Method for analyzing biological sample

Abstract

Provided is a method for analyzing a biological sample, the method comprising: (1) a step for preparing a probe, and a plurality of probe solutions having different ionic strengths or pH, wherein the probe comprises (a) a peptide composed of 3-100 amino acid residues, and having an absolute net charge score of at least 0.15, a hydrophobicity score of at least 0.0, and a total sum of the absolute net charge score and the hydrophobicity score of at least 0.30, and (b) an environment-responsive fluorophore covalently bound to the peptide; (2) a step for mixing the plurality of probe solutions with a biological sample to be analyzed; (3) a step for measuring the fluorescence intensity of a mixed solution prepared in step (2); and (4) a step for comparing a fluorescence intensity pattern obtained in step (3) with a fluorescence intensity pattern obtained for a reference sample.

Description

Methods for analyzing biological samples

[0001] The present invention relates to a method for analyzing a biological sample.

[0002] Biological samples, such as cells, tissues, biofluids, and microbiota, reflect various information about an organism's health, constitution, and disease risk. However, because biological samples contain a wide variety of components and have extremely complex compositions, comprehensive analysis of them is laborious, cost-intensive, and impractical. Therefore, conventionally, only a very limited number of components are analyzed, but this can lead to important information being overlooked and erroneous conclusions being drawn.

[0003] Serum is also used as an essential supplement for cell culture. Because serum significantly influences cell proliferation, phenotypes, and differentiation, strict quality control is required. However, due to its complex composition, it is not fully understood which components in serum affect cell culture and how. Therefore, end users must actually culture the target cells to verify the quality of the serum (so-called lot-to-lot verification), which requires a great deal of time and effort. Furthermore, lot checks depend on the condition of the cells and the skill of the experimenter, making reliable and consistent quality control difficult in many cases.

[0004] Similarly, alcoholic beverages, produced through a fermentation process involving microorganisms, contain a wide variety of components and have extremely complex compositions. Furthermore, the mechanisms by which components affect flavor remain largely unknown. For example, to standardize the quality of sake, the National Tax Agency's analytical methods specify the analysis parameters of alcohol content, amino acid content, acidity, and sake meter value, and specify methods for measuring these parameters. However, the evaluation indices based on these measurements (sweet / dry, rich / light) do not necessarily coincide with sensory evaluation. Therefore, current quality control of alcoholic beverages relies on the intuition of sake craftsmen or sensory tests by expert tasters, making objective and consistent quality evaluation extremely difficult.

[0005] The present inventors have succeeded in using cross-reactive sensing methods to accurately distinguish biological samples such as culture supernatants, microflora, serum, and alcoholic beverages (Patent Documents 1 and 2, and pre-publication Japanese patent applications Nos. 2023-130004 and 2023-178769). These methods enable analysis of samples by simply obtaining a fluorescence intensity pattern reflecting the sum of nonspecific interactions between all components and the probe, without the need for comprehensive analysis of the components in the sample. However, these methods use probes with cationic polymer backbones, and expanding the probe repertoire requires chemical modification of the cationic polymer, which is time-consuming and labor-intensive.

[0006] International Publication No. WO 2018 / 088510 International Publication No. WO 2020 / 262413

[0007] The present invention aims to provide an analytical method based on cross-reactive sensing that can be easily optimized according to the target and purpose of analysis by preparing probes more cheaply and efficiently.

[0008] The present inventors have found that by using a probe having a peptide skeleton with specific properties instead of the conventional cationic polymer-based probe, biological samples can be identified with the same accuracy.

[0009] That is, according to one embodiment, the present invention provides a method for detecting a peptide comprising: (1) preparing a probe and a plurality of probe solutions having different ionic strengths or pHs; wherein the probe is: (a) a peptide consisting of 3 to 100 amino acid residues, the peptide having (i) an absolute net charge score (ANC) of 0.15 to 1.0, (ii) a hydrophobicity score (H) of 0.0 to 0.85, and (iii) a sum of the absolute net charge score and the hydrophobicity score (ANC+H) of 0.30 to 1.0; wherein the absolute net charge score (ANC) is expressed by the formula (I): ANC=|AA ｐｏｓｉｔｉｖｅ -AA ｎｅｇａｔｉｖｅ | / AA ｔｏｔａｌ (I) (Wherein, AA ｐｏｓｉｔｉｖｅis the number of amino acid residues having a positive charge at the pH of the probe solution; ｎｅｇａｔｉｖｅ is the number of negatively charged amino acid residues at the pH of the probe solution; ｔｏｔａｌ is the total number of amino acid residues), and the hydrophobicity score (H) is calculated by the formula (II): H = AA ｈｙｄｒｏｐｈｏｂｉｃ / AA ｔｏｔａｌ (II) (wherein, AA ｈｙｄｒｏｐｈｏｂｉｃ is the number of hydrophobic amino acid residues; AA ｔｏｔａｌ (where Λ is the total number of amino acid residues), (b) an environmentally responsive fluorophore, and wherein the fluorophore is covalently bound to the peptide; (2) a method for analyzing a biological sample, the method comprising: (1) mixing the plurality of probe solutions with a biological sample to be analyzed; (3) measuring the fluorescence intensity of the mixture prepared in step (2); and (4) comparing the fluorescence intensity pattern obtained in step (3) with the fluorescence intensity pattern obtained for a reference sample.

[0010] The peptide preferably consists of 5 to 60 amino acid residues.

[0011] Preferably, the absolute net charge score (ANC) is between 0.20 and 1.0.

[0012] The sum of the absolute net charge score and the hydrophobicity score (ANC+H) is preferably 0.40 to 1.0.

[0013] The environmentally responsive fluorophore is preferably selected from the group consisting of a fluorophore having a naphthalenesulfonic acid skeleton, a fluorophore having a benzofurazan skeleton, a fluorophore having a xanthene skeleton, a fluorophore having a pyrene skeleton, and an aggregation-induced emission fluorophore.

[0014] The measurement of the fluorescence intensity in step (3) is preferably carried out for a plurality of excitation wavelengths and fluorescence wavelengths.

[0015] The biological sample may contain cells or their culture supernatant.

[0016] Alternatively, the biological sample may contain serum or a serum substitute.

[0017] Alternatively, the biological sample may contain a microbiota.

[0018] Alternatively, the biological sample may contain alcoholic beverages.

[0019] According to the method of the present invention, by using a peptide probe, similar to the case of using a conventional cationic polymer probe, the characteristics of a biological sample can be determined with high accuracy, ease, and consistency simply by obtaining a fluorescence intensity pattern that reflects the sum of nonspecific interactions between all components in the biological sample and the probe.

[0020] Furthermore, since the probes used in the method of the present invention have a peptide backbone, they are easier to modify in design and can be prepared more inexpensively than conventional cationic polymer probes, making it easy to build a repertoire of probes optimized for each sample to be analyzed.

