Cell counting method and cell counting device of microfluidic chip

Abstract

The application relates to the technical field of biochemistry, and provides a cell counting method and a cell counting device of a micro-fluidic chip. The cell counting method of the micro-fluidic chip comprises the following steps: controlling a cell solution to fill a plurality of flow channels with height differences; collecting a target image corresponding to a set region in each flow channel, obtaining a cell clumping rate corresponding to each flow channel based on the target image, and determining a flow channel with the lowest cell clumping rate as a calculation flow channel; wherein the cell clumping rate is the ratio of the number of cell groups in the set region to the total number of cells; obtaining a calculation cell number in a target region of the calculation flow channel; obtaining a preset volume corresponding to the target region based on the area of the target region and the height of the calculation flow channel, and determining the ratio of the calculation cell number to the preset volume as the concentration of the cell solution. The cell counting method of the micro-fluidic chip provided by the application provides the cell solution with a flow channel with a suitable height, so that the cell solution is uniformly distributed in the flow channel as much as possible, and the cell counting accuracy is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of biochemistry, and in particular to a cell counting method and a cell counting device of a microfluidic chip. Background Art

[0002] Cell counting technology has been widely used in medicine, biology, agriculture, teaching, science and technology and other fields. It is one of the commonly used experimental techniques in modern scientific biochemistry laboratories.

[0003] At present, cell counting technology can be mainly divided into two technologies: cell counting plate method and algorithm recognition cell counting. First, with respect to the cell counting plate method, cells are counted by artificial observation through the staining method. The cell counting plate lacks consideration of the diversity of cell concentration. When cells of different concentrations flow through, it will produce counting errors caused by concentration reasons (such as high cell concentration causing cell stacking to make image recognition inaccurate). At the same time, the experimental operation is cumbersome and the counting efficiency is low. Second, with respect to the algorithm recognition cell counting method, it is usually to process the collected image first, and then count it by identifying the cells, such as using photosensitive chips, image recognition software, etc. to count cells. It has the problems of high requirements for the required operating environment and strong technical requirements, which makes the cell counting algorithm have certain limitations in application and promotion. Therefore, in view of the shortcomings of the prior art, it is necessary to provide a simplified method that can accurately count cells in solutions of different cell concentrations. Summary of the Invention

[0004] The present invention aims to address at least one of the technical problems existing in the related art. To this end, the present invention proposes a cell counting method using a microfluidic chip. This method utilizes the principle that cell distribution in a cell solution varies within flow channels at different heights. By selecting a flow channel at an appropriate height for the cell solution, the concentration of the cell solution is calculated, resulting in a more accurate and simpler cell counting method.

[0005] The present invention also provides a cell counting device.

[0006] According to a cell counting method of a microfluidic chip according to an embodiment of the first aspect of the present invention, the microfluidic chip is provided with an inlet, an outlet, and two or more flow channels, the flow channels are arranged in parallel between the inlet and the outlet, and a height difference is formed between the flow channels. The cell counting method includes:

[0007] controlling the cell solution to fill the plurality of flow channels having height differences;

[0008] Acquiring a target image corresponding to a set area in each flow channel, obtaining a cell clumping rate corresponding to each flow channel based on the target image, and determining the flow channel with the lowest cell clumping rate as the calculation flow channel; wherein the cell clumping rate is the ratio of the number of cell clusters in the set area to the total number of cells;

[0009] obtaining the number of calculated cells in a target area of ​​the calculation channel;

[0010] Based on the area of ​​the target region and the height of the calculation flow channel, a preset volume corresponding to the target region is obtained, and the ratio of the calculated cell number to the preset volume is determined as the concentration of the cell solution.

[0011] According to one embodiment of the present invention, the step of obtaining the cell agglomeration rate corresponding to each flow channel based on the target image is implemented by a cell recognition model, and the cell recognition model includes:

[0012] The target image is divided into a first grid structure, a target detection frame having cell characteristics is determined based on the first grid structure, the number of cell clusters is determined based on the fact that multiple intersecting target detection frames are a cell cluster; the number of independent target detection frames is determined as the number of independent cells; and the number of target detection frames is determined as the total number of cells.

[0013] According to one embodiment of the present invention, the cell recognition model is trained based on a YOLO model or a VGG16 model to determine the target detection frame.

[0014] According to one embodiment of the present invention, the cell recognition model determines whether the target detection frames are intersecting or independent based on an IOU algorithm, wherein the target detection frame is a rectangular frame.

