Fluorescence image analysis methods, fluorescence image analysis equipment, fluorescence image analysis programs

Abstract

To provide an optical image analysis device or the like that outputs a ratio of positive cells without requiring cell isolation.SOLUTION: A fluorescence image analysis apparatus 10 includes an imaging unit 154 that captures a first image including at least a partial region of a plurality of cells in a sample in which target sites on chromosomes are labeled with a fluorescent dye and a second image including, as an imaging target, fluorescence generated from a fluorescent dye that labels the target site of cells in the first image. A processing unit 11 of the fluorescence image analysis device 10 selects a plurality of test cells, which have specific morphological characteristics and are to be tested, from the plurality of cells based on at least the first image, and extracts bright spots due to fluorescence generated from the fluorescent dye from at least the plurality of test cells based on the second image. The processing unit 11 identifies cells with chromosomal abnormality and / or cells without chromosomal abnormality based on the extracted bright spots, and generates information about a ratio of cells with chromosomal abnormality to the test cells based on the number of cells with chromosomal abnormality and / or the number of cells without chromosomal abnormality.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a fluorescence image analysis method, a fluorescence image analysis apparatus, and a fluorescence image analysis program.

Background Art

[0002] Conventionally, as a method for detecting positive cells in which chromosomal abnormalities such as chromosomal translocations, chromosomal inversions, gene deletions, or gene amplifications have occurred, a method using fluorescence in situ hybridization (FISH method) is known. According to the FISH method, cells are labeled by a pretreatment of hybridizing a labeled probe to a DNA sequence region of a detection target in the cells, and an examiner observes the fluorescence generated by the labeled probe with a fluorescence microscope to determine whether there are positive cells in the sample. In the diagnosis of a subject, depending on the disease, if the ratio of positive cells to the cells to be determined is 1% to several percent, it is diagnosed that treatment or a detailed examination is required. In the above method in which an examiner determines positive cells using a fluorescence microscope, the number of cells determined by the examiner is generally at most 100 to 200 per sample. Therefore, in order not to miss positive cells, specific types of cells are isolated from a biological sample using antibody beads, and the isolated cells are examined. However, isolating specific types of cells from a biological sample has been a cumbersome task for an examiner. As a method that does not require such a cumbersome isolation operation, in Patent Document 1, detection cells labeled with a labeled probe are flowed through a flow cell, and from a two-dimensional fluorescence image obtained by imaging the detection cells flowing through the flow cell, software extracts bright spots for each detection cell, and based on the extracted bright spots, determines whether each detection cell is a positive cell, and outputs the ratio of positive cells to the detection cells. A fluorescence image analysis method is disclosed. According to the analysis method described in Patent Document 1, since the number of detection cells per sample can be increased on the order of several tens to several tens of thousands compared with the above method in which an examiner determines positive cells using a fluorescence microscope, there is no need to isolate specific types of cells from a biological sample.

Prior Art Documents

Patent Documents

[0003] [Patent Document 1] Japanese Patent Publication No. 2017-215311 [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] However, according to the analysis method described in Patent Document 1, depending on the orientation of the cells in the flow cell during imaging, for example, multiple bright spots may overlap in the fluorescence image of negative cells, and the software may mistakenly identify negative cells as positive cells. Although the probability of such a judgment occurring is small and generally does not affect the diagnosis of the subject, if the ratio of positive cells to the cells being judged is 1% to several percent, the impact of the above judgment becomes significant in the diagnosis of diseases requiring treatment or further examination, posing a challenge in making the diagnosis difficult.

[0005] Therefore, the object of the present invention is to provide a fluorescence image analysis method, a fluorescence image analysis apparatus, and a fluorescence image analysis program that facilitate the diagnosis of a subject. [Means for solving the problem]

[0006] The fluorescence image analysis method according to the present invention involves labeling a target site on a chromosome of a plurality of cells contained in a sample with a fluorescent dye, capturing a first image of the plurality of cells in the sample, which includes at least a portion of the cell region as the imaging target, and capturing a second image which includes the fluorescence generated from the fluorescent dye that labeled the target site of the cell in the first image as the imaging target, selecting a plurality of test cells that have specific morphological characteristics and are subject to examination from the plurality of cells based on at least the first image, extracting bright spots due to fluorescence generated from the fluorescent dye from at least the plurality of test cells based on the second image, identifying cells with chromosomal abnormalities and / or cells without chromosomal abnormalities based on the extracted bright spots, and generating information regarding the ratio of cells with chromosomal abnormalities to the test cells based on the number of cells with chromosomal abnormalities and / or the number of cells without chromosomal abnormalities.

[0007] The fluorescence image analyzer according to the present invention comprises an imaging unit and a processing unit that captures, for a plurality of cells in a sample which are contained in the sample and whose target sites on chromosomes are labeled with a fluorescent dye, a first image which includes at least a portion of the cells as the imaging target, and a second image which includes fluorescence generated from the fluorescent dye that labeled the target sites of the cells in the first image as the imaging target, the processing unit performs the following: a process of selecting a plurality of test cells from the plurality of cells which have specific morphological characteristics and are subject to examination based on at least the first image; a process of extracting bright spots due to fluorescence generated from the fluorescent dye from at least the plurality of test cells based on the second image; a process of identifying cells with chromosomal abnormalities and / or cells which do not have chromosomal abnormalities based on the extracted bright spots, and a process of generating information regarding the ratio of cells with chromosomal abnormalities to the test cells based on the number of cells with chromosomal abnormalities and / or the number of cells which do not have chromosomal abnormalities.

[0008] The fluorescence image analysis program according to the present invention causes a computer to perform the following processes for a plurality of cells in a sample, which are contained in the sample and have target sites on chromosomes labeled with a fluorescent dye: a first image in which at least a portion of the cells is included as the imaging target; a second image in which the fluorescence generated from the fluorescent dye that labeled the target sites of the cells in the first image is included as the imaging target; a process to select a plurality of test cells from the plurality of cells that have specific morphological characteristics and are subject to examination, based on at least the first image; a process to extract bright spots due to fluorescence generated from the fluorescent dye from at least the plurality of test cells based on the second image; a process to identify cells with chromosomal abnormalities and / or cells without chromosomal abnormalities based on the extracted bright spots; and a process to generate information regarding the ratio of cells with chromosomal abnormalities to the test cells based on the number of cells with chromosomal abnormalities and / or the number of cells without chromosomal abnormalities. [Effects of the Invention]

[0009] According to the present invention, a fluorescence image analysis method, a fluorescence image analysis apparatus, and a fluorescence image analysis program can be provided that facilitate the diagnosis of a subject. [Brief explanation of the drawing]

[0010] [Figure 1] This diagram schematically shows the configuration of a fluorescence image analyzer, which is one example of an embodiment. [Figure 2] This figure shows an example of the first image, two second images, a third image, and a composite image of the two second images acquired by a fluorescence image analyzer. [Figure 3] This is a schematic composite image of the first and third images, illustrating the method by which the processing unit calculates the nuclear eccentricity value. [Figure 4] This graph plots the nuclear eccentricity and cell area of ​​each cell in a sample prepared from bone marrow fluid of sample 1 onto a two-dimensional coordinate system with nuclear eccentricity on the horizontal axis and cell area on the vertical axis. [Figure 5A]This figure shows nuclear images (a), bright-field images (b), and a composite image (c) of typical multiple myeloma cells. [Figure 5B] This figure shows nuclear images (a), bright-field images (b), and a composite image (c) of non-multiple myeloma cells. [Figure 6] The third image (a), the first second image (b), and the second second image (c) are schematically shown, all acquired from the same region of the sample flowing through the flow cell. [Figure 7] This diagram illustrates the classification of cells based on the bright spot pattern determined by the processing method, and is a schematic diagram showing a simplified bright spot pattern. [Figure 8] This diagram illustrates the method of bright spot extraction and fusion determination performed by the processing unit. [Figure 9] This diagram illustrates the method used by the processing unit to determine fusion. [Figure 10] This flowchart shows an example of the steps involved in the fluorescence image analysis process performed in the processing unit. [Figure 11] Figure 10 is a flowchart showing an example of the detailed procedure for the classification process in step S2. [Figure 12] Figure 10 is a flowchart showing an example of the detailed procedure for the classification process in step S3. [Figure 13] This graph shows the calculated percentage of positive cells for the IGH / CCND1 fusion gene, the IGH / FGFR3 fusion gene, and the IGH / MAF fusion gene in bone marrow samples from multiple myeloma patients with IGH translocations. [Figure 14] This graph shows the calculated percentage of positive cells for the IGH / CCND1 fusion gene, the IGH / FGFR3 fusion gene, and the IGH / MAF fusion gene in bone marrow samples from multiple myeloma patients who do not have IGH translocations. [Figure 15] This graph plots the nuclear area and cell area of ​​each cell in a sample prepared from spleen tissue of a follicular lymphoma patient with IGH translocation on a two-dimensional coordinate system, with nuclear area as the parameter on the x-axis and cell area as the parameter on the y-axis. [Figure 16] A graph showing the calculation results when calculating the positive cell rates of each of the IGH / CCND1 fusion gene, IGH / BCL2 fusion gene, and IGH / MYC fusion gene for a specimen of a patient with the above follicular lymphoma. [Figure 17] A graph in which the nuclear area and cell area of each cell contained in a sample prepared from a lymph node specimen of a diffuse large B-cell lymphoma patient having an IGH translocation are plotted on a two-dimensional coordinate system with the nuclear area as the parameter on the horizontal axis and the cell area as the parameter on the vertical axis. [Figure 18] A graph showing the calculation results when calculating the positive cell rates of each of the IGH / CCND1 fusion gene, IGH / BCL2 fusion gene, and IGH / MYC fusion gene for a specimen of a patient with the above diffuse large B-cell lymphoma. [Figure 19] A flowchart showing a modified example of the classification process in step S3 of FIG. 10. [Figure 20] It is a flowchart showing a modified example of the classification process in step S3 of FIG. 10. [Figure 21] A schematic diagram of a fluorescence image analysis apparatus which is another example of an embodiment. [Figure 22] A schematic diagram of a fluorescence image analysis apparatus which is another example of an embodiment.

Embodiments for Carrying Out the Invention

[0011] Hereinafter, with reference to the drawings, an example of an embodiment of a fluorescence image display method and a fluorescence image analysis apparatus according to the present invention will be described in detail. The embodiments described below are merely examples, and the present invention is not limited to the following embodiments. Also, among the components described below, components not described in the independent claims indicating the most上位 concept are arbitrary components and are not essential components. Note that the present invention is not limited to the embodiments and modified examples described below, and can be appropriately designed and changed without impairing the object of the present invention. For example, selectively combining the components of the plurality of embodiments and modified examples described below is within the scope of the present disclosure.

[0012] Figure 1 is a schematic diagram showing the configuration of a fluorescence image analyzer 10, which is an example of an embodiment. In Figure 1, solid double arrows indicate signal lines, dashed lines indicate light paths, and solid single arrows indicate the direction of light propagation. As shown in Figure 1, the fluorescence image analyzer 10 includes an imaging unit 100 that acquires fluorescence images and bright-field images of cells (hereinafter sometimes referred to as "fluorescently labeled cells") whose target sites are labeled with a fluorescent dye, a fluid circuit unit 15 for supplying a sample 20a to a flow cell 110 (described later), and a processing unit 11 that controls each device such as the imaging unit 100 and the fluid circuit unit 15, and generates information regarding the ratio of cells in which gene fusion due to chromosomal translocation has occurred. The fluorescence image analyzer 10 also includes a storage unit 12, a display unit 13, and an input unit 14 connected to the processing unit 11.