[0021] Figure 1 shows the change in fluorescence spectrum when various concentrations of α1-antitrypsin were added to 500 nM probes (Seq01, Seq03, Seq13) in 20 mM MOPS (pH 7.0). Figure 2 shows the change in fluorescence intensity (520 nm) when various concentrations of α1-antitrypsin were added to 500 nM probes (Seq01, Seq03, Seq13) in 20 mM MOPS (pH 7.0) (mean ± standard error, n = 3). Figure 3 shows the change in fluorescence intensity (480 nm) (I-I) obtained by measuring 8 proteins × 1 solvent condition (pH 7.0) × 8 probes (Seq01 to Seq03, Seq05, Seq11 to Seq14) × 1 wavelength set × 6 times. ０ 4 is a graph showing the change in fluorescence intensity (II-II) obtained by measuring 8 proteins × 1 solvent condition (pH 7.0) × 8 probes (Seq01 to Seq03, Seq05, Seq11 to Seq14) × 2 wavelength sets × 6 times. ０) is a heat map representation of the data from FIG. 4 . FIG. 5 is a graph of the coefficient of variation (CV) of the measurement results for four proteins that showed positive responses overall from the data from FIG. 4 . FIG. 6 is a diagram plotting the first and second discriminant scores obtained by analyzing the data from FIG. 4 (Seq01 / Seq02, Seq03 / Seq05, Seq11 / Seq12, and Seq13 / Seq14) by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. FIG. 7 is a diagram plotting the first and second discriminant scores and the second and third discriminant scores obtained by analyzing the data from FIG. 4 (Seq01 to Seq03, Seq05, or Seq11 to Seq14) by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. FIG. 8 shows the change in fluorescence intensity (480 nm) (II-II) obtained by measuring 7 proteins × 1 solvent condition (pH 7.0) × 4 probes (Seq01, Seq03, Seq19, Seq20) × 1 wavelength set × 6 times. ０ 9 is a graph showing the change in fluorescence intensity (I-I) obtained by measuring 7 proteins x 1 solvent condition (pH 7.0) x 4 probes (Seq01, Seq03, Seq19, Seq20) x 2 wavelength sets x 6 times. ０ ) is a heat map representation. Figure 10 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figures 8 and 9 by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are indicated by bold. Figure 11 shows the change in fluorescence intensity (480 nm) (I-I) obtained by measuring 7 proteins x 1 solvent condition (pH 7.0) x 6 probes (Seq01, Seq02, Seq25 to Seq28) x 1 wavelength set x 6 times. ０ 12 is a graph showing the change in fluorescence intensity (II) obtained by measuring 7 proteins × 1 solvent condition (pH 7.0) × 6 probes (Seq01, Seq02, Seq25 to Seq28) × 2 wavelength sets × 6 times. ０) is a heat map representation. Figure 13 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figures 11 and 12 by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are indicated by bold. Figure 14 shows the change in fluorescence intensity (480 nm) (I-I) obtained by measuring 7 proteins x 1 solvent condition (pH 7.0) x 8 probes (Seq02, Seq03, Seq26, Seq28, Seq29 to Seq32) x 1 wavelength set x 6 times. ０ ) in the amino acid sequence. Cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are indicated by bold. Figure 15 shows the change in fluorescence intensity (480 nm) (I-I) obtained by measuring 7 proteins x 1 solvent condition (pH 7.0) x 4 probes (Seq21 to Seq24) x 1 wavelength set x 6 times. ０ 16 is a graph showing the change in fluorescence intensity (II) obtained by measuring 7 proteins × 1 solvent condition (pH 7.0) × 4 probes (Seq21 to Seq24) × 2 wavelength sets × 6 times. ０ ) is a heat map representation. Figure 17 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figures 15 and 16 by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 18 shows the change in fluorescence intensity (480 nm) (I-I) obtained by measuring 7 proteins x 1 solvent condition (pH 7.0) x 6 probes (Seq01, Seq03, Seq33 to Seq36) x 1 wavelength set x 6 times. ０ 19 is a graph showing the change in fluorescence intensity (II) obtained by measuring 7 proteins × 1 solvent condition (pH 7.0) × 6 probes (Seq01, Seq03, Seq33 to Seq36) × 2 wavelength sets × 6 times. ０) is a heat map representation. Figure 20 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figures 18 and 19 by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 21 shows the change in fluorescence intensity (480 nm) (I-I) obtained by measuring 7 proteins x 1 solvent condition (pH 7.0) x 4 probes (Seq01, Seq02, Seq37, Seq38) x 1 wavelength set x 6 times. ０ 22 is a graph showing the change in fluorescence intensity (II-II) obtained by measuring 7 proteins, 1 solvent condition (pH 7.0), 4 probes (Seq01, Seq02, Seq37, Seq38), 2 wavelength sets, and 6 times. ０ ) is a heat map representation. Figure 23 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figures 21 and 22 by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 24 shows the change in fluorescence intensity (480 nm) (I-I) obtained by measuring 7 proteins x 1 solvent condition (pH 7.0) x 3 probes (Seq01, Seq39, Seq40) x 1 wavelength set x 6 times. ０ 25 is a graph showing the change in fluorescence intensity (II-II) obtained by measuring 7 proteins x 1 solvent condition (pH 7.0) x 3 probes (Seq01, Seq39, Seq40) x 2 wavelength sets x 6 times. ０ ) is a heat map representation. Figure 26 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figures 24 and 25 by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 27 shows the change in fluorescence intensity (480 nm) (I-I) obtained by measuring 7 proteins x 1 solvent condition (pH 7.0) x 6 probes (Seq01, Seq03, Seq41 to Seq44) x 1 wavelength set x 6 times. ０ 28 is a graph showing the change in fluorescence intensity (II) obtained by measuring 7 proteins × 1 solvent condition (pH 7.0) × 6 probes (Seq01, Seq03, Seq41 to Seq44) × 2 wavelength sets × 6 times.０ ) is a heat map representation. Figure 29 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figures 27 and 28 by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 30 shows the change in fluorescence intensity (480 nm) (I-I) obtained by repeating three independent trials of 7 proteins x 1 solvent condition (pH 7.0) x 3 probes (Seq01, Seq02, Seq03) x 2 wavelength sets x 6 measurements. ０ ) is a graph showing the average values ​​of the independent trials in FIG. 30. FIG. 31 is a graph showing the CV values ​​for the average values ​​of the independent trials in FIG. 30. FIG. 32 is a graph plotting the first and second discriminant scores obtained by analyzing the data in FIG. 30 by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. FIG. 33 shows the change in fluorescence intensity (I-I) obtained by measuring 4 proteins x 1 solvent condition (pH 7.0) x 31 probes (Seq01 to Seq03 and Seq19 to Seq46) x 1 wavelength set x 6 times. ０ ) are plots showing the correlation with ANC (top) and ANC+H (bottom). Figure 34 shows the change in fluorescence intensity (I-I) obtained by 8 proteins x 1 solvent condition (pH 7.0) x 4 probes (Seq47 to Seq50) x 2 wavelength sets x 6 measurements. ０ ) is a heat map representation. Figure 35 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 34 by supervised linear discriminant analysis. In the amino acid sequence, cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are indicated by bold. Figure 36 shows the change in fluorescence intensity (I-I) obtained by measuring 8 proteins x 2 solvent conditions (pH 5.0, pH 7.0) x 4 probes (Seq03, Seq16 to Seq18) x 2 wavelength sets x 6 times. ０) is a heat map representation of the results. Figure 37 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 36 by supervised linear discriminant analysis (Seq03 alone, Seq16 alone, and Seq03 / Seq16). In the amino acid sequences, cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are bold. Figure 38 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 36 by supervised linear discriminant analysis (Seq03 / Seq17 and Seq03 / Seq18). In the amino acid sequences, cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are bold. FIG. 39 shows the change in fluorescence intensity (I-I) obtained by 6 nucleotides × 2 solvent conditions (pH 5.0, pH 7.0) × 5 probes (Seq01, Seq03, Seq21, Seq23, Seq36) × 1 wavelength set × 6 measurements. ０ ) is a heat map representation of the results. Figure 40 is a plot of the first and second discriminant scores obtained by analyzing the data in Figure 39 using supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 41 is a diagram showing changes in fluorescence spectra upon addition of conditioned medium from A549 cells diluted to various concentrations to 500 nM of probe (Seq01, Seq03) / 20 mM MOPS (pH 7.0). Figure 42 is a diagram showing changes in fluorescence intensity (520 nm) upon addition of conditioned medium from A549 cells diluted to various concentrations to 500 nM of probe (Seq01, Seq03) / 20 mM MOPS (pH 7.0) (mean ± standard error, n = 3). FIG. 43 shows the change in fluorescence intensity (II) obtained by 6 media × 2 solvent conditions (pH 5.0, pH 7.0) × 3 probes (Seq01, Seq03, Seq05) × 2 wavelength sets × 6 measurements. ０) is a heat map representation. Figure 44 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 43 by supervised linear discriminant analysis. In the amino acid sequence, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 45 shows the change in fluorescence intensity (I-I) obtained from 8 bacteria x 2 solvent conditions (pH 5.0, pH 7.0) x 4 probes (Seq02, Seq07 to Seq09) x 1 wavelength set x 6 measurements. ０ ) is a heat map representation. Figure 46 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 45 by supervised linear discriminant analysis. In the amino acid sequences, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 47 shows the change in fluorescence intensity (I-I) obtained from 8 gut microbiota samples x 2 solvent conditions (pH 5.0, pH 7.0) x 5 probes (Seq07 to Seq10, Seq15) x 1 wavelength set x 6 measurements. ０ ) is a heat map representation. Figure 48 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 47 by supervised linear discriminant analysis. In the amino acid sequence, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 49 shows the change in fluorescence intensity (I-I) obtained by measuring 5 cell suspensions x 2 solvent conditions (pH 5.0, pH 7.0) x 2 probes (Seq01, Seq03) x 2 wavelength sets x 6 times. ０ ) is a heat map representation. Figure 50 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 49 by supervised linear discriminant analysis. In the amino acid sequence, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 51 shows the change in fluorescence intensity (I-I) obtained from 4 sera x 2 solvent conditions (pH 5.0, pH 7.0) x 2 probes (Seq01, Seq03) x 2 wavelength sets x 6 measurements. ０) is a heat map representation. Figure 52 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 51 by supervised linear discriminant analysis. In the amino acid sequence, cationic amino acids are indicated by asterisks, and hydrophobic amino acids are indicated by bold. Figure 53 shows the change in fluorescence intensity (I-I) obtained by measuring 5 media × 2 solvent conditions (pH 5.0, pH 7.0) × 4 probes (Seq01, Seq17, Seq23, Seq38) × 2 wavelength sets × 6 times. ０ ) is a heat map representation. Figure 54 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 53 by supervised linear discriminant analysis. In the amino acid sequence, cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are indicated by bold. Figure 55 shows the change in fluorescence intensity (I-I) obtained by measuring 4 protein pharmaceutical samples x 2 solvent conditions (pH 5.0, pH 7.0) x 4 probes (Seq01, Seq17, Seq23, Seq38) x 2 wavelength sets x 6 times. ０ ) is a heat map representation. Figure 56 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 55 by supervised linear discriminant analysis. In the amino acid sequence, cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are indicated by bold. Figure 57 shows the change in fluorescence intensity (I-I) obtained by measuring 5 broths x 2 solvent conditions (pH 5.0, pH 7.0) x 4 probes (Seq01, Seq17, Seq23, Seq38) x 2 wavelength sets x 6 times. ０ ) is a heat map representation. Figure 58 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 57 by supervised linear discriminant analysis. In the amino acid sequence, cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are indicated by bold. Figure 59 shows the change in fluorescence intensity (I-I) obtained from 8 sake samples x 2 solvent conditions (pH 5.0, pH 7.0) x 4 probes (Seq01, Seq17, Seq23, Seq38) x 2 wavelength sets x 6 measurements. ０) is a heat map representation. Figure 60 is a diagram plotting the first and second discriminant scores obtained by analyzing the data in Figure 59 by supervised linear discriminant analysis. In the amino acid sequence, cationic amino acids are indicated by asterisks, anionic amino acids are underlined, and hydrophobic amino acids are indicated by bold. Figure 61 shows the change in fluorescence intensity (I-I) obtained by 6 proteins x 1 solvent condition (pH 7.0) x 9 probes (Seq51 to Seq56, Seq03, Seq23, Seq35) x 1 wavelength set x 6 measurements. ０ ) is a heat map representation of the first and second discriminant scores obtained by analyzing the data in FIG. 61 by supervised linear discriminant analysis. FIG. 63 shows the change in fluorescence intensity (I-I) obtained by measuring 8 proteins × 2 solvent conditions (pH 5.0, pH 7.0) × 6 probes (Seq57 to Seq62) × 2 wavelength sets × 6 measurements. ０ 64 is a diagram plotting the first discriminant scores and second discriminant scores obtained by analyzing the data in FIG. 63 by supervised linear discriminant analysis.

[0022] The present invention will be described in detail below, but the present invention is not limited to the embodiments described in this specification.

[0023] According to a first embodiment, the present invention provides a method for producing a peptide comprising: (1) preparing a probe and a plurality of probe solutions having different ionic strengths or pHs; wherein the probe is: (a) a peptide consisting of 3 to 100 amino acid residues, the peptide having (i) an absolute net charge score (ANC) of 0.15 to 1.0, (ii) a hydrophobicity score (H) of 0.0 to 0.85, and (iii) a sum of the absolute net charge score and the hydrophobicity score (ANC+H) of 0.30 to 1.0; and wherein the absolute net charge score (ANC) is expressed by the formula (I): ANC=|AA ｐｏｓｉｔｉｖｅ -AA ｎｅｇａｔｉｖｅ | / AA ｔｏｔａｌ (I) (Wherein, AA ｐｏｓｉｔｉｖｅ is the number of amino acid residues having a positive charge at the pH of the probe solution; ｎｅｇａｔｉｖｅis the number of negatively charged amino acid residues at the pH of the probe solution; ｔｏｔａｌ is the total number of amino acid residues), and the hydrophobicity score (H) is calculated by the formula (II): H = AA ｈｙｄｒｏｐｈｏｂｉｃ / AA ｔｏｔａｌ (II) (wherein, AA ｈｙｄｒｏｐｈｏｂｉｃ is the number of hydrophobic amino acid residues; AA ｔｏｔａｌ (where Λ is the total number of amino acid residues), (b) an environmentally responsive fluorophore, and (c) an environmentally responsive fluorophore covalently bonded to the peptide; (2) mixing the plurality of probe solutions with a biological sample to be analyzed; (3) measuring the fluorescence intensity of the mixture prepared in step (2); and (4) comparing the fluorescence intensity pattern obtained in step (3) with the fluorescence intensity pattern obtained for a reference sample.

[0024] First, the probe used in the method of this embodiment will be described. The probe used in the method of this embodiment includes (a) a peptide and (b) an environmentally responsive fluorophore, and the fluorophore is covalently bound to the peptide.

[0025] The peptide (a) used in the probe of this embodiment consists of 3 to 100 amino acid residues. Considering the difficulty and cost of peptide synthesis, peptide (a) preferably consists of 5 to 60 amino acid residues, and more preferably 10 to 40 amino acid residues. The types of amino acids constituting peptide (a) are not particularly limited and may be either natural or unnatural, such as L-amino acids, D-amino acids, β-amino acids, γ-amino acids, or derivatives thereof with modified side chains. Natural amino acids may include not only the 20 standard amino acids constituting proteins, but also non-proteinogenic amino acids (NPAAs). Considering the cost of peptide synthesis, peptide (a) is preferably composed of standard amino acids.

[0026] In this embodiment, peptide (a) is defined by (i) an absolute net charge score (ANC), (ii) a hydrophobicity score (H), and (iii) the sum of the absolute net charge score and the hydrophobicity score (ANC+H).

[0027] In this embodiment, the absolute net charge score (ANC) is a value calculated by dividing the absolute value of the net charge of the entire peptide by the total number of amino acids in the peptide, and indicates the degree of charge of the entire peptide. Specifically, the absolute net charge score (ANC) is a value calculated by the following formula (I): ANC = |AA ｐｏｓｉｔｉｖｅ -AA ｎｅｇａｔｉｖｅ | / AA ｔｏｔａｌ (I) wherein AA ｐｏｓｉｔｉｖｅ is the number of amino acid residues whose side chains have a positive charge, and AA ｎｅｇａｔｉｖｅ is the number of amino acid residues whose side chains have negative charges, and AA ｔｏｔａｌ is the total number of amino acid residues in the peptide. It should be noted that the charge of amino acids changes with pH, ​​so the ANC changes with the pH of the probe solution. Examples of amino acids that are positively charged at pH = 7.0 include, but are not limited to, lysine (K), arginine (R), ornithine, homolysine, homoarginine, diaminobutyric acid, diaminopropionic acid, hydroxylysine, canavanine, hypusine, and thialysine. Examples of amino acids that are negatively charged at pH = 7.0 include, but are not limited to, glutamic acid (E), aspartic acid (D), homoglutamic acid, homoaspartic acid, phosphoserine, phosphotyrosine, aspartyl phosphate, carboxyglutamic acid, cysteic acid, homocysteic acid, and aminoadipic acid.

[0028] In this embodiment, the hydrophobicity score (H) is a value calculated by dividing the number of hydrophobic amino acids in a peptide by the total number of amino acids in the peptide, and indicates the degree of hydrophobicity of the peptide as a whole. Specifically, the hydrophobicity score (H) is a value calculated by the following formula (II): H = AA ｈｙｄｒｏｐｈｏｂｉｃ / AA ｔｏｔａｌ (II) where AA ｈｙｄｒｏｐｈｏｂｉｃ is the number of hydrophobic amino acid residues, and AAｔｏｔａｌ is the total number of amino acid residues. In this embodiment, a "hydrophobic amino acid" refers to an amino acid having an octanol / water partition coefficient (log P) of 0.2 or more. Therefore, examples of hydrophobic amino acids in this embodiment include, but are not limited to, isoleucine (I), leucine (L), methionine (M), phenylalanine (F), tryptophan (W), tyrosine (Y), valine (V), cysteine ​​(C), norvaline, norleucine, alloisoleucine, tert-leucine, cyclohexylalanine, phenylglycine, homophenylalanine, diphenylalanine, allylglycine, cyclobutylglycine, cyclopentylglycine, cyclohexylglycine, cycloheptylglycine, and cyclooctylglycine. The log P value of an amino acid can be calculated using a chemical information analysis tool such as the Percepta platform (ACD / Labs).

[0029] In this embodiment, peptide (a) has (i) an absolute net charge score (ANC) of 0.15 to 1.0, (ii) a hydrophobicity score (H) of 0.0 to 0.85, and (iii) a sum of the absolute net charge score and the hydrophobicity score (ANC+H) of 0.30 to 1.0. Peptides having the above-defined peptide length and the above-mentioned absolute net charge score (ANC) and hydrophobicity score (H) ranges can be designed to cross-react with various affinities with any component in a biological sample. In order to prepare a probe with higher discrimination ability, (i) the absolute net charge score (ANC) is preferably 0.15 to 1.0, more preferably 0.20 to 0.90, and most preferably 0.25 to 0.80; (ii) the hydrophobicity score (H) is preferably 0.00 to 0.85, more preferably 0.00 to 0.70, and most preferably 0.10 to 0.55; and (iii) the sum of the absolute net charge score and the hydrophobicity score (ANC+H) is preferably 0.30 to 1.0, more preferably 0.40 to 1.0, and most preferably 0.50 to 1.0.