[0015] According to one embodiment of the present invention, in the step of obtaining the number of calculated cells in the target area of ​​the calculation channel,

[0016] Determine the total cell number as the calculated cell number,

[0017] Alternatively, a computational image of the computational flow channel is obtained, wherein the clarity of the computational image is higher than that of the target image, computational target detection frames having cell features are identified based on the computational image, and the number of the computational target detection frames is determined as the number of calculated cells.

[0018] According to one embodiment of the present invention, the total cell number is the sum of the number of independent cells and the number of cells in several cell clusters.

[0019] Alternatively, the total cell number is the sum of the cell cluster number and the independent cell number.

[0020] According to one embodiment of the present invention, in the step of determining the flow channel with the lowest cell agglomeration rate as the calculation flow channel,

[0021] The cell agglomeration rates of two or more flow channels are the lowest, and the flow channel with a higher height is determined as the calculation flow channel.

[0022] According to one embodiment of the present invention, the step of controlling the cell solution to fill the plurality of flow channels having height differences includes:

[0023] The cell solution is delivered into the flow channel for a preset time period by a pump body, wherein the starting moment of the preset time period is: the moment when the first sensor of the inlet detects the passage of the fluid;

[0024] Alternatively, the pump body is controlled to deliver the cell solution into the flow channel, and the delivery of the liquid is stopped when the second sensor at the outlet detects the passage of the fluid;

[0025] Alternatively, a preset volume of the cell solution is delivered to the inlet by a pump, where the preset volume is equal to the capacity of the microfluidic chip.

[0026] According to one embodiment of the present invention, the height of the flow channels is between 50um and 450um, and the height difference between the flow channels is between 50um and 150um.

[0027] According to an embodiment of the second aspect of the present invention, a cell counting device includes a microfluidic chip, an electron microscope and a processor, wherein the electron microscope is used to collect images of the flow channel of the microfluidic chip as described above, the electron microscope is communicatively connected to the processor to send the image to the processor, and the processor can be used to execute a computer program. When the processor executes the computer program, it implements the cell counting method for the microfluidic chip as described in any one of the above items.

[0028] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0029] The cell counting method of the microfluidic chip of the embodiment of the present invention provides flow channels of different heights in the microfluidic chip. When the cell solution flows into the microfluidic chip, the cell distribution of the cell solution in different flow channels is different. The cell distribution is characterized by calculating the cell agglomeration rate in each flow channel, and the flow channel with a low cell agglomeration rate is selected for cell counting to avoid the influence of cell stacking on the cell count, thereby making the cell count more accurate. The concentration of the cell solution is then calculated by the ratio of the number of cells to the volume of the corresponding flow channel. The concentration of the cell solution can be calculated more accurately, and the calculation method is simple, highly precise, and more efficient.

[0030] The cell counting device of the embodiment of the present invention is used to implement the cell counting method of the microfluidic chip. It only requires improving the height of the flow channel in the microfluidic chip. The equipment modification is small and has little impact on the production cost.

[0031] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 1 is a schematic diagram of a top view of the microfluidic chip provided in an embodiment of the present invention;

[0034] Figure 2 1 is a schematic cross-sectional structural diagram of a microfluidic chip provided by an embodiment of the present invention, taken along the height of the flow channel;

[0035] Figure 3 This is a flow chart of a cell counting method using a microfluidic chip provided by an embodiment of the present invention;

[0036] Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present invention.

[0037] Reference numerals:

[0038] 1. Inlet; 2. Outlet; 3. Flow channel; 31. First flow channel; 32. Second flow channel; 33. Third flow channel; 34. Fourth flow channel; 35. Fifth flow channel. DETAILED DESCRIPTION

[0039] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0040] In the description of the embodiments of the present invention, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise specified, "plurality," "multiple roots," and "multiple groups" mean two or more.

[0041] In the description of the embodiments of the present invention, it should be noted that, unless otherwise specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; and direct connections or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on the specific circumstances.

[0042] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0043] Before describing the cell counting method of the microfluidic chip of the invention, the microfluidic chip for executing this method is described. Figure 1 and Figure 2 As shown, the microfluidic chip is provided with an inlet 1, an outlet 2 and two or more flow channels 3. The flow channels are arranged in parallel between the inlet 1 and the outlet 2. The cell solution is introduced into the flow channel 3 through the inlet 1 so that the cell solution fills each flow channel 3. The inlet 1 and the outlet 2 are closed to keep the cell solution in the flow channel 3.

