A single-cell imaging analysis system and method for determining acute toxicity of water

Abstract

The present invention provides a single-cell imaging analysis system and method for determining the acute toxicity of water bodies, which relates to the field of environmental monitoring technology. The system comprises a scattered light imaging module for collecting images of a test solution in a cuvette in a time series to obtain multiple video frames of single-cell images to be analyzed; a detection module for detecting particles in each video frame of the single-cell image to be analyzed, the particles being formed by light scattering, and locating the particles in each detected video frame to obtain the location coordinates of each particle, and obtaining the particle's motion trajectory based on each location coordinate; an analysis module for obtaining a characterization of the particle's motion range within the test solution space based on the motion trajectory, and obtaining a characterization of the particle's trajectory change within the test solution space based on the motion trajectory, and obtaining a conclusion on whether the water body is contaminated based on the characterization of the motion range and / or the characterization of the trajectory change. Compared with traditional acute toxicity analysis methods, the present invention has the advantages of high speed and anti-interference.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of environmental monitoring, and in particular to a single-cell imaging analysis system and method for determining the acute toxicity of water bodies. Background Art

[0002] Water quality monitoring and early warning play a vital role in drinking water safety, water pollution control and river basin water environment management. Therefore, strengthening real-time water quality monitoring is an important prerequisite for controlling frequent water pollution and ensuring water quality safety. Developing online water quality monitoring technology and equipment with fast response speed and high sensitivity is an important guarantee for achieving water quality monitoring and early warning.

[0003] Traditional water quality monitoring technologies rely on conventional chemical monitoring of water quality, limited to toxic and hazardous substances such as COD, ammonia nitrogen, heavy metals, and common organic pollutants. This extremely limited number of monitored indicators significantly increases the risk of a large number of unrecognized potential pollutants. Furthermore, pollutants of unknown quantity and type often do not exist in isolation but undergo a series of chemical reactions, including degradation, binding, and transformation, resulting in complex pollution. Therefore, chemical monitoring alone is difficult to implement for early warning water quality monitoring. Biological water quality monitoring methods can, to some extent, address the shortcomings of chemical monitoring, but traditional biological monitoring methods still suffer from low sensitivity, long detection times, high maintenance costs, and difficulties in preserving indicator organisms. In recent years, whole-cell biosensing methods, such as luminescent bacteria and bioelectrochemical systems, have been applied to biological water quality monitoring and show promising application prospects. However, current whole-cell sensing methods require specific luminescent and genetically engineered strains, have poor sensitivity, and are susceptible to environmental interference, making them difficult to meet the needs for real-time water quality monitoring and early warning of accidents. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a single-cell imaging analysis system and method for measuring the acute toxicity of water bodies in response to the deficiencies of the existing technology.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: a single-cell imaging analysis system for determining acute toxicity of water bodies, comprising a scattered light imaging module, a detection module and an analysis module;

[0006] The scattered light imaging module is used to collect images of the solution to be tested in the cuvette in a time series to obtain multiple video frames of single cell images to be analyzed;

[0007] The detection module is used to detect particles in the single cell image to be analyzed in each video frame, wherein the particles are formed by light scattering, and locate the particles detected in each video frame to obtain the location coordinates of each particle, and obtain the movement trajectory of the particle based on each location coordinate;

[0008] The analysis module is used to obtain a representation of the movement range of the particles in the solution space to be tested based on the movement trajectory, and to obtain a representation of the trajectory change of the particles in the solution space to be tested based on the movement trajectory, and to obtain a conclusion on whether the water body is polluted based on the representation of the movement range and / or the representation of the trajectory change.

[0009] Another technical solution of the present invention to solve the above technical problem is as follows: a single-cell imaging analysis method for determining acute toxicity of water, comprising the following steps:

[0010] Capturing images of the solution to be tested in the cuvette in time series to obtain multiple video frames of single cell images to be analyzed;

[0011] Detecting particles in the single-cell image to be analyzed in each video frame, the particles being formed by light scattering, and locating the particles in each detected video frame to obtain the location coordinates of each particle, and obtaining the movement trajectory of the particle based on each location coordinate;

[0012] Based on the motion trajectory, a characterization of the movement range of the particle in the solution space to be tested is obtained, and based on the motion trajectory, a characterization of the trajectory change of the particle in the solution space to be tested is obtained, and based on the characterization of the movement range and / or the characterization of the trajectory change, a conclusion is obtained on whether the water body is polluted.