[0013] As will be described in detail later, in this embodiment, image analysis is performed by software based on a bright-field image as an example of the first image, two fluorescent spot images as an example of the second image, and a nuclear stained image as an example of the third image. Multiple cells contained in the sample are then classified into multiple test cells having specific morphological characteristics and multiple non-test cells not having those specific morphological characteristics. Furthermore, the multiple test cells are classified into cells that cannot be determined, negative cells, and positive cells. In this specification, positive cells mean cells with chromosomal abnormalities, and negative cells mean cells without chromosomal abnormalities. Chromosomal abnormalities include chromosomal translocations, chromosomal inversions, gene deletions, or gene amplifications.

[0014] The imaging unit 100 comprises light sources 121-124 for irradiating fluorescently labeled cells with light, a flow cell 110 for flowing the sample 20a, and an imaging unit 154. The imaging unit 100 also includes focusing lenses 131-134, 151, 153, dichroic mirrors 141, 142, and an optical unit 152. In this embodiment, the imaging unit 100 is provided with four types of light sources 121-124, each with a different wavelength of light output. The imaging unit 154 captures three types of fluorescence images using three types of fluorescent dyes with different wavelengths of excitation light and emitted fluorescence, as well as a bright-field image of the cell. A bright-field image of a cell is an image obtained by uniformly irradiating the entire cell with light and detecting the light that has passed through the cell.

[0015] In this embodiment, bone marrow fluid prepared by a pretreatment process including a step of hybridizing two different types of nucleic acid probes labeled with a first fluorescent dye and a second fluorescent dye with target sites in nucleic acids, and a nuclear staining step of labeling the nucleus of each cell with a nuclear staining dye as a third fluorescent dye, is used as sample 20a. Then, using a fluorescence image analyzer 10, information regarding the number of negative cells and the ratio of positive cells based on the number of positive cells is calculated for multiple test cells selected from multiple cells contained in sample 20a.

[0016] In this embodiment, the positive cells are multiple myeloma cells (hereinafter simply referred to as myeloma cells) in which the IGH gene or FGFR3 gene has fused via chromosomal translocation to produce an IGH / FGFR3 fusion gene, and the negative cells are cells that do not contain the IGH / FGFR3 fusion gene. As will be explained in the later modifications, the fluorescence imaging method and apparatus according to the present invention are not limited to myeloma cells containing the IGH / FGFR3 fusion gene as the measurement target, and the measurement target may also be myeloma cells containing other fusion genes. Alternatively, the measurement target may be cells other than plasma cells (myeloma cells), as long as they are nucleated cells. In the following description, the IGH / FGFR3 fusion gene may be referred to as t(4;14). Here, in this specification, "t" means translocation, and when written as t(x;y), "x" and "y" mean the chromosome numbers in which each gene exists.

[0017] The fluorescence imaging analyzer 10 measures the sample 20a prepared in the pre-processing unit 20 to classify the cells contained in the sample 20a. The pre-processing unit 20 prepares the sample 20a by performing pre-processing that includes a step of labeling target sites of cells with a fluorescent dye and a step of specifically staining the cell nucleus with a nuclear staining dye. In the step of labeling target sites with a fluorescent dye, the nucleic acid probe labeled with the fluorescent dye is hybridized with the target site in the nucleic acid.

[0018] The first nucleic acid probe, which hybridizes with the IGH gene, is labeled with a first fluorescent dye that emits a first fluorescence at wavelength λ21 when irradiated with excitation light at wavelength λ11. Hybridization of this first nucleic acid probe with the IGH gene results in the IGH gene being labeled with the first fluorescent dye. The second nucleic acid probe, which hybridizes with the FGFR3 gene, is labeled with a second fluorescent dye that emits a second fluorescence at wavelength λ22 when irradiated with excitation light at wavelength λ12. Hybridization of this second nucleic acid probe with the FGFR3 gene results in the FGFR3 gene being labeled with the second fluorescent dye. The nucleus is stained with a nuclear staining dye that emits a third fluorescence at wavelength λ23 when irradiated with excitation light at wavelength λ13. In addition, TexasRed® is used as the first fluorescent dye, FITC (Fluorescein isothiocyanate) is used as the second fluorescent dye, and Hoechst® 33342 is used as the nuclear staining dye, but the dyes are not limited to these.

[0019] The flow cell 110 is made of a translucent resin or glass and has a channel 111 for flowing the sample 20a. The flow cell 110 is located in the common optical path of the light sources 121 to 124. In the imaging unit 100, the light sources 121 to 124 irradiate the flow cell 110 with light, and the imaging unit 154 is configured to capture fluorescence images and bright-field images of fluorescently labeled cells flowing through the channel 111 of the flow cell 110.

[0020] In the FISH method using flow cell 110 (hereinafter referred to as "flow FISH method"), a sample 20a containing fluorescently labeled cells is flowed through flow cell 110, and a fluorescence image is obtained by imaging the cells in the fluid. Therefore, compared to the conventional FISH method using microscopic observation, the number of cells to be analyzed can be increased by tens to tens of thousands of times, resulting in high diagnostic accuracy. In particular, reproducibility is improved when the number of positive cells is small.

[0021] As described above, the imaging unit 100 is configured such that light emitted from light sources 121 to 124 irradiates the sample 20a flowing through the flow channel 111 of the flow cell 110. An example of light sources 121 to 123 is a semiconductor laser light source, and an example of light source 124 is a white LED. Light source 121 is a light source for exciting the first fluorescent dye and emits laser light with a wavelength λ11. Light source 122 is a light source for exciting the second fluorescent dye and emits laser light with a wavelength λ12. Light source 123 is a light source for exciting the nuclear staining dye as the third fluorescent dye and emits laser light with a wavelength λ13. Light source 124 emits light for generating a bright-field image of the cell, i.e., white light that penetrates the cell. In this embodiment, the wavelength λ11 is 592 nm, the wavelength λ12 is 488 nm, and the wavelength λ13 is 405 nm.

[0022] The focusing lenses 131-134 are positioned between the light sources 121-124 and the flow cell 110, respectively, to focus the light emitted from the light sources 121-124 onto the flow cell 110. The dichroic mirror 141 transmits light with wavelength λ11 and reflects light with wavelength λ12. The dichroic mirror 142 transmits light with wavelengths λ11 and λ12 and reflects light with wavelength λ13. By providing such an optical system, the light from the light sources 121-124 is irradiated into the flow channel 111 of the flow cell 110. When the sample 20a flowing through the flow channel 111 is irradiated with light with wavelengths λ11-λ13, the fluorescent dyes that label the cells emit fluorescence.

[0023] Specifically, when light of wavelength λ11 is irradiated onto the first fluorescent dye that labels the IGH gene, the first fluorescence at wavelength λ21 is produced from the first fluorescent dye. When light of wavelength λ12 is irradiated onto the second fluorescent dye that labels the FGFR3 gene, the second fluorescence at wavelength λ22 is produced from the second fluorescent dye. When light of wavelength λ13 is irradiated onto the nuclear staining dye that stains the nucleus, the third fluorescence at wavelength λ23 is produced from the nuclear staining dye. In this embodiment, the first fluorescence color is red, the second fluorescence color is green, and the third fluorescence color is blue. Furthermore, when white light from the light source 124 is irradiated onto the sample 20a, a portion of this light penetrates the cells, and a bright-field image is obtained. Note that the wavelengths of each light source and each fluorescent dye are not limited to those described above and may be appropriately selected depending on the gene to be detected.

[0024] Between the flow cell 110 and the imaging unit 154, a focusing lens 151, an optical unit 152, and a focusing lens 153 are arranged in order from the flow cell 110 side along the optical path of the laser beam. The focusing lens 151 focuses the first to third fluorescence generated from the sample 20a and the transmitted light that has passed through the sample 20a onto the optical unit 152. The optical unit 152 is composed of four stacked dichroic mirrors. The four dichroic mirrors reflect the first to third fluorescence at slightly different angles from each other, separating them on the light-receiving surface of the imaging unit 154. The focusing lens 153 focuses the light reflected by the optical unit 152 onto the light-receiving surface of the imaging unit 154.

[0025] The imaging unit 154 is composed of a TDI (Time Delay Integration) camera. The imaging unit 154 captures images formed by the first to third fluorescence and transmitted light, acquiring three types of fluorescence images corresponding to the first to third fluorescence and a bright-field image corresponding to the transmitted light, and stores the acquired images in the storage unit 12 (described later) under the control of the processing unit 11.

[0026] The bright-field image is an example of the first image, the images capturing the first and second fluorescence are examples of the second image, and the image of the nucleus capturing the third fluorescence constitutes the third image. Hereinafter, the image capturing the first fluorescence will be referred to as the first second image, and the image capturing the second fluorescence will be referred to as the second second image. The processing unit 11 corrects each image sent from the imaging unit 154 using software so that the positional relationship between the subject and the pixels matches. It is preferable that the two second images are the same size in order to analyze the overlap of bright spots.

[0027] The TDI camera constituting the imaging unit 154 repeatedly images cells with multiple rows of line sensors along the direction of the flow channel 111 of the flow cell 110, i.e., the direction in which the sample 20a flows, and obtains an image of the cells by accumulating the charge of the line sensors along the direction of sample flow. Therefore, high-quality cell images can be obtained without reducing the movement speed of the sample 20a or shortening the exposure time. Note that the configuration of the imaging unit 100 is not limited to the above and may be changed as appropriate as long as it is possible to capture cell images necessary for image analysis by software. For example, a configuration combining prisms may be adopted as the optical unit 152. As such an optical unit 152, for example, the optical block described in U.S. Patent Publication No. 2020-0271585 can be used, and said publication is incorporated herein by reference. Alternatively, a CCD camera may be adopted as the imaging unit 154.

[0028] The fluid circuit section 15 includes a suction tube for aspirating the sample 20a, a flow path connecting the suction tube and the flow cell 110, and a pump. Under the control of the processing unit 11, the sample 20a is aspirated by the suction tube and supplied to the flow cell 110.

[0029] The processing unit 11 performs image analysis of the fluorescence image acquired by the imaging unit 100 by executing software stored in the memory unit 12, which will be described later, and classifies each cell in the fluorescence image into negative cells, positive cells, and non-determined cells, which are cells in the fluorescence image that do not meet the predetermined conditions for classification into negative or positive cells by the software. The processing unit 11 is composed of a CPU and performs calculation processing related to the processing and analysis of fluorescence images. Based on the program stored in the memory unit 12, the processing unit 11 performs various processes, including image analysis of the first to third images. The processing unit 11 is connected to the imaging unit 100, the memory unit 12, the display unit 13, the input unit 14, and the fluid circuit unit 15, and receives signals from each device to acquire various information and outputs control signals to each device to control it. For example, the processing unit 11 controls the pump of the fluid circuit unit 15 to aspirate the sample 20a through the suction tube and supplies the aspirated sample 20a to the flow cell 110. Furthermore, the processing unit 11 controls the light sources 121-124 and the imaging unit 154 to capture a first image, two second images, and a third image for each cell flowing through the flow cell 110.

[0030] The memory unit 12 consists of RAM, ROM, and a solid-state drive (SSD), etc. The memory unit 12 stores software that the processing unit 11 executes for image analysis of the first to third images. The display unit 13 consists of a liquid crystal display and displays information such as the classification results of each cell, the first to third images, and the ratio of positive cells. The input unit 14 consists of a mouse and keyboard and is used for inputting information such as the sample ID, switching display screens, and selecting the first to third images. The configuration of the memory unit 12, the display unit 13, and the input unit 14 is not particularly limited.

