A method for detecting aortic valve osteoclasts based on single cell nuclear transcriptome sequencing technology

Abstract

The present invention discloses a method for detecting aortic valve osteoclasts based on single cell nuclear transcriptome sequencing technology, which belongs to the field of biotechnology. The steps are as follows: collect aortic valve tissue and store it in a high-glucose culture medium, then use collagenase working solution to lyse the tissue into a single cell suspension, then use a cell sieve to filter non-cellular components, lyse the cells after filtration and separate the nuclei, and use single cell nuclear transcriptome sequencing technology to perform transcriptome detection on the cells. The test results show that compared with the previous application of scRNA, which almost did not capture the results of valve tissue osteoclasts, this scheme captured thousands of cell nuclei that can be identified as osteoclasts or osteoclast precursors in a total of more than 44,000 cell nuclei, and obtained the transcriptome results with an average sequencing depth of 10X. In addition, there was no significant change in the composition and proportion of other cells, which can be used to illustrate that the technology is reliable.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology, and in particular relates to a method for detecting aortic valve osteoclasts based on single-cell nuclear transcriptome sequencing technology. Background Art

[0002] Valvular heart disease is a major and intractable disease that affects human health, with approximately 209 million patients worldwide. Valvular calcification is the core pathological lesion, and there is currently a lack of targeted prevention and treatment strategies. Analyzing the pathophysiological mechanism of valvular calcification is of great significance for the prevention and treatment of calcific valvular heart disease. Single-cell transcriptome sequencing technology (scRNA) captures thousands of heterogeneous cell transcriptomes, and measures the expression of all mRNAs in all cells in a tissue in one test, showing the functional states of various cells in complex tissues, which has greatly improved people's understanding of the disease. In previous studies, many researchers used scRNA technology to analyze the mRNA levels of human aortic valve cells and infer protein expression, and conducted in-depth analysis of the pathophysiological mechanism of aortic valve calcification.

[0003] Osteoclasts play an important role in calcium salt deposition, bone formation and remodeling. They gather near the core of calcium salt deposition, dissolve calcium salts by releasing substances such as cathepsin K and absorb them through lysosome phagocytosis. In orthopedic research, osteoclasts play a very critical role in absorbing and remodeling abnormal calcified tissues, but there is a lack of in-depth research on valve calcification. One of the important constraints is the current lack of transcriptome detection technology for aortic valve cells at the single-cell level. In the current scRNA sequencing process, in order to prevent larger cells or fibers from clogging the sequencing tube, a 40μm-60μm cell sieve is generally used for filtration. However, the diameter of osteoclasts is generally 40μm-100μm, and the above single-cell sequencing step will filter out most of the osteoclasts, resulting in the loss of their transcriptome data. The shortcomings of this technical solution have greatly restricted the comprehensive analysis of the cellular composition of the aortic valve, and have hindered the research process of activating aortic valve osteoclasts to slow down or even reverse calcific valvular disease. Summary of the invention

[0004] Single-cell nuclear transcriptome sequencing technology (snRNA) is similar to scRNA. It extracts the nucleus of the cell to detect the cell transcriptome, and can also analyze the cell transcriptome at the single-cell level. This technology has achieved good detection results in the nervous system and myocardial tissue. In addition, studies have shown that snRNA has similar effects to scRNA in terms of the depth and accuracy of RNA information collection. However, no one has yet applied snRNA technology to detect the transcriptome level of aortic valve tissue at the single cell level. Based on the inspiration of snRNA technology and the technical difficulties of using scRNA to analyze multiple nuclei and irregular cells in valve tissue, we innovatively developed a set of technical solutions to use snRNA technology to detect valve tissue and achieved good results.

