Method for estimating the rate of senescent immune cells

Abstract

The objective is to provide a simple and indirect method for estimating the rate of senescent immune cells (the ratio of immune cells with high SAβgal expression levels among T cells whose surface marker is CD8-positive) using a small amount of blood sample. [Solution] This method estimates the rate of senescent immune cells using the relative expression levels of one or more selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in the blood.

Description

Technical Field

[0001] The present invention relates to a method for estimating the rate of senescent immune cells.

Background Art

[0002] It is known that as the age increases, the ratio of immune cells with a high expression level of SAβgal among T cells, which are a type of immune cells with a surface marker of CD8 positivity, increases (Non-Patent Document 1). Therefore, by determining the ratio of immune cells with a high expression level of SAβgal in CD8-positive T cells (hereinafter sometimes referred to as "the expression level of CD8+T SAβgal"), findings regarding the aging of a subject can be obtained. However, for this purpose, in addition to the large burden of blood sampling, the measurement process is complicated and there are significant restrictions such as storage conditions. Therefore, there is a need for a method for indirectly and simply estimating the ratio of immune cells with a high expression level of CD8+T SAβgal.

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] To provide a method for indirectly and simply estimating the rate of senescent immune cells (the ratio of immune cells with a high expression level of SAβgal among T cells with a surface marker of CD8 positivity) with a small blood sampling volume.

Means for Solving the Problems

[0005] The main components of this invention are as follows: 1. A method for estimating the rate of senescent immune cells using the relative expression levels of one or more proteins selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in the blood. 2. The estimation method described in 1, wherein the rate of senescent immune cells is the ratio obtained by determining the number of cells in the group with high SAβgal expression in a biphasic histogram of a group with high SAβgal expression and a group with low SAβgal expression in T cells with the surface marker CD8, using the trough between the two phases as a threshold, and dividing by the total number of T cells with the surface marker CD8. 3. The estimation method described in 1. or 2., wherein the protein is a protein in plasma or serum. 4. An estimation method described in any of 1. to 3., in which a simple regression equation is obtained with the rate of senescent immune cells as the dependent variable and the relative expression levels of one of the following in the blood: Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), or ferritin, as the independent variables, and the rate of senescent immune cells is estimated from the measured values ​​of the independent variables using this simple regression equation. 5. An estimation method described in any of 1. to 3., in which a multiple regression equation is obtained in which the rate of senescent immune cells is the dependent variable and the relative expression levels of one or more selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in body fluids are used as independent variables, and the rate of senescent immune cells is estimated from the measured values ​​of the independent variables using the multiple regression equation. [Effects of the Invention]

[0006] We were able to provide a simple and indirect method for estimating the senescent immune cell rate (the ratio of immune cells with high SAβgal expression levels among T cells whose surface marker is CD8 positive) using a small amount of blood sample. [Modes for carrying out the invention]

[0007] In this invention, the senescent immune cell rate is the ratio of the number of immune cells with high SAβgal expression levels to the total number of T cells with CD8-positive surface markers, where SAβgal is labeled with a fluorescent dye, with fluorescence intensity on the x-axis and cell number on the y-axis. The number of immune cells with high SAβgal expression levels is defined as the number of cells exceeding the threshold, where the trough between the peaks of the two phases is used as the threshold, in a biphasic histogram of high and low SAβgal expression levels.

[0008] In this invention, CD8+T SAβgal refers to SAβgal (Senescence-associated beta-galactosidase) in T cells whose surface marker is CD8-positive.

[0009] The blood proteins used in this invention are Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin. Granzyme K is a serine protease. α-Klotho is a glycosidase. GDF15 (growth differentiation factor 15) is a growth differentiation factor (cytokine). PAI-1 (plasminogen activator inhibitor type 1), also known as SERPINE1, is a serine protease inhibitor. Ferritin is an iron storage protein.

[0010] The protein used as an explanatory variable in this invention is obtained from blood, and the blood may be plasma or serum, but serum is preferred.

[0011] By using the senescent immune cell rate and the relative expression levels of one of the proteins Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), or ferritin, a simple regression equation can be pre-defined with the senescent immune cell rate as the dependent variable and the relative expression levels of one of the proteins Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), or ferritin as the independent variables. Then, by measuring the relative expression levels of the independent proteins, the senescent immune cell rate can be estimated.