[0031] The processing unit 11 processes the first to third images captured by the imaging unit 154 by executing software stored in the memory unit 12, extracting cell size from the first image, fluorescence spots related to two types of fluorescence from the two second images, and the nuclear region from the third image. Then, the processing unit 11 performs analysis based on the extracted cell size, the two types of fluorescence spots, and the nuclear region, and calculates the number of negative cells and the ratio of positive cells based on the number of positive cells from the multiple cells contained in the sample 20a.

[0032] First, the processing unit 11 uses the first and third images to select cells belonging to a region in the sample 20a where plasma cells to be tested and myeloma cells that have become cancerous are found at a higher frequency compared to other regions (hereinafter, cells belonging to this region may be referred to as "test cells"). The region where plasma cells to be tested and myeloma cells that have become cancerous are found at a higher frequency compared to other regions is the region in the distribution map of the multiple cells contained in the sample 20a, which will be described in detail later. Next, the processing unit 11 analyzes the two second images for the selected test cells to count the number of positive cells and the number of negative cells, and calculates information regarding the ratio of positive cells.

[0033] More specifically, the imaging unit 154 captures the first to third images shown in Figure 2 for each of the multiple cells (for example, all cells) contained in the sample 20a. The first and third images, the two second images, and their composite image shown in Figure 2 have had their grayscale inverted and their color tone changed to gray. The processing unit 11 first calculates the cell area, which represents the size of each cell, for each cell contained in the sample 20a based on the first image (bright-field image). The cell area can be calculated by the processing unit 11 counting the number of pixels in the bright-field image in which a part of the cell is visible.

[0034] In this embodiment, to select multiple test cells from multiple cells contained in sample 20a that belong to a region in which plasma cells and myeloma cells to be examined are found at a higher frequency compared to other regions, cell size and a nuclear eccentricity value, which represents a measure of nuclear eccentricity described below, are used. The selection of test cells using cell size and nuclear eccentricity value will be explained later with reference to Figure 4. The processing unit 11 calculates the nuclear eccentricity value based on the first image and the third image (nuclear staining image). Figure 3 is a schematic composite image of the first and third images, illustrating the method for calculating the nuclear eccentricity value in the processing unit 11. In Figure 3, the circumference of the outer circle f1 indicates the outer edge of the cells identified by the processing unit 11 based on the first image, and the circumference of the circle f2, indicated by the diagonal lines inside circle f1, indicates the outer edge of the nucleus identified by the processing unit 11 based on the third image. The two-dimensional composite image has mutually orthogonal X and Y directions set.

[0035] The processing unit 11 identifies the range x1 in the X direction and the range y1 in the Y direction in which cells exist based on the composite image, identifies the intersection of the center line of the range x1 in the X direction and the center line of the range y1 in the Y direction as the central position z1 of the cell, and calculates the coordinates of the central position z1 of the cell. The processing unit 11 also identifies the range x2 in the X direction and the range y2 in the Y direction in which nuclei exist based on the composite image, identifies the intersection of the center line of the range x2 in the X direction and the center line of the range y2 in the Y direction as the central position z2 of the nucleus, and calculates the coordinates of that central position z2. Using the coordinates of the central position z1 of the cell and the coordinates of the central position z2 of the nucleus, the processing unit 11 calculates the nuclear eccentricity value, which is defined by the distance d between the two central positions z1 and z2.

[0036] The X or Y direction may be aligned with the direction of sample flow. Also, for the sake of explanation, the method for calculating the nuclear eccentricity value in the processing unit 11 was explained using a composite image of the first and third images, but it is sufficient to determine the distance between the center coordinates of the cells in the first image and the center coordinates of the nucleus in the third image, and the creation of a composite image is not essential.

[0037] Figure 4 is a graph plotting the nuclear eccentricity and cell area of ​​each cell in sample 20a, prepared from sample 1 (bone marrow fluid), on a two-dimensional coordinate system with nuclear eccentricity on the horizontal axis and cell area on the vertical axis. In Figure 4, the vertical axis represents the number of pixels determined to be part of a cell, with 1 pixel representing 0.25 μm. 2 This matches. The horizontal axis represents the distance d between the central position z1 of the cell and the central position z2 of the nucleus, and the unit is μm. In this embodiment, in Figure 4, multiple cells included in the examination area, which are indicated as a Plasma Cell Gate with large cell area and nuclear eccentricity, are used as examination cells, and bright spot analysis in the two second images (FISH images) is performed only on the examination cells. The reason for this will be explained next.

[0038] Figure 5A shows nuclear images (a), bright-field images (b), and a composite image (c) of multiple myeloma cells (cancerous plasma cells). Figure 5B shows nuclear images (a), bright-field images (b), and a composite image (c) of non-multiple myeloma cells. Non-multiple myeloma cells are cells that are neither plasma cells nor myeloma cells that have become cancerous. In the composite images of Figure 5A(c) and Figure 5B(c), the nuclear region is shown in black. As shown in Figures 5A(b) and 5B(b), multiple myeloma cells are often larger than non-multiple myeloma cells.

[0039] Furthermore, as shown in Figures 5A(c) and 5B(c), the nuclei of multiple myeloma cells are located off-center, while the nuclei of non-multiple myeloma cells are located in the center. Therefore, the nuclear eccentricity of multiple myeloma cells is often greater than that of non-multiple myeloma cells. Thus, multiple myeloma cells often have a larger cell area and a greater nuclear eccentricity compared to non-multiple myeloma cells. For this reason, in the two-dimensional coordinate graph in Figure 4, a Plasma Cell Gate with both a large cell area and a large nuclear eccentricity can be used as the examination area, and bright spot analysis of cells within that examination area can be performed efficiently to analyze the bright spots of multiple myeloma cells.

[0040] Next, with reference to Figure 2 and Figure 6, the method for extracting fluorescent spots and nuclear regions will be explained. Note that the third image in Figure 6(a), the second image in Figure 6(b), and the second image in Figure 6(c) schematically show images acquired from the same region of the sample 20a flowing through the flow cell 110. In this embodiment, a fluorescent spot is a point of fluorescence emitted by the fluorescent dye of the nucleic acid probe, and refers to a region where the brightness (pixel value) of each pixel constituting the image is higher than the brightness of the surrounding pixels. Fluorescent spots are extracted by the binarization process described later.

[0041] In the example shown in Figure 2, the first second image contains three bright spots of the first fluorescence at wavelength λ21, and the second second image contains three bright spots of the second fluorescence at wavelength λ22. The third image is a fluorescence image corresponding to the third fluorescence at wavelength λ23, which indicates the nuclear region. The composite image is an image obtained by superimposing the two second images, and is used to determine the fused bright spots, which will be explained later. In the flow channel 111 of the flow cell 110, the cells of the sample 20a flow with space between them, so when this is imaged by the imaging unit 154, three fluorescence images and a bright-field image are obtained for each cell, as shown in Figure 2.

[0042] When the third image shown in Figure 6(a) is acquired, the processing unit 11 creates a brightness-frequency graph, as shown in the center of Figure 6(a), based on the brightness of each pixel in the third image. The frequency on the vertical axis indicates the number of pixels. Based on this graph, the processing unit 11 sets a brightness threshold, which is the boundary between the nuclear region and the background. Then, the processing unit 11 performs a binarization process to represent the third image with pixels having a brightness below the threshold and pixels having a brightness above the threshold, and extracts the area where pixels with a brightness above the threshold are distributed as the nuclear region. In the right-hand diagram of Figure 6(a), the circumference of the dotted circle indicates the outer edge of the extracted nuclear region.

[0043] When the first second image shown in Figure 6(b) is acquired, the processing unit 11 creates a brightness and frequency graph, as shown in the center of Figure 6(b), based on the brightness of each pixel in the first image. Similar to the processing of the third image, the processing unit 11 sets a brightness threshold in this graph and extracts the area where pixels with brightness greater than the threshold are distributed as the region of fluorescent spots. In the right-hand diagram of Figure 6(b), the circumference of the circle drawn with a dotted line indicates the outer edge of the extracted region of fluorescent spots.

[0044] When the second image shown in Figure 6(c) is acquired, the processing unit 11 creates a brightness and frequency graph, as shown in the center of Figure 6(c), based on the brightness of each pixel in the second image. Similar to the processing of the third image, the processing unit 11 sets a brightness threshold in this graph and extracts the area where pixels with brightness greater than the threshold are distributed as the region of fluorescent spots. In the right-hand diagram of Figure 6(c), the circumference of the circle drawn with a dotted line indicates the outer edge of the extracted region of fluorescent spots.

[0045] The processing unit 11 may extract the nuclear region from the third image and the fluorescent spot region from the first and second images by calculation in accordance with the procedure described above, without creating graphs as shown in Figures 6(a) to (c). The processing unit 11 extracts the nuclear region from the third image, but may also detect the nuclear region based on the bright-field image. In that case, it is possible to omit the acquisition of the third image.

[0046] Next, with reference to Figure 7, we will explain the classification of cells based on the bright spot pattern by the processing unit 11. Figure 7 is a schematic diagram showing a simplified bright spot pattern. In this simplified bright spot pattern, if a cell is negative, two bright spots corresponding to the first gene appear in the first second image, and two bright spots corresponding to the second gene appear in the second second image.

[0047] Figure 7(a) shows an example of the arrangement of fluorescent spots in negative cells without chromosomal abnormalities, i.e., a negative pattern, and Figures 7(b) to (d) show examples of the arrangement of fluorescent spots in positive cells with chromosomal abnormalities, i.e., a positive pattern. The first and second second images in Figure 7 are superimposed third images showing the nuclear region, and the composite image is an image superimposed on the two second images and the third image. Fluorescence images include the first second image, the second second image, the third image, and the composite image. Hereafter, the first fluorescent spot constituting the first second image will be referred to as the "first fluorescent spot," and the second fluorescent spot constituting the second second image will be referred to as the "second fluorescent spot."

[0048] Figure 7 illustrates the fluorescence spot pattern, consisting of the letters "GRF" and the numbers "0-3". In the fluorescence spot pattern, "G" represents the first green spot in the composite image, "R" represents the second red spot in the composite image, and "F" represents the fused yellow spot in the composite image. The numbers immediately following G, R, and F indicate the number of G, R, and F spots, respectively, included in the composite image.

[0049] For example, the negative pattern "G2R2F0" shown in Figure 7(a) indicates that there are two primary bright spots in the first secondary image, two secondary bright spots in the second secondary image, and zero fused bright spots in the composite image. Similarly, the positive pattern "G2R3F1" shown in Figure 7(b) indicates that there are two primary bright spots in the first secondary image, three secondary bright spots in the second secondary image, and one fused bright spot in the composite image.

[0050] In the example shown in Figure 7(a), if there are no chromosomal abnormalities such as fusion of the first and second loci, each gene exists independently in pairs within a single nucleus. Therefore, in the first 2nd image, there are two first bright spots within one nuclear region, and in the second 2nd image, there are two second bright spots within the nuclear region. When the first and second 2nd images, which were acquired at the same size, are superimposed and combined, the combined image will have two first bright spots and two second bright spots within a single nuclear region without overlapping. The fluorescence spot pattern shown in Figure 7(a), in which two first bright spots and two second bright spots exist within a nuclear region without overlapping, is a negative pattern in which no chromosomal abnormalities are observed.

