Method for preparing nucleic acid derived from skin cells of a subject

Abstract

To provide methods for analyzing RNA of a subject with high accuracy.SOLUTION: Provided is a method for preparing a nucleic acid derived from skin cells of a subject, comprising storing RNA-containing skin surface lipid collected from the subject at 0°C or below. Provided is a method for preparing a nucleic acid derived from skin cells of a subject, comprising converting the RNA contained in the lipid on the skin surface of the subject into cDNA, subsequently amplifying it with multiplex PCR, and purifying the resulting reaction product.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to a method for preparing nucleic acid derived from skin cells of a subject, and a method for analyzing the skin of a subject using said nucleic acid. [Background technology]

[0002] Technologies have been developed to investigate the current and future physiological state of the human body by analyzing molecules (nucleic acids, proteins, metabolites, etc.) in biological samples. Among these, analysis using nucleic acid molecules has the advantage that a wealth of information can be obtained in one analysis because comprehensive analysis methods have been established, and that it is easy to link the analysis results to functions based on the many research reports on single nucleotide polymorphisms and RNA functions.

[0003] Among various tissues of the living body, the skin is in contact with the outside world and has attracted attention as a tissue from which biological samples can be collected with low invasiveness. As a method for non-invasively collecting and analyzing nucleic acids from the skin, a method has been reported in which a human skin sample is collected by wiping the skin surface with a moistened cotton swab (cotton ball) and RNA profiling is performed (Non-Patent Document 1). Patent Document 1 describes that nucleic acids such as RNA derived from the skin cells of a subject are separated from lipids on the skin surface and used as samples for biological analysis. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2018 / 008319 [Non-patent literature]

[0005] [Non-Patent Document 1] Forensic Sci Int Genet,2012, 6(5):565-577 Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention relates to a method for preparing nucleic acid derived from skin cells of a subject, and a method for analyzing the skin of a subject using said nucleic acid. [Means for solving the problem]

[0007] The present invention provides a method for preparing nucleic acid derived from skin cells of a subject, which comprises storing RNA-containing lipids on the skin surface collected from a subject at 0°C or below. The present invention also relates to a method for detecting a subject's skin surface lipids by converting the RNA contained in the lipids to cDNA by reverse transcription, and then subjecting the cDNA to multiplex PCR; purifying the PCR reaction product; The present invention provides a method for preparing nucleic acid from a skin cell of a subject, the method comprising: Furthermore, the present invention provides a method for analyzing the condition of the skin, a site other than the skin, or the entire body of a subject, which comprises analyzing the nucleic acid prepared by the above-mentioned method. The present invention further provides a method for evaluating the effect or efficacy of a topical skin preparation, an intradermal preparation, a patch preparation, an oral preparation, or an injection preparation on a subject, comprising analyzing the nucleic acid prepared by the above-mentioned method. Furthermore, the present invention provides a method for analyzing the concentration of a component in the blood of a subject, which comprises analyzing the nucleic acid prepared by the above-mentioned method. Effect of the Invention

[0008] According to the method of the present invention, a nucleic acid sample derived from skin cells contained in lipids on the skin surface of a subject can be stably stored. Therefore, the present invention improves the accuracy of analysis (e.g., genetic analysis, diagnosis, etc.) using a nucleic acid sample. Furthermore, since the concentration of a component derived from a specific marker gene contained in a nucleic acid sample derived from skin cells prepared by the method of the present invention correlates with the concentration of various components present in blood, the use of the nucleic acid sample enables non-invasive measurement of blood component concentrations. [Brief description of the drawings]

[0009] [Figure 1] Effect of storage temperature on RNA stability during SSL. 18S:18S ribosomal RNA, 28S:28S ribosomal RNA. [Diagram 2] Expression of atopic dermatitis-related markers in SSL-derived RNA. [Diagram 3] Prediction of blood testosterone concentration from the expression level of SSL-derived RNA based on a machine learning model. The vertical axis shows the predicted value, and the horizontal axis shows the actual measured value. [Figure 4] Prediction results of blood concentrations of various components from SSL-derived RNA expression levels based on a machine learning model. [Diagram 5] Subjects were divided into groups based on the expression levels (0 day) of 22 RNA groups whose expression increases with the use of facial cleanser. [Figure 6] Changes in moisture content of the stratum corneum due to use of facial cleanser. [Figure 7] Percentage of people who felt a moisturizing effect after using the facial cleanser, based on survey results. [Figure 8] Expression of BSG and HCAR2 in high and low sebum secretion groups. [Figure 9] Expression of ASPRV1 and PADI3 in high and low hydration groups. [Figure 10] Expression of SOCS3, JUNB, and IL-1B in high and low skin redness groups. [Figure 11] Prediction of skin condition from SSL-derived RNA expression levels based on a machine learning model. The vertical axis represents the predicted value, and the horizontal axis represents the actual measured value. [Figure 12] Prediction of blood cortisol concentration from SSL-derived RNA expression levels based on a machine learning model. The vertical axis shows the predicted value, and the horizontal axis shows the actual measured value. [Figure 13] The predicted cumulative UV exposure time from the amount of SSL-derived RNA expression based on a machine learning model. The vertical axis shows the predicted value, and the horizontal axis shows the calculated value based on a questionnaire given to subjects. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] All patents, non-patent documents, and other publications cited herein are hereby incorporated by reference in their entirety.

[0011] The names of genes disclosed herein are based on the Official Symbols listed in NCBI ([www.ncbi.nlm.nih.gov / ]), while the names of genes in Gene Ontology (GO) are based on the Pathway IDs listed in String ([string-db.org / ]).

[0012] Since RNA is easily degraded, it is usually stored under special low-temperature conditions of -80°C unless it has been specially treated. If a sample is used in which the amount of RNA has decreased due to degradation, the accuracy of the analysis will decrease. Even if RNA is converted to cDNA by reverse transcription and stored, if the original RNA is in an unstable state, a sufficient amount of cDNA cannot be obtained, and the accuracy of the analysis will also decrease.

[0013] The present inventor previously discovered that lipids present on the skin surface (skin surface lipids) contain RNA derived from the skin cells of a subject, and that this can be used to analyze the living body, and filed a patent application (Patent Document 1). This time, the present inventor further discovered that the RNA contained in the skin surface lipids can be stored under general low-temperature conditions, and can be stably stored even without the special low-temperature conditions of -80°C as previously known. Furthermore, the present inventor discovered that by subjecting RNA separated from the skin surface lipids to reverse transcription and PCR under specified conditions, and then purifying it, a sufficient amount of nucleic acid sample for analysis can be obtained even from skin surface lipids with a low RNA content.

[0014] Therefore, in one aspect, the present invention provides a method for preparing nucleic acid derived from skin cells of a subject.In one embodiment, the method for preparing nucleic acid derived from skin cells of a subject according to the present invention comprises storing the RNA-containing skin surface lipid collected from a subject at 0°C or lower.In another embodiment, the method for preparing nucleic acid derived from skin cells of a subject according to the present invention comprises converting the RNA contained in the skin surface lipid of a subject into cDNA by reverse transcription, then subjecting the cDNA to multiplex PCR, and purifying the reaction product of the PCR.

[0015] As used herein, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin, also known as sebum. Generally, SSL mainly contains secretions secreted from exocrine glands such as sebaceous glands in the skin, and exists on the skin surface in the form of a thin layer covering the skin surface.

[0016] As used herein, unless otherwise specified, "skin" is a general term for an area including tissues such as the epidermis, dermis, hair follicles, sweat glands, sebaceous glands and other glands on the body surface.

[0017] The nucleic acid derived from the skin cells of the subject prepared by the method of the present invention is not particularly limited to DNA, RNA, etc., but is preferably RNA or DNA prepared from said RNA. Examples of RNA include mRNA, tRNA, rRNA, small RNA (e.g., microRNA (miRNA), small interfering RNA (siRNA), Piwi-interacting RNA (piRNA), etc.), long intergenic non-coding (linc)RNA, etc. mRNA is RNA that codes for a protein, and most of them have a length of 1000 nt or more. miRNA, siRNA, piRNA, and lincRNA are non-coding (nc)RNA that do not code for a protein. miRNA is a small RNA of about 19-30 nt in length among ncRNAs. LincRNA is a long non-coding RNA that has poly-A like mRNA, and has a length of 200 nt or more (Non-Patent Document 1). More preferably, the RNA prepared by the method of the present invention is RNA having a length of 200 nt or more. Even more preferably, the RNA prepared by the method of the present invention is at least one selected from the group consisting of mRNA and lincRNA. Examples of DNA prepared in the method of the present invention include cDNA prepared from the above-mentioned RNA, and reaction products from said cDNA (eg, PCR products, cloned DNA, etc.).

[0018] The subject in the method of the present invention can be any organism that has SSL on skin.Examples of subject include mammals, including humans and non-human mammals, and preferably humans.Preferably, the subject is a human or non-human mammal that needs or desires to analyze its own nucleic acid.Also preferably, the subject is a human or non-human mammal that needs or desires to analyze gene expression in skin, or to analyze the condition of skin or other sites using nucleic acid.

[0019] SSL collected from a subject contains RNA expressed in the skin cells of the subject, preferably contains RNA expressed in any of the epidermis, sebaceous glands, hair follicles, sweat glands, and dermis of the subject, more preferably contains RNA expressed in any of the epidermis, sebaceous glands, hair follicles, and sweat glands of the subject (see Patent Document 1). Thus, RNA derived from the skin cells of a subject prepared by the method of the present invention is preferably RNA derived from at least one site selected from the epidermis, sebaceous glands, hair follicles, sweat glands, and dermis of the subject, more preferably RNA derived from at least one site selected from the epidermis, sebaceous glands, hair follicles, and sweat glands.

[0020] In one embodiment, the method of the present invention may further include collecting SSL from a subject. The site of the skin from which the SSL is collected includes, but is not limited to, the skin of any part of the body such as the head, face, neck, trunk, hands and feet, skin with diseases such as atopy, acne, dryness, inflammation (redness), tumor, and skin with wounds. In addition, the site of the skin from which the SSL is collected preferably does not include the skin of the palm, back, sole, or fingers.

[0021] Any means used for recovering or removing SSL from the skin can be used to collect SSL from the skin of a subject. Preferably, an SSL absorbent material, an SSL adhesive material, or an instrument for scraping SSL from the skin, as described below, can be used. The SSL absorbent material or SSL adhesive material is not particularly limited as long as it has an affinity for SSL, and examples thereof include polypropylene, pulp, and the like. More detailed examples of procedures for collecting SSL from the skin include a method of absorbing SSL into a sheet-like material such as oil blotting paper or oil blotting film, a method of adhering SSL to a glass plate, tape, or the like, and a method of scraping SSL off and collecting it with a spatula, scraper, or the like. In order to improve the adsorption of SSL, an SSL absorbent material that has been previously impregnated with a highly lipid-soluble solvent may be used. On the other hand, it is preferable that the SSL absorbent material contains a low content of highly water-soluble solvent or water, since the adsorption of SSL is inhibited when the SSL absorbent material contains a highly water-soluble solvent or water. It is preferable to use the SSL absorbent material in a dry state.

