Control of inter-sample analyte variability in complex biological substrates

Abstract

Compositions and methods for normalizing the variability of analyte measurements between samples from biological substrates are described in this application. In some embodiments, the present disclosure relates to a method for normalizing one or more protein levels from urine measured by an aptamer-based assay.

Description

Technology Field

[0001] Cross-reference regarding related applications

[0002] This application claims the benefit of priority of U.S. provisional patent application no. 62 / 849,212 filed on May 17, 2019, which is incorporated by reference in its entirety for any purpose.

[0003] field

[0004] The present disclosure generally relates to controlling the variability of analyte measurements between samples from biological substrates. In some embodiments, the present disclosure relates to a method for controlling the levels of one or more proteins from a urine sample measured by an aptamer-based assay. Background Technology

[0005] Inter-sample variability in analyte measurements in biological samples is a problem for biomarker discovery, metabolic analysis, gene expression analysis, protein pathway analysis, and diagnostic and prognostic tools, particularly when results depend on other quantitative biological signals of a relatively small magnitude. Biological sample types exhibiting high inter-sample variability present a major challenge in working with them. Controlling this variability can provide more consistent and meaningful data sets for experimental and clinical applications.

[0006] Therefore, there is a continuing need for alternative compositions and methods to control analyte measurement variability between samples in biological substrates. The present disclosure meets this need by providing novel compositions and methods for normalizing biological signals of complex substrates to reduce, minimize, or eliminate such variability. means of solving the problem

[0007] Example 1. A method for generating a composite dilution model, wherein

[0008] a) a step of determining the level of an analyte in a first dilution series of a first biological sample containing the analyte, wherein the first dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0009] b) a step of determining the level of the analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0010] c) a step of generating a model based on the levels of the analytes in the first dilution series;

[0011] d) A step of selecting a reference value, wherein the reference value is:

[0012] (i) a second dilution series reference value, which is the analyte level at a specific dilution of the second dilution series;

[0013] (ii) a model reference value, which is the analyte level at a specific dilution of the above model; and

[0014] (iii) a step of selecting from any reference value which is an analyte level at a specific dilution, wherein the analyte level at the specific dilution is not found in the second dilution series or the model;

[0015] e) a step of performing at least one horizontal translation, wherein the at least one horizontal translation is:

[0016] (i) a horizontal translational shift (ΔX) of the second dilution series reference value to the model value, wherein the model value is the analyte level at a specific dilution of the model, and the second dilution series reference value and the model value are identical or substantially identical;

[0017] (ii) a horizontal translational shift (ΔX) of the model reference value to the second dilution series value, wherein the second dilution series value is an analyte level at a specific dilution of the second dilution series, and the model reference value and the second dilution series value are identical or substantially identical; and

[0018] (iii) selected from a horizontal translational shift of a second dilution series value (ΔX) and a model value (ΔY) to an arbitrary reference value, wherein the model value is an analyte level at a specific dilution of the model, the second dilution series value is an analyte level at a specific dilution of the second dilution series, and the second dilution series value, the model value and the arbitrary reference value are the same or substantially the same;

[0019] f) (i) the second dilution series, (ii) the model, or (iii) the step of performing at least one horizontal translational shift of the remainder value in the second dilution series and the model, wherein the horizontal translational shift of the remainder value results in a lined composite translational shift; and

[0020] g) A method comprising the step of forming a synthetic dilution model by fitting a function to an aligned synthetic translational shift series.

[0021] Example 2. In Example 1, the method is:

[0022] d) a step of selecting a second dilution series reference value, which is the analyte level at a specific dilution of the second dilution series;

[0023] e) a step of performing a horizontal translational shift (ΔX) of the second dilution series reference value to a model value, wherein the model value is an analyte level at a specific dilution of the model, and the second dilution series reference value and the model value are identical or substantially identical; and

[0024] f) a step of performing a horizontal translational shift of the remainder value of the second dilution series, wherein the horizontal translational shift of the remainder value results in an aligned composite translational shift.

[0025] Example 3. In Example 1, the method is:

[0026] d) A step of selecting a model reference value, which is the analyte level at a specific dilution of the above model;

[0027] e) a step of performing a horizontal translational shift (ΔX) of the model reference value to a second dilution series value, wherein the second dilution series value is an analyte level at a specific dilution of the second dilution series, and the model reference value and the second dilution series value are the same or substantially the same; and

[0028] f) A method comprising the step of performing a horizontal translational shift of the remainder value in the above model, wherein the horizontal translational shift of the remainder value results in an aligned composite translational shift.

[0029] Example 4. In Example 1, the method is:

[0030] d) a step of selecting an arbitrary reference value, which is an analyte level at a specific dilution, wherein the analyte level at the said specific dilution is not found in the second dilution series or the model;

[0031] e) a step of horizontally translating a second dilution series value (ΔX) and a model value (ΔY) to an arbitrary reference value, wherein the model value is an analyte level at a specific dilution of the model, the second dilution series value is an analyte level at a specific dilution of the second dilution series, and the second dilution series value, the model value, and the arbitrary reference value are the same or substantially the same; and

[0032] f) a step of performing a horizontal translational shift of the remainder value of the second dilution series and the model, wherein the horizontal translational shift of the remainder value results in an aligned composite translational shift.

[0033] Example 5. A method for generating a synthetic dilution model,

[0034] a) a step of determining the level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0035] b) a step of determining the level of an analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0036] c) a step of generating a model based on the levels of the analytes in the first dilution series;

[0037] d) a step of performing at least one horizontal translational movement, wherein the at least one horizontal translational movement is

[0038] (i) Horizontal translational shift (ΔX) of the second dilution series to the above model

[0039] (ii) horizontal translational shift (ΔX) of the model into the second dilution series; and

[0040] (iii) selecting the second dilution series (ΔX) and the model (ΔY) from a horizontal translational shift to an arbitrary reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the said specific dilution is not found in the second dilution series or the model, and wherein the at least one horizontal translational shift results in an aligned synthetic translational shift series; and

[0041] e) A method comprising the step of fitting a function to the above-aligned synthetic translational shift series to form a synthetic dilution model.

[0042] Example 6. The method of Example 5, wherein the method comprises the step of performing a horizontal translational shift (ΔX) of the second dilution series to the model.

[0043] Example 7. The method of Example 5, wherein the method comprises the step of performing a horizontal translational shift (ΔX) of the model to the second dilution series.

[0044] Example 8. The method of Example 5, wherein the method comprises the step of performing a horizontal translational shift of the second dilution series (ΔX) and the model (ΔY) to any reference value which is an analyte level at a specific dilution, and wherein the analyte level at the specific dilution is not found in the second dilution series or the model.

[0045] Example 9. A method for generating a synthetic dilution model,

[0046] a) a step of determining the level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0047] b) a step of determining the level of an analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0048] c) A step for selecting a reference value, wherein the reference value is:

[0049] (i) a second dilution series reference value, which is the analyte level at a specific dilution of the second dilution series; and

[0050] (ii) a step of selecting from any reference value which is an analyte level at a specific dilution, wherein the analyte level at the specific dilution is not found in the first dilution series or the second dilution series;

[0051] d) a step of performing at least one horizontal translational movement, wherein the at least one horizontal translational movement is:

[0052] (i) a horizontal translational shift (ΔX) of the second dilution series reference value to the first dilution series value, wherein the first dilution series value is an analyte level at a specific dilution of the first dilution series, and the second dilution series reference value and the first dilution series value are the same or substantially the same; and

[0053] (ii) The first dilution series (ΔX) and the second dilution series (ΔY) are selected from horizontal translational movement to the above arbitrary reference value, and

[0054] The above at least one horizontal translational movement results in an aligned composite translational movement series; and

[0055] e) A method comprising the step of fitting a function to the above-aligned synthetic translational shift series to form a synthetic dilution model.

[0056] Example 10. The method of Example 9, wherein the method comprises the step of performing a horizontal translational shift (ΔX) of the second dilution series to the first dilution series.

[0057] Example 11. The method of Example 9, wherein the method comprises the step of horizontally translating the first dilution series (ΔX) and the second dilution series (ΔY) to any reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the said specific dilution is not found in the first dilution series or the second dilution series.

[0058] Example 12. A method for generating a synthetic dilution model,

[0059] a) a step of determining the level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0060] b) a step of determining the level of an analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0061] c) a step of performing at least one horizontal translational movement, wherein the at least one horizontal translational movement is:

[0062] (i) horizontal translational shift (ΔX) of the second dilution series into the first dilution series; and

[0063] (ii) determined from the horizontal translational shift of the first dilution series (ΔX) and the second dilution series (ΔY) to an arbitrary reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at that specific dilution is not found in the first dilution series or the second dilution series;

[0064] The step of performing the horizontal translational movement, wherein the above at least one horizontal translational movement results in an aligned composite translational movement series; and

[0065] d) a method comprising the step of forming the synthetic dilution model by fitting a function to the aligned synthetic translational shift series.

[0066] Example 13. The method of Example 12, wherein the method comprises the step of performing a horizontal translational shift (ΔX) of the second dilution series to the first dilution series.

[0067] Example 14. The method of Example 12, wherein the method comprises the step of horizontally translating the first dilution series (ΔX) and the second dilution series (ΔY) to the arbitrary reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the said specific dilution is not found in the first dilution series or the second dilution series.

[0068] Example 15. A method for generating a synthetic dilution model,

[0069] a) a step of determining the level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0070] b) a step of determining the level of an analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0071] c) generating a first model based on the level of the analyte in the first dilution series and generating a second model based on the level of the analyte in the second dilution series;

[0072] d) A step of selecting a reference value, wherein the reference value is:

[0073] (i) a first model reference value, which is the analyte level at a specific dilution of the first model; and

[0074] (ii) a step of selecting from any reference value which is an analyte level at a specific dilution, wherein the analyte level at the specific dilution is not found in the first model or the second model;

[0075] d) a step of performing at least one horizontal translational movement, wherein the at least one horizontal translational movement is:

[0076] (i) horizontal translation (ΔX) of the first model into the second model; and

[0077] (ii) selected from the horizontal translation of the first model (ΔX) and the second model (ΔY) to the above arbitrary reference value; and

[0078] The above horizontal translational movement results in an aligned composite translational movement series, the step of performing the above horizontal translational movement; and

[0079] e) A method for forming a synthetic dilution model by fitting a function to the above-aligned synthetic translational shift series.

[0080] Example 16. The method of Example 15, wherein the method comprises the step of performing a horizontal translation (ΔX) of the first model to the second model.

[0081] Example 17. The method of Example 15, wherein the method comprises the step of horizontally translating the first model (ΔX) and the second model (ΔY) to the arbitrary reference value.

[0082] Example 18. A method for generating a synthetic dilution model,

[0083] a) a step of determining the level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0084] b) a step of determining the level of an analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series includes at least three (3) different dilutions, and the level of the analyte is determined at each of the at least three different dilutions;

[0085] c) generating a first model based on the level of the analyte in the first dilution series and generating a second model based on the level of the analyte in the second dilution series;

[0086] d) a step of performing at least one horizontal translational movement, wherein the at least one horizontal translational movement is:

[0087] (i) horizontal translation (ΔX) of the first model into the second model; and

[0088] (ii) selected from the horizontal translational shift of the first model (ΔX) and the second model (ΔY) to an arbitrary reference value, wherein the analyte level of the specific dilution is not found in the first model or the second model;

[0089] The above horizontal translational movement results in an aligned composite translational movement series, the step of performing the above horizontal translational movement; and

[0090] e) A method comprising the step of fitting a function to the above-aligned synthetic translational shift series to form a synthetic dilution model.

[0091] Example 19. The method of Example 18, wherein the method comprises the step of performing a horizontal translation (ΔX) of the first model to the second model.

[0092] Example 20. The method of Example 18, wherein the method comprises a horizontal translational shift of the first model (ΔX) and the second model (ΔY) to any reference value.

[0093] Example 21. A method in any one of Examples 1 to 20, wherein the level of the analyte is determined at each of at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 different dilutions in the first dilution series.

[0094] Example 22. A method in any one of Examples 1 to 21, wherein the level of the analyte is determined at each of at least eight different dilutions.

[0095] Example 23. A method in which, in any one of Examples 1 to 22, the level of the analyte is determined at each of at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 different dilutions of the second dilution series.

[0096] Example 24. A method in any one of Examples 1 to 23, wherein the level of the analyte is determined at each of at least eight different dilutions of the second dilution series.

[0097] Example 25. A method in any one of Examples 1 to 24, wherein each model is independently selected from a linear regression model, a LOESS curve fitting model, a nonlinear regression model, a spline fit model, a mixed effects regression model, a fixed effects regression model, a generalized linear model, a substrate decomposition model, and a four-parameter logistic regression (4PL) model.

[0098] Example 26. A method in any one of Examples 1 to 25, wherein the level of the analyte is a relative amount of the analyte or a concentration of the analyte.

[0099] Example 27. A method in any one of Examples 1 to 26, wherein the selected reference value is within the linear range of the dilution series or model.

[0100] Example 28. Method of Example 21, wherein the selected reference value is the center point of the linear range.

[0101] Example 29. A method in any one of Examples 1 to 28, wherein the first and second biological samples comprise urine or are derived from urine.

[0102] Example 30. A method in any one of Examples 1 to 29, wherein the first and second biological samples are collected from the same subject.

[0103] Example 31. A method in any one of Examples 1 to 30, wherein the first and second biological samples are collected from different subjects.

[0104] Example 32. The method of Example 31, wherein the first biological sample is collected at a first time point and the second biological sample is collected at a second time point.

[0105] Example 33. The method of Example 32, wherein the first time point and the second time point are different by at least about 0.5 hours, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 13 hours, 14 hours, 15 hours, 16 hours, 17 hours, 18 hours, 19 hours, 20 hours, 21 hours, 22 hours, 23 hours, 24 hours, 36 hours, 48 ​​hours, 60 hours or 72 hours.

[0106] Example 34. A method in any one of Examples 1 to 33, wherein the level of the analyte is measured by an assay using an aptamer, an antibody, a mass spectrophotometer, or a combination thereof.

[0107] Example 35. A method in any one of Examples 1 to 34, wherein the dilution factor for each of the first dilution series and the second dilution series is a constant dilution factor.

[0108] Example 36. A method in any one of Examples 1 to 35, wherein the dilution factor for each of the first dilution series and the second dilution series is at least 2, 3, 4, 5, 6, 7, 8, 9, and 10 times dilution.

[0109] Example 37. A method in any one of Examples 1 to 35, wherein the dilution factor for each of the first dilution series and the second dilution series is an exponential or logarithmic dilution factor.

[0110] Example 38. A method in any one of Examples 1 to 37, wherein the first biological sample and the second biological sample are biological samples of the same type.

[0111] Example 39. A method in any one of Examples 1 to 35, wherein each of the first dilution series and the second dilution series is at least a 5-point serial titration at a titration factor of at least 1:2.

[0112] Example 40. A method comprising, in any one of Examples 1 to 39, further comprising the step of determining the relative dilution of a biological test sample by horizontally translating the level of at least one analyte from the biological test sample to a synthetic dilution model for at least one analyte.

[0113] Example 41. Method in Example 40, wherein the biological test sample used to form the synthetic dilution model and the first and second biological samples are of the same sample type.

[0114] Example 42. Method in Example 40 or Example 41, wherein the biological test sample and the first and second biological samples used to form the synthetic dilution model are urine samples or derived from urine samples.

[0115] Example 43. A method for determining the relative dilution of a biological test sample from a subject, wherein the method determines the relative dilution of the biological test sample from the subject by horizontally translating the level of at least one analyte from the biological test sample to a synthetic dilution model developed for at least one analyte.

[0116] Example 44. In any one of Examples 40 to 43, the step of horizontally translating at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 50, at least 75, at least 100, at least 150, or at least 200 different analytes from a biological test sample for each synthetic dilution model developed for each different analyte to determine the relative dilution of the biological test sample for each different analyte, and A method comprising the step of using relative dilution for each analyte.

[0117] Example 45. A method according to Example 44, wherein the relative dilution of the biological test sample is derived from the central tendency of the relative dilutions of each of the different analytes.

[0118] Example 46. A method in which the relative dilution of the biological test sample in Example 44 or Example 45 is derived from the median, mean, or mode of the relative dilutions of each of the different analytes.

[0119] Example 47. In any one of Examples 43 to 46, the synthetic dilution model is a method developed using the method of any one of Examples 1 to 42.

[0120] Example 48. A method in which, in any one of Examples 43 to 48, the biological test sample used to develop the synthetic dilution model and the sample are of the same sample type.

[0121] Example 49. The method of Example 41, wherein the biological test sample used to develop the synthetic dilution model and the sample are urine samples or derived from urine samples.

[0122] Example 50. A method comprising, in any one of Examples 40 to 49, the step of calculating the relative dilution of the biological test sample using the derived relative dilution coefficient.

[0123] Example 51. A method in which, in any one of Examples 1 to 50, each analyte is a target protein.