[0013] The beneficial effects of the present invention are: by locating particles in single-cell images to be analyzed in multiple video frames and obtaining motion trajectories, the motion range of the particles and the trajectory changes are characterized by the motion trajectories, and a conclusion is drawn on whether the water body is polluted based on the characterization. This has important application value for toxicological research and rapid assessment of water bodies. Compared with traditional acute toxicity analysis methods, the present invention has the advantages of fast speed and anti-interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A block diagram of a single-cell imaging and analysis system according to an embodiment of the present invention;

[0015] Figure 2 A schematic diagram of the process of the single-cell imaging analysis method provided in an embodiment of the present invention;

[0016] Figure 3 This is a structural diagram of a scattered light imaging module provided in an embodiment of the present invention.

[0017] In the accompanying drawings, the names of the components represented by the various symbols are as follows:

[0018] 1. Laser; 2. Prism; 3. Collimating prism; 4. Cuvette; 5. Sample tray; 6. Zoom lens; 7. CCD camera; 8. Thermal insulation shell; 9. Temperature controller. DETAILED DESCRIPTION

[0019] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0020] Example 1:

[0021] like Figure 1 As shown, a single-cell imaging analysis system for determining acute toxicity of water bodies includes a scattered light imaging module, a detection module, and an analysis module;

[0022] The scattered light imaging module is used to collect images of the solution to be tested in the cuvette in a time series to obtain multiple video frames of single cell images to be analyzed;

[0023] The detection module is used to detect particles in the single cell image to be analyzed in each video frame, wherein the particles are formed by light scattering, and locate the particles detected in each video frame to obtain the location coordinates of each particle, and obtain the movement trajectory of the particle based on each location coordinate;

[0024] The analysis module is used to obtain a representation of the movement range of the particles in the solution space to be tested based on the movement trajectory, and to obtain a representation of the trajectory change of the particles in the solution space to be tested based on the movement trajectory, and to obtain a conclusion on whether the water body is polluted based on the representation of the movement range and / or the representation of the trajectory change.

[0025] Specifically, in the detection module, particles in the single cell image to be analyzed in each video frame are detected by:

[0026] The single-cell image to be analyzed is subjected to background noise removal processing to obtain an optimized single-cell image to be analyzed; specifically, the background noise is subtracted by subtracting the first image in the image time series to increase the signal-to-noise ratio of the image.

[0027] The particles in the optimized single cell image to be analyzed are obtained by detecting the particles using Laplace of Gaussian.

[0028] Specifically, in the detection module, the particles detected in each video frame are located to obtain the location coordinates of each particle, and the motion trajectory of the particle is obtained according to each location coordinate, specifically:

[0029] The sub-pixel positioning of the particles in each video frame is performed using a two-dimensional Gaussian function fitting method to obtain the sub-pixel positioning coordinates of each particle;

[0030] The tracker based on the linear distribution method tracks particles in time series and obtains the motion trajectory of each particle in the time dimension.

[0031] Specifically, after obtaining the precise sub-pixel positioning coordinates of each particle in each frame, a tracker based on the linear distribution method is used to track the particles in the time series, and finally the motion trajectory of each particle in the time dimension can be obtained.

[0032] It should be understood that since the grayscale distribution function of the light spot detected by the camera detector can be approximately regarded as a Gaussian distribution, it can be fitted by a two-dimensional Gaussian function. That is, the two-dimensional Gaussian function is where A,x0,σ x ,y0,σ y are the parameters to be fitted.

[0033] Specifically, in the analysis module, the characterization of the movement range of the particle in the solution space to be tested is obtained according to the movement trajectory, specifically:

[0034] The motion trajectory includes a plurality of nodes, each of which is the coordinate of the particle on the trajectory at each time t, and the plurality of nodes form a time series.