[0030] The environmentally responsive fluorophore (b) of the probe in this embodiment may be any fluorophore whose fluorescence properties change depending on the environment around the fluorescent molecule. Examples of such fluorophores include, but are not limited to, fluorophores whose fluorescence properties change depending on the polarity around the fluorescent molecule, fluorophores whose fluorescence properties change depending on the pH around the fluorescent molecule, and fluorophores whose fluorescence properties change depending on the degree of crowding around the fluorescent molecule.

[0031] Examples of fluorophores whose fluorescent properties change depending on the polarity around the fluorescent molecule include fluorophores having a naphthalenesulfonic acid skeleton, such as 5-dimethylaminonaphthalene-1-sulfonyl (dansyl), 1-anilinonaphthalene-8-sulfonic acid (ANS), N-methyl-2-anilinonaphthalene-6-sulfonic acid (MANS), and 2-p-toluidinylnaphthalene-6-sulfonic acid (TNS); fluorophores having a benzofurazan skeleton, such as 4-(N,N-dimethylaminosulfonyl)-2,1,3-benzoxadiazole (DBD), 7-nitro-2,1,3-benzoxadiazole (NBD), 4-(aminosulfonyl)-2,1,3-benzoxadiazole (ABD), and ammonium 2,1,3-benzoxadiazole-4-sulfonate (SBD); and fluorescent derivatives thereof.

[0032] Examples of fluorophores whose fluorescence properties change depending on the pH around the fluorescent molecule include fluorophores having a xanthene skeleton, such as fluorescein, fluorescein isothiocyanate (FITC), carboxyfluorescein (FAM), 2'-7'-bis(carboxyethyl)-5(6)-carboxyfluorescein (BCECF), and seminaphthalodafluorescein (SNARF); fluorophores having a pyrene skeleton, such as 8-hydroxypyrene-1,3,6-trisulfonic acid trisodium salt (HTPS); and fluorescent derivatives thereof.

[0033] Examples of fluorophores whose fluorescence properties change depending on the degree of crowding around the fluorescent molecule include aggregation-induced emission (AIE) fluorophores such as tetraphenylethylene (TPE), 10,10',11,11'-tetrahydro-5,5'-bisbenzo[a,d][7]annulenylidene (THBA), and 1,1,2,3,4,5-hexaphenylsilole (HPS); AIE fluorophores having a triphenylamine skeleton such as N,N-diphenyl-4-(2-thienyl)aniline; and fluorescent derivatives thereof.

[0034] Preferred environmentally responsive fluorophores in this embodiment may be dansyl, NBD, DBD, FITC, FAM or TPE or fluorescent derivatives thereof.

[0035] The probe of this embodiment comprises a peptide (a) and an environmentally responsive fluorophore (b) introduced by a covalent bond. The environmentally responsive fluorophore may be introduced at any one or more positions of the polypeptide, for example, at either or both of the N-terminus and C-terminus, and / or at an amino group, hydroxyl group, carboxyl group, SH group, azide group, alkynyl group, etc. in the side chain of an amino acid residue in the polypeptide. In the probe of this embodiment, the environmentally responsive fluorophore may be preferably introduced at the N-terminus of the peptide and / or the ε-amino group in the side chain of a lysine residue.

[0036] The probe in this embodiment may further include modifications at the N-terminus and / or C-terminus of peptide (a). N-terminus modifications include, for example, acetylation, alkylation, formylation, pyruvylation, lipoylation, carboxylation, succinylation, maleylation, carbamylation, biotinylation, benzoylation, allylation, acrylate, PEGylation, glycosylation, etc. C-terminus modifications include, for example, amidation, methyl esterification, prenylation, glycosylation, hydroxylation, phosphorylation, sulfonation, nedylation, PEGylation, etc.

[0037] The probe of this embodiment can be prepared by synthesizing a peptide and labeling it with a fluorophore by a conventionally known method. For example, the probe of this embodiment can be prepared by chemically or biosynthesizing a peptide and labeling an amino group in the polypeptide with an environment-responsive fluorophore activated by an active ester group such as an N-hydroxysuccinimide (NHS) ester group or a pentafluorophenyl (PFP) ester group, an isothiocyanate group, or a halogenated alkyl group.

[0038] The method of this embodiment uses multiple probe solutions that differ in one or more of the following: probe, ionic strength, and pH. The multiple probe solutions in this embodiment may be prepared, for example, by dissolving one type of probe in multiple solvents with different ionic strengths and / or pHs, or by dissolving multiple types of probes in the same solvent. For example, solvents with two or more different ionic strengths and / or pH conditions can be used, preferably three or more, and particularly preferably six or more different ionic strengths and / or pH conditions can be used.

[0039] In the method of this embodiment, one type of probe may be used, but it is more preferable to use multiple types of probes, for example, two or more types, preferably three or more types, and particularly preferably six or more types of probes. For example, by using two types of solvents and three types of probes, six types of probe solutions can be prepared, resulting in six-dimensional data for one analytical sample. For example, by using five types of solvents and three types of probes, fifteen types of probe solutions can be prepared, resulting in fifteen-dimensional data for one analytical sample. In this way, by increasing the number of solvents and probes, more multidimensional data can be obtained.

[0040] The solvent for dissolving the probe may be an aqueous solvent containing any buffer and / or salt. Examples of the buffer include MES, MOPS, EPPS, HEPES, Tris, phosphoric acid, acetic acid, citric acid, boric acid, glycine, etc. Examples of the salt include NaCl, KCl, MgCl. ２ , Na ２ SO ４ , K. ２ SO ４ , MgSO ４ , NaI, NaSCN, etc. In this embodiment, the pH of the solvent is preferably 4.0 to 10.0, and particularly preferably 5.0 to 9.0. In this embodiment, the ionic strength of the solvent is preferably 10 to 500 mM. In addition, the concentration of the probe in the probe solution is preferably 0.1 to 100 μg / mL.

[0041] Next, the multiple probe solutions are mixed with a biological sample to be analyzed (hereinafter also referred to as an "analytical sample") In this step, the probes interact nonspecifically with any molecules contained in the analytical sample.

[0042] In this embodiment, the term "biological sample" refers to a sample containing a biomolecule. Therefore, the biological sample in this embodiment may be, for example, cells or tissues or their cultures, culture supernatants, body fluids (blood, serum, plasma, saliva, urine, sweat, feces, semen, cerebrospinal fluid, swabs, etc.), or extracts thereof. Compositions such as pharmaceuticals containing purified biomolecules (e.g., proteins, peptides, nucleic acids, etc.) may also be included in the biological sample in this embodiment. The method of this embodiment is particularly suitable for analyzing biological samples containing cells or their culture supernatants, serum or serum substitutes, microbiota, or alcoholic beverages.

[0043] The "cells" may be derived from any living organism, may be derived from animals or plants, or may be microbial cells such as fungal cells or prokaryotic cells. The cells may be primary cultured cells or established cell lines. The animals from which the cells are derived are not particularly limited, but are preferably mammalian, such as mice, rats, rabbits, dogs, monkeys, or humans, and are particularly preferably human.

[0044] The final concentration of cells in the mixture of the analytical sample and the probe solution is, for example, 10 to 10 ８ cells / mL or OD ６００ = 0.002 to 0.500, preferably 10 ２ ~10 ５ cells / mL or OD ６００ = 0.010 to 0.100. When the concentration of cells in the analysis sample is unknown, the sample may be appropriately serially diluted and added to the probe solution.

[0045] The "serum" may be derived from any animal, but is preferably derived from a mammal such as a cow, horse, sheep, goat, pig, rabbit, rat, mouse, or human. The age of the animal from which the serum is derived is not particularly limited, and the animal may be of any age, including a fetus, a newborn, and an adult. The country / region of origin of the serum is also not particularly limited. Sera derived from a wide variety of animals produced in various countries / regions are commercially available, and the method of this embodiment can analyze any of them.

[0046] "Serum replacement" is a general term for serum-free supplements used in place of serum in cell culture. Serum replacements generally contain several to several dozen factors selected from growth factors, cytokines, hormones, etc. Various serum replacements are commercially available, such as N2 supplement (Cell Cult. Neurosci. 1985; pp. 3-43) and its improved products, B27 supplement (J. Neurosci. Res. 1993; 35(5): 567-76) and its improved products, G5 supplement (Int. J. Dev. Neurosci. 1984; 2(6): 575-84) and its improved products, Gibco™ KnockOut™ Serum Replacement (KSR) (Thermo Fisher Scientific), StemSure™ Serum Replacement (SSR) (Fujifilm Wako Pure Chemical Industries), XF212 XerumFree (TNC BIO Examples of serum substitutes include, but are not limited to, human platelet lysate, plant hydrolysate, corn extract, yeast extract, soy extract, etc. Although the specific compositions of many commercially available serum substitutes are not published, the method of the present embodiment can analyze any of them.

[0047] The final concentration of serum or serum substitute in the mixture of the analytical sample and probe solution may be, for example, 0.001 vol% to 99.9 vol%, preferably 0.01 vol% to 10 vol%. When the concentration of serum or serum substitute in the analytical sample is unknown, the sample may be serially diluted as appropriate and added to the probe solution.

[0048] "Microbiota" refers to a collection of multiple microorganisms present in a particular environment. A microbiota may be composed of, for example, at least 100, 300, 500, 700, 1,000, or more types of microorganisms. A microbiota may be composed of various classes and / or types of microorganisms, such as bacteria, fungi, protozoa, and viruses, and the method of this embodiment can be applied to a microbiota composed of any class and / or type of microorganism. Examples of environments in which a microbiota exists include the surface or interior of individual animals and plants, soil, seawater, and river water.

[0049] The method of this embodiment can be applied to any microbiota derived from any environment, but is preferably applied to microbiota derived from an animal body. The animal body from which the microbiota is derived may be any vertebrate or invertebrate body, but is preferably a mammalian body such as a mouse, rat, rabbit, dog, non-human primate, or human, and is particularly preferably a human body. The microbiota in an animal body is present in the epithelium of tissues (e.g., the digestive tract, oral cavity, nasal cavity, skin, respiratory tract, reproductive tract, etc.), and the microbiota analyzed in the method of this embodiment may be a microbiota derived from any tissue epithelium. The microbiota derived from an animal body analyzed in the method of this embodiment is preferably an oral microbiota (oral flora) or an intestinal microbiota (intestinal flora), and is particularly preferably an intestinal microbiota.

[0050] A microbiota sample derived from an animal body can be prepared by a conventionally known method. For example, a sample containing oral microbiota can be prepared from dental plaque or saliva, and a sample containing intestinal microbiota can be prepared from feces. The final concentration of microorganisms in the mixture of the analysis sample and the probe solution can be determined, for example, by OD ６００ = 0.002 to 0.500, preferably OD ６００ = 0.010 to 0.100. When the concentration of microorganisms in the analysis sample is unknown, the sample may be appropriately serially diluted and added to the probe solution.

[0051] The term "alcoholic beverage" refers to any beverage containing ethanol, and may be produced from any raw material, without any particular manufacturing method. Furthermore, the characteristics of the alcoholic beverage (e.g., color, clarity, sparkling quality) are not particularly limited. Therefore, the alcoholic beverage targeted by the method of this embodiment may be any of brewed alcoholic beverages, distilled alcoholic beverages, and mixed alcoholic beverages. Examples of brewed alcoholic beverages include sake (Japanese rice wine), wine, beer, Shaoxing wine, and makgeolli. Examples of distilled alcoholic beverages include shochu, awamori, whiskey, brandy, vodka, and tequila. Examples of mixed alcoholic beverages include, but are not limited to, liqueurs, vermouth, sherry, and mirin. The country / region of origin of the alcoholic beverage is also not particularly limited. A wide variety of alcoholic beverages produced in various countries / regions are commercially available, and the method of this embodiment can analyze any of them.

[0052] The analysis sample in this embodiment may contain a single alcoholic beverage selected from any of the above alcoholic beverages, or a mixture of two or more alcoholic beverages, and the ratio of alcoholic beverages in the mixture of two or more alcoholic beverages may be arbitrary. Furthermore, the analysis sample in this embodiment may consist solely of alcoholic beverages, or may be a beverage containing alcoholic beverages. The beverage containing alcoholic beverages may be, for example, a cocktail made by mixing alcoholic beverages with non-ethanolic beverages such as fruit juice, water, or carbonated water.

[0053] The final concentration of the alcoholic beverage in the mixture of the analytical sample and the probe solution may be, for example, 0.001 to 99.9 v / v %, preferably 0.01 to 95 v / v %, and more preferably 0.1 to 90 v / v %. When the concentration of the alcoholic beverage component in the analytical sample is unknown, the sample may be appropriately serially diluted and added to the probe solution.