[0005] The present invention provides a method for detecting aortic valve osteoclasts based on single-cell nuclear transcriptome sequencing technology. Compared with the results of detecting valve cells using scRNA technology, the scheme of the present invention uses a larger cell sieve to replace the 40-micron small cell sieve, and simultaneously extracts cell nuclei for transcriptome sequencing instead of direct sequencing by whole cell lysis. This is the first time that transcriptome detection of osteoclasts is achieved in aortic valve tissue, solving the previous dilemma of being unable to capture osteoclasts in valve tissue and perform functional enrichment analysis.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for detecting aortic valve osteoclasts based on single cell nuclear transcriptome sequencing technology comprises the following steps:

[0008] S1: Obtain aortic valve tissue and store it in culture medium;

[0009] S2: Use collagenase working solution to lyse the aortic valve tissue to prepare aortic valve tissue single cell suspension;

[0010] S3: The prepared single cell suspension is filtered out of non-cellular components using a cell sieve, the cells are lysed using a cell nucleus extraction kit, and a single cell nucleus suspension is obtained using differential centrifugation;

[0011] S4: The single-cell nuclear suspension was subjected to snRNA detection using the single-cell nuclear transcriptome detection system. The sequencing results were subjected to upstream quality control and processing using cellranger, and downstream analysis was performed using the R software packages Seurat and SCP.

[0012] The aortic valve tissue obtained in step S1 is placed in 5 mL of Dulbecco's Modified Eagle Medium high-glucose culture medium.

[0013] The collagenase used in step S2 is type I collagenase.

[0014] The preparation method of the type I collagenase working solution is as follows: the amount of collagenase is calculated at a concentration of 2 mg / mL, the required collagenase powder is weighed and dissolved in Dulbecco's Modified Eagle Medium high-glucose culture medium, and fully dissolved, and finally a mixed solution of penicillin and streptomycin is added, and the concentration is adjusted so that the concentrations of both penicillin and streptomycin in the final working solution are 100 IU / mL.

[0015] The lysis process parameters in step S2 are as follows: the rinsed valves are transferred to 15 mL centrifuge tubes, 8-10 mL of type I collagenase working solution is added to each tube, and the tube is placed in a 37° C. constant temperature shaker for digestion for 4-6 hours, until the valves are decomposed into flocs, indicating that the digestion and lysis are complete, and then centrifuged at 200-500 g for 5-10 minutes, the upper layer of liquid is discarded, and the tube is resuspended by blowing with 0.2-0.5 mol / L sucrose solution.

[0016] 8 mL of type I collagenase working solution was added to each tube, and the tube was placed in a 37°C constant temperature shaker for digestion for 4 hours. When the valve was decomposed into flocs, indicating that the digestion and lysis were completed, the tube was centrifuged at 200g for 5 minutes, the upper liquid was discarded, and the tube was resuspended by blowing with 0.25 mol / L sucrose solution.

[0017] The pore size of the cell sieve used in step S3 is 100 μm; the differential centrifugation process is to put 3-5 mL of 0.1-0.5 mol / L sucrose solution into a centrifuge tube, and then carefully add 1-5 mL of suspension along the tube wall to cover the upper layer; centrifuge at 500-1000×g for 5-15 minutes, discard the supernatant, wash twice with 2-8 mL of 0.1-0.5 mol / L pre-cooled sucrose solution, centrifuge at 500-1000×g for 10-20 minutes each time, and obtain cell nuclei.

[0018] The specific process parameters of the differential centrifugation method in step S3 are as follows: 4.5 ml of 0.34 mol / L sucrose solution is placed in a centrifuge tube, and then 4.5 ml of the suspension is carefully added along the tube wall to cover the upper layer; centrifuged at 700×g for 10 min, the supernatant is discarded, and the cell is washed twice with 5 mL of 0.25 mol / L pre-cooled sucrose solution, each time at 1000×g for 15 min to obtain the cell nucleus.

[0019] In step S4, the suspension is injected into the BGI DNBelab C4 single nuclear transcriptome detection system for snRNA detection.

[0020] In the analysis process of step S4, CALCR and TNFRSF11A are used as osteoclast markers to identify osteoclasts.

