Organ-like cell aggregation real-time monitoring and dynamic analysis method

Abstract

The invention provides a real-time monitoring and dynamic analysis method for organoid cell aggregation, which realizes a complete process from image preprocessing, feature extraction to data analysis and visual extraction by automatically monitoring an organoid cell aggregation process and processing a cell aggregation image in real time, and extracts morphological useful information from a bright field image. Quantitative analysis is carried out, the rapid coagulation period, the form stable period and the later coagulation period of the organoid cells are clearly monitored in the forms of charts and data files, and support is provided for selection of the optimal induction period of the organoid.

Description

Technical Field

[0001] The present 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 Art

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

[0003] Due to the opacity of organoids, existing imaging technologies and analysis tools often adopt endpoint measurement methods, such as periodic sampling, fixing the samples and then slicing and analyzing them. These tools are unable to track the development process of organoids or their changes under different conditions, and may cause changes in the structure or function of organoids, thereby affecting the comparability and repeatability of experimental results. They are also destructive to organoids, and the experiments are time-consuming and labor-intensive.

[0004] In recent years, researchers have used cell aggregates containing dozens to hundreds of cells instead of dispersed cells to inoculate collagen hydrogels. Their research has shown that even without the supplementation of exogenous growth factors (TGF-β), biomaterials containing aggregates can quickly form cartilage in vivo. Therefore, cell aggregation can be considered an effective method to guide cell fate and enhance the differentiation ability of cells, but the process of cell aggregation cannot be accurately monitored, making it difficult to control the subsequent differentiation. For example, in patent CN 118421456A, the system combines organoid culture and real-time monitoring of culture fluid components, but the system focuses on inoculating mature organoids in special culture wells and monitoring them. It cannot monitor the whole process of organoids from initial cell aggregation to mature organoid structure in real time, and image analysis depends on high-content cell imagers, but the current high-content imagers provide cells with a culture environment, that is, the temperature and carbon dioxide concentration provided, which are not stable enough, and in the early stage of cell aggregation, the environmental requirements are very harsh. It has been verified that in high-content imaging, it is difficult for early cells to go from a discrete state to an aggregated state. Therefore, an obvious challenge at this stage is how to dynamically monitor and evaluate the progression of cell aggregates and organoid development in a non-invasive manner. Summary of the invention

[0005] The purpose of the present invention is to provide a method for real-time monitoring and dynamic analysis of organoid cell aggregation in the early stage of cells. In order to achieve the above purpose, the technical solution adopted by the present invention includes the following steps:

[0006] (I) Cell processing

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

[0008] (2) A layer of agarose with a micro-well structure is laid on the bottom of the cell culture plate, and then chondrogenic induction medium is added to each well of the cell culture plate, and the bubbles in the micro-wells are removed by centrifugation.

[0009] (3) The cell suspension was aspirated and added to a cell culture plate. The cells were centrifuged to settle in the microwells. The cell culture plate was then placed in a 37° C. carbon dioxide constant temperature incubator for culture.

[0010] (II) Image acquisition

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

[0012] (III) Image preprocessing

[0013] Use a self-written python code segment to read the bright field image updated in step (2) in real time at regular intervals for preprocessing and convert the image format. Then crop the original image to an area with only 4 cells, remove the areas on both sides, and use Otsu's binarization method to convert the grayscale image into a binary image to retain the cell ball image information in the image.

[0014] (IV) Cell outline acquisition

[0015] Retrieve the cell sphere contour: Use chain approximation to compress the contour points of the outer contour of the cell sphere in the retrieved binary image and calculate the areas of all detected contours, and find the largest contour area and set it as max_contour_area.

[0016] Morphological operation: According to the different max_contour_area, adjust and select the morphological kernel kernel_size (structural element) of different sizes, create a rectangular structural element with all 1s, and then select the appropriate kernel size to use it to perform closing operations on the binary image to fill small holes in the image and remove noise.

[0017] The image after morphological closing operation is searched for contours again and a minimum contour area is set to filter out contours larger than this area. After creating a new image, the detected contours of each image are drawn and the image is 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 the area, roundness, and density of the cell spheres over time were plotted. The rapid coagulation period, morphological stability period, and late coagulation period were determined, and statistical information and visual charts were generated.

[0020] The self-written Python program checks the input folder every 5 minutes and detects whether there are new image files through set difference operation. If a new image file is found, the Python program will call the function to perform the above operations on the new image, so that the program can automatically process the increasing image data without manual intervention, so as to achieve the effect of real-time monitoring.

[0021] Furthermore, the organoid cells in step (i) may also be derived from adult stem cells and pluripotent stem cells. Furthermore, the mesenchymal stem cells in step (i) may also be induced using a neuron or osteoblast induction medium.