[0044] A height difference is formed between the flow channels 3, that is, the heights of the flow channels 3 are different. The microfluidic chip is formed with multiple microchannels of different heights to provide different volumes for the cell solution. The height difference can be understood as a height difference between the two flow channels 3 that are closest in height.

[0045] Microfluidic chips use a microscope capture-image processing method for cell counting. For cell solutions of the same concentration, the distribution of cells in different microchannels will vary greatly. When the concentration of the cell solution is low, if a microchannel with a lower height is selected for cell counting, the number of cells in the microscope field of view may be less than the actual value, or even no cells may appear, and the statistical data obtained is not true; when a microchannel with a higher height is selected, more cells will be obtained in the microscope field of view, which can effectively reduce the probability of unreliable data. Similarly, when the concentration of the cell solution is high, if a microchannel with a higher height is selected, there may be more cells in the microscope field of view, which will cause the cells to stack and overlap, resulting in inaccurate cell counting results; in this case, a microchannel with a lower height can be selected, which can make the cells flattened in the microchannel, reduce or even eliminate the phenomenon of cell stacking and overlap, and make the data obtained more accurate.

[0046] refer to Figure 1 As shown, the microfluidic chip is provided with five flow channels. The dyed cell solution is introduced into the inlet 1 of the microfluidic chip. After entering the chip, the cell solution can flow into five flow channels of different heights. The degree of dispersion of cells in the flow channels of different heights is different. By selecting the flow channel with less cell clumping for cell counting, the counting can be made more accurate.

[0047] In some embodiments, the height of the flow channels is between 50um and 450um, and the height difference between the flow channels is between 50um and 150um. The range of the height and height difference of the flow channels is wide, and the structure is simple.

[0048] like Figure 1 and Figure 2 As shown, the microfluidic chip is provided with five flow channels, namely the first flow channel 31, the second flow channel 32, the third flow channel 33, the fourth flow channel 34 and the fifth flow channel 35. The height from the first flow channel 31 to the fifth flow channel 35 can be set to 50um, 150um, 250um, 350um, 450um, and the height difference is 100um. The heights of the flow channels are arranged in equal intervals.

[0049] The aforementioned height and height difference may include endpoint values. However, the height and height difference of the flow channel are not limited to the aforementioned ranges, and the height and height difference of the microchannel can be designed according to requirements such as cell morphology and size.

[0050] refer to Figure 1As shown, the flow channel includes a diversion section and a parallel section. A diversion section is provided at both ends of the parallel section. One diversion section of each flow channel is connected to the inlet, and the other is connected to the outlet. The cell solution enters the parallel section through the diversion section, and then enters the parallel section through the diversion section. Part or all of the parallel section can form an observation area. This observation area can be photographed using a microscope to identify and count cells and observe their status.

[0051] It should be noted that the aforementioned flow channels have different height limits, but do not limit their width and shape, which can be selected as needed. A microfluidic chip can be provided with multiple flow channels of varying heights and shapes to detect the concentration of a cell solution. In some cases, the width of each flow channel is the same, and the width of the flow channel needs to be slightly larger than the field of view of the microscope, typically 0.5 to 2 mm greater than the field of view.

[0052] The embodiment of the first aspect of the present invention, referring to Figure 3 As shown, a cell counting method for a microfluidic chip is provided. The cell counting method includes:

[0053] Step 100: Control the cell solution to fill multiple flow channels with height differences;

[0054] The cell solution enters each flow channel through the inlet, ensuring that the flow channel is filled with the cell solution; wherein, the cell solution can be transported from the cell solution bag to the inlet by the driving force of the pump body, which is simple to operate and highly efficient.

[0055] Step 200: Acquire a target image corresponding to a set area in each flow channel, obtain the cell clumping rate corresponding to each flow channel based on the target image, and determine the flow channel with the lowest cell clumping rate as the calculation flow channel; wherein the cell clumping rate is the ratio of the number of cell clusters in the set area to the total number of cells;

[0056] Each pass is captured using an electron microscope, camera, or other equipment to obtain a target image, which displays the cells in the cell solution. Based on the target image, the total cell count and the number of clumped cell clusters are determined. The ratio of the number of cell clusters to the total cell count is then used to determine the cell clumping rate. Flow channels with lower cell clumping rates are used as calculation channels, reducing the probability of cell clumping in the calculation channel and improving cell counting accuracy.