[0012] By using the senescent immune cell rate and the relative expression levels of one or more proteins such as Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin, a multiple regression equation can be pre-defined with the senescent immune cell rate as the dependent variable and the relative expression levels of one or more proteins such as Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin as independent variables. Then, by measuring the independent variables, the senescent immune cell rate can be estimated. In this case, as long as the relative expression levels of one or more proteins such as Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin are included, the expression levels of other proteins or other physiological parameters of the subjects may also be included as independent variables. [Examples]

[0013] (Measurement of the rate of senescent immune cells) Peripheral blood samples of 8 mL were collected from the cubital vein of 107 participants (52 men and 55 women) aged 25 to 69 years, and mononuclear cells were isolated. Using a flow cytometer, killer T cells (CD8-positive) were extracted from the T cell population (CD3-positive). Killer T cells exhibited both high and low SAβgal expression, resulting in a biphasic histogram of fluorescence intensity. Due to differences in brightness between samples, a threshold was visually determined for each sample at the valley of the biphasic curve. The number of cells exceeding this threshold was defined as the number of cells with high CD8+T SAβgal expression. Analysis results were calculated using a flow cytometer (BD FACS Celesta / BD Bioscience) or analysis software (FlowJo / BD Bioscience). CD3 was labeled with the fluorescent dye Alexa Fluor 700, CD8 with the fluorescent dye PerCP-Cy5.5, and SAβgal with the fluorescent dye Alexa Fluor 488 for identification.

[0014] (Measurement of proteins in serum) From a portion of the blood samples taken from 107 individuals aged 25 to 69 (52 men and 55 women), serum was collected. Relative brightness values ​​of Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin were measured by proteomic analysis in the serum of 106 non-hemolyzed samples, and these values ​​were defined as relative expression levels. Proteomic analysis was performed by reacting the proteins with nucleic acid aptamers (SOMAmers) using the SomaScan Assay. 7322 types of proteins were analyzed in the proteomic analysis.

[0015] Measuring the rate of senescent immune cells requires collecting approximately 4 mL of blood from each subject, primarily from the cubital vein, which is burdensome for the subject. Furthermore, the experimental procedure is complex and time-consuming. On the other hand, measuring serum proteins was possible with only about 0.2 mL of blood sample from the subject, and self-collection from the fingertip was also possible, resulting in minimal burden on the subject. Furthermore, when measuring a single protein using ELISA, measurement was possible with only 0.05 mL of blood sample. In addition, the method for measuring serum proteins was simple and could be completed in a short time.

[0016] (Simple linear regression analysis) A simple linear regression analysis was performed with the aging immune cell rate as the dependent variable and the relative expression levels of proteins in the serum as the independent variables. The following regression equation was obtained, and the aging immune cell rate can be estimated using the relative expression levels of any of Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in the serum. Y = a1 × X1 + b Y is the aging immune cell rate. a1 is the coefficient of the independent variable X1 and is shown in Table 1. X1 is the relative expression level of the protein of the independent variable and is indicated by an abbreviation in Table 1. The abbreviations are as follows. GR is the relative expression level of Granzyme K in the serum. KL is the relative expression level of α-Klotho in the serum. GD is the relative expression level of GDF15 in the serum. PA is the relative expression level of PAI-1 (SERPIN1) in the serum. FE is the relative expression level of ferritin in the serum. SI is the relative expression level of SIRT3 (sirtuin 3) in the serum. b is the intercept of the regression equation and is shown in Table 1. The correlation coefficient R-squared value and p-value are shown in Table 1. It can be determined that there is a weak correlation when the value of R-squared is between 0.04 and 0.16, a somewhat correlation when it is between 0.16 and 0.49, and a strong correlation when it is between 0.49 and 1.

[0017] <00​​​​​​​​​​​​The following multiple regression equation can be obtained using two variables, and the rate of senescent immune cells can be estimated by multiple regression analysis including the expression levels of one or more selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in serum. Y = a1 × X1 + a2 × X2 + b Y represents the rate of senescent immune cells. a1 and a2 are the coefficients of the explanatory variables X1 and X2, respectively, as shown in Table 2. X1 and X2 represent the relative expression levels of the explanatory variables proteins, using the same abbreviations as in Table 1. b is the intercept of the regression equation, as shown in Table 2. The correlation coefficients (R-squared) and p-values ​​are shown in Table 2.

