Dynamic evaluation method for integrity of endothelial barrier of organ chip

Abstract

The invention relates to the field of image processing, in particular to a dynamic evaluation method and system for the integrity of an endothelial barrier of an organ chip. According to the method, a two-channel fluorescence image of the cell nucleus and the intercellular connexin is obtained through a confocal microscope, and four key characteristic parameters including the cell coverage rate, the cell nucleus distribution uniformity, the intercellular connexin strength and the connexin integrity are extracted on the basis of an image processing technology. A comprehensive evaluation index is obtained by fusing the parameters and is compared with a preset threshold value, so that whether the endothelial barrier is complete or not is judged. According to the method, interference of a traditional permeability detection method is avoided, multi-dimensional, quantitative and high-reliability evaluation of the barrier structure is achieved, and the method is suitable for various organ chip models.

Description

Technical Field

[0001] This invention relates to the field of image processing, and more specifically to a method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip. Background Technology

[0002] The rapid development of organ-on-a-chip technology has placed higher demands on the functional assessment of in vitro biological models. As the core functional unit, the accurate assessment of the integrity of the endothelial barrier is crucial. Currently, one of the standard methods in this field is based on the detection of permeation function using fluorescent dyes. This method requires adding exogenous tracer dyes into the chip chamber during detection, and indirectly assessing barrier function by measuring the leakage amount in real time. However, this method has inherent drawbacks: First, the addition of exogenous dyes can contaminate the culture system itself, and during permeation, they may interfere with or be toxic to cell activity, thus affecting the functional state of the barrier itself, resulting in the measured leakage data not accurately reflecting the true physiological condition of the barrier; second, this method relies on a single permeability indicator, making the judgment criteria rigid and difficult to adapt to different types and culture standards of organ-on-a-chip models.

[0003] This invention proposes an assessment strategy based on direct morphological analysis. This strategy, through specific fluorescent staining of endothelial barrier cells, shifts the focus of assessment from potentially compromised dynamic functional measurements to the direct observation and quantitative analysis of the stability and coverage of the cell structure itself. The advantage of this method is that even though staining may terminate cell activity, it preserves the complete morphological structure of the sample at the moment of staining to the greatest extent possible. This structural information is stable under visual conditions and unaffected by subsequent analysis, providing an intuitive and reliable data foundation for assessment. More importantly, this method, by extracting multidimensional parameters such as cell coverage, distribution uniformity, and the strength and integrity of connective proteins, can comprehensively reflect the structural state of the barrier. Furthermore, the weights and thresholds of various parameters can be flexibly adjusted according to the specific requirements of different types of organ-on-a-chip, significantly enhancing its versatility and adaptability. Summary of the Invention

[0004] This invention provides a method for dynamic assessment of endothelial barrier integrity on organ-on-a-chip, in order to solve the existing problems: existing organ-on-a-chip endothelial barrier assessment techniques based on dye penetration methods are inaccurate, incomplete and poorly adaptable due to interference from exogenous dyes and reliance on a single functional indicator.

[0005] The present invention provides a method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip, which employs the following technical solution:

[0006] In a first aspect, the present invention provides a method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip, the method comprising the following steps:

[0007] High-resolution images of endothelial barrier cells were acquired using a confocal microscope under both the fluorescence channel and the transmission light channel. Dual labeling of intercellular junction proteins and cell nuclei was performed, and pixels in the cell nucleus region and the intercellular junction protein region were labeled to obtain fluorescence images that simultaneously labeled pixels in the cell nucleus and intercellular junction protein regions.

[0008] Based on the gray values ​​of the translucent images of endothelial barrier cells, the pixels in the translucent images of endothelial barrier cells are clustered into high gray value classes and low gray value classes. The cell coverage W is obtained by the ratio of the number of pixels in the low gray value class to the total number of pixels in the translucent images of endothelial barrier cells.

[0009] The location and number of pixels of the cell nucleus region in the fluorescence image are detected. The Delaunay triangulation is used to obtain the cell nucleus regions that are adjacent to each cell nucleus region. The image is divided into N windows with the farthest distance of all adjacent cell nucleus regions as the window side length. The cell nucleus distribution uniformity parameter is obtained based on the information entropy of the number of cell nucleus regions in different windows.

[0010] The non-nuclear region between the nearest connected domain of each nucleus region is selected and recorded as the connective protein template region between two adjacent nuclei. The mean gray value of all pixels marked as connective proteins in the connective protein template region is used as the intercellular connective protein strength between two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connective protein strength is calculated and normalized to obtain the intercellular connective protein strength parameter.

[0011] Set up an image grid, filter all grids containing pixels marked as intercellular connective proteins, use the pixel with the lowest gray value among the pixels marked as intercellular connective proteins in each grid as the seed point, use the gray values ​​of the eight neighboring pixels of the seed point as the growth condition and update the position of the seed point, find the suspected weak region connected domain in each grid, and obtain the intercellular connective protein integrity parameter based on the ratio of the number of pixels in the suspected weak region connected domain to the number of pixels in the grid.

[0012] The probability parameters of endothelial barrier integrity are obtained by combining parameters such as cell coverage, uniformity of cell nuclear distribution, strength of intercellular junction proteins, and integrity of intercellular junction proteins.

[0013] Set a threshold for the probability parameter of endothelial barrier integrity, and compare the probability parameter of endothelial barrier integrity with the threshold to determine whether the endothelial barrier is intact.

[0014] Furthermore, the method of acquiring high-resolution images of endothelial barrier cells using a confocal microscope under both the fluorescence channel and the transmission channel, performing dual-label staining on intercellular junction proteins and the cell nucleus, and labeling pixels in both the nuclear region and the intercellular junction protein region to obtain fluorescence images simultaneously labeled with pixels in both regions, includes the following specific methods:

[0015] Endothelial barrier cells were double-labeled using antibodies targeting specific intercellular junction proteins and DAPI nuclear dye. High-resolution images were then acquired using confocal microscopy under both fluorescence and transmission light channels, ensuring all imaging parameters remained consistent. The image size was [size missing]. The images were analyzed, and the areas showing fluorescence in the grayscale image scanned by the 405nm laser were marked as the cell nucleus region, and the areas showing fluorescence in the grayscale image scanned by the 488nm laser were marked as the intercellular protein region, thus obtaining the transluminal image and fluorescence image of the endothelial barrier cells.

[0016] Furthermore, the specific method for clustering the pixels of the endothelial barrier cell transmissive image into high gray value classes and low gray value classes based on the gray value of the endothelial barrier cell transmissive image, and obtaining the cell coverage W based on the ratio of the number of pixels in the low gray value class to the total number of pixels in the endothelial barrier cell transmissive image, includes the following:

[0017] The grayscale values ​​of each pixel in the translucent image of endothelial barrier cells are statistically analyzed, and the maximum and minimum values ​​are used as the initial cluster centers. Each pixel is assigned to the category corresponding to the cluster center whose grayscale value is closer to its own. The mean grayscale value of all pixels in each category is recalculated and updated as the new cluster center. The above pixel division and cluster center update process is repeated until the iteration termination condition is met: after a certain iteration, the change in the grayscale values ​​of the two newly calculated cluster centers compared with the previous one is less than 1% of the current cluster center's grayscale value. Based on the last pixel division result, all pixels in the image are clustered into two categories: high grayscale value and low grayscale value. The ratio of the number of pixels in the low grayscale value category to the total number of pixels in the translucent image of endothelial barrier cells is used as the cell coverage rate W. If the ratio of the number of pixels in the category with fewer pixels to the total number of pixels in the translucent image of endothelial barrier cells is less than or equal to 5%, the cell coverage rate W = 1.