[0054] In the method of this embodiment, the analytical sample may be added to each of the plurality of probe solutions, or the plurality of probe solutions may be added to the analytical sample simultaneously.

[0055] Next, the fluorescence intensity of the mixture of the probe solution and the analytical sample is measured. In the method of this embodiment, fluorescence intensity can be measured at an excitation wavelength of 300 to 600 nm and a fluorescence wavelength of 400 to 800 nm. Furthermore, in the method of this embodiment, it is preferable to measure the fluorescence intensity for each measurement target at multiple excitation wavelength / fluorescence wavelength sets (e.g., excitation wavelength (nm) / fluorescence wavelength (nm): 340 / 520, 330 / 480, 345 / 505, 360 / 530, etc.), and the fluorescence intensity can be measured using, for example, two, three, or four sets of excitation wavelength / fluorescence wavelength.

[0056] The fluorescence intensity of the probe solution varies depending on the type, amount, and state of the components in the sample, as well as the conditions of the solvent the probe is dissolved in. Therefore, this step allows us to obtain a fluorescence intensity pattern specific to the sample.

[0057] The fluorescence intensity pattern obtained for the analytical sample is then compared with the fluorescence intensity pattern obtained for the reference sample. The fluorescence intensity pattern for the reference sample may be obtained by measuring the fluorescence intensity in parallel with the analytical sample, or may be a predetermined fluorescence intensity pattern prepared in advance. The comparison of the fluorescence intensity patterns is preferably performed by reducing the number of dimensions using multivariate analysis such as principal component analysis, linear discriminant analysis, or hierarchical cluster analysis, and then compressing and converting the differences between the fluorescence intensity patterns into two or three dimensions for comparison. Alternatively, the comparison of the fluorescence intensity patterns may be performed using machine learning methods such as support vector machines (SVMs), random forests, gradient boosting, k-nearest neighbors, neural networks, naive Bayes classifiers, and logistic regression.

[0058] The method of this embodiment analyzes biological samples based solely on differences in fluorescence intensity patterns. Biological samples such as culture supernatants, serum, microbiota, and alcoholic beverages contain a vast variety of biomolecules, making it extremely difficult and impractical to comprehensively analyze their types, quantities, and states. In contrast, the method of this embodiment acquires a fluorescence intensity pattern that reflects the sum of nonspecific interactions between a wide variety of components and probes, and can identify the overall characteristics of the biological sample simply by comparing it with the fluorescence intensity pattern of a reference sample.

[0059] Therefore, in certain embodiments, for example, a fluorescence intensity pattern obtained for a culture supernatant or lysate (reference sample) of undifferentiated or differentiated cells is compared with a fluorescence intensity pattern obtained for a culture supernatant or lysate (analytical sample) of cells whose differentiation state is unknown. If the fluorescence intensity pattern obtained for the analytical sample is probabilistically close to the distribution of the fluorescence intensity pattern obtained for the culture supernatant or lysate of differentiated cells, it is estimated or identified that the analytical sample is a culture supernatant of differentiated cells.

[0060] In certain embodiments, for example, a fluorescence intensity pattern obtained for a serum or serum substitute currently being used for culture (reference sample) is compared with a fluorescence intensity pattern obtained for a serum or serum substitute (analytical sample) from a different origin / manufacturer A, B, or C. As a result, if the distance between the fluorescence intensity pattern obtained for the serum or serum substitute from origin / manufacturer A and the distribution of the fluorescence intensity pattern obtained for the reference sample is probabilistically closest, it can be estimated or identified that the serum or serum substitute from origin / manufacturer A can be used in place of the serum or serum substitute currently being used.

[0061] In another specific embodiment, for example, a fluorescence intensity pattern obtained for serum or serum substitute (reference sample) immediately after purchase is compared with a fluorescence intensity pattern obtained for serum or serum substitute (analytical sample) from the same place of origin / manufacturer / lot but stored under different conditions A, B, or C (e.g., different storage temperatures and / or periods). As a result, if the distance between the fluorescence intensity pattern obtained for serum or serum substitute stored under condition A and the distribution of the fluorescence intensity pattern obtained for the reference sample is probabilistically closest, it can be estimated or identified that the quality of the serum or serum substitute stored under condition A has not deteriorated.

[0062] In another specific embodiment, for example, a fluorescence intensity pattern obtained for a fermented food sample (reference sample) containing a different bacterial strain A, B, or C is compared with a fluorescence intensity pattern obtained for a fermented food sample (analytical sample) containing an unknown bacterial strain. If the fluorescence intensity pattern obtained for the analytical sample is probabilistically closest to the distribution of the fluorescence intensity pattern obtained for the fermented food sample containing strain A, it can be estimated or identified that the fermented food being analyzed contains strain A.

[0063] In another specific embodiment, for example, a fluorescence intensity pattern obtained for a microbiota (reference sample) from an obese individual or a non-obese individual is compared with a fluorescence intensity pattern obtained for a microbiota (analytical sample) from a subject whose health status or constitution is unknown. If the fluorescence intensity pattern obtained for the analytical sample is closest in probability to the distribution of the fluorescence intensity pattern obtained for the obese individual, the subject from whom the analytical sample was derived can be estimated or identified as an individual with obesity or a high risk of obesity.

[0064] In another specific embodiment, for example, the fluorescence intensity pattern obtained for a different brand of sake A, B, or C (reference sample) is compared with the fluorescence intensity pattern obtained for the analytical sample. If the fluorescence intensity pattern obtained for the analytical sample is probabilistically closest to the distribution of the fluorescence intensity pattern obtained for sake A, then the analytical sample can be estimated or identified as sake A.

[0065] In another specific embodiment, for example, a fluorescence intensity pattern obtained for sake X (reference sample) immediately after purchase is compared with fluorescence intensity patterns obtained for sakes X1, X2, and X3 (analytical samples) of the same brand / specific name classification but stored under different conditions (e.g., storage temperature and / or period). If the fluorescence intensity pattern obtained for sake X1 is probabilistically closest to the distribution of the fluorescence intensity pattern obtained for the reference sample, it can be estimated or determined that the quality of sake X1 has not deteriorated.

[0066] The present invention will be further described below with reference to examples, which should not be construed as limiting the scope of the present invention.

[0067] [A: Probe Characterization] <1. Materials> (1-1) Probes The following nonspecific fluorescent peptide probes (>95% purity) were synthesized by Biologica and used without further purification. In the probes of SEQ ID NOs: 33 to 36, the amino group on the side chain of the lysine residue in the peptide is modified with dansyl (indicated by "K^"). The numbers in "Code" indicate, from left to right, the number of cationic amino acids, the number of anionic amino acids, and the number of hydrophobic amino acids contained in the peptide. In the following examples, "cationic amino acids" refer to amino acids that are positively charged at pH 7.0, and "anionic amino acids" refer to amino acids that are negatively charged at pH 7.0. The absolute net charge score (ANC) and hydrophobic score (H) in the following examples are values ​​at pH 7.0.

[0068] Table 1. Amino acid sequences and modifications of probes

[0069] Table 2. Probe characteristics

[0070] (1-2) Proteins The following proteins were used for characterizing the probes.

[0071] Table 3. Protein list

[0072] Abbreviations for proteins in this example are as follows: Lac: β-lactoglobulin, Ant: α1-antitrypsin, Lip: lipase, BSA: bovine serum albumin, Fib: fibrinogen, HSA: human serum albumin, Tra: transferrin, IgG: immunoglobulin G, Myo: myoglobin, Hem: hemoglobin, Lys: lysozyme, PSA: porcine serum albumin, Pro: proteinase K, Pap: papain, Try: trypsin.

[0073] 2. Protein-Induced Fluorescence Changes of Cationic Probes Seq01, Seq03, and Seq13 were dissolved in 40 mM MOPS (pH 7.0) to prepare three probe solutions (1000 nM). Each probe solution was dispensed into a 384-well microplate (Corning, 3575) at 30 μL per well using an automated pipetting device (Andrew+, Andrew Alliance). Various concentrations of Ant / pure water solutions (30 μL) were added to each well. After incubation at 35°C for 10 minutes, the fluorescence spectrum (fluorescence wavelength 500-650 nm) and fluorescence intensity (fluorescence wavelength 520 nm) at an excitation wavelength of 340 nm were measured using a microplate reader (Cytation 5, BioTek) (final concentrations: 500 nM probe, 0-1600 nM Ant, 20 mM MOPS (pH 7.0)).

[0074] Figure 1 shows the changes in the fluorescence spectra of Seq01, Seq03, and Seq13 upon the addition of 0 to 1600 nM Ant. The fluorescence intensity of Seq01 and Seq03 increased in an Ant concentration-dependent manner, and their peaks shifted to shorter wavelengths (blue shift). In contrast, the fluorescence spectrum of Seq13 showed little change with increasing Ant concentration. Figure 2 shows the changes in fluorescence intensity (520 nm). The fluorescence intensity of Seq01 continued to increase up to an Ant concentration of 1600 nM, whereas the fluorescence intensity of Seq03 saturated at 800 nM Ant. The fluorescence intensity of Seq13 showed little change. These results confirmed that nonspecific fluorescent peptide probes exhibit different responsiveness to the anionic protein α1-antitrypsin depending on the peptide sequence. Here, Seq01 is charged and contains many hydrophobic amino acids (ANC = 0.4, H = 0.5), Seq03 is charged but contains few hydrophobic amino acids (ANC = 0.7, H = 0.2), and Seq13 is neither charged nor hydrophobic compared to seq03 (ANC = 0.4, H = 0). Considering these facts, it is possible that the higher the charge and hydrophobicity of the peptide, the greater the probe response.

[0075] 3. Effect of Cationic and Hydrophobic Amino Acids on Probe Performance (1) Eight probes (Seq01 to Seq03, Seq05, and Seq11 to Seq14) each consisting of 10 amino acids and containing eight different peptides with different ratios of cationic and hydrophobic amino acids were dissolved in 23.1 mM MOPS (pH 7.0) to prepare eight probe solutions (577 nM). Each probe solution was added to a 384-well microplate at 52 μL / well using an automated dispenser. After incubation at 35°C for 10 minutes, the fluorescence intensity (I) was measured using a microplate reader at the following two sets of excitation wavelength (nm) / emission wavelength (nm): ０) were measured: (Ch1) 320 / 560, (Ch2) 360 / 480. Then, using an automated pipetting device, various proteins with different charge and hydrophobicity (Lac, Ant, Lip, BSA, HSA, Tra, IgG, and Tyr; each 3000 nM protein / pure water) were added at 8 μL / well, incubated at 35 °C for 10 minutes, and then the fluorescence intensity was measured under the same conditions (final concentrations: 500 nM probe, 500 nM protein, 20 mM MOPS (pH 7.0)). Each measurement (8 proteins x 1 solvent condition x 8 probes x 2 wavelength sets) was performed six times.

[0076] The change in fluorescence intensity before and after the addition of protein (fluorescence response) (I-I ０ ) are shown in Figures 3 and 4. The responses of Seq01 to Seq03, Seq05, and Seq12 were greater than those of Seq11, Seq13, and Seq14. The low responses of Seq11 (ANC = 0.2, H = 0.5), Seq13 (ANC = 0.4, H = 0.0), and Seq14 (ANC = 0.3, H = 0.0) suggest that both cationic and hydrophobic amino acids are required for the interaction between the probe and the protein.

[0077] To compare the response variability between probes, the coefficient of variation (CV) of the measurement results for Lac, Ant, BSA, and HSA, which generally showed positive responses, was calculated. The results are shown in Figure 5. The CV values ​​for Seq11 to Seq14 were larger than those for Seq01 to Seq03 and Seq05. These results are thought to be due to the extremely small response (fluorescence intensity) for Seq11, Seq13, and Seq14, and the instability of the dissolved state of Seq12 due to its low cationicity and high hydrophobicity. The difference in variability between probes is also evident in Figure 4.

[0078] Focusing on the diversity of responses, Seq01 to Seq03 and Seq05 in particular showed significantly different changes depending on the type of protein, resulting in unique fluorescence patterns for each protein (Figure 4). The data in Figure 4 was analyzed using supervised linear discriminant analysis, and the results up to the second discriminant score are plotted in Figure 6. For the Seq01 / Seq02 and Seq03 / Seq05 combinations, the clusters for each protein were distributed without overlapping, with only a small percentage. Furthermore, analysis of these results using the jackknife algorithm confirmed that each protein could be identified with high accuracy (92% and 96%). In contrast, for the Seq11 / Seq12 and Seq13 / Seq14 combinations, many of the clusters for each protein overlapped, resulting in low accuracy (56% and 69%) when analyzed using the jackknife algorithm.

[0079] The datasets Seq01 to Seq03 and Seq05, as well as the datasets Seq11 to Seq14, were analyzed by linear discriminant analysis. The results, plotted up to the third discriminant score, are shown in Figure 7. The analysis results for Seq01 to Seq03 and Seq05 showed no cluster overlap, confirming that these probes exhibited high discrimination accuracy (98% by jackknife analysis). On the other hand, the analysis results for Seq11 to Seq14 showed significant cluster overlap, the relative distance between clusters was small, and the discrimination accuracy was low (79% by jackknife analysis). These results suggest that peptide probes with high cationic and hydrophobic properties can increase the accuracy of protein discrimination.

