. 3 and 4 , in the non-restricted state, the cells Ce constituting the cell population are dispersed and arranged approximately evenly. On the other hand, in the restricted state, the cells Ce constituting the cell population are concentrated and arranged in a certain area. Therefore, in the restricted state, compared to the non-restricted state, even if the cell density (number of cells per unit area) is the same, the cells are locally densely packed, making it easier for cell-cell contact inhibition to occur.

[0026] 5 to 7 are explanatory diagrams showing the relationship between the local cell confluency and the cell proliferation rate. In FIG. 5, the cell density (1000, 2000, or 3000 cells / cm) is plotted for each of a low local confluency state (non-restrictive state) and a high local confluency state (restrictive state). ２ ) and shows the change in cell number over time from the start of culture. Figure 6 shows the cell number at the start of culture for each cell density for both low and high local confluency states. Figure 7 shows the cell proliferation rate calculated from the change in cell number over time for each cell density for both low and high local confluency states. As shown in Figures 5 to 7, even for the same cell lot, the local confluency is higher under the limiting condition than under the non-limiting condition, resulting in a decrease in cell proliferation rate due to inhibition of cell-cell contact. Based on these results, it can be said that evaluating the risk of cell-cell contact based on local confluency is important in cell culture.

[0027] FIG. 8 is an explanatory diagram showing the relationship between the total cell area and cell number and the cell proliferation rate. FIG. 8 shows the results of a linear regression analysis in which the total cell area and cell number are used as explanatory variables and the cell proliferation rate is used as the response variable. In this linear regression analysis, the relationship between the cell density (1000, 2000, or 3000 cells / cm) and the cell proliferation rate are shown for both the non-limiting and limiting conditions. ２ ) was used. As shown in Figure 8, neither the total cell area nor the cell count was used to predict the cell proliferation rate with high accuracy. This is thought to be because both the total cell area and the cell count evaluate the overall cell confluence, but are unable to evaluate the local cell confluence that affects the cell proliferation rate.

[0028] Thus, in cell culture, the cell proliferation rate differs significantly between low and high local confluency states, making it important to evaluate the local confluency of a cell population. The present inventors conducted extensive research and devised a specific index value, the Neighbor Score (NS), to represent the local confluency of a cell population. The specific index value is, for example, an index value for assessing the risk of cell-to-cell contact inhibition.

[0029] 9 to 13 are explanatory diagrams showing a method for calculating the neighborhood score NS. First, as shown in Fig. 9, an image I0 of a cell population cultured in a culture vessel, captured using, for example, a phase-contrast microscope, is subjected to region segmentation processing to separate regions where cells Ce exist from other regions, thereby generating a segmented image I1. The segmented image I1 is, for example, a binary image, in which regions where cells Ce exist are represented in white (or black) and other regions are represented in black (or white).

[0030] Next, as shown in FIG. 10 , a neighborhood region R1 is set for each cell Ce in the section image I1. The neighborhood region R1 is a region that defines the neighborhood of the cell Ce. In this embodiment, as shown in the lower part of FIG. 10 , a minimum rectangular region RO encompassing the cell Ce is set, and a rectangular region obtained by expanding the minimum rectangular region RO in the circumferential direction (centrifugal direction), more specifically, in the vertical and horizontal directions, by predetermined dimensions L1 and L2, is set as the neighborhood region R1. The dimensions L1 and L2 may be, for example, 10 μm or more and 50 μm or less, 20 μm or more and 40 μm or less, or 30 μm or more. The dimensions L1 and L2 may be the same or different. Instead of the minimum rectangular region RO, a region with the smallest area and a predetermined shape (e.g., a circle) may be used. In this case, the region expanded in the circumferential direction from the minimum rectangular region RO is set as the neighborhood region R1.

[0031] Next, as shown in FIG. 11 , the neighborhood region R1 is divided into multiple small regions, and the presence or absence of other cells in each small region is examined. Any method can be used to divide the neighborhood region R1 into multiple small regions. In this embodiment, the neighborhood region R1 is divided into two equal parts vertically and horizontally, thereby dividing it into four small regions. In the example of FIG. 11 , for cell Ce1, no other cells are located in any of the small regions; for cell Ce2, other cells are located in one small region A; for cell Ce3, other cells are located in two small regions A and B; for cell Ce4, other cells are located in three small regions A, B, and C; and for cell Ce5, other cells are located in all of the small regions.