[0018] Furthermore, the method for detecting the location and number of pixels of the cell nucleus region in the fluorescence image, using a Delaunay triangulation to obtain the cell nucleus regions adjacent to each cell nucleus region, dividing the image into N windows with the farthest distance among all adjacent cell nucleus regions as the window side length, and obtaining the cell nucleus distribution uniformity parameter based on the information entropy of the number of cell nucleus regions in different windows, includes the following specific methods:

[0019] In the fluorescence image, the geometric center of the connected region of each pixel labeled as a cell nucleus is calculated and used as the center point of each cell nucleus. The Delaunay triangulation algorithm is applied to construct a triangular mesh based on the coordinates of all cell nucleus center points. The adjacency relationship between pairs of cell nucleus centers is determined based on the connectivity relationships formed by this triangulation. All connected regions of pixels labeled as cell nucleus regions with adjacency relationships are obtained, and the maximum distance among all pairs of cell nucleus center points is used as the determining factor. The entire fluorescence image is divided according to the side length of the analysis window. The window is segmented, and the number of connected components in the cell nucleus region of each window is recorded. The uniformity parameter of cell nucleus distribution is obtained by the standardized entropy of the distribution of the number of connected components in the cell nucleus region of different windows. The specific method is as follows:

[0020]

[0021] In the formula, A parameter representing the uniformity of cell nucleus distribution, where N represents the number of windows. This represents the total number of connected components in the pixel region of the cell nucleus. Indicates the first The number of connected components in the cell nucleus region of each window.

[0022] Further, the method of selecting the non-nuclear region between the nearest connected domains of each nuclear region and marking this region as the connective protein template region between two adjacent nuclei, using the average gray value of all pixels marked as connective proteins in the connective protein template region as the intercellular connective protein strength between two adjacent nuclei, calculating and normalizing the Pearson correlation coefficient between intercellular distance and intercellular connective protein strength to obtain the intercellular connective protein strength parameter, includes the following specific methods:

[0023] Using the geometric center of the nearest connected domain of each adjacent nucleus region as a baseline, a series of parallel lines are generated by translating pixel by pixel in the vertical direction on both sides of this baseline, until the newly generated parallel lines no longer intersect with the connected domains of two nucleus regions simultaneously. The first intersection point that simultaneously passes through two nuclei and intersects with the connected domains of two nucleus regions is recorded. The pixel containing the line segment between the two points is recorded as the effective connection region on the parallel line. The set of all pixels on all effective connection regions is recorded as the connector protein template region between two adjacent nuclei. The average gray value of all pixels marked as connector proteins in the connector protein template region is used as the intercellular connector protein strength between two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connector protein strength is calculated and normalized. The specific method for obtaining the intercellular connector protein strength parameter is as follows:

[0024] ;

[0025] ;

[0026] In the formula, This represents the linear relationship between internuclear distance and the strength of intercellular junction proteins. This represents the total number of connected components in the pixel region of the cell nucleus. This represents the distance between the connected regions of the i-th cell nucleus pixel and the geometric center of the connected region of its nearest neighbor cell nucleus pixel. This represents the intercellular protein strength between the connected domain of the i-th cell nucleus pixel and the connected domain of its nearest neighboring cell nucleus pixel. This represents the mean distance between the geometric centers of the connected regions of each pixel in the cell nucleus region and the connected regions of its nearest neighboring pixel in the cell nucleus region. This represents the average intercellular protein intensity between the connected regions of each pixel in the cell nucleus region and the connected regions of its nearest neighboring cell nucleus pixel. This represents the strength parameter of intercellular junction proteins.

[0027] Further, the process of setting an image grid, filtering all grids containing pixels marked as intercellular connective proteins, using the pixel with the lowest gray value among the marked intercellular connective protein pixels in each grid as the seed point, updating the seed point position based on the gray values ​​of its eight neighboring pixels, finding suspected weak regions in each grid, and obtaining the intercellular connective protein integrity parameter based on the ratio of the number of pixels in the suspected weak regions to the total number of pixels in the grid, includes the following specific methods:

[0028] Set an image grid in the fluorescence image to divide the fluorescence image into equal parts of size. A grid is created that records all grid points covering intercellular connection protein pixels, denoted as the intercellular protein grid. The total number of pixels within the y-th grid is calculated. Average gray value of each pixel of intercellular connection protein The intercellular protein pixel with the lowest gray value in each intercellular protein grid is recorded as the first pixel of the suspected weak region. Within the eight-neighborhood of the first pixel, if any intercellular protein pixel has a gray value lower than... For pixels with a grayscale value lower than 0, the corresponding pixel will be classified as a pixel with a grayscale value lower than 0. The pixels marked as suspected weak areas in the grid are then analyzed to determine whether there are any intercellular protein pixels with a gray value lower than the eight neighboring pixels of the newly marked suspected weak areas. If there are pixels with gray values ​​lower than 1000, For pixels with a grayscale value lower than 0, the corresponding pixel will be classified as a pixel with a grayscale value lower than 0. The pixels marked as suspected weak areas in the grid are repeated. The process of eight-neighbor judgment and marking suspected weak areas is repeated until all pixels in the grid have been judged. The specific method for calculating the integrity parameters of intercellular connection proteins is as follows:

[0029]

[0030] Parameters representing the integrity of intercellular junction proteins. Indicates a total of A grid covering pixels of intercellular connection proteins. Indicates the first The number of pixels in suspected weak areas within each grid. Indicates the first Within each grid Individual intercellular connection protein pixels.

[0031] Furthermore, the specific method for obtaining the endothelial barrier integrity probability parameter by combining cell coverage, uniformity of cell nuclear distribution, intercellular junction protein strength, and intercellular junction protein integrity parameters includes:

[0032]

[0033] P represents the probability parameter of endothelial barrier integrity, and W represents cell coverage. A parameter representing the uniformity of cell nucleus distribution. This represents a parameter indicating the strength of intercellular junction proteins. This represents a parameter indicating the integrity of intercellular junction proteins.

[0034] Furthermore, the specific method for setting a threshold for the endothelial barrier integrity probability parameter and comparing the endothelial barrier integrity probability parameter with the threshold for determining whether the endothelial barrier is intact includes:

[0035] Set appropriate thresholds based on the specific uses of organ-on-a-chip. As a specific requirement for assessing endothelial barrier integrity, if the endothelial barrier integrity probability parameter Greater than the set threshold If the endothelial barrier integrity is considered good, then the probability parameter of endothelial barrier integrity is considered to be... Less than or equal to the set threshold This indicates poor endothelial barrier integrity.