[0080] 4. Effect of Cationic and Hydrophobic Amino Acids on Probe Performance (2) The responsiveness of Seq19 and Seq20 (comprising a 10-amino acid peptide with an ANC and H of 0.1 or less) was analyzed and compared under the same conditions and procedures as in 3 above, except that proteins (HSA, BSA, PSA, Ant, Fib, Tra, and IgG) were used at a final concentration of 1000 nM. Seq01 and Seq03 were used as controls.

[0081] A graph of the measurement results (7 proteins × 1 solvent condition × 4 probes × 1 wavelength set) is shown in Figure 8 , and a heat map (7 proteins × 1 solvent condition × 4 probes × 2 wavelength sets) is shown in Figure 9 . Seq19 and Seq20 showed little response to any of the proteins. These results suggest that probes with low charge and hydrophobicity hardly interact with proteins. The above data were analyzed using supervised linear discriminant analysis, and the resulting first and second discriminant scores are plotted in Figure 10 . When Seq19 / Seq20 were paired, the clusters for each protein largely overlapped, and analysis using the jackknife algorithm confirmed low discrimination accuracy (60%). These results suggest that when the cationic and hydrophobic nature of the peptide probes are both low, the accuracy of protein discrimination is low.

[0082] 5. Effect of Net Charge on Probe Performance Based on Seq01 (ANC = 0.4, H = 0.5) and Seq02 (ANC = 0.5, H = 0.2), probes (Seq25 and Seq26) containing peptides in which one cationic amino acid was replaced with an anionic amino acid, and probes (Seq27 and Seq28) containing peptides in which two cationic amino acids were replaced with anionic amino acids were prepared. The responsiveness of these probes was analyzed and compared using the same conditions and procedures as in 4 above. Seq01 and Seq02 were used as controls.

[0083] The graph of the measurement results (7 proteins × 1 solvent condition × 6 probes × 1 wavelength set) is shown in Figure 11 , and the heat map (7 proteins × 1 solvent condition × 6 probes × 2 wavelength sets) is shown in Figure 12 . Probe response consistently decreased with decreasing ANC (Seq01 > Seq25 > Seq27; Seq02 > Seq26 > Seq28). These results suggest that probe-protein interaction becomes less likely with decreasing ANC. The above data was analyzed using supervised linear discriminant analysis, and the resulting first and second discriminant scores are plotted in Figure 13 . The cluster overlap was greatest for the Seq27 / Seq28 combination, with the contribution of the first discriminant score (95.2%) approximately 30% higher than for the other combinations. This indicates that the fluorescence pattern became simpler with decreasing ANC. Analysis of the results in Figure 13 using the jackknife algorithm revealed that the discrimination accuracy of Seq01 / Seq02 was significantly higher than that of the other combinations. These results suggest that the higher the ANC of a peptide, the higher the accuracy of protein identification.

[0084] 6. Effect of Hydrophobicity on Probe Performance Based on Seq02 and Seq03 (10 amino acids long, ≥50% cationic amino acids (i.e., ANC≥0.5), ≥20% hydrophobic amino acids (i.e., H≥0.2)), probes (Seq29 and Seq31) containing peptides in which one hydrophobic amino acid was substituted with the hydrophilic amino acid glycine, or probes (Seq30 and Seq32) containing peptides in which two hydrophobic amino acids were substituted with glycine, were prepared. The responsiveness of these probes was analyzed and compared using the same conditions and procedures as in 4 above. Seq26 and Seq28 prepared in 5 above were used as controls.

[0085] The graph of the measurement results (7 proteins × 1 solvent condition × 8 probes × 1 wavelength set) is shown in Figure 14. For Seq02 (i.e., probes with ANC ≥ 0.5), probe response consistently decreased with decreasing proportion of hydrophobic amino acids (Seq02 > Seq29 > Seq30). Seq30, which did not contain hydrophobic amino acids (i.e., H = 0.0), showed a low response comparable to Seq26 and Seq28. In contrast, for probes with ANC = 0.7 (Seq03, Seq31, Seq32), probe response did not change significantly even with decreasing proportion of hydrophobic amino acids. These results suggest that the effect of hydrophobic amino acids on probe response varies depending on the net charge. Specifically, it can be considered as follows: (1) when the ANC is high, sufficient probe performance can be achieved regardless of H; (2) when the ANC is not so high, sufficient probe performance can be achieved if H is high; and (3) when the ANC is low, sufficient probe performance cannot be achieved even if H is high.

[0086] 7. Effect of Peptide Length on Probe Performance Probes (Seq21 and Seq22) comprising 18-amino acid peptides and probes (Seq23 and Seq24) comprising 24-amino acid peptides containing 22% or more cationic amino acids (i.e., ANC≧0.22) and 30% or more hydrophobic amino acids (i.e., H≧0.3) were prepared. The responsiveness of these probes was analyzed and compared using the same conditions and procedures as in 4 above.

[0087] A graph of the measurement results (7 proteins × 1 solvent condition × 4 probes × 1 wavelength set) is shown in Figure 15, and a heat map (7 proteins × 1 solvent condition × 4 probes × 2 wavelength sets) is shown in Figure 16. All of Seq21 to Seq24 responded to the proteins, with Seq23 and Seq24 exhibiting superior responsiveness compared to Seq21 and Seq22. The above data was analyzed using supervised linear discriminant analysis, and the resulting first and second discriminant scores were plotted in Figure 17. Clusters for each protein were distributed without overlapping, with some exceptions. Furthermore, when these results were analyzed using the jackknife algorithm, it was confirmed that each protein could be identified with 100% accuracy. These results suggest that if the proportion of cationic amino acids and hydrophobic amino acids is sufficiently high, fluorescence patterns capable of identifying proteins can be obtained regardless of peptide length.

[0088] 8. Effect of Fluorophore Position on Probe Performance Probes Seq33 to Seq36 were prepared in which the intermediate position of a 10-amino acid peptide containing 30% or more cationic amino acids (i.e., ANC≧0.3) and 15% or more hydrophobic amino acids (i.e., H≧0.15) was modified with a dansyl group. For Seq33 and Seq34, the N-terminus of the peptide was acetylated, and the side chain amino group of the C-terminal lysine residue was modified with a dansyl group. For Seq35 and Seq36, the N-terminus of the peptide was unmodified, and the side chain amino group of the intermediate lysine residue was modified with a dansyl group. The responsiveness of these probes was analyzed and compared using the same conditions and procedures as in 4 above. As controls, Seq01 and Seq03 (probes in which the N-terminus of a 10-amino acid peptide containing 40% or more cationic amino acids and 20% or more hydrophobic amino acids was modified with a dansyl group) were used.

[0089] A graph of the measurement results (7 proteins × 1 solvent condition × 6 probes × 1 wavelength set) is shown in Figure 18, and a heat map (7 proteins × 1 solvent condition × 6 probes × 2 wavelength sets) is shown in Figure 19. All probes responded to proteins regardless of the dansyl group introduction position, and the responsiveness varied depending on the dansyl group introduction position. The above data was analyzed using supervised linear discriminant analysis, and the first and second discriminant scores were plotted as shown in Figure 20. Clusters for each protein were distributed without overlapping, with some exceptions. Furthermore, when these results were analyzed using the jackknife algorithm, it was confirmed that each protein could be identified with 100% accuracy. These results suggest that if the proportion of cationic amino acids and hydrophobic amino acids is sufficiently high, fluorescence patterns capable of identifying proteins can be obtained regardless of the fluorophore introduction position.

[0090] 9. Effect of Cationic Amino Acid and Hydrophobic Amino Acid Arrangement on Probe Performance Based on Seq01 and Seq02, which comprise a 10-amino acid peptide containing 40% or more cationic amino acids (i.e., ANC≧0.4) and 20% or more hydrophobic amino acids (i.e., H≧0.2), probes (Seq37 and Seq38) were prepared, each comprising a peptide in which cationic amino acids were located in the C-terminal region and hydrophobic amino acids were located in the N-terminal region. The responsiveness of these probes was analyzed and compared using the same conditions and procedures as in 4 above. Seq01 and Seq02 were used as controls.

[0091] A graph of the measurement results (7 proteins × 1 solvent condition × 4 probes × 1 wavelength set) is shown in FIG. 21 , and a heat map (7 proteins × 1 solvent condition × 4 probes × 2 wavelength sets) is shown in FIG. 22 . Regardless of the arrangement of cationic and hydrophobic amino acids, all probes responded to the proteins, and the responsiveness varied depending on their arrangement. The above obtained data was analyzed by supervised linear discriminant analysis, and the obtained first and second discriminant scores were plotted. The clusters for each protein were distributed without overlapping, with some exceptions. Furthermore, when these results were analyzed by jackknife analysis, it was confirmed that Seq01 / Seq02 could distinguish each protein with 98% accuracy, and Seq37 / Seq38 could distinguish each protein with 100% accuracy.

[0092] Next, we prepared a probe (Seq39) with one amino acid sequence shifted by one residue (Seq01) and a probe (Seq40) with two amino acid sequences shifted by two residues (Seq01). The responsiveness of these probes was analyzed and compared using the same conditions and procedures as in Section 4 above. Seq01 was used as a control.

[0093] A graph of the measurement results (7 proteins x 1 solvent condition x 3 probes x 1 wavelength set) is shown in Figure 24, and a heat map (7 proteins x 1 solvent condition x 3 probes x 2 wavelength sets) is shown in Figure 25. All probes responded to proteins regardless of the amino acid residue arrangement, and the responsiveness varied depending on their arrangement. The above data was analyzed by supervised linear discriminant analysis, and the first and second discriminant scores were plotted, as shown in Figure 26. The clusters for each protein were distributed without overlapping with some exceptions, and the position of the clusters differed depending on the amino acid residue arrangement.

[0094] These results suggest that if the proportion of cationic amino acids and hydrophobic amino acids is sufficiently high, a fluorescence pattern capable of distinguishing proteins can be obtained regardless of the arrangement of cationic and hydrophobic amino acids, and that different fluorescence patterns can be obtained by changing the arrangement of cationic and hydrophobic amino acids.

[0095] 10. Effect of Amino Acid Sequence Similarity on Probe Performance Based on Seq01 and Seq03, which comprise 10-amino acid peptides containing 40% or more cationic amino acids (i.e., ANC≧0.4) and 20% or more hydrophobic amino acids (i.e., H≧0.2), probe combinations (Seq41 / Seq42, Seq43 / Seq44) with increased amino acid sequence identity were prepared. The amino acid sequence identities of Seq01 / Seq03, Seq41 / Seq42, and Seq43 / Seq44 are 20%, 40%, and 70%, respectively. The responsiveness of these probes was analyzed and compared using the same conditions and procedures as in Section 4 above.

[0096] A graph of the measurement results (7 proteins × 1 solvent condition × 6 probes × 1 wavelength set) is shown in FIG. 27 , and a heat map (7 proteins × 1 solvent condition × 6 probes × 2 wavelength sets) is shown in FIG. 28 . All probes responded to the proteins, but the responsiveness was similar as the amino acid sequence identity increased. The data obtained above was analyzed using supervised linear discriminant analysis, and the resulting first and second discriminant scores were plotted. The results are shown in FIG. 29 . The contribution of the first discriminant score increased with increasing amino acid sequence identity (78.6% (Seq01 / Seq03), 90.3% (Seq41 / Seq42), and 91.7% (Seq43 / Seq44)). These results indicate that the fluorescence pattern became simpler with increasing amino acid sequence identity. Furthermore, analysis of these results using the jackknife algorithm confirmed that each protein could be identified with high accuracy regardless of sequence identity. These results suggest that even if the proportion of cationic amino acids and hydrophobic amino acids is sufficiently high, the fluorescence pattern becomes simpler when probes with high amino acid sequence identity to the peptide are combined.

[0097] 11. Confirmation of Reproducibility Analysis (7 proteins × 3 probes × 2 wavelength sets (n = 6)) was repeated three times using Seq01, Seq02, and Seq03 under the same conditions and procedures as in 4 above. A graph of the measurement results is shown in Figure 30, and a graph of the CV values ​​for the average values ​​of three independent trials is shown in Figure 31. The CV values ​​were in the range of 0.003 to 0.064. The above data was analyzed by supervised linear discriminant analysis, and the results of plotting the first discriminant score and second discriminant score are shown in Figure 32. The clusters for each protein were distributed without overlapping, and the cluster distribution was consistent across the three independent trials. These results confirmed that this method using peptide probes exhibits sufficiently high reproducibility.

