Controlling sample-to-sample analyte variability in complex biological matrices

Abstract

Described herein are compositions and methods for normalizing sample-to-sample variability in analyte measurements from biological matrices.In some embodiments, the disclosure relates to methods for normalizing levels of one or more proteins from urine measured by an aptamer-based assay.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 62 / 849,212, filed on May 17, 2019, which is incorporated herein by reference in its entirety for any purpose. Technical Field

[0003] The present disclosure generally relates to controlling sample-to-sample variability in analyte measurements from biological matrices.In some embodiments, the present disclosure relates to methods for controlling the levels of one or more proteins from a urine sample measured by an aptamer-based assay. Background Art

[0004] Sample-to-sample variability in analyte measurements in biological samples is a problem for biomarker discovery, metabolic profiling, gene expression analysis, protein pathway analysis, and diagnostic and prognostic tools, especially when the results rely on quantitative biological signals with relatively small differences in magnitude. Biological sample types that exhibit high sample-to-sample variability are a major challenge in processing these sample types. Controlling this variability will provide more consistent and meaningful datasets for both experimental and clinical applications.

[0005] Therefore, there continues to be a need for alternative compositions and methods for controlling sample-to-sample variability in analyte measurements in biological matrices. The present disclosure satisfies such needs by providing novel compositions and methods for normalizing biological signals in complex matrices that reduce, minimize or eliminate such variability. Summary of the invention

[0006] Embodiment 1 A method for generating a composite dilution model, the method comprising:

[0007] a) determining the level of an analyte in a first dilution series of a first biological sample comprising the analyte, wherein the first dilution series comprises at least three (3) different dilutions, and determining the level of the analyte in each of the at least three different dilutions;

[0008] b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three (3) different dilutions, and determining the level of the analyte in each of the at least three different dilutions;

[0009] c) generating a model based on the levels of the analyte in the first dilution series;

[0010] d) selecting a reference value, wherein the reference value is selected from:

[0011] (i) a second dilution series reference value, wherein the second dilution series reference value is the level of the analyte at a specific dilution of the second dilution series;

[0012] (ii) a model reference value, which is the level of the analyte at a specific dilution of the model; and

[0013] (iii) an arbitrary reference value, wherein the arbitrary reference value is the level of the analyte at a specific dilution, wherein the level of the analyte at the specific dilution is not found in the second dilution series or in the model;

[0014] e) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from:

[0015] (i) a horizontal shift (ΔX) of the second dilution series reference value to a model value, wherein the model value is the level of the analyte at a specific dilution of the model, wherein the second dilution series reference value and the model value are equal or substantially equal;

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

[0017] (iii) a horizontal shift (ΔX) of a second dilution series value to the arbitrary reference value and a horizontal shift (ΔY) of a model value to the arbitrary reference value, wherein the model value is the level of the analyte at a specific dilution of the model, wherein the second dilution series value is the level of the analyte at a specific dilution of the second dilution series, and wherein the second dilution series value, the model value and the arbitrary reference value are equal or substantially equal;

[0018] f) performing at least one horizontal shift of remaining values ​​in (i) the second dilution series, (ii) the model, or (iii) the second dilution series and the model, wherein the horizontal shift of the remaining values ​​results in a linear composite shift; and

[0019] g) fitting a function to the linear composite translation series to form a composite dilution model.

[0020] Embodiment 2 The method according to embodiment 1, wherein the method comprises:

[0021] d) selecting a second dilution series reference value, wherein the second dilution series reference value is the level of the analyte at a specific dilution of the second dilution series;

[0022] e) performing a horizontal translation (ΔX) of the second dilution series reference value to a model value, wherein the model value is the level of the analyte at a specific dilution of the model, wherein the second dilution series reference value and the model value are equal or substantially equal; and

[0023] f) performing said horizontal shifting of said remaining values ​​in said second dilution series, wherein said horizontal shifting of said remaining values ​​results in a linear compound shift.

[0024] Embodiment 3 The method according to embodiment 1, wherein the method comprises:

[0025] d) selecting a model reference value, the model reference value being the level of the analyte at a specific dilution of the model;

[0026] e) performing a horizontal translation (ΔX) of the model reference value to a second dilution series value, wherein the second dilution series value is the analyte level at a specific dilution of the second dilution series, wherein the model reference value and the second dilution series value are equal or substantially equal; and

[0027] f) performing said horizontal translation of said residual values ​​in said model, wherein said horizontal translation of said residual values ​​results in a linear compound translation.

[0028] Embodiment 4 The method according to embodiment 1, wherein the method comprises:

[0029] d) selecting an arbitrary reference value, said arbitrary reference value being the level of an analyte at a particular dilution, wherein said level of the analyte at said particular dilution is not found in said second dilution series or in said model;

[0030] e) performing a horizontal shift (ΔX) of second dilution series values ​​to the arbitrary reference value and a horizontal shift (ΔY) of model values ​​to the arbitrary reference value, wherein the model value is the analyte level at a specific dilution of the model, wherein the second dilution series values ​​are the analyte levels at a specific dilution of the second dilution series, and wherein the second dilution series values, the model value and the arbitrary reference value are equal or substantially equal; and

[0031] f) performing said horizontal shifting of said remaining values ​​in said second dilution series and said model, wherein said horizontal shifting of said remaining values ​​results in a linear compound shift.

[0032] Embodiment 5 A method for generating a composite dilution model, the method comprising:

[0033] a) determining the level of an analyte in a first dilution series of a first biological sample comprising the analyte, wherein the first dilution series comprises at least three (3) different dilutions, and determining the level of the analyte in each of the at least three different dilutions;

[0034] b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three (3) different dilutions, and determining the level of the analyte in each of the at least three different dilutions;

[0035] c) generating a model based on the levels of the analyte in the first dilution series;

[0036] d) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from:

[0037] (i) a horizontal translation (ΔX) of the second dilution series to the model;

[0038] (ii) a horizontal translation (ΔX) of the model to the second dilution series; and

[0039] (iii) a horizontal shift (ΔX) of the second dilution series to an arbitrary reference value and a horizontal shift (ΔY) of the model to an arbitrary reference value, wherein the arbitrary reference value is the level of the analyte at a specific dilution, wherein the level of the analyte at the specific dilution is not found in the second dilution series or in the model;

[0040] wherein the at least one horizontal translation results in a linear compound series of translations; and

[0041] e) fitting a function to the linear composite translation series to form a composite dilution model.

[0042] Embodiment 6 A method as described in Embodiment 5, wherein the method includes performing a horizontal translation (ΔX) of the second dilution series to the model.

[0043] Embodiment 7 A method as described in Embodiment 5, wherein the method includes performing a horizontal translation (ΔX) of the model to the second dilution series.

[0044] Embodiment 8 is a method as described in Embodiment 5, wherein the method includes performing a horizontal shift (ΔX) of the second dilution series to an arbitrary reference value and a horizontal shift (ΔY) of the model to an arbitrary reference value, wherein the arbitrary reference value is the 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.

[0045] Embodiment 9 A method for generating a composite dilution model, the method comprising:

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

[0047] b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three (3) different dilutions, and determining the level of the analyte in each of the at least three different dilutions;

[0048] c) selecting a reference value, wherein the reference value is selected from:

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

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

[0051] d) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from:

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

[0053] (ii) a horizontal shift (ΔX) of the first dilution series to the arbitrary reference value and a horizontal shift (ΔY) of the second dilution series to the arbitrary reference value;

[0054] wherein the at least one horizontal translation results in a linear compound series of translations; and

[0055] e) fitting a function to the linear composite translation series to form a composite dilution model.

[0056] Embodiment 10 is a method as described in Embodiment 9, wherein the method includes performing a horizontal translation (ΔX) of the second dilution series to the first dilution series.

[0057] Embodiment 11 is a method as described in Embodiment 9, wherein the method includes a horizontal shift (ΔX) of the first dilution series to an arbitrary reference value and a horizontal shift (ΔY) of the second dilution series to an arbitrary reference value, wherein the arbitrary reference value is the 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.

[0058] Embodiment 12 A method for generating a composite dilution model, the method comprising:

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

[0060] b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three (3) different dilutions, and determining the level of the analyte in each of the at least three different dilutions;

[0061] c) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from:

[0062] (i) a horizontal translation (ΔX) of the second dilution series to the first dilution series; and

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

[0064] wherein the at least one horizontal translation results in a linear compound series of translations; and

[0065] d) fitting a function to the linear composite translation series to form a composite dilution model.

[0066] Embodiment 13 is a method as described in Embodiment 12, wherein the method includes performing a horizontal translation (ΔX) of the second dilution series to the first dilution series.

[0067] Embodiment 14 is a method as described in Embodiment 12, wherein the method includes a horizontal shift (ΔX) of the first dilution series to an arbitrary reference value and a horizontal shift (ΔY) of the second dilution series to an arbitrary reference value, wherein the arbitrary reference value is the 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.

[0068] Embodiment 15 A method for generating a composite dilution model, the method comprising:

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

[0070] b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three (3) different dilutions, and determining the level of the analyte in each of the at least three different dilutions;

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

[0072] d) selecting a reference value, wherein the reference value is selected from:

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

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

[0075] d) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from:

[0076] (i) a horizontal translation (ΔX) from the first model to the second model; and

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

[0078] wherein the horizontal translation results in a linear series of compound translations; and

[0079] e) fitting a function to the linear composite translation series to form a composite dilution model.

[0080] Embodiment 16 A method as described in Embodiment 15, wherein the method includes performing a horizontal translation (ΔX) from the first model to the second model.

[0081] Embodiment 17 A method as described in Embodiment 15, wherein the method includes a horizontal translation (ΔX) of the first model to the arbitrary reference value and a horizontal translation (ΔY) of the second model to the arbitrary reference value.

[0082] Embodiment 18 A method for generating a composite dilution model, the method comprising:

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

[0084] b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three (3) different dilutions, and determining the level of the analyte in each of the at least three different dilutions;

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

[0086] d) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from:

[0087] (i) a horizontal translation (ΔX) from the first model to the second model; and

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

[0089] wherein the horizontal translation results in a linear series of compound translations; and

[0090] e) fitting a function to the linear composite translation series to form a composite dilution model.

[0091] Embodiment 19 is a method as described in Embodiment 18, wherein the method includes performing a horizontal translation (ΔX) from the first model to the second model.

[0092] Embodiment 20 A method as described in Embodiment 18, wherein the method includes a horizontal translation (ΔX) of the first model to an arbitrary reference value and a horizontal translation (ΔY) of the second model to an arbitrary reference value.

[0093] Embodiment 21 A method as described in any of the preceding embodiments, wherein the level of the analyte in 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 is determined.

[0094] Embodiment 22 A method as described in any of the preceding embodiments, wherein the level of the analyte in each of at least 8 different dilutions is determined.

[0095] Embodiment 23 A method as described in any of the preceding embodiments, wherein the level of the analyte in 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 is determined.

[0096] Embodiment 24 A method as described in any of the preceding embodiments, wherein the level of the analyte in each of at least 8 different dilutions in the second dilution series is determined.

[0097] Embodiment 25 is a method as described in any of the preceding embodiments, wherein each model is independently selected from a linear regression model, a LOESS curve fitting model, a nonlinear regression model, a spline fitting model, a mixed effects regression model, a fixed effects regression model, a generalized linear model, a matrix decomposition model, and a four-parameter logistic regression (4PL) model.

[0098] Embodiment 26 A method as described in any of the preceding embodiments, wherein the level of the analyte is the relative amount of the analyte or the concentration of the analyte.

[0099] Embodiment 27 A method as described in any of the preceding embodiments, wherein the selected reference value is within the linear range of the dilution series or model.

[0100] Embodiment 28 is a method as described in Embodiment 21, wherein the selected reference value is the center point of the linear range.

[0101] Embodiment 29 A method as described in any of the preceding embodiments, wherein the first biological sample and the second biological sample comprise urine or are derived from urine.

[0102] Embodiment 30 A method as described in any of the preceding embodiments, wherein the first biological sample and the second biological sample are collected from the same subject.

[0103] Embodiment 31 A method as described in any of the preceding embodiments, wherein the first biological sample and the second biological sample are collected from different subjects.

[0104] Embodiment 32 A method as described in Embodiment 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] Embodiment 33 is a method as described in embodiment 32, wherein the first time point and the second time point differ 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] Embodiment 34 A method as described in any of the preceding embodiments, wherein the level of the analyte is measured by an assay using an aptamer, an antibody, a mass spectrophotometer, or a combination thereof.

[0107] Embodiment 35 A method as described in any of the preceding embodiments, wherein the dilution factor of each of the first dilution series and the second dilution series is a constant dilution factor.

[0108] Embodiment 36 A method as described in any of the preceding embodiments, wherein the dilution factor of each of the first dilution series and the second dilution series is at least two-fold, three-fold, four-fold, five-fold, six-fold, seven-fold, eight-fold, nine-fold, or ten-fold dilution.

[0109] Embodiment 37 The method of any one of Embodiments 1 to 35, wherein the dilution factor of each of the first dilution series and the second dilution series is an exponential dilution factor or a logarithmic dilution factor.

[0110] Embodiment 38 A method as described in any of the preceding embodiments, wherein the first biological sample and the second biological sample are the same type of biological samples.

[0111] Embodiment 39 The method of any one of Embodiments 1 to 35, wherein the first dilution series and the second dilution series are each at least 5-point continuous titrations performed with a titration factor of at least 1:2.

[0112] Embodiment 40 A method as described in any of the preceding embodiments, wherein the method further comprises horizontally shifting the level of at least one analyte from the biological test sample to the composite dilution model of the at least one analyte, thereby determining the relative dilution of the biological test sample.

[0113] Embodiment 41 The method of embodiment 40, wherein the biological test sample and the first biological sample and the second biological sample used to form the composite dilution model are the same sample type.

[0114] Embodiment 42 The method of embodiment 40 or embodiment 41, wherein the biological test sample and the first biological sample and the second biological sample used to form the composite dilution model are urine samples or are derived from urine samples.

[0115] Embodiment 43 A method for determining the relative dilution of a biological test sample from a subject, the method comprising horizontally shifting the level of at least one analyte from the biological test sample to a composite dilution model developed for the at least one analyte, thereby determining the relative dilution of the biological test sample from the subject.

[0116] Embodiment 44 The method of any one of Embodiments 40 to 43, comprising adding 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, The levels of 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 are horizontally shifted to a corresponding composite dilution model developed for each of the different analytes to determine the relative dilution of the biological test sample for each of the different analytes, and the relative dilution for each of the different analytes is used to determine the relative dilution of the biological test sample.

[0117] Embodiment 45 The method of Embodiment 44, wherein the relative dilution of the biological test sample is derived from the central trend of the relative dilution of each of the different analytes.

[0118] Embodiment 46 The method of Embodiment 44 or Embodiment 45, wherein the relative dilution of the biological test sample is derived from the median, mean, or mode of the relative dilution of each of the different analytes.

[0119] Embodiment 47 The method of any one of embodiments 43 to 46, wherein the composite dilution model is developed using the method of any one of embodiments 1 to 42.

[0120] Embodiment 48 The method of any one of Embodiments 43 to 48, wherein the biological test sample and the sample used to develop the composite dilution model are the same sample type.

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

[0122] Embodiment 50 The method of any one of Embodiments 40 to 49, further comprising calculating the relative dilution of the biological test sample using the derived relative dilution factor.

[0123] Embodiment 51 A method as described in any of the preceding embodiments, wherein each analyte is a target protein.

