JP7738845B2 - Cell evaluation method and cell analysis device - Google Patents

Description

The present invention relates to a cell evaluation method and a cell analysis device used for such evaluation, and more specifically to an evaluation method and analysis device suitable for non-invasively evaluating the morphology of cells in a population of multiple cells.

In recent years, the field of regenerative medicine has seen active research using pluripotent stem cells such as iPS cells, ES cells, and mesenchymal stem cells. Research and development into regenerative medicine using these pluripotent stem cells requires the mass cultivation of undifferentiated cells that maintain pluripotency. This requires the selection of an appropriate culture environment and stable control of that environment, as well as frequent monitoring of the state of the cells during cultivation. In the field of regenerative medicine, it is important to monitor the state of cells non-destructively and non-invasively. Morphological evaluation using cell images obtained with cell observation devices is a common method of evaluating cell state non-destructively and non-invasively.

Thin and transparent objects such as cells generally absorb little light, making it difficult to recognize their morphology using optical intensity images. For this reason, phase-contrast microscopes have traditionally been widely used for observing cells. Phase-contrast microscopes visualize the amount of phase change that occurs when light passes through an object as contrast, allowing for the production of phase-contrast images that clearly visualize the outlines of transparent cells. However, the pixel values (signal values) of phase-contrast images obtained using phase-contrast microscopes have the limitation that they cannot quantitatively evaluate information about the thickness of an object, specifically the thickness of a cell.

In contrast, cell analysis devices such as those disclosed in Non-Patent Document 1 use a holographic microscope to acquire cell observation images. Holographic microscopes image the physical quantity, i.e., optical thickness, which is the product of the difference in refractive index between the medium in a culture vessel and the cells on the medium and the thickness of the cells. Therefore, the phase image obtained by a holographic microscope contains information about the optical thickness. The cell analysis device disclosed in Non-Patent Document 1 assigns display colors according to a color scale to the optical thickness, thereby creating a color image showing the distribution of optical thickness from the phase image. Non-Patent Document 1 also shows that differences in optical thickness can be used to distinguish between undifferentiated iPS cell colonies and undifferentiated deviation cells.

International Patent Publication No. 2016/084420

"Cell Culture Analysis Device CultureScanner CS-1," [online], [searched May 14, 2020], Shimadzu Corporation, Internet <URL: https://www.an.shimadzu.co.jp/bio/cell/cs1/index.htm>

For example, nuclear accumulation of mRNA and proteins or abnormal lipid metabolism can cause cell nuclei to become enlarged. Being able to distinguish cells with enlarged nuclei from normal cells would be useful for research, development, and commercialization in various cell-related fields, such as regenerative medicine and nucleic acid medicine. However, during cell culture, the morphology of individual cells generally varies considerably. Therefore, when evaluating differences and changes in cell morphology due to differences in culture conditions and culture environments, it is necessary to evaluate a population of cells numbering approximately 100 to ¹⁰ cells rather than individual cells. As described above, conventional cell analysis devices can display images showing the thickness of individual cells on a color scale. This allows for subjective evaluation of multiple samples, such as which one tends to have a larger overall cell thickness. However, because such evaluations are not objective, variations in evaluation results are inevitable depending on the evaluator. Furthermore, because it is not possible to obtain quantitative evaluation results that indicate the degree of difference between multiple samples, it is difficult to compare the degree of changes in cell morphology or the extent of morphological differences.

The present invention was made to solve these problems, and its main purpose is to provide a cell evaluation method and cell analysis device that can objectively and quantitatively compare cell morphologies and understand changes in morphology in a cell population containing a large number of cells.

In order to solve the above problems, one aspect of the cell evaluation method according to the present invention is to
a phase image acquisition step of capturing an image of a group of cells in a container using a holographic microscope to acquire a phase image of a predetermined observation range;
a cell region extraction step of extracting individual cell regions from the phase image;
an individual optical thickness acquisition step of calculating a representative value of the optical thickness for each of the cellular regions in the phase image;
an evaluation index calculation step of creating a frequency distribution of the optical thickness of cells in the observation range from the representative value of the optical thickness obtained for each cell region in the individual optical thickness acquisition step, and using the frequency distribution to calculate an evaluation index of cell thickness for a group of cells present in the observation range;
It has.

In order to solve the above problems, one aspect of the cell analysis device according to the present invention is to
an observation unit which is a holographic microscope;
a phase image creating unit that creates a phase image of a predetermined observation range based on hologram data obtained by the observation unit for the cell population in the container;
a cell region extraction unit that extracts individual cell regions from the phase image;
an individual optical thickness acquisition unit that calculates a representative value of the optical thickness for each of the cellular regions in the phase image;
an evaluation index calculation unit that creates a frequency distribution of the optical thickness of cells in the observation range from the representative value of the optical thickness obtained for each cell region by the individual optical thickness acquisition unit, and calculates an evaluation index of the cell thickness for a group of cells present in the observation range using the frequency distribution;
Equipped with.

The cell evaluation method and cell analysis device of the above aspects of the present invention provide an evaluation index for the cell thickness of a cell population for a large number of cells being cultured in a culture vessel. Therefore, this evaluation index can be used to objectively compare and evaluate the cell thickness of each culture vessel, for example. This allows comparisons and evaluations to be made without relying on the subjectivity of the evaluator. Furthermore, by quantifying and indicating the cell thickness of a cell population, it becomes possible to grasp subtle differences and changes in cell morphology that were previously unclear. For example, the cell nucleus is the part of the cell with a high refractive index, and as this nucleus enlarges, the maximum optical thickness measured for the cell increases. Therefore, a representative optical thickness index, such as the maximum optical thickness of the cell, can be used to distinguish, for example, between normal cells and cells with enlarged nuclei.

