Cell sorting method and cell display method

Abstract

The present application relates to a cell sorting method and a cell display method. The cell sorting method comprises: acquiring cell image data sent by a cell sorting device (202); positioning cells to be sorted in the cell image data by means of a deep learning model (204); on the basis of the cell image data, evaluating the fluorescence signal intensity of the cells to be sorted (206); and on the basis of the fluorescence signal intensity of the cells to be sorted, pipetting from among the cells to be sorted cells meeting a preset sorting condition (208).

Description

Cell sorting methods and cell display methods

[0001] Related applications

[0002] This application claims the following: a) Method, device, computer equipment and storage medium for cell sorting, filed on July 28, 2023, with application number 2023109452145; b) Method, device, computer equipment and storage medium for cell extraction, filed on July 28, 2023, with application number 2023109496001; c) Method, device, computer equipment and storage medium for cell extraction, filed on July 28, 2023, with application number 2023109516931; and c) Display of cells on a chip, filed on July 28, 2023, with application number 2023109516931. The application claims priority to the Chinese patent applications entitled “Cell display method, device, computer equipment and storage medium”, “Cell selection device calibration method, device, computer equipment and storage medium”, and “Cell display method, device, computer equipment and storage medium”, filed on July 28, 2023, with application number 2023109481148, and application number 2023109515426, and application number 2023109515426, which are all incorporated herein by reference in their entirety. Technical Field

[0003] The present application relates to the technical field of cell sorting, and in particular to a cell sorting method and a cell display method. Background Art

[0004] With the development of the biomedical field, cell sorting technology has emerged. Cell sorting technology refers to the separation and processing of a specific cell subpopulation from a mixed cell sample based on the characteristics of the cells, in order to achieve functional analysis of specific cells.

[0005] In traditional technologies, cell sorting is performed through threshold segmentation technology or template matching technology. However, threshold segmentation technology finds it difficult to give a suitable threshold for all backgrounds and cell morphologies, and template matching technology also finds it difficult to accurately match cells, resulting in low cell sorting accuracy.

[0006] Summary of the Invention

[0007] Based on this, it is necessary to provide a cell sorting method and a cell display method to address the above technical problems.

[0008] In a first aspect, the present application provides a cell sorting method, which is applied to a terminal. The method comprises:

[0009] Acquire cell image data sent by a cell sorting device;

[0010] Locate cells to be sorted in cell image data using a deep learning model;

[0011] Evaluate the fluorescence signal intensity of the cells to be sorted based on the cell image data; and

[0012] According to the fluorescence signal intensity of the cells to be sorted, cells that meet the preset sorting conditions are extracted from the cells to be sorted.

[0013] In one embodiment, locating cells to be sorted in cell image data using a deep learning model includes:

[0014] The cell image data is input into the deep learning model, the fluorescence signal characteristics of the cell image data are extracted, and the position data of the cells to be sorted in the cell image data are determined based on the fluorescence signal characteristics.

[0015] In one embodiment, evaluating the fluorescence signal intensity of the cells to be sorted based on the cell image data to obtain the fluorescence signal intensity of the cells to be sorted includes:

[0016] intercepting the cells to be sorted in the cell image data to obtain an image of the cells to be sorted; and

[0017] Grayscale analysis is performed on the image of the cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted.

[0018] In one embodiment, performing grayscale analysis on the image of cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted includes:

[0019] De-noising the image of cells to be sorted;

[0020] Performing threshold segmentation on the denoised image of cells to be sorted; and

[0021] Grayscale analysis is performed on the segmented image of the cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted.

[0022] In one embodiment, the method further comprises:

[0023] Using a deep learning model, first position data of each cell in a first bright field cell image before cell sorting and second position data of each cell in a second bright field cell image after cell sorting are respectively identified;

[0024] comparing the first position data with the second position data; and

[0025] If the comparison is consistent, the cell sorting fails, and the process returns to the step of locating the cells to be sorted in the cell image data through the deep learning model, and continues the cell sorting.

[0026] In a second aspect, the present application further provides a cell display method, which is applied to a cell sorting device, wherein the cell sorting device includes an imaging mechanism; the method comprises:

[0027] When the to-be-sorted member containing the to-be-sorted cells moves to a preset area of ​​the cell sorting device, the imaging mechanism images the to-be-sorted cells in the preset area to obtain cell image data; and

[0028] Send cell image data to the terminal for display.

[0029] In one embodiment, the imaging mechanism includes an imaging lighting unit, an optical path component, and a detection component; the imaging mechanism images the cells to be sorted in a preset area to obtain cell image data, including:

[0030] The imaging lighting unit provides lighting for the parts to be sorted and the optical path components;

[0031] The optical path components image the cells to be sorted under the illumination conditions; and

[0032] The detection component acquires the cell image data generated by the optical path component.

[0033] In one embodiment, the imaging mechanism further comprises a fluorescent component; and the method further comprises:

[0034] The fluorescent component illuminates the cells to be sorted so that the cells to be sorted emit fluorescence;

[0035] The optical path component images the cells to be sorted to obtain a fluorescent image of the cells to be sorted; and

[0036] The detection component obtains the fluorescent image of the cells to be sorted.

[0037] In one embodiment, the fluorescence component further includes a filter switching unit; and the fluorescence component irradiating the cells to be sorted so that the cells to be sorted emit fluorescence includes:

[0038] The filter switching unit in the fluorescent component switches the excitation light of multiple bands; and

[0039] The cells to be sorted are irradiated with excitation light of multiple wavelength bands respectively, so that the cells to be sorted emit different fluorescence.

[0040] In one embodiment, a cell sorting device includes a picking mechanism; the picking mechanism includes an aspirating and placing member; and before acquiring cell image data, the method includes a calibration step of the picking mechanism, including:

[0041] Acquire image data of the pickup and placement part, and determine the clarity of the image data of the pickup and placement part through a calibration program;

[0042] Move the pick-and-place piece according to the definition through a calibration program to obtain a pick-and-place piece image with the best definition; and

[0043] The position data of the pick-and-place part in the pick-and-place part image is adjusted through a calibration program until the preset conditions are met, and the current position data of the pick-and-place part is stored.

[0044] In one embodiment, determining the clarity of the image data of the pickup and placement component through a calibration process includes:

[0045] Performing edge detection on the image data of the pickup and placement component to obtain the brightness distribution of multiple boundary points in the image data; and

[0046] The clarity of the image data of the pickup and placement part is determined based on a preset image clarity evaluation relationship and the brightness distribution of multiple boundary points.

[0047] In one embodiment, the selection mechanism further includes a selection moving component; and moving the pick-and-place piece according to the clarity through a calibration program to obtain an image of the pick-and-place piece with optimal clarity includes:

[0048] Identifying the current position of the suction and placement member in the image data of the suction and placement member through a calibration program, and controlling the selection and movement component to move the suction and placement member according to the current position of the suction and placement member; and

[0049] Return to the step of obtaining the image data of the suction and placement member until the image data of the suction and placement member reaches the optimal clarity, thereby obtaining an image of the suction and placement member with the optimal clarity.

[0050] In one embodiment, the selection mechanism further includes a mounting assembly, the mounting assembly including a fixing unit; the position data of the pickup and placement component in the pickup and placement component image is adjusted through a calibration program until a preset condition is met, and the current position data of the pickup and placement component is stored, including:

[0051] Identify the position data of the pick-and-place part in the pick-and-place part image through a calibration program; and

[0052] The calibration program controls the fixing unit to fine-tune the suction and placement member according to the position data, so that the position data of the suction and placement member is located at the preset position of the suction and placement member image, and the current position data of the suction and placement member is stored as the calibration value.

[0053] In one embodiment, the object to be sorted is a biological culture chip; the method further comprises:

[0054] When the biological culture chip containing cells to be sorted moves to a preset area of ​​the cell sorting device, the objective lens in the imaging mechanism images the cells to be sorted in the preset area to obtain cell image data.

[0055] In one embodiment, the biological culture chip includes a plurality of accommodating chambers, each accommodating chamber contains cells to be sorted, and a preset distance is separated between two adjacent accommodating chambers.

[0056] The details of one or more embodiments of the present application are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the present application will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the disclosed drawings without any creative work.

[0058] FIG1 is a diagram showing an application environment of a cell sorting method according to an embodiment;

[0059] FIG2 is a schematic diagram of a cell sorting method according to an embodiment;

[0060] FIG3 is a schematic diagram of a process for a cell display method according to an embodiment;

[0061] FIG4 is a schematic diagram of a process for obtaining cell image data in one embodiment;

[0062] FIG5 is a schematic diagram of a calibration process of a selection mechanism in one embodiment;

[0063] FIG6 is a schematic flow chart of a cell display method in another embodiment;

[0064] FIG7 is a block diagram of a cell sorting device according to an embodiment;

[0065] FIG8 is a structural block diagram of a cell display device according to an embodiment;

[0066] FIG9 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0068] Cell sorting technology is a technique that separates cells that meet predetermined requirements from a pool of cells to be sorted based on their specific characteristics. When functional analysis of cells that meet certain requirements is required, such as analyzing cell culture supernatants using ELISA (enzyme-linked immunosorbent assay) to detect cellular molecules or performing cell function tests using co-cultures, obtaining high-purity cells that meet the predetermined requirements is a prerequisite.

