Device and method for diagnosing infectious disease using lens-free shadow imaging technology based on t cell response

Abstract

The present invention relates to a composition and imaging technology for evaluating the immune response of T cells, and in particular, to the technological field of T cell activation analysis and infectious disease diagnosis using lens-free shadow imaging. According to the present invention, activation of T cells can be effectively induced through a T cell activation composition composed of a specific component combination (PHA, PMA, ionomycin, etc.), and T cell activation-specific image parameters (MMD_SD etc.) can be acquired from the activated T cells through lens-free shadow imaging technology. Moreover, since a T cell activity analysis device and method using these parameters exhibit significant performance in diagnosing the presence or absence of latent tuberculosis infection, the present invention has the effect of providing a biological composition that can be used for T cell functional analysis, creating a non-labeling immunodiagnostic platform, and presenting a novel analytical index for LTBI diagnosis.

Description

Device and method for diagnosing infectious diseases using lens-free shadow imaging technology based on T cell response

[0001] The present invention relates to a composition and imaging technique for evaluating the immune response of T cells, and particularly to the field of T cell activation analysis and infectious disease diagnosis technique using lens-free shadow imaging.

[0002] The human body has developed a systematic defense system to combat harmful pathogens and microorganisms. This defense system can be divided into two categories: innate immunity and adaptive immunity. Innate immunity is mediated by immune cells such as macrophages, neutrophils, dendritic cells, and natural killer (NK) cells, which are widely distributed throughout the blood and tissues. It acts as the body's first line of defense, inducing a rapid and immediate immune response. Unlike innate immunity, adaptive immunity possesses a characteristic called "memory." Upon first encounter with a pathogen, the immune response is relatively slow. However, upon subsequent encounters with the same pathogen, memory cells generated after the initial attack induce a rapid immune response. T cells are a key cell responsible for adaptive immunity. T cells are a type of white blood cell that plays a crucial role in the immune system, accounting for 80-90% of lymphocytes. T cells play a key role in recognizing and eliminating pathogens (viruses, bacteria, etc.) and abnormal cells such as cancer cells within the body. T cells are divided into CD4 T cells and CD8 T cells. CD4 T cells secrete various cytokines (signaling substances) in response to antigens that have invaded the body, activating other immune cells such as B cells. CD8 cells recognize abnormal cells such as cancer cells and directly attack and eliminate them. T cell dysfunction is associated with diseases such as AIDS, autoimmune diseases (such as lupus and rheumatoid arthritis), and cancer. Therefore, T cell activity analysis plays a crucial role in disease diagnosis and treatment research.

[0003] Meanwhile, tuberculosis (TB) is divided into active tuberculosis and latent tuberculosis (LTBI). Active tuberculosis can be distinguished through imaging analysis and is diagnosed with X-rays, but latent tuberculosis does not show any distinct changes, so a diagnostic method based on the immune response of T cells is required. Latent tuberculosis refers to a state in which the body is infected with the tubercle bacilli but active tuberculosis has not developed. Therefore, in patients with latent tuberculosis, memory T cells for the tubercle bacilli exist, and when they come into contact with specific tuberculosis antigens, they are activated and secrete cytokines (such as IFN-γ). Currently, methods such as ELISA (Interferon-Gamma Release Assay, IGRA) are used based on this, but they have limitations such as a long analysis time (more than 24 hours), expensive reagents and equipment, and the need for skilled experts.

[0004] To provide a T cell activation composition capable of accelerating or enhancing an antigen-specific T cell activation response.

[0005] In addition, a method for inducing an activation response and a change in shadow parameters of T cells using the composition is provided.

[0006] In addition, a measuring device and method capable of visualizing and quantifying changes according to T cell activation using lens-free shadow imaging technology are provided.

[0007] In addition, a method for analyzing the functional activity of T cells based on the above measurement results is provided.

[0008] Furthermore, the present invention provides a device for diagnosing tuberculosis infection, particularly latent tuberculosis, and a method for providing diagnostic information through the above analysis.

[0009] To achieve the above objective, the present invention provides a composition capable of non-specifically activating T cells, said composition may include at least one of phytohemagglutinin (PHA), phorbol myristate acetate (PMA), and ionomycin.

[0010] Furthermore, the present invention provides a method for stimulating and activating peripheral blood mononuclear cells (PBMCs) or T cells in whole blood using the above composition. This method induces an activation response of T cells and provides a basis for objectively measuring the degree of the response.

