Systems and methods for improving biomarker detection results using multiple biomarkers

Abstract

Some embodiments provide for a method for performing a multi-biomarker beta¬ amyloid status determining assay. The method may include: obtaining, for one or more blood or blood-derived samples, values for biomarkers associated with Alzheimer's disease, the values comprising a value for a phosphorylated Tau (p-Tau) and one or more values for one or more additional biomarkers; comparing the value for p-Tau to upper and lower thresholds to determine whether the value for p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; and after determining that the value for p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, further interrogating the intermediate range values that are indicative of borderline cases of amyloid pathology using a statistical model to obtain an output facilitating additional classification of the likelihood that the one or more blood or blood-derived samples are associated with a particular beta-amyloid status.

Description

[0001] SYSTEMS AND METHODS FOR IMPROVING BIOMARKER DETECTION RESULTS USING MULTIPLE BIOMARKERS

[0002] RELATED APPLICATIONS

[0003] This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Serial No. 63 / 710,994, filed October 23, 2024, and entitled “Systems and Methods for Improving Biomarker Detection Results Using Multiple Biomarkers,” and to U.S. Provisional Patent Application Serial No. 63 / 664,378, filed June 26, 2024, and entitled “Systems and Methods for Improving Biomarker Detection Results Using Multiple Biomarkers,” each of which is incorporated herein by reference in its entirety for all purposes.

[0004] TECHNICAL FIELD

[0005] Systems and methods generally related to multi-biomarker assays and improving the results thereof are generally described.

[0006] SUMMARY

[0007] Systems and methods generally related to multi-biomarker assays and improving the results thereof are generally described. The subject matter of the present invention involves, in some cases, interrelated products, alternative solutions to a particular problem, and / or a plurality of different uses of one or more systems and / or articles.

[0008] In one aspect, methods for performing a multi-biomarker beta-amyloid status determining assay are provided. In some embodiments, the method comprises obtaining one or more blood or blood-derived samples previously obtained from a subject; performing a first assay on the one or more blood or blood-derived samples to obtain a value indicative of a concentration of a phosphorylated Tau (p-Tau) in the one or more blood or blood-derived samples; performing one or more additional assays on the one or more blood or blood-derived samples to obtain values of one or more additional biomarkers associated with Alzheimer's disease; and using at least one processor to perform: comparing the value indicative of the concentration of the p-Tau to a lower threshold and an upper threshold to determine whether the value indicative of the concentration of the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; and after determining that the value indicative of the concentration of the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the value indicative of the concentration of the p-Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood-derived samples are associated with a particular beta-amyloid status.

[0009] In another aspect, a system is provided. In some embodiments, the system comprises at least one processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one processor, causes the at least one processor to perform a method for computationally analyzing a multi-biomarker beta-amyloid status determining assay, the method comprising: obtaining, for one or more blood or blood-derived samples previously obtained from a subject, a plurality of values for a respective plurality of biomarkers associated with Alzheimer’s disease, each of the values being indicative of a concentration of its respective biomarker, the plurality of values comprising a value for a phosphorylated Tau (p-Tau) and one or more values for one or more additional biomarkers associated with Alzheimer's disease; comparing the value for the p-Tau to a lower threshold and an upper threshold to determine whether the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; and after determining that the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the value for the p- Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood-derived samples are associated with a particular beta-amyloid status.

[0010] In another aspect, at least one non-transitory computer-readable storage medium is provided. In some embodiments, the at least one non-transitory computer-readable storage medium stores processor-executable instructions that, when executed by at least one processor, causes the at least one processor to perform a method for computationally analyzing a multi-biomarker beta-amyloid status determining assay, the method comprising: obtaining, for one or more blood or blood-derived samples previously obtained from a subject, a plurality of values for a respective plurality of biomarkers associated with Alzheimer’s disease, each of the values being indicative of a concentration of its respective biomarker, the plurality of values comprising a value for a phosphorylated Tau (p-Tau) and one or more values for one or more additional biomarkers associated with Alzheimer's disease; comparing the value for the p-Tau to a lower threshold and an upper threshold to determine whether the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; and after determining that the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the value for the p- Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood-derived samples are associated with a particular beta-amyloid status.

[0011] In another aspect, methods for computationally analyzing a multi-biomarker betaamyloid status determining assay are provided. In some embodiments, the method comprises using at least one processor to perform: obtaining, for one or more blood or blood-derived samples previously obtained from a subject, a plurality of values for a respective plurality of biomarkers associated with Alzheimer’s disease, each of the values being indicative of a concentration of its respective biomarker, the plurality of values comprising a value for a phosphorylated Tau (p-Tau) and one or more values for one or more additional biomarkers associated with Alzheimer's disease; comparing the value for the p-Tau to a lower threshold and an upper threshold to determine whether the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; and after determining that the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the value for the p-Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood-derived samples are associated with a particular betaamyloid status.

[0012] In another aspect, a system is provided. In some embodiments, the system comprises at least one processor; and at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for computationally analyzing a multi-biomarker beta-amyloid status determining assay, the method comprising: obtaining, for one or more blood or blood-derived samples previously obtained from a subject, a value indicative of a concentration of a phosphorylated Tau (p-Tau); comparing the value indicative of the concentration of the p-Tau to a lower threshold and an upper threshold to determine whether the value indicative of the concentration of the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; after determining that the value indicative of the concentration of the p-Tau is greater than the upper threshold or lower than the lower threshold, determining a first likelihood that the one or more blood or blood- derived are associated with a particular beta-amyloid status using the value indicative of the concentration of the p-Tau; after determining that the value indicative of the concentration of the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold: obtaining, for the one or more blood or blood-derived samples, one or more values for a respective one or more additional biomarkers associated with Alzheimer’s disease, each of the one or more values being indicative of a concentration of its respective biomarker; and processing the value indicative of the concentration of the p-Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a second likelihood that the one or more blood or blood-derived samples are associated with a particular beta- amyloid status.

[0013] Other advantages and novel features of the present invention will become apparent from the following detailed description of various non-limiting embodiments of the invention when considered in conjunction with the accompanying figures. In cases where the present specification and a document incorporated by reference include conflicting and / or inconsistent disclosure, the present specification shall control.

[0014] BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Non-limiting embodiments of the present invention will be described by way of example with reference to the accompanying figures, which are schematic and are not intended to be drawn to scale. In the figures, each identical or nearly identical component illustrated is typically represented by a single numeral. For purposes of clarity, not every component is labeled in every figure, nor is every component of each embodiment of the invention shown where illustration is not necessary to allow those of ordinary skill in the art to understand the invention. In the figures:

[0016] FIG. 1A is a diagram of an illustrative technique 100 for performing a multibiomarker beta-amyloid status determining assay, according to some embodiments of the technology described herein. FIG. IB is a block diagram of an example system 150 for computationally analyzing a multi-biomarker beta-amyloid status determining assay, according to some embodiments of the technology described herein.

[0017] FIG. 2A is a flowchart of an illustrative process 200 for performing a multibiomarker beta-amyloid status determining assay, according to a first embodiment of the technology described herein.

[0018] FIG. 2B is a flowchart of an illustrative process 250 for performing a multibiomarker beta-amyloid status determining assay, according to a second embodiment of the technology described herein.

[0019] FIG. 3A is an exemplary embodiment of an assay method that may be used to determine a value indicative of a concentration of at least one of the plurality of biomarkers, according to some embodiments of the technology described herein.

[0020] FIG. 3B is an exemplary embodiment showing a capture object associated with more than one biomarker molecule, according to some embodiments of the technology descried herein.

[0021] FIG. 3C is an exemplary embodiment of a flow-based assay method that may be used to determine a value indicative of a concentration of at least one of the plurality of biomarkers, according to some embodiments of the technology described herein.

[0022] FIG. 4 is a schematic diagram of an illustrative computing system with which aspects of the technology described herein may be implemented.

[0023] FIG. 5A and FIG. 5B are plots showing that performing a multi-biomarker betaamyloid status determining assay, according to embodiments of the technology described herein, increases the number of blood or blood-derived samples for which a positive or negative beta-amyloid status can be determined.

[0024] FIG. 6A, FIG. 6B, FIG. 7A, and FIG. 7B are tables summarizing distributions of probabilities that blood or blood-derived samples are beta-amyloid positive, according to some embodiments of the technology described herein.

[0025] FIG. 8A shows plots of the coefficient of variation (CV) estimated for each of multiple biomarkers used for performing a beta-amyloid status determination assay, according to some embodiments of the technology described herein.

[0026] FIG. 8B shows a table and plot summarizing the modeled imprecision of estimating probabilities that blood or blood-derived samples are beta-amyloid positive, according to some embodiments of the technology described herein. FIG. 9A, FIG. 9B, FIG. 9C, and FIG. 9D show the distribution of biomarker levels across training and validation cohorts, stratified by amyloid status, according to some embodiments of the technology described herein.

[0027] FIG. 10 shows plots demonstrating that performing a multi-biomarker betaamyloid status determining assay, according to some embodiments of the technology described herein, results in improved classification accuracy within a p-Tau 217 intermediate range.

[0028] FIG. 11 A and FIG. 1 IB show the distribution of test results across validation cohorts, according to some embodiments of the technology described herein.

[0029] FIG. 12 shows the precision profiles of biomarkers tested at five different levels across five different days, according to some embodiments of the technology described herein.

[0030] FIG. 13 shows the modeled imprecision of performing a multi-biomarker betaamyloid status determining assay, according to some embodiments of the technology described herein.

[0031] FIG. 14 shows the effect of the addition of vascular dementia (VaD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB) cases on clinical performance metrics, according to some embodiments of the technology described herein.

[0032] FIG. 15A shows the accuracy of amyloid detection across different demographic subgroups, according to some embodiments of the technology described herein.

[0033] FIG. 15B shows the percentages of intermediate results across the different demographic subgroups for which results are shown in FIG. 15 A, according to some embodiments of the technology described herein.

[0034] DETAILED DESCRIPTION

[0035] Embodiments implementing the technology described herein can provide techniques for performing a multi-biomarker beta-amyloid status determining assay. In some embodiments, the techniques involve (a) obtaining, for one or more blood or blood-derived samples, a value indicative of a concentration of a phosphorylated Tau (p- Tau) in the sample(s), and (b) using the value for p-Tau to initially classify the sample(s) as being associated with a beta-amyloid positive status, a beta-amyloid negative status, or an indeterminant beta-amyloid status. In some embodiments, if the sample(s) are classified as being associated with an indeterminant beta-amyloid status, then value(s) for one or more additional blood-based biomarkers associated with Alzheimer’s disease are obtained for the sample(s). In some embodiments, the values for p-Tau and the additional blood-based biomarker(s) are processed using a statistical model (e.g., a machine learning model) to obtain a probability that the sample(s) are associated with a particular beta-amyloid status. The probability may be used to reclassify the sample as being associated with a positive beta-amyloid status, a negative beta-amyloid status, or an indeterminant beta-amyloid status.

[0036] Amyloid pathology refers to the deposition of abnormally folded proteins in tissues. Amyloid pathology can be found in the brains of people with Alzheimer’s disease. Accurately and reliably detecting amyloid pathology can provide important benefits to aid in diagnosing Alzheimer’s disease. The inventors have identified and developed techniques by which accurate beta-amyloid status can be determined for a greater number of patients (e.g., in some instances non-invasively and / or in a less resource-intensive manner) than can be achieved through conventional testing approaches.

[0037] Conventional techniques for detecting amyloid pathology include performing amyloid positron emission tomography (PET) or analyzing a cerebrospinal fluid (CSF) sample. However, these techniques are both invasive and resource intensive. For example, performing a PET scan requires administration of a radioactive tracer, while obtaining a CSF sample requires a lumbar puncture. Both procedures are invasive to the patient and could cause discomfort and / or adverse effects. Furthermore, both procedures are expensive and time consuming.

[0038] Various phosphorylated Tau (p-Tau) proteins such as phosphorylated Tau 217 (p- Tau 217) have been identified as useful blood-based biomarkers that can be potentially used to aid in diagnosing Alzheimer's disease without the need for performing a PET scan or lumbar puncture. In particular, by measuring the concentration of p-Tau (e.g., p- Tau 217) in blood or blood-derived samples (e.g., plasma samples), certain sensitive measurement techniques have been able to successfully classify some samples as being associated with a particular amyloid status (e.g., beta-amyloid positive or beta-amyloid negative). However, there are in certain instances limitations associated with the conventional approaches for determining the amyloid status of a sample using only the concentration of p-Tau. In particular, the conventional approaches are only able to reliably predict the amyloid status of a sample having a concentration above a positive diagnostic threshold (e.g., representative of high amyloid burden ) or a concentration below a negative diagnostic threshold (e.g., representative of very low amyloid burden or non-existent amyloid pathology). Accordingly, patients having blood samples with p-Tau concentrations in an intermediate range between the positive and negative diagnostic thresholds representative of intermediate or borderline levels of amyloid pathology are delivered uncertain results and have an indeterminant amyloid status and are required to seek further, more invasive testing such as a PET scan or CSF testing. With conventional approaches to blood-based testing, the proportion of patients receiving indeterminant results can be substantial, up to approximately one-third of all patients tested.

[0039] Accordingly, the inventors have developed techniques that address the abovedescribed limitations of conventional approaches for determining amyloid pathology in a patient using assays employing blood-based samples. In some embodiments, the techniques developed by the inventors involve performing a multi-biomarker betaamyloid determining assay on one or more blood samples obtained for a subject to determine a likelihood that the sample(s) are associated with a particular beta-amyloid status (e.g., beta-amyloid positive or beta-amyloid negative). In some embodiments, the techniques include (a) performing an assay on at least one of the sample(s) to obtain a value indicative of a concentration of a p-Tau in the sample(s), and (b) determining whether the value falls between an upper and lower threshold (e.g., in an intermediate range) such that the beta-amyloid status cannot be determined using the concentration of p-Tau alone. In some embodiments, if the value is within the intermediate range, then the techniques further involve (a) performing at least one additional assay on at least one of the sample(s) to obtain biomarker values for one or more additional biomarkers associated with Alzheimer’s disease, and (b) processing the value for p-Tau and the value(s) for the additional biomarker(s) using a statistical model to obtain an output indicative of the likelihood that the sample(s) are associated with a particular betaamyloid status. The statistical model may include any suitable type of statistical model, without limitation, such as any of the types of statistical models described herein including at least with respect to the section entitled “Statistical Models.”

[0040] The techniques developed by the inventors are an improvement over conventional approaches for determining a beta-amyloid status from assays testing one or more blood or blood-derived samples because they facilitate the reliable and accurate determination of a beta-amyloid status for blood sample(s) for which an amyloid status could not previously be determined using the concentration of p-Tau alone. This improvement may be achieved in some embodiments by utilizing a multivariate statistical model (e.g., a logistic regression model) to process multiple different bloodbased biomarkers associated with Alzheimer’s disease. Utilizing multiple different biomarkers can allow for a more comprehensive understanding of the subject’s pathology, rather than relying on a single biomarker. Furthermore, because the statistical model may be trained on multiple biomarker values from each of hundreds to thousands of samples, the trained statistical model may be able to learn complex relationships between different biomarkers. Therefore, given a set of multiple biomarker values, certain trained statistical models are able to accurately predict a likelihood that a sample is associated with a particular beta-amyloid status where the human mind or conventional computing techniques using pre-defined rules are not otherwise able to establish relationships between the multiple values and their implications with respect to a beta- amyloid status.

[0041] Further, by obtaining values of one or more additional biomarkers from the blood or blood-based samples for subjects for whom their p-Tau values are indeterminant, the systems and methods herein may reduce or avoid the need for invasive beta-amyloid pathology determination techniques and, in some instances, may reduce the number of assays and / or reagents performed overall (e.g., in embodiments where the values of the one or more additional biomarkers are not obtained from and / or used for subjects for whom their p-Tau value is above or below the diagnostic thresholds discussed above). The improvements over the conventional approaches for determining a betaamyloid status from assays testing one or more blood or blood-derived samples are demonstrated in the section entitled “Examples.” In particular, as shown in Table 7, FIG. 5A, and FIG. 5B, the techniques developed by the inventors facilitated determination of an amyloid status for 190 of 325 samples for which an amyloid status could not be determined using p-Tau 217 measurements alone. 137 samples were re-classified as amyloid positive (accuracy 88%), and 53 samples were re-classified as negative (accuracy 87%), reducing the number of indeterminant samples 2.4-fold from 32.9% to 13.7%. Additionally, as shown in Table 9, the techniques developed by the inventors facilitated determination of an amyloid status for 123 of 185 samples for which an amyloid status could not be determined using p-Tau 217 measurements alone. 74 samples were re-classified as amyloid positive (accuracy 88%), and 49 samples were reclassified as negative (accuracy 92.8%), reducing the number of indeterminant samples about 3-fold from 33.9% to 11.4%. Thus, by increasing the number of samples for which an amyloid status can be accurately determined, the multi-biomarker techniques developed by the inventors can be used to reliably detect amyloid pathology in a significantly greater proportion of patients using minimally invasive blood-based tests, thereby decreasing the number of patients who require further, more invasive testing.

[0042] Following below are descriptions of various concepts related to, and embodiments of, performing a multi-biomarker beta- amyloid status determining assay. It should be appreciated that various implementation details described herein may be altered or substituted in any of numerous ways, as the techniques are not limited in any particular manner of implementation. Exemplary details of implementations are provided herein solely for illustrative purposes. Furthermore, the techniques disclosed herein may be used individually or in any suitable combination, as the technology described herein are not necessarily limited to the use of any particular technique or combination of techniques unless otherwise specifically indicated.

[0043] FIG. 1A is a diagram of an illustrative technique 100 for performing a multibiomarker beta-amyloid status determining assay, according to some embodiments of the technology described herein. As shown in FIG. 1A, illustrative technique 100 includes

[0044] (a) performing one or more assay(s) 105 on one or more blood or blood-derived sample(s) 102 using sample analysis platform 104 to obtain biomarker value(s) 106, and

[0045] (b) processing the biomarker value(s) 106 using computing device(s) 108 to obtain output 110 and / or output 112. For example, output 110 may be indicative of a likelihood that the blood or blood-derived sample(s) 102 are associated with a particular betaamyloid status. Output 112 may be indicative of a classification of the blood or blood- derived sample(s) as beta-amyloid positive, beta-amyloid negative, or indeterminant.

[0046] In some embodiments, any, some, or all of the steps of the illustrative technique 100 may be implemented in a clinical or laboratory setting. For example, certain steps of the illustrative technique 100 may be implemented on a computing device 108 that is located within a clinical or laboratory setting. In some embodiments, the computing device 108 may obtain biomarker value(s) 106 from a sample analysis platform 104 colocated with the computing device 108 within the clinical or laboratory setting. For example, the computing device 108 may be included within the sample analysis platform 104. In some embodiments, the computing device 108 may indirectly obtain the biomarker value(s) 106 from a sample analysis platform 104 that is located externally from or co-located with the computing device 108 within the clinical or laboratory setting. For example, in certain implementations and without limitation, the computing device 108 may obtain the biomarker value(s) 106 via at least one communication network, such as the Internet or any other suitable communication network(s).

[0047] In some embodiments, any, some, or all steps of the illustrative technique 100 may be implemented in a setting that is not located in a clinical or laboratory setting. In such situations, the computing device 108 may indirectly obtain biomarker value(s) 106 from a sample analysis platform 104 located within or external to a clinical or laboratory setting. For example, in certain implementations and without limitation, the biomarker value(s) 106 may be provided to the computing device 108 via at least one communication network, such as the Internet or any other suitable communication network(s).

[0048] In some embodiments, software (e.g. software 160 shown in FIG. IB) on computing device 108 is configured to process the biomarker value(s) 106 to obtain outputs 110 and 112. In some embodiments, this includes (a) determining whether a value indicative of the concentration of a p-Tau in sample(s) 102 is greater than or equal to a lower threshold and less than or equal to an upper threshold, and (b) after determining that the value is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the value for the p-Tau and values indicative of the concentration of one or more additional biomarkers associated with Alzheimer’s disease using a statistical model trained to predict a likelihood that the sample(s) 102 are associated with a particular beta-amyloid status. Example techniques for computationally analyzing biomarker values to determine the likelihood that sample(s) are associated with a particular beta-amyloid status are described herein including at least with respect to FIG. 2A and FIG. 2B.

[0049] As shown in FIG. 1A, the computing device 108 is configured to generate outputs 110 and / or 112. In some embodiments, the outputs may be stored (e.g., in memory), displayed via a user interface, transmitted to one or more other devices, used to generate a report, and / or otherwise processed using any other suitable techniques, the technology described herein is not limited in this respect. For example, the outputs of the computing device 108 may displayed using a graphical user interface (GUI) of a computing device.

[0050] FIG. IB is a block diagram of an example system 150 for computationally analyzing a multi-biomarker beta-amyloid status determining assay, according to some embodiments of the technology described herein. System 150 includes computing device(s) 108. Software 160 is configured to execute on computing device(s) 108 to perform various functions in connection with computationally analyzing a multibiomarker beta-amyloid status determining assay. In some embodiments, software 160 includes a plurality of modules. A module may include processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform function(s) of the module. Such modules are sometimes referred to herein as “software modules,” each of which includes processorexecutable instructions configured to perform one or more acts of one or more processes, such as process 200 shown in FIG. 2A and process 250 shown in FIG. 2B.

[0051] The computing device(s) 108 may be operated by one or more user(s) 170. In some embodiments, the user(s) 170 provides input to computing device(s) 108. For example, the user(s) 170 may provide biomarker data (e.g., biomarker values), data for training a statistical model, and / or any other suitable information, without limitation. Additionally or alternatively, user(s) 170 may provide input specifying processing or other methods to be performed on biomarker data, training data, and / or measurements obtained as a result of performing one or more assays. User(s) 170 my provide input by uploading one or more files, interacting with a user interface, or using any other suitable technique for providing input, without limitation.

[0052] In some embodiments, the initial beta- amyloid status classification module 162 is configured to identify an initial beta- amyloid class for the one or more blood or blood- derived samples. For example, the initial beta-amyloid status classification module 162 may be configured to identify the sample(s) as belonging to a beta-amyloid positive class, a beta-amyloid negative class, or an indeterminant (e.g., intermediate) class. As described herein, in some embodiments, this includes (a) comparing value(s) for one or more biomarkers to respective thresholds, and (b) classifying the one or more blood or blood-derived samples based on a result of the comparing. For example, this may include comparing a value indicative of the concentration of a p-Tau (e.g., p-Tau 217) to lower and upper thresholds. If the value is greater than or equal to the lower threshold and less than or equal to the upper threshold, the one or more blood samples may be classified as belonging to the indeterminant class. If the value is greater than the upper threshold, the one or more blood samples may be classified as belonging to the beta-amyloid positive class. If the value is less than the lower threshold, the one or more blood samples may be classified as belonging to the beta-amyloid negative class. Example techniques for initially classifying one or more blood samples are described herein including at least with respect to acts 204 and 206 of processes 200 and 250 shown in FIGs. 2A and 2B. For example, initial beta-amyloid status classification module 162 may be configured to implement act 204 and / or 206.

[0053] In some embodiments, the initial beta- amyloid status classification module 162 obtains the values for one or more biomarkers (e.g., p-Tau) associated with Alzheimer’s disease from sample analysis platform 104, biomarker data store 152, and / or user(s) 170.

[0054] In some embodiments, the class determined by the initial beta-amyloid status classification module 162 is used by the beta- amyloid status likelihood determination module 163 to determine a likelihood that the blood or blood-derived sample(s) are associated with a particular beta-amyloid status. In some embodiments, the beta-amyloid status is the beta-amyloid positron emission tomography (PET) status. The beta-amyloid PET status refers to beta-amyloid status as would be determined by PET. In some embodiments, the beta-amyloid status is the beta-amyloid cerebrospinal (CSF) status. The beta-amyloid PET status refers to beta-amyloid status as would be determined by CSF (e.g., from a lumbar puncture). PET or CSF are both clinically acceptable methods for determining beta-amyloid status. For example, the beta-amyloid status likelihood determination module 163 may be configured to process biomarker value(s) using one or more statistical models trained to predict the likelihood that the blood or blood-derived sample(s) are associated with the particular beta- amyloid status. The particular trained statistical model used by the beta- amyloid status likelihood determination module 163 to determine the likelihood may depend on the class predicted by the initial beta-amyloid status classification module 162. For example, a first statistical model may be used to determine the likelihood when the indeterminant beta-amyloid class is identified for the one or more blood or blood-derived samples. The first statistical model may be trained to predict the likelihood that the sample(s) are associated with the particular beta-amyloid status based on a plurality of values for a respective plurality of biomarkers (e.g., amyloid P42 / P40 ratio, NfL, GFAP and / or a p-Tau) associated with Alzheimer’s disease. A second trained statistical model may be used to determine the likelihood when the positive or negative beta-amyloid class is identified for the one or more blood or blood- derived samples. The second statistical model may be trained to predict the likelihood that the sample(s) are associated with the particular beta-amyloid status based on one or more values for a respective one or more biomarkers (e.g., a p-Tau) associated with Alzheimer’s disease. Example techniques for determining a likelihood that one or more sample(s) are associated with a particular beta-amyloid status are described herein including at least with respect to act 222 and act 214 of processes 200 and 250 shown in FIG. 2A and FIG. 2B. For example, the beta-amyloid status likelihood determination module 162 may be configured to implement act 222 and / or act 214.

