WO2023163355A1 - Method and apparatus for quantifying car proteins of cell therapy agent by using raman spectroscopy analysis - Google Patents

Method and apparatus for quantifying car proteins of cell therapy agent by using raman spectroscopy analysis Download PDF

Info

Publication number
WO2023163355A1
WO2023163355A1 PCT/KR2022/021423 KR2022021423W WO2023163355A1 WO 2023163355 A1 WO2023163355 A1 WO 2023163355A1 KR 2022021423 W KR2022021423 W KR 2022021423W WO 2023163355 A1 WO2023163355 A1 WO 2023163355A1
Authority
WO
WIPO (PCT)
Prior art keywords
cell therapy
car
signal
sample
raman
Prior art date
Application number
PCT/KR2022/021423
Other languages
French (fr)
Korean (ko)
Inventor
김준기
주미연
Original Assignee
재단법인 아산사회복지재단
울산대학교 산학협력단
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 재단법인 아산사회복지재단, 울산대학교 산학협력단 filed Critical 재단법인 아산사회복지재단
Publication of WO2023163355A1 publication Critical patent/WO2023163355A1/en

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/44—Raman spectrometry; Scattering spectrometry ; Fluorescence spectrometry
    • G01J3/4412—Scattering spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6428—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes"
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6486—Measuring fluorescence of biological material, e.g. DNA, RNA, cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/58—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances
    • G01N33/582—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances with fluorescent label
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/705—Assays involving receptors, cell surface antigens or cell surface determinants
    • G01N2333/70503—Immunoglobulin superfamily, e.g. VCAMs, PECAM, LFA-3
    • G01N2333/7051—T-cell receptor (TcR)-CD3 complex

Definitions

  • the present invention relates to a method and apparatus for quantifying CAR protein of a cell therapeutic agent using Raman spectroscopy.
  • CAR-T Chimeric Antigen Receptor-T, hereinafter referred to as CAR-T
  • CAR-T Chimeric Antigen Receptor-T
  • CAR-T a cell therapy that genetically manipulates the patient's immune cells and re-administers them to the patient, has innovative medicinal effects on B cell-derived relapsed, refractory acute leukemia, and lymphoma. Proven.
  • CAR-T is also an immune cell therapy anticancer agent made by combining genetic information for expressing cancer cell-specific chimeric antigen receptors in patient's T cells.
  • CAR-NK is also used as a cell therapy.
  • CAR-T cell therapy such as CAR-T has side effects of CSC (Cytokine Release Syndrome) and neurotoxicity. It can cause fatal side effects such as death.
  • CSC Cytokine Release Syndrome
  • CAR-T Chimeric Antigen Receptor
  • FDA Food and Drug Administration
  • 3rd and 4th generation CAR-Ts each have two costimulatory domains, and are not widely used in clinical practice due to strong cytokine production and severe toxicity due to the added IL-12 domain. For this reason, the second generation of CAR-T is being used as a stable cell therapy in clinical practice.
  • the problem to be solved by the present invention is to provide a method and apparatus for quantifying CAR protein of a cell therapy using Raman spectroscopy.
  • the problem to be solved by the present invention is the amount of CAR protein expression for cell therapeutics administered to patients such as CAR-T using Raman spectroscopic analysis, or CAR protein expression excluding various extracellular matrices (Extracellular Expression) It is to provide a method and apparatus for quantifying the protein of a cell therapeutic agent capable of detecting the amount.
  • a first sample including immune cells and a second sample including a cell therapy agent are placed in each test kit to perform Raman spectroscopy
  • the first sample includes at least one immune cell of T cell, NK cell, iNKT cell, and ⁇ T cell
  • the second sample includes CAR-T, CAR-NK, CAR-iNKT, and ⁇ CAR- T may include at least one cell therapy agent.
  • the step of detecting the signal specific to the immune cell includes the step of obtaining a first Raman signal for the first sample and a spectrum of the first Raman signal by performing a Raman scattering test.
  • a Raman scattering test is performed to obtain a second Raman signal for the second sample and a spectrum of the second Raman signal.
  • the comparing of the unique signal includes extracting a difference value between the first Raman signal of the first specimen and the second Raman signal of the second specimen.
  • the step of confirming the CAR protein expression level by combining the second sample and fluorescent beads, and counting the number of fluorescent beads through a fluorescence microscope, the capacity of the second sample or the unit of the second sample Determine the number of fluorescent beads bound to the CAR protein per area, and match the Raman signal of the CAR protein increased in the cell therapy with the number of fluorescent beads to quantify the expression level of the CAR protein only by the increased amount of the Raman signal of the CAR protein can do.
  • the step of quantifying the expression level of the CAR protein is to match the increase in Raman signal of the CAR protein, the expression level of the CAR protein, and the detection human information to determine the difference between the Raman signal of the immune cell and the Raman signal of the cell therapy
  • the CAR protein expression level can be quantified only with the value.
  • an apparatus for quantifying protein of a cell therapy agent for solving the above problems is to arrange a first sample including immune cells and a second sample including a cell therapy agent in each test kit to conduct Raman spectroscopy. by performing a test module for detecting the unique signal of the immune cell and the unique signal of the cell therapy, respectively, comparing the unique signal of the immune cell and the unique signal of the cell therapy, and combining the cell therapy with fluorescent beads. Quantification to quantify the expression level of the CAR protein based on the difference between the intrinsic signal of the immune cell and the intrinsic signal of the cell therapy using a test signal analysis module for confirming the CAR protein expression level and a preset machine learning model Contains learning modules.
  • the test module includes a sample test and pre-processing unit for preparing the Raman spectroscopy test by placing the first sample and the second sample in each test kit, and the unique signal of the immune cells and the test sample through the Raman spectroscopy test.
  • a Raman signal detector for detecting each unique signal of a cell therapy agent, and a fluorescent bead that binds the second specimen and the fluorescent beads and binds the CAR protein per unit area of the second specimen or the volume of the second specimen through fluorescence measurement Includes a fluorescence measuring unit for detecting the number of and checking the expression level.
  • the Raman signal detector performs a Raman scattering test to obtain a first Raman signal for the first sample and a spectrum of the first Raman signal, and performs a Raman scattering test to A second Raman signal for the second specimen and a spectrum of the second Raman signal may be obtained.
  • test signal analysis module may include a signal comparison and analysis unit for extracting a difference between the first Raman signal of the first sample and the second Raman signal of the second sample, and a second Raman signal to which the fluorescent beads are coupled.
  • An expression level detector may be included to check the number of fluorescent beads bound to the CAR protein per unit area or volume of the second sample by counting the number of fluorescent beads from the sample.
  • the increased Raman signal of the CAR protein in the cell therapy product is matched with the number of the fluorescent beads, and the increased amount of the Raman signal of the CAR protein can be applied as data for quantifying the expression level of the CAR protein.
  • the quantification learning module matches the increase in the Raman signal of the CAR protein, the expression level of the CAR protein, and the detected human information to determine the CAR protein only with the difference between the Raman signal of the cell therapy and the Raman signal of the immune cell.
  • the expression level can be quantified.
  • the method and apparatus for quantifying CAR protein of a cell therapy agent can detect the CAR protein expression level of cell therapy agents administered to patients, such as CAR-T, using Raman spectroscopic analysis.
  • the expression level of the CAR protein can be quantified through a machine learning model. Accordingly, it is possible to assist in appropriately determining the administration concentration of the cell therapy agent to be administered to the patient.
  • CAR-T cell therapeutics
  • CAR-iNKT CAR-iNKT
  • CAR-NK CAR-NK
  • ⁇ CAR-T cell therapeutics
  • cell therapeutics such as CAR-iNKT, CAR-NK, and ⁇ CAR-T are possible using viral vectors.
  • a non-cell-destructive method not just Raman spectroscopy, it is compatible with other measurement equipment such as flow cytometry and qPCR to analyze protein expression levels for cell therapeutics.
  • FIG. 1 is a flowchart for sequentially explaining a method for quantifying a CAR protein of a cell therapy agent according to the present invention.
  • FIG. 2 is a diagram showing the structure of CAR-T, a second-generation cell therapy applied as a specimen according to the present invention.
  • FIG. 3 is a diagram showing a process for obtaining a Raman signal of a CAR protein by comparing the difference between the intrinsic signal of an immune cell and the intrinsic signal of a cellular therapeutic agent according to the present invention.
  • FIG. 4 is a view showing a process of confirming the expression level of CAR protein per unit area through fluorescent bead binding according to the present invention.
  • FIG. 5 is a block diagram specifically showing the configuration of an apparatus for quantifying a CAR protein of a cell therapeutic agent according to the present invention.
  • the identification code is used for convenience of description, and the identification code does not explain the order of each step, and each step may be performed in a different order from the specified order unless a specific order is clearly described in context. there is.
  • FIG. 1 is a flowchart for sequentially explaining a method for quantifying a CAR protein of a cell therapy agent according to an embodiment of the present invention.
  • the CAR protein quantification method of a cell therapy agent includes a preprocessing step of preparing a Raman spectroscopy test by placing a first sample including immune cells and a second sample including a cell therapy agent in each test kit ( ST1), detecting the unique signal of the immune cell and the unique signal of the cell therapy by performing Raman spectroscopy (ST2), comparing the unique signal of the immune cell and the unique signal of the cell therapy (ST3) ), checking the amount of CAR protein expression through the combination of the cell therapy and fluorescent beads (ST4), and using a pre-set machine learning model to determine the difference between the unique signal of the immune cell and the unique signal of the cell therapy and quantifying the CAR protein expression level based on the step (ST5).
  • the first sample is placed in at least one test kit to prepare for a Raman spectroscopy test, and the at least one test kit in which the second sample is placed is pre-processed.
  • At least one of T cells, NK cells, iNKT cells, and ⁇ T cells may be used as the first sample of immune cells capable of preparing the cell therapy.
  • a first Raman signal of the first sample may be obtained by performing a Raman scattering test on the first sample corresponding to the immune cells.
  • a Raman scattering method is an analysis method that provides molecular-specific information about biological and chemical specimens.
  • a change in intensity of a characteristic peak of a molecule is measured to quantify a constituent material (eg, protein) of the first sample.
  • a second sample used as a cell therapy is placed in at least one test kit to prepare for Raman spectroscopy, and the at least one test kit in which the cell therapy is placed is pretreated.
  • the second specimen used at least one cell therapy agent among CAR-T, CAR-NK, CAR-iNKT, and ⁇ CAR-T may be applied.
  • a Raman scattering test is performed to obtain the second Raman signal for the second specimen corresponding to the cell therapy agent and the second sample.
  • the spectrum of the Raman signal can be obtained.
  • a Raman scattering method is an analysis method that provides molecular-specific information about biological and chemical specimens.
  • a change in intensity of a characteristic peak of a molecule is measured to quantify a component (eg, protein) of a CAR-T sample.
  • FIG. 2 is a diagram showing the structure of CAR-T, a second-generation cell therapy agent applied as a specimen according to the present invention.
  • a Raman scattering method is used, and Raman spectroscopy equipment (eg, 532nm laser 785nm laser Raman Device) is used to acquire Raman signals. It is possible to sample a preset wavelength range, such as 200 cm -1 to 3000 cm -1 , and analyze the spectrum distribution of the sampled wavelength range. In particular, if the spectrum distribution of the Raman signal is analyzed through a preset computer program, the type of protein, lipid, RNA, DNA, etc. can be detected according to the unique Raman spectrum distribution and coverage of organic and inorganic molecules.
  • 3 is a view showing a process for obtaining a Raman signal of a CAR protein by comparing the difference between the intrinsic signal of an immune cell and the intrinsic signal of a cell therapy according to the present invention.
