A method for real-time monitoring and dynamic analysis of organoid cell aggregation

Abstract

The application provides a kind of organoid cell aggregation real-time monitoring and dynamic analysis method, by automatically monitoring organoid cell aggregation process, real-time processing cell aggregation image, realize from image pre-processing, feature extraction to data analysis and visualization extraction complete process, and extract morphological useful information in bright field image, carry out quantitative analysis, and clearly monitor to organoid cell rapid condensation period, morphological stable period and condensation late period in the form of chart and data file, provide support for the best induction period selection of organoid.

Description

Technical Field

[0001] This invention relates to the field of cell and tissue culture technology, and in particular to a method for real-time monitoring and dynamic analysis of organoid cell aggregation. Background Technology

[0002] Organoids are tissue analogs with a certain spatial structure formed by in vitro three-dimensional culture of stem cells (pluripotent stem cells, embryonic stem cells, or adult stem cells).

[0003] Due to the opacity of organoids, existing imaging techniques and analytical tools often adopt endpoint determination methods, such as periodically sampling, fixing samples, and then performing slice analysis. This method cannot track the development process of organoids or changes under different conditions, and may lead to changes in the structure or function of organoids, thereby affecting the comparability and reproducibility of experimental results. Furthermore, it is destructive to organoids, time-consuming, and labor-intensive.

[0004] In recent years, researchers have used cell aggregates containing dozens to hundreds of cells, rather than dispersed cells, to seed collagen hydrogels. Their studies have demonstrated that even without supplementation of exogenous growth factor (TGF-β), biomaterials containing aggregates can rapidly form cartilage in vivo. Therefore, cell aggregation can be considered an effective method to guide cell fate and enhance cell differentiation capacity. However, the inability to precisely monitor the cell aggregation process makes subsequent guided differentiation control difficult. For example, the system in patent CN 118421456A combines organoid culture with real-time monitoring of culture medium composition. However, this system focuses on seeding mature organoids in special culture wells and monitoring them. It cannot monitor the entire process of organoids from initial cell aggregation to mature organoid structure in real time. Furthermore, image analysis relies on high-content cell imagers, but current high-content imagers provide an unstable cell culture environment, including temperature and carbon dioxide concentration. In the early stages of cell aggregation, the environmental requirements are very stringent. It has been verified that in high-content imaging, it is difficult for cells to transition from a discrete state to an aggregated state in the early stages. Therefore, a significant challenge at present is how to dynamically monitor and assess the progress of cell aggregates and organoid development in a non-invasive manner. Summary of the Invention

[0005] The purpose of this invention is to provide a method for real-time monitoring and dynamic analysis of organoid cell aggregation in the early stages of cell development. To achieve this objective, the technical solution adopted by this invention includes the following steps:

[0006] (I) Cell Treatment

[0007] (1) First, digest mesenchymal stem cells with trypsin and repeatedly rinse the culture dish with growth medium to collect cells. Take a small amount of cell suspension and drop it on a cell counting plate. Centrifuge the collected cells and then resuspend them in cartilage induction medium to form a cell suspension.

[0008] (2) Spread a layer of agarose with a microwell structure at the bottom of the cell culture plate, then add cartilage induction medium to each well of the cell culture plate, and centrifuge to remove air bubbles in the microwells.

[0009] (3) Add the cell suspension to the cell culture plate, centrifuge to allow the cells to settle in the microwell, and then place the cell culture plate in a 37°C carbon dioxide constant temperature incubator for culture.

[0010] (II) Image Acquisition

[0011] Place the cell culture plate on a live cell observation instrument, adjust the field of view, and take a bright-field image of the cell aggregation at regular intervals using the live cell observation instrument.

[0012] (III) Image Preprocessing

[0013] Using a self-written Python code snippet, the bright-field image updated in step (II) is read in real time at regular intervals for preprocessing, and the image format is converted. Then, the original image is cropped to a region with only 4 cells clustered together, and the regions on both sides are removed. The cropped image is then converted from grayscale image to binary image using Otsu's binarization method, preserving the cell spheroid image information in the image.

[0014] (iv) Cell contouring

[0015] Retrieve cell sphere contours: The outer contours of cell spheres in the retrieved binary image are compressed using a chain approximation to compress contour points and calculate the area of ​​all detected contours. The largest contour area is then set as max_contour_area.