[0055] In some embodiments, the beta-amyloid status likelihood determination module 163 obtains the values for one or more biomarkers (e.g., p-Tau, amyloid P42 / P40 ratio, NfE, and / or GFAP) associated with Alzheimer’s disease from sample analysis platform 104, biomarker data store 152, user(s) 170, and / or preliminary biomarker evaluation module 162. Additionally or alternatively, beta-amyloid status likelihood determination module 163 may obtain the trained statistical model(s) from statistical model training module 167 and / or statistical model data store 156. Additionally or alternatively, betaamyloid status likelihood determination module 163 may obtain the initial classification for the one or more blood sample(s) from initial beta-amyloid status classification module 162, user(s) 170, and / or one or more data stores.

[0056] In some embodiments, the beta- amyloid status classification module 164 is configured to identify an updated beta- amyloid class for the one or more blood or blood- derived samples based on the likelihood determined by the beta-amyloid status likelihood determination module 163. For example, the beta-amyloid status classification module 164 may classify the one or more blood or blood-derived samples as betaamyloid positive, beta-amyloid negative, or indeterminant. In some embodiments, classifying the one or more blood or blood-derived samples includes (a) comparing the likelihood that the one or more blood or blood-derived samples are associated with the particular beta-amyloid status to an upper beta-amyloid classification threshold and a lower beta-amyloid classification threshold, and (b) classifying the one or more blood or blood-derived samples based on a result of the comparison. For example, the sample(s) may be classified as (i) beta-amyloid positive if the likelihood is greater than the upper classification threshold, (ii) beta-amyloid negative if the likelihood is less than the lower classification threshold, and (iii) indeterminant if the likelihood is greater or equal to the lower classification threshold and less than or equal to the upper classification threshold. Example techniques for classifying blood or blood-derived sample(s) are described herein including at least with respect to act 214 and act 226 of processes 200 and 250 shown in FIG. 2A and FIG. 2B. For example, the beta-amyloid status classification module 164 may be configured to implement act 214 and / or act 216.

[0057] In some embodiments, the beta- amyloid status classification module 164 obtains the likelihood that the blood or blood-derived sample(s) are associated with the particular beta- amyloid status from the beta- amyloid status likelihood determination module 163, user(s) 170, and / or one or more data stores.

[0058] In some embodiments, report generation module 166 is configured to generate a report to convey information related to performing and / or computationally analyzing a multi-biomarker beta-amyloid status determining assay. For example, the report may include an indication of the class (e.g., beta-amyloid positive, beta-amyloid negative, or indeterminant) determined for the one or more blood or blood-derived samples. Additionally or alternatively, the report may indicate the determined likelihood that the one or more blood or blood-derived samples are associated with a particular betaamyloid status. Additionally or alternatively, the report may indicate the initial betaamyloid class identified, by the initial beta- amyloid status classification module for the one or more blood or blood-derived samples. Additionally or alternatively, the report may indicate the value(s) measured for the biomarker(s) associated with Alzheimer's disease. It should be appreciated, however, that the report may include any other suitable information related to performing and / or computationally analyzing a multi-biomarker beta-amyloid status determining assay.

[0059] As shown in FIG. IB, software 160 also includes user interface module 165. User interface module 165 may be configured to generate a graphical user interface (GUI) through which user(s) 170 may provide input and view information generated by software 160. For example, in some embodiments, the user interface module 165 may be a webpage or web application accessible through an Internet browser. In some embodiments, the user interface module 165 may generate a GUI of an app executing on a user’s mobile device. For example, computing device(s) 108 may be the user’s mobile device, and the user interface module 165 may generate a GUI of an app executing thereon. In some embodiments, the user interface module 165 may generate a number of selectable elements through which a user may interact. For example, the user interface module 165 may generate dropdown lists, checkboxes, text fields, or any other suitable element. In some embodiments, the user interface module 165 generates a GUI that includes one or more reports generated by report generation module 166.

[0060] In some embodiments, the statistical model training module 167 is configured to train one or more statistical models to predict a likelihood that one or more blood or blood-derived samples are associated with a particular beta-amyloid status. For example, the statistical model training module 167 may obtain training data and / or validation data from training data store 154, sample analysis platform 104, and / or user(s) 170. For example, the training and / or validation data may include, for each of a plurality of subjects, biomarker value(s) measured for sample(s) obtained from the subject and an indication of the beta- amyloid status associated with those sample(s). The statistical model training module 167 may be configured to use the obtained training and / or validation data to train and / or validate one or more statistical models to predict a likelihood that one or more blood samples are associated with a particular beta-amyloid status. For example, the statistical model training module 167 may be configured to fit a statistical model to the training data. Example techniques for training a statistical model are described herein including at least with respect to the section “Statistical Models.” In some embodiments, the statistical model training module 167 may provide the trained statistical model(s) to statistical model data store 156 for storage thereon. For example, the statistical model training module 167 may provide the values of parameters of the statistical model(s) to the statistical model data store 156 for storage thereon.

[0061] As shown in FIG. IB, exemplary system 150 also includes data stores including the biomarker data store 152, training data store 154, and statistical model data store 156. The biomarker data store 152 may store value(s) for one or more biomarkers associated with Alzheimer's disease. For example, the biomarker data store 152 may obtain the values from sample analysis platform 104 and / or user(s) 170. The training data store 154 may store data used to train and / or validate one or more statistical models. For example, the training data store may obtain training and / or validation data from public data store(s), publication(s), sample analysis platform, user(s) 170, and / or any other suitable source, without limitation. The statistical model data store 154 may store one or more trained statistical models. For example, the statistical model data store 154 may store parameters of one or more trained statistical models. It should be appreciated that system 150 may include one or more other data store(s) configured to store any other suitable information (e.g., reports generated by report generation module 166, intermediate results of computationally analyzing the multi-biomarker assay, etc.), without limitation. Each of the data stores may include any suitable type of data store (e.g., a flat file, a database system, a multi-file, etc.) and may store data in any suitable format, without limitation. The data stores 152, 154, 156 may be part of software 160 (not shown) or excluded from software 160, as shown in FIG. IB.

[0062] FIG. 2A is a flowchart of an illustrative process 200 for performing a multibiomarker beta-amyloid status determining assay, according to a first embodiment of the technology described herein. One or more of the acts of process 200 may be performed automatically by any suitable computing device(s). For example, act(s) may be performed by computing device(s) 108 shown in FIG. 1A and FIG. IB, computing system 400 shown in FIG. 4, a laptop computer, a desktop computer, a mobile device, one or more servers, in a cloud computing environment, and / or in any other suitable way, without limitation

[0063] At act 202, a value indicative of the concentration of a phosphorylated Tau (p- Tau) is obtained for one or more blood or blood-derived samples from a subject. In some embodiments, the p-Tau is one of phosphorylated Tau 217 (p-Tau 217), phosphorylated Tau 181 (p-Tau 181), or phosphorylated Tau 231 (p-Tau 231). In some embodiments, the p-Tau is p-Tau 217. In some embodiments, the value for the p-Tau is obtained by performing at least one assay on at least one of the blood or blood-derived sample(s). Examples of assays performed to obtain a value indicative of a concentration of a p-Tau are described herein including at least with respect to the section entitled “Assays.” In some embodiments, the value for the p-Tau is obtained from a user (e.g., user(s) 170 shown in FIG. IB), a data store (e.g., biomarker data store 152 shown in FIG. IB), and / or a sample analysis platform (e.g., sample analysis platform shown in FIG. 1A and FIG. IB).

[0064] At act 204, the value indicative of the concentration of the p-Tau is compared to upper and lower thresholds. In some embodiments, the upper and lower thresholds are themselves values indicative of respective concentrations of the p-Tau. In some embodiments, the upper threshold is at least 0.080 pg / mE, at least 0.085 pg / mE, at least 0.090 pg / mE, at least 0.095 pg / mE, at least 0.100 pg / mE, or at least any other suitable value indicative of concentration of the p-Tau. In some embodiment, the upper threshold is at most 0.085 pg / mL, at most 0.090 pg / mL, at most 0.095 pg / mL, at most 0.100 pg / mL, at most 0.105 pg / mL, or at most any other suitable value indicative of concentration of the p-Tau. In some embodiments, the upper threshold is between 0.080 pg / mL and 0.105 pg / mL. In some embodiments, the upper threshold is 0.090 pg / mL. In some embodiments, the lower threshold is at least 0.030 pg / mL, at least 0.035 pg / mL, at least 0.040 pg / mL, at least 0.045 pg / mL, at least 0.050 pg / mL, or at least any other suitable value indicative of the concentration of the p-Tau. In some embodiments, the lower threshold is at most 0.035 pg / mL, at most 0.040 pg / mL, at most 0.045 pg / mL, at most 0.050 pg / mL, at most 0.055 pg / mL, or at most any other suitable value indicative of the concentration of p-Tau. In some embodiments, the lower threshold is between 0.030 pg / mL and 0.055 pg / mL. In some embodiments, the lower threshold is 0.040 pg / mL.

[0065] The numerical values for concentration in this paragraph refer to values as measured by a digital immunoassay (e.g., SiMoA® by Quanterix); in instances where the relevant assay is performed using a different assay format that would provide a different numerical concentration value for the same underlying actual concentration, then the numerical values for the upper and lower threshold would instead be the corresponding values as measured by the different assay format for the same underlying actual concentration. As a purely illustrative example, if an assay format other than a digital immunoassay is determined to produce concentrations values for a biomarker lower than digital immunoassays by 10%, then a method / system employing that other assay format using an upper threshold of 0.072 pg / mL would still fall within the scope of the “at least 0.080 pg / mL” threshold because a value of 0.072 pg / mL measured by the different assay format would be correspond to the same actual concentration as the value of 0.080 pg / mL measured by the digital immunoassay.

[0066] In some embodiments, the upper and lower thresholds are values indicative of likelihoods that the one or more blood or blood-derived samples are beta-amyloid positive. In some embodiments, the upper threshold is at least 80%, at least 85%, at least 88%, at least 90%, at least 92%, at least 95%, or at least any other suitable value indicative of the likelihood that the one or more blood or blood-derived samples are betaamyloid positive. In some embodiments, the upper threshold is at most 85%, at most 88%, at most 90%, at most 92%, at most 95%, at most 98% or at most any other suitable value indicative of the likelihood that the one or more blood or blood-derived samples are beta-amyloid positive. In some embodiments, the upper threshold is a value indicative of a likelihood between 80% and 98%. In some embodiments, the lower threshold is at least 5%, at least 8% at least 10%, at least 12%, at least 15%, at least 18%, at least 20%, or at least any other suitable value indicative of the likelihood that the one or more blood or blood-derived samples are beta-positive. In some embodiments, the lower threshold is at most 8%, at most 10%, at most 12%, at most 15%, at most 18%, at most 20%, or at most any other suitable value indicative of the likelihood that the one or more blood or blood-derived samples are beta-positive. In some embodiments, the lower threshold is a value indicative of a likelihood between 5% and 20%.

[0067] At act 206, process 200 involves determining, based on a result of the comparing at act 204, whether the value indicative of the concentration of the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold. If the value for p-Tau is greater than the upper threshold, the one or more blood or blood-derived samples may be initially classified as being associated with a beta-amyloid positive status. If the value for p-Tau is less than the lower threshold, the one or more blood or blood-derived samples may be initially classified as being associated with a beta-amyloid negative beta-amyloid status. If the value for p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, the one or more blood or blood- derived samples may be initially classified as belonging to an intermediate class that is not associated with either the beta-amyloid positive or beta-amyloid negative status.

[0068] As described herein, the techniques developed by the inventors are an improvement over conventional approaches for determining a beta-amyloid status for blood or blood-derived sample(s) because they facilitate the determination of a betaamyloid status for sample(s) initially classified as belonging to the intermediate class, without requiring further expensive and invasive diagnostic testing. Rather, the techniques developed by the inventors utilize at least one statistical model and multiple blood-based biomarkers associated with Alzheimer’s disease to reliably determine a beta-amyloid status for the sample(s). In particular, if the sample(s) are initially classified as being associated with the intermediate class (e.g., the value for p-Tau is determined to be greater than or equal to the lower threshold and less than or equal to the upper threshold) at act 206, process 200 proceeds to acts 212 and 214.

[0069] At act 212, one or more values indicative of concentration for a respective one or more additional biomarkers associated with Alzheimer’s disease are obtained for the one or more blood or blood-derived samples. In some embodiments, the one or more additional biomarkers include one additional biomarker, two additional biomarkers, three additional biomarkers, four additional biomarkers, five additional biomarkers, six additional biomarkers, seven additional biomarkers, or any other suitable number of additional biomarkers, without limitation. Examples of biomarkers associated with Alzheimer's disease are described herein including at least with respect to the section entitled “Biomarkers Associated with Alzheimer's Disease.” As a nonlimiting example, the one or more additional biomarkers may include one or more of amyloid P42 / P40 ratio, neurofilament light (NfL), and Glial Fibrillary Acidic Protein (GFAP). In some embodiments, the values indicative of concentration for the additional biomarkers are obtained by performing at least one assay on at least one of the blood or blood-derived sample(s). Examples of assays performed to obtain such values for biomarkers associated with Alzheimer’s disease are described herein including at least with respect to the section entitled “Assays.” In some embodiments, such values for the additional biomarkers are obtained from a user (e.g., user(s) 170 shown in FIG. IB), a data store (e.g., biomarker data store 152 shown in FIG. IB), and / or a sample analysis platform (e.g., sample analysis platform shown in FIG. 1A and FIG. IB).

[0070] At act 214, the value indicative of the concentration of p-Tau and the one or more values indicative of concentration for the one or more additional biomarkers are processed using a statistical model to obtain an output indicative of a likelihood (e.g., a second likelihood) that the one or more blood or blood-derived samples are associated with the particular beta- amyloid status. For example, the value for the p-Tau and the values for the one or more additional biomarkers may be provided as input to the statistical model, which is trained to predict the likelihood that the one or more blood or blood-derived samples are associated with particular beta-amyloid status given the biomarker values as input. The statistical model may include any of the types of statistical models described herein including at least with respect to the section entitled “Statistical Models,” which also described techniques for training statistical models. In some embodiments, the output indicative of a likelihood that the one or more blood or blood-derived samples are associated with the particular beta-amyloid status is a probability that the one or more blood or blood-derived samples are associated with the particular beta- amyloid status.

[0071] As described herein, if the one or more blood or blood-derived samples are initially classified as being associated with a particular beta-amyloid status (e.g., the value for p-Tau is determined to be greater than the upper threshold or less than the lower threshold), it may be beneficial, in some embodiments, to quantify the likelihood that the blood or blood-derived sample(s) are associated with the particular beta-amyloid status. For example, the likelihood may be relevant to clinical decisions (e.g., treatment administration, monitoring, further diagnostic testing, etc.) that are based on the particular beta-amyloid status determined for the blood or blood-derived sample(s). Accordingly, if at act 206, it is determined that the value is not greater than or equal to the lower threshold and less than or equal to the upper threshold, process 200 proceeds to act 222.

[0072] At act 222, a likelihood (e.g., a first likelihood) that the one or more blood or blood-derived samples are associated with a particular beta-amyloid status (e.g., betaamyloid PET status or beta-amyloid CSF status) is determined using the value indicative of the concentration of p-Tau. In some embodiments, determining the likelihood includes processing the value indicative of the concentration of p-Tau as input to a statistical model (e.g., a second statistical model) to obtain an output indicative of the likelihood. The second statistical model may be different from the statistical model (e.g., the first statistical model) described herein with respect to act 214. For example, the second statistical model may be trained to predict the likelihood given value(s) indicative of concentration for one or more biomarker(s) different from the one or more additional biomarkers described with respect to acts 212 and 214. For example, the second statistical model may be trained to predict the likelihood given only the value indicative of the concentration of the p-Tau as input. It should be appreciated, however, that the value indicative of the concentration of the p-Tau may be converted to likelihood using any other suitable conversion technique, without limitation. For example, the value for p- Tau may be scaled using a suitable conversion factor. In some embodiments, the likelihood that the one or more blood or blood-derived samples are associated with the particular beta-amyloid status is a probability that the one or more blood or blood- derived samples are associated with the particular beta-amyloid status.

[0073] At act 236, the one or more blood or blood-derived samples are classified as betaamyloid positive, beta-amyloid negative, or indeterminant. In some embodiments, the classifying is based on the determined likelihood that the one or more blood or blood- derived samples is associated with the particular beta- amyloid status. For example, depending on the outcome of the decision at act 206, the likelihood may be either the likelihood determined at act 214 (e.g., the second likelihood) or the likelihood determined at act 224 (e.g., the first likelihood). In some embodiments, classifying the sample(s) based on the likelihood includes comparing the likelihood to one or more classification thresholds. For example, the one or more classification thresholds may include an upper classification threshold and a lower classification threshold. In some embodiments, if the likelihood is greater than the upper classification threshold, the sample(s) are classified as beta-amyloid positive. In some embodiments, if the likelihood is less than the lower classification threshold, the sample(s) are classified as betaamyloid negative. In some embodiments, if the likelihood is less than or equal to the upper classification threshold and greater than or equal to the lower classification threshold, the sample(s) are classified as indeterminant. In some embodiments, the upper classification threshold is at least 0.55, at least 0.60, at least 0.65, at least 0.70, at least 0.75, at least 0.80, at least 0.85, at least 0.90, or at least any other suitable value. In some embodiments, the upper classification threshold is at most 0.60, at most 0.65, at most 0.70, at most 0.75, at most 0.80, at most 0.85, at most 0.90, at most 0.95, or at most any other suitable value. In some embodiments, the upper classification threshold is between 0.55 and 0.95. In some embodiments, the upper classification threshold is 0.61. In some embodiments, the upper classification threshold is 0.70. In some embodiments, the lower classification threshold is at least 0.20, at least 0.25, at least 0.30, at least 0.35, at least 0.40, at least 0.45, or at least any other suitable value. In some embodiments, the lower classification threshold is at most 0.25, at most 0.30, at most 0.35, at most 0.40, at most 0.45, or at most any other suitable value. In some embodiments, the lower classification threshold is at most 0.48, at most 0.50, or at most any other suitable value. In some embodiments, the lower classification threshold is between 0.20 and 0.45. In some embodiments, the lower classification threshold is between 0.20 and 0.50. In some embodiments, the lower classification threshold is 0.41. In some embodiments, the lower classification threshold is 0.45.

[0074] FIG. 2B is a flowchart of an illustrative process 250 for performing a multibiomarker beta-amyloid status determining assay, according to a second embodiment of the technology described herein. Process 250 differs from process 200 in that all biomarker values are obtained at act 252, prior to performing act 204. By contrast, process 200 includes act 212, which involves obtaining the value(s) for the additional biomarker(s) associated with Alzheimer's disease only after determining, at act 206, that the concentration of p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold.

[0075] While various exemplary embodiments are described throughout this disclosure referring to direct comparisons of the value indicative of the concentration of the p-Tau in the one or more blood or blood-derived samples to the upper threshold and the lower threshold, any such methods may be performed using a normalized value for the p-Tau based on a normalization value as an alternative to or in addition to just the value indicative of the concentration of the p-Tau alone. For example, in some such embodiments, the method further comprises obtaining a normalization value (e.g., by the processor). Such a normalization may be useful in some instances at least because some physiological / co-morbidity conditions may raise or lower blood (e.g., plasma) biomarker (e.g., protein biomarker) concentrations in a manner unrelated to amyloid pathology status. Accordingly, in some embodiments it may be desirable to obtain a normalization value from, for example, a normalization biomarker that reflects, for example, non- amyloid-related changes that could affect the concentration of the p-Tau in the one or more blood or blood-derived samples.

[0076] In some embodiments, obtaining the normalization value comprises performing a normalization assay on the one or more blood or blood-derived samples to obtain the normalization value. The method may further comprise dividing the value indicative of the concentration of the p-Tau in the one or more blood sample by the normalization value to obtain a normalized p-Tau value (e.g., for a normalization correction).

[0077] In some such embodiments involving the normalized p-Tau value, the comparison of the value indicative of the concentration of the p-Tau to a lower threshold and an upper threshold further comprises comparing the normalized p-Tau value to the lower threshold and the upper threshold to determine whether the normalized p-Tau value is greater than or equal to the lower threshold and less than or equal to the upper threshold (e.g., to determine whether the normalized p-Tau value is in an intermediate range). Such method may further comprise, after determining that the normalized p-Tau value is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the normalized p-Tau value and the one or more values for the one or more additional biomarkers using the statistical model to obtain the output indicative of the likelihood that the one or more blood or blood-derived samples are associated with a particular beta- amyloid status. Any of a variety of normalization values may be employed. For example, in some embodiments, the normalization value is a value indicative a concentration of a normalization biomarker in the one or more blood or blood-derived samples. Any of a variety of normalization biomarkers may be used. In some, but not necessarily all embodiments, the normalization biomarker is not a biomarker associated with Alzheimer’s Disease. In some embodiments, the normalization biomarker is (a) unphosphorylated Tau or (b) total Tau or fragments thereof. In some embodiments, the normalization biomarker is unphosphorylated Tau - that is, the total concentration of all unphosphorylated Tau in the one or more blood or blood-derived samples. For example, in some embodiments in which the p-Tau is p-Tau 217, the normalized p-Tau is the ratio of the value indicative of the concentration of p-Tau 217 in the one or more blood or blood-derived samples to the value indicative of the concentration of unphosphorylated Tau in the one or more blood or blood-derived samples. It is believed that normalization of the value indicative of the p-Tau concentration by, for example, dividing by the value indicative of the concentration of unphosphorylated Tau can, in some instances, provide results more robust with respect to accounting for co-morbidities (e.g., kidney disease) that affect overall Tau levels in the blood or blood-derived samples (e.g., plasma samples). In some embodiments, the normalization biomarker is total Tau or fragments thereof. For example, in some embodiments in which the p-Tau is p-Tau 217, the normalized p-Tau is the ratio of the value indicative of the concentration of p-Tau 217 in the one or more blood or blood-derived samples to the value indicative of the total concentration of Tau or fragments thereof in the one or more blood or blood-derived samples.

[0078] Biomarkers Associated with Alzheimer’s Disease

[0079] As noted above, improved techniques involving the detection of a plurality of biomarkers associated with Alzheimer’s disease are disclosed herein. The plurality biomarkers may comprise a p-Tau protein and one or more additional biomarkers associated with Alzheimer’s disease. Any of a variety of the phosphorylated tau isoforms may be used. In some embodiments, the p-Tau is one of phosphorylated Tau 217 (p-Tau 217), phosphorylated Tau 181 (p-Tau 181), phosphorylated Tau 231 (p-Tau 231), phosphorylated Tau 205 (p-Tau 205), phosphorylated Tau 212 (p-Tau 212), phosphorylated Tau 262 (p-Tau 262), or phosphorylated Tau 356 (p-Tau 356). In some embodiments, the p-Tau is one of phosphorylated Tan 217 (p-Tau 217), phosphorylated Tan 181 (p-Tau 181), or phosphorylated Tau 231 (p-Tau 231). In some embodiments, the p-Tau is p-Tau 217.

