Methods and related aspects for analyzing samples

Abstract

Examples may provide a method of analyzing a sample. The method includes contacting the sample with a composition comprising colloidal plasmonic nanoparticles under conditions sufficient for analyte molecules, if present, in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles to form a colloidal mixture. The methods also include exposing the colloidal mixture to incident excitation electromagnetic radiation such that the surfaces of the colloidal plasmonic nanoparticles exhibit surface plasmon resonance (SPR), and detecting Raman scattering signal emanated from adsorbed analyte molecules, if present, to determine a presence or absence of the analyte molecules in the sample. Related methods, kits, compositions, and systems are also provided.

Description

Attorney Docket No. 0184.0336-PCT (C18473_P 18473-02)METHODS AND RELATED ASPECTS FOR ANALYZING SAMPLESCross-Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Patent Application Ser. No. 63 / 743,671, filed January 10, 2025, the disclosure of which is incorporated herein by reference.Statement Regarding Federally Sponsored Research or Development

[0002] This invention was made with government support under grant GM149272 awarded by the National Institutes of Health and the TEDCO Maryland Innovation Initiative (MH). The government has certain rights in the invention.Field

[0003] This disclosure relates generally to sample analysis, e.g., in the context of surface-enhanced Raman spectroscopy.Background

[0004] Maintaining consistent quality in biopharmaceutical manufacturing is essential for producing complex biologies, such as monoclonal antibodies, viral vectors, and cell therapies. Even small variations in key process parameters or critical quality attributes (CQAs) can result in expensive batch failures, product recalls, and significant regulatory challenges. To mitigate these risks, real-time process analytical technologies (PAT) are essential for monitoring key cell culture parameters, such as metabolite concentrations, and providing actionable feedback on CQAs, including protein aggregation and glycosylation patterns. However, current monitoring methods, such as high-performance liquid chromatography (HPLC) and mass spectrometry, are costly, require extensive offline analysis, and can introduce production delays of up to 48 hours. These delays in real-time data capture and analysis create bottlenecks in bioprocess optimization and batch control, leading to inefficiencies and increased production costs. Such challenges highlight the urgent need for innovative PAT tools that can offer rapid, precise, and cost-effective analytical solutions to enhance biopharmaceutical manufacturing practices.

[0005] Raman spectroscopy is a nondestructive bioanalytical technique with high molecular specificity. It is particularly attractive as a PAT tool, as it is capable of monitoring multiple molecules simultaneously, in-line and at-line, and in both upstream and downstream applications. Building on this capability, surface-enhanced Raman spectroscopy (SERS) leverages plasmonic nanostructures to boost weak Raman signals and provides the sensitivity required for detecting key metabolites, impurities, and other critical process parameters (CPPs). Nevertheless, conventional SERS suffers from considerable intensity fluctuations. This can be attributed to various reasons, such as the inhomogeneous distribution of both hotspots and analytes on a plasmonic substrate, as well as the highly dynamic analyte-metal interactions, which compromises the achievable reproducibility. While single antibody-based spectro-immunoassays displayed strong capability to overcome the SERS intensity fluctuations by transducing frequency-shift signals based on nanomechanical perturbations of antibody-conjugated Raman molecules as a result of antibody-antigen interactions, this approach cannot be easily extended for label-free analysis of cell culture media.

[0006] Recently, a digital SERS protocol for chemical analysis was proposed to overcome the SERS intensity fluctuation issues by converting SERS intensity signals into a digital binary signal in the form or “ON” or “OFF” based on a predefined intensity threshold. This effectively reduces false positives and allows digital visualization of single-molecule events, which significantly facilitates ultrasensitive detection of analytes, particularly at ultralow concentrations where the analyte-metal interactions primarily occur at the single-molecule level. Nevertheless, the performance of substrate-based digital SERS is predicated on rationally designed two-dimensional (2D) plasmonic substrates to maximize SERS enhancement, and therefore, is still vulnerable to the inhomogeneous distribution of hotspots and analytes on the plasmonic substrate. Moreover, the short spatial decay length of the plasmonic fields perpendicular to the substrate limits the effective SERS enhancement to this very thin layer in close proximity to the substrate. Additionally, the dewetting process of analytes on a substrate is time-consuming, often occurs in an uncontrolled manner, and could even prevent the analytes from being in close contact with the substrate owing to thedifference in their respective surface energy. This further underscores the plethora of challenges confronting the substrate-based SERS arrays.