[0016] Morphological operations: Depending on the size of max_contour_area, adjust the kernel_size (structuring element) of different sizes to create a rectangular structuring element that is all 1s. Then, select an appropriate kernel size and use it to perform closing operations on the binary image to fill small holes and remove noise.

[0017] The image after morphological closing operation is re-performed with contour search, and a minimum contour area is set. Contours larger than this area are filtered out. After creating a new image, the detected contours for each image are drawn, and the images are output and saved.

[0018] (V) Real-time cell monitoring

[0019] The contour data of the four cell spheres in each image were averaged, and the changes in morphological information were statistically analyzed. The changes in cell sphere area, roundness, and density over time were plotted to determine the rapid aggregation phase, the morphologically stable phase, and the late aggregation phase. Statistical information and visualization charts were generated.

[0020] A self-written Python program checks the input folder every 5 minutes, using set difference operations to detect new image files. If a new image file is found, the Python program calls a function to perform the aforementioned operations on the new image, enabling the program to automatically process the ever-increasing image data without manual intervention, thus achieving real-time monitoring.

[0021] Furthermore, the organoid cells in step (a) can also be derived from adult stem cells and pluripotent stem cells. Furthermore, the mesenchymal stem cells in step (a) can also be induced using neuronal or osteoblast induction culture media.

[0022] Furthermore, the centrifugation sedimentation time of the cell suspension in step (i) is 1-10 minutes.

[0023] Furthermore, the shooting interval in step (ii) is 1-8 minutes.

[0024] Furthermore, in step (iii), the bright-field image format is converted from TIFF to PNG.

[0025] Furthermore, the self-written Python code for cell contour extraction in step (iii) is as follows:

[0026]

[0027] Furthermore, the self-written Python code for extracting contour properties in step (iii) is as follows:

[0028]

[0029] Furthermore, the self-written Python code for real-time monitoring in step (iii) is as follows:

[0030]

[0031] Compared to existing technologies, the technical advantages of this invention lie in its ability to automatically monitor the organoid cell aggregation process, process organoid cell aggregation images in real time, and realize a complete process from image preprocessing and feature extraction to data analysis and visualization. It extracts useful morphological information from bright-field images, performs quantitative analysis, and presents the data in the form of charts and data files. Furthermore, the three phases defined by the real-time dynamic monitoring system are validated using PCR technology. The morphological information in the output charts corresponds to changes in cell gene expression, allowing for the selection of the optimal time for induced differentiation. This demonstrates that this non-invasive dynamic monitoring supports the cell aggregation process. Detailed analysis of cell morphology, aggregation characteristics, and their changes over time helps in understanding cell behavior, evaluating the effects of experimental conditions or drug treatments, and studying the trends and changes in cell aggregation. Attached Figure Description

[0032] Figure 1 Schematic diagram of a method for real-time online dynamic monitoring of organoid cell aggregation;

[0033] Figure 2 This is a cell outline extraction image of the rapid aggregation phase T1 of cell aggregation in Example 1;

[0034] Figure 3 This is a cell outline extraction image from the T2 phase of rapid cell aggregation and morphological stabilization in Example 1.

[0035] Figure 4 This is a cell outline extraction image of T3 during the late stage of cell aggregation and condensation in Example 1;

[0036] Figure 5 This is an online monitoring graph showing the area, roundness, and density of cell spheroids at the beginning of cell aggregation in Example 1.

[0037] Figure 6 This is an online monitoring graph showing the area, roundness, and density of cell spheroids during the rapid aggregation phase of cells in Example 1.

[0038] Figure 7 This is an online monitoring graph showing the area, roundness, and density of cell spheroids during the stable morphology phase of cell aggregation in Example 1.

[0039] Figure 8 This is an online monitoring graph showing the area, roundness, and density of cell spheroids during the later stages of cell aggregation and condensation in Example 1.

[0040] Figure 9 For comparison, high-content imager was used to analyze cell aggregation in Example 1;

[0041] Figure 10 To compare the expression of chondrogenic differentiation genes in MSCs at time points T1, T2, and T3 in Example 2;

[0042] Figure 11 For comparison, see the N-cadherin immunofluorescence staining image from Example 2; Specific Implementation

[0043] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0044] To facilitate understanding of the invention, certain technical and scientific terms are specifically defined below. Unless otherwise expressly defined elsewhere in this document, all other technical and scientific terms used herein have the meanings commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] In this document, the terms “comprising” or “including” are open-ended expressions, meaning that they include the contents specified in this invention, but do not exclude other aspects.