[0035] After obtaining the coordinates of the particle trajectory, the forward direction of the trajectory is first calculated, and then the distance from each node on the trajectory to the forward direction vector of the trajectory is calculated. The distance is defined as D n-f , to characterize the motion range of particles in the solution space.

[0036] The distance from each node to the trajectory forward direction vector is calculated according to the motion range characterization formula. The distance characterizes the motion range of the particle in the solution space. The motion range characterization formula is:

[0037]

[0038] Among them, x is the horizontal coordinate of the coordinate time series in the trajectory, y is the vertical coordinate of the coordinate time series in the trajectory, x0 is the horizontal coordinate of the starting point of the coordinate time series in the trajectory, y0 is the vertical coordinate of the starting point of the coordinate time series in the trajectory, x max, y max is the coordinate of the point with the maximum distance from the starting point of the trajectory time series; l is the length of the coordinate time series in the trajectory.

[0039] In the above embodiment, the characterization of the movement range of particles in the solution space to be tested can be accurately obtained, which has important application value for toxicological research and rapid assessment of water bodies.

[0040] After obtaining the coordinates of the particle trajectory, first calculate the change in the swing angle of each node on the motion trajectory compared to the motion direction at the previous moment. Then calculate the multi-scale entropy of the swing angle change to characterize the complexity change of the particle motion trajectory, defined as MsEn_A.

[0041] Specifically, in the analysis module, according to the motion trajectory, a characterization of the trajectory change of the particle in the space of the solution to be inspected is obtained, specifically:

[0042] For a one-dimensional swing angle time series {x1, x2, …, x i , …, x N} of length N, first construct, at multiple scales, a coarse-grained time series {y } according to the formula: (τ) where 1 ≤ j ≤ N / τ, where τ is the scale factor, and the length after coarse-graining the sequence is M = int(N / τ). Calculate the sample entropy values under different scale factors respectively according to the following process:

[0043] S1: Construct a set of m-dimensional vectors: Xm(i) = {y i+k : 0 ≤ k ≤ m - 1};

[0044] S2: For each i value, calculate the distance between Xm(i) and the other vectors Xm(j): d[Xm(i), Xm(j)] = max|y (i+k) - y (j+k [[ID=2,6]](0 ≤ k ≤ m - 1, i, j = 1 ~ M - m + 1; i ≠ j);

[0045] S3: Set the tolerance threshold r (r > 0) for the matching process, and then count, for each i, the number B m (i) of d[Xm(i), Xm(j)] < r (i, j = 1 ~ M - m + 1; i ≠ j). B m (i) is the number of template matches, and calculate the ratio to the total number of distances, denoted as:

[0046]

[0047] S4: Calculate the average value of :

[0048]

[0049] S5: Increase the dimension to m + 1, repeat steps S1 - S4, and then calculate

[0050]

[0051] S6: Calculate the average value of :

[0052]

[0053] When M is a finite value, the above steps yield the estimated sample entropy when the sequence length is M, which is recorded as:

[0054]

[0055] S7: Repeat steps S1-S6 to obtain sample entropy values ​​at different scales, and finally obtain the multi-scale entropy of the swing angle time series based on the sample entropy values:

[0056] MsEn_A={τ|SampEn(m,s,τ)}.

[0057] It should be understood that in step S7, the number of loops is determined by the initially set scale factor τ. For example, if τ is set to 10, 10 loop exits are calculated.

[0058] Specifically, in the analysis module, based on the representation of the motion range and / or the representation of the trajectory change, a conclusion on whether the water body is polluted is obtained, specifically:

[0059] If the result of the characterization of the motion range shows an exponential decay change and / or the result of the characterization of the trajectory change is less than a preset value, it is concluded that the water body is polluted.

[0060] In the above embodiment, by calculating the multi-scale entropy of the pendulum angle change, the characterization of the particle trajectory change in the solution space to be tested can be accurately obtained, which has important application value for toxicological research and rapid assessment of water bodies.