[0069] The cell sorting method provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. The cell sorting device 102 communicates with the terminal 104 via a network. The cell sorting device 102 sends the acquired cell image data to the terminal 104. The terminal 104 locates the cells to be sorted in the cell image data using a deep learning model, evaluates the fluorescence signal intensity of the cells to be sorted based on the cell image data, and extracts cells that meet the preset sorting conditions from the cells to be sorted based on the fluorescence signal intensity of the cells to be sorted. The terminal 104 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and image processing devices.

[0070] In one embodiment, as shown in FIG2 , a cell sorting method is provided. The method is described by taking the application of the method to a terminal as an example. The method includes the following steps:

[0071] Step 202: Acquire cell image data sent by the cell sorting device.

[0072] Step 204: Locate the cells to be sorted in the cell image data using a deep learning model.

[0073] The cell image data refers to an image containing the cells to be sorted obtained by imaging the cells to be sorted. The cells to be sorted may be cells that have been specifically screened with a fluorescent dye.

[0074] Specifically, the cell sorting device images the cells to be sorted, acquires cell image data, and transmits the cell image data to the terminal. There may be multiple cells to be sorted. The cell image data may be fluorescence image data. The terminal invokes a deep learning model, inputs the cell image data into the deep learning model, performs target recognition on the cell image data, and determines the location of the cells to be sorted.

[0075] Step 206 : Evaluate the fluorescence signal intensity of the cells to be sorted based on the cell image data.

[0076] After determining the location area of ​​the cells to be sorted in the cell image data, grayscale analysis is performed on the cell image data using image processing technology to obtain grayscale values, and then the fluorescence signal intensity of the cells to be sorted is evaluated based on the grayscale values.

[0077] Step 208 : Based on the fluorescence signal intensity of the cells to be sorted, cells that meet the preset sorting conditions are extracted from the cells to be sorted.

[0078] By identifying whether the fluorescence signal intensity of the cells to be sorted meets preset sorting conditions, cells that meet the preset sorting conditions are screened out from the cells to be sorted, and then the cell sorting device is controlled to aspirate the cells that meet the preset sorting conditions. The preset sorting condition may be that the fluorescence signal intensity exceeds a preset threshold. For example, the aspirated cells may be B cells, hybridoma cells, tumor cells, etc.

[0079] Optionally, the cell sorting device includes a main body, a picking mechanism, a loading mechanism, an imaging mechanism and a control mechanism. The main body is used to support and accommodate other mechanisms in the cell sorting device, forming a storage space for the picking mechanism, the loading mechanism, the imaging mechanism and the control mechanism. The loading mechanism is used to carry the parts to be sorted, and the parts to be sorted are used to accommodate the cells to be sorted. The imaging mechanism is used to image the cells to be sorted and send the cell image data to the terminal. The terminal displays the cell image data, and locates the cells to be sorted in the cell image data through a deep learning model, evaluates the fluorescence signal intensity of the cells to be sorted based on the cell image data, and screens cells that meet the preset sorting conditions from the cells to be sorted based on the fluorescence signal intensity of the cells to be sorted. The picking mechanism is then used to absorb and recover cells that meet the preset sorting conditions in the parts to be sorted, thereby realizing the sorting of cells that meet the preset sorting conditions. The control mechanism is used to control the various mechanisms of the cell sorting device to perform cell sorting operations.

[0080] In the above-mentioned cell sorting method, a method combining deep learning and traditional image processing is used to screen the cells to be sorted and evaluate the fluorescence signal intensity. The deep learning model is used to locate the cells to be sorted, and then image processing technology is used to evaluate the fluorescence signal strength of the located cells to be sorted. Then, based on the fluorescence signal strength of the cells to be sorted, cells that meet the preset sorting criteria are extracted from the cells to be sorted. This solves the problem of low cell positioning accuracy caused by the large difference in fluorescence signals between template matching technology and threshold segmentation technology, which makes it difficult to distinguish fluorescence signals from impurities or background. It achieves accurate cell positioning, and based on this, the signal strength is evaluated, which can greatly improve the accuracy of cell sorting. In addition, the cell sorting device automatically selects cells that meet the preset sorting criteria from the cells to be sorted, and the cells are separated and purified. It has the advantages of multi-species, high throughput, multiple sorting, short sorting cycle, and high sorting accuracy. Compared with traditional manual sorting, sorting cells using a cell sorting device can reduce the interference of human factors, ensure the consistency of cells during the cell sorting process, accurately obtain single cells with complete morphology and high activity, improve sorting efficiency, and achieve high-throughput sorting of single cells.

[0081] In one embodiment, locating cells to be sorted in cell image data using a deep learning model includes: inputting the cell image data into the deep learning model, extracting fluorescence signal features of the cell image data, and determining position data of the cells to be sorted in the cell image data based on the fluorescence signal features.

[0082] The terminal acquires cell image data transmitted by the imaging mechanism of the cell sorting device and displays the cell image data. The cell image data may be fluorescence image data. Fluorescence image data refers to an image with fluorescence characteristics captured when the cells to be sorted are illuminated by bright ambient light and a filter unit is used in imaging the cells to be sorted.

[0083] Specifically, the imaging mechanism of the cell sorting device includes a fluorescence component, an optical path component, and a detection component. The fluorescence component and the optical path component are positioned on the same side of the cells to be sorted. The fluorescence component is used to cause the cells to fluoresce, thereby acquiring fluorescence image data of the cells to be sorted. The optical path component is configured to form an optical path for the imaging mechanism and is equipped with optical elements for forming the optical path to enable imaging of the cells to be sorted. The detection component is configured to acquire image data of the cells to be sorted obtained by the optical path component.

[0084] The terminal inputs the acquired cell image data into a deep learning model, which then extracts fluorescence signal features from the cell image data. These fluorescence signal features can include the outline of the cell to be sorted and the location and number of fluorescent spots within the cell. The outline of the sorted cell is then determined as its positional data. For example, the positional data can be the coordinates of the cell to be sorted.

[0085] Optionally, the deep learning model can be trained by the fluorescence signal features of a large number of cells, and can accurately identify the fluorescence signals to locate the cells.

[0086] In this embodiment, the fluorescence signal features of the cell image data are extracted from the deep learning model, and the position data of the cells to be sorted in the cell image data are determined based on the fluorescence signal features, which can fully utilize the advantages of deep learning technology to achieve accurate cell positioning.

[0087] In one embodiment, the fluorescence signal intensity of the cells to be sorted is evaluated based on the cell image data to obtain the fluorescence signal intensity of the cells to be sorted, including: intercepting the cells to be sorted in the cell image data to obtain an image of the cells to be sorted; and performing grayscale analysis on the image of the cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted.

[0088] The image of cells to be sorted refers to a cell image captured with the position data of the cells to be sorted as the boundary.

[0089] After locating the cells to be sorted in the cell image data, the cells to be sorted are captured to obtain an image of the cells to be sorted. Grayscale analysis is performed on the image of the cells to be sorted to obtain grayscale values ​​of the image of the cells to be sorted. Based on the relationship between the grayscale values ​​and the fluorescence signal intensity, the fluorescence signal intensity of the cells to be sorted is determined.

[0090] Optionally, a deep learning model is used to identify the positional data of the cells to be sorted in each cell image, and the fluorescent spots of the cells to be sorted in each cell image are identified based on the positional data to locate the cells to be sorted. Furthermore, grayscale analysis of the images of the cells to be sorted to obtain the fluorescence signal intensities of the cells to be sorted may include: performing grayscale analysis on the fluorescent spots in the images of the cells to be sorted to obtain grayscale values ​​of the fluorescent spots, and determining the fluorescence signal intensities of the cells to be sorted based on the grayscale values. By intercepting the images of the cells to be sorted, the analysis range of the fluorescence signal intensity can be narrowed, which helps improve the efficiency of the fluorescence signal intensity analysis.

[0091] In this embodiment, grayscale analysis is performed on the image of the cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted. The fluorescence signal intensity is determined based on the relationship between the grayscale value and the fluorescence signal intensity, which can quickly and accurately evaluate the fluorescence signal intensity of the cells to be sorted.

[0092] In the above embodiment, performing grayscale analysis on the image of cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted includes: performing denoising on the image of cells to be sorted; performing threshold segmentation on the denoised image of cells to be sorted; and performing grayscale analysis on the segmented image of cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted.

[0093] Specifically, the denoising process can be an image preprocessing method known to those skilled in the art, and is not limited here. The denoising process is used to remove noise points in the image of cells to be sorted. The denoised image of cells to be sorted is subjected to threshold segmentation to separate the cells to be sorted from the background in the image, so that the image presents a clear black and white effect. Among them, threshold segmentation can also be called binarization processing. Afterwards, the grayscale value of the fluorescent points in the segmented image of cells to be sorted is calculated to obtain the grayscale value of the fluorescent points, and the average grayscale value of the fluorescent points belonging to the same cell to be sorted is calculated. Based on the relationship between the grayscale value and the fluorescence signal intensity and the average grayscale value of the fluorescent points in each cell to be sorted, the fluorescence signal intensity of each cell to be sorted is determined.