[0007] While 2D plasmonic substrates are constrained by those challenges, colloidal plasmonic nanoparticles (e.g. gold, silver, copper, metallic alloy nanoparticles, and the like) could be potentially utilized as a highly reproducible liquid plasmonic platform for digital SERS analysis of various analytes owing to the colloidal homogeneity. Given the stochastic nature of the interactions between colloidal plasmonic nanoparticles and the analytes, especially when the analytes have a low concentration, the digital SERS analysis can accurately capture positive nanoparticleanalyte interaction events and convert them into digital signals not directly affected by the absolute SERS intensity. Recent demonstrations of digital colloid-enhanced Raman spectroscopy validated the feasibility of colloidal digital SERS assays, where reproducible quantification of various analytes was demonstrated with single-molecule counting at very low concentrations, limited only by the Poisson noise of the measurement process. Despite the promise, existing digital colloidal SERS is limited by the modest SERS enhancements from sphere-shaped gold nanoparticles and requires the analytes to possess a characteristic SERS peak, which significantly limits its applicability.

[0008] Accordingly, there exists an unmet need for additional SERS assays with improved reproducibility and reliability relative to conventional SERS assays, among other attributes.Summary

[0009] The present disclosure provides, in certain aspects, a deep learning-powered colloidal digital SERS assay by combining artificial intelligence with gold nanostar-based SERS spectroscopy. Exemplary enabling innovations of this integrated deep learning-SERS assay platform include: first, the homogeneous distribution of the colloidal plasmonic nanoparticles and analytes can deliver a high level of reproducibility, which remains elusive for substrate-based SERS assays. Second, the dynamic colloidal environment allows all the analytes to enjoy concentration-correlated probability to interact with the plasmonic nanoparticles, allowing quantitative digital SERS analysis. In contrast, for substrate-based SERSassays, only those analytes located within the plasmonic field decay length can be effectively detected, while those beyond the decay length are largely missed in the acquired SERS spectra. Third, geometrically heterogeneous colloidal plasmonic nanoparticles, such as gold nanostars, possess significant SERS enhancements and wide spectral tunability, while the prevailing gold nanoparticles with a sphere shape can only provide a modest SERS enhancement with limited spectral tunability. Fourth, digital SERS analysis circumvents the SERS intensity fluctuation issues and eliminates false signals. Leveraging the digital SERS counts to establish the correlation with the analyte concentration also enables single-molecule events to be accurately captured, which could either be missed because of limited sampling for substrate-based SERS assays or obscured by the background after averaging if the mean SERS intensity-based traditional approach was implemented. Fifth, the deep learning regression analysis leverages an artificial neural network (ANN) algorithm to predict the analyte concentration by extracting the hidden features based on studying the entire spectral features, which, without relying on any characteristic peaks, significantly expands the applicability of the colloidal SERS assay. Ultimately, the integrated deep learning-powered colloidal digital SERS assay platform disclosed herein provides a highly scalable strategy for rapid and accurate monitoring various components in cell culture media and other sample types. These and other aspects will be apparent upon a complete review of the present disclosure, including the accompanying figures.

[0010] According to various embodiments, a method of analyzing a sample is presented. The method includes contacting the sample with a composition comprising colloidal plasmonic nanoparticles under conditions sufficient for one or more analyte molecules, if present, in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles to form a colloidal mixture; exposing the colloidal mixture to incident excitation electromagnetic radiation such that the surfaces of the colloidal plasmonic nanoparticles exhibit surface plasmon resonance (SPR); and detecting Raman scattering signal emanated from adsorbed analyte molecules, if present, to determine a presence or absence of the analyte molecules in the sample, thereby analyzing the sample.