[0080] In some embodiments, at least one (or all) of the one or more additional biomarkers is a core Alzheimer’s disease biomarker that is specific to Alzheimer’s disease (alone and / or in combination with one or more other biomarkers (e.g., as a ratio)). In some embodiments, at least one of the one or more additional biomarkers is a non-core Alzheimer’s disease biomarker that is non-specifically associated with Alzheimer’s disease. Non-limiting examples of biomarkers that could be one of the one or more additional biomarkers associated with Alzheimer’s disease include, but are not limited to amyloid P42, amyloid P40, amyloid P42 / P40 ratio, neurofilament light (NfL), Glial Fibrillary Acidic Protein (GFAP), a ratio of the p-Tau with another biomarker (e.g., the concentration of the p-Tau such as p-Tau 217 divided by concentration of one or more of the additional biomarkers such as amyloid P42), brain-derived tau, a different p- Tau, unphosphorylated Tau, total Tau or fragments thereof, MTBR-tau243, SNAP24, GAP-43, and / or TREM2, and / or ratios thereof. For example, non-limiting examples of biomarkers that could be one of the one or more additional biomarkers associated with Alzheimer’ s disease include, but are not limited to amyloid P42, amyloid P40, amyloid P42 / P40 ratio, neurofilament light (NfL), Glial Fibrillary Acidic Protein (GFAP), brain- derived tau, a different p-Tau, unphosphorylated tau, total tau or fragments thereof, MTBR-tau243, SNAP24, GAP-43, and / or TREM2, and / or ratios thereof. In some embodiments, the one or more additional biomarkers associated with Alzheimer’s disease comprises amyloid P42 / P40 ratio. In some embodiments, the one or more additional biomarkers associated with Alzheimer’s disease comprises amyloid P42 / P40 ratio, NfL, and / or GFAP. In some embodiments, the one or more additional biomarkers associated with Alzheimer’s disease comprises amyloid P42 / P40 ratio, NfL, and GFAP. In some embodiments, the one or more additional biomarkers associated with Alzheimer’s disease consists of amyloid P42 / P40 ratio, NfL, and / or GFAP.

[0081] NfL is a cytoskeletal intermediate filament protein that may combine with other proteins to form neurofilaments in neurons and may be released in significant quantity following axonal damage or neuronal degeneration. GFAP is a class-III intermediate filament involved in many central nervous system processes including cell communication and the functioning of the blood brain barrier. Samples and Sample Preparation

[0082] As noted above, some embodiments involve obtaining one or more blood or blood-derived samples obtained from a subject. The one or more assay may be performed on the one or more blood samples, e.g., to determine respective values indicative of the concentrations of the biomarkers. In some embodiments, all assays are performed on a single blood or blood-derived sample. However, in some embodiments at least some of the assays are performed on different blood or blood-derived samples obtained from the subject. For example, in some embodiments, a first assay is performed on a first blood or blood-derived sample obtained from the patient to measure a value for at least one of the biomarkers, and a second assay is performed (at the same time or at a different time) on a second blood or blood-derived sample obtained from the patient to measure a value for at least one different biomarker. In some embodiments where two or more blood or blood-derived samples are obtained from the subject, the two samples may be obtained at the same time (e.g., from a single blood drawing) or at different times from different drawings.

[0083] In some embodiments, the blood or blood-derived sample comprises whole blood. In some embodiments, the blood or blood-derived sample comprises plasma (e.g., plasma is present in the sample in an amount of at least 50 wt%, at least 75 wt%, at least 90 wt%, at least 95 wt%, at least 98 wt%, at least 99 wt%, or 100 wt% by weight of the sample). In some embodiments, the blood or blood-derived sample is prepared by manipulation by a human or machine (e.g., by centrifuging a sample such as a whole blood-containing sample and performing a separation) to such that the desired amount and / or enrichment of plasma is obtained. In some embodiments, the blood or blood- derived sample comprises serum. To obtain serum, a whole blood sample is allowed or induced to clot (coagulate). The sample may then centrifuged or otherwise processed to remove the clot and blood cells, and the resulting liquid (e.g. supernatant) is serum.

[0084] In some embodiments, the blood or blood-derived sample is prepared as a dried blood spot. In some embodiments, the blood or blood-derived sample is prepared as a dried plasma spot. Further description of examples of methods and techniques involving dried physiological samples such as dried blood or dried plasma samples is provided in U.S. Patent Publication No. 2021-0096140, published on April 1, 2021, filed as U.S. Patent Application No. 17 / 046,116 on October 8, 2020, entitled “QUANTIFICATION OF BIOMARKERS PRESENT IN DRIED PHYSIOLOGICAL SAMPLES,” which is incorporated herein by reference in its entirety for all purposes.

[0085] The one or more blood or blood-derived samples on which the assay(s) is performed may have relatively low concentrations of the biomarkers associated with Alzheimer’s disease. In some embodiments, the concentration of one or more biomarkers in the blood or blood-derived sample is (and / or is determined by the assay to be) less than or equal to 50 x 10'12moles per liter (M), less than or equal to 10 x 10'12M, less than or equal to 5 x 10'12M, less than or equal to 1 x 10'12, less than or equal to 500 x 10'15M, less than or equal to 100 x 10'15M, less than or equal to 50 x 10'15M, less than or equal to 10 x 10'15M, and / or as low as 5 x 10'15M, as low as 1 x 10'15M, or less.

[0086] Assays

[0087] Assays comprising any of a variety of methodologies, reagents, and / or formats may be employed to obtain the respective values indicative of the concentrations of the plurality of biomarkers associated with Alzheimer’s disease in the one or more blood or blood-derived samples. In some embodiments, at least one (or all) of the assays is an ultra- sensitive assay. In some embodiments, at least one (or all) of the assays has singlemolecule sensitivity for at least one of the biomarkers. In some embodiments, at least one (or all) of the assays comprise an immunoassay. In some embodiments, at least one (or all) of the assays comprises a mass spectrometry assay. In some embodiments where more than a value for more than one biomarker is obtained, each of the biomarkers are obtained by the same type of assay (e.g., as separate singleplex assays, via a multiplex assay, or via a combination of singleplex and multiplex assays performed either simultaneously or non- simultaneously). However, in some embodiments where more than a value for more than one biomarker is obtained, at least one value for a biomarker is obtained using an assay format that is different than at least one other value (e.g., at least one biomarker is tested using an immunoassay while at least one biomarker is tested using a mass spectrometry assay).

[0088] One example of an assay format / protocol comprises exposing capture objects (e.g., beads) is a digital assay format in the presence of single molecules of the biomarker are resolved and interrogated (e.g., spatially and / or temporally). Nonlimiting examples of embodiments of assays, such as digital immunoassays, are now described. One example of such an assay format / protocol comprises exposing capture objects (e.g., beads) configured to capture a particular type of biomarker to a solution (e.g., a fluid sample comprising or derived from the blood or blood-derived sample) containing or suspected of containing such bio markers. At least some of the biomarker molecules become immobilized with respect to a capture object. The capture objects may each have affinity for a particular type of biomarker. The capture objects may each include a binding surface having affinity for at least one type of biomarker (e.g., a particular type of biomarker). In some cases, the binding surface may comprise a plurality of capture components. A “capture component”, as used herein, is any molecule, other chemical / biological entity, or solid support modification disposed upon a solid support that can specifically attach, bind, or otherwise capture a target molecule or particle (e.g., a biomarker molecule), so the target molecule / particle becomes immobilized with respect to the capture object. In some embodiments, the capture component comprises a biomolecule having specific binding affinity for the particular biomarker. In some embodiments, the capture component comprises an antibody or fragment thereof having specific binding affinity for the particular biomarker. The immobilization may be caused by the association of a biomarker molecule with a capture component on the surface of the capture object. In the context of immobilizing a biomarker molecule with respect to a capture object, “immobilized” means captured, attached, bound, or affixed so as to prevent dissociation or loss of the target biomarker, but does not require absolute immobility with respect to either the capture component or the object.

[0089] The number of biomarker molecules immobilized with respect to a capture object may depend on the ratio of the total number of the biomarker molecules in the sample compared to at least one of the total number, size, and / or surface density of capture components of capture objects provided. In some embodiments, the number of the biomarker molecules immobilized with respect to a single capture object may follow a standard Poisson distribution. In some cases, a statistically significant number of the capture objects associate with a single biomarker molecule from the fluid sample and a statistically significant number of capture objects do not associate with any biomarker from the fluid sample. In some embodiments, the percentage of capture objects which associate with at least one biomarker molecule (e.g., of the particular type of biomarker) is less than or equal to 99.999%, less than or equal to 99.99%, less than or equal to 99.9%, less than or equal to 99%, less than or equal to 98%, less than or equal to 95%, less than or equal to 90%, less than or equal to 80%, less than or equal to 70%, less than or equal to 60%, less than or equal to 50%, less than or equal to 40%, less than or equal to 30%, less than or equal to 20%, less than or equal to 10%, less than or equal to 5%, less than or equal to 1%, less than or equal to 0.5%, less than or equal to 0.1%, or less of the total number of capture objects

[0090] In some embodiments, an assay method employs a step of spatially segregating at least some of the capture objects into a plurality of separate locations to facilitate detection / quantification. However, in some embodiments, the assay method employs a step of temporally segregating at least some of the capture objects to facilitate detection / quantification, such as by flowing the capture objects passed a detection system (e.g., using flow cytometry). In some embodiments in which spatial segregation is performed, the segregation is performed so each location comprises / contains either zero or one or more biomarker molecule from the fluid sample. Additionally, in some embodiments, the locations may be configured in a manner so each location can be individually addressed. In some embodiments, a measure of the concentration of a biomarker in a fluid sample may be determined by detecting biomarker molecules immobilized with respect to a binding surface having affinity for at least one type of biomarker molecule (e.g., a particular type of biomarker molecule). In certain embodiments the binding surface may form (e.g., a surface of an assay site such as a well / reaction vessel on a substrate) or be contained within (e.g., a surface of a capture object, such as a bead, immobilized with respect to an assay site such as a well) one of a plurality of locations (e.g., assay sites such as wells / reaction vessels) on a substrate (e.g., plate, dish, chip, optical fiber end, surface of a channel, disc, surface of an assay consumable, etc.). At least a portion of the locations may be addressed and a measure indicative of the number or fraction of capture objects associated with at least one biomarker molecule from the fluid sample may be made. In some cases, based at least in part upon the measure indicative of the number or fraction, a measure of the concentration of biomarker molecules in the fluid sample may be determined. In some cases, a measure of the concentration may be based at least in part on the number or fraction of locations determined to contain a capture object that is or was associated with at least one biomarker molecule. The measure of the concentration of biomarker molecules in the fluid sample may be determined by a digital analysis method / system optionally employing Poisson distribution adjustment and / or based at least in part on a measured intensity of a signal, as known to those of ordinary skill in the art.

[0091] For example, in some embodiments in which a measure indicative of a number or fraction of capture objects determined to be associated with a biomarker molecule represents a relatively low percentage (e.g., less than or equal to 80%, less than or equal to 70%, less than or equal to 50%, or less), a digital analysis method (optionally employing a Poisson distribution adjustment) may be used, at least in part, to determine a measure of the concentration of the biomarker in the fluid sample. However, in some embodiments in which a measure indicative of a number or fraction of capture objects determined to be associated a biomarker molecule is determined to represent a relatively higher percentage (e.g., greater than or equal 50%, greater than or equal 60%, greater than or equal 70%, greater than or equal to 80%, greater than or equal to 90%), the measure indicative of a concentration of biomarker molecule in the fluid sample can be determined, at least in part, based on a measurement of an intensity level of at least one signal (e.g., fluorescence signal) indicative of the presence of an biomarker molecule. In some embodiments, the method comprises, based upon the measure indicative of the number or fraction of capture objects associated with at least one biomarker molecule from the fluid sample, either determining a measure of the concentration of biomarker molecules in the fluid sample based at least in part on the measure indicative of the number or fraction of capture objects determined to be associated at least one biomarker molecule, or determining a measure of the concentration of the biomarker molecules in the fluid sample based at least in part on a measured intensity level of a signal that is indicative of the presence of a plurality of bio marker molecules. In certain embodiments, an automated system (e.g., part of the platform configured to process the one or more blood samples configured and programmed to perform the assay and determine the measure indicative of a concentration of the biomarker molecule in the fluid sample may be programmed to initially determine a measure indicative of the fraction of capture objects determined associated with biomarker molecule - e.g. the fraction of assay sites displaying a positive signaling status and / or an average intensity level of the capture sites - and to automatically (or manually in response to a prompt provided to a user) switch which measurement and quantification technique is employed (i.e. a digital analysis method - optionally employing a Poisson distribution adjustment, or an analog intensity level based method). The use of such digital and / or “analog” methods for determining a measure indicative of a concentration of an analyte molecule or particle (e.g., a biomarker molecule), alone or in combination, is described, for example, in U.S. Patent Application Serial No. 13 / 037,987, filed March 1, 2011, published as US-2011-0245097 on October 6, 2011, entitled “METHODS AND SYSTEMS FOR EXTENDING DYNAMIC RANGE IN ASSAYS FOR THE DETECTION OF MOLECULES OR PARTICLES,” by Rissin et al., which is incorporated by reference herein in its entirety for all purposes. In some cases, the assay methods and / or systems may be automated.

[0092] It should be understood that while in some instances a measure indicative of the number or fraction of capture objects associated with at least one biomarker molecule may be determined at least in part by addressing the separate locations (e.g., assay sites), other techniques of determining the measure indicative of the number or fraction are possible. For example, in some embodiments at least some of the capture objects subjected to the exposing and immobilizing steps are individually addressed (e.g., by being individually isolated from a remainder of the capture objects). One non-limiting way of individually addressing capture objects without necessarily spatially segregating the capture objects into a plurality of separate locations is by flowing at least some of the capture objects through a channel (e.g., a microchannel having a largest cross-sectional dimension with respect to the direction of flow of less than or equal to 1 mm, less than or equal to 500 micrometers, or less) and addressing the flowed capture objects. For example, the capture objects may flow past a detector (e.g., an optical detector) and be addressed accordingly. As a more specific example, at least some of the capture objects may be flowed through a capillary of a flow cytometry apparatus, and the capture objects may be detected and addressed via flow cytometry techniques (e.g., detecting the capture objects as they flow past a detector (e.g., an optical detector). Examples of flow cytometry embodiments for performing digital assays suitable for detecting analyte molecules such as biomarkers are described in International Patent Application Publication No. WO2023 / 059731, published on April 13, 2023, filed as International Patent Application No. PCT / US2022 / 045798 on October 5, 2022, and entitled “SINGLE MOLECULE ASSAYS FOR ULTRASENSITIVE DETECTION OF ANALYTES,” which is incorporated by reference herein in its entirety. FIG. 3C described below describes one example of such an embodiment. In some embodiments, the capture objects (e.g., some of which may be associated with at least one biomarker molecule and optionally) may be provided as separate droplets or as objects contained within droplets (e.g., by being segregated using fluidic techniques such as microfluidic techniques). In some such embodiments, the capture objects comprise or are each contained within a liquid droplet suspended in a fluid immiscible with the liquid droplets. The liquid droplets may be suspended in a fluid immiscible with the liquid droplets at least during a step of individually addressing the capture objects (e.g., via a detector). In some instances the liquid droplets may be provided as an array (e.g., by being spatially segregated such as on a substantially planar surface). However, in some instances the liquid droplets may be individually addressed by being flowed through a channel (e.g., a microchannel) and interrogated while flowing through the channel. One way the droplets may be interrogated is by flowing the droplets past a detector. For example, the detector may be an optical detector. In some such embodiments the droplets are temporally segregated with respect to a fixed detection location, for example by being flowed through a channel (e.g., during an addressing step) past such a detection location. While the droplets may be flowed single file in some instances, single file flow is not necessary in all cases. For example, the droplets may be collected in a layer and all droplets imaged substantially simultaneously.

[0093] Certain methods and systems which can be used as an assay for the biomarkers, are described in U.S. Patent Application Publication No. US-2007-0259448 (Serial No. 11 / 707,385), filed February 16, 2007, entitled “METHODS AND ARRAYS FOR TARGET ANALYTE DETECTION AND DETERMINATION OF TARGET ANALYTE CONCENTRATION IN SOLUTION,” by Walt et al.; U.S. Patent Application Publication No. US-2007-0259385 (Serial No. 11 / 707,383), filed February 16, 2007, entitled “METHODS AND ARRAYS FOR DETECTING CELLS AND CELLULAR COMPONENTS IN SMALL DEFINED VOLUMES,” by Walt et al.; U.S. Patent Application Publication No. US-2007-0259381 (Serial No. 11 / 707,384), filed February 16, 2007, entitled “METHODS AND ARRAYS FOR TARGET ANALYTE DETECTION AND DETERMINATION OF REACTION COMPONENTS THAT AFFECT A REACTION,” by Walt et al.; International Patent Publication No. WO 2009 / 029073 (International Patent Application No. PCT / US2007 / 019184), filed August 30, 2007, entitled “METHODS OF DETERMINING THE CONCENTRATION OF AN ANALYTE IN SOLUTION,” by Walt et al.; U.S. Patent Application Publication No. US-2010-0075862 (Serial No. 12 / 236484), filed September 23, 2008, entitled “HIGH SENSITIVITY DETERMINATION OF THE CONCENTRATION OF ANALYTE MOLECULES OR PARTICLES IN A FLUID SAMPLE,” by Duffy et al.; U.S. Patent Application Publication No. US-2010-00754072 (Serial No. 12 / 236,486), filed September 23, 2008, entitled “ULTRA-SENSITIVE DETECTION OF MOLECULES ON SINGLE MOLECULE ARRAYS,” by Duffy et al.; U.S. Patent Application Publication No. US-2010-0075439 (Serial No. 12 / 236488), filed September 23, 2008, entitled “ULTRA-SENSITIVE DETECTION OF MOLECULES BY CAPTURE- AND- RELEASE USING REDUCING AGENTS FOLLOWED BY QUANTIFICATION,” by Duffy et al.; International Patent Publication No. W02010 / 039179 (International Patent Application No. PCT / US2009 / 005248), filed September 22, 2009, entitled “ULTRASENSITIVE DETECTION OF MOLECULES OR ENZYMES,” by Duffy et al.; U.S. Patent Application Publication No. US-2010-0075355 (Serial No. 12 / 236490), filed September 23, 2008, entitled “ULTRA-SENSITIVE DETECTION OF ENZYMES BY CAPTURE- AND-RELEASE FOLLOWED BY QUANTIFICATION,” by Duffy et al.; U.S. Patent Application Serial No. 12 / 731,130, filed March 24, 2010, published as US- 2011-0212848 on September 1, 2011, entitled “ULTRA-SENSITIVE DETECTION OF MOLECULES OR PARTICLES USING BEADS OR OTHER CAPTURE OBJECTS,” by Duffy et al.; International Patent Application No. PCT / US2011 / 026645, filed March 1, 2011, published as WO 2011 / 109364 on September 9, 2011, entitled “ULTRASENSITIVE DETECTION OF MOLECULES OR PARTICLES USING BEADS OR OTHER CAPTURE OBJECTS,” by Duffy et al.; International Patent Application No. PCT / US2011 / 026657, filed March 1, 2011, published as WO 2011 / 109372 on September 9, 2011, entitled “ULTRA-SENSITIVE DETECTION OF MOLECULES USING DUAL DETECTION METHODS,” by Duffy et al.; U.S. Patent Application Serial No. 12 / 731135, filed March 24, 2010, published as US-2011-0212462 on September 1, 2011, entitled “ULTRA-SENSITIVE DETECTION OF MOLECULES USING DUAL DETECTION METHODS,” by Duffy et al.; International Patent Application No.

[0094] PCT / US2011 / 026665, filed March 1, 2011, published as WO 2011 / 109379 on September 9, 2011, entitled “METHODS AND SYSTEMS FOR EXTENDING DYNAMIC RANGE IN ASSAYS FOR THE DETECTION OF MOLECULES OR PARTICLES,” by Rissin et al.; U.S. Patent Application Serial No. 12 / 731136, filed March 24, 2010, published as US-2011-0212537 on September 1, 2011, entitled “METHODS AND SYSTEMS FOR EXTENDING DYNAMIC RANGE IN ASSAYS FOR THE DETECTION OF MOLECULES OR PARTICLES,” by Duffy et al.; U.S. Patent Application Serial No. 13 / 035,472, filed February 25, 2011, published as US 2012- 0196774, entitled “SYSTEMS, DEVICES, AND METHODS FOR ULTRASENSITIVE DETECTION OF MOLECULES OR PARTICLES,” by Fournier et al.; U.S. Patent Application Serial No. 13 / 037,987, filed March 1, 2011, published as US- 2011-0245097 on October 6, 2011, entitled “METHODS AND SYSTEMS FOR EXTENDING DYNAMIC RANGE IN ASSAYS FOR THE DETECTION OF MOLECULES OR PARTICLES,” by Rissin et al.; U.S. Patent Application Serial No. 17 / 965,199, filed October 13, 2022, published as US-2023-0109130 on April 6, 2023, entitled “METHODS AND SYSTEMS RELATED TO HIGHLY SENSITIVE ASSAYS AND DELIVERING CAPTURE OBJECTS,” by Rissin et al., each of which are incorporated by reference in their entirety for all purposes.

[0095] In some embodiments involving digital assays such as digital immunoassays (e.g., digital RCA-based immunoassays or digital ELISA), a measure indicative of the number or fraction of locations containing a capture object but not associated with a biomarker molecule is also determined and / or a measure indicative of the number or fraction of locations not containing any capture object is also determined. In some such embodiments, a measure of the concentration of biomarker molecules in the fluid sample may be based at least in part on the ratio of the number of locations determined to contain a capture object associated with a biomarker molecule to the total number of locations determined to contain a capture object not associated with a biomarker molecule, and / or a measure of the concentration of biomarker molecules in the fluid sample may be based at least in part on the ratio of the number of locations determined to contain a capture object associated with a biomarker molecule to the number of locations determined to not contain any capture objects, and / or a measure of the concentration of the biomarker molecule in the fluid sample may be based at least in part on the ratio of the number of locations determined to contain a capture object associated with a biomarker molecule to the number of locations determined to contain a capture object. In yet other embodiments, a measure of the concentration of biomarker molecules in a fluid sample may be based at least in part on the ratio of the number of locations determined to contain a capture object and a biomarker molecule or particle to the total number of locations addressed and / or analyzed. In certain embodiments, at least some of the capture objects (e.g., at least some associated with at least biomarker molecule from the fluid sample) are spatially separated into a plurality of locations, for example, assays sites such as reaction vessels in an array format. The reaction vessels may be formed in, on and / or of any suitable material, and in some cases, the reaction vessels can be sealed or may be formed upon the mating of a substrate with a sealing component, as discussed in more detail below. In certain embodiments, especially where quantization of the capture objects associated with at least one biomarker molecule is desired, the partitioning of the capture objects can be performed so at least some (e.g., a statistically significant fraction; e.g., as described in International Patent Application No. PCT / US2011 / 026645, filed March 1, 2011, published as WO 2011 / 109364 on September 9, 2011, entitled “ULTRASENSITIVE DETECTION OF MOLECULES OR PARTICLES USING BEADS OR OTHER CAPTURE OBJECTS,” by Duffy et al., incorporated by reference herein for all purposes) of the reaction vessels comprise at least one or, in certain cases, only one capture object associated with at least one biomarker molecule and at least some (e.g., a statistically significant fraction) of the reaction vessels comprise a capture object not associated with any biomarker molecules. The capture objects associated with at least one biomarker molecule may be quantified in certain embodiments, thereby allowing for the detection and / or quantification of biomarker molecules or particles in the fluid sample by techniques described in more detail herein.

[0096] An exemplary assay method may proceed as follows. A solution containing or suspected of containing at least one of the plurality of biomarkers. The solution may be the fluid sample. An assay consumable comprising assay sites (e.g., in an array) is exposed to the solution. In some cases, the biomarker molecules are provided in a manner (e.g., at a concentration) so at least some (e.g., a statistically significant fraction) of the assay sites contain a single biomarker molecule and a statistically significant fraction of the assay sites do not contain any biomarker molecules. The assay sites may optionally be exposed to a variety of reagents (e.g., using a reagent loader) and / or rinsed. The assay sites may then optionally be sealed and imaged (using systems or methods described in this disclosure or in, for example, U.S. Patent Application Serial No. 13 / 035,472, filed February 25, 2011, published as US 2012-0196774, entitled “SYSTEMS, DEVICES, AND METHODS FOR ULTRA-SENSITIVE DETECTION OF MOLECULES OR PARTICLES,” by Fournier et al.). The images are then analyzed (e.g., using a computer implemented control system) so a measure of the concentration of the biomarker molecule in the fluid sample may be obtained, based at least in part, by determination of a measure of the number or fraction of assay sites which contain a biomarker molecule and / or the number or fraction which do not contain any biomarker molecules. In some cases, the biomarker molecules are provided in a manner (e.g., at a concentration) so at least some assay sites comprise more than one biomarker molecule. In such embodiments, a measure of the concentration of biomarker molecule in the fluid sample may be obtained at least in part on an intensity level of at least one signal indicative of the presence of a plurality of biomarker molecules at one or more of the assay sites.