[0036] A second aspect of the present invention provides an organ-on-a-chip dynamic assessment system for endothelial barrier integrity. This system includes a data acquisition module, a cell coverage calculation module, a cell nucleus distribution uniformity parameter calculation module, an intercellular junction protein strength parameter calculation module, an intercellular junction protein integrity parameter calculation module, an endothelial barrier integrity probability parameter calculation module, and an endothelial barrier integrity judgment module, wherein:

[0037] The data acquisition module is used to acquire high-resolution images of endothelial barrier cells under both the fluorescence channel and the transmission light channel using a confocal microscope. It performs dual labeling staining on intercellular junction proteins and cell nuclei, and labels the pixels in the cell nucleus region and the pixels in the intercellular junction protein region, and obtains fluorescence images that simultaneously label the pixels in the cell nucleus and the intercellular junction protein region.

[0038] The cell coverage calculation module is used to cluster the pixels of the endothelial barrier cell transparent image into high gray value class and low gray value class according to the gray value of the endothelial barrier cell transparent image, and obtain the cell coverage W according to the ratio of the number of pixels in the low gray value class to the total number of pixels in the endothelial barrier cell transparent image.

[0039] The cell nucleus distribution uniformity parameter calculation module is used to detect the position and number of pixels of the cell nucleus region in the fluorescence image. It uses Delaunay triangulation to obtain the cell nucleus regions that are adjacent to each cell nucleus region. The image is divided into N windows with the farthest distance of all adjacent cell nucleus regions as the window side length. The cell nucleus distribution uniformity parameter is obtained based on the information entropy of the number of cell nucleus regions in different windows.

[0040] The intercellular connective protein strength parameter calculation module is used to select the non-nuclear region between the nearest connected domain of the nuclear region that is adjacent to each nuclear region, and record this region as the connective protein template region between two adjacent nuclei. The average gray value of all pixels marked as connective proteins in the connective protein template region is used as the intercellular connective protein strength between two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connective protein strength is calculated and normalized to obtain the intercellular connective protein strength parameter.

[0041] The intercellular connective protein integrity parameter calculation module is used to filter all grids containing pixels marked as intercellular connective proteins. The pixel with the lowest gray value among the pixels marked as intercellular connective proteins in each grid is used as the seed point. The gray values ​​of the eight neighboring pixels of the seed point are used as the growth condition and the position of the seed point is updated. The module finds the suspected weak region connected domains in each grid and obtains the intercellular connective protein integrity parameter based on the ratio of the number of pixels in the suspected weak region connected domains to the number of pixels in the grid.

[0042] The endothelial barrier integrity probability parameter calculation module is used to combine cell coverage, cell nucleus distribution uniformity parameters, intercellular junction protein strength parameters, and intercellular junction protein integrity parameters to obtain endothelial barrier integrity probability parameters.

[0043] The endothelial barrier integrity assessment module is used to set a threshold for the endothelial barrier integrity probability parameter and compare the endothelial barrier integrity probability parameter with the threshold to determine whether the endothelial barrier is intact.

[0044] In a third aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip.

[0045] In a fourth aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for dynamic assessment of organ-on-a-chip endothelial barrier integrity.

[0046] The beneficial effects of the technical solution of the present invention are:

[0047] It enables automatic identification and quantification of cell coverage areas, avoiding subjective errors from manual interpretation and improving the objectivity and repeatability of the assessment;

[0048] Dual-channel fluorescence imaging and specific wavelength labeling ensured accurate differentiation between cell nuclei and connective proteins, providing a reliable data foundation for subsequent analysis;

[0049] An adaptive clustering method is used to distinguish cells from the background, effectively eliminating noise interference and improving the accuracy of cell coverage calculation.

[0050] By combining Delaunay triangulation with information entropy, the uniformity of cell nucleus distribution can be objectively quantified, avoiding the subjectivity of artificial division and enhancing the reliability of the analysis.

[0051] By constructing a template region for a connector protein and calculating the distance-strength correlation, the functional compensatory capacity of intercellular connections was revealed, providing a dynamic functional assessment indicator.

[0052] By employing a gridding and region growing algorithm, local weak areas are actively identified, which compensates for the shortcomings of overall average analysis and improves the sensitivity of defect detection.

[0053] By integrating multiple parameters to comprehensively evaluate the barrier status, the limitations of a single indicator are avoided, and the comprehensiveness and robustness of the evaluation are improved.

[0054] Setting adjustable thresholds allows the evaluation results to be flexibly adjusted according to specific application scenarios, enhancing the applicability and practicality of the method. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart of the steps of an organ-on-a-chip dynamic assessment method for endothelial barrier integrity according to the present invention.

[0057] Figure 2 This is a structural block diagram of an organ-on-a-chip dynamic assessment system for endothelial barrier integrity according to the present invention. Detailed Implementation

[0058] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an organ-on-a-chip dynamic assessment method for endothelial barrier integrity proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0059] 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.

[0060] The following description, in conjunction with the accompanying drawings, details the specific scheme of the organ-on-a-chip dynamic assessment method for endothelial barrier integrity provided by the present invention.

[0061] Please see Figure 1 It illustrates the first objective of the present invention, a flowchart of a method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip, the method comprising the following steps:

[0062] Step S001: High-resolution images of endothelial barrier cells were acquired using a confocal microscope under both the fluorescence channel and the transmission channel. Intercellular junction proteins and cell nuclei were double-labeled and stained, and pixels in the cell nucleus region and the intercellular junction protein region were labeled. Fluorescence images of pixels in both the cell nucleus and the intercellular junction protein region were obtained.

[0063] To assess the integrity of the endothelial barrier on an organ-on-a-chip, specific images of endothelial barrier cells are first required as a basis for judgment.

[0064] Specifically, high-resolution images of endothelial barrier cells were acquired using a confocal microscope under both the fluorescence channel and the transmitted light channel. Dual labeling of intercellular junction proteins and the cell nucleus was performed, and pixels in both the nuclear and intercellular junction protein regions were labeled. Fluorescence images simultaneously labeled with pixels in both regions were then obtained. The specific method is as follows:

[0065] Endothelial barrier cells were double-labeled using antibodies targeting specific intercellular junction proteins and DAPI nuclear dye. High-resolution images were then acquired using confocal microscopy under both fluorescence and transmission light channels, ensuring all imaging parameters remained consistent. The image size was [size missing]. The images were analyzed, and the areas showing fluorescence in the grayscale image scanned by the 405nm laser were marked as the cell nucleus region, and the areas showing fluorescence in the grayscale image scanned by the 488nm laser were marked as the intercellular protein region, thus obtaining the transluminal image and fluorescence image of the endothelial barrier cells.

[0066] It should be noted that the image size in this embodiment is 1080*1080. This invention does not specifically limit the image size, but it is necessary to ensure that the area occupied by the endothelial barrier in the image is at least 90% of the image area.

[0067] Step S002: Based on the gray values ​​of the endothelial barrier cell transmissive image, cluster the pixels of the endothelial barrier cell transmissive image into a high gray value class and a low gray value class. Obtain the cell coverage W based on the ratio of the number of pixels in the low gray value class to the total number of pixels in the endothelial barrier cell transmissive image.