[0057] The cell clumping rate is used to characterize the degree of cell clumping in the current flow channel. The higher the cell clumping rate, the more likely the cells in the lower layer are to be obscured by the cells in the upper layer, and the accuracy of cell counting is reduced. The lower the cell clumping rate, the more dispersed the cells in the flow channel are, the lower the possibility of cells being obscured, and the higher the accuracy of cell counting.

[0058] The number of cell clusters and the total number of cells in the target image can be automatically identified based on a cell recognition model or manually identified, and the identification method is not limited.

[0059] The total cell number can be the sum of the number of independent cells and the number of cell clusters, or the sum of the number of all cells in a cell cluster and the number of independent cells. The specific number can be selected according to needs and is not limited here.

[0060] Step 300, obtaining the number of calculated cells in the target area of ​​the calculation channel;

[0061] Among them, the target area can be the set area mentioned above, or the target area can be a part of the set area, or the target area can intersect with a part of the set area, or it can be an area reselected in other parts of the flow channel (the target area does not intersect with the preset area). The area range of the target area can be selected according to needs and is not limited here.

[0062] The calculated cell number may be the total cell number mentioned above, or the total number of cells in the target area obtained by re-identification and calculation.

[0063] Step 400 : Based on the area of ​​the target region and the height of the calculated flow channel, a preset volume corresponding to the target region is obtained, and the ratio of the number of cells to the preset volume is calculated to determine the concentration of the cell solution.

[0064] The product of the area of ​​the target region and the height of the calculation channel is the preset volume, and the ratio of the number of calculated cells to the preset volume is the concentration of cells in the cell solution, which is called the cell solution concentration.

[0065] During use, the microfluidic chip adaptively selects the flow channel of the cell solution, judges the cell agglomeration rate corresponding to the flow channel height according to the image information captured by the microscope, selects the flow channel with a lower cell agglomeration rate as the calculation flow channel, extracts the number of cells in the cell solution, and then obtains the number of cells per unit volume by calculation, that is, the cell concentration of the cell solution, to calculate the concentration of the cell solution. Among them, the number of cells obtained by the selected calculation flow channel statistics, and the area (pixel area) of the target area involved in the microscope shooting multiplied by the calculated volume obtained by the height of the calculation flow channel, the ratio of the number of cells to the calculated volume obtains the concentration of the cell solution. The cell counting method of the microfluidic chip of the embodiment of the present invention can not only count cells more clearly and accurately, but also calculate cell concentration more conveniently and accurately.

[0066] In some embodiments, in step 200, the step of obtaining the cell agglomeration rate corresponding to each flow channel based on the target image is implemented by a cell recognition model, and the cell recognition model includes:

[0067] The target image is divided into a first grid structure, a target detection frame having cell characteristics is determined based on the first grid structure, a plurality of intersecting target detection frames are considered as a cell cluster, and the number of cell clusters is determined.

[0068] The cell recognition model can be trained to have the functions of identifying cells and determining target detection frames. The cell recognition model can represent the cells identified in the target image in the form of target detection frames. An independent target detection frame can represent a cell, and multiple intersecting target detection frames can represent a cell cluster. The number of independent cells and the number of cell clusters can be counted, and the number of cell clusters can be recorded as the number of cell clusters.

[0069] Among them, the cell recognition model divides the target detection frame by identifying cell features. Cell features include: cell nucleus, cell membrane, cell wall, cytoplasm, etc. In some cases, identifying a cell nucleus corresponds to counting as one cell, or identifying a cell wall corresponds to counting as one cell, or simultaneously identifying the outlines of the cell nucleus and cell membrane (cell wall) (all outlines and partial outlines are acceptable), then it is counted as one cell. There are many ways to identify cells, which will not be described here. The specific ones can be selected according to needs. The target detection frame can be a rectangular frame, an elliptical frame, a circular frame, or a frame of other shapes. The specific shape of the target detection frame is not limited.

[0070] It should be noted that the cell recognition model can be obtained through training with a large amount of image data, and the cell recognition methods in related technologies can be applied to this cell recognition model, or a cell recognition model with the above-mentioned specific functions in related technologies can also be used.

[0071] Based on the above-mentioned cell recognition model, in step 200, target detection frames with cell characteristics are determined based on the first grid structure, and the number of independent target detection frames is determined as the number of independent cells. Directly obtaining the number of independent cells allows for diverse cell recognition methods and a simple counting process.