[0097] In some cases, the methods optionally comprise exposing the fluid sample to beads (e.g., having affinity for a particular type of biomarker molecule) (e.g., magnetic beads). The total number of beads (e.g., having affinity for a particular type of biomarker molecule) may be relatively small, as described above. At least some of the biomarker molecules are immobilized with respect to a bead. In some cases, the biomarker molecules are provided in a manner (e.g., at a concentration) such that a statistically significant fraction of the beads associate with a single biomarker molecule and a statistically significant fraction of the beads do not associate with any biomarker molecules. At least some of the beads (e.g., those associated with a single biomarker molecule or not associated with any biomarker molecules) may then be spatially separated / segregated such that they are immobilized with respect to assay sites (e.g., of an assay consumable). The assay sites (e.g., comprising reaction vessels) may optionally be exposed to a variety of reagents and / or rinsed. At least some of the assay sites may then be addressed to determine the number of assay sites containing a biomarker molecule. In some cases, the number of assay sites containing a bead not associated with a biomarker molecule, the number of assay sites not containing a bead and / or the total number of assay sites addressed may also be determined. Some such determination(s) may then be used to determine a measure of the concentration of the biomarker molecule in the fluid sample. In some cases, more than one molecules of the biomarker may associate with a bead and / or more than one bead may be present in an assay site. In some cases, the biomarker molecules are exposed to at least one additional reaction component before, concurrent with, and / or following spatially separating at least some of the biomarker molecules such that they are immobilized with respect to the assay sites. The biomarker molecules may be directly detected or indirectly detected. With direct detection, a biomarker molecule may comprise a molecule or moiety that may be directly interrogated and / or detected (e.g., a fluorescent entity). With indirect detection, an additional component is used for determining the presence of the biomarker molecule. For example, the biomarker molecules (e.g., optionally associated with a bead) may be exposed to at least one type of binding ligand. In certain embodiments, a binding ligand may be adapted to be directly detected (e.g., the binding ligand comprises a detectable molecule or moiety) or may be adapted to be indirectly detected (e.g., including a component that can convert a precursor labeling agent into a labeling agent). A component of a binding ligand may be adapted to be directly detected in embodiments where the component comprises a measurable property (e.g., a fluorescence emission, a color, etc.). A component of a binding ligand may facilitate indirect detection, for example, by converting a precursor labeling agent into a labeling agent (e.g., an agent detected in an assay). A “precursor labeling agent” is any molecule, particle, or the like, that can be converted to a labeling agent upon exposure to a suitable converting agent (e.g., an enzymatic component). A “labeling agent” is any molecule, particle, or the like, that facilitates detection, by acting as the detected entity, using a chosen detection technique. In some embodiments, the binding ligand may comprise an enzymatic component (e.g., horseradish peroxidase, beta-galactosidase, alkaline phosphatase, etc.). A first type of binding ligand may or may not be used in conjunction with additional binding ligands (e.g., second type, etc.).

[0098] The labeling agent may comprise a molecule or moiety that can be interrogated and / or detected. The presence or absence of biomarker molecule or binding ligand associated with a capture object and / or at a location may then be determined by determining the presence or absence of a labeling agent in the proximity of the capture object and / or at / in the location. For example, the biomarker molecule and / or binding ligand may comprise an enzymatic component and the precursor labeling agent molecule may be a chromogenic, fluorogenic, or chemiluminescent enzymatic precursor labeling agent molecule which is converted to a chromogenic, fluorogenic, or chemiluminescent product (each an example of a labeling agent) upon exposure to the converting agent. In this instance, the precursor labeling agent may be an enzymatic label, for example, a chromogenic, fluorogenic, or chemiluminescent enzymatic precursor labeling agent, that upon contact with the enzymatic component, is converted into a labeling agent, which is detectable. In some cases, the chromogenic, Anorogenic, or chemiluminescent enzymatic precursor labeling agent is provided in an amount sufficient to contact every capture object and / or location. In some embodiments, an electrochemiluminescent precursor labeling agent is converted to an electrochemiluminescent labeling agent. In some cases, the enzymatic component may comprise beta-galactosidase, horseradish peroxidase, or alkaline phosphatase.

[0099] More than one type of binding may be employed in any assay method, for example, a first type of binding ligand and a second type of binding ligand. In one example, the first type of binding ligand is able to associate with a first type of biomarker molecule and the second type of binding ligand is able to associate with the first binding ligand. In another example, both a first type of binding ligand and a second type of binding ligand may associate with the same or different epitopes of a biomarker molecule.

[0100] In some embodiments, a binding ligand and / or a biomarker molecule may comprise an enzymatic component. The enzymatic component may convert a precursor labeling agent (e.g., an enzymatic substrate) into a labeling agent (e.g., a detectable product). A measure of the concentration of the biomarker in the Auid sample can then be determined based at least in part by determining the number or fraction of capture objects associated with a labeling agent (e.g., by relating the number of locations containing a labeling agent to the number of locations containing a capture object). Other non-limiting examples of systems or methods for detection include embodiments where nucleic acid precursors are replicated into multiple copies or converted to a nucleic acid that can be detected readily (e.g., by introducing a detectable moiety such as a Auorescent moiety). Some such methods include the polymerase chain reaction (PCR), rolling circle amplification (RCA), ligation, Loop-Mediated Isothermal Amplification (LAMP), etc. Such systems and methods will be known to those of ordinary skill in the art, for example, as described in “DNA Amplification: Current Technologies and Applications,” Vadim Demidov et al., 2004.

[0101] Another exemplary embodiment of indirect detection is as follows. In some cases, the biomarker molecules are exposed to a precursor labeling agent (e.g., enzymatic substrate) and the enzymatic substrate is converted to a detectable product (e.g., Auorescent molecule) upon exposure to the biomarker molecule. The assay methods and systems may employ a variety of components, steps, and / or other aspects known and understood by those of ordinary skill in the art. For example, a method may further comprise determining at least one background signal determination (e.g., and further comprising subtracting the background signal from other determinations), wash steps, etc. In some cases, the assays or systems may include the use of at least one binding ligand, as described herein. In some cases, the measure of the concentration of biomarker molecule in a fluid sample is based at least in part on comparison of a measured parameter to a calibration curve. The calibration curve may be developed using samples containing known concentrations of target biomarker molecules. In some instances, the calibration curve is formed at least in part by determination at least one calibration factor.

[0102] In certain embodiments, solubilized, or suspended precursor labeling agents may be employed, wherein the precursor labeling agents are converted to labeling agents that are insoluble in the liquid and / or which become immobilized with respect to a capture object (e.g., directly and / or with respect to a moiety attached to the capture object such as a portion of the binding ligand itself) and / or within / near the location (e.g., within an assay site such as a reaction vessel in which the labeling agent is formed). Some such precursor labeling agents and labeling agents and their use is described in commonly owned U.S. Patent Application Publication No. US-2010-0075862 (Serial No. 12 / 236484), filed September 23, 2008, entitled “HIGH SENSITIVITY DETERMINATION OF THE CONCENTRATION OF ANALYTE MOLECULES OR PARTICLES IN A FLUID SAMPLE,” by Duffy et al., which is incorporated by reference herein for all purposes.

[0103] One example of an embodiment of an assay method that may be used to determine a value indicative of a concentration of at least one of the plurality of biomarkers in certain embodiments is illustrated in FIG. 3A. Capture objects 302 are provided (step (A)). In this example, the capture objects comprise a plurality of beads. The beads are exposed to a fluid sample containing biomarker molecules 303 (e.g., beads 302 are incubated with biomarker molecules 303). At least some of the biomarker molecules are immobilized with respect to a bead. In this example, the biomarker molecules are provided in a manner (e.g., at a concentration) so a statistically significant fraction of the beads associate with a single biomarker molecule and a statistically significant fraction of the beads do not associate with any biomarker molecules. For example, as shown in step (B), biomarker molecule 304 is immobilized with respect to bead 305, thereby forming complex 306, whereas some beads 307 are not associated with any biomarker molecules. It should be understood, in some embodiments, more than one biomarker molecule may associate with at least some of the beads, as described herein. At least some of the plurality of beads (e.g., those associated with a single a biomarker molecule or not associated with any biomarker molecule) may then be spatially separated / segregated into a plurality of separate locations. As shown in step (C), the plurality of locations is illustrated as substrate 308 comprising a plurality of assay sites in the form of wells / reaction vessels 309. In this example, each reaction vessel comprises either zero or one bead. At least some of the reaction vessels may then be addressed (e.g., optically or via other detection means) to determine the number of locations containing a bead associated with a biomarker molecule. For example, as shown in step (D), the plurality of reaction vessels are interrogated optically using light source 315, wherein each reaction vessel is exposed to electromagnetic radiation (represented by arrows 310) from light source 315. The light emitted (represented by arrows 311) from each reaction vessel is determined (and / or recorded) by detector 315 (in this example, housed in the same system as light source 315). A measure indicative of the number or fraction of reaction vessels containing a bead associated with a biomarker molecule (e.g., reaction vessels 312) is determined based on the light detected from the reaction vessels. In some cases, a measure indicative of the number or fraction of reaction vessels containing a bead not associated with a biomarker molecule (e.g., reaction vessel 313), a measure indicative of the number or fraction of wells not containing a bead (e.g., reaction vessel 314) and / or a measure indicative total number of wells addressed may also be determined. Such determination(s) may then be used to determine a measure of the concentration of the biomarker in the fluid sample.

[0104] A non-limiting example of an embodiment where a capture object is associated with more than one biomarker molecule is illustrated in FIG. 3B. Capture objects 320 are provided (step (A)). In this example, the capture objects comprise beads. The beads are exposed to a fluid sample containing biomarker molecules 321 (e.g., beads 320 are incubated with biomarker molecules 321). At least some of the biomarker molecules are immobilized with respect to a bead. For example, as shown in step (B), biomarker molecule 322 is immobilized with respect to bead 324, thereby forming complex 326. Also illustrated is complex 330 comprising a bead immobilized with respect to three biomarker molecules and complex 332 comprising a bead immobilized with respect to two biomarker molecules. Additionally, in some cases, some of the beads may not associate with any biomarker molecule (e.g., bead 328). The beads from step (B) are exposed to binding ligands 331. As shown in step (C), a binding ligand associates with some of the biomarker molecules immobilized with respect to a bead. For example, complex 340 comprises bead 334, biomarker molecule 336, and binding ligand 338. The binding ligands are provided in a manner such that a statistically significant fraction of the beads comprising at least one biomarker molecule become associated with at least one binding ligand (e.g., one, two, three, etc.) and a statistically significant fraction of the beads comprising at least one biomarker molecule do not become associated with any binding ligands. At least some of the plurality of beads from step (C) are then spatially separated into a plurality of separate locations. As shown in step (D), in this example, the locations comprise assay sites in the form of reaction vessels 341 on substrate 342. The plurality of reaction vessels may be exposed to the beads from step (C) so each reaction vessel contains zero or one bead. The substrate may then be analyzed to determine a measure indicative of the number or fraction of reaction vessels containing a binding ligand (e.g., reaction vessels 343), wherein the number or fraction may be related to a measure of the concentration of biomarker molecule in the fluid sample. In some cases, a measure indicative of the number or fraction of reaction vessels containing a bead and not containing a binding ligand (e.g., reaction vessel 344), a measure indicative of the number or fraction number of reaction vessels not containing a bead (e.g., reaction vessel 345), and / or the total number of reaction vessels addressed / analyzed may also be determined. Some such determination(s) may then be used to determine a measure of the concentration of biomarker molecule in the fluid sample.

[0105] Another example of an embodiment of an assay method that may be used to determine a value indicative of a concentration of at least one of the plurality of biomarkers in certain embodiments is illustrated in FIG. 3C. Steps A and B in FIG. 3C may be performed similarly to those in the embodiment shown in FIG. 3A. Capture objects 302 are provided (step (A)). In this example, the capture objects comprise a plurality of beads. The beads are exposed to a fluid sample containing biomarker molecules 303 (e.g., beads 302 are incubated with biomarker molecules 303). At least some of the biomarker molecules are immobilized with respect to a bead. In some embodiments, the biomarker molecules are provided in a manner (e.g., at a concentration) so a statistically significant fraction of the beads associate with a single biomarker molecule and a statistically significant fraction of the beads do not associate with any biomarker molecules. For example, as shown in step (B), biomarker molecule

[0106] 304 is immobilized with respect to bead 305, thereby forming complex 306, whereas some beads 307 are not associated with any biomarker molecules. It should be understood, in some embodiments, more than one biomarker molecule may associate with at least some of the beads, as described herein. At least some of the plurality of beads (e.g., those associated with a single a biomarker molecule or not associated with any biomarker molecule) may then be flowed past a detection system, which may interrogate the beads (e.g., to detect a signal indicative of the presence or absence of a biomarker associated with each of the beads that pass by). One such flow format comprises use of flow cytometry. As shown in step (C), at least some of beads 305 (associated with biomarker 304) and beads 307 are fed to capillary 374 and interrogated by a detection system comprising irradiation source 376 and detector 378. Irradiation source 376 may expose beads 305 or 307 to electromagnetic radiation 377 such as light, e.g., to induce photoluminescence signal 379. Photoluminescence signal 379 may be generated by biomarker 304 or a labeling agent associated with biomarker 304 such as via a binding ligand (not shown). The labeling agent may be immobilized with respect to the biomarker and / or the bead. Detector 378 may detect (and / or record) the photoluminescence signal 379, which may be indicative of the presence of capture object

[0107] 305 (and if so, which type of capture object) and / or the presence or absence of an associated biomarker 304). A measure indicative of the number or fraction of beads associated with a biomarker molecule (e.g., as a complex 306) is determined based on the signal detected by detector 378. In some cases, a measure indicative of the number of beads not associated with a biomarker molecule (e.g., beads 307) and / or a measure indicative of a total number of interrogated beads may also be determined. Such determination(s) may then be used to determine a measure of the concentration of the biomarker in the fluid sample.

[0108] As one example of an embodiment consistent with that shown in FIG. 3C, beads are exposed to a fluid sample containing biomarker molecules. At least some of the biomarker molecules are immobilized with respect to a bead. The biomarker molecules are provided in a manner (e.g., at a concentration) so a statistically significant fraction of the beads associate with a single biomarker molecule and a statistically significant fraction of the beads do not associate with any biomarker molecules. At least some of the plurality of beads are exposed to a binding ligand that directly or indirectly associates with the biomarker molecules associated with the beads. The associated binding ligands are further exposed to a precursor labeling agent that is converted to a labeling agent that becomes immobilized with respect to the bead (e.g., directly or indirectly) via rolling circle amplification (RCA). Further details of RCA are described below. At least some of the plurality of beads (e.g., those associated with a single a biomarker molecule or not associated with any biomarker molecule) may then be flowed past a detection system using a flow cytometry format, as is described in International Patent Application Publication No. WO2023 / 059731, published on April 13, 2023, filed as International Patent Application No. PCT / US2022 / 045798 on October 5, 2022, and entitled “SINGLE MOLECULE ASSAYS FOR ULTRASENSITIVE DETECTION OF ANALYTES,” which is incorporated by reference herein in its entirety. The detection system interrogates the beads to detect a signal indicative of the presence or absence of a biomarker associated with each of the beads that pass by, where the signal indicative of the presence of the biomarker molecule is produced by a detectable entity associated with the beads via the RCA. A measure indicative of the number or fraction of beads associated with a biomarker molecule is determined based on the signal detected by the flow cytometry detector. Such a determination may then be used to determine a measure of the concentration of the biomarker in the fluid sample.

[0109] It should be understood that while in some embodiments a single type of biomarker is detected / quantified (“singleplex”) in one or more of the assays, in other embodiments, more than one type of biomarker is detected / quantified (“multiplex”). For example, in some embodiments, p-Tau is measured in a singleplex assay, and at least some (or all) of the one or more additional biomarkers (e.g., beta- amyloid, GFAP, NfL) are detected / quantified in one or more multiplex assays. In some embodiments, all of the biomarkers are detected / quantified in a single multiplex assay. The use of a multiplex assay in the analysis platform of the systems herein may improve the system’s speed, efficiency, and / or accuracy (e.g., by reducing the number of sample preparation steps and / or sample manipulations).

[0110] In some embodiments, different capture objects for capture of different biomarker molecule targets may be employed. In some cases, different sub-groups of the total group of capture objects have different binding specificity (e.g., by including surfaces with differing binding specificity). In these embodiments, more than one type of biomarker may be quantified and / or detected in a single, multiplex assay method. For example, the capture objects described above may be first capture objects each having affinity for a first type of biomarker molecule or particle, the method may further comprise exposing second capture objects each having an affinity for a second type of biomarker molecule to the solution. Upon exposure to a sample containing the first type of biomarker molecule and the second type of biomarker molecule, the first type of biomarker molecule becomes immobilized with respect to the first capture objects and the second type of biomarker molecule becomes immobilized with respect to the second capture objects. The first capture objects and the second capture objects may be encoded to be distinguishable from each other (e.g., to facilitate differentiation upon detection) by including a differing detectable property. For example, each sub-group of capture object may have a differing fluorescence emission, a spectral reflectivity, shape, a spectral absorption, or an FTIR emission or absorption. In a particular embodiment, each subgroup of the total group of capture objects comprises one or more dye compounds (e.g., fluorescent dyes) but at varying concentration levels, such that each sub-group of capture object has a distinctive signal (e.g., based on the intensity of the fluorescent emission). In some embodiments involving spatial segregation, upon spatially segregating the capture objects after the capture step into a plurality of locations for detection, a location comprising a first capture object associated with a first type of biomarker molecule can be distinguished from a location comprising a second capture object associated with a second type of biomarker molecule via detection of the differing property. The number of locations comprising each sub-group of capture object and / or the number of capture objects associated with a biomarker molecule may be determined, allowing a determination of a measure of the concentration of both the first type of biomarker molecule and the second type of biomarker molecule in the fluid sample based at least in part on these numbers. It should be understood that while some multiplexing methods may involve detection of two different types of biomarker molecule (e.g., a first type of biomarker molecule or particle and a second type of biomarker molecule), some methods further comprise detection of greater numbers of different types of biomarker molecule (e.g., a third type of biomarker molecule, a fourth type biomarker molecule, and so on). A multiplex assay may involve detection of at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 20, at least 50, and / or up to 100, up to 120, up to 150, or more different types of biomarker molecules.

[0111] In some embodiments, a plurality of locations may be addressed and / or a plurality of capture objects and / or species / molecules / particles of interest may be detected substantially simultaneously. “Substantially simultaneously” when used in this context, refers to addressing / detection of the locations / capture objects / species / molecules / particles of interest at approximately the same time such that the time periods during which at least two locations / capture objects / species / molecules / particles of interest are addressed / detected overlap, as opposed to being sequentially addressed / detected, where they would not. Simultaneous addressing / detection can be accomplished by using various techniques, including optical techniques (e.g., CCD or CMOS detectors). Spatially segregating capture objects and biomarker molecules into a plurality of discrete, resolvable locations, according to some embodiments facilitates substantially simultaneous detection by allowing multiple locations to be addressed substantially simultaneously. For example, for embodiments where individual biomarker molecules are associated with capture objects spatially segregated with respect to the other capture objects into a plurality of discrete, separately resolvable locations during detection, substantially simultaneously addressing the plurality of discrete, separately resolvable locations permits individual capture objects, and thus individual biomarker molecules to be resolved. For example, in certain embodiments, individual biomarker molecules of a plurality of biomarker molecule are partitioned across a plurality of reaction vessels so each reaction vessel contains zero or only one species / molecule / particle. In some cases, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, at least 99.5% of all biomarker molecules are spatially separated with respect to other bio marker molecules. A plurality of biomarker molecules may be detected substantially simultaneously within a time period of less than or equal to 1 second, less than or equal to 500 milliseconds, less than or equal to 100 milliseconds, less than or equal to 50 milliseconds, less than or equal to 10 milliseconds less than or equal to 1 millisecond, less than or equal to 500 microseconds, less than or equal to 100 microseconds, less than or equal to 50 microseconds less than or equal to 10 microseconds, less than or equal to 1 microsecond less than or equal to 0.5 microseconds, less than or equal to 0.1 microseconds, less than or equal to 0.01 microseconds, less than or equal to 0.001 microseconds, or less. In some embodiments, the plurality of biomarker molecule may be detected substantially simultaneously within a time period of between about 100 microseconds and about 0.001 microseconds, between about 10 microseconds and about 0.01 microseconds, or less.

[0112] In some embodiments, the capture objects and / or the locations are optically interrogated. The capture objects and / or locations exhibiting changes in their optical signature may be identified by a conventional optical train and optical detection system. Depending on the detected species (e.g., type of fluorescence entity, etc.) and the operative wavelengths, optical filters designed for a particular wavelength may be employed for optical interrogation of the locations. In embodiments where optical interrogation is used, the system may comprise more than one light source and / or a plurality of filters to adjust the wavelength and / or intensity of the light source. In some embodiments, the optical signal from a plurality of locations is captured using a CCD or CMOS camera.

[0113] In some embodiments, the assay sites (e.g., reaction vessels) may be sealed (e.g., after introducing the capture objects, biomarker molecules, binding ligands, and / or precursor labeling agent), for example, through the mating of the substrate and a sealing component. Sealing the assay sites (e.g., reaction vessels) may be such that the contents of each assay site cannot escape the assay site during the remainder of the assay. In some cases, the assay sites (e.g., reaction vessels) may be sealed after adding the capture objects, and, optionally, at least one type of precursor labeling agent to facilitate detection of the biomarker molecules. For embodiments employing precursor labeling agents, by sealing the contents in some or each assay site (e.g., reaction vessel), a reaction to produce the detectable labeling agents can proceed within the assay sites (e.g., reaction vessels), thereby producing a detectable amount of labeling agents retained in the assay site for detection.

[0114] In some embodiments, at least some (e.g., a subset or all) of the assay sites are not sealed (e.g., after introducing the capture objects, biomarker molecule, binding ligands, and / or precursor labeling agent). In some such instances, a detection signal production process of the assay does not produce freely diffusible detectable molecules (e.g., labeling agents), thereby avoiding diffusion-related interference of signal at capture objects resulting from labeling agents diffusing away from other capture objects (which could reduce accuracy of the assay). For example, in some embodiments, labeling agents are generated from precursor labeling agents and immobilized (e.g., via chemical bonds or precipitation) with respect to the capture objects (directly or indirectly) and / or other surfaces at or near the capture objects, as described in more detail below. Such immobilization of labeling agents can result in spatially fixed detectable signals on or in proximity to the signal-generating capture objects that do not appreciably diffuse from the biomarker- signal-generating capture objects (e.g., those associated biomarker molecules) to non-biomarker-signal-generating capture objects (e.g., those not associated with any biomarker molecules).

[0115] The plurality of locations (e.g., assay sites) may be formed may be formed using a variety of methods and / or materials. In some embodiments, the plurality of locations comprises assay sites in the form of reaction vessels / wells on a substrate. In some cases, the reaction vessels may in some instances be formed as an array of depressions on a first surface. In other cases, however, the reaction vessels may be formed by mating a sealing component comprising a plurality of depressions with a substrate that may either have a featureless surface or include depressions aligned with those on the sealing component. Any of the device components, for example, the substrate or sealing component, may be fabricated from a compliant material, e.g., an elastomeric polymer material, to aid in sealing. The surfaces may be or made to be hydrophobic or contain hydrophobic regions. Hydrophobicity may in some instances reduce leakage of aqueous samples from the reaction vessels (e.g., microwells). The reactions vessels, in certain embodiments, may be configured to receive and contain only a single capture object (e.g., bead).

[0116] In some embodiments, the assay sites (e.g., reaction vessels) may all have approximately the same volume. In other embodiments, the assay sites (e.g., reaction vessels) may have differing volumes. The volume of each individual assay site (e.g., reaction vessel) may be selected to be appropriate to facilitate any particular assay protocol. For example, in one set of embodiments where it is desirable to limit the number of capture objects used for biomarker capture immobilized with respect to each site to a small number, the volume of the assay sites (e.g., reaction vessels) may range from attoliters or smaller to nanoliters or larger depending upon the nature of the capture objects, the detection technique and equipment employed, the number and density of the assay sites (e.g., reaction vessels) on the substrate and the expected concentration of capture objects in the fluid applied to the substrate containing the wells. In one embodiment, the size of the assay site (e.g., reaction vessel) may be selected such only a single capture object used for biomarker molecule capture can be fully contained within the assay site (e.g., reaction vessel) (see, for example, U.S. Patent Application Serial No. 12 / 731,130, filed March 24, 2010, published as US-2011-0212848 on September 1, 2011, entitled “ULTRA-SENSITIVE DETECTION OF MOLECULES OR PARTICLES USING BEADS OR OTHER CAPTURE OBJECTS,” by Duffy et al.; International Patent Application No. PCT / US2011 / 026645, filed March 1, 2011, published as WO 2011 / 109364 on September 9, 2011, entitled “ULTRA-SENSITIVE DETECTION OF MOLECULES OR PARTICLES USING BEADS OR OTHER CAPTURE OBJECTS ,” by Duffy et al., each herein incorporated by reference for all purposes).