[0061] Specifically, if Figure 3 As shown, the scattered light imaging module includes an xyz axis translation stage, a laser 1, a prism 2, a collimating lens 3, a cuvette 4, a sample tray 5, a zoom lens 6, a CCD camera 7, a base, an optical frame and a stepping motor.

[0062] The z-axis of the xyz-axis translation stage is fixed to the base, the laser 1 is mounted on the x-axis of the xyz-axis translation stage, the prism 2 and the collimating lens 3 are fixed by the optical frame and then mounted on the x-axis of the xyz-axis translation stage, and the zoom lens 6 is connected to the C-type bayonet of the CCD camera 7 and is also mounted on the y-axis of the xyz-axis translation stage;

[0063] The stepper motor is mounted on the base, the sample tray 5 is mounted on the stepper motor, and the rotation of the sample tray is controlled by the stepper motor, and the cuvette is placed in the sample tray.

[0064] Specifically, the laser 1 is used to generate laser light, the collimating lens 3 is used to collimate the laser light, the prism 2 is used to shape the collimated laser light, and the shaped laser light is irradiated into a cuvette containing a solution to be tested, so that the solution to be tested in the cuvette 4 emits side scattered light. The zoom lens 6 is at a 90-degree angle to the shaped laser light, and the CCD camera 7 captures images of the solution to be tested in a time series at a speed of 30fps through the zoom lens 6 to obtain multiple video frames of single-cell images to be analyzed.

[0065] Specifically, the scattered light imaging module also includes an insulating shell 8 and a temperature controller 9. The insulating shell covers the outside of the sample tray 5, and the temperature controller 9 is installed inside the insulating shell 8. The temperature controller 9 is used to control the temperature inside the insulating shell 8.

[0066] In the above embodiment, Figure 3 As shown, the cuvette can be made of quartz material, and eight quartz cuvettes can be arranged circumferentially.

[0067] In the above embodiment, a wide-field scattered light imaging device is constructed. The wide-field scattered light imaging device uses a 100mW, 637nm fiber-coupled diode laser as the laser light source, and is fixed to the bottom of the device and adjusted in position by an xyz-axis translation stage. The laser generated by the laser is first collimated by a collimating lens, and the collimated laser is shaped by a prism. The collimating lens and prism are fixed by an optical frame and a connecting rod. The shaped light beam transmitted from the prism is irradiated into the sample in the quartz cuvette. Eight quartz cuvettes are placed in a 3D-printed circular sample tray. The sample tray is fixed to a stepper motor below and is controlled to rotate by the stepper motor. It is placed in a 3D-printed thermal insulation shell, and the temperature of the internal sample is maintained at 37±0.1° by a digital temperature controller. Side scattered light emitted from the sample in the cuvette is collected at 30 frames per second by a CMOS camera (CCD camera) at a 90-degree angle to the laser beam. The zoom lens is attached to the camera via a C-mount. The camera and zoom lens are fixed to the bottom of the device and adjusted in position by an xyz-axis translation stage. The imaging volume of this system is determined by the size of the focused beam illuminating the sample, as well as the viewing size and focal length of the optical components. For the experiments described in this study, an observation volume of 10 mm × 5 mm × 2 mm at 2.5x magnification corresponds to 100 μL.

[0068] The present invention utilizes laser light scattering to illuminate bacteria in water samples using a shaped beam. A zoom industrial lens amplifies the scattered light from the sample, which is then captured and recorded by a high-speed camera. This allows for high-speed, real-time imaging of the position and morphology of individual bacteria within the water sample. The signal-to-noise ratio (SNR) of the image is improved by adjusting the imaging field of view, stabilizing the laser light source signal, and isolating environmental noise. According to Mie scattering theory, the scattered light signal of a particle is related to its size, type, and scattering area. Therefore, by reading the scattered light signal of a single bacterium, its morphology, movement, growth, and other characteristics can be determined, establishing a single-cell imaging and analysis system.