[0011] Various optional features of the above embodiments include the following. The method comprises higher levels of reproducibility than a dried substratebased approach to analyzing the sample. The sample comprises a substantially unprocessed sample. The sample comprises a sample type selected from the group consisting of: a clinical sample, a forensics sample, a cell culture sample, an environmental sample, and a food safety sample. The method comprises agitating the sample contacted with the fluidic composition to form the colloidal mixture. The colloidal plasmonic nanoparticles are substantially homogeneously distributed in the colloidal mixture. The colloidal plasmonic nanoparticles comprise metallic plasmonic nanoparticles. The colloidal plasmonic nanoparticles are substantially non-spherical in shape. The colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles. The heterogeneous plasmonic nanoparticles comprise nanostars and other shaped plasmonic nanoparticles, such as rod, triangle, cube, etc. The colloidal plasmonic nanoparticles comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles. The colloidal plasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles. The colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles. The coating shell is not limited to silica, and could be any dielectric or semiconductor shell. The colloidal mixture lacks a dried substrate.

[0012] Various additional optional features of the above embodiments include the following. The method comprises quantifying the analyte molecules when present in the sample. The analyte molecules are present in the sample. The analyte molecules comprise organic molecules. The analyte molecules comprise inorganic molecules. The analyte molecules comprise biomolecules. The biomolecules comprise nucleic acid molecules, protein molecules, carbohydrate molecules, lipid molecules, and / or combinations thereof. The analyte molecules are unlabeled. The analyte molecules are labeled. The analyte molecules are bound to receptor moieties attached to the surfaces of the colloidal plasmonic nanoparticles. The receptor moieties comprise antibodies or antigen binding portions thereof. The receptor moieties comprise aptamers. The sample is obtained from a subject. The presence orabsence of the analyte molecules in the sample is indicative of a property, state, or condition of the subject. The detecting step comprises performing one or more data analysis techniques selected from the group consisting of: conventional surface-enhanced Raman spectroscopy (SERS) intensity-based regression, digital SERS-based regression, and deep learning regression. The detecting step produces concentration-correlated digital surface-enhanced Raman spectroscopy (SERS) counts of the analyte molecules. A kit comprises the colloidal plasmonic nanoparticles contained in a container that is suitable for performing the method.

[0013] According to various embodiments, a kit is presented. The kit comprises a container that contains a composition comprising geometrically heterogeneous colloidal plasmonic nanoparticles that each comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles. In some embodiments, the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles. In some embodiments, the colloidal plasmonic nanoparticles comprise nanostars.

[0014] According to various embodiments, a colloidal mixture is presented. The colloidal mixture comprises a sample that comprises one or more analyte molecules; and, a composition comprising colloidal plasmonic nanoparticles, wherein the colloidal mixture is disposed under conditions sufficient for the analyte molecules in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles.

[0015] Various optional features of the above embodiments include the following. The sample comprises a substantially unprocessed sample. The sample comprises a sample type selected from the group consisting of: a clinical sample, a forensics sample, a cell culture sample, an environmental sample, a forensics sample, and a food safety sample. The colloidal plasmonic nanoparticles are substantially homogeneously distributed in the colloidal mixture. The colloidal plasmonic nanoparticles comprise metallic plasmonic nanoparticles. The colloidal plasmonic nanoparticles are substantially non-spherical in shape. The colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles. The heterogeneous plasmonic nanoparticles comprise nanostars. The colloidal plasmonic nanoparticles comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles. The colloidalplasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles. The colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles. The colloidal mixture lacks a dried substrate. The analyte molecules comprise organic molecules. The analyte molecules comprise inorganic molecules. The analyte molecules comprise biomolecules. The biomolecules comprise nucleic acid molecules, protein molecules, carbohydrate molecules, lipid molecules, and / or combinations thereof. The analyte molecules are unlabeled. The analyte molecules are labeled. The analyte molecules are bound to receptor moieties attached of the surfaces to the colloidal plasmonic nanoparticles. The receptor moieties comprise antibodies or antigen binding portions thereof. The receptor moieties comprise aptamers.

