A cell counting method based on a microfluidic chip

Abstract

The present application relates to the technical field of biology, and particularly relates to a cell counting method based on a micro-fluidic chip. The cell counting method comprises the following steps: introducing dyed cells into a plurality of micro-channels of the micro-fluidic chip; the plurality of micro-channels have different heights; obtaining images of the plurality of micro-channels respectively; determining an optimal micro-channel according to the image results; and counting the cell density in the optimal micro-channel as a cell counting result. The present application selects the optimal micro-channel with the most suitable height to count cells, so that the number of cells in the field of view of the microscope is in the most suitable range, and the most accurate cell counting result is obtained. Furthermore, the optimal micro-channel is selected adaptively, and the concentration of the cell liquid is automatically calculated, so that the practicability of the micro-fluidic chip is significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a cell counting method based on 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] Currently, cell counting techniques can be mainly divided into two types: cell counting plate method and algorithm-based cell counting. The cell counting plate method involves manually observing and counting cells through staining. For example, a variety of cell counting plates are provided in the prior art as devices for manually observing and counting cells. However, these cell counting plates generally fail to consider the diversity of cell concentrations. When cells of different concentrations flow through, they will produce counting errors caused by concentration (for example, high cell concentrations will cause cell stacking, making image recognition inaccurate). At the same time, the experimental operation is cumbersome and the counting efficiency is low.

[0004] Algorithmic cell counting typically involves first processing the captured image and then counting the cells by identifying them. Existing technologies use photosensitive chips or image recognition software to count cells, but these counting methods require a high operating environment and strong technical requirements, which limits their application and promotion. Summary of the Invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a cell counting method based on a microfluidic chip, which improves the applicability of the microfluidic chip and the accuracy of cell counting by adaptively selecting the most suitable microchannel.

[0006] In a first aspect, the present invention provides a cell counting method based on a microfluidic chip, comprising:

[0007] Passing the stained cells into multiple microchannels of a microfluidic chip; the multiple microchannels have different heights;

[0008] Images of each of the plurality of microchannels are obtained, an optimal microchannel is determined based on the image results, and cell density in the optimal microchannel is counted as a cell counting result.

[0009] Furthermore, determining the optimal microchannel according to the image results includes:

[0010] The agglomeration rate and overlap rate of each microchannel are obtained based on the image;

[0011] The optimal microchannel is determined based on the agglomeration rate and overlap rate of each microchannel.

[0012] Furthermore, the agglomeration rate is identified by the following method:

[0013] Shooting a two-dimensional image of the same microchannel to obtain a two-dimensional image;

[0014] Recognizing cell boundaries on the two-dimensional image to identify the number of two-dimensional cells and cell boundaries;

[0015] Calculating the area to be processed enclosed by the identified cell boundaries, and marking the area to be processed that is larger than a preset area as agglomerated cells;

[0016] The agglomeration rate of the microchannel is obtained according to the identified number of two-dimensional cells and the counted number of agglomerated cells.

[0017] Furthermore, the overlap rate is identified by the following method:

[0018] Performing three-dimensional image modeling on the collected images of the same microchannel to obtain a three-dimensional model;

[0019] performing cell number identification on the three-dimensional model to obtain a three-dimensional cell number, and performing overlapping cell identification on the three-dimensional model to obtain a cell overlapping number;

[0020] The overlapping rate of the microchannel is obtained according to the number of overlapping cells and the number of three-dimensional cells.

[0021] Furthermore, determining the optimal microchannel according to the agglomeration rate and overlap rate of each microchannel includes:

[0022] The agglomeration rate and overlap rate of each microchannel were scored to obtain the counting score of each microchannel;

[0023] The microchannel with the highest counting score is selected as the optimal microchannel.

[0024] Furthermore, the counting of the cell density in the optimal microchannel includes:

[0025] Recording the number of three-dimensional cells in the optimal microchannel as the optimal number;

[0026] Converting the area of ​​the three-dimensional image of the optimal microchannel to obtain the cross-sectional area of ​​the optimal microchannel, and calculating the optimal microchannel collection volume based on the height of the optimal microchannel and the cross-sectional area;

[0027] The optimal number is divided by the optimal microchannel collection volume to obtain the cell density.

[0028] Furthermore, the height of the plurality of microchannels is 10 to 1000 μm.

[0029] Furthermore, the step of passing the stained cells into the multiple microchannels of the microfluidic chip includes:

[0030] A bubble sensor is provided at a preset length from the sample inlet; or a bubble sensor is provided at a preset length from the sample outlet; and the stained cells are filled into the multiple microchannels of the microfluidic chip according to the instructions of the bubble sensor.