[0021] Calcific valvular heart disease is a major and intractable disease that affects human health, but the specific composition and function of valve tissue cells have not been fully studied, making it difficult for researchers to explore and analyze the proportion of each valve component cell and the development and changes of the disease at the microscopic level. Single-cell transcriptome sequencing technology (scRNA) is one of the most popular biotechnologies in recent years. It can obtain all the mRNA of each cell in the tissue at one time, and can perform detailed detection and analysis on the functional status of each cell in the tissue. Previously, some scholars applied scRNA to the detection of mRNA expression in aortic valve cells. Although certain results have been achieved, it is still subject to great limitations and restrictions in the study of atypical cells and giant cells.

[0022] Osteoclasts are multinucleated giant cells that play a key role in dissolving calcium salt deposits and maintaining the balance between osteoblasts and osteoclasts. Many scholars have proposed that osteoclasts may be involved in alleviating or even reversing the process of calcification in various cardiovascular calcification diseases, but there is currently no research on the role of osteoclasts in valve calcification. To explore whether there are osteoclasts in the valve and what their functions are, transcriptome sequencing at the single cell level is the most effective and comprehensive tool. However, due to the limitations of scRNA technology, osteoclasts are filtered by the cell screen before entering the sequencing device, and sequencing detection cannot be performed.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] In the technical solution of this patent application, we innovatively proposed for the first time to use single-cell nuclear transcriptome sequencing technology (snRNA) to detect the full mRNA expression level of each osteoclast in the valve. This solution: 1. Improve the existing technology, use a 100uM filter for pre-machine quality control, and retain as many osteoclasts in the valve as possible for transcriptome sequencing while filtering cell impurities; 2. Innovatively extract the cell nuclei in the valve tissue for sequencing. Due to the channel design requirements of the transcriptome sequencing equipment, larger cells cannot pass through the channel to enter the equipment for effective sequencing. Therefore, we extract the cell nuclei of the cells in the valve tissue for snRNA detection, which greatly improves the collection of osteoclast transcriptome information in the valve.

[0025] We used two world-recognized osteoclast markers, CALCR and TNFRSF11A, to determine whether the cells detected by sequencing were osteoclasts. In previous technologies, we could hardly detect any osteoclasts. We could only detect 11 TNFRSF11A-positive cells out of more than 27,000 cells, and no CALCR-positive cells. In our technical solution, we used CALCR and TNFRSF11A as markers to detect 388 and 896 positive cell populations respectively in more than 44,000 cells, and the positioning was highly overlapped, indicating that this technical solution successfully captured a considerable number of osteoclasts and detected their entire transcriptome information.

[0026] This technical solution has solved the technical defects and shortcomings of valve osteoclasts being difficult to detect and their functional status difficult to analyze. In the future, this technology can be used to conduct more in-depth research on the proportion and functional status of osteoclasts in the valve tissue of patients with valve calcification due to different reasons, analyze the pathogenesis of valve calcification from a new perspective, and specifically restore the function of osteoclasts in the valve or enhance the formation and number of osteoclasts in the valve tissue. Activating or increasing the number of osteoclasts is expected to dissolve the calcium salt nodules formed by valve calcification, fundamentally reverse valve calcification, reduce the incidence of aortic valve stenosis, and improve the incidence and survival rate of cardiovascular events in patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a schematic diagram of the overall process of this technology;

[0028] Figure 2 A dimensionality reduction display diagram of single nuclear sequencing results (where b shows the patient from which each cell originates by color, and c shows whether each cell originates from a calcified valve or normal valve tissue by color);

[0029] Figure 3 scRNA sequencing results (where d is the distribution of CALCR cells expressed by scRNA sequencing, and e is the distribution of TNFRSF11A cells expressed by scRNA sequencing);

[0030] Figure 4 Figures 2 and 3 are the results of single nuclear sequencing (where f is the distribution of cells expressing CALCR by single nuclear sequencing, and g is the distribution of cells expressing TNFRSF11A by single nuclear sequencing); DETAILED DESCRIPTION

[0031] In order to better illustrate the present invention, the following embodiments are listed. Obviously, the described embodiments are only a part of the present invention, not all embodiments. Based on the embodiments in the present invention, other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0032] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0033] Example 1