[0124] Embodiment 52 A computer system, the computer system comprising:

[0125] a non-transitory memory storing instructions; and

[0126] one or more hardware processors coupled to the non-transitory memory and configured to read the instructions from the non-transitory memory to cause the computer system to perform operations including:

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

[0128] analyzing the analyte measurement data for respective analyte measurements for each of a plurality of selected analytes; and

[0129] A composite dilution model is generated for each of the selected analytes based on the analysis.

[0130] Embodiment 53 A computer system as described in Embodiment 52, wherein the analyzing comprises:

[0131] For each selected analyte, the biological sample having the greatest linear dilution range is determined.

[0132] Embodiment 54 The computer system of embodiment 53, wherein the analyzing further comprises:

[0133] For each selected analyte, a reference dilution model is generated based on the determined maximum linear range of the corresponding selected analyte.

[0134] Embodiment 55 The computer system of embodiment 54, wherein the analyzing further comprises:

[0135] For each selected analyte, the analyte measurement data associated with the corresponding selected analyte is shifted based on the reference dilution model generated for the corresponding selected analyte.

[0136] Embodiment 56 A computer system as described in any of Embodiments 52 to 55, wherein the biological sample is a urine sample.

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

[0138] Embodiment 58 A non-transitory computer-readable medium comprising computer-readable instructions that, when executed by a processing device, cause the processing device to perform operations comprising:

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

[0140] analyzing the analyte measurement data for respective analyte measurements for each of a plurality of selected analytes; and

[0141] A composite dilution model is generated for each of the selected analytes based on the analysis.

[0142] Embodiment 59 A computer-implemented method for generating a composite dilution model for a biological material, the method comprising:

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

[0144] analyzing, by one or more of the processing devices, respective analyte measurement results in the analyte measurement data for each of a plurality of selected analytes; and

[0145] A composite dilution model is generated by one or more of the processing devices for each of the selected analytes based on the analysis.

[0146] Embodiment 60 A computer system, the computer system comprising:

[0147] a non-transitory memory storing instructions; and

[0148] one or more hardware processors coupled to the non-transitory memory and configured to read the instructions from the non-transitory memory to cause the computer system to perform operations including:

[0149] receiving analyte measurement data of an analyte in a biological sample;

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

[0151] receiving a composite dilution model generated for a respective one of the selected analytes;

[0152] determining, for each of the selected analytes, a predicted relative dilution value based on a corresponding composite dilution model generated for the respective selected analyte; and

[0153] The predicted relative dilution of the biological sample is determined based on the determined predicted relative dilution value of the selected analyte.

[0154] The foregoing and other objects, features and advantages of the present invention will become more apparent from the following detailed description, which proceeds with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0155] Figure 1 The linear range determined by serial dilutions is shown. RFU = relative fluorescence units.

[0156] FIG. 2A to FIG. 2E Analysis of cystatin C (CST3) levels and ephrin type B receptor 6 (EPHB6) are shown. Figure 2A Shown is the measurement of CST3 levels using serial dilutions of urine samples. Figure 2B The use of a weighted linear regression model to generate a regression line for CST3 is shown. Figure 2C A 4-parameter logistic function (4PL) fit of the CST3 data is shown. Figure 2D Shown is the use of serial dilutions of urine samples to measure EPHB6 levels. Figure 2E The use of a weighted linear regression model to generate the regression line for EPHB6 is shown.

[0157] FIG. 3A to FIG. 3C Analysis of platelet-derived growth factor D (PDGFD) levels is shown. Figure 3AShown is the use of serial dilutions of urine samples to measure PDGFD levels. Figure 3B The use of a weighted linear regression model to generate a regression line is shown. Figure 3C A 4PL fit of the PDGFD data is shown.

[0158] FIG. 4A to FIG. 4C Analysis of retinoic acid receptor response factor 2 (RARRES2) levels is shown. Figure 4A Shown is the use of serial dilutions of urine samples to measure RARRES2 levels. Figure 4B The use of a weighted linear regression model to generate a regression line is shown. Figure 4C A 4PL fit of the RARRES2 data is shown.

[0159] FIG. 5A to FIG. 5C Analysis of interleukin 1 receptor-like 2 (IL1RL2) levels is shown. Figure 5A Shown is the use of serial dilutions of urine samples to measure IL1RL2 levels. Figure 5B The use of a weighted linear regression model to generate a regression line is shown. Figure 5C A 4PL fit of the IL1RL2 data is shown.

[0160] FIG. 6A to FIG. 6C Analysis of coagulation factor XI (F11) levels is shown. Fig. 6A Shown is the use of serial dilutions of urine samples to measure F11 levels. Figure 6B The use of a weighted linear regression model to generate a regression line is shown. Figure 6C A 4PL fit of the F11 data is shown.

[0161] 7A to 7C Analysis of sepsis protein 11 (SEPT11) levels is shown. Fig. 7A Shown is the use of serial dilutions of urine samples to measure SEPT11 levels. Figure 7B The use of a weighted linear regression model to generate a regression line is shown. Figure 7C A 4PL fit of the SEPT11 data is shown.

[0162] FIG. 8A to FIG. 8C Analysis of thymopoietin (TMPO) levels is shown. Fig. 8A Shown is the use of serial dilutions of urine samples to measure TMPO levels. Figure 8B The use of a weighted linear regression model to generate a regression line is shown. Figure 8C A 4PL fit of the TMPO data is shown.

[0163] 9A to 9C Analysis of shisa family member 3 (SHISA3) levels is shown. Fig. 9AShown is the measurement of SHISA3 levels using serial dilutions of urine samples. Fig. 9B The use of a weighted linear regression model to generate a regression line is shown. Fig. 9C A 4PL fit of the SHISA3 data is shown.

[0164] FIG. 10A to FIG. 10B Normalized results for analyte levels of iduronidase (IDUA, 10A) and neogenin 1 (NEO1, 10B) are shown.

[0165] Fig.11 The distribution of predicted relative dilutions for 4 serial 1:2 dilutions of a single sample is shown. The lines shown are 0.3125%, 0.625%, 1.25%, 2.5%, 5%, 10%, 20% and 40% from left to right.

[0166] Fig.12 Shown are target protein levels for the 15 study participants before normalization.

[0167] Fig.13 Shown are the predicted relative dilutions of each sample calculated from the composite dilution curves for the 15 study participants.

[0168] Fig.14 A normalized sample of 15 study participants is shown.

[0169] FIG. 15A to FIG. 15D Non-normalized measurements of analytes from urine of a single study participant are shown ( Fig.15A and Fig. 15C ) and normalized measurement results ( Fig. 15B and Fig.15D ).

[0170] FIG. 16A to FIG. 16D Non-normalized measurements of analytes from urine of a single study participant are shown ( Fig.16A and Fig. 16C ) and normalized measurement results ( Fig. 16B and Fig.16D ).

[0171] FIG. 17A to FIG. 17B The F statistics for the non-normalized and normalized data for each study participant are shown ( Fig.17A ), and the fold reduction in the F statistic for each study participant ( Fig. 17B ).

[0172] Fig.18A and Fig.18B Shown are a flow chart (A) and a graph (B) illustrating a first exemplary method of forming a composite dilution model.

[0173] Fig.19A and Fig.19B Shown are a flow chart (A) and a graph (B) illustrating a second exemplary method of forming a composite dilution model.

[0174] Fig. 20A and Fig. 20B Shown are a flow chart (A) and a graph (B) illustrating a third exemplary method of forming a composite dilution model.

[0175] Fig.21A and Fig. 21B Shown are a flow chart (A) and a graph (B) illustrating a fourth exemplary method of forming a composite dilution model.

[0176] Fig.22A and Fig. 22B Shown are a flow chart (A) and a graph (B) illustrating a fifth exemplary method of forming a composite dilution model.

[0177] Fig.23A and Fig. 23B Shown are a flow chart (A) and a graph (B) illustrating a sixth exemplary method of forming a composite dilution model.

[0178] Fig.24A and Fig. 24B Shown are a flow chart (A) and a graph (B) illustrating a seventh exemplary method of forming a composite dilution model.

[0179] Fig.25 is a block diagram illustrating an example system architecture according to various examples of the present disclosure.

[0180] Fig.26 and Fig. 27 is an exemplary flow chart illustrating the generation of a composite dilution model for each of a plurality of analytes present in different biological samples according to examples of the present disclosure.

[0181] Fig.28 is a flow chart showing application of a composite dilution model to a new biological sample to predict the relative dilution of the new sample according to one example of the present disclosure.

[0182] Fig.29 is a block diagram of an exemplary computer system that can perform one or more of the operations described herein. DETAILED DESCRIPTION

[0183] I. Terminology and Methodology

[0184] While the present invention will be described in conjunction with certain representative embodiments, it will be understood that the invention is defined by the claims and is not limited to these embodiments.

[0185] One skilled in the art will recognize that many methods and materials similar or equivalent to those described herein can be used in the practice of the present invention. The present invention is in no way limited to the methods and materials described.

[0186] Unless otherwise limited, the technical and scientific terms used herein have the meanings commonly understood by those of ordinary skill in the art to which the invention belongs. Definitions of commonly used 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). Although any methods, devices and materials similar or equivalent to those described herein can be used in the practice of the present invention, certain methods, devices and materials are described herein.

[0187] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as if each individual publication, published patent document, or patent application was specifically and individually indicated to be incorporated by reference.

[0188] As used in this application, including the appended claims, the singular forms "a," "an," and "the" include plural forms and are used interchangeably with "at least one" and "one or more," unless the context clearly dictates otherwise. Thus, reference to an "aptamer" includes a mixture of aptamers, reference to a "probe" includes a mixture of probes, and so forth.

[0189] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, product, or composition of matter that comprises, includes, or contains an element or list of elements may include other elements not expressly listed.

[0190] It is also to be understood that all base sizes or amino acid sizes and all molecular weights or molecular weight values ​​given for nucleic acids or polypeptides are approximate and are provided for description purposes.

[0191] In addition, ranges provided herein are understood to be shorthand for all values ​​within the range. For example, a range of 1 to 50 is understood to include any number, combination of numbers, or subranges of 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, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50, and fractions thereof, unless the context clearly dictates otherwise.

[0192] Unless otherwise indicated, any concentration range, percentage range, ratio range or integer range should be understood to include the value of any integer within the range, and its fractions (such as one tenth and one hundredth of an integer) when appropriate. In addition, unless otherwise indicated, any numerical range related to any physical feature (such as polymer subunits, size or thickness) described herein should be understood to include any integer within the range.

[0193] As used herein, unless otherwise indicated, "about" or "consisting essentially of" means ± 20% of the indicated range, value, or structure.

[0194] The use of alternatives (eg, "or") should be understood to mean either, both, or any combination of the alternatives.

[0195] To facilitate review of the various embodiments of the present disclosure, the following explanations of specific terms are provided:

[0196] Antibody: The term "antibody" refers to full-length antibodies of any species and fragments and derivatives of such antibodies that retain the ability to bind to an antigen, including Fab fragments, F(ab')2 fragments, single-chain antibodies, Fv fragments, and single-chain Fv fragments. The term "antibody" also includes synthetically derived antibodies, such as phage-displayed derived antibodies and fragments, affibodies, and nanobodies.

[0197] Aptamer: As used herein, "aptamer" refers to a nucleic acid having a specific binding affinity to a target molecule, wherein the binding of the aptamer to the target molecule does not include Watson-Crick base pairing. It is recognized that affinity interactions are a matter of degree; however, in this context, the "specific binding affinity" of an aptamer to its target means that the aptamer generally binds to its target with a much higher affinity than it binds to other components in the test sample. An "aptamer" is a set of copies of a type or species of nucleic acid molecules having a specific nucleotide sequence. An aptamer may contain any suitable number of nucleotides, including any number of chemically modified nucleotides. The plural "aptamer" refers to more than one set of such molecules. Different aptamers may have the same or different numbers of nucleotides. An aptamer may be a DNA or RNA or a chemically modified nucleic acid, and may be single-stranded, double-stranded, or contain both single-stranded and double-stranded regions, and may include higher order structures. An aptamer may also contain photoreactive or chemically reactive functional groups to allow it to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein may include the use of two or more aptamers that specifically bind to the same target molecule. As further described below, the aptamer may include a tag. If the aptamer includes a tag, not all copies of the aptamer need to have the same tag. In addition, if different aptamers each include a tag, each different aptamer may have the same tag or a different tag.

[0198] Biological sample or biological matrix: As used herein, "biological sample" and "biological matrix" refer to any material, solution or mixture obtained from an organism. This includes blood (including whole blood, leukocytes, peripheral blood mononuclear cells, plasma and serum), sputum, exhaled air, urine, semen, saliva, meningeal fluid, amniotic fluid, glandular fluid, lymph fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, cell, cell extract and cerebrospinal fluid. This also includes experimentally separated parts of all the aforementioned substances. The terms "biological sample" and "biological matrix" also include materials, solutions or mixtures containing homogenized solid substances such as from, for example, fecal samples, tissue samples or tissue biopsies. The terms "biological sample" and "biological matrix" also include materials, solutions or mixtures derived from cell lines, tissue cultures, cell cultures, bacterial cultures, viral cultures or cell-free biological systems (such as IVTT).

[0199] Level: As used herein, "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 indicate the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc. of, for, or corresponding to an analyte in a biological sample. The exact nature of the "level" depends on the specific design and composition of the particular analytical method used to detect the analyte.

[0200] C-5 modified pyrimidine: As used herein, the term "C-5 modified pyrimidine" refers to a pyrimidine with a modification at the C-5 position. Examples of C-5 modified pyrimidines include those described in the following documents: U.S. Pat. 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 substitution of deoxyuridine at the C-5 position with a substituent independently selected from the following: benzylcarboxamide (alternatively benzylaminocarbonyl) (Bn), naphthylmethylcarboxamide (alternatively naphthylmethylaminocarbonyl) (Nap), tryptophanaminocarboxamide (alternatively tryptophanaminocarbonyl) (Trp), phenethylcarboxamide (alternatively phenethylaminocarbonyl) (Pe), thienylmethylcarboxamide (alternatively thienylmethylaminocarbonyl) (Th) and isobutylcarboxamide (alternatively isobutylaminocarbonyl) (iBu), as shown directly below.

[0201] Chemical modification of the C-5 modified pyrimidine can also be combined with 2'-position sugar modification, exocyclic amine modification, and 4-thiouridine substitution, etc., alone or in any combination.

[0202] Representative C-5 modified pyrimidines include: 5-(N-benzylcarboxamide)-2'-deoxyuridine (BndU), 5-(N-benzylcarboxamide)-2'-O-methyluridine, 5-(N-benzylcarboxamide)-2'-fluorouridine, 5-(N-isobutylcarboxamide)-2'-deoxyuridine (iBudU), 5-(N-isobutylcarboxamide)-2'-O-methyluridine, 5-(N-phenylethylcarboxamide)-2'-deoxyuridine (PedU), 5-(N-thienylmethylcarboxamide)-2'-deoxyuridine (ThdU), 5-(N-isobutylcarboxamide)-2'-fluorouridine, 5- (N-tryptaminecarboxamide)-2'-deoxyuridine (TrpdU), 5-(N-tryptaminecarboxamide)-2'-O-methyluridine, 5-(N-tryptaminecarboxamide)-2'-fluorouridine, 5-(N-[1-(3-trimethylammonium)propyl]carboxamide)-2'-deoxyuridine chloride, 5-(N-naphthylmethylcarboxamide)-2'-deoxyuridine (NapdU), 5-(N-naphthylmethylcarboxamide)-2'-O-methyluridine, 5-(N-naphthylmethylcarboxamide)-2'-fluorouridine or 5-(N-[1-(2,3-dihydroxypropyl)]carboxamide)-2'-deoxyuridine).

