WO2020261455A1 - Cell function evaluation method and cell analysis device - Google Patents

Abstract

An embodiment of the present invention is a method for evaluating a cell function of test cells which are stem cells, the method comprising: an image acquisition step (S1, S2) for acquiring a cell image obtained by capturing an image of the test cells; a characteristic amount extraction step (S5 to S8) for extracting, from the cell image, information related to a gradient of a contour portion of the test cells and/or information related to the orientation of the test cells as a characteristic amount of the test cells; and an evaluation step (S9, S10) for evaluating a cell function of the test cells on the basis of the characteristic amount of the test cells. Thus, it is possible to quantitatively evaluate cell functions of stem cells, such as undifferentiated-state maintenance, migratory ability and tissue repairability.

Description

Cell function evaluation method and cell analyzer

The present invention relates to a method and a device for evaluating the function of cells, and more specifically, to a method and a device for non-invasively evaluating the function of stem cells.

In recent years, research and industrialization of regenerative medicine and cell therapy have progressed rapidly, and along with this, it is required to establish an evaluation method for accurately ensuring the quality, safety, and effectiveness of cell preparations. .. Since cell preparations are derived from animals, especially humans, there are a wide variety of origins of cell collection tissues, types of stem cells, and production methods. Therefore, it is required to establish a flexible, versatile, and reproducible rational evaluation method based on the characteristics of cell preparations.

Conventionally, as a method for evaluating cell function, analysis of specific genes and proteins expressed in cells, analysis of cell migration ability and proliferative ability, analysis of factors secreted by cells and cellular senescence, etc. are well known. ing. These cell traits and functions are thought to correlate with the therapeutic effect of cell preparations. However, considering that the cells used for evaluation are used as they are for another purpose, for example, they are administered to patients as products such as regenerative medicine, it is necessary to perform invasive or destructive treatment on the cells. The evaluation method cannot be used.

As a method for non-invasively evaluating cells, there is a method of extracting various feature amounts from still images and moving images of cells and evaluating the state of the cells from the feature amounts. For example, Patent Document 1 describes a cell evaluation device that determines the quality of a cell state by comparing various feature amounts obtained from cell images with a predetermined threshold value. In this device, the "good state" of cells means, for example, "well-increasing state", "well-differentiating state", "many undifferentiated cells", "hard-to-differentiate state", and "cells" depending on the purpose. It is shown that it is easy to select (easy to clone).

On the other hand, as another method for non-invasively evaluating cell images, there is also known a method for evaluating the differentiation state of cells by analyzing cell-derived secretory factors contained in the supernatant of cultured cells. For example, Patent Document 2 discloses a method for evaluating a cell differentiation state from the abundance of a specific component contained in a culture supernatant. According to this, by comparing the abundance of the specific component in the reference cell and the test cell, it is possible to evaluate the state of the cell without following the change with time.

Japanese Patent No. 6448129 Japanese Patent No. 6332440 International Patent Publication No. 2016/08424 Pamphlet Japanese Patent No. 3471556

However, although the known non-invasive cell evaluation method can quantitatively evaluate the state of cells in culture, it cannot evaluate the specific function of cells. Therefore, the conventional evaluation of general cell function has been limited to the level that a person in charge who has experience and knowledge of analysis makes a sensory judgment based on the observation image of the cell and the evaluation result of the cell state. .. In such an evaluation method, the reliability and reproducibility of the evaluation result largely depend on the experience and knowledge of the person in charge. It is also difficult to verify whether such an evaluation was appropriate.

The present invention has been made to solve these conventional problems, and its main purpose is to non-invasively and quantitatively evaluate the function and ability of a stem cell based on the image information obtained about the stem cell. It is an object of the present invention to provide a method for evaluating cell function and a cell analyzer capable of performing the same.

One aspect of the method for evaluating cell function according to the present invention, which has been made to solve the above problems, is a method for evaluating cell function of a test cell which is a stem cell.
An image acquisition step to acquire a cell image of a test cell,
A feature amount extraction step for extracting information on the gradient of the contour portion of the test cell and / or information on the orientation of the test cell as the feature amount of the test cell from the cell image.
An evaluation step for evaluating the cell function of the test cell based on the characteristic amount of the test cell, and
It has.

One aspect of the cell analysis device according to the present invention made to solve the above problems is a cell analysis device for evaluating the cell function of a test cell which is a stem cell.
An image acquisition unit that acquires a cell image of a test cell,
A feature amount extraction unit that extracts information on the gradient of the contour portion of the test cell and / or information on the orientation of the test cell as the feature amount of the test cell from the cell image.
An evaluation processing unit that relatively evaluates the cell function of the test cell based on the characteristic amount of the test cell,
Is provided.

The "cell function" here is not the state of the cell itself at that time, but the ability, performance, or property of the cell, especially when it is used for regenerative medicine or cell therapy, or for its use. Functional power, performance, properties, etc. that are useful or adversely act when culturing and proliferating. Specifically, for example, it can include at least one of cell functions such as maintenance of undifferentiated state, migration ability, and tissue repair ability.

By repeating various experiments and analyzes, the present inventors have analyzed the expression level of genes related to various cell functions such as maintenance of undifferentiated stem cells and migration, or the migration ability of cells. The gradient in the thickness direction at the contour (peripheral part) of the stem cell and the orientation (alignment) of the stem cell, which can be obtained based on the measurement result of the stem cell and the observed image of the stem cell obtained by non-invasiveness, are high. It was found to have a correlation. Based on this finding, in the feature amount extraction step in the method for evaluating cell function, which is one aspect of the present invention, an index value indicating the degree of the gradient in the thickness direction in the contour portion of the test cell is obtained from the observation image of the stem cell. , Either one or both of the index value indicating the degree of orientation of the test cell is extracted as the feature amount of the test cell. Then, in the evaluation step, the cell function of the test cell is evaluated based on the index value which is the characteristic amount of the test cell. Specifically, for example, the numerical values of the above index values are compared with respect to two types of test cells having different origins, and it is determined which cell has higher cell function. Further, in the cell analysis device according to one aspect of the present invention, the processing in the feature amount extraction step and the evaluation step can be performed on a computer including a CPU, RAM and the like.

According to the cell function evaluation method and the cell analyzer according to one aspect of the present invention, the function of a stem cell can be quantitatively evaluated based on the image information obtained non-invasively and non-destructively with respect to the stem cell. it can. Since such functional evaluation can be performed using numerical values, even a person in charge who has little experience or knowledge in cell analysis work or evaluation work can perform accurate and highly reliable evaluation. In addition, it is possible to avoid variations in the results depending on the person, which tends to be evaluated based on the senses, and it is easy to verify the reason for the evaluation. In addition, the work efficiency of evaluation of cell function can be improved.