[0072] On the basis of the above-mentioned cell recognition model, in step 200, target detection frames having cell features are determined based on the first grid structure, and the number of target detection frames is determined as the total number of cells.

[0073] It can be understood that each target detection frame is recorded as a cell. Here, it is not limited whether the target detection frames intersect. An independent target detection frame is recorded as a cell, and a target detection frame that has an intersection area with other target detection frames is also recorded as a cell. In this way, the total number of cells in the target image can be obtained. The counting method of the total number of cells is simple, and the total number of cells, the number of independent cells, and the number of cell clusters can be obtained by identifying the target image once, which can simplify the calculation process and improve the calculation efficiency.

[0074] In some embodiments, in step 200, a cell recognition model is trained based on a YOLO model or a VGG16 model to identify a target detection frame. The YOLO model or the VGG16 model divides the target image into grids and can mark cells in the target image using target detection frames, thereby implementing cell recognition and labeling functions in the cell recognition model.

[0075] The YOLO model is a new target detection method. Based on this target detection method, its cell recognition function of the target image is trained to obtain a cell recognition model. The YOLO model can achieve fast detection while also achieving a high accuracy.

[0076] The VGG16 model is well suited for classification and positioning tasks and can be used for initial training, such as training its cell recognition function for target images to obtain a cell recognition model with high accuracy.

[0077] The cell recognition model is trained based on the above model. The process of establishing the cell recognition model is smoother, and the accuracy and detection efficiency of the cell recognition model can be higher.

[0078] In some embodiments, in step 200, the cell recognition model determines whether the target detection boxes are intersecting or independent based on an IOU algorithm. The target detection boxes are rectangular. In two-dimensional target detection, because the target detection boxes are rectangular, calculating IOU is simple, which can simplify the calculation process.

[0079] The IOU (Intersection over Union) algorithm is used to measure the degree of overlap between two target detection frames. When the IOU value is between 0 and 1, it represents the degree of overlap between the two target detection frames. Higher values ​​indicate a higher degree of overlap. When the IOU is 0, the two frames do not overlap and have no intersection, and can be determined to be independent cells. The calculated IOU value can be set to be greater than a preset value, indicating that the two target detection frames intersect. Correspondingly, these two or more cells form a cell cluster. The preset value can be derived based on experience or calculation.

[0080] Of course, the method of determining the degree of overlap of the target detection frames is not limited to the aforementioned IOU algorithm, and can also be implemented through other algorithms.

[0081] In the above content, the agglomeration rate in step 200 is calculated by first collecting two-dimensional images of each flow channel, taking a bird's-eye view of the target image using a microscope, and then identifying cell features in the two-dimensional target image, that is, detecting and counting independent cells and clumped cells. The network structure of the target image can be divided by a YOLO model or a VGG16 model, identifying target detection frames with cell features, and then judging whether the target detection frames overlap based on the IOU algorithm. The overlapping target detection frames are determined to be a cell cluster, and the number of cell clusters is calculated. At the same time, the number of independent cells and the total number of cells can also be determined. The cell agglomeration rate is obtained based on the ratio of the number of cell clusters to the total number of cells.

[0082] In some embodiments, the total cell number is the sum of the number of cell clusters and the number of independent cells, which can be calculated simply.

[0083] Alternatively, the total cell count can be calculated by summing the number of individual cells and the number of cells in several cell clusters. This can be calculated by simply adding the number of target detection frames. There are various ways to calculate the total cell count, and you can choose the method you need.

[0084] In some embodiments, in step 300, i.e., the step of obtaining the number of cells in the target area of ​​the computational flow channel,

[0085] A computational image corresponding to the computational flow channel is obtained, and computational target detection frames having cell features are determined based on the computational image. The number of computational target detection frames is determined as the number of computational cells.

[0086] The computational flow channel is imaged again to obtain a computational image, and target detection is performed on the computational image to identify the number of cells in the computational image, thereby improving the accuracy of cell number recognition. The clarity of the computational image can be improved by changing the magnification of the microscope. The improved clarity of the computational image helps the cell recognition model to more accurately identify cells and improve the accuracy of cell recognition. Alternatively, images of a local area or the entire area within the computational flow channel can be collected as computational images. Multiple computational images can be collected. After analyzing multiple computational images, the maximum number of target detection frames can be selected as the number of cells to be calculated, or the average number of target detection frames corresponding to multiple calculations can be selected as the number of cells to be calculated.