[0098] 12. Correlation between Cationic Amino Acid and Hydrophobic Amino Acid Proportions and Responsiveness Four proteins (HSA, BSA, PSA, and Ant) were analyzed using 31 probes (Seq01-Seq03 and Seq19-Seq46) under the same conditions and procedures as in Section 4 above. The plots of the measurement results and ANC are shown in the upper panel of Figure 33, and the plots of the measurement results and ANC+H are shown in the lower panel of Figure 33. The correlation coefficients of the fluorescence response to ANC were 0.323-0.584 (p>0.01 for HSA and PSA, p>0.05 for BSA). The correlation coefficients of the fluorescence response to ANC+H were 0.582-0.681 (p>0.01 for all). These results indicate that the greater the sum of the cationic amino acid proportion and the hydrophobic amino acid proportion, the higher the fluorescence responsiveness of the probe to the protein.

[0099] 13. Protein Identification Using Anionic Probes Four types of probes (Seq47 to Seq50) were prepared, each containing a peptide consisting of 14 to 19 amino acids, with anionic amino acids of 20% or more (i.e., ANC≧0.2) and anionic amino acids and hydrophobic amino acids of 50% or more (i.e., ANC+H≧0.5). The responsiveness of these probes was analyzed and compared under the same conditions and procedures as in Section 4 above, except that the type of protein used was changed (HSA, BSA, Hem, Lys, Pap, Pro, Tyr, and Myo).

[0100] A heat map of the measurement results (8 proteins × 1 solvent condition × 4 probes × 2 wavelength sets) is shown in Figure 34. All probes responded to the proteins. The above data obtained was analyzed by supervised linear discriminant analysis, and the first and second discriminant scores obtained were plotted. The results are shown in Figure 35. Clusters for each protein were distributed without overlapping. Furthermore, when these results were analyzed by the jackknife method, it was confirmed that each protein could be identified with 98% accuracy. These results suggest that fluorescence patterns capable of identifying proteins can be obtained even with anionic probes.

[0101] 14. Combined Use of Anionic Probe and Cationic Probe The responsiveness of Seq03 (10 amino acids long, ≧40% cationic amino acids (i.e., ANC≧0.4), ≧20% hydrophobic amino acids (i.e., H≧0.2)) and Seq16 to Seq18 (10 amino acids long, 50% anionic amino acids (i.e., ANC≧0.5), 50% hydrophobic amino acids (i.e., H≧0.5)) was analyzed under the same conditions and procedures as in 2 above.

[0102] A heat map of the measurement results (8 proteins x 1 solvent condition x 4 probes x 2 wavelength sets) is shown in Figure 36. The probes responded to the proteins, and unique fluorescence patterns were obtained for each different protein. The data obtained using Seq03 and Seq16 were analyzed using supervised linear discriminant analysis, and the resulting first and second discriminant scores are plotted in Figure 37. When each probe was used alone, there was significant cluster overlap, and the discrimination accuracy based on the jackknife algorithm was low, at 73% and 77%, respectively. In contrast, when the fluorescence responses of Seq03 and Seq16 were combined, the clusters were separated extremely well, and the discrimination accuracy reached 100%. Similarly, when Seq03 and Seq17 and Seq03 and Seq18 were combined, each protein could be discriminated with high accuracy (Figure 38). These results suggest that the accuracy of protein discrimination can be improved by combining anionic and cationic probes with sufficiently high ANC and H.

[0103] 15. Nucleotide Discrimination We tested whether the nonspecific fluorescent peptide probes could distinguish six types of nucleotides (AMP (Sigma, A1752), ADP (Sigma, A2754), ATP (Sigma, A3377), TTP (Sigma, T0251), CTP (Sigma, C1506), and GTP (Sigma, G8877)). Five probes were used: Seq01, Seq03, and Seq36 (10 amino acids long, ≥40% cationic amino acids (i.e., ANC≥0.4), ≥20% hydrophobic amino acids (i.e., H≥0.2)); Seq21 (18 amino acids long, 28% cationic amino acids (i.e., ANC=0.28), 33% hydrophobic amino acids (i.e., H=0.33)); and Seq23 (24 amino acids long, 25% cationic amino acids (i.e., ANC≥0.25), 33% hydrophobic amino acids (i.e., H≥0.33)).

[0104] Ten probe solutions (555 nM) were prepared by dissolving Seq01, Seq03, Seq21, Seq23, and Seq36 in 22.2 mM MOPS (pH 7.0) or 22.2 mM MES (pH 5.0). Each probe solution was added to a 384-well microplate at 54 μL / well using an automated pipetting device. After incubation at 35°C for 10 minutes, the fluorescence intensity (I ) was measured using a microplate reader at an excitation wavelength / emission wavelength of 360 nm / 480 nm. ０ ) was measured. Then, nucleotides (4 mM each in pure water) were added at 6 μL / well using an automatic pipetting device, and the mixture was incubated at 35°C for 10 minutes. Fluorescence intensity was then measured under the same conditions (final concentrations: 500 nM probe, 0.4 mM nucleotide, 20 mM MOPS (pH 7.0) or 20 mM MES (pH 5.0)). Each measurement (6 nucleotides × 5 probes × 2 solvent conditions × 1 wavelength set) was performed six times.

[0105] The change in fluorescence intensity (fluorescence response) before and after the addition of nucleotide (I-I ０ ) is shown in Figure 39. The fluorescence intensity of each probe changed differently depending on the nucleotide, and a unique fluorescence pattern was obtained for each different nucleotide. The above data was analyzed by linear discriminant analysis, and the results up to the second discriminant score were plotted, as shown in Figure 40. The clusters for each nucleotide were distributed without overlapping. Furthermore, when these results were analyzed by the jackknife method, it was confirmed that each nucleotide could be identified with 100% accuracy.

[0106] [B: Analysis of Biological Samples] <16. Analysis of Conditioned Media> (16-1) Preparation of Conditioned Media Conditioned media for human-derived cell lines, A549 cells (provided by JCRB Cell Bank), HeLa cells (provided by Riken BRC), HepG2 cells (provided by JCRB Cell Bank), HuH7 cells (provided by JCRB Cell Bank), and MDA-MB-453 cells (provided by Riken BRC), were prepared by the following procedure. The cells were suspended (2.5 × 10 cells) in D-MEM (high glucose, L-glutamine, and phenol red) (Fujifilm Wako Pure Chemical Industries) supplemented with 10% FBS (GE Life Sciences) and 1% penicillin streptomycin neomycin antibiotic mixture (PSN) (Life Technologies). ４ 100 μL / well of each of the 96-well plates (Greiner Bio-One) was seeded at 37°C, 5% CO ２ After 24 hours of incubation under this condition, the medium was removed and the cells were washed twice with 100 μL of phosphate-buffered saline (PBS) (Fujifilm Wako Pure Chemical Industries, Ltd.). 100 μL of 1x CD CHO Medium (Thermo Fisher Scientific) supplemented with 8 mM L-glutamine was added per well, and the cells were incubated at 37°C for 48 hours under a 5% CO2 environment. The medium was then collected and centrifuged at 3000 × g for 10 minutes. The resulting culture supernatant was used as the conditioned medium.

[0107] (16-2) Changes in Probe Fluorescence with Conditioned Medium. Seq01 and Seq03 (≥40% cationic amino acids, ≥20% hydrophobic amino acids) were dissolved in 40 mM MOPS (pH 7.0) to prepare two probe solutions (1000 nM). Each probe solution was dispensed into a 384-well microplate at 30 μL / well using an automated pipetting device. Then, 30 μL of A549 cell conditioned medium diluted with pure water to various concentrations was added to each well. After incubation at 35°C for 10 minutes, the fluorescence spectra (fluorescence wavelengths 500-650 nm) and fluorescence intensity (fluorescence wavelength 520 nm) at an excitation wavelength of 340 nm were measured using a microplate reader (final concentrations: 500 nM probe, 0-30 vol% conditioned medium, 20 mM MOPS (pH 7.0)).

[0108] Figure 41 shows the changes in the fluorescence spectra of Seq01 and Seq03 upon addition of 0 to 30 vol% conditioned medium. The fluorescence intensity of Seq01 and Seq03 increased and blue-shifted in a conditioned medium concentration-dependent manner. Figure 42 shows the changes in fluorescence intensity (520 nm). The fluorescence intensity of Seq01 and Seq03 continued to increase up to 30 vol% conditioned medium. These results demonstrate that the nonspecific fluorescent peptide probe responds to conditioned medium.

[0109] (16-3) Discrimination of Conditioned Media from Different Cells Seq01, Seq03, and Seq05 (10 amino acids long, ≥40% cationic amino acids (i.e., ANC ≥0.4), ≥20% hydrophobic amino acids (i.e., H ≥0.2)) were dissolved in 22.2 mM MOPS (pH 7.0) or 22.2 mM acetic acid (pH 5.0) to prepare six probe solutions (555 nM). Each probe solution was added to a low-volume 384-well microplate (Corning, 3820) at 22.5 μL / well using an automated pipetting device. After incubation at 35°C for 10 minutes, the fluorescence intensity (I ) was measured using a microplate reader at the following two sets of excitation wavelength (nm) / emission wavelength (nm): ０ ) were measured: (Ch1) 320 / 560, (Ch2) 360 / 480. The conditioned medium prepared above (100 vol%) was then added at 2.5 μL / well using an automated dispenser, and after incubation at 35°C for 10 minutes, fluorescence intensity was measured under the same conditions (final concentrations: 500 nM probe, 10 vol% conditioned medium, 20 mM MOPS (pH 7.0) or 20 mM acetic acid (pH 5.0)). As a control, medium obtained by incubation under the same conditions as above without adding cells was used. Each measurement (6 medium × 3 probe × 2 solvent conditions × 2 wavelength sets) was performed eight times.

[0110] The change in fluorescence intensity before and after adding the medium (fluorescence response) (I-I ０A heat map of the conditioned media is shown in Figure 43. The fluorescence intensity of each probe varied depending on the medium, resulting in a unique fluorescence pattern for each conditioned medium. The data above was analyzed by linear discriminant analysis, and the results up to the second discriminant score were plotted. The clusters for each conditioned medium were distributed without overlapping, with some exceptions. Furthermore, when these results were analyzed by the jackknife method, it was confirmed that each conditioned medium could be distinguished with 94% accuracy.

[0111] 17. Identification of Different Microorganisms Information on the enterobacteria analyzed in this example is shown in Table 4. Enterobacteria other than E. coli were obtained from the culture collections of the Japan Collection of Microorganisms (JCM) and the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ). The E. coli DH5α strain was purchased from GM biolab. The E. coli JM109 strain was purchased from Takara Bio.

[0112] Table 4. List of enterobacteria

[0113] Eight probe solutions (1200 nM) were prepared by dissolving Seq02 and Seq07 to Seq09 (10 amino acids in length, ≥40% cationic amino acids (i.e., ANC ≥ 0.4), ≥20% hydrophobic amino acids (i.e., H ≥ 0.2)) in 24 mM MOPS (pH 7.0) + 180 mM NaCl or 24 mM acetic acid (pH 5.0) + 180 mM NaCl. Each probe solution was added to a 384-well microplate at 50 μL / well using an automated pipetting device. After incubation at 35°C for 10 minutes, the fluorescence intensity (I ) was measured using a microplate reader at an excitation wavelength / emission wavelength = 340 nm / 520 nm. ０ Then, the bacterial suspension (each OD ６００ = 0.24 / pure water) was added at 10 μL / well, and after incubation at 35°C for 10 minutes, the fluorescence intensity was measured under the same conditions (final concentration: 1000 nM probe, OD ６００= 0.04 bacteria, 150 mM NaCl, 20 mM MOPS (pH 7.0) or 20 mM acetic acid (pH 5.0). Each measurement (8 bacteria x 4 probes x 2 solvent conditions x 1 wavelength set) was performed in six replicates.

[0114] Change in fluorescence intensity (fluorescence response) before and after adding the bacterial suspension (I-I ０ A heat map of the results is shown in Figure 45. The fluorescence intensity of each probe varied depending on the type of bacteria and solvent conditions, resulting in a unique fluorescence pattern for each different bacterium. The above data was analyzed using linear discriminant analysis, and the results up to the second discriminant score were plotted. The clusters for each bacterium were distributed without overlapping. Furthermore, when these results were analyzed using the jackknife method, it was confirmed that each bacterium could be identified with 100% accuracy.

[0115] <18. Identification of Gut Microbiota Samples> (18-1) Preparation of a Mouse Model of Immobility A mouse model of immobility was prepared. C3H-HeN mice (male, 6 weeks (approximately 40 days) old, total of 4 mice) were housed in a wheel cage (SW-15, Merquest) at 22°C, 50% humidity, and a 12:12 hour light:dark cycle. They were provided with regular food and water ad libitum and allowed to acclimate for 2 weeks. Subsequently, a locomotion measurement device (nano tag, KISSEI COMTEC) was implanted into each mouse's abdomen (day -34), and each mouse was transferred to an individual wheel cage (day -24) and housed with the wheel open (day -0). The mice were then housed for one week with the wheel fixed (days 1-8), again with the wheel open (days 9-21), and then housed for one week with the wheel fixed (days 22-29). The integrated value of the vibration frequency measured by the locomotion measuring device was obtained as behavioral data of the mice.