The schematic block diagram of one Embodiment of the cell analysis apparatus for carrying out the evaluation method of the cell function which concerns on this invention. The flowchart which shows the work procedure and process flow at the time of evaluating the cell function using the cell analysis apparatus of this embodiment. The conceptual diagram for demonstrating the photographing operation and the image reconstruction processing in the cell analysis apparatus of this embodiment. Schematic flowchart of the process of calculating the cell orientation score. Some detailed flowcharts in the process of calculating the cell orientation score. The figure which shows an example of the differential filter used in the differential processing in the cell analysis apparatus of this embodiment. The figure which shows another example of the differential filter used in the differential processing in the cell analysis apparatus of this embodiment. The figure which shows an example of the IHM phase image created by the cell analysis apparatus of this embodiment, and the differential image obtained from it. The explanatory view of an example of the gradient score calculation method from the differential value histogram in the cell analysis apparatus of this embodiment. Explanatory drawing of another example of the gradient score calculation method from the differential value histogram in the cell analysis apparatus of this embodiment. Explanatory drawing of another example of the gradient score calculation method from the differential value histogram in the cell analysis apparatus of this embodiment. Explanatory drawing of another example of the gradient score calculation method from the differential value histogram in the cell analysis apparatus of this embodiment. The explanatory view of the calculation method of the orientation score in the cell analysis apparatus of this embodiment. The figure which shows an example of the image for evaluation of the orientation of two kinds of samples in the cell analysis apparatus of this embodiment. The figure which shows the comparative example of the orientation score about two kinds of samples in the cell analysis apparatus of this embodiment. Explanatory drawing of the collection site of umbilical cord-derived mesenchymal stem cells in an experimental example. The figure which shows the analysis result of the expression of the cell surface antigen about four kinds of mesenchymal stem cells derived from one sample A. The figure which shows the quantification result of the gene expression level related to various cell functions about 4 kinds of mesenchymal stem cells derived from Specimen A. The figure which shows the measurement result of the migration ability by the scratch assay method about 4 kinds of mesenchymal stem cells derived from Specimen A. The figure which shows the comparison of the orientation score and the gradient score calculated by the cell analysis apparatus of this embodiment about 4 kinds of samples derived from Specimen A. The figure which shows the analysis result of the expression of the cell surface antigen about 4 kinds of mesenchymal stem cells derived from another sample B. The figure which shows the quantification result of the gene expression level related to various cell functions about 4 kinds of mesenchymal stem cells derived from Specimen B. The figure which shows the measurement result of the migration ability by the scratch assay method about 4 kinds of mesenchymal stem cells derived from Specimen B. The figure which shows the comparison of the orientation score and the gradient score calculated by the cell analysis apparatus of this embodiment about 4 kinds of samples derived from Specimen B.

Hereinafter, a method for evaluating cell function, which is an embodiment of the present invention, will be described with reference to the attached drawings.

[Structure of the cell analyzer of the present embodiment]
FIG. 1 is a schematic configuration diagram of an embodiment of a cell analyzer for evaluating a cell function according to the present invention.

The cell analysis device of the present embodiment includes a microscopic observation unit 1, a control / processing unit 2, an input unit 3 and a display unit 4 which are user interfaces.
In the present embodiment, the microscopic observation unit 1 is an in-line holographic microscope, includes a light source unit 10 including a laser diode and the like and an image sensor 11, and includes a cell 13 between the light source unit 10 and the image sensor 11. The culture plate 12 is arranged.

The control / processing unit 2 controls the operation of the microscopic observation unit 1 and processes the data acquired by the microscopic observation unit 1, and includes the imaging control unit 20, the hologram data storage unit 21, and the phase information calculation. Unit 22, image reconstruction unit 23, reconstruction image data storage unit 24, differential image creation unit 25, gradient score calculation unit 26, orientation score calculation unit 27, and cell function evaluation information output unit 28. , Is provided as a functional block.

Usually, the entity of the control / processing unit 2 is a personal computer or a higher-performance workstation in which predetermined software (computer program) is installed, or a high-performance computer connected to such a computer via a communication line. Including computer system. That is, the function of each block included in the control / processing unit 2 executes software installed in a single computer including a CPU, RAM, an external storage device such as an HDD or SDD, or a computer system including a plurality of computers. It can be embodied by the processing using various data stored in the computer or the computer system, which is carried out by the above.

The cell analyzer of the present embodiment can observe and analyze various cells, but here, as an example, it is assumed that the observation target is a mesenchymal stem cell, which is one of the pluripotent stem cells. .. However, the evaluation of cell function as described later is not limited to mesenchymal stem cells, and can be applied to other stem cells that exhibit the same or similar behavior as mesenchymal stem cells.

[Photographing of cells and acquisition of IHM phase image]
FIG. 2 is a flowchart showing a work procedure and a flow of processing in the cell function evaluation work using the cell analysis device of the present embodiment. Further, FIG. 3 is a conceptual diagram for explaining a shooting operation and an image reconstruction process.

FIG. 3A is a schematic top view of the culture plate 12 used in the cell analyzer of the present embodiment. Six wells 12a having a circular shape in the top view are formed on the culture plate 12, and cells are cultured in each well 12a. Here, the entire observation plate 12, that is, the entire rectangular area including the six wells 12a is the observation target area. The microscopic observation unit 1 includes four sets of a light source unit 10 and an image sensor 11. As shown in FIG. 3A, each of the light source unit 10 and the image sensor 11 of each set is responsible for collecting hologram data of four four-division ranges 81 in which the entire culture plate 12 is divided into four equal parts. That is, the four sets of the light source unit 10 and the image sensor 11 share the collection of hologram data over the entire culture plate 12.

As shown in FIGS. 3 (b) and 3 (c), the range in which the set of the light source unit 10 and the image sensor 11 can be photographed at one time includes the well 12a in the four division range 81. It is a range corresponding to one imaging unit 83 obtained by dividing the square-shaped range 82 into 10 equal parts in the X-axis direction and 12 equal parts in the Y-axis direction. One 4-division range 81 includes 15 × 12 = 180 imaging units 83. The four light source units 10 and the four image sensors 11 are respectively arranged in the XY plane including the light source unit 10 and the image sensor 11 near the four vertices of a rectangle having the same size as the four division range 81. The holograms of the four different imaging units 83 on the culture plate 12 are acquired at the same time.

When collecting hologram data, the operator first sets the culture plate 12 in which the cells 13 to be observed are cultured at a predetermined position of the microscopic observation unit 1, and determines the identification number, measurement date and time, etc. that identify the culture plate 12. After inputting the information from the input unit 3, the measurement execution is instructed. In response to this measurement instruction, the imaging control unit 20 controls each unit of the microscopic observation unit 1 to execute imaging (step S1).

That is, one light source unit 10 irradiates a predetermined region (one imaging unit 83) of the culture plate 12 with coherent light having a spread of a minute angle (for example, about 10 °). The coherent light (object light 15) transmitted through the cells 13 on the culture plate 12 interferes with the light (reference light 14) transmitted through the region (usually the medium) around the cells 13 on the culture plate 12 and is an image sensor. Reach 11 The object light 15 is light whose phase changes when it passes through the cell 13, while the reference light 14 is light that does not pass through the cell 13 and therefore does not undergo the phase change caused by the cell 13. Therefore, on the detection surface (image surface) of the image sensor 11, an interference image of the object light 15 whose phase has been changed by the cell 13 and the reference light 14 whose phase has not changed, that is, a hologram is formed, and this hologram is formed. The two-dimensional light intensity distribution data (hologram data) corresponding to the above is output from the image sensor 11.