[0022] In one embodiment of the present invention, the collected RNA-containing SSL is stored under low-temperature conditions of 0° C. or less. In order to minimize the degradation of RNA, the collected RNA-containing SSL is preferably stored under a predetermined low-temperature condition as soon as possible after collection. The temperature condition for storing the RNA-containing SSL in the present invention may be 0° C. or less, but is preferably −20±20° C. to −80±20° C., more preferably −20±10° C. to −80±10° C., even more preferably −20±20° C. to −40±20° C., even more preferably −20±10° C. to −40±10° C., even more preferably −20±10° C., and even more preferably −20±5° C. This temperature condition is considerably milder than the conventional general RNA storage conditions (e.g., −80° C.). Therefore, in order to store the RNA-containing SSL in the present invention under the preferred low-temperature conditions, it is not necessary to use special equipment such as an ultra-low-temperature freezer or a dedicated storage container, and a normal freezer or a freezer compartment of a refrigerator can be used. The storage period of the RNA-containing SSL of the present invention under low temperature conditions is not particularly limited, but is preferably 12 months or less, for example, 6 hours or more and 12 months or less, more preferably 6 months or less, for example, 1 day or more and 6 months or less, and even more preferably 3 months or less, for example, 3 days or more and 3 months or less.

[0023] RNA can be isolated from the collected RNA-containing SSL by methods commonly used for extracting or purifying RNA from biological samples, such as the phenol / chloroform method, the acid guanidinium thiocyanate-phenol-chloroform extraction (AGPC) method, or methods using columns such as TRIzol (registered trademark), RNeasy (registered trademark), or QIAzol (registered trademark), methods using special silica-coated magnetic particles, methods using Solid Phase Reversible Immobilization magnetic particles, or extraction using a commercially available RNA extraction reagent such as ISOGEN.

[0024] In another embodiment of the present invention, the RNA (SSL-derived RNA) separated from the RNA-containing SSL may be used in various analyses in the form of RNA. In a preferred embodiment, the SSL-derived RNA is converted to DNA. Preferably, the SSL-derived RNA is converted to cDNA by reverse transcription, and then the cDNA is subjected to PCR, and the resulting reaction product is purified. For the reverse transcription, a primer targeting a specific RNA to be analyzed may be used, but for more comprehensive nucleic acid storage and analysis, it is preferable to use a random primer. In addition, in the PCR, a primer pair targeting a specific DNA to be analyzed may be used to amplify only the specific DNA, but multiple primer pairs may be used to amplify multiple DNAs. Preferably, the PCR is multiplex PCR. This is a method of simultaneously amplifying multiple gene regions by simultaneously using multiple primer pairs in a PCR reaction system. Multiplex PCR can be performed using a commercially available kit (e.g., Ion AmpliSeq Transcriptome Human Gene Expression Kit; Life Technologies Japan, Inc., etc.).

[0025] A general reverse transcriptase or a reverse transcription reagent kit can be used for reverse transcription of RNA. Preferably, a highly accurate and efficient reverse transcriptase or a reverse transcription reagent kit is used, and examples thereof include M-MLV Reverse Transcriptase and its variants, or a commercially available reverse transcriptase or reverse transcription reagent kit, such as the PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Inc.) and the SuperScript (registered trademark) Reverse Transcriptase series (Thermo Scientific Inc.). SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (registered trademark) VILO cDNA Synthesis kit (both Thermo Scientific Inc.), and the like are preferably used.

[0026] By adjusting the reaction conditions of the reverse transcription and PCR, the yield of the PCR reaction product is further improved, and the accuracy of the analysis using the same is further improved. Preferably, in the extension reaction in the reverse transcription, the temperature is preferably adjusted to 42°C ± 1°C, more preferably 42°C ± 0.5°C, and even more preferably 42°C ± 0.25°C, while the reaction time is preferably adjusted to 60 minutes or more, more preferably 80 to 100 minutes. Also preferably, the temperature of the annealing and extension reaction in the PCR is preferably 62°C ± 1°C, more preferably 62°C ± 0.5°C, and even more preferably 62°C ± 0.25°C. Therefore, in the PCR, the annealing and extension reaction are preferably performed in one step. The time of the annealing and extension reaction steps can be adjusted depending on the size of the DNA to be amplified, etc., but is preferably 14 to 18 minutes. The conditions of the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, but is preferably 95 to 99°C for 10 to 60 seconds. Reverse transcription and PCR at the above-mentioned temperatures and times can be carried out using a thermal cycler commonly used for PCR.

[0027] The purification of the reaction product obtained by the PCR is preferably carried out by size separation of the reaction product. By size separation, the target PCR reaction product can be separated from the primers and other impurities contained in the PCR reaction solution. Size separation of DNA can be carried out, for example, by a size separation column, a size separation chip, magnetic beads that can be used for size separation, etc. A preferred example of magnetic beads that can be used for size separation is Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Ampure XP. When Ampure XP and a DNA solution are mixed, the DNA is adsorbed to the carboxyl groups coated on the surface of the magnetic beads, and the DNA is purified by recovering only the magnetic beads using a magnet. In addition, the molecular size of the DNA adsorbed to the magnetic beads changes when the mixing ratio of the Ampure XP solution and the DNA solution is changed. By utilizing this principle, it is possible to recover DNA of a specific molecular size to be captured on the magnetic beads and purify DNA and impurities of other molecular sizes.

[0028] The purified PCR reaction product may be subjected to further processing required for subsequent analysis. For example, for DNA sequencing or fragment analysis, the purified PCR reaction product may be prepared in an appropriate buffer solution, the PCR primer region contained in the PCR-amplified DNA may be cleaved, or an adapter sequence may be further added to the amplified DNA. For example, the purified PCR reaction product may be prepared in a buffer solution, the PCR primer sequence may be removed from the amplified DNA, and adapter ligation may be performed, and the resulting reaction product may be amplified as necessary to prepare a library for various analyses. These operations may be performed, for example, using the 5×VILO RT Reaction Mix included in the SuperScript (registered trademark) VILO cDNA Synthesis kit (Life Technologies Japan, Inc.), and the 5×Ion AmpliSeq HiFi Mix and Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.), according to the protocol included with each kit.

[0029] The SSL-derived RNA to be subjected to the reverse transcription and PCR may be RNA derived from an RNA-containing SSL immediately after collection from a living organism, or RNA derived from an RNA-containing SSL that has been collected from a living organism and then stored at room temperature or in a refrigerator, but is preferably RNA derived from an RNA-containing SSL that has been collected from a living organism and then stored at 0° C. or below. The storage at 0° C. or below may be storage at −80° C., but is preferably storage at −20±10° C., and more preferably storage at −20±5° C. After separation from the SSL, the SSL-derived RNA may be used immediately for the reverse transcription and PCR, or may be stored in a conventional manner until use.

[0030] Nucleic acid derived from a subject's skin cells and prepared from SSL-derived RNA by the method of the present invention can be used for various analyses or diagnoses using nucleic acid. Therefore, the present invention also provides a method for analyzing nucleic acid, comprising analyzing nucleic acid prepared by the above-mentioned method for preparing nucleic acid according to the present invention. The nucleic acid is prepared by the above-mentioned method for preparing nucleic acid according to the present invention. Examples of analyses and diagnoses that can be performed using nucleic acid prepared by the present invention include the following: (i) gene expression analysis and other genetic analysis of the subject's skin, and functional analysis of the subject's skin based on such analysis, etc.; (ii) analysis of a subject's skin disease or condition, for example, evaluation of the health state of the skin (e.g., skin conditions such as sebum secretion, moisture content, redness, atopic dermatitis, sensitive skin, etc.), prediction of the current or future state of the skin condition, prediction of the past history such as the cumulative time of exposure of the skin to ultraviolet rays, diagnosis or prognosis of skin disease, diagnosis or prognosis of skin cancer, evaluation of subtle changes in the skin, etc.; (iii) Evaluating the effectiveness or efficacy of topical skin preparations, intradermal preparations, patches, oral preparations, injections, etc., using the analysis of the subject's skin disease or condition as described above; (iv) Analysis of the condition of a site other than the skin or the whole body of a subject, for example, evaluation or prediction of the whole body health condition, and diagnosis or prognosis of various diseases such as neurological diseases, cardiovascular diseases, metabolic diseases, and cancer; (v) Analysis of blood constituent concentrations of the subject.

[0031] More detailed examples of analysis and diagnosis using the nucleic acid prepared according to the present invention are given below. Gene expression analysis As disclosed in Patent Document 1, SSL is rich in high molecular weight RNA such as mRNA derived from a subject. SSL is a source of mRNA that can be collected non-invasively from a subject and is useful as a biological sample for gene expression analysis. Furthermore, the mRNA in SSL reflects the gene expression profiles of the sebaceous glands, hair follicles and epidermis (see Examples 1 to 4 of Patent Document 1). Therefore, the nucleic acid prepared by the present invention is suitable as a biological sample for gene expression analysis of the skin, particularly the sebaceous glands, hair follicles and epidermis.

[0032] Skin Analysis The skin of a subject can be analyzed using the nucleic acid prepared by the present invention as a sample. Examples of the analysis of the skin include the above-mentioned gene expression analysis and analysis of skin condition. Examples of the analysis of the skin condition include detection of skin with or without a certain disease or condition. Examples of the certain disease or condition include, but are not limited to, insufficient or excessive sebum amount, insufficient or excessive skin moisture amount, redness, atopic dermatitis, sensitive skin, etc. For example, by analyzing the expression level of a marker gene for a certain disease or condition such as sebum amount, skin moisture amount, redness, atopic dermatitis, sensitive skin, etc. in the skin of a subject from the nucleic acid prepared by the present invention, it can be determined whether the skin of the subject has the certain disease or condition. Preferably, the expression level of a marker gene for a certain disease or condition obtained for a subject can be compared with the expression level of the marker gene in the nucleic acid prepared by the method of the present invention from the SSL of a group with the certain disease or condition (positive control) or a group without the certain disease or condition (negative control), to determine whether the skin of the subject has the certain disease or condition. A known skin condition-related marker gene can be used as the marker gene.

[0033] Another example of skin analysis is prediction of skin condition, and examples of the skin condition include prediction of skin properties, prediction of visual or palpation evaluation of skin, prediction of sebum composition, etc. Examples of the skin properties include stratum corneum water content, transepidermal water loss (TEWL), sebum amount, melanin amount, erythema amount, etc. Examples of the visual or palpation evaluation of the skin include the skin condition usually evaluated by a professional assessor by visual or palpation. More specific examples of the visual evaluation include the presence or absence or degree of "cleanliness", "transparency", "brightness", "luster", "stains", "eyeshadow", "yellowness", "overall redness", "cheek wrinkles", "sagging corners of the mouth", "scale", "acne", "prominent pores on the cheeks", "prominent pores on the nose", etc. Examples of palpation evaluation include the presence or absence or degree of "roughness" and "moisture". Examples of the sebum composition include the amounts of components that make up sebum, such as free fatty acids (FFA), wax esters (WE), cholesterol esters (ChE), squalene (SQ), squalene epoxide (SQepo), squalene oxide (SQOOH), diacylglycerol (DAG), and triacylglycerol (TAG).

[0034] As shown in the Examples below, by performing correlation analysis between the RNA expression profile obtained from the SSL-derived RNA analysis and the data related to the measured values ​​and evaluation values ​​of various skin conditions, genes highly related to various skin conditions can be selected and used to construct a prediction model. Specific related genes used to predict skin conditions include the genes shown in Table 8.

[0035] When a large amount of gene data is used as expression data of highly relevant genes to be used in constructing a prediction model, the data may be compressed by principal component analysis as necessary before constructing the prediction model.

[0036] The algorithm for constructing the prediction model may be a known algorithm such as an algorithm used in machine learning. Examples of machine learning algorithms include a linear regression model, a lasso regression, a random forest, a neural network, a support vector machine with a linear kernel (SVM (linear)), and a support vector machine with an rbf kernel (SVM (rbf)). Verification data is input into the constructed prediction model to calculate a predicted value, and the model with the smallest root mean square error (RMSE) between the predicted value and the actual measured value can be selected as the optimal model.