[0117] The total number of locations and / or density of the locations employed in an assay (e.g., the number / density of reaction vessels in an array) can depend on the composition and end use of the array. As mentioned above, the number of assay sites (e.g., reaction vessels) employed may depend on the number of types of biomarker molecule and / or binding ligand employed, the suspected concentration range of the assay, the method of detection, the size of the capture objects, the type of detection entity (e.g., free labeling agent in solution, precipitating labeling agent, etc.). In some embodiments, the number of capture objects exposed to the solution containing or suspected of containing at least biomarker molecule is less than or equal to the number of locations employed in the assay (e.g., number of assay sites on the surface such as in an array). In some embodiments, the ratio of the number of capture objects exposed to the solution containing or suspected of containing at least one biomarker molecule to the number of separate locations (e.g., assay sites) employed in the assay is less than or equal to 1:1, less than or equal to 1:2, less than or equal to 1:3, less than or equal to 1:4, less than or equal to 1:5, less than or equal to 1: 10, less than or equal to 1:20, less than or equal to 1:30, less than or equal to 1:40, and / or as low as 1:50, as low as 1: 100, as low as 1:1,000, as low as 1:2,000, as low as 1:5,000, or less.

[0118] Arrays containing from about 2 to many billions of assay sites (e.g., reaction vessels) (or total number of reaction vessels) can be made by utilizing a variety of techniques and materials. Increasing the number of assay sites (e.g., reaction vessels optionally in the form of an array) can increase the dynamic range of an assay or to allow multiple samples or multiple types of biomarker molecules to be assayed in parallel. An array may comprise between one thousand and one million assay sites (e.g., reaction vessels) per sample to be analyzed. In some cases, the array comprises greater than one million assay sites (e.g., reaction vessels). In some embodiments, the array comprises between 1,000 and about 50,000, between 1,000 and 1,000,000, between 1,000 and 10,000, between 10,000 and 100,000, between 100,000 and 1,000,000, between 100,000 and 500,000, between 1,000 and 100,000, between 50,000 and 100,000, between 20,000 and 80,000, between 30,000 and 70,000, between 40,000 and 60,000 assay sites (e.g., reaction vessels). In some embodiments, the array comprises 10,000, 20,000, 50,000, 100,000, 150,000, 200,000, 300,000, 500,000, 1,000,000, or more, assay sites (e.g., reaction vessels). The assay sites (e.g., reaction vessels) may have a volume in any of the ranges described above (e.g., greater than or equal to 10 attoliters and less than or equal to 100 picoliters, greater than or equal to 1 femtoliter and less than or equal to 1 picoliter).

[0119] The assay sites (e.g., reaction vessels), optionally in the form of an array, may be arranged on a substantially planar surface or in a non-planar three-dimensional arrangement. The assay sites (e.g., reaction vessels) may be arrayed in a regular pattern or may be randomly distributed. In a specific embodiment, the array is a regular pattern of sites on a substantially planar surface permitting the sites to be addressed in the X-Y coordinate plane.

[0120] In some embodiments, the assay sites (e.g., reaction vessels) are formed on and / or in a solid material. The solid material may be part of, for example, an assay consumable described herein. Such a solid material may be or comprise a hydrophobic material. As will be appreciated by those in the art, the number of potentially suitable materials in which the reaction vessels can be formed is very large, and includes, but is not limited to, glass (including modified and / or functionalized glass), plastics (including acrylics, polystyrene and copolymers of styrene and other materials, polypropylene, polyethylene, polybutylene, polyurethanes, cyclic olefin copolymer (COC), cyclic olefin polymer (COP), Teflon®, polysaccharides, nylon or nitrocellulose, etc.), elastomers (such as poly(dimethyl siloxane) and poly urethanes), composite materials, ceramics, silica or silica-based materials (including silicon and modified silicon), carbon, metals, optical fiber bundles, or the like. The substrate material may be selected to allow for optical detection without appreciable autofluorescence. In certain embodiments, the assay sites (e.g., reaction vessels) may be formed in a flexible material.

[0121] A reaction vessel in a surface (e.g., substrate or sealing component) may be formed using a variety of techniques known in the art, including, but not limited to, photolithography, stamping techniques, molding techniques, etching techniques, or the like. As will be appreciated by those of the ordinary skill in the art, the technique used can depend on the composition and shape of the supporting material and the size and number of reaction vessels. In a particular embodiment, an array of reaction vessels is formed by creating microwells on one end of a fiber optic bundle and utilizing a planar compliant surface as a sealing component.

[0122] The structures of reaction vessels may be fabricated using any of a variety of other methods and materials. For example, the array may be a spotted, printed or photolithographically fabricated substrate produced by techniques known in the art; see for example WO95 / 25116; WO95 / 35505; PCT US98 / 09163; U.S. Patent Nos. 5,700,637, 5,807,522, 5,445,934, 6,406,845, and 6,482,593, each of which are incorporated by reference herein for all purposes. In some cases, the array may be produced using molding, embossing, and / or etching techniques as known to those of ordinary skill in the art.

[0123] In some embodiments, the assays and methods described may be carried out on commercially available systems, for example, the Simoa HD-1 Analyzer™, Simoa HD- X Analyzer™, and Quanterix SR-X™ (Quanterix™, Lexington, Massachusetts). See also U.S. Patent Application Serial No. 13 / 035,472, filed February 25, 2011, published as US 2012-0196774, entitled “SYSTEMS, DEVICES, AND METHODS FOR ULTRASENSITIVE DETECTION OF MOLECULES OR PARTICLES,” by Fournier et al., herein incorporated by reference.

[0124] In some embodiments, the plurality of locations comprises assay sites that are not a plurality of reaction vessels / wells. For example, in embodiments where capture objects are employed, a patterned substantially planar surface may be employed, and the patterned areas form a plurality of locations. In some cases, the patterned areas may comprise substantially hydrophilic surfaces which are substantially surrounded by substantially hydrophobic surfaces. In certain embodiments, a capture objects (e.g., beads) may be substantially surrounded by a substantially hydrophilic medium (e.g., comprising water), and the capture objects may be exposed to the patterned surface so the capture objects associate in the patterned areas (e.g., the hydrophilic locations on the surface), thereby spatially segregating the beads. For example, in one such embodiment, a substrate may be or include a gel or other material able to provide a sufficient barrier to mass transport (e.g., convective and / or diffusional barrier) to prevent capture objects used for biomarker capture and / or precursor labeling agent and / or labeling agent from moving from one location on or in the material to another location to cause interference or cross-talk between spatial locations containing different capture objects during the time frame required to address the locations and complete the assay. For example, in one embodiment, capture objects are spatially separated by dispersing the capture objects on and / or in a hydrogel material. In some cases, a precursor labeling agent may be already present in the hydrogel, thereby facilitating development of a local concentration of the labeling agent (e.g., upon exposure to a binding ligand or biomarker molecule carrying an enzymatic component). As still yet another embodiment, the capture objects may be confined in one or more capillaries. In some cases, the capture objects may be absorbed or localized on a porous or fibrous substrate, for example, filter paper. In some embodiments, the capture objects may be spatially segregated on a uniform surface (e.g., a planar surface), and the capture objects may be detected using precursor labeling agents which are converted to substantially insoluble or precipitating labeling agents that remain localized at or near the location of where the corresponding capture object is localized. In some cases, single biomarker molecules may be spatially segregated into a plurality of droplets. That is, single biomarker molecule may be substantially contained in a droplet containing a first fluid. The droplet may be substantially surrounded by a second fluid, wherein the second fluid is substantially immiscible with the first fluid.

[0125] In some embodiments, precursor labeling agents are converted to labeling agents that become immobilized with respect to the capture objects. As one example, a freely diffusible precursor agent may be exposed to a binding ligand immobilized with respect to a biomarker molecule that is itself immobilized with respect to a capture object (e.g., a bead). That freely diffusible precursor agent can undergo a chemical reaction facilitated by a component of the binding ligand (e.g., an enzymatic component) to form a labeling agent that, upon formation or following a further chemical or physical transformation and / or translocation (e.g., a further chemical reaction and / or deposition), becomes immobilized with respect to such capture object (e.g., bead) such that the labeling agent does not freely diffuse from the capture object. The immobilized labeling agent can produce a detectable signal (e.g., emission of electromagnetic radiation such as from fluorescence) at (e.g., on) the capture object indicative of the presence of at least one biomarker molecule associated with the capture object. In some such embodiments, a measure indicative of the number or fraction of capture objects having at least one immobilized labeling agent can then be determined. A measure of the concentration of a particular biomarker molecule can then be determined based at least in part on that measure indicative of the number or fraction of capture objects determined to have at least one immobilized labeling agent.

[0126] It has been recognized in the context of the present disclosure that immobilized labeling agents (as opposed to freely diffusible labeling agents) can allow for simplified sample handling and / or detection schemes. For example, a lack of freely diffusible labeling agents may facilitate capture object detection methods that do not involve sealing the capture objects and labeling agents in spatially and fluidically isolated assay sites (e.g., sealed reaction vessels such as sealed microwells) because, at least in part, immobilized labeling agents do not appreciably diffuse away from the capture objects with which they are associated to the interfere with signal detection from capture objects not associated with any biomarker molecules (which can lead to inaccurate measures indicative of the number or fraction of capture objects associated with an biomarker molecule and therefore inaccurate measures of the concentration of the biomarker molecule as described above).

[0127] In some embodiments, the process of converting precursor labeling agents into labeling agents immobilized with respect to the capture objects associated with the biomarker molecule occurs prior to spatial segregation of the capture objects into a plurality of separate locations (e.g., separate assay sites such as separate reaction vessels) or prior to flowing the capture objects past a detector (e.g., using flow cytometry or microfluidics). In some embodiments, the process of converting precursor labeling agents into labeling agents immobilized with respect to the capture objects associated with the biomarker molecule occurs after spatial segregation of the capture objects into a plurality of separate locations (e.g., separate assay sites such as separate reaction vessels or separate locations on a planar surface).

[0128] The labeling agent produced from the precursor labeling agent may become immobilized with respect to the capture object in any of a variety of ways. For example, the capture object may have a solid surface on which the labeling agent may become immobilized upon or following formation from the precursor labeling agent. Such immobilization may occur via formation of a chemical bond between the labeling agent and a functional group attached to the capture object (e.g., a functional group attached to the surface of a bead). Such a chemical bond may be a covalent bond. In some embodiments, immobilization of the labeling agent with respect to the capture object occurs via a non-covalent interaction. One such example is an affinity-based specific binding interaction between the labeling agent and a species (e.g., a biomolecule, a functional group) attached to the surface of the capture object. In some embodiments, a detectable moiety is immobilized with respect to the labeling agent following formation of the chemical bond between the labeling agent and a species associated with the capture object. For example, an added detectable moiety may associate with the immobilized labeling agent via a covalent bond or non-covalent interaction (e.g., hybridization or a non-covalent specific affinity association) during and / or after immobilization of the labeling agent. In some embodiments, the labeling agent is immobilized via a non-specific chemical or physical interaction with a surface of the capture object. For example, in some embodiments, the labeling agent is immobilized via formation of a substantially insoluble or precipitating species that binds to or otherwise associates with the capture object. For example, the labeling agent may be substantially insoluble in a liquid in which the capture object is present, or the labeling agent may be present at a local concentration above a solubility limit of the labeling agent such that the labeling agent precipitates or otherwise is deposited on the capture object (e.g., as a film or particulate precipitate on the surface of the capture object).

[0129] As a specific set of illustrative examples of some embodiments involving conversion of a precursor labeling agent into a labeling agent immobilized with respect to a capture object via an enzymatic component of a binding ligand, binding ligands having a component comprising horseradish peroxidase (HRP) will be discussed. HRP is a common enzymatic component for various assays and is known to those of ordinary skill in the art. HRP may be the enzymatic component of a binding ligand capable of associating with a biomarker molecule, and / or another binding ligand (which may in turn be capable of associating with the biomarker molecule or particle). As a non-limiting example, wherein the biomarker molecule is an antigen, a binding ligand may be an HRP-labeled antibody or streptavidin conjugate. In some cases, HRP converts a precursor labeling agent molecule into a labeling agent molecule that is substantially insoluble under the operative conditions and precipitates onto the capture object. Many examples of precursor labeling agents are known and include those typically used in Western blotting applications, such as chloronaphthol and / or diaminobenzidine. In some instances, a precipitate is a darkly colored molecule allowing the precipitate to be detected optically. For example, darkly colored precipitates may be detected using light when the precipitate absorbs light differently than does the surface of a capture object that lacks such darkly colored precipitate.

[0130] A binding ligand that comprises an enzymatic component (e.g., HRP) may be used jointly with a precursor labeling agent molecule (e.g., enzymatic substrate) that may be immobilized (e.g., via formation of a chemical bond with a functional group attached to the surface of the capture object) when converted to a labeling agent molecule (e.g., detectable product). For example, HRP in the presence of hydrogen peroxide catalyzes the conversion of tyramide into an activated tyramide (e.g., as a free radical) that can become immobilized with respect to materials of certain capture objects. For example, the capture objects may have surfaces comprising functional groups (e.g., hydroxycontaining groups such as phenol groups) that can react with free radicals of active tyramide to form covalent bonds that attach the tyramide to the surface of the capture object. Typically short lifetimes (< 1 ms) of the activated tyramide can prevent significant diffusion of the activated tyramide away from the site of its formation (e.g., in some instances the labeling radius is limited to 20 nm). In such manner, most or all tyramide molecules will tend to immobilize locally with respect to capture objects associated with the binding ligands having the horseradish peroxidase components. In some embodiments, a precursor labeling agent such as a tyramide molecule is attached to any variety of molecules or particles that facilitate detection. For example, a tyramide molecule may be attached to a dye (e.g., a fluorescent dye). Therefore, the presence of the dye immobilized with respect to the capture object (e.g., via the immobilized labeling agent) can be used to detect the presence of biomarker molecule associated with such capture object. In some cases, the conversion of tyramide to activated tyramide may cause a component associated with the tyramide to become detectable (e.g., may cause a non-fluorescent component to fluoresce upon activation. Because HRP activates the tyramide molecules catalytically, the HRP component of a single binding ligand immobilized with respect to a capture object (e.g., via a biomarker molecule) can generate numerous activated tyramide molecules (some or all of which may form covalent bonds with or otherwise be come immobilized with respect to the capture object) if a sufficient amount of reactants are provided, which can form an amplified signal at the capture object. Additionally or alternatively, immobilized tyramides may form sites for immobilizing additional binding ligands comprising HRP components having affinity for the tyramides. The additionally bound HRP components can further activate tyramide molecules that become attached to the capture object, further amplifying the signal. For example, tyramide-biotin can be used to label the capture objects, followed by labeling with Streptavidin conjugated to dyes for fluorescence detection.

[0131] Another specific illustrative example of some embodiments involving conversion of a precursor labeling agent into a labeling agent immobilized with respect to a capture object via an enzymatic component of a binding ligand involves binding ligands having a component comprising a phosphatase. As a non-limiting example in which the biomarker molecule is an antigen, a binding ligand may be a phosphatase-labeled antibody or streptavidin conjugate. Phosphatase components can be used, for example, to mediate Enzyme-Labeled Fluorescence (ELF) signal amplification. In ELF detection, a binding ligand may have either an alkaline phosphatase or an acid phosphatase component, and the precursor labeling agent comprises an ELF 97 phosphate molecule (2-(5'-chloro-2-phosphoryloxyphenyl)-6-chloro-4(3H)-quinazolinone). Exposure to the phosphatase component can convert the ELF 97 phosphate, which is a water-soluble molecule with a light blue fluorescence signal, to a water-insoluble ELF 97 alcohol having a bright yellow-green fluorescence. The water-insoluble ELF 97 can act as a labeling agent by forming a fluorescent precipitate that can become immobilized with respect to the capture object (e.g., upon deposition of the ELF 97 alcohol precipitate onto the capture object). Fluorescence from the ELF 97 precipitate on the capture object (or near to the capture object) can indicate that at least one biomarker molecule is associated with that capture object.

[0132] Another illustrative example of conversion of precursor labeling agents into immobilized labeling agents is the use of rolling circle amplification (RCA). In some such embodiments, a binding ligand (e.g., an antibody) comprising an oligonucleotide primer is capable of binding to a biomarker molecule (e.g., associated with a capture object such as a bead). Such a binding ligand may be, for example, an antibody with a single stranded DNA oligonucleotide primer attached to the antibody (e.g., on the end of a heavy chain of the antibody). The binding ligand comprising the oligonucleotide primer when immobilized with respect to the capture object can be exposed to a circular DNA template having a sequence complementary to the primer. The complementary sequence of the circular DNA template can be copied via conversion of incoming added nucleotides (precursor labeling agents) into copies of the complementary sequence (e.g., in the presence of DNA polymerase) that become attached to the binding ligand as an elongated oligonucleotide (or polynucleotide) strand. Numerous (e.g., hundreds) of such copies of the complementary sequence may be made using the circular DNA template resulting in relatively long polynucleotide strands immobilized with respect to the capture object (e.g., via the binding ligand). The resulting single-stranded polynucleotide strands may serve as labeling agents by having detectable moieties (in some instances numerous detectable moieties) such as fluorescent probes attached to added complementary nucleotides bound to some or all of the copied nucleotide sequence in the elongated polynucleotide strand.

[0133] In some embodiments, capture objects associated with immobilized labeling agents are spatially segregated (e.g. by being compartmentalized). In certain cases, the capture objects are compartmentalized into a plurality of assay sites that are in the form of reaction vessels (e.g., microwells). Such spatial segregation may occur prior to or after to the immobilization of the labeling agents.. The reaction vessels may be sealed in some embodiments, but can remain unsealed in other embodiments. In some embodiments, capture objects associated with immobilized labeling agents are confined in liquid droplets. In some such embodiments, the droplets are spatially segregated. In some such instances the droplets are arranged on a planar surface. In some such embodiments the droplets are temporally segregated with respect to a fixed detection location, for example by being flowed through a channel (e.g., during an addressing step) past such a detection location. For example, flow cytometry may be employed. In some embodiments, capture objects associated with immobilized labeling agents are spatially segregated across a planar surface (e.g., to form an ordered array or a random distribution of capture objects, depending on the specific format of the assay).

[0134] While some aspects of the disclosure above are directed to digital assays (e.g., digital immunoassays), it should be understood that the assays performed to obtain values indicative of the concentrations of the biomarkers associated with Alzheimer’s disease are not limited to such digital assays, and other assay formats may be used. For example, in some embodiments, at least some (or all) of the assays performed are a chemiluminescence immunoassay (CLIA), such as a chemiluminescence enzyme immunoassay (CLEIA). A CLIA may comprise immobilizing the biomarker with respect to a solid object (e.g., a capture object such as a bead) via an immunoreaction and then (e.g., after a wash step), exposing the solid object to a binding ligand comprising a moiety (e.g., an enzyme such as alkaline phosphatase) capable of converting a chemical substrate to a chemiluminescent reaction product that emits photons that can be detected. In some embodiments the assay performed to obtain the value indicative of the concentration of the p-Tau (e.g., p-Tau 217, p-Tau 181, and / or p- Tau-231) is a CLIA. In some embodiments at least one of the assays performed to obtain the value indicative of the concentration of the one or more additional biomarkers is a CLIA. The Lumipulse® platform commercialized by Fujirebio is one example of a CLIA platform that can detect and quantify the one or more biomarkers in the blood or blood-derived samples. As one non-limiting example, a CLIA assay for detecting p-Tau 217 in blood plasma is available commercially as the Lumipulse® G pTau 217 Plasma test (Product #81472, Fujirebio). As another non-limiting example, a CLIA assay for detecting p-Tau 181 in blood plasma is available commercially as the Lumipulse® G pTau 181 Plasma test (Product #81288, Fujirebio). As another non-limiting example, a CLIA assay for detecting beta- amyloid 1-40 in blood plasma is available commercially as the Lumipulse® G P-Amyloid 1-40 Plasma test (Product #81298, Fujirebio). As another non-limiting example, a CLIA assay for detecting beta- amyloid 1-40 in blood plasma is available commercially as the Lumipulse® G P-Amyloid 1-42 Plasma test (Product #81301, Fujirebio). As another non-limiting example, a CLIA assay for detecting NfL in blood plasma or serum is available commercially as the Lumipulse® G NfL Blood test (Product #81215, Fujirebio).

[0135] In some embodiments, at least some (or all) of the assays performed are an electrochemiluminescence immunoassay (ECLIA). An ECLIA may comprise immobilizing the biomarker with respect to a capture component (e.g., an antibody such as a biotinylated antibody) via an immunoreaction. The capture component may already be attached to a solid object (e.g., capture object) or may later become attached to the solid object (e.g., via a covalent bond or a non-covalent binding affinity such as biotinstreptavidin binding). A binding ligand (e.g., comprising a luminescent complex such as a ruthenium complex) may be immobilized with respect to some solid objects, and application of an electrical potential may induce generation chemiluminescent reaction product via reaction involving a substrate. The signal may be indicative of the concentration of the biomarker (e.g., directly proportional or inversely proportional in the case of competitive assay formats). In some embodiments the assay performed to obtain the value indicative of the concentration of the p-Tau (e.g., p-Tau 217, p-Tau 181, and / or p-Tau-231) is an ECLIA. In some embodiments at least one of the assays performed to obtain the value indicative of the concentration of the one or more additional biomarkers is an ECLIA. One example of an ECLIA analysis platform that is commercially available for numerous biomarkers is the Elecsys® platform (Roche Diagnostics). For example, Roche’s Elecsys Amyloid Plasma Panel includes p-Tau 181.

[0136] In some embodiments, at least some (or all) of the assays performed are a mass spectrometry assay. Such an assay may comprise producing charged particles from the biomarkers and using a field (e.g., an electric field or a magnetic field) to measure the mass or mass to charge ratios of the particles to determine the values indicative of the concentrations of the biomarkers (e.g., in the one or more blood samples). One example of a high-resolution mass spectrometry analysis platform that is commercially available for numerous biomarkers is from C2N Diagnostics, which includes products directed to p-Tau 217 and p-Tau 181 in blood plasma.

[0137] The assay(s) described in this disclosure for detecting or quantifying the plurality of biomarkers associated with Alzheimer’s disease (e.g., from one or more blood or blood-derived samples) may characterized by relatively low lower limits of detection (LOD) for the biomarkers. The LOD of an assay for detecting an analyte molecule (e.g., a biomarker molecule) refers to the concentration of the analyte molecule or particle at which the signal rises above three standard deviations over the background noise. In some embodiments, at least one (or all) of the assay(s) are characterized by an LOD for at least one (or all) of the plurality of biomarkers (e.g., a p-Tau) of less than or equal to 10 pg / mL, less than or equal to 5 pg / mL, less than or equal to 1 pg / mL, less than or equal to 0.8 pg / mL, less than or equal to 1 pg / mL, less than or equal to 0.5 pg / mL, less than or equal to 0.4 pg / mL, less than or equal to 0.3 pg / mL, less than or equal to 0.2 pg / mL, less than or equal to 0.1 pg / mL, less than or equal to 0.05 pg / mL, less than or equal to 0.02 pg / mL, less than or equal to 0.01 pg / mL, and / or as low as 0.005 pg / mL, as low as 0.002 pg / mL, as low as 0.001 pg / mL, or less.

[0138] The assay(s) described in this disclosure for detecting or quantifying the plurality of biomarkers associated with Alzheimer’s disease (e.g., from one or more blood or blood-derived samples) may characterized by relatively low lower limits of quantification (LOQ) for the plurality of biomarkers. The LOQ of an assay for detecting an analyte molecule generally refers to the lowest concentration above the LOD wherein the coefficient of variation (CV) of the measured concentrations less than about 20%. In some embodiments, at least one (or all) of the assay(s) are characterized by an LOQ for at least one (or all) of the plurality of biomarkers (e.g., a p-Tau) of less than or equal to 10 pg / mL, less than or equal to 5 pg / mL, less than or equal to 1 pg / mL, less than or equal to 0.8 pg / mL, less than or equal to 1 pg / mL, less than or equal to 0.5 pg / mL, less than or equal to 0.4 pg / mL, less than or equal to 0.3 pg / mL, less than or equal to 0.2 pg / mL, less than or equal to 0.1 pg / mL, less than or equal to 0.05 pg / mL, less than or equal to 0.02 pg / mL, less than or equal to 0.01 pg / mL, and / or as low as 0.005 pg / mL, as low as 0.002 pg / mL, as low as 0.001 pg / mL, or less.