  • the step of comparing the unique signal of the immune cell and the unique signal of the cell therapy (ST3), the first Raman signal of the immune cell as the first sample and the second Raman signal of the cell therapy as the second sample are compared, A difference value between the first Raman signal and the second Raman signal according to the comparison result is extracted.
  • immune cells such as T cell and cell therapy agents such as CAR-T are mapped with a high-magnification (x100, x60) immersion objective lens, and then the difference between the two Raman signals can be confirmed and extracted. .
  • FIG. 4 is a view showing a process of confirming the expression level of CAR protein per unit area through fluorescent bead binding according to the present invention.
  • Figure 4 shows the antigen recognition site of the cell therapy agent, which is the second sample according to the present invention, and antigen-fluorescent beads (fluorescent beads combined with the antigen recognition site of the cell therapy agent and an antigen capable of generating an antigen-antibody binding reaction) according to the present invention. It shows the process of confirming the expression level of CAR protein per unit area through antigen-antibody binding reaction.
  • the antigen-recognition site of the second specimen is first bound to the antigen-fluorescent beads.
  • the number of fluorescent beads bound to the antigen recognition site of the CAR protein per unit area of the second sample or the capacity of the second sample is measured through fluorescence measurement using a laser microscope or a fluorescence microscope, and the expression level of the CAR protein detect
  • the second sample and fluorescent beads are combined and then the number of beads is counted through a fluorescence microscope, etc.
  • the expression level of the CAR protein can be quantified only by the increased amount of the Raman signal of the CAR protein by confirming the number of fluorescent beads bound to the protein and matching the increased Raman signal of the CAR protein with the number of fluorescent beads in the cell therapy. .
  • the counted number of beads may be applied as a learning factor representing the expression level of the CAR protein.
  • the increase in the Raman signal of the CAR protein obtained as the difference in Raman signal between the first sample, which is an immune cell, and the second sample, which is a cell therapy, and the expression level of the CAR protein identified by binding the cell therapy and the fluorescent beads, etc. It can be applied as a learning factor for learning.
  • the detected learning factors such as the increase in the Raman signal of the CAR protein and the expression level of the CAR protein, are input values into an input program and a database. It is input so that the CAR protein expression level can be quantified only by the difference between the Raman signal of the cell therapy and the Raman signal of the immune cell.
  • At least one machine learning program among preset machine learning programs may be selected to additionally input specimen-related information, a result of detecting a Raman signal, and a result of detecting a Raman spectrum as a learning factor.
  • Machine learning programs include PCA (Principal Component Analysis)-LDA (Linear Discriminant Analysis) model, NMF (Non-Negative Matrix Factorization)-LDA (Linear Discriminant Analysis) model, RFML (Random Forest Machine Learning) model, K-means clustering , MCR (multivariate curve resolution) analysis model, deep learning (eg, CNN (Convolutional Neural Networks)), etc. can be applied together.
  • PCA Principal Component Analysis
  • NMF Non-Negative Matrix Factorization
  • RFML Random Forest Machine Learning
  • K-means clustering K-means clustering
  • MCR multivariate curve resolution
  • deep learning eg, CNN (Convolutional Neural Networks)
  • the degree of CAR protein expression for cell therapeutics such as CAR-T and CAR-NK cells can be quantified.
  • the Raman signal increase of CAR protein for CAR-T, CAR-NK, CAR-iNKT, ⁇ CAR-T, and CAR protein The expression level and the like can be quantified.
  • FIG. 5 is a block diagram specifically showing the configuration of an apparatus for quantifying a CAR protein of a cell therapeutic agent according to the present invention.
  • the apparatus for quantifying CAR protein of a cell therapy agent shown in FIG. 5 includes a test module 100, a test signal analysis module 200, and a quantification learning module 300.
  • the test module 100 arranges a first sample including immune cells and a second sample including cell therapy in each test kit and performs a Raman spectroscopy test, thereby performing a unique signal for the immune cells and a unique signal for the cell therapy. detect each signal.
  • the inspection module 100 includes a sample inspection and preprocessing unit 101, a Raman signal detection unit 103, and a fluorescence measurement unit 105.
  • the specimen inspection and preprocessing unit 101 arranges the first specimen including immune cells capable of producing a cell therapy agent and the second specimen used as a cell therapy agent in at least one test kit to prepare for a Raman spectroscopy test, and At least one test kit in which the first sample and the second sample are disposed is subjected to pretreatment.
  • At least one of T cells, NK cells, iNKT cells, and ⁇ T cells may be used as the first sample of immune cells capable of preparing the cell therapy.
  • at least one of CAR-T, CAR-NK, CAR-iNKT, and ⁇ CAR-T may be applied as the second specimen used as the cell therapy.
  • the Raman signal detector 103 obtains a first Raman signal of the first specimen by performing a Raman scattering test on the first specimen corresponding to the immune cells, and A second Raman signal of the second specimen and a spectrum of the second Raman signal are obtained by performing a Raman scattering test on the second specimen, and the obtained first Raman signal, the second Save the Raman signals to the database.
  • the fluorescence measurement unit 105 binds the second sample and fluorescent beads, detects the volume of the second sample or the number of fluorescent beads bound to the CAR protein per unit area of the second sample through fluorescence measurement, and Check the expression level. Specifically, the fluorescence measuring unit 105 measures the volume of the second sample or the CAR protein per unit area of the second sample through fluorescence measurement through a laser microscope or a fluorescence microscope when the second sample and the fluorescent beads are combined. The number of bound fluorescent beads is detected and the expression level of the CAR protein is confirmed. At this time, by counting the number of beads through a fluorescence microscope or the like, the capacity of the second sample or the expression level of the CAR protein per unit area of the second sample can be confirmed.
  • the test signal analysis module 200 compares the intrinsic signal of the cell therapy agent and the intrinsic signal of the immune cell, and checks the expression level of the CAR protein through binding of the cell therapy agent and fluorescent beads.
  • the test signal analysis module 200 may include a target signal identification unit 202 , a signal comparison analysis unit 204 , and an expression level detection unit 206 .
  • the target signal identification unit 202 detects each Raman signal to compare the unique signals of the cell therapy and immune cells.
  • the signal comparison and analysis unit 204 compares the first Raman signal of the first specimen, which is an immune cell, and the second Raman signal of the second specimen, which is a cell therapy, and extracts a difference value.
  • the difference value after mapping immune cells such as T cell and cell therapy such as CAR-T with a high magnification (x100, x60) objective lens, the difference between the two Raman signals can be confirmed and extracted. there is.
  • the expression level detection unit 206 counts the number of beads from the fluorescent bead-bound CAR-T sample through a fluorescence microscope, etc., thereby determining the number of fluorescent beads bound to the CAR protein per unit area of the CAR-T sample or the volume of the CAR-T sample. Detect and confirm the expression level of the CAR protein.
  • the counted number of beads can be applied as a learning factor for machine learning that matches the Raman signal increase of the CAR protein and the expression level of the CAR protein.
  • the quantification learning module 300 quantifies the protein expression level using a preset machine learning model.
  • the quantification learning module 300 includes a learning factor input unit 301, a machine learning processing unit 303, a machine learning program input unit 305, and a quantification data generation unit 307.
  • the learning factor input unit 301 inputs learning factors including the amount of Raman signal increase of the CAR protein and the expression level of the CAR protein into an input program and a database as input values.
  • the machine learning processing unit 303 performs machine learning by matching the quantification data input as input values of the machine learning program, the Raman signal increase amount of the CAR protein, the expression level of the CAR protein, and detected human information.
  • the CAR protein expression level can be quantified only by the difference between the Raman signal of the cell therapy and the Raman signal of the immune cell by matching the Raman signal increase of the CAR protein, the expression level of the CAR protein, and the detected human information.
  • the machine learning program input unit 305 stores machine learning programs for each of a plurality of machine learning models, and selects and compiles at least one machine learning model and machine learning program.
  • the machine learning programs include PCA (Principal Component Analysis)-LDA (Linear Discriminant Analysis) model, NMF (Non-Negative Matrix Factorization)-LDA (Linear Discriminant Analysis) model, RFML (Random Forest Machine Learning) model, K- Means clustering, multivariate curve resolution (MCR) analysis model, deep learning (eg, Convolutional Neural Networks (CNN)), etc. may be concurrently applied.
  • PCA Principal Component Analysis
  • NMF Non-Negative Matrix Factorization
  • RFML Random Forest Machine Learning
  • K- Means clustering K- Means clustering
  • MCR multivariate curve resolution
  • CNN Convolutional Neural Networks
  • the quantification data generating unit 307 generates quantification data that quantifies the CAR protein expression level by matching previously calculated quantification data, the Raman signal increase amount of the CAR protein, the expression level of the CAR protein, and the detected human information.
  • the test signal analysis module 200 and the quantification learning module 300 include a memory (not shown) storing data for an algorithm or a program that reproduces an algorithm for controlling the operation of components in a processor of each module, and a memory ( Alternatively, it may include a processor (not shown) that performs the above-described operation using data stored in a database.
  • the memory and the processor may be implemented as separate chips.
  • the memory and the processor may be implemented as a single chip.
  • Each component refers to software and/or hardware components such as Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC).
  • FPGA Field Programmable Gate Array
  • ASIC Application Specific Integrated Circuit
  • the disclosed embodiments may be implemented in the form of a recording medium storing instructions executable by the test signal analysis module 200 and the quantification learning module 300 . Instructions may be stored in the form of program codes, and when executed by a processor, create program modules to perform operations of the disclosed embodiments.
  • the recording medium may be implemented as the test signal analysis module 200 and the quantification learning module 300 or a computer-readable recording medium.
  • the test signal analysis module 200 and the quantification learning module 300, or computer-readable recording media include all types of recording media in which instructions readable by a computer are stored. For example, there may be read only memory (ROM), random access memory (RAM), magnetic tape, magnetic disk, flash memory, optical data storage device, and the like.
  • ROM read only memory
  • RAM random access memory
  • magnetic tape magnetic tape
  • magnetic disk magnetic disk
  • flash memory optical data storage device
  • the above-described method and apparatus for quantifying CAR protein of a cell therapy product according to the present invention can detect the CAR protein expression level of the cell therapy agent administered to patients using Raman spectroscopic analysis.
  • the expression level of the CAR protein can be quantified through a machine learning model.
  • cell therapy is not simply limited to CAR-T, and overall application to cell therapy such as CAR-iNKT, CAR-NK, and ⁇ CAR-T in which a CAR gene is inserted using a viral vector is possible.
  • a non-cell-destructive method not just Raman spectroscopy, it is compatible with other measurement equipment such as flow cytometry and qPCR, enabling analysis of CAR protein expression levels for cell therapeutics.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Chemical & Material Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biochemistry (AREA)
  • Analytical Chemistry (AREA)
  • Pathology (AREA)
  • Molecular Biology (AREA)
  • Urology & Nephrology (AREA)
  • Hematology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Cell Biology (AREA)
  • Biotechnology (AREA)
  • Theoretical Computer Science (AREA)
  • Microbiology (AREA)
  • Software Systems (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Tropical Medicine & Parasitology (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Toxicology (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Optics & Photonics (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

