Methods, apparatuses, devices, and media for determining phagocytic capacity of phagocytes

Abstract

Provided is a method for determining phagocytosis capacity of phagocytes, comprising: obtaining a phagocyte image and a target cell image, the phagocyte image and the target cell image being extracted from high-content images obtained by photographing co-cultured phagocytes and target cells; decomposing the target cell image into a plurality of sub-band images, each sub-band image comprising pixels of the target cell image having luminance values falling into a corresponding luminance sub-band of a plurality of luminance sub-bands, the plurality of luminance sub-bands being obtained by dividing a luminance range of pixels of the target cell image; determining respective numbers of target cell objects contained in the plurality of sub-band images; determining a number of phagocytes contained in the phagocyte image; and determining phagocytosis capacity of the phagocytes on the target cells based on the respective numbers of target cell objects contained in the plurality of sub-band images and the number of phagocytes contained in the phagocyte image.

Description

[0001] This disclosure claims priority to Chinese Patent Application No. 202211634789.7, filed on December 19, 2022, with the State Intellectual Property Office of the People's Republic of China, entitled "Method, Apparatus, Device and Medium for Determining the Phagocytic Capacity of Phagocytic Cells", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of cell activation status determination technology, and specifically to a method, apparatus, electronic device, computer-readable storage medium, and computer program product for determining the phagocytic capacity of phagocytes. Background Technology

[0003] Flow cytometry (FC) uses a gate / sorting technique to determine whether tumor cells phagocytosed by macrophages are positive cells (positive cells indicate the presence of tumor cells within macrophages) based on the output value of fluorescence-activated cell sorting (FACS) (whether the fluorescence intensity exceeds a preset threshold). The number of positive cells is counted to assess the phagocytic capacity of macrophages under drug influence. A higher number of positive cells indicates stronger phagocytic capacity. However, it lacks the ability to differentiate the intensity of positive expression in positive cells, thus preventing quantitative analysis.

[0004] Furthermore, flow cytometry is required to conduct numerous biological experiments to assess the phagocytic capacity of macrophages. However, flow cytometry instruments have low throughput and consume significant human and material resources, making it impossible to conduct large-scale biological experiments for drug screening, thus affecting the experimental scale and efficiency of drug screening. Summary of the Invention

[0005] It would be beneficial to provide a mechanism to alleviate, reduce, or even eliminate one or more of the aforementioned problems.

[0006] According to one aspect of this disclosure, a method for determining the phagocytic capacity of phagocytes is provided, comprising: acquiring a phagocyte image and a target cell image, wherein the phagocyte image contains a plurality of phagocytes, and the target cell image contains a plurality of target cell objects, the phagocyte image and the target cell image are extracted from high-content images obtained by photographing co-cultured phagocytes and target cells; decomposing the target cell image into a plurality of sub-band images, each sub-band image including pixels of the target cell image having a brightness value falling into a corresponding brightness sub-band of a plurality of brightness sub-bands, the plurality of brightness sub-bands being obtained by dividing the brightness range of pixels in the target cell image; determining a corresponding number of target cell objects contained in each of the plurality of sub-band images; determining the number of phagocytes contained in the phagocyte image; and determining the phagocytic capacity of the phagocytes for the target cells based on the corresponding number of target cell objects contained in each of the plurality of sub-band images and the number of phagocytes contained in the phagocyte image.

[0007] According to another aspect of this disclosure, a method is provided for determining the enhancing or inhibiting effect of perturbation conditions on the phagocytic capacity of phagocytes, comprising: acquiring phagocyte images and target cell images, wherein the phagocyte images contain a plurality of phagocytes, and the target cell images contain a plurality of target cell objects, the phagocyte images and target cell images are extracted from high-content images obtained by photographing co-cultured phagocytes and target cells subjected to perturbation conditions, the perturbation conditions including at least one of: exposure to a predetermined concentration of a compound for a predetermined duration, gene editing of candidate genes in the phagocytes and / or target cells; decomposing the target cell images into a plurality of sub-band images, each sub-band image comprising pixels of the target cell image having a brightness value falling into a corresponding brightness sub-band of a plurality of brightness sub-bands, the plurality of brightness sub-bands being obtained by dividing the brightness range of pixels in the target cell image; determining a corresponding number of target cell objects contained in each of the plurality of sub-band images; determining the number of phagocytes contained in the phagocyte image; and determining whether the perturbation conditions enhance or inhibit the phagocytic capacity of phagocytes on target cells based on the corresponding number of target cell objects contained in each of the plurality of sub-band images and the number of phagocytes contained in the phagocyte image.

[0008] According to another aspect of this disclosure, a target screening method is provided, comprising: determining the enhancing or inhibiting effect of multiple different candidate genes in phagocytes and / or target cells on the phagocytic ability of phagocytes to phagocytose target cells, wherein, for each of the multiple different candidate genes, the determination includes: acquiring phagocyte images and target cell images, wherein the phagocyte images and target cell images are extracted from high-content images obtained by photographing phagocytes and target cells co-cultured under perturbed conditions, the perturbed conditions including gene editing of the candidate gene in the phagocytes and / or target cells, the phagocyte images containing multiple phagocytes, and the target cell images containing multiple target cell objects; decomposing the target cell images into multiple sub-band images, each sub-band... The image comprises pixels of a target cell image having brightness values ​​falling into a corresponding brightness sub-band of a plurality of brightness sub-bands, the plurality of brightness sub-bands being obtained by dividing the brightness range of pixels in the target cell image; determining the corresponding number of target cell objects contained in each of the plurality of sub-band images; determining the number of phagocytes contained in a phagocyte image; and, based on the corresponding number of target cell objects contained in each of the plurality of sub-band images and the number of phagocytes contained in the phagocyte image, determining whether gene editing enhances or inhibits the phagocytic capacity of phagocytes on the target cells; and selecting at least one candidate gene and / or the protein expressed by at least one candidate gene as a target from a plurality of different candidate genes, wherein the selected at least one candidate gene is determined to have the function of enhancing phagocytic capacity.

[0009] According to another aspect of this disclosure, an apparatus for determining the phagocytic capacity of phagocytes is provided, comprising: a first module for acquiring phagocyte images and target cell images, wherein the phagocyte images contain a plurality of phagocytes, and the target cell images contain a plurality of target cell objects, the phagocyte images and target cell images being extracted from high-content images obtained by photographing co-cultured phagocytes and target cells; a second module for decomposing the target cell images into a plurality of sub-band images, each sub-band image including pixels of the target cell image having a brightness value falling into a corresponding brightness sub-band of a plurality of brightness sub-bands, the plurality of brightness sub-bands being obtained by dividing the brightness range of the pixels of the target cell image; a third module for determining a corresponding number of target cell objects contained in each of the plurality of sub-band images; a fourth module for determining the number of phagocytes contained in the phagocyte images; and a fifth module for determining the phagocytic capacity of the phagocytes for the target cells based on the corresponding number of target cell objects contained in each of the plurality of sub-band images and the number of phagocytes contained in the phagocyte images.

[0010] According to another aspect of this disclosure, an apparatus is provided for determining the enhancing or inhibiting effect of perturbation conditions on the phagocytic capacity of phagocytes, comprising: a sixth module for acquiring phagocyte images and target cell images, wherein the phagocyte images contain a plurality of phagocytes, and the target cell images contain a plurality of target cell objects, the phagocyte images and target cell images are extracted from high-content images obtained by photographing phagocytes and target cells co-cultured under perturbation conditions, the perturbation conditions including at least one of: exposure to a predetermined concentration of a compound for a predetermined duration, gene editing of candidate genes in the phagocytes and / or target cells; and a seventh module for... The target cell image is decomposed into multiple sub-band images. Each sub-band image includes pixels of the target cell image with brightness values ​​falling into a corresponding brightness sub-band of multiple brightness sub-bands, which are obtained by dividing the brightness range of the pixels of the target cell image. The eighth module is used to determine the corresponding number of target cell objects contained in each of the multiple sub-band images. The ninth module is used to determine the number of phagocytes contained in the phagocyte image. The tenth module is used to determine whether the perturbation condition enhances or inhibits the phagocytic ability of phagocytes on the target cells based on the corresponding number of target cell objects contained in each of the multiple sub-band images and the number of phagocytes contained in the phagocyte image.

[0011] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory storing instructions executable by the processor, the instructions, when executed by the processor, causing the processor to perform the method described in any of the preceding aspects.

[0012] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided that stores instructions which, when executed by a processor, cause the processor to perform the method described in any of the preceding aspects.

[0013] According to another aspect of this disclosure, a computer program product is provided, comprising: instructions, wherein, when executed by a processor, the instructions cause the processor to perform the method described in any of the preceding aspects.

[0014] These and other aspects of this disclosure will be apparent from the embodiments described below, and will be elucidated with reference to the embodiments described below. Attached Figure Description

[0015] Further details, features, and advantages of this disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0016] Figure 1 This is a flowchart illustrating an example process for acquiring phagocyte images and target cell images according to embodiments of the present disclosure;

[0017] Figure 2 These are example diagrams of color images according to embodiments of the present disclosure;

[0018] Figure 3 This is an example diagram of phagocyte images according to embodiments of the present disclosure;

[0019] Figure 4 This is an example image of a target cell according to an embodiment of the present disclosure;

[0020] Figure 5 These are example images of denoised images according to embodiments of the present disclosure;

[0021] Figure 6 This is an example image of a phagocyte after halo removal according to an embodiment of the present disclosure;

[0022] Figure 7A and Figure 7B This is a flowchart of a method for determining the phagocytic capacity of phagocytes according to embodiments of the present disclosure;

[0023] Figure 8 This is an example image of a grayscale phagocyte according to an embodiment of the present disclosure;

[0024] Figure 9 This is an example image of a phagocyte after image segmentation according to an embodiment of the present disclosure;

[0025] Figure 10 This is an example image of a phagocyte after image segmentation according to an embodiment of the present disclosure;

[0026] Figure 11 This is a flowchart of a method for determining the enhancing or inhibiting effect of perturbation conditions on the phagocytic capacity of phagocytes according to embodiments of the present disclosure;

[0027] Figure 12 This is a flowchart of a target screening method according to an embodiment of the present disclosure;

[0028] Figure 13 This is a block diagram of an apparatus for determining the phagocytic capacity of phagocytes according to embodiments of the present disclosure;

[0029] Figure 14 This is a block diagram of an apparatus for determining the enhancing or inhibiting effect of perturbation conditions on the phagocytic capacity of phagocytes, according to embodiments of the present disclosure; and

[0030] Figure 15 This is a block diagram of an electronic device for determining the phagocytic capacity of phagocytes according to embodiments of the present disclosure. Detailed Implementation

[0031] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same example of the element, while in other cases, based on the context, they may refer to different examples.