[0124] Example 52. In a computer system,

[0125] Non-transient memory storing instructions; and

[0126] One or more hardware processors coupled to the above non-transient memory, by reading instructions from the above non-transient memory, the computer system:

[0127] A step of receiving analyte measurement data from a plurality of different biological samples;

[0128] A step of analyzing individual analyte measurements for each of a plurality of selected analytes in the above analyte measurement data; and

[0129] A computer system comprising one or more hardware processors configured to perform an operation including the step of generating a synthetic dilution model for each analyte selected based on the above analysis.

[0130] Example 53. In Example 52, the analyzing step is:

[0131] A computer system comprising the step of determining, for each selected analyte, a biological sample having a maximum linear dilution range.

[0132] Example 54. In Example 53, the analyzing step is:

[0133] A computer system further comprising the step of generating a reference dilution model for each selected analyte based on a maximum linear range determined for each selected analyte.

[0134] Example 55. In Example 54, the analyzing step is:

[0135] A computer system further comprising the step of, for each selected analyte, translating the analyte measurement data associated with each selected analyte based on a reference dilution model generated for each selected analyte.

[0136] Example 56. A computer system in which, in any one of Examples 52 to 55, the biological sample is a urine sample.

[0137] Example 57. A computer system in any one of Examples 52 to 56, wherein the analyte measurement data includes RFU (relative fluorescence unit) measurements.

[0138] Example 58. A non-transient computer-readable medium comprising a computer-readable instruction, wherein when the instruction is executed by a processing device, the processing device causes:

[0139] A step of receiving analyte measurement data from a plurality of different biological samples;

[0140] A step of analyzing individual analyte measurements for each of a plurality of selected analytes in the above analyte measurement data; and

[0141] A non-transient computer-readable medium that enables the operation including the step of generating a synthetic dilution model for each selected analyte based on the above analysis.

[0142] Example 59. A computer-implemented method for generating a synthetic dilution model for a biological substance, wherein

[0143] A step of receiving analyte measurement data from a plurality of different biological samples by one or more processing devices;

[0144] A step of analyzing individual analyte measurements for each of a plurality of selected analytes in the analyte measurement data by one or more processing devices; and

[0145] A computer-implemented method comprising the step of generating a synthetic dilution model for each selected analyte based on the analysis by one or more processing devices.

[0146] Example 60. In a computer system,

[0147] Non-transient memory storing instructions; and

[0148] One or more hardware processors coupled to the above non-transient memory, by reading instructions from the above non-transient memory, the computer system:

[0149] A step of receiving analyte measurement data for an analyte of a biological sample;

[0150] A step of selecting a plurality of analytes to determine the predicted relative dilution of the biological sample;

[0151] A step of receiving a synthetic dilution model generated for each individual analyte of each selected analyte;

[0152] A step of determining a predicted relative dilution value for each selected analyte based on a corresponding synthetic dilution model generated for each selected analyte; and

[0153] A computer system comprising one or more hardware processors configured to perform an operation including the step of determining the predicted relative dilution of the biological sample based on a predicted relative dilution value determined for the selected analyte.

[0154] The above and other objects, features, and advantages of the present invention will become more apparent from the following detailed description with reference to the accompanying drawings. Brief explanation of the drawing

[0155] Figure 1 illustrates the determination of the linear range by serial dilution. RFU = Relative Fluorescence Unit. Figures 2a-2e illustrate the analysis of cystatin C (CST3) levels and ephrine receptor B 6 (EPHB6). Figure 2a illustrates the measurement of CST3 levels using serial dilutions of urine samples. Figure 2b illustrates the generation of regression lines for CST3 using a weighted linear regression model. Figure 2c illustrates the fitting of a four-parameter logistic function (4PL) to the data for CST3. Figure 2d illustrates the measurement of EPHB6 levels using serial dilutions of urine samples. Figure 2e illustrates the generation of regression lines for EPHB6 using a weighted linear regression model. Figures 3a-3c illustrate the analysis of PDGFD (platelet-derived growth factor D) levels. Figure 3a illustrates the measurement of PDGFD levels using serial dilution of urine samples. Figure 3b illustrates the generation of regression lines using a weighted linear regression model. Figure 3c illustrates the 4PL fitting of the data for PDGFD. Figures 4a-4c illustrate the analysis of RARRES2 (retinoic acid receptor responder 2) levels. Figure 4a illustrates the measurement of RARRES2 levels using serial dilution of urine samples. Figure 4b illustrates the generation of regression lines using a weighted linear regression model. Figure 4c illustrates the 4PL fitting of the data for RARRES2. Figures 5a-5c illustrate the analysis of IL1RL2 (interleukin 1 receptor like 2) levels. Figure 5a illustrates the measurement of IL1RL2 levels using serial dilution of urine samples. Figure 5b illustrates the generation of regression lines using a weighted linear regression model. Figure 5c illustrates the 4PL fitting of the data for IL1RL2. Figures 6a-6c illustrate the analysis of F11 (coagulation factor XI) levels. Figure 6a illustrates the measurement of F11 values ​​using serial dilution of urine samples. Figure 6b illustrates the generation of regression lines using a weighted linear regression model. Figure 6c illustrates the 4PL fitting of the data for F11. Figures 7a-7c illustrate the analysis of SEPT11 (septin 11) levels. Figure 7a illustrates the measurement of SEPT11 levels using serial dilution of urine samples. Figure 7b illustrates the generation of regression lines using a weighted linear regression model. Figure 7c illustrates the 4PL fitting of the data for SEPT11. Figures 8a-8c illustrate the analysis of TMPO (thymopoietin) levels. Figure 8a illustrates the measurement of TMPO levels using serial dilution of urine samples. Figure 8b illustrates the generation of regression lines using a weighted linear regression model. Figure 8c illustrates the 4PL fitting of the data for TMPO. Figures 9a-9c illustrate the analysis of SHISA3 (shisa family member 3) levels. Figure 9a illustrates the measurement of SHISA3 levels using serial dilution of urine samples. Figure 9b illustrates the generation of regression lines using a weighted linear regression model. Figure 9c illustrates the 4PL fitting of the data for SHISA3. Figures 10a-10b show the normalization results of the analyte levels for IDUA (iduronidase, 10A) and NEO1 (neogenin 1, 10B). Figure 11 illustrates the distribution of expected relative dilutions for four consecutive 1:2 dilutions of a single sample. The lines shown from left to right are 0.3125%, 0.625%, 1.25%, 2.5%, 5%, 10%, 20%, and 40%. Figure 12 shows the target protein levels before normalization for 15 study participants. Figure 13 shows the expected relative dilution of each sample calculated from the synthetic dilution curve for 15 study participants. Figure 14 shows normalized samples for 15 study participants. Figures 15a-15d illustrate denormalized (15a and 15c) and normalized (15b and 15d) measurements of analytes from the urine of a single study participant. Figures 16a-16d illustrate denormalized (16a and 16c) and normalized (16b and 16d) measurements of analytes from the urine of a single study participant. FIGS. 17a-17b illustrate the F-statistic for the denormalized and normalized data for each study participant (17a) and the fold reduction of the F-statistic for each study participant (17b). FIGS. 18a and 18b illustrate a flowchart (A) and a graph (B) illustrating a first exemplary method for forming a synthetic dilution model. FIGS. 19a and 19b illustrate a flowchart (A) and a graph (B) showing a second exemplary method for forming a synthetic dilution model. FIGS. 20a and 20b illustrate a flowchart (A) and a graph (B) showing a third exemplary method for forming a synthetic dilution model. FIGS. 21a and 21b illustrate a flowchart (A) and a graph (B) showing a fourth exemplary method for forming a synthetic dilution model. FIGS. 22a and 22b illustrate a flowchart (A) and a graph (B) showing a fifth exemplary method for forming a synthetic dilution model. FIGS. 23a and 23b illustrate a flowchart (A) and a graph (B) showing a sixth exemplary method for forming a synthetic dilution model. FIGS. 24a and 24b illustrate a flowchart (A) and a graph (B) showing a seventh exemplary method for forming a synthetic dilution model. FIG. 25 is a block diagram illustrating an exemplary system architecture according to various examples of the present disclosure. FIGS. 26 and 27 are exemplary flowcharts illustrating the generation of a synthetic dilution model for each of a plurality of analytes present in different biological samples according to an example of the present disclosure. FIG. 28 is a flowchart illustrating the application of a synthetic dilution model to a new biological sample for predicting the relative dilution of a new sample according to an example of the present disclosure. FIG. 29 is a block diagram of an exemplary computer system capable of performing one or more of the operations described in the present application. Specific details for implementing the invention

[0156] I. Terms and Methods

[0157] The present invention will be described in connection with specific representative embodiments, but it will be understood that the invention is defined by the claims and is not limited to such embodiments.

[0158] Those skilled in the art will recognize that many methods and materials similar to or equivalent to those described in this application may be used in the practice of the present invention. The present invention is by no means limited to the described methods and materials.

[0159] Unless otherwise defined, technical and scientific terms used in this application have the meanings generally understood by those skilled in the art to which the invention pertains. Definitions of general terms in molecular biology can be found in Benjamin Lewin, Genes V, published by Oxford University Press, 1994 (ISBN 0-19-854287-9); Kendrew et al. (eds.), The Encyclopedia of Molecular Biology, published by Blackwell Science Ltd., 1994 (ISBN 0-632-02182-9); and Robert A. Meyers (ed.), Molecular Biology and Biotechnology: a Comprehensive Desk Reference, published by VCH Publishers, Inc., 1995 (ISBN 1-56081-569-8). Any method, device, and material similar or equivalent to that described in this application may be used in the practice of the invention, but specific methods, devices, and materials are described in this application.

[0160] All publications, disclosed patent documents, and patent applications cited in this application are incorporated by reference to the same extent as each individual publication, disclosed patent document, or patent application is specifically and individually indicated as being incorporated by reference.

[0161] As used in this application, including in the appended claims, the singular forms "a," "an," and "the" include the plural unless the context clearly indicates otherwise, and may be used interchangeably with "at least one" and "one or more." Accordingly, references to "aptamer" include mixtures of aptamers, and references to "probe" include mixtures of probes.

[0162] As used in this application, the terms “comprising,” “comprising,” “comprising,” “comprising,” “containing,” “containing,” and any variations thereof are intended to include non-exclusive inclusion, so that the composition of a product-by-process or material by a process, method, or procedure comprising or containing an element or a list of elements may include other elements not explicitly listed.

[0163] It should be further understood that all base sizes or amino acid sizes, and all molecular weights or molecular weight values ​​given for nucleic acids or polypeptides are approximations and are provided for illustrative purposes.

[0164] Additionally, the range provided in this application is understood as an abbreviation for all values ​​within the range. For example, the range from 1 to 50 is understood to include any number, combination of numbers, or subrange from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50, as well as fractions thereof, unless otherwise specified in the context.

[0165] Any concentration range, percentage range, ratio range, or integer range should be understood to include any integer value within the cited range and, where appropriate, fractions thereof (e.g., tenths and hundredths of an integer) unless otherwise indicated. Additionally, any number range cited in this application with respect to any physical feature, such as polymer subunits, size, or thickness, should be understood to include any integer within the cited range unless otherwise indicated.

[0166] As used in this application, “about” or “essentially composed” means ± 20% of the indicated range, value, or structure unless otherwise indicated.

[0167] The use of alternatives (e.g., “or”) should be understood to mean one, both, or a combination of the alternatives.

[0168] To facilitate the examination of various embodiments of the present disclosure, the following description of specific terms is provided:

[0169] Antibody: The term "antibody" refers to any species of full-length antibody having the ability to bind to an antigen, including Fab fragments, F(ab')2 fragments, single-chain antibodies, Fv fragments, and single-chain Fv fragments, as well as fragments and derivatives of such antibodies. The term "antibody" also includes antibodies and fragments derived from phage displays, and synthetically derived antibodies such as affibodies and nanobodies.

[0170] Aptamer:As used in this application, "aptamer" refers to a nucleic acid having a specific binding affinity for a target molecule, and the binding of the aptamer to the target molecule does not involve Watson-Crick base pairing. Affinity interactions are recognized as a matter of degree; however, in this context, the "specific binding affinity" of an aptamer to a target means that the aptamer binds to its target with a much higher affinity than it would generally bind to other components of a test sample. An "aptamer" is a set of copies of a type or species of nucleic acid molecule having a specific nucleotide sequence. An aptamer may contain any appropriate number of nucleotides, including any number of chemically modified nucleotides. The plural "aptamers" refers to one or more of such a set of molecules. Different aptamers may have the same or different numbers of nucleotides. Aptamers may be DNA, RNA, or chemically modified nucleic acids, may include single-stranded, double-stranded, or both single- and double-stranded regions, and may include higher-order structures. Aptamers may also include photoreactive or chemically reactive functional groups and may be covalently linked to a corresponding target. Any aptamer method disclosed in this application may involve the use of two or more aptamers that specifically bind to the same target molecule. As described below, aptamers may include a tag. If an aptamer includes a tag, it is not necessary for all copies of the aptamer to have the same tag. Additionally, if different aptamers each include a tag, each different aptamer may have the same tag or a different tag.

[0171] Biological sample or biological substrate:As used in this application, “biological sample” and “biological matrix” mean any substance, solution, or mixture obtained from an organism. This includes blood (including whole blood, leukocytes, peripheral blood monocytes, plasma, and serum), sputum, respiration, urine, semen, saliva, meningeal fluid, amniotic fluid, glandular fluid, lymph fluid, nipple aspirates, bronchial aspirates, synovial fluid, joint aspirates, cells, cell extracts, and cerebrospinal fluid. This also includes experimentally isolated fractions of all the above. The terms “biological sample” and “biological matrix” also include substances, solutions, or mixtures containing homogenized solid material, such as, for example, stool samples, tissue samples, or tissue biopsies. The terms “biological sample” and “biological matrix” also include substances, solutions, or mixtures derived from cell lines, tissue cultures, cell cultures, bacterial cultures, viral cultures, or cell-free biological systems (e.g., IVTT).

[0172] Level: As used in this application, “target protein level,” “analyte level,” and “level” refer to measurements made using any analytical method for detecting an analyte (e.g., target protein) in a biological sample, and represent measurements indicating the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of the measured level, etc., of the analyte in the biological sample, or equivalent measurements. The precise nature of the “level” depends on the specific design and components of the specific analytical method used to detect the analyte.

[0173] C-5 modified pyrimidine:As used in this application, the term "C-5 modified pyrimidine" refers to a pyrimidine modified at the C-5 position. Examples of C-5 modified pyrimidines include US Patent Nos. 5,719,273; 5,945,527; 9,163,056; and Dellafiore et al., 2016, Front. Chem., 4:18. Examples of C-5 modifications include substituting the deoxyuridine at the C-5 position with a substituent independently selected from the following: benzylcarboxyamide (alternatively benzylaminocarbonyl) (Bn), naphthylmethylcarboxyamide (alternatively naphthylmethylaminocarbonyl) (Nap), tryptaminocarboxyamide (alternatively tryptaminocarbonyl) (Trp), phenethylcarboxyamide (alternatively phenethylaminocarbonyl) (Pe), thiophenylmethylcarboxyamide (alternatively thiophenylmethylaminocarbonyl) (Th), and isobutylcarboxyamide (alternatively isobutylaminocarbonyl) (iBu) exemplified immediately below.

[0174] Chemical modifications of C-5 modified pyrimidines can also be combined with modifications at the 2'-position, modifications in exocyclic amines, and substitution of 4-thiouridines, either alone or in any combination.

[0175] Representative C-5 modified pyrimidines are: 5-(N-benzylcarboxyamide)-2'-deoxyuridine (BndU), 5-(N-benzylcarboxyamide)-2'-O-methyluridine, 5-(N-benzylcarboxyamide)-2'-fluorouridine, 5-(N-isobutylcarboxyamide)-2'-deoxyuridine (iBudU), 5-(N-isobutylcarboxyamide)-2'-O-methyluridine, 5-(N-phenethylcarboxyamide)-2'-deoxyuridine (PedU), 5-(N-thiophenylmethylcarboxyamide)-2'-deoxyuridine (ThdU), 5-(N-isobutylcarboxyamide)-2'-fluorouridine, 5-(N-tryptaminocarboxyamide)-2'-deoxyuridine (TrpdU), 5-(N-tryptaminocarboxyamide)-2'-O-methyluridine, 5-(N-tryptaminocarboxyamide)-2'-fluorouridine, 5-(N-[1-(3-trimethylammonium)propyl]carboxyamide)-2'-deoxyuridine chloride, 5-(N-naphthylmethylcarboxyamide)-2'-deoxyuridine (NapdU), 5-(N-naphthylmethylcarboxyamide)-2'-O-methyluridine, 5-(N-naphthylmethylcarboxyamide)-2'-fluorouridine or 5-(N-[1-(2),3-dihydroxypropyl)]carboxyamide)-2'-deoxyuridine).

[0176] Nucleotides may be modified before or after oligonucleotide synthesis. The nucleotide sequence of an oligonucleotide may be interrupted by one or more non-nucleotide components. The modified oligonucleotide may be further modified after polymerization, for example, by conjugation with any suitable labeling component.

[0177] The term "at least one pyrimidine" as used in this application refers to one, several, or all pyrimidines of the nucleic acid when referring to a modification of the nucleic acid, and indicates that some or all occurrences of C, T, or U in the nucleic acid may or may not be modified.