[0022] Furthermore, the cell suspension in step (i) is centrifuged for 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 format to PNG format.

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

[0026]

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

[0028]

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

[0030]

[0031] Compared with the prior art: The technical advantage of the present invention is that it can automatically monitor the organoid cell aggregation process, process the organoid cell aggregation image in real time, realize the complete process from image preprocessing, feature extraction to data analysis and visualization extraction, extract useful morphological information from bright field images, perform quantitative analysis, and display it in the form of charts and data files. In addition, the three periods divided by the real-time dynamic monitoring system are verified by PCR technology. The morphological information in the output chart file and the changes in cell gene expression can form a corresponding explanation, and the best time can be selected to induce differentiation, indicating that the non-invasive dynamic monitoring provides support for the cell aggregation process. The detailed analysis of cell morphology, aggregation characteristics and their changes over time helps to understand cell behavior, evaluate the effects of experimental conditions or drug treatment, and study the trends and changes in cell aggregation. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0034] Figure 3 This is a cell outline extraction diagram of the cell aggregation fast morphology stable period T2 in Example 1;

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

[0036] Figure 5 This is an online monitoring diagram of the cell sphere area, roundness, and density at the beginning of cell aggregation in Example 1;

[0037] Figure 6 This is an online monitoring diagram of the cell sphere area, roundness, and density during the rapid aggregation phase of cell aggregation in Example 1;

[0038] Figure 7 This is an online monitoring diagram of the cell sphere area, roundness, and density during the stable period of cell aggregation morphology in Example 1;

[0039] Figure 8 This is an online monitoring diagram of the cell sphere area, roundness, and density at the late stage of cell aggregation and coagulation in Example 1;

[0040] Fig. 9 For comparison with Example 1, imaging analysis of cell aggregation using a high-content imager was performed;

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

[0042] Fig.11 This is the N-cadherin immunofluorescence staining diagram of Comparative Example 2; Specific embodiments

[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 understood as limiting the present invention.

[0044] In order to make the present invention more easily understood, certain technical and scientific terms are specifically defined below. Unless otherwise clearly defined elsewhere in this document, all other technical and scientific terms used herein have the meanings commonly understood by those skilled in the art to which the present invention belongs.

[0045] In this document, the terms “include” or “comprising” are open expressions, that is, including the contents specified in the present invention but not excluding other contents.

[0046] Example 1

[0047] (I) Cell processing

[0048] (1) When the P2 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 the cell suspension is dropped on a cell counting plate, and 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 resuspended in chondrogenic induction medium to form a cell suspension.

[0050] (3) A layer of agarose with a micro-well structure is laid on the bottom of the cell culture plate, and then chondrogenic induction medium is added to each well of the cell culture plate, and the bubbles in the micro-wells are removed by centrifugation.

[0051] (4) The cell suspension was aspirated and added to a cell culture plate. The cells were centrifuged for 3 minutes to allow the cells to settle in the microwells. The cell culture plate was then placed in a 37° C. carbon dioxide constant temperature incubator for culture.

[0052] (II) Image acquisition

[0053] The cell culture plate was placed on a living cell observer, and the field of view was adjusted. A bright field image of cell aggregation was taken every 5 minutes by the living cell observer. There were four main areas of cell aggregation in the field of view.

[0054] (III) Image preprocessing

[0055] Use a self-written python code segment to read the bright field image updated in step 1 in real time every 5 minutes for preprocessing, convert the image format, and convert the TIFF format to PNG format. Then crop the original image to an area with only 4 cells gathered, remove the area on both sides, and use Otsu's binarization method to convert the grayscale image into a binary image to retain the cell ball image information in the image.

[0056]

[0057] (IV) Cell outline acquisition

[0058] Retrieve the cell sphere contour: Next, retrieve the outer contour of the cell sphere in the binary image, use chain approximation to compress the contour points and calculate the area of ​​all detected contours, and find the largest contour area and set it as max_contour_area.

[0059] Morphological operation: According to the difference of max_contour_area, adjust and select different sizes of morphological kernel kernel_size (structural element), which will be used for morphological operation. Create a rectangular structural element with all 1s, and then select an appropriate kernel size to use it to close the binary image to fill the small holes in the image and remove noise.

[0060] The image after morphological closing operation is contour searched again and a minimum contour area is set to filter out contours larger than this area. This step is to further accurately locate the cell sphere contours in the analysis and processing image and remove the non-cell sphere contour area. After creating a new image, draw the contours detected in each picture and output the saved image.

