Control of sample-to-sample analyte variability in complex biological matrices

Abstract

To provide a method for reducing inter-sample variability of analyte measurements in biological samples.SOLUTION: A) determining levels of an analyte in a first dilution series of a first biological sample comprising the analyte, b) determining levels of the analyte in a second dilution series of a second biological sample comprising the analyte, c) generating a model based on the levels of the analyte of the first dilution series, d) selecting a reference value, e) performing a horizontal translation, f) performing a horizontal translation of the second dilution series, residual values, wherein the horizontal translation of the residual values results in a sequence of compound translations, g) fitting a function to the sequence of compound translations in a sequence, thereby forming a compound dilution model.SELECTED DRAWING: Figure 18A

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Patent Application No. 62 / 849,212, filed May 17, 2019, which is incorporated by reference in its entirety for all purposes.

[0002] The present disclosure relates generally to controlling variability in sample-to-sample 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 as measured by an aptamer-based assay. [Background technology]

[0003] Sample-to-sample variability in analyte measurements in biological samples is problematic in biomarker discovery, metabolic analysis, gene expression analysis, protein pathway analysis, and diagnostic and prognostic tools, especially when the results depend on quantitative biological signals with relatively small differences. Biological sample types that exhibit high sample-to-sample variability present a major challenge in dealing with such sample types. Controlling such variability would result in more consistent and meaningful data sets in experimental and clinical applications.

[0004] Thus, there is a continuing need for alternative compositions and methods for controlling sample-to-sample variability in analyte measurements in biological matrices. The present disclosure meets this need by providing novel compositions and methods for normalizing biological signals in complex matrices, which compositions and methods reduce, minimize, or eliminate such variability. Summary of the Invention

[0005] Embodiment 1. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) generating a model based on the levels of the analyte in the first dilution series; and d) selecting a reference value, wherein the reference value is: (i) a second dilution series reference value, which is the analyte level at a particular dilution of said second dilution series; (ii) a model reference value, which is the analyte level at a particular dilution of the model; and (iii) selecting from any reference value that is an analyte level at a particular dilution, the analyte level at that particular dilution not found in the second dilution series or the model; e) performing at least one horizontal translation, said at least one horizontal translation comprising: (i) a horizontal translation (ΔX) of the second dilution series reference value to a model value, the model value being the analyte level at a particular dilution of the model, the second dilution series reference value and the model value being equal or substantially equal; (ii) a horizontal translation (ΔX) of the model reference value to a second dilution series value, the second dilution series value being an analyte level at a particular dilution of the second dilution series, the model reference value and the second dilution series value being equal or substantially equal; and (iii) performing a horizontal translation of the second dilution series value (ΔX) and the model value (ΔY) to the arbitrary reference value, wherein the model value is the analyte level at a particular dilution of the model, and the second dilution series value is the analyte level at a particular dilution of the second dilution series, and the second dilution series value, the model value, and the arbitrary reference value are equal or substantially equal; f) performing a horizontal translation of at least one of (i) the second dilution series, (ii) the model, or (iii) residual values ​​in the second dilution series and the model, wherein the horizontal translation of the residual values ​​results in a series of complex translations; and g) fitting a function to the series of complex translations, thereby forming a complex dilution model.

[0006] Embodiment 2. (d) selecting a second dilution series reference value that is the analyte level at a particular dilution of said second dilution series; (e) performing a horizontal translation (ΔX) of the second dilution series reference value to a model value, the model value being the analyte level at a particular dilution of the model, the second dilution series reference value and the model value being equal or substantially equal; f) performing a horizontal translation of the residual values ​​in the second dilution series, wherein the horizontal translation of the residual values ​​results in a single line of compound translation.

[0007] Embodiment 3. d) selecting a model reference value that is the analyte level at a particular dilution of the model; e) performing a horizontal translation (ΔX) of the model reference value to a second dilution series value, the second dilution series value being an analyte level at a particular dilution of the second dilution series, the model reference value and the second dilution series value being equal or substantially equal; f) performing a horizontal translation of residual values ​​in the model, wherein the horizontal translation of the residual values ​​results in a line of composite translations.

[0008] Embodiment 4. d) selecting an arbitrary reference value that is an analyte level at a particular dilution, said analyte level at that particular dilution not found in said second dilution series or said model; e) performing a horizontal translation of the second dilution series values ​​(ΔX) and the model values ​​(ΔY) to the arbitrary reference value, wherein the model value is the analyte level at a particular dilution of the model, the second dilution series values ​​are the analyte levels at a particular dilution of the second dilution series, and the second dilution series values, the model values, and the arbitrary reference value are equal or substantially equal; f) performing a horizontal translation of residual values ​​in the second dilution series and the model, wherein the horizontal translation of the residual values ​​results in a single line of combined translation.

[0009] Embodiment 5. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) generating a model based on the levels of the analyte in the first dilution series; and d) performing at least one horizontal translation, wherein the at least one horizontal translation comprises: (i) the horizontal translation (ΔX) of the second dilution series onto the model; (ii) the horizontal translation (ΔX) of the model to the second dilution series; and (iii) a horizontal translation of the second dilution series (ΔX) and the model (ΔY) to an arbitrary reference value, the arbitrary reference value being an analyte level at a particular dilution, the analyte level at that particular dilution being selected from the horizontal translations not found in the second dilution series or the model; performing said at least one horizontal translation resulting in a sequence of composite translations; e) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model.

[0010] Embodiment 6. The method of embodiment 5, comprising performing a horizontal translation (ΔX) of the second dilution series into the model.

[0011] Embodiment 7. The method of embodiment 5, comprising performing a horizontal translation (ΔX) of the model to the second dilution series.

[0012] Embodiment 8. The method of embodiment 5, comprising performing a horizontal translation of the second dilution series (ΔX) and the model (ΔY) to an arbitrary reference value that is an analyte level at a particular dilution, where the analyte level at that particular dilution is not found in the second dilution series or the model.

[0013] Embodiment 9. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) selecting a reference value, said reference value being: (i) a second dilution series reference value, which is the analyte level at a particular dilution of said second dilution series; and (ii) selecting from any reference value that is an analyte level at a particular dilution, the analyte level at that particular dilution not found in the first dilution series or the second dilution series; d) performing at least one horizontal translation, teeth, (i) a horizontal translation (ΔX) of the second dilution series reference value to a first dilution series value, the first dilution series value being an analyte level at a particular dilution of the first dilution series, the second dilution series reference value and the first dilution series value being equal or substantially equal; and (ii) a horizontal translation of the first dilution series (ΔX) and the second dilution series (ΔY) to the arbitrary reference value; performing said at least one horizontal translation resulting in a sequence of composite translations; e) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model.

[0014] Embodiment 10. The method of embodiment 9, comprising performing a horizontal translation (ΔX) of the second dilution series onto the first dilution series.

[0015] Embodiment 11. The method of embodiment 9, comprising horizontal translation of the first dilution series (ΔX) and the second dilution series (ΔY) to an arbitrary reference value, wherein the arbitrary reference value is an analyte level at a particular dilution that is not found in the first dilution series or the second dilution series.

[0016] Embodiment 12. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) performing at least one horizontal translation, said at least one horizontal translation comprising: (i) the horizontal translation (ΔX) of the second dilution series relative to the first dilution series, and (ii) horizontal translations of the first dilution series (ΔX) and the second dilution series (ΔY) to an arbitrary reference value, the arbitrary reference value being an analyte level at a particular dilution that is not found in the first dilution series or the second dilution series; performing said at least one horizontal translation resulting in a sequence of composite translations; d) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model.

[0017] Embodiment 13. The method of embodiment 12, comprising performing a horizontal translation (ΔX) of the second dilution series onto the first dilution series.

[0018] Embodiment 14. The method of embodiment 12, comprising horizontal translation of the first dilution series (ΔX) and the second dilution series (ΔY) to an arbitrary reference value, wherein the arbitrary reference value is an analyte level at a particular dilution that is not found in the first dilution series or the second dilution series.

[0019] Embodiment 15. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) generating a first model based on the levels of the analyte in the first dilution series and a second model based on the levels of the analyte in the second dilution series; d) selecting a reference value, said reference value being: (i) a first model reference value, which is the analyte level at a particular dilution of the first model; and (ii) selecting from any reference value that is an analyte level at a particular dilution, the analyte level at the particular dilution not found in the first model or the second model; d) performing at least one horizontal translation, said at least one horizontal translation comprising: (i) the horizontal translation (ΔX) of the first model relative to the second model; and (ii) a horizontal translation of the first model (ΔX) and the second model (ΔY) to the arbitrary reference value; performing said horizontal translation resulting in a sequence of composite translations; e) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model.

[0020] Embodiment 16. The method of embodiment 15, comprising performing a horizontal translation (ΔX) of the first model onto the second model.

[0021] Embodiment 17. The method of embodiment 15, comprising horizontally translating the first model (ΔX) and the second model (ΔY) to the given reference value.

[0022] Embodiment 18. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) generating a first model based on the levels of the analyte in the first dilution series and a second model based on the levels of the analyte in the second dilution series; d) performing at least one horizontal translation, said at least one horizontal translation comprising: (i) the horizontal translation (ΔX) of the first model relative to the second model; and (ii) horizontal translations of the first model (ΔX) and the second model (ΔY) to any reference value, where the analyte level at that particular dilution is not found in the first model or the second model, are selected from the horizontal translations; performing said horizontal translation resulting in a sequence of composite translations; e) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model. and

[0023] Embodiment 19. The method of embodiment 18, comprising performing a horizontal translation (ΔX) of the first model onto the second model.

[0024] Embodiment 20. The method of embodiment 18, comprising horizontally translating the first model (ΔX) and the second model (ΔY) to an arbitrary reference value.

[0025] Embodiment 21. The method of any one of the preceding embodiments, wherein the level of the analyte is determined at each of at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 different dilutions in the first dilution series.

[0026] Embodiment 22. The method of any one of the preceding embodiments, wherein the level of the analyte is determined in each of at least eight different dilutions.

[0027] Embodiment 23. The method of any one of the preceding embodiments, wherein the level of the analyte is determined at each of at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 different dilutions in the second dilution series.

[0028] Embodiment 24. The method of any one of the preceding embodiments, wherein the level of the analyte is determined at each of at least eight different dilutions in the second dilution series.

[0029] Embodiment 25. The method of any one 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.

[0030] Embodiment 26 The method of any one of the preceding embodiments, wherein the level of the analyte is a relative amount of the analyte or a concentration of the analyte.

[0031] Embodiment 27. The method of any one of the preceding embodiments, wherein the selected reference value is within the linear range of the dilution series or model.

[0032] Embodiment 28. The method of embodiment 21, wherein the selected reference value is the midpoint of the linear range.

[0033] Embodiment 29. The method of any one of the preceding embodiments, wherein the first and second biological samples comprise or are obtained from urine.

[0034] Embodiment 30. The method of any one of the preceding embodiments, wherein the first and second biological samples are collected from the same subject.

[0035] Embodiment 31. The method of any one of the preceding embodiments, wherein the first and second biological samples are collected from different subjects.

[0036] Embodiment 32 The method of 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.

[0037] Embodiment 33. The first time point and the second time point are separated by at least about 0.5 hours, 1 hour, or 33. The method of embodiment 32, wherein the time intervals are 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.

[0038] Embodiment 34 The method of any one 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.

[0039] Embodiment 35. The method of any one 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.

[0040] Embodiment 36. The method of any one of the preceding embodiments, wherein the dilution factor of each of the first dilution series and the second dilution series is at least a 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, or 10-fold dilution.

[0041] 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 or logarithmic dilution factor.

[0042] Embodiment 38 The method of any one of the preceding embodiments, wherein the first biological sample and the second biological sample are the same type of biological sample.

[0043] 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 serial titrations with a titration factor of at least 1:2.

[0044] Embodiment 40. The method of any one of the preceding embodiments, further comprising horizontally translating the level of at least one analyte from the biological test sample to a complex dilution model of said at least one analyte, thereby determining the relative dilution of said biological test sample.

[0045] Embodiment 41. The method of embodiment 40, wherein the biological test sample and the first and second biological samples used to form the complex dilution model are the same sample type.

[0046] Embodiment 42. The method of embodiment 40 or 41, wherein the biological test sample and the first and second biological samples used to form the complex dilution model are urine samples or are obtained from urine samples.

[0047] Embodiment 43. A method for determining the relative dilution of a biological test sample from a subject, comprising horizontally translating the level of at least one analyte from the biological test sample to a complex dilution model developed for the at least one analyte, thereby determining the relative dilution of the biological test sample from the subject.

[0048] Embodiment 44. 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 44. The method of any one of embodiments 40 to 43, comprising horizontally translating levels of at least 200 different analytes to a respective complex 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 using the relative dilution for each of the different analytes to determine the relative dilution of the biological test sample.

[0049] Embodiment 45. The method of embodiment 44, wherein the relative dilution of the biological test sample is derived from the central tendency of the relative dilution of each of the different analytes.

[0050] Embodiment 46. The method of embodiment 44 or 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.

[0051] Embodiment 47. The method of any one of embodiments 43 to 46, wherein the complex dilution model is developed using the method of any one of embodiments 1 to 42.

[0052] Embodiment 48. The method of any one of embodiments 43 to 48, wherein the biological test sample and the sample used to develop the complex dilution model are the same sample type.

[0053] Embodiment 49 The method of embodiment 41, wherein the biological test sample and the sample used to develop the complex dilution model are or are obtained from a urine sample.

[0054] Embodiment 50. The method of any one of embodiments 40 to 49, further comprising calculating the relative dilution of the biological test sample with the derived relative dilution factor.

[0055] Embodiment 51 The method of any one of the preceding embodiments, wherein each analyte is a target protein.

[0056] Embodiment 52. A computer system, comprising: a non-transitory memory for storing instructions; a non-transitory memory coupled to said non-transitory memory and reading said instructions from said non-transitory memory to said computer system; receiving analyte measurement data from a plurality of different biological samples; analyzing a corresponding analyte measurement value for each of a plurality of selected analytes in the analyte measurement data; and generating a complex dilution model for each of the selected analytes based on the analyzing.

[0057] Embodiment 53. The analyzing comprises: 53. The computer system of embodiment 52, comprising determining, for each selected analyte, the biological sample having the largest linear dilution range.

[0058] Embodiment 54. The analyzing comprises: 54. The computer system of embodiment 53, further comprising, for each selected analyte, generating a reference dilution model based on the determined maximum linear range of the corresponding selected analyte.

[0059] Embodiment 55. The analyzing comprises: 55. The computer system of embodiment 54, further comprising, for each selected analyte, translating the analyte measurement data associated with the corresponding selected analyte based on the reference dilution model generated for the corresponding selected analyte.

[0060] Embodiment 56. The computer system of any one of embodiments 52 to 55, wherein the biological sample is a urine sample.