[0139] In some embodiments, kits for use in determining a measure of the concentration of at least one biomarker in a sample (e.g., a blood or blood-derived sample) are provided. In some embodiments, the biomarkers being detected are a p-Tau (e.g., p-Tau 217) and the one or more additional biomarkers. For example, in some embodiments, the biomarkers being detected are a p-Tau (e.g., p-Tau 217) one or more additional biomarkers comprising amyloid P42, amyloid P40, NfL, and GFAP. An exemplary kit is directed to determination of a measure of the concentration of a biomarker panel comprising at least p-Tau (e.g., p-Tau 217), amyloid P42 / P40 ratio, NfL, and GFAP. The kit may be configured such that the p-Tau is detected as part of the same test as the one or more additional biomarkers or separately). In some embodiments, the kit comprises plurality of capture objects (e.g., beads, optionally magnetic beads), each having a binding surface comprising a plurality of capture components. The plurality of capture components may comprises a plurality of an antibodies having specific affinity for the respective biomarkers being detected. For example, each capture object (e.g., bead) may comprises a plurality of types of capture objects, each type of capture object having specific affinity for a particular biomarker (e.g., a plurality of antibodies having specific affinity for p-Tau and a plurality of antibodies having specific affinity for at least one other biomarker selected from the group consisting of, amyloid P42, amyloid P40, NfL, and GFAP). In some embodiments, the kit also comprises a plurality of types of binding ligands having specific affinity for the at least one biomarker. The binding ligands may be directly or indirectly detectable. In some embodiments, the kit comprises a plurality of a first type of binding ligand having affinity for a p-Tau (e.g., p-Tau 217 and a plurality of a second type of binding ligand having affinity for at least one other biomarker selected from the group consisting of amyloid P42, amyloid P40, NfL, and GFAP. In some embodiments, the kit comprises i) a plurality of capture objects, each having a binding surface comprising a plurality of capture components; ii) a plurality of a first type of binding ligand having affinity for the p-Tau; and iii) a plurality of a second type of binding ligand and a third type of binding ligand having affinity for at least two other biomarker selected from the group consisting of, amyloid P42, amyloid P40, NfL, and GFAP. In some embodiments, the kit comprises i) a plurality of capture objects, each having a binding surface comprising a plurality of capture components; ii) a plurality of a first type of binding ligand having affinity for p-Tau; iii) a plurality of a second type of binding ligand having affinity amyloid P42; iv) a plurality of a third type of binding ligand having affinity amyloid P40; v) a plurality of a fourth type of binding ligand having affinity for NfL, and vi) a plurality of a fifth type of binding ligand having affinity for GFAP. Any of the kits described here may further comprise one or more components for performing the assays. For example, when a binding ligand is indirectly detectable and comprises an enzyme as the label, a substrate of the enzyme can be included in the kit. Further, the kit may also comprise an instruction manual providing guidance for using the kit to perform any one of the detection assay provided herein.

[0140] While aspects of the disclosure above are directed to embodiments in which the biomarker for which a value indicative of a concentration is obtained is a p-Tau (e.g., p- Tau 217) and compared to an upper threshold and a lower threshold and a likelihood of a particular beta-amyloid status is determined, other embodiments are possible. More generally, some embodiments involve obtaining and comparing the value indicative of the concentration of a first biomarker (which may be p-Tau or a different biomarker) to a lower threshold and an upper threshold to determine whether the value indicative of the concentration of the first biomarker is greater than or equal to the lower threshold and less than or equal to the upper threshold. Then, after determining that the value indicative of the concentration of the first biomarker is greater than or equal to the lower threshold and less than or equal to the upper threshold, the value indicative of the concentration of the first biomarker and one or more values for one or more additional biomarkers (other than the first biomarker) are processed using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood-derived samples are associated with a particular Alzheimer’s pathology status (e.g., beta- amyloid status or a status associated with a different pathophysiology associated with Alzheimer’s disease). Statistical Models

[0141] In some embodiments, at least one statistical model is used to process value(s) obtained for biomarker(s) associated with Alzheimer’s disease using a statistical model to obtain an output indicative of a likelihood that one or more blood or blood-derived samples are associated with a particular beta-amyloid status. In some embodiments, the statistical model is a machine learning model. In some embodiments, the statistical model is a generalized linear model (e.g., a logistic regression model, a probit regression model, etc.), a support vector machine, a decision tree model, a gradient-boosted decision tree model, a Gaussian mixture model, a random forest model, a neural network model, or any other suitable type of statistical models, without limitation. For example, the at least one statistical model may be a logistic regression model.

[0142] In some embodiments, the statistical model(s) may be trained using any suitable training technique(s), including supervised techniques, semi- supervised techniques, unsupervised techniques, or any suitable combination thereof without limitation.

[0143] In some embodiments, regardless of the type of statistical model, the statistical model is trained using training data for a plurality of samples. The training data for a particular sample may include (a) values for biomarkers obtained for the sample, and (c) a label indicating the beta-amyloid status for the sample as measured by PET or cerebrospinal fluid (CSF) testing. For example, the training data may include samples obtained from the Bio-Hermes Study described by Mohs, Richard C., et al ("The BioHermes Study: Biomarker database developed to investigate blood-based and digital biomarkers in community -based, diverse populations clinically screened for Alzheimer's disease." Alzheimer's & Dementia (2024).), which is incorporated by reference herein in its entirety. Additionally or alternatively, the training data may include samples obtained from the Amsterdam Dementia Cohort described by van Der Flier, W. M., & Scheltens, P. (“Amsterdam dementia cohort: performing research to optimize care.” Journal of Alzheimer’s Disease, 62(3), 1091-1111. (2018). DOI: 10.3233 / JAD-170850) and van der Flier, W. M., et al. (Optimizing patient care and research: the Amsterdam Dementia Cohort. Journal of Alzheimer’s disease, 41(1), 313-327. (2014). DOI: 10.3233 / JAD- 132306), each of which is incorporated by reference herein in its entirety. In some embodiments, the statistical model is trained using at least 700 samples, at least 800 samples, at least 900 samples, at least 1,000 samples, at least 1,100 samples, at least 1,200 samples, at least 1,300 samples, at least 1,400 samples, at least 1,500 samples, at least 1,600 samples, at least 1,800 samples, at least 1,900 samples, at least 2,000 samples, at least 2,500 samples, at least 3,000 samples, at least 4,000 samples, at least 5,000 samples, or at least any other suitable number of samples, without limitation.

[0144] As described above, in some embodiments, the statistical model(s) may be implemented as a generalized linear model. For example, the generalized linear model may be a logistic regression model. The generalized linear model may be trained by fitting the model to training data to estimate parameters for the model. In some embodiments, the parameters are estimated using maximum likelihood estimation (MLE). In some embodiments, the parameters are estimated using Bayesian estimation. However, it should be appreciated that any suitable parameter estimation techniques may be used, without limitation.

[0145] In some embodiments, a decision tree classifier may be used. Any suitable type of decision tree classifier may be used and may be trained using any suitable supervised decision tree learning technique. For example, the decision tree classifier may be trained by the iterative dichotomizer technique (e.g., the ID3 algorithm as described, for example, in Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81-106)), the C4.5 technique (e.g., as described, for example, in Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993), the classification and regression tree (CART) technique (e.g., as described, for example, in Breiman, Leo; Friedman, J. H.; Olshen, R. A.; Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks / Cole Advanced Books & Software). It should be appreciated that a decision tree classifier may be trained using any other suitable training method, without limitation.

[0146] In some embodiments, a gradient-boosted decision tree classifier may be used. The gradient-boosted decision tree classifier may be an ensemble of multiple decision tree classifiers (sometimes called "weak learners"). The prediction (e.g., classification) generated by the gradient-boosted decision tree classifier is formed based on the predictions generated by the multiple decision trees part of the ensemble. The ensemble may be trained using an iterative optimization technique involving calculation of gradients of a loss function (hence the name "gradient" boosting). Any suitable supervised training algorithm may be applied to training a gradient-boosted decision tree classifier including, for example, any of the algorithms described in Hastie, T.; Tibshirani, R.; Friedman, J. H. (2009). "10. Boosting and Additive Trees". The Elements of Statistical Learning (2nd ed.). New York: Springer, pp. 337-384. In some embodiments, the gradient-boosted decision tree classifier may be implemented using any suitable publicly available gradient boosting framework such as XGBoost (e.g., as described, for example, in Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). New York, NY, USA: ACM.). The XGBoost software may be obtained from http: / / xgboost.ai, for example). Another example framework that may be employed is LightGBM (e.g., as described, for example, in Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146-3154.). The LightGBM software may be obtained from https: / / lightgbm.readthedocs.io / , for example).

[0147] In some embodiments, a neural network classifier may be used. The neural network classifier may be trained using any suitable neural network optimization software. The optimization software may be configured to perform neural network training by gradient descent, stochastic gradient descent, or in any other suitable way. In some embodiments, the Adam optimizer (Kingma, D. and Ba, J. (2015) Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015)) may be used.

[0148] In some embodiments, a support vector machine (SVM) may be used. The SVM may be implemented using any suitable techniques such as, for example, any of the techniques described by Cristianini, N., and Shawe-Taylor, J. (“An introduction to support vector machines and other kernel-based learning methods.” Cambridge university press, 2000.), which is incorporated by reference herein in its entirety.

[0149] In some embodiments, a Gaussian mixture model may be used. The Gaussian mixture model may be implemented using any suitable techniques such as, for example, any of the techniques described by Reynolds, D. ("Gaussian mixture models." Encyclopedia of biometrics 741.659-663 (2009)), which is incorporated by reference herein in its entirety.

[0150] In some embodiments, a random forest model may be used. The random forest model may be implemented using any suitable techniques such as, for example, any of the techniques described by Biau, G. ("Analysis of a random forests model." The Journal of Machine Learning Research 13.1 (2012): 1063-1095.), which is incorporated by reference herein in its entirety.

[0151] Computer Implementation

[0152] An illustrative implementation of a computer system 400 that may be used in connection with any of the embodiments of the technology described herein (e.g., such as the processes of FIG. 2A and FIG. 2B) is shown in FIG. 4. The computer system 400 includes one or more processors 410 and one or more articles of manufacture that comprise non-transitory computer-readable storage media (e.g., memory 420 and one or more non-volatile storage media 430). The processor 410 may control writing data to and reading data from the memory 420 and the non-volatile storage media 430 in any suitable manner, as the aspects of the technology described herein are not limited to any particular techniques for writing or reading data. To perform any of the functionality described herein, the processor 410 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 420), which may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor 410.

[0153] Computing system 400 may include a network input / output (I / O) interface 440 via which the computing device may communicate with other computing devices. Such computing devices may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.

[0154] Computing system 400 may also include one or more user I / O interfaces 450, via which the computing device may provide output to and receive input from a user. The user I / O interfaces may include devices such as a keyboard, a mouse, a microphone, a display device (e.g., a monitor or touch screen), speakers, a camera, and / or various other types of I / O devices.

[0155] Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer, as examples. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smartphone, a tablet, or any other suitable portable or fixed electronic device.

[0156] The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software, or a combination thereof. When implemented in software, the software code can be executed on any suitable processor (e.g., a microprocessor) or collection of processors, whether provided in a single computing device or distributed among multiple computing devices. It should be appreciated that any component or collection of components that perform the functions described above can be generically considered as one or more controllers that control the above-described functions. The one or more controllers can be implemented in numerous ways, such as with dedicated hardware, or with general purpose hardware (e.g., one or more processors) that is programmed using microcode or software to perform the functions recited above.

[0157] In this respect, it should be appreciated that one implementation of the embodiments described herein comprises at least one computer-readable storage medium (e.g., RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible, non- transitory computer-readable storage medium) encoded with a computer program (i.e., a plurality of executable instructions) that, when executed on one or more processors, performs the above-described functions of one or more embodiments. The computer- readable medium may be transportable such that the program stored thereon can be loaded onto any computing device to implement aspects of the techniques described herein. In addition, it should be appreciated that the reference to a computer program which, when executed, performs any of the above-described functions, is not limited to an application program running on a host computer. Rather, the terms computer program and software are used herein in a generic sense to reference any type of computer code (e.g., application software, firmware, microcode, or any other form of computer instruction) that can be employed to program one or more processors to implement aspects of the techniques described herein.

[0158] The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects as described above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present disclosure need not reside on a single computer or processor but may be distributed in a modular fashion among a number of different computers or processors to implement various aspects of the present disclosure.

[0159] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0160] Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

[0161] When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.

[0162] The foregoing description of implementations provides illustration and description but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the implementations. In other implementations the methods depicted in these figures may include fewer operations, different operations, differently ordered operations, and / or additional operations. Further, non-dependent blocks may be performed in parallel.

[0163] It will be apparent that example aspects, as described above, may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. U.S. Provisional Patent Application Serial No. 63 / 710,994, filed October 23, 2024, and entitled “Systems and Methods for Improving Biomarker Detection Results Using Multiple Biomarkers,” and U.S. Provisional Patent Application Serial No. 63 / 664,378, filed June 26, 2024, and entitled “Systems and Methods for Improving Biomarker Detection Results Using Multiple Biomarkers,” are each incorporated herein by reference in its entirety for all purposes.

[0164] Examples

[0165] The following examples are intended to illustrate certain embodiments of the present invention, but do not exemplify the full scope of the invention.

[0166] Examples 1 and 2 demonstrate the performance of the embodiments of the technology described herein for classifying 730 samples as being associated with a particular beta-amyloid status. The samples were obtained from subjects diagnosed with mild cognitive impair (MCI) and subjects diagnosed with Alzheimer’s disease (AD). The training data was obtained from the Bio-Hermes study and the Amsterdam Dementia Cohort. As demonstrated, relative to conventional approaches which require more resource-intensive and invasive testing (e.g., PET, CSF testing, etc.), the techniques developed by the inventors increase the number of samples that can be reliably identified as being associated with a particular beta-amyloid status using blood-based biomarkers.

[0167] In both Example 1 and Example 2, a value indicative of the concentration of p- Tau 217 was obtained for each sample (e.g., act 202 shown in FIG. 2A and act 255 shown in FIG. 2B). The value was compared to an upper threshold of 0.09 pg / mL and a lower threshold of 0.04 pg / mL (e.g., act 204 shown in FIG. 2A and FIG. 2B). Samples having values for p-Tau that were greater than 0.09 pg / mL were initially classified as being associated with a beta-amyloid positive status. Samples having values for p-Tau that were less than 0.04 pg / mL were initially classified as being associated with a betaamyloid negative status. Samples having values for p-Tau greater than or equal to 0.04 pg / mL and less than or equal to 0.09 pg / mL were initially classified as being associated with an indeterminant beta-amyloid status.

[0168] For each sample classified as being associated with a beta-amyloid positive status or beta-amyloid negative beta-amyloid status, the value indicative of the concentration of p-Tau for the particular sample was converted to a probability that the sample is associated with the particular beta-amyloid status (e.g., beta-amyloid positive or betaamyloid negative). The probability was determined by processing the value indicative of the concentration of p-Tau 217 using a logistic regression model trained to predict the probability, logistic regression model was fitted to a training set comprising concentrations of p-Tau 217 for the 730 samples. Equation 1 is the fitted logistic regression model and Equation 2 is the logistic function.

[0169] Logist — 2.550605518 + 49.610883705 ■ pTau217 [pg / mL]) (Equation 1) where: (Equation 2)

[0170] For samples classified as being associated with an indeterminant beta-amyloid status, Examples 1 and 2 each involve processing multiple biomarkers using a statistical model trained to predict the likelihood that the samples are associated with a betaamyloid positive status. However, as described herein, Examples 1 and 2 demonstrate (a) the use of different biomarkers associated with Alzheimer’s disease, and (b) the use of different threshold classification thresholds to ultimately classify a sample as being associated with a particular beta-amyloid status.

[0171] EXAMPLE 1

[0172] In the first example, a value for amyloid P42 / 40 ratio was obtained for each of the samples classified as being associated with an indeterminant beta-amyloid status. The values for p-Tau 217 and amyloid P42 / 40 ratio were processed using a logistic regression model trained to predict the probability that a sample is associated with a particular betaamyloid status. The logistic regression model was fitted to the values for p-Tau 217 and amyloid P42 / 40 ratio for the 730 samples obtained from subjects diagnosed with MCI and subjects diagnosed with AD. The parameters estimated for the logistic regression model are shown in Table 1. The fitted logistic regression model was used to estimate the probability that each sample is associated with a beta-amyloid positive status.

[0173] Table 1. Parameters estimated for the two-marker logistic regression model.

[0174] Accordingly, probabilities were determined for all 730 samples either based on the value for p-Tau 217 (e.g., for samples initially classified as being associated with a beta-amyloid positive or negative status) or based on the multiple biomarker values (e.g., for samples classified as being associated with an indeterminant beta-amyloid status). The probabilities determined for the samples were then used to classify the samples as being associated with a particular beta-amyloid status. If the probability determined for a particular sample was greater than a threshold of 0.68, the sample was classified as being associated with a beta-amyloid positive status (e.g., high risk). If the probability determined for a particular sample was less than a threshold of 0.49, the sample was classified as being associated with a beta-amyloid negative status (e.g., low risk). If the probability was greater than or equal to 0.49 and less than or equal to 0.68, the sample was classified as being associated with an indeterminant beta-amyloid status.

[0175] The classification thresholds were determined based on the distribution of probabilities determined for beta-amyloid positive samples (FIG. 6A) and the distribution of probabilities determined for beta-amyloid negative samples (FIG. 6B). FIG. 6A shows the distribution of the probabilities determined for samples classified as being associated with a beta-amyloid negative status. As shown in FIG. 6A, an 8% false positive rate is estimated using the threshold of 0.68. As shown in FIG. 6B, an 8% false positive rate is estimated using the threshold of 0.49.

[0176] As shown in Table 2, only 10.7% of the samples were classified as indeterminant.

[0177] 8.2% of the samples were classified as false negatives and 8.3% of the samples were classified as false positives. This aligns with the estimates shown in FIG. 6 A and FIG.

[0178] 6B.

[0179] Table 2. Performance of classifying samples as being associated with a particular betaamyloid status.

[0180] EXAMPLE 2

[0181] In the second example, values for amyloid P42 / 40 ratio, NfL, and GFAP were obtained for each of the samples classified as being associated with an indeterminant beta-amyloid status. The logistic regression model was fitted to the values for p-Tau 217, amyloid P42 / 40 ratio, GFAP, and NfL for the 730 samples obtained from subjects diagnosed with MCI and subjects diagnosed with AD. The parameters estimated for the logistic regression model are shown in Table 3. The fitted logistic regression model was used to estimate the probability that each sample is associated with a beta-amyloid positive status.

[0182] Table 3. Parameters estimated for the four-marker logistic regression model.

[0183] Accordingly, probabilities were determined for all 730 samples either based on the value for p-Tau 217 (e.g., for samples initially classified as being associated with a beta-amyloid positive or beta-amyloid negative status) or based on the multiple biomarker values (e.g., for samples classified as being associated with an indeterminant beta-amyloid status). The probabilities determined for the samples were then used to classify the samples as being associated with a particular beta-amyloid status. If the probability determined for a particular sample was greater than a threshold of 0.67, the sample was classified as being associated with a beta-amyloid positive status (e.g., high risk). If the probability determined for a particular sample was less than a threshold of 0.50, the sample was classified as being associated with a beta-amyloid negative status (e.g., low risk). If the probability was greater than or equal to 0.49 and less than or equal to 0.68, the sample was classified as being associated with an indeterminant betaamyloid status.

[0184] The classification thresholds were determined based on the distribution of probabilities determined for beta-amyloid positive samples (FIG. 7A) and the distribution of probabilities determined for beta-amyloid negative samples (FIG. 7B). FIG. 6A shows the distribution of the probabilities determined for samples classified as being associated with a beta-amyloid negative status. As shown in FIG. 7A, an 8% false positive rate is estimated using the threshold of 0.67. As shown in FIG. 7B, an 8% false positive rate is estimated using the threshold of 0.50.

[0185] As shown in Table 4, with the classification thresholds set at 0.67 and 0.50, 6.85% of the samples were classified as indeterminant, 9.53% of the samples were classified as false negatives, and 9.26% of the samples were classified as false positives.

[0186] Table 4. Performance of classifying samples as being associated with a particular betaamyloid status.

[0187] The classification thresholds were widened to 0.42 and 0.7 in an effort to obtain false positive and negative rates closer to 8%. As shown in Table 5, with the adjusted classification thresholds, 11.5% of the samples were classified as indeterminant, 8.2% of the samples were classified false negatives, and 7.9% of the samples were classified as false positives.

[0188] Table 5. Performance of classifying samples as being associated with a particular betaamyloid status.

[0189] EXAMPLE 3

[0190] Example 3 demonstrates the performance of the embodiments of the technology described herein for classifying 986 samples as being associated with a particular betaamyloid status. The samples were obtained from subjects diagnosed with mild cognitive impair (MCI), Alzheimer’s disease (AD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB). The data was obtained from the Bio-Hermes study and the Amsterdam Dementia Cohort. As demonstrated, relative to conventional approaches which require more resource-intensive and invasive testing (e.g., PET, CSF testing, etc.), the techniques developed by the inventors increase the number of samples that can be reliably identified as being associated with a particular beta-amyloid status using bloodbased biomarkers.

[0191] A value indicative of the concentration of p-Tau 217 was obtained for each sample. The value was compared to an upper threshold of 0.09 pg / mL and a lower threshold of 0.04 pg / mL. Samples having values for p-Tau that were greater than 0.09 pg / mL were initially classified as being associated with a beta-amyloid positive status. Samples having values for p-Tau that were less than 0.04 pg / mL were initially classified as being associated with a beta-amyloid negative status. Samples having values for p-Tau greater than or equal to 0.04 pg / mL and less than or equal to 0.09 pg / mL were initially classified as being associated with an indeterminant beta-amyloid status.

[0192] Table 6 shows the initial classifications. As shown, 32.9% of the samples are classified as being associated with an indeterminant (e.g., uncertain) beta- amyloid status. This corresponds to the region labeled “grey zone” shown in FIG. 5A.

[0193] Table 6. Initial classifications.

[0194] For each sample classified as being associated with a beta-amyloid positive status or beta-amyloid negative status, the value indicative of the concentration of p-Tau for the particular sample was converted to a probability that the sample is associated with the particular beta-amyloid status (e.g., beta-amyloid positive or beta-amyloid negative). The probability was determined by processing the value indicative of the concentration of p- Tau 217 using a logistic regression model trained to predict the probability. The logistic regression model was fitted to a training set comprising concentrations of p-Tau 217 for the 986 samples.

[0195] For samples classified as being associated with an indeterminant beta-amyloid status, values for additional biomarkers associated with Alzheimer’s disease were obtained. In particular, values for amyloid P42 / 40 ratio, NfL, and GFAP were obtained for each of the samples classified as being associated with an indeterminant beta-amyloid status.

[0196] A logistic regression model was fitted to the values for p-Tau 217, amyloid P42 / 40 ratio, GFAP, and NfL for the 986 samples. The fitted logistic regression model was used to estimate the probability that each sample is associated with a beta-amyloid positive status.

[0197] Accordingly, probabilities were determined for all 986 samples either based on the value for p-Tau 217 (e.g., for samples initially classified as being associated with a beta-amyloid positive or negative status) or based on the multiple biomarker values (e.g., for samples classified as being associated with an indeterminant beta-amyloid status). The probabilities determined for the samples were then used to classify the samples as being associated with a particular beta-amyloid status. If the probability determined for a particular sample was greater than a threshold of 0.61, the sample was classified as being associated with a beta-amyloid positive status (e.g., high risk). If the probability determined for a particular sample was less than a threshold of 0.41, the sample was classified as being associated with a beta-amyloid negative status (e.g., low risk). If the probability was greater than or equal to 0.41 and less than or equal to 0.61, the sample was classified as being associated with an indeterminant beta-amyloid status (e.g., uncertain).