[0011] The present invention further provides an apparatus and method for quantitatively analyzing T cell activity by utilizing lens-free shadow imaging technology based on changes in cell shape or optical properties of the cell background resulting from T cell activation. The apparatus may include a light source, a sample holder, an image sensor, and an image analysis module.

[0012] Furthermore, the present invention includes a diagnostic device and an information-providing method capable of evaluating the presence of tuberculosis infection, particularly the possibility of the existence of latent tuberculosis, based on the results of the T cell activity analysis. This enables faster and more intuitive immunodiagnosis than existing blood-based diagnostic methods.

[0013] According to the present invention, T cell activation can be effectively induced through a T cell activation composition composed of a specific combination of ingredients (PHA, PMA, Ionomycin, etc.). In addition, T cell activation-specific image parameters (MMD_SD, etc.) can be obtained from the activated T cells through lens-free shadow imaging technology, and a T cell activity analysis device and method utilizing these parameters exhibit significant performance in diagnosing latent tuberculosis infection. Therefore, the present invention has the effects of providing a biological composition that can be utilized for T cell function analysis, implementing a non-labeling type immune diagnosis platform, and suggesting a new analysis index for diagnosing latent tuberculosis (LTBI).

[0014] Furthermore, this technology can be expanded to various infectious disease and immune disease diagnosis fields, and can also be used as a basic technology for the development of point-of-care (POC) immune diagnosis devices.

[0015] Figure 1 shows the morphological changes of cells after T cell activation confirmed by Hema-3 staining:

[0016] Nil: Negative control group;

[0017] PHA: Phytohemagglutinin treatment group;

[0018] CD3 / CD28: CD3 / CD28 antibody treatment group; and

[0019] PMA+Ionomycin: Group treated with phorbol myristate acetate and Ionomycin combination.

[0020] Figure 2 shows the analysis of the expression of T cell activation markers CD69 and CD25 by flow cytometry after T cell activation:

[0021] Nil: Negative control group;

[0022] PHA: Phytohemagglutinin treatment group;

[0023] CD3 / CD28: CD3 / CD28 antibody treatment group;

[0024] PHA 5 + CD3 / CD28: PHA 5 μg / mL and CD3 / CD28 antibody 50 μL / mL combination treatment group;

[0025] PHA 10 + CD3 / CD28: PHA 10 μg / mL and CD3 / CD28 antibody 50 μL / mL combination treatment group;

[0026] PHA 5 + P / I: PHA 5 μg / mL, PMA (50 ng / mL), and Ionomycin (500 ng / mL) combination treatment group; and

[0027] PHA 10 + P / I: PHA 10 μg / mL, PMA (50 ng / mL), and Ionomycin (500 ng / mL) combination treatment group.

[0028] Figure 3 is a diagram analyzing the gene expression levels of IL-8 and IFN-γ by real-time PCR after T cell activation:

[0029] CMV: Central Maxima Value, a lens-free shadow image analysis parameter;

[0030] PPD: Lens-free shadow image analysis parameter Peak-to-Peak Distance;

[0031] MMD: Lens-free shadow image analysis parameter Maximum-to-Minimum Distance;

[0032] MMD_SD: Standard deviation of lens-free shadow image analysis parameter MMD;

[0033] WCM: Lens-free shadow image analysis parameter Width of Central Maximum;

[0034] WSM: Lens-free shadow image analysis parameter Width of Secondary Maxima;

[0035] WCM_SD: Standard deviation of lens-free shadow image analysis parameter WCM;

[0036] WSM_SD: Standard deviation of lens-free shadow image analysis parameter WSM;

[0037] a: real-time PCR analysis results; and

[0038] b: Lens-free shadow image analysis parameters.

[0039] Figure 4 shows the results of flow cytometry analysis after activation of CD4+ T cells isolated from human peripheral blood.

[0040] Here is a comparison analysis of the parameters obtained by taking a lens-free shadow image:

[0041] 3h: T cells were activated for 3 hours by combined treatment with PHA, PMA, and Ionomycin;

[0042] 6h: T cells were activated for 6 hours by combined treatment with PHA, PMA, and Ionomycin;

[0043] a: Flow cytometry results; and

[0044] b: Lens-free shadow image analysis parameters.