[0061] Embodiment 57. The computer system of any one of embodiments 52 to 56, wherein the analyte measurement data comprises relative fluorescence unit (RFU) measurements.

[0062] Embodiment 58. A non-transitory computer-readable medium containing computer-readable instructions that, when executed by a processing device, cause the processing device to: receiving analyte measurement data from a plurality of different biological samples; analyzing a corresponding analyte measurement value for each of a plurality of selected analytes in the analyte measurement data; generating a complex dilution model for each of the selected analytes based on the analyzing.

[0063] Embodiment 59. A computer-implemented method for generating a complex dilution model of a biological material, 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, a corresponding analyte measurement value for each of a plurality of selected analytes in the analyte measurement data; generating, by one or more of the processing devices, a complex dilution model for each of the selected analytes based on the analyzing.

[0064] Embodiment 60. A computer system, comprising: a non-transitory memory for storing instructions; a non-transitory memory coupled to said non-transitory memory and reading said instructions from said non-transitory memory to said computer system; receiving analyte measurement data for an analyte in a biological sample; selecting a plurality of said analytes for determining an expected relative dilution of said biological sample; receiving a composite dilution model generated for each corresponding one of the selected analytes; determining, for each one of the selected analytes, a predicted relative dilution value based on the corresponding composite dilution model generated for the corresponding selected analyte; determining the predicted relative dilution of the biological sample based on the determined predicted relative dilution value of the selected analyte.

[0065] 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 explanation of the drawings]

[0066] [Figure 1] Determination of the linear range by serial dilution is shown. RFU = relative fluorescence units. [Figure 2A] Analysis of cystatin C (CST3) levels and ephrin type B receptor 6 (EPHB6) is shown. Measurement of CST3 levels using serial dilutions of urine samples is shown. [Figure 2B] Analysis of cystatin C (CST3) levels and ephrin type B receptor 6 (EPHB6) levels is shown. Regression lines for CST3 are generated using a weighted linear regression model. [Figure 2C] Analysis of cystatin C (CST3) levels and ephrin type B receptor 6 (EPHB6) levels is shown. A four-parameter logistic function (4PL) fit of the CST3 data is shown. [Figure 2D] Analysis of cystatin C (CST3) levels and ephrin type B receptor 6 (EPHB6) is shown. Measurement of EPHB6 levels using serial dilutions of urine samples is shown. [Figure 2E] Analysis of cystatin C (CST3) levels and ephrin type B receptor 6 (EPHB6) is shown. Regression line generation for EPHB6 using a weighted linear regression model is shown. [Figure 3A] 1 shows an analysis of platelet-derived growth factor D (PDGFD) levels. Measurement of PDGFD levels using serial dilutions of urine samples is shown. [Figure 3B] 1 shows an analysis of platelet-derived growth factor D (PDGFD) levels. 2 shows the generation of a regression line using a weighted linear regression model. [Figure 3C] 1 shows an analysis of platelet-derived growth factor D (PDGFD) levels. A 4PL fit of the PDGFD data is shown. [Figure 4A] 1 shows an analysis of retinoic acid receptor responder 2 (RARRES2) levels. Measurement of RARRES2 levels using serial dilutions of urine samples is shown. [Figure 4B] Analysis of retinoic acid receptor responder 2 (RARRES2) levels. Generation of a regression line using a weighted linear regression model. [Figure 4C] Analysis of retinoic acid receptor responder 2 (RARRES2) levels is shown. A 4PL fit of the RARRES2 data is shown. [Figure 5A] 1 shows an analysis of interleukin 1 receptor-like 2 (IL1RL2) levels. Measurement of IL1RL2 levels using serial dilutions of urine samples. [Figure 5B] 1 shows an analysis of interleukin 1 receptor-like 2 (IL1RL2) levels. Generation of a regression line using a weighted linear regression model is shown. [Figure 5C] Analysis of interleukin 1 receptor-like 2 (IL1RL2) levels is shown. A 4PL fit of the IL1RL2 data is shown. [Figure 6A] 1 shows the analysis of coagulation factor XI (F11) levels. Measurement of F11 levels using serial dilutions of urine samples. [Figure 6B] 1 shows an analysis of coagulation factor XI (F11) levels. 2 shows the generation of a regression line using a weighted linear regression model. [Figure 6C] Analysis of coagulation factor XI (F11) levels is shown. A 4PL fit of the F11 data is shown. [Figure 7A] 1 shows an analysis of Septin 11 (SEPT11) levels. Measurement of SEPT11 levels using serial dilutions of urine samples. [Figure 7B]Analysis of Septin 11 (SEPT11) levels is shown. Generation of a regression line using a weighted linear regression model is shown. [Figure 7C] Analysis of Septin 11 (SEPT11) levels is shown. A 4PL fit of the SEPT11 data is shown. [Figure 8A] 1 shows an analysis of thymopoietin (TMPO) levels. Measurement of TMPO levels using serial dilutions of urine samples. [Figure 8B] 1 shows an analysis of thymopoietin (TMPO) levels. 2 shows the generation of a regression line using a weighted linear regression model. [Figure 8C] Analysis of thymopoietin (TMPO) levels is shown. A 4PL fit of the TMPO data is shown. [Figure 9A] Analysis of shisa family member 3 (SHISA3) levels. Measurement of SHISA3 levels using serial dilutions of urine samples. [Figure 9B] Analysis of shisa family member 3 (SHISA3) levels is shown. Generation of a regression line using a weighted linear regression model is shown. [Figure 9C] Analysis of shisa family member 3 (SHISA3) levels is shown. A 4PL fit of the SHISA3 data is shown. [Figure 10] AB show the results of normalization of analyte levels for iduronidase (IDUA, 10A) and neogenin 1 (NEO1, 10B). [Figure 11] The distribution of predicted relative dilutions for four 1:2 serial dilutions of a single sample is shown. The lines shown are, from left to right, 0.3125%, 0.625%, 1.25%, 2.5%, 5%, 10%, 20%, and 40%. [Figure 12] Pre-normalized target protein levels for 15 study participants are shown. [Figure 13] The predicted relative dilution of each sample calculated from the composite dilution curve is shown for the 15 study participants. [Figure 14] Normalized samples of 15 study participants are shown. [Figure 15A]Non-normalized measurements of analytes from the urine of a single study participant are shown. [Figure 15B] Normalized measurements of analytes from the urine of a single study participant are shown. [Figure 15C] Non-normalized measurements of analytes from the urine of a single study participant are shown. [Figure 15D] Normalized measurements of analytes from the urine of a single study participant are shown. [Figure 16A] Non-normalized measurements of analytes from the urine of a single study participant are shown. [Figure 16B] Normalized measurements of analytes from the urine of a single study participant are shown. [Figure 16C] Non-normalized measurements of analytes from the urine of a single study participant are shown. [Figure 16D] Normalized measurements of analytes from the urine of a single study participant are shown. [Figure 17] A to B show the F statistic (A) for the non-normalized data and normalized data of each study participant, and the reduction factor of the F statistic (B) for each study participant. [Figure 18A] 1 shows a flow diagram illustrating a first exemplary method for forming a complex dilution model. [Figure 18B] 1 shows graphs illustrating a first exemplary method for forming a complex dilution model. [Figure 19A] 10 shows a flow diagram illustrating a second exemplary method for forming a complex dilution model. [Figure 19B] 10 shows graphs illustrating a second exemplary method for forming a complex dilution model. [Figure 20A] 10 shows a flow diagram illustrating a third exemplary method for forming a complex dilution model. [Figure 20B] 10 shows graphs illustrating a third exemplary method for forming a complex dilution model. [Figure 21A] 10 shows a flow diagram illustrating a fourth exemplary method for forming a complex dilution model. [Figure 21B] 10 shows graphs illustrating a fourth exemplary method for forming a complex dilution model. [Figure 22A]10 shows a flow diagram illustrating a fifth exemplary method for forming a complex dilution model. [Figure 22B] 10 shows a graph illustrating a fifth exemplary method for forming a complex dilution model. [Figure 23A] 10 shows a flow diagram illustrating a sixth exemplary method for forming a complex dilution model. [Figure 23B] 10 shows a graph illustrating a sixth exemplary method for forming a complex dilution model. [Figure 24A] 10 shows a flow diagram illustrating a seventh exemplary method for forming a complex dilution model. [Figure 24B] 10 shows a graph illustrating a seventh exemplary method for forming a complex dilution model. [Figure 25] FIG. 1 is a block diagram illustrating an exemplary system architecture, according to various examples of the present disclosure. [Figure 26] FIG. 1 is an illustrative flow diagram illustrating the generation of a composite dilution model for each of multiple analytes present in different biological samples, according to examples of the present disclosure. [Figure 27] FIG. 1 is an illustrative flow diagram illustrating the generation of a composite dilution model for each of multiple analytes present in different biological samples, according to examples of the present disclosure. [Figure 28] FIG. 1 is a flow diagram showing application of a composite dilution model to novel biological samples to predict the relative dilution of new samples, according to an example of the present disclosure. [Figure 29] FIG. 1 is a block diagram of an example computer system capable of implementing one or more of the operations described herein. DETAILED DESCRIPTION OF THE INVENTION

[0067] I. Terminology and Methods While the present invention will be described in connection with certain exemplary embodiments, it will be understood that the invention is not limited to these embodiments, as defined by the claims.

[0068] One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention, and the present invention is in no way limited to the methods and materials described.

[0069] Unless otherwise defined, technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which this invention belongs. Definitions of common 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.

[0070] All publications, published patent documents, and patent applications cited in this specification are herein 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.

[0071] As used in this application, including the appended claims, the singular forms "a," "an," and "the" include the plural and can be used interchangeably with "at least one" and "one or more," unless the content clearly dictates otherwise. Thus, reference to an "aptamer" includes a mixture of aptamers, reference to a "probe" includes a mixture of probes, and so on.

[0072] As used herein, the terms "comprise," "comprising," "include," "including," "contain," "containing," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, product-by-process, or composition that comprises, includes, or contains an element or list of elements may include other elements not expressly listed.

[0073] Furthermore, it should be understood that all base or amino acid sizes and all molecular weight or molecular mass values ​​given for nucleic acids or polypeptides are approximate and are provided for illustrative purposes.

[0074] Additionally, ranges provided herein should be understood to be shorthand notations for all values ​​within that range. For example, a range of 1 to 50 should be understood to include any number, combination of numbers, or subrange from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50, as well as fractions thereof, unless the context clearly dictates otherwise.

[0075] Any concentration range, percentage range, ratio range, or integer range is Unless otherwise indicated, any numerical range for any physical feature described herein, e.g., polymer subunits, size, or thickness, should be understood to include any integer within the recited range, as well as fractions thereof, where appropriate (such as tenths and hundredths of an integer). Also, any numerical range for any physical feature described herein, e.g., polymer subunits, size, or thickness, should be understood to include any integer within the recited range, unless otherwise indicated.

[0076] As used herein, "about" or "consisting essentially of" means ±20% of the indicated range, value, or structure, unless otherwise indicated.

[0077] The use of the alternative (eg, "or") should be understood to mean either one, both, or any combination thereof of the alternatives.

[0078] To facilitate review of the various embodiments of the disclosure, the following explanations of specific terms are provided.

[0079] Antibody: The term "antibody" refers to full-length antibodies of any species that retain the ability to bind to an antigen, as well as fragments and derivatives of such antibodies, 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-display derived antibodies and fragments, affibodies, and nanobodies.

[0080] Aptamer: As used herein, "aptamer" refers to a nucleic acid having specific binding affinity for a target molecule, where the binding of the aptamer to the target molecule does not involve Watson-Crick base pairing. While it is recognized that affinity interactions are a matter of degree, in this context, the "specific binding affinity" of an aptamer for its target means that the aptamer binds to its target with a degree of affinity that is generally much higher than the aptamer binds to other components in the test sample. An "aptamer" is a set of copies of one type or species of nucleic acid molecule having a specific nucleotide sequence. An aptamer can contain any suitable number of nucleotides (including any number of chemically modified nucleotides). A plurality of "aptamers" refers to two or more sets of such molecules. Different aptamers can have either the same or different numbers of nucleotides. Aptamers can be DNA or RNA or chemically modified nucleic acids, and can be single-stranded, double-stranded, or contain both single- and double-stranded regions, and can include higher-order structures. Aptamers may also contain photoreactive or chemically reactive functional groups that allow them to covalently bind to their corresponding targets. Any aptamer method disclosed herein may include the use of two or more aptamers that specifically bind to the same target molecule. As will be further described below, aptamers may contain tags. When an aptamer contains a tag, it is not necessary for all copies of the aptamer to have the same tag. Furthermore, when different aptamers each contain a tag, each different aptamer can have either the same tag or a different tag.

[0081] Biological sample or biological matrix: As used herein, "biological sample" and "biological matrix" refer to any material, solution, or mixture obtained from an organism. Biological samples or biological matrices include blood (including whole blood, leukocytes, peripheral blood mononuclear cells, plasma, and serum), sputum, exhaled breath, urine, semen, saliva, cerebrospinal fluid, amniotic fluid, glandular fluid, lymph, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, cells, cell extracts, and cerebrospinal fluid. Biological samples or biological matrices also include all of the above fractions separated in an experiment. The terms "biological sample" and "biological matrix" also include materials, solutions, or mixtures containing homogenized solid material (e.g., fecal samples, tissue samples, or tissue biopsies). The terms "biological sample" and "biological matrix" also include materials, solutions, or mixtures containing homogenized solid material (e.g., fecal samples, tissue samples, or tissue biopsies). The term also includes materials, solutions, or mixtures derived from cell lines, tissue cultures, cell cultures, bacterial cultures, viral cultures, or cell-free biological systems (eg, IVTT).

[0082] Level: As used herein, "target protein level," "analyte level," and "level" refer to a measurement made using any analytical method for detecting an analyte (e.g., a target protein) in a biological sample, which measurement indicates the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc., of, or corresponding to, the analyte in the biological sample. The exact nature of the "level" depends on the specific design and components of the particular analytical method used to detect the analyte.

[0083] C-5 modified pyrimidine: As used herein, the term "C-5 modified pyrimidine" refers to a pyrimidine having a modification at the C-5 position. Examples of C-5 modified pyrimidines include those described in U.S. Patent Nos. 5,719,273, 5,945,527, 9,163,056, and Dellafiore et al., 2016, Front. Chem., 4:18. Examples of C-5 modifications include substitution of deoxyuridine at the C-5 position with substituents independently selected from benzylcarboxamide (or benzylaminocarbonyl) (Bn), naphthylmethylcarboxamide (or naphthylmethylaminocarbonyl) (Nap), tryptaminocarboxamide (or tryptaminocarbonyl) (Trp), phenethylcarboxamide (or phenethylaminocarbonyl) (Pe), thiophenylmethylcarboxamide (or thiophenylmethylaminocarbonyl) (Th), and isobutylcarboxamide (or isobutylaminocarbonyl) (iBu), as illustrated immediately below.