[0198] Table 7 shows updated classifications. As shown in Table 7, FIG. 5A, and FIG. 5B, the techniques developed by the inventors, which utilize a multivariate logistic regression model that processes multiple biomarkers associated with Alzheimer's disease, enabled classifications for 190 of 325 samples that were initially classified as being associated with an indeterminant beta-amyloid status using p-Tau 217 alone. 137 samples were re-classified as amyloid positive (accuracy 88%), and 53 samples were reclassified as negative (accuracy 87%), reducing the number of indeterminant samples 2.4-fold from 32.9% to 13.7%. Overall test accuracy, sensitivity, and specificity across all 987 samples was greater than 90%. In FIG. 5 A, amyloid negative samples are shown as hollow circles, while amyloid positive samples are shown with “+”symbols.

[0199] Table 7. Updated classifications.

[0200] EXAMPLE 4

[0201] Example 4 demonstrates the performance of the embodiments of the technology described herein for classifying samples as being associated with a particular betaamyloid status. As demonstrated, relative to conventional approaches which require more resource-intensive and invasive testing (e.g., PET, CSF testing, etc.), the techniques developed by the inventors increase the number of samples that can be reliably identified as being associated with a particular beta-amyloid status using bloodbased biomarkers.

[0202] In this example, 545 validation samples were tested for values indicative of the concentration of p-Tau 217. The samples were a randomized subset of additional samples obtained from objectively symptomatic patients (e.g., patients having mild cognitive impairment (MCI) and Alzheimer’s disease) drawn from the Bio-Hermes and Amsterdam Dementia Cohort, thereby forming a cross-sectional clinical validation sample set. The value obtained for each sample was compared to an upper threshold of 0.09 pg / mL and a lower threshold of 0.04 pg / mL. Samples having values for p-Tau 217 that were greater than 0.09 pg / mL were initially classified as being associated with a beta-amyloid positive status. Samples having values for p-Tau 217 that were less than 0.04 pg / mL were initially classified as being associated with a beta-amyloid negative status. Samples having values for p-Tau 217 greater than or equal to 0.04 pg / mL and less than or equal to 0.09 pg / mL were initially classified as being associated with an indeterminant beta-amyloid status.

[0203] The thresholds of 0.04 pg / mL and 0.09 pg / mL were determined based on 730 test samples to achieve greater than or equal to 90% accuracy for objectively symptomatic patients (e.g., patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD)) across two independent cohorts (Bio-Hermes, n=235, Amsterdam Dementia Cohort, n=495, diagnostic thresholds 0.04 and 0.09 pg / mL). These cohorts represent diversity in clinical settings, comparator methods, and demographic profiles. Across this diversity mix, 31.2% of the 730 test samples fell in the intermediate (indeterminant) zone between the thresholds.

[0204] Table 8 shows the initial classifications of the 545 validation samples. As shown, 33.9% of the samples were classified as being associated with an indeterminant betaamyloid status (i.e., in the intermediate zone), labeled in Table 8 as “Uncertain.”

[0205] Table 8. Initial classifications.

[0206] For each sample classified as being associated with a beta-amyloid positive status or beta-amyloid negative status, the value indicative of the concentration of p-Tau 217 for the particular sample was converted to a probability that the sample is associated with the particular beta-amyloid status (e.g., beta-amyloid positive or beta-amyloid negative). The probability was determined by processing the value indicative of the concentration of p-Tau 217 using a logistic regression model trained to predict the probability.

[0207] For samples classified as being associated with an indeterminant beta-amyloid status, values for additional biomarkers associated with Alzheimer’s disease were obtained. In particular, values for amyloid P42 / 40 ratio, NfL, and GFAP were obtained for each of the samples classified as being associated with an indeterminant beta-amyloid status. A logistic regression model was fitted to the values for p-Tau 217, amyloid P42 / 40 ratio, GFAP, and NfL for these samples. The fitted logistic regression model was used to estimate the probability that each sample is associated with a beta-amyloid positive status.

[0208] Accordingly, probabilities were determined for all 545 validation samples either based on the value for p-Tau 217 (for samples initially classified as being associated with a beta-amyloid positive or negative status) or based on the multiple biomarker values (for samples classified as being associated with an indeterminant beta-amyloid status). The probabilities determined for the samples were then used to classify the samples as being associated with a particular beta-amyloid status. If the probability determined for a particular sample was greater than a threshold of 0.70, the sample was classified as being associated with a beta-amyloid positive status (e.g., high risk). If the probability determined for a particular sample was less than a threshold of 0.45, the sample was classified as being associated with a beta-amyloid negative status (e.g., low risk). If the probability was greater than or equal to 0.45 and less than or equal to 0.70, the sample was classified as being associated with an indeterminant beta-amyloid status (e.g., uncertain). The second set of thresholds (e.g., 0.70 and 0.45) represented 90% accuracy.

[0209] Table 9 shows updated classifications for the 545 validation samples. As shown in Table 9, the techniques developed by the inventors, which utilize a multivariate logistic regression model that processes multiple biomarkers associated with Alzheimer's disease, enabled classifications for 123 of 185 samples that were initially classified as being associated with an indeterminant beta-amyloid status using p-Tau 217 alone. 74 samples were re-classified as amyloid positive (accuracy 92.4%), and 49 samples were re-classified as negative (accuracy 85%), reducing the number of indeterminant samples (i.e., in the intermediate zone) about 3-fold from 33.9% to 11.4% (as shown in the column labeled “Uncertain”).

[0210] Table 9. Updated classifications. Additionally, to ensure the robustness of validation, the techniques developed by the inventors for classifying samples as amyloid positive, amyloid negative, or indeterminant, were further validated with an independent sampling from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. This cohort included 537 longitudinal samples from 129 individuals across up to a 15-year timeframe. To assess the performance with individual patients over time, a subset of 107-115 patients for which samples representing baseline, month 24, and month 48 were available were compared.

[0211] Table 10 summarizes the clinical performance parameters of the techniques developed by the inventors for classifying samples as amyloid positive, amyloid negative, or indeterminant. Column 1 of Table 10 summarizes the performance of the techniques in classifying the 730 samples in the test set. Column 2 of Table 10 summarizes the performance of the techniques in classifying the 545 samples in the first validation set from BioHermes and Amsterdam Dementia Cohort. Column 3 of Table 10 summarizes the performance of the techniques in classifying 537 samples in the second validation set. Column 4 of Table 10 summarizes the combined performance of the two validation sets. The results demonstrate reproducible performance across three highly diverse cohorts and prevalences. Positive predictive value (PPV) remained >90% from prevalences ranging from 52.5% to 70.4%. Combined validation statistics met target accuracy standards of 90%. Intermediate range (11.9%), corresponding to indeterminant samples, was well below the recommended maximum of 20%.

[0212] Table 10

[0213] Cross sectional Longitudinal Combined

[0214] Cohorts Training Validation Validation Validation n AUC Prevalence False neg rate False pos rate % in Intermediate Accuracy Sensitivity Specificity PPV NPV Table 11 summarizes the performance of the techniques in classifying the samples obtained over time for the subset of individuals from the ADNI cohort. The results illustrate the reproducibility of the techniques in smaller longitudinal samplings. These results, on the same individuals repeatedly sampled over 4 years, illustrate the performance levels and apparent performance variation expected in smaller sampling sizes.

[0215] Table 11

[0216] ADNI, single sample / individual

[0217] Baseline 24 mo 48 mo

[0218] 115 107 111

[0219] 0.941 0.939 0.883

[0220] 46.0% 46.7% 50.5%

[0221] 10.9% 10.0% 14.3%

[0222] 7.5% 5.3% 12.7%

[0223] 10.8% 8.4% 13.5%

[0224] 89.8% 91.8% 84.4%

[0225] 87.7% 89.1% 84.0%

[0226] 91.4% 94.2% 84.8%

[0227] 90.4% 93.2% 85.7%

[0228] 89.2% 90.7% 83.0%

[0229] Additionally, in this example, modeling was performed to evaluate the precision of the techniques described herein for estimating probabilities that blood or blood- derived samples are beta-amyloid positive, according to some embodiments of the technology described herein. As demonstrated herein, the modeled level of imprecision is expected to have very little impact on classification accuracy, with a predicted misclassification rate of only 1%.

[0230] In this example, the modeling was performed as follows: 1) The imprecision of all the biomarkers used in the multi-biomarker beta- amyloid status determining assay was estimated through a multi-day precision study; 2) the variance estimates from the precision study were used to define a normal error for each biomarker; 3) error was randomly added / subtracted for each biomarker; 4) simulated biomarker values with error added were used to calculate logistic multi-marker scores; 5) 10 simulations were performed for each of 1,305 samples (to mimic an in-lab study of 10 runs / sample); 6) imprecision of risk score output (e.g., the estimated probability that a particular sample is beta-amyloid positive) and its effect on amyloid classification accuracy was evaluated. FIG. 8A shows the coefficient of variation (CV) profiles for the four biomarkers. FIG. 8B shows the modeled imprecision of the risk score across 13,050 error simulations based on actual variances of each biomarker. The overall mean CV was 7.33%. At this level of imprecision relative to the thresholds for classifying samples as amyloid positive, amyloid negative, and indeterminant, the potential low-to-high or high-to-low misclassification rate is 1%.

[0231] EXAMPLE 5

[0232] Example 5 demonstrates the performance of embodiments of the technology described herein for classifying samples as being associated with a particular betaamyloid status. As demonstrated, relative to conventional approaches which require more resource-intensive and invasive testing (e.g., PET, CSF testing, etc.), the techniques developed by the inventors increase the number of samples that can be reliably identified as being associated with a particular beta-amyloid status using bloodbased biomarkers.

[0233] Methods

[0234] Cohorts and. Reference Methods: Amsterdam Dementia Cohort

[0235] The Alzheimer Center Amsterdam (ADC) cohort comprised patients referred for cognitive evaluation by general practitioners or specialists. All patients underwent a standardized, multidisciplinary work-up, including neurological history and examination, vital function assessment, informant history, dementia nurse consultation, neuropsychological testing, brain MRI, EEG, standard laboratory tests, and either lumbar puncture for CSF biomarker analysis or amyloid PET imaging. Diagnoses were determined via multidisciplinary consensus, with Alzheimer’s dementia requiring abnormal CSF biomarkers or positive amyloid PET. Amyloid PET scans utilized [18F]Florbetaben or [18F]Florbetapir, with positivity defined by visual assessment of neocortical fibrillary amyloid by a nuclear medicine physician. CSF amyloid positivity was determined using Roche Elecsys p-Tau 181 / AP42 assays (cut-off 0.02) or Fujirebio Innotest p-Tau 181 / AP42 ELIS As (cut-off 0.06). As the intended use population is objectively impaired patients, MCI (n=403) and AD (n=336) cases constituted 68% of the training and 25% of the validation sample sets (demographic characteristics previously reported). Demographic characteristics of the sub cohort used for validation (n=274) are shown in Table 12. Demographic characteristics of the combined validation cohorts (ADC, Bio-Hermes (BH), and ADNI) stratified by amyloid status are shown in Table 13.

[0236] Table 12

[0237] Table 13

[0238] For analysis of amyloid detection accuracy in mixed pathology cases, subgroups of symptomatic individuals diagnosed with vascular dementia (VaD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB), some of whom also had amyloid pathology, were examined. Demographic data for these subgroups are provided in Table 14.

[0239] Table 14 Cohorts and Reference Methods: Bio-Hermes Cohort

[0240] From April 2021 to November 2022, 17 clinical trial sites recruited community -based participants for the Bio-Hermes cohort, aiming to enrich ethnic / racial diversity. Participants, meeting established inclusion criteria, were categorized as cognitively unimpaired, MCI, or mild AD. MCI participants had a documented MCI diagnosis (NIA- AA criteria) or met screening criteria: MMSE 24-30, RAVLT-delayed recall >1 SD below age-adjusted mean, and minimal functional impairment (FAQ). Mild AD participants had a probable AD diagnosis (NIA-AA criteria) or met screening criteria: MMSE 20-24, RAVLT-delayed recall >1 SD below age-adjusted mean, and evidence of functional decline (FAQ). All participants underwent [18F]Florbetapir amyloid PET scans, interpreted centrally by IXICO Technologies Inc. Underrepresented groups (Hispanic and non-Hispanic Black) comprised 27.8% of the symptomatic subcohort (MCI and mild AD). Consistent with the intended use population, MCI (n=285) and mild AD (n=221) cases constituted 32% of the training and 25% of the validation sample sets. Details of these subgroups have been previously reported. Demographic characteristics of the sub cohort used for validation (n=271) are shown in Table 12.

[0241] Cohorts and. Reference Methods: ADNI Cohort

[0242] The ADNI cohort, derived from the ADNI database (adni.loni.usc.edu), comprised participants from a FNIH Biomarker Consortium study examining longitudinal trajectories of blood-based biomarkers in relation to amyloid PET. ADNI, initiated in 2003, aims to assess the progression of MCI and early AD using multimodal biomarkers. For this validation study, participants with plasma samples collected within 6 months of amyloid PET at three distinct time points were selected. From 406 subjects with 1231 samples (2-5 time points), 1010 had amyloid status and all five biomarker assays. Exclusion of samples outside the intended use population yielded 537 samples from 236 individuals, spanning up to 180 months. A subset of 107-115 individuals with contiguous baseline, 24-month, and 48-month time points was analyzed separately to assess test result consistency over time. The ADNI cohort was not utilized in training and constituted 50% of the validation sample set. Demographic characteristics of the ADNI cohort are shown in Table 12.

[0243] Cohorts and Reference Methods: Validation and Training Cohorts p-Tau 217 clinical thresholds were established using approximately 50% of the ADC cohort 2 and a randomized subset of the Bio-Hermes training and validation cohorts. All p-Tau 217 testing for this threshold determination was performed at the Quanterix CLIA laboratory using a single reagent / calibrator lot. Multi-analyte algorithm thresholds were derived from N4PE data on ADC cohort 1 (n=495, Neurochemistry Laboratory Amsterdam) and the Bio-Hermes training sub-cohort (n=235, Quanterix CLIA laboratory). To account for different reagent lots used between laboratories, a crossover set of 100 samples was tested at both sites. Bridging of individual biomarkers was made using Passing-Bablok regression, with an average bridging adjustment of 15-22% depending on the biomarker. The validation of the multi-analyte thresholds was conducted exclusively at the Quanterix CLIA laboratory, using ADC cohort 2 (n=274) combined with the Bio-Hermes validation sub-cohort (n=271), as well as the independent ADNI longitudinal cohort (n=537).

[0244] Plasma Sample Analysis: Instrumentation

[0245] All assay testing was performed on the Simoa HD-X® instrument, a fully automated digital immunoassay analyzer utilizing Simoa® technology for isolation and counting of single molecules.

[0246] Plasma Sample Analysis: Assay Principle and Protocol

[0247] Simoa® technology, a digitized bead-based ELISA, achieves attomolar sensitivity through single-molecule detection within 40-femtoliter microwells. By confining fluorescent reporter molecule diffusion, single enzyme labels generate detectable signals within 30 seconds. Arrays of 216,000 wells permit rapid, simultaneous counting, resulting in assay completion within 45-60 minutes.

[0248] The Simoa® p-Tau 217 assay, a 3-step sandwich immunoassay, involves capture, sandwich formation with biotinylated detector antibodies, and labeling with a streptavidin-P-galactosidase conjugate. Following magnetic bead collection and washing at each step, beads are resuspended in resorufin P-D-galactopyranoside substrate. Digital processing occurs upon bead transfer to the Simoa® array disc, where captured p-Tau 217 leads to substrate hydrolysis and fluorescence. p-Tau 217 concentration is determined via 4-parameter logistic curve interpolation, with assay completion in approximately one hour.

[0249] The commercially available Simoa® Neurology 4-plex E Kit (N4PE) simultaneously measures Ap40, Ap42, GFAP, and NfL using a 2-step digital immunoassay. Capture beads and detector antibodies are combined, with analyte- specific beads pre-coated with distinct fluorescent dyes. Following substrate resuspension and array transfer, bandpass filters identify analyte- specific beads. Concentrations are determined through 4- parameter logistic regression fitting, with a processing time of approximately one hour. Plasma Sample Analysis: Sample Collection and Testing

[0250] K2EDTA plasma was collected via venipuncture for all three cohorts. For the Amsterdam Dementia Cohort (ADC), samples were centrifuged within 2 hours (1800xg, 10 min, room temperature), aliquoted (<0.5mL), and stored at -80°C until transfer / shipment to either the Neurochemistry Laboratory Amsterdam or Quanterix. In Amsterdam, samples were thawed, centrifuged (10,000xg, 10 min), and analyzed in singlicate for Ap40, Ap42, GFAP, and NfL using the Simoa® N4PE kit on the Simoa® HD-X analyzer, with calibration and quality controls in duplicate. Inter-assay coefficients of variation were <15%. For the Bio-Hermes cohort, 2.0 mL whole blood was transferred to conical tubes and centrifuged (1500xg, >15 min, room temperature), with plasma aliquoted and frozen at -80°C within 4 hours. For the ADNI cohort, plasma was centrifuged within 1 hour (1500xg, 15 min, room temperature), aliquoted, and frozen at -80°C. For testing at Quanterix, samples were thawed (60 min, room temperature), centrifuged (10,000xg, 10 min), and analyzed), with calibrators, controls, and quality controls according to Quanterix SOPs.

[0251] Quality control samples, included in all p-Tau 217 and N4PE runs, demonstrated acceptable precision, consistent with previous reports. The precision of the composite multi-analyte Amyloid Risk Score, incorporating all five biomarkers, was estimated by evaluating inter-assay imprecision from a multi-day precision study for each assay. The expected composite score imprecision was then modeled from the combined biomarkers.

[0252] The precision of the LucentAD® Complete logistic risk score (an example of a multianalyte method of this disclosure) relies on the precision of each constituent assay. LucentAD® Complete measures five analytes. Notably, four of these (Ap40, Ap42, GFAP, and NfL) are measured within a single multiplexed immunoassay, minimizing the additive variability that would arise from separate immunoassays. This consolidation enhances the overall precision of the algorithm. To evaluate the impact of logistic risk score imprecision on amyloid classification accuracy, a modeling study was performed. This involved: 1) estimating biomarker imprecision through a multi-day precision study (FIG. 12); 2) defining a normal error distribution for each biomarker using the precision study's variance estimates; 3) randomly adding or subtracting error from each biomarker; 4) calculating logistic multi-marker scores using the simulated biomarker values with added error; 5) conducting 10 simulations for each of 1,305 samples, mimicking 10 runs per sample in a laboratory setting (FIG. 13); and 6) assessing the resulting risk score imprecision and its effect on amyloid classification accuracy.

[0253] As shown in FIG. 13, the overall expected mean inter-day imprecision (%CV) of the composite Amyloid Risk Score across all simulations was 7.33% (95% CI: 6.87-7.80%). At this level of precision relative to clinical cutoffs, the potential low to high or high to low amyloid risk misclassification was estimated to be 1%.

[0254] Statistical Methods

[0255] All data analysis was performed using JMP Pro 18 (JMP Statistical Discovery LLC, Cary, NC). Discovery analyses, employing multiple modeling techniques, graphical data interrogations, and performance metrics, resulted in the LucentAD® Complete test configuration. Test performance was subsequently validated in three independent cohorts, both individually and combined. Performance metrics, including 95% confidence intervals, are reported, with specific mention of instances where indeterminate zone results were excluded.

[0256] Initially, a logistic regression model was constructed using all five biomarkers across the training dataset. A separate logistic regression model transformed p-Tau 217 levels into a model score. These two models were then combined using a decision tree based on the p- Tau 217 thresholds. Thresholds for the resulting multi-analyte model score were determined by fitting best- fit distributions to the negative and positive training samples. Specifically, the upper threshold was set at the 93rd percentile of a sinh-arcsinh distribution fit to the negative samples (corresponding to a 7% false positive rate), and the lower threshold was set at the 7th percentile of a beta distribution fit to the positive samples (corresponding to a 7% false negative rate). These 7% false negative and false positive rates were selected to establish robust thresholds, aiming for 10% false negative and false positive rates in independent validation cohorts.

[0257] Results

[0258] Demographic and Clinical Characteristics Demographic and clinical characteristics of the three validation cohorts, and the combined validation set stratified by amyloid status, are detailed in Table 12 and Table 13, respectively. The combined validation cohort had a mean age of 69.9 years (SD 7.8, range 44-86), with 48.0% female representation. Mean age varied across cohorts: ADC, 64.9 years (SD 7.7, range 44-83); Bio-Hermes, 73.4 years (SD 6.8, range 60-85); and ADNI, 70.6 years (SD 7.1, range 55-86). The validation set was predominantly White (93.3%), with Bio-Hermes contributing the majority of underrepresented minorities (25.5% within-cohort proportion).

[0259] All participants were symptomatic, with diagnoses of MCI (71.3%), mild AD (11.3%), or AD (17.5%). 43.6% were ApoE s4 carriers. Amyloid prevalence varied significantly across cohorts and clinical subgroups: ADC, 67.3% in MCI and >99% in AD dementia (CSF positivity required for selection); Bio-Hermes, 34.7% in MCI and 61.5% in dementia (PET confirmed); and ADNI, 45.1% in MCI and 88.2% in dementia (PET confirmed). The combined validation set had an overall amyloid prevalence of 55.4%. These variations likely resulted from differences in the clinical determination of diagnostic categories (MCI, AD dementia), CSF requirement for diagnosis in ADC, and Bio-Hermes diagnoses preceding PET testing.

[0260] Analyte Measurement in Plasma Samples

[0261] K2EDTA plasma samples from ADC (n=769), Bio-Hermes (n=506), and ADNI (n=537) were analyzed for p-Tau 217, AP42 / AP40, GFAP, and NfE, with results compared to reference amyloid status (CSF or PET). FIG. 9A, FIG. 9B, FIG. 9C, and FIG. 9D display biomarker results stratified by amyloid status. All samples were above the assay limit of detection (EoD), and 99.5% of p-Tau 217 samples exceeded the lower limit of quantification (EEoQ), ensuring quantifiable data. All other assays yielded 100% EEoQ- exceeding samples. Median p-Tau 217 concentration was 3.14-fold higher in amyloidpositive participants (negative: 0.035 pg / mE, IQR 0.02; positive: 0.11 pg / mE, IQR 0.08; p<0.0001), with an overall AUC of 0.91 (95% CI: 0.89-0.92). Amyloid status differences were statistically significant for AP42 / 40, GFAP, and NfE (p<0.05), with AUCs of 0.72 (95% CI: 0.70-0.75), 0.75 (95% CI: 0.72-0.77), and 0.55 (95% CI: 0.53-0.58), respectively. Although NfE demonstrated limited independent classification of amyloid status, it significantly contributed to classifying p-Tau 217 borderline cases. Development of Multi-analyte Algorithm

[0262] While combining p-Tau 217 with amyloid ratio or the full N4PE panel showed no statistically significant improvement in AUC in preliminary trials, a stepwise increase in classification accuracy was observed with more biomarkers specifically for cases within the intermediate range of previously optimized 2-cutoff thresholds for p-Tau 217. To further explore multivariate modeling within this intermediate zone, eight model types (Lasso regression, Bootstrap Forest, etc.) were tested using various biomarker combinations, APOE status, sex, and age as inputs in an exploratory cohort of 735 symptomatic individuals (approximately 2 / 3 from the ADC and 1 / 3 from the Bio-Hermes cohort). Across model variations, AUCs remained statistically indistinguishable, averaging approximately 0.90, with Youden index-derived sensitivity and specificity converging around 85%. A logistic regression model was selected for subsequent data fitting, which was used to refine the understanding of how the additional biomarkers enhanced classification in the p-Tau 217 intermediate zone. Models were fit to predict amyloid status based on p-Tau 217 and AP42 / 40 ratio, and with all five biomarkers. Non-tau pathology markers (AP42 / 40 ratio, NfL, and GFAP) exhibited statistically significant, non-zero parameters in the models, with improved model metrics (R2, AIC, BIC) upon their inclusion. Notably, despite NfL’s limited standalone amyloid discrimination, it demonstrated the strongest statistical contribution to the all-biomarker multivariate model (p < 0.0001), surpassing AP42 / 40 ratio (p < 0.0048). Applying these 3-and 5-biomarker models to the exploratory cohort, slight but non-significant increases in AUC (0.91 to 0.92) was observed across models (FIG. 10). The inset of FIG. 10 highlights the midrange of the ROC curves where an incremental improvement in specificity with the addition of biomarkers suggested enhanced classification accuracy for cases within the p-Tau 217 intermediate zone.