[0032] Next, as shown in Figure 12, the number of small regions in which other cells are located is defined as the neighborhood score NS of each cell Ce. The neighborhood score NS of each cell Ce is a five-level score ranging from 0 to 4. It can be said that the higher the neighborhood score NS of a cell Ce, the more other cells Ce are present in the neighborhood of that cell Ce. In the example of Figure 12, the neighborhood score NS of cell Ce1 is 0 (zero), the neighborhood score NS of cell Ce2 is 1, the neighborhood score NS of cell Ce3 is 2, the neighborhood score NS of cell Ce4 is 3, and the neighborhood score NS of cell Ce5 is 4.

[0033] The calculation of the neighborhood score NS of each cell Ce as described above is performed for all cells Ce in the image. As a result, all cells Ce are classified into five categories (five categories with neighborhood scores NS = 0 to 4) based on their neighborhood scores NS. The neighborhood scores NS of each cell Ce are then tallied to determine the neighborhood score NS of the cell population. In this embodiment, as shown in FIG. 13 , the neighborhood score NS of the cell population is determined as a frequency distribution obtained by tallying the number of cells for each of the five neighborhood scores NS (0 to 4). The neighborhood score NS calculated in this manner indicates the number of cells Ce belonging to each of the five categories, which differ from one another in the number of other cells Ce located near the cell Ce, and can be said to be a score representing the local density of the cell population.

[0034] It is considered that the relationship between the neighborhood score NS of a cell Ce and the risk of cell-cell contact is not linear, but rather the higher the neighborhood score NS of a cell Ce, the greater the risk of cell-cell contact. Therefore, instead of a simple frequency distribution of the neighborhood scores NS of each cell Ce, a weighted frequency distribution in which a larger weight is assigned to a higher neighborhood score NS may be used as the neighborhood score NS of a cell population.

[0035] Some or all of the above steps for calculating the neighborhood score NS may be performed automatically using image processing software running on a computer, or may be performed manually by a person.

[0036] In this embodiment, as shown in the upper part of FIG. 14 , the neighborhood region R1 is set as a region obtained by expanding the minimum rectangular region RO encompassing the cell Ce by predetermined dimensions L1 and L2. Therefore, the larger the area of ​​the cell Ce, the larger the area of ​​the neighborhood region R1. However, the method for setting the neighborhood region R1 is not limited to this. For example, the neighborhood region R1 may be set as a region obtained by expanding the outline of the cell Ce outward by a predetermined dimension. Even in this case, the larger the area of ​​the cell Ce, the larger the area of ​​the neighborhood region R1. Furthermore, as shown in the lower part of FIG. 14 , a rectangular region with predetermined dimensions (e.g., length L2 × 2, width L1 × 2) centered on the center of gravity of the cell Ce may be set as the neighborhood region R1. In this case, the area of ​​the neighborhood region R1 is constant regardless of the area of ​​the cell Ce.

[0037] 15 is an explanatory diagram showing the relationship between the neighborhood score NS and the cell proliferation rate. In FIG. 15, the neighborhood score NS (frequency distribution of five categories) of a cell population is plotted against five explanatory variables X １ ~X ５ The graph shows the results of multiple regression analysis using the cell proliferation rate as the objective variable. In this multiple regression analysis, the cell density (1000, 2000, or 3000 cells / cm) was calculated for each of the non-limiting and limiting conditions. ２) was used. As shown in Figure 15, the prediction accuracy of the cell proliferation rate when using the cell population neighborhood score NS is higher than the prediction accuracy when using the total cell area or cell number shown in Figure 8. Based on this result, it can be said that the cell population neighborhood score NS can quantitatively evaluate the local density that affects the cell proliferation rate.

[0038] 16 and 17 are explanatory diagrams showing the relationship between morphological information of cells Ce and the neighborhood score NS. As shown in FIG. 16, multiple cells Ce contained in multiple image data I (all-condition, time-course binding data) are classified into five categories based on the neighborhood score NS. In the example of FIG. 16, there are 3,000 cells Ce classified into each of the five categories. Note that in FIG. 16, each of the five categories is distinguished by a type of hatching, and this distinction is also common to FIG. 17. Morphological information for 24 indices is obtained by calculating the average value and standard deviation of 12 indices (e.g., area, contour length, etc.) that represent the morphology of each cell Ce.