[0046] Example 1

[0047] (I) Cell Treatment

[0048] (1) When the P2 generation mesenchymal stem cells proliferate to about 70% of the culture dish area, the cells are digested with trypsin and the culture dish is repeatedly rinsed with growth medium to collect the cells. A small amount of cell suspension is dropped onto a cell counting plate, the number of cells is counted under a microscope, and the total number of collected cells is calculated.

[0049] (2) The collected cells were centrifuged and then resuspended in cartilage induction medium to form a cell suspension.

[0050] (3) Spread a layer of agarose with a microwell structure at the bottom of the cell culture plate, then add cartilage induction medium to each well of the cell culture plate, and centrifuge to remove air bubbles in the microwells.

[0051] (4) Add the cell suspension to the cell culture plate, centrifuge for 3 minutes to allow the cells to settle in the microwell, and then place the cell culture plate in a 37°C carbon dioxide constant temperature incubator for culture.

[0052] (II) Image Acquisition

[0053] Place the cell culture plate on the live cell observation instrument, adjust the field of view, and take a bright field image of the cell aggregation every 5 minutes using the live cell observation instrument. There are four main cell aggregation areas in the field of view.

[0054] (III) Image Preprocessing

[0055] Using a self-written Python code snippet, the bright-field image updated in step 1 is read in real time every 5 minutes for preprocessing. The image format is converted from TIFF to PNG. Then, the original image is cropped to a region with only 4 cells clustered together, and the areas on both sides are removed. The cropped image is then converted from grayscale to binary image using Otsu's binarization method, preserving the cell spheroid image information in the image.

[0056]

[0057] (iv) Cell contouring

[0058] Retrieving cell sphere contours: Next, the outer contours of cell spheres in the binary image are retrieved. The chain approximation is used to compress the contour points and calculate the area of ​​all detected contours. The largest contour area is then set as max_contour_area.

[0059] Morphological operations: Depending on the `max_contour_area`, adjust the selected morphological kernel `kernel_size` (structuring element) to different sizes. These kernels will be used for morphological operations. Create a rectangular structuring element consisting entirely of 1s, and then select an appropriate kernel size to perform closing operations on the binary image, filling in small holes and removing noise.

[0060] The image after morphological closing is re-performed with contour finding, and a minimum contour area is set. Contours larger than this area are filtered out. This step is to further accurately locate and analyze cell spheroid contours in the image, removing non-cell spheroid contour regions. After creating a new image, the detected contours for each image are plotted, and the image is output and saved.

[0061]

[0062] (V) Cell morphology statistics

[0063] The contour data of four cell spheres in each image were averaged, and changes in morphological information were statistically analyzed. The changes in cell sphere area, roundness, and density over time were plotted, and statistical information and visualization charts were generated. A self-written Python program checked the input folder every 5 minutes, using set difference operations to detect new image files. If a new image file was found, the Python program called functions to perform the aforementioned operations on the new image, enabling the program to automatically process the continuously increasing image data without manual intervention, thus achieving real-time monitoring.

[0064]

[0065] The charts and graphs output by the real-time monitoring system described above allow for visual monitoring of the entire cell aggregation process, which can be seen from the charts.

[0066] from Figures 2-4 It can be seen that after obtaining bright-field images of the cell spheroidization process using a self-written Python program, the images are processed in real time. For each bright-field image obtained, the outer contours of the four spheres in the image are extracted. The contour images at time points T1, T2, and T3 are clear, indicating that image processing can obtain the approximate contours of the cell spheres.