As described above, coherent light is emitted from the four light source units 10 toward the culture plate 12 at substantially the same time, and the four image sensors 11 acquire hologram data of regions corresponding to different imaging units 83 on the culture plate 12. Will be done. Each time the measurement at one measurement position is completed, the light source unit 10 and the image sensor 11 are moved in the X-axis direction and the Y-axis direction by a moving unit (not shown) by a distance corresponding to one imaging unit 83 in the XY plane. It is moved sequentially in steps. As a result, the measurement is performed by 180 imaging units 83 included in the 4-division range 81, and the measurement of the entire culture plate 12 is executed by the four sets of the light source unit 10 and the image sensor 11 as a whole. The hologram data thus obtained by the four image sensors 11 of the microscopic observation unit 1 is stored in the hologram data storage unit 21 together with the attribute information such as the measurement date and time.

When the series of measurements (photographing) as described above is completed, the phase information calculation unit 22 sequentially reads hologram data from the hologram data storage unit 21 and performs light wave propagation calculation processing (phase recovery processing) in a two-dimensional manner. Restore the phase information and amplitude information at each position. The spatial distribution of this information is obtained for each imaging unit 83. If the phase information and amplitude information of all the imaging units 83 are obtained, the image reconstruction unit 23 forms a phase image of the entire observation target region, that is, an IHM phase image, based on the phase information and amplitude information. (Step S2).

That is, the image reconstruction unit 23 reconstructs the IHM phase image of each imaging unit 83 based on the spatial distribution of the phase information calculated for each imaging unit 83. Then, by performing a tiling process (see FIG. 3D) for connecting the IHM phase images in the narrow range, an IHM phase image for the observation target region, that is, the entire culture plate 12 is formed. The data constituting the IHM phase image is stored in the reconstructed image data storage unit 24. At the time of the tiling process, it is advisable to perform an appropriate correction process so that the IHM phase images at the boundary of the imaging unit 83 are smoothly connected.

When calculating the phase information and reconstructing the image as described above, a well-known algorithm disclosed in Patent Documents 3 and 4 and the like may be used. Generally, in an IHM phase image, it becomes easy to see the outline (boundary) of a cell and the pattern inside the cell, which are transparent and difficult to see with an optical microscope.

In addition to the phase information, the intensity information and the pseudo-phase information obtained by merging the phase information and the intensity information are also calculated based on the hologram data, and the image reconstruction unit 23 calculates the reproduced image (IHM) based on these. Intensity image, IHM pseudo-phase image) can also be created.

Next, the image reconstruction unit 23 executes a process of removing the background region in which it is clear that the cells do not exist in the created IHM phase image (step S3).
Various methods can be adopted as the background removal method, and as an example, texture analysis can be used. Texture analysis is roughly divided into structural texture analysis and statistical texture analysis, and the latter is suitable for extracting patterns and pattern features of local parts in an image. There are various methods for statistical texture analysis, such as a concentration histogram method using a concentration histogram, a concentration level difference method using a difference statistic, a spatial concentration level dependence method using a simultaneous occurrence matrix, and a method using a concentration co-occurrence matrix. , The appropriate method may be selected. The pixel value of the portion removed as the background area may be a fixed value such as 0.

Specifically, the entire IHM phase image is divided into a plurality of small small areas, and a predetermined texture analysis is executed for each small area to calculate the texture feature amount. Since there is a clear difference in the texture feature amount between the case where the small area is only the background area and the case where the small area includes at least a part of the cell area, the small area corresponding to the background area is selected from the texture feature amount. You can find it and exclude it. Of course, such background removal processing is not essential and may be omitted.

Further, the image reconstruction unit 23 executes a process of removing noise components from the IHM phase image after the background removal process by using a Gaussian filter or the like (step S4). This noise removal process can also be omitted as in the background removal process.
By the above processing, noise and background are removed, that is, an IHM phase image in which the target cell can be observed well can be obtained.

[Calculation of gradient score of cell contour based on IHM phase image]
Then, based on the created IHM phase image, a gradient score is calculated as an index of the degree of gradient in the thickness direction in the contour portion of the cell.
That is, the differential image creation unit 25 applies a differential filter to the signal value (pixel value) of each pixel of the IHM phase image after removing noise or the like as described above, and calculates the differential value for each pixel. Then, a differential image composed of the differential values of all the pixels is created (step S5). As the differential filter, for example, a Laplacian filter often used for edge detection of an image can be used.

FIG. 6 is an example of a general 3 × 3 pixel Laplacian filter using pixels in the vicinity of four pixels on the top, bottom, left, and right. Further, the Sobel filter, which is a combination of the vertical filter and the horizontal filter shown in FIGS. 7A and 7B, may be used as the differential filter. If a differential value is obtained for each pixel, a differential image corresponding to the IHM phase image is created using it. The Sobel filter is also used when calculating the cell orientation score, which will be described later.

FIG. 8 is an example of an IHM phase image of human umbilical cord-derived mesenchymal stem cells (Umbilical Cord derived-Mesenchymal Stem Cells) and a differential image obtained from the IHM phase image. The signal value of each pixel in the IHM phase image indicates the amount of phase lag of light that has passed through the cell, which reflects the optical thickness of the cell. Therefore, the steeper the gradient in the thickness direction of the contour portion of each cell, the larger the differential value of the pixel corresponding to the contour portion. In the differential image of FIG. 8B, the display luminance range is appropriately adjusted, whereby the contour portion of each cell is clearly depicted as compared with the inner side.

The gradient score calculation unit 26 creates a differential value histogram in which the horizontal axis is the differential value and the vertical axis is the number of occurrences (number of pixels) based on the differential values of all the pixels constituting the differential image (step S6). In order to make it easier to visually grasp the change in the number of occurrences with respect to the increase in the differential value, the horizontal axis of the histogram should be the linear axis and the vertical axis should be the logarithmic axis.

FIG. 9 shows an example of the differential value histogram. A and B in the figure show two samples having different culture conditions (specifically, the presence or absence of an activator that activates cells). As shown in FIG. 9, in the differential value histogram, there are left-right asymmetric peaks in which the slope on the low differential value side (left in the figure) is steep and the slope on the high differential value side (right in the figure) is gentle. appear. It is considered that the pixels included in the range on the low differential value side including the peak top of this peak are mainly pixels existing in a background region other than the cell or a region having a relatively flat thickness in the cell. On the other hand, the pixels included in the range of the gentle slope on the high differential value side are considered to be the pixels mainly present in the contour portion of the cell. Therefore, the degree of slope slope on the high derivative side in the differential value histogram reflects the degree of slope in the thickness direction at the contour of the cell.

That is, among the large number of cells appearing in the original IHM phase image, the larger the proportion of cells having a steeper thickness gradient in the outline of the cells, the greater the proportion of pixels having a relatively high differential value. It can be said that it will increase. Then, as the proportion of pixels having a relatively high differential value increases, the slope on the right side of the peak in the differential value histogram becomes gentler. In FIG. 9, it can be said that the peak of sample 1 has a gentler slope than the peak of sample 2, and the proportion of cells having a steep thickness-direction gradient in the contour portion of the cells is large. Therefore, in the cell analysis apparatus of the present embodiment, an index value that allows the degree of slope inclination to be compared with each other in different differential value histograms is calculated as a gradient score.