Provided are a method and apparatus for quantifying chimeric antigen receptor (CAR) proteins of a cell therapy agent by using Raman spectroscopy analysis. The method for quantifying CAR proteins of a cell therapy agent, according to the present invention, comprises the steps of: performing pretreatment to prepare a Raman spectroscopy test by placing a first sample including immune cells and a second sample including a cell therapy agent in each test kit; performing the Raman spectroscopy test to respectively detect unique signals of the immune cells and unique signals of the cell therapy agent; comparing the unique signals of the immune cells with the unique signals of the cell therapy agent; identifying an expression level of CAR proteins through a combination of the cell therapy agent with fluorescent beads; and quantifying the expression level of CAR proteins on the basis of a difference value between the unique signals of the immune cells and the unique signals of the cell therapy agent by using a preset machine learning model.

Description

라만 분광 분석을 이용한 세포 치료제의 CAR 단백질 정량화 방법 및 장치Method and device for quantifying CAR protein in cell therapy using Raman spectroscopy
본 발명은 라만 분광 분석을 이용한 세포 치료제의 CAR 단백질 정량화 방법 및 장치에 관한 것이다.The present invention relates to a method and apparatus for quantifying CAR protein of a cell therapeutic agent using Raman spectroscopy.
환자의 면역 세포를 유전자 조작하여 다시 환자에게 투여하는 세포 치료제로서의 CAR-T(Chimeric Antigen Receptor-T, 이하 CAR-T)는 B 세포 유래의 재발성, 불응성 급성 백혈병, 및 림프종 등에 혁신적인 약효를 입증하였다. 이러한, CAR-T는 환자의 T 세포에 암세포 특이적인 키메릭 항원 수용체를 발현시키는 유전 정보를 조합하여 만든 면역세포치료 항암제이기도 하다. CAR-T 외에도 CAR-NK 등이 세포 치료제로 사용되기도 한다. CAR-T (Chimeric Antigen Receptor-T, hereinafter referred to as CAR-T), a cell therapy that genetically manipulates the patient's immune cells and re-administers them to the patient, has innovative medicinal effects on B cell-derived relapsed, refractory acute leukemia, and lymphoma. Proven. Such, CAR-T is also an immune cell therapy anticancer agent made by combining genetic information for expressing cancer cell-specific chimeric antigen receptors in patient's T cells. In addition to CAR-T, CAR-NK is also used as a cell therapy.
하지만, CAR-T 등의 세포 치료제는 CSC(Cytokine Release Syndrome)와 신경 독성이라는 부작용을 가지고 있으며, 암세포에만 특이적으로 발현되는 항원을 타겟으로 하는게 아니라면, 정상 세포에 존재하는 항원을 타겟으로 하여 환자를 사망에 이르게 하는 등 치명적인 부작용을 일으킬 수도 있다. However, cell therapy such as CAR-T has side effects of CSC (Cytokine Release Syndrome) and neurotoxicity. It can cause fatal side effects such as death.
종래 기술에 따른 CAR(Chimeric Antigen Receptor, 이하 CAR)를 이용한 항원 표적화는 CAR 설계에서 중요한 문제이며, CAR의 구조는 1세대에서 4세대까지 발전하였다. 현재 FDA(Food and Drug Administration)의 승인을 받은 CAR-T는 2세대의 세포 치료제로서 하나씩의 신호전달 도메인과 공동자극 도메인을 가지고 있다. 하지만, 3세대 및 4세대 CAR-T의 경우 각각 두 개의 공동자극 도메인을 가지며, 추가된 IL-12 도메인 등으로 인해 강력한 사이토카인 생산과 심각한 독성으로 인해 임상에서 많이 사용되고 있지 않다. 이러한 이유로 임상에서는 2세대의 CAR-T를 안정적인 세포 치료제로 사용하고 있다.Antigen targeting using a Chimeric Antigen Receptor (CAR) according to the prior art is an important issue in CAR design, and the structure of CAR has been developed from the 1st generation to the 4th generation. CAR-T, currently approved by the Food and Drug Administration (FDA), is a second-generation cell therapy that has one signaling domain and one costimulatory domain. However, 3rd and 4th generation CAR-Ts each have two costimulatory domains, and are not widely used in clinical practice due to strong cytokine production and severe toxicity due to the added IL-12 domain. For this reason, the second generation of CAR-T is being used as a stable cell therapy in clinical practice.
다만, 종래 기술로서 CAR-T를 세포 치료제로 사용함에 있어서, 바이러스 벡터를 이용한 CAR-T 세포 치료제에 CAR 유전자를 삽입하는 방법은 성공 확률이 높지 않아 성공적으로 만들어진 CAR-T의 양을 가늠하기 어려우며, 환자에게 투여하는 CAR-T 중에서는 CAR 단백질이 얼마나 발현되는지 예측하기 어려운 문제가 있었다.However, in using CAR-T as a cell therapy as a conventional technology, the method of inserting a CAR gene into a CAR-T cell therapy using a viral vector has a low probability of success, making it difficult to estimate the amount of successfully produced CAR-T. However, it is difficult to predict how much CAR protein is expressed among CAR-Ts administered to patients.
본 발명이 해결하고자 하는 과제는 라만 분광 분석을 이용한 세포 치료제의 CAR 단백질 정량화 방법 및 장치를 제공하는 것이다. The problem to be solved by the present invention is to provide a method and apparatus for quantifying CAR protein of a cell therapy using Raman spectroscopy.
또한, 본 발명이 해결하고자 하는 과제는 라만 분광 분석을 이용하여 CAR-T 등 환자들에게 투여되는 세포 치료제들에 대한 CAR 단백질 발현량, 또는 다양한 세포외 기질(Extracellular Expression)들이 제외된 CAR 단백질 발현량을 검출할 수 있는 세포 치료제의 단백질 정량화 방법 및 장치를 제공하는 것이다. In addition, the problem to be solved by the present invention is the amount of CAR protein expression for cell therapeutics administered to patients such as CAR-T using Raman spectroscopic analysis, or CAR protein expression excluding various extracellular matrices (Extracellular Expression) It is to provide a method and apparatus for quantifying the protein of a cell therapeutic agent capable of detecting the amount.
또한, 기계 학습 모델을 통해 세포 치료제들에 대한 CAR 단백질의 발현량을 정량화할 수 있는 세포 치료제의 단백질 정량화 방법 및 장치를 제공하는 것이다.In addition, to provide a method and apparatus for quantifying the protein of a cell therapy product capable of quantifying the expression level of the CAR protein for the cell therapy product through a machine learning model.
본 발명이 해결하고자 하는 과제들은 이상에서 언급된 과제로 제한되지 않으며, 언급되지 않은 또 다른 과제들은 아래의 기재로부터 통상의 기술자에게 명확하게 이해될 수 있을 것이다.The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.
상술한 과제를 해결하기 위한 본 발명의 일 면에 따른 세포 치료제의 CAR 단백질 정량화 방법은 면역세포를 포함하는 제1 검체 및 세포 치료제를 포함하는 제2 검체를 각각의 검사 키트에 배치하여 라만 분광 검사를 준비하는 전처리 단계, 라만 분광 검사를 수행하여, 상기 면역세포에 대한 고유 시그널 및 상기 세포 치료제에 대한 고유 시그널을 각각 검출하는 단계, 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널을 비교하는 단계, 상기 세포 치료제와 형광 비드의 결합을 통한 CAR 단백질 발현량을 확인하는 단계, 및 미리 설정된 기계 학습 모델을 이용하여 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널의 차이값을 기반으로 CAR 단백질 발현량을 정량화하는 단계를 포함한다. In the method for quantifying the CAR protein of a cell therapy agent according to an aspect of the present invention for solving the above problems, a first sample including immune cells and a second sample including a cell therapy agent are placed in each test kit to perform Raman spectroscopy A preprocessing step of preparing, a step of performing a Raman spectroscopy test to detect the unique signal for the immune cells and the unique signal for the cell therapy, respectively, comparing the unique signal of the immune cells and the unique signal of the cell therapy Step, confirming the CAR protein expression level through the combination of the cell therapy and fluorescent beads, and using a pre-set machine learning model to determine the CAR based on the difference between the unique signal of the immune cell and the unique signal of the cell therapy. quantifying the protein expression level.
이때, 상기 제1 검체는, T cell, NK cell, iNKT cell, 및 γδT cell 중 적어도 하나의 면역세포를 포함하고, 상기 제2 검체는 CAR-T, CAR-NK, CAR-iNKT, 및 γδCAR-T 중 적어도 하나의 세포 치료제를 포함할 수 있다. At this time, the first sample includes at least one immune cell of T cell, NK cell, iNKT cell, and γδT cell, and the second sample includes CAR-T, CAR-NK, CAR-iNKT, and γδCAR- T may include at least one cell therapy agent.
또한, 상기 면역세포에 대한 고유 시그널을 검출하는 단계는, 라만 산란(Raman scattering) 방식의 검사를 수행하여 상기 제1 검체에 대한 제1 라만 시그널 및 상기 제1 라만 시그널의 스펙트럼을 획득하는 단계를 포함하고, 상기 세포 치료제에 대한 고유 시그널을 검출하는 단계는, 라만 산란(Raman scattering) 방식의 검사를 수행해서 상기 제2 검체에 대한 제2 라만 시그널 및 상기 제2 라만 시그널의 스펙트럼을 획득한다. In addition, the step of detecting the signal specific to the immune cell includes the step of obtaining a first Raman signal for the first sample and a spectrum of the first Raman signal by performing a Raman scattering test. In the step of detecting the unique signal for the cell therapy, a Raman scattering test is performed to obtain a second Raman signal for the second sample and a spectrum of the second Raman signal.
또한, 상기 고유 시그널을 비교하는 단계는, 상기 제1 검체의 상기 제1 라만 시그널과 상기 제2 검체의 상기 제2 라만 시그널 간의 차이값을 추출하는 단계를 포함한다.In addition, the comparing of the unique signal includes extracting a difference value between the first Raman signal of the first specimen and the second Raman signal of the second specimen.
또한, 상기 CAR 단백질 발현량을 확인하는 단계는, 상기 제2 검체와 형광 비드를 결합시키고, 형광 현미경을 통해 상기 형광 비드의 개수를 카운팅함으로써, 상기 제2 검체의 용량 또는 상기 제2 검체의 단위 면적당 CAR 단백질과 결합한 형광 비드의 개수를 확인하고, 상기 세포 치료제에서 증가된 CAR 단백질의 라만 시그널과 상기 형광 비드의 개수를 매칭하여 상기 CAR 단백질의 라만 시그널의 증가량 만으로 상기 CAR 단백질의 발현량을 정량화할 수 있다.In addition, the step of confirming the CAR protein expression level, by combining the second sample and fluorescent beads, and counting the number of fluorescent beads through a fluorescence microscope, the capacity of the second sample or the unit of the second sample Determine the number of fluorescent beads bound to the CAR protein per area, and match the Raman signal of the CAR protein increased in the cell therapy with the number of fluorescent beads to quantify the expression level of the CAR protein only by the increased amount of the Raman signal of the CAR protein can do.
또한, 상기 CAR 단백질의 발현량을 정량화하는 단계는, 상기 CAR 단백질의 라만 시그널 증가량, 상기 CAR 단백질의 발현량 및 검출 인적 정보를 매칭시켜서 상기 면역세포의 라만 시그널 및 상기 세포 치료제의 라만 시그널의 차이값 만으로 상기 CAR 단백질 발현량을 정량화할 수 있다.In addition, the step of quantifying the expression level of the CAR protein is to match the increase in Raman signal of the CAR protein, the expression level of the CAR protein, and the detection human information to determine the difference between the Raman signal of the immune cell and the Raman signal of the cell therapy The CAR protein expression level can be quantified only with the value.
또한, 상술한 과제를 해결하기 위한 본 발명의 일 면에 따른 세포 치료제의 단백질 정량화 장치는, 면역세포를 포함한 제1 검체 및 세포 치료제를 포함한 제2 검체를 각각의 검사 키트에 배치하여 라만 분광 검사를 수행하여 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널을 각각 검출하는 검사 모듈과, 상기 면역세포의 고유 시그널과 상 세포 치료제의 고유 시그널을 비교하고, 상기 세포 치료제와 형광 비드 결합을 통한 CAR 단백질 발현량을 확인하는 검사 신호 분석 모듈, 및 미리 설정된 기계 학습 모델을 이용하여 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널의 차이값을 기반으로 상기 CAR 단백질의 발현량을 정량화하는 정량화 학습 모듈을 포함한다.In addition, an apparatus for quantifying protein of a cell therapy agent according to an aspect of the present invention for solving the above problems is to arrange a first sample including immune cells and a second sample including a cell therapy agent in each test kit to conduct Raman spectroscopy. by performing a test module for detecting the unique signal of the immune cell and the unique signal of the cell therapy, respectively, comparing the unique signal of the immune cell and the unique signal of the cell therapy, and combining the cell therapy with fluorescent beads. Quantification to quantify the expression level of the CAR protein based on the difference between the intrinsic signal of the immune cell and the intrinsic signal of the cell therapy using a test signal analysis module for confirming the CAR protein expression level and a preset machine learning model Contains learning modules.
이때, 상기 검사 모듈은, 상기 제1 검체 및 상기 제2 검체를 각각의 검사 키트에 배치하여 라만 분광 검사를 준비시키는 검체 검사 및 전처리부와, 라만 분광 검사를 통해 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널을 각각 검출하는 라만 시그널 검출부, 및 상기 제2 검체와 상기 형광 비드를 결합시키고, 형광 측정을 통해 상기 제2 검체의 용량 또는 상기 제2 검체의 단위 면적당 CAR 단백질과 결합한 형광 비드의 개수를 검출 및 발현량을 확인하는 형광 측정부를 포함한다.At this time, the test module includes a sample test and pre-processing unit for preparing the Raman spectroscopy test by placing the first sample and the second sample in each test kit, and the unique signal of the immune cells and the test sample through the Raman spectroscopy test. A Raman signal detector for detecting each unique signal of a cell therapy agent, and a fluorescent bead that binds the second specimen and the fluorescent beads and binds the CAR protein per unit area of the second specimen or the volume of the second specimen through fluorescence measurement Includes a fluorescence measuring unit for detecting the number of and checking the expression level.