[0039] As shown on the left side of Figure 17, when we look at each indicator of the morphological information of cells Ce, we see significant differences among the five categories classified by the neighborhood score NS. Furthermore, as shown on the right side of Figure 17, when we perform principal component analysis using the 24 indicators of the morphological information of cells Ce, we find that the five categories of neighborhood score NS are clearly separated from each other along the direction of the first principal component PC1. This result demonstrates the influence of differences in local density, represented by the neighborhood score NS of cell populations, on the morphological information of cells Ce.

[0040] 18 and 19 are explanatory diagrams showing an example in which the neighborhood score NS is applied to a large-scale dataset. The upper part of Fig. 18 shows the change in cell count over time for each lot included in three datasets (datasets A to C) related to cell culture. The number of lots included in datasets A to C is 40, 37, and 12, respectively, for a total of 89 lots. The lower part of Fig. 18 shows the cell proliferation rate calculated from the change in cell count over time for each lot included in each dataset.

[0041] Figure 19 shows the results of classifying data from a total of 89 lots into a group of 15 lots in a restricted state (with restriction) and a group of 74 lots in a non-restricted state (without restriction) based on a heat map of the neighborhood score NS of the cell population. As shown in Figure 19, the group in the restricted state had a lower cell proliferation rate due to the influence of local congestion compared to the group in the non-restricted state.

[0042] In this way, by using the neighborhood score NS of a cell population, for example, it is possible to extract data that is not affected (or is only slightly affected) by local density from a dataset of morphological information of a cell population (data cleansing). Furthermore, by using the neighborhood score NS of a cell population, for example, it is possible to classify a dataset of morphological information of a cell population into multiple (e.g., five) categories with different degrees of local density. Furthermore, by performing statistical processing or machine learning using data on morphological information of a cell population to which the neighborhood score NS has been added, it is possible to perform prediction and classification with high accuracy.

[0043] (Configuration of Information Processing Device) Fig. 20 is an explanatory diagram showing the configuration of an information processing device (information processing system) 100 for calculating a neighborhood score NS as a specific index value. The information processing device 100 is configured by a computer (PC, server, etc.). The information processing device 100 includes a control unit 110, a storage unit 120, a display unit 130, an operation input unit 140, and an interface unit 150. These units are connected to each other via a bus 190 so as to be able to communicate with each other.

[0044] The display unit 130 of the information processing device 100 is configured, for example, by a liquid crystal display or the like, and displays various images and information. The operation input unit 140 is configured, for example, by a keyboard, mouse, buttons, a microphone, a trackpad, or the like, and accepts operations and instructions from an administrator. The display unit 130 may also function as the operation input unit 140 by being equipped with a touch panel. The interface unit 150 is configured, for example, by a LAN interface, a USB interface, or the like, and communicates with other devices via wired or wireless connections.

[0045] The storage unit 120 of the information processing device 100 is configured, for example, with a ROM, RAM, hard disk drive (HDD), etc., and is used to store various programs and data, and as a work area when executing various programs, and as a temporary storage area for data. For example, the storage unit 120 stores a data processing program CP, which is a computer program for executing the index value calculation process described below. The data processing program CP is provided in a state stored on a computer-readable recording medium (not shown), such as a CD-ROM, DVD-ROM, or USB memory, or is provided in a state that can be obtained from an external device (for example, a cloud server or other terminal device) via the interface unit 150, and is stored in the storage unit 120 in a state that is operable on the information processing device 100.

[0046] The control unit 110 of the information processing device 100 is configured with, for example, a CPU, and controls the operation of the information processing device 100 by executing a computer program read from the storage unit 120. For example, the control unit 110 reads and executes a data processing program CP from the storage unit 120, thereby functioning as an index value calculation unit 111 that executes an index value calculation process described below, a specific process execution unit 115, and a result output unit 116. The index value calculation unit 111 has an image data acquisition unit 112, a neighboring region setting unit 113, and a region division unit 114. The functions of each of these units will be described in conjunction with the explanation of the index value calculation process described below.