[0051] In the example shown in Figure 7(b), a translocation causes a portion of the second gene to fuse with the first gene, resulting in two first-order bright spots in the nucleus of the first second image and three second-order bright spots in the second second image. When the first and second second images are combined, the combined image will contain one first-order bright spot, two second-order bright spots, and one fused bright spot where the first and second bright spots overlap, all within a single nucleus. The fluorescence pattern shown in Figure 7(b) is a positive pattern indicating a chromosomal abnormality.

[0052] Fusion spots, which occur when the first and second fluorescent spots overlap, appear yellow in the composite image. The presence or absence of fusion spots, along with the number of fluorescent spots, is an important classification indicator in FISH testing.

[0053] In the example shown in Figure 7(c), translocation causes a portion of gene 1 to fuse with gene 2, and a portion of gene 2 to fuse with gene 1, resulting in three primary bright spots in the nucleus in the first secondary image and three secondary bright spots in the second secondary image. When the first secondary image and the second secondary image are combined, the combined image will contain one primary bright spot, one secondary bright spot, and two fused bright spots where the primary and secondary bright spots overlap, all within a single nucleus. The fluorescence pattern shown in Figure 7(c) is a positive pattern indicating a chromosomal abnormality.

[0054] In the example shown in Figure 7(d), translocation causes the entirety of the second gene to fuse with the first gene, resulting in two first-order bright spots in the nucleus of the first second image and two second-order bright spots in the second second image. When the first second image and the second second second image are combined, the combined image will contain one first-order bright spot, one second-order bright spot, and one fused bright spot where the first and second bright spots overlap, all within a single nucleus. The fluorescence pattern shown in Figure 7(d) is a positive pattern indicating a chromosomal abnormality.

[0055] According to the FISH method, as described above, it is possible to determine whether each cell is a positive cell with a chromosomal abnormality based on the number of red and green fluorescent spots and the number of fused spots in the composite image of the first and second second images. The processing unit 11 counts the number of first fluorescent spots in the first second image of each cell, the number of second fluorescent spots in the second second image, and the number of fused spots where the first and second fluorescent spots overlap when the first and second second images are combined. For example, the processing unit 11 determines whether a cell is a positive or negative cell by classifying the cell into one of the images in Figures 7(a) to (d) based on the counted number of first fluorescent spots (G), the number of second fluorescent spots (R), and the number of fused spots (F).

[0056] In this embodiment, multiple bright spot patterns for cases where the first gene is the IGH gene and the second gene is the FGFR3 gene are stored in the memory unit 12. More specifically, multiple bright spot patterns corresponding to the negative pattern "G2R2F0" and the positive patterns "G2R3F1", "G3R3F2", and "G2R2F1" illustrated in Figure 7, for cases where the first gene is the IGH gene and the second gene is the FGFR3 gene, are stored in the memory unit 12. The processing unit 11 counts the number of first bright spots (G), the number of second bright spots (R), and the number of fused bright spots (F) from the first second image, the second second image, and the composite image of the two second images of the cell to be analyzed. The processing unit 11 compares the results with the negative and positive patterns stored in the memory unit 12. If the count result corresponds to a positive pattern, the cell is determined to be a positive cell; if it corresponds to a negative pattern, the cell is determined to be a negative cell.

[0057] Next, the method for extracting bright spots and determining fused bright spots by the processing unit 11 will be described with reference to Figures 8 and 9. Figure 8(A) shows the first and second second images obtained by the imaging unit 154. As shown in Figure 8(B), the processing unit 11 performs noise reduction on the fluorescence images (two second images). Since fluorescence images generally contain noise, the processing unit 11 performs noise reduction processing using noise reduction means such as a top-hat filter.

[0058] The processing unit 11 performs a binarization process on the denoised fluorescence image, as shown in Figure 8(C). The threshold setting for the binarization process is as explained using Figure 6. The processing unit 11 calculates the centroid coordinates of the fluorescence spots extracted by the binarization process, as shown in Figure 8(D). The centroid coordinates represent the coordinates of the geometric centroid of the fluorescence spot and can be calculated based on a predetermined formula. As shown in Figure 8(E), the processing unit 11 performs a fusion determination to determine whether the closely spaced first and second fluorescence spots are fused fluorescence spots, based on the distance D between the centroids of the first and second fluorescence spots. The first and second fluorescence spots located within the circle drawn in the composite image in Figure 8(E) are close to each other and are determined to be fused fluorescence spots caused by a translocation of the IGH gene or FGFR3 gene.

[0059] Figure 9 is a schematic diagram illustrating the fusion determination in Figure 8(E) in detail. As shown in Figure 9, the processing unit 11 calculates the distance D between the centroid coordinates C1 of the first bright spot and C2 of the second bright spot, and compares the distance D with a threshold. The threshold used is a distance equivalent to the diameter of one standard fluorescent spot. A fixed value is used for the threshold regardless of the sample. However, the threshold may not be a fixed value, but may be set to be variable depending on the sample. For example, representative values ​​may be calculated from the diameters of multiple fluorescent spots extracted from multiple cells contained in sample 20a, and these representative values ​​may be applied to the threshold. The representative value may be the mean, median, or mode.

[0060] The processing unit 11 determines that the first and second bright spots are fused if the distance D between their centroids is less than or equal to a threshold. If the distance D between their centroids is greater than the threshold, the processing unit 11 determines that the first and second bright spots are not fused. For all first bright spots in the nuclear region, the processing unit 11 calculates the distance D between their centroids and each second bright spot, compares it to the threshold, and performs a fusion determination. The processing unit 11 counts the number of fused bright spots for each cell. Note that in Figures 8(E) and 9, for the sake of explanation, the fusion determination is shown as being performed on a composite image of the first and second second images. However, for the fusion determination by the processing unit 11, it is sufficient to determine the distance between the centroid coordinates of each bright spot in the first second image and the centroid coordinates of each bright spot in the second second image, and the creation of a composite image is not essential.

[0061] The determination of whether the first and second bright spots overlap may be made using the distance between the center points of the first and second bright spots instead of the centroid-to-centroid distance D. In this specification, the center point is the point with the highest brightness among the fluorescent spots, and means the pixel with the highest brightness. The processing unit 11 may determine that the first and second bright spots are fused if the distance between the center points of the first and second bright spots is less than or equal to a predetermined threshold, and may determine that they are not fused if the distance between the center points is greater than the predetermined threshold.

[0062] Furthermore, it is possible to perform fusion detection by comparing the proportion of the overlapping regions between the first and second bright spots, that is, the proportion of pixels constituting the first bright spot that are in the same position (same coordinates) as the pixels constituting the second bright spot, with a threshold value.

[0063] Next, the fluorescence image analysis process performed by the processing unit 11 in this embodiment will be described using Figures 10 to 12. Figure 10 is a flowchart showing an example of the procedure for the fluorescence image analysis process performed by the processing unit 11. The processing shown in Figure 10 is performed when the processor of the processing unit 11 executes a program stored in the storage unit 12. When the sample 20a obtained by the operator using the pre-processing unit 20 is set in the fluorescence image analyzer 10 and an instruction to start measurement is given via the input unit 14, the processing unit 11 starts a series of analysis processes.

[0064] In step S1, the processing unit 11 controls the fluid circuit section 15 of the fluorescence image analyzer 10 to flow the sample 20a into the flow cell 110. The processing unit 11 causes the light sources 121-124 to emit light. This irradiates the cells in the sample 20a flowing through the flow cell 110. The processing unit 11 causes the imaging unit 154 to capture fluorescence images and bright-field images of the cells. This acquires a fluorescence image and a bright-field image (first image) for each cell. As fluorescence images, a first second image corresponding to the first fluorescence, a second second image corresponding to the second fluorescence, and a third image (nuclear staining image) corresponding to the third fluorescence are captured. The processing unit 11 stores the three fluorescence images and bright-field images for each cell in the storage unit 12, associating them with cell IDs assigned in the order of acquisition to identify each cell.

[0065] In step S2, the processing unit 11 performs image analysis on the first and third images and selects multiple cells from the sample 20a (in this embodiment, all cells in the sample 20a) that have a high proportion of plasma cells and myeloma cells. More specific processing performed by the processing unit 11 in step S2 will be explained in detail later with reference to Figure 11.

[0066] Once step S2 is completed, the process moves to step S3. In step S3, the processing unit 11 performs image analysis on the fluorescence images (first second image, second second image) of the multiple cells that were classified as test cells in step S2, and classifies the cells in the fluorescence images as positive cells, negative cells, or unclassified cells.

[0067] Once step S3 is completed, the process moves to step S4. In step S4, the processing unit 11 calculates the positive cell rate for gene fusions to be tested based on the number of positive and negative cells obtained in step S3 using the following equation (1). The positive cell rate is an example of information regarding the proportion of cells in which gene fusion has occurred. Number of positive cells / (number of negative cells + number of positive cells)...(1) Equation (1) excludes non-detectable cells corresponding to unclear fluorescence images, and calculates the positive cell rate using only the number of negative and positive cells. This allows for the output of a more accurate positive cell rate.

[0068] Next, the processes of steps S2 and S3 in Figure 10 will be described in detail. Figure 11 is a flowchart showing an example of the detailed procedure for the classification process in step S2 of Figure 10. Referring to Figure 11, in the classification process of tested cells and non-tested cells, first, in step S11, the processing unit 11 selects one cell from multiple cells in the sample before classification, using the cell ID as the key, and retrieves (reads from) the first and third images stored in association with the selected cell from the storage unit 12. Here, the cell to be selected first is the cell corresponding to the image captured earliest, but is not limited to this.

[0069] In the next step, S12, the processing unit 11 calculates the cell area and nuclear eccentricity value for the selected cell before classification. Specifically, the processing unit 11 calculates the cell area by counting the number of pixels in which a portion of the cell is visible in the bright-field image (first image). The processing unit 11 also calculates the nuclear eccentricity value based on the bright-field image and the nuclear stained image (third image) using the method explained with reference to Figure 3.

[0070] In the next step, S13, the processing unit 11 determines whether the calculated cell area is greater than a predetermined value (threshold area) and whether the calculated nuclear eccentricity value is greater than a predetermined value (threshold nuclear eccentricity value). In the example shown in Figure 4, the threshold area is set to the area of ​​90 pixels and the threshold nuclear eccentricity value is set to 1.2 μm, but the threshold area and threshold nuclear eccentricity value are not limited to these values. Also, in the example shown in Figure 4, upper limits for the cell area and nuclear eccentricity value are set as conditions for a positive determination in step S13, but these upper limits do not need to be set.

[0071] If a positive result is obtained in step S13, in step S15, the processing unit 11 classifies the selected cell as a test cell. Specifically, in step S15, the processing unit 11 associates a label indicating that it is a test cell with the cell ID of the selected cell and stores it in the storage unit 12. When step S15 is completed, the process moves to step S16, where the processing unit 11 determines whether the classification of all cells in the sample has been completed. If a negative result is obtained in step S16, steps S11 and below are repeated. If a positive result is obtained in step S16, the process in step S2 is completed, and the process returns to step S3 in Figure 10.

[0072] On the other hand, if a negative determination is made in step S13, in step S14, the processing unit 11 classifies the selected cell as a non-tested cell. Specifically, in step S14, the processing unit 11 associates a label indicating that it is a non-tested cell with the cell ID of the selected cell and stores it in the storage unit 12. When step S14 is completed, the process proceeds to step S16.