[0067] from Figure 6 It can be seen that after approximately 350 minutes of monitoring, cell-cell interactions increased, and the morphologically, the cell aggregation area gradually changed from square to round, with a significant and rapid decrease in area. Roundness and density also increased rapidly during this stage. The final time point of this stage is designated as Time 1 (T1): the rapid aggregation phase. Figure 7 It can be seen that after approximately 1800 minutes of monitoring, the cell-cell binding becomes tighter, the cell spheroid area slowly decreases and approaches stability, and the cell aggregation areas continuously adjust their positions during the interaction process, forming a spherical structure that begins to stabilize. The roundness and density fluctuate within an acceptable range. This final stage is designated as Time 2 (T2): the morphological stabilization period. Figure 8 It can be seen that after monitoring for about 3000 minutes, as the time of cell aggregation increases, the cell spheres do not maintain a spherical structure indefinitely, but rather the tightly packed spheres slowly loosen. The morphological manifestation of the cell spheres entering this stage is that the cell sphere area gradually increases, while the roundness and density gradually decrease. The end point of this stage is divided into Time 3 (T3): the late stage of aggregation.

[0068] Comparative Example 1

[0069] This embodiment uses a high-content imager to observe and test organoid cell aggregation. The specific method is as follows:

[0070] (1) Spread a layer of agarose with a microwell structure at the bottom of a confocal dish, then add cartilage induction medium to each well in a cell culture plate, and centrifuge to remove air bubbles from the microwells.

[0071] (2) Aspirate the cell suspension and add it to a confocal dish. Add Hoechest 3342 to stain the cell nuclei to facilitate subsequent cell localization. Centrifuge to allow the cells to settle in the microwell. Then place the confocal dish in a 37°C carbon dioxide constant temperature incubator and a stage with an external carbon dioxide and temperature controller for culture.

[0072] (3) After culturing for 5 hours, the samples were photographed using a high-memory imager and a cell analyzer.

[0073] from Figure 9 It can be seen that in the early stages of high-content imaging organoids, it is difficult to observe the transition of cells from a discrete state to an aggregated state.

[0074] Comparative Example 2

[0075] This embodiment uses traditional biological techniques, namely early chondrogenesis-related gene expression (quantitative real-time PCR), to verify the various stages defined in the above-mentioned technical solution. The specific operation process is as follows:

[0076] Step 1: RNA Extraction: Cell spheres were collected at time points T1 (rapid aggregation phase), T2 (morphostationary phase), and T3 (late aggregation phase) using PBS solution. After centrifugation, the supernatant was discarded, and 1 ml of Trizol solution was added to lyse the cell spheres for 10 min. The spheres were then homogenized in a homogenizer. 200 μL of chloroform was added to each sample, and the mixture was allowed to stand for 10 min before centrifuging at 12000 rpm for 10 min at 4°C. The solution separated into layers, with RNA on the top. The RNA layer was aspirated and added to a new EP tube, followed by an equal volume of chloroform solution. The mixture was then centrifuged at 7500 rpm for 5 min at 4°C to allow the RNA to precipitate at the bottom. The supernatant was discarded, and the cells were washed with 75% ethanol solution. After washing out the ethanol, the EP tubes were placed in a fume hood to air dry. After drying, 20 μL of enzyme-free water was added to dissolve the RNA from the cells at time points T1, T2, and T3.

[0077] Step 2: RNA concentration and purity test: The concentration and purity of RNA in the sample were determined using Nano Drop 2000 (260 / 280 should be between 1.8 and 2.0).

[0078] Step 3: Reverse transcription of RNA into cDNA: Strictly follow the instructions of the RevertAid First Strand cDNA Synthesis Kit to obtain the RNA extracted in Step 1. Store the resulting cDNA at -20°C.

[0079] Step 4: Real-time quantitative PCR: After mixing the cDNA solution with the primers and PCR supermix solution at time points T1, T2, and T3, the expression of CDH2, SOX9, COL1A2, and COL2A1 related genes was detected in the real-time quantitative PCR detection system.

[0080] CDH2 is the gene expressing N-cadherin, which promotes further aggregation of mesenchymal stem cells and their chondrogenic differentiation. One of the main functions of the SOX9 gene is to promote chondrocyte differentiation and development; it is a key gene in the initiation of chondrogenic differentiation by mesenchymal stem cells. COL1A2 is the gene expressing type I collagen, which is associated with cell adhesion, migration, and osteogenic differentiation. COL2A1 is the gene expressing type II collagen and is a hyaline cartilage-specific gene; this gene can indicate whether mesenchymal stem cells have initiated chondrogenic differentiation. Figure 10 It can be seen that at time points T1, T2, and T3, the expression of CDH2, SOX9, COL1, and COL2 genes decreased with the extension of cell aggregation time. At T1, the expression of chondrogenic genes was highest, and N-cadherin, SOX9, and COL2 genes were highly expressed, indicating strong cell adhesion in the early stage and early initiation of chondrogenic differentiation, consistent with the morphological changes observed in the real-time monitoring system. At T3, the expression of all genes decreased, indicating a decline in cell adhesion levels at this stage, leading to cell separation from the aggregated cell spheroids. Figure 11 It can also be seen that immunofluorescence staining analysis of N-cadherin secreted by cytospheric cells at time points T1, T2, and T3 showed that positive expression of N-cadherin was marked in green, while the cell nucleus was marked in blue. Compared with T3, T1 and T2 showed more positive staining areas of N-cadherin, and the intensity of green fluorescence gradually decreased with T1, T2, and T3. Therefore, the morphological changes in cell aggregation and the changes in cell gene expression can be correlated.