Specifically, the gradient score calculation unit 26 first executes a smoothing process by taking a moving average in the changing direction of the differential value in order to reduce variations and errors in the differential value histogram. In the high differential value range where the difference in the gradient in the thickness direction at the contour of the cell is prominent in the differential value histogram, the decreasing slope can be approximated by an exponential function. As described above, when the vertical axis is the logarithmic axis, the slope becomes almost linear, so that the exponential function can be approximated by performing linear approximation on the graph. Therefore, here, two arbitrary differential values are selected within a predetermined high differential value range, and the slope between the two differential values is set as y = a · e ^-bx (where a and b are). Approximate with an exponential function of any constant). That is, the constants a and b having the smallest approximation error with respect to the slope may be searched. The constant b of the exponent part obtained at this time reflects the degree of slope gradient, and since it reflects the tendency of the degree of gradient in the thickness direction in the contour portion of each cell, the constant b of this exponent part Can be used as the gradient score. Since the gradient score at this time is not affected by the cell density, it is convenient for comparison between different samples.

[Other examples of how to calculate the gradient score]
When calculating the gradient score from the differential value histogram, the method is not limited to the above method, and other methods such as the following may be used.

10 to 12 are explanatory views of other examples of the method of calculating the gradient score.
In FIG. 10, the half width of the peak of the differential value histogram is used as the gradient score. The half-value width referred to here is the width of the differential value at half the number of appearances of the peak top of the peak. For example, in FIG. 10, the half-value width Wa and the peak of sample 2 are set with respect to the peak of sample 1. On the other hand, the half width Wb can be obtained. However, as described above, since only the slope on the high differential value side is meaningful here, the differential value at the peak top and the horizontal line indicating (the number of occurrences of the peak top) × (1/2). The difference between the differential value and the differential value corresponding to the intersection of the slope on the higher differential value side may be used instead of the half width.

In FIG. 11, the rate of decrease in the number of occurrences in the decrease slope of the peak on the differential value histogram is used as the gradient score. Specifically, in a predetermined high differential value range, an arbitrary k (where k is an integer of 2 or more) differential values are selected, and the ratio of the number of occurrences corresponding to the k differential values is calculated as the gradient score. Calculate as. Generally, as shown in FIG. 11, k may be 2, and for the two differential values D1 and D2, the appearance number ratio Xa [%] to the peak of sample 1 and the appearance number ratio Xb to the peak of sample 2. [%] Can be obtained as the gradient score.

In FIG. 12, the difference in the differential value corresponding to a certain difference in the number of appearances is used as the gradient score. Specifically, in the number of occurrences range corresponding to the slope of the high differential value range, an arbitrary L number of appearances (where L is an integer of 2 or more) is selected, and the derivative corresponding to the number of L appearances is selected. The difference between the values (width of the differential value) is calculated as the gradient score. Generally, as shown in FIG. 12, L may be 2, and for the two occurrence numbers P1 and P2, the differential value difference La is obtained from the peak of sample 1 and the differential value difference Lb is obtained from the peak of sample 2. Since the absolute value of the number of appearances depends on the cell density, in order to eliminate the influence of the difference in cell density, for example, a process for standardizing the number of appearances may be performed.

Further, according to the experimental study of the present inventor, in the differential value histogram, in the high differential value range in which the difference in the gradient in the thickness direction in the contour portion of the cell appears remarkably, the differential value is particularly high. , The number of appearances is known to be proportional to the number of cells or the confluency (the ratio of the area of cells to the total image area). Therefore, if the number of occurrences in such a high differential value range is normalized by the number of cells or the confluence, a gradient score that does not depend on them is obtained.

Therefore, the confluence or the number of cells in the cell image (IHM phase image) to be observed is obtained by a measurement method different from the measurement method by this device, that is, an independent measurement method. As a measurement method, for example, cell counting by a hemocytometer or the like can be used. Then, the number of appearances at an arbitrary differential value in the high differential value range is obtained from the differential value histogram, and the number of appearances is divided by the number of cells or confluency obtained as described above to obtain a gradient of the normalized number of appearances. It may be calculated as a score.

[Calculation of cell orientation score based on IHM phase image]
Next, an orientation score is calculated that indexes the orientation of a large number of cells observed in the IHM phase image. 3 and 4 are flowcharts showing the procedure and processing for calculating the orientation score from the IHM phase image, and FIG. 13 is an explanatory diagram of the calculation method of the orientation score.

The orientation score calculation unit 27 reads the IHM phase image from which noise has been removed in step S4 (step S21), and divides this image into a plurality of rectangular small regions as shown in FIG. 13A (step S21). Step S22). In FIG. 13A, the number of divisions is 5 × 7 = 35, which is an example, and the size and number of small regions are appropriately determined according to the cell size, cell density, etc. in the phase image. It is good to set.

Next, the orientation score calculation unit 27 calculates the orientation strength and orientation of the cells contained in the small regions for each small region obtained by the division (step S23). The processing in one small area will be specifically described with reference to FIG.

The orientation score calculation unit 27 first reads a small area image (step S31) and performs noise removal processing (step S32). This may be the same processing as in step S4, and may be omitted. After that, contour detection processing using a differential filter or the like is executed for the signal value (pixel value) of each pixel. Here, the vertical contour detection using the vertical sobel filter shown in FIG. 7 (a) and the horizontal contour detection using the horizontal sobel filter shown in FIG. 7 (b) are described above. Contour detection is performed for each pixel (steps S33 and S34), and the strength and direction of the change in luminance in that pixel are calculated (steps S35 and S36).

Specifically, the signal value after processing by the sobel filter in the vertical direction with respect to the signal value org (x, y) of a certain pixel is sobel H (x, y), and after processing by the sobel filter in the horizontal direction. When the signal value is sobel V (x, y), the strength of the luminance change Strength (x, y) is given by the following equation (1), and the direction of the luminance change Angle (x, y) is given by the following equation (2). Can be calculated with.
Strength (x, y) = √ (sobelH (x, y) ² + sobelV (x, y) ² )… (1)
Angle (x, y) = tan ^-1 (sobelH (x, y) / sobelV (x, y))… (2)

When the processes of steps S33 to S36 are executed for all the pixels included in the small region, the distribution of the angle of the brightness change is first calculated in order to convert the information on the direction of the brightness change into the information on the direction of the cells ( Step S37). To do so, first, Angle (x, y), which is a continuous numerical value obtained by Eq. (2), is rounded off and converted into a numerical value at a fixed angle interval, for example, every 1 degree. Then, the distribution of the angle after the conversion, that is, the histogram is obtained. In that case, the number of pixels whose brightness change strength Strength (x, y) is equal to or greater than a predetermined threshold value may be counted for each angle, and the counted value may be used as the frequency of the angle distribution. Further, for each angle, the sum of the strengths of the brightness changes (x, y) in all the pixels whose brightness change direction is the angle may be calculated, and the value of the sum may be used as the frequency in the angle distribution. ..