[0084] Chemical modifications of C-5 modified pyrimidines can also be combined, alone or in any combination, with 2' sugar modifications, modifications with exocyclic amines, and 4-thiouridine substitutions.

[0085] Representative C-5 modified pyrimidines include 5-(N-benzylcarboxamido)-2'-deoxyuridine (BndU), 5-(N-benzylcarboxamido)-2'-O-methyluridine, 5-(N-benzylcarboxamido)-2'-fluorouridine, 5-(N-isobutylcarboxamido)-2'-deoxyuridine (iBudU), 5-(N-isobutylcarboxamido)-2'-O-methyluridine, 5-(N-phenethylcarboxamido)-2'-deoxyuridine (PedU), 5-(N-thiophenylmethylcarboxamido)-2'-deoxyuridine (ThdU), 5-(N-isobutylcarboxamido)-2'-fluorouridine, 5-(N-tryptamino ...tryptaminocarboxamido)-2'-deoxyuridine (ThdU), 5-(N-tryptaminocarboxamido)-2'-fluorouridine, 5-(N-tryptaminocarboxamido)-2'-deoxyuridine (ThdU), 5-(N-tryptaminocarboxamido)-2'-fluorouridine, 5-(N-tryptaminocarboxamido)-2'-deoxyuridine (ThdU), 5-(N-trypt Examples of suitable uridine derivatives include 5-(N-naphthylmethylcarboxyamido)-2'-deoxyuridine (TrpdU), 5-(N-tryptaminocarboxamido)-2'-O-methyluridine, 5-(N-tryptaminocarboxamido)-2'-fluorouridine, 5-(N-[1-(3-trimethylammonium)propyl]carboxamido)-2'-deoxyuridine chloride, 5-(N-naphthylmethylcarboxyamido)-2'-deoxyuridine (NapdU), 5-(N-naphthylmethylcarboxyamido)-2'-O-methyluridine, 5-(N-naphthylmethylcarboxyamido)-2'-fluorouridine, and 5-(N-[1-(2,3-dihydroxypropyl)]carboxamido)-2'-deoxyuridine).

[0086] Nucleotides can be modified before or after the synthesis of oligonucleotides.The sequence of nucleotides in oligonucleotides can be interrupted by one or more non-nucleotide components.Modified oligonucleotides can be further modified after polymerization, for example, by conjugation with any suitable labeling component.

[0087] As used herein, the term "at least one pyrimidine," when referring to modification of a nucleic acid, refers to one, some, or all pyrimidines in a nucleic acid, indicating that any or all occurrences of any or all C, T, or U in a nucleic acid may be modified or unmodified.

[0088] Capture Reagent: As used herein, a "capture agent" or "capture reagent" refers to a molecule capable of specifically binding to an analyte (e.g., a biomarker, a protein, and / or a peptide). A "target protein capture reagent" refers to a molecule capable of specifically binding to a target protein. Non-limiting exemplary capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, and modifications or fragments of any of the above capture reagents. In some embodiments, the capture reagent is selected from an aptamer and an antibody.

[0089] Control level: The "control level" of a target molecule refers to the level of the target molecule in the same sample type from an individual who does not have a disease or condition, or from an individual who is not suspected of or at risk of having a disease or condition, or from an individual who has a non-progressive form of the disease or condition. Furthermore, a "control level" can refer to a reference material based on an average or what is considered to be within normal or healthy parameters. A "control level" can also refer to a reference level taken at a previous time and used to compare with a later measured or detected level of the target. For example, the level of the target can be detected at time A, then at time B, where time B is after time A. In a more specific example, time point A is considered to be time 0 or day 0, and time point B is determined as a certain number of minutes after time point A (e.g., 10, 20, 30, 40, 50, or 60 minutes after time point A), a certain number of 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), or a certain number of days (e.g., 1, 2, 3, 4, 5, 6, or 7 days after time point A). The "control level" of a target molecule need not be determined each time a method of the invention is performed, but may be a predetermined level used as a reference or threshold to determine whether the level in a particular sample is higher or lower than normal levels.

[0090] Corresponding correlation: "Corresponding correlation" or "concordance correlation coefficient" measures the agreement between two continuous variables X and Y (e.g., predicted, estimated, or determined, and actual measured values). "Corresponding correlation" assesses the extent to which a pair lies on a 45-degree line and includes a measure of accuracy and precision (i.e., "Lin's agreement"). Further information can be found in Lin, Biometrics, Vol. 45, No. 1 (March, 1989), pp. 255-268 (incorporated herein by reference). Other methods for quantifying correlation that can be used herein include, but are not limited to, Pearson 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 agreement, Pearson correlation coefficient, paired t-test, least-squares analysis of slope (=1) and intercept (=0), coefficient of variation, and intraclass correlation coefficient.

[0091] Detect: As used herein, "detect" or "quantify" in relation to analyte levels. " includes the use of both an instrument used to observe and record a signal corresponding to an analyte level as well as the material(s) necessary to generate that signal. 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, nuclear magnetic resonance, quantum dots, and the like.

[0092] Diagnosis: "Diagnosing," "diagnosis," "diagnosis," and variations thereof, refer to detecting, determining, or recognizing the health state or condition of an individual based on one or more signs, symptoms, data, or other information associated with that individual. An individual's health state may be diagnosed as healthy / normal (i.e., a diagnosis of the absence of a disease or condition) or diseased / abnormal (i.e., a diagnosis of the presence or characterization of a disease or condition). The terms "diagnosing," "diagnosis," "diagnosis," and the like, with respect to a particular disease or condition, encompass the initial detection of disease, characterization or classification of disease, detection of disease progression, remission, or recurrence, and detection of disease response following administration of a treatment or therapy to an individual.

[0093] Dilution: "Dilution," "dilution series," and variations thereof encompass several different types of dilution, including, but not limited to, stepwise dilution, serial dilution, and combinations thereof. As an example of stepwise dilution, if the dilution factor is 1000 (1:1000 dilution), the user can first perform a 1:10 dilution (dilution factor 10), then a 1:100 dilution (dilution factor 100), and diluting from the 1:10 dilution with 1 part solute and 99 parts diluent can result in a dilution factor of 1000 or a 1:1000 dilution of solute. Serial dilution involves a series of stepwise dilutions, each with the same dilution factor, where diluted material from the previous step is used to make the subsequent dilution. As an example of a serial dilution, making a five-point 1:2 serial dilution involves using 1 part solute and combining with 1 part diluent to make the first dilution in the dilution series (point 1 of 5), followed by using 1 part solute from the first dilution and combining with 1 part diluent to make the second dilution in the serial dilution series (point 2 of 5), and so on until the fifth consecutive serial dilution is reached.

[0094] Dilution Factor: "Dilution factor" refers to the ratio of parts solute to parts diluent. For example, a dilution factor of 2 means a 1:2 dilution, 1 part solute to 1 part diluent for a total of 2 parts. A dilution factor of 10 means a 1:10 dilution, 1 part solute to 9 parts diluent for a total of 10 parts.

[0095] Evaluate: "Evaluate," "assessing," "evaluation," and variations thereof encompass both "diagnosis" and "prognosis," and include the judgment or prediction of the future course of a disease or condition in an individual who has the disease or condition, as well as the judgment or prediction of the likelihood of a disease or condition occurring in an individual who has not previously been diagnosed with the disease or condition, and the judgment or prediction of the likelihood of a disease or condition recurring in an individual who is in remission or thought to be cured of the disease. The term "evaluating" also encompasses assessing an individual's response to a therapy, e.g., predicting whether an individual is likely to respond favorably or unlikely to respond to a therapeutic agent (or, for example, experience toxicity or other undesirable side effects), selecting a therapeutic agent to administer to the individual, or monitoring or determining an individual's response to a therapy that has been or is being administered to the individual.

[0096] Individual: As used herein, "individual" and "subject" are used interchangeably to 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 is not detected by conventional diagnostic methods.

[0097] Linear Regression: As used herein, the term "linear regression" refers to an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables, denoted x. In the case of one explanatory variable, it is called simple linear regression. For multiple explanatory variables, it is called multiple linear regression. Generally, linear regression can be used to fit a predictive model to an observed data set of y and x values. After developing such a model, if a value of x is added without the accompanying value of y, the fitted model can be used to predict the value of y.

[0098] Marker: As used herein, "marker" and "biomarker" are used interchangeably and refer to a target molecule (or analyte) that indicates or is indicative of a normal or abnormal process in an individual, or of 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 are detectable and measurable by a variety of methods, including laboratory assays and medical imaging. In some embodiments, a biomarker is a target protein.

[0099] Modified: As used herein, the terms "modify," "modified," "modified," and any variations thereof, when used in reference to an oligonucleotide, mean that at least one of the four constituent nucleotide bases (i.e., A, G, T / U, and C) of the oligonucleotide is an analog or ester of a natural nucleotide. In some embodiments, the modified nucleotide confers nuclease resistance to the oligonucleotide. In some embodiments, the modified nucleotide provides predominantly hydrophobic interactions between the aptamer and the protein target, resulting in high binding efficiency and stable co-crystal complexes. A pyrimidine with a substitution at the C-5 position is an example of a modified nucleotide. Modifications can include backbone modifications, methylation, and rare base-pairing combinations, such as the isobases isocytidine and isoguanidine. Modifications can also include 3' and 5' modifications, such as capping. Other modifications can include substitution of one or more natural nucleotides with analogs, internucleotide modifications such as uncharged linkages (e.g., methylphosphonates, phosphotriesters, phosphoamidates, carbamates, etc.) and charged linkages (e.g., phosphorothioates, phosphorodithioates, etc.), intercalators (e.g., acridine, psoralen, etc.), chelators (e.g., metals, radioactive metals, boron, metal oxides, etc.), alkylators, and modified linkages (e.g., alpha-anomeric nucleic acids, etc.). Additionally, any of the hydroxyl groups normally present on the sugar of the nucleotide can be replaced with phosphonate or phosphate groups, protected by standard protecting groups, or activated to provide for additional linkages to additional nucleotides or to solid supports.The 5'- and 3'-terminal OH groups may be phosphorylated or substituted with amines, organic capping group moieties of about 1 to about 20 carbon atoms, polyethylene glycol (PEG) polymers (in some embodiments, in the range of about 10 to about 80 kDa), PEG polymers (in some embodiments, in the range of about 20 to about 60 kDa), or other hydrophilic or hydrophobic biological or synthetic polymers. In one embodiment, the modification is at the C-5 position of the pyrimidine. Such modifications may be made via a direct amide bond at the C-5 position or by other types of linkages.

[0100] Polynucleotides may also contain analogous forms of ribose or deoxyribose sugars known in the art, including 2'-O-methyl-, 2'-O-allyl, 2'-O-diamino- ... Examples of suitable nucleoside analogs include 2'-fluoro- or 2'-azido-ribose, carbocyclic sugar analogs, α-anomeric sugars, epimeric sugars (e.g., arabinose, xylose, or lyxose), pyranose sugars, furanose sugars, sedoheptulose, acyclic analogs, and abasic nucleoside analogs (e.g., methyl riboside). As noted above, one or more phosphodiester linkages may be replaced with alternative linking groups. Such alternative linking groups include embodiments in which phosphate is replaced with P(O)S ("thioate"), P(S)S ("dithioate"), (O)NR2 ("amidate"), P(O)R, P(O)OR', CO, or CH2 ("formacetal"), where each R or R' is independently H or substituted or unsubstituted alkyl (1-20C) (optionally containing an ether (-O-) linkage), aryl, alkenyl, cycloalkoxy, cycloalkenyl, or araldyl. Not all linkages in a polynucleotide need be identical. Substitution of analogous forms of sugars, purines, and pyrimidines can be advantageous in the design of the final product, as can alternative backbone structures such as, for example, polyamide backbones.

[0101] Nucleic Acid: As used herein, "nucleic acid," "oligonucleotide," and "polynucleotide" are used interchangeably to refer to polymers of nucleotides, including DNA, RNA, DNA / RNA hybrids, and modifications of these types of nucleic acids, oligonucleotides, and polynucleotides, 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; thus, although the terms nucleic acid, oligonucleotide, and polynucleotide include polymers of nucleotides that are aptamers, the terms nucleic acid, oligonucleotide, and polynucleotide are not limited to aptamers.

[0102] Least Squares: As used herein, "Least Squares" or "OLS" or "Linear Least Squares" refers to a method for estimating unknown parameters in a linear regression model. This method minimizes the sum of squared perpendicular distances between the observed responses in a dataset and the responses predicted by a linear fit. The resulting estimator can be expressed as a simple equation, especially when there is a single regressor on the right-hand side.

[0103] Prognosis: "Predict," "prognosis," "prognosis," and variations thereof, refer to the prediction of the future course of a disease or condition in an individual having the disease or condition (e.g., a prediction of how long a patient will survive), and such terms encompass the assessment of disease response during and / or after the administration of a treatment or therapy to an individual.

[0104] SELEX: The terms "SELEX" and "SELEX method" are used interchangeably herein and generally refer to the combination of (1) the selection of aptamers that interact with a target molecule in a desired manner (e.g., bind with high affinity to a protein) and (2) the amplification of the selected nucleic acids. The SELEX method can be used to identify aptamers with high affinity for a particular analyte, such as a target protein.

[0105] Sequence identity: As used herein in reference to 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 two or more sequences. Sequence comparison and determination of percent identity between two or more sequences can be performed using mathematical algorithms such as BLAST and Gapped BLAST programs with default parameters (e.g., Altschul et al., J. Mol. Biol. 215:403, 1990; also see www.ncbi.nlm.nih. (See also BLASTN at .gov / BLAST.) For sequence comparison, typically one sequence acts as a reference sequence, to which test sequences are compared. When using a sequence comparison algorithm, test and reference sequences are input into a computer, subsequence coordinates are designated, if necessary, and sequence algorithm program parameters are designated. The sequence comparison algorithm then calculates the percent sequence identity for the test sequence(s) relative to the reference sequence, based on the designated program parameters. Optimal alignment of sequences for comparison can be achieved, 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, F. M. et al., Current Protocols in Molecular Biology, pub. by Greene Publishing Assoc. and Wiley-Interscience (1987)). As used herein, when describing the percent identity of a nucleic acid (e.g., an aptamer) whose sequence is, for example, at least about 95% identical to a reference nucleotide sequence, it is intended that the nucleic acid sequence is identical to the reference sequence except that it may contain up to 5 point mutations per 100 nucleotides of the reference nucleic acid sequence.In other words, to obtain a desired nucleic acid sequence that is at least about 95% identical in sequence to a reference nucleic acid sequence, up to 5% of the nucleotides in the reference sequence can be deleted or substituted with alternative nucleotides, or a number of nucleotides up to 5% of the total number of nucleotides in the reference sequence can be inserted into the reference sequence (referred to herein as insertions). These mutations of the reference sequence to generate the desired sequence can occur at the 5' or 3' terminal position of the reference nucleotide sequence, or anywhere between these terminal positions, and can be interspersed individually among nucleotides in the reference sequence or interspersed within one or more contiguous groups in the reference sequence.