[0178] Capture Reagent:As used in this application, “capture agent” or “capture reagent” means a molecule capable of specifically binding to an analyte such as a biomarker, protein, and / or peptide. “Target protein capture reagent” means a molecule capable of specifically binding to a target protein. Non-limiting exemplary capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-coupled receptors, imprinted polymers, avimers, peptide mimics, hormone receptors, cytokine receptors, synthetic receptors, and modifications and fragments of any of the aforementioned capture reagents. In some embodiments, the capture reagent is selected from aptamers and antibodies.

[0179] Control Level:The "control level" of a target molecule refers to the level of the target molecule in the same sample type from individuals free of disease or condition, from individuals not suspected of or at risk of disease or condition, or from individuals with a non-progressive form of disease or condition. Additionally, the "control level" may refer to a reference considered within average, normal, or healthy parameters. The "control level" may also refer to a reference level taken at a previous time, which is used for comparison with a later measured or detected level of the target. For example, the level of the target may be detected at time point A and then at time point B, where time point B is after time point A. In a more specific example, time point A may be considered as 0 o'clock (0) or 0 days (0), and time point B is minutes (e.g., 10, 20, 30, 40, 50, 60 minutes), hours (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours), days (e.g., 1, 2, 3, 4, 5, 6, or 7 days), weeks (e.g., 1, 2, 3, or 4 weeks), and months (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, It may be 10, 11, or 12 months) and even years (e.g., time points A 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 years). The "control level" of the target molecule does not need to be determined each time this method is performed and may be a predetermined level used as a reference or threshold to determine whether the level of a specific sample is higher or lower than the normal level.

[0180] Correlation:"Correspondence correlation" or "concordance correlation coefficient" measures the agreement between two continuous variables X and Y (e.g., prediction, estimate, or decision, actual). "Correspondence correlation" is 45 pairs o The degree of belonging to the line is evaluated and includes measurements of accuracy and precision (or "Lin's Concordance"). Additional information can be found in Lin, Biometrics, Vol. 45, No. 1 (March, 1989), 255–268, which is incorporated by reference into this application. Other methods for determining correlation that may be used in this application include, but are not limited to, Pearson correlation coefficients, paired t-tests, least squares analysis of slope (=1) and intercept (=0), coefficients of variation, and within-class correlation coefficients. In certain embodiments, the paired correlation is determined by a method selected from Lin's Concordance, Pearson correlation coefficients, paired t-tests, least squares analysis of slope (=1) and intercept (=0), coefficients of variation, and within-class correlation coefficients.

[0181] Detection: As used in this application, "detection" or "determination" with respect to an analyte level includes the use of both an instrument used to observe and record a signal corresponding to the analyte level and a material required to generate said signal. In various embodiments, the level is detected using any suitable method including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic wave, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, etc.

[0182] diagnosis:"Diagnosis," "diagnosing stage," "diagnosis," and variations thereof refer to detecting, determining, or recognizing the health status or condition of an individual based on one or more signs, symptoms, data, or other information concerning the individual. The health status of an individual may be diagnosed as healthy / normal (i.e., diagnosis of the absence of disease or condition) or as disease / abnormal (i.e., diagnosis of the presence of disease or condition or evaluation of characteristics). Terms such as "diagnose," "diagnosing," and "diagnose" include the initial detection of a disease in relation to a specific disease or condition; the characterization or classification of a disease; the detection of disease progression, remission, or recurrence; and the detection of a disease response following the administration of a treatment or therapy to the individual.

[0183] Dilution: "Dilution," "dilution series," and variations thereof include various other types of dilution, including but not limited to step dilutions, serial dilutions, and combinations thereof. As an example of a step dilution, where the dilution factor is 1000 (1:1000 dilution), the user may first perform a 1:10 dilution (dilution factor 10) and then perform a 1:100 dilution (dilution factor 100) using 1 part solute and 99 parts diluent at the 1:10 dilution, resulting in a dilution factor of 1000 or a 1:1000 dilution of the solute. Serial dilutions include successive step dilutions, each having the same dilution ratio, wherein the diluted material from the previous step is used to create the subsequent dilution. For example, in the case of a series of dilutions, to create a 5-point 1:2 series of dilutions, the first dilution of the series (the first point of the 5-point series) is created by combining the 1 part solute with the 1 part diluent, and then the second dilution of the series (the second point of the 5-point series) is created by combining the 1 part solute of the first dilution with the 1 part diluent, and so on, until the fifth series of dilutions is reached.

[0184] Dilution factor: The "dilution factor" represents the ratio of the solute portion to the diluent portion. For example, a dilution factor of 2 means a 1:2 dilution, with 1 part solute and 1 part diluent for a total of 2 parts; a dilution factor of 10 means a 1:10 dilution, with 1 part solute and 9 parts diluent for a total of 10 parts.

[0185] evaluate: “Evaluate,” “evaluating,” “evaluate,” and variations thereof include both “diagnosis” and “prediction,” and include determination or prediction regarding the future course of a disease or condition in an individual having the disease or condition, as well as determination and prediction regarding the likelihood of the disease or condition occurring in an individual who has not previously been diagnosed with the disease or condition, as well as determination or prediction regarding the likelihood of the disease or condition recurring in an individual who is in remission of the disease or condition or who is believed to have been cured. The term “evaluate” also includes evaluating an individual’s response to treatment, e.g., predicting whether an individual is likely to respond favorably to a therapeutic agent or is unlikely to respond to a therapeutic agent (or, e.g., experience toxicity or other undesirable side effects), selecting a therapeutic agent to administer to an individual, and monitoring or determining an individual’s response to a therapy that has been or is being administered to an individual.

[0186] individual:As used in this application, "individual" and "subject" are used interchangeably to refer to a test subject or patient. An individual may be a mammal or a non-mammalian. In various embodiments, the individual is a mammal. A mammalian individual may be human or non-human. In various embodiments, the individual is a human. A healthy or normal individual is an individual for whom the disease or condition of interest cannot be detected by existing diagnostic methods.

[0187] Linear Regression: As used in this application, the term "linear regression" refers to an approach for modeling the relationship between one or more explanatory variables denoted by scalar dependent variables y and x. The case with one explanatory variable is called simple linear regression. The case with one or more explanatory variables is called multiple linear regression. In general, linear regression can be used to fit a prediction model to an observed dataset of y and x values. After developing such a model, the fitted model can be used to predict the value of y when given a value of y accompanied by an additional value of x.

[0188] Marker: As used in this application, “marker” and “biomarker” are used interchangeably to refer to target molecules (or analytes) that are signs of or indicate normal or abnormal processes of an individual or of a disease or other condition of an individual. More specifically, a “marker” or “biomarker” is an anatomical, physiological, biochemical, or molecular parameter associated with the presence of a specific physiological state or process, whether normal or abnormal, and, if abnormal, chronic or acute. Biomarkers can be detected and measured by various methods, including laboratory assays and medical imaging. In some embodiments, the biomarker is a target protein.

[0189] Modified:As used in this application, the terms “modify,” “modified,” “modified,” and any variations thereof, when used in relation to oligonucleotides, mean that at least one of the four constituent nucleotide bases (i.e., A, G, T / U, and C) of the oligonucleotide is an analog or ester of a naturally occurring nucleotide. In some embodiments, the modified nucleotide confers nuclease resistance to the oligonucleotide. In some embodiments, the modified nucleotide induces dominant hydrophobic interactions between the protein target and the aptamer, resulting in high binding efficiency and a stable co-crystal complex. Pyrimidines with substitutions at the C-5 position are examples of modified nucleotides. Modifications may include backbone modifications, methylation, specific base pair combinations such as the isobases isocitidine and isoguanidine, etc. Modifications may also include 3' and 5' modifications such as capping. Other modifications may include substitution with analogs of one or more naturally occurring nucleotides, internucleotide modifications such as those having uncharged bonds (e.g., methyl phosphonate, phosphotriester, phosphoamidate, carbamate, etc.) and those having charged bonds (e.g., phosphorothioate, phosphorodithioate, etc.), those having intercalators (e.g., acridine, psoralen, etc.), those containing chelating agents (e.g., metal, radioactive metal, boron, oxidizing metal, etc.), those containing alkylating groups, and those having modified bonds (e.g., alpha-anomer nucleic acids, etc.). Additionally, any hydroxyl group typically present in the sugar of the nucleotide may be replaced with a phosphonate group or a phosphate group; may be protected by a standard protecting group; or activated to prepare for additional linkage to an additional nucleotide or solid support.The 5' and 3' terminal OH groups may be phosphorylated or substituted with an amine, an organic capping moiety of about 1 to about 20 carbon atoms, a polyethylene glycol (PEG) polymer in the range of about 10 to about 80 kDa in some embodiments, a PEG polymer in the range of about 20 to about 60 kDa in some embodiments, or other hydrophilic or hydrophobic biological or synthetic polymers. In one embodiment, the modification is to the C-5 position of the pyrimidine. This modification may be produced through a direct amide linkage at the C-5 position or by other types of linkage.

[0190] Polynucleotides may also contain similar forms of ribose or deoxyribose sugars generally known in the art, including 2'-O-methyl-, 2'-O-allyl, 2'-fluoro-, or 2'-azido-ribose, carbon-cyclic sugar analogs, α-anomer sugars, epimer sugars, e.g., arabinose, xylose, or lyxose, pyranose sugars, furanose sugars, sedoheptulose, acyclic analogs, and basic nucleoside analogs such as methyl riboside. As mentioned above, one or more phosphodiester linkages may be replaced with alternative linkers. These alternative linkers include examples in which the phosphate is replaced with P(O)S("thioate"), P(S)S("dithioate"), (O)NR2("amidate"), P(O)R, P(O)OR', CO, or CH2("formacetal"), wherein each R or R' is independently a substituted or unsubstituted alkyl (1-20 C) including an H or ether (-O-) linkage, or optionally an aryl, alkenyl, cycloalkyl, cycloalkenyl, or arardyl. Not all linkages of the polynucleotide need to be identical. Substitution of similar forms of sugars, purines, and pyrimidines may be advantageous for designing the final product, as with alternative backbone structures such as, for example, polyamide backbones.

[0191] Nucleic acid:As used in this application, “nucleic acid,” “oligonucleotide,” and “polynucleotide” are used interchangeably to refer to polymers of nucleotides, including DNA, RNA, DNA / RNA hybrids, and modifications of these types of nucleic acids, oligonucleotides, and polynucleotides, wherein the attachment of various entities or moieties to nucleotide units at any position is included. The terms “polynucleotide,” “oligonucleotide,” and “nucleic acid” include double or single-stranded molecules and triple helix molecules. Since nucleic acid, oligonucleotide, and polynucleotide are broader terms than aptamers, the terms nucleic acid, oligonucleotide, and polynucleotide include polymers of nucleotides that are aptamers, but the terms nucleic acid, oligonucleotide, and polynucleotide are not limited to aptamers.

[0192] Ordinary least squares: As used in this application, "least squares method," "OLS," or "linear least squares" refers to a method for estimating unknown parameters in a linear regression model. This method minimizes the sum of the squared vertical distances between the observed response in the dataset and the response predicted by linear approximation. The resulting estimate can be expressed by a simple formula, particularly for a single regression variable on the right.

[0193] Prognose: "Prognosis," "prognosis," "prognosis," and variations thereof mean a prediction of the future course of a disease or condition of an individual with the disease or condition (e.g., prediction of patient survival), and these terms include an evaluation of the disease response during and / or after the administration of treatment or therapy to the individual.

[0194] SELEX:The terms “SELEX” and “SELEX process” are generally used interchangeably in this application to denote a combination of (1) the selection of an aptamer that interacts with a target molecule in a desirable manner, e.g., (2) binding with high affinity for a protein, along with the amplification of a selected nucleic acid. The SELEX process can be used to identify aptamers that have high affinity for a specific analyte, such as a target protein.

[0195] Sequence IdentityWith respect to two or more nucleic acid sequences, the sequence identity used in this application is a function of the number of identical nucleotide positions shared by the sequences, taking into account the number of gaps that must be introduced to optimize the alignment of two or more sequences and the length of each gap (i.e., %identity = number of identical positions / total number of positions of the reference sequence × 100). In default parameters, the comparison of sequences and the determination of the percentage of identity between two or more sequences can be performed using mathematical algorithms such as BLAST and Gapped BLAST programs (e.g., Altschul et al., J. Mol. Biol. 215:403, 1990; see BLASTN at www.ncbi.nlm.nih.gov / BLAST). For sequence comparison, typically one sequence serves as the reference sequence against which the test sequence is compared. When using a sequence comparison algorithm, the test sequence and the reference sequence are input into a computer, subsequence coordinates are specified as necessary, and sequence algorithm program parameters are specified. The sequence comparison algorithm then calculates the percentage of sequence identity of the test sequence(s) relative to the reference sequence based on specified program parameters. Optimal sequence alignment for comparison is determined, for example, by the local homology algorithm of Smith and Waterman, Adv. Appl. Math., 2:482, 1981; by the homology alignment algorithm of Needleman and Wunsch, J. Mol. Biol., 48:443, 1970; by similarity search of Pearson and Lipman, Proc. Nat'l. Acad. Sci. USA 85:2444, 1988; and by computerized implementations of these algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Dr.This can be performed by visual inspection (see Ausubel, FM et al., Current Protocols in Molecular Biology, pub. by Greene Publishing Assoc. and Wiley-Interscience (1987)). When describing the percentage of identity of nucleic acids, such as aptamers, as used in this application, the nucleic acid sequence is intended to be identical to the reference sequence, except that the sequence is, for example, at least about 95% identical to the reference nucleotide sequence, and the nucleic acid sequence may contain up to 5 point mutations per 100 nucleotides of the reference nucleic acid sequence. In other words, to obtain the desired nucleic acid sequence, so that the sequence is at least about 95% identical to the reference nucleic acid sequence, up to 5% of the nucleotides in the reference sequence may be deleted or substituted with other nucleotides, or some nucleotides, up to 5% of the total number of nucleotides in the reference sequence, may be inserted into the reference sequence (referred to as insertion in this application). These mutations in the reference sequence to generate the desired sequence may occur individually between nucleotides of the reference sequence or interspersed among one or more adjacent groups within the reference sequence at the 5' or 3' terminal positions of the reference nucleotide sequence, or at any position between these terminal positions.

[0196] SOMAmer:In this application, the terms somamer or somamer reagent refer to aptamers with improved off-rate characteristics. SOMAmer reagents are alternatively referred to as Slow Off-Rate Modified Aptamers and may be selected via the improved SELEX method described in US Publication No. 20090004667 under the heading "Method for Generating Aptamers with Improved Off-Rates," the entirety of which is incorporated by reference. In some embodiments, a slow off-rate aptamer (including an aptamer comprising at least one nucleotide having a hydrophobic modification) has an off-rate (t½) of ≥ 2 min, ≥ 4 min, ≥ 5 min, ≥ 8 min, ≥ 5 min, ≥ 10 min, ≥ 15 min, ≥ 30 min, ≥ 60 min, ≥ 90 min, ≥ 120 min, ≥ 150 min, ≥ 180 min, ≥ 210 min, or ≥ 240 min.

[0197] Substantially identical: The phrase “substantially equivalent” as used in this application indicates a sufficiently high degree of similarity between two numeric values ​​such that a person skilled in the art may consider the difference between the two values ​​to have little or no biological and / or statistical significance within the context of the characteristic measured by the value. More specifically, the difference between the two values ​​(e.g., the difference between a reference value and a regression model reference value) is preferably less than about 25%, or less than about 20%, or less than about 15%, or less than about 10%, or less than about 5%, or less than about 4%, or less than about 3%, or less than about 2.5%, or less than about 2%, or less than about 1%.

[0198] Target molecule:"Target," "Target molecule," and "Analyte" are used interchangeably in this application to refer to any molecule of interest that may be present in a sample. These terms include any minor modification of a specific molecule, e.g., in the case of proteins, e.g., minor modification of the amino acid sequence, formation of disulfide bonds, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, e.g., conjugation with a labeling component that does not substantially alter the identity of the molecule. "Target molecule," "Target," and "Analyte" represent a set of copies of one type or species of molecule or multiple molecular structure. "Target molecule," "Target," and "Analyte" represent one or more types or species of molecule or multiple molecular structures. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimics, viruses, pathogens, toxic substances, substrates, metabolites, transitional state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and fragments or parts of the foregoing. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as a "target protein."

[0199] The above and other objects, features, and advantages of the present invention will become more apparent from the following detailed description with reference to the accompanying drawings.

[0200] Methods and materials similar to or equivalent to those described in this application may be used to practice or test the present disclosure, but suitable methods and materials are described below. Furthermore, materials, methods, and examples are illustrative only and are not intended to be limiting.