[0037] Another example of skin analysis is the prediction of the cumulative UV exposure time of the skin. In general, the cumulative UV exposure time is calculated by predicting the UV exposure time based on a questionnaire survey on lifestyle habits and outdoor leisure activities. As shown in the examples below, a correlation analysis is performed between the RNA expression profile obtained from the SSL-derived RNA analysis and the calculated data of the cumulative UV exposure time associated therewith, thereby selecting genes highly related to the cumulative UV exposure time, and constructing a prediction model. The procedure for constructing the model is the same as described above.

[0038] Alternatively, the expression levels of nucleic acids prepared from SSLs of a group with the specified disease or condition (positive control) and a group without the disease (negative control) can be analyzed, and genes showing significant variations in expression levels between the two groups can be used as skin condition-related marker genes. Specifically, examples of marker genes for atopic dermatitis include one or more genes selected from a group of 1,911 genes whose expression was significantly reduced in atopic dermatitis patients compared to healthy subjects in Test Example 6 described below (A in Tables 7-1 to 7-24), as well as one or more genes selected from a group of 370 OR genes whose expression was reduced in atopic dermatitis patients compared to healthy subjects (B in Tables 7-1 to 7-11) among olfactory receptors (ORs) included in GO:0050911, a biological process (BP) that has been shown to be strongly associated with atopic dermatitis, as well as one or more genes selected from a group of 368 OR genes (C in Tables 7-1 to 7-11) and a group of 284 OR genes (D in Tables 7-1 to 7-11) whose expression was reduced depending on the severity of dermatitis. Examples of marker genes for sensitive skin include one or more genes selected from a group of 693 genes (Tables 7-1 to 7-20 (E)) whose expression was significantly decreased in a group with subjective symptoms of sensitive skin compared to a group without subjective symptoms of sensitive skin in Test Example 7 described later, and one or more genes selected from a group of 344 OR genes (Tables 7-1 to 7-10 (F)) whose expression was decreased in a group with subjective symptoms of sensitive skin compared to a group without subjective symptoms of sensitive skin among olfactory receptors (ORs) included in GO:0050911, which is a biological process (BP) that has been found to be strongly associated with sensitive skin. Examples of marker genes for redness include one or more genes selected from a group of 703 genes (Tables 7-1 to 7-20 (G)) whose expression was significantly changed between a group with strong redness and a group with weak redness in Test Example 8 described later. Marker genes for skin moisture content include one or more genes selected from the group of 553 genes (H in Tables 7-1 to 7-16) whose expression levels were significantly different between groups with high and low stratum corneum moisture content in Test Example 8 described below.Examples of marker genes for sebum amount include one or more genes selected from the group of 594 genes (Tables 7-1 to 7-17 (I)) whose expression significantly differed between the groups with high and low sebum amounts in Test Example 8 described below.

[0039] Furthermore, based on the analysis of the above-mentioned skin disease or condition in the subject, the effect or efficacy of a given skin topical agent, intradermal agent, patch, oral agent, injection agent, etc. on the subject can be evaluated.For example, by examining the expression of the marker gene for the above-mentioned skin disease or condition in the skin of the subject, the effect or efficacy of the use of the skin care product on the skin of the subject can be evaluated.Examples of the marker for the skin disease or condition used in this evaluation include one or more genes selected from BNIP3, CALML3, GAL, HSPA5, JUNB, KIF13B, KRT14, KRT17, KRT6A, OVOL1, PPIF, PRDM1, RBM3, RPLP1, RPS4X, SEPT9, SOAT1, SPNS2, UBB, VCP, WIPI2, and YPEL3.

[0040] Analysis of various component concentrations in blood The concentration of various components present in the blood of a subject can be analyzed using the nucleic acid prepared by the present invention as a sample. As shown in the examples described later, the blood component concentration of a subject can be predicted from the RNA expression level derived from the relevant marker gene in the SSL-derived RNA of the subject using a machine learning model constructed based on the RNA expression level derived from the relevant marker gene in the SSL-derived RNA of the subject and the concentration data of various components in the blood. Therefore, the concentration of various components in the blood can be quantified based on the RNA expression level derived from the relevant marker gene in the SSL-derived RNA. The machine learning model can be constructed according to the procedure for constructing the skin condition prediction model described above. Examples of various components present in the blood that are analyzed by the present invention include hormones, insulin, neutral lipids, γ-GTP, LDL-cholesterol, etc. Examples of the blood hormones include androgens such as testosterone, dihydrotestosterone, androstenedione, and dehydroepiandrosterone, estrogens such as estrone and estradiol, progesterone, cortisol, etc. Among these, testosterone and cortisol are preferred. RNA derived from a relevant marker gene in the SSL-derived RNA used for quantifying the concentrations of various components in blood can be selected from those whose expression levels show a relatively high correlation with the blood component concentrations. Preferably, the expression levels of the SSL-derived RNA and the blood concentrations of the target components are measured for a population, the correlation between the expression levels of each RNA and the blood component concentrations is examined, and RNA showing a relatively high correlation is selected. For example, as an example of RNA derived from a relevant marker gene in SSL-derived RNA used for quantifying the concentrations of testosterone, insulin, triglyceride, γ-GTP, and LDL-cholesterol in blood, at least one, preferably all, selected from the group of RNAs derived from the following human genes can be used: (testosterone) SCARNA16, PRSS27, RDBP, PSMB10, SBNO1, EMC3, MAR9, C20orf112, C14orf2, and CCDC90B; (insulin) EAPP, SDE2, LYAR, ZNF493, PSMB10, FAM71A, GPANK1, FGD4, MRPL43, and CMPK1; (neutral fat) CCDC9, C6orf106, CERK, HSD3B2, SUN2, FNDC4, GRAMD1C, DGAT2, ALPL, HOMER3, MTHFS, ADIPOR1, RBM3, EXOC8, and ARHGEF37; (γ-GTP)TMEM38A, BTN3A2, NAP1L2, ABCA2, ALPL, SECTM1, C17orf62, GNB2, R3HDM4, LRG1, SBNO2, CD14, MLLT1, NINJ2, and LIMD2; (LDL-cholesterol) THTPA, LOC100506023, ZNF700, TAB3, PLEKHA1, ZNF845, FXC1, CUL4A, NDUFV1, and AMZ2.

[0041] A preferred procedure for quantifying blood component concentrations using SSL-derived RNA will be described below with blood testosterone concentration quantification as an example: First, the RNA expression data of the above 10 genes (SCARNA16, PRSS27, RDBP, PSMB10, SBNO1, EMC3, MAR9, C20orf112, C14orf2, and CCDC90B) contained in SSL-derived RNA obtained from a human population and highly correlated with blood testosterone concentration is used as an explanatory variable, and an optimal prediction model for predicting blood testosterone concentration from the expression amount of the RNA is constructed by machine learning using blood testosterone concentration data obtained from the population as a target variable. Meanwhile, SSL-derived RNA is collected from a human subject whose blood testosterone concentration is to be examined. Based on the constructed model, the predicted value of the blood testosterone concentration of the subject can be calculated from the RNA expression amount data of the above 10 genes in the SSL-derived RNA of the subject.

[0042] Pathological diagnosis According to a recent report, about 63% of the RNAs whose expression changes in cancer cells are mRNAs that code for proteins (Cancer Res. 2016, 76, 216-226). Therefore, by measuring the expression state of mRNA, it is possible to more faithfully capture changes in the physiological state of cells due to diseases such as cancer, and more accurate diagnosis of physical conditions is possible. SSLs are rich in mRNAs, including SOD2 mRNA, which has been reported to be related to cancer (Physiol Genomics, 2003, 16, 29-37;Cancer Res, 2001, 61, 6082-6088). Therefore, SSLs are useful as biological samples for the diagnosis or prognosis of cancers such as skin cancer.

[0043] In recent years, it has been reported that the expression of molecules in the skin fluctuates in patients with diseases in tissues other than the skin, such as obesity, Alzheimer's, breast cancer, and heart disease, and therefore the skin can be said to be a "window to the body's health" (Eur J Pharm Sci. 2013, 50, 546-556). Therefore, by measuring the expression state of mRNA in SSL, it may be possible to analyze the physiological state of sites other than the skin or the physiological state of the whole body of a subject.

[0044] non-coding RNA analysis In recent years, the involvement of non-coding (nc)RNAs such as miRNAs and lincRNAs in cellular gene expression has been attracting attention, and research into this has been actively conducted. In addition, non-invasive or minimally invasive methods for diagnosing cancer and other diseases using miRNAs in urine or serum have been developed (e.g., Proc Natl Acad Sci USA, 2008, 105, 10513-10518; Urol Oncol, 2010, 28, 655-661). ncRNAs such as miRNAs and lincRNAs prepared from SSLs can be used as samples for the above research and diagnosis.

[0045] Screening or detection of nucleic acid markers Nucleic acid markers for diseases or conditions can be screened or detected using nucleic acids prepared from SSL as samples. In the present specification, a nucleic acid marker for a disease or condition refers to a nucleic acid whose expression serves as an indicator for determining a given disease or condition or for determining the risk of the disease or condition. Preferably, the nucleic acid marker is an RNA marker, and the RNA is preferably mRNA, miRNA, or lincRNA. Diseases or conditions targeted by the nucleic acid marker include, but are not limited to, various skin diseases (such as atopic dermatitis), skin health conditions (sensitive skin, photoaging, inflammation (redness), dryness, moisture or oil content, skin firmness, dullness, etc.), and cancers such as skin cancer, and diseases in tissues other than the skin, such as obesity, Alzheimer's, breast cancer, and heart disease, as described above in the section "Pathological Diagnosis". Analysis of nucleic acid expression can be performed according to known means such as real-time PCR, microarrays, and RNA expression analysis using a next-generation sequencer.

[0046] One example is a method for selecting a nucleic acid marker for a disease or condition. In this method, a population having a certain disease or condition or a risk thereof is used as a subject, and nucleic acid derived from the skin cells of the subject is prepared according to the nucleic acid preparation method of the present invention. The expression (expression level, etc.) of the nucleic acid prepared from the population is compared with the expression of a control. Examples of the control include a population not having the certain disease or condition or a risk thereof, and statistical data based thereon. A nucleic acid that shows a different expression compared to the control can be selected as a marker or candidate for the certain disease or condition. Examples of nucleic acid markers or candidates for the certain disease or condition selected in this way include the marker genes listed in Tables 7-1 to 7-24.

[0047] Another example is a method for detecting a nucleic acid marker for a disease or condition, or a method for determining a disease or condition, or a risk thereof, based on the detection of the marker. In this method, nucleic acid derived from skin cells of a subject who desires or needs to be determined for a certain disease or condition, or a risk thereof, is prepared from the subject according to the nucleic acid preparation method of the present invention. Then, a nucleic acid marker for the certain disease or condition is detected from the prepared nucleic acid. The disease or condition of the subject, or the risk thereof, is determined based on the presence or absence or expression level of the nucleic acid marker.

[0048] Analysis of the nucleic acid prepared according to the present invention can be carried out by conventional methods used in nucleic acid analysis, such as real-time PCR, RT-PCR, microarray, sequencing, chromatography, etc., but the nucleic acid analysis techniques according to the present invention are not limited to these.

[0049] As exemplary embodiments of the present invention, the following substances, manufacturing methods, uses, methods, etc. are further disclosed in this specification, but the present invention is not limited to these embodiments.

[0050] [1] A method for preparing nucleic acid derived from skin cells of a subject, comprising storing RNA-containing skin surface lipids collected from the subject at 0°C or below. [2] The method according to [1], wherein the storage temperature is preferably -20±20°C to -80±20°C, more preferably -20±10°C to -80±10°C, even more preferably -20±20°C to -40±20°C, even more preferably -20±10°C to -40±10°C, even more preferably -20±10°C, and even more preferably -20±5°C. [3] The method according to [1] or [2], wherein the storage period is preferably 12 months or less, for example, 6 hours or more and 12 months or less, more preferably 6 months or less, for example, 1 day or more and 6 months or less, even more preferably 3 months or less, for example, 3 days or more and 3 months or less.