[0032] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. As used herein, the term "multiple" means two or more, and the term "based on" should be interpreted as "at least partially based on". Furthermore, the terms "and / or" and "at least one of..." cover any one of the listed items and all possible combinations thereof.

[0033] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with their meanings in the relevant field and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0034] As used herein, the term "phagocyte" refers to cells capable of phagocytosis, including but not limited to macrophages and neutrophils. The term "macrophage" (abbreviated Mφ) is a type of white blood cell located in tissues, derived from monocytes. The term "target cell" refers to the target of immune cells and immune factors, that is, the phagocytosed object, such as cells, viruses, bacteria, etc., that can be phagocytosed by phagocytes. It is not limited to cells; for example, if the target is a virus, the target cell could be a virus; if the target is bacteria, the target cell could be bacteria; if the target is a tumor, the target cell could be tumor cells. The term "target" is a life science concept. A biological target is a structure located within an organism that can be recognized or bound by other substances (ligands, drugs, etc.). Common targets include proteins, nucleic acids, and ion channels. The term "high-content cell imaging system" refers to a high-resolution microscopy imaging system used to capture images of cells. Correspondingly, the term "high-content image" refers to a microscopic image acquired using such a microscopy imaging system.

[0035] In related technologies, the biological experimental protocol includes: staining tumor cells with a specific fluorescent dye; when tumor cells are phagocytosed by macrophages, the fluorescence intensity of the phagocytosed tumor cells increases, meaning the tumor cells exhibit high fluorescence intensity within the macrophages. The macrophages in the experiment are screened using flow cytometry, and the data is used by corresponding computational software to plot a set of two-dimensional dot plots. The horizontal axis represents the fluorescence staining intensity of tumor cells within the macrophages, and the vertical axis represents the number of macrophages. The dot plots are separated and analyzed by gates. The gated macrophages are considered positive samples, i.e., the number of macrophages with strong fluorescence intensity. Dividing this number by the total number of macrophages yields the output value for fluorescence-activated cell sorting. This output value can be used to determine the strength of the macrophages' phagocytic capacity.

[0036] Figure 1 This is a flowchart of an example process 100 for acquiring phagocyte images and target cell images according to embodiments of the present disclosure. Figure 1 As shown, the process 100 includes steps 110 to 140.

[0037] In step 110, high-content images of co-cultured phagocytes and / or target cells are acquired using a high-content cell imaging system.

[0038] In the example, phagocytes and target cells were co-cultured at a 1:1 ratio.

[0039] In this example, phagocytes and target cells may be subjected to perturbation conditions and staining. Perturbation conditions may include exposure to a predetermined concentration of a compound for a predetermined duration. Staining may include staining the phagocytes and target cells separately with fluorescent dyes of different colors.

[0040] In the example, the high-content image contains multiple phagocytes and target cell objects. The high-content image has a first color channel and a second color channel. The first color channel corresponds to phagocytes. The second color channel corresponds to target cell objects.

[0041] In the example, the target cell is the target of the phagocyte, such as a tumor cell.

[0042] In step 120, the high-content image is preprocessed to generate a color image.

[0043] High-content images can have pixel values ​​ranging from 0 to 65535 and two color channels. These images require preprocessing to convert them into color images with pixel values ​​between 0 and 255 and three color channels for direct observation. Examples of such color images are shown below. Figure 2 As shown (for ease of illustration, it is shown as a grayscale image).

[0044] In the example, preprocessing may include: normalizing the high-content image, for example, performing max-min normalization on the pixels of the high-content image to ensure that the value of each pixel is within the range of [0, 1], and then multiplying each pixel by 255 so that the pixel value range of the normalized high-content image is 0~255. Further, before normalization, pixels in the high-content image whose pixel values ​​exceed a first pixel threshold and / or are less than a second pixel threshold may be removed. For example, the pixels in the high-content image may first be sorted from highest to lowest pixel value, and then pixels representing a predetermined proportion (e.g., the top 1% and bottom 1% of pixel values) may be removed.

[0045] Furthermore, preprocessing may include: applying median filtering to the high-content image to remove a small number of Gaussian noise points. This removes noise while preserving edge information, resulting in a smoother high-content image. Median filtering is a non-linear smoothing technique that sets the gray value of each pixel to the median of the gray values ​​of all pixels within a neighborhood window of that pixel.

[0046] Furthermore, preprocessing may also include removing overexposed pixels from the high-content image. Overexposed pixels have brightness values ​​exceeding a brightness threshold. In one example, the brightness threshold may be set empirically. For example, when pixel values ​​range from 0 to 255, the brightness threshold might be set to 243. For instance, the pixels in the high-content image could first be sorted from highest to lowest brightness value, and then a predetermined proportion of pixels in the high-content image (e.g., those with brightness values ​​in the top 3‰) could be removed as overexposed pixels.

[0047] In step 130, a phagocyte image is generated based on the image information corresponding to the first color channel of the color image. An example of such a phagocyte image is shown below. Figure 3 As shown (for ease of illustration, it is shown as a grayscale image).

[0048] In step 140, a target cell image is generated based on the image information corresponding to the second color channel of the color image. An example of such a target cell image is shown below. Figure 4 As shown (for ease of illustration, it is shown as a grayscale image).

[0049] In the example, the first color channel can be the green channel, and the second color channel can be the red channel. The green channel G in the color image is used as the green channel G1 of the phagocyte image, and the red channel R1 and blue channel B1 of the phagocyte image are set to zero. The red channel R in the color image is used as the red channel R2 of the target cell image, and the green channel G2 and blue channel B2 of the target cell image are set to zero.

[0050] Due to the imaging principles of high-content machines, the central region of the resulting phagocyte images often contains halos, affecting image quality. Therefore, in some embodiments, halo removal processing can be performed on the phagocyte images. This process may include the following steps:

[0051] The first step is to obtain a denoised image. The denoised image is the same size as the phagocyte image. Pixels in the central region of the denoised image have pixel values ​​used to compensate for halo noise. An example of such a denoised image is shown below. Figure 5 As shown.

[0052] In this example, the method for setting up the denoised image can include: First, creating an image with a center pixel value of 1 and all other pixels valued at 0, with a size of 81x81. Then, performing a Gaussian filter on the image, for example, setting the Gaussian standard deviation (sigma) to 16. Next, normalizing the Gaussian-filtered image, for example, using max-min normalization, to ensure that each pixel value falls within the range of [0, 1]. Then, multiplying each pixel by a magnification factor increases the overall image brightness, outputting a brighter image. Next, removing pixels from the outer edges of the brighter image by a predetermined percentage (e.g., 25%), retaining the pixels in the central region, thus enhancing the halo effect. Then, multiplying each pixel in the enhanced image by 255, setting pixels with values ​​less than 0 to 0, and pixels with values ​​greater than 255 to 255, to ensure the pixel value range is between 0 and 255, resulting in a grayscale denoised image. Finally, adjusting the size of the denoised image to match the size of the phagocytic cell image.

[0053] The second step involves subtracting the pixel values ​​of pixels in the denoised image that are at the same relative position as the pixels in the phagocyte image from the pixel values ​​of each pixel in the phagocyte image to obtain the halo-reduced phagocyte image. An example of such a halo-reduced phagocyte image is shown below. Figure 6 As shown (for ease of illustration, it is shown as a grayscale image).

[0054] The imaging principle of high-content cell imaging systems results in halos in the captured phagocyte images, affecting image quality and consequently the accuracy of subsequent phagocyte counts based on these images. Therefore, this example demonstrates how removing halo noise from phagocyte images can improve the accuracy of phagocyte counts.

[0055] In one example, the method 800 for determining the phagocytic capacity of phagocytes according to embodiments of the present disclosure includes steps 810 to 830, such as... Figure 7A As shown.

[0056] In step 810, a target cell image is acquired; the target cell image is extracted from a high-content image obtained by photographing co-cultured phagocytes and target cells; the fluorescence intensity of the target cell changes after being phagocytosed by the phagocytes.

[0057] For example, a pH-sensitive dye (such as pHrodo) can be used to stain target cells. Target cells exhibit a certain intensity of fluorescence when not phagocytosed. After the target cells are phagocytosed, the pH of the phagocytic environment differs from the pH of the target cells before phagocytosis, causing a change in the fluorescence intensity of the target cells. In one example, the environment inside the phagocytic cell is acidic; the phagocytosed target cells exhibit a higher intensity of fluorescence in the acidic environment within the phagocytic cell.

[0058] For details on the process of acquiring target cell images, please refer to the relevant description of acquiring target cell images in process 100.

[0059] In step 820, based on the pixel values ​​of the pixels in the target cell image, the number of pixels in the target cell image that fall into at least one of the multiple brightness sub-bands is determined; and / or, based on the pixel values ​​of the pixels in the target cell image, the number of target cells in the target cell image that fall into at least one of the multiple brightness sub-bands is determined; the multiple brightness sub-bands are obtained by dividing according to fluorescence intensity thresholds, and the first fluorescence intensity threshold in the fluorescence intensity threshold is used to distinguish between phagocytosed target cells and non-phagocytosed target cells.

[0060] The fluorescence intensity of a target cell changes after it is phagocytosed by a phagocyte. For example, the change in fluorescence intensity is greater when a target cell is phagocytosed by a phagocyte with a weaker phagocytic ability compared to one phagocytosed by a phagocyte with a stronger phagocytic ability. Therefore, a first fluorescence intensity threshold can be set to distinguish between phagocytosed and unphagocytosed target cells. Other fluorescence intensity thresholds can also be set to distinguish whether a target cell is phagocytosed by a phagocyte with a strong or weak phagocytic ability. The total brightness range (e.g., 0-255) can be divided into multiple brightness sub-bands based on the fluorescence intensity thresholds. The number of pixels and / or the number of target cells falling into different brightness sub-bands can characterize how many target cells are phagocytosed, how many are not, how many are phagocytosed by phagocytes with strong phagocytic ability, and how many are phagocytosed by phagocytes with weak phagocytic ability. Thus, the phagocytic ability of a phagocyte can be measured by statistically analyzing the number of pixels and / or the number of target cells falling into at least one brightness sub-band.

