A method and application for assessing the motility activity of marine parasitic dinoflagellates-Hymenopteran sporozoites using trajectory analysis

Abstract

This invention belongs to the field of microalgal cell motility analysis and detection, specifically relating to a method and application for assessing the spore motility activity of the marine parasitic dinoflagellate *Heliotropium indicum* using trajectory analysis. The method includes steps such as microscopic image acquisition, video tag re-identification for image sequence grouping, image preprocessing (grayscale conversion, filtering and noise reduction, thresholding, hole filling, and overlap segmentation), trajectory detection based on the nearest neighbor algorithm, and visualization of trajectory and velocity data output. The spore cell motility activity detection method proposed in this invention can be used to evaluate the spread and infective capacity of *Heliotropium indicum*, as well as assess the killing effect of different drug types and dosages, providing technical support for taking corresponding control measures. This detection technology is practical, efficient, accurate, and reliable, and is expected to be widely applied in practice, promoting the healthy and sustainable development of aquaculture.

Description

Technical Field

[0001] This invention belongs to the field of microalgal cell motility detection and analysis, specifically involving a method and application for assessing the motility activity of marine parasitic dinoflagellates-Hymenopteran sporozoites using trajectory analysis. Background Technology

[0002] Marine parasitic dinoflagellates, specifically the bloodwort dinoflagellates, are a type of generalized parasitic pathogen that primarily infects marine crustaceans. They can parasitize over 40 species of decapod crabs and shrimp, including important wild and farmed economic species such as the American blue crab, the spider crab, and the Chinese swimming crab (Portunus trituberculatus) and the mud crab (Scylla serrata). These diseases cause epidemics such as "milk disease" and "yellow water disease" in Chinese coastal swimming crab farming, with infection rates exceeding 90%. This can lead to a reduction of over 80% in crab production in affected ponds, and even complete crop failure, causing severe economic losses to the local crab farming industry along my country's coast.

[0003] Hematodinium, belonging to the class Dinophyta, order Parasiticales, family Parasiticaceae, and genus Hematodinium, is the only known stage in the life cycle of this parasitic dinoflagellate that allows for free movement. The spores spread through water, infecting crustacean hosts and causing large-scale epidemics in aquaculture. Currently, the assessment of Hematodinium spore activity relies primarily on manual observation under a microscope to roughly evaluate its motility. However, this method suffers from low efficiency, high subjectivity, and insufficient accuracy, making it difficult to accurately assess the motility of Hematodinium spores at the cellular level. This hinders the provision of necessary technical support for evaluating the spread and infectivity of the pathogen and assessing the effectiveness of drug treatments. Summary of the Invention

[0004] In order to quantitatively assess the motility of *Hemiberlesia oleracea* spores, thereby evaluating the pathogen's spread and infectivity, and accurately assessing the effectiveness of drug treatment, this invention provides a method and application for evaluating the motility of marine parasitic dinoflagellates *Hemiberlesia oleracea* spores using the computer image processing technology Cell-Tracking.

[0005] The technical solution adopted by the present invention to achieve the above objectives is as follows:

[0006] A method for assessing the motility activity of marine parasitic dinoflagellates, specifically *Hemiberlesia lataniae* spores, involves acquiring video images of spore locations in the water body under test, analyzing the movement trajectories of individual spores over a time period, and determining motility activity. The method includes the following steps:

[0007] Step 1: Prepare the sample, observe it under a microscope, and record video.

[0008] Step 2: Tag the video and group the single-frame image sequence using tag re-identification;

[0009] Step 3: Obtain the position of the spores in each group on a continuous single-frame image through image processing to obtain the continuous movement trajectory of the individual spores;

[0010] Step 4: Visualize the movement trajectory and motility data of the spores.

[0011] It uses a high-speed camera to capture video images of the four corners and five central grid areas of the blood cell counting plate; it records 10 frames per second, with each field of view recorded for at least 10 seconds, with a pixel size of 800×800 pixels or higher, a conversion rate of 0.5-1μm / pixel, and an actual field of view of 400μm×400μm or higher.

[0012] The video tagging method involves using video editing software to mask non-target segments in the video that have no analytical value, in order to distinguish them from target segments that can be analyzed.

[0013] The non-target section refers to the overall swaying and unfocused field of view caused by moving the glass slide.

[0014] The occlusion is a layer marker with a set shape and color, and it is occluded according to a preset field of view occlusion ratio, so as to effectively occlude non-target sections.