[0068] It should be noted that the integrity of the endothelial barrier is mainly determined by the continuity of intercellular junction structures and the intensity of protein expression. These microscopic morphological features can be directly presented and quantified in images through specific fluorescent staining. Therefore, by analyzing cell morphology, distribution density, and fluorescence signals of junctional proteins, the integrity of barrier function can be assessed non-invasively and with high precision, thereby enabling reliable inference of its physiological state.

[0069] It is further important to note that only by confirming that cells have formed a continuous and highly covered monolayer structure can the fundamental failure of the barrier due to cell loss be effectively ruled out. This avoids misleading subsequent microscopic analyses of cell distribution uniformity and the functional strength of connective proteins. In transmitted light imaging, the absorption and scattering of light by cell structures significantly reduces the intensity of transmitted light, resulting in a lower grayscale value in the corresponding imaging area. Conversely, blank areas without cell coverage exhibit high grayscale values ​​because of their good light transmittance and almost no attenuation of transmitted light. Given this grayscale difference, the image is segmented into two parts: a cell-covered area and a non-cell-covered area. The coverage degree of the cell layer is then accurately assessed by calculating their area ratio.

[0070] Specifically, based on the grayscale values ​​of the endothelial barrier cell transmissive images, the pixels in the images are clustered into high grayscale value classes and low grayscale value classes. The cell coverage W is obtained based on the ratio of the number of pixels in the low grayscale value class to the total number of pixels in the endothelial barrier cell transmissive images. The specific method is as follows:

[0071] The grayscale values ​​of each pixel in the translucent image of endothelial barrier cells are statistically analyzed, and the maximum and minimum values ​​are used as the initial cluster centers. Each pixel is assigned to the category corresponding to the cluster center whose grayscale value is closer to its own. The mean grayscale value of all pixels in each category is recalculated and updated as the new cluster center. The above pixel division and cluster center update process is repeated until the iteration termination condition is met: after a certain iteration, the change in the grayscale values ​​of the two newly calculated cluster centers compared with the previous one is less than 1% of the current cluster center's grayscale value. Based on the last pixel division result, all pixels in the image are clustered into two categories: high grayscale value and low grayscale value. The ratio of the number of pixels in the low grayscale value category to the total number of pixels in the translucent image of endothelial barrier cells is used as the cell coverage rate W. If the ratio of the number of pixels in the category with fewer pixels to the total number of pixels in the translucent image of endothelial barrier cells is less than or equal to 5%, the cell coverage rate W = 1.

[0072] It should be noted that this step achieves automatic identification and quantification of cell-covered areas. Its core lies in utilizing the inherent differences in light transmittance between cell structures and background areas: cellular regions significantly reduce transmitted light intensity due to light absorption and scattering, exhibiting lower grayscale values; while cell-free blank areas have good light transmittance, displaying higher grayscale values. Based on this physical characteristic, this method uses an unsupervised clustering algorithm to automatically classify the pixel grayscale of the transmitted light image, dividing the image into cell-covered and cell-free areas.

[0073] Step S003: Detect the position and number of pixels of the cell nucleus region in the fluorescence image, use Delaunay triangulation to obtain the cell nucleus regions that are adjacent to each cell nucleus region, use the farthest distance of all adjacent cell nucleus regions as the window side length, divide the image into N windows, and obtain the cell nucleus distribution uniformity parameter based on the information entropy of the number of cell nucleus regions in different windows.

[0074] It is important to note that the integrity of the endothelial barrier fundamentally depends on the spatial order of its cellular monolayer. Therefore, the uniformity of cell distribution is a key criterion for assessing barrier function. A uniformly spread cell layer ensures continuous physical coverage, avoiding inherent permeability defects caused by localized sparseness. Simultaneously, this structural uniformity is also the basis for the stable mechanical coupling of intercellular junction proteins, enabling the barrier to uniformly distribute external stress and maintain overall stability. Theoretically, all cell nuclei are completely uniformly distributed within the cell layer. However, due to external factors, this uniformity changes. Therefore, calculating the uncertainty in the location of this distribution is used as a basis for judging its uniformity.

[0075] Specifically, the location and number of pixels in the cell nucleus region of the fluorescence image are detected. A Delaunay triangulation is used to obtain the cell nucleus regions that are adjacent to each cell nucleus region. The image is divided into N windows, with the farthest distance between all adjacent cell nucleus regions used as the window side length. The uniformity parameter of cell nucleus distribution is obtained based on the information entropy of the number of cell nucleus regions in different windows. The specific method is as follows:

[0076] In the fluorescence image, the geometric center of the connected region of each pixel labeled as a cell nucleus is calculated and used as the center point of each cell nucleus. The Delaunay triangulation algorithm is applied to construct a triangular mesh based on the coordinates of all cell nucleus center points. The adjacency relationship between pairs of cell nucleus centers is determined based on the connectivity relationships formed by this triangulation. All connected regions of pixels labeled as cell nucleus regions with adjacency relationships are obtained, and the maximum distance among all pairs of cell nucleus center points is used as the determining factor. The entire fluorescence image is divided according to the side length of the analysis window. The window is segmented, and the number of connected components in the cell nucleus region of each window is recorded. The uniformity parameter of cell nucleus distribution is obtained by the standardized entropy of the distribution of the number of connected components in the cell nucleus region of different windows. The specific method is as follows:

[0077]

[0078] In the formula, A parameter representing the uniformity of cell nucleus distribution, where N represents the number of windows. This represents the total number of connected components in the pixel region of the cell nucleus. Indicates the first The number of connected components in the cell nucleus region of each window.

[0079] It is important to note that quantifying the homogeneity of the endothelial cell layer by analyzing the spatial distribution of cell nuclei is a key structural indicator for assessing barrier integrity. This method utilizes a Delaunay triangulation to objectively define the spatial adjacency relationships between cells, ensuring the accuracy of adjacency determination and avoiding the subjectivity of manual division. Using the farthest adjacency distance as the window size is highly advantageous: it adapts to the current cell distribution density, ensuring that each analysis window covers a typical local region, making the analysis results comparable under different samples or density conditions. Finally, homogeneity is assessed by calculating and standardizing the information entropy of cell number distribution within each window. The higher the information entropy, the more random and uneven the cell distribution across different windows; conversely, the larger the p1 value obtained after standardization and negative sign processing, the more uniform the cell distribution. This parameter can sensitively capture the distribution heterogeneity caused by cell aggregation or sparseness, and this structural disorder is often an early sign of impaired barrier function.

[0080] Step S004: Select the non-nuclear region between the nearest connected domain of each nucleus region that is adjacent to it, and record this region as the connective protein template region between two adjacent nuclei. Use the average gray value of all pixels marked as connective proteins in the connective protein template region as the intercellular connective protein strength between two adjacent nuclei. Calculate and normalize the Pearson correlation coefficient between intercellular distance and intercellular connective protein strength to obtain the intercellular connective protein strength parameter.

[0081] It's important to note that structural homogeneity only reflects the spatial arrangement of cells, while the barrier's function essentially depends on the effective adhesion formed by intercellular junction proteins. Increased internuclear distance indicates active cell contraction or passive separation, representing structural separation; junction protein strength represents the functional compensation the cell makes to maintain connections. A negative correlation between the two indicates that cells are resisting external forces and striving to maintain barrier continuity—a healthy stress response demonstrating the barrier's self-stabilizing capacity.