[0106] SOMAmer: As used herein, the term SOMAmer or SOMAmer reagent refers to an aptamer with improved off-rate characteristics. SOMAmer reagents are alternatively referred to as slow off-rate modified aptamers and can be selected via the improved SELEX method described in U.S. Patent 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 containing at least one nucleotide with a hydrophobic modification) have an off-rate (t) of 2 minutes or more, 4 minutes or more, 5 minutes or more, 8 minutes or more, 10 minutes or more, 15 minutes or more, 30 minutes or more, 60 minutes or more, 90 minutes or more, 120 minutes or more, 150 minutes or more, 180 minutes or more, 210 minutes or more, or 240 minutes or more.

[0107] Substantially equivalent: As used herein, the phrase "substantially equivalent" refers to two values ​​that are sufficiently similar that one of ordinary skill in the art would consider the difference between the two values ​​to have little or no biological and / or statistical significance within the context of the characteristics measured by the two 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%.

[0108] Target molecule: "Target," "target molecule," and "analyte" are used interchangeably herein and refer to any molecule of interest that may be present in a sample. The terms may include any slight change in a particular molecule, e.g., in the case of a protein, a slight change in the amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, e.g., conjugation with a labeling moiety, that does not substantially change the identity of the molecule. A "target molecule," "target," or "analyte" refers to a set of copies of one type or species of molecule or multimolecular structure. A "target molecule," "target," and "analyte" refer to two or more types 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, toxicants, substrates, metabolites, transition-state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the above. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as a "target protein."

[0109] 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.

[0110] Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of this disclosure, suitable methods and materials are described below. Additionally, the materials, methods, and examples are illustrative only and not intended to be limiting.

[0111] Detection and Quantification of Analytes and Analyte Levels The analyte levels of the analytes described herein can be detected using any of a variety of known analytical methods. In one embodiment, the analyte levels are detected using a capture reagent. In various embodiments, the capture reagent can be exposed to the analyte in solution or while the capture reagent is immobilized on a solid support. In some embodiments, the capture reagent includes a feature that is reactive to a secondary feature on the solid support. In such embodiments, the capture reagent can be exposed to the analyte in solution, and then the feature on the capture reagent can be used in conjunction with the secondary feature on the solid support to immobilize the analyte on the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture reagents include, but are not limited to, aptamers, antibodies, adnectins, ankyrins, other antibody mimetics 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, avimers, peptidomimetics, hormone receptors, cytokine receptors, and synthetic receptors, including modifications and fragments of any of these.

[0112] In some embodiments, the analyte level is detected using an analyte / capture reagent complex.

[0113] In some embodiments, the analyte level originates from the analyte / capture reagent complex and is detected indirectly (e.g., as a result of a reaction following analyte / capture reagent interaction), but is dependent on the formation of the analyte / capture reagent complex.

[0114] In some embodiments, the analyte level is detected directly from the analyte in the biological sample.

[0115] In some embodiments, the analytes are selected from the group consisting of analytes, analytes, and analytes. The analytes are detected using a multiplexed format that allows for simultaneous processing of the analytes. In some embodiments of the multiplexed format, the capture reagents are immobilized directly or indirectly, covalently or non-covalently, at individual locations on a solid support. In some embodiments, the multiplexed format uses individual solid supports, with each solid support having a unique capture reagent (e.g., quantum dots) associated with it. In some embodiments, individual devices are used to detect each one of the multiple analytes to be detected in the biological sample. The individual devices can be configured to allow each analyte in the biological sample to be processed simultaneously. For example, a microtiter plate can be used, with each well in the plate being used to analyze one or more of the multiple analytes to be detected in the biological sample.

[0116] In one or more embodiments described herein, a fluorescent tag can be used to label a component of an analyte / capture reagent complex to enable detection of the analyte level. 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 the fluorescent label can then be used to detect the corresponding analyte level. 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.

[0117] In some embodiments, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule comprises at least one substituted indolium ring system, wherein the substituent on the 3-carbon of the indolium ring comprises a chemically reactive group or a conjugation agent. In some embodiments, the dye molecule comprises an AlexFluor molecule, e.g., AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In some embodiments, the dye molecule comprises a first type and a second type of dye molecule, e.g., two different AlexaFluor molecules. In some embodiments, the dye molecule comprises a first type and a second type of dye molecule, wherein the two dye molecules have different emission spectra.

[0118] Fluorescence can be measured using a variety of instrumentation compatible with a wide range of assay formats. For example, spectrofluorometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Fluorescence Spectroscopy (J.R. Lakowicz, Springer Science + Business Media, Inc., 2004). See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

[0119] In one or more embodiments, a chemiluminescent tag can optionally be used to label components of the analyte / capture complex to allow for 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, peroxyoxalates, aryloxalates, acridinium esters, dioxetanes, and the like.

[0120] In some embodiments, the detection method provides a detectable signal corresponding to the analyte level. The enzymes used in the detection of chromogenic markers include enzyme / substrate combinations that produce chromogenic markers. Generally, the enzyme catalyzes a chemical change in a chromogenic substrate, which can be measured using a variety of 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, and microperoxidase.

[0121] In some embodiments, the detection method may be a combination of fluorescence, chemiluminescence, radionuclide, and / or enzyme / substrate combinations that generate a measurable signal. In some embodiments, multimodal signaling may have unique and advantageous features in analyte assay formats.

[0122] In some embodiments, the analyte levels of the analytes described herein can be detected using any analytical method, including singleplex aptamer assays, multiplexed aptamer assays, singleplex or multiplexed immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc., as discussed below.

[0123] Quantifying analyte levels using aptamer-based assays Assays directed at the detection and quantification of physiologically important molecules in biological and other samples are important tools in scientific research and healthcare. One class of such assays involves the use of microarrays containing one or more aptamers immobilized on a solid support. Each aptamer is capable of binding to a target molecule in a highly specific manner and with very high affinity. See, for example, U.S. Pat. No. 5,475,096, entitled "Nucleic Acid Ligands." See also, for example, U.S. Pat. Nos. 6,242,246, 6,458,543, and 6,503,715, each entitled "Nucleic Acid Ligand Diagnostic Biochip." Once the microarray is contacted with a sample, the aptamers bind to the respective target molecules present in the sample, thereby enabling quantification of the corresponding analyte levels.

[0124] In one embodiment, an 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 embodiments, aptamers are from about 25 to about 100 nucleotides in length (i.e., 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, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135 , 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 nucleotides in length) or about 25-50 nucleotides in length (i.e., about 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 nucleotides in length).

[0125] Aptamers can be identified using any known method, including SELEX. Once identified, aptamers can be prepared or synthesized according to any known method, including chemical and enzymatic synthesis. In some embodiments, aptamers contain at least one nucleotide with a hydrophobic modification (e.g., a hydrophobic base modification), which allows hydrophobic contact with the target protein. Such hydrophobic contacts, in some embodiments, contribute to increased affinity and / or slower off-rate binding by the aptamer. In some embodiments, aptamers contain at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotides with hydrophobic modifications, each of which may be the same or different from the others. In some embodiments, the hydrophobic base modification is a C-5 modified pyrimidine. Non-limiting exemplary C-5 modified pyrimidines are described herein and / or known in the art.

[0126] In some assay formats, aptamers are immobilized on a solid support before contacting with a sample. However, under certain circumstances, immobilizing aptamers before contacting with a sample may not provide an optimal assay. For example, in some cases, pre-immobilizing aptamers may cause inefficient mixing of the aptamer with the target molecule on the surface of the solid support, which may result in a longer reaction time, so the incubation period may be extended to allow the aptamer to efficiently bind to its target molecule. Furthermore, when photoaptamers are used in the assay, and depending on the material used as the solid support, the solid support may tend to scatter or absorb the light used to form a covalent bond between the photoaptamer and its target molecule. Furthermore, depending on the method used, the detection of target molecules bound to aptamers may be inaccurate. This is because the surface of the solid support may also be exposed to and affected by any labeling agent used. Finally, immobilization of aptamers onto a solid support generally involves a step of preparing the aptamer (i.e., immobilization) prior to exposing the aptamer to a sample, and this preparation step may affect the activity or functionality of the aptamer.

[0127] Aptamer assays or "aptamer-based assay(s)" have also been described that allow an aptamer to capture its target in solution, followed by a separation step designed to remove specific components of the aptamer-target mixture prior to detection (see, e.g., U.S. Patent Publication No. 2009 / 0042206, entitled "Multiplexed Analyses of Test Samples"). The described aptamer assay methods allow for 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 create nucleic acid surrogates (i.e., aptamers) for detecting and quantifying non-nucleic acid targets, thereby allowing a wide variety of nucleic acid technologies, including amplification, to be applied to a wider range of desired targets, including protein targets.

[0128] Aptamers can be constructed to facilitate separation of assay components from the aptamer-analyte complex (or photoaptamer-analyte covalent complex), allowing for isolation of the aptamer for detection and / or quantification. In one embodiment, such constructs can include a cleavable or releasable element in the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, such as a label or detectable moiety, a spacer moiety, or a specific binding tag or immobilization element. For example, an aptamer can include a tag connected to the aptamer via a cleavable moiety, a label, a spacer moiety that separates the label, and a cleavable moiety. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin moiety and a spacer moiety, and can be used for derivatization of amines. The aptamer may contain an NHS group for biotinylation, which can be used to introduce a biotin group into the aptamer, allowing for subsequent release of the aptamer in an assay.

[0129] Homogeneous assays, which in some embodiments are performed with all assay components in solution, may not require separation of sample and reagents prior to detection of a signal. These methods are fast and easy to use. In some embodiments, the signal generation method utilizes anisotropic signal changes due to the interaction of a fluorophore-labeled capture reagent with its specific analyte target. When the labeled capture agent reacts with its target, the increased molecular weight causes rotational motion of the fluorophore bound to the complex, which changes the anisotropy value much more slowly. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the analyte in solution. Other methods include fluorescence polarization assays, molecular beacon techniques, time-resolved fluorescence quenching, chemiluminescence, and fluorescence resonance energy transfer.

[0130] An exemplary solution-based aptamer assay that can be used to detect analyte levels in a biological sample includes: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first tag and has specific affinity for the analyte, whereby if the analyte is present in the sample, an aptamer affinity complex is formed; (b) exposing the mixture to a first solid support that includes a first capture element, causing the first tag to associate with the first capture element; (c) removing components of the mixture that are not associated with the first solid support; and (d) removing the aptamer affinity complex. (e) binding a second tag to the analyte component of the aptamer affinity complex; (f) exposing the released aptamer affinity complex to a second solid support containing a second capture element, and associating the second tag with the second capture element; (g) removing the uncomplexed aptamer from the mixture by partitioning the uncomplexed aptamer from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the analyte by detecting the aptamer component of the aptamer affinity complex. For example, the protein concentration or level in a sample can be expressed as relative fluorescence units (RFU), which can be the product of detecting the aptamer component of the aptamer affinity complex (e.g., an aptamer complexed with a target protein creates an aptamer affinity complex). That is, in an aptamer-based assay, the protein concentration or level correlates with the RFU.

[0131] A non-limiting exemplary method for detecting an analyte in a biological sample using aptamers is described in Kraemer et al., PLoS One 6(10):e26332.

[0132] Quantifying analyte levels using immunoassays Immunoassays are based on the reaction of antibodies with corresponding targets or analytes and can detect the analyte in a sample depending on the specific assay format. To improve 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 higher affinity for targets 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.

[0133] Quantitative results are obtained by using a standard curve generated with known concentrations of the particular analyte to be detected. The response or signal from an unknown sample is plotted against the standard curve to establish the corresponding amount or level of the target in the unknown sample.

[0134] Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative in detecting an analyte. The method relies on the attachment of a label to either the analyte or the antibody, where the label component comprises an enzyme, either directly or indirectly. ELISA tests can be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods include, for example, the use of radioisotopes (I 125 ) or rely on labels such as fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidity, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assays, etc. (ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0135] Exemplary assay formats include enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Exemplary procedures for detecting analytes include analyte immunoprecipitation followed by quantitative methods that allow size and peptide level differentiation (e.g., gel electrophoresis, capillary electrophoresis, planar electrochromatography, etc.).

[0136] Methods for detecting and / or quantifying a detectable label or signal-generating material depend on the nature of the label. The product of the reaction catalyzed by an appropriate enzyme (when the detectable label is an enzyme (see above)) may be, but is not limited to, fluorescent, luminescent, or radioactive, or such product may 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, luminometers, and densitometers.

[0137] All detection methods can be performed in any format that allows for any suitable preparation, processing, and analysis of the reaction. This can be, for example, a multi-well assay plate (e.g., 96-well or 386-well) or any suitable array or microarray. Stock solutions for various agents can be made manually or robotically, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample readout, data collection, and analysis can be performed robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting the detectable label.

[0138] Quantifying Analyte Levels Using Gene Expression Profiling Measurement of mRNA in a biological sample can, in some embodiments, be used as a surrogate for detecting the level of the corresponding protein in the biological sample. Thus, in some embodiments, an analyte or analyte panel described herein can be detected by detecting the appropriate RNA.

[0139] In some embodiments, mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed by qPCR). RT-PCR is used to generate cDNA from mRNA. The cDNA can be used in a qPCR assay to generate fluorescence as the DNA amplification process progresses. By comparison with a standard curve, qPCR can yield absolute measurements, such as the number of mRNA copies per cell. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure mRNA expression levels in samples. Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0140] Analyte Level Quantification Using Mass Spectrometry Mass spectrometers of various configurations can be used to detect analyte levels. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and an instrument control system, as well as a data system. The differences in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, the inlet may be a capillary column liquid chromatography source or a direct probe or stage such as those used in 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. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, New York (2000)).

[0141] 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 (called ultraflex III 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.

[0142] Sample preparation strategies are used to label and enrich samples prior to mass spectrometric characterization of protein analytes and quantitation of analyte levels. Labeling methods include, but are not limited to, isobaric tagging for relative and absolute quantitation (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich samples for candidate analyte proteins prior to mass spectrometric analysis include, but are not limited to, aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g., diabodies), imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modifications and fragments thereof.

[0143] kit Any combination of analytes described herein can be detected using a suitable kit, e.g., a kit for use in practicing the methods disclosed herein. Additionally, any kit can include one or more detectable labels (e.g., fluorescent moieties, etc.) described herein.

[0144] Method for normalizing analyte measurements in biological matrices - Patent Application 20070122999 Provided herein are methods for normalizing analyte measurements in biological matrices.