[0201] Detection and measurement of analytes and analyte levels

[0202] The analyte level for the analyte described in this application can be detected using any various known analytical methods. In one embodiment, the analyte level is detected using a capture reagent. In various embodiments, the capture reagent may be exposed to the analyte in solution or may be exposed to the analyte while the capture reagent is immobilized on a solid support. In some embodiments, the capture reagent comprises a second feature and a reactive feature on the solid support. In these embodiments, the capture reagent may be exposed to the analyte in solution, and then the feature on the capture reagent may be used in conjunction with the second feature on the solid support to immobilize the analyte on the solid support. The capture reagent is selected according to the type of analysis to be performed. Capture reagents include, but are not limited to, aptamers, antibodies, adnectins, ankyrins, other antibody mimics and other protein scaffolds, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-coupled receptors, affibodies, nanobodies, imprinted polymers, avimers, peptide mimics, hormone receptors, cytokine receptors and synthetic receptors, and include modifications and fragments of any of these.

[0203] In some embodiments, the analyte level is detected using an analyte / capture reagent complex.

[0204] In some embodiments, the analyte level is derived from the analyte / capture reagent complex and is detected indirectly, for example, as a result of a reaction that follows an analyte / capture reagent interaction but depends on the formation of the analyte / capture reagent complex.

[0205] In some embodiments, the analyte level is detected directly from the analyte of the biological sample.

[0206] In some embodiments, analytes are detected using a multiplexed format that allows for the simultaneous detection of two or more analytes in a biological sample. In some embodiments of the multiplexed format, capture reagents are immobilized directly or indirectly, covalently or non-covalently, at distinct locations on a solid support. In some embodiments, multiple formats use individual solid supports, each having a unique capture reagent associated with the solid support, such as, for example, quantum dots. In some embodiments, individual devices are used for the detection of each of the multiple analytes to be detected in the biological sample. Individual devices can be configured so that each analyte of the biological sample is processed simultaneously. For example, a microtiter plate can be used so that each well of the plate is used to analyze one or more of the multiple analytes to be detected in the biological sample.

[0207] In one or more embodiments described in this application, components of an analyte / capture reagent complex may be labeled to enable detection of analyte levels using fluorescent tags. In various embodiments, the fluorescent label may be conjugated to a capture reagent specific to any analyte described in this application using known techniques, and the corresponding analyte level may then be detected using the fluorescent label. Suitable fluorescent labels include rare earth chelates, fluorescein and derivatives thereof, rhodamine and derivatives thereof, dancil, allophycocyanin, PBXL-3, Qdot 605, lisamine, phycoerythrin, Texas Red, and other such compounds.

[0208] In some embodiments, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule comprises at least one substituted indolium ring system in which a substituent on the 3-carbon of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule comprises an AlexFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In some embodiments, the dye molecule comprises a first type and a second type dye molecule, such as two different AlexaFluor molecules. In some embodiments, the dye molecule comprises a first type and a second type dye molecule, and the two dye molecules have different emission spectra.

[0209] Fluorescence can be measured using various instruments compatible with a wide range of calibration formats. For example, spectrofluorometers are designed to analyze microquantification plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy by JR Lakowicz, Springer Science + Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

[0210] In one or more embodiments, a chemiluminescent tag may be optionally used to label components of the analyte / capture complex to enable detection at the analyte level. Suitable chemiluminescent materials include oxalyl chloride, rhodamine 6G, and Ru(BP)3 2+It includes any of the following: TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxyoxalate, aryl oxalate, acridinium ester, dioxetane, etc.

[0211] In some embodiments, the detection method comprises an enzyme / substrate combination that generates a detectable signal corresponding to an analyte level. Generally, the enzyme catalyzes a chemical alteration of the chromogenic substrate that can be measured using various techniques including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, HRPO (horseradish peroxidase), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, etc.

[0212] In some embodiments, the detection method may be a combination of fluorescence, chemiluminescence, radionuclides, and / or enzyme / substrate combinations that generate a measurable signal. In some embodiments, multiple mode signalings may have unique and advantageous properties in the analyte analysis format.

[0213] In some embodiments, analyte levels for the analytes described in this application may be detected using any analytical method including monocellular aptamer assays, multiplexed aptamer assays, monocellular or multiplexed immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc., and those discussed below.

[0214] Analyte level measurement using aptamer-based analysis

[0215] Assays for the detection and quantification of physiologically important molecules in biological and other samples are important tools in scientific research and medical fields. One class of such assays involves the use of microarrays containing one or more aptamers immobilized on a solid support. Each aptamer can bind to a target molecule with very high affinity in a highly specific manner. For example, see U.S. Patent No. 5,475,096 titled “Nucleic Acid Ligands”; U.S. Patents No. 6,242,246, 6,458,543, and 6,503,715, each titled “Nucleic Acid Ligand Diagnostic Biochips”. When the microarray comes into contact with a sample, the aptamers bind to each target molecule present in the sample to determine the level of the analyte corresponding to that analyte.

[0216] In one embodiment, the aptamer may comprise up to about 100 nucleotides, up to about 95 nucleotides, up to about 90 nucleotides, up to about 85 nucleotides, up to about 80 nucleotides, up to about 75 nucleotides, up to about 70 nucleotides, about 65 or fewer nucleotides, about 60 or fewer nucleotides, about 55 or fewer nucleotides, about 50 or fewer nucleotides, about 45 or fewer nucleotides, about 40 or fewer nucleotides, about 35 or fewer nucleotides, about 30 or fewer nucleotides, about 25 nucleotides, and up to about 20 nucleotides. In a related embodiment, the aptamer is of a length of about 25 to about 100 nucleotides (or about 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, It may be 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 nucleotide lengths) or about 25 to 50 nucleotide lengths (or about 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 nucleotide lengths).

[0217] Aptamers can be identified using any known method, including the SELEX process. Once identified, aptamers can be prepared or synthesized according to any known method, including chemical synthesis methods and enzymatic synthesis methods. In some embodiments, the aptamer comprises at least one nucleotide having a hydrophobic modification, such as a hydrophobic base modification, to allow hydrophobic contact with the target protein. In some embodiments, such hydrophobic contact contributes to greater affinity and / or slower off-rate binding by the aptamer. In some embodiments, the aptamer comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotides having a hydrophobic modification, wherein each hydrophobic modification may be the same as or different from the others. In some embodiments, the hydrophobic base modification is a C-5 modified pyrimidine. Non-limiting exemplary C-5 modified pyrimidines are described in this application and / or are known in the art.

[0218] In some assay formats, aptamers are immobilized on a solid support before contacting the sample. However, in certain situations, immobilizing aptamers prior to contact with the sample may not provide an optimal assay. For example, in some cases, pre-immobilization of aptamers can lead to inefficient mixing of the aptamer with the target molecule on the solid support surface, which may result in longer reaction times and consequently require a longer incubation period to allow the aptamer to efficiently bind to the target molecule. Furthermore, depending on whether a photoaptamer is used in the assay and the material used as the solid support, the solid support may tend to scatter or absorb the light used to influence the formation of covalent bonds between the photoaptamer and the target molecule. Moreover, depending on the method used, the detection of target molecules bound to their aptamers may be inaccurate because the surface of the solid support may also be exposed to and affected by the labeling agents used. Finally, the immobilization of aptamers on a solid support generally involves an aptamer preparation step (i.e., immobilization) before exposing the aptamers to a sample, and this preparation step can affect the activity or function of the aptamers.

[0219] Aptamer assays or “aptamer-based assay(s)” have also been described, in which aptamers can capture targets in solution and use a separation step designed to remove specific components of the aptamer-target mixture before detection (see, for example, U.S. Publication No. 2009 / 0042206 titled “Analysis of Multiple Test Samples”). The described aptamer assay method enables the detection and quantification of non-nucleic acid targets (e.g., protein targets) in test samples by detecting and quantifying nucleic acids (i.e., aptamers). The described method allows for the application of various nucleic acid techniques, including amplification, to a wider range of desired targets, including protein targets, by generating nucleic acid substitutes (i.e., aptamers) for detecting and quantifying non-nucleic acid targets.

[0220] Aptamers may be configured to enable the separation of assay components from an aptamer analyte complex (or photo-aptamer analyte covalent complex) and to allow the separation of aptamers for detection and / or quantification. In one embodiment, these constructs may include cleavable or releaseable elements within the aptamer sequence. In other embodiments, additional functions, such as labeled or detectable components, spacer components, or specific binding tags or immobilization elements, may be introduced into the aptamers. For example, the aptamer may include a cleavable moiety, a label, a spacer component separating the label, and a tag connected to the aptamer via the cleavable moiety. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker may be attached to a biotin moiety and a spacer section, may include an NHS group for the derivatization of an amine, and may be used to introduce a biotin group into the aptamer, thereby allowing the release of the aptamer later in the assay method.

[0221] In some embodiments, a homogeneous assay performed using all assay components in solution may not require separation of the sample and reagent prior to signal detection. This method is fast and easy to use.

[0222] In some embodiments, the signal generation method utilizes anisotropic signal changes resulting from the interaction between a fluorescently labeled capture reagent and its specific analyte target. When the labeled capture reacts with the target, the rate at which the rotational motion of the fluorescent dye attached to the complex changes the anisotropic value becomes much slower due to the increased molecular weight. By monitoring the isotropic change, the analyte in the solution can be quantitatively measured using binding events. Other methods include fluorescence polarization assays, molecular beacon methods, time-resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, etc.

[0223] An exemplary solution-based aptamer assay that can be used to detect analyte levels in a biological sample comprises: (a) preparing a mixture by contacting a biological sample with an aptamer comprising a first tag and having specific affinity for the analyte, wherein an aptamer affinity complex is formed when the analyte is present in the sample; (b) exposing the mixture to a first solid support comprising a first capture element and causing the first tag to bind to the first capture element; (c) removing any component of the mixture not associated with the first solid support; (d) attaching a second tag to the analyte component of the aptamer affinity complex; (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support comprising a second capture element and causing the second tag to associate with the second capture element; (g) removing any non-complex aptamer from the mixture by splitting the non-complex aptamer from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the analyte by detecting the aptamer component of the aptamer affinity complex. For example, the protein concentration or level of the sample may be expressed in RFU (relative fluorescence units), which may be the product of detecting the aptamer component of the aptamer affinity complex (e.g., aptamers complexed with a target protein form an aptamer affinity complex). That is, in the case of an aptamer-based assay, the protein concentration or level is correlated with the RFU.

[0224] A non-limiting exemplary method for detecting analytes in biological samples using aptamers is described in Kraemer et al., PLoS One 6(10): e26332.

[0225] Determination of analyte levels using immunoassays

[0226] Immunoassays are based on the response of antibodies to corresponding targets or analytes and can detect analytes in samples depending on the specific assay format. To enhance the specificity and sensitivity of immunoreactivity-based assays, monoclonal antibodies and their fragments are often used due to specific epitope recognition. Polyclonal antibodies have been successfully used in various immunoassays because they exhibit increased affinity for targets compared to monoclonal antibodies. Immunoassays are designed for use with a wide range of biological sample substrates. Immunoassay formats are designed to provide qualitative, semi-quantitative, and quantitative results.

[0227] Quantitative results are generated using a standard curve created with the known concentration of the specific analyte to be detected. The response or signal of the unknown sample is plotted on the standard curve, and the amount or level corresponding to the target of the unknown sample is established.

[0228] Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of analytes. This method relies on attaching labels to analytes or antibodies, and the label components directly or indirectly contain enzymes. ELISA tests can be formulated for direct, indirect, competitive, or sandwich detection of analytes. Other methods include, for example, radioisotopes (I 125 It relies on labels such as ) or fluorescence. Additional techniques include, for example, agglutination, nephelometrium, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, etc. (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0229] Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Examples of procedures for detecting analytes include quantitative methods that allow for size and peptide level differentiation, such as gel electrophoresis, capillary electrophoresis, and planar electrochromatography following analyte immunoprecipitation.

[0230] The method for detecting and / or quantifying detectable labels or signal-generating substances depends on the characteristics of the label. Reaction products catalyzed by appropriate enzymes (where the detectable label is an enzyme; see above) may be fluorescent, luminescent, or radioactive without limitation, or may absorb visible light or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, without limitation, X-ray films, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.

[0231] Any detection method can be performed in any format that allows for the appropriate preparation, processing, and analysis of the reaction. This can be, for example, in a multi-well assay plate (e.g., 96-well or 386-well) or by using any suitable array or microarray. Stock solutions for various agents can be prepared manually or robotically, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample reading, data collection, and analysis can be performed robotically using commercial analysis software, robotics, and detection instruments that detect detectable labels.

[0232] Determination of analyte levels using gene expression profiling

[0233] Measuring mRNA in a biological sample may be used as a surrogate for detecting the level of a corresponding protein in a biological sample in some embodiments. Accordingly, in some embodiments, the analyte or panel of analytes described in this application may be detected by detecting appropriate RNA.

[0234] In some embodiments, mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed by qPCR). RT-PCR is used to generate cDNA from mRNA. The cDNA can be used in qPCR analysis to generate fluorescence as the DNA amplification process proceeds. Compared to a standard curve, qPCR can produce absolute measurements, such as the number of mRNA copies per cell. Northern blot, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis were all used to measure mRNA expression levels in samples. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0235] Measurement of analyte levels using the Mass Spectrometry Method

[0236] Mass spectrometers of various configurations can be used to detect analyte levels. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, a mass spectrometer has key components such as a sample inlet, an ion source, a mass spectrometer, a detector, a vacuum system, an instrument control system, and a data system. The differences between the sample inlet, the ion source, and the mass spectrometer generally define the instrument type and function. For example, the inlet can be a capillary column liquid chromatography source, or a direct probe or stage, such as those used for substrate-assisted laser desorption. Common ion sources are, for example, nanosprays and microsprays, or electrosprays, which include substrate-assisted laser desorption. Common mass spectrometers include quadrupole mass filters, ion trap mass spectrometers, and time-of-flight mass analyzers. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, New York (2000)).

[0237] Protein analytes and analyte levels can be detected and measured via one of the following: Electron Spray Ionization Mass Spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, Substrate-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS), Surface-Enhanced Laser Desorption / Ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS), Silicon Desorption / Ionization (DIOS), Secondary Ion Mass Spectrometry (SIMS), Quadrupole Time of Flight (Q-TOF), Tandem Time of Flight (TOF / TOF) technology known as Ultraflex III TOF / TOF, Atmospheric Pressure Chemical Ionization Mass Spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS) N, Atmospheric Pressure Photoionization Mass Spectrometer (APPI-MS), APPI-MS / MS and APPI-(MS) N , quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0238] Sample preparation strategies are used to label and enrich samples prior to mass spectroscopic characterization of protein analytes and determination of analyte levels. Labeling methods include, but are not limited to, isobaric tags (iTRAQ) for relative and absolute quantification and stable isotope labeling (SILAC) using amino acids in cell culture. Capture reagents used to selectively enrich samples for candidate analyte proteins prior to mass spectroscopic analysis include, but are not limited to, aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-coupled receptors, affibodies, nanobodies, ankyrins, domain antibodies, polymers imprinted with alternative antibody scaffolds (e.g., diabodies, etc.), avimers, peptidomimetic compounds, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, and modifications and fragments thereof.

[0239] Kit

[0240] Any combination of analytes described in this application may be detected, for example, using a suitable kit for use in carrying out the method described in this application. Additionally, any kit may contain one or more detectable labels described in this application, such as fluorescent moiety, etc.

[0241] Method for normalizing analyte measurements in biological substrates

[0242] A method for normalizing analyte measurements in biological substrates is provided in this application.

[0243] In certain embodiments, a method for developing a synthetic dilution model is provided. The synthetic dilution model can be used to determine the relative dilution of one or more biological samples, such as urine. For example, see FIG. 10. Then, the relative dilution is used to normalize multiple samples to minimize differences in sample concentration (e.g., which may arise from differences in the hydration levels of subjects) and to detect differences in analyte levels caused by factors other than sample concentration. These factors other than sample concentration that may affect analyte levels include, but are not limited to, increases and decreases in analyte expression, changes in analyte stability, and changes in the rate or amount of analyte secreted. In some embodiments, these differences in analyte levels may indicate a disease or condition or the likelihood of a disease or condition developing.

[0244] In certain embodiments, a synthetic dilution model is developed by the exemplary method illustrated in FIGS. 18a and 18b. The levels of an analyte are measured in a first biological sample dilution series and a second biological sample dilution series. The first and second biological samples may be biological samples of the same individual at different points in time or biological samples of different individuals. The dilution series includes levels of analytes at various dilutions of the sample. A model is created based on the first dilution series. A reference value for the second dilution series is selected. In some embodiments, the selected reference value for the second dilution series is, for example, near the midpoint of the second dilution series in a part of the series that appears substantially linear. The reference value for the second dilution series is translated by ΔX to a point in the model. The remaining points of the second dilution series (i.e., levels of analytes at various dilutions) are translated by the same amount ΔX to form an aligned synthetic translation. The synthetic dilution model is created by fitting a function to the aligned synthetic translation series. This synthetic dilution model can be used to determine the relative dilution of a subject's biological test sample.

[0245] In certain embodiments, a synthetic dilution model is developed by the exemplary method illustrated in FIGS. 19a and 19b. The levels of an analyte are measured in a first biological sample dilution series and a second biological sample dilution series. The first and second biological samples may be biological samples of the same individual at different points in time or biological samples of different individuals. The dilution series contains levels of the analyte at various dilutions of the sample. A model is created based on the first dilution series. A reference value for the model is selected. In some embodiments, the model reference value is the analyte level at a specific dilution of the model. The reference value of the model is then translated by an amount ΔX to a point in the second dilution series. The remaining values ​​of the model are translated by the same amount ΔX to form an aligned synthetic translational series. The synthetic dilution model is created by fitting a function to the linear synthetic translational series. This synthetic dilution model can be used to determine the relative dilution of a subject's biological test sample.