[0094] In this embodiment, the image of the cells to be sorted is denoised to avoid the influence of noise points, and the denoised image of the cells to be sorted is threshold segmented. Thereafter, the image of the cells to be sorted is subjected to grayscale analysis to obtain the fluorescence signal intensity of the cells to be sorted. Threshold segmentation can separate the cells to be sorted from the background in the image, making the image present a clear black and white effect, which is conducive to improving the accuracy of the cell fluorescence signal intensity.

[0095] In one embodiment, when the cell image data is a plurality of fluorescent images, cells meeting preset sorting conditions are extracted from the cells to be sorted according to the fluorescent signal intensities of the fluorescent spots in the plurality of fluorescent images.

[0096] Specifically, for each fluorescent image, the average grayscale value of the fluorescent spots belonging to the same cell to be sorted is calculated. Based on the relationship between grayscale value and fluorescence signal intensity and the average grayscale value of the fluorescent spots in each cell to be sorted, the fluorescence signal intensity of each cell to be sorted is determined. The fluorescence signal intensities of the multiple cells to be sorted are then sorted, and cells that meet preset sorting conditions are determined based on the sorting results corresponding to the multiple cell images. The suction and release components on the sorting mechanism are controlled to absorb the cells that meet the preset sorting conditions. The fluorescence signal intensities of each cell to be sorted include the DAPI (4',6-diamidino-2-phenylindole) fluorescence value, the TRITC (tetramethylrhodamine isothiocyanate) fluorescence value, and the FITC (fluorescein isothiocyanate) fluorescence value. The sorting results corresponding to the multiple cell images include the DAPI fluorescence value sorting result, the TRITC fluorescence value sorting result, and the FITC fluorescence value sorting result.

[0097] Furthermore, impurities without cell nuclei are removed according to the DAPI fluorescence value sorting results, and cells with TRITC fluorescence values ​​less than a preset low value in the total cells are marked according to the TRITC fluorescence value sorting results, and sorted according to the FITC fluorescence value. If the FITC fluorescence value of the marked cell is greater than the preset high value, it is determined that the cell meets the preset sorting conditions.

[0098] In one embodiment, the method further includes: using a deep learning model to respectively identify the first position data of each cell in the first bright field cell image before cell sorting and the second position data of each cell in the second bright field cell image after cell sorting; comparing the first position data with the second position data; if the comparison is consistent, the cell sorting fails, and the process returns to the step of locating the cells to be sorted in the cell image data using the deep learning model to continue cell sorting.

[0099] The cell sorting device may also acquire a first bright field cell image before cell sorting and transmit the first bright field cell image to the terminal. The terminal inputs the first bright field cell image into the deep learning model to identify the first position data of each cell in the first bright field cell image.

[0100] After the control suction and release component completes cell sorting, a second bright field cell image is obtained through the cell sorting device and sent to the terminal. The terminal inputs the second bright field cell image into the deep learning model to identify the second position data of each cell in the second bright field cell image. The first position data is compared with the second position data. If the comparison is consistent, it indicates that the cell absorption has failed. At this time, the process returns to the step of locating the cells to be sorted in the cell image data through the deep learning model and continues cell sorting. If the second position data lacks the data in the first position data, that is, the comparison is inconsistent, it indicates that the cell existed before sorting and does not exist after sorting, and the cell absorption is successful.

[0101] In this embodiment, a deep learning model is used to identify the cell positions before and after cell sorting. By comparing the cell positions, it is determined whether the cells are successfully aspirated, which can ensure the accuracy of cell aspiration and avoid the problem of cell omission.

[0102] In one embodiment, as shown in FIG3 , a cell display method is provided, which is described by taking the method applied to the cell sorting device in FIG1 as an example, including the following steps:

[0103] Step 302: When the to-be-sorted member containing the to-be-sorted cells moves to a preset area of ​​the cell sorting device, the imaging mechanism images the to-be-sorted cells in the preset area to obtain cell image data.

[0104] Step 304: Send the cell image data to the terminal for display.

[0105] The preset area refers to a location where an image with optimal clarity can be acquired. The cell image data refers to an image containing cells to be sorted obtained by imaging the cells to be sorted.

[0106] Specifically, the loading mechanism of the cell sorting device includes a loading assembly, which includes a first loading member and a second loading member. The first loading member is used to accommodate a sample containing cells to be sorted, and the second loading member is used to hold the first loading member. The second loading member is driven by the loading movement assembly to move relative to the base of the main body, so that the sample to be sorted moves relative to the base of the main body to a predetermined area.

[0107] The optical path assembly includes an objective lens and an imaging lens. The objective lens has a specific focal length. When the cell to be sorted containing the cells to be sorted is moved to a predetermined area of ​​the cell sorting device, the objective lens images the cells to be sorted in the predetermined area. For example, a 10x objective lens can be used. It is understood that the magnification of the objective lens can be set according to actual needs and is not limited here.

[0108] Optionally, the optical path assembly may include multiple objective lenses to obtain image data of the cells to be sorted at different magnifications.

[0109] In one embodiment, during the imaging process, the optical path component adjusts the focal length between the objective lens and the cells to be sorted so that the objective lens is focused and the cells to be sorted are imaged.

[0110] Specifically, the optical path assembly further includes an objective lens moving unit, which is used to focus the objective lens and change the focal length between the objective lens and the cells to be sorted, so as to make the imaging of the cells to be sorted clear and enable the imaging mechanism to obtain clear image data of the cells to be sorted.

[0111] In the above-mentioned cell display method, when the cell to be sorted containing the cells to be sorted is moved to a predetermined area of ​​the cell sorting device, the imaging mechanism in the cell sorting device images the cells to be sorted in the predetermined area to obtain image data of the cells to be sorted, which is then displayed on the terminal. Because the terminal can visually display the cells to be sorted and easily adjust the image clarity, it can accelerate cell sorting efficiency.

[0112] In one embodiment, as shown in FIG4 , the imaging mechanism includes an imaging lighting unit, an optical path component, and a detection component; the imaging mechanism images the cells to be sorted in a preset area to obtain cell image data of the cells to be sorted, including:

[0113] Step 402: The imaging lighting unit provides lighting for the parts to be sorted and the optical path components.

[0114] Step 404 : The optical path component images the cells to be sorted under the illumination condition to obtain cell image data of the cells to be sorted.

[0115] Step 406: The detection component obtains cell image data of the cells to be sorted.

[0116] The cell image data of the cells to be sorted may include brightfield image data. Brightfield image data refers to an image of the cells to be sorted that is captured under bright ambient light and without using a filter channel for imaging the cells to be sorted, resulting in a normal color image. The optical path assembly is used to form the optical path of the imaging mechanism and is equipped with optical elements for forming the optical path to enable imaging of the cells to be sorted. The detection assembly is used to acquire the cell image data of the cells to be sorted obtained by the optical path assembly.

[0117] Specifically, the objects to be sorted are placed between the imaging illumination unit and the optical path assembly. The imaging illumination unit provides brightfield illumination for both the objects and the optical path assembly. Transmitted or reflected light is then applied to the cells to be sorted. The transmitted or reflected light then passes through the objects and enters the optical path assembly, where it then reaches the detection assembly. The optical path assembly transmits the generated brightfield image data to the detection assembly, which then acquires the brightfield image data of the cells to be sorted. The detection assembly is connected to a terminal, where it transmits the brightfield image data of the cells to be sorted to the terminal for display.

[0118] In one embodiment, the imaging lighting unit includes an imaging light source, a light shielding member, and a light-transmitting member. The selection mechanism of the cell sorting device includes a suction and release member for sucking and releasing cells that meet preset sorting conditions. The light emitted by the imaging light source passes through the light shielding member and the light-transmitting member to the cells to be sorted, and then illuminates the cells to be sorted along the periphery of the suction and release member. It is understood that the position of the imaging light source can be set according to actual conditions.

[0119] Optionally, the imaging light source is a ring light source, providing bright, uniform illumination for a large field of view. Further, the imaging light source may include, but is not limited to, an LED (Light Emitting Diode) light source. The ring LED light source may be a whole ring light source, or may be formed by arranging multiple light sources evenly spaced circumferentially, so that the imaging light source illuminates the area of ​​cells to be sorted from the radial periphery of the suction and placement member.

[0120] Optionally, the brightness of the imaging light source can be adjusted, so that the imaging light source is applicable to more cell sorting scenarios and facilitates brightness adjustment of image data.

[0121] In this embodiment, the imaging lighting unit provides lighting for the cells to be sorted and the optical path component. Under the lighting conditions, the optical path component images the cells to be sorted to obtain image data of the cells to be sorted. The detection component obtains the image data of the cells to be sorted, and a clear cell image can be obtained.

[0122] In one embodiment, the imaging mechanism further includes a fluorescent component; the method further includes: the fluorescent component irradiates the cells to be sorted so that the cells to be sorted emit fluorescence; the optical path component images the cells to be sorted to obtain a fluorescent image of the cells to be sorted; and the detection component obtains the fluorescent image of the cells to be sorted.

[0123] The fluorescence image refers to an image with fluorescence characteristics captured when the cells to be sorted are illuminated by bright ambient light and a filter channel is used in the imaging of the cells to be sorted.