[0061]

[0062] (V) Cell morphology statistics

[0063] 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. Statistical information and visualization charts were also generated. A self-written python program was used to check the input folder every 5 minutes, and a set difference operation was performed to detect whether there were new image files. If a new image file was found, the python program would call a function to perform the above operations on the new image, so that the program could automatically process the increasing image data without manual intervention, so as to achieve the effect of real-time monitoring.

[0064]

[0065] The entire cell aggregation process can be visually monitored through the chart file output by the real-time monitoring system, and the cell aggregation process can be seen from the chart.

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

[0067] from Figure 6 It can be seen that at around 350 minutes of monitoring, the cell-cell interaction increased, and the cell aggregation area gradually changed from square to round in morphology. The area decreased significantly and the roundness and density also increased rapidly in this stage. The end time point of this stage is divided into Time 1 (T1): rapid aggregation period. Figure 7 It can be seen that around 1800 minutes of monitoring, the cells are more closely bound together, the area of ​​the cell sphere slowly decreases and approaches stability, the cell aggregation area constantly adjusts its position during the interaction process, the spherical structure formed begins to stabilize, and the roundness and density fluctuate within an acceptable range. The end of this stage is divided into Time 2 (T2): morphological stability period. Figure 8 It can be seen that at around 3000 minutes of monitoring, as the time course of cell aggregation prolonged, the cell sphere did not always maintain a spherical structure, but the tight sphere slowly loosened. The morphology of the cell sphere entering this stage was characterized by a gradual increase in the area of ​​the cell sphere and a gradual decrease in the roundness and density. The final time point of this stage was divided into Time 3 (T3): the late stage of aggregation.

[0068] Comparative Example 1

[0069] In this example, a high-content imager was used to observe and test the aggregation of organoid cells. The specific method is as follows:

[0070] (1) A layer of agarose with micro-well structure was laid on the bottom of the confocal dish, and then chondrogenic induction medium was added to each well of the cell culture plate. The bubbles in the micro-wells were removed by centrifugation.

[0071] (2) The cell suspension was aspirated and added to a confocal dish. Hoechest 3342 was added to stain the cell nucleus to facilitate subsequent cell positioning. The cells were centrifuged to settle in the microwells. The confocal dish was then placed in a 37°C carbon dioxide constant temperature incubator and a stage connected to an external carbon dioxide and temperature controller for incubation.

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

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

[0074] Comparative Example 2

[0075] This embodiment uses traditional biological technology, namely early cartilage-related gene expression (fluorescence quantitative PCR technology) to verify the various periods divided in the technical solution above. The specific operation process is as follows:

[0076] Step 1 RNA extraction: Use PBS solution to collect cell balls at the rapid aggregation period T1, morphological stability period T2, and late aggregation period T3 time points, pour out the supernatant after centrifugation, add 1ml Trizol solution to lyse the cell balls for 10 minutes, and then place them in a grinder to crush. Add 200ul chloroform to each sample and let it stand for 10 minutes, then use a 4℃ centrifuge to centrifuge at 12000 rpm for 10 minutes. The solution is layered, RNA is on the upper layer, the RNA layer is drawn into a new EP tube, and then an equal volume of chloroform solution is added to mix, and the RNA is precipitated at the bottom of the tube using a 4℃ centrifuge at 7500 rpm for 5 minutes. After pouring out the supernatant, add 75% ethanol solution to wash, and after washing out the ethanol solution, place the EP in a fume hood to air dry, and after air drying, add 20ul enzyme-free water to dissolve to obtain RNA solutions of cells at T1, T2, and T3 time points.

[0077] Step 2 RNA concentration and purity test: Nano Drop 2000 was used to measure the concentration and purity of RNA in the sample (260 / 280 should be between 1.8-2.0).

[0078] Step 3: Reverse transcription of RNA into cDNA: The RNA extracted in step 1 was operated strictly according to the instructions of the reverse transcription kit (RevertAidFirst Strand cDNA Synthesis Kit). The cDNA obtained was stored at -20°C.

[0079] Step 4: Fluorescence quantitative PCR: The cDNA solution at time points T1, T2, and T3 was mixed with the front and rear primers and PCR supermix, and the expression of CDH2, SOX9, COL1A2, and COL2A1 related genes was measured in a real-time fluorescence quantitative PCR detection system.