[0061] Understandably, a luminance sub-band can be a luminance range.

[0062] For example, the fluorescence intensity threshold is E1…E n Where n is greater than or equal to 1, at least one fluorescence intensity threshold is used to distinguish between phagocytosed and unphagocytosed target cells; this fluorescence intensity threshold may be referred to as the first fluorescence intensity threshold. For example, the fluorescence intensity of a target cell increases after it has been phagocytosed by another phagocytic cell. The fluorescence intensity threshold is used to distinguish between phagocytosed and unphagocytosed target cells. The fluorescence intensity threshold can be a single value, such that target cells below the fluorescence intensity threshold E are considered unphagocytosed, and target cells above the fluorescence intensity threshold E are considered phagocytosed. Phagocytic cells with strong phagocytic capacity have a higher acidity level in vivo. When using a pH-sensitive staining agent, the fluorescence intensity change is greater after target cells with strong phagocytic capacity are phagocytosed. Therefore, fluorescence intensity thresholds other than the first fluorescence intensity threshold are used to characterize the phagocytic intensity of the phagocytic cell that has phagocytosed the target cell. For example, the fluorescence intensity threshold can be multiple values ​​E1…E1…E2 ...…E2……E2……E2……E2…… n Fluorescence intensity threshold E1 other than the first fluorescence intensity threshold E1 2- E nThe defined brightness ranges indicate whether the target cell was phagocytosed by phagocytic cells with weak phagocytic capacity, moderate phagocytic capacity, or strong phagocytic capacity. Fluorescence intensity thresholds can be preset or determined statistically from relevant images. For example, the first fluorescence intensity threshold can be determined statistically from the brightness values ​​of unphagocytosed target cells. This can be achieved by staining individually cultured target cells, obtaining high-content images of the individually cultured target cells, identifying images of the individually cultured target cells based on the high-content images, statistically analyzing the distribution of fluorescence intensity values ​​of the target cells in the images, and determining the first fluorescence intensity threshold based on the fluorescence intensity distribution. Alternatively, fluorescence intensity thresholds other than the first fluorescence intensity threshold can be determined statistically from the fluorescence intensity of target cells phagocytosed by phagocytic cells with different phagocytic capacities. For example, flow cytometry can be used to determine the phagocytic capacity of phagocytes under different perturbation conditions. For instance, phagocytes may have weak phagocytic capacity under perturbation condition A and strong phagocytic capacity under perturbation condition B. High-content images of phagocytes and target cells co-cultured under perturbation condition A are obtained. Target cell images are extracted from these high-content images, and the fluorescence intensity distribution of phagocytosed target cells in the target cell images is statistically analyzed. A second fluorescence intensity threshold is determined based on this distribution. Similarly, images of phagocytes and target cells co-cultured under perturbation condition B are taken to determine a third fluorescence intensity threshold. The first, second, and third fluorescence intensity thresholds divide the entire brightness range into four brightness intervals, corresponding to unphagocytosed target cells, target cells phagocytosed by weakly phagocytic cells, target cells phagocytosed by strongly phagocytic cells, and target cells phagocytosed by even more strongly phagocytic cells, respectively.

[0063] For example, there are n fluorescence intensity thresholds, and the entire brightness range is divided into n+1 brightness intervals by the fluorescence intensity thresholds. Multiple brightness sub-bands can be one of the n+1 brightness intervals, or they can be the union of multiple brightness intervals. For example, when determining the phagocytic capacity of a phagocyte for a target cell does not involve the number of phagocytes, it is necessary to be able to determine the number of pixels and the number of target cells corresponding to each of the n+1 brightness intervals based on the number of pixels corresponding to at least one brightness sub-band and / or the number of target cells. In this case, at least one brightness sub-band can be any one of the multiple brightness sub-bands. For example, two brightness sub-bands can be set: (0 E1) and (0 255). Cells falling into (0 E1) are un-phagocytosed target cells, while those falling into (0 255) are all target cells. The difference between the two represents the number of phagocytosed target cells. For instance, when determining the phagocytic capacity of phagocytes for target cells, the number of phagocytes is also used. Therefore, it is necessary to determine the number of pixels and / or target cells corresponding to the brightness intervals of the n+1 brightness intervals corresponding to the phagocytosed target cells, based on the number of pixels corresponding to at least one brightness sub-band and / or the number of target cells. In this case, at least one brightness sub-band can be a brightness sub-band corresponding to the brightness interval of the phagocytosed target cells.

[0064] For example, determining the number of pixels in a target cell image that fall within at least one of multiple brightness sub-bands based on the pixel values ​​of pixels in the target cell image may include one of the following steps:

[0065] Step 8201: Count the number of pixels in the target cell image whose pixel values ​​fall into at least one of the multiple brightness sub-bands.

[0066] Step 8202: Count the number of pixels in the target cell image whose pixel values ​​fall into at least one of the multiple brightness sub-bands.

[0067] At this point, it is necessary to first preprocess the target cell image and perform image segmentation to determine the region where the target cell is located. Then, the number of pixels in the region where the target cell is located that fall into at least one of the multiple brightness sub-bands is counted. Understandably, it is possible that some pixels corresponding to the same target cell may fall into one brightness sub-band and some pixels may fall into another brightness sub-band.

[0068] Step 8203: Determine the region where the target cell is located in the target cell image, calculate the brightness value corresponding to each target cell, and for at least one brightness sub-band among multiple brightness sub-bands, count the sum of the number of pixels corresponding to the target cells whose brightness values ​​fall into that brightness sub-band.

[0069] At this point, it is necessary to first preprocess the target cell image and perform image segmentation to determine the region where each target cell is located. Then, calculate the brightness value corresponding to each target cell (for example, take the average pixel value of the region where the target cell is located as the brightness value of the cell). After that, divide the target cells into a brightness sub-band according to the brightness value corresponding to the target cells, and then determine which target cells correspond to at least one brightness sub-band. Finally, calculate the sum of the number of pixels of these target cells, which is the number of pixels corresponding to the brightness sub-band.

[0070] For example, determining the number of target cells in a target cell image that fall into at least one of multiple brightness sub-bands based on the pixel values ​​of pixels in the target cell image may include: determining the region where the target cells are located in the target cell image, calculating the brightness value corresponding to each target cell, and for at least one of the multiple brightness sub-bands, counting the number of target cells whose brightness values ​​fall into that brightness sub-band.

[0071] In step 830, the phagocytic capacity of the phagocytes for the target cells is determined based on the number of pixels falling into at least one of the multiple brightness subbands and / or the number of target cells falling into at least one of the multiple brightness subbands.

[0072] In the example, the ability of a phagocyte to phagocytose a target cell can be determined by at least one of the following methods.

[0073] Method 1: Based on the number of pixels falling into at least one of the multiple brightness sub-bands, determine the number of first pixels corresponding to the brightness interval of the target cell in the n+1 brightness intervals and the number of second pixels corresponding to all target cells (including those that have been phagocytosed and those that have not been phagocytosed) (for example, the region where each target cell is located can be determined, and the number of pixels in these regions is equal to the number of second pixels). The phagocytic capacity of the phagocyte to the target cell is determined based on the ratio of the number of first pixels to the number of second pixels.

[0074] For example, multiple brightness subbands are (E 255] (the target cell corresponding to this brightness subband is the target cell that is being engulfed) and (0 255] (the target cell falling into this brightness subband is all the target cells). The number of pixels falling into at least one of the multiple brightness subbands is the number of pixels of the target cells that are being engulfed and the number of pixels corresponding to all the target cells.

[0075] Understandably, when there are multiple brightness ranges corresponding to the target cell being phagocytosed (for example, multiple brightness ranges corresponding to the target cell being phagocytosed by phagocytic cells with low phagocytic intensity, phagocytic cells with medium phagocytic intensity, and phagocytic cells with high phagocytic intensity respectively), the first pixel count is a weighted sum of the pixel counts corresponding to multiple brightness ranges, indicating that the brightness range corresponding to the target cell being phagocytosed by phagocytic cells with low phagocytic intensity has a smaller weight.

[0076] Method 2: Based on the number of target cells falling into at least one of the multiple brightness subbands, determine the number of first cells corresponding to the brightness interval of the target cells in the n+1 brightness intervals and the number of second cells corresponding to all target cells (including those that have been phagocytosed and those that have not). Determine the phagocytic capacity of the phagocytes for the target cells based on the ratio of the number of first cells to the number of second cells.

[0077] Method 3: Based on the number of target cells falling into at least one of the multiple brightness subbands, determine the number of the first cell in the brightness interval corresponding to the target cell in each of the n+1 brightness intervals. Then, determine the phagocytic capacity of the phagocytes for the target cells based on the ratio of the number of the first cells to the number of phagocytes. Understandably, this also requires acquiring an image of the phagocytes and determining the number of phagocytes contained within that image.

[0078] Understandably, for methods 2 and 3, when there are multiple brightness intervals corresponding to the target cells being phagocytosed (for example, multiple brightness intervals corresponding to the target cells being phagocytosed by phagocytic cells with low phagocytic intensity, phagocytic cells with medium phagocytic intensity, and phagocytic cells with high phagocytic intensity respectively), the first cell number is the weighted sum of the number of target cells corresponding to multiple brightness intervals, indicating that the brightness interval corresponding to the target cells being phagocytosed by phagocytic cells with low phagocytic intensity has a smaller weight.

[0079] Understandably, methods 1 and 2 require a sufficient number of phagocytes co-cultured with the target cells to prevent the number of phagocytes from becoming a bottleneck that prevents the proportion of phagocytes in all target cells from increasing.

[0080] For example, when two or more of the methods 1-3 are selected, the ratios corresponding to the selected methods can be weighted and summed, and the phagocytic capacity of the phagocytes on the target cells can be determined based on the weighted summation result.

