JP5848885B2 - Chemical screening method - Google Patents

Description

  The present invention relates to a method for screening chemical substances that cause morphological changes in cells.

  Conventionally, a simple system has been used for high-throughput screening of physiologically active substances using cells. For example, many of the screening methods for low molecular weight compounds exhibiting an anticancer effect include methods for quantifying the number of viable cells in one well and calculating the survival rate, such as an MTT (methyl thiazol tetrazorium) assay and an alamarBlue assay. It was done.

  In recent years, screening methods for drug discovery targets and screening methods for low molecular weight compounds have become more complex with the development of instruments and imaging techniques. Microscope technology has been applied to screening, and a plurality of parameters have been analyzed from one type of cell. Such a screening method is called High content screening (hereinafter referred to as HCS) (see Non-Patent Document 1).

  HCS is a screening method that uses multiple phenomena in a cell as an index by multiple fluorescent staining of a plurality of targets in the cell and analyzing these fluorescences simultaneously. According to this method, by showing a physiologically active substance that acts on a specific function of a cell, it is possible to screen for those that show medicinal effects. Therefore, HCS is superior from the viewpoint of eliminating side effects, off-targets, and the like, compared to a screening method using only toxicity as an index.

  HCS is suitably used for searching for drugs that cause morphological changes in cells or for genes involved in morphological changes. The change in cell morphology is observed, for example, by allowing a drug or the like to act on a molecule involved in the cytoskeleton or a molecule involved in the division phase in the cell cycle (see Non-Patent Document 2 and Non-Patent Document 3).

Krausz, Mol. BioSystem. Vol.3, pp.232-240, 2007 Kiger et al., J Biol, Volume 2, 27.1-27.15, 2003 Neumann et al., Nat Methods. , Volume 3, 385-390, 2006

The screening method for molecules involved in morphological changes listed in Non-Patent Document 2 is a method for fixing cells and fluorescent staining. Since fluorescent staining requires a certain cost, there is a problem in cost when using a huge number of low molecular compound libraries and siRNA libraries.
In addition, uneven staining that occurs during the staining process may affect the screening results. Furthermore, since it is necessary to immobilize cells in order to introduce fluorescent dyes into cells, it is troublesome to increase the number of samples in order to analyze the passage of time.

On the other hand, in Non-Patent Document 3, GFP (
There is a method of preparing a cell line in which Histone cells are stably expressed in HeLa cells fused with Green Fluorescent Protein) and performing time-lapse analysis using the behavior of the introduced fluorescent protein as an index.
However, since it is essential to produce a stable expression strain, this method cannot use cells that are not suitable for gene transfer for screening, and is not suitable for the case of using many types of cells for screening.

  The present invention has been made in view of the above circumstances, and provides a method for screening a chemical substance that causes a morphological change in cells, which enables inexpensive and rapid analysis without fixing and staining the cells. For the purpose.

In order to solve the above-described problems, the present inventors have conducted extensive research and found that the problems can be solved by using an analysis apparatus having a configuration described later. That is, the present invention provides the following (1) to (13).
(1) The method for screening a chemical substance of the present invention is a method for screening a chemical substance that causes morphological changes in cells,
(A) contacting a cell with a first chemical substance to be screened;
(B) reading a plurality of images taken in time series of cells brought into contact with the first chemical substance;
(C) obtaining a feature value indicating a morphological feature of each cell included in the image in (b) from the image;
(D) obtaining statistical processing data that is a frequency distribution of the feature values corresponding to each of the images; and (e) changing the statistical processing data between the images based on the first chemistry. Generating evaluation information for evaluating a morphological change of a cell contacted with a substance;
It is characterized by including.
(2) In the method for screening a chemical substance of the present invention, after (e),
(F) contacting the cell contacted with the first chemical substance with the first chemical substance or an additional chemical substance to be screened that does not correspond to the first chemical substance;
(G) a step of reading a plurality of images obtained by imaging the cells contacted with the additional chemical substance in time series;
(H) obtaining a feature value indicating a morphological feature of each cell included in the image in (g) from the image;
(I) obtaining statistical processing data that is a frequency distribution of the feature values corresponding to each of the images; and (j) changing the statistical processing data between the images based on the change of the statistical processing data. Generating evaluation information for evaluating the morphological change of the contacted cells;
(K) determining whether to repeat (f) to (j) based on the evaluation information in the cell contacted with the additional chemical substance;
Preferably, the method includes a step of repeating at least once .
(3 ) In the method for screening a chemical substance of the present invention, the first chemical substance and the additional chemical substance are each independently a low molecular compound, antibody, protein, peptide, antisense nucleic acid, ribozyme, shRNA, and siRNA. It is preferably at least one selected from the group consisting of
( 4 ) In the chemical substance screening method of the present invention, the first chemical substance is preferably siRNA.
( 5 ) In the chemical substance screening method of the present invention, the first chemical substance is preferably a low-molecular compound.
( 6 ) In the chemical substance screening method of the present invention, it is preferable that (e) and (j) are steps for generating the evaluation information using a difference in the frequency distribution between the images. .

(8) In the method for screening a chemical substance of the present invention, (c) and (h) are steps for obtaining a plurality of types of feature values for each of the images, and (d) and (i) Is a step of obtaining the frequency distribution for each type of the feature value, and (e) and (j) generate the evaluation information using information on a plurality of types of changes in the frequency distribution. It is preferable that it is a process.
(9) In the chemical substance screening method of the present invention, (d) and (i) respectively normalize the frequency classification in each frequency distribution using the standard deviation for each type of the characteristic value. It is preferable that it is a process to perform.
(10) In the method for screening a chemical substance of the present invention, the (e) and the (j) are the combinations of the frequency distributions in which the image capturing time and the characteristic value are different from each other. It is preferable that the step of extracting a combination of the frequency distributions used for generating the evaluation information and obtaining a calculation model of the evaluation information.
(11) In the chemical substance screening method of the present invention, it is preferable that (e) and (j) are steps for obtaining the calculation model by supervised learning.
(12) The method for producing a chemical substance of the present invention or a pharmaceutical composition containing the chemical substance is a method for producing a chemical substance that causes morphological changes in cells or a pharmaceutical composition containing the chemical substance,
(L) selecting a chemical substance that causes morphological changes in cells by the screening method described above;
(M) producing a selected chemical substance or a pharmaceutical composition comprising said chemical substance;
It is characterized by including.
(13) The method for producing a chemical substance of the present invention or a pharmaceutical composition containing the chemical substance is a method for producing a chemical substance or a pharmaceutical composition containing the chemical substance that causes morphological changes in cells,
(L) selecting a chemical substance that causes morphological changes in cells by the screening method described above;
(N) optimizing the structure of the selected chemical substance;
(O) producing an optimized chemical or a pharmaceutical composition comprising said chemical;
It is characterized by including.

ADVANTAGE OF THE INVENTION According to this invention, the screening method of the chemical substance which causes a morphological change to a cell which enables a cheap and quick analysis without fixing and dye | staining a cell can be provided.
Since the screening method of the chemical substance of the present invention is a non-destructive system, it can be suitably used for time-lapse analysis, and the sample itself used for screening can be used for another evaluation system, and the screening result can be used for another system. Can be evaluated from.

It is a block diagram which shows the outline | summary of the incubator used for this invention. It is a front view of the incubator used for the present invention. It is a top view of the incubator used for the present invention. It is a flowchart which shows an example of observation operation | movement with an incubator. It is a flowchart which shows an example of the production | generation process of a calculation model. It is a figure which shows the outline | summary of each feature value. (A)-(c): Histogram showing an example of change over time of “Shape Factor”, (d)-(f): Histogram showing an example of change over time of “Fiber Length”. (A)-(c): It is a figure which shows the microscope image which observed the time-lapse observation of the normal cell group. (A)-(c): It is a figure which shows the microscope image which observed the time-lapse observation of the cell group which mixed the cancer cell in the normal cell. It is a figure which shows the outline | summary of ANN. It is a figure which shows the outline | summary of FNN. It is a figure which shows the sigmoid function in FNN. It is a flowchart which shows the operation example of a culture state evaluation process. It is the flowchart figure which showed the one aspect | mode of the screening method of this invention. It is the flowchart figure which showed the one aspect | mode of the screening method of this invention, and the conventional screening method. It is the flowchart figure which showed the one aspect | mode of the screening method of this invention. It is the histogram which shows the time-dependent change of the microscopic image which observed the time-lapse observation of the normal cell in the Example or the cell which processed siRNA which targets p53, and the "Length" analyzed based on these images. It is the graph which showed the relative value when 0 hour is set to 1 about the expression level of mRNA of p53 and RhoA after each time of the cell which processed siRNA which targets p53 in an Example. It is the microscope image which observed the time-lapse observation of the cell which processed siRNA which targets RhoA in an Example, and negative control. It is the microscope image which observed the time-lapse observation of the cell which processed siRNA which targets RhoA in an Example, and negative control. It is the histogram which shows the time-dependent change of the "roundness" analyzed based on the microscopic image which performed the time-lapse observation of the cell which processed siRNA which targets RhoA in an Example, or negative control.