[0047] (Index Value Calculation Process) Fig. 21 is a flowchart showing the index value calculation process. First, the image data acquisition unit 112 of the information processing device 100 acquires an image I0 to be used for calculating the neighborhood score NS (S110, see Fig. 9). Next, the index value calculation unit 111 of the information processing device 100 performs region segmentation processing to separate regions where cells Ce exist from other regions, thereby generating a partitioned image I1 (S120, see Fig. 9). The partitioned image I1 is, for example, a binary image, in which regions where cells Ce exist are represented in white (or black), and other regions are represented in black (or white).

[0048] Next, the index value calculation unit 111 of the information processing device 100 acquires the coordinates of each cell Ce in the image (S130). Next, the neighboring region setting unit 113 of the information processing device 100 sets a neighboring region R1 for each cell Ce appearing in the divided image I1 (S140, see FIG. 10).

[0049] Next, the index value calculation unit 111 and the region division unit 114 calculate the neighborhood score NS for each cell Ce (S150). Specifically, the region division unit 114 divides the neighborhood region R1 into multiple small regions (see FIG. 11). Thereafter, the index value calculation unit 111 counts the number of small regions in which other cells are located and sets this number as the neighborhood score NS for each cell Ce (see FIG. 12). The index value calculation unit 111 calculates the neighborhood score NS for all cells Ce in the image and calculates the neighborhood score NS for the cell population by aggregating the neighborhood scores NS for each cell Ce (S160, see FIG. 13). Through the above steps, the neighborhood score NS is calculated as a specific index value representing the local density of the cell population.

[0050] The specific processing execution unit 115 of the information processing device 100 executes specific information processing using the calculated neighborhood score NS (S170). Examples of specific information processing include a process of classifying morphological information representing the morphology of each cell Ce constituting a cell population based on the neighborhood score NS, and a process of predicting the proliferation rate of the cell population using the morphological information representing the morphology of each cell Ce constituting the cell population and the neighborhood score NS. The result output unit 116 outputs the execution results of the above-mentioned processing (e.g., the calculated neighborhood score NS, the target image, the result of image processing, etc.) via, for example, the display unit 130 (S180).

[0051] (Effects of this embodiment) As described above, the information processing device 100 of this embodiment includes the index value calculation unit 111 that calculates a specific index value that represents the local density of a cell population. According to this embodiment, the local density of a cell population can be appropriately evaluated using the specific index value, and for example, a cell proliferation rate can be predicted with high accuracy.

[0052] In this embodiment, the specific index value is an index value for evaluating, for example, the risk of cell-cell contact inhibition. According to this embodiment, the specific index value can be used to appropriately evaluate the risk of cell-cell contact inhibition, and for example, can accurately predict the cell proliferation rate.

[0053] In this embodiment, the specific index value is a neighborhood score NS that indicates, for each cell Ce constituting the cell population, the number of other cells Ce located in the vicinity of the cell Ce. According to this embodiment, the neighborhood score NS can be calculated as an index value that appropriately represents the local density of cells.

[0054] In this embodiment, the index value calculation unit 111 includes a neighborhood region setting unit 113 that sets a neighborhood region R1 for each cell Ce that constitutes the cell population, and a region division unit 114 that divides the neighborhood region R1 into a plurality of small regions. The index value calculation unit 111 calculates a neighborhood score NS that indicates the number of small regions in which other cells Ce are located. According to this embodiment, it is possible to calculate a neighborhood score NS that appropriately represents the local density of cells while avoiding complex processing.

[0055] In this embodiment, the neighborhood region setting unit 113 sets the neighborhood region R1 so that the area of ​​the neighborhood region R1 increases as the area of ​​the cell Ce increases. According to this embodiment, it is possible to set the neighborhood region R1 of an appropriate size according to the area of ​​the cell Ce, and as a result, it is possible to calculate the neighborhood score NS that appropriately represents the local density of the cells.

[0056] In this embodiment, the neighborhood region setting unit 113 sets a minimum rectangular region RO that encompasses the cell, and then sets a region obtained by expanding the minimum rectangular region RO in the circumferential direction by a predetermined dimension as the neighborhood region R1. This embodiment makes it possible to set a neighborhood region R1 of an appropriate size according to the area of ​​the cell Ce while avoiding complex processing.