[0116] (18-2) Preparation of Gut Microbiota Samples Twelve days after the wheel was opened (at day 20) and five days after the wheel was fixed (at day 27), excreted feces were promptly collected in a microtube and frozen in liquid nitrogen. The samples were then stored at -80°C until analysis. A gut microbiota sample was prepared from the frozen feces using a method based on the report by Benno et al. (Sci. Rep., 2011, 2:233). The fecal sample was weighed and phosphate-buffered saline (PBS) was added to obtain a 40 mg / mL suspension. This suspension was mixed for 1 minute and allowed to stand at 4°C for 5 minutes, and this process was repeated multiple times. The sample was then centrifuged at 8000 x g for 10 minutes at 4°C, and the supernatant was removed. The resulting pellet was suspended in PBS and centrifuged again at 8000 x g for 10 minutes at 4°C, and the supernatant was removed. The resulting pellet was suspended in PBS and filtered through a pluriStrainer™ (mesh size 40 μm, manufactured by pluriSelect) to prepare an intestinal microbiota sample (0.16 mg / mL, 0.5 vol% PBS).

[0117] (18-3) Analysis of Gut Microbiota Samples Seq07 to Seq10 and Seq15 (10 amino acids long, ≥40% cationic amino acids (i.e., ANC≥0.4), ≥20% hydrophobic amino acids (i.e., H≥0.2)) were dissolved in 22.9 mM MOPS + 171.4 mM NaCl (pH 7.0) or 22.9 mM acetic acid + 171.4 mM NaCl (pH 5.0) to prepare 10 probe solutions (1143 nM). Each probe solution was added to a 384-well microplate at 52.5 μL / well using an automated pipetting device. After incubation at 35°C for 10 minutes, the fluorescence intensity (I ) was measured using a microplate reader at an excitation wavelength / emission wavelength = 360 nm / 530 nm. ０) was measured. Then, using an automated pipetting device, 7.5 μL / well of the gut microbiota sample prepared above was added, and after incubation at 35°C for 10 minutes, the fluorescence intensity was measured under the same conditions (final concentrations: 1000 nM probe, 20 μg / mL gut microbiota sample, 20 mM MOPS + 150 mM NaCl (pH 7.0) or 20 mM acetic acid + 150 mM NaCl (pH 5.0)). Each measurement (8 gut microbiota samples × 2 solvent conditions × 5 probes × 1 wavelength set) was performed six times.

[0118] Change in fluorescence intensity (fluorescence response) before and after adding the intestinal microbiota sample (I-I ０ A heat map of the results is shown in Figure 47. The fluorescence intensity of each probe varied depending on the gut microbiota sample and solvent conditions, resulting in unique fluorescence patterns for each sample. The above data was analyzed using linear discriminant analysis, and the results up to the second discriminant score were plotted as shown in Figure 48. The clusters for each gut microbiota sample were distributed without overlapping. Furthermore, when these results were analyzed using the jackknife algorithm, it was confirmed that individual mice and the presence or absence of their exercise restriction could be identified with 96% accuracy. These results demonstrate that the exercise habits of individual animals can be determined by analyzing gut microbiota suspensions using peptide probes.

[0119] 19. Identification of Different Cell Populations Human cell lines A431, HL60, HeLa, MCF-7, and MDA-MB-453 cells (all provided by Riken BRC) were cultured and cell suspensions were prepared based on the inventors' previous report (ACS Appl. Mater. Interfaces, 2019;11(7):6751-6758). Seq01 and Seq03 (10 amino acids long, ≥40% cationic amino acids (i.e., ANC ≥0.4), ≥20% hydrophobic amino acids (i.e., H ≥0.2)) were dissolved in 23.1 mM MOPS (pH 7.0) or 23.1 mM acetic acid (pH 5.0) to prepare six probe solutions (577 nM). Each probe solution was added to a 384-well microplate at 52 μL / well using an automatic dispenser. After incubation at 35° C. for 10 minutes, the fluorescence intensity (I) was measured using a microplate reader at the following two sets of excitation wavelength (nm) / emission wavelength (nm). ０ ) were measured: (Ch1) 320 / 560, (Ch2) 360 / 480. Then, using an automatic pipetting device, the cell suspension (2.25 × 10 ５ After incubation at 35°C for 10 minutes, the fluorescence intensity was measured under the same conditions (final concentrations: 500 nM probe, 30,000 cells / mL cell suspension, 20 mM MOPS (pH 7.0) or 20 mM acetic acid (pH 5.0)). Each measurement (5 cell suspensions x 2 solvent conditions x 2 probes x 2 wavelength sets) was performed six times.

[0120] The change in fluorescence intensity (fluorescence response) before and after adding the cell suspension (II) ０ A heat map of the results is shown in Figure 49. The fluorescence intensity of each probe varied depending on the cell type and solvent conditions, resulting in a unique fluorescence pattern for each cell suspension. The data above was analyzed using linear discriminant analysis, and the results obtained up to the second discriminant score were plotted. The clusters for each cell suspension were distributed without overlapping. Furthermore, when these results were analyzed using the jackknife algorithm, it was confirmed that each cell suspension could be identified with 97% accuracy.

[0121] 20. Discrimination of Fetal Bovine Serum from Different Origins and Lots. Fetal bovine serum from Australia (Corning, 35-076-CV) and fetal bovine serum from Chile (Biosera, FB-1365 / 500) (two lots each) were analyzed using Seq01 and Seq03 (10 amino acids long, ≥40% cationic amino acids (i.e., ANC ≥0.4), ≥20% hydrophobic amino acids (i.e., H ≥0.2)). Seq01 and Seq03 were dissolved in 23.1 mM MOPS (pH 7.0) or 23.1 mM acetic acid (pH 5.0) to prepare four probe solutions (577 nM). Each probe solution was dispensed into a 384-well microplate at 52 μL per well using an automated pipetting device. After incubation at 35°C for 10 minutes, the fluorescence intensity (I) was measured using a microplate reader at the following two sets of excitation wavelength (nm) / emission wavelength (nm). ０ The fluorescence intensity was measured: (Ch1) 320 / 560, (Ch2) 360 / 480. Then, using an automated dispenser, 8 μL of serum (0.75 vol% each in pure water) was added to each well and incubated at 35°C for 10 minutes. The fluorescence intensity was then measured under the same conditions (final concentrations: 500 nM probe, 0.10 vol% serum, 20 mM MOPS (pH 7.0) or 20 mM acetic acid (pH 5.0)). Each measurement (4 sera x 2 solvent conditions x 2 probes x 2 wavelength sets) was performed six times.

[0122] Change in fluorescence intensity (fluorescence response) before and after addition of serum (I-I ０ A heat map of the results is shown in Figure 51. The fluorescence intensity of each probe varied depending on the type of serum and solvent conditions, resulting in unique fluorescence patterns for fetal bovine serum from different origins and lots. The above data was analyzed using linear discriminant analysis, and the results obtained up to the second discriminant score were plotted. The clusters for each serum were distributed without overlapping. Furthermore, when these results were analyzed using the jackknife method, it was confirmed that each fetal bovine serum could be distinguished with 96% accuracy.

[0123] 21. Identification of media containing serum substitutes heat-treated under different conditions (21-1) Preparation of serum substitute-containing media Table 5 shows the media and reagents used in this example.

[0124] Table 5. Media and Reagents

[0125] Serum substitutes, N-2 supplement (hereafter referred to as "N2") and B-27 supplement (hereafter referred to as "B27"), were dispensed into microtubes and treated in a sealed, light-protected state under the following conditions: 45°C, 55°C, 65°C, or 75°C for 30 minutes. Untreated or heat-treated N2 and B27 were used to prepare cardiac differentiation induction medium 1 (DM / F-12 supplemented with 1% N2, 2% B27, 1% NEAA, 1% GlutaMAX, 1% PS, and 0.1 mM 2-ME) (hereafter referred to as "CDM1").

[0126] (21-2) Differentiation induction using serum replacement-containing medium. HCN4-EGFP_409B2 cells (Regen. Ther. 2022;21:239-249) (409B2 cells (human iPS cells) transfected with a BAC vector that knocked in EGFP at the HCN4 locus) were suspended in mouse embryonic fibroblast-conditioned medium (MEF-CM) and plated on Geltrex (Thermo Fisher Scientific, A1413302). The following day, the medium was replaced with CDM1 supplemented with 100 ng / mL Noggin and 3.3 μM CHIR99021, and then replaced with fresh CDM1 until day 3. On day 4, the medium was replaced with CDM1 supplemented with 3.3 μM CHIR99021 and 5 μM IWP-2. On day 5, the cells were dispersed and collected by trypsinization and seeded at a density of 1 x 10 cells / well onto PrimeSurface™ 96U low-attachment plates using cardiac differentiation induction medium 2 (RPMI 1640 supplemented with 1% pyruvate, 1% GlutaMAX, 1% ITS-G supplement, 2 mM L-ascorbic acid, 10 mM nicotinamide, 0.2 μM dexamethasone, 0.5% FBS, 10 ng / mL bFGF, 10 ng / mL BMP4, and 1% PS) (hereinafter referred to as "CDM2"). The medium was then replaced with fresh CDM2 every 2-3 days. GFP fluorescence was observed on day 19 or 20 using a fluorescence microscope (Olympus, IX73). Then, 2 mg / mL collagenase II (Worthington, CLS2) was added to the cells and incubated overnight at room temperature. The cells were dispersed and collected, and GFP-positive (i.e., HCN4-positive) cells were counted using a FACSAria™ III cell sorter (BD biosciences). The differentiation-inducing ability of CDM1 was evaluated based on the quantification of GFP-positive cells.

[0127] (21-3) Identification of Serum Replacement-Containing Media. Eight probe solutions (577 nM) were prepared by dissolving Seq01 and Seq38 (10 amino acids long, ≥40% cationic amino acids (i.e., ANC ≥ 0.4), ≥20% hydrophobic amino acids (i.e., H ≥ 0.2)), Seq23 (24 amino acids long, 25% cationic amino acids (i.e., ANC = 0.25), 33% hydrophobic amino acids (i.e., H = 0.33)), and Seq17 (10 amino acids long, 50% anionic amino acids (i.e., ANC = 0.5), 50% hydrophobic amino acids (i.e., H = 0.5)) in 23.1 mM MOPS (pH 7.0) or 23.1 mM acetic acid (pH 5.0). Each probe solution was dispensed into a 384-well microplate at 52 μL per well using an automated pipetting system. After incubation at 35°C for 10 minutes, the fluorescence intensity (I) was measured using a microplate reader at the following two sets of excitation wavelength (nm) / emission wavelength (nm). ０ The fluorescence intensity was measured: (Ch1) 320 / 560, (Ch2) 360 / 480. Then, using an automated pipetting device, 8 μL / well of CDM1 containing untreated or heat-treated N2 and B27 (3.75 vol% CDM1 / pure water) was added. After incubation at 35°C for 10 minutes, the fluorescence intensity was measured under the same conditions (final concentrations: 500 nM probe, 0.50 vol% CDM1, 20 mM MOPS (pH 7.0) or 20 mM acetic acid (pH 5.0)). Each measurement (5 media x 2 solvent conditions x 4 probes x 2 wavelength sets) was performed six times.

[0128] Change in fluorescence intensity before and after addition of CDM1 (fluorescence response) (I-I ０A heat map of the results is shown in Figure 53. The fluorescence intensity of each probe varied depending on the treatment conditions and solvent conditions of the serum substitute, resulting in a unique fluorescence pattern for each CDM1 containing serum substitute treated under different conditions. Compared to untreated CDM1 containing N2 and B27, CDM1 treated at 45°C showed little change in differentiation induction ability or fluorescence pattern, labeled "Good." CDM1 treated at 55°C showed a significant change in the fluorescence pattern but little change in differentiation induction ability, labeled "Acceptable." CDM1 treated at 65°C and 75°C, which almost completely lost differentiation induction ability, was labeled "Unacceptable." The results were analyzed using linear discriminant analysis, and the resulting second discriminant scores are plotted in Figure 54. The clusters for each performance were distributed without overlapping. Furthermore, when these results were analyzed using the jackknife algorithm, it was confirmed that the differentiation induction ability of CDM1 could be identified with 100% accuracy.

[0129] 22. Identification of Denatured Protein Drugs Asparaginase from Escherichia coli (Sigma-Aldrich) (MW = 138,000, pI = 4.7) and collagenase from Clostridium histolyticum (Sigma-Aldrich) (MW = 110,000, pI = 5.6) were dissolved in PBS (pH 7.4) (0.5 mg / mL). The asparaginase and collagenase solutions were dispensed into microcentrifuge tubes and heat-treated at 55°C for 4 hours using a thermoelectric incubator (Gingko Biotech). Based on visual observation and far-UV CD spectra (Jasco J-720W spectrophotometer), denatured but non-aggregated protein drug samples were identified.