또한, 상기 라만 시그널 검출부는, 라만 산란(Raman scattering) 방식의 검사를 수행하여 상기 제1 검체에 대한 제1 라만 시그널 및 상기 제1 라만 시그널의 스펙트럼을 획득하고, 라만 산란 방식의 검사를 수행해서 상기 제2 검체에 대한 제2 라만 시그널 및 상기 제2 라만 시그널의 스펙트럼을 획득할 수 있다.In addition, the Raman signal detector performs a Raman scattering test to obtain a first Raman signal for the first sample and a spectrum of the first Raman signal, and performs a Raman scattering test to A second Raman signal for the second specimen and a spectrum of the second Raman signal may be obtained.
또한, 상기 검사 신호 분석 모듈은, 상기 제1 검체의 상기 제1 라만 시그널과 상기 제2 검체의 상기 제2 라만 시그널 간의 차이값을 추출하는 신호 비교 분석부, 및 상기 형광 비드가 결합된 제2 검체로부터 상기 형광 비드의 개수를 카운팅하여 상기 제2 검체의 용량 또는 단위 면적당 상기 CAR 단백질과 결합된 형광 비드의 개수를 확인하는 발현량 검출부를 포함할 수 있다.In addition, the test signal analysis module may include a signal comparison and analysis unit for extracting a difference between the first Raman signal of the first sample and the second Raman signal of the second sample, and a second Raman signal to which the fluorescent beads are coupled. An expression level detector may be included to check the number of fluorescent beads bound to the CAR protein per unit area or volume of the second sample by counting the number of fluorescent beads from the sample.
이때, 상기 세포 치료제에서 증가된 CAR 단백질의 라만 시그널과 상기 형광 비드의 개수가 매칭되어, 상기 CAR 단백질의 라만 시그널의 증가량이 상기 CAR 단백질의 발현량을 정량화하는 데이터로 적용될 수 있다.In this case, the increased Raman signal of the CAR protein in the cell therapy product is matched with the number of the fluorescent beads, and the increased amount of the Raman signal of the CAR protein can be applied as data for quantifying the expression level of the CAR protein.
또한, 상기 정량화 학습 모듈은, 상기 CAR 단백질의 라만 시그널 증가량, 상기 CAR 단백질의 발현량 및 검출 인적 정보를 매칭시켜서, 상기 세포 치료제의 라만 시그널 및 상기 면역세포의 라만 시그널의 차이값 만으로 상기 CAR 단백질 발현량을 정량화할 수 있다.In addition, the quantification learning module matches the increase in the Raman signal of the CAR protein, the expression level of the CAR protein, and the detected human information to determine the CAR protein only with the difference between the Raman signal of the cell therapy and the Raman signal of the immune cell. The expression level can be quantified.
본 발명의 기타 구체적인 사항들은 상세한 설명 및 도면들에 포함되어 있다.Other specific details of the invention are included in the detailed description and drawings.
본 발명의 실시예에 따른 세포 치료제의 CAR 단백질 정량화 방법 및 장치는 라만 분광 분석을 이용하여 CAR-T 등 환자들에게 투여되는 세포 치료제들에 대한 CAR 단백질 발현량을 검출할 수 있다. 그리고 기계 학습 모델을 통해 CAR 단백질의 발현량을 정량화할 수 있다. 이에, 환자에게 투여되는 세포 치료제의 투여 농도를 적당하게 결정할 수 있도록 지원할 수 있다. The method and apparatus for quantifying CAR protein of a cell therapy agent according to an embodiment of the present invention can detect the CAR protein expression level of cell therapy agents administered to patients, such as CAR-T, using Raman spectroscopic analysis. In addition, the expression level of the CAR protein can be quantified through a machine learning model. Accordingly, it is possible to assist in appropriately determining the administration concentration of the cell therapy agent to be administered to the patient.
특히, CAR-T에 단순하게 국한되지 않고, 바이러스 벡터를 이용해 CAR-iNKT, CAR-NK, γδCAR-T 등의 세포 치료제들에 대한 전반적인 응용이 가능하다. 또한, 단순히 라만 분광법뿐만이 아닌 세포 비파괴적인 방법으로서 유세포 분석, qPCR 등의 다른 측정 장비들과 호환해서 세포 치료제들에 대한 단백질 발현량 분석이 가능하다. In particular, it is not simply limited to CAR-T, and overall applications to cell therapeutics such as CAR-iNKT, CAR-NK, and γδ CAR-T are possible using viral vectors. In addition, as a non-cell-destructive method, not just Raman spectroscopy, it is compatible with other measurement equipment such as flow cytometry and qPCR to analyze protein expression levels for cell therapeutics.
본 발명의 효과들은 이상에서 언급된 효과로 제한되지 않으며, 언급되지 않은 또 다른 효과들은 아래의 기재로부터 통상의 기술자에게 명확하게 이해될 수 있을 것이다.The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.
도 1은 본 발명에 따른 세포 치료제의 CAR 단백질 정량화 방법을 순서대로 설명하기 위한 순서도이다. 1 is a flowchart for sequentially explaining a method for quantifying a CAR protein of a cell therapy agent according to the present invention.
도 2는 본 발명에 따른 검체로 적용되는 2세대의 세포 치료제인 CAR-T 구조를 나타낸 도면이다. 2 is a diagram showing the structure of CAR-T, a second-generation cell therapy applied as a specimen according to the present invention.
도 3은 본 발명에 따른 면역세포의 고유 시그널 및 세포 치료제의 고유 시그널의 차이를 비교하여 CAR 단백질의 라만 시그널 획득을 위한 과정을 나타낸 도면이다. 3 is a diagram showing a process for obtaining a Raman signal of a CAR protein by comparing the difference between the intrinsic signal of an immune cell and the intrinsic signal of a cellular therapeutic agent according to the present invention.
도 4는 본 발명에 따른 형광 비드 결합을 통한 단위 면적당 CAR 단백질의 발현량 확인 과정을 나타낸 도면이다. 4 is a view showing a process of confirming the expression level of CAR protein per unit area through fluorescent bead binding according to the present invention.
도 5은 본 발명에 따른 세포 치료제의 CAR 단백질 정량화 장치를 구체적으로 나타낸 구성 블록도이다.5 is a block diagram specifically showing the configuration of an apparatus for quantifying a CAR protein of a cell therapeutic agent according to the present invention.
명세서 전체에 걸쳐 동일 참조 부호는 동일 구성요소를 지칭한다. 본 명세서가 실시예들의 모든 요소들을 설명하는 것은 아니며, 본 발명이 속하는 기술분야에서 일반적인 내용 또는 실시예들 간에 중복되는 내용은 생략한다. 명세서에서 사용되는 '부, 모듈, 부재, 블록'이라는 용어는 소프트웨어 또는 하드웨어로 구현될 수 있으며, 실시예들에 따라 복수의 '부, 모듈, 부재, 블록'이 하나의 구성요소로 구현되거나, 하나의 '부, 모듈, 부재, 블록'이 복수의 구성요소들을 포함하는 것도 가능하다. Like reference numbers designate like elements throughout the specification. This specification does not describe all elements of the embodiments, and general content or overlapping content between the embodiments in the technical field to which the present invention belongs is omitted. The term 'unit, module, member, or block' used in the specification may be implemented as software or hardware, and according to embodiments, a plurality of 'units, modules, members, or blocks' may be implemented as one component, It is also possible that one 'part, module, member, block' includes a plurality of components.
어떤 부분이 어떤 구성요소를 "포함"한다고 할 때, 이는 특별히 반대되는 기재가 없는 한 다른 구성요소를 제외하는 것이 아니라 다른 구성요소를 더 포함할 수 있는 것을 의미한다. When a certain component is said to "include", this means that it may further include other components, not excluding other components unless otherwise stated.
제1, 제2 등의 용어는 하나의 구성요소를 다른 구성요소로부터 구별하기 위해 사용되는 것으로, 구성요소가 전술된 용어들에 의해 제한되는 것은 아니다. Terms such as first and second are used to distinguish one component from another, and the components are not limited by the aforementioned terms.
단수의 표현은 문맥상 명백하게 예외가 있지 않는 한, 복수의 표현을 포함한다.Expressions in the singular number include plural expressions unless the context clearly dictates otherwise.
각 단계들에 있어 식별부호는 설명의 편의를 위하여 사용되는 것으로 식별부호는 각 단계들의 순서를 설명하는 것이 아니며, 각 단계들은 문맥상 명백하게 특정 순서를 기재하지 않는 이상 명기된 순서와 다르게 실시될 수 있다. In each step, the identification code is used for convenience of description, and the identification code does not explain the order of each step, and each step may be performed in a different order from the specified order unless a specific order is clearly described in context. there is.
이하 첨부된 도면들을 참고하여 본 발명의 작용 원리 및 실시예들에 대해 설명한다. Hereinafter, the working principle and embodiments of the present invention will be described with reference to the accompanying drawings.
도 1은 본 발명의 실시예에 따른 세포 치료제의 CAR 단백질 정량화 방법을 순서대로 설명하기 위한 순서도이다. 1 is a flowchart for sequentially explaining a method for quantifying a CAR protein of a cell therapy agent according to an embodiment of the present invention.
도 1에 도시된 일 실시예에 따른 세포 치료제의 CAR 단백질 정량화 방법으로는 환자에게 투여하는 세포 치료제에 대한 CAR 단백질 발현량을 확인 및 정량화한다. 이를 위해, 일 실시예에 따른 세포 치료제의 CAR 단백질 정량화 방법은 면역세포를 포함하는 제1 검체 및 세포 치료제를 포함하는 제2 검체를 각각의 검사 키트에 배치하여 라만 분광 검사를 준비하는 전처리 단계(ST1), 라만 분광 검사를 수행하여 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널을 각각 검출하는 단계(ST2), 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널을 비교하는 단계(ST3), 상기 세포 치료제와 형광 비드의 결합을 통한 CAR 단백질 발현량을 확인하는 단계(ST4), 및 미리 설정된 기계 학습 모델을 이용하여 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널의 차이값을 기반으로 상기 CAR 단백질 발현량을 정량화하는 단계(ST5)를 포함한다.In the method for quantifying the CAR protein of the cell therapy agent according to an embodiment shown in FIG. 1, the CAR protein expression level of the cell therapy agent administered to the patient is confirmed and quantified. To this end, the CAR protein quantification method of a cell therapy agent according to an embodiment includes a preprocessing step of preparing a Raman spectroscopy test by placing a first sample including immune cells and a second sample including a cell therapy agent in each test kit ( ST1), detecting the unique signal of the immune cell and the unique signal of the cell therapy by performing Raman spectroscopy (ST2), comparing the unique signal of the immune cell and the unique signal of the cell therapy (ST3) ), checking the amount of CAR protein expression through the combination of the cell therapy and fluorescent beads (ST4), and using a pre-set machine learning model to determine the difference between the unique signal of the immune cell and the unique signal of the cell therapy and quantifying the CAR protein expression level based on the step (ST5).
구체적으로, 상기 전처리 단계(ST1)에서는 상기 제1 검체를 적어도 하나의 검사 키트에 배치하여 라만 분광 검사를 준비하고, 상기 제2 검체가 배치된 적어도 하나의 검사 키트를 전처리한다. Specifically, in the pre-processing step (ST1), the first sample is placed in at least one test kit to prepare for a Raman spectroscopy test, and the at least one test kit in which the second sample is placed is pre-processed.
상기 세포 치료제를 만들 수 있는 면역세포의 제1 검체로는 T cell, NK cell, iNKT cell, 및 γδT cell 중 적어도 하나의 면역세포가 적용될 수 있다. At least one of T cells, NK cells, iNKT cells, and γδ T cells may be used as the first sample of immune cells capable of preparing the cell therapy.
상기 고유 신호를 검출하는 단계(ST2)에서는 상기 면역세포에 해당하는 상기 제1 검체에 대해 라만 산란(Raman scattering) 방식의 검사를 수행해서 상기 제1 검체의 제1 라만 시그널을 획득할 수 있다. In the step of detecting the unique signal (ST2), a first Raman signal of the first sample may be obtained by performing a Raman scattering test on the first sample corresponding to the immune cells.
일 실시예에 따른 라만 산란 방식은 생물학적 및 화학적 검체들에 대한 분자 특이적 정보를 제공하는 분석법이다. 라만 산란 방식의 검사로는 분자의 특징적인 피크의 세기(intensity) 변화를 측정하여 제1 검체에 대한 구성 물질(예를 들어, 단백질)을 정량화한다. A Raman scattering method according to an embodiment is an analysis method that provides molecular-specific information about biological and chemical specimens. In the Raman scattering test, a change in intensity of a characteristic peak of a molecule is measured to quantify a constituent material (eg, protein) of the first sample.
또한, 상기 전처리 단계(ST1)에서는 세포 치료제로 이용되는 제2 검체를 적어도 하나의 검사 키트에 배치하여 라만 분광 검사를 준비하고, 세포 치료제가 배치된 적어도 하나의 검사 키트를 전처리한다.세포 치료제로 이용되는 상기 제2 검체로는 CAR-T, CAR-NK, CAR-iNKT, 및 γδCAR-T 중 적어도 하나의 세포 치료제가 적용될 수 있다. In addition, in the pre-processing step (ST1), a second sample used as a cell therapy is placed in at least one test kit to prepare for Raman spectroscopy, and the at least one test kit in which the cell therapy is placed is pretreated. As the second specimen used, at least one cell therapy agent among CAR-T, CAR-NK, CAR-iNKT, and γδ CAR-T may be applied.
라만 분광 검사를 통해 세포 치료제의 고유 시그널을 검출하는 단계(ST2)에서는 라만 산란(Raman scattering) 방식의 검사를 수행하여 상기 세포 치료제에 해당하는 상기 제2 검체에 대한 제2 라만 시그널 및 상기 제2 라만 시그널의 스펙트럼을 획득할 수 있다.In the step of detecting the unique signal of the cell therapy agent through Raman spectroscopy (ST2), a Raman scattering test is performed to obtain the second Raman signal for the second specimen corresponding to the cell therapy agent and the second sample. The spectrum of the Raman signal can be obtained.