[0081] For example, method 800 also includes:

[0082] In step 840, a blank control group image is obtained, which is extracted from a high-content image obtained by photographing the target cells cultured separately.

[0083] In phagocytic function experiments, a blank control group and multiple experimental groups can be set up. The experimental groups include the group corresponding to the perturbation to be tested and the negative control group. Under either the perturbation condition to be tested or the negative control condition, phagocytes are co-cultured with tumor cells, and high-content images are captured using a high-content microscopy imaging system.

[0084] For example, the distribution of the blank control group and experimental group in the experimental plate is shown in Table 1. DLD1only is the blank control group, containing only target cells. Mφ only contains only phagocytes. anti-Her2, anti-EGFR, anti-CD47, anti-Her2 / CD47, and anti-EGFR / CD47 are the test perturbation conditions applied to each of the five test perturbations in the experimental group (where anti-Her2 represents the application of Her2 blocking antibody alone, anti-EGFR represents the application of EGFR blocking antibody alone, anti-CD47 represents the application of CD47 blocking antibody alone, anti-Her2 / CD47 represents the application of both Her2 and CD47 blocking antibodies simultaneously, and anti-EGFR / CD47 represents the application of both EGFR and CD47 blocking antibodies simultaneously). Ctrl (Isotype lgG1 / lgG4) applies a perturbation that is determined not to affect the phagocytic ability of phagocytes and serves as the negative control group.

[0085] Table 1

[0086]

[0087] In this example, the blank control group image is an image that includes only the target cells. See step 100 for how to obtain the blank control group image.

[0088] In step 850, a first fluorescence intensity threshold is determined based on the fluorescence intensity of the target cells in the blank control group image.

[0089] For example, the blank control group image can be preprocessed (e.g., bilateral filtering, adaptive thresholding, morphological opening operation) to segment the target cells from the preprocessed blank control group image (e.g., the image is divided into a foreground region and a background region, the foreground region is the cell region, and then the foreground region is segmented into multiple sub-regions by a contour search algorithm, filtering out sub-regions with an area smaller than a specific threshold, and retaining the more reasonable sub-regions, each sub-region being a target cell instance), and the first fluorescence intensity threshold is determined based on the brightness value (i.e. fluorescence intensity value) corresponding to each target cell.

[0090] The first fluorescence intensity threshold is determined based on the brightness value corresponding to each target cell. For example, the brightness value corresponding to each target cell is calculated (the sum of the pixel values ​​of all pixels corresponding to a target cell instance is calculated and divided by the number of pixels corresponding to the target cell, which is the brightness value corresponding to the target cell instance). All target cells in the blank control group image are sorted from smallest to largest according to their average brightness, and the average brightness value of the 99th percentile is selected as the first fluorescence intensity threshold corresponding to the blank control group image.

[0091] For example, multiple blank control group images can be captured and their respective first fluorescence intensity thresholds can be calculated. The average value of the first fluorescence intensity thresholds corresponding to the multiple blank control images can be taken as the final first fluorescence intensity threshold.

[0092] Understandably, the fluorescence intensity threshold determined using this method is the first fluorescence intensity threshold. The significance of using blank control group images to determine the first fluorescence intensity threshold is that target cells with brightness values ​​less than the first fluorescence intensity threshold are negative cells (i.e., unphagocytosed target cells), while target cells with brightness values ​​greater than the first fluorescence intensity threshold are positive cells (i.e., phagocytosed target cells). Dynamically calculating the first fluorescence intensity threshold based on blank control group images, compared to a fixed threshold, avoids experimental errors caused by different well plates, different experimental conditions, or different imaging conditions, greatly improving the stability and generalization of phagocytic capacity evaluation.

[0093] Figure 7B This is a flowchart of a method 700 for determining the phagocytic capacity of phagocytes according to an embodiment of this disclosure. Figure 7B As shown, method 700 includes steps 710 to 750.

[0094] In step 710, images of phagocytes and target cells are acquired. The phagocyte image contains multiple phagocytes. The target cell image contains multiple target cell objects. The phagocyte and target cell images are extracted from high-content images obtained by photographing co-cultured phagocytes and target cells.

[0095] In the example, phagocyte and target cell images can be acquired through process 100. Alternatively, pre-obtained phagocyte and target cell images can be read from local storage or downloaded from remote storage.

[0096] In step 720, the target cell image is decomposed into multiple sub-band images. Each sub-band image includes pixels of the target cell image that have brightness values ​​falling into a corresponding brightness sub-band among the multiple brightness sub-bands. The multiple brightness sub-bands are obtained by dividing the brightness range of the pixels in the target cell image.

[0097] In the example, the brightness range of pixels in the target cell image can be divided into multiple brightness sub-bands. Then, pixels with brightness values ​​in each of the multiple brightness sub-bands can be selected from the target cell image to obtain corresponding sub-band images. For example, the multiple brightness sub-bands may include a first brightness sub-band, a second brightness sub-band, a third brightness sub-band, a fourth brightness sub-band, and a fifth brightness sub-band. The brightness range corresponding to the first brightness sub-band is [0, 32]. The brightness range corresponding to the second brightness sub-band is [32, 74]. The brightness range corresponding to the third brightness sub-band is [74, 128]. The brightness range corresponding to the fourth brightness sub-band is [128, 192]. The brightness range corresponding to the fifth brightness sub-band is [192, 255]. Accordingly, the multiple sub-band images may include a first sub-band image, a second sub-band image, a third sub-band image, a fourth sub-band image, and a fifth sub-band image. The first sub-band image contains pixels in the target cell image that fall within the brightness range [0, 32]. The second sub-band image contains pixels in the target cell image that fall within the brightness range [32, 74]. The third sub-band image contains pixels in the target cell image that fall within the brightness range [74, 128]. The fourth sub-band image contains pixels in the target cell image that fall within the brightness range [128, 192]. The fifth sub-band image contains pixels in the target cell image that fall within the brightness range [192, 255].

[0098] In step 730, the number of target cell objects contained in each of the multiple sub-band images is determined.

[0099] In the example, the number of target cell objects contained in each of the multiple sub-band images can be determined by the following steps.

[0100] The first step involves applying Gaussian blur and grayscale to each sub-band image from the multiple sub-band images. Gaussian blur transforms sharp edges in the image into smooth transitions. Grayscale conversion facilitates subsequent image processing and reduces computational load. It will be understood that in some embodiments, Gaussian blur is not necessary.

[0101] The second step involves performing image segmentation on each sub-band image from the multiple grayscale sub-band images using an adaptive thresholding algorithm to obtain a first image containing a first number of first sub-regions. The first sub-regions indicate target cell regions. The first number is the number of target cell objects contained in the sub-band image.

[0102] Image thresholding can be achieved using an adaptive thresholding algorithm, separating the target region from the background region in a grayscale image (the sub-band image after grayscale conversion). Pixels below the threshold correspond to the background, while pixels above the threshold correspond to objects of interest in the image. The adaptive thresholding method calculates local thresholds based on the brightness distribution of different regions of the image. It adaptively calculates different thresholds for different regions, hence the name. This method ensures that the threshold of each pixel in the image changes with the changes in its surrounding neighboring pixels, making it suitable for images with uneven lighting.

[0103] Various methods can be used to automatically determine the threshold, such as the bimodal algorithm, iterative algorithms, the OTSU algorithm, and the gray-level extension-based OTSU algorithm. More information on automatic thresholding algorithms can be found in Yu Liu's paper entitled "Study on Automatic Threshold Selection Algorithm of Sensor Images," Physics Procedia, Volume 25, 2012, Pages 1769-1775, which is incorporated herein by reference in its entirety. In one example, a clustering method can be used to cluster the grayscale sub-band image into background and foreground regions. The foreground region (the first sub-region) is considered the target cell region. That is, the adaptive thresholding algorithm can determine both the foreground region (the first sub-region) and some individual target cell instance regions.

[0104] The third step involves segmenting the first image using the watershed algorithm to obtain a second image containing a second number of first sub-regions. The second number is greater than or equal to the first number. The second number represents the number of target cell objects contained in that sub-region image.

[0105] Based on the foreground region determined using an adaptive thresholding algorithm, a watershed algorithm is further used to segment larger areas within the foreground region (containing multiple adhered target cell instances). This allows for finer segmentation, achieving the desired segmentation of adhered cell instances. Furthermore, using an adaptive thresholding algorithm to determine the foreground region first can prevent oversegmentation, a problem that can easily occur with the watershed algorithm.

[0106] The watershed algorithm is an image region segmentation method. During segmentation, it uses the similarity between neighboring pixels as a key reference, connecting pixels that are spatially close and have similar grayscale values ​​(gradient calculation) to form a closed contour. This is beneficial for segmenting larger regions of an image into smaller, more accurate regions. More information about the watershed algorithm can be found on Lidija. omi The paper titled "Watershed Algorithms" by [Authors' Name], Morphological Modeling of Terrains and Volume Data, pp. 59–68, is found here and is incorporated into this paper in its entirety by reference.

[0107] Fourth, filter out at least one first sub-region from the second number of first sub-regions to obtain a third number of first sub-regions. The area of ​​each of at least one first sub-region is less than a first area threshold and / or the area of ​​each of at least one first sub-region is greater than a second area threshold. The third number is the number of target cell objects contained in the sub-band image.

[0108] In the example, a first number of first sub-regions can be segmented from the second image using a contour-finding algorithm (a method for finding object contours based on image edge extraction, such as the Moore-Neighbor algorithm). Then, from the second number of first sub-regions, first sub-regions with areas smaller than a first area threshold (e.g., for an image size of 1080x1080, the first area threshold is 100 consecutive pixels) and / or areas larger than a second area threshold (e.g., for an image size of 1080x1080, the second area threshold is 2000 consecutive pixels) are filtered to obtain a third number of first sub-regions. This preserves first sub-regions that are more closely aligned with the actual size of the target cell object.

[0109] In step 740, the number of phagocytes contained in the phagocyte image is determined.

[0110] In the example, the number of target cell objects contained in each of the multiple sub-band images can be determined by the following steps.

[0111] The first step is to perform Gaussian blurring and grayscale conversion on the phagocyte image. An example of such a Gaussian blurred and grayscale-converted phagocyte image is shown below. Figure 8 As shown. It will be understood that in some embodiments, Gaussian blurring is not necessary.