[0203] Nucleotides can be modified before or after oligonucleotide synthesis. The nucleotide sequence in the oligonucleotide can be interrupted by one or more non-nucleotide components. Modified oligonucleotides can be further modified after polymerization, such as, for example, by conjugation with any suitable labeling component.

[0204] As used herein, when referring to modification of a nucleic acid, the term "at least one pyrimidine" refers to one, several or all pyrimidines in a nucleic acid, thereby indicating that any or all occurrences of any or all C, T or U in the nucleic acid may or may not be modified.

[0205] Capture agent: As used herein, "capture agent" or "capture reagent" refers to a molecule that can specifically bind to an analyte such as a biomarker, protein and / or peptide. "Target protein capture reagent" refers to a molecule that can specifically bind to a target protein. Non-limiting exemplary capture agents include aptamers, antibodies, adnectins, ankyrins, other antibody mimics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, high-affinity multimers (avimers), peptide mimics, hormone receptors, cytokine receptors, synthetic receptors, and modifications and fragments of any of the above capture agents. In some embodiments, the capture agent is selected from aptamers and antibodies.

[0206] Control level: A "control level" of a target molecule refers to the level of a target molecule in the same sample type from an individual who does not suffer from a disease or condition, or from an individual who is not suspected of suffering from a disease or condition or is not at risk of suffering from a disease or condition, or from an individual suffering from a non-progressive disease or condition. In addition, a "control level" may refer to a reference value based on an average value or considered within normal or healthy parameters. A "control level" may also refer to a reference level taken at a previous time and used to compare with a target level measured or detected later. For example, the level of a 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 can be considered to be time zero (0) or day zero (0), and time point B can be a few minutes after time point A (e.g., 10, 20, 30, 40, 50, 60 minutes after time point A), a few 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 after time point A), a few days (e.g., A "control level" of a target molecule need not be determined each time a method of the invention is performed, and can be a previously determined level used as a reference value or threshold to determine whether the level in a particular sample is above or below normal.

[0207] Corresponding correlation: "Corresponding correlation" or "Concordance correlation coefficient" measures the consistency between two continuous variables X and Y (e.g., predicted, estimated or determined and actual). "Corresponding correlation" assesses the degree to which the pairing falls on the 45° line, and includes the accuracy and precision of the measurement (or "Lin's consistency"). Additional information can be found in Lin, Biometrics, Vol. 45, No. 1 (March 1989), 255-268, which is hereby incorporated by reference. Other methods for determining correlation that can be used herein include, but are not limited to, Pearson's correlation coefficient, paired t-test, least squares analysis of slope (=1) and intercept (=0), coefficient of variation, and intraclass correlation coefficient. In certain embodiments, the corresponding correlation is determined by a method selected from: Lin's consistency, Pearson's correlation coefficient, paired t-test, least squares analysis of slope (=1) and intercept (=0), coefficient of variation, and intraclass correlation coefficient.

[0208] Detection: As used herein, "detection" or "determination" of the level of an analyte includes both the use of instruments for observing and recording signals corresponding to the level of the analyte and the materials required to generate the signals. In various embodiments, the level is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, etc.

[0209] Diagnosis: "Diagnose", "diagnosing", "diagnosis" and variations thereof refer to the detection, determination or identification of the health state or condition of an individual based on one or more signs, symptoms, data or other information related to the individual. The health state of an individual can be diagnosed as healthy / normal (i.e., the absence of a disease or condition is diagnosed) or diagnosed as sick / abnormal (i.e., the presence of a disease or condition is diagnosed or the characteristics of a disease or condition are assessed). The terms "diagnose", "diagnosing", "diagnosis" and the like, with respect to a specific disease or condition, encompass the initial detection of a disease; the characterization or classification of a disease; the detection of disease progression, remission or recurrence; and the detection of disease response after treatment or therapy is administered to an individual.

[0210] Dilution: "Dilution", "dilution series" and variations thereof include several different types of dilutions, including but not limited to step dilutions, serial dilutions, and combinations thereof. Using a step dilution as an example, if the dilution factor is 1000 (a 1:1000 dilution), the user may first make a 1:10 dilution (a dilution factor of 10), then use 1 part of solute from the 1:10 dilution and 99 parts of diluent to make a 1:100 dilution (a dilution factor of 100), thereby producing a dilution factor of 1000 or a 1:1000 dilution of the solute. A serial dilution includes a series of step dilutions, each with the same dilution factor, where the dilution material from the previous step is used to make the subsequent dilution. Using a serial dilution as an example, to prepare a 5-point 1:2 serial dilution, one would use 1 part of solute and mix with 1 part of diluent to make the first dilution in the dilution series (point 1 of 5), then use 1 part of solute from the first dilution and mix with 1 part of diluent to make the second dilution in the serial dilution series (point 2 of 5), and so on, until the fifth serial dilution is reached.

[0211] Dilution Factor: "Dilution factor" refers to the ratio of parts of solute to parts of diluent. For example, a dilution factor of 2 means a 1:2 dilution, where there is 1 part solute and 1 part diluent, for a total of 2 parts; and a dilution factor of 10 means a 1:10 dilution, where there is 1 part solute and 9 parts diluent, for a total of 10 parts.

[0212] Evaluate: "Evaluate," "evaluating," and "evaluation" and variations thereof encompass both "diagnosis" and "prognosis," and encompass determinations or predictions regarding the future course of a disease or condition in an individual who has the disease or condition, as well as determinations and predictions regarding the likelihood that a disease or condition will occur in an individual who has not previously been diagnosed with the disease or condition, and determinations or predictions regarding the likelihood that a disease or condition will recur in an individual who is in remission or is believed to be cured of the disease. The term "evaluate" also encompasses assessing an individual's response to a therapy, such as, for example, predicting whether an individual is likely to respond favorably to a therapeutic agent or is unlikely to respond to a therapeutic agent (or, for example, will experience toxicity or other undesirable side effects), selecting a therapeutic agent for administration to an individual, or monitoring or determining an individual's response to a therapy that has been or is being administered to an individual.

[0213] Individual: As used herein, "individual" and "subject" are used interchangeably and refer to a test subject or patient. An individual can be a mammal or a non-mammal. In various embodiments, the individual is a mammal. A mammalian individual can be a human or a non-human. In various embodiments, the individual is a human. A healthy or normal individual is one in which the disease or condition of interest cannot be detected by conventional diagnostic methods.

[0214] Linear regression: As used herein, the term "linear regression" refers to a method for modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted as x. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, it is called multiple linear regression. In general, linear regression can be used to fit a predictive model to an observed data set of y and x values. After such a model is developed, if additional values ​​of x are given without their accompanying y values, the fitted model can be used to predict the y values.

[0215] Marker: As used herein, "marker" and "biomarker" are used interchangeably and refer to a target molecule (or analyte) that indicates or characterizes a normal or abnormal process in an individual or a disease or other condition in an individual. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical or molecular parameter associated with the presence of a particular physiological state or process, whether normal or abnormal, and if abnormal, whether chronic or acute. Biomarkers can be detected and measured by a variety of methods, including laboratory assays and medical imaging. In some embodiments, a biomarker is a target protein.

[0216] Modified: As used herein, the terms "modify," "modified," "modification," and any variations thereof, when used in reference to an oligonucleotide, mean that at least one of the four constituent nucleotide bases of the oligonucleotide (i.e., A, G, T / U, and C) is an analog or ester of a naturally occurring nucleotide. In some embodiments, the modified nucleotides impart nuclease resistance to the oligonucleotide. In some embodiments, the modified nucleotides result in a predominantly hydrophobic interaction of the aptamer with the protein target, resulting in a high binding efficiency and a stable co-crystal complex. A pyrimidine with a substitution at the C-5 position is an example of a modified nucleotide. Modifications may include backbone modifications, methylation, unusual base pairing combinations, such as the isobases isocytidine and isoguanidine, and the like. Modifications may also include 3' and 5' modifications, such as capping. Other modifications may include substitution of one or more naturally occurring nucleotides with an analog; internucleotide modifications such as, for example, those with uncharged linkages (e.g., methylphosphonates, phosphotriesters, phosphoamidates, cabamates, etc.) and those with charged linkages (e.g., phosphorothioates, phosphorodithioates, etc.), those with intercalators (e.g., acridine, psoralen, etc.), those containing chelators (e.g., metals, radioactive metals, boron, oxidized metals, etc.), those containing alkylators, and those with modified linkages (e.g., alpha anomeric nucleic acids, etc.). In addition, any hydroxyl group normally present on the sugar of a nucleotide may be replaced by a phosphonate group, a phosphate group; protected by standard protecting groups; or activated to prepare additional linkages with another nucleotide or with a solid support. The 5' and 3' terminal OH groups may be phosphorylated or substituted with amines, organic capping moieties of about 1 to about 20 carbon atoms, polyethylene glycol (PEG) polymers in the range of about 10 kDa to about 80 kDa in some embodiments, PEG polymers in the range of about 20 kDa 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. These modifications may be made by amide bonding directly at the C-5 position or by other types of bonding.

[0217] The polynucleotide may also contain analogous forms of ribose or deoxyribose sugars generally known in the art, including 2'-O-methyl-, 2'-O-allyl, 2'-fluoro- or 2'-azido-ribose, carbocyclic sugar analogs, α-anomeric sugars, epimeric sugars (such as arabinose, xylose or lyxose), pyranose, furanose, sedoheptulose, acyclic analogs and abasic nucleoside analogs (such as methyl nucleosides). As described above, one or more phosphodiester bonds may be replaced by alternative linking groups. These alternative linking groups include embodiments in which the phosphate is replaced by P(O)S ("thioester"), P(S)S ("dithioester"), (O)NR2 ("amidate"), P(O)R, P(O)OR', CO or CH2 ("methylal"), wherein each R or R' is independently H, or a substituted or unsubstituted alkyl (1-20 C), aryl, alkenyl, cycloalkyl, cycloalkenyl or aralkyl optionally containing an ether (-O-) linkage. Not all linkages in a polynucleotide need be identical. Analogous substitutions of sugars, purines and pyrimidines may be advantageous in designing the final product, for example, alternative backbone structures such as polyamide backbones may also be advantageous in designing the final product.

[0218] Nucleic Acid: As used herein, "nucleic acid," "oligonucleotide," and "polynucleotide" are used interchangeably to refer to polymers of nucleotides, and include DNA, RNA, DNA / RNA hybrids, and modifications of these types of nucleic acids, oligonucleotides, and polynucleotides, including the attachment of various entities or moieties to the nucleotide units at any position. The terms "polynucleotide," "oligonucleotide," and "nucleic acid" include double-stranded or single-stranded molecules, as well as triple-helical molecules. Nucleic acid, oligonucleotide, and polynucleotide are broader terms than the term aptamer, and thus 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.

[0219] Ordinary Least Squares: As used herein, "ordinary least squares" or "OLS" or "linear least squares" refers to a method for estimating the unknown parameters in a linear regression model. This method minimizes the sum of the squared vertical distances between the observed response in the data set and the response predicted by the linear approximation. The resulting estimator can be expressed using a simple formula, especially when there is only one regressor on the right-hand side.

[0220] Prognosis: "Prognose," "prognosing," "prognosis," and variations thereof refer to a prediction of the future course of a disease or condition in an individual suffering from the disease or condition (e.g., prediction of patient survival), and such terms encompass the assessment of disease response during and / or after administration of treatment or therapy to an individual.

[0221] SELEX: The terms "SELEX" and "SELEX process" are used interchangeably herein and generally refer to the combination of (1) selecting aptamers that interact with a target molecule in a desired manner, such as binding to a protein with high affinity, and (2) amplifying those selected nucleic acids. The SELEX process can be used to identify aptamers that have high affinity for a particular analyte, such as a target protein.

[0222] Sequence identity: As used herein, in the context of two or more nucleic acid sequences, sequence identity is a function of the number of identical nucleotide positions shared by the sequences (i.e., % identity = number of identical positions / total number of positions in the reference sequence × 100), taking into account the number of gaps and the length of each gap that needs to be introduced to optimize the alignment of the two or more sequences. The comparison of sequences and the determination of the percent identity between two or more sequences can be performed using mathematical algorithms such as BLAST and Gapped BLAST programs under their default parameters (e.g., Altschul et al., J. Mol. Biol. 215: 403, 1990; see also BLASTN, at www.ncbi.nlm.nih.gov / BLAST). For sequence comparison, one sequence usually serves as a reference sequence for comparison with a test sequence. When using a sequence comparison algorithm, the test sequence and the reference sequence are entered into a computer, subsequence coordinates are specified if necessary, and sequence algorithm program parameters are specified. Then, the sequence comparison algorithm calculates the percent sequence identity of the test sequence relative to the reference sequence based on the specified program parameters. Optimal alignment of sequences for comparison can be performed as follows: 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 the similarity search method of Pearson and Lipman, Proc. Nat'l. Acad. Sci. USA 85:2444, 1988; by computerized implementations of these algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Dr., Madison, Wis.); or by visual inspection (see generally, Ausubel, FM et al., Current Protocols in Molecular Biology, published by Greene Publishing Assoc. and Wiley-Interscience (1987)). As used herein, when describing the percent identity of a nucleic acid, such as an aptamer, whose sequence is at least, for example, about 95% identical to a reference nucleotide sequence, it means that the nucleic acid sequence is identical to the reference sequence, except that the nucleic acid sequence may contain up to five point mutations per every 100 nucleotides of the reference nucleic acid sequence.In other words, in order to obtain a desired nucleic acid sequence, whose sequence is at least about 95% identical to a reference nucleic acid sequence, up to 5% of the nucleotides in the reference sequence may be deleted or substituted with another nucleotide, or a certain number of nucleotides up to 5% of the total number of nucleotides in the reference sequence may be inserted into the reference sequence (referred to herein as insertions). These mutations of the reference sequence that generate the desired sequence may occur at the 5' or 3' terminal position of the reference nucleotide sequence or anywhere between these terminal positions, or may be interspersed individually between nucleotides in the reference sequence, or may be interspersed in one or more consecutive groups within the reference sequence.

[0223] SOMAmer: As used herein, the term SOMAmer or SOMAmer agent refers to an aptamer with improved off-rate characteristics. SOMAmer agents are alternatively referred to as slow off-rate modified aptamers and can be selected via the improved SELEX method described in U.S. Publication No. 20090004667, entitled "Method for Generating Aptamers with Improved Off-Rates", which is incorporated by reference in its entirety. In some embodiments, slow off-rate aptamers (including aptamers comprising at least one nucleotide with a hydrophobic modification) have an off-rate (t1 / 2) of ≥2 minutes, ≥4 minutes, ≥5 minutes, ≥8 minutes, ≥10 minutes, ≥15 minutes, ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes.

[0224] Substantially equivalent: As used herein, the phrase "substantially equivalent" means a sufficiently high degree of similarity between two values ​​that a person skilled in the art would consider the difference between the two values ​​to have little or no biological and / or statistical significance within the range of properties measured by these values. 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%.

[0225] Target molecule: "target", "target molecule" and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a sample. The term includes any subtle changes in a particular molecule, such as in the case of a protein, for example, subtle changes in the amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component that does not substantially change the identity of the molecule. "Target molecule", "target" or "analyte" refers to a set of copies of a type or species of molecule or multimolecular structure. "Target molecule", "target" and "analyte" refer to more than one type or species of molecule or multimolecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimetics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the foregoing substances. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as a "target protein".

[0226] The foregoing and other objects, features and advantages of the present invention will become more apparent from the following detailed description, which proceeds with reference to the accompanying drawings.

[0227] Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure, suitable methods and materials are described below. In addition, the materials, methods, and examples are illustrative only and are not intended to be limiting.