[0019] [Table 2]

[0020] The following multiple regression equation can be obtained using the three variables, and the rate of senescent immune cells can be estimated by multiple regression analysis including the expression levels of one or more selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in serum. Y = a1 × X1 + a2 × X2 + a3 × X3 + b Y represents the rate of senescent immune cells. a1, a2, and a3 are the coefficients of the explanatory variables X1, X2, and X3, respectively, as shown in Table 3. X1, X2, and X3 represent the relative expression levels of the explanatory variables proteins, using the same abbreviations as in Table 1. b is the intercept of the regression equation, as shown in Table 3. The correlation coefficients (R-squared) and p-values ​​are shown in Table 3.

[0021] [Table 3]

[0022] The following multiple regression equation is obtained using the four variables, and the rate of senescent immune cells can be estimated by multiple regression analysis including the expression levels of one or more selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in serum. Y = a1 × X1 + a2 × X2 + a3 × X3 + a4 × X4 + b Y represents the rate of senescent immune cells. a1, a2, a3, and a4 are the coefficients of the explanatory variables X1, X2, X3, and X4, respectively, as shown in Table 4. X1, X2, X3, and X4 represent the relative expression levels of the explanatory variables proteins, using the same abbreviations as in Table 1. b is the intercept of the regression equation, as shown in Table 4. The correlation coefficients (R-squared) and p-values ​​are shown in Table 4.

[0023] [Table 4]

[0024] The following multiple regression equation was obtained using the five variables, and the rate of senescent immune cells can be estimated by multiple regression analysis including the expression levels of one or more selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in serum. Y = a1 × X1 + a2 × X2 + a3 × X3 + a4 × x4 + a5 × x5 + b Y represents the rate of senescent immune cells. a1, a2, a3, a4, and a5 are the coefficients of the explanatory variables X1, X2, X3, X4, and X5, respectively, and are shown in Table 5. X1, X2, X3, X4, and X5 represent the relative expression levels of the explanatory variables proteins, using the same abbreviations as in Table 1. b is the intercept of the regression equation, as shown in Table 5. The correlation coefficients (R-squared) and p-values ​​are shown in Table 5.

[0025] [Table 5]

[0026] The following multiple regression equation was obtained using the six variables, and the rate of senescent immune cells can be estimated by multiple regression analysis including the expression levels of one or more selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in serum. Y = a1 × X1 + a2 × X2 + a3 × X3 + a4 × X4 + a5 × X5 + a6 × X6 + b Y represents the rate of senescent immune cells. a1, a2, a3, a4, a5, and a6 are the coefficients of the explanatory variables X1, X2, X3, X4, X5, and X6, respectively, and are shown in Table 6. X1, X2, X3, X4, X5, and X6 represent the relative expression levels of the explanatory variables proteins, using the same abbreviations as in Table 1. b is the intercept of the regression equation, as shown in Table 6. The correlation coefficient (R-squared) and p-value are shown in Table 6.

[0027] [Table 6]

Claims

1. A method for estimating the rate of senescent immune cells using the relative expression levels of one or more proteins selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in the blood, The aforementioned senescent immune cell rate is estimated by determining the number of cells in the group with high SAβgal expression in a biphasic histogram of T cells with CD8-positive surface markers, using the trough between the two phases as a threshold, and dividing this by the total number of T cells with CD8-positive surface markers.

2. The estimation method according to claim 1, wherein the protein is a protein in plasma or serum.

3. The target variable is the rate of senescent immune cells, and the blood levels of Granzyme K, α-Klotho, GDF15, and PAI-1 (SERPI) are used. N1), find a simple regression equation using the relative expression level of any of the ferritins as the explanatory variable, and then find the simple regression equation The method described in claim 1 or 2, which estimates the rate of senescent immune cells from the measured values ​​of explanatory variables using the method described in claim 1 or 2. Estimation method.

4. The estimation method according to claim 1 or 2, comprising obtaining a multiple regression equation in which the rate of senescent immune cells is the dependent variable and the relative expression levels of one or more selected from Granzyme K, α-Klotho, GDF15, PAI-1 (SERPIN1), and ferritin in the blood are used as independent variables, and using the multiple regression equation to estimate the rate of senescent immune cells from the measured values ​​of the independent variables.

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