[0112] The second step involves image segmentation of the grayscale phagocyte image using an adaptive thresholding algorithm to obtain a fourth image containing a fourth number of second sub-regions. The second sub-regions indicate the phagocyte regions. The fourth number represents the number of phagocytes contained in the phagocyte image.

[0113] In the example, a clustering method can be used to cluster the grayscale phagocyte image into background and foreground regions. The foreground region (second sub-region) is considered as the phagocyte region. That is, the adaptive thresholding algorithm can determine the foreground region and also identify some individual phagocyte instance regions. An example of such a segmented phagocyte image is shown below. Figure 9 As shown (for ease of illustration, it is shown as a grayscale image).

[0114] The third step involves segmenting the fourth image using the watershed algorithm to obtain a fifth image containing a fifth number of sub-regions. The fifth number is greater than or equal to the fourth number. The fifth number represents the number of phagocytes contained in the phagocyte image. An example of such a segmented phagocyte image is shown below. Figure 10 As shown (for ease of illustration, it is shown as a grayscale image).

[0115] In this example, after determining the foreground region using an adaptive thresholding algorithm, a watershed algorithm is further used to segment larger areas within the foreground region (containing multiple adherent phagocyte instances). This achieves fine-grained segmentation of adherent cell instances. Furthermore, using an adaptive thresholding algorithm to determine the foreground region first can prevent oversegmentation, a problem that can easily occur with the watershed algorithm.

[0116] Fourth step: Filter out at least one second sub-region from the fifth number of second sub-regions to obtain the sixth number of second sub-regions. The area of ​​each of at least one second sub-region is less than the third area threshold and / or the area of ​​each of at least one first sub-region is greater than the fourth area threshold.

[0117] In the example, a fifth number of second sub-regions can be segmented from the fourth image using a contour-finding algorithm (a method for finding object contours based on image edge extraction, such as the Moore-Neighbor algorithm). Then, from these fifth number of second sub-regions, second sub-regions with areas smaller than a third area threshold and / or areas larger than a fourth area threshold are filtered out to obtain a sixth number of second sub-regions. This preserves second sub-regions that are more closely aligned with the actual size of the phagocytes.

[0118] In step 750, the phagocytic capacity of the phagocytes for the target cells is determined based on the number of target cell objects contained in the multiple sub-band images and the number of phagocytes contained in the phagocyte images.

[0119] In the example, the weighted sum of the respective numbers of target cell objects contained in multiple sub-band images can be calculated as a ratio to the number of phagocytes. This ratio indicates the phagocytic capacity of the phagocytes for the target cells.

[0120] In the example, the weighting coefficients corresponding to the number of target cells contained in each of the multiple sub-band images are positively correlated with the brightness range of the corresponding brightness sub-band. When a target cell is engulfed by a phagocyte image, its fluorescence intensity increases; the stronger the intensity, the stronger the phagocytic ability of the phagocyte. Since fluorescence intensity is represented by pixel brightness in the image, the weighting coefficients corresponding to the number of target cells contained in the sub-band images of the corresponding brightness sub-band can be set based on the influence of the brightness range of the brightness sub-band on determining the phagocytic ability of the phagocyte. A higher brightness range in the brightness sub-band indicates a stronger phagocytic ability of the phagocyte, and therefore a larger weighting coefficient corresponding to the number of target cells contained in the sub-band images of the corresponding brightness sub-band.

[0121] Continuing the previous example, multiple sub-band images can include a first sub-band image, a second sub-band image, a third sub-band image, a fourth sub-band image, and a fifth sub-band image. The number of target cell objects contained in the first sub-band image is *a*. The number of target cell objects contained in the second sub-band image is *b*. The number of target cell objects contained in the third sub-band image is *c*. The number of target cell objects contained in the fourth sub-band image is *d*. The number of target cell objects contained in the fifth sub-band image is *e*. The weighted sum of the corresponding numbers of target cell objects contained in each of the multiple sub-band images is A = a*k1 + b*k2 + c*k3 + d*k4 + e*k5. Where k1, k2, k4, and k5 are the weight coefficients corresponding to a, b, c, d, and e, respectively. In this example, k1 is set to 0, k2 to 1, k3 to 2, k4 to 3, and k5 to 4. Since the brightness sub-band of the first sub-band image has the lowest brightness range, the target cells it contains are considered negative cells, so its weight coefficient is set to 0.

[0122] Continuing the previous example, let B be the number of phagocytes contained in the phagocyte image. Calculate the weighted sum of the corresponding numbers of target cell objects contained in multiple sub-band images, and the ratio A / B to the total number of phagocytes. This ratio indicates the phagocytic capacity of the phagocytes for the target cells. The larger the ratio, the stronger the phagocytic capacity of the phagocytes for the target cells.

[0123] Method 700 decomposes the target cell image into multiple sub-band images, and then counts the number of target cells under different brightness intensities based on the multiple sub-band images, thereby achieving quantitative analysis of the positive intensity of target cells by distinguishing brightness intensities.

[0124] Method 700, based on multiple sub-band images decomposed from phagocyte and target cell images, determines the number of phagocytes in the phagocyte image and the corresponding number of target cells in each of the multiple sub-band images, thus performing quantitative analysis of cell phenotype and converting cell images into numerical data. The weighted sum of the corresponding number of target cells in each of the multiple sub-band images, as well as the ratio to the number of phagocytes, is then used as a measure of the phagocytic capacity of phagocytes towards target cells. A weighting coefficient is introduced when calculating the number of target cells, based on the influence of the positive intensity of target cells on the phagocytic capacity of phagocytes, thereby achieving quantitative analysis of the phagocytic capacity of phagocytes towards target cells.

[0125] In one example, a method 1000 for determining the enhancing or inhibiting effect of a perturbation condition to be tested on the phagocytic capacity of phagocytes, according to an embodiment of the present disclosure, includes steps 1010 and 1020.

[0126] In step 1010, according to method 700 or method 800, the phagocytic capacity of phagocytes under the test perturbation condition is determined, wherein the target cell image corresponding to the test perturbation condition is extracted from a high-content image obtained by photographing co-cultured phagocytes and target cells subjected to the test perturbation condition, and the test perturbation condition includes at least one of the following: exposure to a predetermined concentration of a compound for a predetermined duration, or gene editing of candidate genes in the phagocytes and / or the target cells. Further description of the test perturbation condition is given in the description of method 1100.

[0127] In step 1020, based on the phagocytic capacity of the phagocytes under the test perturbation condition, the enhancing or inhibiting effect of the test perturbation condition on the phagocytic capacity of the phagocytes is determined.

[0128] For example, the enhancing or inhibiting effect of the perturbation condition on the phagocytic capacity of phagocytes can be determined based on the absolute value of the phagocytic capacity of phagocytes under the perturbation condition. For instance, a perturbation condition where the phagocytic capacity of phagocytes under the perturbation condition is greater than a threshold is considered to enhance the phagocytic capacity of phagocytes. Alternatively, the enhancing or inhibiting effect of the perturbation condition on the phagocytic capacity of phagocytes can be determined based on the relative value of the phagocytic capacity of phagocytes under the perturbation condition compared to the negative control group. For instance, a perturbation condition where the phagocytic capacity of phagocytes under the perturbation condition is increased by more than N% compared to the negative control group with no perturbation or perturbation that has no effect on the phagocytic capacity of phagocytes is considered to enhance the phagocytic capacity of phagocytes.

[0129] In one example, step 1020 may also include steps 1021 and 1022.

[0130] In step 1021, the phagocytic capacity of phagocytes under negative control conditions is determined according to method 700 or method 800, wherein the target cell image corresponding to the negative control conditions is extracted from a high-content image obtained by taking pictures of phagocytes and target cells co-cultured under negative control conditions. The negative control conditions include unperturbed conditions or perturbed conditions that have been determined to have no effect on the phagocytic capacity of phagocytes (e.g., Ctrl: Isotype lgG1 / lgG4).

[0131] In step 1022, the phagocytic capacity of phagocytes under the test perturbation condition is compared with that under the negative control condition to determine the enhancing or inhibiting effect of the test perturbation condition on the phagocytic capacity of phagocytes.

[0132] Understandably, step 1010 yields the absolute phagocytic capacity of phagocytes under the tested perturbation condition, while step 1022 yields the "relative" phagocytic capacity of phagocytes under the tested perturbation condition relative to the negative control group. For example, if step 1010 yields an absolute phagocytic capacity of phagocytes under the tested perturbation condition of 0.7, and step 1022 yields a phagocytic capacity of phagocytes under the negative control condition of 0.5, then the enhancement effect of the tested perturbation condition on the phagocytic capacity of phagocytes is 120%.

[0133] Figure 11 This is a flowchart of a method 1100 for determining the enhancing or inhibiting effect of a test perturbation condition on the phagocytic capacity of phagocytes, according to an embodiment of this disclosure. Figure 11 As shown, method 1100 includes steps 1110 to 1150.

[0134] In step 1110, phagocyte images and target cell images are acquired. The phagocyte image contains multiple phagocytes, and the target cell image contains multiple target cell objects.

[0135] The phagocyte and target cell images were extracted from high-content images obtained by photographing co-cultured phagocytes and target cells subjected to a test perturbation condition. The test perturbation condition included at least one of the following: exposure to a predetermined concentration of a compound for a predetermined duration, or gene editing of a candidate gene in the phagocytes and / or target cells.

[0136] In the example, phagocyte images and target cell images can be extracted from a high-content image through the operation of process 100.

[0137] In one example, the perturbation condition to be tested may include exposure to a predetermined concentration of a compound for a predetermined duration. The compound may be, for example, anti-CD47, anti-PD-L1, anti-CD24, anti-LILRB1, etc. Anti-CD47 is a blocking antibody targeting the CD47 membrane protein. Anti-PD-L1 is a blocking antibody targeting the PD-L1 membrane protein. Anti-CD24 is a blocking antibody targeting the CD24 membrane protein. Anti-LILRB1 is a blocking antibody targeting the LILRB1 membrane protein.