[0130] The eight probe solutions (577 nM) prepared in (21-3) above were used. Each probe solution was added to a 384-well microplate at 52 μL / well using an automatic dispenser. After incubation at 35°C for 10 minutes, the fluorescence intensity (I ) was measured using a microplate reader at the following two sets of excitation wavelength (nm) / emission wavelength (nm): ０) were measured: (Ch1) 320 / 560, (Ch2) 360 / 480. Then, using an automated pipetting device, native or denatured protein drug samples (150 μg / mL each) were added at 8 μL / well, and the mixture was incubated at 35°C for 10 minutes. The fluorescence intensity was then measured under the same conditions (final concentrations: 500 nM probe, 20 μg / mL protein, 20 mM MOPS (pH 7.0) or 20 mM acetic acid (pH 5.0)). Each measurement (4 protein drug samples x 2 solvent conditions x 4 probes x 2 wavelength sets) was performed six times.

[0131] The change in fluorescence intensity before and after adding the sample (fluorescence response) (I-I ０ A heat map of the results is shown in Figure 55. The fluorescence intensity of each probe varied depending on the type of protein drug, treatment conditions, or solvent conditions, resulting in a unique fluorescence pattern for each protein drug sample. The above data was analyzed using linear discriminant analysis, and the results up to the second discriminant score were plotted as shown in Figure 56. The clusters for each sample were distributed without overlapping. Furthermore, when these results were analyzed using the jackknife algorithm, it was confirmed that each protein drug sample could be identified with 100% accuracy.

[0132] <23. Identification of Microbial Broth> GAM (Accudia ＴＭ GAM Broth (Shimadzu Diagnostics, 05422), LB (LB Broth, Miller (Nacalai Tesque, 20068-04)), NB (BD Difco ＴＭ Nutrient Broth (BD Diagnostics, 234000)) and R2A (R2A Broth "Daigo" (Fujifilm Wako Pure Chemical Industries, 395-01681)) were dissolved in a specified amount of purified water (GAM was dissolved in five times the specified amount of purified water) and sterilized in an autoclave (120°C, 15 minutes).

[0133] Probe solutions (3000 nM) were prepared by dissolving Seq01 and Seq38 (10 amino acids long, ≥40% cationic amino acids (i.e., ANC ≥0.4), ≥20% hydrophobic amino acids (i.e., H ≥0.2)), Seq23 (24 amino acids long, 25% cationic amino acids (i.e., ANC = 0.25), 33% hydrophobic amino acids (i.e., H = 0.33)), and Seq17 (10 amino acids long, 50% anionic amino acids (i.e., ANC = 0.5), 50% hydrophobic amino acids (i.e., H = 0.5)) in purified water. The broth prepared above was diluted with 24 mM MOPS (pH 7.0) or 24 mM acetic acid (pH 5.0) (1.2 vol% each) and added to a 384-well microplate at 50 μL / well using an automated pipetting device. After incubation at 35°C for 10 minutes, the fluorescence intensity (I) was measured using a microplate reader at the following two sets of excitation wavelength (nm) / emission wavelength (nm). ０ The fluorescence intensity was measured: (Ch1) 320 / 560, (Ch2) 360 / 480. Then, using an automated pipetting device, 10 μL of probe solution was added per well, and the mixture was incubated at 35°C for 10 minutes. The fluorescence intensity was then measured under the same conditions (final concentrations: 500 nM probe, 1.0 vol% broth, 20 mM MOPS (pH 7.0) or 20 mM acetic acid (pH 5.0)). Each measurement (5 broths x 2 solvent conditions x 4 probes x 2 wavelength sets) was performed six times.

[0134] The change in fluorescence intensity before and after adding the probe (fluorescence response) (I-I ０ A heat map of the results is shown in Figure 57. The fluorescence intensity of each probe varied depending on the type of broth and solvent conditions, resulting in a unique fluorescence pattern for each broth. The data above was analyzed using linear discriminant analysis, and the results up to the second discriminant score were plotted. The clusters for each broth were distributed without overlapping. Furthermore, when these results were analyzed using the jackknife algorithm, it was confirmed that each broth could be identified with 100% accuracy.

[0135] 24. Analysis of Sake Information on the sake analyzed in this example is shown in Table 1. The sakes S1 to S12 are from different breweries and are different brands.

[0136] Table 1. Sake (S1-S8)

[0137] Eight probe solutions (3000 nM) were prepared using the method described in 23 above. Sake was diluted with 24 mM MOPS (pH 7.0) or 24 mM acetic acid (pH 5.0) (9.6 vol.%) and added to a 384-well microplate at 50 μL / well using an automatic dispenser. After incubation at 35°C for 10 minutes, the fluorescence intensity (I) was measured using a microplate reader at the following two sets of excitation wavelength (nm) / emission wavelength (nm): ０ The fluorescence intensity was measured: (Ch1) 320 / 560, (Ch2) 360 / 480. Then, using an automated dispenser, 10 μL of probe solution was added per well, and the mixture was incubated at 35°C for 10 minutes. The fluorescence intensity was then measured under the same conditions (final concentrations: 500 nM probe, 8.0 vol% sake, 20 mM MOPS (pH 7.0) or 20 mM acetic acid (pH 5.0)). Each measurement (8 sake samples x 2 solvent conditions x 4 probe samples x 2 wavelength sets) was performed six times.

[0138] The change in fluorescence intensity before and after adding the probe (fluorescence response) (I-I ０ A heat map of the results is shown in Figure 59. The fluorescence intensity of each probe varied depending on the type of sake and the solvent conditions, resulting in a unique fluorescence pattern for each sake. The above data was analyzed using linear discriminant analysis, and the results up to the second discriminant score were plotted. The clusters for each sake were distributed without overlapping. Furthermore, when these results were analyzed using the jackknife algorithm, it was confirmed that each sake could be identified with 100% accuracy.

[0139] [A-2: Probe Characterization] <25. Effect of the Number of Fluorophores on Probe Performance> Probes Seq51 to Seq56 were prepared in which the N-terminal amino group and the side chain amino group of a lysine residue at the middle position of a peptide consisting of 11 to 24 amino acids and having an ANC of 0.22 or more and an H of 0.09 were modified with a dansyl group. The responsiveness of these probes was measured under the same conditions and procedures as in Section 3 above, except that proteins (Ant, BSA, HSA, IgG, PSA, and Tra) were used at a final concentration of 1,000 nM: (Ch1) 360 / 480. As controls, the following three probe combinations were used, which were expected to produce diverse responses: Seq03 (10 amino acids long, 70% cationic amino acids (i.e., ANC = 0.7), 20% hydrophobic amino acids (i.e., H = 0.2), N-terminus modified with a dansyl group); Seq23 (24 amino acids long, 25% cationic amino acids (i.e., ANC = 0.25), 33% hydrophobic amino acids (i.e., H = 0.33), N-terminus modified with a dansyl group); and Seq35 (10 amino acids long, 30% cationic amino acids (i.e., ANC = 0.3), 50% hydrophobic amino acids (i.e., H = 0.5), N-terminus unmodified, side chain amino group of lysine residue in the middle position of the peptide modified with a dansyl group).

[0140] Table 7. Amino acid sequences and modifications of probes

[0141] Table 8. Probe characteristics

[0142] A heat map of the measurement results (6 proteins × 1 solvent condition × 9 probes × 1 wavelength set) is shown in Figure 61. The above data was analyzed by supervised linear discriminant analysis for three probe combinations (combination 1: Seq51, Seq52, Seq53; combination 2: Seq54, Seq55, Seq56; combination 3 (control): Seq03, Seq23, Seq35). The first and second discriminant scores were plotted and shown in Figure 62. In combinations 1 and 2, the clusters for each protein were distributed without overlapping. In contrast, in combination 3, the BSA and PSA clusters partially overlapped, and the relative distance between the clusters tended to be close. Furthermore, when these results were analyzed by jackknife analysis, it was confirmed that combinations 1 and 2 could identify each protein with 100% accuracy, while combination 3 had a low identification accuracy (97%). These results suggest that when the proportion of cationic amino acids and hydrophobic amino acids is sufficiently high, a fluorescence pattern capable of identifying proteins can be obtained regardless of the number of fluorophores introduced, and that the accuracy of protein identification improves by increasing the number of fluorophores introduced.

[0143] 26. Effect of Peptide Length on Probe Performance (2) Based on Seq57 and Seq58 (7 amino acids long, ≥43% cationic amino acids (i.e., ANC ≥0.43), ≥29% hydrophobic amino acids (i.e., H ≥0.29), N-terminally modified with a dansyl group), probes shortened by one amino acid from the C-terminus (Seq59 and Seq60) and by two amino acids (Seq61 and Seq62) were prepared. Seq59 and Seq60: ANC ≥0.50, H ≥0.33; Seq61 and Seq62: ANC ≥0.60, H ≥0.20. The reactivity of these probes was measured under the same conditions and procedures as in 3 above, except that proteins (Ant, BSA, Fib, HSA, Hem, Myo, PSA, and Tryptophan) were used at a final concentration of 2000 nM: (Ch1) 320 / 560, (Ch2) 360 / 480.

[0144] Table 9. Amino acid sequences and modifications of probes

[0145] Table 10. Probe characteristics

[0146] A heat map of the measurement results (8 proteins x 2 solvent conditions x 6 probes x 2 wavelength sets) is shown in Figure 63. The above data was analyzed by supervised linear discriminant analysis for three probe combinations (combination 4: Seq57, Seq58; combination 5: Seq59, Seq60; combination 6: Seq61, Seq62). The first and second discriminant scores were plotted and shown in Figure 64. In all combinations, the clusters for each protein were distributed without overlapping, with some exceptions. Furthermore, when these results were analyzed by jackknife analysis, it was confirmed that each protein could be identified with 100%, 96%, and 100% accuracy, respectively. These results suggest that when the proportion of cationic amino acids and hydrophobic amino acids is sufficiently high, a fluorescence pattern capable of identifying proteins can be obtained even when the amino acid length is shortened to five.

Claims

1. (1) A step of preparing a probe, a plurality of probe solutions having different ionic strengths or pHs, wherein the probe is: (a) a peptide consisting of 3 to 100 amino acid residues, wherein the peptide (i) has an absolute net charge score (ANC) of 0.15 to 1.0, (ii) a hydrophobicity score (H) of 0.0 to 0.85, and (iii) the sum of the absolute net charge score and the hydrophobicity score (ANC + H) is 0.30 to 1.0, wherein the absolute net charge score (ANC) is expressed by the formula (I): ANC = |AA ｐｏｓｉｔｉｖｅ -AA ｎｅｇａｔｉｖｅ | / AA ｔｏｔａｌ (I) (Wherein, AA ｐｏｓｉｔｉｖｅ is the number of amino acid residues having a positive charge at the pH of the probe solution; ｎｅｇａｔｉｖｅ is the number of negatively charged amino acid residues at the pH of the probe solution; ｔｏｔａｌ is the total number of amino acid residues), and the hydrophobicity score (H) is calculated by the formula (II): H=AA ｈｙｄｒｏｐｈｏｂｉｃ / AA ｔｏｔａｌ (II) (wherein, AA ｈｙｄｒｏｐｈｏｂｉｃ is the number of hydrophobic amino acid residues; AA ｔｏｔａｌ (a) a peptide containing a plurality of probe solutions and a biological sample to be analyzed; (b) an environmentally responsive fluorophore; and (c) a peptide containing a plurality of probe solutions and a biological sample to be analyzed; (d) a probe solution containing a plurality of probe solutions and a biological sample to be analyzed; and (e) a probe solution containing a plurality of probe solutions and a biological sample to be analyzed; and (f) a probe solution containing a plurality of probe solutions and a biological sample to be analyzed; and (g) a probe solution containing a plurality of probe solutions and a biological sample to be analyzed; and (h) a probe solution containing a plurality of probe solutions and a biological sample to be analyzed; and (i) a probe solution containing a plurality of probe solutions and a biological sample to be analyzed; and (j ...f) a probe solution containing a plurality of probe solutions and a biological sample to be analyzed; and (f) a probe solution containing a plurality of probe solutions and a biological sample to be analyzed; and 2. The method of claim 1, wherein the peptide consists of 5 to 60 amino acid residues.

3. The method of claim 1 or 2, wherein the absolute net charge score (ANC) is between 0.20 and 1.

0.

4. The method of any one of claims 1 to 3, wherein the sum of the absolute net charge score and the hydrophobicity score (ANC+H) is 0.40 to 1.

0.

5. The method according to any one of claims 1 to 4, wherein the environmentally responsive fluorophore is selected from the group consisting of fluorophores having a naphthalenesulfonic acid skeleton, fluorophores having a benzofurazan skeleton, fluorophores having a xanthene skeleton, fluorophores having a pyrene skeleton, and aggregation-induced emission fluorophores.

6. The method according to any one of claims 1 to 5, wherein the measurement of fluorescence intensity in step (3) is performed for a plurality of excitation wavelengths and fluorescence wavelengths.

7. The method according to any one of claims 1 to 6, wherein the biological sample contains cells or their culture supernatant.

8. The method of any one of claims 1 to 6, wherein the biological sample contains serum or a serum substitute.

9. The method according to any one of claims 1 to 6, wherein the biological sample contains a microbiota.

10. The method according to any one of claims 1 to 6, wherein the biological sample contains alcoholic beverages.

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