[0015] The re-identification process of grouping single-frame image sequences includes:

[0016] The re-identification steps are as follows: each frame of the image is identified pixel by pixel. If the pixel color value is within the range of the occlusion pattern color value, it is determined to be an occluded pixel; otherwise, it is a non-occluded pixel. If the cumulative proportion of occluded pixels in a single frame exceeds a set threshold, the frame is determined to contain an occlusion pattern and is a non-target segment; otherwise, it is a target segment.

[0017] b. Grouping steps: Divide the video into segments using non-target segments as segmentation nodes, and extract consecutive single-frame images of the target segments as a group of images; thus obtaining grouped image data for several regions on the hemocytometer counting area.

[0018] The image processing involves sequentially performing the following operations on consecutive single-frame images of each target segment:

[0019] a. Grayscale conversion;

[0020] b. Select the analysis area;

[0021] c. Use a filter to highlight edges;

[0022] d. Automatic thresholding to separate foreground cells from the background;

[0023] e. To fill the cavities in algal cells;

[0024] f. The watershed algorithm is used to segment overlapping cells to obtain a binary image sequence to be analyzed;

[0025] g. The nearest neighbor algorithm is used for trajectory detection to obtain the motion trajectory and coordinate information.

[0026] The visualized data on the movement trajectory and motility activity of marine parasitic dinoflagellates-Hymenopteran spores include:

[0027] By concatenating the trajectory position coordinates, the coordinate time series data of all objects can be obtained;

[0028] Calculate the Euclidean displacement distance of the same spore in adjacent frames within a unit sampling rate to obtain the distance-time correlation sequence;

[0029] Calculate the average velocity and acceleration within a unit sampling rate to assess the motility of individual spores, and sum the average velocities of individual spores to assess the motility of the population of spores.

[0030] Using graphing software, the coordinate data of spores are visualized as movement trajectories to visually demonstrate their trajectory morphology.

[0031] One method for assessing the motility of marine parasitic dinoflagellates, specifically *Hemiberlesia lataniae* spores, is applied to evaluate the spread and infectivity of the pathogen.

[0032] The method is used to evaluate the application of pathogen-inhibiting drugs in pathogen suppression.

[0033] The present invention has the following beneficial effects and advantages:

[0034] This invention provides a method for assessing the motility of marine parasitic dinoflagellates, specifically *Heliotropium indicum* spores, using cell-tracking. This method is highly efficient, objective, and accurate. Compared to manual observation methods, this method is not only more efficient but also more objective and accurate, allowing for a refined assessment of *Heliotropium indicum* spore activity at the cellular level. Furthermore, this method can detect the spore trajectory, providing a more direct observation of spore movement behavior. Using this invention to assess *Heliotropium indicum* spore motility can be used to evaluate the pathogen's spread and infectivity, assess the cytotoxic effects of different drug types and dosages, provide technical support for implementing appropriate control measures, effectively prevent large-scale outbreaks of *Heliotropium indicum* epidemics, and thus ensure the healthy development of economically important crustacean aquaculture. Attached Figure Description

[0035] Figure 1 This is a flowchart of the method of the present invention.

[0036] Figure 2 This is a diagram showing the optimal velocity setting for the trajectory calculated by the nearest neighbor algorithm for the movement of *Hemiberlesia spp.*

[0037] Figure 3 This is a graph showing the results of Cell-Tracking detection of *Helicobacter erythrozoa* spore motility under different salinity levels. Detailed Implementation

[0038] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0040] like Figure 1 The image illustrates a method for assessing the motility of marine parasitic dinoflagellates, specifically *Heliotropium hainanense* spores, using Cell-Tracking. Samples are prepared using a hemocytometer, observed under a microscope, and recorded. A video tagging and re-identification method is employed to group the image sequences, followed by Cell-Tracking for trajectory recognition and processing of the results to quantitatively assess the motility of *Heliotropium hainanense* spores. Details are as follows.

[0041] (1) Microscopic image acquisition

[0042] Shake the water sample thoroughly, then drop 10 μL onto an XB-K-25 hemocytometer (100 μm depth) to prepare the sample. Observe the central region of the counting chamber under a microscope. If the concentration is too high, dilute with seawater (25°C, 30 ppt) to a final concentration of 10. 7 cells / mL or less, 10 6 -10 7The ideal cell / mL ratio is [number of cells / mL]. Use an OLYMPUS-DP73 high-speed camera, running Cell SensStandard software synchronously, capturing 10 frames per second, recording for at least 10 seconds per field of view, with a resolution of 800×800 pixels or higher, a conversion rate of 0.5-1 μm / pixel, and an actual field of view of 400μm×400μm or higher. Record the five central grid areas at the four corners and center of the hemocytometer's counting area. Record one video per sample, containing all five recorded areas. Name the video using date, grouping information, etc., separated by underscores for use in subsequent grouping steps.