1 is a schematic diagram of a cell analysis device according to one embodiment of the present invention. 10 is a flowchart showing the flow of processing when morphological evaluation of a cell population is performed in the cell analysis device of this embodiment. 3 is a flowchart showing the flow of a phase inversion correction process in the flowchart shown in FIG. 2 . 3A and 3B are conceptual diagrams for explaining the imaging operation and image reconstruction processing in the cell analysis device of the present embodiment. 1A and 1B are diagrams showing an example of an IHM phase image obtained by the cell analysis device of the present embodiment and an extracted cell region image obtained from the IHM phase image; 4A and 4B are diagrams showing an image and a phase delay distribution for explaining the operation of the correction process for the phase inversion portion shown in FIG. 3 . 1A and 1B are diagrams showing IHM phase images and optical thickness pseudocolor images for six types of samples in Experimental Example 1. 1 is a scatter plot showing the relationship between the area and optical thickness of individual cells for six types of samples in Experimental Example 1. 1 is a heat map showing the relationship between the area and optical thickness of individual cells for six types of samples in Experimental Example 1. FIG. 10 is a graph showing the frequency distribution of the optical thickness of individual cells for six types of samples in Experimental Example 1. FIG. 11 is a diagram showing the proportions of thin cells and thick cells obtained from the frequency distribution shown in FIG. 10 . FIG. 10 is a graph showing the frequency distribution of the area of individual cells for six types of samples in Experimental Example 1. FIG. 13 is a diagram showing the proportions of small cells and large cells obtained from the frequency distribution shown in FIG. 12 . 10 is a flowchart showing the flow of a process for calculating an index value indicating the gradient degree of the outer edge of a cell, which can be additionally performed in the cell analysis device of this embodiment. FIG. 10 is an explanatory diagram of an example of a method for calculating an index value indicating the gradient degree of the outer edge of a cell. FIG. 10 is a graph showing gradient index values of the outer edges of individual cells for six types of samples in Experimental Example 1. 10A and 10B are diagrams showing phase-contrast microscope images of six types of samples in Experimental Example 2. 10A and 10B are diagrams showing IHM phase images of six types of samples in Experimental Example 2. 10A and 10B are diagrams showing optical thickness pseudo-color images of six types of samples in Experimental Example 2. 10 is a heat map showing the relationship between the area and optical thickness of individual cells for six types of samples in Experimental Example 2. 10A and 10B are diagrams showing phase-contrast microscope images of six types of samples in Experimental Example 3. 10A and 10B are diagrams showing IHM phase images of six types of samples in Experimental Example 3. 10A and 10B are diagrams showing optical thickness pseudo-color images of six types of samples in Experimental Example 3. 10 is a heat map showing the relationship between the area and optical thickness of individual cells for six types of samples in Experimental Example 3.

One embodiment of a cell analysis device according to the present invention will be described below with reference to the accompanying drawings.

[Configuration of the cell analysis device of this embodiment]
FIG. 1 is a schematic diagram of the cell analysis device according to this embodiment.

The cell analysis device of this embodiment includes a microscopic observation unit 1, a control and processing unit 2, an input unit 3 serving as a user interface, and a display unit 4.
In this embodiment, the microscopic observation unit 1 is an inline holographic microscope, and is equipped with a light source unit 10 including a laser diode or the like and an image sensor 11, and a culture plate 12 containing cells 13 to be analyzed is placed between the light source unit 10 and the image sensor 11.

The control/processing unit 2 controls the operation of the microscope observation unit 1 and processes the data acquired by the microscope observation unit 1, and includes the following functional blocks: an imaging control unit 20, a hologram data storage unit 21, a phase information calculation unit 22, an image reconstruction unit 23, a phase inversion correction unit 24, a cell region extraction unit 25, an optical thickness/area calculation unit 26, an evaluation index calculation unit 27, and a display processing unit 28.

The control/processing unit 2 is typically a personal computer or a high-performance workstation with specific software (computer programs) installed, or a computer system including a high-performance computer connected to such a computer via a communications line. In other words, the functions of each block included in the control/processing unit 2 can be realized by executing software installed on a single computer or a computer system including multiple computers, and processing various data stored on that computer or computer system.

[Cell evaluation process]
The cell analysis device of this embodiment can analyze various types of cells, but as an example, the cells to be analyzed are assumed to be intestinal epithelial cells. The following describes the process of acquiring observation images of cells for the purpose of cell analysis required when culturing intestinal epithelial cells, and evaluating the morphology of the cells during culture from the observation images.

Figure 2 is a flowchart showing the processing flow for cell evaluation work using the cell analysis device of this embodiment. Also, Figure 4 is a conceptual diagram for explaining the imaging operation and image reconstruction processing in the cell analysis device of this embodiment.

<Acquisition of IHM phase images of cells>
FIG. 4A is a schematic top view of a culture plate 12 used in the cell analysis device of this embodiment. Six wells 12a, each circular in top view, are formed in the culture plate 12, and cells are cultured in each well 12a. In this example, the entire culture plate 12, i.e., the entire rectangular area including the six wells 12a, is the observation area. The microscope observation unit 1 includes four pairs of light source units 10 and image sensors 11. As shown in FIG. 4A, each pair of light source units 10 and image sensors 11 is responsible for collecting hologram data for four quadrant areas 81, which are obtained by dividing the entire culture plate 12 into four equal parts. In other words, the four pairs of light source units 10 and image sensors 11 are responsible for collecting hologram data across the entire culture plate 12.

As shown in Figures 4(B) and (C), the range that can be photographed by one set of light source units 10 and image sensors 11 at a time is the range equivalent to one imaging unit 83 obtained by dividing a substantially square area 82 containing one well 12a within a quadrant 81 into 10 equal parts in the X-axis direction and 12 equal parts in the Y-axis direction. One quadrant 81 contains 15 x 12 = 180 imaging units 83. The four light source units 10 and four image sensors 11 are each positioned near the four vertices of a rectangle the same size as the quadrant 81 within the X-Y plane containing the light source units 10 and image sensors 11, and hologram data is acquired for four different imaging units 83 on the culture plate 12 approximately simultaneously.

When collecting hologram data, the operator first places the culture plate 12 in a predetermined position in the microscope observation unit 1, inputs information such as an identification number that identifies the culture plate 12 and the measurement date and time through the input unit 3, and then issues a command to perform measurement. Upon receiving this command, the imaging control unit 20 controls each component of the microscope observation unit 1 to perform imaging (step S1).

More specifically, one light source unit 10 irradiates a predetermined area (one imaging unit 83) of the culture plate 12 with coherent light having a spread of a predetermined angle (e.g., approximately 10°). The coherent light (object light 15) that has passed through the cells 13 on the culture plate 12 interferes with light (reference light 14) that has passed through the area (usually the culture medium) surrounding the cells 13 on the culture plate 12, and reaches the image sensor 11. The object light 15 is light whose phase has changed when it passed through the cells 13, while the reference light 14 is light that does not pass through the cells 13 and therefore does not undergo a phase change due to the cells 13. Therefore, an interference image, i.e., a hologram, between the object light 15 whose phase has been changed by the cells 13 and the reference light 14 whose phase has not been changed is formed on the detection plane (image plane) of the image sensor 11, and two-dimensional light intensity distribution data (hologram data) corresponding to this hologram is output from the image sensor 11.

As described above, the four light source units 10 emit coherent light toward the culture plate 12 substantially simultaneously, and the four image sensors 11 acquire hologram data of areas on the culture plate 12 corresponding to different imaging units 83. Each time a measurement is completed at one measurement position, the light source units 10 and image sensors 11 are sequentially moved in steps in the X-axis and Y-axis directions within the X-Y plane by a distance equivalent to one imaging unit 83 using a moving unit (not shown). This allows measurements to be taken at 180 imaging units 83 included in the quadrant 81, and the entire culture plate 12 is measured using all four sets of light source units 10 and image sensors 11. The hologram data obtained in this way by the four image sensors 11 of the microscopic observation unit 1 is stored in the hologram data storage unit 21 along with attribute information such as the date and time of measurement.