[0051] [4] Converting RNA contained in the subject's skin surface lipids into cDNA by reverse transcription, and then subjecting the cDNA to multiplex PCR; and purifying the PCR reaction product; A method for preparing nucleic acid derived from skin cells of a subject, comprising: [5] The method according to [4], wherein the temperature of the annealing and extension reaction in the multiplex PCR is preferably 62°C±1°C, more preferably 62°C±0.5°C, and even more preferably 62°C±0.25°C. [6] The method according to [4] or [5], wherein the extension reaction in the reverse transcription is preferably carried out under the following conditions: 42°C ± 1°C for at least 60 minutes; 42°C ± 1°C for 80-100 minutes; 42°C ±0.5°C for at least 60 minutes; 42°C ± 0.5°C for 80-100 minutes; 42°C ±0.25°C for at least 60 minutes; or 42°C ±0.25°C for 80 to 100 minutes. [7] The method according to any one of [4] to [6], wherein the purification of the PCR reaction product is preferably by size separation. [8] The method according to any one of [4] to [7], wherein the RNA contained in the lipids on the skin surface of the subject is preferably prepared by isolating RNA from the lipids on the skin surface of the subject. [9] The method according to any one of [4] to [8], wherein the lipids on the skin surface of the subject have been stored at preferably 0°C or below, more preferably -20±20°C to -80±20°C, more preferably -20±10°C to -80±10°C, even more preferably -20±20°C to -40±20°C, even more preferably -20±10°C to -40±10°C, even more preferably -20±10°C, and even more preferably -20±5°C.

[10] The method described in [9], wherein the lipids on the skin surface of the subject have been stored for preferably 12 months or less, for example, 6 hours or more and 12 months or less, more preferably 6 months or less, for example, 1 day or more and 6 months or less, even more preferably 3 months or less, for example, 3 days or more and 3 months or less.

[0052]

[11] A method for analyzing the condition of the skin, a site other than the skin, or the entire body of a subject, comprising analyzing a nucleic acid prepared by the method according to any one of [1] to

[10] .

[12] The above analysis Preferably, the analysis of a skin disease or condition, More preferably, Whether it is detection of skin with or without redness, sensitive skin or atopic dermatitis; Whether it is detection of skin with or without high or low sebum or skin moisture content; a prediction of skin condition, such as a prediction of skin physical properties, a prediction of visual or palpable assessment of the skin, or a prediction of sebum composition; A prediction of cumulative UV exposure time of the skin. The analysis method described in

[11] .

[13] Preferably, The analysis is detection of skin with or without atopic dermatitis, and the nucleic acid is at least one, more preferably all, of the genes listed in (B), (C), and (D) of Tables 7-1 to 7-11; The analysis is for detecting skin having mild or moderate atopic dermatitis or having no atopic dermatitis, and the nucleic acid is at least one, more preferably all, of the genes listed in (C) and (D) of Tables 7-1 to 7-11; The analysis is a detection of skin with or without sensitive skin, and the nucleic acid is at least one, more preferably all, of the genes listed in (E) of Tables 7-1 to 7-20; The analysis is detection of skin with or without sensitive skin, and the nucleic acid is at least one, more preferably all, of the genes listed in (F) of Tables 7-1 to 7-10; The analysis is detection of skin with or without redness, and the nucleic acid is at least one, more preferably all, of the genes listed in (G) of Tables 7-1 to 7-20; The analysis is for detecting skin with high or low moisture content, and the nucleic acid is at least one, more preferably all, of the genes listed in (H) of Tables 7-1 to 7-16; The analysis is for detecting skin with a high or low amount of sebum, and the nucleic acid is at least one, more preferably all, of the genes listed in (I) of Tables 7-1 to 7-17; The analysis is a prediction of skin properties, a prediction of visual or palpation evaluation of skin, or a prediction of sebum composition, and the nucleic acid is at least one, more preferably all, of the genes listed in Table 8. The method described in

[11] .

[14] A method for evaluating the effect or efficacy of a topical skin preparation, an intradermal preparation, a patch preparation, an oral preparation, or an injection preparation on a subject, the method comprising analyzing a nucleic acid prepared by the method according to any one of [1] to

[10] .

[15] The method described in

[14] , wherein the effect or efficacy of the topical skin preparation, intradermal preparation, patch, oral preparation or injection on the subject is preferably an effect of improving the skin condition of the subject by a skin care product, and the nucleic acid is preferably at least one selected from BNIP3, CALML3, GAL, HSPA5, JUNB, KIF13B, KRT14, KRT17, KRT6A, OVOL1, PPIF, PRDM1, RBM3, RPLP1, RPS4X, SEPT9, SOAT1, SPNS2, UBB, VCP, WIPI2, and YPEL3, more preferably all of them.

[16] A method for analyzing a concentration of a component in the blood of a subject, comprising analyzing a nucleic acid prepared by the method according to any one of [1] to

[10] .

[17] The analysis method according to

[16] , wherein the blood component is preferably a hormone, insulin, neutral fat, γ-GTP, or L-cholesterol.

[18] The hormone, Preferably, it is testosterone, dihydrotestosterone, androstenedione, dehydroepiandrosterone, estrone, estradiol, progesterone, or cortisol; More preferably, it is testosterone or cortisol. The analysis method described in

[17] .

[19] The method of analysis described in

[16] , wherein the blood component is preferably testosterone, and the nucleic acid is at least one selected from 10 types of RNA derived from 10 genes, preferably consisting of SCARNA16, PRSS27, RDBP, PSMB10, SBNO1, EMC3, MAR9, C20orf112, C14orf2, and CCDC90B, more preferably the 10 types of RNA.

[20] The method of analysis described in

[16] , wherein the blood component is preferably insulin, and the nucleic acid is at least one selected from 10 types of RNA derived from 10 genes, preferably consisting of EAPP, SDE2, LYAR, ZNF493, PSMB10, FAM71A, GPANK1, FGD4, MRPL43, and CMPK1, more preferably the 10 types of RNA.

[21] The method of analysis described in

[16] , wherein the blood component is preferably triglyceride, and the nucleic acid is at least one selected from 15 types of RNA derived from 15 genes consisting of CCDC9, C6orf106, CERK, HSD3B2, SUN2, FNDC4, GRAMD1C, DGAT2, ALPL, HOMER3, MTHFS, ADIPOR1, RBM3, EXOC8, and ARHGEF37, more preferably the 15 types of RNA.

[22] The method of analysis described in

[16] , wherein the blood component is preferably γ-GTP, and the nucleic acid is at least one selected from 15 types of RNA derived from 15 genes consisting of TMEM38A, BTN3A2, NAP1L2, ABCA2, ALPL, SECTM1, C17orf62, GNB2, R3HDM4, LRG1, SBNO2, CD14, MLLT1, NINJ2, and LIMD2, more preferably the 15 types of RNA.

[23] The method of analysis described in

[16] , wherein the blood component is preferably LDL-cholesterol, and the nucleic acid is at least one selected from 10 types of RNA derived from 10 genes consisting of THTPA, LOC100506023, ZNF700, TAB3, PLEKHA1, ZNF845, FXC1, CUL4A, NDUFV1, and AMZ2, more preferably the 10 types of RNA.

[0053]

[24] A method for analyzing a concentration of a blood component in a subject, comprising: Obtaining an expression level of RNA derived from a gene having a high correlation with a blood component concentration from a nucleic acid of a subject prepared by any one of the methods described in [1] to

[10] ; analyzing the concentration of a blood component of the subject using a machine learning model based on the expression amount of RNA derived from a gene having a high correlation with the blood component concentration; Including, The machine learning model is constructed using expression amount data of RNA derived from a gene having a high correlation with the blood component concentration contained in RNA derived from lipids on the skin surface obtained from a human population as an explanatory variable, and using concentration data of the blood component obtained from the human population as a response variable. method.

[25] Preferably, the blood component is a hormone, insulin, neutral fat, γ-GTP, or LDL-cholesterol; The hormone is preferably testosterone or cortisol. The method described in

[24] .

[26] The blood component is preferably testosterone, and the genes having a high correlation with the blood component concentration are preferably at least one selected from SCARNA16, PRSS27, RDBP, PSMB10, SBNO1, EMC3, MAR9, C20orf112, C14orf2, and CCDC90B, more preferably all of them; The blood component is preferably insulin, and the genes having a high correlation with the blood component concentration are preferably at least one selected from EAPP, SDE2, LYAR, ZNF493, PSMB10, FAM71A, GPANK1, FGD4, MRPL43, and CMPK1, more preferably all of them; The blood component is preferably triglyceride, and the genes having a high correlation with the blood component concentration are preferably at least one selected from CCDC9, C6orf106, CERK, HSD3B2, SUN2, FNDC4, GRAMD1C, DGAT2, ALPL, HOMER3, MTHFS, ADIPOR1, RBM3, EXOC8, and ARHGEF37, more preferably all of them; The blood component is preferably γ-GTP, and the genes having a high correlation with the blood component concentration are preferably at least one selected from TMEM38A, BTN3A2, NAP1L2, ABCA2, ALPL, SECTM1, C17orf62, GNB2, R3HDM4, LRG1, SBNO2, CD14, MLLT1, NINJ2, and LIMD2, more preferably all of them; The blood component is preferably LDL-cholesterol, and the genes having a high correlation with the blood component concentration are preferably at least one selected from THTPA, LOC100506023, ZNF700, TAB3, PLEKHA1, ZNF845, FXC1, CUL4A, NDUFV1, and AMZ2, more preferably all of them. The method described in

[25] .

[27] A database for constructing a machine learning model for analyzing concentrations of blood components, comprising: Expression data of RNA derived from genes that are highly correlated with blood component concentrations contained in RNA derived from lipids on the skin surface obtained from a human population, and concentration data for said blood constituents from said human population; Including, The blood component is preferably testosterone, and the genes having a high correlation with the blood component concentration are preferably at least one selected from SCARNA16, PRSS27, RDBP, PSMB10, SBNO1, EMC3, MAR9, C20orf112, C14orf2, and CCDC90B, more preferably all of them; The blood component is preferably insulin, and the genes having a high correlation with the blood component concentration are preferably at least one selected from EAPP, SDE2, LYAR, ZNF493, PSMB10, FAM71A, GPANK1, FGD4, MRPL43, and CMPK1, more preferably all of them; The blood component is preferably triglyceride, and the genes having a high correlation with the blood component concentration are preferably at least one selected from CCDC9, C6orf106, CERK, HSD3B2, SUN2, FNDC4, GRAMD1C, DGAT2, ALPL, HOMER3, MTHFS, ADIPOR1, RBM3, EXOC8, and ARHGEF37, more preferably all of them; The blood component is preferably γ-GTP, and the genes having a high correlation with the blood component concentration are preferably at least one selected from TMEM38A, BTN3A2, NAP1L2, ABCA2, ALPL, SECTM1, C17orf62, GNB2, R3HDM4, LRG1, SBNO2, CD14, MLLT1, NINJ2, and LIMD2, more preferably all of them; The blood component is preferably LDL-cholesterol, and the genes having a high correlation with the blood component concentration are preferably at least one selected from THTPA, LOC100506023, ZNF700, TAB3, PLEKHA1, ZNF845, FXC1, CUL4A, NDUFV1, and AMZ2, more preferably all of them. Database.

[28] A program for carrying out the analysis method according to any one of

[24] to

[26] .

[29] An apparatus for carrying out the analysis method according to any one of

[24] to

[26] . EXAMPLES

[0054] The present invention will be described in more detail below based on examples, but the present invention is not limited to these examples.