[0145] In certain embodiments, a method for developing a complex dilution model is provided. The complex dilution model can be used to determine the relative dilution of one or more biological samples, such as urine. See, for example, FIG. 10. The relative dilution can then be used to normalize multiple samples, thereby minimizing differences in sample concentrations (such as those that may arise due to differences in subject hydration levels) and thereby detecting differences in analyte levels due to factors other than sample concentration. Other factors other than sample concentration that can affect analyte levels include, but are not limited to, increases and decreases in analyte expression, changes in analyte stability, changes in the rate or amount of analyte secreted, etc. Such differences in analyte levels, in some embodiments, can be indicative of a disease or condition or the likelihood of developing a disease or condition.

[0146] In certain embodiments, a composite dilution model is developed according to the exemplary method shown in Figures 18A and 18B. Analyte levels are measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first and second biological samples can be biological samples from the same individual at different time points or from different individuals. The dilution series includes analyte levels at various dilutions of the sample. A model is generated based on the first dilution series. A reference value in the second dilution series is selected. In some embodiments, the selected reference value in the second dilution series is near the midpoint of the second dilution series, e.g., in a portion of the series that appears substantially linear. The reference value in the second dilution series is then translated by an amount ΔX to a point on the model. The remaining points in the second dilution series (i.e., analyte levels at various dilutions) are translated by the same amount ΔX to form a series of composite translations. The composite dilution model is generated by fitting a function to the series of composite translations. This composite dilution model can be used to determine the relative dilutions of a biological test sample from a subject.

[0147] In certain embodiments, a complex dilution model is developed according to the exemplary method shown in Figures 19A and 19B. Analyte levels are measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first and second biological samples can be biological samples from the same individual at different time points or from different individuals. The dilution series includes the analyte levels 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 analyte level at a particular dilution of the model. The model reference value is then translated by an amount ΔX to a point in the second dilution series. The remaining values ​​of the model are translated by the same amount ΔX to form a series of complex translation series. The complex dilution model is generated by fitting a function to the series of complex translation series. This complex dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0148] In certain embodiments, a complex dilution model is developed by the exemplary method shown in Figures 20A and 20B. Analyte levels are measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first and second biological samples can be biological samples from the same individual at different time points or from different individuals. The dilution series includes analyte levels at various dilutions of the sample. A reference value in the second dilution series is selected. In some embodiments, the selected reference value in the second dilution series is near the midpoint of the second dilution series, e.g., in a portion of the series that appears substantially linear. The reference value in the second dilution series is then translated by an amount ΔX to the value in the first dilution series. The remaining points in the second dilution series (i.e., analyte levels at various dilutions) are translated by the same amount ΔX to form a series of complex translation series. A complex dilution model is generated by fitting a function to the series of complex translation series. This complex dilution model is generated by fitting a function to the series of complex translation series. The complex dilution model is generated by fitting a function to the series of complex translation series. It can be used to determine the relative dilution of the test sample.

[0149] In certain embodiments, a composite dilution model is developed according to the exemplary method shown in Figures 21A and 21B. Analyte levels are measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first and second biological samples can be biological samples from the same individual at different time points or from different individuals. The model is generated based on the first dilution series. An arbitrary reference value is selected, which is the analyte level at a dilution value. In some embodiments, the arbitrary reference value is the analyte level at a specific dilution not found in the second dilution series or model. Next, points on the mathematical model are translated by an amount ΔX to the arbitrary reference value, and points on the second dilution series are translated by an amount ΔY to the arbitrary reference value. The remaining points of the second dilution series (i.e., the analyte levels at 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 series of composite translations. The composite dilution model is generated by fitting a function to the series of composite translations. This complex dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0150] In certain embodiments, a composite dilution model is developed according to the exemplary method shown in FIG. 22A. Analyte levels are measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first and second biological samples can be biological samples from the same individual at different time points or from different individuals. The dilution series indicates the analyte levels at various dilutions of the sample. An arbitrary reference value is selected, which is the analyte level at a dilution value. In some embodiments, the arbitrary reference value is the analyte level at a specific dilution not found in the first or second dilution series. Next, points on the first dilution series are translated by an amount ΔX to the arbitrary reference value, and points on the second dilution series are translated by an amount ΔY to the 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 series of composite translations. A complex dilution model is generated by fitting a function to a series of complex translational series, which can be used to determine the relative dilution of a biological test sample from a subject.

[0151] In certain embodiments, a composite dilution model is developed according to the exemplary method shown in Figures 23A and 23B. Analyte levels are measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first and second biological samples can be biological samples from the same individual at different time points or from different individuals. The dilution series indicates the analyte levels at various dilutions of the sample. A first model is generated based on the analyte levels in the first dilution series, and a second model is generated based on the analyte levels in the second dilution series. Values ​​from the first model are then translated by an amount ΔX to values ​​in the second model. The remaining values ​​from the first model are then translated by the same amount ΔX to form a series of composite translations. The composite dilution model is generated by fitting a function to the series of composite translations. This composite dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0152] In certain embodiments, a composite dilution model is developed by the exemplary method shown in Figures 24A and 24B. Analyte levels are measured in a dilution series from a first biological sample and a dilution series from a second biological sample. The first and second biological samples can be biological samples from the same individual at different time points or from different individuals. The dilution series indicates the analyte levels at various dilutions of the sample. A first model is generated based on the analyte levels in the first dilution series, and a second model is generated based on the analyte levels in the second dilution series. Arbitrary reference values ​​are selected that are the analyte levels at the dilution values. Several In some embodiments, the arbitrary reference value is an analyte level at a particular dilution not found in the first dilution series or the second dilution series. Then, the points on the first model are translated by an amount ΔX to the arbitrary reference value, and the points on the second model are translated by an amount ΔY to the arbitrary reference value. The residual values ​​of the first model are translated by the same amount ΔX, and the residual values ​​of the second model are translated by the same amount ΔY to form a series of composite translations. The composite dilution model is generated by fitting a function to the series of composite translations. This composite dilution model can be used to determine the relative dilution of a biological test sample from a subject.

[0153] Any suitable model can be used in the methods described herein, including models generated from the analyte levels in the first and / or second dilution series, as well as models used to generate the composite dilution model. Exemplary models that can be used in the methods include, but are not limited to, linear regression models, LOESS curve-fitting models, nonlinear regression models, spline-fitting models, mixed-effect regression models, fixed-effect regression models, generalized linear models, matrix decomposition models, and / or four-parameter logistic regression (4PL) models. The models generated from the analyte levels in the first and / or second dilution series can be the same or different. Similarly, the models used to generate the composite dilution model can be the same or different from one or more of the models generated from the analyte levels. Those skilled in the art can select an appropriate model depending on the particular application, and many such models are known in the art.

[0154] In some embodiments, the relative dilution of a biological test sample can be determined following the development of a composite dilution model or using a previously developed composite dilution model. In some such embodiments, the level of at least one analyte from the biological test sample is horizontally translated to the composite dilution model developed for the at least one analyte. This allows the relative dilution of the biological test sample to be determined. In some embodiments, the composite dilution model can be developed using a set of analytes, such as at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, or 200 analytes. The relative dilution of the biological test sample is then determined for a subset of the analytes or for each analyte. The relative dilution of the biological test sample is then determined, in some embodiments, using an average, mean, median, or the like of the relative dilution for each analyte. In some embodiments, analytes are selected whose levels are not expected to vary between individuals, e.g., between individuals with a disease or condition and individuals without a disease or condition.

[0155] Computer Devices, Methods, and Software In one aspect, the system further comprises one or more devices for providing input data to the one or more processors. The system further comprises a memory for storing the dataset of ranked data elements.

[0156] In another aspect, the device for providing input data comprises a detector for detecting characteristics of the data elements, such as, for example, a mass spectrometer or a gene chip reader.

[0157] The system may further comprise a database management system. User requests or queries may be formatted in an appropriate language understood by the database management system, which processes the queries and extracts relevant information from the database of training sets.

[0158] The system may be connectable to a network to which a network server and one or more clients are connected. The network may be a local area network (LAN) or a wide area network (WAN), as known in the art. Preferably, the server accesses database data to process user requests. This includes the necessary hardware to execute a computer program product (e.g., software) to

[0159] The system may include an operating system (e.g., UNIX or Linux) for executing instructions from the database management system. In one aspect, the operating system operates on a global communications network, such as the Internet, and may connect to such a network using a global communications network server.

[0160] The system may include one or more devices with a graphical display interface including interface elements such as buttons, pull-down menus, scroll bars, text entry fields, etc., commonly found in graphical user interfaces known in the art. Requests entered at the user interface may be sent to application programs within the system for formatting to search for relevant information in one or more of the system databases. User-entered requests or queries may be formulated in any suitable database language.

[0161] A graphical user interface may be generated by graphical user interface code as part of the operating system and may be used to input data and / or display input data. The results of processed data may 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 provided in the form of a computer-readable medium.

[0162] The system can be in communication with an input device for providing data regarding the data elements (e.g., values ​​of expressions) 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 an array reader.

[0163] The computer system may be a stand-alone system or may be part of a computer network that includes a server and one or more databases.

[0164] Some embodiments described herein can be implemented to include a computer program product, which may include a computer-readable medium having computer-readable program code embodied in the medium for executing an application program on a computer with a database.

[0165] As used herein, a "computer program product" refers to an organized set of instructions in the form of natural language or programming language statements contained on a physical medium of any nature (e.g., written, electronic, magnetic, optical, or other) and usable by a computer or other automated data processing system. These programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to operate in accordance with the specific content of the statements. Computer program products include, but are not limited to, source and object code embedded in a computer-readable medium and / or programs in test or data libraries. Furthermore, computer program products that enable a computer system or data processing device to operate in a preselected manner may be provided in many forms, including, but not limited to, original source code, assembly code, object code, machine code, encrypted or compressed versions of the foregoing, and all equivalents.

[0166] While various embodiments have been described as methods or apparatus, it should be understood that the embodiments can be implemented via code coupled to a computer, e.g., code resident on or accessible by a computer. For example, software and databases can be utilized to implement many of the methods described above. Thus, in addition to embodiments achieved via hardware, it should also be noted that these embodiments can be achieved via the use of an article of manufacture comprising a computer-usable medium having computer-readable program code embodied thereon that enables the functions disclosed herein to be performed. Accordingly, the embodiments should also be considered protected by this patent in their program code means.

[0167] Further, the embodiments may be embodied as code stored in virtually any type of computer-readable memory, including but not limited to RAM, ROM, magnetic, optical, or magneto-optical media. Still more generally, the embodiments may be implemented in software, hardware, or any combination thereof, including but not limited to software running on a general purpose processor, microcode, programmable logic array (PLA), or application-specific integrated circuit (ASIC).

[0168] It is also contemplated that embodiments may be achieved as computer signals embodied in carrier waves, as well as signals propagated over transmission media (e.g., electrical and optical). Thus, the various types of information discussed above may be formatted into structures, such as data structures, and transmitted as electrical signals over transmission media or stored on computer-readable media.

[0169] 25 illustrates an exemplary system architecture 2500 in which embodiments 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 analysis 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.

[0170] 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 analysis of 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 diagnostic devices involved in processing biological samples, such as an Affymetrix® or Illumina® microarray machine.

[0171] In one example, the robot 2504 drives, processes, and / or delivers biological samples as part of the processing of the biological samples within the diagnostic machine 2502 or between multiple diagnostic machines 2502. For example, the robot 2504 may generally include, but is not limited to, a TECAN® robot 2502 or any other robotic machine that provides automated or semi-automated processing of biological samples in a laboratory environment.

[0172] In one example, the diagnostic machine 2502 communicates with one or more data store(s) 2508 and one or more server machine(s) via one or more network(s) 2506. 2510. Network 2506 may generally 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, network 2506 may include the Internet and / or one or more intranets, a landline network, a wireless network, and / or other suitable types of communication networks. In one example, network 2506 may comprise a wireless communication network (e.g., a cellular network) adapted to communicate with other communication networks, such as the Internet.

[0173] The data store 2508 is a persistent storage device that can store various types of data, such as alphanumeric text, audio, video, and / or image content, etc. In some examples, the data store 2508 may be a networked file server, while in other examples, the data store 2508 may be another type of persistent storage device, such as an object-oriented database, a relational database, etc.

[0174] In one example, diagnostic machine 2502 can store and access various types of data, including analyte measurement data 2580, via network 2506. Diagnostic machine 2502 can also store and access such data on one or more local data store(s) 2508 associated with diagnostic machine 2502 (not shown) and / or one or more data store(s) 2508 locally connected to server machine 2510 or one or more other computing systems (not shown). In one example, analyte measurement data 2580 can include, but is not limited to, relative fluorescence unit measurements for each of multiple analytes or proteins associated with one or more biological samples.

[0175] The data store 2508 can also receive, store, and provide a composite dilution model 2590 for use in controlling sample-to-sample analyte variability in complex biological matrices. The composite dilution model 2590 may be generated from the analyte measurement data 2580 or may be provided directly or indirectly from another source. Furthermore, 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.

[0176] 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. Server machine 2510 may include a web server 2520, an application server 2522, and a script detection system 2530. In some embodiments, each of web server 2520, application server 2522, and / or script detection system 2530 may run on one or more different server machine(s) 2510.

[0177] The web server 2520 can serve text, audio, video, and image content from the server machine 2510 and / or data store 2508 to one or more client device(s) 2570. The web server 2520 can also provide web-based application services and business logic to the client device(s) 2570. The client device(s) 2570 can use applications such as a web browser to search, access, and consume various forms of content and services from the web server 2520. The web server 2520 can also serve existing and new distributions from the client device(s) 2570. Analysis measurement data 2580 may be received, such as text, audio, video, and image content, which is stored in one or more data store(s) 2508 for purposes that may include analyzing, transforming, processing, persisting, and distributing such content.

[0178] In one example, the web server 2520 is coupled to one or more application server(s) 2522 that provide applications and services to the client device(s) 2570, either directly or with the assistance of the web server 2520. For example, the web server 2520 may provide the client device(s) 2570 with access to one or more specialized software applications in the field of biotechnology. Such functionality may also be provided, for example, as one or more different web applications, standalone applications, computer systems, plug-ins, web browser extensions, and application programming interfaces (APIs). In some examples, plug-ins and extensions, individually or collectively, may be referred to as add-ons.

[0179] The client device 2570 may be a personal computer (PC), laptop, mobile phone, tablet computer, or generally any other computing device. The client device 2570 may execute an operating system (OS) that manages the hardware and software of the client device 2570. A browser (not shown) may execute on the client device 2570. The browser may be a web browser that can access services and / or content provided by the server machine 2510, the web server 2520, the application server 2522, the data store 2508, etc. Additionally, other types of computer programs and computer scripts may also execute on the client device 2570. For example, the client device 2570 may use an application (i.e., an "app") to access such content and communicate with the server machine 2510 without visiting or otherwise utilizing a web page.