[0073] Regarding the selection of cells in step S11, the order of cell selection may be the order in which the cells are stored in the memory unit 12, rather than the order in which they are captured. Also, in the flow chart of Figure 11, steps S11 to S16 do not require that the processing in step S16 be completed for the cells selected in step S11 before selecting the next cells; multiple cells may be processed in parallel. For example, the next cells may be selected at the same time that the processing in step S12 begins for the cells selected in step S11. In step S2, the processing unit 11 may perform any processing that classifies all cells in the sample 20a into tested cells and non-tested cells. As explained using Figures 5A and 5B, step S2 is performed to efficiently extract plasma cells and myeloma cells.

[0074] Figure 12 is a flowchart showing an example of the detailed procedure for the classification process in step S3 of Figure 10. Referring to Figure 12, in the classification process of positive cells, negative cells, and unclassified cells, first, in step S20, the processing unit 11 selects one test cell before classification from the multiple test cells selected in step S2 of Figure 10, and retrieves (reads from) the second image (both the first second image and the second second image) stored in association with the selected cell from the storage unit 12. The processing unit 11 can identify cells as test cells if the label indicating that it is a test cell is associated with the cell ID.

[0075] In step S21, the processing unit 11 removes noise from the captured image and extracts fluorescent spots by binarization, as explained with reference to Figures 6 and 8. As fluorescent spots, the first spot is extracted from the binarized first second image, and the second spot is extracted from the second second image. Then, in the next step S22, the processing unit 11 determines whether it is possible to determine the number of fluorescent spots in the second image (both the first second image and the second second image) for the selected test cell. If the processing unit 11 determines in step S22 that it is not possible, the process moves to step S23, where the processing unit 11 increments the count of non-determined cells stored in the memory unit 12 by 1 and stores the updated number of non-determined cells in the memory unit 12. Note that before step S3 in Figure 10 starts, the number of non-determined cells is stored as 0 in the memory unit 12.

[0076] The processing unit 11 determines whether or not to determine the number of bright spots as follows. Specifically, if the shape of the fluorescent spot is elliptical or other shape that deviates from a perfect circle, the processing unit 11 determines that it is not subject to classification by the software and classifies the fluorescence image as ineligible. The first and second bright spots are usually close to a perfect circle, but they can sometimes be elliptical or other shapes that deviate from a perfect circle. One reason for this is that the movement speed of the cells flowing through the channel 111 of the flow cell 110 does not match the charge transfer speed of the line sensor of the TDI camera, causing the fluorescent spot to extend or contract in the Y direction, which is the direction of sample flow. In this case, it is difficult to determine the exact centroid coordinates of the fluorescent spot, and the software may make an incorrect fusion determination, so cells corresponding to such fluorescence images are determined to be non-determined cells.

[0077] In this determination, the processing unit 11 classifies a cell as an exclusion cell if, for example, the ratio (Y / X) of the length of the fluorescent spot in the Y direction to the length in the X direction perpendicular to the Y direction exceeds a predetermined threshold. Alternatively, another indicator representing the shape of the fluorescent spot, such as the circularity of the fluorescent spot, may be used instead of the length ratio (Y / X).

[0078] Furthermore, the processing unit 11 excludes fluorescence images from software classification if the size of the fluorescent spot exceeds a predetermined threshold, and classifies those fluorescence images as excluded. The first and second fluorescent spots are usually of similar size, but in some cases, only a few fluorescent spots may be imaged as larger. This is because the fluorescent labeling sites of cells are located far apart in the depth direction along the optical axis of the TDI camera. In this case, it is difficult to determine the exact centroid coordinates of the fluorescent spot, and the software may make an incorrect fusion determination, so such fluorescence images are excluded from cell classification. For example, the processing unit 11 classifies fluorescence images as excluded if the area of ​​the fluorescent spot exceeds a predetermined threshold.

[0079] Furthermore, the processing unit 11 classifies a fluorescence image as ineligible if the brightness of the fluorescent spots is lower than a predetermined threshold, and excludes it from software classification. The maximum brightness values ​​in the two second images are usually about the same, but the brightness may decrease significantly due to the effects of pre-processing, etc. Sample 20a is prepared by pre-processing to fluorescently label target sites of cells, but it is generally difficult to perform homogeneous pre-processing on all cells, and so-called poorly stained cells that are not fluorescently labeled to the desired level may occur. For example, the processing unit 11 classifies a fluorescence image as ineligible if at least one of the maximum brightness value of the first second image and the maximum brightness of the second second image is below a predetermined threshold.

[0080] Once step 23 is completed, the process moves to step S27, where the processing unit 11 determines whether all classifications of the multiple test cells have been completed. If the processing unit 11 determines that the classification is complete in step S27, it terminates the process in step S3 of Figure 10 and returns to step S4. On the other hand, if the processing unit 11 determines that the classification is complete in step S27, it moves to step S20, where it selects one new test cell before classification and performs the processes from step S21 onwards for that selected test cell.

[0081] On the other hand, if a positive determination is made in step S22, the process moves to step S24-1, where the processing unit 11 determines from the composite image of the second image (both the first second image and the second second image) whether or not gene fusion has occurred in the selected test cell. This fusion determination is performed based on the method described with reference to Figures 8 and 9. In step S24-2, the processing unit 11 counts the first bright spot, the second bright spot, and the fused bright spot for the selected test cell. In step S24-3, the processing unit 11 determines whether the count value from step S24-2 corresponds to either a positive pattern or a negative pattern stored in the memory unit 12. This determination is performed based on the method described with reference to Figure 7. If it is determined in step S24-3 that it corresponds to a positive pattern, the process moves to step S25, where a label indicating that it is a positive cell is associated with the cell ID of that cell and stored in the memory unit 12. Furthermore, the processing unit 11 increments the number of positive cells stored in the memory unit 12 by 1 and stores the updated number of positive cells in the memory unit 12. Note that before step S3 in Figure 10 is started, the number of positive cells in the memory unit 12 is stored as 0. When step S25 is completed, the process moves to step S27.

[0082] On the other hand, if it is determined in step S24-3 that the cell matches a positive pattern, the process moves to step S26, where a label indicating that the cell is negative is associated with the cell ID of that cell and stored in the memory unit 12. The processing unit 11 also increments the count of negative cells stored in the memory unit 12 by 1 and stores the updated number of negative cells in the memory unit 12. Note that before step S3 in Figure 10 is started, the memory unit 12 stores the number of negative cells as 0. When step S26 is completed, the process moves to step S27. Note that the determination in step S24-3 is not limited to the above method of comparing the count values ​​of the first bright spot, the second bright spot, and fused bright spots with the positive and negative patterns. For example, the processing unit 11 may count the fused bright spots and determine that the cell is positive if there is one or more fused bright spots, and negative if there are no fused bright spots.

[0083] Figure 13 is a graph showing the calculation results of the positive cell rates for the IGH / CCND1 fusion gene, the IGH / FGFR3 fusion gene, and the IGH / MAF fusion gene, respectively, calculated using the method of this embodiment described above, in a bone marrow sample (sample 1) from a multiple myeloma patient with an IGH translocation. Plasma cells were not isolated from sample 1. Sample 1 is a positive sample in which the IGH / FGFR3 fusion gene is present. IGH / CCND1 fusion gene and IGH / MAF gene fusion are not present in sample 1. Figure 14 is a graph showing the calculation results of the positive cell rates for the IGH / CCND1 fusion gene, the IGH / FGFR3 fusion gene, and the IGH / MAF fusion gene, respectively, calculated using the method of this embodiment described above, in a bone marrow sample (sample 2) from a multiple myeloma patient without an IGH translocation. Plasma cells were not isolated from sample 2. Sample 2 is a negative sample in which none of the IGH / CCND1 fusion genes, IGH / FGFR3 fusion genes, or IGH / MAF fusion genes are present. Figures 13 and 14 also show, as a comparative example, the calculation results obtained by omitting step S2 in Figure 10 and performing steps S3 and S4 in Figure 10 on all cells in sample 20a without selecting test cells, and calculating the positive cell rate. In Figures 13 and 14, the positive cell rate by the method of this embodiment is represented by a bar graph with hatching, and the positive cell rate of the comparative example is represented by a bar graph without hatching. In the following description, the IGH / CCND1 fusion gene and the IGH / MAF fusion gene may be referred to as t(11;14) and t(14;16), respectively.

[0084] Comparing the positive cell rates calculated by the method of this embodiment between sample 1, which has an IGH translocation, and sample 2, which does not have an IGH translocation, as shown in Figures 13 and 14, the positive cell rate for sample 1 with respect to the IGH / FGFR3 fusion gene is 71.84%, while the positive cell rate for sample 2 is 20.45%, a difference of more than 50%. Therefore, it becomes possible to set a threshold between 20.45% and 71.84%, and by generating and outputting the positive cell rate as an indicator for the diagnosis of multiple myeloma, diagnosis can be facilitated.

[0085] On the other hand, comparing the positive cell rates calculated using the comparative example method for sample 1 and sample 2, as shown in Figures 13 and 14, the positive cell rate for sample 1 with respect to the IGH / FGFR3 fusion gene was 28.36%, while the positive cell rate for sample 2 was 19.79%, a difference of less than 9%. Thus, when the difference in positive cell rates between samples with and without IGH translocations is small, the positive cell rate often cannot be used as an indicator for diagnosing multiple myeloma.

[0086] Regarding the IGH / FGFR3 fusion gene in sample 2, as shown in Figure 14, the positive cell rate calculated by the method of this embodiment was 20.45%, while the positive cell rate calculated by the method of the comparative example was 19.79%. Since sample 2 is a negative sample that does not produce an IGH / FGFR3 fusion gene, it is thought that approximately 20% of the negative cells were judged as positive cells (hereinafter referred to as false positives) in both the method of this embodiment and the method of the comparative example. From this, it is thought that the positive cell rate of 71.84% calculated by the method of this embodiment and the positive cell rate of 28.36% calculated by the method of the comparative example for the IGH / FGFR3 fusion gene in sample 1 each include approximately 20% of false positives. Here, regarding the IGH / FGFR3 fusion gene in sample 1, even if 20% is subtracted from the positive cell rate calculated by the method of this embodiment, the positive cell rate still exceeds 50%, whereas if 20% is subtracted from the positive cell rate calculated by the method of the comparative example, the positive cell rate falls below 9%. This demonstrates that, in the method of this embodiment, false positive results do not affect the diagnosis of multiple myeloma, and the diagnosis can be made easier, whereas in the comparative example method, false positive results do affect the diagnosis, making it difficult to diagnose multiple myeloma.

[0087] Regarding the IGH / FGFR3 fusion gene in sample 1, as shown in Figure 13, the positive cell rate calculated by the method of this embodiment was 71.84%, while the positive cell rate calculated by the method of the comparative example was 28.36%. In the formula for the positive cell rate calculated by the method of this embodiment (71.84%), the numerator is 574 and the denominator is 799. On the other hand, in the formula for the positive cell rate calculated by the method of the comparative example (28.36%), the numerator is 3674 and the denominator is 12954. This indicates that by setting up the Plasma Cell Gate shown in Figure 4, the numerator in the formula for calculating the positive cell rate shows that 574 cells were selected from 3674 cells, i.e., the selection rate was 15.6%, and the denominator shows that 799 cells were selected from 12954 cells, i.e., the selection rate was 6.2%. Thus, in the method of this embodiment, by setting the Plasma Cell Gate, the cell selection rate in the numerator of the formula for calculating the positive cell rate becomes larger than the cell selection rate in the denominator, and positive cells are efficiently extracted.