일 실시예에 따른 라만 산란 방식은 생물학적 및 화학적 검체들에 대한 분자 특이적 정보를 제공하는 분석법이다. 라만 산란 방식의 검사로는 분자의 특징적인 피크의 세기(intensity) 변화를 측정하여 CAR-T 검체에 대한 구성 물질(예를 들어, 단백질)을 정량화한다. A Raman scattering method according to an embodiment is an analysis method that provides molecular-specific information about biological and chemical specimens. In the Raman scattering test, a change in intensity of a characteristic peak of a molecule is measured to quantify a component (eg, protein) of a CAR-T sample.
도 2는 본 발명에 따른 검체로 적용되는 2세대의 세포 치료제 CAR-T 구조를 나타낸 도면이다. 2 is a diagram showing the structure of CAR-T, a second-generation cell therapy agent applied as a specimen according to the present invention.
도 2를 참조하면, 면역세포 및 세포 치료제의 고유 시그널을 검출하는 단계(ST2)에서는 라만 산란 방식을 이용하며, 라만 시그널 획득시에는 라만 분광 장비(예를 들어, 532nm laser 785nm laser Raman Device)를 이용하여 200cm-1 ~ 3000cm-1 등 미리 설정된 파장 범위를 샘플링하고, 샘플링된 파장 범위의 스펙트럼 분포를 분석할 수 있다. 특히, 라만 시그널의 스펙트럼 분포를 컴퓨터의 미리 설정된 프로그램을 통해 분석하면, 유기 및 무기 분자의 고유 라만 스펙트럼 분포 및 포함 범위에 따라 단백질의 종류, 지질, RNA, DNA 등을 검출할 수 있다. 도 3은 본 발명에 따른 면역세포의 고유 시그널 및 세포 치료제의 고유 시그널의 차이를 비교하여 CAR 단백질의 라만 시그널 획득을 위한 과정을 나타낸 도면이다.Referring to Figure 2, in the step of detecting the unique signals of immune cells and cell therapy (ST2), a Raman scattering method is used, and Raman spectroscopy equipment (eg, 532nm laser 785nm laser Raman Device) is used to acquire Raman signals. It is possible to sample a preset wavelength range, such as 200 cm -1 to 3000 cm -1 , and analyze the spectrum distribution of the sampled wavelength range. In particular, if the spectrum distribution of the Raman signal is analyzed through a preset computer program, the type of protein, lipid, RNA, DNA, etc. can be detected according to the unique Raman spectrum distribution and coverage of organic and inorganic molecules. 3 is a view showing a process for obtaining a Raman signal of a CAR protein by comparing the difference between the intrinsic signal of an immune cell and the intrinsic signal of a cell therapy according to the present invention.
면역세포의 고유 시그널 및 세포 치료제의 고유 시그널을 비교하는 단계(ST3)에서는 상기 제1 검체인 면역세포의 상기 제1 라만 시그널 및 상기 제2 검체인 세포 치료제의 상기 제2 라만 시그널을 비교하고, 상기 비교된 결과에 따른 상기 제1 라만 시그널 및 상기 제2 라만 시그널의 차이값을 추출한다. In the step of comparing the unique signal of the immune cell and the unique signal of the cell therapy (ST3), the first Raman signal of the immune cell as the first sample and the second Raman signal of the cell therapy as the second sample are compared, A difference value between the first Raman signal and the second Raman signal according to the comparison result is extracted.
상기 차이값을 추출 시에는 T cell 등의 면역세포와 CAR-T 등의 세포치료제를 각각 고배율(x100, x60)의 침지대물렌즈로 맵핑한 뒤 두 라만 시그널의 차이 값을 확인 및 추출할 수 있다. When extracting the difference value, immune cells such as T cell and cell therapy agents such as CAR-T are mapped with a high-magnification (x100, x60) immersion objective lens, and then the difference between the two Raman signals can be confirmed and extracted. .
도 4는 본 발명에 따른 형광 비드 결합을 통한 단위 면적당 CAR 단백질의 발현량 확인 과정을 나타낸 도면이다. 4 is a view showing a process of confirming the expression level of CAR protein per unit area through fluorescent bead binding according to the present invention.
즉, 도 4는 본 발명에 따른 상기 제2 검체인 세포 치료제의 항원 인지 부위와 항원-형광 비드(세포 치료제의 항원 인지 부위와 항원-항체 결합 반응을 발생시킬 수 있는 항원과 결합한 형광 비드)의 항원-항체 결합 반응을 통한 단위 면적당 CAR 단백질의 발현량을 확인하는 과정을 나타내고 있다.That is, Figure 4 shows the antigen recognition site of the cell therapy agent, which is the second sample according to the present invention, and antigen-fluorescent beads (fluorescent beads combined with the antigen recognition site of the cell therapy agent and an antigen capable of generating an antigen-antibody binding reaction) according to the present invention. It shows the process of confirming the expression level of CAR protein per unit area through antigen-antibody binding reaction.
도 4를 참조하면, 상기 제2 검체인 세포 치료제의 항원 인지 부위와 항원(CD19, CD22, CD33, CD147, CD340 중 어느 하나)-형광 비드의 항원-항체 결합 반응을 통한 CAR 단백질 발현량 확인 단계(ST4)에서는, 먼저 제2 검체의 항원 인지 부위와 항원-형광 비드를 결합시킨다. 그리고, 레이저 현미경 또는 형광 현미경 등을 통한 형광 측정을 통해 상기 제2 검체의 용량 또는 상기 제2 검체의 단위 면적당 CAR 단백질의 항원 인지 부위와 결합된 형광 비드의 개수를 확인 및 CAR 단백질의 발현량을 검출한다.Referring to FIG. 4, the step of confirming the CAR protein expression level through the antigen-antibody binding reaction between the antigen recognition site of the cell therapy agent, which is the second specimen, and the antigen (any one of CD19, CD22, CD33, CD147, and CD340)-fluorescent beads In (ST4), the antigen-recognition site of the second specimen is first bound to the antigen-fluorescent beads. In addition, the number of fluorescent beads bound to the antigen recognition site of the CAR protein per unit area of the second sample or the capacity of the second sample is measured through fluorescence measurement using a laser microscope or a fluorescence microscope, and the expression level of the CAR protein detect
이와 같이, CAR 단백질 발현량 확인 단계(ST4)에서 상기 제2 검체와 형광 비드를 결합시킨 다음 형광 현미경 등을 통해 비드 개수를 카운팅함으로써, 상기 제2 검체의 용량 또는 상기 제2 검체의 단위 면적당 CAR 단백질과 결합한 형광 비드의 개수를 확인하고, 상기 세포 치료제에서 증가된 CAR 단백질의 라만 시그널과 형광 비드의 개수를 매칭하여 상기 CAR 단백질의 라만 시그널의 증가량 만으로 상기 CAR 단백질의 발현량을 정량화할 수 있다.As such, in the CAR protein expression level confirmation step (ST4), the second sample and fluorescent beads are combined and then the number of beads is counted through a fluorescence microscope, etc. The expression level of the CAR protein can be quantified only by the increased amount of the Raman signal of the CAR protein by confirming the number of fluorescent beads bound to the protein and matching the increased Raman signal of the CAR protein with the number of fluorescent beads in the cell therapy. .
여기서, 상기 카운팅 된 비드 개수는 CAR 단백질의 발현량을 나타내는 학습 인자로 적용될 수 있다. 이러한, 면역세포인 상기 제1 검체 및 세포 치료제인 상기 제2 검체 간의 라만 시그널의 차이 값으로 획득되는 CAR 단백질의 라만 시그널 증가량, 세포 치료제와 형광 비드 결합으로 확인된 CAR 단백질의 발현량 등은 기계 학습을 위한 학습 인자로 적용될 수 있다. Here, the counted number of beads may be applied as a learning factor representing the expression level of the CAR protein. The increase in the Raman signal of the CAR protein obtained as the difference in Raman signal between the first sample, which is an immune cell, and the second sample, which is a cell therapy, and the expression level of the CAR protein identified by binding the cell therapy and the fluorescent beads, etc. It can be applied as a learning factor for learning.
미리 설정된 기계 학습 모델을 이용하여 단백질 발현량을 정량화하는 단계(ST5)에서는 상기 CAR 단백질의 라만 시그널 증가량, 및 상기 CAR 단백질의 발현량등의 검출된 학습 인자들이 입력 프로그램과 데이터 베이스에 입력 값들로 입력되어 상기 세포 치료제의 라만 시그널과 상기 면역세포의 라만 시그널 차이값 만으로도 상기 CAR 단백질 발현량을 정량화 할 수 있도록 한다. In the step of quantifying the protein expression level using a pre-set machine learning model (ST5), the detected learning factors, such as the increase in the Raman signal of the CAR protein and the expression level of the CAR protein, are input values into an input program and a database. It is input so that the CAR protein expression level can be quantified only by the difference between the Raman signal of the cell therapy and the Raman signal of the immune cell.
학습 인자들의 입력시, 미리 설정된 기계 학습 프로그램들 중 적어도 하나의 기계 학습 프로그램을 선택해서 검체 관련 정보를 비롯한 라만 시그널 검출 결과 및 라만 스펙트럼 검출 결과를 학습 인자로 추가 입력할 수 있다. When inputting learning factors, at least one machine learning program among preset machine learning programs may be selected to additionally input specimen-related information, a result of detecting a Raman signal, and a result of detecting a Raman spectrum as a learning factor.
기계 학습 프로그램으로는 PCA(Principal component analysis)-LDA(Linear Discriminant Analysis)모델, NMF(Non-Negative Matrix Factorization)-LDA(Linear Discriminant Analysis)모델, RFML(Random Forest Machine Learning) 모델, K-means clustering, MCR(multivariate curve resolution)분석 모델, 딥러닝(예를 들어, CNN(Convolutional Neural Networks)) 등이 동반적으로 적용될 수 있다. 학습 인자 입력시에는 결과적인 분류 정보들을 명확히 구분하기 위해 CAR 단백질의 라만 시그널 증가량, CAR 단백질의 발현량 외에도 검출 인적 정보(예를 들어, CAR 유전자 삽입량 등)을 각각 추가 입력할 수 있다. Machine learning programs include PCA (Principal Component Analysis)-LDA (Linear Discriminant Analysis) model, NMF (Non-Negative Matrix Factorization)-LDA (Linear Discriminant Analysis) model, RFML (Random Forest Machine Learning) model, K-means clustering , MCR (multivariate curve resolution) analysis model, deep learning (eg, CNN (Convolutional Neural Networks)), etc. can be applied together. When inputting the learning factor, in addition to the amount of Raman signal increase of the CAR protein and the expression amount of the CAR protein, detection human information (eg, CAR gene insertion amount, etc.) may be additionally input to clearly distinguish the resultant classification information.
단백질 발현량을 정량화하는 단계(ST5)에서는 기계 학습 프로그램의 입력 값으로 입력된 기 산출되었었던 정량화 데이터들, CAR 단백질의 라만 시그널 증가량, CAR 단백질의 발현량, 및 검출 인적 정보를 매칭시켜서 기계 학습이 수행되도록 한다. In the step of quantifying the protein expression amount (ST5), machine learning let this be done
최종적으로 산출되는 정량화 데이터들을 통해서는 CAR-T, CAR-NK cell 등 세포 치료제들에 대한 CAR 단백질 발현 정도를 정량화 할 수 있다. Through the finally calculated quantification data, the degree of CAR protein expression for cell therapeutics such as CAR-T and CAR-NK cells can be quantified.
CAR-T, CAR-NK, CAR-iNKT, γδCAR-T 등을 제2 검체로 적용해서는 CAR-T, CAR-NK, CAR-iNKT, γδCAR-T에 대한 CAR 단백질의 라만 시그널 증가량, CAR 단백질의 발현량 등을 정량화할 수 있다. By applying CAR-T, CAR-NK, CAR-iNKT, γδCAR-T, etc. as a second sample, the Raman signal increase of CAR protein for CAR-T, CAR-NK, CAR-iNKT, γδCAR-T, and CAR protein The expression level and the like can be quantified.
도 5은 본 발명에 따른 세포 치료제의 CAR 단백질 정량화 장치를 구체적으로 나타낸 구성 블록도이다. 5 is a block diagram specifically showing the configuration of an apparatus for quantifying a CAR protein of a cell therapeutic agent according to the present invention.
도 5에 도시된 세포 치료제의 CAR 단백질 정량화 장치는 검사 모듈(100), 검사 신호 분석 모듈(200), 및 정량화 학습 모듈(300)을 포함한다. The apparatus for quantifying CAR protein of a cell therapy agent shown in FIG. 5 includes a test module 100, a test signal analysis module 200, and a quantification learning module 300.
검사 모듈(100)은 면역세포를 포함한 제1 검체 및 세포 치료제를 포함한 제2 검체를 각각의 검사 키트에 배치하여 라만 분광 검사의 수행을 통해, 상기 면역세포에 대한 고유 시그널 및 상기 세포 치료제의 고유 시그널을 각각 검출한다. 이를 위해, 검사 모듈(100)은 검체 검사 및 전처리부(101), 라만 시그널 검출부(103), 및 형광 측정부(105)를 포함한다. The test module 100 arranges a first sample including immune cells and a second sample including cell therapy in each test kit and performs a Raman spectroscopy test, thereby performing a unique signal for the immune cells and a unique signal for the cell therapy. detect each signal. To this end, the inspection module 100 includes a sample inspection and preprocessing unit 101, a Raman signal detection unit 103, and a fluorescence measurement unit 105.
검체 검사 및 전처리부(101)는 세포 치료제를 만들수 있는 면역세포를 포함한 상기 제1 검체와 세포 치료제로 이용되는 상기 제2 검체를 적어도 하나의 검사 키트에 배치하여 라만 분광 검사를 준비하고, 상기 제1 검체 및 제2 검체가 배치된 적어도 하나의 검사 키트를 전처리시킨다. The specimen inspection and preprocessing unit 101 arranges the first specimen including immune cells capable of producing a cell therapy agent and the second specimen used as a cell therapy agent in at least one test kit to prepare for a Raman spectroscopy test, and At least one test kit in which the first sample and the second sample are disposed is subjected to pretreatment.
상기 세포 치료제를 만들 수 있는 면역세포의 제1 검체로는 T cell, NK cell, iNKT cell, 및 γδT cell 중 적어도 하나의 면역세포가 적용될 수 있다. 또한, 세포 치료제로 이용되는 상기 제2 검체로는 CAR-T, CAR-NK, CAR-iNKT, 및 γδCAR-T 중 적어도 하나의 세포 치료제가 적용될 수 있다. At least one of T cells, NK cells, iNKT cells, and γδ T cells may be used as the first sample of immune cells capable of preparing the cell therapy. In addition, at least one of CAR-T, CAR-NK, CAR-iNKT, and γδ CAR-T may be applied as the second specimen used as the cell therapy.