Once the series of measurements (photographing) described above is complete, the phase information calculation unit 22 sequentially reads out the hologram data from the hologram data storage unit 21 and performs a light wave propagation calculation process (phase recovery process) to restore the phase information and amplitude information at each two-dimensional position. The spatial distribution of this information is determined for each imaging unit 83. Once the phase information and amplitude information for all imaging units 83 has been obtained, the image reconstruction unit 23 creates a phase image of the entire observation area based on that phase information and amplitude information (step S2). Here, because an inline holographic microscope is used as the microscopic observation unit 1, the resulting phase image is called an IHM phase image.

That is, the image reconstruction unit 23 reconstructs an IHM phase image of each imaging unit 83 based on the spatial distribution of phase information calculated for each imaging unit 83. Then, a tiling process (see FIG. 4D ) is performed to connect the IHM phase images of the limited range together to create an IHM phase image of the entire observation range, that is, the entire culture plate 12. During the tiling process, it is advisable to perform appropriate correction processing so that the IHM phase images are smoothly connected at the boundaries of the imaging units 83.
When calculating the phase information and reconstructing the image as described above, a well-known algorithm disclosed in Patent Document 1 or the like can be used.

<Phase inversion correction processing>
Due to the principles of holographic microscope measurements, pixel values in the IHM phase image obtained by phase recovery processing are limited to a range of 2π, and values outside this range are phase-inverted. For example, if pixel values (phase delay amounts) are expressed in the range of -π to π, even if a pixel's true value is 1.2π, values exceeding π cannot be expressed, resulting in a pixel value of -0.8π. This phenomenon is referred to as "phase inversion." While phase inversion is not a problem when only thin cells are observed, phase inversion is likely to occur when observing relatively thick cells such as intestinal epithelial cells, hindering accurate determination of cell thickness. Therefore, in this example, regions where phase inversion occurs on the IHM phase image are detected and the pixel values in those regions are corrected. That is, the phase inversion correction unit 24 detects regions where phase inversion occurs in the uncorrected IHM phase image obtained in step S2 and corrects the pixel values in those regions (step S3).

Figure 3 is a flowchart showing the flow of the phase inversion correction process in the flowchart shown in Figure 2. Figure 6 is a diagram showing an image and phase delay distribution to explain the operation of the phase inversion correction process shown in Figure 3. The phase inversion correction process will be explained using Figures 3 and 6.

The phase inversion correction unit 24 reads the uncorrected IHM phase image (step S31) and first extracts the region P with a low phase delay (step S32). Figure 6(A) is an IHM phase image of a single cell, and (Aa) shows the phase delay distribution of the region indicated by line 90 on the IHM phase image. Intestinal epithelial cells typically have a raised central shape, and although the thickness of the contour region is in the range of -π to π, the thickness of the central region exceeds π, resulting in phase inversion. In the phase delay distribution shown in Figure 6(Aa), the phase delay at the cell contour region is significantly higher than the background region, but in most regions of the cell excluding the contour region, the phase delay is significantly lower than the background region. Therefore, it is sufficient to detect regions where the phase delay is lower than a predetermined threshold, for example.

Next, the phase inversion correction unit 24 extracts pixel Q, which has a lower brightness than the surrounding pixels (step S33). Then, pixels that are both in the above region P and pixel Q are selected as pixels with inverted phase (step S34). Figure 6(B) is an image in which the pixels selected in step S34 are shown in black and the other pixels in white, representing a binary image. This binary image roughly shows the lines indicating the periphery of the cell (strictly speaking, lines indicating the area slightly inside the outer edge of the cell).

The phase inversion correction unit 24 then creates a correction target mask image by filling in areas surrounded by closed curves in the binary image (step S35). For example, since the line representing the periphery of the cell depicted in the binary image shown in Figure 6(B) is a closed curve, the area surrounded by this curve, i.e., the area that occupies the majority of the cell, is filled in, resulting in a correction target mask image as shown in Figure 6(C). Then, the pixel values of the pixels in the mask area (white area in Figure 6(C)) on the correction target mask image in the original IHM phase image are corrected by adding 2π to the pixel values (step S36). An IHM phase image with corrected pixel values in the phase inversion area is output (step S37).

Figure 6(D) is an image (corrected IHM phase image) after the phase inversion correction process described above has been performed on the IHM phase image shown in Figure 6(A), and Figure 6(Da) shows the phase delay distribution in the region indicated by line 91 on the corrected IHM phase image. As shown in Figure 6(Da), the phase delay amount in the cellular region has a favorable shape with a raised center.
In this way, the pixel values of the regions where phase inversion occurs in each cell are corrected, and the effects of phase inversion can be avoided. Note that, as already mentioned, phase inversion may not occur depending on the thickness of the cell to be analyzed, so the phase inversion correction process is not essential and can be omitted as appropriate depending on the type of cell to be analyzed, etc.

<Calculation of optical thickness and area of individual cells>
Returning to FIG. 2 , the explanation will be continued. Once the corrected IHM phase image is obtained, the cellular region extraction unit 25 performs noise removal processing on the corrected IHM phase image using a Gaussian filter or the like (step S4). This removes noise mainly from background regions where no cells exist. Note that this noise removal processing may be omitted. Furthermore, any appropriate noise removal method other than using a Gaussian filter may be used.

The cell region extraction unit 25 then extracts individual cell regions by performing simple binarization processing on the noise-removed IHM phase image using a predetermined threshold as a judgment criterion (step S5). This results in a cell region extraction image in which, for example, cell regions are shown in white and the background region (culture medium region) is shown in black. Figure 5 shows an example of a corrected IHM phase image (A) of intestinal epithelial cells and the corresponding cell region extraction image (B). It can be seen from Figure 5 that the cell regions corresponding to the cells on the IHM phase image have been generally appropriately extracted.

During the above binarization process, it is necessary to avoid mistakenly recognizing background regions as cell regions. It is also desirable to separate multiple cells forming a colony into individual cell regions as much as possible, rather than recognizing them as a single cell region. Therefore, it is preferable to appropriately determine the threshold value for binarization depending on the optical thickness of the background region and the three-dimensional shape of the cell type being analyzed. Therefore, this threshold value can be a fixed value preset in the device (or software), but it is also desirable to allow the user to change or select it as appropriate.

In many cell types, the outer edge of the cell does not rise steeply in the thickness direction, but has a gradient where the thickness gradually increases from the outer edge toward the center. Therefore, if simple binarization is performed using a certain threshold as a reference, part of what would normally be a cell may not be included in the cell region, meaning that part of the outer periphery of the cell may be missing from the cell region. However, such missing parts do not pose a problem when calculating the optical thickness of the cell, as described below. On the other hand, missing cell regions do affect the area of the cell. However, in the cell evaluation performed here, the strictness of the absolute values of the area of individual cells is not very important; all that is required is a relative quantitative evaluation of the cell population, so missing cell regions as described above do not actually pose a problem when calculating the area.