[0124] Specifically, the fluorescent component and the optical path component are arranged on the same side of the cells to be sorted. The fluorescent component is used to make the cells to be sorted emit fluorescence to obtain a fluorescent image of the cells to be sorted. The fluorescent component includes an excitation light source and a filter unit. Among them, the excitation light source refers to a light source that can make the cells to be sorted emit fluorescence. The cells to be sorted are irradiated with the excitation light source and made to emit fluorescence. For example, the excitation light source can be a xenon lamp, a mercury lamp or other lamps, various lasers such as argon lasers, and light-emitting diodes. The filter unit is the core component of the imaging mechanism to realize fluorescence imaging. The excitation light source is filtered by the filter unit, so that the excitation light source emits excitation light, so that the cells to be sorted emit fluorescence, and the fluorescence passes through the optical path component. The fluorescence image generated by the optical path component is transmitted to the detection component, and the detection component obtains the fluorescence image of the cells to be sorted. The detection component is connected to the terminal, and the detection component sends the fluorescence image of the cells to be sorted to the terminal.

[0125] In this embodiment, the cells to be sorted are irradiated by the fluorescent component, which can quickly stimulate the cells to be sorted to emit fluorescence, thereby improving the efficiency of acquiring the fluorescence image of the cells to be sorted.

[0126] In an optional manner of the above embodiment, the fluorescent component further includes a filter switching unit; the fluorescent component irradiates the cells to be sorted so that the cells to be sorted emit fluorescence, including: the filter switching unit in the fluorescent component switches the excitation light of multiple bands; and the cells to be sorted are irradiated with the excitation light of multiple bands respectively so that the cells to be sorted emit different fluorescence.

[0127] Specifically, there may be multiple filter units in the fluorescent component, and the filter units are used to excite the cells to be sorted for specific screening by adding a culture medium of a preset type of fluorescent dye to emit fluorescence. Among them, the preset types of fluorescent dyes may include fluorescent dyes that can generate blue fluorescence, fluorescent dyes that can generate green fluorescence, and fluorescent dyes that can generate red fluorescence. For example, the fluorescent dye that can generate blue fluorescence may be DAPI fluorescent dye, the fluorescent dye that can generate red fluorescence may be TRITC fluorescent dye, and the fluorescent dye that can generate green fluorescence may be FITC fluorescent dye.

[0128] The type of filter unit corresponds to the type of fluorescent dye. The filter unit may include a first filter unit, a second filter unit, and a third filter unit. After the excitation light passes through the first filter unit, the cells to be sorted emit a first fluorescence. After the excitation light passes through the second filter unit, the cells to be sorted emit a second fluorescence. After the excitation light passes through the third filter unit, the cells to be sorted emit a third fluorescence. Specifically, the cells to be sorted are specifically screened by adding a culture medium containing a fluorescent dye capable of generating blue fluorescence, a fluorescent dye capable of generating green fluorescence, and a fluorescent dye capable of generating red fluorescence. Short-wavelength light will excite the generation of long-wavelength light. Therefore, ultraviolet excitation generates blue light, blue light excitation generates green light, and green light excitation generates red light. The cells to be sorted cultured with a fluorescent dye capable of generating blue fluorescence use an ultraviolet filter unit. The excitation light causes the cells to be sorted to emit blue fluorescence. The blue fluorescence passes through the optical path component, and the optical path component generates a DAPI fluorescence image. Cells to be sorted that have been cultured with a fluorescent dye capable of generating green fluorescence use a blue light filter unit. The excitation light causes the cells to emit green fluorescence. The green fluorescence passes through the optical path component, which generates a green fluorescence image, namely a FITC fluorescence image. Cells to be sorted that have been cultured with a fluorescent dye capable of generating red fluorescence use a green light filter unit. The excitation light causes the cells to emit red fluorescence. The red fluorescence passes through the optical path component, which generates a TRITC fluorescence image. Cells to be sorted are specifically screened with a preset type of fluorescent dye, which helps to more accurately select cells that meet the preset requirements. It is understood that different filter units are selected according to different fluorescent dyes, and the types of fluorescent dyes and filter units are not limited.

[0129] By switching different filter units through the filter switching unit, excitation light of different bands is obtained after passing through different filter units. The excitation light of each band can stimulate the cells to be sorted to emit different fluorescence, so that the imaging mechanism can meet the culture requirements of the cells to be sorted in various situations and increase the applicability of the cell sorting equipment.

[0130] In this embodiment, the cells to be sorted are irradiated with excitation light of multiple wavelengths, causing the cells to emit different fluorescence to obtain multiple fluorescence images of the cells to be sorted, and the cells to be sorted can be accurately located according to the fluorescence signal characteristics of the cells.

[0131] In one embodiment, the terminal can also acquire and display brightfield image data of the cells to be sorted obtained by the cell sorting device. The brightfield image data is used to determine whether the cell sorting is successful. The brightfield image data refers to an image with normal color captured when the cells to be sorted are illuminated by bright ambient light and without using a filter unit in the imaging of the cells to be sorted.

[0132] Furthermore, the imaging mechanism of the cell sorting device also includes an imaging lighting unit. The object to be sorted is placed between the imaging lighting unit and the optical path component. The imaging lighting unit provides a brightfield illumination source for the object to be sorted and the optical path component, irradiating the cells to be sorted with transmitted light or reflected light. The transmitted light or reflected light enters the optical path component after passing through the object to be sorted, and then reaches the detection component through the optical path component. The optical path component transmits the generated brightfield image data to the detection component, which obtains the brightfield image data of the cells to be sorted. The detection component is connected to a terminal, and the detection component sends the brightfield image data of the cells to be sorted to the terminal for display.

[0133] In one embodiment, the imaging lighting unit includes an imaging light source, a light shielding member, and a light-transmitting member. The selection mechanism of the cell sorting device includes a suction and release member for sucking and releasing cells that meet preset sorting conditions. The light emitted by the imaging light source passes through the light shielding member and the light-transmitting member to the cells to be sorted, and then illuminates the cells to be sorted along the periphery of the suction and release member. It is understood that the position of the imaging light source can be set according to actual conditions.

[0134] In one embodiment, the detection assembly includes a brightfield image data detector and a fluorescence image data detector. The brightfield image data detector is used to detect image data based on light transmitted or reflected by the imaging light source through the cells to be sorted. The fluorescence image data detector is used to detect fluorescence image data emitted by the cells to be sorted.

[0135] The specific operation for acquiring brightfield image data of the cells to be sorted is as follows: the imaging illumination unit is turned on, allowing light from the imaging light source to pass through the cells to be sorted, the objective lens, the filter unit, and the first imaging lens in the imaging lens into the brightfield image data detector. The brightfield image data detector transmits the acquired image data of the cells to be sorted to the terminal. Based on the image data displayed by the terminal, the objective lens movement unit is controlled to move the objective lens to make the image data of the cells to be sorted clear.

[0136] The specific operation of obtaining the fluorescence image data of the cells to be sorted is as follows: turn off the imaging light source, turn on the excitation light source, and the light emitted by the excitation light source passes through the filter unit and the objective lens to the cells to be sorted, so that the cells to be sorted that have been specifically screened by the fluorescent dye emit fluorescence. The fluorescence enters the fluorescence image data detector through the objective lens, the filter unit and the second imaging lens in the imaging lens, and the fluorescence image data detector transmits the fluorescence image data of the cells to be sorted to the terminal. The image data obtained by the terminal controls the objective lens moving unit to move the position of the objective lens to make the image data clear. Different filter units are replaced by the filter switching unit, and the above operation is repeated to transmit different fluorescence image data of the cells to be sorted to the terminal. The terminal can find cells that meet the preset sorting conditions based on the fluorescence image data.

[0137] Optionally, the switching between the imaging light source and the excitation light source can be achieved by a control mechanism in the cell sorting device, or by manual switching.

[0138] Optionally, the cells to be sorted can be automatically moved under the control of a control mechanism, and the fluorescent component is provided with different filter units. The filter units are automatically switched by the filter switching unit, and automatic focusing is achieved by the objective lens moving unit. The automatic scanning of the imaging mechanism is achieved, and a large number of cells to be sorted can be automatically scanned and imaged, which greatly improves the working efficiency of the cell sorting equipment and expands its application range.

[0139] In one embodiment, as shown in FIG5 , the cell sorting device includes a sorting mechanism; the sorting mechanism includes a suction and placement member; and before acquiring cell image data of the cells to be sorted, a calibration step of the sorting mechanism is also included, including:

[0140] Step 502: Acquire image data of the pickup and placement component, and determine the clarity of the image data of the pickup and placement component through a calibration program.

[0141] Step 504 , moving the pickup and placement component according to the definition through a calibration program to obtain an image of the pickup and placement component with the best definition.

[0142] Step 506: Adjust the position data of the pick-and-place component in the pick-and-place component image through a calibration program until a preset condition is met, and store the current position data of the pick-and-place component.

[0143] The image data of the suction and placement member refers to the image data projected by the suction and placement member.

[0144] Specifically, the imaging mechanism in the cell sorting device acquires image data of the suction and placement member and sends the acquired image data of the suction and placement member to the terminal. The terminal displays the image data of the suction and placement member and determines the clarity of the image data through a calibration program.