[0043] (2) Video tag re-identification for image sequence grouping

[0044] The first step in processing the recorded video is to mark the target segments in five regions. Using video editing software, such as FastStone Capture, the video is processed. After importing the video, non-target segments, including but not limited to those caused by overall visual distortion due to slide movement or out-of-focus areas, are marked. A circular white shape occupying approximately 3 / 4 of the screen is added to the center of the image to obscure these segments, marking them as unwanted. The video is then saved. The edited video is converted into an image sequence using Python's cv2 library. For each frame of the image, the cv2 library is used to identify the proportion of pure white pixels (R>240, G>240, B>240) in the center region of the image. This allows for precise location of the previously set pattern markers. A proportion greater than 90% is recorded as 1, indicating a white pattern has been identified; less than 90% is recorded as 0, indicating no white pattern has been identified, representing the desired target segment. The image sequence is identified as a sequence of 0s and 1s. Images with more than 20 0s following the 1 (i.e., at least 2 seconds) are grouped and extracted into five groups. The image sequences of the five hemocytometer counting regions from each sample are then grouped and stored.

[0045] (3) Process the image sequence and perform trajectory tracking

[0046] The acquired images were divided into 5 groups based on the 5 fields of view required for counting on the hemocytometer. Each group of images was processed and analyzed sequentially. The image sequence was imported into ImageJ (File→Import→Image Sequence...), and then converted to grayscale (Image→Type→8-bit). The counting region within the image sequence was selected (Set Tool→Rectangle Duplicate...). Edges were highlighted using a filter (Process→Fitters→Variance...), with a radius set to the pixel size corresponding to 2μm. Automatic thresholding was used to separate cells from the background (Image→Adjust→AutoThreshold...→Li Method). Holes on algal cells were filled (Process→Binary→FillHoles). Overlapping cells were segmented using the watershed algorithm (Process→Binary→Watershed), ultimately converting the images into a binary image sequence for analysis, used to locate cell centers. Using the nearest neighbor algorithm, Mtrack2 processes the binarized image for cell center trajectory detection (Plugins→Tracking→MTrack2), with specific parameters: minSize = 3.1415*(4.5μm). 2 maxSize = 3.1415 * (11 μm) 2 maxSpeed=(20-30)μm, minTrackLength=0μm, track coordinate information is stored and exported.

[0047] (4) Trajectory visualization and velocity data output

[0048] In the exported text file, columns store different moving objects, and rows store the position of each object in each frame. The data has a maximum of 255 columns; any columns exceeding this limit will start a new line after the frame description ends. Therefore, the data is first transformed and concatenated in Python to obtain the trajectory coordinates of all objects. The Pandas library is used to process the coordinate time series data, calculating the Euclidean displacement distance between adjacent points and dividing by the time difference (0.1s per frame) to obtain sequence information including velocity changes over time. The average velocity is calculated to assess the kinetic activity of individual spores, and the average velocities of individual spores are accumulated to assess the kinetic activity of the spore population. The Matplotlib library is used to visualize the spore coordinate displacement data as motion trajectories, analyzing parameters such as the morphology, velocity, and acceleration of the motion trajectories to visually observe and quantitatively evaluate the spore's motion behavior.

[0049] Example 1:

[0050] The host releases spores into the aquatic environment, which are then sampled, recorded, and converted into image sequences for analysis.

[0051] Import the image sequence into ImageJ (File→Import→Image Sequence...), convert the image sequence to grayscale (Image→Type→8-bit), select the counting regions contained in this image sequence (Set Tool→Rectangle Duplicate...), use a filter to highlight edges (Process→Fitters→Variance...), set the radius to the pixel size corresponding to 2μm, use automatic thresholding to separate cells from the background (Image→Adjust→AutoThreshold...→Li Method), fill the holes appearing on the algal cells (Process→Binary→FillHoles), and use the watershed algorithm to segment overlapping cells (Process→Binary→Watershed). Use the nearest neighbor algorithm, Mtrack2, to process the binarized image sequence for trajectory detection (Plugins→Tracking→MTrack2), with the specific parameters being minSize=3.1415*(4.5μm). 2 maxSize = 3.1415 * (11 μm) 2 The `minTrackLength` parameter is set to 0 μm, and `maxSpeed` is set to different values ​​(5, 10, 20, 30, 40, 50) for trajectory calculation. The trajectory recognition results are then saved. Figure 2 ).