≪Culture state evaluation device≫
In the method for screening a chemical substance of the present invention, the steps (b) to (e) and (g) to (k) after contacting a chemical substance that causes morphological changes in cells are cultures incorporating these steps. This is performed using a state evaluation device. Therefore, first, an incubator including the culture state evaluation apparatus will be described.

  FIG. 1 is a block diagram showing an outline of an incubator used in the present invention. 2 and 3 are a front view and a plan view of the incubator used in the present invention.

  The incubator 11 used in the present invention has an upper casing 12 and a lower casing 13. In the assembled state of the incubator 11, the upper casing 12 is placed on the lower casing 13. Note that the internal space between the upper casing 12 and the lower casing 13 is vertically divided by a base plate 14.

  First, an outline of the configuration of the upper casing 12 will be described. A constant temperature chamber 15 for culturing cells is formed inside the upper casing 12. The temperature-controlled room 15 includes a temperature adjusting device 15a and a humidity adjusting device 15b, and the temperature-controlled room 15 is maintained in an environment suitable for cell culture (for example, an atmosphere having a temperature of 37 ° C. and a humidity of 90%) ( Note that illustration of the temperature adjusting device 15a and the humidity adjusting device 15b in FIGS. 2 and 3 is omitted).

  A large door 16, a middle door 17 and a small door 18 are arranged on the front surface of the temperature-controlled room 15. The large door 16 covers the front surfaces of the upper casing 12 and the lower casing 13. The middle door 17 covers the front surface of the upper casing 12 and isolates the environment between the temperature-controlled room 15 and the outside when the large door 16 is opened. The small door 18 is a door for carrying in and out a culture vessel 19 for culturing cells, and is attached to the middle door 17. It is possible to suppress environmental changes in the temperature-controlled room 15 by carrying the culture container 19 in and out of the small door 18. The large door 16, the middle door 17, and the small door 18 are maintained airtight by the packings P1, P2, and P3, respectively.

  In the temperature-controlled room 15, a stocker 21, an observation unit 22, a container transfer device 23, and a transfer table 24 are arranged. Here, the conveyance stand 24 is disposed in front of the small door 18, and carries the culture container 19 in and out of the small door 18.

  The stocker 21 is disposed on the left side of the temperature-controlled room 15 when viewed from the front surface of the upper casing 12 (the lower side in FIG. 3). The stocker 21 has a plurality of shelves, and each shelf of the stocker 21 can store a plurality of culture vessels 19. Each culture container 19 contains cells to be cultured together with a medium.

  The observation unit 22 is disposed on the right side of the temperature-controlled room 15 when viewed from the front surface of the upper casing 12. The observation unit 22 can execute time-lapse observation of cells in the culture vessel 19.

  Here, the observation unit 22 is disposed by being fitted into the opening of the base plate 14 of the upper casing 12. The observation unit 22 includes a sample stage 31, a stand arm 32 protruding above the sample stage 31, and a main body portion 33 containing a microscopic optical system for phase difference observation and an imaging device (34). The sample stage 31 and the stand arm 32 are disposed in the temperature-controlled room 15, while the main body portion 33 is accommodated in the lower casing 13.

  The sample stage 31 is made of a translucent material, and the culture vessel 19 can be placed thereon. The sample stage 31 is configured to be movable in the horizontal direction, and the position of the culture vessel 19 placed on the upper surface can be adjusted. The stand arm 32 includes an LED light source 35. And the imaging device 34 can acquire the microscope image of a cell by imaging the cell of the culture container 19 permeate | transmitted and illuminated by the stand arm 32 from the upper side of the sample stand 31 via a microscopic optical system.

  The container transport device 23 is disposed in the center of the temperature-controlled room 15 when viewed from the front surface of the upper casing 12. The container transport device 23 delivers the culture container 19 between the stocker 21, the sample table 31 of the observation unit 22, and the transport table 24.

  As shown in FIG. 3, the container transport device 23 includes a vertical robot 34 having an articulated arm, a rotary stage 35, a mini stage 36, and an arm unit 37. The rotary stage 35 is attached to the tip of the vertical robot 34 via a rotary shaft 35a so as to be capable of rotating 180 ° in the horizontal direction. Therefore, the rotary stage 35 can make the arm portions 37 face the stocker 21, the sample table 31, and the transport table 24.

  The mini stage 36 is attached to the rotation stage 35 so as to be slidable in the horizontal direction. An arm part 37 that holds the culture vessel 19 is attached to the mini stage 36.

  Next, the outline of the configuration of the lower casing 13 will be described. Inside the lower casing 13, the main body portion 33 of the observation unit 22 and the control device 41 of the incubator 11 are accommodated.

  The control device 41 is connected to the temperature adjustment device 15a, the humidity adjustment device 15b, the observation unit 22, and the container transport device 23, respectively. The control device 41 comprehensively controls each part of the incubator 11 according to a predetermined program.

  As an example, the control device 41 controls the temperature adjustment device 15a and the humidity adjustment device 15b, respectively, to maintain the inside of the temperature-controlled room 15 at a predetermined environmental condition. The control device 41 controls the observation unit 22 and the container transport device 23 based on a predetermined observation schedule, and automatically executes the observation sequence of the culture vessel 19. Furthermore, the control device 41 executes a culture state evaluation process for evaluating the culture state of the cells based on the image acquired in the observation sequence.

  Here, the control device 41 includes a CPU 42 and a storage unit 43. The CPU 42 is a processor that executes various arithmetic processes of the control device 41. The CPU 42 functions as a feature value calculation unit 44, a frequency distribution calculation unit 45, and an evaluation information generation unit 46 by executing the program (note that the feature value calculation unit 44, the frequency distribution calculation unit 45, and the evaluation information generation unit). The operation 46 will be described later).

  The storage unit 43 is configured by a hard disk, a nonvolatile storage medium such as a flash memory, or the like. The storage unit 43 stores management data related to each culture vessel 19 stored in the stocker 21 and data of a microscope image captured by the imaging device. Further, the storage unit 43 stores a program executed by the CPU 42.

  The management data includes (a) index data indicating individual culture containers 19, (b) the storage position of the culture container 19 in the stocker 21, and (c) the type and shape of the culture container 19 (well plate, (Dish, flask, etc.), (d) type of cells cultured in culture vessel 19, (e) observation schedule of culture vessel 19, (f) imaging conditions during time-lapse observation (magnification of objective lens, observation in vessel) Point, etc.). In addition, for the culture container 19 that can simultaneously culture cells in a plurality of small containers such as a well plate, management data is generated for each small container.

≪Example of observation operation in culture state evaluation device≫
Next, an example of the observation operation in the incubator 11 in the culture state evaluation apparatus used in the present invention will be described with reference to the flowchart of FIG. FIG. 4 shows an operation example in which the culture container 19 carried into the temperature-controlled room 15 is time-lapse observed according to a registered observation schedule.

  Step S101: The CPU 42 compares the observation schedule of the management data in the storage unit 43 with the current date and time to determine whether or not the observation start time of the culture vessel 19 has come. When it is the observation start time (YES side), the CPU 42 shifts the process to S102. On the other hand, when it is not the observation time of the culture vessel 19 (NO side), the CPU 42 waits until the next observation schedule time.

  Step S102: The CPU 42 instructs the container transport device 23 to transport the culture container 19 corresponding to the observation schedule. Then, the container transport device 23 carries the instructed culture container 19 out of the stocker 21 and places it on the sample stage 31 of the observation unit 22. Note that, when the culture vessel 19 is placed on the sample stage 31, an entire observation image of the culture vessel 19 is captured by a bird view camera (not shown) built in the stand arm 32.

  Step S103: The CPU 42 instructs the observation unit 22 to take a microscopic image of the cell. The observation unit 22 turns on the LED light source 35 to illuminate the culture vessel 19 and drives the imaging device 34 to take a microscopic image of the cells in the culture vessel 19.

  At this time, the imaging device 34 captures a microscopic image based on the imaging conditions specified by the user (magnification of the objective lens, observation point in the container) based on the management data stored in the storage unit 43. For example, when observing a plurality of points in the culture container 19, the observation unit 22 sequentially adjusts the position of the culture container 19 by driving the sample stage 31 and picks up a microscope image at each point. The microscopic image data acquired in S103 is read by the control device 41 and recorded in the storage unit 43 under the control of the CPU.

  Step S104: The CPU 42 instructs the container transport device 23 to transport the culture container 19 after the observation schedule is completed. Then, the container transport device 23 transports the designated culture container 19 from the sample stage 31 of the observation unit 22 to a predetermined storage position of the stocker 21. Thereafter, the CPU 42 ends the observation sequence and returns the process to S101. Above, description of the flowchart of FIG. 4 is complete | finished.