[0228] Detection and determination of analytes and analyte levels

[0229] Any of a variety of known analytical methods can be used to detect the analyte level of the analyte described herein. In one embodiment, a capture reagent is used to detect the analyte level. In various embodiments, the capture reagent can be exposed to the analyte in the solution or can be exposed to the analyte when the capture reagent is fixed on a solid support. In some embodiments, the capture reagent comprises a feature that reacts with a secondary feature on a solid support. In these embodiments, the capture reagent can be exposed to the analyte in the solution, and then the feature on the capture reagent can be used in combination with the secondary feature on the solid support to fix 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, Adenectin, ankyrin, 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 binding receptors, affibodies, nanobodies, imprinted polymers, high-affinity polymers, peptide mimics, hormone receptors, cytokine receptors and synthetic receptors, including any of these modifiers and fragments.

[0230] In some embodiments, analyte levels are detected using an analyte / capture reagent complex.

[0231] In some embodiments, the analyte level is derived from the analyte / capture agent complex and is detected indirectly, such as, for example, as a result of a reaction subsequent to the analyte / capture agent interaction, but dependent upon the formation of the analyte / capture agent complex.

[0232] In some embodiments, analyte levels are detected directly from the analyte in a biological sample.

[0233] In some embodiments, multiple formats are used to detect analytes in two or more analytes in biological samples simultaneously. In some embodiments of multiple formats, capture reagents are directly or indirectly, covalently or non-covalently fixed to discrete positions on solid supports. In some embodiments, multiple formats use discrete solid supports, wherein each solid support has a unique capture reagent associated with the solid support, such as, for example, quantum dots. In some embodiments, a separate device is used to detect each of the multiple analytes to be detected in biological samples. A single device can be configured to allow each analyte in a biological sample to be processed simultaneously. For example, a microtiter plate can be used so that each hole in the plate is used to analyze one or more of the multiple analytes to be detected in a biological sample.

[0234] In one or more embodiments described herein, a fluorescent label can be used to label the components of the analyte / capture reagent complex to enable detection of analyte levels. In various embodiments, a fluorescent label can be conjugated to a capture reagent specific for any of the analytes described herein using known techniques, and then the corresponding analyte levels can be detected using the fluorescent label. Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, lissamine, phycoerythrin, Texas Red, and other such compounds.

[0235] In some embodiments, fluorescent label is fluorescent dye molecule.In some embodiments, fluorescent dye molecule includes at least one substituted indole ring system, wherein the substituent on the 3-carbon of indole ring contains chemically reactive group or conjugated material.In some embodiments, dye molecule includes AlexFluor molecule, such as for example AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680 or AlexaFluor 700.In some embodiments, dye molecule includes the dye molecule of the first type and the second type, such as for example two different AlexaFluor molecules.In some embodiments, dye molecule includes the dye molecule of the first type and the second type, and these two dye molecules have different emission spectra.

[0236] Fluorescence can be measured with a variety of instruments compatible with a wide range of assay formats. For example, fluorescence spectrophotometers have been designed for analyzing microtiter 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, eds., World Scientific Publishing Company, January 2002.

[0237] In one or more embodiments, chemiluminescent tags may be optionally used to label components of the analyte / capture complex to enable detection of analyte levels. Suitable chemiluminescent materials include oxalyl chloride, rhodamine 6G, Ru(bipy)3 2+, TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxy oxalate, aryl oxalate, acridinium ester, dioxetane, etc.

[0238] In some embodiments, detection methods include enzyme / substrate combinations that generate detectable signals corresponding to analyte levels. Typically, enzymes catalyze chemical changes in chromogenic substrates, which can be measured using various techniques, including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, etc.

[0239] In some embodiments, the detection method can be a combination of fluorescence, chemiluminescence, radionuclides, and / or enzyme / substrate combinations that generate a measurable signal. In some embodiments, multimodal signals can have unique and advantageous features in analyte assay formats.

[0240] In some embodiments, the analyte levels of the analytes described herein can be detected using any analytical method, including singleplex aptamer assays, multiplex aptamer assays, singleplex or multiplex immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc., and as described below.

[0241] Determine analyte levels using aptamer-based assays

[0242] Many assays for the detection and quantification of physiologically important molecules in biological samples and other samples are important tools in scientific research and health care fields. One type of such assay involves the use of a microarray comprising one or more aptamers fixed on a solid support. Each of these aptamers can bind to a target molecule in a highly specific manner and with extremely high affinity. See, for example, U.S. Patent No. 5,475,096 entitled "Nucleic Acid Ligands", see, for example, U.S. Patent No. 6,242,246, U.S. Patent No. 6,458,543 and U.S. Patent No. 6,503,715, each of which is entitled "Nucleic Acid Ligand Diagnostic Biochip". Once the microarray contacts the sample, the aptamer binds to their corresponding target molecules present in the sample, and thus enables the analyte level corresponding to the analyte to be determined.

[0243] In one aspect, the aptamer can 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, up to about 65 nucleotides, up to about 60 nucleotides, up to about 55 nucleotides, up to about 50 nucleotides, up to about 45 nucleotides, up to about 40 nucleotides, up to about 35 nucleotides, up to about 30 nucleotides, up to about 25 nucleotides, and up to about 20 nucleotides. In related aspects, an aptamer can be about 25 to about 100 nucleotides long (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, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 41 , 42, 43, 44, 45, 46, 47, 48, 49, or 50 nucleotides long).

[0244] Any known method can be used, including the SELEX method to identify aptamers. After identification, aptamers can be prepared or synthesized according to any known method, including chemical synthesis methods and enzymatic synthesis methods. In some embodiments, the aptamer includes at least one nucleotide with hydrophobic modification (such as hydrophobic base modification), thereby allowing hydrophobic contact with the target protein. In some embodiments, such hydrophobic contact helps the greater affinity and / or slower dissociation rate of the aptamer to bind. In some embodiments, the aptamer includes 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 10 nucleotides with hydrophobic modification, wherein each hydrophobic modification can be the same or different from other. In some embodiments, the hydrophobic base modification is a pyrimidine modified by C-5. The pyrimidine modified by non-limiting exemplary C-5 is described herein and / or is known in the art.

[0245] In some assay formats, the aptamer is fixed on a solid support before contact with the sample. However, in some cases, fixing the aptamer before contact with the sample may not provide the best assay. For example, in some cases, the pre-fixation of the aptamer may result in inefficient mixing of the aptamer with the target molecule on the surface of the solid support, which may result in a long reaction time, and thus extend the incubation period to allow the aptamer to effectively bind to its target molecule. In addition, when a photoaptamer is used in the assay and depending on the material used as a solid support, the solid support may tend to scatter or absorb light used to affect the formation of a covalent bond between the photoaptamer and its target molecule. In addition, depending on the method used, the detection of the binding of the target molecule to its aptamer may not be accurate because the surface of the solid support may also be exposed to and affected by any labeling agent used. Finally, the fixation of the aptamer on the solid support generally involves an aptamer preparation step (i.e., fixation) before the aptamer is exposed to the sample, and the preparation step may affect the activity or function of the aptamer.

[0246] Aptamer assays or "aptamer-based assays" have also been described, which allow an aptamer to capture its target in solution and then employ separation steps designed to remove specific components of the aptamer-target mixture prior to detection (see, e.g., U.S. Publication No. 2009 / 0042206, entitled "Multiplexed Analyses of Test Samples"). The described aptamer assay methods enable the detection and quantification of non-nucleic acid targets (e.g., protein targets) in a test sample by detecting and quantifying nucleic acids (i.e., aptamers). The described methods generate nucleic acid surrogates (i.e., aptamers) for the detection and quantification of non-nucleic acid targets, thereby allowing the application of a variety of nucleic acid techniques, including amplification, to a wider range of desired targets, including protein targets.

[0247] Aptamers can be constructed to facilitate separation of assay components from aptamer-analyte complexes (or photoaptamer-analyte covalent complexes) and allow separation of aptamers for detection and / or quantification. In one embodiment, these constructs can include cleavable or releasable elements within the aptamer sequence. In other embodiments, additional functions can be introduced into the aptamer, such as labeled or detectable components, spacer components, or specific binding tags or fixed elements. For example, the aptamer can include a label, a label, a spacer component that separates the label, and a cleavable portion that is connected to the aptamer via a cleavable portion. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker can be connected to the biotin portion and the spacer segment, can include an NHS group for amine derivatization, and can be used to introduce the biotin group into the aptamer, thereby allowing the aptamer to be released later in the assay method.

[0248] Homogeneous assays performed with all assay components in solution in some embodiments may not require separation of sample and reagents prior to detecting the signal. These methods are rapid and easy to use.

[0249] In some embodiments, the method for signal generation utilizes the anisotropic signal change caused by the interaction of the capture reagent labeled with a fluorophore with its specific analyte target. When the labeled capture reacts with its target, the increased molecular weight causes the rotational motion of the fluorophore attached to the complex to become much slower, thereby changing the anisotropy value. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the analyte in the solution. Other methods include fluorescence polarization determination, molecular beacon method, time-resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, etc.

[0250] An exemplary solution-based aptamer assay that can be used to detect the level of an analyte in a biological sample comprises the following steps: (a) preparing a mixture by contacting the biological sample with an aptamer comprising a first tag and having a 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 allowing the first tag to associate with the first capture element; (c) removing any components of the mixture that are 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 allowing the second tag to associate with the second capture element; (g) removing any non-complexed aptamers from the mixture by separating the non-complexed aptamers from the aptamer affinity complex; (h) eluting the aptamers 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 in a sample can be expressed as relative fluorescence units (RFU), which can be the product of the aptamer component of the detection aptamer affinity complex (e.g., the aptamer complexed with the target protein produces the aptamer affinity complex). That is, for aptamer-based assays, protein concentration or level is correlated to RFU.

[0251] Non-limiting exemplary methods for using aptamers to detect analytes in biological samples are described in Kraemer et al., PLoS One 6(10):e26332.

[0252] Determination of analyte levels using immunoassays

[0253] Immunoassays are based on the reaction of antibodies with their corresponding targets or analytes and can detect analytes in samples depending on the specific assay format. To increase the specificity and sensitivity of immunoreactivity-based assays, monoclonal antibodies and their fragments are often used due to their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays due to their increased affinity for the target compared to monoclonal antibodies. Immunoassays are designed for use with a wide range of biological sample matrices. Immunoassay formats are designed to provide qualitative, semi-quantitative, and quantitative results.

[0254] Quantitative results are generated by using a standard curve generated from known concentrations of the specific analyte to be detected. The response or signal from the unknown sample is plotted onto the standard curve and the amount or level corresponding to the target in the unknown sample is established.

[0255] A variety of immunoassay formats have been designed. ELISA or EIA can quantitatively detect analytes. The method relies on attaching a label to the analyte or antibody, and the label component directly or indirectly includes an enzyme. The ELISA test format can be used for direct, indirect, competitive or sandwich detection of analytes. Other methods rely on labels, such as, for example, radioisotopes (I 125 ) or fluorescence. Other techniques include, for example, agglutination, turbidimetry, nephelometry, Western blotting, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, etc. (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0256] 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 analyte immunoprecipitation followed by quantitative methods that allow size and peptide level differentiation, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.

[0257] The method of detecting and / or quantifying the detectable label or signal generating material depends on the nature of the label. The reaction products catalyzed by the appropriate enzyme (wherein the detectable label is an enzyme; see above) can be, but are not limited to, fluorescent, luminescent or radioactive, or they can absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, but are not limited to, x-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, photometers and densitometers.

[0258] Any detection method can be carried out in any form that allows any suitable reaction preparation, processing and analysis. This can be, for example, in a multi-well assay plate (e.g., 96-well or 386-well) or using any suitable array or microarray. Stock solutions of various reagents can be prepared manually or automatically, and all subsequent pipetting, dilution, mixing, distribution, washing, incubation, sample readout, data collection and analysis can be automatically completed using commercially available analysis software, robots and detection instruments capable of detecting detectable labels.

[0259] Determine analyte levels using gene expression profiling

[0260] In some embodiments, measuring mRNA in a biological sample can be used as a surrogate for detecting the level of the corresponding protein in the biological sample. Thus, in some embodiments, an analyte or group of analytes described herein can be detected by detecting the appropriate RNA.

[0261] 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. cDNA can be used in qPCR assays to generate fluorescence as the DNA amplification process proceeds. By comparing with a standard curve, qPCR can produce absolute measurements, such as the number of mRNA copies per cell. Northern blotting, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure the expression level of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, edited by Richard A. Shimkets, Humana Press, 2004.

[0262] Determination of analyte levels using mass spectrometry

[0263] Mass spectrometers of various configurations can be used to detect analyte levels. Several types of mass spectrometers are available, or they can be produced in different configurations. Typically, a mass spectrometer has the following main components: sample inlet, ion source, mass analyzer, detector, vacuum system, instrument control system, and data system. The differences between sample inlet, ion source, and mass analyzer usually define the instrument type and its capabilities. For example, the inlet can be a capillary column liquid chromatography source, or can be a direct probe or a platform such as for matrix-assisted laser desorption. Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass analyzers, and time-of-flight mass analyzers. Other mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70: 647R-716R (1998); Kinter and Sherman, New York (2000)).

[0264] Protein analytes and analyte levels can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-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), desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) technology (known as ultraflexIII TOF / TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS) N , atmospheric pressure photoionization mass spectrometry (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.

[0265] Sample preparation strategies are used to label and enrich samples before the mass spectrometry characterization of protein analytes and the 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) of amino acids in cell culture. Capture reagents for selectively enriching candidate analyte proteins in samples before mass spectrometry 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 binding receptors, affibodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g., double antibodies, etc.), imprinted polymers, high-affinity polymers, peptide mimetics, peptidomimetics, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modifiers and fragments thereof.

[0266] Reagent test kit

[0267] Any combination of analytes described herein can be detected using a suitable kit, such as for performing the methods disclosed herein. In addition, any kit can contain one or more detectable labels as described herein, such as fluorescent moieties, etc.

[0268] Methods for normalizing analyte measurements in biological matrices

[0269] Provided herein are methods for normalizing analyte measurements in biological matrices.

[0270] In certain embodiments, a method for developing a composite dilution model is provided. The composite dilution model can be used to determine the relative dilution of one or more biological samples (such as urine). See, for example, Figure 10. Relative dilution can then be used to normalize multiple samples so that the difference in sample concentration (such as may occur by the difference in the subject's hydration level) can be minimized, and therefore the difference in analyte levels due to factors other than sample concentration can be detected. Such factors that may affect analyte levels other than sample concentration include, but are not limited to, an increase and decrease in analyte expression, a change in analyte stability, a change in the rate or amount of secreted analytes, etc. In some embodiments, such differences in analyte levels can indicate a disease or illness, or the possibility of suffering from a disease or illness.