[0057] The information processing device 100 of this embodiment further includes a specific processing execution unit 115 that executes specific information processing using the neighborhood score NS, and a result output unit 116 that outputs the results of the specific information processing. According to this embodiment, the specific information processing can be executed with high accuracy using the neighborhood score NS that represents the local density of the cell population, and the processing results can be output.

[0058] The specific information processing may be a process of classifying morphological information representing the morphology of each cell Ce constituting the cell population based on the neighborhood score NS. In this way, the morphological information representing the morphology of each cell Ce constituting the cell population can be classified according to the local density.

[0059] The specific information processing may be a process of predicting the proliferation rate of a cell population using morphological information representing the morphology of each cell Ce constituting the cell population and the neighborhood score N. In this way, the proliferation rate of the cell population can be predicted with high accuracy using the neighborhood score N representing the local density of the cell population.

[0060] (Another specific index value 1) The inventors of the present application have devised a closed region change characteristic BD as another specific index value representing the local density of a cell population. The closed region change characteristic BD will be described below.

[0061] 22 is an explanatory diagram showing a method for calculating the closed region change characteristic BD. When calculating the closed region change characteristic BD, as in the calculation of the neighborhood score NS, an image of a cell population being cultured in a culture vessel, captured using, for example, a phase-contrast microscope, is used. The closed region change characteristic BD is an index value represented by the appearance and disappearance of a closed region Re surrounded by multiple cell inclusion regions Ci when the cell inclusion region Ci, which is a region of a predetermined shape centered on each cell Ce constituting the cell population, is gradually expanded. In this embodiment, the cell inclusion region Ci is a circular region centered on the center of gravity of the cell Ce.

[0062] The leftmost diagram in Fig. 22 shows a plurality of cells Ce that make up a cell population. In this state, a cell inclusion area Ci has not been set for each cell Ce, and the radius r of the cell inclusion area Ci is 0.

[0063] The second diagram from the left in Fig. 22 shows the initial state of the cell inclusion area Ci for each cell Ce. The initial value ri of the radius of the cell inclusion area Ci is set appropriately. Thereafter, each cell inclusion area Ci is gradually enlarged. Specifically, a process of increasing the radius r of each cell inclusion area Ci by a preset pitch (e.g., one pixel) is repeatedly executed.

[0064] By repeatedly performing the process of enlarging each cell inclusion region Ci, a closed region Re surrounded by multiple cell inclusion regions Ci is generated, as shown in the third diagram from the left in Fig. 22. The radius rm of each cell inclusion region Ci at this time is called the closed region birth radius rb.

[0065] When the process of enlarging each cell inclusion area Ci is further repeated, the closed area Re becomes smaller as shown in the fourth diagram from the left in Fig. 22, and eventually the closed area Re disappears as shown in the rightmost diagram in Fig. 22. The radius rx of each cell inclusion area Ci at this time is called the closed area disappearance radius rd.

[0066] In this way, by repeating the process of increasing the radius r of each cell inclusion region Ci by a predetermined pitch, closed regions Re are generated and disappear at various locations in the cell population. Figure 23 is an explanatory diagram showing an example of a duration diagram PD showing the relationship between the closed region birth radius rb and the closed region disappearance radius rd for each closed region Re. Each plot in the duration diagram PD specifies the closed region birth radius rb and the closed region disappearance radius rd for one closed region Re.

[0067] FIG. 24 is an explanatory diagram showing the relationship between the local density of a cell population and the duration diagram PD. FIG. 24 shows an example of a duration diagram PD for each combination of the number of cells in the cell population and the local density. The greater the number of cells, the greater the number of closed regions Re generated, resulting in a greater number of plots. Regardless of the number of cells, when the local density of the cell population is high, the variance of each plot in the duration diagram PD increases. Conversely, when the local density of the cell population is low, the variance of each plot in the duration diagram PD decreases. Therefore, an index value (e.g., variance or standard deviation) representing the magnitude of the variance of the plots in the duration diagram PD can be used as the closed region change characteristic BD representing the local density of the cell population.