[0016] According to various embodiments, a system is presented. The system comprises a sample container receiving area configured to receive a colloidal mixture that comprises a sample and a composition comprising colloidal plasmonic nanoparticles, wherein the colloidal mixture is disposed under conditions sufficient for one or more analyte molecules, if present, in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles; an electromagnetic radiation source configured to provide incident excitation electromagnetic radiation such that the surfaces of the colloidal plasmonic nanoparticles exhibit surface plasmon resonance (SPR); an electromagnetic radiation detector configured to detect Raman scattering signal emanated from adsorbed analyte molecules, if present, in the sample; and, a controller operably connected to the electromagnetic radiation source and to the electromagnetic radiation detector, which controller comprises a processor, and a memory communicatively coupled directly or remotely to the processor, the memory storing non-transitory computer executable instructions which, when executed by the processor, perform operations comprising: detecting the Raman scattering signal emanated from the adsorbed analyte molecules, if present, to determine a presence or absence of the analyte molecules in the sample.

[0017] Various additional optional features of the above embodiments include the following. The colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles. The colloidal plasmonic nanoparticlescomprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles. The colloidal plasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles. The colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles. The colloidal mixture lacks a dried substrate. The non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising: quantifying the analyte molecules when present in the sample. The sample is obtained from a subject and wherein the non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising: identifying a property, state, or condition of the subject based at least in part on the presence or absence of the analyte molecules determined in the sample. The non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising: performing one or more data analysis techniques selected from the group consisting of: conventional surface-enhanced Raman spectroscopy (SERS) intensity-based regression, digital SERS-based regression, and deep learning regression. The non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising: producing concentration-correlated digital surface-enhanced Raman spectroscopy (SERS) counts of the analyte molecules when present in the sample.Drawings

[0018] The above and / or other aspects and advantages will become more apparent and more readily appreciated from the following detailed description of examples, taken in conjunction with the accompanying drawings, in which:

[0019] Fig. 1 is a flow chart that schematically shows exemplary method steps of analyzing a sample according to some aspects disclosed herein.

[0020] Fig. 2 is a schematic diagram of an exemplary system suitable for use with certain aspects disclosed herein.

[0021] Figs. 3A-3G. Scheme for deep learning-powered SERS for cell culture media monitoring, (a) Schematic of a bioreactor, (b) mixing of media analytes andcolloidal gold nanostars (inset: a TEM image of a gold nanostar) and loading of the mixture into a quartz capillary tube, (c) protocol for Raman spectroscopy measurements and digital SERS analysis (d) schematic SERS dataset, (e) artificial neural networks-based deep learning model, (f) regression analysis, and (g) concentration prediction.

[0022] Figs. 4A-4I. Colloidal SERS assay for detection of R6G in D.l. water, (a-c) SERS intensity analysis, where (a) represents the acquired SERS spectra with various R6G concentrations as specified, (b) the SERS intensity in relation to R6G concentration, and (c) the corresponding coefficient of variations (CV). (d-f) Digital SERS analysis, where (d) represents the distribution of digital SERS signals with various R6G concentrations across three repeats, (e) percentage of positive digital SERS counts in relation to the R6G concentration, and (f) the corresponding CV. (g-i) Deep learning analysis, where (g) represents the side-by-side comparison and (h) the correlation between the true and predicted concentrations, (i) the corresponding CV.

[0023] Figs. 5A-5F. Digital SERS analysis of (a-b) glucose and (c-d) tryptophan, both in D. I. water, (e-f) Deep learning analysis of glutathione in D.l. water.

[0024] Figs. 6A-6I. Cell culture media monitoring (AMBIC media 1.1). Detection of (a-c) tryptophan, (d-f) phenylalanine, and (g-i) glucose in cell culture media.Definitions

[0025] In order for the present disclosure to be more readily understood, certain terms are first defined below. Additional definitions for the following terms and other terms may be set forth throughout the specification. If a definition of a term set forth below is inconsistent with a definition in an application or patent that is incorporated by reference, the definition set forth in this application should be used to understand the meaning of the term.

[0026] As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, a reference to “a method” includes one or more methods, and / or steps of the type described herein and / or which will become apparent to those persons skilled in the art upon reading this disclosure and so forth.