라만 시그널 검출부(103)는 상기 면역세포에 해당하는 상기 제1 검체에 대해 라만 산란(Raman scattering) 방식의 검사를 수행해서 상기 제1 검체의 제1 라만 시그널을 획득하고, 상기 세포 치료제에 해당하는 상기 제2 검체에 대해 라만 산란(Raman scattering) 방식의 검사를 수행해서 상기 제2 검체의 제2 라만 시그널 및 상기 제2 라만 시그널의 스펙트럼을 획득하고, 상기 획득된 제1 라만 시그널, 상기 제2 라만 시그널을 데이터 베이스에 저장한다. The Raman signal detector 103 obtains a first Raman signal of the first specimen by performing a Raman scattering test on the first specimen corresponding to the immune cells, and A second Raman signal of the second specimen and a spectrum of the second Raman signal are obtained by performing a Raman scattering test on the second specimen, and the obtained first Raman signal, the second Save the Raman signals to the database.
형광 측정부(105)는 상기 제2 검체와 형광 비드를 결합시키고, 형광 측정을 통해 상기 제2 검체의 용량 또는 상기 제2 검체의 단위 면적당 CAR 단백질과 결합한 형광 비드의 개수를 검출하고 CAR 단백질의 발현량을 확인한다. 구체적으로, 형광 측정부(105)는 상기 제2 검체와 형광 비드가 결합되면, 레이저 현미경 또는 형광 현미경 등을 통한 형광 측정을 통해 상기 제2 검체의 용량 또는 상기 제2 검체의 단위 면적당 CAR 단백질과 결합한 형광 비드의 개수를 검출하고 CAR 단백질의 발현량을 확인한다. 이때, 형광 현미경 등을 통해 비드 개수를 카운팅함으로써, 상기 제2 검체의 용량 또는 상기 제2 검체의 단위 면적당 CAR 단백질의 발현량을 확인할 수 있다. The fluorescence measurement unit 105 binds the second sample and fluorescent beads, detects the volume of the second sample or the number of fluorescent beads bound to the CAR protein per unit area of the second sample through fluorescence measurement, and Check the expression level. Specifically, the fluorescence measuring unit 105 measures the volume of the second sample or the CAR protein per unit area of the second sample through fluorescence measurement through a laser microscope or a fluorescence microscope when the second sample and the fluorescent beads are combined. The number of bound fluorescent beads is detected and the expression level of the CAR protein is confirmed. At this time, by counting the number of beads through a fluorescence microscope or the like, the capacity of the second sample or the expression level of the CAR protein per unit area of the second sample can be confirmed.
검사 신호 분석 모듈(200)은 상기 세포 치료제의 고유 시그널과 상기 면역세포의 고유 시그널을 비교하고, 상기 세포 치료제와 형광 비드 결합을 통한 CAR 단백질 발현량을 확인한다. 이를 위해, 검사 신호 분석 모듈(200)은 타겟 신호 확인부(202), 신호 비교 분석부(204), 및 발현량 검출부(206)를 포함할 수 있다. The test signal analysis module 200 compares the intrinsic signal of the cell therapy agent and the intrinsic signal of the immune cell, and checks the expression level of the CAR protein through binding of the cell therapy agent and fluorescent beads. To this end, the test signal analysis module 200 may include a target signal identification unit 202 , a signal comparison analysis unit 204 , and an expression level detection unit 206 .
타겟 신호 확인부(202)는 세포 치료제와 면역세포의 고유 신호 비교를 위해 각각의 라만 시그널을 검출한다.The target signal identification unit 202 detects each Raman signal to compare the unique signals of the cell therapy and immune cells.
신호 비교 분석부(204)는 면역세포인 제1 검체의 제1 라만 시그널과 세포 치료제인 제2 검체의 제2 라만 시그널을 비교하여 그 차이값을 추출한다. 여기서, 상기 차이 값을 추출 시에는 T cell 등의 면역세포와 CAR-T 등의 세포 치료제를 각각 고배율(x100, x60)의 대물렌즈로 맵핑한 뒤 두 라만 시그널의 차이 값을 확인 및 추출할 수 있다. The signal comparison and analysis unit 204 compares the first Raman signal of the first specimen, which is an immune cell, and the second Raman signal of the second specimen, which is a cell therapy, and extracts a difference value. Here, when extracting the difference value, after mapping immune cells such as T cell and cell therapy such as CAR-T with a high magnification (x100, x60) objective lens, the difference between the two Raman signals can be confirmed and extracted. there is.
발현량 검출부(206)는 형광 비드가 결합된 CAR-T 검체로부터 형광 현미경 등을 통해 비드 개수를 카운팅함으로써, CAR-T 검체 용량 또는 CAR-T 검체의 단위 면적당 CAR 단백질과 결합한 형광 비드의 개수를 검출하고 CAR 단백질의 발현량을 확인한다. 여기서, 카운팅 된 비드 개수는 CAR 단백질의 라만 시그널 증가량과 CAR 단백질의 발현량을 매칭시키는 기계 학습을 위한 학습 인자로 적용될 수 있다. The expression level detection unit 206 counts the number of beads from the fluorescent bead-bound CAR-T sample through a fluorescence microscope, etc., thereby determining the number of fluorescent beads bound to the CAR protein per unit area of the CAR-T sample or the volume of the CAR-T sample. Detect and confirm the expression level of the CAR protein. Here, the counted number of beads can be applied as a learning factor for machine learning that matches the Raman signal increase of the CAR protein and the expression level of the CAR protein.
정량화 학습 모듈(300)은 미리 설정된 기계 학습 모델을 이용하여 단백질 발현량을 정량화한다. 이를 위해, 정량화 학습 모듈(300)은 학습 인자 입력부(301), 기계학습 처리부(303), 기계학습 프로그램 입력부(305), 및 정량화 데이터 생성부(307)를 포함한다. The quantification learning module 300 quantifies the protein expression level using a preset machine learning model. To this end, the quantification learning module 300 includes a learning factor input unit 301, a machine learning processing unit 303, a machine learning program input unit 305, and a quantification data generation unit 307.
학습 인자 입력부(301)는 CAR 단백질의 라만 시그널 증가량, CAR 단백질의 발현량을 포함하는 학습 인자들을 입력 프로그램과 데이터 베이스에 입력 값들로 입력한다. The learning factor input unit 301 inputs learning factors including the amount of Raman signal increase of the CAR protein and the expression level of the CAR protein into an input program and a database as input values.
기계학습 처리부(303)는 기계 학습 프로그램의 입력 값으로 입력된 정량화 데이터들, CAR 단백질의 라만 시그널 증가량, CAR 단백질의 발현량, 검출 인적 정보들을 매칭시켜서 기계 학습을 수행한다. 그리고 CAR 단백질의 라만 시그널 증가량, CAR 단백질의 발현량, 검출 인적 정보들을 매칭시켜서 세포 치료제의 라만 시그널과 면역세포의 라만 시그널 차이 값 만으로도 CAR 단백질 발현량을 정량화시킬 수 있다. 기계학습 프로그램 입력부(305)는 복수의 기계 학습 모델별 기계 학습 프로그램을 저장하고, 적어도 하나의 기계 학습 모델 및 기계 학습 프로그램을 선택해서 컴파일한다. 이때, 기계 학습 프로그램으로는 PCA(Principal component analysis)-LDA(Linear Discriminant Analysis)모델, NMF(Non-Negative Matrix Factorization)-LDA(Linear Discriminant Analysis)모델, RFML(Random Forest Machine Learning) 모델, K-means clustering, MCR(multivariate curve resolution)분석 모델, 딥러닝(예를 들어, CNN(Convolutional Neural Networks)) 등이 동반적으로 적용될 수 있다. The machine learning processing unit 303 performs machine learning by matching the quantification data input as input values of the machine learning program, the Raman signal increase amount of the CAR protein, the expression level of the CAR protein, and detected human information. In addition, the CAR protein expression level can be quantified only by the difference between the Raman signal of the cell therapy and the Raman signal of the immune cell by matching the Raman signal increase of the CAR protein, the expression level of the CAR protein, and the detected human information. The machine learning program input unit 305 stores machine learning programs for each of a plurality of machine learning models, and selects and compiles at least one machine learning model and machine learning program. At this time, the machine learning programs include PCA (Principal Component Analysis)-LDA (Linear Discriminant Analysis) model, NMF (Non-Negative Matrix Factorization)-LDA (Linear Discriminant Analysis) model, RFML (Random Forest Machine Learning) model, K- Means clustering, multivariate curve resolution (MCR) analysis model, deep learning (eg, Convolutional Neural Networks (CNN)), etc. may be concurrently applied.
정량화 데이터 생성부(307)는 기 산출되었었던 정량화 데이터들, CAR 단백질의 라만 시그널 증가량, CAR 단백질의 발현량, 검출 인적 정보들을 매칭시켜서 CAR 단백질 발현량을 정량화시킨 정량화 데이터를 생성한다. The quantification data generating unit 307 generates quantification data that quantifies the CAR protein expression level by matching previously calculated quantification data, the Raman signal increase amount of the CAR protein, the expression level of the CAR protein, and the detected human information.
검사 신호 분석 모듈(200) 및 정량화 학습 모듈(300)은 각 모듈의 프로세서 내 구성요소들의 동작을 제어하기 위한 알고리즘 또는 알고리즘을 재현한 프로그램에 대한 데이터를 저장하는 메모리(미도시), 및 메모리(또는, 데이터 베이스)에 저장된 데이터를 이용하여 전술한 동작을 수행하는 프로세서(미도시)를 포함할 수 있다. 이때, 메모리와 프로세서는 각각 별개의 칩으로 구현될 수 있다. 또는, 메모리와 프로세서는 단일 칩으로 구현될 수도 있다. The test signal analysis module 200 and the quantification learning module 300 include a memory (not shown) storing data for an algorithm or a program that reproduces an algorithm for controlling the operation of components in a processor of each module, and a memory ( Alternatively, it may include a processor (not shown) that performs the above-described operation using data stored in a database. In this case, the memory and the processor may be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.
각각의 구성요소는 소프트웨어 및/또는 Field Programmable Gate Array(FPGA) 및 주문형 반도체(ASIC, Application Specific Integrated Circuit)와 같은 하드웨어 구성요소를 의미한다. Each component refers to software and/or hardware components such as Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC).
한편, 개시된 실시예들은 검사 신호 분석 모듈(200) 및 정량화 학습 모듈(300)에 의해 실행 가능한 명령어를 저장하는 기록매체의 형태로 구현될 수 있다. 명령어는 프로그램 코드의 형태로 저장될 수 있으며, 프로세서에 의해 실행되었을 때, 프로그램 모듈을 생성하여 개시된 실시예들의 동작을 수행할 수 있다. 기록매체는 검사 신호 분석 모듈(200) 및 정량화 학습 모듈(300)이나, 컴퓨터로 읽을 수 있는 기록매체로 구현될 수 있다. Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing instructions executable by the test signal analysis module 200 and the quantification learning module 300 . Instructions may be stored in the form of program codes, and when executed by a processor, create program modules to perform operations of the disclosed embodiments. The recording medium may be implemented as the test signal analysis module 200 and the quantification learning module 300 or a computer-readable recording medium.
검사 신호 분석 모듈(200) 및 정량화 학습 모듈(300)이나, 컴퓨터가 읽을 수 있는 기록매체로는 컴퓨터에 의하여 해독될 수 있는 명령어가 저장된 모든 종류의 기록 매체를 포함한다. 예를 들어, ROM(Read Only Memory), RAM(Random Access Memory), 자기 테이프, 자기 디스크, 플래쉬 메모리, 광 데이터 저장장치 등이 있을 수 있다. The test signal analysis module 200 and the quantification learning module 300, or computer-readable recording media include all types of recording media in which instructions readable by a computer are stored. For example, there may be read only memory (ROM), random access memory (RAM), magnetic tape, magnetic disk, flash memory, optical data storage device, and the like.
전술한 본 발명에 따른 세포 치료제의 CAR 단백질 정량화 방법 및 장치로는 라만 분광 분석을 이용하여 환자들에게 투여되는 세포 치료제들에 대한 CAR 단백질 발현량을 검출할 수 있다. 그리고 기계 학습 모델을 통해 CAR 단백질의 발현량을 정량화할 수 있다. 이에, 환자에게 투여되는 세포 치료제의 투여 농도를 적당하게 결정할 수 있도록 지원하여, 세포 치료제들에 대한 안정성을 향상시킬 수 있다. 특히, 세포 치료제가 CAR-T에 단순하게 국한되지 않고, 바이러스 벡터를 이용해 CAR gene을 삽입한 CAR-iNKT, CAR-NK, γδCAR-T 등의 세포 치료제들에 대한 전반적인 응용이 가능하다. 또한, 단순히 라만 분광법뿐만이 아닌 세포 비파괴적인 방법으로서 유세포 분석, qPCR 등의 다른 측정 장비들과 호환해서 세포 치료제들에 대한 CAR 단백질 발현량 분석이 가능하다. The above-described method and apparatus for quantifying CAR protein of a cell therapy product according to the present invention can detect the CAR protein expression level of the cell therapy agent administered to patients using Raman spectroscopic analysis. In addition, the expression level of the CAR protein can be quantified through a machine learning model. Thus, it is possible to improve the stability of cell therapeutics by supporting the proper determination of the administration concentration of the cell therapeutics administered to the patient. In particular, cell therapy is not simply limited to CAR-T, and overall application to cell therapy such as CAR-iNKT, CAR-NK, and γδ CAR-T in which a CAR gene is inserted using a viral vector is possible. In addition, as a non-cell-destructive method, not just Raman spectroscopy, it is compatible with other measurement equipment such as flow cytometry and qPCR, enabling analysis of CAR protein expression levels for cell therapeutics.
이상에서와 같이 첨부된 도면을 참조하여 개시된 실시예들을 설명하였다. 본 발명이 속하는 기술분야에서 통상의 지식을 가진 자는 본 발명의 기술적 사상이나 필수적인 특징을 변경하지 않고도, 개시된 실시예들과 다른 형태로 본 발명이 실시될 수 있음을 이해할 것이다. 개시된 실시예들은 예시적인 것이며, 한정적으로 해석되어서는 안 된다.As above, the disclosed embodiments have been described with reference to the accompanying drawings. Those skilled in the art to which the present invention pertains will understand that the present invention can be implemented in a form different from the disclosed embodiments without changing the technical spirit or essential features of the present invention. The disclosed embodiments are illustrative and should not be construed as limiting.