[0068] 25 is an explanatory diagram showing a landscape function created based on a persistence diagram PD. The landscape function is a function that indicates the landscape value F[b, d](x) at time x. The landscape function F[b, d](x) is defined by the following equation (1). In equation (1), b is the closed region birth radius, and d is the closed region extinction radius. The landscape function is equivalent to a visually enhanced function that is created by rotating the persistence diagram PD by 45 degrees and drawing the function that is at its maximum.

[0069] ​In FIG. 25 , lines L1, L2, and L3 represent landscape functions for cell culture times of 0, 15, and 60 minutes, respectively, using the heterogeneous seeding method shown in FIG. 2 . Lines L01 and L02 represent landscape functions obtained from ideal images (pseudo cell seeding images) of reference arrangement patterns P01 and P02 shown in FIG. 26 . Reference arrangement pattern P01 is obtained by allocating a predetermined number of cells using perfectly uniform coordinates. Reference arrangement pattern P02 is obtained by allocating a predetermined number of cells using random selection of arrangement coordinates. In the landscape function, the height of the peaks indicates the degree of cell placement unevenness (dispersion), and the number of peaks indicates the amount of cell unevenness. For example, compared to the landscape function for a cell culture time of 0 minutes, represented by line L1, the landscape functions for a cell culture time of 15 and 60 minutes, represented by lines L2 and L3, have higher peak heights and a larger number of peaks. By comparing with the landscape functions of the reference arrangement patterns P01 and P02, the unevenness in seeding in the landscape functions (lines L1, L2, L3) of each state can be quantitatively evaluated.

[0070] 27 is an explanatory diagram showing a silhouette (Silhouette) function converted from a landscape function. The silhouette function is a function obtained by smoothing the landscape function, and is defined by the following equation (2). In equation (2), φ is the silhouette function, Λ(t) is a landscape function indicating the landscape value at time t, and p is a predetermined parameter.

[0071] FIG. 27 shows the cells with cell densities of 10,000, 20,000, and 30,000 cells / cm, from top to bottom. ２ In each figure, lines L1, L2, L3, L01, and L02 correspond to the states of lines L1, L2, L3, L01, and L02 in Figure 25, respectively. By converting the landscape function to a silhouette function, the lines are smoothed and simplified, making them easier to interpret.

[0072] ​28 is an explanatory diagram showing the Wasserstein distance Lw as a score calculated from the silhouette function. The Wasserstein distance Lw is a known index that quantifies the similarity between two functions. The shorter the Wasserstein distance Lw, the higher the similarity between the two functions.

[0073] FIG. 28 shows the cells with cell densities of 10,000, 20,000, and 30,000 cells / cm, from top to bottom. ２ The left half of each figure shows the Wasserstein distance Lw when compared with the reference arrangement pattern P01, and the right half of each figure shows the Wasserstein distance Lw when compared with the reference arrangement pattern P02. For example, by using the Wasserstein distance Lw as a score, it becomes possible to quantitatively evaluate uneven cell seeding.

[0074] 29 is a flowchart showing a process for calculating the closed region change characteristic BD as another specific index value representing the local density of a cell population. The processes of S110 to S130 in FIG. 29 are the same as the processes of S110 to S130 in FIG.

[0075] Following the process of S130, the index value calculation unit 111 acquires vector information describing the contour of each cell Ce (S132). Next, the index value calculation unit 111 calculates the closed region change characteristic BD while expanding the contour of each cell Ce according to the method described above (S134). Thereafter, the same processes as those of S170 to S180 in FIG. 21 are executed.

[0076] (Another Specific Index Value 2) The inventors of the present application have devised a cell region change characteristic CC as another specific index value that represents the local density of a cell population. The cell region change characteristic CC will be described below.

[0077] 30 is an explanatory diagram showing an example of a method for calculating the cell region change characteristic CC. When calculating the cell region change characteristic CC, as in the calculation of the neighborhood score NS, an image of a cell population cultured in a culture vessel, captured by, for example, a phase-contrast microscope, is used. The cell region change characteristic CC is an index value represented by the change in the number or area ratio of cell regions Rc when multiple cell regions Rc, which are regions occupied by each cell Ce that constitutes the cell population, are gradually expanded to connect the multiple cell regions Rc to each other.