[0082] Specifically, a non-nuclear region adjacent to the nearest connected domain of each nucleus region is selected and designated as the connective protein template region between two adjacent nuclei. The average gray value of all pixels marked as connective proteins in the connective protein template region is used as the intercellular connective protein strength between two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connective protein strength is calculated and normalized to obtain the intercellular connective protein strength parameter. The specific methods include:

[0083] Using the geometric center of the nearest connected domain of each adjacent nucleus region as a baseline, a series of parallel lines are generated by translating pixel by pixel in the vertical direction on both sides of this baseline, until the newly generated parallel lines no longer intersect with the connected domains of two nucleus regions simultaneously. The first intersection point that simultaneously passes through two nuclei and intersects with the connected domains of two nucleus regions is recorded. The pixel containing the line segment between the two points is recorded as the effective connection region on the parallel line. The set of all pixels on all effective connection regions is recorded as the connector protein template region between two adjacent nuclei. The average gray value of all pixels marked as connector proteins in the connector protein template region is used as the intercellular connector protein strength between two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connector protein strength is calculated and normalized. The specific method for obtaining the intercellular connector protein strength parameter is as follows:

[0084] ;

[0085]

[0086] In the formula, This represents the linear relationship between internuclear distance and the strength of intercellular junction proteins. This represents the total number of connected components in the pixel region of the cell nucleus. This represents the distance between the connected regions of the i-th cell nucleus pixel and the geometric center of the connected region of its nearest neighbor cell nucleus pixel. This represents the intercellular protein strength between the connected domain of the i-th cell nucleus pixel and the connected domain of its nearest neighboring cell nucleus pixel. This represents the mean distance between the geometric centers of the connected regions of each pixel in the cell nucleus region and the connected regions of its nearest neighboring pixel in the cell nucleus region. This represents the average intercellular protein intensity between the connected regions of each pixel in the cell nucleus region and the connected regions of its nearest neighboring cell nucleus pixel. This represents the strength parameter of intercellular junction proteins.

[0087] It's important to note that this step assesses the functional integrity of the endothelial barrier, not just its static structural state. By quantifying the statistical relationship between internuclear distance and the strength of corresponding connective proteins, it reveals the cells' compensatory ability to maintain barrier integrity in the face of external stress or stimuli. Its advantages are: First, by generating a "connective protein template region," it precisely defines the effective area to be analyzed between each pair of adjacent cells, avoiding the inclusion of the entire intercellular region or irrelevant background in the calculation, ensuring data accuracy and specificity; second, it uses the Pearson correlation coefficient ρ to objectively measure the linear relationship between structural separation (increased distance) and functional compensation (changes in connective protein strength). The formula logic is: a healthy, resilient barrier compensates for slight cell separation by enhancing connective protein expression or aggregation, resulting in a negative correlation between distance and strength (ρ is negative). This is achieved through normalization transformation. Mapping the correlation coefficient ρ to the [0,1] interval, and making this ideal negative correlation (ρ→-1) correspond to a higher parameter value ( →1), thus intuitively indicating good barrier function strength; conversely, a positive correlation or no correlation (ρ≥0) indicates barrier dysfunction or loss of compensatory ability, corresponding to a lower p_2 value. This provides a dynamic and sensitive indicator of functional strength.

[0088] Step S005: Set up an image grid, filter all grids containing pixels marked as intercellular connective proteins, use the pixel with the lowest gray value among the pixels marked as intercellular connective proteins in each grid as the seed point, use the gray values ​​of the eight neighboring pixels of the seed point as the growth condition and update the seed point position, find the suspected weak region connected domain in each grid, and obtain the intercellular connective protein integrity parameter based on the ratio of the number of pixels in the suspected weak region connected domain to the number of pixels in the grid.

[0089] It should be noted that local integrity analysis, through precise subunit-level quantification of each cell junction, enables spatial analysis of the distribution, continuity, and signal intensity of intercellular connection proteins, effectively identifying microscopic structural weaknesses that cannot be reflected by statistical averages. This microscopic scale can mutually verify and complement macroscopic conclusions based on overall correlation, compensating for potential biases arising from relying solely on statistical trends, and ultimately providing a more comprehensive, reliable, and spatially resolved assessment of the integrity of endothelial barrier function.

[0090] Specifically, an image grid is set up, and all grids containing pixels marked as intercellular connective proteins are selected. The pixel with the lowest gray value among the marked intercellular connective proteins in each grid is used as the seed point. The gray values ​​of the eight neighboring pixels of the seed point are used as growth conditions to update the seed point's position. Suspected weak regions in each grid are found, and the integrity parameter of intercellular connective proteins is obtained based on the ratio of the number of pixels in the suspected weak regions to the total number of pixels in the grid. The specific methods include:

[0091] Set an image grid in the fluorescence image to divide the fluorescence image into equal parts of size. A grid is created that records all grid points covering intercellular connection protein pixels, denoted as the intercellular protein grid. The total number of pixels within the y-th grid is calculated. Average gray value of each pixel of intercellular connection protein The intercellular protein pixel with the lowest gray value in each intercellular protein grid is recorded as the first pixel of the suspected weak region. Within the eight-neighborhood of the first pixel, if any intercellular protein pixel has a gray value lower than... For pixels with a grayscale value lower than 0, the corresponding pixel will be classified as a pixel with a grayscale value lower than 0. The pixels marked as suspected weak areas in the grid are then analyzed to determine whether there are any intercellular protein pixels with a gray value lower than the eight neighboring pixels of the newly marked suspected weak areas. If there are pixels with gray values ​​lower than 1000, For pixels with a grayscale value lower than 0, the corresponding pixel will be classified as a pixel with a grayscale value lower than 0. The pixels marked as suspected weak areas in the grid are repeated. The process of eight-neighbor judgment and marking suspected weak areas is repeated until all pixels in the grid have been judged. The specific method for calculating the integrity parameters of intercellular connection proteins is as follows:

[0092]

[0093] In the formula, Parameters representing the integrity of intercellular junction proteins. Indicates a total of A grid covering pixels of intercellular connection proteins. Indicates the first The number of pixels in suspected weak areas within each grid. Indicates the first Within each grid Individual intercellular connection protein pixels.

[0094] It should be noted that this invention does not specifically limit the size of the grid. The purpose of selecting the grid size is to reduce the influence of noise. In this embodiment, the grid size is 108*108 pixels. This step aims to actively detect local structural defects in the connecting protein network of the endothelial barrier at the microscopic scale. These defects may be masked in the overall average intensity analysis, but they are potential starting points for barrier function failure. The advantages of this method are: by decomposing the global image into local analysis units through gridding, it ensures that the entire barrier region is scanned without omission; then, an algorithm based on region growing is used, with the average gray value of the connecting protein pixels in each grid as a dynamic threshold, starting from the darkest pixel, to search for and mark all continuously distributed low-intensity pixel regions (i.e., suspected weak regions). This method can accurately identify tiny regions of insufficient or broken protein expression, rather than relying solely on the overall gray value; the formula logic is: first, calculate the percentage of weak region pixels in each grid. The higher this ratio, the worse the local integrity of the mesh. Then, the average of this ratio for all meshes is calculated to obtain the overall weakness. Finally, this overall weakness is subtracted from 1 to convert the index into an intuitive integrity parameter. The closer the value is to 1, the better the overall continuity of the linker protein network and the fewer local weak points; conversely, the lower the value, the more microscopic defects exist in the barrier and the poorer its integrity.