[0145] A hollow pipe is provided in the suction and release member to enable cells to enter the suction and release member and to allow the sucked cells to be stored in the suction and release member. The size of the suction and release member can be gradually reduced along the radial direction of the suction and release member to form a suction and release port at one end of the suction and release member, and the size of the suction and release port matches the size of the cells to be sucked. During suction, the cells enter the hollow pipe through the suction and release port, and during release, the cells leave the suction and release port through the hollow pipe. The design of the suction and release port facilitates accurate and effective cell suction, thereby improving the sorting efficiency of the cell sorting equipment. The material and size of the suction and release member can be selected according to the size of the cells to be sorted, such as a 1 mm glass capillary tube is selected for the suction and release member.

[0146] Furthermore, the picking mechanism includes an installation component, a picking movement component, a picking lighting unit, a cleaning component and a recycling component. The installation component is used for installing and fixing the suction and release component to prevent the suction and release component from falling off during use. Accordingly, the installation component can also be used for replacing the suction and release component. The picking movement component is used to drive the suction and release component to move relative to the main body. The picking movement component includes a picking drive component, a picking transmission component and a picking movement component. Under the drive of the picking drive component and the rotation of the picking transmission component, the picking movement component drives the suction and release component to move. The cleaning component is used for cleaning the suction and release component before use, so that the activity of the cells can be guaranteed during the cell absorption and release process without damaging the normal state of the cells. The recycling component is used for the absorption and release of cells that meet the preset sorting conditions.

[0147] The selection mechanism is used to select cells that meet the preset sorting conditions from the objects to be sorted. The automated setting of the selection mechanism realizes automatic cell absorption, improves the efficiency of cell sorting, avoids damage to cells caused by manual operation, is conducive to achieving high-throughput cell sorting, and further can realize single cell sorting.

[0148] The selection lighting unit is connected to the mounting assembly and is located at an end of the suction and placement member away from the mounting mechanism. The selection lighting unit includes a selection light source connected to the control mechanism. Light emitted by the selection light source can pass through the hollow tube of the suction and placement member to form a shadow of the suction and placement member, thereby facilitating the positioning of the suction and placement member.

[0149] In one embodiment, obtaining image data of the suction and placement component includes: when light emitted by the selected light source passes through the hollow tube of the suction and placement component to form a projection of the suction and placement component, the projection of the suction and placement component is identified by the imaging structure to obtain image data of the suction and placement component; obtaining the image data of the suction and placement component sent by the imaging mechanism and displaying it.

[0150] Specifically, the control mechanism controls the selected light source to emit light, and the light passes through the hollow pipe of the suction and release part and passes through the suction and release part through the suction and release port. The suction and release part projection is obtained according to the light passing through the suction and release port, and the image data (such as a circular aperture) of the suction and release part projection is obtained through the imaging mechanism, and the image data of the suction and release part projection obtained is sent to the terminal for display. The terminal performs image edge detection on the image data of the suction and release part through a calibration program to obtain the clarity of the image data of the suction and release part. The purpose of edge detection is to extract edge information in the image, that is, the boundary between pixels in the image. It is understandable that the method of image edge detection can be an edge detection method known to those skilled in the art.

[0151] Through the calibration program, according to the clarity of the image data of the suction and placement part, the selection moving component is controlled to move the suction and placement part, the clarity of the image data of the suction and placement part is determined again, and the steps of moving the suction and placement part and obtaining the image data of the suction and placement part are repeated until the suction and placement part image with the optimal clarity is obtained.

[0152] The preset conditions refer to the position calibration conditions for the pickup and placement parts.

[0153] After adjusting the position of the suction and placement member to obtain an image of the suction and placement member with optimal clarity, the position data of the suction and placement member is adjusted through a calibration program until the suction and placement member is located at a preset position in the image. For example, the preset position can be a cross cursor. The suction and placement member is displayed as a circular aperture in the image, that is, the middle position of the circular aperture can be adjusted to the cross cursor. At this time, the current position data of the suction and placement member is marked and stored. In the subsequent cell sorting process, based on this position as a reference, the control mechanism controls the selection moving component to drive the suction and placement member to move in order to absorb cells that meet the preset sorting conditions.

[0154] In this embodiment, the clarity of the image data of the suction and release member is determined by a calibration program, and the suction and release member is moved according to the clarity through the calibration program to obtain an image of the suction and release member with optimal clarity. The position data of the suction and release member in the suction and release member image is adjusted through the calibration program until the preset conditions are met, and the current position data of the suction and release member is stored. The position of the suction and release member is automatically calibrated through the calibration program, so that the suction and release member can accurately and quickly absorb cells that meet the preset sorting conditions, thereby improving the efficiency and accuracy of cell sorting. In addition, this method can also avoid damage to the suction and release member and cells that meet the preset sorting conditions by manual operation, ensure the biological activity of cells that meet the preset sorting conditions, and achieve high-throughput cell sorting.

[0155] In one embodiment, determining the clarity of the image data of the suction and placement object through a calibration program includes: performing edge detection on the image data of the suction and placement object to obtain the brightness distribution of multiple boundary points in the image data; and determining the clarity of the image data of the suction and placement object based on a preset image clarity evaluation relationship and the brightness distribution of multiple boundary points.

[0156] The boundary points refer to the boundary points among the pixels in the image data.

[0157] The calibration program converts the image data of the pickup and placement process into grayscale, generating a grayscale image. Edge detection is then performed on the grayscale image to obtain the brightness distribution of the boundary points in the image data. The brightness distribution indicates changes in image clarity.

[0158] There is a corresponding relationship between the brightness distribution of the boundary points and the clarity. The clarity of the image data of the pickup and placement object is determined by a calibration program based on a preset image clarity evaluation relationship and the brightness distribution of the multiple boundary points obtained. For example, the preset image clarity evaluation relationship can be an image clarity evaluation function known to those skilled in the art, which can achieve clarity evaluation based on brightness, and this embodiment is not limited to this.

[0159] In this embodiment, edge detection is performed on the image data of the suction and placement component to obtain the brightness distribution of multiple boundary points in the image data. Since the brightness distribution can represent the change in clarity, the clarity of the suction and placement component image data can be accurately and reliably obtained based on the preset image clarity evaluation relationship.

[0160] In one embodiment, moving the suction and placement part according to the clarity through a calibration program to obtain an image of the suction and placement part with optimal clarity includes: identifying the current position of the suction and placement part in the image data of the suction and placement part through a calibration program, and controlling the selection of a moving component to move the suction and placement part according to the current position of the suction and placement part; returning to the step of obtaining the image data of the suction and placement part until the image data of the suction and placement part reaches the optimal clarity, thereby obtaining an image of the suction and placement part with optimal clarity.

[0161] Specifically, the selection moving assembly comprises a selection driving member, a selection transmission member and a selection moving member. Under the driving of the selection driving member and the rotation of the selection transmission member, the selection moving member drives the suction and placement member to move.

[0162] The suction and placement member is located in the image data of the suction and placement member through a calibration program to determine the current position of the suction and placement member. Based on the current position of the suction and placement member, the control mechanism is instructed to drive the selection drive member and rotate the selection transmission member. Under the drive of the selection drive member and the rotation of the selection transmission member, the selection moving member drives the suction and placement member to move. Returning to the step of obtaining the image data of the suction and placement member, the clarity of the image data of the suction and placement member is determined through the calibration program until the clarity of the image data of the suction and placement member is maximized, thereby obtaining an image of the suction and placement member with optimal clarity.

[0163] In this embodiment, the current position of the suction and placement component is identified through a calibration program, and the suction and placement component is moved according to the current position so that the image of the suction and placement component reaches the optimal clarity, which can ensure that the position of the suction and placement component can accurately absorb cells that meet the preset sorting conditions without damaging the cells that meet the preset sorting conditions.

[0164] In one embodiment, the selection mechanism also includes an installation component, which includes a fixing unit; the position data of the suction and placement component in the suction and placement component image is adjusted through a calibration program until a preset condition is met, and the current position data of the suction and placement component is stored, including: identifying the position data of the suction and placement component in the suction and placement component image through a calibration program; controlling the fixing unit according to the position data through the calibration program to fine-tune the suction and placement component so that the position data of the suction and placement component is located at the preset position of the suction and placement component image, and storing the current position data of the suction and placement component as a calibration value.

[0165] Specifically, the fixing unit may include a first fixing member and a second fixing member. The suction and release member in the image data of the suction and release member is located by a calibration program, and the position data of the suction and release member is determined. According to the current position indication of the suction and release member, the control mechanism controls the first fixing member and the second fixing member to fine-tune the suction and release member so that the position data of the suction and release member is located at a preset position of the suction and release member image. The preset position may be a cross cursor. At this time, the current position data of the suction and release member is marked, and the current position data is stored as a calibration value. In the subsequent cell sorting process, based on this position as a reference, the control mechanism controls the selection moving component to drive the suction and release member to move, so as to absorb cells that meet the preset sorting conditions.