[0055] Test Example 1 Effect of storage temperature on RNA stability in SSL 1) Stability of RNA during SSL Sebum was collected from the entire face of a healthy subject using an oil blotting film (5 x 8 cm, made of polypropylene, 3M). The oil blotting film was transferred to a glass vial and left at 4°C for several hours, after which the RNA in the SSL contained in the film was purified. In RNA purification, the oil blotting film was cut to an appropriate size, and RNA was extracted using QIAzol (registered trademark) Lysis Reagent (Qiagen) according to the attached protocol. Based on the extracted RNA, reverse transcription was performed at 42°C for 30 minutes using SuperScript (registered trademark) VILO cDNA Synthesis kit (Life Technologies Japan, Inc.) to synthesize cDNA. The random primers included in the kit were used as primers for the reverse transcription reaction. A library containing DNA derived from the 20802 gene was prepared from the obtained cDNA by multiplex PCR. Multiplex PCR was performed using the Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.) under the following conditions: 99°C, 2 minutes → (99°C, 15 seconds → 60°C, 16 minutes) × 20 cycles → 4°C, hold. The prepared library was measured using TapeStation (Agilent Technologies, Inc.) and High Sensitivity D1000 ScreenTape (Agilent Technologies, Inc.), but no peaks derived from the library were detected. The reason for the failure to detect peaks was thought to be that the amount of sebum collected from the subjects was small, and that the amount of purified RNA was small due to decomposition caused by leaving the sample at 4°C after collection before purification.

[0056] 2) Effects of storage temperature To investigate the effect of storage temperature on human RNA in SSL, 40 ng of RNA was applied to the oil-blotting film from which sebum was collected in 1) and then stored at (i) room temperature (RT), (ii) 4°C, (iii) -20°C, or (iv) -80°C for 4 days. The human epidermal cell-derived RNA solution was prepared by dissolving RNA extracted from frozen NHEK (NB) (Kurabo) in 50% (v / v) ethanol solution. The oil-blotting film after storage was cut to an appropriate size, and RNA was extracted using QIAzol (registered trademark) Lysis Reagent (Qiagen) according to the attached protocol. The extracted RNA was measured using TapeStation (Agilent Technologies) and High Sensitivity RNA Screen Tape (Agilent Technologies).

[0057] The results of the measurements are shown in Figure 1. In the RNA in SSL stored at RT or 4°C, the peaks of human-derived RNA (28S and 18S ribosomal RNA) were significantly reduced, and peaks of other RNAs were hardly detected. On the other hand, in the RNA in SSL stored at -20°C or -80°C, peaks of 28S and 18S ribosomal RNA were detected, indicating that the RNA was stably stored. Furthermore, since the peak areas of 28S and 18S ribosomal RNA were larger when stored at -20°C compared to when stored at -80°C, it was estimated that -20°C is a more suitable storage temperature for RNA in SSL than -80°C, which is the conventional storage temperature for RNA.

[0058] Test Example 2 Nucleic acid preparation from SSL-derived RNA by multiplex PCR 1) Optimization of reverse transcription reaction conditions Sebum was collected from the entire face of a healthy subject with a low amount of sebum using an oil blotting film (3M), and RNA was extracted from the oil blotting film using the same procedure as in Test Example 1. The extracted RNA was reverse transcribed to synthesize cDNA. Reverse transcription reaction was performed using SuperScript (registered trademark) VILO cDNA Synthesis kit (Thermo Scientific). Random primers included in the kit were used as primers for reverse transcription reaction. The extension temperature and time conditions for reverse transcription were (i) 40°C, 60 minutes, (ii) 40°C, 90 minutes, (iii) 42°C, 60 minutes, or (iv) 42°C, 90 minutes (temperature accuracy ±0.25°C). Using the obtained cDNA, multiplex PCR was performed under the same conditions as in Test Example 1. The obtained PCR product was purified using Ampure XP (Beckman Coulter, Inc.). The concentration of the PCR product in the purified solution was quantified using TapeStation (Agilent Technologies, Inc.) and High Sensitivity D1000 ScreenTape (Agilent Technologies, Inc.). The results are shown in Table 1. The largest amount of PCR product was obtained when reverse transcription was performed at 42°C for 90 minutes.

[0059] [Table 1]

[0060] 2) Optimization of PCR conditions Sebum was collected from the entire face of a healthy subject with a low amount of sebum using an oil blotting film (3M), and RNA was extracted from the oil blotting film using the same procedure as in Test Example 1. Using the extracted RNA, cDNA synthesis and multiplex PCR were performed using the same procedure as in 1), except that the annealing and extension temperatures in the PCR were changed. The reverse transcription conditions were 42°C and 90 minutes. The annealing and extension temperatures were (i) 60°C, (ii) 62°C, (iii) 63°C, and (iv) 64°C (temperature accuracy ±0.25°C). The obtained PCR products were purified using Ampure XP (Beckman Coulter, Inc.) and quantified using TapeStation (Agilent Technologies, Inc.) and High Sensitivity D1000 ScreenTape (Agilent Technologies, Inc.). The results are shown in Table 2. The largest amount of PCR product was obtained when the annealing and extension temperatures were 62°C.

[0061] [Table 2]

[0062] 3) Purification of PCR products Sebum was collected from the entire face of a healthy subject with a small amount of sebum using an oil blotting film (3M), and RNA was extracted from the oil blotting film using the same procedure as in Test Example 1. Using the extracted RNA, cDNA synthesis and multiplex PCR were performed using the same procedure as in 1). The reverse transcription conditions were 42°C and 30 minutes. The annealing and extension temperatures were 60°C (temperature accuracy ±0.25°C). The obtained PCR product was divided into two, one of which was purified using Ampure XP (Beckman Coulter, Inc.), and the other was not purified. Each sample solution was mixed with 5x VILO RT Reaction Mix included with the SuperScript® VILO cDNA Synthesis kit, and 5x Ion Ampliseq HiFi Mix and Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.) and Ion AmpliSeq Transcriptome Human Gene Expression Core Panel to reconstitute the buffer composition, and the library was prepared by digesting the primer sequence, ligating the adapter, purifying, and amplifying according to the protocol included with each kit. The concentration of the obtained library was quantified using TapeStation (Agilent Technologies, Inc.) and High Sensitivity D1000 ScreenTape (Agilent Technologies, Inc.). The results are shown in Table 3. No library was detected in the sample that was not purified.

[0063] [Table 3]

[0064] From the results of 1) and 2) above, when preparing a nucleic acid sample from RNA in SSL, the optimal conditions for reverse transcription reaction were approximately 42°C and 90 minutes, and the optimal conditions for annealing and extension temperature of multiplex PCR were approximately 62°C. It was considered that the yield of nucleic acid sample from RNA in SSL could be increased by performing multiplex RT-PCR under these conditions. In addition, the result of 3) showed that the yield of nucleic acid sample was increased by adding a purification process after PCR, and it became possible to prepare a nucleic acid sample from SSL with a small amount of RNA. Furthermore, as shown in Test Example 1, it was considered that the denaturation of RNA could be suppressed and the yield of nucleic acid sample could be further increased by storing the RNA in SSL collected from the subject at -20°C until it was used for preparing a nucleic acid sample. The reverse transcriptase used in the above reverse transcription reaction was SuperScript (registered trademark) III Reverse Transcriptase, and the primers used were Random Primers. The enzyme used in PCR was AmpliSeq HiFi Mix Plus, and the primers used were AmpliSeq Transcriptome Panel Human Gene Expression CORE.

[0065] Test Example 3 Detection of atopic dermatitis using SSL-derived RNA SSL collection Twenty healthy subjects (male, aged 20-39, BMI 18.5 to 25.0) and eleven atopic dermatitis (AD) patients (male, aged 20-39, BMI 18.5 to 25.0) were used as subjects. The healthy subjects had been confirmed by a dermatologist to have no abnormal skin, and the AD subjects had been diagnosed with atopic dermatitis by a dermatologist. After taking a full face photograph, sebum was collected from the entire face of each subject using an oil blotting film (3M). The oil blotting film was transferred to a glass vial and stored at -80°C for about one month until it was used for RNA extraction. In the following test examples including this test example, the SSL collected from the subject was stored at -80°C, which is a general storage condition, until it was used for RNA extraction. However, as shown in Test Example 1, if it is stored under conditions (-20°C) that allow RNA in SSL to be stored more stably, RNA expression analysis data can be obtained more stably, and it is considered that similar or better analysis results can be obtained.

[0066] RNA preparation and sequence analysis RNA was extracted from the stored oil blotting film using the same procedure as in Test Example 1. The extracted RNA was reverse transcribed at 42°C for 90 minutes, and multiplex PCR was performed at annealing and extension temperatures of 62°C. The PCR products obtained were purified using Ampure XP (Beckman Coulter, Inc.), and then the same procedures as in Test Examples 2 and 3) were used to reconstitute the buffer, digest the primer sequence, ligate the adapter, purify, and amplify the PCR products to prepare a library. The prepared library was loaded onto an Ion 540 Chip and sequenced using an Ion S5 / XL system (Life Technologies Japan, Inc.).

[0067] RNA expression analysis The expression levels of SSL-derived RNA species confirmed by sequencing were compared between healthy subjects and AD subjects. The RNA species used for comparison were 19 immune response-related RNAs and 17 keratinization-related RNAs. It has been reported in the literature (J Allergy Clin Immunol, 2011, 127:954-964) that the expression levels of these RNA species differ between affected and non-affected areas of AD skin tissue compared to healthy subjects.

[0068] The results are shown in Figure 2. The values ​​in the figure represent the ratio of expression level in AD to healthy subjects measured in this test example, and the ratio of expression level in affected and non-affected areas of AD to healthy subjects measured in the above literature. In the figure, RNAs whose expression was increased in AD are shown in light gray, and RNAs whose expression was decreased are shown in dark gray. Student's t-test was used to test for significance. As reported in the above literature, the expression levels of many of the immune response-related RNAs in the SSL-derived RNA were increased in AD compared to healthy subjects. On the other hand, as reported in the above literature, the expression levels of many of the keratinization-related RNAs in the SSL-derived RNA were decreased in AD compared to healthy subjects. These results showed that SSL-derived RNA contains atopic dermatitis-related markers that reflect increased inflammatory states and decreased barrier function, and that atopic dermatitis patients can be identified based on the expression of these markers in the SSL-derived RNA.

[0069] Test Example 4 Prediction of blood component concentrations using SSL-derived RNA subject The subjects were 38 healthy men (aged 20-59, BMI 18.5 or greater and less than 25.0) who had been previously confirmed by a dermatologist to have no skin abnormalities.

[0070] Blood sampling and quantitative determination of blood component concentrations 3mL of blood was collected from each subject's arm using a vacuum blood collection tube, and serum was separated and stored at -80℃. Serum testosterone concentration was quantified from the stored serum using a Testosterone ELISA Kit (Cyman) according to the attached protocol. Serum insulin, neutral lipid, γ-GTP, and LDL-cholesterol concentrations were quantified by an external testing agency (LSI Medience Corporation).

[0071] Preparation of SSL-derived RNA and sequence analysis After taking a photograph of the entire face, sebum was collected from the entire face of each subject using an oil blotting film (3M). The oil blotting film was transferred to a glass vial and stored at -80°C for about one month. From the stored oil blotting film, RNA in the SSL was extracted in the same manner as in Test Example 3, a library was prepared, and RNA species were identified by sequencing and their expression levels were measured.