≪Culture state evaluation process in culture state evaluation device≫
Next, an example of the culture state evaluation process in the culture state evaluation apparatus will be described. In this example, an example will be described in which the control device 41 estimates the mixing ratio of cancer cells in the cultured cells in the culture container 19 using a plurality of microscope images acquired by time-lapse observation of the culture container 19.

  The control device 41 in the culture state evaluation process obtains a frequency distribution of feature values indicating the morphological features of the cells from the above-described microscopic image. And the control apparatus 41 produces | generates the evaluation information which estimated the mixing rate of the cancer cell based on the time-dependent change of this frequency distribution. The control device 41 generates the evaluation information by applying the obtained evaluation information to a calculation model generated in advance by supervised learning.

≪Example of calculation model generation process≫
Hereinafter, an example of a calculation model generation process that is a preprocess of the culture state evaluation process will be described with reference to the flowchart of FIG. In this calculation model generation process, the control device 41 determines a combination of frequency distributions used for generating evaluation information from combinations of a plurality of frequency distributions having different image capturing times and feature value types.

  In the example of FIG. 5, a culture vessel 19 in which a group of cancer cells is cultured is time-lapse-observed under the same imaging conditions with the same field of view by an incubator 11, and a microscope image group of samples is prepared in advance. In the microscopic image of the above sample, the total number of cells and the number of cancer cells included in each image are known experimentally, not from the image.

  In the example of FIG. 5, the time-lapse observation of the culture vessel 19 is performed every 8 hours until the 72nd hour after the first 8 hours from the start of the culture. Therefore, in the example of FIG. 5, nine sets of microscope images (8h, 16h, 24h, 32h, 40h, 48h, 56h, 64h, 72h) of samples having the same culture container 19 and observation point are acquired as one set. Will be. In the example of FIG. 5, it is assumed that a plurality of sets of microscope images of the above samples are prepared by time-lapse observation of the plurality of culture vessels 19. Moreover, you may make it handle the some microscope image which image | photographed several points (for example, 5 points | piece observation or the whole culture container 19) of the same culture container 19 in the same observation time zone as the image for one time lapse observation.

  Step S <b> 201: The CPU 42 reads from the storage unit 43 microscopic image data of a sample prepared in advance. Note that the CPU 42 in S201 also acquires information indicating the total number of cells and the number of cancer cells corresponding to each image at this time.

  Step S202: The CPU 42 designates an image to be processed from the microscopic image (S201) of the sample. Here, it is assumed that the CPU 42 in S202 sequentially designates all of the sample microscope images prepared in advance as processing targets.

  Step S203: The feature value calculation unit 44 extracts cells included in the processing target microscope image (S202). For example, when a cell is imaged with a phase-contrast microscope, a halo appears in the vicinity of a region with a large phase difference change such as a cell wall. Therefore, the feature value calculation unit 44 extracts the halo corresponding to the cell wall by a known edge extraction method, and estimates the closed space surrounded by the edges by the contour tracking process as a cell. Thereby, individual cells can be extracted from the above-mentioned microscopic image.

  Step S204: The feature value calculation unit 44 obtains a feature value indicating a morphological feature of the cell for each cell extracted from the image in S203. The feature value calculation unit 44 in S204 obtains the following 16 types of feature values for each cell.

・ Total area (see (a) of FIG. 6)
“Total area” is a value indicating the area of the cell of interest. For example, the feature value calculation unit 44 can obtain the value of “Total area” based on the number of pixels in the cell area of interest.

・ Hole area (see FIG. 6B)
“Hole area” is a value indicating the area of Hole in the cell of interest. Here, Hole refers to a portion where the brightness of the image in the cell is equal to or greater than a threshold value due to contrast (a portion that is close to white in phase difference observation). For example, stained lysosomes of intracellular organelles are detected as Hole. Further, depending on the image, a cell nucleus and other organelles can be detected as Hole. The feature value calculation unit 44 may detect a group of pixels in which the luminance value in the cell is equal to or greater than the threshold as Hole, and obtain the value of “Hole area” based on the number of pixels of the Hole.

・ Relative hole area (see (c) of FIG. 6)
“Relative hole area” is a value obtained by dividing the value of “Hole area” by the value of “Total area” (relative hole area = Hole area / Total area). The “relative hole area” is a parameter indicating the ratio of the organelle in the cell size, and the value varies depending on, for example, enlargement of the organelle or deterioration of the shape of the nucleus.

・ Perimeter (see (d) of FIG. 6)
“Perimeter” is a value indicating the length of the outer periphery of the cell of interest. For example, the feature value calculation unit 44 can acquire the value of “Perimeter” by contour tracking processing when extracting cells.

Width (see (e) of FIG. 6)
“Width” is a value indicating the length of the cell of interest in the horizontal direction (X direction) of the image.

・ Height (see (f) of FIG. 6)
“Height” is a value indicating the length of the cell of interest in the image vertical direction (Y direction).

-Length (see (g) of FIG. 6)
“Length” is a value indicating the maximum value (the total length of the cell) of the lines crossing the cell of interest.

・ Breadth (see (h) of FIG. 6)
“Breadth” is a value indicating the maximum value (lateral width of the cell) of lines orthogonal to “Length”.

・ Fiber Length (see (i) of FIG. 6)
“Fiber Length” is a value indicating the length when the target cell is assumed to be pseudo linear. The feature value calculation unit 44 calculates the value of “Fiber Length” by the following equation (1).

However, in the formula of this specification, “P” indicates the value of Perimeter. Similarly, “A” indicates the value of Total Area.

・ Fiber Breath (see (j) of FIG. 6)
“Fiber Breath” is a value indicating a width (a length in a direction orthogonal to Fiber Length) when the target cell is assumed to be pseudo linear. The feature value calculation unit 44 obtains a value of “Fiber Breath” by the following equation (2).

・ Shape Factor (see (k) of FIG. 6)
“Shape Factor” is a value indicating the circularity of the cell of interest (cell roundness). The feature value calculation unit 44 calculates the value of “Shape Factor” by the following equation (3).

Elliptical form factor (see (l) in FIG. 6)
“Elliptical form factor” is a value obtained by dividing the value of “Length” by the value of “Breadth” (Elliptical form Factor = Length / Breadth), and is a parameter indicating the degree of length of a cell of interest.

・ Inner radius (see (m) in FIG. 6)
“Inner radius” is a value indicating the radius of the inscribed circle of the cell of interest.

・ Outer radius (see (n) of FIG. 6)
“Outer radius” is a value indicating the radius of the circumscribed circle of the cell of interest.

・ Mean radius (see (o) of FIG. 6)
“Mean radius” is a value indicating the average distance between all the points constituting the outline of the cell of interest and its center of gravity.

Equivalent radius (see (p) of FIG. 6)
“Equivalent radius” is a value indicating the radius of a circle having the same area as the cell of interest. The parameter “Equivalent radius” indicates the size when the cell of interest is virtually approximated to a circle.

  Here, the feature value calculation unit 44 may obtain each of the feature values by adding an error to the number of pixels corresponding to the cell. At this time, the feature value calculation unit 44 may obtain the feature value in consideration of imaging conditions of the microscope image (imaging magnification, aberration of the microscopic optical system, etc.). Note that when calculating “Inner radius”, “Outer radius”, “Mean radius”, and “Equivalent radius”, the feature value calculation unit 44 calculates the center of gravity of each cell based on a known center of gravity calculation method. What is necessary is just to obtain | require each parameter on the basis of a gravity center point.

  Step S205: The feature value calculation unit 44 records 16 types of feature values (S204) of each cell in the storage unit 43 for the microscope image (S202) to be processed.

  Step S206: The CPU 42 determines whether or not all the microscopic images have been processed (a state in which the feature values of each cell have been acquired in the microscopic images of all the samples). If the above requirement is satisfied (YES side), the CPU 42 shifts the process to S207. On the other hand, when the above requirement is not satisfied (NO side), the CPU 42 returns to S202 and repeats the above operation with another unprocessed microscope image as a processing target.

  Step S207: The frequency distribution calculation unit 45 obtains a frequency distribution of feature values for each feature value type for each microscope image. Therefore, the frequency distribution calculation unit 45 in S207 obtains the frequency distribution of 16 types of feature values for the microscope image acquired by one observation. In each frequency distribution, the number of cells corresponding to each category of feature values is obtained as a frequency (%).

  Further, the frequency distribution calculation unit 45 in S207 normalizes the frequency classification in the frequency distribution using the standard deviation for each type of feature value. Here, as an example, a case will be described in which a division in the frequency distribution of “Shape Factor” is determined.

  First, the frequency distribution calculation unit 45 calculates the standard deviation of all “Shape Factor” values obtained from the respective microscopic images. Next, the frequency distribution calculation unit 45 substitutes the value of the above standard deviation into the Fisher equation to obtain a reference value for the frequency classification in the frequency distribution of “Shape Factor”. At this time, the frequency distribution calculation unit 45 divides the standard deviation (S) of all “Shape Factor” values by 4, and rounds off the third decimal place to obtain the above-described reference value. In addition, the frequency distribution calculation part 45 in one Embodiment makes a monitor etc. draw the area for 20 series, when frequency distribution is illustrated as a histogram.