Claims (13)

  1. 세포 치료제의 단백질 정량화 장치에 의해 수행되는 방법에 있어서,In the method performed by the protein quantification device for cell therapy,
    면역세포를 포함하는 제1 검체 및 세포 치료제를 포함하는 제2 검체를 각각의 검사 키트에 배치하여 라만 분광 검사를 준비하는 전처리 단계;A preprocessing step of preparing a Raman spectroscopy test by arranging a first sample containing immune cells and a second sample including a cell therapy agent in each test kit;
    라만 분광 검사를 수행하여, 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널을 각각 검출하는 단계; Performing Raman spectroscopy to detect the unique signals of the immune cells and the unique signals of the cell therapy, respectively;
    상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널을 비교하는 단계; Comparing the unique signal of the immune cell and the unique signal of the cell therapy;
    상기 세포 치료제와 형광 비드의 결합을 통한 CAR 단백질 발현량을 확인하는 단계; 및Checking the amount of CAR protein expression through the combination of the cell therapy and fluorescent beads; and
    미리 설정된 기계 학습 모델을 이용하여 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널의 차이값을 기반으로 상기 CAR 단백질 발현량을 정량화하는 단계를 포함하는 세포 치료제의 CAR 단백질 정량화 방법.A method for quantifying a CAR protein of a cell therapy agent comprising quantifying the CAR protein expression level based on a difference between the intrinsic signal of the immune cell and the intrinsic signal of the cell therapy using a preset machine learning model.
  2. 제1 항에 있어서, According to claim 1,
    상기 제1 검체는, T cell, NK cell, iNKT cell, 및 γδT cell 중 적어도 하나의 면역세포 포함하고,The first sample includes at least one immune cell among T cells, NK cells, iNKT cells, and γδ T cells,
    상기 제2 검체는, CAR-T, CAR-NK, CAR-iNKT, 및 γδCAR-T 중 적어도 하나의 세포 치료제를 포함하는 세포 치료제의 CAR 단백질 정량화 방법.The second sample is a CAR protein quantification method of a cell therapy agent comprising at least one cell therapy agent among CAR-T, CAR-NK, CAR-iNKT, and γδCAR-T.
  3. 제2 항에 있어서, According to claim 2,
    상기 면역세포의 고유 시그널을 검출하는 단계는, 라만 산란(Raman scattering) 방식의 검사를 수행하여 상기 제1 검체에 대한 제1 라만 시그널 및 상기 제1 라만 시그널의 스펙트럼을 획득하는 단계를 포함하고,The step of detecting the unique signal of the immune cell includes obtaining a first Raman signal for the first specimen and a spectrum of the first Raman signal by performing a Raman scattering test,
    상기 세포 치료제의 고유 시그널을 검출하는 단계는, 라만 산란(Raman scattering) 방식의 검사를 수행해서 상기 제2 검체에 대한 제2 라만 시그널 및 상기 제2 라만 시그널의 스펙트럼을 획득하는 단계를 포함하는 세포 치료제의 CAR 단백질 정량화 방법.The step of detecting the unique signal of the cell therapy may include obtaining a second Raman signal for the second sample and a spectrum of the second Raman signal by performing a Raman scattering test. Methods for quantifying CAR proteins in therapeutics.
  4. 제3 항에 있어서, According to claim 3,
    상기 고유 시그널을 비교하는 단계는, Comparing the unique signal,
    상기 제1 검체의 상기 제1 라만 시그널과 상기 제2 검체의 상기 제2 라만 시그널 간의 차이값을 추출하는 단계를 포함하는 세포 치료제 CAR 단백질 정량화 방법.A method for quantifying a cell therapy drug CAR protein comprising extracting a difference value between the first Raman signal of the first specimen and the second Raman signal of the second specimen.
  5. 제3 항에 있어서, According to claim 3,
    상기 CAR 단백질 발현량을 확인하는 단계는, The step of checking the CAR protein expression level,
    상기 제2 검체와 형광 비드를 결합시키고 형광 현미경을 통해 상기 형광 비드의 개수를 카운팅함으로써, 상기 제2 검체의 용량 또는 상기 제2 검체의 단위 면적당 CAR 단백질과 결합한 형광 비드의 개수를 확인하고, 상기 세포 치료제에서 증가된 CAR 단백질의 라만 시그널과 형광 비드의 개수를 매칭하여 상기 CAR 단백질의 라만 시그널의 증가량 만으로 상기 CAR 단백질의 발현량을 정량화하는 것을 특징으로 하는 세포 치료제의 CAR 단백질 정량화 방법. By combining the second sample and fluorescent beads and counting the number of fluorescent beads through a fluorescence microscope, the capacity of the second sample or the number of fluorescent beads bound to the CAR protein per unit area of the second sample is confirmed, and the A method for quantifying the CAR protein of a cell therapy, characterized in that the expression level of the CAR protein is quantified only by the increased amount of the Raman signal of the CAR protein by matching the Raman signal of the CAR protein increased in the cell therapy with the number of fluorescent beads.
  6. 제5 항에 있어서, According to claim 5,
    상기 CAR 단백질의 발현량을 정량화하는 단계는, The step of quantifying the expression level of the CAR protein,
    상기 CAR 단백질의 라만 시그널 증가량, 상기 CAR 단백질의 발현량 및 검출 인적 정보를 매칭시켜서, 상기 세포 치료제의 라만 시그널 및 상기 면역세포의 라만 시그널의 차이값 만으로 상기 CAR 단백질 발현량을 정량화하는 것을 특징으로 하는 세포 치료제의 CAR 단백질 정량화 방법.By matching the increase in Raman signal of the CAR protein, the expression level of the CAR protein, and detection human information, only the difference between the Raman signal of the cell therapy and the Raman signal of the immune cell is characterized in that the CAR protein expression level is quantified A method for quantifying the CAR protein of a cell therapeutic agent.
  7. 하드웨어인 컴퓨터와 결합되어, 제1 항에 따른 세포 치료제의 CAR 단백질 정량화 방법을 수행하기 위한 프로그램이 저장된 컴퓨터 판독 가능한 기록매체.A computer-readable recording medium in which a program for performing the CAR protein quantification method of a cell therapy agent according to claim 1 is stored in combination with a hardware computer.
  8. 면역세포를 포함한 제1 검체 및 세포 치료제를 포함한 제2 검체를 각각의 검사 키트에 배치하여 라만 분광 검사를 수행하여 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널을 각각 검출하는 검사 모듈; A test module configured to place a first sample including immune cells and a second sample including cell therapy into respective test kits and perform Raman spectroscopy to detect signals unique to the immune cells and signals unique to the cell therapy, respectively;
    상기 면역세포의 고유 시그널과 상 세포 치료제의 고유 시그널을 비교하고, 상기 세포 치료제와 형광 비드 결합을 통한 CAR 단백질 발현량을 확인하는 검사 신호 분석 모듈; 및 A test signal analysis module that compares the unique signal of the immune cells and the unique signal of the phase cell therapy, and checks the amount of CAR protein expression through the combination of the cell therapy and the fluorescent beads; and
    미리 설정된 기계 학습 모델을 이용하여 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널의 차이값을 기반으로 상기 CAR 단백질의 발현량을 정량화하는 정량화 학습 모듈을 포함하는 세포 치료제의 CAR 단백질 정량화 장치.A CAR protein quantification device for cell therapy comprising a quantification learning module for quantifying the expression level of the CAR protein based on the difference between the unique signal of the immune cell and the unique signal of the cell therapy using a preset machine learning model.
  9. 제8 항에 있어서, According to claim 8,
    상기 검사 모듈은, The inspection module,
    상기 제1 검체 및 상기 제2 검체를 각각의 검사 키트에 배치하여 라만 분광 검사를 준비시키는 검체 검사 및 전처리부; a sample inspection and pre-processing unit preparing the first sample and the second sample for a Raman spectroscopy test by placing the first sample and the second sample in each test kit;
    라만 분광 검사를 통해 상기 면역세포의 고유 시그널 및 상기 세포 치료제의 고유 시그널을 각각 검출하는 라만 시그널 검출부; 및 a Raman signal detector for detecting the unique signals of the immune cells and the unique signals of the cell therapy, respectively, through Raman spectroscopy; and
    상기 제2 검체와 상기 형광 비드를 결합시키고, 형광 측정을 통해 상기 제2 검체의 용량 또는 상기 제2 검체의 단위 면적당 CAR 단백질과 결합한 형광 비드의 개수를 검출 및 발현량을 확인하는 형광 측정부를 포함하는 세포 치료제의 CAR 단백질 정량화 장치.A fluorescence measuring unit that binds the second sample and the fluorescent beads, detects the volume of the second sample or the number of fluorescent beads bound to the CAR protein per unit area of the second sample through fluorescence measurement, and checks the expression level A device for quantifying CAR proteins of cell therapy products.
  10. 제9 항에 있어서, According to claim 9,
    상기 라만 시그널 검출부는, The Raman signal detector,
    라만 산란(Raman scattering) 방식의 검사를 수행하여 상기 제1 검체에 대한 제1 라만 시그널 및 상기 제1 라만 시그널의 스펙트럼을 획득하고,Obtaining a first Raman signal and a spectrum of the first Raman signal for the first specimen by performing a Raman scattering test,
    라만 산란 방식의 검사를 수행해서 상기 제2 검체에 대한 제2 라만 시그널 및 상기 제2 라만 시그널의 스펙트럼을 획득하는 세포 치료제의 CAR 단백질 정량화 장치.An apparatus for quantifying a CAR protein of a cell therapy product that acquires a second Raman signal for the second sample and a spectrum of the second Raman signal by performing a Raman scattering test.
  11. 제9 항에 있어서, According to claim 9,
    상기 검사 신호 분석 모듈은,The test signal analysis module,
    상기 제1 검체의 상기 제1 라만 시그널과 상기 제2 검체의 상기 제2 라만 시그널 간의 차이값을 추출하는 신호 비교 분석부; 및a signal comparison and analysis unit extracting a difference value between the first Raman signal of the first specimen and the second Raman signal of the second specimen; and
    상기 형광 비드가 결합된 제2 검체로부터 상기 형광 비드의 개수를 카운팅하여 상기 제2 검체의 용량 또는 단위 면적당 상기 CAR 단백질과 결합된 형광 비드의 개수를 확인하는 발현량 검출부를 포함하는 세포 치료제의 CAR 단백질 정량화 장치.CAR of cell therapy comprising an expression level detection unit for counting the number of fluorescent beads from the second sample to which the fluorescent beads are bound and confirming the number of fluorescent beads bound to the CAR protein per unit area or volume of the second sample Protein quantification device.
  12. 제11 항에 있어서, According to claim 11,
    상기 세포 치료제에서 증가된 CAR 단백질의 라만 시그널과 상기 형광 비드의 개수가 매칭되어, 상기 CAR 단백질의 라만 시그널의 증가량이 상기 CAR 단백질의 발현량을 정량화하는 데이터로 적용되는 것을 특징으로 하는 세포 치료제의 CAR 단백질 정량화 장치.In the cell therapy, the increased Raman signal of the CAR protein and the number of fluorescent beads are matched, and the increased amount of the Raman signal of the CAR protein is applied as data for quantifying the expression level of the CAR protein. CAR protein quantification device.
  13. 제12 항에 있어서, According to claim 12,
    상기 정량화 학습 모듈은,The quantification learning module,
    상기 CAR 단백질의 라만 시그널 증가량, 상기 CAR 단백질의 발현량 및 검출 인적 정보를 매칭시켜서, 상기 세포 치료제의 라만 시그널 및 상기 면역세포의 라만 시그널의 차이값 만으로 상기 CAR 단백질 발현량을 정량화하는 것을 특징으로 하는 세포 치료제의 CAR 단백질 정량화 장치.By matching the increase in Raman signal of the CAR protein, the expression level of the CAR protein, and detection human information, only the difference between the Raman signal of the cell therapy and the Raman signal of the immune cell is characterized in that the CAR protein expression level is quantified A device for quantifying CAR proteins of cell therapy products.
PCT/KR2022/021423 2022-02-23 2022-12-27 Method and apparatus for quantifying car proteins of cell therapy agent by using raman spectroscopy analysis WO2023163355A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR1020220023812A KR20230126523A (en) 2022-02-23 2022-02-23 Method and device for quantification of car protein in cell therapy products using raman spectroscopic analysis
KR10-2022-0023812 2022-02-23