[0087] Among them, when the number of calculated target detection frames is different from the total number of cells, the larger number can be selected as the number of calculated cells.

[0088] In some cases, in step 300, that is, the step of obtaining the number of cells in the target area of ​​the calculation channel,

[0089] A computational image of the computational flow channel is obtained, and the computational image is divided into a second grid structure based on a cell recognition model. The density of the second grid structure is higher than that of the first grid structure. Based on the second grid structure, computational target detection frames with cell characteristics are determined, and the number of computational target detection frames is determined as the number of computational cells.

[0090] The grid structure division of the computational image is more refined, the determination of the computational target detection frame in the computational image is more accurate, and thus the result of the cell number calculation is more accurate, thereby improving the calculation accuracy of the concentration of the cell solution.

[0091] Of course, in step 300, the total cell number in step 200 can also be selected as the calculated cell number. For example, when the cell clumping rate is small enough to be ignored, the calculated cell number can be the total cell number mentioned above, without the need for secondary image acquisition and secondary calculation, which can simplify the calculation process.

[0092] The method for calculating the cell count in the above step 300 can be selected as needed. For example, for scenarios where high cell counting accuracy is required, secondary image acquisition and secondary calculation can be performed. For scenarios where low cell counting accuracy is required, the total cell count can be used as the calculated cell count.

[0093] In some embodiments, in step 200, in the step of determining the flow channel with the lowest cell clumping rate as the calculation flow channel,

[0094] The cell agglomeration rate of cells with two or more flow channels is the lowest, and the flow channel with a higher height is determined as the calculation flow channel.

[0095] The cells are more evenly distributed in the flow channel with a higher height, which can more accurately characterize the cell agglomeration rate.

[0096] In some embodiments, step 100, i.e., the step of controlling the cell solution to fill the plurality of flow channels with height differences, includes:

[0097] The cell solution is delivered into the flow channel for a preset time period through the pump body, wherein the starting moment of the preset time period is the moment when the first sensor at the inlet detects the passage of the fluid.

[0098] The first sensor may be a bubble sensor. When the first sensor detects that the cell solution is flowing into the microfluidic chip, it starts timing. When the time for delivering the cell solution reaches a preset time, the pump body is closed to complete the filling of the cell solution. The structure is simple and easy to operate. The pump body may be a peristaltic pump.

[0099] The method of filling each flow channel in the microfluidic chip can be: after detecting the flow of liquid through the bubble sensor (or liquid inlet sensor) at the inlet and outlet, the cell solution is delivered to the microfluidic chip for a preset time and then the liquid delivery is stopped, and the microfluidic chip is determined to be filled;

[0100] In other embodiments, step 100, i.e., the step of controlling the cell solution to fill the plurality of flow channels with height differences, includes:

[0101] A preset volume of cell solution is delivered to the inlet through the pump body, and the preset volume is equal to the volume of the microfluidic chip.

[0102] The cell solution is transferred from the cell solution bag into the inlet through a peristaltic pump; the peristaltic pump is controlled to input a preset volume of cell solution into the microfluidic chip. The cell solution will flow to fill each flow channel in the microfluidic chip, and a liquid storage bag will be connected to the outlet.

[0103] In other embodiments, step 100, i.e., the step of controlling the cell solution to fill the plurality of flow channels with height differences, includes:

[0104] The pump body is controlled to deliver the cell solution into the flow channel, and the liquid is stopped when the second sensor at the outlet detects that the fluid has passed.

[0105] Another method for filling each flow channel in the microfluidic chip is to provide a second sensor at a predetermined distance from the outlet, and stop the liquid inlet when the second sensor senses liquid flowing through. The second sensor can be a bubble sensor or a liquid outlet sensor.

[0106] There are various ways to control the cell solution to fill each flow channel. The above-mentioned ways to control the cell solution to fill each flow channel can also be combined with each other, but the way to control the cell solution to fill each flow channel is not limited to the above-mentioned ways, and can be selected according to needs.