[0078] 30 shows the change over time in the number nc of cell regions Rc when the cell regions Rc are gradually expanded and connected to each other for each of a state where the local density of the cell population is high (high local density state) and a state where the local density is low (low local density state). In the initial state (expansion count ne = 0), the number nc of cell regions Rc is 7 in both the high local density state and the low local density state.

[0079] As the expansion of the cell region Rc is repeated and the number of expansions ne increases, the cell regions Rc are connected to each other, and the number nc of cell regions Rc decreases. Eventually, the number nc of cell regions Rc becomes 1. The expansion of the cell region Rc is performed, for example, by extending the outline of the cell region Rc outward by a predetermined pitch (for example, one pixel).

[0080] The right side of Figure 30 shows a connection diagram CD that shows the change in the number nc of cell regions Rc as the number ne of cell region Rc increases. When the local density of a cell population is high, many connections of cell regions Rc occur at a relatively early stage. Therefore, in the connection diagram CD, the area Sr of the region defined by the straight lines showing the change in the number nc of cell regions Rc is relatively small. Conversely, when the local density of a cell population is low, few connections of cell regions Rc occur at a relatively early stage, and many connections of cell regions Rc occur at the end. Therefore, in the connection diagram CD, the area Sr of the region defined by the straight lines showing the change in the number nc of cell regions Rc is relatively large. Therefore, for example, the area Sr in the connection diagram CD can be used as a cell region change characteristic CC that represents the local density of a cell population.

[0081] 31 is an explanatory diagram showing another example of a method for calculating the cell region change characteristic CC. Fig. 31 shows the change over time in the area ratio ar of the cell region Rc to the total area when the cell regions Rc are gradually expanded and connected to each other for each of a high local density state and a low local density state. In the initial state (expansion count ne = 0), the area ratio ar of the cell region Rc is 20% in both the high local density state and the low local density state.

[0082] As the expansion of the cellular region Rc is repeated and the number of expansion times ne increases, the cellular regions Rc are connected to each other, and the area ratio ar of the cellular region Rc increases. Finally, the area ratio ar reaches 100%.

[0083] The right side of Figure 31 shows a connection diagram CD showing the change in the area ratio ar of the cell region Rc as the number of expansions ne of the cell region Rc increases. When the local density of the cell population is high, many connections of the cell region Rc occur at a relatively early stage. Therefore, in the connection diagram CD, the area Sr of the region defined by the straight line showing the change in the area ratio ar of the cell region Rc becomes relatively large. Conversely, when the local density of the cell population is low, few connections of the cell region Rc occur at a relatively early stage, and many connections of the cell region Rc occur at the end. Therefore, in the connection diagram CD, the area Sr of the region defined by the straight line showing the change in the area ratio ar of the cell region Rc becomes relatively small. Therefore, for example, the area Sr in the connection diagram CD can be used as the cell region change characteristic CC representing the local density of the cell population.

[0084] 32 is a flowchart showing the process of calculating the cell region change characteristic CC as another specific index value representing the local density of a cell population. The processes of S110 to S132 in FIG. 32 are the same as the processes of S110 to S132 in FIG.

[0085] Following the processing of S132, the index value calculation unit 111 calculates the cell region change characteristic CC while expanding the contour of each cell Ce according to the method described above (S136). As described above, methods for calculating the cell region change characteristic CC include the method shown in FIG. 30 and the method shown in FIG. 31. The index value calculation unit 111 calculates the cell region change characteristic CC by either method. Thereafter, processing similar to the processing of S170 to S180 in FIG. 29 is executed.

[0086] (Modifications) The technology disclosed in this specification is not limited to the above-described embodiments, and can be modified into various forms without departing from the spirit thereof, for example, the following modifications are also possible.

[0087] The configuration of the information processing device 100 in the above embodiment is merely an example and can be modified in various ways. For example, part of the configuration of the information processing device 100 may be included in another device instead of the information processing device 100.

[0088] The content of the score calculation process in the above embodiment is merely an example and can be modified in various ways. For example, in the above embodiment, the neighborhood score NS of each cell Ce is a score of five levels, but the number of levels of the neighborhood score NS of each cell Ce may be four levels, or six levels or more. Furthermore, the neighborhood score NS may be a continuous value instead of a discrete value.