[0166] In this embodiment, the position data of the suction and placement component in the suction and placement component image is identified through a calibration program, and the fixing unit is controlled to fine-tune the suction and placement component according to the position data, so that the position data of the suction and placement component is located at the preset position of the suction and placement component image, which can further improve the calibration accuracy of the suction and placement component.

[0167] In another embodiment, as shown in FIG6 , a cell display method is provided, comprising:

[0168] Step 602: When the biological culture chip containing the cells to be sorted moves to a preset area of ​​the cell sorting device, the objective lens in the imaging mechanism images the cells to be sorted in the preset area to obtain cell image data of the cells to be sorted.

[0169] Step 604: Send the cell image data of the cells to be sorted to the terminal for display.

[0170] Among them, the biological culture chip refers to a biological culture chip that has undergone cell-specific screening.

[0171] Specifically, if the cell to be sorted is a bio-culture chip that has undergone cell-specific screening, the bio-culture chip containing the cells to be sorted needs to be placed on a loading mechanism of the cell sorting apparatus. The loading mechanism includes a loading assembly, which includes a first loading member and a second loading member. The first loading member is used to hold the bio-culture chip containing the cells to be sorted, and the second loading member is used to hold the first loading member. Driven by the loading movement assembly, the second loading member is capable of moving relative to the base of the main body, allowing the bio-culture chip to move relative to the base of the main body to a predetermined area.

[0172] The imaging mechanism of the cell sorting device includes an optical path assembly. The optical path assembly includes an objective lens and an imaging lens. The objective lens has a specific focal length. When the biological culture chip containing the cells to be sorted is moved to a preset area of ​​the cell sorting device, the objective lens in the optical path assembly images the cells to be sorted in the preset area. The image data of the cells to be sorted acquired by the optical path assembly is then transmitted to a terminal, which displays the image data of the cells to be sorted. For example, a 10x objective lens can be selected. It is understood that the magnification of the objective lens can be set according to actual needs and is not limited here.

[0173] Optionally, the optical path assembly may include multiple objective lenses to obtain image data of the cells to be sorted at different magnifications.

[0174] In one embodiment, because the biological culture chip is relatively thin, soft, and brittle, making it difficult to directly secure it in a cell sorting device, the cell sorting device further includes a chip securing assembly that can be placed on a loading mechanism. The chip securing assembly includes a chip holder that is configured to hold the biological culture chip. The biological culture chip is first secured in the chip holder, which is then placed on the loading mechanism of the cell sorting device.

[0175] Furthermore, to ensure the normal use of the biological culture chip in the cell sorting device, the cell sorting device also includes a fixing mechanism, which includes a chip fixing component and a loading and limiting component. The chip fixing component is used to fix the biological culture chip in the chip container, and the loading and limiting component is used to fix the chip container to the loading mechanism. The use of the biological culture chip improves the sorting efficiency and accuracy of the cell sorting device.

[0176] In this embodiment, the bio-culture chip leverages microscopy and nanotechnology to precisely control cell capture, fixation, and culture on the chip, thereby improving cell sorting accuracy. Furthermore, the terminal's ability to visualize the cells to be sorted and easily adjust image clarity accelerates cell sorting efficiency.

[0177] In one embodiment, the biological culture chip includes a plurality of accommodating chambers, each accommodating chamber contains cells to be sorted, and a preset distance is separated between two adjacent accommodating chambers.

[0178] Specifically, the bio-culture chip may include a matrix and a cavitation chamber, wherein the matrix is ​​formed of a matrix material, the cavitation chamber is formed on the surface of the matrix, and the cavitation chamber is open on the surface of the matrix. The cavitation chamber defines a bio-culture space for culturing cell biomaterials. The structure and size of the cavitation chamber are not particularly limited, as long as it can provide a space for culturing or growing a single cell. The cavitation chamber may have a structure such as a cuboid, a cube, or a cylinder.

[0179] Optionally, the matrix material is a microporous soft matrix with good biocompatibility, cell culture medium compatibility, and mechanical tension, which can more efficiently capture and culture cells (including single cells and multi-cells aggregated in a controlled number). The soft matrix is ​​easy to affinity and fuse with different extracellular matrix environments, providing a three-dimensional culture space for cells, thereby creating a microenvironment for the cells in the two-dimensional array that is conducive to survival, proliferation, migration, protein expression and secretion, stem cell differentiation, etc., as well as conducive to the detection of biological functions of array cells. It should be noted that the specific type of matrix material is not limited, and hard matrix materials such as glass, silicon wafers, plastics, etc. are also within the scope of protection of this application.

[0180] Optionally, the opening diameter of the accommodating cavity is smaller than the bottom diameter of the accommodating cavity. In this way, it is convenient to capture single cells for culture without the occurrence of cell escape during the culture process. For example, the opening diameter of the accommodating cavity is at most 80% of the bottom diameter of the accommodating cavity. For example, the opening diameter of the accommodating cavity can be 50% of the bottom diameter of the accommodating cavity. The diameter refers to the diameter of the maximum area circle that the opening or bottom can accommodate. The opening diameter of the accommodating cavity is 8-25 microns and the depth is 15-35 microns. It should be noted that different opening diameters can be set for different cells. For example, if the biomaterial is B cells, the opening diameter is 8-12 microns; if the biomaterial is hybridoma cells or tumor cells, the opening diameter is 15-25 microns.

[0181] Optionally, the cavities in the biological culture chip are discretely distributed, with a preset distance between two adjacent cavities. The cavities form a predetermined pattern, and the distance between two adjacent cavities is 10-100 microns. As a result, the culture of multiple single cells can be processed in batches. When the distance between the cavities is 10-100 microns, the cells cultured in the cavities will not interfere with each other between the cavities, thereby improving the uniformity of batch processing, improving the accuracy and efficiency of cell biological function detection, and facilitating high-throughput acquisition and analysis of cell signal data. The cavities on the biological culture chip can form an array, for example, there can be an array of tens of thousands of cavities or even hundreds of thousands of cavities or more on a chip.

[0182] In this embodiment, by accommodating cells to be sorted in cavities separated by a preset distance, the culture of multiple single cells can be processed in batches, and the cells in different cavities will not interfere with each other, which can improve the accuracy and efficiency of cell culture and subsequent cell selection.

[0183] In one embodiment, the cell-specific screening process of the biological culture chip is as follows:

[0184] Place the chip in the chip holder. Because the biological culture chip needs to be stored in a low-temperature environment, such as in an environment of 0-5°C. Therefore, before use, the biological culture chip needs to be left at room temperature for a period of time to allow the temperature to return to room temperature. Because the biological culture chip is thin, the material is soft and brittle, it is usually placed in a chip holder. After disinfecting and sterilizing the outside of the chip holder where the biological culture chip is placed, transfer it to a sterile environment, remove the chip preservation solution with a pipette, and wash it several times with sterile 0.01M PBS (phosphate buffered solution). It should be noted that do not touch the biological culture chip during the cleaning process.

[0185] Add cells. Add the pre-prepared cells to be sorted to the chip holder containing the bio-culture chip. Preferably, to ensure optimal single-cell array efficiency, the ratio of cell number to chip throughput is between 5:1 and 10:1, and the cell suspension volume is 1.5 ml. If the chip throughput is 100,000, a cell suspension containing 500,000 to 1,000,000 cells is used for arraying. Allow the cells to stand in a cell culture incubator for a predetermined time, such as 15 minutes, to form an array on the bio-culture chip. The bio-culture chip's holding chamber is configured as an array.

[0186] First separation: Use a pipette to recover the arrayed cell suspension and then wash it multiple times with 0.01M PBS buffer to remove any remaining cells. Tilt the chip holder appropriately during washing, maintaining gentle manipulation throughout the entire process to separate any cells that have not formed an array. After washing, observe the array on the cell arrayed chip under a microscope.

[0187] Prepare screening medium. Prepare complete cell culture medium. Add the desired fluorescent dyes to the complete cell culture medium at the desired dilution factor. Vortex at low speed to mix thoroughly. Add substances such as pre-labeled fluorescent dye antigens to the thoroughly mixed complete culture medium at a desired dilution ratio. Vortex at low speed to mix thoroughly. This will produce screening medium.

[0188] Add screening medium. Add pre-configured screening medium to the chip after the cell array, then place the chip in a cell incubator for incubation. Substances in the screening medium specifically bind to secretions from the arrayed cells to be sorted. The incubation time varies depending on the project, such as 3-8 hours for cell line development projects and at least 8 hours for antibody discovery.

[0189] Second separation: After the incubation is completed, the screening culture medium is recovered and the chip is washed 3-5 times with 0.01M PBS buffer until all suspended cells are basically removed, and the cells to be sorted after being cultured with fluorescent dye are obtained in the biological culture chip.

[0190] In one embodiment, after obtaining image data of cells to be sorted contained in a biological culture chip, the terminal uses a deep learning model to locate the containing cavities in the image data of the cells to be sorted according to the positioning marks of each containing cavity pre-set in the biological culture chip, and locates the cells to be sorted in the located containing cavities.

[0191] Specifically, the bio-culture chip is provided with positioning markers formed on the substrate. These positioning markers can be used to locate the multiple cavities. The form of the positioning markers is not limited; for example, coordinate lines can be set to locate each cavity. Once cells meeting the preset sorting criteria are observed, each cavity containing cells meeting the preset sorting criteria can be quickly located and recorded. This further facilitates the subsequent separation of cells or other components within the cavities that meet the preset sorting criteria.