[0107] In the cell counting method of the above-mentioned microfluidic chip, a plurality of flow channels arranged in parallel are adopted, and the heights of different flow channels are different. Due to the flow channels with different heights, it is possible to adaptively select the most suitable flow channel with the highest degree of sensitivity to perform cell counting when the concentration of the cell solution is unknown, thereby greatly improving the applicability of the microfluidic chip. For the counting of high-concentration cell solutions, in order to avoid cell overlap when identifying cells, a microflow channel with a lower degree of sensitivity can be adopted, and a tiled image of a single cell can be obtained within the visual field, making the cell counting result more accurate. For the counting of low-concentration cell solutions, in order to avoid fewer cells within the visual field when identifying cells, a microflow channel with a higher degree of sensitivity can be adopted, and a relatively large number of cells can be obtained within the visual field, making the cell counting result more accurate. The cell counting method can automatically calculate the concentration of the cell solution without the need for manual selection of flow channels, thereby improving the accuracy and precision of cell counting.

[0108] An embodiment of the second aspect of the present invention provides a cell counting device, comprising a microfluidic chip, an electron microscope, and a processor. The electron microscope is used to capture images of the flow channel of the microfluidic chip. The electron microscope is communicatively connected to the processor to send the images to the processor. The processor can be used to execute a computer program. When the processor executes the computer program, it implements any of the above-mentioned cell counting methods for the microfluidic chip.

[0109] The electron microscope is used to collect the above-mentioned target image, calculate the image, and transmit the image to the processor. The processor can execute the above-mentioned cell counting method of the microfluidic chip, analyze the image, and obtain the concentration of the cell solution. The microfluidic chip has a simple structure and a simple calculation method, and has high calculation accuracy and efficiency.

[0110] Of course, the cell counting device may also include the aforementioned pump body, cell solution bag, etc.

[0111] The cell counting device is in use. First, the flow channel of the microfluidic chip is controlled by the pump body to carry out cell solution delivery. When the flow channel is filled with cell solution, the liquid inlet is stopped. Then, the target image of each flow channel is taken by a microscope, and the target image of each flow channel is identified by the cell agglomeration rate. The process of its identification can adopt the above-mentioned cell recognition model. Once again, the agglomeration rate of each flow channel is sorted, and the flow channel with the lowest agglomeration rate in the sorting is taken as the calculation flow channel for cell counting. Wherein, for the flow channel with the same cell agglomeration rate, the flow with higher height is used as the calculation flow channel. Afterwards, the two-dimensional calculation image of the calculation flow channel is subjected to more accurate target detection (also adopting the above-mentioned cell recognition model), the identification of the number of cells is carried out, and the number of cells is calculated. Of course, the total cell number can also be obtained in the calculation step of the cell agglomeration rate as the calculation number of cells. Finally, the number of cells is divided by the actual area corresponding to the calculation image to obtain the result. Then the result is multiplied by the height corresponding to the calculation flow channel to obtain the concentration of the cell solution, i.e., the number of cells / unit volume (1mL or 1 microliter). The concentration calculation process of the cell solution is simple and has high calculation efficiency.

[0112] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the cell counting method of the microfluidic chip: including

[0113] Step 100, controlling the cell solution to fill the plurality of flow channels with height differences;

[0114] Step 200, collecting a target image corresponding to a set area in each of the flow channels, obtaining a cell clumping rate corresponding to each of the flow channels based on the target image, and determining the flow channel with the lowest cell clumping rate as the calculation flow channel; wherein the cell clumping rate is the ratio of the number of cell clusters in the set area to the total number of cells;

[0115] Step 300, obtaining the number of calculated cells in the target area of ​​the calculation channel;

[0116] Step 400: Based on the area of ​​the target area and the height of the calculation channel, a preset volume corresponding to the target area is obtained, and the ratio of the calculated cell number to the preset volume is determined as the concentration of the cell solution.

[0117] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the relevant technology, or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0118] Furthermore, an embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the cell counting method for a microfluidic chip provided in the above-mentioned method embodiments, for example, including:

[0119] Step 100, controlling the cell solution to fill the plurality of flow channels with height differences;

[0120] Step 200, collecting a target image corresponding to a set area in each of the flow channels, obtaining a cell clumping rate corresponding to each of the flow channels based on the target image, and determining the flow channel with the lowest cell clumping rate as the calculation flow channel; wherein the cell clumping rate is the ratio of the number of cell clusters in the set area to the total number of cells;

[0121] Step 300, obtaining the number of calculated cells in the target area of ​​the calculation channel;

[0122] Step 400: Based on the area of ​​the target area and the height of the calculation channel, a preset volume corresponding to the target area is obtained, and the ratio of the calculated cell number to the preset volume is determined as the concentration of the cell solution.