[0246] In certain embodiments, a synthetic dilution model is developed by the exemplary method illustrated in FIGS. 20a and 20b. The levels of an analyte are measured in a first biological sample dilution series and a second biological sample dilution series. The first and second biological samples may be biological samples of the same individual at different points in time or biological samples of different individuals. The dilution series contains the levels of the analyte at various dilutions of the sample. A reference value for the second dilution series is selected. In some embodiments, the selected reference value for the second dilution series is, for example, near the midpoint of the second dilution series in a portion of the series that appears substantially linear. The reference value for the second dilution series is translated by ΔX to the value of the first dilution series. The remaining points of the second dilution series (i.e., the levels of the analyte at various dilutions) are translated by the same amount ΔX to form an aligned synthetic translational series. The synthetic dilution model is generated by fitting a function to the linear synthetic translational series. This synthetic dilution model can be used to determine the relative dilution of a subject's biological test sample.

[0247] In certain embodiments, a synthetic dilution model is developed by the exemplary method illustrated in FIGS. 21a and 21b. The levels of an analyte are measured in a dilution series of a first biological sample and a dilution series of a second biological sample. The first and second biological samples may be biological samples of the same individual at different points in time or biological samples of different individuals. A model is generated based on the first dilution series. An arbitrary reference value, which is the analyte level at a dilution value, is selected. In some embodiments, the arbitrary reference value is the analyte level at a specific dilution not found in the second dilution series or the model. Then, one point in the mathematical model is translated by a positive ΔX to the arbitrary reference value, and one point in the second dilution series is translated by a positive ΔY to the arbitrary reference value. The remaining points in the second dilution series (i.e., levels of analyte at various dilutions) are translated by the same amount (ΔY), and the remaining values ​​in the model are translated by the same amount (ΔX) to form an aligned synthetic translation. The synthetic dilution model is generated by fitting a function to a linear synthetic translation series. This synthetic dilution model can be used to determine the relative dilution of a subject's biological test sample.

[0248] In certain embodiments, a synthetic dilution model is developed by the exemplary method illustrated in FIG. 22a. The level of an analyte is measured by a first biological sample dilution series and a second biological sample dilution series. The first and second biological samples may be biological samples of the same individual at different points in time or of different individuals. The dilution series illustrates the level of an analyte at various dilutions of the sample. An arbitrary reference value is selected that is the analyte level at a dilution value. In some embodiments, the arbitrary reference value is the analyte level at a specific dilution that is not found in the first dilution series or the second dilution series. Then, one point in the first dilution series is translated by a positive ΔX to the arbitrary reference value, and one point in the second dilution series is translated by a positive ΔY to the arbitrary reference value. The remaining points in the first dilution series (i.e., levels of analyte at various dilutions) are translated by the same amount, ΔX, and the remaining points in the second dilution series (i.e., levels of analyte at various dilutions) are translated by the same amount, ΔY, to form an aligned synthetic translation. A synthetic dilution model is generated by fitting a function to an aligned synthetic translational shift series. This synthetic dilution model can be used to determine the relative dilution of a subject's biological test sample.

[0249] In a specific embodiment, a synthetic dilution model is developed by the exemplary method illustrated in FIGS. 23a and 23b. The levels of an analyte are measured in a dilution series of a first biological sample and a dilution series of a second biological sample. The first and second biological samples may be biological samples of the same individual at different times or biological samples of different individuals. The dilution series plots the levels of analytes at various dilutions of the sample. The first model is generated based on the levels of analytes in the first dilution series, and the second model is generated based on the levels of analytes in the second dilution series. Then, the values ​​of the first model are translated by a positive ΔX to the values ​​of the second model. Then, the remaining values ​​of the first model are translated by the same positive ΔX to form an aligned synthetic translation. The synthetic dilution model is generated by fitting a function to the aligned synthetic translation series. This synthetic dilution model can be used to determine the relative dilution of a subject's biological test sample.

[0250] In certain embodiments, a synthetic dilution model is developed by the exemplary method illustrated in FIGS. 24a and 24b. The levels of an analyte are measured in a dilution series of a first biological sample and a dilution series of a second biological sample. The first and second biological samples may be biological samples of the same individual at different points in time or of different individuals. The dilution series illustrates the levels of analytes at various dilutions of the sample. The first model is created based on the analyte levels of the first dilution series, and the second model is created based on the analyte levels of the second dilution series. An arbitrary reference value, which is the analyte level at the dilution value, is selected. In some embodiments, the arbitrary reference value is the analyte level at a specific dilution that is not found in the first dilution series or the second dilution series. Then, a point in the first model is translated by a positive ΔX to the arbitrary reference value, and a point in the second model is translated by a positive ΔY to the arbitrary reference value. The remaining value of the first model is translated by the same amount (ΔX), and the remaining value of the second model is translated by the same amount (ΔY) to form an aligned synthetic translation. A synthetic dilution model is generated by fitting a function to an aligned synthetic translational shift series. This synthetic dilution model can be used to determine the relative dilution of a subject's biological test sample.

[0251] Any suitable model may be used in the method described in this application, including the model generated at the analyte level of the first and / or second dilution series and the model used to generate the synthetic dilution model. Exemplary models that may be used in the method include, but are not limited to, linear regression models, LOESS curve fitting models, nonlinear regression models, spline fitting models, mixed-effects regression models, fixed-effects regression models, generalized linear models, substrate decomposition models, and / or four-parameter logistic regression (4PL) models. The model generated at the analyte level in the first and / or second dilution series may be the same or different. Similarly, the model used to generate the synthetic dilution model may be the same or different from one or more models generated at the analyte level. Those skilled in the art may select a suitable model depending on the specific application, and such models are well known in the art.

[0252] In some embodiments, the relative dilution of a biological test sample may be determined after developing a synthetic dilution model or using a synthetic dilution model developed previously. In some of these embodiments, the level of at least one analyte from the biological test sample is translated horizontally into a synthetic dilution model developed for one or more analytes. This allows the relative dilution of the biological test sample to be determined. In some embodiments, the synthetic dilution model may be developed using a set of analytes, for example, at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, or 200 analytes. The relative dilution of the biological test sample is then determined for a subset of analytes or for each analyte. In some embodiments, the relative dilution of the biological test sample is then determined using the average, mean, median, etc. of the relative dilutions based on each analyte. In some embodiments, an analyte is selected in which levels are not expected to differ between individuals, such as between individuals with or without a disease or condition.

[0253] Computer devices, methods, and software

[0254] In one embodiment, the system further includes one or more devices for providing input data to one or more processors. The system further includes memory for storing a data set of ranked data elements.

[0255] In another embodiment, a device for providing input data includes a detector for detecting characteristics of data elements, such as a mass spectrometer or a gene chip reader.

[0256] The system may additionally include a database management system. User requests or queries may be formatted in an appropriate language that can be understood by the database management system, which processes the queries to extract relevant information from the database of the training set.

[0257] The system may be connected to a network in which a network server and one or more clients are connected. The network may be a Local Area Network (LAN) or a Wide Area Network (WAN) known in the art. Preferably, the server includes hardware necessary to execute a computer program product (e.g., software) to access database data for processing user requests.

[0258] The system is an operating system for executing commands of the database management system (e.g., UNIX ® It may include Linux. In one embodiment, the operating system may operate on a global communication network such as the Internet and may connect to such a network by utilizing a global communication network server.

[0259] The system may include one or more devices comprising a graphical display interface that includes interface elements such as buttons, pull-down menus, scroll bars, and fields for entering text, which are routinely found in graphical user interfaces known in the art. Requests entered into the user interface may be transmitted to applications of the system for formatting to retrieve relevant information from one or more system databases. Requests or queries entered by the user may be composed in an appropriate database language.

[0260] A graphical user interface can be generated by graphical user interface code as part of an operating system and can be used to input data and / or display input data. The results of the processed data may be displayed on the interface, printed on a printer communicating with the system, stored in a memory device, transmitted over a network, or provided in the form of a computer-readable medium.

[0261] The system may communicate with an input device to provide the system with data regarding data elements (e.g., expression values). In one embodiment, the input device may include a gene expression profiling system including, for example, a mass spectrometer, a gene chip, or an array reader.

[0262] A computer system may be a standalone system or part of a computer network that includes a server and one or more databases.

[0263] Some embodiments described in this application may be implemented to include a computer program product. The computer program product may include a computer-readable medium having computer-readable program code implemented on the medium to enable an application to be executed on a computer having a database.

[0264] As used in this application, the term "computer program product" refers to a set of instructions organized in the form of natural or programming language statements contained in a physical medium of any nature (e.g., written, electronic, magnetic, optical, or others) and capable of being used with a computer or other automated data processing system. Such programming language statements cause the computer or data processing system to operate according to specific contents of the statements when executed by the computer or data processing system. Computer program products include, but are not limited to, programs of source and object code and / or test or data libraries embedded in a computer-readable medium. Additionally, computer program products that enable a computer system or data processing equipment device to operate in a predetermined manner may be provided in various forms, including but not limited to original source code, assembly code, object code, machine code, encrypted or compressed versions, and any and all equivalents.

[0265] Although various embodiments have been described as methods or devices, it should be understood that the embodiments may be implemented through code combined with a computer, for example, code residing in or accessible by a computer. For example, software and databases may be utilized to implement many of the methods discussed above. Accordingly, in addition to embodiments achieved by hardware, these embodiments may be achieved through the use of manufactured articles comprising computer-readable media having computer-readable program code implemented therein that enables the functions disclosed herein. Accordingly, it is desirable to consider the embodiments as being protected by this patent in the means of program code as well.

[0266] Additionally, the embodiments may be implemented as code stored in almost any type of computer-readable memory, including, without limitation, RAM, ROM, magnetic media, optical media, or magneto-optical media. Much more generally, the embodiments may be implemented in software or hardware, including but not limited to software running on a general-purpose processor, microcode, a programmable logic array (PLA), or an application-specific integrated circuit (ASIC), or any combination thereof.

[0267] Furthermore, it is conceived that the embodiments can be achieved not only as computer signals implemented as carrier waves but also as signals propagated through a transmission medium (e.g., electrical and optical). Accordingly, the various information described above can be formatted into a structure such as a data structure and transmitted as an electrical signal through a transmission medium or stored on a computer-readable medium.

[0268] FIG. 25 illustrates an exemplary system architecture (2500) in which examples of the present disclosure may be implemented. The system architecture (2500) includes a diagnostic machine (2502), a robot (2504), a network (2506), a data storage (2508), a server machine (2510), a web server (2520), an application server (2522), an analyte variability control system (2530), a biological sample analyzer module (2540), a composite relative dilution model generator module (2550), a relative dilution prediction module (2560), a client device (2570), analyte measurement data (2580), and a composite dilution model (2590).

[0269] The diagnostic machine (2502) is a diagnostic device that receives and analyzes one or more biological samples. For example, various types of biological samples may include, but are not limited to, urine, blood, plasma, serum, cerebrospinal fluid, or generally other types of biological fluids. The diagnostic machine (2502) may collect or generate analyte measurement data (2580) based on the analysis of the biological samples, and such data may be stored or transmitted to one or more other internal or external machines for processing, analysis, or use. Additionally, the diagnostic machine (2502) is generally an Affymetrix ® or Illumina ® It may refer to one or more parts of diagnostic equipment involved in processing biological samples, such as microarray machines.

[0270] In one example, the robot (2504) drives, processes, and / or transfers biological samples within a diagnostic machine (2502) or between multiple diagnostic machines (2502) as part of biological sample processing. For example, the robot (2504) is a TECAN that generally provides automated or semi-automatic processing of biological samples in a laboratory setting. ® It may include, but is not limited to, a robot (2502) or any other robot machine.

[0271] In one example, the diagnostic machine (2502) communicates with one or more data storage(s) (2508) and one or more server machine(s) (2510) through one or more network(s) (2506). The network (2506) may generally be a public network (e.g., the Internet), a private network (e.g., a LAN (Local Area Network) or a WAN (Wide Area Network)), or a combination thereof. In one example, the network (2506) may include the Internet and / or one or more intranets, wired networks, wireless networks, and / or other suitable types of communication networks. In one example, the network (2506) may include a wireless telecommunications network (e.g., a cellular phone network) configured to communicate with other communication networks, such as the Internet.

[0272] The data store (2508) is a persistent storage capable of storing various types of data, such as alphanumeric text, audio, video and / or image content. In some examples, the data store (2508) may be a network-attached file server, whereas in other examples, the data store (2508) may be some other type of persistent storage, such as an object-oriented database, a relational database, etc.

[0273] In one example, the diagnostic machine (2502) may store and access various types of data, including analyte measurement data (2580), through a network (2506). The diagnostic machine (2502) may also store and access such data in one or more local data storage(s) (2508) associated with the diagnostic machine (2502) (not shown) and / or in one or more data storage(s) (2508) local to the server machine (2510) or one or more other computing systems (not shown). In one example, the analyte measurement data (2580) may include, but is not limited to, relative fluorescence unit measurements for each of a plurality of analytes or proteins associated with one or more biological samples.

[0274] The data repository (2508) may also receive, store, and provide a synthetic dilution model (2590) for use in controlling the variability of analytes between samples in a complex biological substrate. The synthetic dilution model (2590) may be generated from analyte measurement data (2580) or provided directly or indirectly from other sources. Additionally, the synthetic dilution model (2590) may be used to determine the predicted relative dilution for a biological sample type, which may then be used to adjust the relative dilution or other aspects of one or more different, added, or newly received biological samples.

[0275] The server machine (2510) may generally be specialized diagnostic hardware, a rackmount server, a personal computer, a portable digital terminal, a mobile phone, a laptop computer, a tablet computer, a netbook, a desktop computer, or any combination thereof. The server machine (2510) may include a web server (2520), an application server (2522), and a script detection system (2530). In some examples, each of the web server (2520), the application server (2522), and / or the script detection system (2530) may be run on one or more different server machine(s) (2510).

[0276] The web server (2520) may provide text, audio, video, and image content from a server machine (2510) and / or a data store (2508) to one or more client device(s) (2570). The web server (2520) may also provide web-based application services and business logic to the client device(s) (2570). The client device(s) (2570) may use an application, such as a web browser, to find, access, and consume various forms of content and services from the web server (2520). The web server (2520) may receive text, such as existing and new analytical measurement data (2580), audio, video, and image content from the client device(s) (2570) that is stored in one or more data stores (2508) for purposes that may include analyzing, converting, processing, sustaining, and distributing such content.

[0277] In one example, the web server (2520) is coupled to one or more application server(s) (2522) that provide applications and services to client device(s) (2570) directly or with the help of the web server (2520). For example, the web server (2520) may provide access to one or more specialized software applications in the field of biotechnology to the client device(s) (2570). Such functionality may also be provided, for example, as one or more different web applications, standalone applications, computer systems, plugins, web browser extensions, and application programming interfaces (APIs). In some examples, plugins and extensions may be referred to individually or collectively as add-ons.

[0278] The client device (2570) may be a personal computer (PC), a laptop, a mobile phone, a tablet computer, or any other computing device in general. The client device (2570) may run an operating system (OS) that manages the hardware and software of the client device (2570). A browser (not shown) may run on the client device (2570). The browser may be a web browser capable of accessing services and / or content provided by the server machine (2510), a web server (2520), an application server (2522), a data storage (2508), etc. Additionally, other types of computer programs and computer scripts may also run on the client device (2570). For example, the client device (2570) may use an application (i.e., "app") to access such content or to communicate with the server machine (2510) without visiting or otherwise using web pages.

[0279] In one example, the functions and features of the server machine (2510) may also be performed wholly or partially by the client device (2570). Additionally, functions assigned to a specific component may be performed by different components or multiple components working together. The server machine (2510) may also be accessed as a service provided to other systems or devices through an application programming interface, and thus is not limited to use on a website.

[0280] The server machine (2510) also includes an analyte variability control system (2530). The analyte variability control system (2530) generally refers to specialized computer hardware and / or software for controlling analyte variability between samples in complex biological substrates. For example, the analyte variability control system (2530) may receive and analyze biological samples associated with different subjects, generate a synthetic dilution model (2590) for each of a plurality of analytes within the biological samples, determine a relative dilution prediction model for the biological sample type based on the generated plurality of synthetic dilution models (2590), receive and analyze a new biological sample of the biological sample type, and adjust one or more aspects of the newly received biological sample based on the relative dilution prediction model determined for the biological sample type.

[0281] Examples of services provided by the analyte variability control system (2530) include “Example 1: Generation of an empirical substrate-specific standard curve,” “Example 2: Application of an empirical substrate-specific standard curve,” and “Example 3: Design of a pilot study to characterize and normalize changes in analyte signals due to the hydration status of human subjects,” which are further described in the drawings and the following paragraphs related to the present disclosure.