  As an example, when the standard deviation S of “Shape Factor” is 259, the reference value is “64.75” from 259/4 = 64.750. Then, when calculating the frequency distribution of the “Shape Factor” of the image of interest, the frequency distribution calculation unit 45 classifies the cells into each class set from 0 to 64.75 in accordance with the value of “Shape Factor”. Then, the number of cells in each class may be counted.

  As described above, since the frequency distribution calculation unit 45 normalizes the frequency distribution section with the standard deviation for each type of feature value, the tendency of the frequency distribution can be roughly approximated between different feature values. Therefore, in one embodiment, it is relatively easy to obtain a correlation between a cell culture state and a change in frequency distribution between different feature values.

  Here, FIG. 7A is a histogram showing a change with time of “Shape Factor” when the initial mixing ratio of cancer cells is 0%. FIG. 7B is a histogram showing the change with time of “Shape Factor” when the initial mixing ratio of cancer cells is 6.7%. FIG. 7C is a histogram showing the change over time of “Shape Factor” when the initial mixing ratio of cancer cells is 25%.

  FIG. 7D is a histogram showing the change with time of “Fiber Length” when the initial mixing ratio of cancer cells is 0%. However, in this figure, for ease of understanding, the histogram is omitted up to Fiber Length = 323. FIG. 7E is a histogram showing the change with time of “Fiber Length” when the initial mixing ratio of cancer cells is 6.7%. FIG. 7 (f) is a histogram showing the change with time of “Fiber Length” when the initial mixing ratio of cancer cells is 25%.

  Step S208: The evaluation information generation unit 46 obtains the change over time of the frequency distribution for each type of feature value.

  The evaluation information generation unit 46 in S208 combines two frequency distributions having the same feature value type and different shooting times from the frequency distributions (9 × 16) acquired from one set of microscope images. As an example, for the nine frequency distributions having the same feature value type and different shooting times, the evaluation information generation unit 46 includes the frequency distributions at the 8th and 16th hours, and the frequency distribution at the 8th and 24th hours. , Frequency distribution at 8th and 32nd hours, frequency distribution at 8th and 40th hours, frequency distribution at 8th and 48th hours, frequency distribution at 8th and 56th hours, 8 The frequency distribution at the time and the 64th hour is combined with the frequency distribution at the 8th and 72th hours, respectively. That is, when attention is paid to one type of feature value in one set, a total of eight combinations are generated for the frequency distribution of the feature value.

  Then, the evaluation information generation unit 46 obtains the amount of change in the frequency distribution (integrated absolute value of the difference in the frequency distribution between the images) for each of the above eight combinations using the following equation (4).

  However, in Expression (4), “Control” indicates the frequency (number of cells) for one section in the initial frequency distribution (8th hour). “Sample” indicates the frequency of one section in the frequency distribution to be compared. “I” is a variable indicating a frequency distribution interval.

The evaluation information generation unit 46 can acquire eight types of change amounts of the frequency distribution for all feature values by performing the above calculation for each type of feature value. In other words, for a set of microscope images, combinations of 16 frequency distributions of 128 types × 8 can be taken.
Hereinafter, in the present specification, one of the combinations of the frequency distributions is simply referred to as “index”. Needless to say, the evaluation information generation unit 46 in S208 obtains the amount of change in the 128 frequency distribution corresponding to each index in a set of a plurality of microscope images.

  Here, the reason why attention is paid to the change over time in the frequency distribution will be described. FIG. 8 shows microscopic images when normal cell groups (initial mixing ratio of cancer cells 0%) are cultured in the incubator 11 and time-lapse observed. In addition, the histogram which shows the frequency distribution of "Shape Factor" calculated | required from each microscope image of FIG. 8 is shown to Fig.7 (a).

  In this case, in each image of FIG. 8, the number of cells increases with time, but in the “Shape Factor” histogram shown in FIG. 7A, the frequency distribution corresponding to each image is almost the same. It keeps its shape.

  On the other hand, FIG. 9 shows a microscopic image when a normal cell group previously mixed with 25% of cancer cells is cultured in the incubator 11 and time-lapse observed. In addition, the histogram which shows the frequency distribution of "Shape Factor" calculated | required from each microscope image of FIG. 9 is shown in FIG.7 (c).

  In this case, in each image of FIG. 9, the ratio of cancer cells (rounded cells) having different shapes from normal cells increases with time. Thereby, in the “ShapeFactor” histogram shown in FIG. 7C, a large change appears in the shape of the frequency distribution corresponding to each image as time passes. Therefore, it turns out that the time-dependent change of frequency distribution has a strong correlation with mixing of cancer cells. Based on the above findings, the present inventors evaluate the state of cultured cells from information on changes over time in the frequency distribution.

  Step S209: The evaluation information generation unit 46 designates one or more indices out of 128 indices that well reflect the cell culture state by multivariate analysis. In addition to the nonlinear model equivalent to a fuzzy neural network described later, the use of a linear model is also effective in selecting the combination of indices according to the complexity of the cell and its form. Along with the selection of such a combination of indices, the evaluation information generation unit 46 obtains a calculation model for deriving the number of cancer cells from the microscope image using one or more of the designated indices.

  Here, the evaluation information generation unit 46 in S209 obtains the above calculation model by fuzzy neural network (FNN) analysis.

  FNN is a method in which an artificial neural network (ANN) and fuzzy inference are combined. In this FNN, an ANN is incorporated in fuzzy inference to automatically determine the membership function, which is a drawback of fuzzy inference. ANN (see FIG. 10), which is one of the learning machines, is a mathematical model of a neural network in a living brain and has the following characteristics. The learning in the ANN uses learning data (input value: X) having a target output value (teacher value), and uses a back propagation (BP) method to calculate the teacher value and the output value (Y). This is a process of constructing a model so that the coupling load in a circuit connecting between nodes (indicated by circles in FIG. 10) is changed so that the error becomes small, and the output value approaches the teacher value. Using this BP method, the ANN can automatically acquire knowledge by learning. Finally, by inputting data that is not used for learning, the versatility of the model can be evaluated. Conventionally, membership function determination has relied on human senses, but by incorporating ANN as described above into fuzzy inference, membership function can be automatically identified. This is FNN. In the FNN, like the ANN, the input / output relationship given to the network by using the BP method can be automatically identified and modeled by changing the coupling weight. The FNN has a feature that it can acquire knowledge as a linguistic rule that is easy for humans to understand like fuzzy inference (see the balloon at the lower right in FIG. 11 as an example) by analyzing the model after learning. In other words, FNN automatically determines the optimal combination of fuzzy reasoning in the combination of variables such as numerical values representing the morphological characteristics of the cell from its structure and characteristics, and provides an estimate of the prediction target and an effective index for prediction. Rules that indicate combinations can be generated simultaneously.

  The structure of the FNN is “input layer”, “membership function part (preceding part)” that determines parameters Wc and Wg included in the sigmoid function, Wf is determined, and the relationship between input and output is taken out as a rule It consists of four possible fuzzy rule portions (consequent portions) and “output layers” (see FIG. 11). There are Wc, Wg, and Wf as bond loads that determine the model structure of FNN. The combined load Wc determines the center position of the sigmoid function used for the membership function, and Wg determines the inclination at the center position (see FIG. 12). In the model, the input value is expressed by a fuzzy function with flexibility close to human senses (see the balloon at the lower left in FIG. 11 as an example). The combined load Wf represents a contribution to the estimation result of each fuzzy region, and a fuzzy rule can be derived from Wf. That is, the structure in the model can be deciphered later and written as a rule (see the balloon at the lower right in FIG. 11 as an example).

  A Wf value, which is one of the coupling loads, is used to create a fuzzy rule in the FNN analysis. If the Wf value is positive and large, the unit contributes greatly to being determined to be “effective for prediction”, and the index applied to the rule is determined to be “effective”. If the Wf value is negative and small, the unit has a large contribution to being determined as “invalid for prediction”, and the index applied to the rule is determined as “invalid”.

  As a more specific example, the evaluation information generation unit 46 in S209 obtains the above calculation model by the following processes (A) to (H).

  (A) The evaluation information generation unit 46 selects one index from among 128 indexes.

  (B) The evaluation information generation unit 46 calculates the number of cancer cells (predicted value) in each set of microscopic images by a calculation formula using the amount of change in the frequency distribution according to the index selected in (A) as a variable. Ask.

Assuming that the calculation formula for obtaining the number of cancer cells from one index is “Y = αX ₁ ” (where “Y” is a calculated value of cancer cells (for example, a value indicating the increase in the number of cancer cells), “X”. ₁ ”represents the amount of change in the frequency distribution corresponding to the selected index (obtained in S208), and“ α ”represents the coefficient value corresponding to X ₁ ). At this time, the evaluation information generation unit 46 is configured to assign any value to alpha, if the values are the variation of the frequency distribution in each set to X _1. Thereby, the calculated value (Y) of the cancer cell in each set is obtained.