[0088] For the IGH / CCND1 fusion gene in sample 1, as shown in Figure 13, the positive cell rate calculated by the method of this embodiment was 30.54%, while the positive cell rate calculated by the comparative example method was 20.11%, a difference of less than 11%. For the IGH / MAF fusion gene in sample 1, as shown in Figure 13, the positive cell rate calculated by the method of this embodiment was 16.90%, while the positive cell rate calculated by the comparative example method was 13.83%, a difference of less than 4%. Thus, with the method of this embodiment, even when the Plasma Cell Gate shown in Figure 4 is set, the positive cell rate can be output with the same accuracy as when the Plasma Cell Gate is not set for samples that do not have an IGH translocation.

[0089] As described above, in the method of this embodiment, the difference between the positive cell rate in samples with IGH translocations and the positive cell rate in samples without IGH translocations becomes large, and the positive cell rate can be output as an indicator for the diagnosis of multiple myeloma. Therefore, the diagnosis of the subject can be facilitated.

[0090] In other words, according to this embodiment, the fluorescence image analyzer 10 automatically performs a process based on software to select multiple test cells that belong to a region in which the target cells (in this embodiment, plasma cells and myeloma cells) are frequently contained from among multiple cells in the sample. Therefore, even if the proportion of plasma cells and myeloma cells to be tested relative to all cells in the sample 20a is small, the positive cell rate can be output without having to perform a process to isolate those cells from the sample 20a.

[0091] [Variations regarding the selection of test cells] In the above embodiment, cell area and nuclear eccentricity were calculated as selection information using the first image, which is a bright-field image, and the third image, which is a nuclear-stained image, and plasma cells and myeloma cells, which are the cells to be tested, were efficiently extracted from these two pieces of selection information. However, the selection information for selecting the cells to be tested is not limited to cell area and nuclear eccentricity, but may be any other information relating to the characteristics of the cells. Alternatively, multiple cells to be tested may be selected from multiple cells in the sample without using selection information. Next, a modified example of the selection of cells to be tested will be described.

[0092] <First variation> As images from which selection information can be extracted, the first image, which is a bright-field image, and the third image, which is a fluorescence image of nuclear staining, may be used. Furthermore, the cell area extracted from the first image and the nuclear area extracted from the third image may be used as selection information.

[0093] Next, we will describe a modified example 1 in which the positive cell rate was calculated using cell area and nuclear area as selection information in one sample of follicular lymphoma (pancreas) in which the IGH / BCL2 fusion gene was present.

[0094] In this modified example 1, sample 20a was prepared by applying a pretreatment to the pancreatic juice of the sample described in 1 above, which included the steps of hybridizing a nucleic acid probe labeled with a first fluorescent dye with a target site in the nucleic acid, hybridizing a nucleic acid probe labeled with a second fluorescent dye with a target site in the nucleic acid, and nuclear staining in which the nucleus of each cell was labeled with a nuclear staining dye as a third fluorescent dye.

[0095] Imaging processing was performed on the sample 20a in the same manner as in the above embodiment, and a first image (bright-field image), two second images, and a third image (nuclear-stained image) were obtained for each cell in the sample 20a. Then, for each cell, the cell area was calculated from the first image and the nuclear area was calculated from the third image. The cell area can be calculated by the processing unit 11 identifying the number of pixels in the bright-field image in which a part of the cell is visible. The nuclear area can be calculated by the processing unit 11 identifying the number of pixels in the nuclear-stained image in which a part of the nucleus is visible.

[0096] Figure 15 is a graph plotting the nuclear area and cell area of ​​each cell in sample 20a on a two-dimensional coordinate system, with nuclear area as the parameter on the horizontal axis and cell area as the parameter on the vertical axis. In Figure 15, the vertical axis represents the number of pixels in the region identified as a cell, with 1 pixel representing 0.25 μm. 2 This matches. The horizontal axis shows the number of pixels in the region identified as the nucleus, where one pixel represents 0.25 μm. 2 It matches.

[0097] In this modified example 1, cells in the region excluding the area with a nuclear area of ​​less than 55 pixels and a cell area of ​​less than 80 pixels in the graph of Figure 15 were designated as test cells. Bright spot analysis was performed only on multiple test cells in that region, and cells with one or more target gene fusions were designated as positive cells.

[0098] Figure 16 is a graph showing the calculation results of the positive cell rates for the IGH / CCND1 fusion gene, the IGH / BCL2 fusion gene, and the IGH / MYC fusion gene in a spleen tissue sample from a follicular lymphoma patient with an IGH translocation, calculated using the method of Modification 1 described above. This sample is a positive sample in which the IGH / BCL2 fusion gene is present. In this sample, the IGH / CCND1 fusion gene and the IGH / MYC fusion gene are not present. In this graph, as with Figures 13 and 14, a comparative example is also shown in which step S2 in Figure 10 is omitted, and the positive cell rate is calculated by performing steps S3 and S4 in Figure 10 on all cells in sample 20a without selecting any test cells. In Figure 16, the positive cell rate using the method of Modification 1 is represented by a hatched bar graph, and the positive cell rate of the comparative example is represented by an unhatched bar graph. In the following explanation, the IGH / CCND1 fusion gene, the IGH / BCL2 fusion gene, and the IGH / MYC fusion gene may be referred to as t(11;14), t(14;18), and t(8;14), respectively.

[0099] Regarding the IGH / BCL2 fusion gene in this sample, as shown in Figure 16, the positive cell rate calculated using the method of Modification 1 was 79.6%, while the positive cell rate calculated using the method of the Comparative Example was 53.3%. In the formula for calculating the positive cell rate (79.6%) using the method of Modification 1, the numerator is 9944 and the denominator is 12492. On the other hand, in the formula for calculating the positive cell rate (53.3%) using the method of the Comparative Example, the numerator is 11238 and the denominator is 21079. This indicates that, by setting the region described above as shown in Figure 15, the numerator in the formula for calculating the positive cell rate shows that 9944 cells were selected from 11238 cells, i.e., the selection rate was 88.4%, and the denominator shows that 12492 cells were selected from 21079 cells, i.e., the selection rate was 59.3%. Thus, in the method of this modified example 1, by setting the region as described above as shown in Figure 15, the cell selection rate in the numerator of the formula for calculating the positive cell rate becomes larger than the cell selection rate in the denominator, and positive cells are efficiently extracted.

[0100] For the IGH / CCND1 fusion gene in this sample, as shown in Figure 16, the positive cell rate calculated using the method of Modification 1 was 28.4%, while the positive cell rate calculated using the method of the Comparative Example was 26.0%, a difference of less than 3%. For the IGH / MYC fusion gene in this sample, as shown in Figure 16, the positive cell rate calculated using the method of Modification 1 was 19.4%, while the positive cell rate calculated using the method of the Comparative Example was 16.3%, a difference of less than 4%. Thus, with the method of Modification 1, even when the region described above shown in Figure 15 is set, the positive cell rate can be output with the same accuracy as when the region described above shown in Figure 15 is not set, for samples that do not have an IGH translocation.

[0101] As described above, in the method of this modified example 1, the difference between the positive cell rate in samples with IGH translocations and the positive cell rate in samples without IGH translocations becomes large, and the positive cell rate can be output as an indicator for the diagnosis of follicular lymphoma. Therefore, the diagnosis of the subject can be facilitated.

[0102] Next, we will describe another example using the method of Modification 1, specifically an example in which the positive cell rate was calculated by using cell area and nuclear area as selection information in lymph node samples from diffuse large B-cell lymphoma patients with IGH translocations.

[0103] In this example, sample 20a was prepared by performing a pretreatment on the lymph node sample, which included a step of hybridizing a nucleic acid probe labeled with a first fluorescent dye with a target site in the nucleic acid, a step of hybridizing a nucleic acid probe labeled with a second fluorescent dye with a target site in the nucleic acid, and a nuclear staining step of labeling the nucleus of each cell with a nuclear staining dye as a third fluorescent dye.

[0104] Imaging was performed on sample 20a to obtain a first image (bright-field image), two second images, and a third image (nuclear-stained image) for each cell in sample 20a. For each cell, the cell area was calculated from the first image, and the nuclear area was calculated from the third image.

[0105] Figure 17 is a graph plotting the nuclear area and cell area of ​​each cell in sample 20a on a two-dimensional coordinate system, with nuclear area as the parameter on the horizontal axis and cell area as the parameter on the vertical axis. In Figure 17, the vertical axis represents the number of pixels in the area identified as a cell, with 1 pixel representing 0.25 μm. 2 This matches. The horizontal axis shows the number of pixels in the region identified as the nucleus, where one pixel represents 0.25 μm. 2 It matches.

[0106] In this example, in the graph of Figure 17, cells located outside the region where the nuclear area is less than 55 pixels and the cell area is less than 80 pixels were designated as test cells. Bright spot analysis was performed only on multiple test cells located in this region, and cells containing one or more target gene fusions were designated as positive cells.

[0107] Figure 18 is a graph showing the calculation results of the positive cell rates for the IGH / CCND1 fusion gene, the IGH / BCL2 fusion gene, and the IGH / MYC fusion gene, respectively, in lymph node samples from a patient with diffuse large B-cell lymphoma who has an IGH translocation, using the method of another example described in Modification 1 above. This sample is a positive sample in which the IGH / BCL2 fusion gene is present. In this sample, the IGH / CCND1 fusion gene and the IGH / MYC fusion gene are not present. In this graph, as with Figures 13 and 14, a comparative example is also shown, in which step S2 in Figure 10 is omitted and no test cells are selected, and the positive cell rate is calculated by performing steps S3 and S4 in Figure 10 on all cells in sample 20a. In Figure 18, the positive cell rate of the comparative example and the positive cell rate using the other example method of Modification 1 are represented by hatched bar graphs and unhatched bar graphs, respectively.

[0108] Regarding the IGH / BCL2 fusion gene in this sample, as shown in Figure 18, the positive cell rate calculated using the other method in Modification 1 was 63.6%, while the positive cell rate calculated using the comparative example method was 29.0%. In the formula for calculating the positive cell rate (63.6%) using the other method in Modification 1, the numerator is 1236 and the denominator is 1944. On the other hand, in the formula for calculating the positive cell rate (29.0%) using the comparative example method, the numerator is 4939 and the denominator is 17058. This indicates that, by setting the region described above as shown in Figure 17, the numerator in the formula for calculating the positive cell rate was 1236 cells selected from 4939 cells, i.e., the selection rate was 25.0%, and the denominator was 1944 cells selected from 17058 cells, i.e., the selection rate was 11.4%. Thus, in the alternative method of Modification 1, by setting the region as described above as shown in Figure 17, the cell selection rate in the numerator of the formula for calculating the positive cell rate becomes larger than the cell selection rate in the denominator, and positive cells are efficiently extracted.

[0109] For the IGH / CCND1 fusion gene in this sample, as shown in Figure 18, the positive cell rate calculated using the other method in Modification 1 was 18.1%, while the positive cell rate calculated using the comparative example method was 24.1%, a difference of less than 7%. For the IGH / MYC fusion gene in this sample, as shown in Figure 18, the positive cell rate calculated using the other method in Modification 1 was 10.6%, while the positive cell rate calculated using the comparative example method was 12.3%, a difference of less than 2%. Thus, with the other method in Modification 1, even when the region described above shown in Figure 17 is set, the positive cell rate can be output with the same accuracy as when the region described above shown in Figure 17 is not set, for samples that do not have an IGH translocation.

[0110] As described above, in the alternative method of Modification 1, the difference between the positive cell rate in samples with IGH translocations and the positive cell rate in samples without IGH translocations becomes larger, and the positive cell rate can be output as an indicator for the diagnosis of diffuse large B-cell lymphoma. Therefore, the diagnosis of the subject can be facilitated.