[0052] Figure 2 This is a graph showing the optimal velocity values ​​for the trajectory calculations using the nearest neighbor algorithm for the movement of *Helicobacter erythropoietinus* spores. The graph includes Cell-Tracking detection results for *Helicobacter erythropoietinus* spores under maxSpeed ​​conditions of 5, 10, 20, 30, 40, and 50 μm. Figure 2 The results showed that when the maximum speed was set to 5 μm and 10 μm, the trajectories of *Heliotropium indicum* spores detected by Cell-Tracking were discontinuous, with the same trajectory being identified as multiple trajectories due to excessive speed. When the maximum speed was set to 40 μm and 50 μm, the trajectories detected by Cell-Tracking were chaotic, with different trajectories being identified interchangeably. When the maximum speed was set to 20 μm and 30 μm, the trajectories detected by Cell-Tracking were stable and clear, with 20-30 μm being a stable and optimal range.

[0053] Example 2:

[0054] Seawater samples containing *Heliotropium ovale* spores were collected for motility testing of the spores, and the changes in motility were measured under low salinity (22) and high salinity (30) seawater environments.

[0055] The specific steps are as follows:

[0056] (1) Blood oviduct spores were released from crustacean hosts into the aquatic environment. 50 mL of seawater was collected, and the initial concentration was determined to be 10 on an XB-K-25 hemocytometer. 8 Cells / mL, 3 mL of spore solution was mixed with 27 mL of seawater (21.5 ± 0.5 salinity) at a ratio of 1:9 and added to a 50 mL cell culture flask. The same was done for the 30 salinity seawater treatment group. Each treatment was repeated in triplicate. The prepared sample slides were placed under a microscope for observation, and the field of view was moved to the central counting chamber of the hemocytometer. Images were acquired using an OLYMPUS-DP73 high-speed camera and Cell Sens Standard 1.7 software, with one frame acquired every 0.1 s, and each field of view recorded for 25 s. The resolution was 1185 × 875 pixels, the conversion rate was 0.6 μm / pixel, and the actual field of view was 711 μm × 525 μm. The four corners and the five central grid areas of the hemocytometer counting area were recorded. Two treatments, three replicates, a total of six groups, and 30 fields of view spore motility videos were recorded.

[0057] (2) Import the obtained video into FastStone Capture 9.2 and mark the non-target areas that are out of focus or have overall shaky vision caused by the movement of the glass slide with circular white patterns. Convert the edited video into an image sequence using Python's cv2 library. Use the cv2 library to identify the proportion of pure white pixels (R>240, G>240, B>240) in the center of each frame of the image. Using 90% as the threshold, mark the non-target areas (represented by 0) and target areas (represented by 1) as 01 sequences. Select the 25 images with 0s following the 1 and group them segment by segment.

[0058] (3) The acquired images underwent two processing methods, with three repetitions and five fields of view, resulting in a total of 30 images processed and analyzed sequentially. (File→Import→Image Sequence...), (Image→Type→8-bit), (Set Tool→RectangleDuplicate...), (Process→Fitters→Variance...), with the radius set to the pixel size corresponding to 2μm. (Image→Adjust→Auto Threshold...→Li Method), (Process→Binary→Fill Holes), (Process→Binary→Watershed), converting the images into a binary image sequence to be analyzed. (Plugins→Tracking→MTrack2), with the specific parameter being minSize=3.1415*(4.5μm). 2 maxSize = 3.1415 * (11 μm) 2 maxSpeed ​​= 20μm, minTrackLength = 0μm, track coordinate information is stored and exported.

[0059] (4) Trajectory visualization and velocity data output

[0060] In Python, the exported text data is transformed and concatenated to obtain the trajectory coordinates of all objects. The Pandas library is used to process the coordinate time series data, calculating the Euclidean displacement distance between adjacent points, dividing by the 0.1s time difference between each two points to obtain sequence information including velocity changes over time. The average velocity is calculated to assess the kinetic activity of individual spores, and the average velocities of individual spores are accumulated to assess the kinetic activity of the spore population. The Matplotlib library is used to visualize the spore coordinate displacement data as motion trajectories. Figure 3 ).