[0031] Furthermore, if a bubble sensor is provided at a preset length from the injection port, the liquid injection is stopped after a preset time period after the bubble sensor detects the liquid flowing through;

[0032] If a bubble sensor is provided at a preset length from the sample outlet, the liquid inlet is stopped after the bubble sensor detects the liquid flowing through.

[0033] In a second aspect, the present invention provides a cell counting system, comprising a microfluidic chip, an imaging device, and a cell counting device, wherein the cell counting device is used to pass stained cells into multiple microchannels of the microfluidic chip; the multiple microchannels have different heights;

[0034] The imaging device is connected to the cell counting device and is used to obtain images of each of the multiple microchannels and determine the optimal microchannel based on the image results;

[0035] Then, the cell counting device counts the cell density in the optimal microchannel as the cell counting result.

[0036] The present invention has the following beneficial effects:

[0037] The present invention adaptively selects a microchannel with the most suitable height from microchannels of different heights as the optimal microchannel for cell counting without manual selection of the microchannel, thereby greatly improving the applicability of the microfluidic chip.

[0038] The present invention screens microchannels based on microchannel images, and the obtained optimal microchannel has a suitable height, which avoids cell overlap and a small number of cells in the field of view, thereby significantly improving the efficiency and accuracy of cell counting. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0040] Figure 1 Schematic top view of the microfluidic chip provided in an embodiment of the present invention.

[0041] Figure 2 Schematic cross-sectional view of the microfluidic chip provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] An embodiment of the present invention provides a cell counting method based on a microfluidic chip, comprising:

[0044] Passing the stained cells into multiple microchannels of a microfluidic chip; the multiple microchannels have different heights;

[0045] Images of each of the plurality of microchannels are obtained, an optimal microchannel is determined based on the image results, and cell density in the optimal microchannel is counted as a cell counting result.

[0046] Furthermore, the microfluidic chip includes PDMS and a substrate (the substrate can be a glass sheet), which are bonded together. The PDMS contains any number of microchannels arranged in parallel. These microchannels have different heights. Figure 1 For example, a microfluidic chip includes an inlet, five microchannels, and an outlet. The cell fluid flows into five microchannels of different heights through the inlet, and is guided into a parallel observation area. The images of each microchannel are taken by an electron microscope. For each microchannel, different microchannel heights can be designed according to the requirements of the morphology and size of the statistical cells. The preferred height is between 10um and 1000um, and the height difference between different microchannels is not less than 50um. Figure 2 For example, the five microchannels have different heights, which can be set to 50um, 150um, 250um, 350um and 450um.

[0047] Furthermore, when the stained cells are passed into the multiple microchannels of the microfluidic chip, the capacity is preset, and a liquid storage bag is provided at the sample outlet.

[0048] Furthermore, the stained cells are passed through the multiple microchannels of the microfluidic chip to fill the multiple microchannels. This can be achieved by providing a bubble sensor (or liquid inlet sensor) at the inlet or at the outlet. For example, a bubble sensor can be provided at the inlet, and a preset time period can be set. After the sensor detects liquid flowing through, the liquid inlet is stopped after the preset time period to confirm that all microchannels have been filled. For example, a bubble sensor can be provided at a position of a preset length at the outlet. After the bubble sensor detects liquid flowing through, the liquid inlet is stopped to confirm that all microchannels have been filled.

[0049] Furthermore, for the stained cells, various existing staining methods can be used, such as trypan blue staining or fluorescent staining.

[0050] Further, determining the optimal microchannel according to the image results includes:

[0051] The agglomeration rate and overlap rate of each microchannel are obtained based on the image;

[0052] The optimal microchannel is determined based on the agglomeration rate and overlap rate of each microchannel.

[0053] Furthermore, the agglomeration rate is identified by the following method:

[0054] Shooting a two-dimensional image of the same microchannel to obtain a two-dimensional image;

[0055] Recognizing cell boundaries on the two-dimensional image to identify the number of two-dimensional cells and cell boundaries;

[0056] Calculate the area to be processed enclosed by the identified cell boundaries, and mark the area to be processed larger than the preset area as agglomerated cells;

[0057] The agglomeration rate of the microchannel is obtained according to the number of the identified two-dimensional cells and the number of the counted agglomerated cells.