Next, the cell region extraction unit 25 labels each cell region on the extracted cell region image (step S6). "Labeling" here refers to numbering, and for example, a unique number may be assigned to each cell region on the extracted cell region image, and a list may be created that pairs the number with the coordinates of the center position of each cell region.

The optical thickness/area calculation unit 26 uses the corrected and noise-removed IHM phase image obtained in step S4 and the cell region extraction image obtained in step S5 to determine a representative optical thickness value for each labeled cell region (step S7). Specifically, for example, for each cell region in the IHM phase image, the maximum pixel value among the pixel values of the multiple pixels contained in that region can be found, and this value can be used as the representative optical thickness value for the cell corresponding to that cell region. Of course, instead of simply selecting the maximum value, the representative optical thickness value can also be determined using other methods, such as using the average optical thickness values at the position where the maximum value is found and multiple positions surrounding it. For example, the average optical thickness or the median optical thickness value within a pre-specified range including the pixel showing the maximum optical thickness (e.g., a 3 x 3 pixel range centered on the pixel showing the maximum optical thickness) can be used as the representative optical thickness value. Alternatively, the average optical thickness or the median optical thickness value of all pixels contained in a region determined to be a cell region by binarization processing can be used as the representative optical thickness value.

The optical thickness/area calculation unit 26 calculates the area value of each labeled cell region based on the extracted cell region image (step S8). This can be done by simply counting the number of pixels included in the cell region and calculating the area value from the counted value.
By the processing of steps S7 and S8, the optical thickness value and area value are determined for each labeled cell region (that is, cell) included in the entire observation range.

<Calculation of evaluation indexes of optical thickness and area in cell populations>
Thereafter, the evaluation index calculation unit 27 creates or calculates various evaluation indexes based on the optical thickness value and area value obtained for each cell region, that is, for each cell, over the entire observation range.
First, the evaluation index calculation unit 27 creates a scatter diagram showing the relationship between the optical thickness value and the area value of each cell for the entire observation range, and further converts the scatter diagram into a heat map (step S9).

In other words, since each labeled cell can be represented as a single point on a two-dimensional graph with optical thickness and area values as mutually orthogonal axes, a scatter diagram can be created by plotting points corresponding to all labeled cells within the observation range on the two-dimensional graph. Furthermore, by dividing the scatter diagram into small rectangular regions of a specified size, counting the number of points in each region, and assigning a display color according to a color scale to the count, the scatter diagram can be converted into a heat map. In other words, this heat map is a two-dimensional graph that shows the concentration (density) of points on the scatter diagram. Specific examples of these graphs will be described later.

The evaluation index calculation unit 27 also calculates the frequency distribution of the optical thickness values of all labeled cells within the observation range and the frequency distribution of the area values of all those cells (step S10). Then, from these frequency distributions, it calculates index values that characterize the trends in the optical thickness values and area values of the cell population (step S11).

As an example, the ratio of the number of cells whose optical thickness or area values fall within a specific range to the total number of labeled cells within the observation range can be calculated from the frequency distribution and used as the index value. Furthermore, if a peak is formed in the frequency distribution, the optical thickness or area value corresponding to the top position of the peak and its peak width can also be used as the index value. Alternatively, a range of optical thickness values or area values that includes a predetermined ratio of cells to the total number of cells can be determined and used as the index value. Specific examples of these index values will be described later.

The display processing unit 28 displays the scatter diagram and heat map created in step S10, the frequency distribution of the optical thickness values and area values obtained in step S11, and the index values calculated from the frequency distribution as analysis results on the screen of the display unit 4 (step S12). Note that, in response to an instruction from the user via the input unit 3, only necessary graphs and information can be displayed.
As described above, the cell analysis device of this embodiment can provide the user with evaluation indices relating to the optical thickness and area of cells as a cell population during culture.

Several experimental examples in which cells were evaluated using the above method will be described.
[Experimental Example 1]
In this experiment, rat intestinal epithelial cells were analyzed, and differences (changes) in cell morphology were evaluated depending on whether or not the cells were stimulated with a cytotoxic cytokine (TNFα: Tumor Necrosis Factor-α), and whether or not a cytoprotective agent was added under TNFα stimulation.
The samples are of the following six types:
TNFα0: A sample obtained by culturing rat intestinal epithelial cells for 72 hours without adding TNFα. TNFα66: A sample obtained by culturing rat intestinal epithelial cells for 72 hours after adding 66 ng/mL of TNFα. TNFα100: A sample obtained by culturing rat intestinal epithelial cells for 72 hours after adding 100 ng/mL of TNFα. TNFα200: A sample obtained by culturing rat intestinal epithelial cells for 72 hours after adding 200 ng/mL of TNFα. TNFα200+A: A sample obtained by culturing rat intestinal epithelial cells for 72 hours after adding 200 ng/mL of TNFα and cytoprotectant A. TNFα200+B: A sample obtained by culturing rat intestinal epithelial cells for 72 hours after adding 200 ng/mL of TNFα and cytoprotectant B.

FIG. 7 shows IHM phase images obtained for the six types of samples and pseudocolor images of the optical thicknesses calculated from the IHM phase images (note that the color images are shown here converted to grayscale).
The IHM phase image shown in FIG. 7 allows the following qualitative evaluation.
In the sample without TNFα, the cells were densely packed and arranged in a paving-stone pattern. Furthermore, the brightness (halo) of individual cells was unclear, and the cells were relatively thin. In contrast, in the sample with TNFα, the brightness of individual cells increased, and the cells were thicker. Furthermore, the area of individual cells increased, and the spacing between cells increased. These changes were more pronounced with higher TNFα concentrations. Furthermore, the addition of a cytoprotective agent reduced these changes.

Furthermore, the following qualitative evaluation is possible from the pseudo-color image of the optical thickness shown in FIG.
The addition of TNFα resulted in a decrease in cells with small area and thin optical thickness. Conversely, the addition of a cytoprotective agent reduced the number of cells with large area and thick optical thickness. Furthermore, in samples without TNFα, the cell outlines were unclear.

Figure 8 shows scatter plots created from IHM phase images of six samples according to the procedure described above. Figure 9 shows heat maps (originally color plots, but converted to grayscale for this illustration) derived from these scatter plots. When creating the heat maps, the density of data points was normalized by the number of data points.
While a scatter plot cannot adequately represent the density of data points, a heat map allows for a qualitative comparison of the density, making it possible to confirm that differences in culture conditions affect the density of cells with small areas and thin optical thickness, and cells with large areas and thick optical thickness.