  (C) The evaluation information generation unit 46 obtains an error between the calculated value Y obtained in (B) and the actual number of cancer cells (teacher value) for each set of microscope images. In addition, said evaluation value production | generation part 46 calculates | requires said teacher value based on the information of the number of cancer cells read by S201.

  Then, the evaluation information generation unit 46 corrects the coefficient α of the above calculation formula by supervised learning so that the error of the calculated value in each set of microscope images becomes smaller.

  (D) The evaluation information generation unit 46 repeats the processes (B) and (C) described above, and acquires the calculation formula model that minimizes the average error of the calculated values for the index (A).

  (E) The evaluation information generation unit 46 repeats the processes (A) to (D) for 128 indices. Then, the evaluation information generation unit 46 compares the average errors of the calculated values of the 128 indexes, and sets the index with the lowest average error as the first index used for generating the evaluation information.

  (F) The evaluation information generation unit 46 obtains a second index to be combined with the first index obtained in (E) above. At this time, the evaluation information generation unit 46 pairs the first index and the remaining 127 indices one by one. Next, the evaluation information generation unit 46 obtains a cancer cell prediction error by a calculation formula in each pair.

Suppose that the calculation formula for obtaining the number of cancer cells from two indices is “Y = αX ₁ + βX ₂ ” (where “Y” corresponds to the calculated value of cancer cells, and “X ₁ ” corresponds to the first index. “Α” is a coefficient value corresponding to X ₁ , “X ₂ ” is a change amount of the frequency distribution corresponding to the selected index, and “β” is a coefficient value corresponding to X 2. Show.) Then, the evaluation information generation unit 46 obtains the values of the coefficients α and β so that the average error of the calculated values is minimized by the same processing as the above (B) and (C).

  Thereafter, the evaluation information generation unit 46 compares the average errors of the calculated values obtained for each pair, and obtains the pair having the lowest average error. Then, the evaluation information generation unit 46 sets the pair index having the lowest average error as the first and second indices used for generating the evaluation information.

  (G) The evaluation information generation unit 46 ends the calculation process when a predetermined end condition is satisfied. For example, the evaluation information generation unit 46 compares the average errors of the respective calculation formulas before and after increasing the index. And the evaluation information generation part 46, when the average error by the calculation formula after increasing the index is higher than the average error by the calculation formula before increasing the index (or when the difference between the two is within the allowable range), The calculation process in S209 is terminated.

  (H) On the other hand, when the termination condition is not satisfied in (G), the evaluation information generation unit 46 further increases the number of indices by one and repeats the same processing as in (F) and (G) above. Thereby, when obtaining the above calculation model, the index is narrowed down by stepwise variable selection.

  Step S210: The evaluation information generation unit 46 records the information of the calculation model obtained in S209 (information indicating each index used in the calculation formula, information on the coefficient value corresponding to each index in the calculation formula, etc.) in the storage unit 43. To do. This is the end of the description of FIG.

  Here, as an index of the above calculation model, “a combination of frequency distributions at 8th and 72th hours of“ Shape Factor ”” and “a combination of frequency distributions at 8th and 24th hours of“ Perimeter ”” And “the combination of frequency distributions at 8th and 72th hours of“ Length ””, a calculation model with a cancer cell prediction accuracy of 93.2% could be obtained. .

  In addition, as an index of the above calculation model, “a combination of frequency distributions at 8 hours and 72 hours of“ Shape Factor ”” and “a combination of frequency distributions at 8 hours and 56 hours of“ Fiber Breath ”” “Combination of frequency distributions at 8th and 72th hours of“ relative hole area ””, “Combination of frequency distributions at 8th and 24th hours of“ Shape Factor ””, and “Breadth” When six combinations of “frequency combination of 8th and 72th hours” and “combination of frequency distribution of 8th and 64th hours of“ Breadth ”” are used, the prediction accuracy of cancer cells is 95. A calculation model of 5% was obtained.

In the process of step S208 described above, the evaluation information generation unit 46 obtains eight types of indices for one type of feature value with reference to the eighth hour, and for 16 types of feature values. A total of 128 indicators (16 types × 8 patterns) were obtained. However, the index is not limited to this, and the evaluation information generation unit 46 performs the 8th, 16th, 24th, 32th, 40th, 48th, 56th, and 64th hours. 36 (= 9 × 8/2) indicators are obtained for one type of feature value based on each of eyes and 72 hours, and a total of 576 (16 types) is obtained for 16 types of feature values. (X36 types) of indicators may be obtained.

≪Example of culture state evaluation process≫
Next, an exemplary operation of the culture state evaluation process will be described with reference to the flowchart of FIG.

  Step S301: The CPU 42 reads data of a plurality of microscope images to be evaluated from the storage unit 43. Here, it is assumed that the microscopic image to be evaluated was acquired by observing the culture container 19 in which the cell group was cultured with the incubator 11 in the same visual field under the same imaging conditions. In this case, the time lapse observation is performed every 8 hours up to the 72nd hour after the lapse of 8 hours from the start of the culture in order to align the conditions with the example of FIG.

  Step S302: The CPU 42 reads calculation model information (recorded in S210 of FIG. 5) in the storage unit 43.

  Step S303: The feature value calculation unit 44, the frequency distribution calculation unit 45, and the evaluation information generation unit 46 obtain a change amount of the frequency distribution for each index corresponding to the variable of the calculation model. The processing in S303 corresponds to S203, S204, S207, and S208 in FIG.

  Step S304: The evaluation information generation unit 46 performs the calculation by substituting the change amount of the frequency distribution of each index obtained in S303 into the calculation model read out in S302. And the evaluation information generation part 46 produces | generates the evaluation information which shows the mixing rate of the cancer cell in the microscope image of evaluation object based on said calculation result. Thereafter, the evaluation information generation unit 46 displays this evaluation information on a monitor (not shown) or the like. Above, description of FIG. 13 is complete | finished.

According to the incubator used in the present invention, the control device 41 can accurately predict the mixture ratio of cancer cells from the temporal change of the frequency distribution of feature values, using a microscope image acquired by time-lapse observation. Moreover, since the control apparatus 41 can make a cell as an evaluation object, it is very useful, for example, when evaluating the cell cultured by the use of a pharmaceutical screening or regenerative medicine.
As described above, in the incubator used in the present invention, the description is limited to “frequency distribution”, but there is no limitation as long as it is about “statistical processing data”.

≪Chemical screening method≫
The screening method for chemical substances of the present invention is a screening method for chemical substances that cause morphological changes in cells,

(A) contacting a cell with a first chemical substance to be screened;
(B) reading a plurality of images taken in time series of cells brought into contact with the first chemical substance;
(C) obtaining a feature value indicating a morphological feature of each cell included in the image in (b) from the image;
(D) obtaining statistical processing data of the feature value corresponding to each of the images;
(E) generating evaluation information for evaluating a morphological change of a cell contacted with the first chemical substance based on a change in the statistical processing data between the images;
It is characterized by including.
Hereinafter, each step will be described.
In the following description, a case where “statistical processing data” is “frequency distribution” will be described.

In the present invention, the “chemical substance” is a target that targets a gene in a cell or a protein that is a gene product thereof, and indicates an organic compound, an inorganic compound, or a complex thereof. Specific “chemical substances” include proteins, peptides, nucleic acids, low molecular compounds, and the like. Furthermore, examples of the nucleic acid include antisense nucleic acid, ribozyme, siRNA, shRNA and the like. Among the above, the chemical substance is a low molecular compound, an antibody,
It is preferably at least one selected from the group consisting of proteins, peptides, antisense nucleic acids, ribozymes, shRNAs and siRNAs.
In the screening method of the present invention, the first chemical substance used for screening is referred to as “first chemical substance”. When the target protein is an enzyme, the first chemical substance is more preferably a low molecular compound. When targeting a gene, the first chemical substance is more preferably siRNA.

  In the present invention, “inducing a morphological change in a cell” means directly or indirectly modulating a pathway involved in the morphological change in the cell. Therefore, the chemical substance to be administered may not be intended to act on the morphological change of the cell, and may include a substance that results in a morphological change in the cell. For example, the morphological change is brought about by inhibiting a gene or its gene product involved in cytoskeleton, mitotic phase in the cell cycle, apoptosis or the like. In the method for screening a chemical substance of the present invention, for example, when a chemical substance having cytotoxic activity is used as a target substance, it is not merely a cytotoxic substance, but a mechanism whose action mechanism is clear to some extent can be obtained. .