[0045] Figure 5 shows CD4 cells isolated after treating human peripheral blood with T cell activating substances. + The results of flow cytometry analysis using T cells (CD69) and lens-free shadow image parameters are compared and analyzed:

[0046] 3h: T cells were activated for 3 hours by combined treatment with PHA, PMA, and Ionomycin; and

[0047] 6h: T cells were activated for 6 hours by combined treatment with PHA, PMA, and Ionomycin.

[0048] Figure 6 is a diagram showing T cell activity analysis using lens-free shadow imaging technology after classifying latent tuberculosis infected and non-infected individuals:

[0049] Nil: Negative control group;

[0050] TB1: Tuberculosis-specific antigen-treated group;

[0051] Mitogen: Positive control;

[0052] LTBI-negative: individuals not infected with latent tuberculosis; and

[0053] LTBI-positive: Latent tuberculosis infected person.

[0054] Figure 7 is a diagram showing the results of logistic regression analysis using shadow image parameters and evaluating diagnostic performance:

[0055] a: Regression coefficients of key variables, odds ratios, P values, and goodness-of-fit indices of the prediction model;

[0056] b: ROC analysis results for the Cellytics-based prediction score (P1) (AUC = 0.920, Sensitivity = 81.8%, Specificity = 94.1%); and

[0057] c: Sensitivity, specificity, accuracy, and predictive value indices through comparative analysis of QuantiFERON-TB and Cellytics results.

[0058] Hereinafter, the present invention will be described in detail with reference to the attached drawings and embodiments thereof. However, the following embodiments are provided as examples of the present invention. If a detailed description of a technology or configuration well known to those skilled in the art is judged to unnecessarily obscure the gist of the present invention, such detailed description may be omitted, and the present invention is not limited thereby. The present invention is capable of various modifications and applications within the scope of the following claims and equivalents interpreted therefrom.

[0059] Additionally, the terminology used in this specification is intended to appropriately express preferred embodiments of the present invention, and may vary depending on the intent of the user or operator, or the practices of the field to which the present invention pertains. Therefore, the definitions of these terms should be based on the contents throughout this specification.

[0060] Throughout the specification, when a part is described as "comprising" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice of testing the present invention, the preferred materials and methods are described herein.

[0062]

[0063] In one aspect, the present invention relates to a composition for inducing non-specific activation of T cells comprising phytohemagglutinin (PHA), phorbol myristate acetate (PMA), and ionomycin as active ingredients.

[0064] In one embodiment, the composition may contain phytohemagglutinin, phorbol myristate acetate, and ionomycin in a weight ratio of 100 to 200:1:10.

[0065] In one embodiment, the composition comprises CD4 + Helper T cells, CD8 +It is most desirable to apply this to a cell population including killer T cells, naive T cells, memory T cells, central memory T cells, operator memory T cells, memory stem T cells, immutable T cells, cytokine-induced killer T cells, or gamma delta (g / d) T cells.

[0066] In one embodiment, the composition can induce or promote activation of T cells.

[0067] In one embodiment, the composition can increase the expression of CD69, CD25, IL-8 or IFN-γ in T cells.

[0068] In one embodiment, the composition may further comprise a CD3 / CD28 antibody.

[0069] In one embodiment, the composition may contain phytohemagglutinin at 1 to 50 μg / mL, phorbol myristate acetate at 10 to 200 ng / mL, and ionomycin at 100 ng / mL to 2 μg / mL.

[0070] In one embodiment, the concentrations of phytohemagglutinin, phorbol myristate acetate, and ionomycin in the composition can be varied and appropriately adjusted depending on the specific type or amount of target T cells, degree of activation, activation induction time, activation effect, etc.

[0071] In one aspect, the present invention relates to a method for activating T cells comprising the step of treating T cells or peripheral blood containing T cells with a composition of the present invention to induce activation.

[0072] In one embodiment, the T cell is CD4 + Helper T cells, CD8 +They can be killer T cells, naive T cells, memory T cells, central memory T cells, effector memory T cells, memory stem T cells, invariant T cells, cytokine-induced killer T cells, or gamma-delta (g / d) T cells, and CD4 + Most preferably, it is a helper T cell.

[0073] In one embodiment, the method can increase the expression of CD69, CD25, IL-8 or IFN-γ in T cells.