[0027] It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. Further, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In describing and claiming the methods, systems, and computer readable media, the following terminology, and grammatical variants thereof, will be used in accordance with the definitions set forth below.

[0028] Classifier. As used herein, “classifier” generally refers to algorithm computer code that receives, as input, test data and produces, as output, a classification of the input data as belonging to one or another class.

[0029] Data set: As used herein, “data set” refers to a group or collection of information, values, or data points related to or associated with one or more objects, records, and / or variables. In some embodiments, a given data set is organized as, or included as part of, a matrix or tabular data structure. In some embodiments, a data set is encoded as a feature vector corresponding to a given object, record, and / or variable, such as a given test or reference subject. For example, a medical data set for a given subject can include one or more observed values of one or more variables associated with that subject.

[0030] Artificial neural network: As used herein, “artificial neural network” refers to a machine learning algorithm or model that includes layers of at least partially interconnected artificial neurons (e.g., perceptrons or nodes) organized as input and output layers with one or more intervening hidden layers that together form a network that is or can be trained to classify data, such as test subject medical data sets (e.g., medical images or the like).

[0031] Labeled: As used herein, “labeled” or “assigned” in the context of data sets or points refers to data that is classified as, or otherwise associated with, having or lacking a given characteristic or property.

[0032] Machine Learning Algorithm: As used herein, "machine learning algorithm" generally refers to an algorithm, executed by computer, that automates analytical model building, e.g., for clustering, classification or pattern recognition. Machine learning algorithms may be supervised or unsupervised. Learning algorithms include, for example, artificial neural networks (e.g., back propagation networks),discriminant analyses (e.g., Bayesian classifier or Fisher’s analysis), multiple-instance learning (MIL), support vector machines, decision trees (e.g., recursive partitioning processes such as CART -classification and regression trees, or random forests), linear classifiers (e.g., multiple linear regression (MLR), partial least squares (PLS) regression, and principal components regression), hierarchical clustering, and cluster analysis. A dataset on which a machine learning algorithm learns can be referred to as "training data." A model produced using a machine learning algorithm is generally referred to herein as a “machine learning model.”

[0033] Subject: As used herein, “subject” or “test subject” refers to an animal, such as a mammalian species (e.g., human) or avian (e.g., bird) species. More specifically, a subject can be a vertebrate, e.g., a mammal such as a mouse, a primate, a simian ora human. Animals include farm animals (e.g., production cattle, dairy cattle, poultry, horses, pigs, and the like), sport animals, and companion animals (e.g., pets or support animals). A subject can be a healthy individual, an individual that has or is suspected of having a disease or pathology or a predisposition to the disease or pathology, or an individual that is in need of therapy or suspected of needing therapy. The terms “individual” or “patient” are intended to be interchangeable with “subject.” A “reference subject” refers to a subject known to have or lack specific properties (e.g., a known pathology, such as melanoma and / or the like).

[0034] System: As used herein, "system" in the context of analytical instrumentation refers a group of objects and / or devices that form a network for performing a desired objective.

[0035] Value: As used herein, “value” generally refers to an entry in a dataset that can be anything that characterizes the feature to which the value refers. This includes, without limitation, numbers, words or phrases, symbols (e.g., + or -) or degrees.Description of the Embodiments

[0036] Reference will now be made in detail to example implementations. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention and it is to be understood that other embodiments may beutilized and that changes may be made without departing from the scope of the invention. The following description is, therefore, merely exemplary.

[0037] I. Description of Example Embodiments

[0038] Advanced analytical techniques are essential in various domains, such as chemical analysis, biomedical diagnostics, biopharmaceutical monitoring, environmental surveillance, food safety, forensic sciences, and homeland security. Over the past decades, surface-enhanced Raman spectroscopy (SERS) has established itself as a powerful tool for both labelled and label-free analysis of a wide range of analytes. Particularly, augmented by significant electromagnetic field enhancements on plasmonic substrates, SERS enables analysis of trace amounts of analytes with a high molecular specificity in a non-contact and non-destructive manner.