[0080] CDH2 is an N-cadherin expression gene, which can promote the further aggregation of mesenchymal stem cells and promote cell chondrogenic differentiation. One of the main functions of the SOX9 gene is to promote the differentiation and development of chondrocytes. It is a key gene in the initiation of chondrogenic differentiation program by mesenchymal stem cells. COL1A2 is an expression gene of type I collagen, which is related to cell adhesion, migration and osteogenic differentiation. COL2A1 is an expression gene of type II collagen, which is a specific gene related to hyaline cartilage. This gene can indicate whether mesenchymal stem cells have initiated chondrogenic differentiation. Fig.10 It can be seen that at T1, T2, and T3 time points, the expression of CDH2, SOX9, COL1, and COL2 genes decreased as the cell aggregation time increased. At T1, the expression of chondrogenesis-related genes was the highest, and N-cadherin, SOX9, and COL2 genes were highly expressed, indicating that cells had strong cell adhesion in the early stage and initiated chondrogenic differentiation at an early stage, which was consistent with the morphological changes of the real-time monitoring system. At T3, the expression of all genes decreased, indicating that the cell adhesion level decreased during this period, causing the cells to separate from the aggregated cell spheres. Fig.11 It can also be seen that the N-cadherin secreted by the cell spheres at T1, T2, and T3 time points was analyzed by immunofluorescence staining. N-cadherin positive expression was marked in green, and the cell nucleus was marked in blue. Compared with T3, T1 and T2 had more N-cadherin positive staining areas, and the green fluorescence intensity gradually weakened with T1, T2, and T3. Therefore, the morphological changes of cell aggregation and the changes in cell gene expression can correspond.

Claims

1. A method for real-time monitoring and dynamic analysis of organoid cell aggregation, characterized in that The following steps are involved: (I) Cell processing (1) First, digest the mesenchymal stem cells with trypsin, and repeatedly rinse the culture dish with growth medium to collect the cells. Take a small amount of the cell suspension and drop it on a cell counting plate. Centrifuge the collected cells and resuspend them in chondrogenic induction medium to form a cell suspension. (2) A layer of agarose with a micro-well structure is laid on the bottom of the cell culture plate, and then chondrogenic induction medium is added to each well of the cell culture plate, and the bubbles in the micro-wells are removed by centrifugation. (3) The cell suspension was aspirated and added to a cell culture plate. The cells were centrifuged to settle in the microwells. The cell culture plate was then placed in a 37° C. carbon dioxide constant temperature incubator for culture. (II) Image acquisition Place the cell culture plate on the living cell observer, adjust the field of view, and take a bright field image of cell aggregation at regular intervals using the living cell observer. (III) Image preprocessing Use a self-written python code segment to read the bright field image updated in step (2) in real time at regular intervals for preprocessing and convert the image format. Then crop the original image to an area with only 4 cells, remove the areas on both sides, and use Otsu's binarization method to convert the grayscale image into a binary image to retain the cell ball image information in the image. (IV) Cell outline acquisition Retrieve the cell sphere contour: Use chain approximation to compress the contour points of the outer contour of the cell sphere in the retrieved binary image and calculate the areas of all detected contours, and find the largest contour area and set it as max_contour_area. Morphological operation: According to the different max_contour_area, adjust and select the morphological kernel kernel_size (structural element) of different sizes, create a rectangular structural element with all 1s, and then select the appropriate kernel size to use it to perform closing operations on the binary image to fill small holes in the image and remove noise. The image after morphological closing operation is searched for contours again and a minimum contour area is set to filter out contours larger than this area. 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, and the changes in morphological information were statistically analyzed. The changes in the area, roundness, and density of the cell spheres over time were plotted. The rapid coagulation period, morphological stability period, and late coagulation period were determined, and statistical information and visual charts were generated. The self-written Python program checks the input folder every 5 minutes and detects whether there are new image files through set difference operation. If a new image file is found, the Python program will call the function to perform the above operations on the new image, so that the program can automatically process the increasing image data without manual intervention, so as to achieve the effect of real-time monitoring.

2. A method for real-time monitoring and dynamic analysis of organoid cell aggregation according to claim 1, characterized in that: The source of organoid cells in step (i) can also be adult stem cells and 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 (i) can also be induced using neuron and 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 cell suspension in step (i) is centrifuged for 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 (ii) is 1-8 minutes.

6. The method for real-time monitoring and dynamic analysis of organoid cell aggregation according to claim 1, characterized in that: In step (iii), the bright field image format is converted from TIFF format to PNG format.

7. The method for real-time monitoring and dynamic analysis of organoid cell aggregation according to claim 1, characterized in that: The self-written python code for cell contour extraction in step (iii) is:

8. The method for real-time monitoring and dynamic analysis of organoid cell aggregation according to claim 1, characterized in that: The self-written Python code for extracting contour properties in step (iii) is:

9. The method for real-time monitoring and dynamic analysis of organoid cell aggregation according to claim 1, characterized in that: The self-written Python code for real-time monitoring in step (iii) is:

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