[0069] Example 2:

[0070] like Figure 2 As shown, a single-cell imaging analysis method for determining acute toxicity of water includes the following steps:

[0071] Capturing images of the solution to be tested in the cuvette in time series to obtain multiple video frames of single cell images to be analyzed;

[0072] Detecting particles in the single-cell image to be analyzed in each video frame, the particles being formed by light scattering, and locating the particles in each detected video frame to obtain the location coordinates of each particle, and obtaining the movement trajectory of the particle based on each location coordinate;

[0073] Based on the motion trajectory, a characterization of the movement range of the particle in the solution space to be tested is obtained, and based on the motion trajectory, a characterization of the trajectory change of the particle in the solution space to be tested is obtained, and based on the characterization of the movement range and / or the characterization of the trajectory change, a conclusion is obtained on whether the water body is polluted.

[0074] The following is a verification of the water body early warning principle based on single-cell analysis of the present invention:

[0075] Representative pollutants were selected as model pollutants, simulated water samples were prepared, and the water samples were imaged under the constructed single-cell imaging and analysis system to study the stress behavior response of microorganisms under the action of multiple pollutants; the changes in phenotypic characteristics such as bacterial movement, growth and morphology in water samples were examined, and various phenotypic characteristics related to the degree of water pollution were preliminarily screened; the universality and response range of the technology were verified by examining the response relationship between each phenotypic characteristic and different types and degrees of simulated polluted water bodies; the feasibility was verified by comparing with the standard method for determining the acute toxicity of water bodies - the luminescent bacteria method, which laid the foundation for using the changes in various phenotypic characteristics of single bacteria for water body monitoring and early warning.

[0076] The present invention proposes three indicators for analyzing changes in bacterial trajectories: the semi-quantitative distance of each node in the motion trajectory perpendicular to the forward direction and the specific distribution of the swing angle during travel; and the multi-scale entropy of the swing angle of the quantitative indicator trajectory. Rapid monitoring of wastewater toxicity is achieved using a wide-field scattering imaging system, which has important application value for toxicological research and rapid assessment of water bodies.

[0077] Compared to traditional acute toxicity analysis methods, this method offers the advantages of speed and robustness. Because this system combines wide-field imaging with single-particle tracking, it can generate more convincing statistical information. Furthermore, because it collects scattered light information, the image has a high signal-to-noise ratio and is less susceptible to interference from static matrix backgrounds. Furthermore, the analysis results are less susceptible to the influence of solution color, salinity, or hardness.

[0078] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0079] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another system, or ignoring or not implementing certain features.