[0058] Further, the two-dimensional image is used to identify the cell boundaries, which can be identified by a trained neural network model, or by image recognition technology (for example, identification of cell boundaries based on OpenCV). Specifically, the two-dimensional image can be used for the statistics of the agglomeration rate. For a single cell, after identifying the boundary, the area surrounded by the boundary is the area of ​​the single cell. However, for agglomerated cells, due to the agglomeration of multiple cells, the boundary in the middle of the agglomeration cannot be identified. Therefore, after boundary identification, the area surrounded by the boundary exceeds the single cell. Therefore, a preset area can be set. If the preset area is exceeded, it can be considered that agglomerated cells have occurred. The preset area can be determined according to the area size of the actual cell, for example, 1.5 times the area of ​​a single cell. The final statistics obtain a number of agglomerated cells whose areas exceed the preset area, as well as the total number of two-dimensional cells. The number of agglomerated cells is divided by the total number of two-dimensional cells, which can be recorded as the agglomeration rate of the microchannel.

[0059] Furthermore, the overlap rate is identified by the following method:

[0060] Performing three-dimensional image modeling on the collected images of the same microchannel to obtain a three-dimensional model;

[0061] performing cell number identification on the three-dimensional model to obtain a three-dimensional cell number, and performing overlapping cell identification on the three-dimensional model to obtain a cell overlapping number;

[0062] The overlapping rate of the microchannel is obtained according to the number of overlapping cells and the number of three-dimensional cells.

[0063] Furthermore, the collected image can be a photographic image or a non-photographic image, for example, a plurality of two-dimensional images can be obtained by collecting with different focusing by a microscope, and then the collected two-dimensional images are constructed to obtain a three-dimensional model. Specifically, when building a three-dimensional model, the interval between the layers of the two-dimensional images obtained by each focusing can be adjusted according to the diameter of the cell. For example, a plurality of layers of collected images separated by half a cell diameter can be established for modeling, and then the position of a single cell can be identified based on the obtained three-dimensional model, and then the number of cells overlapping in the axial direction and the total number of three-dimensional cells can be determined. The axial direction can be perpendicular to the axial direction of the microfluidic chip, or it can be the axial direction of the horizontal direction of the microfluidic chip. Finally, the overlap rate of the microchannel can be obtained by dividing the number of all overlapping cells by the total number of three-dimensional cells.

[0064] Furthermore, in addition to the above-mentioned method of collecting two-dimensional images under different focusing conditions and finally building a three-dimensional model, other three-dimensional model construction methods in the existing technology can also be used to establish a three-dimensional model. For example, two detection devices with imaging functions are used to perform imaging at the same time at a certain distance, and finally a three-dimensional model is constructed based on the collected images.

[0065] Furthermore, determining the optimal microchannel according to the agglomeration rate and the overlap rate results includes:

[0066] The agglomeration rate and overlap rate of each microchannel were scored to obtain the counting score of each microchannel;

[0067] The microchannel with the highest counting score is selected as the optimal microchannel.

[0068] Furthermore, the counting score of each microchannel is obtained by weighted calculation of the agglomeration rate and overlap rate results of the microchannel.

[0069] Specifically, after obtaining the clumping rate and overlap rate of each microchannel through the aforementioned process, each microchannel can be scored according to the clumping rate and overlap rate results, for example, the technical score = 100-30×clumping rate-20×overlap rate. This score can reflect the cell clumping and overlap of the microchannel to a certain extent. Based on the scoring result, the microchannel most suitable for cell counting can be selected. Minimizing cell clumping and overlap can make the cells in the microscope field of view as flat as possible in the microchannel, so that the results obtained when counting cells are more accurate.

[0070] Preferably, the skill score = A × clumping rate + B × overlap rate, where A + B = 1, A is the weight of the clumping rate (0-1), and B is the weight of the overlap rate (0-1). Alternatively, the skill score = X × (mean of all clumping rates - clumping rate of the microchannel) + Y × (mean of all overlap rates - overlap rate of the microchannel), where X + Y = 1, X is the weight of the difference between the mean of all clumping rates and the clumping rate of the microchannel (0-1), and Y is the weight of the difference between the mean of all overlap rates and the overlap rate of the microchannel (0-1). This scoring method can optimally reflect the cell state in the microchannel and screen out the microchannel with the highest accuracy.

[0071] In fact, what is used in cell counting is the method of microscope shooting-image processing, and the distribution of cells in different microchannels for the same concentration of cell solution will be very different. When the concentration of the cell solution is low, if a low-altitude microchannel is selected, the number of cells may be less than the true value or even no cells may appear in the microscope field of view, and the data obtained by statistics are untrue; when a high-altitude microchannel is selected, the cell situation of more solution volume 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 high-altitude microchannel is selected, the number of cells may be large in the microscope field of view, which will cause the cells to produce a stacked covering phenomenon, thereby causing the result of cell counting to be inaccurate; in this case, a low-altitude microchannel can be selected, which can make the cells tiled in the microchannel, reducing or even not causing the cell stacking covering situation, making the data obtained more accurate. The above-mentioned scoring method provided by the embodiment of the present invention can adaptively select the most suitable microchannel with a high degree of cell count when the concentration is unknown, comprehensively considering the agglomeration rate and the overlap rate, thereby greatly improving the applicability of the chip.