Figure 10 shows the frequency distribution of the optical thickness values of cells for six types of samples, and Figure 11 shows a comparison of the percentages of thin and thick cells calculated from this frequency distribution. In Figure 11, thin cells are cells with an optical thickness in the range of 0.1 to 0.2π, and thick cells are cells with an optical thickness in the range of 0.5 to 1.0π. Here, these are index values for evaluating optical thickness, respectively.
Figure 12 shows the frequency distribution of area values for six types of samples, and Figure 13 compares the proportions of small and large cells calculated from this frequency distribution. In Figure 13, small cells are cells with area values in the range of 100 to 200 ^μm2 , and large cells are cells with area values in the range of 500 to 1000 ^μm2 . Here, these are index values for evaluation of area values, respectively.
10 and 12, the frequency on the vertical axis is normalized using the total number of data. Cells with an area less than 100 ^μm2 are excluded when calculating the frequency distribution of optical thickness.

The comparison of the index values for optical thickness shown in Figure 11 reveals that the addition of TNFα significantly reduces thin cells and increases thick cells, and that the addition of a cytoprotective agent reduces this change to some extent.
On the other hand, the comparison of the index values related to area values shown in Figure 13 reveals that the addition of TNFα caused a significant change, with a decrease in small cells and an increase in large cells. Furthermore, the addition of a cytoprotective agent reduced this change, and the degree of reduction was greater than the reduction in cell thickness.

As mentioned above, the evaluation results based on index values calculated from the frequency distribution of optical thickness and the frequency distribution of area values are generally consistent with the qualitative evaluation results based on IHM phase images and optical thickness pseudocolor images. However, because the evaluation is objective and quantitative, it is possible to perform accurate and reliable evaluations at all times without being influenced by the evaluator's subjectivity. Furthermore, when comparing the results of multiple samples or examining changes in a certain sample over time, the extent of the differences can be quantitatively shown, making it easy to visualize the comparison results.

[Experimental Example 2]
In this experiment, the cells analyzed were human colon cancer cells, and 5-fluorouracil (hereinafter referred to as "5-FU"), a type of metabolic antagonist (pyrimidine antagonist), was used to evaluate the differences (changes) in cell morphology caused by cell stimulation with various concentrations of 5-FU.
The samples are of the following six types:
・5-FU 0 μM: A sample of human colon cancer cells cultured for 48 hours without adding 5-FU. ・5-FU 2.5 μM: A sample of human colon cancer cells cultured for 48 hours with 2.5 μM 5-FU added. ・5-FU 5 μM: A sample of human colon cancer cells cultured for 48 hours with 5-FU added at a concentration of 5 μM. ・5-FU 10 μM: A sample of human colon cancer cells cultured for 48 hours with 10 μM 5-FU added. ・5-FU 20 μM: A sample of human colon cancer cells cultured for 48 hours with 20 μM 5-FU added. ・5-FU 40 μM: A sample of human colon cancer cells cultured for 48 hours with 40 μM 5-FU added.

Fig. 17 shows phase-contrast microscope images obtained for the six types of samples. The following qualitative evaluations are possible from the phase-contrast microscope images shown in Fig. 17.
In samples without added 5-FU (5-FU 0 μM), cells adhere to each other with almost no gaps. In other words, the cells are densely packed and arranged in a paving-stone pattern. In contrast, adding 5-FU to the sample changes the shape of individual cells to spindle shapes or polygonal shapes with extended processes. As the concentration of 5-FU added to the sample increases, intercellular adhesion decreases, the distance between cells increases, and the number of cells with large halos increases. When the 5-FU concentration exceeds 20 μM, cells with large areas and extended processes coexist with cells with small areas and high halos, resulting in significant unevenness in cell size.

FIG. 18 shows IHM phase images obtained for the six types of samples.
The IHM phase image shown in FIG. 18 allows the following qualitative evaluation.
In samples without 5-FU (5-FU 0 μM), cells adhered to each other to form colonies. By adding 5-FU to the sample, the number of cells forming colonies decreased as the concentration increased. Furthermore, up to a concentration of 10 μM 5-FU, cell processes were observed to elongate, and the area of each individual cell expanded. When the 5-FU concentration exceeded 20 μM, small, isolated cells appeared in addition to the large cells.

FIG. 19 shows pseudo-color images (however, here, the color images are converted into grayscale images) of the optical thicknesses obtained from the IHM phase images obtained for the above six types of samples.
The pseudo-color image of the optical thickness shown in FIG. 19 allows the following qualitative evaluation.
In samples without added 5-FU (5-FU 0 μM), many cells have small cell area and thin optical thickness. By adding 5-FU to the sample, the number of cells with large cell area and thick optical thickness increases. The more 5-FU added, the more cells with thick optical thickness increase. At high concentrations of 5-FU above 20 μM, only the thick optical thickness portion remains, and the number of cells in which the thin optical thickness cell protrusion portion has disappeared increases.

Figure 20 shows heat maps (originally color images, but converted to grayscale) derived from scatter plots created from IHM phase images of six samples according to the procedure described above. When creating the heat maps, the density of data points was normalized by the number of data points.
This heat map allows for qualitative comparison of cell density, revealing that differences in culture conditions affect the density of cells with small areas and thin optical thickness, and cells with large areas and thick optical thickness.
In Experimental Example 2, since the optical thickness of the cells was thick overall, the scale of the optical thickness was doubled, and the threshold for extracting the cell region was increased from 0.075π [rad] to 0.15π [rad] compared to Experimental Example 1.

The pseudocolor image of the heat map shown in FIG. 20 allows the following qualitative evaluation.
In samples without added 5-FU (0 μM 5-FU), many of the cells had small cell areas and thin optical thicknesses. In contrast, when 5-FU was added to the sample, as the amount added increased, the number of small cells decreased and the number of large cells increased. In addition, the number of cells with thick optical thickness also increased.
Furthermore, since the mode of optical thickness can be extracted from the pseudocolor image of the created heat map, the mode can be calculated as an index value to evaluate the state of the cells reflected in the image. In this case, not only the above-mentioned qualitative evaluation but also a quantitative evaluation can be performed.

[Experimental Example 3]
In this experiment, the cells analyzed were human colon cancer cells, and oxaliplatin (hereinafter referred to as "Oxa"), a type of platinum compound, was used to evaluate the differences (changes) in cell morphology caused by cell stimulation with various concentrations of Oxa.
The samples are of the following six types:
Oxa 0 μM: A sample of human colon cancer cells cultured for 48 hours without adding Oxa. Oxa 2.5 μM: A sample of human colon cancer cells cultured for 48 hours with Oxa added at a concentration of 2.5 μM. Oxa 5 μM: A sample of human colon cancer cells cultured for 48 hours with Oxa added at a concentration of 5 μM. Oxa 10 μM: A sample of human colon cancer cells cultured for 48 hours with Oxa added at a concentration of 10 μM. Oxa 20 μM: A sample of human colon cancer cells cultured for 48 hours with Oxa added at a concentration of 20 μM. Oxa 40 μM: A sample of human colon cancer cells cultured for 48 hours with Oxa added at a concentration of 40 μM.