[0095] Step S006: Combine parameters such as cell coverage, uniformity of cell nucleus distribution, strength of intercellular junction proteins, and integrity of intercellular junction proteins to obtain the probability parameter of endothelial barrier integrity.

[0096] It should be noted that the integrity of the endothelial barrier is a multi-dimensional and comprehensive property. It depends on a sufficient number of cells to form a continuous cover, requires orderly spatial arrangement of cells, and depends on the functional strength and structural continuity of intercellular connection proteins. The absence or weakness of any aspect may lead to impaired barrier function. Therefore, this step combines the various parameters and indicators of the above steps to comprehensively evaluate the true state of the barrier.

[0097] Specifically, the probability parameters of endothelial barrier integrity are obtained by combining parameters such as cell coverage, uniformity of cell nuclear distribution, strength of intercellular junction proteins, and integrity of intercellular junction proteins. The specific methods include:

[0098]

[0099] In the formula, P represents the probability parameter of endothelial barrier integrity, and W represents the cell coverage. A parameter representing the uniformity of cell nucleus distribution. This represents a parameter indicating the strength of intercellular junction proteins. This represents a parameter indicating the integrity of intercellular junction proteins.

[0100] It should be noted that multiplication can sensitively amplify the defects of any sub-item. All parameters are normalized to the [0,1] interval, making the P-value itself a probabilistic indicator between 0 and 1. A higher value indicates a more complete barrier, making the evaluation results intuitive and reliable. This ensemble method greatly improves the robustness and accuracy of the evaluation, avoiding misjudgments that may occur when relying on a single indicator, and providing a reliable and quantifiable decision-making basis for subsequent barrier integrity assessments.

[0101] Step S007: Set the threshold for the endothelial barrier integrity probability parameter, and compare the endothelial barrier integrity probability parameter with the threshold to determine whether the endothelial barrier is intact.

[0102] It should be noted that this step transforms the continuous probability parameter P into a clear binary judgment by setting a specific threshold ω. The advantage lies in its flexibility and interpretability. Users can customize the threshold ω according to the specific application scenario of organ-on-a-chip, such as the blood-brain barrier requiring extremely high integrity while other barriers allow for slightly lower permeability, or the specific requirements of the experiment. This allows the evaluation criteria to be closely linked to the biological functional requirements. The threshold is obtained by collecting a large number of sample images of known states based on historical data or control experiments, calculating their P values ​​and drawing distribution maps, and finding the critical point of the optimal classification state P value as the threshold. In this embodiment, the threshold value is 0.16. In other embodiments, the threshold value depends on the specific implementation.

[0103] Specifically, a threshold for the probability parameter of endothelial barrier integrity is set, and the integrity of the endothelial barrier is determined by comparing the probability parameter with the threshold. The specific method is as follows:

[0104] Set appropriate thresholds based on the specific uses of organ-on-a-chip. As a specific requirement for assessing endothelial barrier integrity, if the endothelial barrier integrity probability parameter Greater than the set threshold If the endothelial barrier integrity is considered good, then the probability parameter of endothelial barrier integrity is considered to be... Less than or equal to the set threshold This indicates poor endothelial barrier integrity.

[0105] Please see Figure 2 It illustrates a structural block diagram of an organ-on-a-chip dynamic assessment system for endothelial barrier integrity, the second objective of the present invention, which includes the following modules:

[0106] The data acquisition module is used to acquire high-resolution images of endothelial barrier cells under both the fluorescence channel and the transmission light channel using a confocal microscope. It performs dual labeling staining on intercellular junction proteins and cell nuclei, and labels the pixels in the cell nucleus region and the pixels in the intercellular junction protein region, and obtains fluorescence images that simultaneously label the pixels in the cell nucleus and the intercellular junction protein region.

[0107] The cell coverage calculation module is used to cluster the pixels of the endothelial barrier cell transparent image into high gray value class and low gray value class according to the gray value of the endothelial barrier cell transparent image, and obtain the cell coverage W according to the ratio of the number of pixels in the low gray value class to the total number of pixels in the endothelial barrier cell transparent image.

[0108] The cell nucleus distribution uniformity parameter calculation module is used to detect the position and number of pixels of the cell nucleus region in the fluorescence image. It uses Delaunay triangulation to obtain the cell nucleus regions that are adjacent to each cell nucleus region. The image is divided into N windows with the farthest distance of all adjacent cell nucleus regions as the window side length. The cell nucleus distribution uniformity parameter is obtained based on the information entropy of the number of cell nucleus regions in different windows.

[0109] The intercellular connective protein strength parameter calculation module is used to select the non-nuclear region between the nearest connected domain of the nuclear region that is adjacent to each nuclear region, and record this region as the connective protein template region between two adjacent nuclei. The average gray value of all pixels marked as connective proteins in the connective protein template region is used as the intercellular connective protein strength between two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connective protein strength is calculated and normalized to obtain the intercellular connective protein strength parameter.

[0110] The intercellular connective protein integrity parameter calculation module is used to filter all grids containing pixels marked as intercellular connective proteins. The pixel with the lowest gray value among the pixels marked as intercellular connective proteins in each grid is used as the seed point. The gray values ​​of the eight neighboring pixels of the seed point are used as the growth condition and the position of the seed point is updated. The module finds the suspected weak region connected domains in each grid and obtains the intercellular connective protein integrity parameter based on the ratio of the number of pixels in the suspected weak region connected domains to the number of pixels in the grid.

[0111] The endothelial barrier integrity probability parameter calculation module is used to combine cell coverage, cell nucleus distribution uniformity parameters, intercellular junction protein strength parameters, and intercellular junction protein integrity parameters to obtain endothelial barrier integrity probability parameters.

[0112] The endothelial barrier integrity assessment module is used to set a threshold for the endothelial barrier integrity probability parameter and compare the endothelial barrier integrity probability parameter with the threshold to determine whether the endothelial barrier is intact.

[0113] Beneficial effects of this invention:

[0114] Dual-channel fluorescence imaging and specific wavelength labeling ensured accurate differentiation between cell nuclei and connective proteins, providing a reliable data foundation for subsequent analysis;

[0115] An adaptive clustering method is used to distinguish cells from the background, effectively eliminating noise interference and improving the accuracy of cell coverage calculation.

[0116] By combining Delaunay triangulation with information entropy, the uniformity of cell nucleus distribution can be objectively quantified, avoiding the subjectivity of artificial division and enhancing the reliability of the analysis.

[0117] By constructing a template region for a connector protein and calculating the distance-strength correlation, the functional compensatory capacity of intercellular connections was revealed, providing a dynamic functional assessment indicator.