[0072] Prediction of blood testosterone levels using SSL-derived RNA The data on the expression level of SSL-derived RNA from the subjects measured above (reads per million mapped reads: RPM value) was randomly divided into 33 subjects and 5 subjects. Using the SSL-derived RNA expression level (RPM value) and serum testosterone concentration for the 33 subjects, a prediction model for serum testosterone concentration was constructed by machine learning. First, 10 types of RNA (SCARNA16, PRSS27, RDBP, PSMB10, SBNO1, EMC3, MAR9, C20orf112, C14orf2, and CCDC90B-derived RNA) with the highest correlation with serum testosterone concentration were selected based on the RPM value. The expression levels (RPM values) of SSL-derived RNA for the 10 types of RNA selected above for the 33 individuals were used as explanatory variables as learning data, and the serum testosterone concentrations of the 33 individuals were used as objective variables to construct and select the optimal predictive model using Visual Mining Studio software (NTT Mathematical Systems, Inc.). Using the selected prediction model, predicted values ​​for blood testosterone concentrations were calculated from the expression levels of SSL-derived RNA in the remaining five subjects. As a result, as shown in Figure 3, the calculated predicted values ​​were highly correlated with the actual measured values ​​of serum testosterone concentrations (correlation coefficient = 0.93). This demonstrated that SSL-derived RNA contains important information for predicting blood testosterone concentrations.

[0073] Prediction of blood insulin, neutral lipids, γ-GTP and LDL-cholesterol concentrations using SSL-derived RNA The data on the expression levels of SSL-derived RNA (RPM values) from the subjects measured above were randomly divided into 31 subjects and 7 subjects. Using the SSL-derived RNA expression levels (RPM values) and serum insulin, neutral lipids, γ-GTP, and LDL-cholesterol concentrations for the 31 subjects, machine learning was used to construct prediction models for serum insulin, neutral lipids, γ-GTP, and LDL-cholesterol concentrations for each. First, RNA derived from the molecules shown below was selected as RNA with the highest correlation with serum concentrations of 1) insulin, 2) neutral lipids, 3) γ-GTP, and 4) LDL-cholesterol based on the RPM values. 1) Insulin: EAPP, SDE2, LYAR, ZNF493, PSMB10, FAM71A, GPANK1, FGD4, MRPL43, and CMPK1 2) Neutral fat: CCDC9, C6orf106, CERK, HSD3B2, SUN2, FNDC4, GRAMD1C, DGAT2, ALPL, HOMER3, MTHFS, ADIPOR1, RBM3, EXOC8, and ARHGEF37 3) γ-GTP: TMEM38A, BTN3A2, NAP1L2, ABCA2, ALPL, SECTM1, C17orf62, GNB2, R3HDM4, LRG1, SBNO2, CD14, MLLT1, NINJ2, and LIMD2 4) LDL-cholesterol: THTPA, LOC100506023, ZNF700, TAB3, PLEKHA1, ZNF845, FXC1, CUL4A, NDUFV1, and AMZ2 The expression levels (RPM values) of SSL-derived RNA for each of the 31 individuals selected above were used as explanatory variables for the learning data, and the insulin, triglyceride, γ-GTP or LDL-cholesterol concentrations in the blood of the 31 individuals were used as objective variables.The optimal prediction model was constructed and selected using Visual Mining Studio software (NTT Mathematical Systems, Inc.). Using the selected prediction model, predicted values ​​for blood insulin, triglyceride, γ-GTP, and LDL-cholesterol concentrations were calculated from the expression levels of SSL-derived RNA in the remaining seven subjects. As a result, as shown in Figure 4, the calculated predicted values ​​had a positive correlation with the actual measured values ​​of serum insulin, triglyceride, γ-GTP, and LDL-cholesterol concentrations, respectively. This demonstrated that SSL-derived RNA is a useful indicator for predicting blood insulin, triglyceride, γ-GTP, and LDL-cholesterol concentrations.

[0074] Test Example 5 Evaluation of topical skin preparations (facial cleansers) (1) Identification of RNA species used to predict the effects of facial cleansers on the skin Subjects and SSL collection Nine healthy male subjects (aged 20-39) participated in the study. The test product used was a facial cleanser (Biore Ouchi de Esthe, Kao Corporation) that breaks down and removes keratin plugs. The subjects used an appropriate amount (approximately 1g) of the test product to wash their entire face twice a day (morning and night) for one week. SSL was collected from the subjects' entire face using an oil blotting film (3M) before starting to use the test cleanser (day 0) and two days after starting use.

[0075] Preparation of SSL-derived RNA and sequence analysis The oil blotting films containing the SSL collected above were transferred to glass vials and stored at -80°C for approximately one month. RNA in the SSL was extracted from the stored oil blotting films using the same procedure as in Test Example 3, a library was prepared, and RNA species were identified by sequencing and their expression levels were measured.

[0076] 4. Data Analysis Based on the SSL-derived RNA expression levels (RPM values) measured before and 2 days after the start of use of the cleanser, 22 types of RNA derived from molecules, BNIP3, CALML3, GAL, HSPA5, JUNB, KIF13B, KRT14, KRT17, KRT6A, OVOL1, PPIF, PRDM1, RBM3, RPLP1, RPS4X, SEPT9, SOAT1, SPNS2, UBB, VCP, WIPI2, and YPEL3, were identified as RNAs with a p-value of 0.05 or less and a 2-fold increase in RPM value 2 days after the start of use compared to day 0 (Table 4). Among these molecules were molecules related to terminal keratinization of the skin, such as BNIP3, OVOL1, KRT14, and KRT17, and molecules related to anti-inflammatory effects, such as JUNB and PRDM1. The expression levels of these molecules increased when the cleanser was used, suggesting that they may be markers indicating improvement in skin condition.

[0077] [Table 4]

[0078] (2) Prediction of the effectiveness of facial cleansers Subjects, skin condition data and SSL collection Eighteen healthy males (aged 20-48 years) were used as subjects. A facial cleanser with the effect of breaking down and removing keratin plugs (Biore Ouchi de Esthe, Kao Corporation) was used as the test product. As in "(1) Identification of RNA species used to predict the effect of facial cleanser on the skin" above, the subjects washed their entire faces with an appropriate amount of the test product (approximately 1 g) twice a day (morning and night) for one week. Before starting to use the test product facial cleanser (day 0) and one week after starting to use it, SSL samples were taken from the subjects and their skin conditions were measured as follows. i) SSL was collected from the entire face using oil blotting film (3M). ii) After washing the face, the subject was allowed to acclimate for 15 minutes in a temperature-controlled room (20±5°C, 40% RH). iii) After taking a magnified image of the cheek, the moisture content of the stratum corneum was measured at one location on the left cheek using a Corneometer (MPA580, Courage+Khazaka, Germany). iv) A questionnaire regarding skin condition was conducted.

[0079] Preparation of SSL-derived RNA and sequence analysis The oil blotting films containing the SSL collected above were transferred to glass vials and stored at -80°C for approximately one month. RNA in the SSL was extracted from the stored oil blotting films using the same procedure as in Test Example 3, a library was prepared, and RNA species were identified by sequencing and their expression levels were measured.

[0080] 4. Data Analysis Using the RPM values ​​on day 0 for the 22 RNA groups selected in "(1) Identification of RNA species used to predict the effects of facial cleanser on skin" above (Table 4), the 22 RNA groups were divided into two groups using the Self-Organizing Map function of Gene Spring (Tommy Digital Biology) software: one group tending to have high overall expression (high value group) and one group tending to have low expression (low value group) (Figure 5).

[0081] Francesco Iorio et al. have reported "signature reversion" as a disease improvement method in which drugs or the like that have the effect of increasing the expression of RNA groups whose expression has been reduced by disease or the like are used (Drug Discov Today, 2013, 18(7-8):350-357). Based on this method, it was predicted that the low value group would have a more significant increase in the 22 types of RNA groups compared to the high value group, and that the skin condition would improve by using this test product. In fact, the results of comparing the change in stratum corneum moisture content after one week of use of the test product (value after one week of use - value on day 0) are shown in Figure 6. As for the stratum corneum moisture content, the change in stratum corneum moisture content was a negative value, and the stratum corneum moisture content tended to decrease, due to the influence of a large drop in temperature on the test day one week after the start of use, compared to the test day on day 0 of this test. However, compared to the decrease in stratum corneum moisture content in the high value group, the decrease in the low value group was smaller, and a tendency for the decrease in moisture content to be suppressed was shown. Furthermore, in a questionnaire survey about usage experience, a higher proportion of people in the low value group felt that their skin's moisturizing condition had improved compared to the high value group (Figure 7). From these results, it is suggested that by using SSL-derived RNA analysis technology, it is possible to predict the effect of skin external preparation before starting to use the product.For example, by identifying in advance the SSL-derived RNA (for example, the 22 kinds of RNA found in this embodiment) that is characteristically expressed or not expressed in the person who is likely to enjoy the effect of a certain skin external preparation, and then by examining the expression amount of the SSL-derived RNA in the subject, it is possible to predict whether the subject will obtain the effect when using the skin external preparation.

[0082] Test Example 6 Detection of novel atopic dermatitis marker molecules using SSL-derived RNA subject The subjects were 55 healthy subjects (20-49 years old, male, BMI 18.5 to less than 25.0), 15 patients with mild atopic dermatitis (20-39 years old, male, BMI 18.5 to less than 25.0), and 25 patients with moderate atopic dermatitis (20-39 years old, male, BMI 18.5 to less than 25.0). The healthy subjects had been confirmed by a dermatologist to have no abnormalities in their skin, and the patients with atopic dermatitis had been diagnosed with atopic dermatitis by a dermatologist.

[0083] Preparation of SSL-derived RNA and sequence analysis Sebum was collected from the entire face of each subject using an oil blotting film (3M). The oil blotting film was transferred to a glass vial and stored at -80°C for about one month until it was used for RNA extraction. RNA in the SSL was extracted from the stored oil blotting film in the same manner as in Test Example 3, a library was prepared, and RNA species were identified by sequencing and their expression levels were measured.

[0084] 4. Data Analysis Based on the SSL-derived RNA information (RPM value), the RPM value converted to a logarithmic value of base 2 was subjected to data analysis. A group of 1911 genes was extracted in which the RPM value converted to a logarithmic value of base 2 was reduced by more than 2-fold in patients with atopic dermatitis compared to healthy subjects, and the p-value was 0.05 or less in the Student's t-test (Tables 7-1 to 7-24 (A)). Next, using the RPM value converted to a logarithmic value of base 2, a search for biological process (BP) was performed according to previously published literature (Nature Protoc, 2009, 4:44-57; Nucleic Acids Res, 2009, 37:1-13) with a false discovery rate (FDR) level of 5%. As a result, 19 BPs were obtained that were associated with genes whose expression was reduced in patients with atopic dermatitis, and among them, GO:0050911 (detection of chemical stimulus involved in sensory perception of smell) was shown to be the most strongly associated (Table 5). The RNA that constitutes GO:0050911 contains approximately 400 olfactory receptors (ORs), and the expression of 370 ORs was statistically significantly reduced in patients with atopic dermatitis compared to healthy subjects, and the reduced expression of these ORs contributed to the significance of the GO. This suggests that the expression levels of these 370 ORs contained in the SSL-derived RNA information may be useful markers for distinguishing healthy subjects from patients with atopic dermatitis. Furthermore, when the RNA expression of ORs was compared between healthy individuals and patients with mild atopic dermatitis, and between patients with mild atopic dermatitis and patients with moderate atopic dermatitis, it was found that 368 ORs were reduced in patients with mild atopic dermatitis compared to healthy individuals, and furthermore, 284 ORs were reduced in patients with moderate atopic dermatitis compared to patients with mild atopic dermatitis (Tables 7-1 to 7-11 (B) to (D)).These results revealed that the expression levels of the ORs shown in (B) to (D) of Tables 7-1 to 7-11 in SSL decreased as the symptoms of atopic dermatitis worsened, suggesting that the severity of atopic dermatitis may be grasped by using the expression levels of these ORs as indicators.