[0111] <Second variation> The images used to extract selection information may include a first image, which is a bright-field image, and a third image, which is a fluorescence image of the nucleus. The selection information may also include information about the size of the cell other than the cell area extracted from the first image, such as the diameter or width of the cell. Furthermore, the selection information may also include information about the size of the nucleus other than the nuclear area extracted from the third image, such as the diameter or width of the nucleus.

[0112] <Third variation> The first image, which is a bright-field image, may be used as the image from which selection information is extracted. Alternatively, other selection information may be obtained from signals instead of images. Specifically, the processing unit 11 may calculate information about cell size, such as cell area, based on the first image. The processing unit 11 may also use the intensity of fluorescence from a nucleus-labeled staining dye, which occurs when the cell is irradiated with excitation light, as selection information. The magnitude of the fluorescence intensity from the nucleus-labeled staining dye indicates the size of the nucleus.

[0113] <Fourth variation> As the first image used to extract selection criteria, a fluorescence image of the nucleus may be used, and one or more selection information items may be information about the nucleus calculated by the processing unit 11 based on the fluorescence image of the nucleus, for example, the area of ​​the nucleus. In addition, the processing unit 11 may obtain other selection information from signals instead of images, and the processing unit 11 may use the intensity of scattered light (preferably forward scattered light) as the selection information.

[0114] Forward scattering, also known as low-angle scattering, refers to the light scattered forward when laser light is shone on a cell. The magnitude of forward scattering is approximately proportional to the size of the cell.

[0115] <Fifth variation> In the above embodiment and all its variations in which selection information is acquired using bright-field images, dark-field images may be used instead of bright-field images. That is, a dark-field image may be used as the first image, and the processing unit 11 may acquire selection information based on the dark-field image. A dark-field image is an image based on light scattered by cells. Even when using dark-field images, information about the size of cells, such as cell area, can be acquired.

[0116] <Sixth variation> In the above embodiment, the case was described in which, with respect to multiple cells contained in sample 20a, each cell is photographed by the imaging unit 100, and one cell is photographed in each image. However, for one or more of the first to third images, multiple cells may be photographed in each image, and information about multiple cells may be obtained by analyzing each image. For example, multiple cells may be captured in one bright-field image, or multiple bright spots of cells may be captured in one second image, or multiple nuclei may be captured in one nuclear-stained image.

[0117] <7th variation> Regarding one or more of the first to third images, each image may only capture a portion of a cell, and information about multiple cells may be obtained by analyzing each image. For example, a single nuclear staining image does not need to show the entire cell; it may only show a portion of the cell containing the nucleus.

[0118] <8th variation> In all the embodiments and modifications described above, the case in which selection information is obtained using at least the first image has been explained. However, it is not necessary to obtain selection information. Specifically, whether or not a cell is a test cell may be determined by inputting the pixel data extracted from the image into a classification algorithm having a neural network (preferably a deep learning algorithm).

[0119] An image is data, and each pixel in the image contains brightness information and hue information. In this case, the pixel data, such as information on the magnitude of luminance (brightness information) and hue information, is directly input into a classification algorithm that has a neural network, and the algorithm determines whether or not it corresponds to a test cell.

[0120] Specifically, first, a large number of images of tested cells and non-tested cells are prepared, and a classification algorithm with a neural network is trained using these numerous images. The trained classification algorithm will be able to determine whether or not a cell is a tested cell with considerable accuracy. Therefore, by setting a threshold (for example, 80%), only cells that the classification algorithm determines to be tested cells with a probability of above that threshold can be extracted as tested cells.

[0121] [Example of calculation method for positive cell percentage] In the above embodiment, the positive cell rate was calculated using equation (1) described above. However, the positive cell rate may be calculated using other equations. Below, we will describe a modified example in which the positive cell rate is calculated using an equation different from equation (1).

[0122] <First variation> The percentage of positive cells may also be calculated using the following equation (2). Positive cell rate = Number of positive cells / (Number of tested cells - Number of unclassified cells) ... (2) In calculating the positive cell rate using equation (2), as in calculating the positive cell rate using equation (1), unclassified cells corresponding to unclear fluorescence images are excluded, and the positive cell rate is calculated using only the number of negative and positive cells. This allows for the output of more accurate information on the number of positive cells.

[0123] <Second variation> In the above embodiment and the first modified example, the positive cell rate was calculated considering undetermined cells, but the positive cell rate may also be calculated using the following equation (3) without considering undetermined cells. Positive cell rate = Number of positive cells / Number of tested cells ... (3)

[0124] <Third variation> In the above embodiments and their respective modifications, only linear values ​​for one or more variables among the number of positive cells, the number of tested cells, and the number of unclassified cells appeared in the formula for positive cells. However, the formula for the positive cell rate may include a function other than a linear function in which one or more of the number of positive cells, the number of tested cells, and the number of unclassified cells are variables. For example, the formula for the positive cell rate may include the square of one or more of the variables among the number of positive cells, the number of tested cells, and the number of unclassified cells. In one specific example, the positive cell rate may be calculated by dividing the square of the number of positive cells by the number of tested cells.

[0125] Thus, even if the formula for the positive cell rate includes a function other than a linear function (for example, a higher-order function of degree two or higher, an exponential function, a logarithmic function, etc.) with one or more of the following as variables: the number of positive cells, the number of tested cells, and the number of undetermined cells, it still falls under the category of calculating information regarding the proportion of cells in which gene fusion has occurred.

[0126] <Fourth variation> In the positive cell rate of the above embodiment and each modified example, the number of positive cells may be replaced with (number of tested cells - number of negative cells) to calculate the positive cell rate. Figure 19 is a flowchart showing a modified image processing of the processing unit 11 in step S3 of Figure 10. The flowchart shown in Figure 19 differs from the flowchart in Figure 12, which shows the image processing procedure of the processing unit 11 in step S3 of the above embodiment, in that steps S22 and S23 are omitted.

[0127] As shown in Figure 19, in step S3 of Figure 10, each cell included in the multiple tested cells may be classified into either a positive cell count or a negative cell count. In this case, since there are no unclassified cells, accurate determination can be made by replacing the number of positive cells with (number of tested cells - number of negative cells) in the positive cell rate of the above embodiment and each modified example. Even if there are unclassified cells, the positive cell rate may be calculated by replacing the number of positive cells with (number of tested cells - number of negative cells) in the positive cell rate of the above embodiment and each modified example.

[0128] [Other variations] In the above embodiment, no further processing was performed on the multiple cells classified as non-test cells in step S14 shown in Figure 11. However, further processing may be performed on the multiple cells classified as non-test cells in step S14.

[0129] More specifically, as shown in Figure 20, in step S20', the processing unit 11 selects one cell from all the imaged cells and acquires a second image (both the first second image and the second second image) stored in association with the selected cell from the storage unit 12. The processing in steps S21, S22', and steps 23 to S24-3 differs from steps S21 to S24-3 of the above embodiment in that in step S22', one of the cells being examined is changed to one cell. The processing in steps S21, S22', and steps 23 to S24-3 is basically the same as the processing in steps S21 to S24-3 of the above embodiment, so a description is omitted. In step S25', if the cell ID of the cell has a label indicating that it is a target for testing, the processing unit 11 associates it with a label indicating that it is a positive cell and stores it in the storage unit 12, increments the count of positive cells by 1, and stores the updated number of positive cells in the storage unit 12. If the cell does not have a label indicating that it is a target for testing, the count of positive cells is not changed. In step S26', if the cell ID of the cell has a label indicating that it is a target for testing, the processing unit 11 associates it with a label indicating that it is a negative cell and stores it in the storage unit 12, increments the count of negative cells by 1, and stores the updated number of negative cells in the storage unit 12. If the cell does not have a label indicating that it is a target for testing, the count of positive cells is not changed. In step S27', the processing unit 11 determines whether processing has been completed for all the imaged cells. If the determination in step S27' is positive, the processing unit 11 returns to step S4 in Figure 10; otherwise, it returns to step S20'.

[0130] The above embodiments and modifications describe the case in which fluorescence dot fusion is determined. However, cells included in the image may be classified into positive and negative cells based on the number of fluorescence dots without determining fluorescence dot fusion. The method of classifying cells, i.e., the index used for classification, differs depending on the cells to be analyzed, the target site, the measurement items, the fluorescent label, etc. Therefore, it is necessary to use an appropriate classification index according to the subject of analysis.

[0131] Furthermore, as shown in Figure 21, the fluorescence imaging analyzer 10 may also include a sample preprocessing unit 20 within the device. The processing unit 11 is connected to the sample preprocessing unit 20 and is configured to control the sample preprocessing unit 20. When a sample 10a, which has been collected from a subject and processed by centrifugation or other means, is set in the preprocessing unit 20, the preprocessing unit 20 performs preprocessing on the sample 10a to prepare a sample 20a containing cells with fluorescently labeled target sites. The other configurations are the same as those shown in Figure 1. As illustrated in Figure 21, if the fluorescence imaging analyzer 10 is equipped with a sample preprocessing unit 20, the operator can simply set the sample 10a in the device, and the preprocessing will be performed automatically, and the prepared sample 20a will be automatically analyzed.

[0132] Furthermore, as shown in Figure 22, the imaging unit of the fluorescence image analyzer may include an imaging unit 300, which includes a fluorescence microscope, instead of the imaging unit 100 exemplified in Figure 1. The imaging unit 300 comprises light sources 301-303, a mirror 304, dichroic mirrors 305 and 306, a shutter 311, a quarter-wave plate 312, a beam expander 313, a focusing lens 314, a dichroic mirror 315, an objective lens 316, a stage 320, a focusing lens 331, an imaging unit 332, and controllers 341 and 342.

[0133] The stage 320 is a support platform on which the slide glass 321 is placed, and is driven by the controller 342. The slide glass 321 placed on the stage 320 is on which the sample 20a prepared in the pre-processing unit 20 is placed. That is, in the imaging unit 300, the fluorescently labeled cells on the slide glass 321 are imaged by a fluorescence microscope equipped with an imaging unit 332, and a fluorescence image is acquired.

[0134] Light sources 301 to 303 are the same as light sources 121 to 123 shown in Figure 1. Mirror 304 reflects light from light source 301. Dichroic mirror 305 transmits light from light source 301 and reflects light from light source 302. Dichroic mirror 306 transmits light from light sources 301 and 302 and reflects light from light source 303. The optical axes of the light from light sources 301 to 303 are aligned with each other by mirror 304 and dichroic mirrors 305 and 306.

[0135] The shutter 311 is driven by the controller 341 and switches between a state that allows light emitted from light sources 301-303 to pass through and a state that blocks light emitted from light sources 301-303. This adjusts the light irradiation time for the sample 20a. The quarter-wave plate 312 converts linearly polarized light emitted from light sources 301-303 into circularly polarized light. The fluorescent dye bound to the nucleic acid probe reacts to light in a predetermined polarization direction. Therefore, by converting the excitation light emitted from light sources 301-303 into circularly polarized light, the polarization direction of the excitation light is more likely to match the polarization direction to which the fluorescent dye reacts. This allows the fluorescent dye to be excited efficiently.

[0136] The beam expander 313 widens the light illumination area on the slide glass 321. The focusing lens 314 focuses the light so that parallel light is irradiated onto the slide glass 321 from the objective lens 316. The dichroic mirror 315 reflects the light emitted from the light sources 301-303 and transmits the fluorescence generated from the sample 20a. The objective lens 316 guides the light reflected by the dichroic mirror 315 to the slide glass 321.