[0123] On the other hand, an embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the cell counting method for the microfluidic chip provided in each of the above embodiments is implemented, for example, including:

[0124] Step 100, controlling the cell solution to fill the plurality of flow channels with height differences;

[0125] Step 200, collecting a target image corresponding to a set area in each of the flow channels, obtaining a cell clumping rate corresponding to each of the flow channels based on the target image, and determining the flow channel with the lowest cell clumping rate as the calculation flow channel; wherein the cell clumping rate is the ratio of the number of cell clusters in the set area to the total number of cells;

[0126] Step 300, obtaining the number of calculated cells in the target area of ​​the calculation channel;

[0127] Step 400: Based on the area of ​​the target area and the height of the calculation channel, a preset volume corresponding to the target area is obtained, and the ratio of the calculated cell number to the preset volume is determined as the concentration of the cell solution.

[0128] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0129] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A cell counting method using a microfluidic chip, characterized in that: The microfluidic chip is provided with an inlet, an outlet and two or more flow channels, the flow channels are provided in parallel between the inlet and the outlet, and a height difference is formed between the flow channels. The cell counting method includes: controlling the cell solution to fill the plurality of flow channels having height differences; Acquiring a target image corresponding to a set area in each flow channel, obtaining a cell clumping rate corresponding to each flow channel based on the target image, and determining the flow channel with the lowest cell clumping rate as the calculation flow channel; wherein the cell clumping rate is the ratio of the number of cell clusters in the set area to the total number of cells; obtaining the number of calculated cells in a target area of ​​the calculation channel; Based on the area of ​​the target area and the height of the calculation channel, a preset volume corresponding to the target area is obtained, and the ratio of the calculated cell number to the preset volume is determined as the concentration of the cell solution; The step of obtaining the cell agglomeration rate corresponding to each flow channel based on the target image is achieved by a cell recognition model, and the cell recognition model includes: The target image is divided into a first grid structure, a target detection frame having cell characteristics is determined based on the first grid structure, a plurality of intersecting target detection frames are considered as a cell cluster, and the number of cell clusters is determined; the number of independent target detection frames is determined as the number of independent cells; and the number of target detection frames is determined as the total number of cells; The cell recognition model is trained based on the YOLO model or the VGG16 model to determine the target detection frame; The cell recognition model determines whether the target detection frames are intersecting or independent according to the IOU algorithm, wherein the target detection frame is a rectangular frame; In the step of obtaining the number of calculated cells in the target area of ​​the calculation channel, Determine the total cell number as the calculated cell number, Alternatively, a calculation image of the calculation flow channel is obtained, wherein the clarity of the calculation image is higher than that of the target image, and calculation target detection frames having cell features are identified based on the calculation image, and the number of the calculation target detection frames is determined as the number of calculated cells; The total cell number is the sum of the number of independent cells and the number of cells in several cell clusters. Alternatively, the total cell number is the sum of the cell cluster number and the independent cell number.

2. The cell counting method using a microfluidic chip according to claim 1, wherein: In the step of determining the flow channel with the lowest cell agglomeration rate as the calculation flow channel, The cell agglomeration rates of two or more flow channels are the lowest, and the flow channel with a higher height is determined as the calculation flow channel.

3. The cell counting method of the microfluidic chip according to claim 1, characterized in that: The step of controlling the cell solution to fill the plurality of flow channels having height differences comprises: The cell solution is delivered into the flow channel for a preset time period by a pump body, wherein the starting moment of the preset time period is: the moment when the first sensor of the inlet detects the passage of the fluid; Alternatively, the pump body is controlled to deliver the cell solution into the flow channel, and the delivery of the liquid is stopped when the second sensor at the outlet detects the passage of the fluid; Alternatively, a preset volume of the cell solution is delivered to the inlet by a pump, where the preset volume is equal to the capacity of the microfluidic chip.

4. The cell counting method using a microfluidic chip according to any one of claims 1 to 3, characterized in that: The height of the flow channels is between 50um and 450um, and the height difference between the flow channels is between 50um and 150um.

5. A cell counting device, characterized in that: The invention comprises a microfluidic chip, an electron microscope, and a processor, wherein the electron microscope is used to collect an image of the flow channel of the microfluidic chip in the cell counting method according to any one of claims 1 to 4, the electron microscope is communicatively connected to the processor to send the image to the processor, and the processor can be used to execute a computer program. When the processor executes the computer program, the cell counting method for the microfluidic chip according to any one of claims 1 to 4 is implemented.

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