[0039] Nevertheless, the prevailing SERS assays are substrate-based, and the data collection is performed on the dried substrate. This often requires a well-designed two-dimensional (2D) nanostructure, ora non-plasmonic substrate (e.g. quartz, silicon, or the like) on which plasmonic nanoparticles are captured and dried prior to SERS spectral collection, or a combination of both. While the substrate-based design is well suited to maximize SERS enhancement by rationally tailoring the 2D plasmonic nanostructures, it is limited by the heterogeneity of the distributions of both the plasmonic hotspots and the analytes, undermining the desired reproducibility. The short spatial decay length of ~20 nm of the plasmonic fields perpendicular to the substrate also limits the effective SERS enhancement to this very thin layer of analytes in close proximity to the substrate. Moreover, the dewetting process is timeconsuming, often occurs in an uncontrolled manner, and can even prevent the analytes from being in close contact with the substrate owing to the surface energy difference. Additionally, it has been recently observed that the dynamic and complex interactions between analytes and 2D metallic substrates can give rise to marked SERS intensity fluctuations, particularly when there is a significant SERS enhancement, further underscoring the plethora of challenges confronting the design of substrate-based SERS arrays.

[0040] Accordingly, in some aspects, the present disclosure provides a colloidal plasmonic assay for label-free SERS sensing. In some embodiments, colloidalplasmonic nanoparticles (e.g., gold, silver, copper, metallic alloys, and the like) are utilized as a liquid platform for SERS detection of various analytes, which is fundamentally different from the substrate-based SERS assay. Owing to the localized surface plasmon resonance supported by colloidal plasmonic nanoparticles, they can provide significant SERS enhancement to detect analytes in their close proximity. In some embodiments, however, bare colloidal plasmonic nanoparticles tend to aggregate when mixed with real-world samples, such as human fluids (e.g., saliva, serum, urine, or the like), as the salts and complex biological matrices screen electrostatic repulsion which stabilizes ligands-conjugated colloidal nanoparticles. This can limit the applicability of bare nanoparticle-based colloidal plasmonic assays. In some embodiments, to address this challenge and expand the scope of applicability, the methods and related aspects disclosed herein utilize conformationally encapsulated colloidal plasmonic nanoparticles with an ultrathin silica or other layer, which has minimal, if any, effect on the SERS enhancement. Given the well-known stability and biocompatibility of silica shells, silica-shell encapsulated colloidal plasmonic nanoparticles provide a highly stable liquid platform for sensitive SERS detection of a wide variety of real-world samples unconstrained by the sample matrix.

[0041] In some embodiments, the collected SERS spectra of the assays disclosed herein are subject to three types of data analysis to establish a robust calibration curve, including conventional SERS intensity-based regression, digital SERS-based regression, and deep learning regression analysis. In conventional SERS intensity-based analysis, the SERS intensity of the peak characteristic of the analytes is correlated with the analyte concentration. In digital SERS analysis, each SERS spectrum is converted to a digital signal in the form of “on” or “off” based on a predefined signal threshold. This effectively filters the background, eliminates false signals, and circumvents the SERS intensity fluctuation issue, noted above. Given the stochastic nature of the interactions between colloidal plasmonic nanoparticles and the analytes, especially when the analytes have a low concentration, the digital SERS analysis can accurately capture those positive nanoparticle-analyte interaction events and convert them into positive digital signals regardless of the absolute SERS intensity. In deep learning regression analysis, the artificial neural network algorithm is implemented to analyze the collected SERS spectra, where, in some embodiments,80% of the data is used to train the artificial neural network model while the remaining 20% of the data is used fortesting. In some embodiments, prior to training the artificial neural network model, two types of outlier rejection methods, i.e. , Robust Principal Component Analysis (RPCA) and / or Interquartile Range (IQR) methods, are also utilized. RPCA is a multivariate method that decomposes the data matrix into a low-rank matrix and a sparse matrix, where the sparse matrix captures the outliers. IQR is a simple and effective method for univariate outlier detection, which is suitable for datasets where outliers can be detected in individual features. Given that these three complementary strategies provide incremental sophistication for SERS spectral analysis, the colloidal SERS assays disclosed herein deliver desired levels of performance for applications in various domains.