[0192] In one embodiment, during the imaging process, an optical path component in the imaging mechanism adjusts the focal length between the objective lens and the biological culture chip to focus the objective lens and image the cells to be sorted. By changing the focal length between the objective lens and the biological culture chip, the image of the cells to be sorted is clear, allowing the imaging mechanism to obtain clear image data of the cells to be sorted.

[0193] Furthermore, the sorting mechanism includes a mounting assembly mounted on the main body of the cell sorting device, a sorting movement assembly, and a recovery assembly. The mounting assembly is used to mount the suction and placement member, the sorting movement assembly drives the suction and placement member to move toward or away from the loading mechanism, and the recovery assembly is connected to the suction and placement member, enabling the suction and placement member to absorb cells that meet preset sorting conditions. This arrangement can automatically absorb cells that meet the preset sorting conditions, thereby improving cell sorting efficiency.

[0194] Furthermore, the recovery component includes a drive unit, a connecting piece, and a passage connector. The suction and release piece includes a hollow pipe. Under the control of the control mechanism, the selection movement component drives the suction and release port of the suction and release piece to move to a set position. At this time, there is a preset distance between the suction and release port and the cells that meet the preset sorting conditions. The control mechanism controls the drive unit to generate a pressure difference at the suction and release port of the suction and release piece, thereby sucking the cells that meet the preset sorting conditions into the hollow pipe, thereby completing the suction of the cells that meet the preset sorting conditions. Among them, the preset distance is the distance that enables the suction and release piece to suck the cells that meet the preset sorting conditions into the hollow pipe.

[0195] Furthermore, the sorting mechanism includes a sorting illumination unit connected to the mounting assembly and located at an end of the pick-and-place member distal from the first carrier. Light emitted by the sorting illumination unit can pass through the pick-and-place member. The provision of the sorting illumination unit enables calibration of the pick-and-place member and determines its position, facilitating accurate pick-up of cells meeting preset requirements by the pick-and-place member, thereby improving the accuracy of cell sorting.

[0196] In one embodiment, the carrier assembly further includes a storage unit disposed on the first carrier. The storage unit is used to store and hold cells that meet the preset sorting criteria, facilitating subsequent culturing of the cells that meet the preset sorting criteria. Cells that meet the preset sorting criteria are then aspirated by the selection mechanism and placed in the storage unit. The storage unit is then transferred to an incubator for further culturing of the cells that meet the preset sorting criteria.

[0197] In one embodiment, during the cell sorting process, the method for operating the cell sorting device includes the following steps:

[0198] Install the pick-and-place unit to the mounting assembly on the picking mechanism.

[0199] Calibrate the position of the suction and release member. Specifically, under the control of the control mechanism, turn on the selection light source, and select the moving component to drive the suction and release member to move until the image data projected by the suction and release member is clearly presented on the terminal. Use the first fixing member and the second fixing member to fine-tune the suction and release member so that the projection mark is located at the preset position of the projection mapping of the suction and release member. Then, place the part to be sorted without the cells to be sorted in the fixed groove of the first carrier. The moving component drives the suction and release member to move until the image data of the suction and release port is clearly presented on the terminal. Record the position information of the suction and release member at this time to complete the position calibration of the suction and release member.

[0200] The pick-and-place parts are cleaned for the first time by the cleaning fluid.

[0201] Place the objects to be sorted and the storage unit. Place the objects containing the cells to be sorted in the loading section of the first loading section. Pull the elastic member on one side of the loading section to the other side, biasing the elastic member against the objects to be sorted, thereby placing and securing the objects on the first loading section. Place the storage unit containing an appropriate amount of culture medium in the placement slot of the first loading section.

[0202] Find cells that meet the preset sorting conditions. Under the control of the control mechanism, turn on the imaging light source, and the objective lens moving unit drives the objective lens to move until the image data of the cells to be sorted to be observed is clearly presented on the terminal, and the bright field image data of the cells to be sorted is recorded. Turn off the imaging light source, turn on the excitation light source, and allow the light emitted by the excitation light source to pass through the first filter unit, so that the cells to be sorted emit a first fluorescence, and record the first fluorescence image data of the cells to be sorted. Control the filter switching unit to switch to the second filter unit, so that the cells to be sorted emit a second fluorescence, and record the second fluorescence image data of the cells to be sorted. Control the filter switching unit to switch to the third filter unit, so that the cells to be sorted emit a third fluorescence, and record the third fluorescence image data of the cells to be sorted. Then turn off the excitation light source and turn on the imaging light source. Drive the cells to be sorted to move to the preset area and replace the cells to be sorted to be observed. Repeat the above process to record the bright field image data and fluorescence image data of the cells to be sorted in different areas. Process the acquired bright field image data and fluorescence image data of the cells to be sorted, and analyze to find cells that meet the preset sorting conditions.

[0203] Absorb and recover cells that meet the preset sorting conditions. Under the control of the control mechanism, the selection moving component drives the suction and release component to move until the suction and release component reaches a position where it can absorb cells. This position is marked as the preset position, and pre-absorption is completed. The selection moving component drives the suction and release component to move to the preset position, and controls the suction and release component to absorb cells into the hollow pipe of the suction and release component. The selection moving component drives the suction and release component to move in a direction away from the carrier component. The selection moving component drives the suction and release component to move so that there is a preset distance between the suction and release port and the storage portion of the storage unit. The preset distance is the distance that satisfies the suction and release component to release cells that meet the preset requirements to the storage portion. The suction and release component is controlled to release the cells that meet the preset sorting conditions absorbed in the hollow pipe into the storage portion. The selection moving component drives the suction and release component to move in a direction away from the carrier component. Repeat the above process so that different cells that meet the preset sorting conditions are absorbed, released, and recovered into different storage portions, until the absorption and release of all cells that meet the preset sorting conditions in the part to be sorted are completed. The parts to be sorted are recovered and stored, and the storage unit is transferred to the incubator for further cultivation.

[0204] After the cell sorting is completed, the aspiration and placement component is cleaned a second time with cleaning fluid.

[0205] In this embodiment, the cell partitioning device selectively absorbs the cells that meet the preset sorting conditions in the parts to be sorted and releases them into the corresponding storage part of the storage unit. This process is automatically completed under the control of the control mechanism, thereby improving the efficiency of cell sorting, avoiding damage to the aspiration and release parts and cells that meet the preset sorting conditions due to manual operations, ensuring the biological activity of cells that meet the preset sorting conditions, and realizing high-throughput cell sorting.

[0206] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0207] Based on the same inventive concept, the present application also provides a cell sorting device for implementing the aforementioned cell sorting method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more cell sorting device embodiments provided below can be found in the above-mentioned limitations of the cell sorting method and will not be further elaborated here.

[0208] In one embodiment, as shown in FIG7 , a cell sorting device is provided, comprising: a positioning module 702 , an evaluation module 704 , and a sorting module 706 , wherein:

[0209] The positioning module 702 is used to locate the cells to be sorted in the cell image data through a deep learning model.

[0210] The evaluation module 704 is used to evaluate the fluorescence signal intensity of the cells to be sorted based on the cell image data.

[0211] The sorting module 706 is used to extract cells that meet preset sorting conditions from the cells to be sorted according to the fluorescence signal intensity of the cells to be sorted.

[0212] In one embodiment, the positioning module 702 is also used to obtain cell image data; input the cell image data into the deep learning model, extract the fluorescence signal characteristics of the cell image data, and determine the position data of the cells to be sorted in the cell image data based on the fluorescence signal characteristics.

[0213] In one embodiment, the evaluation module 704 is further configured to intercept the cells to be sorted in the cell image data to obtain images of the cells to be sorted; and perform grayscale analysis on the images of the cells to be sorted to obtain the fluorescence signal intensities of the cells to be sorted.

[0214] In one embodiment, the evaluation module 704 is further configured to perform denoising on the image of cells to be sorted; perform threshold segmentation on the denoised image of cells to be sorted; and perform grayscale analysis on the segmented image of cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted.

[0215] In one embodiment, the apparatus further comprises:

[0216] The comparison module is used to identify the first position data of each cell in the first bright field cell image before cell sorting and the second position data of each cell in the second bright field cell image after cell sorting through a deep learning model; compare the first position data with the second position data; if the comparison is consistent, the cell sorting fails and the cell sorting process is repeated.

[0217] Each module in the cell sorting device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0218] In one embodiment, as shown in FIG8 , a cell display device is provided, including: an imaging module 802 and a display module 804 , wherein:

[0219] Imaging module 802 is used to image the cells to be sorted in the preset area when the sorting member containing the cells to be sorted moves to the preset area to obtain cell image data of the cells to be sorted.

[0220] The display module 804 is used to send the cell image data of the cells to be sorted to the terminal for display.

[0221] In one embodiment, the imaging mechanism includes an imaging lighting unit, an optical path component, and a detection component; the imaging module 802 is also used for the imaging lighting unit to provide lighting for the cells to be sorted and the optical path component; the optical path component images the cells to be sorted under the lighting conditions to obtain image data of the cells to be sorted; and the detection component obtains image data of the cells to be sorted.