Publications (1)

Publication Number Publication Date
WO2023163355A1 true WO2023163355A1 (en) 2023-08-31

Family

ID=87766212

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2022/021423 WO2023163355A1 (en) 2022-02-23 2022-12-27 Method and apparatus for quantifying car proteins of cell therapy agent by using raman spectroscopy analysis

Country Status (2)

Country Link
KR (1) KR20230126523A (en)
WO (1) WO2023163355A1 (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20070006728A (en) * 2003-12-30 2007-01-11 인텔 코포레이션 Methods for using raman spectroscopy to obtain a protein profile of a biological sample
KR20200018177A (en) * 2018-08-10 2020-02-19 삼성전자주식회사 Apparatus and method for estimating analyte concentration, Apparatus and method for generating analyte concentration estimation model
KR20200118262A (en) * 2019-04-03 2020-10-15 고려대학교 산학협력단 Method for Raman Spectroscopy Based Protein Quantification, System and Method for Raman Spectroscopy Based Bio-Marker Quantification
JP6855382B2 (en) * 2015-03-26 2021-04-14 ユニバーシティー オブ ヒューストン システム Integrated functionality of cells and profiling of molecules
WO2021184060A1 (en) * 2020-03-16 2021-09-23 Proteomics International Pty Ltd Endometriosis biomarkers

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101444995B1 (en) 2012-05-31 2014-10-01 인제대학교 산학협력단 Method for detecting protein-protein interaction of cytosolic proteins

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20070006728A (en) * 2003-12-30 2007-01-11 인텔 코포레이션 Methods for using raman spectroscopy to obtain a protein profile of a biological sample
JP6855382B2 (en) * 2015-03-26 2021-04-14 ユニバーシティー オブ ヒューストン システム Integrated functionality of cells and profiling of molecules
KR20200018177A (en) * 2018-08-10 2020-02-19 삼성전자주식회사 Apparatus and method for estimating analyte concentration, Apparatus and method for generating analyte concentration estimation model
KR20200118262A (en) * 2019-04-03 2020-10-15 고려대학교 산학협력단 Method for Raman Spectroscopy Based Protein Quantification, System and Method for Raman Spectroscopy Based Bio-Marker Quantification
WO2021184060A1 (en) * 2020-03-16 2021-09-23 Proteomics International Pty Ltd Endometriosis biomarkers

Also Published As

Publication number Publication date
KR20230126523A (en) 2023-08-30

Similar Documents

Publication Publication Date Title
Nassar et al. Label‐free identification of white blood cells using machine learning
Su et al. Single cell proteomics in biomedicine: High‐dimensional data acquisition, visualization, and analysis
Amir et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
US10867706B2 (en) Multi-scale complex systems transdisciplinary analysis of response to therapy
Wang et al. Fluorescence microscopy of living cells in culture, part a: Fluorescent analogs, labeling cells and basic microscopy
US11726031B2 (en) Fluorescent spectrum correcting method and fluorescent spectrum measuring device
Valet et al. Cytomics—new technologies: towards a human cytome project
Kimoto et al. Further development of the rat Pig‐a mutation assay: Measuring rat Pig‐a mutant bone marrow erythroids and a high throughput assay for mutant peripheral blood reticulocytes
CN111118167B (en) Tumor mutation load standard substance and preparation method and kit thereof
Brodin The biology of the cell–insights from mass cytometry
WO2013165065A1 (en) Device for analyzing protein-protein interactions at single molecular level in cell environment
Tárnok Slide‐based cytometry for cytomics—A minireview
Keating et al. Opportunities and challenges in implementation of multiparameter single cell analysis platforms for clinical translation
Giudice et al. Optimization and standardization of fluorescent cell barcoding for multiplexed flow cytometric phenotyping
WO2016117812A1 (en) Apparatus and method for evaluating performance of cancer biomarker
Heinemann et al. Deep morphology learning enhances ex vivo drug profiling-based precision medicine
WO2012144859A2 (en) Method and apparatus for analyzing protein-protein interaction on single-molecule level within the cellular environment
CN108319815A (en) A kind of method and its system virtually dyed for cell
WO2023163355A1 (en) Method and apparatus for quantifying car proteins of cell therapy agent by using raman spectroscopy analysis
Barnard Flow cytometry: a flexible tool for biomarker research
Solorzano et al. Machine learning for cell classification and neighborhood analysis in glioma tissue
CN115058388A (en) Mesenchymal stem cell wound repair advantage function subset and identification and application thereof
Siegerist et al. ScoMorphoFISH: A deep learning enabled toolbox for single‐cell single‐mRNA quantification and correlative (ultra‐) morphometry
Tsai et al. High throughput pSTAT signaling profiling by fluorescent cell barcoding and computational analysis
Misevic Single-cell omics analyses with single molecular detection: challenges and perspectives

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22929102

Country of ref document: EP

Kind code of ref document: A1