[0085] [Table 5]

[0086] Test Example 7 Detection of novel sensitive skin marker molecules using SSL-derived RNA subject Forty-two healthy women (ages 20-59, BMI 18.5 to 25.0) who had been confirmed by a dermatologist in advance to have no skin abnormalities were selected as subject candidates. These candidates were given a questionnaire about whether they were aware of their sensitive skin (choosing one of four options: "worried," "not very bothered," "not bothered," or "not bothered at all"). The 10 subjects who chose "not bothered" or "not bothered at all" were divided into a group with no subjective symptoms of sensitive skin, and the 13 subjects who chose "worried" were divided into a group with subjective symptoms of sensitive skin.

[0087] Preparation of SSL-derived RNA and sequence analysis Sebum was collected from the entire face of each subject using an oil blotting film (3M). The oil blotting film was transferred to a glass vial and stored at -80°C for about one month until it was used for RNA extraction. RNA in the SSL was extracted from the stored oil blotting film in the same manner as in Test Example 3, a library was prepared, and RNA species were identified by sequencing and their expression levels were measured.

[0088] 4. Data Analysis Based on the SSL-derived RNA information (RPM value), the RPM value converted to a logarithmic value of base 2 (Log2RPM value) was subjected to data analysis. 693 gene groups were extracted in which the Log2RPM value was reduced by more than 2-fold in the group with sensitive skin symptoms compared to the group without sensitive skin symptoms, and the p-value was 0.05 or less in the Student's t-test (Tables 7-1 to 7-20 (E)). Next, using the Log2RPM value, a search for BPs was performed by gene ontology enrichment analysis according to the above-mentioned published literature, with the FDR level set to 5%. As a result, four BPs associated with the gene group whose expression was reduced in the group with sensitive skin symptoms were obtained, and GO:0050911 was shown to be most strongly associated (Table 6). Of the approximately 400 ORs contained in GO:0050911, the expression of 344 ORs (Tables 7-1 to 7-10 (F)) was statistically significantly decreased in people with sensitive skin symptoms compared to people without sensitive skin symptoms, and this decrease in expression contributed to the significance of the GO. This suggests that the expression levels of these ORs contained in SSL-derived RNA information may be useful markers for detecting sensitive skin symptoms.

[0089] [Table 6]

[0090] Test Example 8 Detection of marker molecules related to sebum secretion, moisture content, and redness using SSL-derived RNA subject The subjects were 38 healthy men (aged 20-59, BMI 18.5 or greater and less than 25.0) who had been previously confirmed by a dermatologist to have no skin abnormalities.

[0091] Skin properties measurement After taking a photograph of the entire face, the amount of casual sebum from the forehead of each subject before washing was measured using a Sebumeter (MPA580, Courage+Khazaka, Germany). Then, the subject washed their face and was allowed to acclimate in an environmentally variable room (temperature 20°C (±2°C), humidity 50% (±5%)) for 15 minutes. After acclimatization, the moisture content of the cheeks was measured using a Corneometer (MPA580, Courage+Khazaka, Germany).

[0092] Preparation of SSL-derived RNA and sequence analysis After the measurement of the amount of casual sebum in the above-mentioned skin property measurement, sebum was collected from the entire face of each subject using an oil blotting film (3M). The oil blotting film was transferred to a glass vial and stored at -80°C for about one month. From the stored oil blotting film, RNA in SSL was extracted in the same manner as in Test Example 3, library preparation was performed, and RNA species were identified by sequencing and their expression levels were measured.

[0093] 4. Data Analysis 1) Sebum secretion-related molecules The subjects were divided into a low group and a high group, with the casual sebum amount measured using a Sebumeter being less than 100 and 150 or more, respectively. The expression levels (RPM values) of SSL-derived RNA were compared between the low and high groups using the same procedure as in Test Example 7, and it was shown that 594 types of RNA were statistically significantly expressed (Tables 7-1 to 7-17 (I)). Among them, as shown in Figure 8, it was revealed that the expression level of Basigin (BSG) was statistically significantly increased (Student's t-test, p<0.05) in the high sebum secretion group compared to the low group. It has been reported that BSG knockout mice show a phenotype in which lipid accumulation is reduced and the meibomian glands, which are biologically and structurally similar to sebaceous glands, are atrophied (Cell Death and Disease, 2015, 6:e1726). Furthermore, it was revealed that the expression level of hydroxycarboxylic acid receptor 2 (HCAR2) was statistically significantly decreased in the high sebum secretion group compared to the low sebum secretion group. It has also been reported that HCAR2 has the effect of suppressing lipid accumulation in macrophages (J Lipid Res, 2014, 2501-2508). These findings suggest that the RNA contained in SSL is a useful indicator of the amount of sebum secreted.

[0094] 2) Moisture-related molecules Based on the results of the Corneometer measurement, the top 15 subjects with the highest stratum corneum moisture content (high value group) and the bottom 15 subjects with the lowest stratum corneum moisture content (low value group) were selected. The expression levels (RPM values) of SSL-derived RNA were compared between the two groups, the high value group and the low value group, using the same procedure as in Test Example 7, and it was shown that 553 types of RNA were statistically significantly expressed (Tables 7-1 to 7-16 (H)). It has been reported that natural moisturizing factors play an important role in maintaining the moisturizing power of the skin (Dermatol Ther, 17 Suppl, 2004, 1:43-48). When the expression of factors related to the production of natural moisturizing factors was examined, it was revealed that the expression levels of Aspartic peptidase retroviral like 1 (ASPRV1) and Peptidyl arginine deiminase 3 (PADI3), etc. were statistically significantly (Student's t-test, p<0.05) decreased in the low value group compared to the high value group, as shown in Figure 9. It has been reported that mice lacking ASPRV1 actually have reduced stratum corneum moisture content (EMBO Mol Med, 2011, 3:320-333). On the other hand, it has been reported that PADI3 also plays an important role in the production of natural moisturizing factors (J Invest Dermatol, 2005, 124:384-393). These findings suggest that the RNA contained in SSL is a useful indicator of skin moisture content.

[0095] 3) Redness-related molecules Based on the results of visual evaluation of the facial images, eight people with strong redness (high value group) and six people with weak redness (low value group) were selected. The expression levels (RPM values) of SSL-derived RNA were compared between the two groups, the high value group and the low value group, using the same procedure as in Test Example 7, and it was shown that 703 types of RNA showed statistically significant changes in expression (Tables 7-1 to 7-20 (G)). Among them, as shown in Figure 10, it was revealed that the expression levels of Suppressor of cytokine shignaling 3 (SOCS3) and JunB proto-oncogene (JUNB) were statistically significantly reduced (Student's t-test, p<0.05) in the high redness group compared to the low redness group. It has been reported that inflammation is induced in the skin in JUNB and SOCS3 knockout mice (Proc Natl Acad Sci USA, 2009, 106:20423-20428, PLoS One, 2012, 7:e40343). Furthermore, although no statistically significant difference was confirmed, it was revealed that IL-1B, which is known to induce inflammation in the skin, tended to increase in the high redness group compared to the low redness group. These findings suggest that RNA contained in SSL is a useful indicator of skin redness.

[0096] [Table 7-1]

[0097] [Table 7-2]

[0098] [Table 7-3]

[0099] [Table 7-4]

[0100]

Table 7-5

[0101]

Table 7-6

[0102]

Table 7-7

[0103]

Table 7-8

[0104]

Table 7-9

[0105]

Table 7-10

[0106]

Table 7-11

[0107]

Table 7-12

[0108]

Table 7-13

[0109]

Table 7-14

[0110]

Table 7-15

[0111] [Table 7-16]

[0112] [Table 7-17]

[0113] [Table 7-18]

[0114] [Table 7-19]

[0115] [Table 7-20]

[0116] [Table 7-21]

[0117] [Table 7-22]

[0118] [Table 7-23]

[0119] [Table 7-24]

[0120] Test Example 9 Prediction of skin condition using SSL-derived RNA subject The subjects were 39 healthy women (in their 30s) with no skin problems on their face, fingers, or upper arms.

[0121] Sebum collection Before washing their faces, sebum was collected from the entire face of each subject using an oil blotting film (5cm x 8cm, 3M) and stored at -80°C for approximately one month as a sample for SSL-derived RNA analysis.

[0122] Visual and palpation evaluation of the skin After collecting the sebum samples, the subjects washed their faces with a commercially available facial cleanser and were allowed to acclimate for 10 minutes in an environmentally variable room (temperature 20°C ± 1°C, humidity 40% ± 5%). During this acclimatization period, the condition of the subjects' facial skin was evaluated visually and by palpation. Visual evaluation items: "Clearance," "Transparency," "Brightness," "Yellowness," "Overall redness," "Spots," "Scales," "Gloss," "Cheek wrinkles," "Prominent dark circles," "Sagging corners of the mouth," "Acne," "Prominent pores (cheeks)," and "Prominent pores (nose)." Palpation evaluation: "rough feeling", "moist feeling" For each evaluation item, three expert evaluators gave scores based on the evaluation criteria (3: very much, 2: some, 1: somewhat, 0: none), and the average of the three evaluators was used as the evaluation value.

[0123] Skin properties measurement After the acclimation, the subjects were measured for stratum corneum moisture content using a Corneometer (MPA580, Courage+Khazaka, Germany) and Skicon (Yayoi Co., Ltd.), transepidermal water loss (TEWL) using a Tewameter (MPA580, Courage+Khazaka, Germany), sebum content using a Sebumeter (MPA580, Courage+Khazaka, Germany), and melanin and erythema content using a CM26000d (Konica Minolta Japan). All measurements were taken on the cheeks except for the forehead, where sebum content was measured.

[0124] Sebum composition analysis From the subjects more than 1 hour after face washing, in the supine position, two cigarette papers (1.7 cm × 1.7 cm, Rizla: Rizla Blue Double) degreased with chloroform / methanol = 1 / 1 were arranged near the center of the forehead so as not to overlap, and gently pressed for 10 seconds to collect sebum. The cigarette papers on which sebum was collected were placed in a screw tube, methanol was immediately added, and stored frozen at -80°C until analysis.

[0125] The solvent was distilled off from the screw tube under a nitrogen stream, and then 1 mL of chloroform / methanol = 1 / 1 was added into the screw tube. After confirming that the cigarette paper was sufficiently immersed in the solvent in the screw tube, lipid extraction was performed by ultrasonic treatment for 5 minutes to obtain a sebum solution. 20 μL of a lipid internal standard mixed solution for Direct-MS / MS measurement at 100 μmol / L was dried in a microvial, and 100 μL of the sebum solution prepared by the above procedure was added thereto, and dissolved and mixed to prepare a sebum sample solution containing an internal standard. From the prepared sample solution, the amounts of free fatty acid (FFA), wax ester (WE), cholesterol ester (ChE), squalene (SQ), squalene epoxide (SQepo), oxidized squalene (SQOOH), diacylglycerol (DAG), and triacylglycerol (TAG) were measured for each subject by Direct-MS / MS, and the absolute amounts were calculated based on the internal standard.

[0126] <Direct-MS / MS Measurement Conditions> Measurement was performed under the following conditions according to the method described in the literature (Patent No. 6482215). Apparatus: LC / Agilent 1200 series, mass spectrometer / 6460 triple quadrupole (manufactured by Agilent) Mobile phase: chloroform / methanol = 1 / 1 containing 15 mmol / L ammonium acetate Flow rate: 0.2 mL / min Injection volume: 1 μL Detection conditions: ionization method = ESI, drying gas temperature = 300 °C, drying gas flow rate = 5 L / min, nebulizer pressure = 45 psi, sheath gas temperature = 250 °C, sheath gas flow rate = 11 L / min, nebulizer voltage = 0 V, capillary voltage = 3500 V (Detection mode of mass spectrometer) FFA:Scan (Negative mode) WE: Precursor Ion Scan to detect molecules from product ions derived from constituent fatty acids ChE: Precursor Ion Scan to detect molecules from product ions derived from cholesterol backbone SQ, SQepo, SQOOH:MRM DAG: Neutral Loss Scan to detect molecules from eliminated hydroxyl groups TAG: Neutral Loss Scan to detect molecules from fatty acids released as neutral molecules

[0127] Preparation of SSL-derived RNA and sequence analysis RNA in the SSL was extracted from the stored oil blotting film in the same manner as in Test Example 3, a library was prepared, RNA species were identified by sequencing, and their expression levels were measured.