[0263] To better characterize the potential of the additional biomarkers to classify amyloid status in the p-Tau 217 intermediate zone, thresholds were derived from the best-fit sinharcsinh distributions of the multianalyte score for amyloid-negative and -positive samples. These thresholds, targeting approximately 7% false negative and 7% false positive rates, yielded lower thresholds of 0.43 and 0.45, and upper thresholds of 0.74 and 0.70 for the 3- and 5-biomarker models, respectively. Compared to p-Tau 217 alone, the intermediate zone decreased from 31.2% to 14.9% of samples in the exploratory sample set with the 3-biomarker model, and further to 10.5% with the 5-biomarker model (Tables 15-17). This model’s stability and marker necessity were confirmed via forward stepwise selection, self-validating ensemble model, and multinomial regression, with all biomarkers demonstrating significant contributions (p < 0.0083 for GFAP, p < 0.0001 for others). Therefore, the 5-biomarker model was selected for subsequent validation.

[0264] Table 15

[0265] Table 16

[0266] Table 17 To perform the test, samples were processed concurrently on the p-Tau 217 and N4PE 4- plex assays. A multi-marker model score was calculated using a decision tree, initially stratifying samples based on p-Tau 217 concentration. Samples with p-Tau 217 concentrations below 0.04 pg / mL or above 0.09 pg / mL received a logistic risk score derived solely from p-Tau 217. Samples in the >0.04 to <0.09 pg / mL p-Tau 217 range had their risk score determined by the logistic multi-analyte model, incorporating p-Tau 217 and the N4PE biomarkers. This is represented by the equation:

[0267] (p — Tau 217 [pg / mL] < 0.04 => P(A +) p - Tau 217

[0268] If p — Tau 2 Y1 [pg / mL] > 0.09 => P(A +) p - Tau 217 Else ^P(Af +) p — Tau 217, A / 342 / 40, G

[0269] Where P(Ap+) represents the probability of amyloid positivity, ranging from 0 to 1. FIG. 5A illustrates the test's workflow: identifying p-Tau 217 intermediate zone cases and subsequently analyzing these cases with the multi-analyte algorithm. Although p-Tau 217 concentrations are shown in the illustration, the test's output for all samples is a risk score between 0 and 1. For user convenience, the risk score is multiplied by 100 and rounded to the nearest whole number.

[0270] Clinical Performance Validation

[0271] Clinical performance of the multi-analyte algorithmic test was assessed with the predefined thresholds. The validation datasets included the ADC cohort (n=274), BioHermes validation sub-cohort (n=271), and the ADNI cohort (n=571), as detailed in Table 12. The overall prevalence of amyloid positivity was 55.4%. All 1,082 validation samples were tested in the Quanterix CLIA laboratory according to established laboratory procedures. In addition to the full sub cohort of ADNI samples, the individuals from this set having two or three longitudinal intervals from baseline in common (24 and 48 months) were analyzed separately at each timepoint as an assessment of the consistency of the test results across longitudinal samplings within individuals. FIGS. 11A and 11B depict the results across the three validation cohorts. FIG. 11 A depicts an aggregate density plot, and risk score data are stratified by source and amyloid status in FIG. 1 IB. The distributions of amyloid positive cases were qualitatively similar across the three cohorts, while there were more false positive results among the Bio-Hermes and ADNI cohorts than from the ADC cohort. It is unclear if the higher false positive rate may reflect lower sensitivity by visual PET (used in BioHermes and ADNI) relative to CSF (used in ADC). Overall, 88% of the results were actionably outside of the intermediate grey zone. Tables 18-20 summarize the clinical performance characteristics of the multi-analyte test. Overall, the combined validation statistics met the target accuracy of 90% and exhibited an intermediate range of 11.9%, well below the proposed maximum of 20%. Importantly, the accuracy was statistically indistinguishable from the accuracy of p-Tau 217 alone as reported previously with a similar validation cohort, while the intermediate range was reduced from 30.9%. The results demonstrate robust and reproducible performance across the diversity of the three cohorts and in relation to amyloid prevalence rates. This diversity included variations in race / ethnicity, age, geography, clinical settings, and reference methods. The test capability included maintaining positive predictive values (PPV) above 90% across amyloid prevalence rates ranging from 41% to 70%. Model-derived PPVs and negative predictive values (NPVs) across a broader range of amyloid prevalence rates, from 20% (cognitively normal older adults) to 65% (high-prevalence dementia population) were also calculated. Tables 18 and 19 illustrate the effect of power to reduce the impact of variability in apparent performance metrics.

[0272] Table 18

[0273] Table 19

[0274] Table 20 presents likelihood ratios: a high likelihood ratio for high-risk test results calls (lower 95% CI > 6) indicating a strong positive signal for amyloid-positive cases, a likelihood ratio close to 1 for the intermediate zone suggesting no significant difference in positive and negative test results, and a low likelihood ratio of 0.12 for low-risk test results calls indicating a much higher likelihood of negative calls in amyloid-negative individuals than false-positive calls.

[0275] Table 20

[0276] Performance in Subgroups

[0277] Sub cohorts of cases diagnosed with VaD (n=60), FTD (n=172), and DLB (n=144) were tested in the LucentAD® Complete assay. A proportion of these samples were also amyloid positive, and the accuracy of the test for detection of amyloid in these mixed pathology cases was characterized. Demographic and clinical characteristics of these samples are summarized in Table 14.

[0278] These subgroups showed amyloid positivity rates of 36.0% (VaD), 19.8% (FTD), and 47.6% (DLB) by CSF, consistent with expected prevalence ranges for these dementia syndromes. LucentAD® Complete results compared with CSF amyloid status for the VaD, FTD, and DLB cases are summarized in Tables 21-23.

[0279] Table 21

[0280] Table 22

[0281] Table 23

[0282] Amyloid detection accuracies for the LucentAD® Complete test were 90.6% for VaD, 87.3% for FTD, and 76.9% for DLB, as listed in Table 24. While detection accuracy for AD pathology in VaD and FTD was statistically consistent to the combined validation cohorts, the accuracy for DLB (76.9%) was significantly lower. In addition, the percentage of cases in the intermediate zone (25%) in DLB patients was statistically higher than for the other two dementia types and the overall validation estimate (11.9%) (Table 23). These results suggest the amyloid signal in plasma in DLB cases is weaker than in other non- AD dementia categories.

[0283] Table 24 The impact of inclusion of these non- AD and co-pathology cases into the combined validation cohort (n=l,082) was assessed. Two different incidence levels were examined: “typical” percentages as reported in memory clinics, and “high” levels as might be encountered with under-diagnoses and among a younger population with high percentages of FTD. Table 25 summarizes the percentages that were included.

[0284] Table 25

[0285] Only a maximum of 4.8% of VaD cases could be included due to a limitation on the number of available samples (60). However, because high accuracy for amyloid detection in VaD cases was observed (90.6%), inclusion of additional VaD samples is not expected to alter the overall diagnostic performance of the test. The effect of the addition of up to 300 non-AD dementia cases is depicted in FIG. 14.

[0286] Despite the lower accuracy for amyloid detection in DLB cases, there was no significant difference in the clinical performance of LucentAD® Complete in classifying amyloid status with up to 22% (300 / 1,382) of non-AD dementia cases (Table 26).

[0287] Table 26

[0288] Except for DLB, accuracy of amyloid detection across demographic subgroups ranged from 87% to 95% (FIG. 15A) with the percent of intermediate results ranging from 3.5% to 14% (FIG. 15B). Although the accuracy estimate for non- White participants (95%) was numerically higher than for White participants, this difference was not statistically significant. Similarly, age, sex, and AP0E4 carrier status did not significantly impact test accuracy (FIG. 15A).

[0289] Model-Derived PPV, NPVs Across Amyloid Prevalence Rates Table 27 shows positive predictive value (PPV) and negative predictive value (NPV) of EucentAD® Complete by amyloid prevalence for different populations. At lower prevalence rates, the test may be particularly helpful for ruling out. At higher prevalence rates, the test may be particularly useful for ruling in. Bolded values indicate > 90% performance. Table 27 Discussion

[0290] This multi-cohort study demonstrates the clinical validity of the LucentAD® Complete test, a blood based assay for amyloid pathology detection that is a non-limiting embodiment of the multi-analyte test of this disclosure. The findings build upon the established value of p-Tau 217 as a highly accurate single biomarker but also address the persistent challenge of classifying individuals within the p-Tau 217 intermediate zone. This multi-analyte approach demonstrated an AUC of 0.92 and 90.9% accuracy, while reducing the intermediate zone approximately 3-fold to 12%. This improvement enhances diagnostic confidence and can reduce the need for more invasive and costly procedures like CSF analysis or PET scans. While p-Tau 217 alone offers excellent discrimination for many individuals, the presence of an indeterminate zone necessitates further investigation, a challenge addressed by this assay.

[0291] The addition of four readily measurable plasma biomarkers (AP42 / AP40, GFAP, and NfL) significantly enhances amyloid classification specifically within this important intermediate zone. This improvement can be rationalized by the multifactorial nature of Alzheimer's disease, which extends beyond amyloid plaque deposition and p-Tau production. Using biomarkers representing diverse pathological processes improves the identification of patients exhibiting multiple disease-related biomarkers. This enhancement is achieved without compromising the high accuracy of p-Tau 217 in clearly classified amyloid-positive or -negative samples. This targeted approach, utilizing multi-analyte techniques solely within the p-Tau 217 intermediate range, positions the LucentAD® Complete test well for diagnostic power while reducing complexity. The observed 3-fold reduction in the intermediate zone (31.2 to 10.5% in the training set) translates to a greater proportion of patients receiving clear and actionable results, thereby improving clinical workflow and potentially accelerating access to appropriate interventions.

[0292] The multi-analyte algorithm and established cut-offs demonstrated robust performance across three independent cohorts, comprising diverse demographics, clinical characteristics, and amyloid positivity prevalence rates. This heterogeneity, including variations in age, geography, race / ethnicity, and clinical presentation, strengthens the generalizability of these findings and suggests that LucentAD® Complete can be reliably applied across a broad spectrum of clinical settings. Notably, the test maintained a positive predictive value exceeding 90% across amyloid prevalence rates ranging from 41% to 70%, an important attribute for clinical utility. The inclusion of the Bio-Hermes cohort, designed to enrich for racial and ethnic diversity, is an important step towards ensuring equitable access to advanced diagnostic tools. The longitudinal analysis within the ADNI cohort provides evidence for the test's consistency over time, an important consideration for monitoring disease progression.

[0293] While several embodiments of the present invention have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and / or structures for performing the functions and / or obtaining the results and / or one or more of the advantages described herein, and each of such variations and / or modifications is deemed to be within the scope of the present invention. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and / or configurations will depend upon the specific application or applications for which the teachings of the present invention is / are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, the invention may be practiced otherwise than as specifically described and claimed. The present invention is directed to each individual feature, system, article, material, and / or method described herein. In addition, any combination of two or more such features, systems, articles, materials, and / or methods, if such features, systems, articles, materials, and / or methods are not mutually inconsistent, is included within the scope of the present invention.

[0294] As used herein in the specification and in the claims, the phrase “at least a portion” means some or all.

[0295] The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one. The phrase “and / or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Other elements may optionally be present other than the elements specifically identified by the “and / or” clause, whether related or unrelated to those elements specifically identified unless clearly indicated to the contrary. Thus, as a non-limiting example, a reference to “A and / or B,” when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A without B (optionally including elements other than B); in another embodiment, to B without A (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

[0296] As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating items in a list, “or” or “and / or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0297] As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and / or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

[0298] Unless clearly indicated to the contrary, concentrations described herein are on a mass basis.

[0299] As used herein, “wt%” is an abbreviation of weight percentage.

[0300] Some embodiments may be embodied as a method, of which various examples have been described. The acts performed as part of the methods may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include different (e.g., more or less) acts than those that are described, and / or that may involve performing some acts simultaneously, even though the acts are shown as being performed sequentially in the embodiments specifically described above.

[0301] Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.

[0302] In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of’ and “consisting essentially of’ shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03.

Claims

CLAIMSWhat is claimed is:

1. A method for performing a multi-biomarker beta-amyloid status determining assay, the method comprising: obtaining one or more blood or blood-derived samples previously obtained from a subject; performing a first assay on the one or more blood or blood-derived samples to obtain a value indicative of a concentration of a phosphorylated Tau (p-Tau) in the one or more blood or blood-derived samples; performing one or more additional assays on the one or more blood or blood- derived samples to obtain values of one or more additional biomarkers associated with Alzheimer's disease; and using at least one processor to perform: comparing the value indicative of the concentration of the p-Tau to a lower threshold and an upper threshold to determine whether the value indicative of the concentration of the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; and after determining that the value indicative of the concentration of the p- Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the value indicative of the concentration of the p-Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood-derived samples are associated with a particular betaamyloid status.

2. The method of claim 1, wherein the p-Tau is one of phosphorylated Tau 217 (p- Tau 217), phosphorylated Tau 181 (p-Tau 181), phosphorylated Tau 231 (p-Tau 231), phosphorylated Tau 205 (p-Tau 205), phosphorylated Tau 212 (p-Tau 212), phosphorylated Tau 262 (p-Tau 262), or phosphorylated Tau 356 (p-Tau 356).

3. The method of any one of claims 1-2, wherein the p-Tau is one of phosphorylated Tan 217 (p-Tau 217), phosphorylated Tau 181 (p-Tau 181), or phosphorylated Tau 231 (p-Tau 231).

4. The method of any one of claims 1-3, wherein the p-Tau is p-Tau 217.

5. The method of any one of claims 1-4, wherein the beta- amyloid status is a betaamyloid PET status or a beta- amyloid CSF status.

6. The method of any one of claims 1-5, further comprising: comparing the likelihood that the one or more blood or blood-derived samples are associated with the particular beta-amyloid status to an upper beta-amyloid classification threshold and a lower beta-amyloid classification threshold to classify the one or more blood or blood-derived samples as beta-amyloid positive, beta-amyloid negative, or indeterminant.

7. The method of claim 6, wherein the upper beta-amyloid classification threshold is greater than or equal to 0.55 and less than or equal to 0.95, and wherein the lower beta-amyloid classification threshold is greater than or equal to 0.20 and less than or equal to 0.50.

8. The method of claim 6, wherein the upper beta-amyloid classification threshold is greater than or equal to 0.60 and less than or equal to 0.80, and wherein the lower beta-amyloid classification threshold is greater than or equal to 0.35 and less than or equal to 0.50.

9. The method of claim 6, wherein the upper beta-amyloid classification threshold is greater than or equal to 0.55 and less than or equal to 0.65, and wherein the lower beta-amyloid classification threshold is greater than or equal to 0.35 and less than or equal to 0.45.

10. The method of any one of claims 1-9, wherein the particular beta-amyloid status is beta-amyloid positive or beta-amyloid negative.

11. The method of any one of claims 1-10, wherein the one or more additional biomarkers associated with Alzheimer’s disease comprise one or more of amyloid P42 / P40 ratio, neurofilament light (NfL), and Glial Fibrillary Acidic Protein (GFAP).

12. The method of any one of claims 1-11, wherein the one or more additional biomarkers associated with Alzheimer’s disease comprise amyloid P42 / P40 ratio, neurofilament light (NfL), and Glial Fibrillary Acidic Protein (GFAP).

13. The method of any one of claims 1-12, wherein the upper threshold and the lower threshold are values indicative of respective concentrations of the p-Tau.

14. The method of any one of claims 1-13, wherein the upper threshold is greater than or equal to 0.085 pg / mL and less than or equal to 0.095 pg / mL, and wherein the lower threshold is greater than or equal to 0.035 pg / mL and less than or equal to 0.045 pg / mL.

15. The method of any one of claims 1-14, wherein the upper threshold is a first value indicative of a first likelihood that the one or more blood or blood-derived samples are beta-amyloid positive, wherein the first likelihood is greater than or equal to 80%, wherein the lower threshold is a second value indicative of a second likelihood that the one or more blood or blood-derived samples are beta-amyloid positive, wherein the second likelihood is less than or equal to 20%.

16. The method of any one of claims 1-15, wherein the one or more blood or blood- derived samples comprise one or more plasma samples.

17. The method of any one of claims 1-16, further comprising:obtaining, for one or more second blood or blood-derived samples previously obtained from a second subject, one or more values for a respective one or more of the one or more additional biomarkers associated with Alzheimer’s disease, wherein each of the one or more values is indicative of a concentration of a respective biomarker of the one or more additional biomarkers associated with Alzheimer’s disease; and determining that each of the one or more values is greater than or equal to a respective upper threshold or less than or equal to a respective lower threshold.

18. The method of claim 17, further comprising: determining, based on the one or more values, a likelihood that the one or more second blood or blood-derived samples are associated with the particular beta-amyloid status.

19. The method of any one of claims 17-18, wherein determining the likelihood that the one or more second blood or blood-derived samples comprises: processing the one or more values using a second statistical model to obtain the likelihood that the one or more second blood or blood-derived samples are associated with the particular beta- amyloid status.

20. The method of any one of claims 1-19, wherein the statistical model is a machine learning model.

21. The method of any one of claims 1-20, wherein the statistical model is a generalized linear model.

22. The method of claim 21, wherein the generalized linear model is a logistic regression model.

23. The method of any one of claims 1-22, further comprising: obtaining a normalization value; and dividing the value indicative of a concentration of the p-Tau by the normalization value to obtain a normalized p-Tau value; wherein:comparing the value indicative of the concentration of the p-Tau to a lower threshold and an upper threshold further comprises comparing the normalized p-Tau value to the lower threshold and the upper threshold to determine whether the normalized p-Tau value is greater than or equal to the lower threshold and less than or equal to the upper threshold; and the method further comprises, after determining that the normalized p-Tau value is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the normalized p-Tau value and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood- derived samples are associated with a particular beta-amyloid status.

24. The method of claim 23, wherein the normalization value is a value indicative a concentration of a normalization biomarker in the one or more blood or blood-derived samples.

25. The method of claim 24, wherein the normalization biomarker is (a) unphosphorylated Tau or (b) total Tau or fragments thereof.

26. The method of any one of claims 23-25, wherein obtaining the normalization value comprises performing a normalization assay on the one or more blood or blood- derived samples to obtain the normalization value.

27. The method of any one of claims 1-26, wherein performing the one or more additional assays comprises performing the one or more additional assays after determining that the value indicative of the concentration of the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold.

28. The method of any one of claims 1-27, further comprising obtaining the one or more blood or blood-derived samples from the subject.

29. The method of any one of claims 1-28, further comprising:generating a report indicating the likelihood that the one or more blood or blood- derived samples are associated with a particular beta-amyloid status.

30. The method of claim 29, wherein generating the report indicating the likelihood comprises generating a graphical user interface (GUI) that indicates the likelihood.

31. The method of any one of claims 1-30, wherein performing the first assay comprises: exposing capture objects, each having affinity for molecules of the p-Tau, to a solution containing or derived from the one or more blood or blood-derived samples, immobilizing the molecules of the p-Tau with respect to the capture objects such that at least some of the capture objects associate with at least one molecule of the p-Tau from the one or more blood samples and a statistically significant fraction of the capture objects do not associate with any of molecules of the p-Tau from the one or more blood samples; determining a measure indicative of the number or fraction of capture objects associated with at least one molecule of the p-Tau from the one or more blood samples; and determining the value indicative of the concentration of the p-Tau in the one or more blood or blood-derived samples based at least in part on the measure indicative of the number or fraction of capture objects determined to be associated with at least one molecule of the p-Tau.

32. A system, comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing processorexecutable instructions that, when executed by the at least one processor, causes the at least one processor to perform a method for computationally analyzing a multibiomarker beta-amyloid status determining assay, the method comprising: obtaining, for one or more blood or blood-derived samples previously obtained from a subject, a plurality of values for a respective plurality of biomarkers associated with Alzheimer’s disease, each of the values being indicative of a concentration of its respective biomarker, the plurality of valuescomprising a value for a phosphorylated Tau (p-Tau) and one or more values for one or more additional biomarkers associated with Alzheimer's disease; comparing the value for the p-Tau to a lower threshold and an upper threshold to determine whether the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; and after determining that the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the value for the p-Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood-derived samples are associated with a particular beta- amyloid status.

33. The system of claim 32, further comprising a sample analysis platform, wherein obtaining the plurality of values for the respective plurality of biomarkers comprises processing the one or more blood or blood-derived samples using the sample analysis platform to obtain the plurality of values.

34. The system of any one of claims 32-33, wherein the method further comprises performing the method of any one of claims 2-25 and 29-30.

35. At least one non-transitory computer-readable storage medium storing processorexecutable instructions that, when executed by at least one processor, causes the at least one processor to perform a method for computationally analyzing a multi-biomarker beta-amyloid status determining assay, the method comprising: obtaining, for one or more blood or blood-derived samples previously obtained from a subject, a plurality of values for a respective plurality of biomarkers associated with Alzheimer’s disease, each of the values being indicative of a concentration of its respective biomarker, the plurality of values comprising a value for a phosphorylated Tau (p-Tau) and one or more values for one or more additional biomarkers associated with Alzheimer's disease; comparing the value for the p-Tau to a lower threshold and an upper threshold to determine whether the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; andafter determining that the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the value for the p- Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood-derived samples are associated with a particular beta-amyloid status.

36. The at least one non-transitory computer-readable storage medium of claim 35, wherein the method further comprises performing the method of any one of claims 2-25 and 29-30.

37. A method for computationally analyzing a multi-biomarker beta- amyloid status determining assay, the method comprising: using at least one processor to perform: obtaining, for one or more blood or blood-derived samples previously obtained from a subject, a plurality of values for a respective plurality of biomarkers associated with Alzheimer’s disease, each of the values being indicative of a concentration of its respective biomarker, the plurality of values comprising a value for a phosphorylated Tau (p-Tau) and one or more values for one or more additional biomarkers associated with Alzheimer's disease; comparing the value for the p-Tau to a lower threshold and an upper threshold to determine whether the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; and after determining that the value for the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold, processing the value for the p-Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a likelihood that the one or more blood or blood-derived samples are associated with a particular beta- amyloid status.

38. The method of claim 37, further comprising performing the method of any one of claims 2-25 and 29-30.

39. A system, comprising:at least one processor; and at least one non-transitory computer-readable storage medium storing processorexecutable instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for computationally analyzing a multibiomarker beta-amyloid status determining assay, the method comprising: obtaining, for one or more blood or blood-derived samples previously obtained from a subject, a value indicative of a concentration of a phosphorylated Tau (p-Tau); comparing the value indicative of the concentration of the p-Tau to a lower threshold and an upper threshold to determine whether the value indicative of the concentration of the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; after determining that the value indicative of the concentration of the p- Tau is greater than the upper threshold or less than the lower threshold, determining a first likelihood that the one or more blood or blood-derived are associated with a particular beta-amyloid status using the value indicative of the concentration of the p-Tau; and after determining that the value indicative of the concentration of the p- Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold: obtaining, for the one or more blood or blood-derived samples, one or more values for a respective one or more additional biomarkers associated with Alzheimer’s disease, each of the one or more values being indicative of a concentration of its respective biomarker; and processing the value indicative of the concentration of the p-Tau and the one or more values for the one or more additional biomarkers using a statistical model to obtain an output indicative of a second likelihood that the one or more blood or blood-derived samples are associated with a particular beta-amyloid status.

40. The system of claim 39, wherein the method comprises performing the method of any one of claims 2-16, 20-25, and 28-30.

41. The system of any one of claims 39-40, wherein the method further comprises: obtaining, for one or more second blood or blood-derived samples previously obtained from a second subject, a second value indicative of a second concentration of p- Tau; comparing the second value indicative of the second concentration of the p-Tau to the lower threshold and the upper threshold to determine whether the second value indicative of the second concentration of the p-Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold; and after determining that the value indicative of the second concentration of the p- Tau is greater than the upper threshold or less than the lower threshold, determining a first likelihood that the one or more second blood or blood-derived are associated with the particular beta-amyloid status using the value indicative of the second concentration of the p-Tau.

42. The system of claim 41, wherein the method further comprises: after determining that the value indicative of the second concentration of the p- Tau is greater than or equal to the lower threshold and less than or equal to the upper threshold: obtaining, for the one or more second blood or blood-derived samples, one or more second values for the respective one or more additional biomarkers associated with Alzheimer’s disease, each of the one or more second values being indicative of a concentration of its respective biomarker; and processing the second value indicative of the second concentration of the p-Tau and the one or more second values for the one or more additional biomarkers using the statistical model to obtain an output indicative of a second likelihood that the one or more second blood or blood-derived samples are associated with the particular beta-amyloid status.

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