[0089] In the above embodiment, the treatment for a single cell Ce can be similarly applied to a mass of an aggregated cell population. In this specification, a single cell Ce or a mass of an aggregated cell population is referred to as a cell unit. Masses of an aggregated cell population include colonies, spheroids, organoids, microcarriers to which cells are attached, etc.

[0090] In the above embodiment, calculation of the neighborhood score NS in a cell culture situation has been described, but the technology disclosed in this specification is not limited to cell culture situations and can be used in general situations to evaluate the state of a cell population. The technology disclosed in this specification is not limited to populations of adherent cells, and can be similarly applied to any cell population, including populations of suspension cells.

[0091] 100: Information processing device 110: Control unit 111: Index value calculation unit 112: Image data acquisition unit 113: Neighborhood region setting unit 114: Region division unit 115: Specific processing execution unit 116: Result output unit 120: Storage unit 130: Display unit 140: Operation input unit 150: Interface unit CP: Data processing program CR: Cloning ring NS: Neighborhood score R1: Neighborhood region RO: Minimum rectangular region SC: Culture vessel

Claims

1. An information processing device comprising an index value calculation unit that calculates a specific index value that represents the local density of a cell population.

2. An information processing device according to claim 1, wherein the specific index value is an index value for evaluating the risk of cell-to-cell contact inhibition.

3. An information processing device according to claim 1 or claim 2, wherein the specific index value is a neighborhood score indicating the number of other cell units located in the vicinity of each cell unit, which is a single cell or a mass of an aggregated cell population that constitutes the cell population.

4. An information processing device according to claim 3, wherein the index value calculation unit includes: a neighborhood area setting unit that sets a neighborhood area for each cell unit that constitutes the cell population; and an area division unit that divides the neighborhood area into a plurality of small areas, and calculates the neighborhood score that indicates the number of small areas in which other cell units are located.

5. An information processing device according to claim 4, wherein the neighboring region setting unit sets the neighboring region so that the area of ​​the neighboring region increases as the area of ​​the cell unit increases.

6. An information processing device according to claim 5, wherein the neighboring region setting unit sets a region of a predetermined shape with a minimum area that encompasses the cell unit, and sets an area obtained by expanding the region in the circumferential direction by a predetermined dimension as the neighboring region.

7. An information processing device according to claim 5, wherein the neighboring region setting unit identifies the contour of each cell, and sets the region obtained by extending the contour outward by a predetermined dimension as the neighboring region.

8. An information processing device according to claim 1 or claim 2, wherein the specific index value is a closed region change characteristic represented by the manner in which a closed region surrounded by a plurality of cell inclusion regions is created and disappears when the cell inclusion region is gradually expanded, the cell inclusion region being a region of a predetermined shape centered on each cell unit, which is a single cell or a mass of an aggregated cell population that constitutes the cell population.

9. An information processing device according to claim 1 or claim 2, wherein the specific index value is a cell region change characteristic expressed by the manner in which the number or area ratio of the cell regions, which are areas occupied by individual cell units that constitute the cell population or clumps of the cell population in an aggregated state, change when a plurality of the cell regions are connected to each other by gradually expanding the cell regions.

10. An information processing device according to claim 1 or claim 2, further comprising: a specific processing execution unit that executes specific information processing using the specific index value; and a result output unit that outputs the result of the specific information processing.

11. An information processing device according to claim 10, wherein the specific information processing includes processing for classifying, based on the specific index value, morphological information representing the morphology of each cell unit, which is a single cell constituting the cell population or a mass of an aggregated cell population.

12. An information processing device according to claim 10, wherein the specific information processing includes processing for predicting the proliferation rate of the cell population using morphological information representing the morphology of each cell unit, which is a single cell or a mass of an aggregated cell population that constitutes the cell population, and the specific index value.

13. An information processing device according to claim 1 or 2, wherein the cell population is a population of adhesive animal cells.

14. An information processing system comprising: an image acquisition unit that acquires a photographed image of a cell population; an index value calculation unit that calculates a specific index value that represents the local density of the cell population using the photographed image; and a display unit that displays the calculated specific index value.

15. An information processing method comprising a step of calculating a specific index value representing the local density of a cell population.

16. A computer program that causes a computer to perform a process of calculating a specific index value that represents the local density of a cell population.

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