[0074] In one aspect, the present invention relates to a T cell activity measuring device comprising: an image sensor for capturing a shadow image of a T cell; and a processor comprising an algorithm configured to analyze whether or not the T cell is activated by calculating parameters such as Peak-to-Peak Distance (PPD), Maximum-to-Minimum Distance (MMD), Width of Central Maximum (WCM), Width of Secondary Maxima (WSM), Standard Deviation of MMD (MMD), Standard Deviation of WCM (WCM), or Standard Deviation of WSM (WSM) from at least a portion of the lens-free shadow image captured through the image sensor.

[0075] In one implementation, the algorithm may be configured to quantitatively analyze the degree of activation of T cells.

[0076] In one implementation, the processor may include an algorithm configured to analyze whether or not a T cell is activated by calculating one or more parameters selected from the group consisting of Peak-to-Peak Distance (PPD), Maximum-to-Minimum Distance (MMD), Width of Central Maximum (WCM), Width of Secondary Maxima (WSM), Standard Deviation of MMD (MMD_SD), Standard Deviation of WCM (WCM_SD), and Standard Deviation of WSM (WSM_SD).

[0077] In one embodiment, the algorithm may be a T cell activity-specific algorithm, and the algorithm may be an algorithm configured to analyze the activity of T cells based on the result of an operation or combination between one or more shadow image parameters.

[0078] In one implementation, the algorithm may be configured to analyze the activity of T cells based on a result of combining the PPD parameter and the MMD_SD parameter, and may be configured to analyze the activity of T cells based on a result of multiplying the PPD parameter and the MMD_SD parameter.

[0079] In one embodiment, the T cell is CD4 + Helper T cells, CD8 + They can be killer T cells, naive T cells, memory T cells, central memory T cells, effector memory T cells, memory stem T cells, invariant T cells, cytokine-induced killer T cells, or gamma-delta (g / d) T cells.

[0080] In one embodiment, the image sensor is configured to capture a lens-free shadow image of a T cell, and the image sensor can capture a lens-free shadow image by irradiating light onto a T cell in which a light-emitting diode is placed.

[0081] In one embodiment, the processor can calculate parameters by performing data processing or calculations on a portion of a lens-free shadow image using an algorithm configured to analyze the activity of T cells.

[0082] In one embodiment, the device may further include a machine learning server configured to perform machine learning on T cell images.

[0083] In one aspect, the present invention relates to a latent tuberculosis (LTBI) diagnosis device, comprising: an image sensor for capturing a shadow image of a T cell; and a processor including an algorithm configured to analyze whether latent tuberculosis infection is present by calculating an MMD_SD or WCM_SD parameter from a lens-free shadow image acquired through the image sensor.

[0084] In one embodiment, the T cell is CD4 + Helper T cells, CD8 + It is most desirable that they be killer T cells, naive T cells, memory T cells, central memory T cells, operator memory T cells, memory stem T cells, immutable T cells, cytokine-induced killer T cells, or gamma delta (g / d) T cells.

[0085] In one aspect, the present invention relates to a method for analyzing T cell activity, comprising the steps of: capturing a shadow image of a T cell; calculating a PPD, MMD, WCM, WSM, MMD_SD, WCM_SD or WSM_SD parameter from at least a portion of the acquired lens-free shadow image; and comparing the parameter with a parameter of a non-activated control T cell.

[0086] In one embodiment, the method may further include a step of evaluating that the T cells are activated if the calculated parameter increases compared to the parameter of the control T cells.

[0087] In one aspect, the present invention relates to a method for providing information for diagnosing latent tuberculosis, comprising the steps of: isolating T cells from blood separated from a subject; capturing a lens-free shadow image of the T cells; calculating an MMD_SD parameter from the acquired lens-free shadow image; and evaluating a subject as infected with latent tuberculosis if the parameter increases compared to the MMD_SD parameter of T cells of a control group.

[0088] In one embodiment, the control T cells may be T cells isolated from blood isolated from a normal control subject.

[0089] In one embodiment, the method may further include a step of activating T cells by treating blood with a tuberculosis-specific antigen prior to isolating the T cells.

[0090] In one embodiment, the tuberculosis-specific antigen may be early secretary antigen taget-6 (ESAT-6), culture filtrate protein-10 (CFP-10), or a synthetic peptide of ESAT-6 and CFP-10.

[0091] In one embodiment, the method may further comprise treating the T cells with antibodies to CD28 or CD49d prior to isolating the T cells.