[0081] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present invention.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A single-cell imaging analysis system for determining acute toxicity of water, characterized in that: It includes a scattered light imaging module, a detection module and an analysis module; The scattered light imaging module is used to collect images of the solution to be tested in the cuvette in a time series to obtain multiple video frames of single cell images to be analyzed; The detection module is used to detect particles in the single cell image to be analyzed in each video frame, wherein the particles are formed by light scattering, and locate the particles detected in each video frame to obtain the location coordinates of each particle, and obtain the movement trajectory of the particle based on each location coordinate; The analysis module is configured to obtain a representation of the range of motion of the particle in the test solution space based on the motion trajectory, obtain a representation of the change in the trajectory of the particle in the test solution space based on the motion trajectory, and obtain a conclusion on whether the water body is polluted based on the representation of the range of motion and / or the representation of the change in the trajectory; In the detection module, the particles detected in each video frame are located to obtain the location coordinates of each particle, and the motion trajectory of the particle is obtained according to each location coordinate, specifically: The sub-pixel positioning of the particles in each video frame is performed using a two-dimensional Gaussian function fitting method to obtain the sub-pixel positioning coordinates of each particle; The tracker based on the linear distribution method tracks particles in time series and obtains the motion trajectory of each particle in the time dimension; In the analysis module, the characterization of the movement range of the particle in the solution space to be tested is obtained according to the movement trajectory, specifically: The motion trajectory includes multiple nodes, each of which is the coordinate of the particle on the trajectory at each time t. The multiple nodes form a time series. The distance from each node to the trajectory forward direction vector is calculated according to the motion range characterization formula. The distance is characterized as the motion range of the particle in the solution space. The motion range characterization formula is: x max ,y max =argmax((x i -x0) 2 +(y i -y0) 2 ),i=1,2,3...,l, Among them, x is the horizontal coordinate of the coordinate time series in the trajectory, y is the vertical coordinate of the coordinate time series in the trajectory, x0 is the horizontal coordinate of the starting point of the coordinate time series in the trajectory, y0 is the vertical coordinate of the starting point of the coordinate time series in the trajectory, x max ,y max is the coordinate of the point with the maximum distance from the starting point of the trajectory time series; l is the length of the coordinate time series in the trajectory, D n-f The distance from each node to the trajectory forward direction vector; In the analysis module, the characterization of the trajectory change of the particle in the solution space to be tested is obtained according to the motion trajectory, specifically: For a one-dimensional pendulum angle time series {x1, x2, ..., x i ,…,x N } ,First, follow the formula at multiple scales: Where 1≤j≤N / τ, construct a coarse-grained time series {y (τ) }, where τ is the scale factor, and the length of the coarse-grained sequence is M = int(N / τ). The sample entropy values ​​under different scale factors are calculated according to the following process: S1: Construct a set of m-dimensional vectors: Xm(i) = {y i+k :0≤k≤m-1}; S2: For each value of i, calculate the distance between Xm(i) and the remaining vectors Xm(j): d[Xm(i),Xm(j)]=max|y (i+k) -y (j+k) |,(0≤k≤m-1,i,j=1~M-m+1;i≠j); S3: Set the tolerance threshold r (r>0) of the matching process, and calculate d[Xm(i),Xm(j)] for each i <r,(i,j=1~M-m+1; The number B of i≠j) m (i), B m (i) is the number of template matches, and the ratio to the total distance is calculated as: S4: Calculation The average value of: S5: Increase the dimension to m+1, repeat steps S1-S4, and calculate S6: Calculation The average value of: When M is a finite value, the estimated sample entropy when the sequence length is M is recorded as: S7: Repeat steps S1-S6 to obtain sample entropy values ​​at different scales, and finally obtain the multi-scale entropy of the swing angle time series based on the sample entropy values: MsEn_A={τ|SampEn(m,s,τ)}.

2. The single cell imaging analysis system according to claim 1, characterized in that In the detection module, particles in the single cell image to be analyzed in each video frame are detected, specifically: performing background noise removal processing on the single-cell image to be analyzed to obtain an optimized single-cell image to be analyzed; The particles in the optimized single cell image to be analyzed are detected using the Laplacian of Gaussian operator.

3. The single cell imaging analysis system according to claim 1, characterized in that In the analysis module, a conclusion on whether the water body is polluted is obtained based on the representation of the motion range and / or the representation of the trajectory change, specifically: If the result of the characterization of the motion range shows an exponential decay change and / or the result of the characterization of the trajectory change is less than a preset value, it is concluded that the water body is polluted.

4. The single-cell imaging analysis system according to claim 1, characterized in that The scattered light imaging module includes an xyz axis translation stage, a laser, a prism, a collimating lens, a cuvette, a sample tray, a zoom lens, a CCD camera, a base, an optical frame and a stepping motor. The z-axis of the xyz-axis translation stage is fixed to the base, the laser is mounted on the x-axis of the xyz-axis translation stage, the prism and the collimating lens are fixed by the optical frame and then mounted on the x-axis of the xyz-axis translation stage, and the zoom lens is connected to the C-type bayonet of the CCD camera and is also mounted on the y-axis of the xyz-axis translation stage; The stepper motor is mounted on the base, the sample tray is mounted on the stepper motor, and the sample tray is rotated by controlling the stepper motor, and the cuvette is placed in the sample tray.

5. The single cell imaging analysis system according to claim 4, characterized in that: The laser is used to generate laser light, the collimating lens is used to collimate the laser light, the prism is used to shape the collimated laser light, and the shaped laser light is irradiated into a cuvette containing a solution to be tested, so that the solution to be tested in the cuvette emits side scattered light. The zoom lens is at a 90-degree angle to the shaped laser light, and the CCD camera uses the zoom lens to capture images of the solution to be tested in a time series at a speed of 30fps to obtain multiple video frames of single-cell images to be analyzed.