In the step (a), the cell is brought into contact with the first chemical substance to be screened.
FIG. 14 is a flowchart showing one embodiment of the screening method of the present invention when the target chemical substance is siRNA. The method for transfecting siRNA into cells differs depending on the transfection reagent used, but an example will be described. First, cells are seeded one day before transfection and cultured in an incubator. The next day, replace the medium with low serum medium for 1 hour. Next, siRNA is dripped in a culture solution, for example by the liposome method, and it culture | cultivates in an incubator. After 6 hours, the low serum medium is replaced with a normal medium and cultured in an incubator including the above-described culture state evaluation apparatus. In addition, you may prepare the cell which performed the same operation, without mixing siRNA as a comparison object.

  As described above, in the method for screening a chemical substance of the present invention, the steps (b) to (e) and (g) to (k) after contacting a chemical substance that causes a morphological change in a cell are performed in these steps. This is performed using an incubator including a culture state evaluation apparatus incorporating the process. Since the incubator including the culture state evaluation apparatus has already been described, the description thereof is omitted.

Next, time-lapse observation is performed according to the registered observation schedule (step (b)). In the step (b), a plurality of images obtained by imaging the cells brought into contact with the first chemical substance in time series are read. The captured image data is read into the control device 41.
As shown in FIG. 14, the expression of the target gene after siRNA treatment varies with time. Since the screening method of the present invention acquires image data at a predetermined time, the siRNA treatment does not miss the timing at which the target gene expression is efficiently suppressed.

  Next, in step (c), feature values indicating the morphological features of each cell included in the image read in step (b) are obtained from the image. The feature value calculation unit 44 extracts the cells included in the captured image, and obtains 16 types of feature values for each extracted cell.

  Next, in step (d), the frequency distribution of the feature values corresponding to each of the images is obtained. The frequency distribution calculation unit 45 obtains a frequency distribution of 16 types of feature values for the captured image acquired by observation at each time.

Next, in step (e), based on the change in the frequency distribution between the images, evaluation information for evaluating a change in the shape of the cell contacted with the first chemical substance is generated. The evaluation information generation unit 46 obtains a time-dependent change in the frequency distribution for each of the 16 types of feature values described above, and designates one or more indices that reflect the cell culture state well from among each index by multivariate analysis. To do. For example, in FIG. 14, a large change appears in the “Length” histogram after 42 hours and 66 hours after siRNA treatment. As described above, in addition to the non-linear model equivalent to FNN, a linear model may be used according to the complexity of the cell and its form in selecting the combination of indices.
In addition, the steps (b) to (d) are also performed for the cells not contacted with the first chemical substance, and the frequency between the images in the cells contacted with the first chemical substance in the step (e). The change in the distribution and the change in the frequency distribution between the images in the cells not contacted with the first chemical substance are compared, and the result is evaluated for the morphological change of the cells contacted with the first chemical substance. You may add to evaluation information.

Since the chemical substance screening method of the present invention is non-destructive, steps (a) to (e) can be performed, and the analyzed sample can be used in another evaluation system. For example, in an experiment using siRNA, a sample analyzed for morphological changes may be used as it is for Western blotting to confirm that the target gene is silenced at the protein level, or used for real-time PCR. It may be confirmed that silencing is performed at the gene level.
According to the screening method of the present invention, since the sample used for the analysis can be used as it is for another evaluation system, further reliability can be given to the analysis result.

  Further, as shown in FIG. 15, when a fluorescent image is acquired and measured every time after chemical substance (for example, siRNA) processing in the conventional method, the number of samples for each sampling point is necessary. Since the chemical substance screening method of the present invention is non-destructive as described above, it is possible to measure changes in cell morphology over time without increasing the number of samples.

The method for screening a chemical substance of the present invention comprises the step (e),
(F) contacting the cell contacted with the first chemical substance with the first chemical substance or an additional chemical substance to be screened that does not correspond to the first chemical substance;
(G) a step of reading a plurality of images obtained by imaging the cells contacted with the additional chemical substance in time series;
(H) obtaining a feature value indicating a morphological feature of each cell included in the image in (g) from the image;
(I) obtaining statistical processing data of the feature values corresponding to each of the images;
(J) generating evaluation information for evaluating a morphological change of a cell contacted with the additional chemical substance based on a change in the statistical processing data between the images;
(K) determining whether to repeat (f) to (j) based on the evaluation information in the cell contacted with the additional chemical substance;
Preferably, the method includes a step of repeating at least once.
Hereinafter, each step will be described.
In the following description, a case where “statistical processing data” is “frequency distribution” will be described.

After the step (e), in the step (f), the cell contacted with the first chemical substance is further subjected to additional chemistry that is a screening target not corresponding to the first chemical substance or the first chemical substance. Contact the substance.
Since the screening method for chemical substances according to the present invention is a non-destructive system, the cells are treated with the first chemical substance and analyzed, and then the additional chemical substances are contacted with the cells to evaluate the drug efficacy. can do.
In the present invention, the “additional chemical substance” means the same chemical substance as the first chemical substance or a chemical substance to be screened that does not correspond to the first chemical substance.

Examples of making the additional chemical substance the same as the first chemical substance include the following.
When siRNA or a low molecular weight compound that targets a gene or a protein that is a gene product thereof is brought into contact with a cell and the effect is analyzed, the effect may not be observed in a short time of inhibition. In order to observe the effect of long-term inhibition, it is necessary to add chemical substances to the medium as needed, taking into consideration that these chemical substances are decomposed in the medium. In the present invention, the first chemical substance as an additional chemical substance can be brought into contact with the cell several times and the result can be analyzed.

  Examples of the additional chemical substance not corresponding to the first chemical substance include the following. In FIG. 16A, the first chemical substance is made into siRNA, brought into contact with the cell, and the process up to step (e) is performed to confirm the effect of treating the first compound substance. As will be described later in Examples, when siRNA targeting p53 is used as the first chemical substance, morphological change is observed, and it can be confirmed that p53 is silenced. Next, as an additional chemical substance, for example, siRNA targeting a low molecular compound or other molecule is brought into contact with the cell. When the steps (f) to (j) are performed and apoptosis is induced in the cell, the additional chemical substance may be a low molecular compound or a target molecule that has an effect on p53 inactivated cancer cells. I understand. In the screening method of the present invention, chemical substances that cause morphological changes in cells from which a specific gene has been removed can be screened.

  Further, when the first chemical substance is a low molecular compound, a low molecular compound or a target molecule that enhances the medicinal effect of the first chemical substance can be screened from the additional chemical substance.

  Furthermore, as shown in FIG. 16B, there may be a plurality of additional substances. For example, in iPS cells established by gene transfer, the expression of various transcription factors changes over time, whereby the direction of differentiation is determined. Thus, the differentiation process can be analyzed by adding siRNAs for one or more molecules involved in transcription at different timings and inhibiting the expression of genes that contribute to universality.

Steps (g) to (j) are steps in which an additional substance is brought into contact with the cell brought into contact with the first chemical substance, and operations similar to those in the above-described steps (b) to (e) are performed. Step (k) is a step of determining whether or not to repeat steps (f) to (j) again based on the evaluation information obtained in step (j). Since steps (f) to (j) are repeated at least once, the cells analyzed in steps (g) to (j) are those in which the first chemical substance and one or more additional chemical substances are brought into contact. As the number of repetitions increases, the type of additional chemical substance in contact with the cell contacted with the first chemical substance, or the concentration of the additional chemical substance in the medium increases if the medium is not changed.
Since the screening method of the present invention is a non-destructive system, the experiment plan can be updated in real time by the step (k), unlike the conventional method in which the experiment plan is determined in advance.

≪Method for producing chemical substance or pharmaceutical composition containing the chemical substance≫
The method for producing a chemical substance of the present invention or a pharmaceutical composition containing the chemical substance is a method for producing a chemical substance that causes morphological changes in cells or a pharmaceutical composition containing the chemical substance,
(L) selecting a chemical substance that causes morphological changes in cells by the screening method described above;
(M) producing a selected chemical substance or a pharmaceutical composition comprising said chemical substance;
It is characterized by including.
Moreover, the method for producing a chemical substance of the present invention or a pharmaceutical composition containing the chemical substance is a method for producing a chemical substance or a pharmaceutical composition containing the chemical substance that causes morphological changes in cells,
(L) selecting a chemical substance that causes morphological changes in cells by the screening method described above;
(N) optimizing the structure of the selected chemical substance;
(O) producing an optimized chemical or a pharmaceutical composition comprising said chemical;
It is characterized by including.
Hereinafter, each step will be described.

  In step (l), a chemical substance that causes morphological changes in cells is selected by the screening method described above. A chemical selected by the screening method of the present invention is referred to as a “Hit” chemical.

Next, in step (n), the structure of the selected chemical substance is optimized. That is, (n) is a step of optimizing the “Hit” chemical substance and creating a “Lead” chemical substance. This step is performed using a known method for drug development.
For example, if the “Hit” chemical is a low molecular weight compound, the side chain of the compound is derivatized to attempt to increase activity on the target molecule or decrease activity on unrelated molecules (especially related molecules of the target molecule). Furthermore, it tries to improve ADME (absorption / distribution / metabolism / excretion) and induces a compound having excellent pharmacokinetics in an in vivo test.