[0271] In certain embodiments, by Fig.18A and Fig.18BThe exemplary method shown in Develop a composite dilution model. The level of an analyte is measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first biological sample and the second biological sample can be biological samples from different time points of the same individual, or biological samples from different individuals. The dilution series includes the analyte level at various dilutions of the sample. Generate a model based on the first dilution series. Select a reference value in the second dilution series. In some embodiments, the selected reference value in the second dilution series is near the midpoint of the second dilution series, for example, in a portion of the series that appears to be substantially linear. The reference value in the second dilution series is then translated by a certain amount ΔX so as to translate to a point on the model. The remaining points of the second dilution series (i.e., the analyte level at various dilutions) are translated by the same amount ΔX to form a linear composite translation. A composite dilution model is generated by fitting a function to a linear composite translation series. The composite dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0272] In certain embodiments, by Fig.19A and Fig.19B The exemplary method shown in develops a composite dilution model. The level of an analyte is measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first biological sample and the second biological sample can be biological samples from the same individual at different time points, or biological samples from different individuals. The dilution series includes the level of the analyte at various dilutions of the sample. A model is generated based on the first dilution series. A reference value in the model is selected. In some embodiments, the model reference value is the level of the analyte at a specific dilution of the model. The reference value in the model is then translated by a certain amount ΔX so as to be translated to a point in the second dilution series. The remaining values ​​of the model are translated by the same amount ΔX to form a linear composite translation series. A composite dilution model is generated by fitting a function to a linear composite translation series. The composite dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0273] In certain embodiments, by Fig. 20A and Fig. 20BThe exemplary method shown in Develop a composite dilution model. The level of an analyte is measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first biological sample and the second biological sample can be biological samples from different time points of the same individual, or biological samples from different individuals. The dilution series includes the analyte levels at various dilutions of the sample. Select a reference value in the second dilution series. In some embodiments, the selected reference value in the second dilution series is near the midpoint of the second dilution series, for example, in a portion of the series that appears to be substantially linear. The reference value in the second dilution series is then translated by a certain amount ΔX so as to translate to the value in the first dilution series. The remaining points of the second dilution series (i.e., the analyte levels at various dilutions) are translated by the same amount ΔX to form a linear composite translation series. A composite dilution model is generated by fitting a function to a linear composite translation series. The composite dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0274] In certain embodiments, by Fig.21A and Fig. 21B The exemplary method shown in Develop a composite dilution model. The level of the analyte is measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first biological sample and the second biological sample can be biological samples from different time points of the same individual, or biological samples from different individuals. The model is generated based on the first dilution series. Select an arbitrary reference value, which is the analyte level under the dilution value. In some embodiments, the arbitrary reference value is the analyte level under a specific dilution not found in the second dilution series or the model. Then the points on the mathematical model are translated by a certain amount ΔX to translate to an arbitrary reference value, and the points on the second dilution series are translated by a certain amount ΔY to translate to an arbitrary reference value. The remaining points of the second dilution series (i.e., the analyte level under various dilutions) are translated by the same amount ΔY, and the remaining values ​​of the model are translated by the same amount ΔX to form a linear composite translation. The composite dilution model is generated by fitting a function to a linear composite translation series. The composite dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0275] In certain embodiments, by Fig.22AThe exemplary method shown in Develop a composite dilution model. The level of the analyte is measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first biological sample and the second biological sample can be biological samples from different time points of the same individual, or biological samples from different individuals. These dilution series show the analyte levels at various dilutions of the sample. Select an arbitrary reference value, which is the analyte level under the dilution value. In some embodiments, the arbitrary reference value is the analyte level under a specific dilution not found in the first dilution series or the second dilution series. Then the points on the first dilution series are translated by a certain amount ΔX to translate to an arbitrary reference value, and the points on the second dilution series are translated by a certain amount ΔY to translate to an arbitrary reference value. The remaining points of the first dilution series (i.e., the analyte levels at various dilutions) are translated by the same amount ΔX, and the remaining points of the second dilution series (i.e., the analyte levels at various dilutions) are translated by the same amount ΔY to form a linear composite translation. The composite dilution model is generated by fitting a function to a linear composite translation series. The composite dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0276] In certain embodiments, by Fig.23A and Fig. 23B The exemplary method shown in develops a composite dilution model. The level of an analyte is measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first biological sample and the second biological sample can be biological samples from the same individual at different time points, or biological samples from different individuals. These dilution series show the analyte levels at various dilutions of the samples. A first model is generated based on the analyte levels of the first dilution series, and a second model is generated based on the analyte levels of the second dilution series. The values ​​from the first model are then translated by a certain amount ΔX to translate to the values ​​in the second model. The remaining values ​​of the first model are then translated by the same amount ΔX to form a linear composite translation. A composite dilution model is generated by fitting a function to a linear composite translation series. The composite dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0277] In certain embodiments, by Fig.24A and Fig. 24BThe exemplary method shown in Develop a composite dilution model. The level of the analyte is measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first biological sample and the second biological sample can be biological samples from different time points of the same individual, or biological samples from different individuals. These dilution series show the analyte levels at various dilutions of the sample. A first model is generated based on the analyte level of the first dilution series, and a second model is generated based on the analyte level of the second dilution series. An arbitrary reference value is selected, which is the analyte level at the dilution value. In some embodiments, the arbitrary reference value is the analyte level at a specific dilution not found in the first dilution series or the second dilution series. Then the points on the first model are translated by a certain amount ΔX to translate to the arbitrary reference value, and the points on the second model are translated by a certain amount ΔY to translate to the arbitrary reference value. The remaining values ​​of the first model are translated by the same amount ΔX, and the remaining values ​​of the second model are translated by the same amount ΔY to form a linear composite translation. The composite dilution model is generated by fitting a function to a linear composite translation series. The composite dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0278] Any suitable model can be used in the methods described herein, including models generated by the analyte levels in the first dilution series and / or the second dilution series, and models for generating a composite dilution model. Exemplary models that can 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, matrix decomposition models, and / or four-parameter logistic regression (4PL) models, etc. The models generated by the analyte levels in the first dilution series and / or the second dilution series can be the same or different. Similarly, the model for generating a composite dilution model can be the same or different from one or more models in the model generated by the analyte level. One of ordinary skill in the art can select a suitable model according to the specific application, and many such models are known in the art.

[0279] In some embodiments, after developing a composite dilution model, or in the case of using a previously developed composite dilution model, the relative dilution of a biological test sample can be determined. In some such embodiments, the level of at least one analyte from a biological test sample is translated to the composite dilution model developed for the at least one analyte. The relative dilution of a biological test sample can thus be determined. In some embodiments, a group of analytes (such as at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180 or 200 analytes) can be used to develop a composite dilution model. The relative dilution of a biological test sample is then determined for a subset of analytes or each analyte. In some embodiments, the relative dilution of a biological test sample is then determined using the mean value, mean, median, etc. of the relative dilution based on each analyte. In some embodiments, an analyte that is expected to not have a level change between individuals (such as between individuals with and without a disease or illness) is selected.

[0280] Computer device, method and software

[0281] In one aspect, the system further comprises one or more means for providing input data to the one or more processors.The system further comprises a memory for storing the data set of sorted data elements.

[0282] In another aspect, the means for providing input data comprises a detector for detecting characteristics of data elements, such as a mass spectrometer or a gene chip reader.

[0283] The system may additionally include a database management system.User requests or queries can be formatted in an appropriate language understood by the database management system, which processes the query to extract relevant information from the training set database.

[0284] The system may be capable of connecting to a network server and a network to which one or more clients are connected. As is known in the art, the network may be a local area network (LAN) or a wide area network (WAN). Preferably, the server includes the hardware necessary to run a computer program product (e.g., software) to access database data for processing user requests.

[0285] The system may include an operating system (e.g., In one aspect, the operating system may operate on a global communications network, such as the Internet, and utilize a global communications network server to connect to such a network.

[0286] The system may include one or more devices including a graphical display interface including interface elements such as buttons, drop-down menus, scroll bars, fields for entering text, etc., such as those commonly found in graphical user interfaces known in the art. Requests entered on the user interface may be transmitted to an application in the system for formatting to search for relevant information in one or more system databases. User-entered requests or queries may be constructed in any suitable database language.

[0287] The graphical user interface can be generated by a graphical user interface code that is part of the operating system and can be used to input data and / or display the inputted data. The results of the processed data can be displayed in the interface, printed on a printer in communication with the system, stored in a memory device and / or transmitted over a network, or can be provided in the form of a computer readable medium.

[0288] The system can communicate with an input device for providing data (e.g., expression values) about data elements to the system. In one aspect, the input device can include a gene expression profiling system, including, for example, a mass spectrometer, a gene chip or array reader, and the like.

[0289] The computer system may be a stand-alone system, or part of a computer network including a server and one or more databases.

[0290] Some embodiments described herein may be implemented to include a computer program product. The computer program product may include a computer readable medium having a computer readable program code embodied in the medium for causing an application to be executed on a computer having a database.

[0291] As used herein, "computer program product" refers to a set of instructions organized in the form of natural or programming language statements, which are contained on a physical medium of any nature (e.g., written, electronic, magnetic, optical or other), and can be used with a computer or other automatic data processing system. When executed by a computer or data processing system, such programming language statements cause the computer or data processing system to act according to the specific content of these statements. Computer program products include, but are not limited to, programs in the form of source code and object code and / or test libraries or databases embedded in a computer-readable medium. In addition, computer program products that enable computer systems or data processing equipment to operate in a pre-selected manner can be provided in a variety of forms, including but not limited to original source code, assembly code, object code, machine language, encrypted or compressed versions of the aforementioned forms and any and all equivalents.

[0292] Although various embodiments have been described as methods or devices, it should be understood that the embodiments can be implemented by codes coupled to a computer (e.g., codes resident on or accessible to a computer). For example, many of the methods discussed above can be implemented using software and databases. Therefore, in addition to the embodiments implemented by hardware, it should also be noted that these embodiments can be implemented using products including computer-usable media, which have computer-readable program codes embodied therein, which enable the functions disclosed in this specification to be implemented. Therefore, it is expected that these embodiments are also considered to be protected by this patent in the form of their program codes. In addition, these embodiments can be embodied as codes stored in virtually any type of computer-readable memory (including but not limited to RAM, ROM, magnetic media, optical media, or magneto-optical media). Even more generally, these embodiments can be implemented in software, hardware, or any combination thereof, including but not limited to software, microcode, programmable logic array (PLA) or application-specific integrated circuit (ASIC) running on a general-purpose processor.

[0293] It is also contemplated that embodiments may be implemented as computer signals embodied in carrier waves, as well as signals (e.g., electrical and optical) propagating through transmission media. Thus, the various types of information discussed above may be formatted in structures such as data structures and transmitted as electrical signals through transmission media or stored on computer-readable media.

[0294] Fig.25 An example system architecture 2500 is shown 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 store 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 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.

[0295] 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 any other type of biological fluid. Diagnostic machine 2502 may collect or generate analyte measurement data 2580 based on analyzing the biological samples, and such data may be stored or transferred to one or more other internal or external machines for processing, analysis, or use. Additionally, diagnostic machine 2502 may generally refer to one or more components of a diagnostic device involved in processing a biological sample, such as a or Microarray machine.

[0296] In one example, the robot 2504 drives, manipulates, and / or delivers a biological sample within a diagnostic machine 2502 or between multiple diagnostic machines 2502 as part of processing a biological sample. For example, the robot 2504 may generally include, but is not limited to Robot 2502 or any other robotic machine that provides automated or semi-automated processing of biological samples in a laboratory environment.

[0297] In one example, the diagnostic machine 2502 communicates with one or more data stores 2508 and one or more server machines 2510 via one or more networks 2506. The network 2506 can typically be a public network (e.g., the Internet), a private network (e.g., a local area network (LAN) or a wide area network (WAN)), or a combination thereof. In one example, the network 2506 can include the Internet and / or one or more intranets, landline networks, wireless networks, and / or other appropriate types of communication networks. In one example, the network 2506 can include a wireless telecommunications network (e.g., a cellular telephone network) suitable for communicating with other communication networks (such as the Internet).

[0298] Data storage 2508 is a permanent storage device capable of storing various types of data, such as alphanumeric text, audio, video, and / or image content. In some examples, data storage 2508 may be a network-attached file server, while in other examples, data storage 2508 may be some other type of permanent storage device, such as an object-oriented database, a relational database, etc.

[0299] In one example, the diagnostic machine 2502 can store and access various types of data, including analyte measurement data 2580, over the network 2506. The diagnostic machine 2502 can also store and access such data on one or more local data stores 2508 associated with the diagnostic machine 2502 (not shown) and / or on one or more data stores 2508 local to the server machine 2510 or one or more other computing systems (not shown). In one example, the analyte measurement data 2580 can 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.

[0300] The data storage 2508 can also receive, store and provide a composite dilution model 2590 for controlling the analyte variability between samples in a complex biological matrix. The composite dilution model 2590 can be generated from the analyte measurement data 2580, or can be provided directly or indirectly from another source. In addition, the composite dilution model 2590 can be used to determine the predicted relative dilution of a biological sample type, which can then be used to adjust the relative dilution or other aspects of any one or more different, additional or newly received biological samples.

[0301] The server machine 2510 may generally be dedicated diagnostic hardware, a rack-mounted server, a personal computer, a portable digital assistant, 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 run on one or more different server machines 2510.

[0302] The web server 2520 can provide text, audio, video, and image content from the server machine 2510 and / or data storage 2508 to one or more client devices 2570. The web server 2520 can also provide web-based application services and business logic to the client devices 2570. The client devices 2570 can use applications (such as web browsers) to locate, access, and consume various forms of content and services from the web server 2520. The web server 2520 can also receive text (such as existing and new analyte measurement data 2580), audio, video, and image content from the client devices 2570, which is stored in one or more data storages 2508 for a number of purposes that may include analyzing, converting, processing, persisting, and distributing such content.

[0303] In one example, the web server 2520 is coupled to one or more application servers 2522, which provide applications and services to the client device 2570 directly or with the assistance of the web server 2520. For example, the web server 2520 can provide the client device 2570 with access to one or more specialized software applications in the field of biotechnology. Such functionality can also be provided as, for example, one or more different web applications, stand-alone applications, computer systems, plug-ins, web browser extensions, and application programming interfaces (APIs). In some examples, plug-ins and extensions can be individually or collectively referred to as add-ons.

[0304] The client device 2570 may be a personal computer (PC), a laptop computer, a mobile phone, a tablet computer, or generally any other computing device. 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 be running 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, the web server 2520, the application server 2522, the data storage 2508, etc. In addition, other types of computer programs and computer scripts may also be run on the client device 2570. For example, the client device 2570 may use an application program (i.e., an "app") to access such content or communicate with the server machine 2510 without accessing or otherwise utilizing a web page.

[0305] In one example, the functions and features of the server machine 2510 may also be performed in whole or in part by the client device 2570. In addition, the functions attributed to a particular component may be performed by a different component or multiple components operating together. The server machine 2510 may also be accessed as a service provided to other systems or devices via an application programming interface and is therefore not limited to use in a website.

[0306] 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 sample-to-sample analyte variability in a complex biological matrix. For example, the analyte variability control system 2530 can receive and analyze biological samples associated with different subjects, generate a composite dilution model 2590 for each of a plurality of analytes in the biological samples, determine a relative dilution prediction model for a biological sample type of the biological sample based on a plurality of generated composite 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 determined relative dilution prediction model for the biological sample type.

[0307] In the figures associated with the present disclosure and the following paragraphs, the present disclosure further describes examples of services provided by the analyte variability control system 2530, including "Example 1: Generating a matrix-specific empirical standard curve", "Example 2: Applying a matrix-specific empirical standard curve", and "Example 3: Preliminary study design for characterizing and normalizing analyte signal changes due to hydration status of human subjects".

[0308] In one example, the analyte variability control system 2530 includes a biological sample analyzer module 2540, a compound dilution model generator module 2550, and a relative dilution prediction module 2560. In other examples, the functionality associated with the biological sample analyzer module 2540, the compound dilution model generator module 2550, and the relative dilution prediction module 2560 can be combined, divided, and organized in various arrangements.

[0309] In one example, the biological sample analyzer module 2540 can generally receive and analyze analyte measurement data 2580 associated with biological samples from different subjects. The composite dilution model generator module 2550 can generally perform one or more steps to generate a composite dilution model 2590 for each analyte in a selected set of analytes from the analyte measurement data 2580. The relative dilution prediction module 2560 can then generally determine a predicted relative dilution for a biological sample type of the biological sample based on the selected composite dilution model 2590 generated from the biological sample. The relative dilution prediction module 2560 can then generally adjust one or more aspects of the analyte measurement data 2580 associated with a different and / or newly received biological sample based on the predicted relative dilution determined by the selected composite dilution model 2590.