Next, the angle of the angle distribution obtained in step S37 is rotated by 90 degrees (step S38). This is because the direction of change in brightness and the direction of cells are orthogonal to each other. The shape of the mesenchymal stem cells assumed here is an elongated elliptical or needle-like shape, and the longitudinal direction thereof is the direction of the cells. Of course, the order of steps S37 and S38 can be exchanged. After that, the frequency of the angle distribution is normalized so that numerical evaluation is possible (step S39), and the accuracy of the distribution is improved by calculating the moving average of the angle distribution (step S40).

FIG. 13 (d) shows an example of the angular distribution of the case where the cells are oriented in the same direction (see 13 (b)) and the case where the cells are not oriented (see 13 (c)). As shown in this figure, when the cells are oriented in the same direction as a whole, the frequency at a specific angle is high, so that the frequency differs between the case where the cells are oriented in the same direction and the case where the cells are not oriented. Therefore, the orientation score calculation unit 27 obtains the most frequent angle, that is, the most frequent angle in the angle distribution after performing the moving average, and determines this as the orientation direction of the cells in the small region (step S41). Further, the frequency corresponding to the mode is defined as the strength of cell orientation in the small region (step S42).

By performing the above treatment for each small region, it is possible to obtain a numerical value indicating the orientation and strength of cell orientation for each small region. The orientation score calculation unit 27 calculates the average value of the orientation strength in all the small regions as the orientation score indicating the tendency of the cell orientation in the entire phase image, and also calculates the orientation strength of the orientation score. The maximum value is calculated as the orientation score. In addition, an evaluation image is created for the user to intuitively evaluate the orientation (step S24). Specifically, the orientation score is visualized by a two-dimensional vector in which the orientation direction is the direction of the vector and the orientation strength is the length (scalar amount) of the vector for each small region. Then, an image displayed by superimposing the two-dimensional vector on the IHM phase image is created as an evaluation image.

FIG. 14 is a diagram showing an example of an image for evaluating orientation of two types of samples, one in which the cells are oriented in the same direction and the other in which the cells are not oriented. The division of small regions is shown on the IHM phase image, and straight lines indicating the strength and direction of orientation are superimposed and displayed in each small region. The cells of sample 2 are more aligned than those of sample 1 when viewed locally. This difference in features on the image appears in the difference in the length of the lines drawn in each subregion. Therefore, by displaying this evaluation image on the display unit 4 and presenting it to the user, the user can intuitively grasp the degree of alignment of the cells.

FIG. 15 is a diagram showing a comparative example of orientation scores for the two types of samples shown in FIG. The difference in the above-mentioned features on the image is clearly shown in the orientation scores of both the mean value and the maximum value. That is, according to the orientation score, it is possible to numerically compare and evaluate the degree of alignment of cell orientation.

[Evaluation of cell function]
The cell function evaluation information output unit 28 displays the gradient score and the orientation score calculated as described above, and further, the evaluation image related to the orientation on the display unit 4 in a predetermined format. The user evaluates cell function based on these indications. The cell functions that can be evaluated here can include three types: maintenance of undifferentiated state, migration ability, and tissue repair ability. It will be explained based on the experimental results conducted by the inventors that it is possible to evaluate these cell functions using the above scores.

The evaluation target in this experimental example is human umbilical cord-derived mesenchymal stem cells (hereinafter, may be abbreviated as UC-MSC). The method for collecting the target cells and the method for identifying the cell function are as follows.

FIG. 16 is an optical observation image of the tissue at the cell collection site. This is a cross section of the umbilical cord. In the following description, cells collected from the amniotic membrane side of the umbilical cord will be referred to as Am-MSC, and cells collected from the interstitial side of the umbilical cord will be referred to as Wj-MSC.
Tissues collected from each of the above parts of the umbilical cord were cut to a size of about 3 to 5 mm square and placed in a cell culture dish. This petri dish is allowed to stand in an incubator at 37 ° C. and a 5% CO ₂ atmosphere for 30 minutes, and when the tissue pieces adhere to the bottom of the culture dish, a basal medium containing fetal bovine serum (FBS) is added and cultured. went. The cells that escaped from the tissue pieces and adhered to the culture dish were collected, and then the cells were subcultured and cultured.

For cultured cells in which passage was repeated 5 times, cells with and without a cell activator (extract derived from Wharton's jelly, hereinafter sometimes referred to as WJ) are added in order to cause a clear difference in cell function. The cells were separately cultured. The cells obtained by adding WJ to Am-MSC are referred to as Am-MSC + WJ, and the cells obtained by adding WJ to Wj-MSC are referred to as Wj-MSC + WJ. Am-MSC and Wj-MSC are reference cells, and Am-MSC + WJ and Wj-MSC + WJ are test cells.

For a sample containing four types of cells (derived from one sample A), Am-MSC, Wj-MSC, Am-MSC + WJ, and Wj-MSC + WJ, first, the cell function and the like were confirmed by an existing method.
(1) The expression of the surface antigen of UC-MSC when cultured for 48 hours with or without addition of WJ was analyzed by flow cytometry. The analysis result is shown in FIG. In FIG. 17, WJ-is shown without WJ, and WJ + is shown with WJ. Further, CD (Cluster of Differentiation) 90, CD73, CD105, CD34, and CD45 are types of antigens. As is well known, the left side of the two peaks on the left and right is the isotope control, and the right side is the target antigen. When the antigen is not expressed, both peaks completely overlap.

In the results shown in FIG. 17, all of Am-MSC, Wj-MSC, Am-MSC + WJ, and Wj-MSC + WJ express three types of antigens, CD90 antigen, CD73 antigen, and CD105 antigen, while CD34 antigen and CD45. It did not express the antigen. In general, it has been reported that human-derived mesenchymal stem cells are positive for CD90 antigen, CD73 antigen, and CD105 antigen, and negative for CD34 antigen and CD45 antigen. The above analysis results are in agreement with this report, and from this, the UC-MSC used in this experiment has the characteristics of the cell surface antigen of human-derived mesenchymal stem cells, and WJ was added. It was confirmed that the characteristics did not change.

(2) Gene expression related to the three functions of cell migration ability, tissue repair ability, and maintenance of undifferentiated state was quantified using the real-time PCR (Polymerase Chain Reaction) method. The result is shown in FIG. The test cells Am-MSC + WJ and Wj-MSC + WJ to which WJ was added are OCT4 (Octamer-binding transcription factor 4), which is a factor indicating maintenance of cell undifferentiation with respect to reference cells Am-MSC and Wj-MSC, respectively. And NANOG, SDF-1 (Stromal derived factor-1) and CXCR4, which are factors indicating high chemotaxis, and TGFb (Transforming growth factor-β) and MCP-1 (MCP-1), which are factors indicating high tissue repair ability. The expression of Monocyte chemoattractant protein-1) was increased, and it was found that both of these cell functions were improved by WJ.

In addition, in general, αSMA (α-smooth muscle actin), which is a cell shape-related factor, is excessively accumulated in cells, causing the cell body to expand and enlarge, resulting in a decrease in cell migration ability and in vivo. It is known that the distribution of cells in the target organ is inhibited (see, for example, Non-Patent Document 1). According to the quantitative results shown in FIG. 18, the expression level of αSMA was decreased in the test cells Am-MSC + WJ and Wj-MSC + WJ to which WJ was added, respectively. In addition, the morphology of these cells has changed to an elongated spindle shape, confirming that the addition of WJ improved the migration ability of the cells.