  Moreover, when the chemical substance optimized in this way is a low molecular weight compound, this low molecular weight compound can also be used as its pharmaceutically acceptable salt or ester. Typical examples of pharmaceutically acceptable salts include salts with alkali metals such as sodium and potassium, hydrochlorides, sulfates, nitrates, phosphates, carbonates, bicarbonates, perchlorates and the like. Inorganic acid salts; organic acid salts such as acetate, propionate, lactate, maleate, fumarate, tartrate, malate, citrate, and ascorbate; for example, methanesulfonate, isethion Examples thereof include sulfonates such as acid salts, benzenesulfonates, and toluenesulfonates; and acidic amino acid salts such as aspartate and glutamate.

  The method for producing a pharmaceutically acceptable salt of a low molecular weight compound can be carried out by appropriately combining methods usually used in the field of synthetic organic chemistry. Specifically, neutralization titration of a free type solution of a low molecular compound with an alkaline solution or an acidic solution can be mentioned.

  Examples of the low molecular compound ester include methyl ester and ethyl ester. These esters can be produced by esterifying a free carboxy group according to a conventional method.

  Depending on the number and quality of the library used for screening, the “Hit” chemical may not be derivatized and may already be a “Lead” chemical. In such a case, the process (m) is performed without performing the process (n).

  Next, in the step (m), a selected chemical substance or a pharmaceutical composition containing the chemical substance is produced. Alternatively, in the step (o), an optimized chemical substance or a pharmaceutical composition containing the chemical substance is produced. In the present invention, the pharmaceutical composition refers to a pharmaceutical composition containing the selected chemical substance or the optimized chemical substance as a main component and formulated by a known pharmaceutical method. In the present invention, the chemical substance itself may be directly administered to the patient without requiring formulation.

  Examples of formulation in a pharmaceutical composition include those used orally as tablets, capsules, elixirs, and microcapsules with sugar coating as necessary. Or what is used parenterally in the form of a sterile solution with water or other pharmaceutically acceptable liquid, or an injection of suspension. Furthermore, a pharmacologically acceptable carrier or medium, specifically, sterile water or physiological saline, vegetable oil, emulsifier, suspension, surfactant, stabilizer, flavoring agent, excipient, vehicle, preservative And those formulated by mixing in a unit dosage form generally required for pharmaceutical practice, in appropriate combination with agents, binders and the like.

  Additives that can be mixed into tablets and capsules include, for example, binders such as gelatin, corn starch, tragacanth gum, gum arabic, excipients such as crystalline cellulose, swelling such as corn starch, gelatin, and alginic acid Agents, lubricants such as magnesium stearate, sweeteners such as sucrose, lactose or saccharin, flavoring agents such as peppermint, red mono oil or cherry. When the dispensing unit form is a capsule, the above material can further contain a liquid carrier such as fats and oils. Sterile compositions for injection can be formulated according to normal pharmaceutical practice using a vehicle such as distilled water for injection.

  Examples of the aqueous solution for injection include isotonic solutions containing physiological saline, glucose and other adjuvants such as D-sorbitol, D-mannose, D-mannitol, and sodium chloride, and suitable solubilizers such as You may use together with alcohol, specifically ethanol, polyalcohol, for example, propylene glycol, polyethyleneglycol, nonionic surfactant, for example, polysorbate 80 (TM), HCO-50.

  Examples of the oily liquid include sesame oil and soybean oil, which may be used in combination with benzyl benzoate or benzyl alcohol as a solubilizing agent. Moreover, you may mix | blend with buffer, for example, phosphate buffer, sodium acetate buffer, a soothing agent, for example, procaine hydrochloride, stabilizer, for example, benzyl alcohol, phenol, antioxidant. The prepared injection solution is usually filled into a suitable ampoule.

  For example, intraarterial injection, intravenous injection, subcutaneous injection, etc., as well as intranasal, transbronchial, intramuscular, percutaneous, or oral administration to patients are performed by methods known to those skilled in the art. sell. The dose varies depending on the weight and age of the patient, the administration method, etc., but those skilled in the art can appropriately select an appropriate dose. In addition, if the compound can be encoded by DNA, it may be possible to incorporate the DNA into a gene therapy vector and perform gene therapy. The dose and administration method vary depending on the weight, age, symptoms, etc. of the patient, but can be appropriately selected by those skilled in the art.

  The dose of the compound varies depending on the symptom, but in the case of oral administration, it is generally about 0.1 to 100 mg, preferably about 1.0 to 50 mg per day for an adult (with a body weight of 60 kg). Preferably it is considered to be about 1.0 to 20 mg.

  When administered parenterally, the single dose varies depending on the administration subject, target organ, symptom, and administration method. For example, in the form of an injection, it is usually 1 for an adult (with a body weight of 60 kg). It may be convenient to administer about 0.01 to 30 mg per day, preferably about 0.1 to 20 mg, more preferably about 0.1 to 10 mg by intravenous injection.

A tumor is mentioned as a suitable example with which the therapeutic effect of the chemical substance which concerns on this invention, or the pharmaceutical composition containing the said chemical substance is anticipated, for example, a human solid cancer etc. are mentioned. Examples of human solid cancer include brain cancer, head and neck cancer, esophageal cancer, thyroid cancer, small cell cancer, non-small cell cancer, breast cancer, stomach cancer, gallbladder / bile duct cancer, and liver. Cancer, pancreatic cancer, colon cancer, rectal cancer, ovarian cancer, choriocarcinoma cancer, endometrial cancer, cervical cancer, renal pelvis / ureter cancer, bladder cancer, prostate cancer, penis Cancer, testicular cancer, fetal cancer, Wilms cancer, skin cancer, malignant melanoma, neuroblastoma, osteosarcoma, Ewing's tumor, soft tissue sarcoma and the like.
Also included are neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease.

  EXAMPLES Hereinafter, although an Example demonstrates this invention further more concretely, this invention is not limited to a following example.

(Measurement of morphological change of NHDF cells after siRNA treatment)
1-1 Materials
The following materials were purchased from Invitrogen.
(1) p53 siRNA (catalog number: VHS40367)

1-2 Transfection The day before transfection, NHDF cells were seeded so as to be 30-50% confluent when siRNA was introduced, and cultured in a medium not containing antibiotics. The next day, siRNA and LIPOFECTAMINE 2000 were diluted with Opti-MEM I Reduced-Serum Medium (Invitrogen) and incubated at room temperature for 5 minutes. Next, the OptiMEM / siRNA mixture and the OptiMEM / LIPOFECTAMINE 2000 mixture were mixed and allowed to stand at room temperature. This mixture was added to the medium and the culture plate was incubated in a Biostation CT (Nikon) 37 ° C., 5% CO ₂ incubator.

  Time-lapse observation was performed on the transfected and untreated cells up to the third day at 2-hour intervals. The upper part of FIG. 17 shows captured images after 42 hours and 66 hours of each cell (p53 siRNA-treated cells (p53 mutation) and untreated cells (normal)), and the lower part of FIG. 17 is analyzed based on the captured images. In addition, histograms of “Length” after 42 hours and after 66 hours are shown. Compared with the change in the shape of the histogram in untreated cells (normal cells), the change in the shape of the histogram in the transfected cells (p53 mutation) is significant. In the upper part of FIG. 17, the morphology of the cells after 66 hours of siRNA treatment with p53 is clearly different from the morphology after 42 hours, and is rounded. From this, it was confirmed that the analysis result reflected the state of morphological change of the cells in the captured image.

The upper part of FIG. 18 quantifies the expression level of the knockdown target gene p53 in the p53 mutant cell in FIG. 17 by RT-PCR. It is the graph which showed the relative value of the gene expression level of each time when 0 hour was set to 1 (corrected by GAPDH). It can be seen that in the time zone just before the change of the histogram shown in FIG. 17 appears, the gene expression in the cell is actually suppressed by siRNA. Although this effect recovers with time, it can be said that the change after 36 hours was detected as a change in morphology distribution between 42 and 66 hours later as a change in morphology.
The lower part of FIG. 18 shows the quantitative evaluation of the gene expression level of RhoA, which is a gene under the control (downstream) of the p53 gene, by RT-PCR. It can be confirmed that the siRNA of the p53 gene was able to knock down the target p53 surely after 36 hours, and its downstream gene, RhoA, was also controlled for expression almost simultaneously. The knockdown of the p53 gene causes the regulation of RhoA, a protein involved in cytoskeletal regulation, which is considered to have caused a change in shape. This data shows the certainty of the experiment that p53, which was the target of siRNA knockdown, was reliably knocked down to include downstream genes, and the effect of siRNA that suppressed p53, which was only a transcription factor. It is also a piece of data that physiologically shows why it appeared after 42 to 66 hours as a morphological change.