Claims

1. A method for real-time monitoring and dynamic analysis of organoid cell aggregation, characterized in that... Includes the following steps: (I) Cell Treatment (1) First, digest mesenchymal stem cells with trypsin and repeatedly rinse the culture dish with growth medium to collect cells. Take a small amount of cell suspension and drop it on a cell counting plate. Centrifuge the collected cells and then resuspend them in cartilage induction medium to form a cell suspension. (2) Spread a layer of agarose with a microwell structure at the bottom of the cell culture plate, then add cartilage induction medium to each well of the cell culture plate, and centrifuge to remove air bubbles in the microwells; (3) Add the cell suspension to the cell culture plate, centrifuge to allow the cells to settle in the microwell, and then place the cell culture plate in a 37°C carbon dioxide constant temperature incubator for culture. (II) Image Acquisition Place the cell culture plate on the live cell observation instrument, adjust the field of view, and take a bright field image of the cell aggregation at regular intervals using the live cell observation instrument. (III) Image Preprocessing The bright field image updated in step (II) is read in real time at regular intervals using a self-written Python code segment. The TIFF format is converted to PNG format, and then the image is cropped to a region with only 4 cells. The regions on both sides are removed, and the cropped image is converted to a grayscale image. Then, Otsu's binarization method is used to convert the grayscale image to a binary image to retain the cell sphere image information in the image. (iv) Cell contouring Retrieve cell sphere contours: The outer contours of cell spheres in the retrieved binary images are compressed using a chain approximation to compress contour points and calculate the area of ​​all detected contours. The largest contour area is then set as max_contour_area. Morphological operations: Depending on the size of max_contour_area, different morphological kernels (kernel_size) are selected. These kernels will be used for morphological operations. For larger max_contour_areas, a relatively smaller kernel_size is used for fine repair, while for smaller max_contour_areas, a larger kernel_size is used for enhanced connectivity and noise removal. This is done to perform closing operations on the binary image to fill small holes and remove noise. The image after morphological closing operation is re-contour searched and a minimum contour area is set. Contours larger than this area are filtered out. After creating a new image, the detected contours of each image are drawn and the image is output and saved. (V) Real-time cell monitoring The contour data of the four cell spheres in each image were averaged, the changes in morphological information were statistically analyzed, and the changes in cell sphere area, roundness, and density over time were plotted to determine the rapid aggregation phase, the morphologically stable phase, and the late aggregation phase. Statistical information and visualization charts were also generated. A self-written Python program checks the input folder every 5 minutes, using set difference operations to detect new image files. If a new image file is found, the Python program calls a function to perform the aforementioned operations on the new image, enabling the program to automatically process the ever-increasing image data without manual intervention, thus achieving real-time monitoring.

2. The method for real-time monitoring and dynamic analysis of organoid cell aggregation according to claim 1, characterized in that: The organoid cells in step (i) can also be derived from adult stem cells or pluripotent stem cells.

3. The method for real-time monitoring and dynamic analysis of organoid cell aggregation according to claim 1, characterized in that: The mesenchymal stem cells in step (1) can also be resuspended in a neuronal or osteoblast induction culture medium.

4. The method for real-time monitoring and dynamic analysis of organoid cell aggregation according to claim 1, characterized in that: The centrifugation and sedimentation time of the cell suspension in step (1) is 1-10 minutes.

5. The method for real-time monitoring and dynamic analysis of organoid cell aggregation according to claim 1, characterized in that: The shooting interval in step (two) is 1-8 minutes.

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