[0072] Furthermore, calculating the cell density in the optimal microchannel includes:

[0073] The number of three-dimensional cells in the optimal microchannel was recorded as the optimal number;

[0074] Converting the area of ​​the three-dimensional image of the optimal microchannel to obtain the cross-sectional area of ​​the optimal microchannel, and calculating the optimal microchannel collection volume based on the height and cross-sectional area of ​​the optimal microchannel;

[0075] The optimal number and the optimal microchannel collection volume are divided to obtain the cell density.

[0076] Specifically, after determining the optimal microchannel, the cell density in the optimal microchannel can be used as the final cell density result. For example, according to the aforementioned three-dimensional modeling, the three-dimensional cell number of the optimal microchannel can be statistically obtained and recorded as the optimal number. The area conversion is performed based on the image obtained by collecting the optimal microchannel to obtain its cross-sectional area. The cross-sectional area is multiplied by the height of the optimal microchannel to obtain the collection volume. The number of cells / collection volume is the calculated cell density of the optimal microchannel, which is also the final calculation result of the cell technology method provided by the embodiment of the present invention.

[0077] The cell counting method provided by the embodiment of the present invention can adaptively determine the optimal microchannel, and after the determination is completed, directly calculate the concentration of cells in the optimal microchannel, which has high technical efficiency and accuracy.

[0078] 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 based on a microfluidic chip, characterized in that: include: Pass the stained cells into multiple microchannels of the microfluidic chip; The plurality of microchannels have different heights; Obtaining images of each of the plurality of microchannels, obtaining agglomeration rates and overlap rates of each microchannel based on the images, scoring the agglomeration rates and overlap rates of each microchannel to obtain a count score for each microchannel, and selecting the microchannel with the highest count score as the optimal microchannel; Counting the cell density in the optimal microchannel as a cell counting result; The agglomeration rate is identified by the following method: Shooting a two-dimensional image of the same microchannel to obtain a two-dimensional image; Recognizing cell boundaries on the two-dimensional image to identify the number of two-dimensional cells and cell boundaries; Calculating the area to be processed enclosed by the identified cell boundaries, and marking the area to be processed that is larger than a preset area as agglomerated cells; Obtaining a clustering rate of the microchannel according to the identified number of two-dimensional cells and the counted number of clustered cells; The overlap rate is identified by the following method: Performing three-dimensional image modeling on the collected images of the same microchannel to obtain a three-dimensional model; performing cell number identification on the three-dimensional model to obtain a three-dimensional cell number, and performing overlapping cell identification on the three-dimensional model to obtain a cell overlapping number; The overlapping rate of the microchannel is obtained according to the number of overlapping cells and the number of three-dimensional cells.

2. The cell counting method according to claim 1, wherein The counting of the cell density in the optimal microchannel comprises: Recording the number of three-dimensional cells in the optimal microchannel as the optimal number; Converting the area of ​​the three-dimensional image of the optimal microchannel to obtain the cross-sectional area of ​​the optimal microchannel, and calculating the optimal microchannel collection volume based on the height of the optimal microchannel and the cross-sectional area; The optimal number is divided by the optimal microchannel collection volume to obtain the cell density.

3. The cell counting method according to claim 1 or 2, characterized in that The height of the multiple microchannels is 10-1000 μm.

4. The cell counting method according to claim 1, wherein The step of passing the stained cells into the multiple microchannels of the microfluidic chip includes: A bubble sensor is provided at a preset length from the sample inlet; or a bubble sensor is provided at a preset length from the sample outlet; and the stained cells are filled into the multiple microchannels of the microfluidic chip according to the instructions of the bubble sensor.

5. The cell counting method according to claim 4, characterized in that If a bubble sensor is provided at a preset length from the injection port, the liquid inlet is stopped after a preset time period after the bubble sensor detects the liquid flowing through; or, If a bubble sensor is provided at a preset length from the sample outlet, the liquid inlet is stopped after the bubble sensor detects the liquid flowing through.

6. A cell counting system for the cell counting method according to any one of claims 1 to 5, characterized in that: The cell counting system includes a microfluidic chip, an imaging device, and a cell counting device. The cell counting device is used to pass the stained cells into multiple microchannels of the microfluidic chip; the multiple microchannels have different heights; The imaging device is connected to the cell counting device and is used to obtain images of each of the multiple microchannels and determine the optimal microchannel based on the image results; Then, the cell counting device counts the cell density in the optimal microchannel as the cell counting result.

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