[0310] Fig.26 26 is a flow chart showing the generation of a composite dilution model for each of a plurality of analytes present in different biological samples according to an example of the present disclosure. The example method 2600 may be performed by a processing logic device that may include dedicated hardware (circuitry, dedicated logic device, programmable logic device, microcode, etc.), dedicated software (such as instructions running on a general purpose computer system, a dedicated machine or a hardware processing device), firmware, or any combination thereof.

[0311] In general, support for example method 2600 is provided throughout this disclosure, including in conjunction with the non-limiting examples described above: "Example 1: Generating a Matrix-Specific Empirical Standard Curve," "Example 2: Applying a Matrix-Specific Empirical Standard Curve," and "Example 3: Preliminary Study Design for Characterizing and Normalizing Analyte Signal Changes Due to Hydration State of Human Subjects."

[0312] The method 2600 begins at block 2602 when the analyte variability control system 2530 receives analyte measurement data 2580 associated with different biological samples. In one example, the biological sample analyzer module 2540 of the analyte variability control system 2530 receives the analyte measurement data 2580 from any of the diagnostic machine 2502, the data storage 2508, the server machine 2510, the client device 2570, or one or more other computer systems or storage devices.

[0313] In one example, biological sample analyzer module 2540 receives analyte measurement data 2580 generated by diagnostic machine 2502. For example, diagnostic machine 2502 may receive biological samples provided from a plurality of different human subjects. Such biological samples may include urine, blood, plasma, serum, cerebrospinal fluid, or generally any other type of biological fluid.

[0314] In one example, the diagnostic machine 2502 measures analytes or proteins associated with each of the biological samples from different subjects to generate analyte measurement data 2580 measured in relative fluorescence units (RFU) or any other suitable measurement unit. Thus, in some examples, the analyte measurement data 2580 may include respective relative fluorescence unit measurements of 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 storage 2508 or any other permanent storage device and subsequently provided to one or more other computer systems, such as a server machine 2510.

[0315] In one example, the biological sample analyzer module 2540 can generally convert, adjust, and / or process the analyte measurement data 2580 (e.g., raw RFU data) in any number of steps in preparation for further processing by the analyte variability control system 2530. For example, the raw or partially processed analyte measurement data 2580 can be cleansed, formatted, normalized, calibrated, or manipulated in any of one or more different steps. In other examples, the analyte measurement data 2580 received by the biological sample analyzer module can be pre-processed and ready for analysis when received without further pre-processing or manipulation.

[0316] At block 2604, the analyte variability control system 2530 analyzes the analyte measurement data for each of the plurality of selected analytes for a corresponding analyte measurement result. In one example, the biological sample analyzer module 2540 of the analyte variability control system 2530 analyzes each of the plurality of selected analytes with the analyte measurement data 2508. For example, the biological sample analyzer module 2540 can analyze each analyte in a subset of available measurable analytes from a plurality of different biological samples. In one example, the biological sample analyzer module 2540 can analyze the analyte measurement data 2580 for each analyte in the different biological samples individually or in parallel (e.g., processing two or more analytes in different biological samples simultaneously).

[0317] In one example, the biological sample analyzer module 2540 analyzes each analyte in preparation for generating a composite dilution model 2590 corresponding to each respective analyte. For example, the biological sample analyzer module 2540 can analyze the analytes of a plurality of biological samples that have been serially diluted. In one example, the biological sample analyzer module 2540 determines the sample with the largest linear dilution range for the analyte and then fits a weighted linear regression model to the data in that linear dilution range. The associated regression line can then be used as a reference against which other samples are registered.

[0318] At block 2606, the analyte variability control system 2530 generates a composite dilution model 2590 for each of the selected analytes based on the analysis performed at block 2604. In one example, the composite dilution model generator module 2550 of the analyte variability control system 2530 translates the respective analyte measurement data 2508 of the analyte based on the corresponding reference dilution model generated by the biological sample analyzer module 2540. In one example, the composite dilution model generator module 2550 then fits a 4-parameter logistic function (4PL) to the registration data of the analyte.

[0319] In one example, the composite dilution model generator module 2550 then generates a corresponding composite dilution model 2590 for the analyte based on the 4PL associated with the analyte. The composite dilution model generator module 2550 further generates a composite dilution model 2590 for each remaining analyte in the selected subset of analytes found in the analyte measurement data 2508. The composite dilution model 2590 generated for the selected subset of analytes can then be used to adjust various aspects of additional, different, or newly received biological samples to control for sample-to-sample analyte variability.

[0320] Fig. 2727 is a flow chart showing the generation of a composite dilution model for each of a plurality of analytes present in different biological samples according to an example of the present disclosure. The example method 2700 may be performed by a processing logic device that may include dedicated hardware (circuitry, dedicated logic device, programmable logic device, microcode, etc.), dedicated software (such as instructions running on a general purpose computer system, a dedicated machine or a hardware processing device), firmware, or any combination thereof.

[0321] In general, support for example method 2700 is provided throughout this disclosure, including as described above in conjunction with the non-limiting examples: "Example 1: Generating a Matrix-Specific Empirical Standard Curve," "Example 2: Applying a Matrix-Specific Empirical Standard Curve," and "Example 3: Preliminary Study Design for Characterizing and Normalizing Analyte Signal Changes Due to Hydration State of Human Subjects."

[0322] 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 the analyte measurement data 2580 from the data storage 2508. Generally speaking, such analyte measurement data may include relative fluorescence unit (RFU) measurements of each of a plurality of different proteins detected in each of a plurality of different biological samples of the same biological sample type that have been collected from different subjects.

[0323] At block 2704, the analyte variability control system 2530 analyzes the corresponding analyte measurement results for each of the 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 at block 2704. For example, a subset of available analytes from the analyte measurement data 2508 may be selected for generating a corresponding composite dilution model 2590 for controlling sample-to-sample analyte variability in a complex biological matrix.

[0324] At box 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 at box 2704. For example, in a non-limiting example, the biological sample analyzer module 2540 can determine which sample of a plurality of samples associated with the corresponding analyte has the maximum linear dilution range. Then, the biological sample analyzer module 2540 can fit a weighted linear regression model to the data in the linear dilution range. In addition, the biological sample analyzer module 2540 can similarly generate a weighted linear regression model for each of the other selected analytes, which will be used at least in part to generate a corresponding composite dilution model 2590 associated with each corresponding selected analyte. Further description and examples are provided in the present disclosure, for example, at least in the non-limiting example "Example 1: Generate a matrix-specific empirical standard curve".

[0325] At block 2708, the analyte variability control system 2530 shifts the corresponding analyte measurement data for each selected analyte based on the corresponding reference dilution model generated for each corresponding analyte. In one example, the composite dilution model generator module 2550 of the analyte variability control system 2530 shifts the corresponding analyte measurement data 2508 for each of the selected analytes based on the 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 the non-limiting example "Example 1: Generating a Matrix-Specific Empirical Standard Curve".

[0326] At block 2710, the analyte variability control system 2530 generates a composite dilution model 2590 for each of the selected analytes based on the translated analyte measurement data. In one example, the composite dilution model generator module 2550 fits a 4-parameter logistic function (4PL) to the translated data for each of the selected analytes. For example, the composite dilution model generator module 2550 generates a corresponding composite dilution model 2590 for each of the selected analytes based on the 4PL. Thus, the composite dilution model generator module 2550 generates a collection or series of composite dilution models 2590 that constitute a composite solution module for each of the selected analytes.

[0327] Further descriptions and examples of describing block 2710 and applying the generated composite dilution model 2590 to other biological samples are described in the present disclosure, for example, at least in the following non-limiting examples: “Example 1: Generating a Matrix-Specific Empirical Standard Curve” and “Example 2: Applying a Matrix-Specific Empirical Standard Curve”.

[0328] In some examples, the composite dilution model generator module 2550 further generates one or more reports containing information and details about various aspects associated with processing the analyte measurement data 2580 and generating the composite dilution model 2590. For example, such 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 composite dilution model 2590; and determination of predicted relative dilutions. In some examples, the analyte variability control system 2530 stores the generated reports in the data storage 2508 and may provide these reports and the corresponding analyte measurement data 2580 and / or composite dilution model 2590 to the client device 2570.

[0329] Fig.28 28 is a flow chart showing the application of a composite dilution model to a new biological sample to predict the relative dilution of the new sample according to an example of the present disclosure. The example method 2800 may be performed by a processing logic device that may include dedicated hardware (circuitry, dedicated logic device, programmable logic device, microcode, etc.), dedicated software (such as instructions running on a general purpose computer system, a dedicated machine or a hardware processing device), firmware, or any combination thereof.

[0330] In general, support for example method 2800 is provided throughout this disclosure, including as described above in conjunction with the non-limiting examples: "Example 1: Generating a Matrix-Specific Empirical Standard Curve," "Example 2: Applying a Matrix-Specific Empirical Standard Curve," and "Example 3: Preliminary Study Design for Characterizing and Normalizing Analyte Signal Changes Due to Hydration State of Human Subjects."

[0331] The method 2800 begins at block 2802 when the analyte variability control system 2530 receives analyte measurement data for an analyte in a biological sample. In one example, the biological sample analyzer module 2540 of the analyte variability control system 2530 can receive analyte measurement data 2580 for one or more new biological samples for analysis. For example, the new biological sample can generally describe analyte measurement data 2580 for the biological sample that was not included in or considered during the generation of a composite dilution model 2590 for the analyte of the biological sample type (e.g., urine). Thus, the new biological sample is different from the biological sample used to generate the composite dilution model, and such associated analyte measurement data 2580 can be received before or after such model is generated.

[0332] At block 2804, the analyte variability control system 2530 selects a plurality of analytes to determine a predicted relative dilution of the 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 the new biological sample. For example, the biological sample analyzer module 2540 can select a subset of analytes in the analyte measurement data 2580 based on user preferences and / or one or more thresholds associated with analyte relative fluorescence unit (RFU) measurements in the analyte measurement data 2580.

[0333] In one example, the biological sample analyzer module 2540 can select the highest number of analytes in a new biological sample based on the "goodness of fit" compared to the corresponding composite dilution model for the biological sample type. In another example, the biological sample analyzer module 2540 can also select multiple analytes with the ability to flatten a series of consecutive titrations of the same sample. Further description and examples are provided in the present disclosure, for example, at least in the non-limiting example "Example 2: Application of Matrix-Specific Empirical Standard Curves".

[0334] At block 2806, the analyte variability control system 2530 receives a composite dilution model 2590 for each of the selected analytes. In one example, the relative dilution prediction module 2560 receives a composite dilution model 2590 for each of the plurality of analytes selected for determining a predicted relative solution of the biological sample. For example, the relative dilution prediction module 2560 receives a composite dilution model 2590 generated for the selected analytes. Such a composite dilution model 2590 can be generated based on the method 2600, the method 2700, or other examples of the present disclosure.

[0335] At block 2808, the analyte variability control system 2530 determines a predicted relative dilution value for each of the selected analytes based on the corresponding composite dilution model associated with each respective analyte. In one example, for each of the selected analytes, the relative dilution prediction module 2560 projects the relative fluorescence unit (RFU) measurement of the respective analyte onto the generated composite dilution model corresponding to the respective analyte, thereby generating a predicted relative dilution for each of the respective analytes.

[0336] For example, the relative dilution prediction module 2560 can perform a first RFU measurement on a first analyte in a new biological sample, project the first RFU measurement result of the first analyte onto a first composite dilution model generated for the first analyte, and determine the predicted relative dilution value of the first analyte of the new biological sample based on the projection. Similarly, the relative dilution prediction module 2560 can perform a second RFU measurement on a second analyte in the same new biological sample, project the second RFU measurement result of the second analyte onto a second composite dilution model generated for the second analyte, and determine the predicted relative dilution value of the second analyte of the new biological sample based on the projection (and so on for each of the other selected analytes). Further descriptions and examples are provided in the present disclosure, for example, at least in the non-limiting example "Example 2: Application of a matrix-specific empirical standard curve".

[0337] At block 2810, the analyte variability control system 2530 determines a predicted relative dilution for the biological sample based on the predicted relative dilution values ​​determined for each of the selected analytes. In one example, the relative dilution prediction module 2560 creates a distribution of the predicted relative dilution values ​​generated at block 2808 for the selected analytes of the new biological sample. The relative dilution prediction module 2560 can then determine and select the predicted relative dilution values ​​from those predicted relative dilution values ​​to use when adjusting multiple aspects of the new biological sample. For example, the relative dilution prediction module 2560 can discard one or more groups of generated predicted relative dilution values ​​to create a last group of generated predicted relative dilution values ​​for adjusting the new biological sample.

[0338] In one example, the relative dilution prediction module 2560 trims the tail from the distribution of predicted relative dilution values ​​and uses the middle percentage of the remaining values ​​to determine the predicted relative dilution of the new biological sample. The relative dilution prediction module 2560 then determines the predicted relative dilution of the biological sample. For example, the relative dilution prediction module 2560 can analyze the remaining predicted relative dilution values ​​and generate a predicted relative dilution of the new biological sample.

[0339] In one example, the relative dilution prediction module 2560 determines the predicted relative dilution of the new biological sample based on the median of the remaining predicted relative dilution values. The relative dilution prediction module 2560 can also generally determine the predicted relative dilution of the new biological sample based on a formula or other analysis using the remaining predicted relative dilution values. Further description and examples are provided in the present disclosure, for example, at least in the non-limiting example "Example 2: Application of a matrix-specific empirical standard curve".

[0340] In one example, the analyte variability control system 2530 adjusts one or more aspects of the 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 can normalize or otherwise adjust the analyte measurement data 2580 of the new biological sample based on the predicted relative dilution of the new biological sample determined by the relative dilution prediction module 2560. Additionally, in some examples, the analyte variability control system 2530 can generate an associated report describing the associated processing and adjustments. The adjusted analyte measurement data 2580 of the new biological sample can then be further examined and analyzed based on the adjustments.

[0341] Fig.29 A diagram of a machine in an exemplary form of a computer system 2900 is shown, wherein a group of instructions for making the machine perform any one or more methods discussed herein can be executed. In one example, the machine can be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. The machine can operate with the capabilities of a server or client machine in a client-server network environment, or operate as a peer machine in a peer-to-peer (or distributed) network environment. For example, the machine can be a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a cellular phone, a wearable computing device, a web device, a server machine, a dedicated diagnostic laboratory machine, or any other machine capable of executing a group of instructions (in order or otherwise) specifying the action to be taken by the machine. In addition, although only a single machine is shown, the term "machine" should also be understood to include any collection of machines that individually or jointly execute a group (or multiple groups) of instructions to perform any one or more methods discussed herein.

[0342] The exemplary computer system 2900 includes a processing device (processor) 2902, a main memory 2904 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM), double data rate (DDR SDRAM) or DRAM (RDRAM), etc.), a static memory 2906 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 2918 that communicate with each other via a bus 2930.

[0343] Processor 2902 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, etc. More specifically, processor 2902 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor that implements other instruction sets or multiple processors that implement a combination of instruction sets. 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. Processor 2902 is configured to execute instructions 2922 for performing the operations and steps discussed herein.

[0344] The computer system 2900 may also 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).

[0345] The data storage device 2918 may include a computer-readable storage medium 2928 on which is stored one or more sets of instructions 2922 (e.g., software) embodying any one or more of the methods or functions described herein. The instructions 2922 may also reside completely or at least partially within the main memory 2904 and / or within the processor 2902 during execution by the computer system 2900, the main memory 2904 and the processor 2902 also constituting computer-readable storage media. The instructions 2922 may also be transmitted or received over the network 2920 via the network interface device 2908.