[0138] In one example, exposure to a predetermined concentration of anti-CD47 for a predetermined duration is used as an exemplary perturbation condition, with macrophages selected as the phagocytes and tumor cells (e.g., DLD1) selected as the target cells. The co-culture of phagocytes and target cells may include the following steps.

[0139] The first step was to resuscitate human peripheral blood mononuclear cells (PBMCs), culture them in a culture flask and let them stand for 1.5 hours. The suspended cells in the supernatant were removed for other experiments. The adherent cells were stimulated with M-CSF for 5 days to obtain M0 type macrophages.

[0140] The second step involves staining the macrophages using methods commonly used in this field. For example, staining with carboxyfluorescein succinimidyl ester (CFSE) for 20 minutes, then removing excess dye. After digestion with trypsin in the culture flask, the macrophages are counted. Then, a predetermined number of macrophages are evenly distributed into the wells of each 96-well plate and incubated for 12 hours.

[0141] The third step involves staining the tumor cells using methods commonly used in the field. For example, a certain number of prepared cells are stained with pHrod Red Succinimidyl Ester (SE) for 30 minutes, and excess dye is removed. The cells are then digested with trypsin in a culture flask and counted. Finally, a certain number of tumor cells (e.g., DLD-1 (human colorectal adenocarcinoma epithelial cells)) are evenly distributed into each well of a 96-well plate containing macrophages.

[0142] The fourth step is to add a predetermined concentration of anti-CD47 and co-culture macrophages and tumor cells for 1.5 hours.

[0143] Furthermore, phagocytes and target cells in a 96-well plate can be placed in a high-content cell imaging system to capture images of the phagocytes and target cells, thereby obtaining high-content images.

[0144] In one example, the perturbation condition to be tested may include gene editing of candidate genes in phagocytes and / or target cells. Gene editing includes gene overexpression, gene knockout, and gene knockdown. Gene overexpression (OE) refers to the artificial upregulation of gene expression through cell transfection methods such as lentivirus or electroporation, resulting in excessive transcription and translation of the gene, leading to gene expression products exceeding normal levels. Typical gene overexpression includes APOA1 gene OE, CALR gene OE, CRLF2 gene OE, etc. Gene knockout (KO) refers to the inactivation or deletion of a specific gene through certain methods (e.g., gene editing methods such as CRISPR / Cas9). Typical targets of gene knockout include APMAP sgRNA, B2M sgRNA, CD47 sgRNA, etc. In contrast to gene overexpression, gene knockdown (KD) refers to the downregulation of gene expression levels. The expression levels of membrane proteins on the phagocytic or target cell side can be affected by specific genes such as OE, KO, or KD.

[0145] In one example, using gene knockout as an example, macrophages are selected as the phagocytes, and tumor cells (e.g., DLD1) are selected as the target cells. The co-culture of phagocytes and target cells may include the following steps.

[0146] The first step was to resuscitate human peripheral blood mononuclear cells (PBMCs), culture them in a culture flask and let them stand for 1.5 hours. The suspended cells in the supernatant were removed for other experiments. The adherent cells were stimulated with M-CSF for 5 days to obtain M0 type macrophages.

[0147] The second step involves staining the macrophages using methods commonly used in this field. For example, staining with carboxyfluorescein succinimidyl ester (CFSE) for 20 minutes, then removing excess dye. After digestion with trypsin in the culture flask, the macrophages are counted. Then, a predetermined number of macrophages are evenly distributed into the wells of each 96-well plate and incubated for 12 hours.

[0148] The third step involves staining the tumor cells using methods commonly used in the field. For example, a certain number of prepared cells are stained with pHrod Red Succinimidyl Ester (SE) for 30 minutes, and excess dye is removed. The cells are then digested with trypsin in a culture flask and counted. Finally, a certain number of tumor cells (e.g., DLD-1 (human colorectal adenocarcinoma epithelial cells)) are evenly distributed into each well of a 96-well plate containing macrophages.

[0149] The fourth step involves knocking out APMAP sgRNA in tumor cells. Macrophages and tumor cells are then co-cultured for 1.5 hours.

[0150] Furthermore, phagocytes and target cells in a 96-well plate can be placed in a high-content cell imaging system to capture images of the phagocytes and target cells, thereby obtaining high-content images.

[0151] In step 1120, the target cell image is decomposed into multiple sub-band images. Each sub-band image includes pixels of the target cell image that have brightness values ​​falling into a corresponding brightness sub-band among the multiple brightness sub-bands. The multiple brightness sub-bands are obtained by dividing the brightness range of the pixels in the target cell image. The specific operation of step 1120 is basically the same as that of step 720, and will not be described again here.

[0152] In step 1130, the number of target cell objects contained in each of the multiple sub-band images is determined. The specific operation of step 1130 is basically the same as that of step 730, and will not be described again here.

[0153] In step 1140, the number of phagocytes contained in the phagocyte image is determined. The specific operation of step 1140 is basically the same as that of step 740, and will not be described again here.

[0154] In step 1150, based on the number of target cell objects contained in the multiple sub-band images and the number of phagocytes contained in the phagocyte image, it is determined whether the perturbation condition enhances or inhibits the phagocytic ability of phagocytes on target cells. The specific operation of step 1150 is basically the same as that of step 750, and will not be repeated here. It should be noted that the enhancement or inhibition of the phagocytic ability of phagocytes on target cells is relative to the case where no perturbation condition is applied.

[0155] In one example, knocking out APMAP in tumor cells enhanced the phagocytic ability of macrophages against tumor cells.

[0156] Method 1100, based on the number of target cells contained in multiple sub-band images and the number of phagocytes contained in the phagocyte image, can indicate whether the perturbation conditions under test enhance or inhibit the phagocytic ability of phagocytes on target cells. Compared to the errors easily introduced by manual measurement in traditional drug screening tasks, Method 1100 can improve the prediction accuracy and screening efficiency of phagocyte phagocytic ability. Compared to the large amount of manpower and resources required in traditional drug screening tasks, Method 1100 can save a significant amount of experimental manpower and costs.

[0157] Figure 12 This is a flowchart of a target screening method 1200 according to an embodiment of the present disclosure. Figure 12 As shown, method 1200 includes steps 1210 and 1220.

[0158] In step 1210, the effects of multiple different candidate genes in phagocytes and / or target cells on enhancing or inhibiting the phagocytic ability of phagocytes to engulf target cells are determined.

[0159] In the example, for each of the multiple different candidate genes, the following steps are performed:

[0160] The first step is to acquire images of phagocytes and target cells. The phagocyte image contains multiple phagocytes. The target cell image contains multiple target cell objects. Both images are extracted from high-content images obtained by photographing co-cultured phagocytes and target cells under perturbed conditions. The perturbed conditions include gene editing of the candidate gene in the phagocytes and / or target cells. The specific operations of this step are essentially the same as those in step 1110, and will not be repeated here.

[0161] The second step involves decomposing the target cell image into multiple sub-band images. Each sub-band image comprises pixels of the target cell image that have brightness values ​​falling within a corresponding brightness sub-band of the multiple brightness sub-bands. The multiple brightness sub-bands are obtained by dividing the brightness range of the pixels in the target cell image. The specific operation of this step is basically the same as that of step 720, and will not be repeated here.

[0162] The third step is to determine the corresponding number of target cell objects contained in each of the multiple sub-band images. The specific operation of this step is basically the same as that of step 730, and will not be described again here.

[0163] The fourth step is to determine the number of phagocytes contained in the phagocyte image. The specific operation of this step is basically the same as that of step 740, and will not be repeated here.

[0164] The fifth step involves determining whether gene editing enhances or inhibits the phagocytic capacity of target cells by using the number of target cells contained in the multiple subband images and the number of phagocytes contained in the phagocyte images. The specific operation of this step is essentially the same as that of step 750, and will not be repeated here.

[0165] In step 1220, at least one candidate gene and / or the protein expressed by at least one candidate gene is selected as a target from a plurality of different candidate genes. The selected candidate gene is determined to have the function of enhancing phagocytic ability.

[0166] Method 1200 can select proteins expressed by candidate genes that enhance phagocytic ability from multiple different candidate genes as targets, thereby achieving target discovery and screening.

[0167] This disclosure also discloses a target screening method 1200A, which includes steps 1230 and 1240.

[0168] In step 1230, gene editing of candidate genes in phagocytes and / or target cells is used as the perturbation to be tested, and the effect of the perturbation condition on the phagocytic capacity of phagocytes is determined by method 1000 or method 1100.

[0169] For details on how to perform gene editing on candidate genes, please refer to the description of step 1210.

[0170] In step 1240, in response to the candidate gene being identified as having a set degree of enhanced phagocytic ability, the candidate gene and / or the protein expressed by the candidate gene are used as targets.

[0171] The effect of enhancing phagocytic ability by a set degree is, for example, a 10% increase in phagocytic ability compared to the negative control group; or, for example, a candidate gene having the greatest phagocytic ability enhancement of 10% among all candidate genes.

[0172] Figure 13 This is a block diagram of an apparatus 1300 for determining the phagocytic capacity of a phagocyte according to an embodiment of the present disclosure. Figure 13 As shown, the device 1300 for determining the phagocytic capacity of phagocytes includes a first module 1310, a second module 1320, a third module 1330, a fourth module 1340, and a fifth module 1350.

[0173] The first module 1310 is used to acquire phagocyte images and target cell images, wherein the phagocyte images contain multiple phagocytes and the target cell images contain multiple target cell objects, and the phagocyte images and target cell images are extracted from high-content images obtained by photographing co-cultured phagocytes and target cells.

[0174] The second module 1320 is used to decompose the target cell image into multiple sub-band images. Each sub-band image includes pixels of the target cell image that have brightness values ​​falling into a corresponding brightness sub-band of multiple brightness sub-bands. The multiple brightness sub-bands are obtained by dividing the brightness range of the pixels of the target cell image.

[0175] The third module 1330 is used to determine the corresponding number of target cell objects contained in each of the multiple sub-band images.

[0176] The fourth module 1340 is used to determine the number of phagocytes contained in the phagocyte image.

[0177] The fifth module 1350 is used to determine the phagocytic capacity of phagocytes for target cells based on the corresponding number of target cell objects contained in multiple sub-band images and the number of phagocytes contained in the phagocyte image.