[0180] In one example, the functions and features of server machine 2510 may also be performed in whole or in part by client device 2570. Furthermore, functionality attributed to a particular component may be performed by different or multiple components working together. Server machine 2510 may also be accessed as a service offered to other systems or devices via an application programming interface, and is therefore not limited to use with websites.

[0181] 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 complex biological matrices. For example, the analyte variability control system 2530 can receive and analyze biological samples associated with different subjects, generate composite dilution models 2590 for each of multiple analytes in the biological samples, determine a relative dilution prediction model for a biological sample type of the biological samples based on the multiple generated composite dilution models 2590, receive and analyze new biological samples 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.

[0182] Examples of services provided by the analyte variability control system 2530 are further described in this disclosure, including in "Example 1: Generation of an Empirical Matrix-Specific Standard Curve," "Example 2: Application of an Empirical Matrix-Specific Standard Curve," "Example 3: Pilot Study Design for Characterizing and Normalizing Analyte Signal Variation Due to Hydration Status in Human Subjects," figures associated with this disclosure, and the following paragraphs.

[0183] In one example, the analyte variability control system 2530 includes a biological sample analysis module 2540, a composite dilution model generator module 2550, and a relative dilution prediction module 2560. In other examples, the functionality associated with the biological sample analysis module 2540, the composite dilution model generator module 2550, and the relative dilution prediction module 2560 can be combined, divided, and organized in various arrangements.

[0184] In one example, the biological sample analysis module 2540 may generally receive and analyze analyte measurement data 2580 associated with biological samples from different subjects. The composite dilution model generator module 2550 may generally perform one or more steps to generate a composite dilution model 2590 for each analyte in a group of analytes selected from the analyte measurement data 2580. The relative dilution prediction module 2560 may then generally determine a predicted relative dilution of a biological sample type for a biological sample based on the selected composite dilution model 2590 generated from the biological sample. The relative dilution prediction module 2560 may 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 from the selected composite dilution model 2590.

[0185] 26 is a flow diagram illustrating the generation of a composite dilution model for each of multiple analytes present in different biological samples, according to an example of the present disclosure. Exemplary method 2600 may be performed by processing logic that may comprise specialized hardware (circuitry, dedicated logic, programmable logic, microcode, etc.), specialized software (such as instructions running on a general-purpose computer system, a dedicated machine, or a hardware processing device), firmware, or any combination thereof.

[0186] In general, support for exemplary method 2600 is provided throughout this disclosure, including in connection with the non-limiting Examples "Example 1: Generation of an Empirical Matrix-Specific Standard Curve," "Example 2: Application of an Empirical Matrix-Specific Standard Curve," and "Example 3: Pilot Study Design for Characterizing and Normalizing Analyte Signal Variation Due to Hydration Status in Human Subjects," discussed above.

[0187] 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 analysis module 2540 of the analyte variability control system 2530 receives the analyte measurement data 2580 from either the diagnostic machine 2502, the data store 2508, the server machine 2510, the client device 2570, or one or more other computer systems or storage devices.

[0188] In one example, the biological sample analysis module 2540 receives analyte measurement data 2580 generated from the diagnostic machine 2502. For example, the diagnostic machine 2502 can receive biological samples provided by 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.

[0189] In one example, 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 unit(s). Thus, in some examples, analyte measurement data 2580 may include relative fluorescence unit measurements corresponding to each of a plurality of proteins associated with one or more different biological samples. Such analyte measurement data 2580 may be saved in data store 2508 or any other persistent storage device. , which may later be provided to one or more other computer systems, such as server machine 2510.

[0190] In one example, the biological sample analysis module 2540 may generally convert, condition, 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 may be cleansed, formatted, normalized, calibrated, or otherwise manipulated in any of one or more different steps. In other examples, the analyte measurement data 2580 received by the biological sample analysis module may be preprocessed and ready for analysis upon receipt without further preprocessing or manipulation.

[0191] At block 2604, the analyte variability control system 2530 analyzes the corresponding analyte measurements for each of the multiple selected analytes in the analyte measurement data. In one example, the biological sample analysis module 2540 of the analyte variability control system 2530 analyzes each of the multiple selected analytes in the analyte measurement data 2508. For example, the biological sample analysis module 2540 can analyze each analyte from a subset of available measured analytes from multiple different biological samples. In one example, the biological sample analysis module 2540 can analyze the analyte measurement data 2580 for each analyte across different biological samples individually or in parallel (e.g., two or more analytes across different biological samples are processed simultaneously).

[0192] In one example, the biological sample analysis module 2540 analyzes each analyte in preparation for generating a composite dilution model 2590 corresponding to each respective analyte. For example, the biological sample analysis module 2540 can analyze an analyte for multiple serially diluted biological samples. In one example, the biological sample analysis 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 for the linear dilution range. The associated regression line can then be used as a reference for overlaying other samples.

[0193] 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 analyte measurement data 2508 for each of the analytes based on the corresponding reference dilution model generated by the biological sample analysis module 2540. In one example, the composite dilution model generator module 2550 then fits a four-parameter logistic function (4PL) to the overlaid data for the analytes.

[0194] In one example, the composite dilution model generator module 2550 then generates a composite dilution model 2590 corresponding to 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 analyte subset found in the analyte measurement data 2508. The composite dilution models 2590 generated for the selected analyte subset can then be used to adjust aspects of additional, different, or newly received biological samples to control for sample-to-sample analyte variability.

[0195] 27 is a flow diagram illustrating the generation of a composite dilution model for each of multiple analytes present in different biological samples, according to an example of the present disclosure. The exemplary method 2700 may be implemented using specialized hardware (circuitry, dedicated logic, programmable logic, microcode, etc.), specialized software (general-purpose computer systems, dedicated machines, or hardware processing). The processing logic may comprise hardware, software, or any combination thereof, such as instructions executed by a device.

[0196] Generally, support for exemplary method 2700 is provided throughout this disclosure, including in connection with the non-limiting Examples "Example 1: Generation of an Empirical Matrix-Specific Standard Curve," "Example 2: Application of an Empirical Matrix-Specific Standard Curve," and "Example 3: Pilot Study Design for Characterizing and Normalizing Analyte Signal Variation Due to Hydration Status in Human Subjects" discussed above.

[0197] The method 2700 begins at block 2702 when the analyte variability control system 2530 receives analyte measurement data 2580 relating to different biological samples of the same biological sample type. In one example, the biological sample analysis module 2540 of the analyte variability control system 2530 receives the analyte measurement data 2580 from the data store 2508. Generally, such analyte measurement data may include relative fluorescence unit (RFU) measurements for each of a plurality of different proteins detected in each of a plurality of different biological samples of the same biological sample type collected from different subjects.

[0198] At block 2704, the analyte variability control system 2530 analyzes the corresponding analyte measurements for each of the plurality of selected analytes in the analyte measurement data 2580. In one example, the biological sample analysis 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 the available analytes may be selected from the analyte measurement data 2508 to generate a corresponding composite dilution model 2590 for use in controlling sample-to-sample analyte variability in a complex biological matrix.

[0199] At block 2706, the analyte variability control system 2530 generates a reference dilution model for each selected analyte. In one example, the biological sample analysis module 2540 generates a reference dilution model for each selected analyte based on the analysis performed at block 2704. For example, in one non-limiting example, the biological sample analysis module 2540 can determine which sample from the plurality of samples associated with each analyte has the largest linear dilution range. The biological sample analysis module 2540 can then fit a weighted linear regression model to the linear dilution range data. Furthermore, the biological sample analysis module 2540 can similarly generate weighted linear regression models for each of the other selected analytes, which are each used, at least in part, in generating a corresponding composite dilution model 2590 associated with each respective selected analyte. Further description and examples are provided in the present disclosure, for example, at least in the non-limiting example "Example 1: Generation of an Empirical Matrix-Specific Standard Curve."

[0200] At block 2708, the analyte variability control system 2530 translates the analyte measurement data for each selected analyte based on the corresponding reference dilution model generated for each respective analyte. In one example, the composite dilution model generator module 2550 of the analyte variability control system 2530 translates the analyte measurement data 2508 corresponding to each selected analyte based on the corresponding reference dilution model generated by the biological sample analysis module 2540. Further description and examples are provided in the present disclosure, for example, at least in the non-limiting example "Example 1: Generation of an empirical matrix-specific standard curve."

[0201] 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 four-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 include composite solution modules for each of the selected analytes.

[0202] Further explanations and examples illustrating block 2710 and applying the generated composite dilution model 2590 to other biological samples are provided in the present disclosure, for example, in at least non-limiting Examples "Example 1: Generating an Empirical Matrix-Specific Standard Curve" and "Example 2: Applying an Empirical Matrix-Specific Standard Curve."

[0203] In some examples, the composite dilution model generator module 2550 further generates one or more reports containing information and details regarding various aspects related to the processing of the analyte measurement data 2580 and the generation of 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, pre-processing 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 dilution. In some examples, the analyte variability control system 2530 may store the generated report in the data store 2508 and provide the report and the corresponding analyte measurement data 2580 and / or composite dilution model 2590 to the client device 2570.

[0204] 28 is a flow diagram illustrating application of a composite dilution model to a novel biological sample to predict the relative dilution of the novel sample, according to an example of the present disclosure. Exemplary method 2800 can be performed by processing logic that can comprise specialized hardware (circuitry, dedicated logic, programmable logic, microcode, etc.), specialized software (such as instructions executed on a general-purpose computer system, a dedicated machine, or a hardware processing device), firmware, or any combination thereof.

[0205] Generally, support for exemplary method 2800 is provided throughout this disclosure, including in connection with the non-limiting Examples "Example 1: Generation of an Empirical Matrix-Specific Standard Curve," "Example 2: Application of an Empirical Matrix-Specific Standard Curve," and "Example 3: Pilot Study Design for Characterizing and Normalizing Analyte Signal Variation Due to Hydration Status in Human Subjects" discussed above.

[0206] The method 2800 begins at block 2802 when the analyte variability control system 2530 receives analyte measurement data for analytes in a biological sample. In one example, the biological sample analysis module 2540 of the analyte variability control system 2530 can receive analyte measurement data 2580 of one or more new biological samples for analysis. For example, the new biological sample may describe analyte measurement data 2580 of a biological sample that is not generally included in or considered in the generation of a complex dilution model 2590 of analytes for a certain biological sample type (e.g., urine). Thus, the new biological sample differs from the biological sample used to generate the complex dilution model, and such relevant analyte measurement data 2580 may be received before or after the generation of such model.

[0207] In block 2804, the analyte variability control system 2530 selects a plurality of analytes for determining the expected relative dilution of the biological sample. In one example, the biological sample analysis 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 analysis module 2540 may analyze the analyte measurement data 2580 based on user preferences and / or relative fluorescence unit (RFU) measurements of the analytes in the analyte measurement data 2580. A subset of analytes in the analyte measurement data 2580 can be selected based on one or more thresholds related to the analytes.

[0208] In one example, the biological sample analysis module 2540 can select a top number of analytes in a new biological sample based on their "goodness of fit" compared to the corresponding complex dilution model for the biological sample type. In another example, the biological sample analysis module 2540 can also select several analytes that have the ability to smooth a series of serial titrations of the same sample. Further explanation and examples are provided in the present disclosure, for example, at least in the non-limiting example "Example 2: Application of an Empirical Matrix-Specific Standard Curve."

[0209] 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 generates a predictive relative dilution model for the biological sample. For each of a plurality of analytes selected for determining a relative dilution solution, the relative dilution prediction module 2560 receives a composite dilution model 2590. For example, the relative dilution prediction module 2560 receives a composite dilution model 2590 generated for the selected analytes. Such composite dilution model 2590 may be generated according to method 2600, method 2700, or other embodiments of the present disclosure.

[0210] 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) measurements of each analyte onto the generated composite dilution model corresponding to each analyte to generate a predicted relative dilution for each of the respective analytes.

[0211] For example, the relative dilution prediction module 2560 can perform a first RFU measurement of a first analyte in a new biological sample, project the first RFU measurement of the first analyte onto the first composite dilution model generated for the first analyte, and determine a predicted relative dilution value of the first analyte in the new biological sample based on the projection. Similarly, the relative dilution prediction module 2560 can perform a second RFU measurement of a second analyte in the same new biological sample, project the second RFU measurement of the second analyte onto the second composite dilution model generated for the second analyte, and determine a predicted relative dilution value of the second analyte in the new biological sample based on the projection (and similarly for each of the other selected analytes). Further description and examples are provided in the present disclosure, for example, at least in the non-limiting example "Example 2: Application of an Empirical Matrix-Specific Standard Curve."

[0212] At block 2810, the analyte variability control system 2530 determines a predicted relative dilution of 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 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 which of these predicted relative dilution values ​​to use when adjusting aspects of the new biological sample. For example, the relative dilution prediction module 2560 can discard one or more sets of generated predicted relative dilution values ​​to create a final set of generated predicted relative dilution values ​​for adjusting the new biological sample.

[0213] In one example, the relative dilution prediction module 2560 trims the tails from the distribution of predicted relative dilution values ​​and uses the middle percentage of the remaining values ​​to determine the predicted relative dilution of a new biological sample. The relative dilution prediction module 2560 then calculates the predicted relative dilution of the biological sample. For example, the relative dilution prediction module 2560 can analyze the remaining predicted relative dilution values ​​to generate a predicted relative dilution for the new biological sample.

[0214] In one example, 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. 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 of the remaining predicted relative dilution values. Further explanation and examples are provided in the present disclosure, for example, at least in the non-limiting example "Example 2: Application of an Empirical Matrix-Specific Standard Curve."

[0215] 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. Further, 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 to take the adjustments into account.

[0216] FIG. 29 shows a diagram of a machine in the exemplary form of a computer system 2900 upon which a set of instructions may be executed to cause the machine to perform any one or more of the methodologies described herein. In one example, the machine may be connected (e.g., networked) to other machines on a LAN, an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server or a client machine in a client / server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. For example, the machine may be a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a mobile phone, a wearable computing device, a web appliance, a server machine, a dedicated diagnostic laboratory machine, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be performed by that machine. Furthermore, while only a single machine is illustrated, the term “machine” should also be understood to include any collection of machines that individually or jointly execute a set (or sets) of instructions to perform any one or more of the methodologies discussed herein.

[0217] 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 dynamic random access memory (SDRAM), double data rate (DDR SDRAM), or DRAM (RDRAM)), a static memory 2906 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 2918, which communicate with each other via a bus 2930.

[0218] Processor 2902 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, or the like. More specifically, processor 2902 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computer (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or 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, or the like. Processor 2902 is configured to execute instructions 2922 to perform the operations and steps discussed herein.