[0346] In one example, instructions 2922 include instructions for controlling an analyte variability control system (e.g., Fig.25 Analyte variability control system 2530) and / or instructions of a software library including a method for calling an analyte variability control system. Although computer-readable storage medium 2928 (machine-readable storage medium) is shown as a single medium in the example, the term "computer-readable storage medium" should be understood to include a single medium or multiple media (e.g., centralized or distributed databases and / or associated caches and servers) storing the one or more sets of instructions. The term "computer-readable storage medium" should also be understood to include any medium that can store, encode or carry a set of instructions for execution by a machine and cause the machine to execute any one or more methods of the present disclosure. In addition, the term "computer-readable storage medium" should be understood accordingly to include, but is not limited to, solid-state memory, optical media, and magnetic media.

[0347] In the foregoing description, many details are set forth. However, it will be apparent to those of ordinary skill in the art who benefit from the present disclosure that the present disclosure may be practiced without these specific details. In some cases, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the present disclosure.

[0348] Some portions of the detailed description have been presented in terms of steps leading to one or more desired results. Typically, such steps are those requiring physical manipulations of physical quantities. Although not necessarily, these quantities typically take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0349] It should be borne in mind, however, that all of these and similar terms are intended to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless otherwise noted, as will be apparent from the following discussion, it will be understood that throughout this specification, discussions utilizing terms such as "compute," "compare," "apply," "create," "sort," "classify," and the like refer to the actions and processes of a special purpose computer system or similar special purpose electronic computing device that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system's memories or registers or other such information storage, transmission, or display devices.

[0350] Certain examples of the present disclosure also relate to devices for performing the operations herein. The device may be constructed for the intended purpose, or it may include special-purpose computer hardware and / or special-purpose computer programs that are selectively installed, activated, or configured to perform the intended purpose. Such computer programs may be stored in a computer-readable storage medium, such as, but not limited to, any type of disk, including floppy disks, optical disks, CD-ROMs and magneto-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of medium suitable for storing electronic instructions.

[0351] It should be understood that the above description is intended to be illustrative and non-restrictive. After reading and understanding the above description, many other examples will be apparent to those skilled in the art. Therefore, the scope of the present disclosure should be determined with reference to the full scope of the equivalents authorized by the attached claims and these claims.

[0352] Example

[0353] The following examples are provided to illustrate certain specific features and / or embodiments. These examples should not be construed as limiting the disclosure to the specific features or embodiments described.

[0354] Example 1: Generation of matrix-specific empirical standard curves

[0355] This example provides a method by which an exemplary composite dilution model is generated from a complex biological matrix (eg, urine) using relative protein measurements as determined with capture agents (eg, aptamers).

[0356] By convention, protein levels in urine are normalized to physiologically based measurements (e.g., total urine volume, creatinine concentration, or albumin:creatinine ratio) or to restricted median normalization, which identifies a subset of capture reagents (e.g., aptamers) that exhibit dilution linearity (or proportionality to the protein content of the sample) and uses this information to perform standard median normalization.

[0357] A new normalization method has been developed, which is based on a titration series of multiple samples from a complex matrix (such as urine) to generate a composite dilution curve for each analyte (measured protein). Many composite dilution curves can then be used to more accurately estimate the total dilution of protein levels. Protein measurement is performed in urine using an assay based on an aptamer, whereby protein levels are represented by relative amounts (RFU or relative fluorescence units). Due to the inter-sample variability of protein levels commonly observed, urine is selected as an example matrix, and the inter-sample variability is caused by, for example, the hydration level of an individual when urine is collected. The hydration level may confuse the relationship between the protein level measured from urine and the clinical evaluation of the subject being tested. Therefore, by generating a composite dilution model that compensates for inter-sample variability, consistent and clinically relevant information about the test subject can be obtained by the protein level in urine. In addition, once a composite dilution model is generated for the matrix, the same composite dilution model can be used to compensate for the inter-sample variability in the samples from many subjects, and consistent and clinically relevant information can be obtained by the analyte measurement results of these subjects.

[0358] By way of background information, the quantitative input to a function will be denoted as X, and the output will be denoted as the variable Y. If X or Y is a matrix, then you can specify the matrix by subscripting X ij to access the individual components. For example, the jth sample of the i-th aptamer would be X ij The next sample of the same aptamer will be X i(j+1) Vectors of values ​​will be represented as upper case, while scalar values ​​will be represented as lower case.

[0359] Analyze the protein levels of at least three (3) serial titrations of at least two (2) samples from the biological matrix of interest, where the i analyte (aptamer) of j samples is represented as the dilution x when performing k serial titrations ijk and the corresponding RFU value Y ijk . For each analyte (or protein), a linear dilution range is defined, which in the case of aptamer-based assays is the range of dilutions in which the measured RFU values ​​are approximately linearly proportional to the dilution of the sample. Starting from the lowest dilution, the RFU measurements of each dilution are used to establish a nominal level to which subsequent dilutions can be compared. The percent recovery is defined as the measured RFU value divided by the expected RFU value at the specified n-fold dilution. Starting from the k-th dilution in the series, the percent recovery of the m-th subsequent dilution is defined as

[0360]

[0361] The acceptable dilution range has a percent recovery within 50% of the nominal value determined at the highest dilution in the linear range. A minimum of three serial dilutions starting from the nominal value are required to define the linear range. The linear range (5 data points from 5 serial dilutions) is within Figure 1 The data points are shown as vertical dashed lines in (note that the scale is logarithmic for visualization purposes and does not represent log-linearity).

[0362] The analyte cystatin C (CST3) was used as a specific example. Nineteen (19) urine samples were serially diluted and the analyte CST3 levels were measured and plotted (see Figure 2A ). Thus, for each analyte i (e.g., CST3), the sample j (out of 19 samples) with the largest linear dilution range was identified. A weighted linear regression model was fit to the data in this linear dilution range, with the weights being proportional to the RFU measurements Y ij The linear model of the i-th analyte is as follows:

[0363]

[0364] The analyte ephrin type B receptor 6 (EPHB6) was used as another specific example. Nineteen (19) urine samples were serially diluted and the analyte EPHB6 levels were measured and plotted (see Figure 2D ). Thus, for each analyte i (e.g., EPHB6), the sample j (out of 19 samples) with the largest linear dilution range was identified. A weighted linear regression model was fit to the data in this linear dilution range, with the weights being proportional to the RFU measurements Y ij The linear model of the i-th analyte is as follows:

[0365]

[0366] Now, this regression line (or regression model) is the reference against which all other curves will be registered ( Figure 2B The black wire in is for CST3, and Figure 2E The black lines in the figure are for EPHB6).

[0367] For each analyte i, the linear range y ci The RFU value of the center point of the reference (or reference value) is mapped (or horizontally translated) to the regression line (regression model) to determine its relative dilution relative to the reference (relative reference dilution) to register the titration curve. For the jth sample of the i-th analyte, the relative reference dilution is as follows:

[0368]

[0369] The center point falls on the reference regression line. Alternatively, another way to achieve this is to fit a linear model to each titration series and register the curves by setting the intercept to the same value (e.g. 1). As shown in the right figure above, this process aligns these curves relative to the reference.

[0370]

[0371] A 4-parameter logistic function (4PL) was fitted to the registered data for each analyte. The 4PL equation consists of parameters for the lower asymptote L, the upper asymptote U, the inflection point k, and the Hill slope b. The model is symmetric about the inflection point and is fitted using a nonlinear least squares method.

[0372]

[0373] This generates an empirical standard curve for each analyte in the matrix of interest in the presence of all capture reagents (e.g., aptamers) and their respective target proteins. Figure 2C Each 4PL fit (compound dilution model) can be ranked by its goodness of fit using the Akaike Information Criterion (AIC).

[0374] Figure 3A-3C to Figure 9A-9C Similar analysis is shown for platelet-derived growth factor D (PDGFD, Figure 3A-3C ), retinoic acid receptor response factor 2 (RARRES2, Figure 4A-4C ), interleukin 1 receptor-like 2 (IL1RL2, Figure 5A-Figure 5C ), coagulation factor XI (F11, Figure 6A-6C ), mitotic protein 11 (SEPT11, Figure 7A-7C ), thymopoietin (TMPO, Figure 8A-8C ) and shisa family member 3 (SHISA3, Figure 9A-9C ).

[0375] Example 2: Use of matrix-specific empirical standard curves

[0376] This example provides a method for using the matrix-specific empirical standard curve (or composite dilution model or 4PL curve) generated in Example 1 for normalizing the analyte levels measured in an assay.

[0377] In general, the method requires selecting a subset of i analytes (aptamer-based protein measurements) to use for normalization calculations.

[0378] Feature selection can be performed in a variety of ways. Three examples are: 1) selecting the top i analytes ranked by AIC by 4PL goodness of fit; 2) feature selection for i analytes that have the ability to flatten a series of consecutive titrations of the same sample, and / or 3) selecting analytes with signal levels above background (e.g., 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, or 10-fold levels above background).

[0379] For example, relative to Option 2 above, for each 1:2 dilution, the expected normalization scaling factor will increase by a factor of two. Then, for each sample to be normalized, find the predicted relative dilution by inversely solving the 4PL equation for all i analytes in the normalized subset. For the i-th analyte (of n total) in sample k, estimate the relative dilution for

[0380]

[0381] Fig. 10A and Fig. 10B Application of the general method to 2 analytes, Iduronidase [IDUA] and Neogene 1 [NEO1], is shown. The RFU measurements of the normalized samples were projected onto the 4PL curve to generate two predicted relative dilutions.

[0382] This information can then be used to find the estimated relative dilution of the sample. In some embodiments, the median predicted relative dilution of all i-th analytes is determined. This process results in a distribution of n predicted relative dilutions for each sample to be normalized. The median of the distribution is the predicted relative dilution for the sample, and the 4PL normalization scale factor for the sample is 1, which exceeds the predicted relative dilution.

[0383] In some embodiments, the median, mean or value obtained from the central tendency of the estimated relative dilution is selected. The estimated relative dilution of each sample can then be used to normalize the sample. In some embodiments, the proportionality factor of each sample is generated by dividing the reference value by the estimated relative dilution of the sample. In some embodiments, the reference value can be 1 (making the proportionality factor the inverse of the estimated relative dilution), or the median estimated relative dilution of all samples to be normalized.

[0384] Fig.11 The distribution of predicted relative dilutions for four serial 1:2 dilutions of a single sample is shown, with the median observed to be perfectly proportional to the dilution of the sample using the 200 analytes with the best AIC.

[0385] Example 3: Preliminary Study Design for Characterizing and Normalizing Analyte Signal Changes Due to Hydration Status in Human Subjects

[0386] This example provides an overview of preliminary studies used to characterize and validate a normalization scheme for changes in analyte signal measurements in human subjects under controlled hydration conditions.

[0387] A gender-balanced cohort of 16 study participants aged 26 to 57 years (mean age 33 years) was recruited for this study. Informed consent for this study was included in the IRB-approved SomaLogic Biorepository research protocol or WIRB #20150206. All participants signed the informed consent form and completed a brief health questionnaire, where metadata was collected, including age, gender, height, weight, and other demographic information. All metadata remained anonymous in this study.

[0388] Collection plan: Clinical information obtained from urine samples may be affected by the collection method and handling. Therefore, to reduce the incidence of cellular and microbial contamination, the collection procedure used for this study was a "clean midstream urine collection." Participants were asked to void the first part of their urine stream into the toilet, which both flushed the urethra and significantly reduced the chance of contaminants entering the urine stream.

[0389] Midstream urine was collected into a 90 ml preservative-free, pre-labeled sterile collection cup with a secure, leak-proof closure. Samples collected at SomaLogic were placed in pre-labeled biohazard bags and immediately placed in a laboratory freezer designated for biological samples for later aliquoting and storage at -80°C.

[0390] To maintain sample conditions similar to those received from future prospective subjects, no pretreatment or centrifugation was performed prior to aliquoting. The collection time of all samples was recorded. In addition to the 4 to 6 assay aliquots, one 8 ml aliquot was produced for urine analysis.

[0391] Samples were anonymized by assigning a unique alphanumeric identifier to each study participant, and all collection tubes and biohazard bags were labeled with this identifier along with the time and date of collection. All metadata and proteomic data were entered into a database using this identifier. This process created an anonymity barrier that prevented metadata or proteomic data from being associated with a participant. A master key that linked this identifier to a participant was created in-house and is kept confidential, with access limited to selected individuals with a need to know.

[0392] Participants were asked to avoid taking anti-inflammatory medications for 48 hours before the study and to abstain from drinking fluids after 10:00 pm the night before the study. Urinating at night was allowed if necessary, but only the morning "first void" was collected.

[0393] Study plan: All participants were asked to avoid drinking any fluids (water, coffee, etc.) or exercising in the morning. The first voided urine sample collected at home was immediately placed on ice until arriving at SomaLogic where it was placed in a refrigerator at 2°C to 8°C.

[0394] Participants were fed a calorically balanced breakfast and were asked to consume water equal to 1.5% of the participant's body weight within 30 minutes (a "hydration challenge"). For a 125 lb individual, this amount was equivalent to approximately 850 ml, and for a 175 lb individual, this amount was equivalent to approximately 1120 ml. Rather than prescribing food amounts for each participant, food intake was controlled by simply asking the participants to eat conservative amounts.

[0395] From 9 am until noon, a portion of each urination was collected, and the final "exit" urine sample was collected at about 12:00 noon. No drinking water was allowed between 9:00 am and 12:00 noon. Two to five series of samples were collected after the hydration challenge, and the last sample was collected at about 12:00 noon. In addition, 15 participants provided uncontrolled samples at 12:00 noon the next day. A total of 81 urine samples were collected from 15 participants.

[0396] All samples were collected “midstream” according to the collection protocol and sent to the clinical laboratory for a complete urinalysis that included specific gravity, total protein, urea, total salts, and microbial titers. Total urine volume was not recorded.

[0397] Analytes measured from collected samples:Urine samples from subjects in the study were assayed using an aptamer-based assay that measures levels of more than a thousand proteins. For each study participant, Fig.12 Data for each aptamer before normalization (represented as a line across multiple time points) are shown, representing the log of the median RFU signal relative to that aptamer. 10 There was a general bias in the non-normalized data due to the hydration status of the participants, where the signal for the first urine sample in the morning (the most concentrated) was higher compared to the median. This signal then decreased due to the hydration challenge, and then increased due to the study participants avoiding drinking water after the hydration challenge.

[0398] This data was used to demonstrate the efficacy of the composite dilution model to compensate for subject hydration levels and to extract consistent and clinically relevant information about the test subjects. The composite dilution model was applied to all serial samples for each study participant. Box plots of the predicted relative dilutions for all serial samples for each study participant are shown in Fig.13 These values ​​were calculated from the composite dilution curves of all aptamers. The normalization process then calculated the scaling factor for each sample by shifting the median of each box plot to 1. Fig.14 The resulting time series are shown after normalization using a compound dilution model to compensate for the subjects' hydration levels, expressed as changes in RFU relative to the median level for each aptamer.

[0399] The box plots and cumulative distribution functions (CDFs) in Figures 15 and 16 show the signal distribution at each time point in two independent study subjects. Fig.15A / C and Fig.16A / C shows the signal level deviation before normalization, Fig. 15B / D and Fig. 16B / D shows the signal level distribution after normalization. Each line in the upper figure represents the aptamer signal level in a sample.