[0282] In one example, the analyte variability control system (2530) includes a biological sample analyzer module (2540), a synthetic dilution model generator module (2550), and a relative dilution prediction module (2560). In another example, the functions associated with the biological sample analyzer module (2540), the synthetic dilution model generator module (2550), and the relative dilution prediction module (2560) may be combined, divided, and configured in various arrangements.

[0283] In one example, a biological sample analyzer module (2540) can generally receive and analyze analyte measurement data (2580) associated with biological samples from different subjects. A synthetic dilution model generator module (2550) can generally perform one or more steps to generate a synthetic dilution model (2590) for each analyte of a selected analyte group from the analyte measurement data (2580). A relative dilution prediction module (2560) can generally determine a predicted relative dilution for a biological sample type based on a selected synthetic dilution model (2590) generated from the biological sample. Then, the relative dilution prediction module (2560) can generally adjust one or more aspects of the analyte measurement data (2580) associated with different and / or newly received biological samples based on the predicted relative dilution determined from the selected synthetic dilution model (2590).

[0284] FIG. 26 is a flowchart illustrating the generation of a synthetic dilution model for each of a plurality of analytes present in different biological samples according to an example of the present disclosure. An exemplary method (2600) may be performed by processing logic that may include special hardware (circuits, dedicated logic, programmable logic, microcode, etc.), special software (instructions executed on a general-purpose computer system, dedicated machine, or hardware processing device, etc.), firmware, or a combination thereof.

[0285] Generally, support for exemplary methods (2600) is provided throughout the present disclosure in relation to non-limiting examples, “Example 1: Generation of an empirical substrate-specific standard curve”, “Example 2: Application of an empirical substrate-specific standard curve” and “Example 3: Design of a pilot study to characterize and normalize changes in analyte signals due to the hydration status of human subjects” described above.

[0286] The method (2600) begins at block (2602) when the analyte variability control system (2530) receives analyte measurement data (2580) associated with a different biological sample. In one example, the biological sample analyzer module (2540) of the analyte variability control system (2530) receives the analyte measurement data (2580) from any one of a diagnostic machine (2502), a data repository (2508), a server machine (2510), a client device (2570), or one or more other computer systems or storage devices.

[0287] In one example, the biological sample analyzer module (2540) receives analyte measurement data (2580) generated from the diagnostic machine (2502). For example, the diagnostic machine (2502) may receive biological samples provided from a number of different human subjects. These biological samples may include urine, blood, plasma, serum, cerebrospinal fluid, or generally other types of biological fluids.

[0288] In one example, the diagnostic machine (2502) measures an analyte or protein associated with each biological sample from different subjects to generate analyte measurement data (2580) measured in relative fluorescence units (RFU) or any other appropriate measurement unit(s). Thus, in some examples, the analyte measurement data (2580) may include respective relative fluorescence unit measurements for each of a plurality of proteins associated with one or more different biological samples. Such analyte measurement data (2580) may be stored in a data repository (2508) or any other permanent repository and may later be provided to one or more other computer systems, such as a server machine (2510).

[0289] In one example, the biological sample analyzer module (2540) may convert, adjust, and / or process the analyte measurement data (2580) (e.g., raw RFU data) at any number of steps to prepare for further processing by the analyte variability control system (2530). For example, the raw or partially processed analyte measurement data (2580) may be purified, formatted, normalized, calibrated, or manipulated at one or more other steps. In another example, the analyte measurement data (2580) received by the biological sample analyzer module may be pre-processed upon receipt and prepared for analysis without further pre-processing or manipulation.

[0290] In block (2604), the analyte variability control system (2530) analyzes each analyte measurement for each of a plurality of selected analytes in the analyte measurement data. In one example, the biological sample analyzer module (2540) of the analyte variability control system (2530) analyzes each of a plurality of selected analytes in the analyte measurement data (2508). For example, the biological sample analyzer module (2540) may analyze each analyte from a subset of available measured analytes from a plurality of different biological samples. In one example, the biological sample analyzer module (2540) may analyze the analyte measurement data (2580) for each analyte across different biological samples individually or in parallel (e.g., two or more analytes are processed simultaneously across different biological samples).

[0291] In one example, the biological sample analyzer module (2540) analyzes each analyte in preparation for generating a synthetic dilution model (2590) corresponding to each analyte. For example, the biological sample analyzer module (2540) may analyze analytes for multiple biological samples that are serially diluted. In one example, the biological sample analyzer module (2540) determines a sample with a maximum linear dilution range for the analyte and then fits a weighted linear regression model to the data of the linear dilution range. The associated regression line can be used as a reference to which other samples are fitted.

[0292] In block (2606), the analyte variability control system (2530) generates a synthetic dilution model (2590) for each selected analyte based on the analysis performed in block (2604). In one example, the synthetic dilution model generator module (2550) of the analyte variability control system (2530) translates individual analyte measurement data (2508) for the analyte based on the corresponding reference dilution model generated by the biological sample analyzer module (2540). In one example, the synthetic dilution model generator module (2550) then fits a 4-parameter logistic function (4PL) to the matched data for the analyte.

[0293] In one example, the synthetic dilution model generator module (2550) generates a corresponding synthetic dilution model (2590) for an analyte based on the 4PL associated with the analyte. The synthetic dilution model generator module (2550) further generates synthetic dilution models (2590) for each remaining analyte in a selected subset of analytes found in the analyte measurement data (2508). The synthetic dilution models (2590) generated for the selected subset of analytes can be used to adjust aspects of additional, different newly received biological samples to control analyte variability between samples.

[0294] FIG. 27 is a flowchart illustrating the generation of a synthetic dilution model for each of a plurality of analytes present in different biological samples according to an example of the present disclosure. An exemplary method (2700) may be performed by processing logic that may include special hardware (circuits, dedicated logic, programmable logic, microcode, etc.), special software (instructions executed on a general-purpose computer system, a dedicated machine, or a hardware processing device), firmware, or a combination thereof.

[0295] Generally, support for exemplary methods (2700) is provided throughout the present disclosure in relation to the non-limiting examples described above, “Example 1: Generation of an empirical substrate-specific standard curve”, “Example 2: Application of an empirical substrate-specific standard curve”, and “Example 3: Design of a pilot study to characterize and normalize changes in analyte signals due to the hydration status of human subjects”.

[0296] The method (2700) begins at block (2702) when the analyte variability control system (2530) receives analyte measurement data (2580) associated with different biological samples of the same biological sample type. In one example, the biological sample analyzer module (2540) of the analyte variability control system (2530) receives analyte measurement data (2580) from a data repository (2508). Generally, such analyte measurement data may include relative fluorescence units (RFU) measurements for each of a number of different proteins detected in each of a number of different biological samples of the same biological sample type collected from different subjects.

[0297] In block (2704), the analyte variability control system (2530) analyzes each analyte measurement for each of a plurality of selected analytes in the analyte measurement data (2580). In one example, the biological sample analyzer module (2540) of the analyte variability control system (2530) analyzes each of the plurality of selected analytes from the analyte measurement data (2508) received in block (2704). For example, a subset of analytes available from the analyte measurement data (2508) may be selected to generate a corresponding synthetic dilution model (2590) to be used to control analyte variability between samples in a complex biological substrate.

[0298] In block (2706), the analyte variability control system (2530) generates a reference dilution model for each selected analyte. In one example, the biological sample analyzer module (2540) generates a reference dilution model for each selected analyte based on the analysis performed in block (2704). For example, in one non-limiting example, the biological sample analyzer module (2540) can determine which of the multiple samples associated with each analyte has the maximum linear dilution range. The biological sample analyzer module (2540) can fit a weighted linear regression model to the data of the linear dilution range. Additionally, the biological sample analyzer module (2540) can generate a weighted linear regression model for each of the other selected analytes, which is similarly used at least partially to generate a corresponding synthetic dilution model (2590) associated with each selected analyte. Further descriptions and examples are provided in the disclosure in at least non-limiting examples, such as, for example, “Example 1: Generation of an Empirical Substrate-Specific Standard Curve”.

[0299] In block (2708), the analyte variability control system (2530) translates the respective analyte measurement data for each selected analyte based on a corresponding reference dilution model generated for each analyte. In one example, the synthetic dilution model generator module (2550) of the analyte variability control system (2530) translates the respective analyte measurement data (2508) for each selected analyte based on a corresponding reference dilution model generated by the biological sample analyzer module (2540). Further description and examples are provided in the present disclosure, for example, at least in non-limiting examples, in “Example 1: Generation of an Empirical Substrate-Specific Standard Curve”.

[0300] In block (2710), the analyte variability control system (2530) generates a synthetic dilution model (2590) for each selected analyte based on translated analyte measurement data. In the example, the synthetic dilution model generator module (2550) fits a 4-parameter logistic function (4PL) to the translated data for each selected analyte. For example, the synthetic dilution model generator module (2550) generates a corresponding synthetic dilution model (2590) for each selected analyte based on the 4PL. Thus, the synthetic dilution model generator module (2550) generates a set or series of synthetic dilution models (2590) including a composite solution module for each selected analyte.

[0301] Further descriptions and examples of describing block (2710) and applying the generated synthetic dilution model (2590) to other biological samples are described in the present disclosure, for example, in the non-limiting examples “Example 1: Generation of an empirical substrate-specific standard curve” and “Example 2: Application of an empirical substrate-specific standard curve”.

[0302] In some examples, the synthetic dilution model generator module (2550) further generates one or more reports containing information and details regarding various aspects related to processing analyte measurement data (2580) and generating a synthetic dilution model (2590). For example, these generated reports may include descriptions of various findings, including potential or actual data anomalies in the analyte measurement data (2580), preprocessing performed on the analyte measurement data (2580), analysis of the analyte measurement data (2580), generation of the synthetic dilution model (2590), and determination of predicted relative dilution. In some examples, the analyte variability control system (2530) may store the generated reports in a data repository (2508) and provide the reports and the corresponding analyte measurement data (2580) and / or synthetic dilution model (2590) to a client device (2570).

[0303] FIG. 28 is a flowchart illustrating the application of a synthetic dilution model to a new biological sample to predict the relative dilution of the new sample according to an example of the present disclosure. An exemplary method (2800) may be performed by processing logic that may include special hardware (circuits, dedicated logic, programmable logic, microcode, etc.), special software (instructions executed on a general-purpose computer system, a dedicated machine, or a hardware processing device), firmware, or a combination thereof.

[0304] Generally, support for exemplary methods (2800) is provided throughout this disclosure in relation to non-limiting examples, such as “Example 1: Generation of an empirical substrate-specific standard curve”, “Example 2: Application of an empirical substrate-specific standard curve” and “Example 3: Design of a pilot study to characterize and normalize changes in analyte signals due to the hydration status of human subjects” described above.

[0305] The method (2800) begins at block (2802) when the analyte variability control system (2530) receives analyte measurement data for an analyte of a biological sample. In the example, the biological sample analyzer module (2540) of the analyte variability control system (2530) may receive analyte measurement data (2580) of one or more new biological samples for analysis. For example, the new biological sample may describe analyte measurement data (2580) of a biological sample that is not typically included in or considered in the creation of a synthetic dilution model (2590) for analytes of a biological sample type (e.g., urine). As such, the new biological sample is different from the biological sample used to create the synthetic dilution model, and this relevant analyte measurement data (2580) may be received before or after the creation of this model.

[0306] In block (2804), the analyte variability control system (2530) selects a plurality of analytes to determine the predicted relative dilution of a biological sample. In one example, the biological sample analyzer module (2540) of the analyte variability control system (2530) analyzes the analyte measurement data (2580) of a new biological sample. For example, the biological sample analyzer module (2540) may select a subset of analytes from the analyte measurement data (2580) based on one or more thresholds and / or user preferences related to the relative fluorescence unit (RFU) measurement of the analytes in the analyte measurement data (2580).

[0307] In one example, the biological sample analyzer module (2540) may select the largest number of analytes from a new biological sample based on the "goodness of fit" compared to a corresponding synthetic dilution model for the biological sample type. In another example, the biological sample analyzer module (2540) may also select many analytes with the ability to flatten a series of titrations of the same sample. Further description and examples are provided in the disclosure, for example, in at least a non-limiting example, "Example 2: Application of an Empirical Substrate-Specific Standard Curve".

[0308] In block (2806), the analyte variability control system (2530) receives a synthetic dilution model (2590) for each selected analyte. In one example, a relative dilution prediction module (2560) receives a synthetic dilution model (2590) for each of a plurality of selected analytes to determine the predicted relative solution of a biological sample. For example, the relative dilution prediction module (2560) receives a synthetic dilution model (2590) generated for the selected analytes. Such a synthetic dilution model (2590) may be generated based on the method (2600), method (2700), or other examples of the present disclosure.

[0309] In block (2808), the analyte variability control system (2530) determines a predicted relative dilution value for each selected analyte based on a corresponding synthetic dilution model associated with each individual analyte. In one example, for each selected analyte, a relative dilution prediction module (2560) projects a relative fluorescence unit (RFU) measurement for each analyte onto a generated synthetic dilution model corresponding to each analyte, thereby generating a predicted relative dilution for each analyte.

[0310] For example, the relative dilution prediction module (2560) may take a first RFU measurement for a first analyte in a new biological sample, project the first RFU measurement for the first analyte onto a first synthetic dilution model generated for the first analyte, and determine a predicted relative dilution value for the first analyte in the new biological sample based on the projection. Similarly, the relative dilution prediction module (2560) may perform a second RFU measurement for a second analyte in the same new biological sample, project the second RFU measurement for the second analyte onto a second synthetic dilution model generated for the second analyte, and determine a predicted relative dilution value for the second analyte in the new biological sample based on the projection (etc. for each of the other selected analytes). Further descriptions and examples are provided in the disclosure, for example, in at least a non-limiting example “Example 2: Application of an Empirical Substrate-Specific Standard Curve”.

[0311] In block (2810), the analyte variability control system (2530) determines the predicted relative dilution of the biological sample based on the predicted relative dilution values ​​determined for each selected analyte. In one example, the relative dilution prediction module (2560) generates a distribution of the predicted relative dilution values ​​generated in block (2808) for the selected analytes of the new biological sample. The relative dilution prediction module (2560) can determine and select the predicted relative dilution values ​​to use when adjusting aspects of the new biological sample. For example, the relative dilution prediction module (2560) can discard one or more sets of generated predicted relative dilution values ​​to generate a final set of generated predicted relative dilution values ​​for adjusting the new biological sample.

[0312] In one example, the relative dilution prediction module (2560) trims the tail from the distribution of predicted relative dilution values ​​and uses the median percentage of the remaining values ​​to determine the predicted relative dilution for a new biological sample. The relative dilution prediction module (2560) then determines the predicted relative dilution for the biological sample. For example, the relative dilution prediction module (2560) can analyze the remaining predicted relative dilution values ​​and generate the predicted relative dilution for a new biological sample.

[0313] In one example, the relative dilution prediction module (2560) determines the predicted relative dilution for a new biological sample based on the median of the remaining predicted relative dilution values. The relative dilution prediction module (2560) may also determine the predicted relative dilution for a new biological sample using a formula or other analysis of the remaining predicted relative dilution values. Further description and examples are provided in the disclosure, for example, in at least a non-limiting example, “Example 2: Application of an Empirical Substrate-Specific Standard Curve”.

[0314] In one example, the analyte variability control system (2530) adjusts one or more aspects of a new biological sample based on the predicted relative dilution determined by the relative dilution prediction module (2560). For example, the analyte variability control system (2530) may normalize or otherwise adjust the analyte measurement data (2580) of the new biological sample based on the predicted relative dilution for the new biological sample determined by the relative dilution prediction module (2560). Additionally, in some examples, the analyte variability control system (2530) may generate a relevant report describing the relevant processing and adjustment. The adjusted analyte measurement data (2580) for the new biological sample may be further investigated and analyzed in terms of adjustment.

[0315] FIG. 29 illustrates a diagram of a machine in an exemplary form of a computer system (2900) on which a set of instructions to perform any one or more of the methodologies discussed herein may be executed. In one example, the machine may be connected (e.g., networked) to other machines on a LAN, intranet, extranet, or the Internet. The machine may operate as a server or client machine in a client-server network environment or as a peer machine in a peer-to-peer (or distributed) network environment. For example, the machine may be a personal computer (PC), tablet PC, personal digital assistant (PDA), mobile phone, wearable computing device, web device, server machine, professional diagnostic laboratory machine, or any other machine capable of executing a series of instructions (sequential or non-sequential) specifying actions to be taken by said machine. Additionally, although only a single machine is exemplified, the term “machine” should be considered to include a set of machines that execute a set of instructions (or a number of sets) individually or jointly to perform any one or more of the methodologies discussed in this application.

[0316] An exemplary computer system (2900) includes a processing device (processor) (2902), main memory (2904) (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), e.g., synchronous DRAM (SDRAM), dual data rate (DDR SDRAM), or DRAM (RDRAM), etc.), static memory (2906) (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device (2918), which communicate with each other via a bus (2930).

[0317] The processor (2902) represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, etc. More specifically, the processor (2902) may be a composite instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing another instruction set or a combination of instruction sets. The processor (2902) may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. The processor (2902) is configured to execute instructions (2922) for performing the operations and steps discussed in this application.