(3) The migration ability of cells was evaluated by a very common scratch assay (Scratched assay) method. The measurement result of the migration ability by the scratch assay method is shown in FIG. This is a measurement of the ratio of the area of the open area due to the scratch wound 9 hours after the linear scratch wound was given, and the higher the cell migration ability, the smaller the area of the open area. Become. As can be seen from FIG. 19, it was confirmed that the cell migration ability of the test cells was improved as compared with the reference cells, and that the addition of WJ improved the cell migration ability.

FIG. 20 shows the four types of MSC gradient scores and orientation scores (here, the average value of the strength of cell orientation in all small regions) derived from the sample A, calculated by the cell analyzer of the above embodiment. It is a figure which shows the comparison result. According to FIG. 20A, it can be seen that the gradient scores of the test cells Am-MSC + WJ and Wj-MSC + WJ are clearly reduced with respect to the reference cells Am-MSC and Wj-MSC, respectively. Since this gradient score indicates the slope of the peak slope in the above-mentioned differential value histogram, a decrease in the gradient score indicates an increase in cells having steep contours. On the other hand, according to FIG. 20 (b), it can be seen that the orientation scores of the test cells Am-MSC + WJ and Wj-MSC + WJ are clearly increased with respect to the reference cells Am-MSC and Wj-MSC, respectively. .. An increase in this orientation score indicates that the cells are aligned.

FIGS. 17 to 20 show the actual measurement results of one sample derived from sample A, but the same actual measurement was carried out for another sample B. FIG. 21 shows the results of flow cytometry analysis of the expression of surface antigens derived from sample B, and FIG. 22 shows the expression levels of genes related to various cell functions of four types of mesenchymal stem cells derived from sample B. It is a figure which shows the quantitative result of. FIG. 23 is a diagram showing the measurement results of migration ability of four types of mesenchymal stem cells derived from sample B by the scratch assay method. FIG. 24 is a diagram showing a comparison of orientation scores and gradient scores calculated by the cell analyzer of the present embodiment for four types of samples derived from sample B. Comparing the result of Specimen A and the result of Specimen B, it can be seen that the tendency of increase or decrease of the numerical value at + WJ is exactly the same for both the test cell and the reference cell.

As described above, many cells with high cell functions of cell migration ability, tissue repair ability, and maintenance of undifferentiated cells have steeper contours than cells with relatively low cell functions. In addition, the cell orientation is high. That is, it can be said that these cell functions have a clear correlation with the gradient score and the orientation score. Therefore, even if the cell function is unknown, the cell undifferentiated, migratory ability, and tissue of the test cell with respect to the reference cell can be compared by comparing the gradient score and the orientation score between the reference cell and the test cell. It is possible to evaluate whether or not the cell function such as repair ability is high.

In the cell analysis apparatus of the present embodiment, the cell function evaluation information output unit 28 uses, for example, a gradient score and orientation score obtained for the test cell, an evaluation image related to orientation, and a gradient score obtained for the reference cell. , The orientation score, and the evaluation image related to the orientation are displayed on the display unit 4 in a predetermined format for easy comparison (step S9). In addition to the IHM phase image, the differential image created in step S5, the differential value histogram created in step S6, and the like may also be displayed. The user relatively evaluates cell functions such as cell undifferentiation, migration ability, and tissue repair ability of the test cell with respect to the reference cell based on the displayed gradient score, orientation score, or evaluation image. (Step S10).

After completing the step of evaluating the cell function as described above, for example, the following step can be carried out.
That is, cells are sorted based on the evaluation result of cell function. For example, a fluorescence activated cell sorting device (FACS) can be used for cell sorting. Then, cells having different evaluation results of cell function are recultured in different culture modes. By subculturing this culture, it is confirmed that cells unsuitable for transplantation, such as low migration and low tissue repair function, do not grow (adhere or proliferate) in the next generation.
As described above, the evaluation of cell function is useful for confirming and verifying the proliferative ability due to the difference in cell function.

In addition, the user does not evaluate the cell function based on the displayed image or score, but for example, by comparing the gradient score and the orientation score between the reference cell and the test cell to determine the magnitude relationship. , The process of relatively evaluating cell functions such as cell undifferentiation, migration ability, and tissue repair ability of the test cell with respect to the reference cell is to be performed in the control / processing unit 2, that is, on a computer including a CPU or the like. It may be. In that case, only the final evaluation result may be output to the display unit 4, or the evaluation result may be output together with the score and the image. In addition, various machine learning methods such as deep learning can be used for such determination and evaluation.

The test cell to be evaluated and the reference cell may be different types of cells, but in actual application, they are the same type of cells with different culture conditions in many cases. Further, of course, the test cell and the reference cell do not have to be one by one, and for example, three or more test cells may be compared with each other to perform a relative evaluation of cell function. In addition, data such as a gradient score and orientation score for reference cells, and evaluation images related to orientation are stored in advance in the control / processing unit 2 as evaluation criteria, and the saved evaluation criteria and the saved evaluation criteria are used. The gradient score and the orientation score newly calculated based on the hologram data obtained by the microscopic observation unit 1 may be displayed on the test cell in a comparable format.

Further, in the cell analysis apparatus shown in FIG. 1, all the processing is performed by the control / processing unit 2, but in general, a huge amount of processing is performed for calculation of phase information based on hologram data and image reconstruction processing. Calculation is required. Therefore, a personal computer connected to the microscopic observation unit 1 is used as a terminal device, and a computer system in which this terminal device and a server, which is a high-performance computer, are connected via a communication network such as the Internet or an intranet is used. Complicated calculations and processing such as are performed by a high-performance computer, and the roles are divided so that the control of the microscopic observation unit 1 and the display processing using the processed data are executed by a relatively low-performance personal computer. May be good.

Further, in the cell analysis apparatus of the above embodiment, an in-line holographic microscope is used as the microscopic observation unit 1, but other microscopes such as an off-axis type and a phase shift type can be used as long as the microscope can obtain a hologram. It goes without saying that it can be replaced with a holographic microscope of the type.

Further, in the cell analysis apparatus of the above embodiment, both the score of the gradient in the thickness direction in the contour portion of the cell and the score indicating the orientation of the cell are calculated and presented to the user, but at least one of them. One may be calculated and presented to the user, and the user may evaluate the cell function based on the calculation. In order to calculate the gradient score in the thickness direction at the contour of the cell, the observation image of the cell needs to include information in the thickness direction (that is, three-dimensional shape information) such as an IHM phase image. On the other hand, in order to calculate the score indicating the orientation of the cells, information in the thickness direction is not required, and it is sufficient to obtain an image in which the cells can be clearly observed. Therefore, in that case, the microscope observation unit 1 does not have to be a holographic microscope, and a general phase-contrast microscope or the like may be used.