[0034] During the operation, 2 normal non-calcified aortic valves from two heart transplant patients and 5 valves from five patients with calcified aortic valvular disease confirmed by preoperative ultrasound and intraoperative pathology were collected. After the valves were obtained, they were placed in 5 mL Dulbecco's Modified Eagle Medium high-glucose culture medium for storage and transported to the laboratory, and then the valve tissue was lysed using type I collagenase working solution. The preparation method of the type I collagenase working solution is as follows: the amount of collagenase is calculated at a concentration of 2 mg / mL, the required collagenase powder is weighed and dissolved in Dulbecco's Modified Eagle Medium high-glucose culture medium, fully dissolved, and finally a mixed solution of penicillin and streptomycin is added, and the concentration is adjusted so that the concentrations of penicillin and streptomycin in the final working solution are both 100 IU / mL. The valve tissue was rinsed and transferred to a 15 mL centrifuge tube. 8 mL of type I collagenase working solution was added to each tube and digested in a 37 ° C constant temperature shaker for 4 hours. The valve was decomposed into flocs, indicating that the digestion and lysis were completed. Then, it was centrifuged at 200g for 5 minutes, the upper liquid was discarded, and it was resuspended by blowing with 0.25 mol / L sucrose solution. The non-cellular components in the suspension were filtered using a 100 μm cell sieve with a larger pore size to retain possible osteoclasts. The prepared and filtered single cell suspension was lysed using a cell nuclear extraction kit, and a single cell nuclear suspension of human aortic valve tissue was obtained by differential centrifugation. The specific process of differential centrifugation is as follows: put 4.5ml of 0.34mol / L sucrose solution into a centrifuge tube, and then carefully add 4.5ml of suspension along the tube wall to cover the upper layer; centrifuge at 700×g for 10min, discard the supernatant, wash twice with 5mL0.25mol / L pre-cooled sucrose solution, centrifuge at 1000×g for 15min each time to obtain the cell nucleus. Finally, inject the suspension into the BGI DNBelab C4 single nuclear transcriptome detection system for snRNA detection. Cellranger is used for upstream quality control and processing of the sequencing results off the machine, and R software packages Seurat and SCP are used for downstream analysis. See below for the specific process flow. Figure 1 .

[0035] The experiment captured a total of 44,707 nuclear transcriptome data. The LIGERUMAP2D dimensionality reduction map shows that calcified aortic valve cells and normal aortic valve cell subpopulations are distributed in each cell subpopulation, and the batch effect between samples is small. Figure 2 b and Figure 2 c shows that under unsupervised clustering with a resolution of 0.4, valvular cells can be divided into 16 types (subpopulations), and there is no increase or decrease in subpopulation types compared with previous studies. We used the classic osteoclast markers CALCR and TNFRSF11A to identify the presence or absence of osteoclasts and the distribution ratio. We used the R language SCP package to analyze the specific gene expression and applied the FeatureDimPlot() function for visual display. In order to identify possible osteoclasts, we focused on the classic osteoclast markers CALCR and TNFRSF11A as the input of the FeatureDimPlot() function, displayed the distribution of marker genes in the UMAP dimensionality reduction diagram, and counted the proportion of positive cell populations. According to Figure 3-4 The results show that when comparing this result with the results of Xu et al. using scRNA to detect valve tissue cells, only 0.04% of the cells expressed TNFRSF11A in the previous data, and no cells expressed CALCR. This shows that almost no osteoclasts were captured in the scRNA test; in our test this time, 0.87% of the cells expressed CALCR and 2% of the cells expressed TNFRSF11A, see Figure 3-Figure 4 This suggests that osteoclasts in valve tissue were effectively captured in our assay and can be further subjected to functional analysis and trajectory differentiation studies.

[0036] The test results showed that compared with the previous results of scRNA, which almost did not capture osteoclasts in valve tissue, this solution captured thousands of cell nuclei that can be identified as osteoclasts or osteoclast precursors out of a total of more than 44,000 cell nuclei, and obtained transcriptome results with an average sequencing depth of 10X. In addition, there was no significant change in the composition and proportion of other cells, which shows that the technology is reliable.