[0118] By employing a gridding and region growing algorithm, local weak areas are actively identified, which compensates for the shortcomings of overall average analysis and improves the sensitivity of defect detection.

[0119] By integrating multiple parameters to comprehensively evaluate the barrier status, the limitations of a single indicator are avoided, and the comprehensiveness and robustness of the evaluation are improved.

[0120] Setting adjustable thresholds allows the evaluation results to be flexibly adjusted according to specific application scenarios, enhancing the applicability and practicality of the method.

[0121] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0122] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip, characterized in that, The method includes the following steps: High-resolution images of endothelial barrier cells were acquired using a confocal microscope under both the fluorescence channel and the transmission light channel. Dual labeling of intercellular junction proteins and cell nuclei was performed, and pixels in the cell nucleus region and the intercellular junction protein region were labeled to obtain fluorescence images that simultaneously labeled pixels in the cell nucleus and intercellular junction protein regions. Based on the gray values ​​of the translucent images of endothelial barrier cells, the pixels in the translucent images of endothelial barrier cells are clustered into high gray value classes and low gray value classes. The cell coverage W is obtained by the ratio of the number of pixels in the low gray value class to the total number of pixels in the translucent images of endothelial barrier cells. The location and number of pixels of the cell nucleus region in the fluorescence image are detected. The Delaunay triangulation is used to obtain the cell nucleus regions that are adjacent to each cell nucleus region. The image is divided into N windows with the farthest distance of all adjacent cell nucleus regions as the window side length. The cell nucleus distribution uniformity parameter is obtained based on the information entropy of the number of cell nucleus regions in different windows. The non-nuclear region between the nearest connected domain of each nucleus region is selected and recorded as the connective protein template region between two adjacent nuclei. The mean gray value of all pixels marked as connective proteins in the connective protein template region is used as the intercellular connective protein strength between two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connective protein strength is calculated and normalized to obtain the intercellular connective protein strength parameter. Set up an image grid, filter all grids containing pixels marked as intercellular connective proteins, use the pixel with the lowest gray value among the pixels marked as intercellular connective proteins in each grid as the seed point, use the gray values ​​of the eight neighboring pixels of the seed point as the growth condition and update the position of the seed point, find the suspected weak region connected domain in each grid, and obtain the intercellular connective protein integrity parameter based on the ratio of the number of pixels in the suspected weak region connected domain to the number of pixels in the grid. The probability parameters of endothelial barrier integrity are obtained by combining parameters such as cell coverage, uniformity of cell nuclear distribution, strength of intercellular junction proteins, and integrity of intercellular junction proteins. Set a threshold for the probability parameter of endothelial barrier integrity, and compare the probability parameter of endothelial barrier integrity with the threshold to determine whether the endothelial barrier is intact.

2. The method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip according to claim 1, characterized in that, The method for acquiring high-resolution images of endothelial barrier cells using a confocal microscope under both fluorescence and transmission light channels, performing dual-labeling staining on intercellular junction proteins and the cell nucleus, and labeling pixels in both the nuclear and intercellular junction protein regions to obtain fluorescence images simultaneously labeled with pixels in both regions, includes the following specific steps: Endothelial barrier cells were double-labeled using antibodies targeting specific intercellular junction proteins and DAPI nuclear dye. High-resolution images were then acquired using confocal microscopy under both fluorescence and transmission light channels, ensuring all imaging parameters remained consistent. The image size was [missing information]. The images were analyzed, and the areas showing fluorescence in the grayscale image scanned by the 405nm laser were marked as the cell nucleus region, and the areas showing fluorescence in the grayscale image scanned by the 488nm laser were marked as the intercellular protein region, thus obtaining the transluminal image and fluorescence image of the endothelial barrier cells.

3. The method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip according to claim 1, characterized in that, The specific method for clustering pixels in the translucent image of endothelial barrier cells into high gray value classes and low gray value classes based on gray values, and obtaining the cell coverage W based on the ratio of the number of pixels in the low gray value class to the total number of pixels in the translucent image of endothelial barrier cells, includes the following: The grayscale values ​​of each pixel in the translucent image of endothelial barrier cells are statistically analyzed, and the maximum and minimum values ​​are used as the initial cluster centers. Each pixel is assigned to the category corresponding to the cluster center whose grayscale value is closer to its own. The mean grayscale value of all pixels in each category is recalculated and updated as the new cluster center. The above pixel division and cluster center update process is repeated until the iteration termination condition is met: after a certain iteration, the change in the grayscale values ​​of the two newly calculated cluster centers compared with the previous one is less than 1% of the current cluster center's grayscale value. Based on the last pixel division result, all pixels in the image are clustered into two categories: high grayscale value and low grayscale value. The ratio of the number of pixels in the low grayscale value category to the total number of pixels in the translucent image of endothelial barrier cells is used as the cell coverage rate W. If the ratio of the number of pixels in the category with fewer pixels to the total number of pixels in the translucent image of endothelial barrier cells is less than or equal to 5%, the cell coverage rate W = 1.

4. The method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip according to claim 1, characterized in that, The method involves detecting the location and number of pixels in the cell nucleus region of the fluorescence image, using a Delaunay triangulation to obtain the cell nucleus regions adjacent to each cell nucleus region, dividing the image into N windows with the farthest distance between all adjacent cell nucleus regions as the window side length, and obtaining the cell nucleus distribution uniformity parameter based on the information entropy of the number of cell nucleus regions in different windows. The specific methods include: In the fluorescence image, the geometric center of the connected region of each pixel labeled as a cell nucleus is calculated and used as the center point of each cell nucleus. The Delaunay triangulation algorithm is applied to construct a triangular mesh based on the coordinates of all cell nucleus center points. The adjacency relationship between pairs of cell nucleus centers is determined based on the connectivity relationships formed by this triangulation. All connected regions of pixels labeled as cell nucleus regions with adjacency relationships are obtained, and the maximum distance among all pairs of cell nucleus center points is used as the determining factor. The entire fluorescence image is divided according to the side length of the analysis window. The window is segmented, and the number of connected components in the cell nucleus region of each window is recorded. The uniformity parameter of cell nucleus distribution is obtained by the standardized entropy of the distribution of the number of connected components in the cell nucleus region of different windows. The specific method is as follows: In the formula, A parameter representing the uniformity of cell nucleus distribution, where N represents the number of windows. This represents the total number of connected components in the pixel region of the cell nucleus. Indicates the first The number of connected components in the cell nucleus region of each window.