FIG. 21 shows phase-contrast microscope images obtained for the six types of samples.
The phase contrast microscope image shown in FIG. 21 allows the following qualitative evaluation.
In the sample without Oxa (0 μM Oxa), there were many small cells with small halos, and the cells were adhered to each other with few gaps.
Addition of Oxa causes individual cells to assume a spindle or polygonal shape with extended processes, increasing the cell area. As the concentration of Oxa added increases, the distance between cells increases, and colony formation is not observed at concentrations above 10 μM. Furthermore, at Oxa concentrations above 10 μM, round-shaped cells with large halos appear. Furthermore, at Oxa concentrations above 20 μM, cell processes shorten, cell area decreases, and the number of cells with large halos increases.

FIG. 22 shows IHM phase images obtained for the above six types of samples.
The IHM phase image shown in FIG. 22 allows the following qualitative evaluation.
In the sample without Oxa (0 μM Oxa), the area of each cell was small and the cells were in close contact with each other to form colonies.
The addition of Oxa causes cells to separate, and the number of cells forming colonies decreases as the concentration increases. Furthermore, increasing the Oxa concentration leads to the extension of cell processes and an increase in the area of individual cells. When the Oxa concentration exceeds 20 μM, small, isolated cells appear among the large cells. When the Oxa concentration exceeds 40 μM, the majority of cells have shortened or lost their cell processes, resulting in a decrease in cell area, and the individual cells become isolated and scattered.

FIG. 23 shows pseudo-color images (however, here, the color images are converted into grayscale images) of the optical thicknesses obtained from the IHM phase images obtained for the above six types of samples.
The pseudo-color image of the optical thickness shown in FIG. 23 allows the following qualitative evaluation.
In the sample without Oxa (0 μM Oxa), many cells have small cell areas and thin optical thicknesses.
When Oxa is added to the sample, the number of cells with large cell area and thick optical thickness increases depending on the amount of Oxa added up to a concentration of 20 μM. However, at concentrations above 40 μM, isolated cells with small cell area and thin optical thickness reappear.

Figure 24 shows a heat map (originally a color image, but converted to grayscale for this illustration) derived from a scatter plot created according to the procedure described above from the IHM phase images obtained for the six samples. When creating the heat map, the density of data points was normalized by the number of data points.
The heat map allows for qualitative comparison of the degree of cell density. This makes it possible to confirm that differences in culture conditions affect the degree of cell density between cells with small areas and thin optical thickness and cells with large areas and thick optical thickness. In Experimental Example 2, the scale of the optical thickness was doubled, and the threshold for extracting the cell region was changed, as in Experimental Example 2.

The pseudocolor image of the heat map shown in FIG. 24 allows the following qualitative evaluation.
In samples without Oxa or with Oxa added at low concentrations, many cells had small cell area and optical thickness. When Oxa was added at a concentration of 5 μM or higher, the number of small area cells decreased and the number of large area cells increased as the concentration increased. Furthermore, the number of cells with thin optical thickness decreased and the number of cells with thick optical thickness increased. On the other hand, when the Oxa concentration increased to 40 μM or higher, the cell area decreased again and the number of cells with thin optical thickness increased. However, unlike in the case of Oxa not being added, these cells showed large variations in both cell area and optical thickness.
As described in Experimental Example 2, in Experimental Example 3, the most frequent value of the optical thickness can be determined from the pseudocolor image of the heat map, and quantitative evaluation can be performed using this value.

In Experimental Examples 2 and 3 above, the evaluation results based on index values calculated from the frequency distribution of cell optical thickness shown in Figure 10 and the frequency distribution of cell area values shown in Figure 12 in Experimental Example 1 generally match the qualitative evaluation results based on IHM phase images and optical thickness pseudocolor images. In other words, the cell evaluation method in the cell analysis device of the above embodiment can also be applied to human diseased cells such as human colon cancer cells.

[Modification]
The cell analysis device of the above embodiment can provide index values for cell thickness and size (area) as cell morphology, but by further combining an index value that quantifies the thickness gradient (inclination) at the outer edge of the cell, as proposed in the applicant's prior applications, Japanese Patent Application No. 2019-119042 and PCT/JP2019/025489, it may be possible to perform an evaluation based on more multifaceted morphological information about the cell.

Here, we will briefly explain an example of a method for calculating an index value for the thickness gradient at the outer edge of a cell. Figure 14 shows an example of the procedure for calculating an index value for the thickness gradient at the outer edge of a cell. In this case, the evaluation index calculation unit 27 includes a differential image creation unit, a differential value histogram creation unit, and a gradient index value calculation unit.

The differential image creation unit applies a differential filter to the signal value (pixel value) of each pixel in the noise-removed IHM phase image to calculate a differential value for each pixel (step S100). A Laplacian filter, which is often used for image edge detection, can be used as the differential filter. Once the differential value for each pixel has been obtained, it is used to create a differential image corresponding to the IHM phase image (step S101). As mentioned above, each pixel value in the IHM phase image reflects the optical thickness of the cell, so the steeper the gradient in the thickness direction at the outer edge of the cell, the larger the differential value at that outer edge.

Based on the differential values of all pixels constituting the differential image, the differential value histogram creation unit creates a histogram with the differential value on the horizontal axis and the occurrence count (number of pixels) on the vertical axis (step S102). To easily visually grasp the change in the occurrence count relative to an increase in differential value, it is recommended to use a linear axis for the horizontal axis and a logarithmic axis for the vertical axis. Figure 15 shows an example of a differential value histogram. As shown in Figure 15, the differential value histogram exhibits an asymmetric peak with a steep slope on the low differential value side and a gentle slope on the high differential value side. Pixels falling within the low differential value range, including the peak top, are considered to be pixels located primarily in non-cellular background regions or in areas of cells with a relatively flat thickness. On the other hand, pixels falling within the gentle slope on the high differential value side are considered to be pixels located primarily on the outer edges of cells. Therefore, the degree of gradient of the slope on the high derivative value side of the derivative value histogram reflects the degree of gradient in the thickness direction of the posterior edge of the cell.

In other words, among the many cells appearing in an IHM phase image, the greater the proportion of cells with a steep gradient in the thickness direction at the cell's outer periphery, the greater the proportion of pixels with relatively high derivative values. Furthermore, when the proportion of pixels with relatively high derivative values is high, the slope to the right of the peak in the derivative value histogram becomes gentler. In Figure 15, the peak indicated by A has a gentler slope than the peak indicated by B, and it can be said that there is a greater proportion of cells with a steep gradient in the thickness direction at the cell's outer periphery. Therefore, an index value representing the degree of the gradient of the above slope in the derivative value histogram is calculated, and this is used as the gradient index value for the thickness direction at the cell's outer periphery.