[0178] It should be understood that Figure 13 The various modules of the apparatus 1300 shown can be correlated with the various steps in the method 700 described above with reference to FIG. 7. Therefore, the operations, features, and advantages described above for method 700 also apply to apparatus 1300 and its included modules. For the sake of brevity, some operations, features, and advantages will not be repeated here.

[0179] This disclosure also discloses another device 1600 for determining the phagocytic capacity of phagocytes, comprising:

[0180] The first acquisition module 1610 is used to acquire target cell images; the target cell images are extracted from high-content images obtained by photographing co-cultured phagocytes and target cells; the fluorescence intensity of the target cells changes after being phagocytosed by phagocytes;

[0181] The first determining module 1620 is used to determine the number of pixels in the target cell image that fall into at least one of the multiple brightness sub-bands based on the pixel values ​​of the pixels in the target cell image; and / or, based on the pixel values ​​of the pixels in the target cell image, determine the number of target cells in the target cell image that fall into at least one of the multiple brightness sub-bands; the multiple brightness sub-bands are obtained by dividing according to a fluorescence intensity threshold, which is used to distinguish between phagocytosed target cells and unphagocytosed target cells.

[0182] The second determining module 1630 is used to determine the phagocytic capacity of the phagocytes for the target cells based on the number of pixels falling into at least one of the multiple brightness subbands and / or the number of target cells falling into at least one of the multiple brightness subbands.

[0183] It should be understood that the various modules of apparatus 1600 can correspond to the various steps in method 800 described above. Therefore, the operations, features, and advantages described above for method 800 also apply to apparatus 1600 and its included modules. For the sake of brevity, some operations, features, and advantages will not be repeated here.

[0184] Figure 14 This is a block diagram of an apparatus 1400 for determining the enhancing or inhibiting effect of a test perturbation condition on the phagocytic capacity of phagocytes, according to an embodiment of this disclosure. Figure 14 As shown, the device 1400 for determining the enhancing or inhibiting effect of perturbation conditions on the phagocytic capacity of phagocytes includes a sixth module 1410, a seventh module 1420, an eighth module 1430, a ninth module 1440, and a tenth module 1450.

[0185] Module 6 1410 is used to acquire images of phagocytes and target cells, wherein the images of phagocytes contain multiple phagocytes and the images of target cells contain multiple target cell objects. The images of phagocytes and target cells are extracted from high-content images obtained by photographing co-cultured phagocytes and target cells subjected to a test perturbation condition, which includes at least one of the following: exposure to a predetermined concentration of a compound for a predetermined duration, or gene editing of candidate genes in phagocytes and / or target cells.

[0186] The seventh module 1420 is used to decompose the target cell image into multiple sub-band images, each sub-band image including pixels of the target cell image having brightness values ​​that fall into a corresponding brightness sub-band of multiple brightness sub-bands, the multiple brightness sub-bands being obtained by dividing the brightness range of the pixels of the target cell image.

[0187] Module 8 1430 is used to determine the corresponding number of target cell objects contained in each of the multiple sub-band images.

[0188] Module 9, 1440, is used to determine the number of phagocytes contained in a phagocyte image.

[0189] Module 10, 1450, is used to determine the ability of the perturbation condition to be tested to enhance or inhibit the phagocytosis of target cells by phagocytes based on the corresponding number of target cell objects contained in multiple sub-band images and the number of phagocytes contained in the phagocyte image.

[0190] It should be understood that Figure 14 The various modules of the device 1400 shown can be referenced above. Figure 11The steps in method 1100 described correspond to each other. Therefore, the operations, features, and advantages described above for method 1100 also apply to device 1400 and its included modules. For the sake of brevity, some operations, features, and advantages will not be repeated here.

[0191] This disclosure also discloses another device 1700 for determining the enhancing or inhibiting effect of perturbation conditions on the phagocytic capacity of phagocytes, comprising:

[0192] The third determining module 1710 is used to determine the phagocytic capacity of phagocytes under a test perturbation condition according to method 700 or method 800, wherein the target cell image corresponding to the test perturbation condition is extracted from a high-content image obtained by taking pictures of co-cultured phagocytes and target cells subjected to the test perturbation condition, and the test perturbation condition includes at least one of the following: exposure to a predetermined concentration of a compound for a predetermined duration, or gene editing of candidate genes in phagocytes and / or target cells;

[0193] The fourth determination module 1720 determines the enhancing or inhibiting effect of the perturbation condition on the phagocytic capacity of the phagocytes based on the phagocytic capacity of the phagocytes under the perturbation condition to be tested.

[0194] It should be understood that the various modules of apparatus 1700 can correspond to the various steps in method 1000 described above. Therefore, the operations, features, and advantages described above for method 1000 also apply to apparatus 1700 and its included modules. For the sake of brevity, some operations, features, and advantages will not be repeated here.

[0195] While specific functions have been discussed above with reference to specific modules, it should be noted that the functions of the modules discussed herein can be divided into multiple modules, and / or at least some functions of multiple modules can be combined into a single module. The specific actions performed by the modules discussed herein include the specific module itself performing the action, or alternatively, the specific module calling or otherwise accessing another component or module that performs the action (or performs the action in conjunction with the specific module). Therefore, a specific module performing an action can include the specific module performing the action itself and / or another module that performs the action, called or otherwise accessed by the specific module.

[0196] It should also be understood that this document can describe various techniques in the general context of software hardware elements or program modules. The various modules described above can be implemented in hardware or in hardware combined with software and / or firmware. For example, these modules can be implemented as computer program code / instructions configured to execute in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules can be implemented as hardware logic / circuit.

[0197] According to one aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and at least one memory communicatively connected to the at least one processor, the at least one memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform the method described above.

[0198] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided that stores instructions which, when executed by at least one processor of a computer, cause the computer to perform the methods described above.

[0199] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method described above.

[0200] Figure 15 An example configuration of an electronic device 1500 that can be used to implement the methods described herein is shown.

[0201] Electronic device 1500 can be a variety of different types of devices. Examples of electronic device 1500 include, but are not limited to: desktop computers, server computers, laptop or netbook computers, mobile devices (e.g., tablet computers, cellular or other wireless phones (e.g., smartphones), notebook computers, mobile stations), wearable devices (e.g., glasses, watches), entertainment devices (e.g., entertainment appliances, set-top boxes communicatively coupled to display devices, game consoles), televisions or other display devices, automotive computers, and so on.

[0202] Electronic device 1500 may include at least one processor 1502, memory 1504, multiple communication interfaces 1506, display device 1508, other input / output (I / O) devices 1510, and one or more mass storage devices 1512 capable of communicating with each other, such as via system bus 1514 or other suitable connections.

[0203] Processor 1502 may be a single processing unit or multiple processing units, and all processing units may include single or multiple computing units or multiple cores. Processor 1502 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. Among other capabilities, processor 1502 may be configured to acquire and execute computer-readable instructions stored in memory 1504, mass storage device 1512, or other computer-readable media, such as program code of operating system 1516, program code of application program 1518, program code of other program 1520, etc.

[0204] Memory 1504 and mass storage device 1512 are examples of computer-readable storage media for storing instructions executed by processor 1502 to perform the various functions described above. For example, memory 1504 may generally include both volatile and non-volatile memory (e.g., RAM, ROM, etc.). Furthermore, mass storage device 1512 may generally include hard disk drives, solid-state drives, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CDs, DVDs), storage arrays, network-attached storage, storage area networks, etc. Both memory 1504 and mass storage device 1512 may be collectively referred to herein as memory or computer-readable storage media, and may be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code, which may be executed by processor 1502 as a specific machine configured to perform the operations and functions described in the examples herein.

[0205] Multiple programs may be stored on mass storage device 1512. These programs include operating system 1516, one or more application programs 1518, other programs 1520, and program data 1522, and they may be loaded into memory 1504 for execution. Examples of such application programs or program modules may include, for example, computer program logic (e.g., computer program code or instructions) for implementing the following components / functions: first module 1310, second module 1320, third module 1330, fourth module 1340, fifth module 1350, sixth module 1410, seventh module 1420, eighth module 1430, ninth module 1440, tenth module 1450, first acquisition module 1610, first determination module 1620, second determination module 1630, third determination module 1710, fourth determination module 1720, method 700 (including any suitable steps of method 700), method 1100 (including any suitable steps of method 1100), and / or other embodiments described herein.

[0206] Although Figure 15 The modules 1516, 1518, 1520, and 1522, or portions thereof, are illustrated as being stored in memory 1504 of electronic device 1500. However, modules 1516, 1518, 1520, and 1522 may be implemented using any form of computer-readable medium accessible by electronic device 1500. As used herein, “computer-readable medium” includes at least two types of computer-readable media: computer-readable storage media and communication media.

[0207] Computer-readable storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD, or other optical storage devices, magnetic cassettes, magnetic tapes, disk storage devices or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by electronic devices. In contrast, communication media can embody computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms. Computer-readable storage media as defined herein do not include communication media.

[0208] One or more communication interfaces 1506 are used for exchanging data with other devices, such as via a network, direct connection, etc. Such communication interfaces can be one or more of the following: any type of network interface (e.g., a network interface card (NIC)), wired or wireless (such as IEEE 802.11 Wireless LAN (WLAN)) wireless interface, Wi-MAX interface, Ethernet interface, Universal Serial Bus (USB) interface, cellular network interface, Bluetooth™ interface, Near Field Communication (NFC) interface, etc. Communication interface 1506 can facilitate communication across various network and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the Internet, etc. Communication interface 1506 can also provide communication with external storage devices (not shown), such as storage arrays, network-attached storage, storage area networks, etc.

[0209] In some examples, a display device 1508, such as a monitor, may be included for displaying information and images to the user. Other I / O devices 1510 may be devices that receive various inputs from the user and provide various outputs to the user, and may include touch input devices, gesture input devices, cameras, keyboards, remote controls, mice, printers, audio input / output devices, and so on.