[0092] In one embodiment, the method may react blood (300 to 500 µl) separated from a subject in QuantiFeron-TB containing anti-CD28 antibody and anti-CD49d antibody for 5 to 7 hours, and then take a lens-free shadow image of the separated T cells.

[0093] In one embodiment, the method for providing information for diagnosing latent tuberculosis may include the steps of treating blood isolated from a subject with a tuberculosis-specific antigen; isolating T cells; capturing a lens-free shadow image of the T cells; calculating an MMD_SD parameter from the obtained lens-free shadow image; and evaluating the subject as infected with latent tuberculosis if the parameter increases compared to the MMD_SD parameter of T cells isolated from blood (Nil tube) that has not been treated with a tuberculosis-specific antigen.

[0094] In one embodiment, the step of treating with an antibody against CD28 or CD49d before isolating the T cells may be further included.

[0095] In the present invention, the term "diagnosis" means confirming the existence or characteristics of a pathological condition. In the present invention, diagnosis may mean confirming the presence of latent tuberculosis infection, or furthermore, predicting the prognosis of latent tuberculosis or distinguishing it from other forms of tuberculosis.

[0096] As used herein, the term "subject" or "patient" refers to any single individual requiring treatment, including humans, apes, monkeys, cows, dogs, guinea pigs, rabbits, chickens, insects, etc. In addition, any subject participating in a clinical research trial that does not exhibit any clinical findings of a disease, a subject participating in an epidemiological study, or a subject used as a control group is included as a subject.

[0097] The present invention is described in more detail through the following examples. However, the following examples are intended only to concretize the content of the present invention and are not intended to limit the present invention.

[0098] Example 1. T cell activation assay

[0099] 1-1. Morphological analysis

[0100] Jurkat cells, a human peripheral blood T cell line, were cultured in RPMI-1640 medium containing 10% FBS and 1% Penicillin / Streptomycin at 37℃ and 5% CO2. PHA (Phytohemagglutinin) (1 μg / mL, 5 μg / mL, 10 μg / mL, or 20 μg / mL), CD3 / CD28 antibody (50 μL / mL), and PMA (phorbol myristate acetate) (50 ng / mL) + Ionomycin (500 ng / mL) were treated, respectively, and cultured for 6 hours to induce T cell activation. After that, cells in each group were stained with Hema-3 staining, and morphological changes were observed at ×400 magnification.

[0101] As a result, the cell size was found to have increased in the groups treated with the activating substance compared to the negative control group (Nil) (Fig. 1).

[0102] 1-2. Flow cytometry

[0103] To analyze T cell activation using a flow cytometer, Jurkat cell lines cultured under the same conditions as in Example 1-1 were treated with PHA (1 μg / mL, 5 μg / mL, 10 μg / mL, or 20 μg / mL), CD3 / CD28 antibody (50 μL / mL), PMA (phorbol myristate acetate) (50 ng / mL) + Ionomycin (500 ng / mL), PHA (5 μg / mL or 10 μg / mL) + CD3 / CD28 antibody (50 μL / mL), and PHA (5 μg / mL or 10 μg / mL) + PMA (50 ng / mL) + Ionomycin (500 ng / mL), respectively, and cultured for 6 hours to induce T cell activation. After inducing activation, cells were obtained, fluorescently stained for CD69 and CD25, which are T cell activation markers, and flow cytometry was performed.

[0104] As a result, it was found that the expression levels of CD69 and CD25 increased depending on the concentration upon PHA treatment, leading to an increase in the proportion of activated T cells, and CD69 + CD25 - / + As a result of cell analysis of the distribution, Nil was approximately 0-1%, PHA 1 μg / mL was approximately 75%, PHA 5 μg / mL was approximately 79-81%, PHA 10 μg / mL was approximately 81-85%, PHA 20 μg / mL was approximately 77%, CD3 / CD28 was approximately 69%, and PMA+Ionomycin (P / I) was approximately 96% (Fig. 2). In particular, CD69 was significantly higher in the PHA (10 μg / mL)+PMA (50 ng / mL)+Ionomycin (500 ng / mL) combination treatment group than in the PHA 10 μg / mL single treatment group (approximately 85%). + CD25 - / + It was shown that the number of cells in the distribution increased significantly (approximately 96%) (Fig. 2b), and it was confirmed that activated T cells increased significantly when PHA, PMA, and Ionomycin were combined and treated compared to when PHA was treated alone.