[0219] The computer system 2900 may further include a network interface device 2908. The computer system 2900 may also include a video display unit 2910 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 2912 (e.g., a keyboard), a cursor control device 2914 (e.g., a mouse), and a signal generation device 2916 (e.g., a speaker).

[0220] The data storage device 2918 may include a computer-readable storage medium 2928 on which one or more sets of instructions 2922 (e.g., software) embodying any one or more of the methods or functions described herein are stored. The instructions 2922 may also reside, completely or at least partially, within the main memory 2904 and / or within the processor 2902 during execution thereof, with the computer system 2900, main memory 2904, and processor 2902 also constituting computer-readable storage media. The instructions 2922 may further be transmitted or received over the network 2920 via the network interface device 2908.

[0221] In one example, instructions 2922 include a software library containing instructions for an analyte variability control system (e.g., analyte variability control system 2530 of FIG. 25) and / or methods for invoking an analyte variability control system. While computer-readable storage medium 2928 (machine-readable storage medium) is shown in one example to be a single medium, the term "computer-readable storage medium" should be interpreted to include a single medium or multiple media (e.g., centralized or distributed databases and / or associated caches and servers) that store one or more sets of instructions. The term "computer-readable storage medium" should also be interpreted to include any medium that can store, encode, or transmit a set of instructions for execution by a machine and that cause a machine to perform any one or more of the methods of the present disclosure. Furthermore, the term "computer-readable storage medium" should be interpreted to include, but is not limited to, solid-state memory, optical media, and magnetic media.

[0222] In the above description, numerous details have been set forth. However, it will be apparent to one skilled in the art having the benefit of this disclosure that the present disclosure may be practiced without such specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present disclosure.

[0223] Some portions of the detailed descriptions are presented in terms of steps leading to one or more desired results. Generally, these steps require physical manipulations of physical quantities. Usually, though not necessarily, these quantities 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.

[0224] It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. As will become apparent from the discussion that follows, unless otherwise indicated, throughout the description, the discussion utilizing terms such as "calculating," "comparing," "applying," "producing," "ranking," "classifying," and the like refers to the operation of a specialized computer system or similar specialized computing device that manipulates and converts data represented as physical (e.g., electronic) quantities in the registers and memory of a computer system into other data similarly represented as physical quantities in the memory or registers of the computer system, or in other such information storage, transmission, or display devices. It should be understood that the term refers to the actions and processes of a computer-implemented electronic computing device.

[0225] Certain examples of the present disclosure also relate to apparatus for performing the operations herein. This apparatus may be constructed for an intended purpose, or may include specialized computer hardware and / or specialized computer programs selectively installed, activated, or configured to perform an intended purpose. Such computer programs may be stored on 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 memory (ROM), random-access memory (RAM), EPROM, EEPROM, magnetic or optical cards, or any type of medium suitable for storing electronic instructions.

[0226] It is to be understood that the above description is intended to be illustrative, and not limiting. Many other examples will become apparent to those skilled in the art upon reading and understanding the above description. The scope of the present disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. [Example]

[0227] The following examples are presented to illustrate certain particular features and / or embodiments, and should not be construed as limiting the disclosure to the particular features or embodiments described.

[0228] Example 1: Generation of an empirical matrix-specific standard curve This example provides a means by which exemplary complex dilution models can be generated from complex biological matrices such as urine, using relative protein measurements as assayed with capture reagents, such as aptamers.

[0229] Traditionally, protein levels in urine are normalized to a physiologically based measure (e.g., total urine volume, creatinine concentration, or albumin:creatinine ratio) or a restricted median normalization that identifies a subset of capture reagents (e.g., aptamers) that exhibit dilution linearity (or are proportional to the protein content of the sample) and uses this information for standard median normalization.

[0230] A novel normalization approach was developed that creates a composite dilution curve for each analyte (measured protein) based on a titration series of multiple samples from a complex matrix, such as urine. The many composite dilution curves can then be used to more accurately estimate the overall dilution of protein levels. Protein measurements were performed in urine using an aptamer-based assay, whereby protein levels were expressed as relative quantities (RFU or relative fluorescence units). Urine was chosen as an exemplary matrix due to the commonly observed sample-to-sample variability in protein levels, resulting, for example, from an individual's hydration level at the time the urine was collected. Hydration levels can confound the relationship between protein levels measured from urine and the clinical assessment of the subject being tested. Therefore, by generating a composite dilution model that corrects for sample-to-sample variability, consistent and clinically meaningful information about a test subject can be derived from protein levels in urine. Furthermore, once a composite dilution model for a matrix is ​​generated, the same composite dilution model can be used to correct for sample-to-sample variability across samples from many subjects, allowing consistent and clinically meaningful information to be derived from the analyte measurements of those subjects.

[0231] As background information, the quantitative inputs to a function are denoted by X and the outputs are denoted by the variable Y. If Y is a matrix, the individual components are denoted by the subscript X ij For example, the output value of the jth sample for the ith aptamer can be accessed by X ij The next sample for the same aptamer is X i(j+1) Vectors of values ​​are denoted in uppercase, scalar values ​​in lowercase.

[0232] Analyze protein levels from at least three serial titrations from at least two samples of the biological matrix of interest, where the i analyte (aptamer) in j samples containing k serial titrations is measured at dilution X. ijk and the corresponding RFU value Y ijk The RFU values ​​were expressed as: For each analyte (or protein), a linear dilution range is defined, which, for aptamer-based assays, is the range of dilutions where the measured RFU values ​​are approximately linearly proportional to the dilution of the sample. Starting with the lowest dilution, the RFU measurements for 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 for an n-fold dilution. Starting with the kth dilution in the series, the percent recovery for the mth subsequent dilution is defined as:

number

[0233] An acceptable dilution range has a recovery within 50% of the nominal value determined at the highest dilution in the linear range. A minimum of three serial dilutions from the nominal value is required to define the linear range. The linear range (five data points from five serial dilutions) is shown in Figure 1 as the data points between the vertical dashed lines (note that the scale is logarithmic for visualization purposes and does not imply log-linearity).

[0234] As a specific example, the analyte cystatin C (CST3) was used. Nineteen 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 the 19 samples) with the largest linear dilution range was identified. A weighted linear regression model was fit to the data in the linear dilution range, where the weights are the RFU measurements Y ij The linear model for the i-th analyte is:

number

[0235] As another example, the analyte ephrin type B receptor 6 (EPHB6) was used. Nineteen 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 the linear dilution range, where the weights are the RFU measurements Y ij The linear model for the i-th analyte is:

number

[0236] This regression line (or regression model) is the reference on which all other curves are superimposed (black line in Figure 2B for CST3 and in Figure 2E for EPHB6).

[0237] For each analyte i, its linear range y ci Regression of the RFU value (or reference value) at the center point of Superimpose the titration curve by mapping (or horizontally translating) it onto a line (regression model) and its relative dilution to the reference

number

number

[0238] Each titration curve Y for a given analyte ij and its relative dilution value

number

number

number

[0239] A four-parameter logistic function (4PL) is fitted to the overlaid data for each analyte. The 4PL equation consists of the following parameters: lower asymptote L, upper asymptote U, inflection point k, and Hill's slope b. The model is symmetric around the inflection point and is fitted using nonlinear least-squares.

number

[0240] This generates an empirical calibration curve for each analyte in the matrix of interest in the presence of all capture reagents (e.g., aptamers) and their respective target proteins. This is shown in Figure 2C for CST3. Each 4PL fit (complex dilution model) can be ranked by its goodness of fit using the Akaike Information Criterion (AIC).

[0241] Figures 3A-3C to 9A-9C show similar analyses of platelet-derived growth factor D (PDGFD, Figures 3A-3C), retinoic acid receptor responder 2 (RARRES2, Figures 4A-4C), interleukin-1 receptor-like 2 (IL1RL2, Figures 5A-5C), coagulation factor XI (F11, Figures 6A-6C), septin 11 (SEPT11, Figures 7A-7C), thymopoietin (TMPO, Figures 8A-8C), and shisa family member 3 (SHISA3, Figures 9A-9C).

[0242] Example 2: Application of an empirical matrix-specific standard curve This example provides the method used to normalize the levels of analytes measured in an assay using the empirical matrix-specific standard curve (or complex dilution model or 4PL curve) generated in Example 1.

[0243] In general, this method requires selecting a subset of i analytes (aptamer-based protein measurements) to use in the normalization calculation.

[0244] Feature selection can be performed in many ways. Three examples are: 1) by selecting the top i analytes by 4PL fitness ranked by AIC; 2) performing feature selection of i analytes with the ability to smooth a serial titration series of the same sample; and / or 3) selecting analytes with signal levels higher than background (e.g., 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, or 10x higher than background).

[0245] For example, per option 2 above, we would expect the normalization scale factor to increase by a factor of 2 for every 1:2 dilution. Then, for each sample to normalize, we find the predicted relative dilution by inversely solving the 4PL equation for all i analytes in the normalization subset. For the i th analyte in sample k (of a total of n), the estimated relative dilution is

number

number

[0246] 10A and 10B show the application of the general process to two analytes (iduronidase [IDUA] and neogenin 1 [NEO1]). Normalized sample RFU measurements were projected onto the 4PL curve to generate two predicted relative dilutions.

[0247] This information can then be used to find the estimated relative dilution for that sample. In some embodiments, the median of the predicted relative dilutions of all i th analytes is determined. This process results in a distribution of n predicted relative dilutions for each sample that is normalized. The median of this distribution is the predicted relative dilution for that sample, and the 4PL normalized scale factor for that sample is 1 over the predicted relative dilutions.

[0248] In some embodiments, a median, mean, or value derived from the central tendency of the estimated relative dilution is selected. The estimated relative dilution of each sample can then be used to normalize that sample. In some embodiments, a scaling factor for each sample is generated by dividing the reference value by the estimated relative dilution of that sample. In some embodiments, this reference value can be 1 (making the scaling factor the reciprocal of the estimated relative dilution) or the median estimated relative dilution across all samples to be normalized.

[0249] Figure 11 shows the distribution of predicted relative dilutions for four 1:2 serial dilutions of a single sample, and it is observed that the median is perfectly proportional to the dilution of the sample using the 200 analytes with the highest AIC.

[0250] Example 3: Characterizing and normalizing analyte signal variation due to hydration status in human subjects Pilot study design to This example provides an overview of a pilot study used to characterize and validate a normalization scheme for variability in analyte signal measurements in human subjects under controlled hydration conditions.

[0251] A gender-balanced cohort of 16 study participants, aged 26-57 years (mean age 33 years), was recruited for the study. Informed consent for this study fell under the IRB-approved SomaLogic Biorepository Research Protocol, or WIRB#20150206. All participants signed consent forms and completed a brief health questionnaire, from which metadata including age, gender, height, weight, and other demographic information was collected. All metadata was kept anonymous within the study.

[0252] Collection Protocol: Clinical information obtained from urine specimens can be affected by collection methods and handling. Therefore, the collection procedure adopted in this study was a "midstream clean catch" to reduce the incidence of cellular and microbial contamination. Participants were requested to release the first portion of their urinary stream into the toilet, which flushes the urethra and significantly reduces the opportunity for contaminants to enter the stream.

[0253] Midstream urine was collected in preservative-free, pre-labeled, 90 ml sterile collection cups with secure, leak-proof lids. Samples collected at the SomaLogic were placed in pre-labeled biohazard bags and immediately placed in a -80°C laboratory refrigerator designated for biological samples for later aliquoting and storage.

[0254] To maintain sample conditions similar to those received from prospective subjects, no pretreatment or centrifugation was performed prior to aliquoting. Collection times were recorded for all samples. Four to six assay aliquots were made, plus one 8 ml aliquot for urinalysis.

[0255] Samples were de-identified by assigning a unique alphanumeric identifier to each study participant, and all collection tubes and biohazard bags were labeled with this identifier and the date and time 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 linked to participants. Master keys linking identifiers to participants were generated internally, kept confidential, and accessible only to selected individuals on a need-to-know basis.

[0256] Participants were asked to refrain from anti-inflammatory medications for 48 hours before the study and to stop drinking fluids at 10:00 pm the night before the study. They were allowed to urinate overnight if needed, but their morning "first-effort" urine was collected.

[0257] Study Protocol: All participants were asked to refrain from drinking liquids (water, coffee, etc.) or exercising in the morning. Initial release samples collected at home were immediately placed on ice until they arrived at SomaLogic, where they were then placed in a refrigerator at 2-8°C.

[0258] Participants were given a calorie-balanced breakfast and asked to consume 1.5% of their body weight in water over a 30-minute period (the "hydration challenge"). This totaled approximately 850 ml for a 125 lb individual and 1120 ml for a 175 lb individual. Instead of pre-specifying the amount of food for each participant, food intake was controlled by simply asking participants to eat modest amounts.

[0259] A portion of each urination was collected from 9:00 AM to noon, with a final "exit" urine sample collected around noon. Water was not permitted between 9:00 AM and noon. Two to five consecutive samples followed the hydration challenge, with the final sample occurring around noon. In addition, 15 participants provided a non-control sample at noon the following day. A total of 81 urine samples were collected from the 15 participants.

[0260] According to the collection protocol, all samples were collected "midstream" and sent to the clinical laboratory for complete urinalysis, including specific gravity, total protein, urea, total salts, and microbial titers. Total urine volume was not recorded.

[0261] Analyte Measurement from Collected Samples: Urine samples from the study subjects were assayed using an aptamer-based assay that measures the levels of over 1000 proteins. For each study participant, Figure 12 shows the log from the median RFU signal for that aptamer. 10 Data for each aptamer before normalization is shown (represented as a line across multiple time points), expressed as a change in concentration. The non-normalized data exhibit a common bias due to the participants' hydration status, with the first morning release sample (highest concentration) having a higher signal compared to the median. This signal subsequently decreases due to the hydration challenge, followed by an increase as study participants refrained from drinking water after the hydration challenge.

[0262] This data was used to demonstrate the effectiveness of the composite dilution model for correcting for subject hydration levels and extracting consistent, clinically meaningful information about study subjects. The composite dilution model was applied to all serial samples for each study participant. Boxplots of the predicted relative dilutions for all serial samples for each study participant are shown in Figure 13. These values ​​were calculated from the composite dilution curves for all aptamers. The normalization process then calculates a scale factor for each sample by shifting the median value of each boxplot to 1. Figure 14 shows the resulting time series after normalization, using the composite dilution model to correct for subject hydration levels, expressed as the change in RFU from the median level for each aptamer.

[0263] The boxplots and cumulative distribution functions (CDFs) in Figures 15 and 16 show the distribution of signal at each time point in two separate study individuals. Figures 15A / C and 16A / C show the bias in signal levels before normalization, while Figures 15B / D and 16B / D show the distribution of signal levels after normalization. Each line in the top plot represents the level of aptamer signal across samples.