Specifically, the gradient index value calculation unit first performs a smoothing process on the differential value histogram by taking a moving average in the direction of change in the differential value to reduce variations and errors. As shown in Figure 15, in the high differential value range where the difference in the thickness direction gradient of the outer edge of the cell is significant, the decrease in slope can be approximated by an exponential function. Therefore, two arbitrary differential values are selected within the high differential value range, and the slope between the two differential values is approximated by an exponential function of y = a·e ^−bx (where a and b are arbitrary constants). Here, the constants a and b that minimize the approximation error can be found. The constant b in the exponent part reflects the degree of the slope gradient and is not affected by cell density. Therefore, this constant b in the exponent part is used as the gradient index value.
However, the present invention is not limited to this, and the gradient index value may be obtained by other methods such as those described in the above-mentioned prior application, for example, by determining the half-width of the peak shown in FIG.

Figure 16 shows the results of calculating the gradient index values for the six types of samples shown in the experimental example above. Figure 16 shows that the addition of TNFα significantly reduces the thickness gradient at the outer periphery of the cells. Furthermore, as mentioned above, while the changes in cell thickness and size caused by the addition of TNFα are clearly reduced by the addition of a cytoprotective agent, the change in the thickness gradient of the cells is hardly reduced by the addition of a cytoprotective agent. In this way, by adding another evaluation index, the thickness gradient at the outer periphery, in addition to cell thickness and size, it is possible to evaluate morphology that reflects the state and function of the cells in more detail.

In the cell analysis device shown in Figure 1, all processing is performed in the control and processing unit 2. However, calculating phase information based on hologram data and reconstructing images generally require a huge amount of calculation. Furthermore, the load of determining the cell state is also heavy. Therefore, a computer system can be used in which a personal computer connected to the microscope observation unit 1 serves as a terminal device, and this terminal device is connected to a high-performance computer server via a communications network such as the Internet or an intranet. The above-mentioned complex calculations and processing can be performed by the high-performance computer, while control of the microscope observation unit 1 and display processing using processed data can be performed by a relatively low-performance personal computer.

Furthermore, in the cell observation device of the above embodiment, an inline holographic microscope was used as the microscopic observation unit 1, but this can be replaced with other types of holographic microscopes, such as off-axis or phase-shifting microscopes, as long as they can obtain holograms.

Furthermore, the above-described embodiment and various modifications are merely examples of the present invention, and it is clear that further modifications, alterations, and additions made within the spirit and scope of the present invention are also encompassed within the scope of the claims.

Various aspects
It will be appreciated by those skilled in the art that the exemplary embodiments described above are examples of the following aspects.

(Item 1) One aspect of the cell evaluation method according to the present invention comprises:
a phase image acquisition step of capturing an image of a group of cells in a container using a holographic microscope to acquire a phase image of a predetermined observation range;
a cell region extraction step of extracting individual cell regions from the phase image;
an individual optical thickness acquisition step of calculating a representative value of the optical thickness for each of the cellular regions in the phase image;
an evaluation index calculation step of creating a frequency distribution of the optical thickness of cells in the observation range from the representative value of the optical thickness obtained for each cell region in the individual optical thickness acquisition step, and using the frequency distribution to calculate an evaluation index of cell thickness for a group of cells present in the observation range;
and evaluating the cell population based on the evaluation index.

(Item 8) One aspect of the cell analysis device according to the present invention is
an observation unit which is a holographic microscope;
a phase image generating unit that performs image reconstruction processing on the cell population in the container based on the hologram data obtained by the observation unit, and generates a phase image for a predetermined observation range;
a cell region extraction unit that extracts individual cell regions from the phase image;
an individual optical thickness acquisition unit that calculates a representative value of the optical thickness for each of the cellular regions in the phase image;
an evaluation index calculation unit that creates a frequency distribution of the optical thickness of cells in the observation range from the representative value of the optical thickness obtained for each cell region by the individual optical thickness acquisition unit, and calculates an evaluation index of the cell thickness for a group of cells present in the observation range using the frequency distribution;
It is equipped with the following.

The cell evaluation method described in paragraph 1 and the cell analysis device described in paragraph 8 make it possible to obtain an evaluation index for the thickness of cells as a cell population for a large number of cells being cultured in a culture vessel. Therefore, using this evaluation index, it is possible to objectively and quantitatively compare and evaluate, for example, the thickness of cells in each culture vessel. This allows for accurate comparisons and evaluations that do not rely on the subjectivity of the evaluator. Furthermore, by quantifying and indicating the thickness of cells as a cell population, it becomes possible to grasp subtle differences and changes in cell morphology that were previously unclear.

(Items 2 and 9) In the cell evaluation method described in Item 1 and the cell analysis device described in Item 8, the evaluation index for cell thickness can be the ratio of the number of cells within a predetermined range of optical thickness to the number of cells in the observation area.

The cell evaluation method described in paragraph 2 and the cell analysis device described in paragraph 9 make it possible to easily obtain an index that accurately represents the trend in cell thickness in a cell population containing a large number of cells.

(Item 3) In the method for evaluating cells according to item 1 or 2,
An individual area acquisition step of calculating the area of each cell region extracted in the cell region extraction step,
In the evaluation index calculation step, a frequency distribution of the area values of cells in the observation range can be created from the area values obtained for each cell region in the individual area acquisition step, and the frequency distribution can be used to determine an evaluation index for the cell area of a cell population present in the observation range.

(Item 10) In the cell analysis device according to item 8 or 9,
an individual area acquisition unit that calculates the area of each cell region extracted by the cell region extraction unit,
The evaluation index calculation unit can create a frequency distribution of the area values of cells in the observation range from the area values obtained for each cell region by the individual area acquisition unit, and use the frequency distribution to determine an evaluation index for the cell area of a cell population present in the observation range.

The cell evaluation method described in paragraph 3 and the cell analysis device described in paragraph 10 can provide the user with an index value for cell size in addition to an index value for cell thickness in a cell population. This enables a more multifaceted evaluation of cell morphology.

(Item 4) In the method for evaluating a cell according to any one of Items 1 to 3,
An individual area acquisition step of calculating the area of each cell region extracted in the cell region extraction step,
In the evaluation index calculation step, the optical thickness value obtained for each cell region in the individual optical thickness acquisition step and the area value obtained for each cell region in the individual area acquisition step can be used to create a scatter plot showing the relationship between the optical thickness value and the area value of each cell in the observation range.

(Item 11) In addition, in the cell analysis device according to any one of items 8 to 10,
an individual area acquisition unit that calculates the area of each cell region extracted by the cell region extraction unit,
The evaluation index calculation unit can use the optical thickness value obtained for each cell region by the individual optical thickness acquisition unit and the area value obtained for each cell region by the individual area acquisition unit to create a scatter plot showing the relationship between the optical thickness value and area value of each cell in the observation range.

The cell evaluation method described in paragraph 4 and the cell analysis device described in paragraph 11 make it possible to visually grasp the relationship between the thickness and size of individual cells contained in a cell population.

(5) In the cell evaluation method described in 4, the evaluation index calculation step may involve creating a heat map from the scatter plot that shows the density of points in two dimensions.