[0105] 1-3. Analysis of IL-8 and IFN-γ expression levels

[0106] To analyze T cell activation by real-time PCR, Jurkat cell lines cultured under the same conditions as in Example 1-1 were treated with PHA (5 μg / mL or 10 μg / mL), PHA (5 μg / mL or 10 μg / mL) + CD3 / CD28 antibody (50 μL / mL), and PHA (5 μg / mL or 10 μg / mL) + PMA (50 ng / mL) + Ionomycin (500 ng / mL), respectively, and cultured for 6 hours to induce T cell activation. After inducing activation, cells were obtained, lysed to extract RNA, and cDNA was synthesized by reverse transcription PCR. The expression levels of the genes IL-8 and IFN-γ, which are expressed upon T cell activation, were analyzed by real-time PCR analysis.

[0107] As a result, the expression levels of IL-8 and IFNγ were found to have significantly increased in the group treated with PHA, PMA, and Ionomycin (Fig. 3a).

[0108] Example 2. Analysis of shadow image parameters according to T cell activation

[0109] 2-1. Comparative Analysis of IL-8 and IFN-γ Expression Levels and Shadow Image Parameters

[0110] To verify whether T cell activation can be detected using lens-free shadow imaging technology, group images were obtained from cells in which T cell activation was induced in Examples 1-3 using PHA (5μg / mL or 10μg / mL), PHA (5μg / mL or 10μg / mL) + CD3 / CD28 antibody (50μL / mL), and PHA (5μg / mL or 10μg / mL) + PMA (50ng / mL) + Ionomycin (500ng / mL), using a Lens-free Shadow Imaging Technology (LSIT) system composed of a single-wavelength LED light source with a central wavelength of approximately 470nm, an irradiation unit equipped with micro-pinholes, a cell chip, a CMOS image sensor, and an image processing algorithm. The parameters extracted / detected / calculated from these images, namely CMV (Central Maxima Value), PPD (Peak-to-Peak Distance), and MMD (Maximum-to-Minimum Distance, WCM (Width of Central Maximum), WSM (Width of Secondary Maxima), MMD_SD (Standard Deviation of MMD), WCM_SD (Standard Deviation of WCM), and WSM_SD (Standard Deviation of WSM) were compared and analyzed with the expression levels of the genes IL-8 and IFN-γ, which increased upon T cell activation in Examples 1-3 above. At this time, the distance between the light source and the sensor of the LSIT system was set to approximately 50 mm, and T cells inserted into the cell chip chamber induced diffraction and interference phenomena by the irradiated light to form a Digital Inline Hologram (DIH), and this pattern was directly recorded on the CMOS sensor. A single captured original image contained approximately 200 or more cells, which were individually separated through an automated algorithm.Specifically, the average of the background pixel values ​​in the image was calculated, and background illumination was removed by subtracting the average brightness value from the corresponding pixels. Subsequently, pixels were masked by classifying them into center (CENTRAL_POSITION), periphery (AROUND_POSITION), and background (BACKGROUND) based on a threshold value. Based on these masked images, connected center pixels were searched to form clusters, and cell candidates were extracted by calculating the center coordinates and size of each cluster. Overlap between cells was determined based on the distance between centers, and cells that overlapped or crossed image boundaries were excluded. Finally, the extracted cells were assigned unique numbers and bounding boxes that could be visualized; if necessary, they were saved as separate images. This process was fully automated and utilized as training data for subsequent parameter analysis. Parameter extraction was performed based on diffraction pattern analysis; intensity profiles were extracted for a total of 12 directions at 15-degree intervals relative to the center of each cell, and various parameters were calculated based on these profiles. The CMV, representing the maximum brightness value near the center, reflects the clarity of the cell structure, and the brightness difference between this maximum and the bidirectional minimums is defined as PPD. The brightness difference between the maximum and minimum values ​​within one half is defined as MMD, and MMD_SD, the standard deviation of the 12 directional measurements of this value, was calculated. WCM is derived from the pixel distance between the minimums located around the center, and WSM is derived from the pixel distance between the two maximums; these also complementarily provide information on directionality and asymmetry through WCM_SD and WSM_SD, respectively.

[0111] As a result, the expression levels of IL-8 and IFN-γ, which change due to T cell activation, and the shadow image parameters PPD, MMD_SD, WCM, WSM, WCM_SD, and WSM_SD showed similar trends (Fig. 3b).