[0061] Figure 3 This image shows the results of cell-tracking detection of *Hemiberlesia spp.* sporulation at different salinities. It includes the initial state at 0 h, the sporulation state at 22 salinity after 6 h, and the sporulation state at 30 salinity after 6 h. The evaluation results show that the total sporulation velocity of the spore cells in the grid area at 0 h, 30 salinity after 6 h, and 105.47 μm / s at 22 salinity after 6 h are 747.10 μm / s, 523.63 μm / s, and 105.47 μm / s, respectively. Based on the comprehensive analysis of other sampling areas and replicates, it can be concluded that the motility of *Hemiberlesia spp.* sporulation decreases slowly after 6 hours at 30 salinity, while the decrease is faster at 22 salinity. This indicates that the sporulation cells cannot adapt to low-salinity environments, and the threat to the host is reduced in low-salinity environments.

[0062] Application of the method of this invention: The method of evaluating the motility of Hemangiomae spores using this invention can be used to evaluate the spread and infectivity of the pathogen, assess the killing effect of different types and dosages of drugs, provide technical support for taking corresponding prevention and control measures, effectively avoid the large-scale occurrence of Hemangiomae spore epidemics, and thus ensure the healthy development of economic crustacean farming.

[0063] The embodiments described above will help those skilled in the art to further understand the present invention, but do not limit the present invention in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

Claims

1. A method for assessing the motility activity of marine parasitic dinoflagellate - Crypthecodinium cohnii spores using trajectories, characterized by, The method comprises the following steps: Step 1, preparing a sample, observing under a microscope and collecting a video; Step 2, marking the video, and grouping single-frame image sequences through re-identification by marking; The video marking is to shield the non-target section in the video which has no analysis value by using video editing software, so as to distinguish it from the target section which can be analyzed; the non-target section is the overall shaking of the visual field caused by the moving slide and the unfocused visual field; the shielding is the layer marking of the shape and color, and the shielding is carried out according to the pre-set visual field shielding ratio, so that the non-target section is effectively shielded; the shielding ratio is 3 / 4; The re-identification grouping of single-frame image sequences comprises: a. The step of re-identification: identifying each pixel of each frame of image, if the pixel color value is within the shielding pattern color value range, it is determined as a shielding pixel position, the shielding pattern color value range is: R>240, G>240, B>240, otherwise it is a non-shielding pixel position; if the shielding pixel proportion of a single-frame image exceeds the set threshold value 90%, it is determined that this frame contains a shielding pattern, which is a non-target section, otherwise it is a target section; b. The grouping step: dividing the video section with the non-target section as the division node, and extracting the continuous single-frame images of the target section as a group of images, that is, obtaining more than 20 continuous stable pictures within 2s; a plurality of grouped image data of the counting area on the blood cell counting plate are obtained; Step 3, obtaining the position of each group of spores in the continuous single-frame images through image processing, and obtaining the continuous motion trajectory of single spores; Step 4, visualizing the motion trajectory and motion activity data of spores, comprising: Splicing the trajectory position coordinates to obtain the coordinate time sequence data of all objects; Calculating the Euclidean distance of the same spore in adjacent frames within a unit sampling rate to obtain the distance-time correlation sequence; Calculating the average speed and acceleration within a unit sampling rate to evaluate the motion activity of a single spore, and accumulating the average speed of a single spore to evaluate the motion activity of a group of spores; Using plotting software to visualize the coordinate data of spores as motion trajectories for intuitive display of the trajectory shape.

2. The method for evaluating the motility of marine parasitic dinoflagellate - Okazatospora ovum spore using trajectory according to claim 1, characterized in that, The video images of the four corners and the center five grid areas of the counting area of the blood cell counting plate are collected by using a high-speed camera; 10 frames are recorded per second, each field of view is recorded for at least 10s, the pixel is more than 800*800 pixels, the conversion rate is 0.5-1µm / pixel, and the actual observation field of view range is more than 400µm*400µm.

3. The method for evaluating the motility of marine parasitic dinoflagellate - Okazatospora ovum spore using trajectory according to claim 1, characterized in that, The image processing comprises the following operations on the continuous single-frame images of each target section in sequence: a. Grayscale processing; b. Selecting the analysis area range; c. Highlighting the edge using a filter; the radius is set to 2µm corresponding to the pixel size; d. Automatically dividing the foreground cells and the background by threshold value; the threshold value is divided by Li Method; e. Filling the holes on the algal cells; f. Dividing the overlapping cells by watershed algorithm to obtain a binary image sequence for analysis; g.The nearest neighbor algorithm is used to detect the trajectory and obtain the coordinate information. The maximum speed is set to 20-30 µm.

4. Use of the method according to claim 1, characterized in that The use of the method in evaluating the spread and infection ability of pathogens.

5. The use according to claim 4, characterized in that, The use of the method for evaluating the inhibition of pathogens by pathogen inhibition drugs.

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