[0128] Building a Machine Learning Model Usage Data In the data on the expression level of SSL-derived RNA from the subjects measured above (read count value), data with a read count of less than 10 was treated as missing values, and the data was converted to RPM values ​​corrected for the difference in the total number of reads between samples, and the missing values ​​were imputed by Singular Value Decomposition (SVD) imputation. However, only genes for which expression level data that was not missing was obtained from 80% or more of all subjects were used in the following analysis. In order to approximate the RPM value that follows a negative binomial distribution to a normal distribution, RPM values ​​converted to logarithmic values ​​with base 2 (Log2RPM value) were used to construct the machine learning model. In addition, the evaluation values ​​and measurement values ​​for each prediction target item (data from the above-mentioned visual and palpation evaluation of the skin, skin physical property measurement, and sebum composition analysis, Table 8) were converted to standard deviations within each target data set, and these values ​​were used as target values.

[0129] · Dataset division Of the RNA profile datasets obtained from the subjects, 80% (31 subjects) of the RNA profile data were used as training data for the skin condition prediction model, and the remaining 20% ​​(8 subjects) of the RNA profile data were used as test data to evaluate the accuracy of the model. To split the training data and test data, two splitting methods were considered: one that would result in a uniform age distribution (split 1), and one that would result in uniform target values ​​for the prediction items (split 2).

[0130] Feature gene selection The absolute value of the Spearman's correlation coefficient (rho) between the target value and the Log2RPM value of the prediction target item in the training data was calculated, and the top 10 genes (or 5 genes) shown in Table 8 were selected as feature genes for each prediction target item.

[0131] Model building Model construction was performed using the caret package in the statistical analysis environment R. A prediction model was constructed using the SSL-derived RNA expression data (Log2RPM value) from the training data as explanatory variables and the target values ​​of each item to be predicted as objective variables. For each item to be predicted, six algorithms were used: linear regression model, lasso regression, random forest, neural network, support vector machine with a linear kernel (SVM (linear)), and support vector machine with an rbf kernel (SVM (rbf)). A 10-fold cross-validation was performed to train a predictive model. For each algorithm, the expression levels (Log2RPM values) of SSL-derived RNA from the test data were input into the trained model to calculate the target prediction values ​​for each prediction item. The root mean square error (RMSE) of the difference between the predicted value and the actual value was calculated for each prediction item, and the model with the smallest value was selected as the optimal prediction model.

[0132] [Table 8]

[0133] (result) Table 9 shows the data partitioning method, algorithm used, and RMSE that gave the minimum RMSE for each prediction target item. Figure 11 shows a scatter plot of the predicted and actual measured values ​​of the target value in the optimal prediction model. Note that R in the figure indicates the correlation coefficient in Pearson's correlation analysis between the predicted and actual values. Positive correlation coefficients were obtained for all prediction target items, demonstrating that it is possible to predict skin condition using data on the expression level of SSL-derived RNA.

[0134] [Table 9]

[0135] Test Example 10: Prediction of blood cortisol concentration using SSL-derived RNA subject The subjects were 128 healthy women (in their 20s to 50s) with no skin problems on their face, fingers, or upper arms.

[0136] Sebum collection and SSL-RNA sequencing Sebum was collected from the entire face of each subject before washing using an oil blotting film (5 cm x 8 cm, 3M) and stored at -80°C for approximately one month as a sample for SSL-derived RNA analysis. RNA in the SSL was extracted from the stored oil blotting film using the same procedure as in Test Example 3, a library was prepared, RNA species were identified by sequencing, and their expression levels were measured.

[0137] Measurement of blood cortisol levels 15 mL of blood was collected from the arm of each subject using a vacuum blood collection tube, and the serum was separated and stored at -80°C. The cortisol concentration in the stored serum was quantified by a chemiluminescence immunoassay (CLIA method) outsourced to an external testing agency (LSI Medience Corporation).

[0138] Building a Machine Learning Model Usage Data As in Test Example 9, data on the expression level of SSL-derived RNA from subjects with a read count of less than 10 (read count value) was treated as missing values, and the data was converted to RPM values ​​corrected for the difference in the total number of reads between samples, after which the missing values ​​were filled in by SVD imputation. In constructing the machine learning model, only genes for which expression level data other than missing values ​​was obtained for 80% or more of all subjects were used in the analysis, and Log2RPM values ​​were used as expression level data.

[0139] · Dataset division From the RNA profile dataset obtained from the subjects, 102 subjects (80%) were randomly extracted and used as training data for the blood cortisol concentration prediction model, while the remaining 20% ​​(26 subjects) were used as test data to evaluate the accuracy of the model.

[0140] Feature gene selection We extracted 1,000 genes that had a large Pearson correlation coefficient with blood cortisol concentration in the training data.

[0141] · Algorithm used (candidate hyperparameter values) Support vector machine (C: [0.1, 1, 10], kernel: ['linear', 'rbf', 'poly']) Random forest (max_depth: [1, 2, 3], max_features: [1, 2], n_estimators: [10, 100]) Multilayer perceptron (solver: ['lbfgs', 'adam'], alpha: [0.1, 1, 10])

[0142] Model building The model was built using the Python machine learning library scikit-learn. ··A predictive model was trained by performing 10-fold cross-validation using the SSL-derived RNA expression data (Log2RPM value) from the training data as the explanatory variable and the blood cortisol concentration as the objective variable. Within the cross-validation, the expression data of 1,000 genes extracted as feature genes were compressed into the first through tenth principal components by principal component analysis, and then the model was trained by performing a grid search for each algorithm and candidate hyperparameter value. After training, the expression levels (Log2RPM values) of SSL-derived RNA from the test data were input into each model to calculate predicted values, and the model with the smallest RMSE from the actual measured values ​​was selected as the optimal predictive model.

[0143] (result) Figure 12 shows a scatter plot of predicted values ​​of blood cortisol concentration obtained by inputting the expression levels (Log2RPM values) of SSL-derived RNA from the test data into a prediction model using random forest (max_depth=2, max_features=2, n_estimator=100) that obtained the smallest RMSE, against the actual measured values. The figure also shows the correlation coefficient (R) and RMSE value in the Pearson correlation analysis between the predicted values ​​and the actual measured values. As shown in the figure, a positive correlation coefficient was obtained, indicating that it is possible to predict blood cortisol concentration using data on the expression levels of SSL-derived RNA.

[0144] Test Example 11 Prediction of cumulative UV exposure time using SSL-derived RNA subject The subjects were 130 healthy women (in their 20s to 50s) with no skin problems on their face, fingers, or upper arms.

[0145] Sebum collection and SSL-RNA sequencing Sebum was collected from the entire face of each subject before washing using an oil blotting film (5 cm x 8 cm, 3M) and stored at -80°C for approximately one month as a sample for SSL-derived RNA analysis. RNA in the SSL was extracted from the stored oil blotting film using the same procedure as in Test Example 3, a library was prepared, RNA species were identified by sequencing, and their expression levels were measured.

[0146] Calculating cumulative UV exposure time The standard time that subjects were exposed to sunlight within a certain age range was predicted based on a questionnaire about their lifestyle and outdoor leisure activities, and the cumulative UV exposure time (hours) was calculated taking into account their actual age. The questions in the questionnaire were based on the photoexposure history questionnaire published by the US Cancer Center (Arch. Dermatol. 144, 217-22 (2008)).

[0147] Building a Machine Learning Model As in Test Example 9, data on the expression level of SSL-derived RNA from subjects with a read count of less than 10 (read count value) was treated as missing values, and the data was converted to an RPM value corrected for the difference in the total number of reads between samples, after which the missing values ​​were filled in by SVD imputation. In constructing the machine learning model, only genes for which expression level data other than missing values ​​was obtained for 80% or more of all subjects were used in the analysis, and Log2RPM was used as the expression level data.

[0148] · Dataset division From the RNA profile dataset obtained from the subjects, 104 subjects (80%) were randomly extracted and used as training data for the cumulative UV exposure time prediction model, while the remaining 26 subjects (20%) were used as test data to evaluate the accuracy of the model.

[0149] Feature gene selection We extracted 1,000 genes with the highest Pearson correlation coefficient between cumulative UV exposure time and the training data. In addition to these 1,000 genes, we added the subject's age as a feature.

[0150] · Algorithm used (candidate hyperparameter values) The same algorithm as in Test Example 10 was used.

[0151] Model building As in Test Example 10, 10-fold cross-validation was performed, and the model with the smallest RMSE of the difference from the actual measured value was selected as the optimal prediction model. However, in addition to the 1000 genes selected above, the subject's age was used as a feature.

[0152] (result) Figure 13 shows a scatter plot of predicted cumulative UV exposure time obtained by inputting the expression levels (Log2RPM values) of SSL-derived RNA from the test data into a prediction model using a support vector machine (C=10, kernel='linear') that obtained the smallest RMSE, against the calculated values ​​based on the questionnaire. The figure also shows the correlation coefficient (R) and RMSE value in the Pearson correlation analysis between the predicted and calculated values. As shown in the figure, a positive correlation coefficient was obtained, indicating that by using data on the expression levels of SSL-derived RNA, it is possible to predict cumulative UV exposure time without relying on a questionnaire.

Claims

1. Converting RNA contained in the subject's skin surface lipids into cDNA by reverse transcription, and then subjecting the cDNA to multiplex PCR; and purifying the PCR reaction product; A method for preparing nucleic acid derived from a skin cell of a subject, comprising: The extension reaction in the reverse transcription is carried out at 42° C.±0.25° C. for 80 to 100 minutes, The temperature of the annealing and extension reaction in the multiplex PCR is 62° C.±0.25° C.; method.

2. The method of claim 1, wherein the RNA contained in the lipids on the skin surface of the subject is prepared by isolating RNA from the lipids on the skin surface of the subject.

3. The method according to claim 1 or 2, wherein the lipids on the surface of the skin of the subject have been stored at or below 0°C.

4. A method for analyzing the condition of the skin, a site other than the skin, or the whole body of a subject, comprising analyzing a nucleic acid prepared by the method according to any one of claims 1 to 3.

5. 5. The method of claim 4, wherein the analysis is detection of skin with or without redness, sensitive skin or atopic dermatitis, or detection of skin with or without high or low sebum or skin moisture content.

6. The analysis method according to claim 4 , wherein the analysis is a prediction of a skin condition.

7. The method of claim 6 , wherein the prediction of the skin condition is a prediction of skin physical properties, a prediction of visual or palpation evaluation of the skin, or a prediction of sebum composition.

8. The method according to claim 4 , wherein the analysis is a prediction of cumulative UV exposure time of the skin.

9. A method for evaluating the effect or efficacy of a topical skin preparation, an intradermal preparation, a patch preparation, an oral preparation or an injection preparation on a subject, comprising analyzing a nucleic acid prepared by the method according to any one of claims 1 to 3.

10. A method for analyzing the concentration of a component in the blood of a subject, comprising analyzing a nucleic acid prepared by the method according to any one of claims 1 to 3.

11. 11. The method according to claim 10, wherein the blood component is a hormone, insulin, neutral fat, γ-GTP, or LDL-cholesterol.

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