[0222] In one embodiment, the imaging mechanism further includes a fluorescent component; the imaging module 802 is further used to illuminate the cells to be sorted with the fluorescent component so that the cells to be sorted emit fluorescence; the optical path component images the cells to be sorted to obtain a fluorescent image of the cells to be sorted; and the detection component obtains the fluorescent image of the cells to be sorted.

[0223] In one embodiment, the fluorescent component further includes a filter switching unit; the imaging module 802 is also used for the filter switching unit in the fluorescent component to switch the excitation light of multiple bands; the cells to be sorted are irradiated with the excitation light of multiple bands respectively, so that the cells to be sorted emit different fluorescence.

[0224] In one embodiment, the display module 804 is also used to locate the cells to be sorted in the image data of the cells to be sorted through a deep learning model; evaluate the fluorescence signal intensity of the cells to be sorted based on the image data; and extract cells that meet preset sorting conditions from the cells to be sorted based on the fluorescence signal intensity of the cells to be sorted.

[0225] In one embodiment, the apparatus further comprises:

[0226] The clarity determination module is used to obtain image data of the pickup and placement component and determine the clarity of the image data of the pickup and placement component through a calibration program.

[0227] The adjustment module is used to move the pickup and placement parts according to the clarity through a calibration program to obtain the pickup and placement part image with the optimal clarity.

[0228] The calibration module is used to adjust the position data of the pickup and placement parts in the pickup and placement parts image through a calibration program until the preset conditions are met, and store the current position data of the pickup and placement parts.

[0229] In one embodiment, the clarity determination module is also used to perform edge detection on the image data of the suction and placement piece to obtain the brightness distribution of multiple boundary points in the image data; and determine the clarity of the image data of the suction and placement piece based on the preset image clarity evaluation relationship and the brightness distribution of multiple boundary points.

[0230] In one embodiment, the device also includes a picking and moving component; the adjustment module is also used to identify the current position of the suction and placement component in the image data of the suction and placement component through a calibration program, and control the picking and moving component to move the suction and placement component according to the current position of the suction and placement component; return to the step of obtaining the image data of the suction and placement component until the image data of the suction and placement component reaches the optimal clarity, thereby obtaining an image of the suction and placement component with the optimal clarity.

[0231] In one embodiment, the device also includes an installation component, which includes a fixing unit; the calibration module is also used to identify the position data of the suction and placement component in the suction and placement component image through a calibration program; the calibration program controls the fixing unit according to the position data to fine-tune the suction and placement component so that the position data of the suction and placement component is located at a preset position in the suction and placement component image, and the current position data of the suction and placement component is stored as a calibration value.

[0232] In one embodiment, the imaging module 802 is also used to image the cells to be sorted in the preset area through the objective lens in the imaging mechanism when the biological culture chip containing the cells to be sorted is moved to the preset area of ​​the cell sorting device to obtain image data of the cells to be sorted; the display module 804 is also used to send the image data of the cells to be sorted to the terminal for display.

[0233] In one embodiment, the biological culture chip includes a plurality of accommodating chambers, each accommodating chamber contains cells to be sorted, and a preset distance is separated between two adjacent accommodating chambers.

[0234] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in Figure 9. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication, and the wireless communication may be achieved via Wi-Fi, a mobile cellular network, NFC (near field communication), or other technologies. When the computer program is executed by the processor, a cell sorting method is implemented. The display unit of the computer device is used to form a visually visible image, and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0235] Those skilled in the art will understand that the structure shown in Figure 9 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0236] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0237] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0238] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0239] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0240] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0241] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A cell sorting method, applied to a terminal, comprising: Acquire cell image data sent by a cell sorting device; Locating the cells to be sorted in the cell image data by using a deep learning model; Evaluating the fluorescence signal intensity of the cells to be sorted according to the cell image data; and According to the fluorescence signal intensity of the cells to be sorted, cells meeting the preset sorting conditions are extracted from the cells to be sorted.

2. The method according to claim 1, wherein: The method of locating the cells to be sorted in the cell image data by using a deep learning model comprises: The cell image data is input into a deep learning model, the fluorescence signal characteristics of the cell image data are extracted, and the position data of the cells to be sorted in the cell image data are determined according to the fluorescence signal characteristics.

3. The method according to claim 1, wherein: The step of evaluating the fluorescence signal intensity of the cells to be sorted according to the cell image data to obtain the fluorescence signal intensity of the cells to be sorted comprises: Intercepting the cells to be sorted in the cell image data to obtain an image of the cells to be sorted; and Grayscale analysis is performed on the image of the cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted.

4. The method according to claim 3, wherein: The grayscale analysis of the image of the cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted comprises: Performing denoising on the image of cells to be sorted; Performing threshold segmentation on the denoised image of cells to be sorted; and Grayscale analysis is performed on the segmented image of the cells to be sorted to obtain the fluorescence signal intensity of the cells to be sorted.

5. The method according to claim 1, further comprising: Using a deep learning model, first position data of each cell in a first bright field cell image before cell sorting and second position data of each cell in a second bright field cell image after cell sorting are respectively identified; comparing the first position data with the second position data; and If the comparison is consistent, the cell sorting fails, and the process returns to the step of locating the cells to be sorted in the cell image data by using the deep learning model to continue the cell sorting.

6. A cell display method, wherein: Applied to a cell sorting device, the cell sorting device includes an imaging mechanism; the method includes: When the to-be-sorted member containing the to-be-sorted cells moves to a preset area of ​​the cell sorting device, the imaging mechanism images the to-be-sorted cells in the preset area to obtain cell image data; and The cell image data is sent to a terminal for display.

7. The method according to claim 6, wherein: The imaging mechanism comprises an imaging lighting unit, an optical path component and a detection component; the imaging mechanism images the cells to be sorted in the preset area to obtain cell image data, including: The imaging lighting unit provides lighting for the to-be-sorted parts and the optical path component; The optical path component images the cells to be sorted under illumination conditions; and The detection component acquires the cell image data generated by the optical path component.

8. The method according to claim 6, wherein: The imaging mechanism further includes a fluorescent component; the method further includes: The fluorescent component irradiates the cells to be sorted so that the cells to be sorted emit fluorescence; The optical path component images the cells to be sorted to obtain a fluorescent image of the cells to be sorted; and The detection component acquires the fluorescent image of the cells to be sorted.

9. The method according to claim 8, wherein: The fluorescent component further includes a filter switching unit; the fluorescent component irradiates the cells to be sorted so that the cells to be sorted emit fluorescence, which includes: The filter switching unit in the fluorescent component switches the excitation light of multiple bands; and The cells to be sorted are irradiated respectively with excitation lights of multiple wavelength bands, so that the cells to be sorted emit different fluorescence.

10. The method according to claim 6, wherein: The cell sorting device includes a selection mechanism; the selection mechanism includes a suction and release member; before acquiring cell image data, the method includes a calibration step of the selection mechanism, including: Acquire image data of the suction and placement component, and determine the clarity of the image data of the suction and placement component through a calibration program; Move the suction and placement member according to the definition through a calibration program to obtain a suction and placement member image with optimal definition; and The position data of the suction and placement member in the suction and placement member image is adjusted through a calibration program until a preset condition is met, and the current position data of the suction and placement member is stored.

11. The method according to claim 10, wherein: Determining the clarity of the image data of the suction and placement member by a calibration procedure includes: Performing edge detection on the image data of the suction and placement member to obtain brightness distribution of multiple boundary points in the image data; and The clarity of the image data of the suction and placement member is determined according to a preset image clarity evaluation relationship and the brightness distribution of a plurality of boundary points.

12. The method according to claim 10, wherein: The selection mechanism also includes a selection moving component; the step of moving the suction and placement component according to the clarity through a calibration program to obtain a suction and placement component image with optimal clarity includes: Identifying the current position of the suction and placement member in the image data of the suction and placement member through a calibration program, and controlling the selection and movement component to move the suction and placement member according to the current position of the suction and placement member; and Return to the step of obtaining the image data of the suction and placement object until the image data of the suction and placement object reaches the optimal clarity, thereby obtaining an image of the suction and placement object with the optimal clarity.

13. The method according to claim 10, wherein: The selection mechanism further includes an installation component, and the installation component includes a fixing unit; the position data of the suction and placement component in the suction and placement component image is adjusted by a calibration program until a preset condition is met, and the current position data of the suction and placement component is stored, including: Identify the position data of the suction and placement component in the suction and placement component image through a calibration procedure; and The fixing unit is controlled to fine-tune the suction and placement member according to the position data through a calibration program, so that the position data of the suction and placement member is located at a preset position of the suction and placement member image, and the current position data of the suction and placement member is stored as a calibration value.

14. The method according to claim 6, wherein: The object to be sorted is a biological culture chip; the method further comprises: When the biological culture chip containing the cells to be sorted moves to a preset area of ​​the cell sorting device, the objective lens in the imaging mechanism images the cells to be sorted in the preset area to obtain cell image data.

15. The method according to claim 14, wherein: The biological culture chip comprises a plurality of accommodating chambers, each accommodating chamber contains cells to be sorted, and two adjacent accommodating chambers are spaced at a preset distance.

Images