[0210] The technologies described herein can be supported by these various configurations of electronic device 1500, and are not limited to specific examples of the technologies described herein. For example, the functionality can also be implemented wholly or partially on a “cloud” using a distributed system. A cloud includes and / or represents a platform for resources. The platform abstracts the underlying functionality of the cloud’s hardware (e.g., servers) and software resources. Resources may include applications and / or data that can be used when performing computational processing on a server remote from electronic device 1500. Resources may also include services provided via the Internet and / or via subscriber networks such as cellular or Wi-Fi networks. The platform can abstract resources and functionality to connect electronic device 1500 to other electronic devices. Therefore, the implementation of the functionality described herein can be distributed throughout the cloud. For example, the functionality can be implemented partly on electronic device 1500 and partly through the platform that abstracts the functionality of the cloud.

[0211] Although this disclosure has been described and illustrated in detail in the accompanying drawings and the foregoing description, such description and illustration should be considered illustrative and suggestive, not restrictive; this disclosure is not limited to the disclosed embodiments. By studying the drawings, the disclosure, and the appended claims, those skilled in the art will be able to understand and implement variations of the disclosed embodiments in practice with respect to the claimed subject matter. In the claims, the word "comprising" does not exclude other elements or steps not listed, the indefinite article "a" or "an" does not exclude a plurality, and the term "a plurality" means two or more. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be beneficial.

Claims

1. A method for determining the phagocytic capacity of phagocytes, comprising: Acquire target cell images; The target cell image was extracted from a high-content image obtained by photographing co-cultured phagocytes and target cells; the fluorescence intensity of the target cell changed after being phagocytosed by the phagocytes. Acquire phagocyte images and determine the number of phagocytes contained in the phagocyte images; the phagocyte images contain multiple phagocytes and are extracted from high-content images obtained by photographing co-cultured phagocytes and target cells; The target cell image is decomposed into multiple sub-band images, each sub-band image including pixels of the target cell image having a brightness value that falls into a corresponding brightness sub-band among the multiple brightness sub-bands; The number of target cell objects contained in each of the plurality of sub-band images is determined; the plurality of brightness sub-bands are obtained by dividing according to fluorescence intensity thresholds, wherein the first fluorescence intensity threshold in the fluorescence intensity thresholds is used to distinguish between phagocytosed target cells and non-phagocytosed target cells; as well as The phagocytic capacity of the phagocytes for the target cells is determined based on the number of target cells falling into at least one of the multiple brightness subbands, including: Calculate the weighted sum of the corresponding number of target cell objects contained in the multiple sub-band images and the ratio to the number of phagocytes, wherein the ratio indicates the phagocytic capacity of the phagocytes for the target cells, and the weight coefficients corresponding to the corresponding number of target cell objects contained in the multiple sub-band images are positively correlated with the brightness range of the corresponding brightness sub-band.

2. The method as described in claim 1, wherein, The method further includes: Images of the blank control group were obtained, which were extracted from high-content images obtained by photographing target cells cultured alone; The first fluorescence intensity threshold is determined based on the fluorescence intensity of the target cells in the blank control group image.

3. The method as described in claim 1 or 2, wherein, The determination of the phagocytic capacity of the phagocyte for the target cells based on the number of target cells falling into at least one of the multiple brightness subbands further includes: The phagocytic capacity of the phagocytes for the target cells is determined based on the number of target cells falling into at least one of the multiple brightness subbands and the number of the phagocytes.

4. The method of claim 1, wherein, Determining the number of target cell objects contained in each of the plurality of sub-band images includes: The multiple sub-band images are converted to grayscale. For each sub-band image among the multiple sub-band images after grayscale conversion, the sub-band image is segmented based on an adaptive threshold algorithm to obtain a first image containing a first number of first sub-regions, wherein the first sub-regions indicate target cell regions, and the first number is the number of target cell objects contained in the sub-band image.

5. The method of claim 4, wherein, The step of determining the number of target cell objects corresponding to the multiple sub-band images further includes: The first image is segmented based on the watershed algorithm to obtain a second image containing a second number of first sub-regions, wherein the second number is greater than or equal to the first number, and the second number is the number of target cell objects contained in the sub-region image.

6. The method of claim 5, wherein, The step of determining the number of target cell objects corresponding to the multiple sub-band images further includes: At least one first sub-region is filtered out from the second number of first sub-regions to obtain a third number of first sub-regions, wherein the area of ​​each of the at least one first sub-region is less than a first area threshold and / or the area of ​​each of the at least one first sub-region is greater than a second area threshold, and the third number is the number of target cell objects contained in the sub-band image.

7. The method of claim 1, wherein, Determining the number of phagocytes contained in the phagocyte image includes: The phagocytic cell image is then converted to grayscale. The grayscale image of the phagocytes is segmented based on an adaptive thresholding algorithm to obtain a fourth image containing a fourth number of second sub-regions, wherein the second sub-regions indicate phagocyte regions, and the fourth number is the number of phagocytes contained in the phagocyte image.

8. The method of claim 7, wherein, Determining the number of phagocytes contained in the phagocyte image further includes: The fourth image is segmented based on the watershed algorithm to obtain a fifth image containing a fifth number of sub-regions, wherein the fifth number is greater than or equal to the fourth number, and the fifth number is the number of phagocytes contained in the phagocyte image.

9. The method of claim 8, wherein, Determining the number of phagocytes contained in the phagocyte image further includes: At least one second sub-region is filtered out from the fifth number of second sub-regions to obtain a sixth number of second sub-regions, wherein the area of ​​each of the at least one second sub-region is less than a third area threshold and / or the area of ​​each of the at least one second sub-region is greater than a fourth area threshold.

10. The method of claim 1, wherein, The acquisition of the target cell image includes: A high-content cell imaging system is used to acquire high-content images of co-cultured phagocytes and target cells, wherein the high-content images have a second color channel, and the second color channel corresponds to the target cell object; The high-content image is preprocessed to generate a color image; The target cell image is generated based on the image information corresponding to the second color channel of the color image.

11. The method of claim 1, further comprising: Before determining the number of phagocytes contained in the phagocyte image: A denoised image is obtained, wherein the denoised image has the same size as the phagocyte image, and the pixels in the middle region of the denoised image have pixel values ​​for compensating for halo noise. The halo-removed phagocyte image is obtained by subtracting the pixel values ​​of pixels in the denoised image that are in the same relative position as the pixels in the phagocyte image from the pixel values ​​of each pixel in the phagocyte image.

12. The method of claim 1, wherein, The target cells include tumor cells.

13. A method for determining the enhancing or inhibiting effect of a test perturbation condition on the phagocytic capacity of phagocytes, comprising: The method according to any one of claims 1-12 determines the phagocytic capacity of phagocytes under the test perturbation condition, wherein the target cell image corresponding to the test perturbation condition is extracted from a high-content image obtained by photographing co-cultured phagocytes and target cells subjected to the test perturbation condition, and the test perturbation condition includes at least one of the following: exposure to a predetermined concentration of a compound for a predetermined duration, gene editing of candidate genes in the phagocytes and / or the target cells; Based on the phagocytic capacity of phagocytes under the tested perturbation conditions, determine whether the tested perturbation conditions enhance or inhibit the phagocytic capacity of phagocytes.

14. The method of claim 13, wherein, The step of determining the enhancing or inhibiting effect of the perturbation condition on the phagocytic capacity of phagocytes based on the phagocytic capacity of phagocytes under the perturbation condition includes: The phagocytic capacity of phagocytes under negative control conditions was determined, wherein the target cell image corresponding to the negative control conditions was extracted from high-content images obtained by taking pictures of phagocytes and target cells co-cultured under negative control conditions, and the negative control conditions included unperturbed conditions or perturbed conditions that were determined to have no effect on the phagocytic capacity of phagocytes. By comparing the phagocytic capacity of phagocytes under the test perturbation condition with that under the negative control condition, the enhancing or inhibiting effect of the test perturbation condition on the phagocytic capacity of phagocytes can be determined.

15. The method of claim 13, wherein, The gene editing includes gene knockout, gene overexpression, or gene knockdown.

16. An apparatus for determining the phagocytic capacity of phagocytes, comprising: The first acquisition module is used to acquire target cell images; The target cell image was extracted from a high-content image obtained by photographing co-cultured phagocytes and target cells; the fluorescence intensity of the target cell changed after being phagocytosed by the phagocytes. The second acquisition module is used to acquire phagocyte images and determine the number of phagocytes contained in the phagocyte images; the phagocyte images contain multiple phagocytes and are extracted from high-content images obtained by photographing co-cultured phagocytes and target cells; The decomposition module is used to decompose the target cell image into multiple sub-band images, each sub-band image including pixels of the target cell image having a brightness value that falls into a corresponding brightness sub-band of the multiple brightness sub-bands; The first determining module is used to determine the corresponding number of target cell objects contained in the plurality of sub-band images; the plurality of brightness sub-bands are obtained according to fluorescence intensity thresholds, and the first fluorescence intensity threshold in the fluorescence intensity thresholds is used to distinguish between phagocytosed target cells and unphagocytosed target cells. The second determining module is used to determine the phagocytic capacity of the phagocytes for the target cells based on the number of target cells falling into at least one of the multiple brightness subbands, including: Calculate the weighted sum of the corresponding number of target cell objects contained in the multiple sub-band images and the ratio to the number of phagocytes, wherein the ratio indicates the phagocytic capacity of the phagocytes for the target cells, and the weight coefficients corresponding to the corresponding number of target cell objects contained in the multiple sub-band images are positively correlated with the brightness range of the corresponding brightness sub-band.

17. An apparatus for determining the enhancing or inhibiting effect of perturbation conditions on the phagocytic capacity of phagocytes, comprising: The third determining module is used to determine the phagocytic capacity of phagocytes under a test perturbation condition according to any one of claims 1-12, wherein the target cell image corresponding to the test perturbation condition is extracted from a high-content image obtained by taking pictures of co-cultured phagocytes and target cells subjected to the test perturbation condition, and the test perturbation condition includes at least one of the following: exposure to a predetermined concentration of a compound for a predetermined duration, gene editing of candidate genes in the phagocytes and / or the target cells; The fourth determining module is used to determine the enhancing or inhibiting effect of the perturbation condition on the phagocytic capacity of the phagocyte based on the phagocytic capacity of the phagocyte under the perturbation condition to be tested.

18. An electronic device comprising: processor; as well as A memory storing instructions executable by the processor, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1-15.

19. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1-15.

20. A computer program product comprising instructions, wherein, when executed by a processor, the instructions cause the processor to perform the method according to any one of claims 1-15.

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