[0318] The computer system (2900) may further include a network interface device (2908). The computer system (2900) may also include a video display unit (2910) (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device (2912) (e.g., a keyboard), a cursor control device (2914) (e.g., a mouse), and a signal generating device (2916) (e.g., a speaker).

[0319] The data storage device (2918) may include a computer-readable storage medium (2928) in which one or more instruction sets (2922) (e.g., software) that implement any one or more of the methodologies or functions described in this application are stored. The instruction (2922) may also reside in the main memory (2904) and / or the processor (2902) wholly or at least partially during execution by the computer system (2900), the main memory (2904), and the processor (2902) that constitutes the computer-readable storage medium. The instruction (2922) may further be transmitted or received through the network (2920) via the network interface device (2908).

[0320] In one example, the instruction (2922) includes instructions to a software library that includes an analyte variability control system (e.g., the analyte variability control system (2530) of FIG. 25) and / or a method for calling the analyte variability control system. Although the computer-readable storage medium (2928) (machine-readable storage medium) is exemplified as a single medium, the term “computer-readable storage medium” should be considered to include a single medium or multiple media (e.g., a centralized or distributed database and / or associated cache and server) that store one or more sets of instructions. The term “computer-readable storage medium” should be considered to include any medium capable of storing, encoding, or transmitting a set of instructions for execution by a machine and enabling the machine to perform one or more methodologies of the present disclosure. Additionally, the term “computer-readable storage medium” should be considered to include, but not be limited to, solid-state memory, optical media, and magnetic media.

[0321] In the foregoing description, many details are described. However, it will be apparent to those skilled in the art to have the advantage of the present disclosure that the present disclosure can be practiced without these specific details. In some examples, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the present disclosure.

[0322] Parts of the detailed description have been presented in terms of steps leading to one or more desired results. Generally, these steps are those requiring the physical manipulation of physical quantities. These quantities typically take the form of electrical or magnetic signals capable of storage, transmission, combination, comparison, and other operations, though this is not always the case. For common usage, it has sometimes proven convenient to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc.

[0323] However, it should be kept in mind that all these terms and similar terms must relate to appropriate physical quantities and are merely convenient labels applied to such quantities. Unless explicitly stated otherwise in the following discussion, it is recognized that any discussion using terms such as "calculation," "comparison," "application," "generation," "ranking," "classification," etc. throughout the description refers to the operations and processes of a specialized computer system or similar specialized electronic computing device that manipulate and convert data represented as physical (e.g., electronic) quantities within the registers and memory of a computer system into other data similarly represented as physical quantities within the computer system memory or registers or other information storage, transmission, or display devices.

[0324] Specific examples of the present disclosure also relate to an apparatus for performing the operation of the present application. The apparatus may include special computer hardware and / or special computer programs that may be configured for an intended purpose or optionally installed, activated, or configured to perform an intended purpose. Such computer programs may be stored on computer-readable storage media such as floppy disks, optical disks, CD-ROMs and magneto-optical disks, read-only memory (ROM), Random Access Memory (RAM), EPROM, EEPROM, magnetic or optical cards, or any type of disk suitable for storing electronic instructions, but not limited thereto.

[0325] It should be understood that the foregoing description is for illustrative purposes only and is not limiting. Many other examples will be apparent to those skilled in the art upon reading and understanding the foregoing description. Accordingly, the scope of this disclosure should be determined by reference to the appended claims and together with the full scope of equivalents corresponding to such claims.

[0326] Examples

[0327] The following examples are provided to illustrate specific features and / or embodiments. These examples should not be construed as limiting the present disclosure to the specific features or embodiments described.

[0328] Example 1: Generation of an Empirical Temperament Specific Standard Curve

[0329] This embodiment provides means for generating an exemplary synthetic dilution model from a complex biological substrate, e.g., urine, using relative protein measurements validated with a capture reagent, e.g., an aptamer.

[0330] Traditionally, urine protein levels are normalized by physiologically based measurements (e.g., total urine volume, creatinine concentration, or albumin:creatinine ratio) or by restricted median normalization, which identifies a subset of capture reagents (e.g., aptamers) exhibiting dilution linearity (or scaling to the protein content of the sample) and uses this information for standard median normalization.

[0331] A novel normalization approach has been developed to generate synthetic dilution curves for each analyte (measured protein) based on a series of titrations of multiple samples in complex substrates such as urine. By utilizing multiple synthetic dilution curves, the total dilution of protein levels can be estimated more accurately. Protein measurements were performed in urine, allowing for aptamer-based assays where protein levels are expressed in relative amounts (RFU or Relative Fluorescence Units). For example, urine was selected as an example substrate due to the inter-sample variability in protein levels typically observed when collecting urine, which stems from the hydration levels of the subjects. Hydration levels can confound the relationship between protein levels measured in urine and the clinical assessment of the subjects being tested. Therefore, by generating synthetic dilution models that compensate for inter-sample variability, consistent and clinically relevant information regarding the test subjects can be derived from protein levels in urine. Furthermore, once a synthetic dilution model is generated for a substrate, the same synthetic dilution model can be used to compensate for inter-sample variability across many subjects' samples, and consistent and clinically relevant information can be derived from the analyte measurements for those subjects.

[0332] Based on background information, the quantitative input to the function is denoted by X, and the output by the variable Y. If X or Y is a substrate, the individual component is subscript X ij It can be accessed as. For example, the output value of the j-th sample for the i-th aptamer is X ij is. The next sample for the same aptamer is X i(j+1) Vectors of values ​​are represented in uppercase, and scalar values ​​are represented in lowercase.

[0333] Protein levels obtained from at least three (3) consecutive titrations on at least two (2) samples of biological substrates of interest were analyzed, and for sample j with k consecutive titrations, analyte i (aptamer) was diluted to Xijk and corresponding RFU value Y ijk It is indicated as follows. For each analyte (or protein), a linear dilution range is defined, which, for aptamer-based assays, is a dilution range in which the measured RFU value is nearly linearly proportional to the sample dilution. Starting from the lowest dilution, the RFU measurement for each dilution is used to establish a nominal level for comparison with subsequent dilutions. Percent recovery is defined as the measured RFU value divided by the expected RFU value provided for an n-fold dilution. Starting from the k-th dilution of the series, the recovery of the m-th subsequent dilution is defined as follows.

[0334]

[0335] The allowable dilution range has a recovery rate within 50% of the nominal value determined at the highest dilution of the linear range. To define the linear range, at least three consecutive dilutions from the nominal value are required. The linear range (five data points at five consecutive dilutions) is shown in Fig. 1 as data points between the vertical dashed lines (scaling is logarithmic for visualization purposes and does not suggest log-linearity).

[0336] The analyte cystatin C (CST3) was used as a specific example. Nineteen (19) urine samples were serially diluted, and the levels of the analyte CST3 were measured and plotted (see Fig. 2a). Thus, for each analyte i (e.g., CST3), the sample j (of the 19 samples) with the largest linear dilution range is identified. RFU measurement Y ij A weighted linear regression model is fitted to the data in the linear dilution range using weights inversely proportional to . The linear model of the i-th analysis is as follows:

[0337]

[0338] Another specific example used was the analyte ephrine type B receptor 6 (EPHB6). Nineteen (19) urine samples were serially diluted, and the levels of the analyte EPHB6 were measured and plotted (see FIG. 2d). Thus, for each analyte i (e.g., EPHB6), the sample j (of the 19 samples) with the largest linear dilution range was identified. RFU measurement Y ij A weighted linear regression model is fitted to the data in the linear dilution range using weights inversely proportional to . The linear model of the i-th analysis is as follows:

[0339]

[0340] This regression line (or regression model) is now the standard upon which all other curves will be fitted (the black line in Fig. 2b for CST3, and Fig. 2e for EPHB6).

[0341] Linear range y for each analyte i ci The RFU value at the center point of the reference value (or reference value) is mapped to the regression line (regression model) (or horizontally translated) to fit the appropriate curve and reference Determine the relative dilution for (relative reference dilution). The relative reference dilution of the i-th analyte for the j-th sample is as follows:

[0342]

[0343] Each titration curve Y for the given analyte ij relative dilution value (or Translated curve after horizontal translation to X) A (synthetic dilution model) is generated, where the center point of the linear range corresponds to the reference regression line. Alternatively, another method for this is to fit a linear model for each titration series and align the curves by setting the intercept to the same value (e.g., 1). This process aligns the curves with respect to the reference, as shown in the plot on the right above.

[0344]

[0345] For each analysis, a four-parameter logistic function (4PL) is fitted to the matched data. The 4PL equation consists of parameters for the lower asymptote L, the upper asymptote U, the inflection point k, and the Hill's slope b. The model is symmetric with respect to the inflection point and is fitted using nonlinear least squares.

[0346]

[0347] This generates an empirical standard curve for the presence of each analyte of the substrate of interest, all capture reagents (e.g., aptamers), and each target protein. This is shown in Figure 2c for CST3. Each 4PL fit (synthetic dilution model) can be ranked according to its fit using the Akaike information criterion (AIC).

[0348] Figures 3a-3c to 9a-9c illustrate similar analyses of platelet-derived growth factor D (PDGFD, Figs. 3a-3c), retinoic acid receptor responder 2 (RARRES2, Figs. 4a-4c), interleukin 1 receptor-like 2 (IL1RL2, Figs. 5a-5c), coagulation factor XI (F11, Figs. 6a-6c), Septin 11 (SEPT11, Figs. 7a-7c), Thymopoietin (TMPO, Figs. 8a-8c) and Shisa family member 3 (SHISA3, Figs. 9a-9c).

[0349] Example 2: Application of the empirical temperament-specific standard curve

[0350] This example provides a method used to normalize the levels of analytes measured in a test using the empirical substrate-specific standard curve (or synthetic dilution model or 4PL curve) generated in Example 1.

[0351] Generally, this method involves selecting a subset of i analytes (aptamer-based protein measurements) to be used for normalization calculations.

[0352] Feature selection can be performed in several ways. Three examples are as follows: 1) selecting the top i analytes based on 4PL goodness of fit ranked by AIC, 2) performing feature selection on i analytes capable of flattening a series of consecutive titrations of the same sample, and / or 3) selecting analytes with signal levels higher than the background (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10 times the background level).

[0353] For example, regarding Option 2 above, we expect the normalization scale factor to double for every 1:2 dilution. Then, for each sample to be normalized, we inversely solve the 4PL equation for all i analytes in the normalization subset to find the predicted relative dilution. The estimated relative dilution for the i-th analyte of sample k (out of a total of n). is as follows.

[0354]

[0355] Figures 10a and 10b illustrate the application of a general process to two analytes (iduronidase [IDUA] and neogenin 1 [NEO1]). RFU measurements for normalized samples were projected onto a 4PL curve to generate two predicted relative dilutions.

[0356] Then, this information can be used to find the expected relative dilution for the corresponding sample. In some embodiments, the median of the predicted relative dilution for every i-th analyte is determined. This process results in a distribution of n predicted relative dilutions for each sample to be normalized. The median of this distribution is the expected relative dilution for the corresponding sample, and the 4PL normalization scale factor for the corresponding sample is 1 relative to the predicted relative dilution.

[0357] In some embodiments, a value, median, and mean derived from the central tendency value of the estimated relative dilution are selected. Then, the sample can be normalized using the estimated relative dilution for each sample. In some embodiments, a scaling factor for each sample is generated by dividing a reference value by the estimated relative dilution for that sample. This reference value may be 1 in some embodiments (making the scaling factor the reciprocal of the estimated relative dilution) or the median estimated relative dilution for all samples to be normalized.

[0358] Figure 11 shows the distribution of expected relative dilutions for four consecutive 1:2 dilutions of a single sample, and the median is observed to scale perfectly with the dilutions of the sample using 200 analytes with the best AIC.

[0359] Example 3: Design of a pilot study to characterize and normalize changes in analyte signals due to the hydration status of human subjects

[0360] This example provides an overview of a pilot study used to characterize and validate normalization techniques for changes in analyte signal measurements in human subjects under controlled hydration conditions.

[0361] A gender-balanced cohort of 16 participants aged between 26 and 57 (average age 33) was recruited for the study. Informed consent for this study corresponds to IRB approval of the SomaLogic Biorepository Research Protocol or WIRB #20150206. All participants signed a consent form and completed a simple health questionnaire to collect metadata including age, gender, height, weight, and other demographic information. All metadata was kept anonymous within the study.

[0362] Collection Protocol:Clinical information obtained from urine specimens can be affected by collection methods and handling. Therefore, the collection procedure used in this study was "clean midstream collection" to reduce the incidence of cellular and microbial contamination. Participants were asked to flush the first portion of their urine stream into a toilet, which flushed the urethra and significantly reduced the chance of contaminants entering the stream.

[0363] Urine midstream was collected in a 90 ml preservative-free, pre-labeled sterile collection cup with a safe, leak-proof lid. The samples collected at SomaLogic were placed in a pre-labeled biohazard bag and immediately placed in a laboratory refrigerator designated for biological samples so that they could be aliquoted later and stored at -80°C.

[0364] To maintain sample conditions similar to those obtained from future potential subjects, no pretreatment or centrifugation was performed prior to aliquoting. The collection time was recorded for all samples. In addition to 4 to 6 analytical aliquots, one 8 ml aliquot was generated for urine testing.

[0365] Samples were anonymized by assigning a unique alphanumeric identifier to each study participant, and this identifier, along with the collection time and date, was marked on all collection tubes and biohazard bags. All metadata and proteomic data were entered into the database using this identifier. This process created an anonymity barrier that prevented metadata or proteomic data from being linked to the participants. The master key linking the participants to the identifiers was generated internally, kept confidential, and accessed only by selected individuals when necessary.

[0366] Participants were asked to refrain from anti-inflammatory drugs for 48 hours prior to the study and to stop fluid intake at 10:00 PM the night before the study. Urinary collection was permitted overnight if necessary, but the "first void" urine was collected in the morning.

[0367] Research Protocol: All participants were asked to refrain from drinking beverages (water, coffee, etc.) or exercising in the morning. The first urine sample collected at home was placed on ice immediately upon arrival at SomaLogic and then placed in a refrigerator at 2 to 8°C.

[0368] Participants were provided with a calorie-balanced breakfast and asked to consume 1.5% of their body weight in water over a 30-minute period ("hydration challenge"). This amounted to approximately 850 ml for a 125 lb individual and approximately 1120 ml for a 175 lb individual. Instead of pre-specifying the amount of food for each participant, food intake was controlled by asking participants to eat a conservative amount.

[0369] A portion of each urine was collected from 9:00 AM to noon, and the final 'excretion' urine sample was collected at approximately 12:00 PM. Water could not be consumed between 9:00 AM and 12:00 PM. Two to five consecutive samples followed the hydration challenge, with the last sample occurring at approximately 12:00 PM. Additionally, 15 participants provided a non-control sample at 12:00 PM the following day. A total of 81 urine samples were collected from 15 participants.

[0370] In accordance with the collection protocol, all samples were collected in the "midstream" and sent to a clinical laboratory for complete urine analysis, including specific gravity, total protein, elements, total salts, and microbial titration concentrations. Total urine volume was not recorded.

[0371] Measurement of analytes in collected samples:Urine samples from study subjects were analyzed using an aptamer-based assay measuring over 1,000 protein levels. For each study participant, Figure 12 shows the log of the median RFU signal for the corresponding aptamer. 10 The data for each aptamer before normalization, indicated by change, are plotted (indicated by lines spanning multiple time points). There is a general bias in the unnormalized data due to the participants' hydration status, with the signal for the first morning urine sample (most concentrated) being higher than the median. This signal decreases due to the hydration challenge and increases because the study participants refrained from attempting to drink water after hydration.

[0372] This data was used to demonstrate the efficacy of a synthetic dilution model for compensating for the subjects' hydration levels and extracting consistent and clinically relevant information about the test subjects. The synthetic dilution model was applied to all consecutive samples of each study participant. Box plots of the expected relative dilution for all consecutive samples of each study participant are shown in Fig. 13. These values ​​were calculated from the synthetic dilution curves for all aptamers. The normalization process then calculates the scaling factor for each sample by shifting the median of each box plot by 1. Fig. 14 shows the resulting time series after normalization using the synthetic dilution model to compensate for the subjects' hydration levels, represented by changes in RFU from the median of each aptamer.

[0373] The box plots and cumulative distribution functions (CDFs) in Figures 15 and 16 illustrate the signal distributions at each time point for two individual study subjects. Figures 15a / c and 16a / c illustrate the bias of signal levels before normalization, and Figures 15b / d and 16b / d illustrate the distribution of signal levels after normalization. Each line in the top plot represents the aptamer signal level across the samples.

[0374] Figures 15b and 16b illustrate the reduction in systematic bias / variance quantified by the repeated measures ANOVA F statistic, which can be interpreted as the ratio of within-group differences to between-group differences. High F statistics result from large differences between time points, while low F statistics indicate that the differences between time points are similar to what would be expected given any opportunity. F statistics were calculated for the denormalized and normalized data for each study participant and are shown in Figure 17a. The normalization procedure resulted in a very significant reduction in longitudinal variance (p < 1E-8, N=15). Figure 17b illustrates the fold reduction of the F statistic for each study participant, with a median of 81.88 and a range of 13.12–524.96.