  Since the screening method of the present invention is non-destructive, it is not necessary to increase the number of samples. Further, according to the screening method of the present invention, since the sample used for the analysis can be used as it is for another evaluation system, it is possible to give more reliability to the analysis result. For example, Western blot or real-time PCR can be performed using p53 mutant cells in which morphological changes have been observed to confirm that p53 in the cells is indeed silenced.

(Measurement of morphological change of HT1080 cells after siRNA continuous treatment)
2-1 Materials
The following materials were purchased from Invitrogen.
(1) RhoA siRNA (catalog number: VHS40471)

2-2 Transfection (first time)
The day before transfection, HT1080 was seeded so as to be 30-50% confluent when siRNA was introduced, and cultured in a medium not containing antibiotics. The next day, siRNA and LIPOFECTAMINE 2000 were diluted with Opti-MEM I Reduced-Serum Medium (Invitrogen) and incubated at room temperature for 5 minutes. Next, the OptiMEM / siRNA mixture and the OptiMEM / LIPOFECTAMINE 2000 mixture were mixed and allowed to stand at room temperature. This mixture was added to the medium and the culture plate was incubated in a Biostation CT (Nikon) 37 ° C., 5% CO ₂ incubator.
In the same transfection treatment as described above, a sample using water instead of siRNA was used as a negative control.

  Time-lapse observation was performed on the cells into which RhoA siRNA was introduced (hereinafter referred to as RhoA siRNA-treated cells 1) and negative control cells (hereinafter referred to as negative control 1) until the third day at intervals of 2 hours. FIG. 19 shows captured images at 0 hours and 48 hours after each cell (RhoA siRNA-treated cells 1 and negative control 1). No significant change in cell morphology was observed in both cells after 0 hour. On the other hand, after 48 hours, morphological changes were observed only in RhoA siRNA-treated cells 1, and the morphology of RhoA siRNA-treated cells 1 showed a long and thin shape.

2-3 Transfection (second time)
The day before the second transfection, the RhoA siRNA-treated cells 1 after 96 hours were seeded so as to be 30-50% confluent at the time of siRNA introduction, and cultured in a medium not containing antibiotics. The next day, siRNA and LIPOFECTAMINE 2000 were diluted with Opti-MEM I Reduced-Serum Medium (Invitrogen) and incubated at room temperature for 5 minutes. Next, the OptiMEM / siRNA mixture and the OptiMEM / LIPOFECTAMINE 2000 mixture were mixed and allowed to stand at room temperature. This mixture was added to the medium and the culture plate was incubated in a Biostation CT (Nikon) 37 ° C., 5% CO ₂ incubator.
The negative control 1 was treated in the same manner as the transfection treatment except that water was used instead of siRNA (negative control 2).

  Time-lapse observation was performed on the cells into which RhoA siRNA was introduced (hereinafter referred to as RhoA siRNA-treated cell 2) and negative control cells (hereinafter referred to as negative control 2) until the third day at intervals of 2 hours. The second transfection time is defined as 0 hour (120 hours in total). FIG. 20 shows captured images after 0 hour (total 120 hours) and 48 hours (total 168 hours) of each cell (RhoA siRNA-treated cells 2 and negative control 2). In both cells after 0 hour (120 hours in total), no significant change in cell morphology was observed. On the other hand, after 48 hours (total 168 hours), morphological changes were observed only in RhoA siRNA-treated cells 2, and the morphology of RhoA siRNA-treated cells 2 showed a long and narrow shape. This result is the same as the result of the first transfection, and the effect of RhoA siRNA introduced into HT1080 cells is completely lost after the second transfection (120 hours in total) after transfection. It was observed that the cells into which RhoA siRNA was introduced had recovered to the form prior to siRNA introduction.

Based on the captured images in the time lapse observation, the “circularity” of each cell after 0 hours and 48 hours (120 hours and 168 hours in the case of RhoA siRNA-treated cells 2 and negative control 2) is analyzed. did. The results are shown in FIG.
Change in shape of histogram between RhoA siRNA-treated cells 1 and negative control 1 after 0 hours, or change in shape of histogram between RhoA siRNA-treated cells 2 and negative control 2 after 0 hours (total 120 hours) (Fig. 21 upper row) was not confirmed.
On the other hand, it was confirmed that the peak of the histogram was shifted to the right between the negative control 1 after 48 hours and the RhoA siRNA-treated cell 1 (lower part of FIG. 21). This shape change is also reproduced between the negative control 2 after 48 hours (after 168 hours in total) and the RhoA siRNA-treated cells 2. From this, it was confirmed that the analysis result reflected the state of cell shape change in the captured image.

Since the screening method for chemical substances according to the present invention is a non-destructive system, the cells are treated with the first chemical substance and analyzed, and then the additional chemical substances are contacted with the cells to evaluate the drug efficacy. Obviously you can.
According to the chemical substance screening method of the present invention, for example, the behavior of a gene whose expression changes over time in a cell can be analyzed in detail.

DESCRIPTION OF SYMBOLS 11 ... Incubator, 15 ... Constant temperature chamber, 19 ... Culture container, 22 ... Observation unit, 34 ... Imaging device, 41 ... Control device, 42 ... CPU, 43 ... Memory | storage part, 44 ... Feature value calculating part, 45 ... Frequency distribution calculation 46, evaluation information generation unit

Claims (12)

A screening method for chemical substances that cause morphological changes in cells,
(A) contacting a cell with a first chemical substance to be screened;
(B) reading a plurality of images taken in time series of cells brought into contact with the first chemical substance;
(C) obtaining a feature value indicating a morphological feature of each cell included in the image in (b) from the image;
(D) obtaining statistical processing data that is a frequency distribution of the feature values corresponding to each of the images; and (e) changing the statistical processing data between the images based on the first chemistry. Generating evaluation information for evaluating a morphological change of a cell contacted with a substance;
A method for screening a chemical substance, comprising: After (e),
(F) contacting the cell contacted with the first chemical substance with the first chemical substance or an additional chemical substance to be screened that does not correspond to the first chemical substance;
(G) a step of reading a plurality of images obtained by imaging the cells contacted with the additional chemical substance in time series;
(H) obtaining a feature value indicating a morphological feature of each cell included in the image in (g) from the image;
(I) obtaining statistical processing data that is a frequency distribution of the feature values corresponding to each of the images; and (j) changing the statistical processing data between the images based on the change of the statistical processing data. Generating evaluation information for evaluating the morphological change of the contacted cells;
(K) determining whether to repeat (f) to (j) based on the evaluation information in the cell contacted with the additional chemical substance;
The chemical substance screening method according to claim 1, comprising a step of repeating at least once. The first chemical substance and the additional chemical substance are each independently at least one selected from the group consisting of a low molecular weight compound, an antibody, a protein, a peptide, an antisense nucleic acid, a ribozyme, shRNA, and siRNA. 3. A method for screening a chemical substance according to 1 or 2 . The method for screening a chemical substance according to claim 3 , wherein the first chemical substance is siRNA. The chemical substance screening method according to claim 3 , wherein the first chemical substance is a low molecular compound. Wherein (e) and the (j), using the difference of the frequency distribution between the image, chemicals according to any one of claims 2 to 5, which is a step of generating the evaluation information Screening method. (C) and (h) are steps for obtaining a plurality of types of feature values for each of the images, and (d) and (i) are the frequency distributions for each type of feature values. a step of calculating, the (e) and the (j), using the information of the change of the frequency distribution of the plurality of types, any one of claims 2-6 is a step of generating the evaluation information A method for screening a chemical substance according to 1. The chemical substance according to claim 7 , wherein (d) and (i) are steps of normalizing frequency divisions in the respective frequency distributions using a standard deviation for each type of the characteristic value. Screening method. In (e) and (j), the combination of the frequency distributions used for generating the evaluation information is extracted from a plurality of combinations of the frequency distributions having different image capturing times and different types of feature values. The method for screening a chemical substance according to claim 7 or 8 , which is a step of obtaining a calculation model of the evaluation information. 10. The chemical substance screening method according to claim 9 , wherein (e) and (j) are steps of obtaining the calculation model by supervised learning. A method for producing a chemical substance that causes a morphological change in a cell or a pharmaceutical composition comprising the chemical substance,
(L) a step of selecting a chemical substance that causes a morphological change in a cell by the screening method according to any one of claims 1 to 10 ;
(M) producing a selected chemical substance or a pharmaceutical composition comprising said chemical substance;
A method for producing a chemical substance or a pharmaceutical composition comprising the chemical substance. A method for producing a chemical substance that causes a morphological change in a cell or a pharmaceutical composition comprising the chemical substance,
(L) a step of selecting a chemical substance that causes a morphological change in a cell by the screening method according to any one of claims 1 to 10 ;
(N) optimizing the structure of the selected chemical substance;
(O) producing an optimized chemical or a pharmaceutical composition comprising said chemical;
A method for producing a chemical substance or a pharmaceutical composition comprising the chemical substance.