[0037] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for detecting aortic valve osteoclasts based on single cell nuclear transcriptome sequencing technology, characterized in that: The following steps are involved: S1: Obtain aortic valve tissue and store it in culture medium; S2: Use collagenase working solution to lyse the aortic valve tissue to prepare aortic valve tissue single cell suspension; S3: The prepared single cell suspension is filtered out of non-cellular components using a cell sieve, the cells are lysed using a cell nucleus extraction kit, and a single cell nucleus suspension is obtained using differential centrifugation; S4: The single-cell nuclear suspension was subjected to snRNA detection using the single-cell nuclear transcriptome detection system, the sequencing results were subjected to upstream quality control and processing using cellranger, and downstream analysis was performed using the R software packages Seurat and SCP; The pore size of the cell sieve used in step S3 is 100 μm; the differential centrifugation process is to put 3-5 mL of 0.1-0.5 mol / L sucrose solution into a centrifuge tube, and then carefully add 1-5 mL of suspension along the tube wall to cover the upper layer; centrifuge at 500-1000×g for 5-15 minutes, discard the supernatant, wash twice with 2-8 mL of 0.1-0.5 mol / L pre-cooled sucrose solution, centrifuge at 500-1000×g for 10-20 minutes each time, and obtain cell nuclei.

2. The detection method according to claim 1, characterized in that The aortic valve tissue obtained in step S1 is placed in 5 mL of Dulbecco's Modified Eagle Medium high-glucose culture medium.

3. The detection method according to claim 1, characterized in that The collagenase used in step S2 is type I collagenase.

4. The detection method according to claim 3, characterized in that The preparation method of the type I collagenase working solution is as follows: the amount of collagenase is calculated at a concentration of 2 mg / mL, the required collagenase powder is weighed and dissolved in Dulbecco's Modified Eagle Medium high-glucose culture medium, and fully dissolved, and finally a mixed solution of penicillin and streptomycin is added, and the concentration is adjusted so that the concentrations of penicillin and streptomycin in the final working solution are both 100 IU / mL.

5. The detection method according to claim 1, characterized in that The lysis process parameters in step S2 are as follows: the rinsed valves are transferred to 15 mL centrifuge tubes, 8-10 mL of type I collagenase working solution is added to each tube, and the tube is placed in a 37° C. constant temperature shaker for digestion for 4-6 hours, until the valves are decomposed into flocs, indicating that the digestion and lysis are complete, and then centrifuged at 200-500 g for 5-10 minutes, the upper layer of liquid is discarded, and the tube is resuspended by blowing with 0.2-0.5 mol / L sucrose solution.

6. The detection method according to claim 5, characterized in that 8 mL of type I collagenase working solution was added to each tube, and the tube was placed in a 37°C constant temperature shaker for digestion for 4 hours. When the valve was decomposed into flocs, indicating that the digestion and lysis were completed, the tube was centrifuged at 200g for 5 minutes, the upper liquid was discarded, and the tube was resuspended by blowing with 0.25 mol / L sucrose solution.

7. The detection method according to claim 1, characterized in that: The specific process parameters of the differential centrifugation method in step S3 are as follows: 4.5 ml of 0.34 mol / L sucrose solution is placed in a centrifuge tube, and then 4.5 ml of the suspension is carefully added along the tube wall to cover the upper layer; centrifugation is performed at 700×g for 10 min, the supernatant is discarded, and the cell nucleus is obtained by washing twice with 5 mL of 0.25 mol / L pre-cooled sucrose solution, and centrifugation is performed at 1000×g for 15 min each time.

8. The detection method according to claim 1, characterized in that In step S4, the suspension is injected into the BGI DNBelabC4 single nuclear transcriptome detection system for snRNA detection.

9. The detection method according to claim 1, characterized in that: In the analysis process of step S4, CALCR and TNFRSF11A are used as osteoclast markers to identify osteoclasts.

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