[0112] 2-2. Comparative Analysis of Flow Cytometry Results and Shadow Image Parameters Under Conditions of Isolated and Activated T Cells

[0113] To determine whether T cell activation can be detected using lens-free shadow imaging technology, CD4+ T cells (purity 90±3%) isolated from human peripheral blood were treated with PHA (5μg / mL)+PMA (50ng / mL)+Ionomycin (500ng / mL) to induce activation for 3 or 6 hours, and then CD69 and CD25 were fluorescently stained and measured using a flow cytometer. Images were obtained from the same cells using a lens-free shadow imaging device, and the parameters derived therefrom were compared and analyzed.

[0114] Flow cytometry analysis showed that CD69 expression increased over time compared to the negative control group (Nil) after T cell activation (Fig. 4a), which showed a similar trend to the results of analysis using a combination of PPD and MMD_SD (PPD×MMD_SD) among the parameters of lens-free shadow imaging analysis (Fig. 4b).

[0115] 2-3. Comparative Analysis of CD69 Expression Levels and Shadow Image Parameters Under Conditions of Isolation After T-Cell Activation from Whole Blood

[0116] To determine whether T cell activation can be detected using lens-free shadow imaging technology, human peripheral blood was treated with PHA (5 μg / mL) + PMA (50 ng / mL) + Ionomycin (500 ng / mL) to induce activation for 3 or 6 hours, after which CD4 + T cells (purity 90±5%) were isolated, and the expression level of CD69, a marker expressed upon T cell activation, was measured using a flow cytometer. Additionally, images were obtained from the same cells using a lens-free shadow imaging device, and the parameters PPD and MMD_SD were calculated from them.

[0117] As a result, the expression level of CD69 was found to be significantly increased in the group in which activation induction was performed for 6 hours compared to the group in which activation induction was performed for 3 hours, and this showed a similar tendency to the result of combining the shadow image parameters PPD*MMD_SD (Fig. 5).

[0118] Through this, it was confirmed that T cell activation can be detected by a combination of parameters PPD and MMD_SD derived from lens-free shadow imaging analysis results.

[0119] Example 3. Analysis of the Diagnostic Effectiveness of Latent Tuberculosis (LTBI) Using Shadow Image Parameters

[0120] 3-1. Shadow Imaging Analysis

[0121] To evaluate whether the morphological changes that appear when T cells react with tuberculosis-specific antigens can be diagnosed using lens-free shadow imaging technology, experiments were performed using blood from subjects without latent tuberculosis infection (LTBI-negative) and subjects with latent tuberculosis infection (LTBI-positive). Blood from each subject was dispensed into Nil tubes (gray, negative control), TB antigen tubes (green), and Mitogen tubes (purple, positive control) used in the QuantiFERON-TB test, and reacted at 37°C for 6 hours. Co-stimulatory antibodies (CD28 / CD49d) were added to all tubes to improve reaction efficiency. After the reaction, blood was collected from each tube and CD4 + After isolating T cells, cell shadow images were captured using a lens-free shadow imaging device. Image parameters such as CMV, PPD, MMD, MMD_SD, and WCM_SD were extracted and changes between groups were analyzed. This experiment was repeated twice.

[0122] As a result, in all subjects, the parameters of CMV, PPD, MMD, MMD_SD, and WCM_SD were significantly increased in the Mitogen tube compared to the Nil tube (Fig. 6), suggesting that Mitogen induced a normal immune response as a positive control. On the other hand, no significant changes were observed in the TB antigen tube in LTBI-negative subjects, but in LTBI-positive subjects, the MMD_SD parameter in the TB antigen tube increased significantly compared to the Nil tube (Fig. 6).

[0123] 3-2. Analysis of Latent Tuberculosis Prediction Performance

[0124] Based on this, logistic regression analysis was performed using shadow image parameters as independent variables. As a result, the MMD_SD parameter was identified as a significant independent variable in predicting tuberculosis infection (P = 0.0083, Odds Ratio = 553.38, 95% CI = 5.08 ~ 60218.57), and WCM and WSM_SD also showed a significant trend as auxiliary variables. In addition, ROC analysis with a cut-off of 0.54 applied to the predicted value (P1) of the image-based diagnostic algorithm (Cellytics) showed an AUC of 0.920, Sensitivity of 81.8%, Specificity of 94.1%, and Accuracy of 89.2% (Fig. 7), confirming high predictive performance even when compared to the existing QuantiFERON results. In the auxiliary analysis, it showed an AUC of 0.880, a PPV of 90.3%, and an NPV of 88.6%, demonstrating the potential for Cellytics-based analysis to be used for label-free LTBI diagnosis.