[0400] Fig. 15B and Fig. 16B The reduction in systematic bias / variance is shown to quantify the repeated measures ANOVA F statistic, which can be interpreted as the ratio of within-group differences to between-group differences. A high F statistic is caused by large differences between time points, while a low F statistic indicates that the differences between time points are similar to what would be expected given random chance. The F statistic was calculated for the non-normalized and normalized data for each study participant and is shown in Fig.17A The normalization process resulted in a highly significant reduction in the longitudinal variance (p<1E-8, N=15). Fig. 17B The fold reduction of the F statistic for each study participant is shown, with a median value of 81.88 and a range of 13.12 to 524.96.

Claims

1. A method for generating a composite dilution model, the method comprising: a) determining the level of an analyte in a first dilution series of a first biological sample comprising the analyte, wherein the first dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions; b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions, wherein the first biological sample and the second biological sample are the same type of sample; c) generating a model based on the levels of the analyte in the first dilution series; d) selecting a reference value, wherein the reference value is selected from: (i) a second dilution series reference value, wherein the second dilution series reference value is the level of the analyte at a specific dilution of the second dilution series; (ii) a model reference value, which is the level of the analyte at a specific dilution of the model; as well as (iii) an arbitrary reference value, wherein the arbitrary reference value is the level of the analyte at a specific dilution, wherein the level of the analyte at the specific dilution is not found in the second dilution series or in the model; e) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from: (i) a horizontal translation ΔX of the second dilution series reference value to a model value, wherein the model value is the level of the analyte at a specific dilution of the model, wherein the second dilution series reference value and the model value are equal or substantially equal; (ii) a horizontal translation ΔX of the model reference value to a second dilution series value, wherein the second dilution series value is the level of the analyte at a particular dilution of the second dilution series, wherein the model reference value and the second dilution series value are equal or substantially equal; as well as (iii) a horizontal shift ΔX of the second dilution series values ​​to the arbitrary reference value and a horizontal shift ΔY of the model value to the arbitrary reference value, wherein the model value is the analyte level at a specific dilution of the model, wherein the second dilution series values ​​are the analyte levels at a specific dilution of the second dilution series, and wherein the second dilution series values, the model value and the arbitrary reference value are equal or substantially equal; f) performing at least one horizontal shift of remaining values ​​in (i) the second dilution series, (ii) the model, or (iii) the second dilution series and the model, wherein the horizontal shift of the remaining values ​​results in a linear composite shift; and g) Fit the function to a linear compound translation series to form a compound dilution model.

2. The method of claim 1, wherein the method comprises: d) selecting a second dilution series reference value, the second dilution series reference value being the level of the analyte at a specific dilution of the second dilution series; e) performing a horizontal translation ΔX of the second dilution series reference value to a model value, wherein the model value is the analyte level at a specific dilution of the model, wherein the second dilution series reference value and the model value are equal or substantially equal; and f) performing said horizontal shifting of said remaining values ​​in said second dilution series, wherein said horizontal shifting of said remaining values ​​results in a linear compound shift.

3. The method of claim 1, wherein the method comprises: d) selecting a model reference value, the model reference value being the level of the analyte at a specific dilution of the model; e) performing a horizontal translation ΔX of the model reference value to a second dilution series value, wherein the second dilution series value is the analyte level at a specific dilution of the second dilution series, wherein the model reference value and the second dilution series value are equal or substantially equal; and f) performing said horizontal translation of said residual values ​​in said model, wherein said horizontal translation of said residual values ​​results in a linear compound translation.

4. The method of claim 1, wherein the method comprises: d) selecting an arbitrary reference value, the arbitrary reference value being the level of the analyte at a specific dilution, wherein the level of the analyte at the specific dilution is not found in the second dilution series or in the model; e) performing a horizontal translation ΔX of second dilution series values ​​to the arbitrary reference value and a horizontal translation ΔY of model values ​​to the arbitrary reference value, wherein the model value is the analyte level at a specific dilution of the model, wherein the second dilution series values ​​are the analyte levels at a specific dilution of the second dilution series, and wherein the second dilution series values, the model value and the arbitrary reference value are equal or substantially equal; and f) performing said horizontal shifting of said second dilution series and said residual values ​​in said model, wherein said horizontal shifting of said residual values ​​results in a linear compound shift.

5. A method for generating a composite dilution model, the method comprising: a) determining the level of an analyte in a first dilution series of a first biological sample comprising the analyte, wherein the first dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions; b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions, wherein the first biological sample and the second biological sample are the same type of sample; c) generating a model based on the levels of the analyte in the first dilution series; d) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from: (i) a horizontal translation ΔX of the second dilution series to the model; (ii) a horizontal translation ΔX of the model to the second dilution series; as well as (iii) a horizontal shift ΔX of the second dilution series to an arbitrary reference value and a horizontal shift ΔY of the model to an arbitrary reference value, wherein the arbitrary reference value is the level of the analyte at a specific dilution, wherein the level of the analyte at the specific dilution is not found in the second dilution series or in the model; wherein the at least one horizontal translation results in a linear compound series of translations; and e) fitting a function to the linear composite translation series to form a composite dilution model. The method of claim 5 , wherein the method comprises performing a horizontal translation ΔX of the second dilution series to the model.

7. The method of claim 5, wherein the method comprises performing a horizontal translation of the model to the second dilution series by ΔX.

8. The method of claim 5, wherein the method comprises performing a horizontal translation ΔX of the second dilution series to an arbitrary reference value and a horizontal translation ΔY of the model to an arbitrary reference value, wherein the arbitrary reference value is the 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.

9. A method for generating a composite dilution model, the method comprising: a) determining the level of an analyte in a first dilution series of a first biological sample comprising the analyte, wherein the first dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions; b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions, wherein the first biological sample and the second biological sample are the same type of sample; c) selecting a reference value, wherein the reference value is selected from: (i) a second dilution series reference value, the second dilution series reference value being the level of the analyte at a specific dilution of the second dilution series; and (ii) an arbitrary reference value, wherein the arbitrary reference value is a level of an analyte at a specific dilution, wherein the level of the analyte at the specific dilution is not found in the first dilution series or the second dilution series; d) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from: (i) a horizontal translation ΔX of the second dilution series reference value to a first dilution series value, wherein the first dilution series value is the level of the analyte at a particular dilution of the first dilution series, wherein the second dilution series reference value and the first dilution series value are equal or substantially equal; and (ii) a horizontal translation ΔX of the first dilution series to the arbitrary reference value and a horizontal translation ΔY of the second dilution series to the arbitrary reference value; wherein the at least one horizontal translation results in a linear compound series of translations; and e) fitting a function to the linear composite translation series to form a composite dilution model.

10. The method of claim 9, wherein the method comprises performing a horizontal translation of the second dilution series to the first dilution series by ΔX.

11. The method of claim 9, wherein the method comprises a horizontal shift ΔX of the first dilution series to an arbitrary reference value and a horizontal shift ΔY of the second dilution series to an arbitrary reference value, wherein the arbitrary reference value 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.

12. A method for generating a composite dilution model, the method comprising: a) determining the level of an analyte in a first dilution series of a first biological sample comprising the analyte, wherein the first dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions; b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions, wherein the first biological sample and the second biological sample are the same type of sample; c) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from: (i) a horizontal translation ΔX from the second dilution series to the first dilution series; and (ii) a horizontal shift ΔX of the first dilution series to an arbitrary reference value and a horizontal shift ΔY of the second dilution series to an arbitrary reference value, wherein the arbitrary reference value is the level of the analyte at a specific dilution, wherein the level of the analyte at the specific dilution is not found in the first dilution series or the second dilution series; wherein the at least one horizontal translation results in a linear compound series of translations; as well as d) fitting a function to the linear composite translation series to form a composite dilution model.

13. The method of claim 12, wherein the method comprises performing a horizontal translation of the second dilution series to the first dilution series by ΔX.

14. The method of claim 12, wherein the method comprises a horizontal shift ΔX of the first dilution series to an arbitrary reference value and a horizontal shift ΔY of the second dilution series to an arbitrary reference value, wherein the arbitrary reference value 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.

15. A method for generating a composite dilution model, the method comprising: a) determining the level of an analyte in a first dilution series of a first biological sample comprising the analyte, wherein the first dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions; b) determining the level of the analyte in a second dilution series of a second biological sample comprising the analyte, wherein the second dilution series comprises at least three different dilutions, and determining the level of the analyte in each of the at least three different dilutions, wherein the first biological sample and the second biological sample are the same type of sample; c) generating a first model based on the levels of the analyte in the first dilution series, and generating a second model based on the levels of the analyte in the second dilution series; d) performing at least one horizontal translation, wherein the at least one horizontal translation is selected from: (i) a horizontal translation ΔX from the first model to the second model; as well as (ii) a horizontal shift ΔX of the first model to an arbitrary reference value and a horizontal shift ΔY of the second model to an arbitrary reference value, the arbitrary reference value being the level of the analyte at a specific dilution, wherein the level of the analyte at the specific dilution is not found in the first model or the second model; and wherein the horizontal translation results in a linear series of compound translations; and e) fitting a function to the linear composite translation series to form a composite dilution model.

16. The method of claim 15, wherein the method comprises performing a horizontal translation ΔX of the first model to the second model.

17. The method of claim 15, wherein the method comprises a horizontal translation ΔX of the first model to an arbitrary reference value and a horizontal translation ΔY of the second model to an arbitrary reference value.

18. The method of any one of claims 1-17, wherein the level of the analyte is determined in 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.

19. The method of any one of claims 1-17, wherein the level of the analyte is determined in 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.

20. The method of any one of claims 1-17, wherein each model is independently selected from a linear regression model, a LOESS curve fitting model, a nonlinear regression model, a spline fitting model, a mixed effects regression model, a fixed effects regression model, a generalized linear model, a matrix decomposition model, and a four-parameter logistic regression model.

21. The method of any one of claims 1-17, wherein the level of the analyte is the relative amount of the analyte or the concentration of the analyte.

22. The method of any one of claims 1-17, wherein the selected reference value is within the linear range of the dilution series or model.

23. The method of claim 22, wherein the selected reference value is a center point of the linear range.

24. The method of any one of claims 1-17, wherein the first biological sample and the second biological sample comprise or are derived from urine.

25. The method of any one of claims 1-17, wherein the first biological sample and the second biological sample are collected from the same subject.

26. The method of any one of claims 1-17, wherein the first biological sample and the second biological sample are collected from different subjects.

27. The method of claim 26, wherein the first biological sample is collected at a first time point and the second biological sample is collected at a second time point.

28. The method of claim 27, wherein the first time point and the second time point differ 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.

29. The method of any one of claims 1-17, wherein the level of the analyte is measured by an assay using an aptamer, an antibody, a mass spectrophotometer, or a combination thereof.

30. The method of any one of claims 1-17, wherein the dilution factor of each of the first dilution series and the second dilution series is a constant dilution factor.

31. The method of any one of claims 1-17, wherein the dilution factor of each of the first dilution series and the second dilution series is at least two-fold, three-fold, four-fold, five-fold, six-fold, seven-fold, eight-fold, nine-fold, ten-fold dilution.

32. The method of any one of claims 1 to 17, wherein the dilution factor of each of the first dilution series and the second dilution series is an exponential dilution factor or a logarithmic dilution factor.

33. The method of any one of claims 1 to 17, wherein the first dilution series and the second dilution series are each at least 5-point serial titrations performed with a titration factor of at least 1 :

2.

34. The method of any one of claims 1-17, wherein the method further comprises level shifting the level of at least one analyte from a biological test sample to the composite dilution model for the at least one analyte, thereby determining a relative dilution of the biological test sample.

35. The method of claim 34, wherein the biological test sample and the first and second biological samples used to form the composite dilution model are the same sample type.

36. The method of claim 34, wherein the biological test sample and the first and second biological samples used to form the composite dilution model are urine samples or are derived from urine samples.

37. A method for determining the relative dilution of a biological test sample from a subject, the method comprising level shifting the level of at least one analyte from the biological test sample to a composite dilution model developed for the at least one analyte, thereby determining the relative dilution of the biological test sample from the subject, wherein the composite dilution model is developed using the method of any one of claims 1 to 36.

38. The method of claim 37, comprising horizontally shifting the levels of 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 the biological test sample to a corresponding composite dilution model developed for each of the different analytes to determine a relative dilution of the biological test sample to each of the different analytes, and using the relative dilution to each of the different analytes to determine the relative dilution of the biological test sample.

39. The method of claim 38, wherein the relative dilution of the biological test sample is derived from a central trend of the relative dilution of each of the different analytes.

40. The method of claim 38, wherein the relative dilution of the biological test sample is derived from the median, mean, or mode of the relative dilution of each of the different analytes.

41. The method of any one of claims 37 to 40, wherein the biological test sample and the sample used to develop the composite dilution model are the same sample type.

42. The method of any one of claims 37 to 40, wherein the biological test sample and the sample used to develop the composite dilution model are urine samples or are derived from urine samples.

43. The method of any one of claims 37 to 40, further comprising calculating the relative dilution of the biological test sample using the derived relative dilution factor.

44. The method of any one of claims 1-17 or 37-40, wherein each analyte is a target protein.

45. A computer system, the computer system comprising: a non-transitory memory for storing instructions; as well as one or more hardware processors coupled to the non-transitory memory and configured to read the instructions from the non-transitory memory to cause the computer system to perform operations including: receiving analyte measurement data from a plurality of different biological samples; analyzing the analyte measurement data for respective analyte measurements for each of a plurality of selected analytes; and A composite dilution model is generated for each of the selected analytes based on the analysis, wherein the composite dilution model is developed using the method of any one of claims 1-36.

46. ​​The computer system of claim 45, wherein the analyzing comprises: For each selected analyte, the biological sample having the greatest linear dilution range is determined.

47. The computer system of claim 46, wherein the analyzing further comprises: For each selected analyte, a reference dilution model is generated based on the determined maximum linear dilution range of the corresponding selected analyte.

48. The computer system of claim 47, wherein the analyzing further comprises: For each selected analyte, the analyte measurement data associated with the corresponding selected analyte is shifted based on the reference dilution model generated for the corresponding selected analyte.

49. The computer system of any one of claims 45 to 48, wherein the biological sample is a urine sample.

50. The computer system of any one of claims 45 to 48, wherein the analyte measurement data comprises relative fluorescence unit (RFU) measurements.

51. A non-transitory computer readable medium comprising computer readable instructions that, when executed by a processing device, cause the processing device to perform operations comprising: receiving analyte measurement data from a plurality of different biological samples; analyzing the analyte measurement data for respective analyte measurements for each of a plurality of selected analytes; as well as A composite dilution model is generated for each of the selected analytes based on the analysis, wherein the composite dilution model is developed using the method of any one of claims 1-36.

52. A computer-implemented method for generating a complex dilution model for a biological material, the method comprising: receiving, by one or more processing devices, analyte measurement data from a plurality of different biological samples; analyzing, by one or more of the processing devices, respective analyte measurement results in the analyte measurement data for each of a plurality of selected analytes; as well as A composite dilution model is generated by one or more of the processing devices for each of the selected analytes based on the analysis, wherein the composite dilution model is developed using the method of any one of claims 1-36.

53. A computer system, the computer system comprising: a non-transitory memory for storing instructions; as well as one or more hardware processors coupled to the non-transitory memory and configured to read the instructions from the non-transitory memory to cause the computer system to perform operations including: receiving analyte measurement data of an analyte in a biological sample; selecting a plurality of said analytes to determine a predicted relative dilution of said biological sample; receiving a composite dilution model generated for a respective one of the selected analytes; determining, for each of the selected analytes, a predicted relative dilution value based on a corresponding composite dilution model generated for the respective selected analyte; and The predicted relative dilution of the biological sample is determined based on the determined predicted relative dilution values ​​of the selected analytes, wherein the composite dilution model is developed using the method of any one of claims 1 to 36.

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