In addition, the above-mentioned cell function evaluation method can be applied to stem cells of other types other than mesenchymal stem cells for the following reasons.
That is, according to the study by the present inventors, the actin fibers in the cell decrease from a flat and widened shape to a thin spindle shape, and the localization of organelles and proteins also changes to increase the thickness of the cell. (Increased gradient), as a result, it is clear that the function of sensing shear stress, which exists on the near side surface near the nucleus apex, works more sensitively, is aligned, and the cell migration ability and tissue repair ability are enhanced. became. In other words, it is expected that the function of sensing cell shear stress is related to cell migration and cell repair ability, and the orientation is evaluated in evaluating the function of sensing shear stress. It became clear by the present invention that this is important. From these facts, it is clear that the present invention can be applied not only to mesenchymal stem cells but also to cells having a function of sensing shear stress, and cell functions such as cell migration and tissue repair ability can be evaluated.

Furthermore, the above-described embodiment and various modifications are examples of the present invention, and it is clear that even if modifications, modifications, and additions are made as appropriate within the scope of the present invention, they are included in the claims of the present application. is there.

[Various aspects]
Those skilled in the art will appreciate that the exemplary embodiments described above are specific examples of the following embodiments.

(Item 1) The method for evaluating cell function according to one aspect of the present invention is a method for evaluating cell function of a test cell which is a stem cell.
An image acquisition step to acquire a cell image of a test cell,
A feature amount extraction step of extracting information on the gradient of the contour portion of the test cell and / or information on the orientation of the test cell as the feature amount of the test cell from the cell image.
An evaluation step for evaluating the cell function of the test cell based on the characteristic amount of the test cell, and
It has.

(Item 10) The cell analysis device according to one aspect of the present invention is a cell analysis device for evaluating the cell function of a test cell which is a stem cell.
An image acquisition unit that acquires a cell image of a test cell,
A feature amount extraction unit that extracts information on the gradient of the contour portion of the test cell and / or information on the orientation of the test cell as the feature amount of the test cell from the cell image.
An evaluation processing unit that relatively evaluates the cell function of the test cell based on the characteristic amount of the test cell,
Is provided.

(2) In the method for evaluating cell function according to paragraph 1, the cell function can include at least one of undifferentiated maintenance, migration ability, and tissue repair ability.

(Item 11) In the cell analyzer according to item 10, the cell function can include at least one of undifferentiated maintenance, migration ability, and tissue repair ability.

According to the method for evaluating cell function according to the first item and the cell analyzer according to the tenth item, the function of the stem cell, for example, not yet, is based on the image information obtained non-invasively and non-destructively with respect to the stem cell. It is possible to quantitatively evaluate the maintenance of differentiation, migration ability, tissue repair ability, and the like. Since such cell function can be evaluated numerically, even a person in charge who has little experience or knowledge of cell analysis work and evaluation work can perform accurate and highly reliable evaluation. In addition, it is possible to avoid variations in results depending on the person, which tends to be evaluated based on the sense of the person in charge. In addition, the work of verifying the reason for evaluation and the like becomes easy. Furthermore, the work efficiency of evaluation of cell function can be improved.

(Section 3) In the method for evaluating cell function according to paragraph 1 or 2, the cell image can be regarded as a phase image.

In the cell analyzer according to (12) Item 10 or 11, the cell image can be regarded as a phase image.

In the phase image, cells that are generally transparent and difficult to see with an optical microscope can be captured relatively clearly. Therefore, according to the cell function evaluation method described in the third item and the cell analysis device described in the twelfth item, it is possible to accurately extract the feature amount of the test cell and accurately evaluate the cell function. it can.

(Item 4) In the method for evaluating cell function according to item 3, the image acquisition step is performed.
A measurement step of irradiating a test cell with a laser beam to detect interference light between the light passing through the test cell and the light passing around the test cell to acquire hologram data.
A phase image creation step of performing image reconstruction processing based on the hologram data to create a phase image of the test cell, and
Can be included.

(Item 13) In the cell analyzer according to item 12, the image acquisition unit is
A measurement execution unit that irradiates a test cell with a laser beam, detects interference light between the light that has passed through the test cell and the light that has passed around the test cell, and acquires hologram data.
A phase image creating unit that performs image reconstruction processing based on the hologram data and creates a phase image of the test cell.
Can be included.

The phase image created based on the hologram data in the phase image creation step includes not only the two-dimensional information of the test cell but also the information in the cell thickness direction. Therefore, according to the method for evaluating cell function according to item 4 and the cell analyzer according to item 13, information on the gradient of the contour portion of the test cell can be accurately extracted from the phase image. Based on the information, it is possible to accurately evaluate cell functions such as maintenance of undifferentiated state, migration ability, and tissue repair ability described above.

(Item 5) In the method for evaluating cell function according to any one of items 1 to 4,
A reference image acquisition step for acquiring a reference image, which is a cell image of a reference cell whose cell function is known,
A reference feature amount extraction step for extracting a feature amount corresponding to the feature amount extracted in the feature amount extraction step from the reference image, and a reference feature amount extraction step.
In the evaluation step, the cell function of the test cell can be evaluated by comparing the feature amount with respect to the test cell and the feature amount with respect to the reference cell.

(Item 14) In the cell analyzer according to any one of items 10 to 13, the evaluation processing unit knows the feature amount for the test cell and the previously acquired cell function. By comparing the feature amount extracted from the cell image of a reference cell with the feature amount, the cell function of the test cell can be relatively evaluated.

The reference cell referred to here does not necessarily have to be a cell different from the test cell. That is, for example, the cell image of the reference cell may be a cell image acquired in the past for the test cell. According to the method for evaluating cell function according to item 5 and the cell analyzer according to item 14, the test cells have the ability to maintain undifferentiated state, migrate, and repair tissues as compared with the reference cells. It is possible to accurately evaluate whether the cell function is excellent or inferior.

(Section 6) In the method for evaluating cell function according to the third or fourth paragraph, the feature amount extraction step is performed.
A differential image generation step of obtaining a spatial differential value for each pixel from the phase image and generating a differential image,
A gradient information extraction step for extracting information related to the gradient in the thickness direction in the contour portion of the test cell from the cell region containing the test cell in the differential image.
Can be included.

(Item 15) In the cell analyzer according to the item 12 or 13, the feature amount extraction unit obtains a spatial differential value for each pixel from the phase image, generates a differential image, and generates the differential image. Information related to the gradient of the contour portion of the test cell can be extracted from the cell region containing the test cell in the image.

According to the method for evaluating cell function according to item 6 and the cell analyzer according to item 15, information on the gradient of the contour portion of the test cell can be accurately extracted, and based on that information, information can be extracted. It is possible to accurately evaluate cell functions such as maintenance of undifferentiated state, migration ability, and tissue repair ability described above.

(Item 7) In the method for evaluating cell function according to item 6,
The feature amount extraction step
A pixel number acquisition step for calculating the number of pixels corresponding to the cell region in the differential image, and
The number of cells or the number of cells to obtain the confluency from the phase image / the confluency acquisition step,
The information related to the gradient of the contour portion of the test cell can be obtained by dividing the number of pixels by the number of cells or the confluence.

(Item 16) In the cell analyzer according to item 15, the feature amount extraction unit calculates the number of pixels corresponding to the cell region in the differential image, and calculates the number of cells or confluence from the phase image. It can be obtained and the information related to the gradient of the contour portion of the test cell can be obtained by dividing the number of pixels by the number of the cells or the confidence.