[0137] The fluorescence emitted from the sample 20a passes through the objective lens 316 and then through the dichroic mirror 315. The focusing lens 331 focuses the fluorescence that has passed through the dichroic mirror 315 and guides it to the imaging surface 332a of the imaging unit 332. The imaging unit 332 captures an image of the fluorescence irradiated onto the imaging surface 332a and generates a fluorescence image. The imaging unit 332 is composed of, for example, a CCD.

[0138] Controllers 341 and 342 and the imaging unit 332 are connected to the processing unit 11 described above. The processing unit 11 controls the controllers 341 and 342 and the imaging unit 332 and receives the fluorescence images captured by the imaging unit 332. Note that the fluorescence images captured by the imaging unit 332 may show cells in a densely packed state, unlike when using the flow cell 110. Therefore, the processing unit 11 performs processes such as dividing the acquired fluorescence image by cell nucleus or setting a region in the fluorescence image that corresponds to the nucleus of a single cell.

[0139] In a fluorescence image analyzer equipped with an imaging unit 300, the acquired fluorescence images are used to classify the cells being examined, determine the occurrence of gene fusion in the cells, and calculate the percentage of positive cells. This allows information on the ratio of positive cells to be output without isolating specific types of cells. Therefore, the diagnosis of the subject can be facilitated.

[0140] Furthermore, when implementing the fluorescence image analysis method of the present invention, a fluorescence image analysis program that causes a computer to perform the following processes (a) to (e) may be used. (a) For multiple cells in a sample that are contained in the sample and whose target sites on chromosomes are labeled with a fluorescent dye, a process to acquire a first image that includes at least a portion of the cells as the imaging target, and a second image that includes the fluorescence generated from the fluorescent dye that labeled the target sites of the cells in the first image as the imaging target. (b) A process for selecting multiple cells to be examined from a group of cells based on at least the first image. (c) Based on the second image, a process to extract fluorescent spots from at least multiple test cells that are caused by fluorescence from a fluorescent dye. (d) A process to identify cells with chromosomal abnormalities and / or cells without chromosomal abnormalities based on the extracted bright spots. (e) A process that generates information regarding the proportion of cells with chromosomal abnormalities, based on the number of cells tested and / or the number of cells without chromosomal abnormalities.

[0141] Furthermore, the gene fusions that can be tested by the method of this disclosure are not limited to the gene fusions used in the description of the embodiments above, but may be any other gene fusion. For example, the method of this disclosure may detect t(14;20), t(8;14), or IGH split(14q32). t(14;20), t(8;14), and IGH split(14q32), like t(11;14), t(4;14), and t(14;16), are gene translocations that cause multiple myeloma. The method of this disclosure may also detect t(3;14), MYC split(8q24), BCL2 split(18q21), or BCL6 split(3q27). t(3;14), MYC split(8q24), BCL2 split(18q21), and BCL6 split(3q27), like t(11;14), t(14;18), and t(8;14), are gene translocations that cause malignant lymphoma.

[0142] Furthermore, the method disclosed herein may also detect chromosomal abnormalities other than gene fusion due to chromosomal translocation, and may detect chromosomal inversions, gene deletions, or gene amplifications as chromosomal abnormalities. For example, del(17p), del(13q), or del(1p) may be detected as gene deletions, and for example, gain(1q21) may be detected as gene amplification. Here, del means deletion, gain means amplification, and the numbers in parentheses represent the gene locus of each gene. When detecting a gene deletion, the processing unit 11 omits the processing in step S24-1 in Figure 12, and in step S24-3, compares the number of bright spots counted in step S24-2 with the number of the gene in the negative cells. If the two are the same number, the processing proceeds to step S26; if the counted number of bright spots is less than the number of the gene in the negative cells, the processing proceeds to step S25. When detecting gene amplification, the processing unit 11 omits the process in step S24-1 in Figure 12, and in step S24-3, compares the number of bright spots counted in step S24-2 with the number of the gene in the negative cells. If the two numbers are the same, the process proceeds to step S26. If the number of bright spots counted is greater than the number of the gene in the negative cells, the process proceeds to step S25.

[0143] Furthermore, it goes without saying that the diseases that can be examined by the method of this disclosure are not limited to the diseases mentioned in the embodiments and modifications described above. [Explanation of Symbols]

[0144] 10. Fluorescence imaging analyzer 11 Processing Section 12 Storage section 13 Display section 14 Input section 20 Pre-processing units 20a Sample 100,300 imaging units 110 Flow Cell 111 channel 121~124 Light source 131-134, 151, 153 Focusing lenses 141,142 Dichroic mirror 152 Optical Units 154 Imaging Unit

Claims

1. For multiple cells contained in the sample, a target site on the chromosome is labeled with a first fluorescent dye, and the nucleus is labeled with a second fluorescent dye having a different output wavelength from the first fluorescent dye. For a plurality of cells in the sample in which the target region and the nucleus are labeled, a first image is captured including at least a portion of the cells as the imaging target, a second image is captured including fluorescence generated from the first fluorescent dye that labeled the target region of the cells in the first image as the imaging target, and a third image is captured including fluorescence generated from the second fluorescent dye that labeled the nucleus of the cells in the first image as the imaging target. Based on at least the first and third images, multiple cells are selected from the plurality of cells that have specific morphological characteristics and are to be examined. Based on the second image, bright spots due to fluorescence generated from the fluorescent dye are extracted from at least a number of the tested cells. Based on the extracted bright spots, cells exhibiting chromosomal abnormalities and / or cells not exhibiting chromosomal abnormalities are identified. Based on the number of cells exhibiting the chromosomal abnormality and / or the number of cells not exhibiting the chromosomal abnormality, information is generated regarding the ratio of cells exhibiting the chromosomal abnormality to the cells being tested. In selecting the cells to be tested, selection information is obtained from the first image and the third image, and the plurality of cells to be tested are selected based on the obtained selection information. The selection information includes a value relating to the size of the cell and a value representing the eccentric distribution of the nucleus within the cell. A fluorescence imaging method wherein the ratio of the number of plasma cells and myeloma cells to the number of multiple cells being tested is higher than the ratio of the number of plasma cells and myeloma cells to the number of multiple cells not selected as being tested.

2. The fluorescence image analysis method according to claim 1, wherein, in the extraction of bright spots, the cells to be extracted are a plurality of selected test cells.

3. The fluorescence image analysis method according to claim 1 or 2, wherein the first image is a bright-field image obtained by detecting light transmitted through the cells.

4. The fluorescence image analysis method according to any one of claims 1 to 3, wherein the first image includes the cytoplasm and nucleus of the cell as the imaging target.

5. In selecting the cells to be tested, selection information is obtained from the first image and the third image, and the plurality of cells to be tested are selected based on the obtained selection information. The first image is a bright-field image obtained by detecting light that has passed through the cells. The fluorescence image analysis method according to any one of claims 1 to 4, wherein the selection information includes a value relating to the cell area obtained from the first image and a value representing the eccentricity of the nucleus obtained from the first image and the third image.

6. The fluorescence imaging method according to any one of claims 1 to 5, wherein the chromosomal abnormality is at least one of a chromosomal translocation, a chromosomal inversion, a gene deletion, and a gene amplification.

7. The fluorescence imaging method according to any one of claims 1 to 6, wherein the chromosomal abnormality is a gene translocation caused by multiple myeloma or malignant lymphoma.

8. The fluorescence image analysis method according to claim 7, wherein the fusion gene resulting from the chromosomal translocation is an IGH / FFFR3 fusion gene, an IGH / CCND1 fusion gene, an IGH / MAF fusion gene, an IGH / BCL2 fusion gene, or an IGH / MYC fusion gene.

9. A fluorescence image analysis method according to any one of claims 1 to 8, wherein, in generating information on the ratio, the number of tested cells for which the cells based on the bright spots could not be identified is not included in the number of tested cells when generating information on the ratio.

10. In generating information regarding the ratio, the ratio of cells exhibiting the chromosomal abnormality to the tested cells is, A fluorescence image analysis method according to any one of claims 1 to 9, wherein the number of cells exhibiting the chromosomal abnormality is generated by dividing the number of cells selected as test cells and exhibiting the chromosomal abnormality by the sum of the number of cells selected as test cells and not exhibiting the chromosomal abnormality.

11. A fluorescence image analysis method according to any one of claims 1 to 10, wherein the first image and the second image are captured while the sample is in a flowing state.

12. The fluorescence image analysis method according to any one of claims 1 to 11, wherein, in the imaging of the first image and the second image, the first image and the second image are captured for each of the plurality of cells.

13. An imaging unit that captures, for a plurality of cells in a sample in which a target region on the chromosome is labeled with a first fluorescent dye and the nucleus is labeled with a second fluorescent dye having a different output wavelength from the first fluorescent dye, a first image that includes at least a portion of the cells as the imaging target, a second image that includes fluorescence generated from the first fluorescent dye that labeled the target region of the cells in the first image as the imaging target, and a third image that includes fluorescence generated from the second fluorescent dye that labeled the nucleus of the cells in the first image as the imaging target, A processing unit, and The aforementioned processing unit, A process of selecting multiple cells from a plurality of cells that have specific morphological characteristics and are subject to examination, based on at least the first and third images, Based on the second image, a process is performed to extract bright spots due to fluorescence generated from the fluorescent dye from at least a number of the tested cells, A process to identify cells with chromosomal abnormalities and / or cells without chromosomal abnormalities based on the extracted bright spots, and to generate information regarding the ratio of cells with chromosomal abnormalities to the number of cells examined, based on the number of cells with chromosomal abnormalities and / or the number of cells without chromosomal abnormalities, Execute, In selecting the cells to be tested, selection information is obtained from the first image and the third image, and the plurality of cells to be tested are selected based on the obtained selection information. The selection information includes a value relating to the size of the cell and a value representing the eccentric distribution of the nucleus within the cell. A fluorescence imaging analyzer in which the ratio of plasma cells and myeloma cells to the number of multiple cells being tested is higher than the ratio of plasma cells and myeloma cells to the number of multiple cells not selected as being tested.

14. A process to acquire, for a plurality of cells in a sample in which a target region on the chromosome is labeled with a first fluorescent dye and the nucleus is labeled with a second fluorescent dye having a different output wavelength from the first fluorescent dye, a first image including at least a portion of the cells as the imaging target, a second image including fluorescence generated from the first fluorescent dye that labeled the target region of the cells in the first image as the imaging target, and a third image including fluorescence generated from the second fluorescent dye that labeled the nucleus of the cells in the first image as the imaging target, A process of selecting multiple cells from a plurality of cells that have specific morphological characteristics and are subject to examination, based on at least the first and third images, Based on the second image, a process is performed to extract bright spots due to fluorescence generated from the fluorescent dye from at least a number of the tested cells, A process to identify cells with chromosomal abnormalities and / or cells without chromosomal abnormalities based on the extracted bright spots, A process to generate information regarding the ratio of cells exhibiting the chromosomal abnormality to the cells being tested, based on the number of cells exhibiting the chromosomal abnormality and / or the number of cells not exhibiting the chromosomal abnormality; Have the computer do it, In selecting the cells to be tested, selection information is obtained from the first image and the third image, and the plurality of cells to be tested are selected based on the obtained selection information. The selection information includes a value relating to the size of the cell and a value representing the eccentric distribution of the nucleus within the cell. A fluorescence imaging analysis program in which the ratio of plasma cells and myeloma cells to the number of multiple cells being tested is higher than the ratio of plasma cells and myeloma cells to the number of multiple cells not selected as being tested.

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