[0125] These results strongly suggest that lens-free shadow image parameters, especially MMD_SD, can function as biomarkers reflecting T cell activation responses in patients with latent tuberculosis infection, and show that the analytical method of the present invention can be utilized as a new LTBI diagnostic platform that replaces or complements existing diagnostic methods.

Claims

1. Phytohemagglutinin (PHA), phorbol myristate acetate A composition for inducing non-specific activation of T cells, comprising phorbol myristate acetate (PMA) and ionomycin as active ingredients.

2. A T cell activation composition according to claim 1, wherein the weight ratio of phytohemagglutinin, phorbol myristate acetate, and ionomycin is 100 to 200:1:

10.

3. In the first paragraph, the composition comprises CD4 + Helper T cells, CD8 + A T cell activating composition applied to a cell population comprising killer T cells, naive T cells, memory T cells, central memory T cells, effector memory T cells, memory stem T cells, invariant T cells, cytokine-induced killer T cells, or gamma delta (g / d) T cells.

4. A method for activating T cells, comprising a step of treating the composition of paragraph 1 to T cells or peripheral blood containing T cells to induce activation. 5.1) An image sensor that captures a shadow image of a T cell; and 2) A T cell activity measuring device including a processor including an algorithm configured to analyze whether or not T cells are activated by calculating the PPD (Peak-to-Peak Distance), MMD (Maximum-to-Minimum Distance), WCM (Width of Central Maximum), WSM (Width of Secondary Maxima), MMD_SD (Standard Deviation of MMD), WCM_SD (Standard Deviation of WCM), or WSM_SD (Standard Deviation of WSM) parameters from at least a portion of a lens-free shadow image captured through an image sensor.

6. In the fifth paragraph, the algorithm is one or more shadow image parameters. A T cell activity measuring device configured to analyze the activity of T cells based on the results of an operation or combination thereof.

7. In paragraph 5, the T cell is CD4 + Helper T cells, CD8 + A device for measuring T cell activity, which is a killer T cell, naive T cell, memory T cell, central memory T cell, effector memory T cell, memory stem T cell, invariant T cell, cytokine-induced killer T cell, or gamma delta (g / d) T cell. 8.1) An image sensor that captures a shadow image of a T cell; and 2) A latent tuberculosis (LTBI) diagnosis device including a processor including an algorithm configured to analyze whether latent tuberculosis infection is present by calculating MMD_SD or WCM_SD parameters from a lens-free shadow image acquired through the image sensor.

9. In paragraph 8, the T cell is CD4 + Helper T cells, CD8 + A latent tuberculosis diagnostic device, which is a killer T cell, a naive T cell, a memory T cell, a central memory T cell, an effector memory T cell, a memory stem T cell, an invariant T cell, a cytokine-induced killer T cell, or a gamma delta (g / d) T cell. 10.1) Step of taking a shadow image of T cells; 2) a step of calculating PPD, MMD, WCM, WSM, MMD_SD, WCM_SD or WSM_SD parameters from at least a portion of the acquired lens-free shadow image; and 3) A method for analyzing T cell activity, comprising a step of comparing the above parameters with parameters of non-activated control T cells.

11. A method for analyzing T cell activity, further comprising a step of evaluating T cells as activated when the calculated parameters increase compared to parameters of control T cells in the 10th paragraph. 12.1) A step of isolating T cells from blood separated from the subject; 2) A step of capturing a lens-free shadow image of the T cell; 3) A step of calculating MMD_SD parameters from the acquired lens-free shadow image; and 4) A method for providing information for diagnosing latent tuberculosis, comprising a step of evaluating that a patient is infected with latent tuberculosis if the parameter increases compared to the MMD_SD parameter of T cells of the control group.

13. A method for providing information for diagnosing latent tuberculosis, further comprising a step of activating T cells by treating blood with a tuberculosis-specific antigen before isolating the T cells in claim 12.

14. A method for providing information for diagnosing latent tuberculosis, further comprising a step of treating antibodies against CD28 or CD49d before isolating T cells in claim 12.

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