According to the cell function evaluation method described in Section 7 and the cell analyzer described in Section 16, the densities of the test cells to be evaluated and the reference cells used as the reference for comparison are different. However, it is possible to accurately compare and evaluate cell functions.

(Item 8) In the method for evaluating cell function according to any one of items 1 to 7, the feature amount extraction step is performed.
An image division step of dividing the cell image into a plurality of small regions, and
For each of the small areas, a differential filter is applied to each pixel included in the small area to calculate the direction in which the brightness changes and the magnitude of the change, and from the direction and magnitude of the brightness change in each small area. The orientation information calculation step for obtaining information on the orientation of the test cells, and
Can be included.

(Item 17) In the cell analyzer according to any one of items 9 to 16, the feature amount extraction unit divides the cell image into a plurality of small regions, and each small region is divided into a plurality of small regions. A differential filter is applied to each pixel included in the small region to calculate the direction in which the brightness changes and the magnitude of the change, and the orientation of the test cells is calculated from the direction and magnitude of the brightness change in each small region. Information about sex can be requested.

According to the method for evaluating cell function according to item 8 and the cell analyzer according to item 17, it is possible to obtain reliable quantitative information regarding cell orientation, that is, cell orientation. It is possible, so that an accurate evaluation of cell function can be performed.

(Section 9) In the method for evaluating cell function according to any one of paragraphs 1 to 8, the stem cell can be assumed to be a mesenchymal stem cell.

(Item 18) In the cell analyzer according to any one of paragraphs 10 to 17, the stem cells can be assumed to be mesenchymal stem cells.

According to the method for evaluating cell function according to item 9 and the cell analyzer according to item 18, it is possible to accurately evaluate cell function in particular.

1 ... Microscopic observation unit 10 ... Light source unit 11 ... Image sensor 12 ... Culture plate 12a ... Well 13 ... Cell 14 ... Reference light 15 ... Object light 2 ... Control / processing unit 20 ... Imaging control unit 21 ... Hologram data storage unit 22 ... Phase information calculation unit 23 ... Image reconstruction unit 24 ... Reconstructed image data storage unit 25 ... Differential image creation unit 26 ... Gradient score calculation unit 27 ... Orientation score calculation unit 28 ... Cell function evaluation information output unit 3 ... Input unit 4 … Display

Claims (18)

1. A method for evaluating the cell function of test cells, which are stem cells.
   An image acquisition step to acquire a cell image of a test cell,
   A feature amount extraction step for extracting information on the gradient of the contour portion of the test cell and / or information on the orientation of the test cell as the feature amount of the test cell from the cell image.
   An evaluation step for evaluating the cell function of the test cell based on the characteristic amount of the test cell, and
   A method for evaluating cell function having.
2. The method for evaluating cell function according to claim 1, wherein the cell function includes at least one of undifferentiated maintenance, migration ability, and tissue repair ability.
3. The method for evaluating cell function according to claim 1 or 2, wherein the cell image is a phase image.
4. The image acquisition step is
   A measurement step of irradiating a test cell with a laser beam to detect interference light between the light passing through the test cell and the light passing around the test cell to acquire hologram data.
   A phase image creation step of performing image reconstruction processing based on the hologram data to create a phase image of the test cell, and
   3. The method for evaluating cell function according to claim 3.
5. A reference image acquisition step for acquiring a reference image, which is a cell image of a reference cell whose cell function is known,
   A reference feature amount extraction step for extracting a feature amount corresponding to the feature amount extracted in the feature amount extraction step from the reference image, and a reference feature amount extraction step.
   In the evaluation step, the cell function of the test cell is evaluated by comparing the feature amount with respect to the test cell and the feature amount with respect to the reference cell, according to any one of claims 1 to 4. The method for evaluating cell function according to item 1.
6. The feature amount extraction step
   A differential image generation step of obtaining a spatial differential value for each pixel from the phase image and generating a differential image,
   A gradient information extraction step for extracting information on the gradient of the contour portion of the test cell from the cell region containing the test cell in the differential image, and
   The method for evaluating cell function according to claim 3 or 4, which comprises.
7. The feature amount extraction step
   A pixel number acquisition step for calculating the number of pixels corresponding to the cell region in the differential image, and
   The number of cells or the number of cells to obtain the confluency from the phase image / the confluency acquisition step,
   The method for evaluating cell function according to claim 6, further comprising the method for obtaining information on the gradient of the contour portion of the test cell by dividing the number of pixels by the number of cells or the confluence.
8. The feature amount extraction step
   An image division step of dividing the cell image into a plurality of small regions, and
   For each of the small areas, a differential filter is applied to each pixel included in the small area to calculate the direction in which the brightness changes and the magnitude of the change, and from the direction and magnitude of the brightness change in each small area. The orientation information calculation step for obtaining information on the orientation of the test cells, and
   The method for evaluating cell function according to any one of claims 1 to 7, which comprises.
9. The method for evaluating cell function according to any one of claims 1 to 8, wherein the stem cell is a mesenchymal stem cell.
10. A cell analyzer that evaluates the cell function of test cells, which are stem cells.
    An image acquisition unit that acquires a cell image of a test cell,
    A feature amount extraction unit that extracts information on the gradient of the contour portion of the test cell and / or information on the orientation of the test cell as the feature amount of the test cell from the cell image.
    An evaluation processing unit that relatively evaluates the cell function of the test cell based on the characteristic amount of the test cell,
    A cell analyzer equipped with.
11. The cell analysis apparatus according to claim 10, wherein the cell function includes at least one of undifferentiated maintenance, migration ability, and tissue repair ability.
12. The cell analyzer according to claim 10 or 11, wherein the cell image is a phase image.
13. The image acquisition unit
    A measurement execution unit that irradiates a test cell with a laser beam, detects interference light between the light that has passed through the test cell and the light that has passed around the test cell, and acquires hologram data.
    A phase image creating unit that performs image reconstruction processing based on the hologram data and creates a phase image of the test cell.
    12. The cell analyzer according to claim 12.
14. The evaluation processing unit compares the feature amount with respect to the test cell with the feature amount extracted from the cell image of the reference cell whose cell function is known, which has been acquired in advance, and thereby the test cell. The cell analyzer according to any one of claims 10 to 13, which relatively evaluates cell function.
15. The feature amount extraction unit obtains a spatial differential value for each pixel from the phase image and generates a differential image, and the gradient of the contour portion of the test cell from the cell region containing the test cell in the differential image. The cell analyzer according to claim 12 or 13, which extracts information about the cell.
16. The feature amount extraction unit calculates the number of pixels corresponding to the cell region in the differential image, acquires the number of cells or confluency from the phase image, and divides the number of pixels by the number of cells or confluence. The cell analyzer according to claim 15, wherein the information regarding the gradient of the contour portion of the test cell is obtained thereby.
17. The feature amount extraction unit divides the cell image into a plurality of small regions, and applies a differential filter to each pixel included in the small region for each small region to change the direction in which the brightness changes and its change. The cell analyzer according to any one of claims 10 to 16, which calculates the size and obtains information on the orientation of the test cells from the direction and magnitude of the brightness change for each small region.
18. The cell analyzer according to any one of claims 10 to 17, wherein the stem cell is a mesenchymal stem cell.

Images