Claims

Claim 1 A method for generating a composite dilution model comprises: a) a step of determining a level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; b) a step of determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; c) a step of generating a model based on the level of the analyte in the first dilution series; and d) a step of selecting a reference value, wherein the reference value is (i) a second dilution series reference value which is the level of the analyte at a specific dilution of the second dilution series. (ii) a model reference value which is an analyte level at a specific dilution of the above model; and (iii) a step of selecting an arbitrary reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the said specific dilution is selected from an arbitrary reference value that is not found in the second dilution series or the above model;e) a step of performing at least one horizontal translation, wherein the at least one horizontal translation comprises: (i) a horizontal translation (ΔX) of the second dilution series reference value to the model value, wherein the model value is the analyte level at a specific dilution of the model, and the second dilution series reference value and the model value are identical or substantially identical; (ii) a horizontal translation (ΔX) of the model reference value to the second dilution series value, wherein the second dilution series value is the analyte level at a specific dilution of the second dilution series, and the model reference value and the second dilution series value are identical or substantially identical; and (iii) a step of performing at least one horizontal translational shift of a second dilution series value (ΔX) and a model value (ΔY) to an arbitrary reference value, wherein the model value is an analyte level at a specific dilution of the model, and the second dilution series value is an analyte level at a specific dilution of the second dilution series, and the second dilution series value, the model value, and the arbitrary reference value are identical or substantially identical, selected from the horizontal translational shift of the second dilution series value (ΔX) and the model value (ΔY) to an arbitrary reference value; f) a step of performing at least one horizontal translational shift of a remainder value in (i) the second dilution series, (ii) the model, or (iii) the second dilution series and the model, wherein the horizontal translational shift of the remainder value results in a lined composite translational shift;and g) a method comprising the step of forming a synthetic dilution model by fitting a function to an aligned synthetic translational shift series.; Claim 2 The method of claim 1 comprises: d) selecting a second dilution series reference value which is an analyte level at a specific dilution of the second dilution series; e) performing a horizontal translational shift (ΔX) of the second dilution series reference value to a model value, wherein the model value is an analyte level at a specific dilution of the model, and the second dilution series reference value and the model value are identical or substantially identical; and f) performing a horizontal translational shift of the remainder value in the second dilution series, wherein the horizontal translational shift of the remainder value results in an aligned composite translational shift; Or the method comprises: d) selecting a model reference value which is an analyte level at a specific dilution of the model; e) performing a horizontal translational shift (ΔX) of the model reference value to a second dilution series value, wherein the second dilution series value is an analyte level at a specific dilution of the second dilution series, and the model reference value and the second dilution series value are identical or substantially identical; and f) performing a horizontal translational shift of the remainder value in the model, wherein the horizontal translational shift of the remainder value results in an aligned composite translational shift; or the method comprises: d) selecting an arbitrary reference value which is an analyte level at a specific dilution, wherein the analyte level at the said specific dilution is not found in the second dilution series or the model;e) a step of performing a horizontal translational shift of a second dilution series value (ΔX) and a model value (ΔY) to an arbitrary reference value, wherein the model value is an analyte level at a specific dilution of the model, the second dilution series value is an analyte level at a specific dilution of the second dilution series, and the second dilution series value, the model value, and the arbitrary reference value are identical or substantially identical; and f) a step of performing a horizontal translational shift of a remainder value in the second dilution series and the model, wherein the horizontal translational shift of the remainder value results in an aligned composite translational shift, comprising a method. Claim 3 A method for generating a synthetic dilution model, wherein the method comprises: a) determining a level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; b) determining a level of an analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; c) generating a model based on the level of the analyte in the first dilution series; and d) performing at least one horizontal translational shift, wherein the at least one horizontal translational shift comprises: (i) a horizontal translational shift (ΔX) of the second dilution series into the model; and (ii) a horizontal translational shift (ΔX) of the model into the second dilution series. and (iii) a horizontal translational shift of the second dilution series (ΔX) and the model to any reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the specific dilution is selected from the horizontal translational shift of the second dilution series (ΔX) and the model to any reference value that is not found in the second dilution series or the model, and the at least one horizontal translational shift results in an aligned synthetic translational shift series; and e) a step of fitting a function to the aligned synthetic translational shift series to form a synthetic dilution model, the method comprising Claim 4 The method of claim 3 comprises: (i) performing a horizontal translational shift (ΔX) of the second dilution series to the model; or (ii) performing a horizontal translational shift (ΔX) of the model to the second dilution series; or (iii) performing a horizontal translational shift of the second dilution series (ΔX) and the model to any reference value which is an analyte level at a specific dilution, wherein the analyte level at the specific dilution is not found in the second dilution series or the model. Claim 5 A method for generating a synthetic dilution model, wherein the method comprises: a) a step of determining a level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; b) a step of determining a level of an analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; c) a step of selecting a reference value, wherein the reference value is a second dilution series reference value which is the level of the analyte at a specific dilution of the second dilution series; and (ii) a step of selecting a reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the specific dilution is selected from an arbitrary reference value that is not found in the first dilution series or the second dilution series; d) a step of performing at least one horizontal translational shift, wherein the at least one horizontal translational shift is a horizontal translational shift (ΔX) of the second dilution series reference value to the first dilution series value, wherein the first dilution series value is an analyte level at a specific dilution of the first dilution series, and the second dilution series reference value and the first dilution series value are identical or substantially identical, and the second dilution series reference value and the first dilution series value areand (ii) performing at least one horizontal translational shift selected from the horizontal translational shifts of the first dilution series (ΔX) and the second dilution series (ΔY) to the arbitrary reference value, wherein the at least one horizontal translational shift results in an aligned synthetic translational shift series; and e) fitting a function to the aligned synthetic translational shift series to form a synthetic dilution model, the method comprising; Claim 6 The method of claim 5 comprises: (i) performing a horizontal translational shift (ΔX) of the second dilution series to the first dilution series; or (ii) performing a horizontal translational shift of the first dilution series (ΔX) and the second dilution series (ΔY) to any reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the said specific dilution is not found in the first dilution series or the second dilution series. Claim 7 A method for generating a synthetic dilution model, wherein the method comprises: a) a step of determining a level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; b) a step of determining a level of an analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; c) a step of performing at least one horizontal translational shift, wherein the at least one horizontal translational shift comprises: (i) a horizontal translational shift (ΔX) of the second dilution series into the first dilution series; and (ii) a horizontal translational shift of the first dilution series (ΔX) and the second dilution series (ΔY) to any reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the specific dilution is selected from the horizontal translational shift of the first dilution series (ΔX) and the second dilution series (ΔY) to any reference value that is not found in the first dilution series or the second dilution series, and wherein the at least one horizontal translational shift results in an aligned synthetic translational shift series; and d) a step of fitting a function to the aligned synthetic translational shift series to form the synthetic dilution model, the method comprising Claim 8 The method of claim 7 comprises: (i) performing a horizontal translational shift (ΔX) of the second dilution series to the first dilution series; or (ii) performing a horizontal translational shift of the first dilution series (ΔX) and the second dilution series (ΔY) to the arbitrary reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the said specific dilution is not found in the first dilution series or the second dilution series. Claim 9 A method for generating a synthetic dilution model, wherein the method comprises: a) a step of determining a level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; b) a step of determining a level of an analyte in a second dilution series of a second biological sample containing an analyte, wherein the second dilution series comprises at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; c) a step of generating a first model based on the level of the analyte in the first dilution series and generating a second model based on the level of the analyte in the second dilution series; and d) a step of selecting a reference value, wherein the reference value is a first model reference value which is the level of the analyte at a specific dilution of the first model. and (ii) selecting a reference value, wherein the reference value is an analyte level at a specific dilution, and the analyte level at the specific dilution is not found in the first model or the second model; e) performing at least one horizontal translational shift, wherein the at least one horizontal translational shift comprises: (i) a horizontal translational shift of the first model to the second model (ΔX); and (ii) a horizontal translational shift selected from the horizontal translational shifts of the first model and the second model to the arbitrary reference value, wherein the horizontal translational shift results in an aligned synthetic translational shift series; and f) fitting a function to the aligned synthetic translational shift series to form a synthetic dilution model, the method comprising Claim 10 The method of claim 9, wherein the method comprises: (i) performing a horizontal translation (ΔX) of the first model to the second model; or (ii) performing a horizontal translation of the first model and the second model to the arbitrary reference value. Claim 11 A method for generating a synthetic dilution model, wherein the method comprises: a) a step of determining a level of an analyte in a first dilution series of a first biological sample containing an analyte, wherein the first dilution series includes at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; b) a step of determining a level of an analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series includes at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; c) a step of generating a first model based on the level of the analyte in the first dilution series and generating a second model based on the level of the analyte in the second dilution series; and d) a step of performing at least one horizontal translational shift, wherein the at least one horizontal translational shift comprises: (i) a horizontal translational shift (ΔX) of the first model to the second model; and (ii) a horizontal translational shift of the first model and the second model to an arbitrary reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the specific dilution is selected from the horizontal translational shift of the first model and the second model to an arbitrary reference value that is not found in the first model or the second model, and the horizontal translational shift results in an aligned synthetic translational shift series; and e) a step of fitting a function to the aligned synthetic translational shift series to form a synthetic dilution model, the method comprising Claim 12 The method of claim 11, wherein the method comprises: (i) performing a horizontal translation (ΔX) of the first model to the second model; or (ii) performing a horizontal translation of the first model and the second model to any reference value. Claim 13 A method according to any one of claims 1 to 12, wherein the level of the analyte is determined at each of at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 different dilutions in the first dilution series and / or; and the level of the analyte is determined at each of at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 different dilutions in the second dilution series. Claim 14 A method according to any one of claims 1 to 12, wherein each model is independently selected from a linear regression model, a LOESS curve fitting model, a nonlinear regression model, a spline fit model, a mixed effects regression model, a fixed effects regression model, a generalized linear model, a substrate decomposition model, and a four-parameter logistic regression (4PL) model. Claim 15 A method according to any one of claims 1 to 12, wherein the level of the analyte is a relative amount of the analyte or a concentration of the analyte. Claim 16 A method according to any one of claims 1 to 12, wherein the selected reference value is within the linear range of the dilution series or model. Claim 17 A method according to any one of claims 1 to 12, wherein the first and second biological samples comprise urine or are derived from urine, wherein the first and second biological samples are collected from the same subject or different subjects. Claim 18 A method according to claim 17, wherein the first biological sample is collected at a first time point and the second biological sample is collected at a second time point. Claim 19 A method according to any one of claims 1 to 12, wherein the level of the analyte is measured by an assay using an aptamer, an antibody, a mass spectrophotometer, or a combination thereof. Claim 20 A method according to any one of claims 1 to 12, wherein the dilution factor for each of the first dilution series and the second dilution series is a constant dilution factor. Claim 21 A method according to any one of claims 1 to 12, wherein the first biological sample and the second biological sample are biological samples of the same type. Claim 22 A method according to any one of claims 1 to 12, further comprising the step of determining the relative dilution of a biological test sample by horizontally translating the level of at least one analyte from a biological test sample to a synthetic dilution model for at least one analyte. Claim 23 A method according to claim 22, wherein the biological test sample used to form the synthetic dilution model and the first and second biological samples are of the same sample type. Claim 24 A method for determining the relative dilution of a biological test sample from a subject, wherein the method comprises the step of determining the relative dilution of the biological test sample from the subject by horizontally translating the level of at least one analyte from the biological test sample to a synthetic dilution model developed for at least one analyte, wherein the synthetic dilution model for the at least one analyte comprises: a) a step of determining the level of the analyte in a first dilution series of a first biological sample containing the analyte, wherein the first dilution series includes at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; b) a step of determining the level of the analyte in a second dilution series of a second biological sample containing the analyte, wherein the second dilution series includes at least three (3) different dilutions and the level of the analyte is determined at each of the at least three different dilutions; and c) a step of the analyte in the first dilution series A step of generating a model based on levels; d) a step of selecting a reference value, wherein the reference value is (i) a second dilution series reference value which is an analyte level at a specific dilution of the second dilution series; (ii) a model reference value which is an analyte level at a specific dilution of the model; and (iii) a step of selecting a reference value which is an arbitrary reference value, wherein the arbitrary reference value is an analyte level at a specific dilution, and the analyte level at the said specific dilution is not found in the second dilution series or the model.e) a step of performing at least one horizontal translational shift, wherein the at least one horizontal translational shift comprises: (i) a horizontal translational shift (ΔX) of the second dilution series reference value to the model value, wherein the model value is the analyte level at a specific dilution of the model, and the second dilution series reference value and the model value are identical or substantially identical; (ii) a horizontal translational shift (ΔX) of the model reference value to the second dilution series value, wherein the second dilution series value is the analyte level at a specific dilution of the second dilution series, and the model reference value and the second dilution series value are identical or substantially identical; and (iii) a step of performing at least one horizontal translational shift of a second dilution series value (ΔX) and a model value (ΔY) to an arbitrary reference value, wherein the model value is an analyte level at a specific dilution of the model, and the second dilution series value is an analyte level at a specific dilution of the second dilution series, and the second dilution series value, the model value, and the arbitrary reference value are identical or substantially identical, selected from the horizontal translational shift of the second dilution series value (ΔX) and the model value (ΔY) to an arbitrary reference value; f) a step of performing at least one horizontal translational shift of a remainder value in (i) the second dilution series, (ii) the model, or (iii) the second dilution series and the model, wherein the horizontal translational shift of the remainder value results in an aligned composite translational shift;and g) a method generated using a method comprising the step of fitting a function to an aligned synthetic translational shift series to form a synthetic dilution model.; Claim 25 In claim 22, the step of horizontally translating at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 50, at least 75, at least 100, at least 150, or at least 200 levels of different analytes from a biological test sample for each synthetic dilution model developed for each different analyte to determine the relative dilution of the biological test sample for each different analyte; and the step of using the relative dilution for each different analyte to determine the relative dilution of the biological test sample. Method including. Claim 26 A method according to claim 25, wherein the relative dilution of the biological test sample is derived from the central tendency of the relative dilution of each of the different analytes. Claim 27 A method according to claim 24, wherein the biological test sample used to develop the synthetic dilution model and the sample are of the same sample type. Claim 28 The method of claim 22 further comprises the step of calculating the relative dilution of the biological test sample using a derived relative dilution coefficient, wherein the derived relative dilution coefficient is derived from a central tendency value of the relative dilution of each of the different analytes, or the relative dilution coefficient is derived from the median, mean, or mode of the relative dilution of each of the different analytes. Claim 29 A method according to any one of claims 1 to 12 and claim 24, wherein each analyte is a target protein. Claim 30 A computer system comprising: a non-transient memory for storing instructions; and one or more hardware processors coupled to the non-transient memory, wherein the computer system reads instructions from the non-transient memory and is configured to perform an operation comprising: receiving analyte measurement data from a plurality of different biological samples; analyzing individual analyte measurements for each of a plurality of selected analytes in the analyte measurement data; and generating a synthetic dilution model for each of the selected analytes according to the method of any one of claims 1 to 12 based on the analysis. Claim 31 A computer system according to claim 30, wherein the analyzing step comprises determining a biological sample having a maximum linear dilution range for each selected analyte. Claim 32 A non-transient computer-readable medium comprising a computer-readable command, wherein, when the command is executed by a processing device, the processing device performs an operation comprising: receiving analyte measurement data from a plurality of different biological samples; analyzing individual analyte measurements for each of a plurality of selected analytes in the analyte measurement data; and generating a synthetic dilution model for each of the selected analytes according to the method of any one of claims 1 to 12 based on the analysis. Claim 33 A computer-implemented method for generating a synthetic dilution model for a biological substance, comprising: receiving analyte measurement data from a plurality of different biological samples by one or more processing devices; analyzing individual analyte measurements for each of a plurality of selected analytes in the analyte measurement data by one or more processing devices; and generating a synthetic dilution model for each of the selected analytes according to the method of any one of claims 1 to 12 by one or more processing devices based on the analysis. Claim 34 A computer system comprising: a non-transient memory for storing instructions; and one or more hardware processors coupled to the non-transient memory, wherein the computer system reads instructions from the non-transient memory and is configured to perform operations including: receiving analyte measurement data for analytes of a biological sample; selecting a plurality of analytes to determine the predicted relative dilution of the biological sample; receiving a synthetic dilution model generated by the method of any one of claims 1 to 12, which is generated for each individual analyte of the selected analytes; determining a predicted relative dilution value for each selected analyte based on a corresponding synthetic dilution model generated for each individual selected analyte; and determining the predicted relative dilution of the biological sample based on the predicted relative dilution value determined for the selected analytes. Claim 35 delete Claim 36 delete Claim 37 delete Claim 38 delete Claim 39 delete Claim 40 delete Claim 41 delete Claim 42 delete Claim 43 delete Claim 44 delete Claim 45 delete Claim 46 delete Claim 47 delete Claim 48 delete Claim 49 delete Claim 50 delete Claim 51 delete Claim 52 delete Claim 53 delete Claim 54 delete Claim 55 delete Claim 56 delete Claim 57 delete Claim 58 delete Claim 59 delete Claim 60 delete

Images