[0264] Figures 15B and 16B show the systematic bias / variance reduction quantified by the repeated measures ANOVA F-statistic, which can be interpreted as the ratio of within-group to between-group differences. A high F-statistic results from large differences between time points, whereas a low F-statistic indicates that the differences between time points are similar to those expected by random chance. F-statistics were calculated for the non-normalized and normalized data for each study participant and are shown in Figure 17A. The normalization procedure significantly reduced longitudinal variance (p<1E-8, N=15). Figure 17B shows the fold reduction of the F-statistic for each study participant, which had a median of 81.88 and a range of 13.12 to 524.96.

Claims

1. 1. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte being determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) generating a model based on the levels of the analyte in the first dilution series; and d) selecting a reference value, wherein the reference value is: (i) a second dilution series reference value, which is the analyte level at a particular dilution of said second dilution series; (ii) a model reference value, which is the analyte level at a particular dilution of the model; and (iii) selecting from any reference value that is an analyte level at a particular dilution, the analyte level at that particular dilution not found in the second dilution series or the model; e) performing at least one horizontal translation, said at least one horizontal translation comprising: (i) a horizontal translation (ΔX) of the second dilution series reference value to a model value, the model value being the analyte level at a particular dilution of the model, the second dilution series reference value and the model value being equal or substantially equal; (ii) a horizontal translation (ΔX) of the model reference value to a second dilution series value, the second dilution series value being an analyte level at a particular dilution of the second dilution series, the model reference value and the second dilution series value being equal or substantially equal; and (iii) performing a horizontal translation of the second dilution series value (ΔX) and the model value (ΔY) to the arbitrary reference value, wherein the model value is the analyte level at a particular dilution of the model, the second dilution series value is the analyte level at a particular dilution of the second dilution series, and the second dilution series value, the model value, and the arbitrary reference value are selected from the horizontal translations, where the model value is the analyte level at a particular dilution of the second dilution series, and the second dilution series value, the model value, and the arbitrary reference value are equal or substantially equal; f) performing a horizontal translation of at least one of (i) the second dilution series, (ii) the model, or (iii) residual values ​​in the second dilution series and the model, wherein the horizontal translation of the residual values ​​results in a series of complex translations; and g) fitting a function to the series of complex translations, thereby forming a complex dilution model.

2. (d) selecting a second dilution series reference value that is the analyte level at a particular dilution of said second dilution series; (e) performing a horizontal translation (ΔX) of the second dilution series reference value to a model value, the model value being the analyte level at a particular dilution of the model, the second dilution series reference value and the model value being equal or substantially equal; f) performing a horizontal translation of the residual values ​​in the second dilution series, wherein the horizontal translation of the residual values ​​results in a single line of composite translations.

3. d) selecting a model reference value that is the analyte level at a particular dilution of said model; 、 e) performing a horizontal translation (ΔX) of the model reference value to a second dilution series value, the second dilution series value being an analyte level at a particular dilution of the second dilution series, the model reference value and the second dilution series value being equal or substantially equal; f) performing a horizontal translation of residual values ​​in the model, the horizontal translation of the residual values ​​resulting in a line of compound translations.

4. d) selecting an arbitrary reference value that is an analyte level at a particular dilution, said analyte level at that particular dilution not found in said second dilution series or said model; e) performing a horizontal translation of the second dilution series values ​​(ΔX) and the model values ​​(ΔY) to the arbitrary reference value, wherein the model values ​​are the analyte levels at a particular dilution of the model, the second dilution series values ​​are the analyte levels at a particular dilution of the second dilution series, and the second dilution series values, the model values, and the arbitrary reference value are equal or substantially equal; f) performing a horizontal translation of the residual values ​​in the second dilution series and the model, wherein the horizontal translation of the residual values ​​results in a single line of combined translations.

5. 1. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte being determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) generating a model based on the levels of the analyte in the first dilution series; and d) performing at least one horizontal translation, the at least one horizontal translation comprising: (i) the horizontal translation (ΔX) of the second dilution series onto the model; (ii) the horizontal translation (ΔX) of the model to the second dilution series; and (iii) a horizontal translation of the second dilution series (ΔX) and the model (ΔY) to an arbitrary reference value, the arbitrary reference value being an analyte level at a particular dilution, the analyte level at that particular dilution being selected from the horizontal translations that are not found in the second dilution series or the model; performing said at least one horizontal translation resulting in a sequence of composite translations; e) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model.

6. 6. The method of claim 5, comprising performing a horizontal translation (ΔX) of the second dilution series onto the model.

7. 6. The method of claim 5, comprising performing a horizontal translation (ΔX) of the model to the second dilution series.

8. 6. The method of claim 5, comprising performing a horizontal translation of the second dilution series (ΔX) and the model (ΔY) to an arbitrary reference value that is an analyte level at a particular dilution, where the analyte level at that particular dilution is not found in the second dilution series or the model.

9. 1. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte being determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) selecting a reference value, said reference value being: (i) a second dilution series reference value, which is the analyte level at a particular dilution of said second dilution series; and (ii) selecting from any reference value that is an analyte level at a particular dilution, the analyte level at that particular dilution not found in the first dilution series or the second dilution series; d) performing at least one horizontal translation, said at least one horizontal translation comprising: (i) a horizontal translation (ΔX) of the second dilution series reference value to a first dilution series value, the first dilution series value being an analyte level at a particular dilution of the first dilution series, the second dilution series reference value and the first dilution series value being equal or substantially equal; and (ii) a horizontal translation of the first dilution series (ΔX) and the second dilution series (ΔY) to the arbitrary reference value; performing said at least one horizontal translation resulting in a sequence of composite translations; e) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model.

10. 10. The method of claim 9, comprising performing a horizontal translation (ΔX) of the second dilution series to the first dilution series.

11. 10. The method of claim 9, comprising horizontal translation of the first dilution series (ΔX) and the second dilution series (ΔY) to an arbitrary reference value, the arbitrary reference value being an analyte level at a particular dilution that is not found in the first dilution series or the second dilution series.

12. 1. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte being determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) performing at least one horizontal translation, said at least one horizontal translation comprising: (i) the horizontal translation (ΔX) of the second dilution series to the first dilution series, and (ii) horizontal translations of the first dilution series (ΔX) and the second dilution series (ΔY) to an arbitrary reference value, the arbitrary reference value being an analyte level at a particular dilution that is not found in the first dilution series or the second dilution series; performing said at least one horizontal translation resulting in a sequence of composite translations; d) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model.

13. 13. The method of claim 12, comprising performing a horizontal translation (ΔX) of the second dilution series to the first dilution series.

14. 13. The method of claim 12, comprising horizontal translation of the first dilution series (ΔX) and the second dilution series (ΔY) to an arbitrary reference value, the arbitrary reference value being an analyte level at a particular dilution that is not found in the first dilution series or the second dilution series.

15. 1. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte being determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) generating a first model based on the levels of the analyte in the first dilution series and a second model based on the levels of the analyte in the second dilution series; d) selecting a reference value, said reference value being: (i) a first model reference value, which is the analyte level at a particular dilution of the first model; and (ii) selecting from any reference value that is an analyte level at a particular dilution, the analyte level at the particular dilution not found in the first model or the second model; d) performing at least one horizontal translation, said at least one horizontal translation comprising: (i) the horizontal translation (ΔX) of the first model to the second model; and (ii) a horizontal translation of the first model (ΔX) and the second model (ΔY) to the given reference value; performing said horizontal translation resulting in a sequence of composite translations; e) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model.

16. The method of claim 15, comprising performing a horizontal translation (ΔX) of the first model onto the second model.

17. The horizontal alignment of the first model (ΔX) and the second model (ΔY) to the arbitrary reference value 16. The method of claim 15, comprising:

18. 1. A method for generating a complex dilution model, comprising: a) determining a level of an analyte in a first dilution series of a first biological sample containing the analyte, the first dilution series including at least three different dilutions, and the level of the analyte being determined in each of the at least three different dilutions; b) determining a level of the analyte in a second dilution series of a second biological sample containing the analyte, the second dilution series including at least three different dilutions, and the level of the analyte is determined in each of the at least three different dilutions; c) generating a first model based on the levels of the analyte in the first dilution series and a second model based on the levels of the analyte in the second dilution series; d) performing at least one horizontal translation, said at least one horizontal translation comprising: (i) the horizontal translation (ΔX) of the first model to the second model; and (ii) horizontal translations of the first model (ΔX) and the second model (ΔY) to any reference value, where the analyte level at that particular dilution is not found in the first model or the second model, are selected from the horizontal translations; performing said horizontal translation resulting in a sequence of composite translations; e) fitting a function to the array of complex translational sequences, thereby forming a complex dilution model.

19. 20. The method of claim 18, comprising performing a horizontal translation (ΔX) of the first model onto the second model.

20. 20. The method of claim 18, comprising horizontal translation of the first model (ΔX) and the second model (ΔY) to an arbitrary reference value.

21. 10. The method of any one of the preceding claims, wherein the level of the analyte is determined at each of at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 different dilutions in the first dilution series.

22. 10. The method of any one of the preceding claims, wherein the level of the analyte is determined at each of at least eight different dilutions.

23. 10. The method of any one of the preceding claims, wherein the level of the analyte is determined at each of at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 different dilutions in the second dilution series.

24. 10. The method of any one of the preceding claims, wherein the level of the analyte is determined at each of at least eight different dilutions in the second dilution series.

25. 10. The method of any one of the preceding claims, wherein each model is independently selected from a linear regression model, a LOESS curve-fitting model, a non-linear 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.

26. 10. The method of any one of the preceding claims, wherein the level of the analyte is the relative amount of the analyte or the concentration of the analyte.

27. 10. The method of any one of the preceding claims, wherein the selected reference value is within the linear range of the dilution series or model.

28. 22. The method of claim 21, wherein the selected reference value is the midpoint of the linear range.

29. 10. The method of any one of the preceding claims, wherein the first and second biological samples comprise or are obtained from urine.

30. 10. The method of any one of the preceding claims, wherein the first and second biological samples are collected from the same subject.

31. 10. The method of any one of the preceding claims, wherein the first and second biological samples are collected from different subjects.

32. 32. The method of claim 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.

33. 33. The method of claim 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.

34. 10. The method of any one of the preceding claims, wherein the level of the analyte is measured by an assay using an aptamer, an antibody, a mass spectrophotometer, or a combination thereof.

35. 10. The method of claim 1, wherein the dilution factor of each of the first and second dilution series is a constant dilution factor.

36. 10. The method of claim 1, wherein the dilution factor of each of the first and second dilution series is at least a 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, or 10-fold dilution.

37. 36. The method of any one of claims 1 to 35, wherein the dilution factor for each of the first and second dilution series is an exponential or logarithmic dilution factor.

38. 10. The method of any one of the preceding claims, wherein the first biological sample and the second biological sample are biological samples of the same type.

39. 36. The method of any one of claims 1 to 35, wherein the first dilution series and the second dilution series are each at least 5-point serial titrations with a titration factor of at least 1:

2.

40. 10. The method of claim 1, further comprising horizontally translating a level of at least one analyte from a biological test sample to a complex dilution model of the at least one analyte, thereby determining a relative dilution of the biological test sample.

41. 41. The method of claim 40, wherein the biological test sample and the first and second biological samples used to form the complex dilution model are of the same sample type.

42. 42. The method of claim 40 or 41, wherein the biological test sample and the first and second biological samples used to form the complex dilution model are urine samples or are derived from urine samples.

43. A method for determining the relative dilution of a biological test sample from a subject, the method comprising horizontally translating the level of at least one analyte from the biological test sample to a complex dilution model developed for the at least one analyte, thereby determining the relative dilution of the biological test sample from the subject.

44. 44. The method of any one of claims 40-43, comprising: horizontally translating 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 respective 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 using the relative dilution for each of the different analytes to determine the relative dilution of the biological test sample.

45. 45. The method of claim 44, wherein the relative dilution of the biological test sample is derived from a central tendency of the relative dilution of each of the different analytes.

46. 46. ​​The method of claim 44 or 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.

47. 47. The method of any one of claims 43 to 46, wherein the complex dilution model is developed using a method of any one of claims 1 to 42.

48. 49. The method of any one of claims 43 to 48, wherein the biological test sample and the sample used to develop the multiple dilution model are the same sample type.

49. 42. The method of claim 41, wherein the biological test sample and the sample used to develop the complex dilution model are or are derived from urine samples.

50. 50. The method of any one of claims 40 to 49, further comprising calculating the relative dilution of the biological test sample with the derived relative dilution factor.

51. 10. The method of any one of the preceding claims, wherein each analyte is a target protein.

52. 1. A computer system comprising: a non-transitory memory for storing instructions; a non-transitory memory coupled to said non-transitory memory and reading said instructions from said non-transitory memory to said computer system; receiving analyte measurement data from a plurality of different biological samples; analyzing a corresponding analyte measurement value for each of a plurality of selected analytes in the analyte measurement data; and generating a complex dilution model for each of the selected analytes based on the analyzing.

53. The analyzing 53. The computer system of claim 52, further comprising determining, for each selected analyte, the biological sample having the largest linear dilution range.

54. The analyzing 54. The computer system of claim 53, further comprising: for each selected analyte, generating a reference dilution model based on the determined maximum linear range of the corresponding selected analyte.

55. The analyzing 55. The computer system of claim 54, further comprising, for each selected analyte, translating the analyte measurement data associated with the corresponding selected analyte based on the reference dilution model generated for the corresponding selected analyte.

56. 56. The computer system of any one of claims 52 to 55, wherein the biological sample is a urine sample.

57. 57. The computer system of any one of claims 52 to 56, wherein the analyte measurement data comprises relative fluorescence unit (RFU) measurements.

58. A non-transitory computer-readable medium containing computer-readable instructions that, when executed by a processing device, cause the processing device to: receiving analyte measurement data from a plurality of different biological samples; analyzing a corresponding analyte measurement value for each of a plurality of selected analytes in the analyte measurement data; generating a complex dilution model for each of the selected analytes based on the analyzing.

59. 1. A computer-implemented method for generating a complex dilution model of a biological material, 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, a corresponding analyte measurement value for each of a plurality of selected analytes in the analyte measurement data; generating, by one or more of the processing devices, a complex dilution model for each of the selected analytes based on the analyzing.

60. 1. A computer system comprising: a non-transitory memory for storing instructions; a non-transitory memory coupled to said non-transitory memory and reading said instructions from said non-transitory memory to said computer system; receiving analyte measurement data for an analyte in a biological sample; selecting a plurality of said analytes for determining an expected relative dilution of said biological sample; receiving a composite dilution model generated for each corresponding one of the selected analytes; For each one of the selected analytes, a corresponding one of the selected analytes is generated for the corresponding selected analyte. determining predicted relative dilution values ​​based on the corresponding composite dilution model obtained; determining the predicted relative dilution of the biological sample based on the determined predicted relative dilution value of the selected analyte.

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