(Item 12) In the cell analysis device described in Item 11, the evaluation index calculation unit may create a heat map that two-dimensionally shows the density of points from the scatter plot.

The cell evaluation method described in paragraph 5 and the cell analysis device described in paragraph 12 make it possible to more accurately grasp the degree of imbalance in the relationship between the thickness and size of individual cells contained in a cell population.

(Item 6) In the method for evaluating a cell according to any one of Items 1 to 5,
a differential image creating step of calculating a spatial differential value for each pixel from the phase image to create a differential image;
a second index value calculation step of creating a histogram showing an appearance frequency distribution of signal values of each pixel included in a range corresponding to the target cell in the differential image, and calculating an index value that quantifies the gradient of a slope on the high signal value side of a peak appearing in the histogram;
The above structure may further include:

(Item 13) In addition, in the cell analysis device according to any one of Items 8 to 12,
a differential image creating unit that calculates a spatial differential value for each pixel from the phase image and creates a differential image;
The image processing device may further include a second index value calculation unit that creates a histogram showing the frequency distribution of signal values of each pixel included in a range corresponding to a target cell in the differential image, and calculates an index value that quantifies the gradient of the slope on the high signal value side of a peak appearing in the histogram.

The cell evaluation method described in paragraph 6 and the cell analysis device described in paragraph 13 can provide the user with index values representing the thickness and size of cells in a cell population, as well as index values representing the gradient of the thickness of the outer periphery of the cells. This enables more multifaceted evaluation of cell morphology.

(Item 7) In the cell evaluation method described in any one of Items 1 to 6, the phase image acquisition step can acquire a phase image by performing a correction process to correct pixel values that have been inverted because their values exceed a predetermined phase range.

(Item 14) In the cell analysis device described in any one of Items 8 to 13, the phase image creation unit can acquire a phase image by performing a correction process to correct pixel values that have been inverted because their values exceed a predetermined phase range.

The cell evaluation method described in paragraph 7 and the cell analysis device described in paragraph 14 make it possible to obtain appropriate pixel values through correction processing and evaluate the thickness of a cell, even when the cell is so thick that it exceeds the phase range of the optical thickness observable with a holographic microscope.

DESCRIPTION OF SYMBOLS 1...Microscopic observation section 10...Light source section 11...Image sensor 12...Culture plate 12a...Well 13...Cell 14...Reference light 15...Object light 2...Control/processing section 20...Photography control section 21...Hologram data storage section 22...Phase information calculation section 23...Image reconstruction section 24...Phase inversion correction section 25...Cell region extraction section 26...Optical thickness/area calculation section 27...Evaluation index calculation section 28...Display processing section 3...Input section 4...Display section

Claims (10)

a phase image acquisition step of capturing an image of a group of cells in a container using a holographic microscope to acquire a phase image of a predetermined observation range;
a cell region extraction step of extracting individual cell regions from the phase image;
an individual optical thickness acquisition step of calculating a representative value of the optical thickness for each of the cellular regions in the phase image;
an individual area acquisition step of calculating an area for each of the cell regions extracted in the cell region extraction step;
an evaluation index calculation step of creating a frequency distribution of the optical thickness of cells in the observation range from the representative value of the optical thickness obtained for each cell region in the individual optical thickness acquisition step, and using the frequency distribution to determine the ratio of the number of cells included in a predetermined range of optical thickness to the number of cells in the observation range as an evaluation index of cell thickness for a group of cells present in the observation range, and creating a frequency distribution of the area values of cells in the observation range from the area values obtained for each cell region in the individual area acquisition step, and using the frequency distribution to determine an evaluation index of cell area for a group of cells present in the observation range;
The cell evaluation method comprises: evaluating the cell population based on the evaluation index for cell thickness and the evaluation index for cell area. 2. The cell evaluation method according to claim 1, wherein the evaluation index calculation step uses the optical thickness value obtained for each cell region in the individual optical thickness acquisition step and the area value obtained for each cell region in the individual area acquisition step to create a scatter plot showing the relationship between the optical thickness value and the area value of each cell in the observation range. The cell evaluation method according to claim 2 , wherein the evaluation index calculation step creates a heat map that two-dimensionally shows the density of points from the scatter diagram. a differential image creating step of calculating a spatial differential value for each pixel from the phase image to create a differential image;
a second index value calculation step of creating a histogram showing an appearance frequency distribution of signal values of each pixel included in a range corresponding to the target cell in the differential image, and calculating an index value that quantifies the gradient of a slope on the high signal value side of a peak appearing in the histogram;
The method for evaluating a cell according to any one of claims 1 to 3 , further comprising: The cell evaluation method according to any one of claims 1 to 4 , wherein in the phase image acquisition step, a phase image is acquired by performing a correction process to correct pixel values that have been inverted because their values have exceeded a predetermined phase range. an observation unit which is a holographic microscope;
a phase image generating unit that performs image reconstruction processing on the cell population in the container based on the hologram data obtained by the observation unit, and generates a phase image for a predetermined observation range;
a cell region extraction unit that extracts individual cell regions from the phase image;
an individual optical thickness acquisition unit that calculates a representative value of the optical thickness for each of the cellular regions in the phase image;
an individual area acquisition unit that calculates an area for each of the cellular regions extracted by the cellular region extraction unit;
an evaluation index calculation unit that creates a frequency distribution of the optical thickness of cells in the observation range from the representative value of the optical thickness obtained for each cell region by the individual optical thickness acquisition unit, and uses the frequency distribution to determine the ratio of the number of cells included in a predetermined range of optical thickness to the number of cells in the observation range as an evaluation index of cell thickness for a group of cells present in the observation range, and creates a frequency distribution of the area values of cells in the observation range from the area values obtained for each cell region by the individual area acquisition unit, and uses the frequency distribution to determine an evaluation index of cell area for a group of cells present in the observation range;
A cell analysis device comprising: 7. The cell analysis device according to claim 6 , wherein the evaluation index calculation unit uses the optical thickness value obtained for each cell region by the individual optical thickness acquisition unit and the area value obtained for each cell region by the individual area acquisition unit to create a scatter plot showing the relationship between the optical thickness value and the area value of each cell in the observation range . The cell analysis device according to claim 7 , wherein the evaluation index calculation unit creates a heat map that two-dimensionally indicates the density of points from the scatter diagram. a differential image creating unit that calculates a spatial differential value for each pixel from the phase image and creates a differential image;
The cell analysis device according to any one of claims 6 to 8, further comprising: a second index value calculation unit that creates a histogram showing an appearance frequency distribution of signal values of each pixel included in a range corresponding to a target cell in the differential image, and calculates an index value that quantifies the gradient of a slope on a high signal value side of a peak appearing in the histogram. The cell analysis device according to any one of claims 6 to 9, wherein the phase image creation unit acquires a phase image by performing a correction process to correct pixel values that have been inverted because their values have exceeded a predetermined phase range.