6. The single cell imaging analysis system according to claim 4, characterized in that: The scattered light imaging module further includes a heat-insulating shell and a temperature controller. The heat-insulating shell covers the outside of the sample tray, and the temperature controller is installed inside the heat-insulating shell. The temperature controller is used to control the temperature inside the heat-insulating shell.

7. A single-cell imaging analysis method for determining acute toxicity of water bodies, applied to the single-cell imaging analysis system for determining acute toxicity of water bodies according to any one of claims 1 to 6, characterized in that: The steps include: Capturing images of the solution to be tested in the cuvette in time series to obtain multiple video frames of single cell images to be analyzed; Detecting particles in the single-cell image to be analyzed in each video frame, the particles being formed by light scattering, and locating the particles in each detected video frame to obtain the location coordinates of each particle, and obtaining the movement trajectory of the particle based on each location coordinate; Obtaining a characterization of the range of motion of the particle within the test solution space based on the motion trajectory, and obtaining a characterization of the trajectory change of the particle within the test solution space based on the motion trajectory, and obtaining a conclusion on whether the water body is contaminated based on the characterization of the motion range and / or the characterization of the trajectory change; The particles detected in each video frame are located to obtain the location coordinates of each particle, and the motion trajectory of the particle is obtained based on each location coordinate, specifically: The sub-pixel positioning of the particles in each video frame is performed using a two-dimensional Gaussian function fitting method to obtain the sub-pixel positioning coordinates of each particle; The tracker based on the linear distribution method tracks particles in time series and obtains the motion trajectory of each particle in the time dimension; The characterization of the particle's motion range in the solution space to be tested is obtained based on the motion trajectory, specifically: The motion trajectory includes multiple nodes, each of which is the coordinate of the particle on the trajectory at each time t. The multiple nodes form a time series. The distance from each node to the trajectory forward direction vector is calculated according to the motion range characterization formula. The distance is characterized as the motion range of the particle in the solution space. The motion range characterization formula is: x max ,y max =argmax((x i -x0) 2 +(y i -y0) 2 ),i=1,2,3...,l, Among them, x is the horizontal coordinate of the coordinate time series in the trajectory, y is the vertical coordinate of the coordinate time series in the trajectory, x0 is the horizontal coordinate of the starting point of the coordinate time series in the trajectory, y0 is the vertical coordinate of the starting point of the coordinate time series in the trajectory, x max ,y max is the coordinate of the point with the maximum distance from the starting point of the trajectory time series; l is the length of the coordinate time series in the trajectory, D n-f The distance from each node to the trajectory forward direction vector; In the analysis module, the characterization of the trajectory change of the particle in the solution space to be tested is obtained according to the motion trajectory, specifically: For a one-dimensional pendulum angle time series {x1, x2, ..., x i ,…,x N }, first follow the formula at multiple scales: Where 1≤j≤N / τ, construct a coarse-grained time series {y (τ) }, where τ is the scale factor, and the length of the coarse-grained sequence is M = int(N / τ). The sample entropy values ​​under different scale factors are calculated according to the following process: S1: Construct a set of m-dimensional vectors: Xm(i) = {y i+k :0≤k≤m-1}; S2: For each value of i, calculate the distance between Xm(i) and the remaining vectors Xm(j): d[Xm(i),Xm(j)]=max|y (i+k) -y (j+k) |,(0≤k≤m-1,i,j=1~M-m+1;i≠j); S3: Set the tolerance threshold r (r>0) of the matching process, and calculate d[Xm(i),Xm(j)] for each i <r,(i,j=1~M-m+1; The number B of i≠j) m (i), B m (i) is the number of template matches, and the ratio to the total distance is calculated as: S4: Calculation The average value of: S5: Increase the dimension to m+1, repeat steps S1-S4, and calculate S6: Calculation The average value of: When M is a finite value, the estimated sample entropy when the sequence length is M is recorded as: S7: Repeat steps S1-S6 to obtain sample entropy values ​​at different scales, and finally obtain the multi-scale entropy of the swing angle time series based on the sample entropy values: MsEn_A={τ|SampEn(m,s,τ)}.

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