5. The method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip according to claim 1, characterized in that, The process involves selecting a non-nuclear region adjacent to the nearest connected domain of each nucleus region and designating this region as the connective protein template region between two adjacent nuclei. The average grayscale value of all pixels marked as connective proteins within this template region is used as the intercellular connective protein strength between the two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connective protein strength is calculated and normalized to obtain the intercellular connective protein strength parameter. The specific methods include: Using the geometric center of the nearest connected domain of each adjacent nucleus region as a baseline, a series of parallel lines are generated by translating pixel by pixel in the vertical direction on both sides of this baseline, until the newly generated parallel lines no longer intersect with the connected domains of two nucleus regions simultaneously. The first intersection point that simultaneously passes through two nuclei and intersects with the connected domains of two nucleus regions is recorded. The pixel containing the line segment between the two points is recorded as the effective connection region on the parallel line. The set of all pixels on all effective connection regions is recorded as the connector protein template region between two adjacent nuclei. The average gray value of all pixels marked as connector proteins in the connector protein template region is used as the intercellular connector protein strength between two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connector protein strength is calculated and normalized. The specific method for obtaining the intercellular connector protein strength parameter is as follows: ； In the formula, This represents the linear relationship between internuclear distance and the strength of intercellular junction proteins. This represents the total number of connected components in the pixel region of the cell nucleus. This represents the distance between the connected regions of the i-th cell nucleus pixel and the geometric center of the connected region of its nearest neighbor cell nucleus pixel. This represents the intercellular protein strength between the connected domain of the i-th cell nucleus pixel and the connected domain of its nearest neighboring cell nucleus pixel. This represents the mean distance between the geometric centers of the connected regions of each pixel in the cell nucleus region and the connected regions of its nearest neighboring pixel in the cell nucleus region. This represents the average intercellular protein intensity between the connected regions of each pixel in the cell nucleus region and the connected regions of its nearest neighboring cell nucleus pixel. This represents the strength parameter of intercellular junction proteins.

6. The method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip according to claim 1, characterized in that, The process of setting up an image grid involves filtering all grids that contain pixels marked as intercellular connective proteins. The pixel with the lowest grayscale value among these labeled pixels in each grid is designated as the seed point. The seed point's position is updated based on the grayscale values ​​of its eight neighboring pixels. This process identifies potential weak regions within each grid and obtains the intercellular connective protein integrity parameter based on the ratio of the number of pixels in these potential weak regions to the total number of pixels in the grid. The specific methods include: Set an image grid in the fluorescence image to divide the fluorescence image into equal parts of size. A grid is created that records all grid points covering intercellular connection protein pixels, denoted as the intercellular protein grid. The total number of pixels within the y-th grid is calculated. Average gray value of each pixel of intercellular connection protein The intercellular protein pixel with the lowest gray value in each intercellular protein grid is recorded as the first pixel of the suspected weak region. Within the eight-neighborhood of the first pixel, if any intercellular protein pixel has a gray value lower than... For pixels with a grayscale value lower than 0, the corresponding pixel will be classified as a grayscale value lower than 0. The pixels marked as suspected weak areas in the grid are then analyzed to determine whether there are any intercellular protein pixels with a gray value lower than the eight neighboring pixels of the newly marked suspected weak areas. If there are pixels with gray values ​​lower than 1000, For pixels with a grayscale value lower than 0, the corresponding pixel will be classified as a grayscale value lower than 0. The pixels marked as suspected weak areas in the grid are repeated. The process of eight-neighbor judgment and marking suspected weak areas is repeated until all pixels in the grid have been judged. The specific method for calculating the integrity parameters of intercellular connection proteins is as follows: In the formula, Parameters representing the integrity of intercellular junction proteins. Indicates a total of A grid covering pixels of intercellular connection proteins. Indicates the first The number of pixels in suspected weak areas within each grid. Indicates the first Within each grid Individual intercellular connection protein pixels.

7. The method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip according to claim 1, characterized in that, The specific method for obtaining the endothelial barrier integrity probability parameter by combining parameters such as cell coverage, uniformity of cell nuclear distribution, strength of intercellular junction proteins, and integrity of intercellular junction proteins includes: In the formula, P represents the probability parameter of endothelial barrier integrity, and W represents the cell coverage. A parameter representing the uniformity of cell nucleus distribution. This represents a parameter indicating the strength of intercellular junction proteins. This represents a parameter indicating the integrity of intercellular junction proteins.

8. The method for dynamic assessment of endothelial barrier integrity on an organ-on-a-chip according to claim 1, characterized in that, The specific method for determining whether the endothelial barrier is intact by setting a threshold for the endothelial barrier integrity probability parameter and comparing the endothelial barrier integrity probability parameter with the threshold is as follows: Set appropriate thresholds based on the specific uses of organ-on-a-chip. As a specific requirement for assessing endothelial barrier integrity, if the endothelial barrier integrity probability parameter Greater than the set threshold If the endothelial barrier integrity is considered good, then the probability parameter of endothelial barrier integrity is considered to be... Less than or equal to the set threshold This indicates poor endothelial barrier integrity.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for dynamic assessment of organ-on-a-chip endothelial barrier integrity as described in any one of claims 1 to 8.

10. An organ-on-a-chip dynamic assessment system for endothelial barrier integrity, characterized in that, The system includes the following modules: The data acquisition module is used to acquire high-resolution images of endothelial barrier cells under both the fluorescence channel and the transmission light channel using a confocal microscope. It performs dual labeling staining on intercellular junction proteins and cell nuclei, and labels the pixels in the cell nucleus region and the pixels in the intercellular junction protein region, and obtains fluorescence images that simultaneously label the pixels in the cell nucleus and the intercellular junction protein region. The cell coverage calculation module is used to cluster the pixels of the endothelial barrier cell transparent image into high gray value class and low gray value class according to the gray value of the endothelial barrier cell transparent image, and obtain the cell coverage W according to the ratio of the number of pixels in the low gray value class to the total number of pixels in the endothelial barrier cell transparent image. The cell nucleus distribution uniformity parameter calculation module is used to detect the position and number of pixels of the cell nucleus region in the fluorescence image. It uses Delaunay triangulation to obtain the cell nucleus regions that are adjacent to each cell nucleus region. The image is divided into N windows with the farthest distance of all adjacent cell nucleus regions as the window side length. The cell nucleus distribution uniformity parameter is obtained based on the information entropy of the number of cell nucleus regions in different windows. The intercellular connective protein strength parameter calculation module is used to select the non-nuclear region between the nearest connected domain of the nuclear region that is adjacent to each nuclear region, and record this region as the connective protein template region between two adjacent nuclei. The average gray value of all pixels marked as connective proteins in the connective protein template region is used as the intercellular connective protein strength between two adjacent nuclei. The Pearson correlation coefficient between intercellular distance and intercellular connective protein strength is calculated and normalized to obtain the intercellular connective protein strength parameter. The intercellular connective protein integrity parameter calculation module is used to filter all grids containing pixels marked as intercellular connective proteins. The pixel with the lowest gray value among the pixels marked as intercellular connective proteins in each grid is used as the seed point. The gray values ​​of the eight neighboring pixels of the seed point are used as the growth condition and the position of the seed point is updated. The module finds the suspected weak region connected domains in each grid and obtains the intercellular connective protein integrity parameter based on the ratio of the number of pixels in the suspected weak region connected domains to the number of pixels in the grid. The endothelial barrier integrity probability parameter calculation module is used to combine cell coverage, cell nucleus distribution uniformity parameters, intercellular junction protein strength parameters, and intercellular junction protein integrity parameters to obtain endothelial barrier integrity probability parameters. The endothelial barrier integrity assessment module is used to set a threshold for the endothelial barrier integrity probability parameter and compare the endothelial barrier integrity probability parameter with the threshold to determine whether the endothelial barrier is intact.

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