[0042] Exemplary attributes the colloidal SERS assays of the present disclosure include, but are not limited to, the following: First, the homogeneous distribution of the colloidal plasmonic nanoparticles and analytes can deliver a high level of reproducibility, which is a major issue for substrate-based SERS assays. Second, the dynamic colloidal environment allows all the analytes to enjoy a concentrationdependent chance to interact with the plasmonic nanoparticles, thus producing concentration-correlated digital SERS counts. In contrast, for substrate-based SERS assays, only those analytes located within the plasmonic field decay length can be effectively detected, while those beyond the decay length are largely unenhanced in the acquired SERS spectra. Third, geometrically heterogeneous colloidal plasmonic nanoparticles, such as gold nanostars, possess significant SERS enhancements and wide spectral tunability, while the prevailing gold nanoparticles with a sphere shape can only provide modest SERS enhancement with limited spectral tunability. Fourth, the strategy of conformational encapsulation with, for example, an ultrathin silica shell stabilizes the colloidal plasmonic nanoparticles, significantly extending the colloidal SERS assays for a wide range of applications. Fifth, the digital SERS analysis circumvents the SERS intensity fluctuation issues and eliminates false signals. Leveraging the digital SERS counts to establish the correlation with the concentration also provides for single-molecule events to be accurately captured, which can either be missed because of limited sampling for substrate-based SERS assays or obscured by the background after averaging if the mean SERS intensity-based traditionalapproach is used. Sixth, while complementing the SERS intensity- and digital SERS count-based regression analysis based on a single spectral peak, the deep learning regression analysis of the present disclosure leverages the artificial neural network algorithm to model and predict analyte concentration by extracting the hidden features based on studying the entire spectral features. Moreover, the assays of the present disclosure can be implemented in various ways, including, for example, in diagnostic test kits that deliver efficient and accurate results to guide early diagnosis of diseases, help combat epidemic outbreaks, address drug abuse issues, perform cell culture media monitoring, conduct environmental and food safety surveillance, and deployed for enhancing homeland security tasks, among other applications.

[0043] To illustrate, Fig. 1 is a flow chart that schematically shows exemplary method steps of analyzing a sample according to some aspects disclosed herein. As shown, method 100 includes contacting the sample with a composition comprising colloidal plasmonic nanoparticles under conditions sufficient for analyte molecules, if present, in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles to form a colloidal mixture (step 102). Method 100 also includes exposing the colloidal mixture to incident excitation electromagnetic radiation such that the surfaces of the colloidal plasmonic nanoparticles exhibit surface plasmon resonance (SPR) (step 104). In addition, method 100 also includes detecting Raman scattering signal emanated from adsorbed analyte molecules, if present, to determine a presence or absence of the analyte molecules in the sample (step 106). Typically, method 100 comprises higher levels of reproducibility than a dried substrate-based approach to analyzing the sample.

[0044] In some embodiments, the sample comprises a substantially unprocessed sample. In some embodiments, the sample comprises a sample type, such as, a clinical sample, a forensics sample, a cell culture sample, an environmental sample, a forensics sample, and a food safety sample, among other sample types. In some embodiments, method 100 includes agitating the sample contacted with the fluidic composition to form the colloidal mixture. In some embodiments, the sample is obtained from a subject and the presence or absence of the analyte molecules in the sample is indicative of a property, state, or condition of the subject.

[0045] The colloidal plasmonic nanoparticles utilized in method 100 include various embodiments. In some embodiments, for example, the colloidal plasmonic nanoparticles are substantially homogeneously distributed in the colloidal mixture. In some embodiments, the colloidal plasmonic nanoparticles comprise metallic plasmonic nanoparticles. In some embodiments, the colloidal plasmonic nanoparticles are substantially non-spherical in shape. In some embodiments, the colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles. In some embodiments, the heterogeneous plasmonic nanoparticles comprise nanostars. In some embodiments, the colloidal plasmonic nanoparticles comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles. In some embodiments, the colloidal plasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles. In some embodiments, the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles. In some embodiments, the colloidal mixture lacks a dried substrate.

[0046] In some embodiments, method 100 includes quantifying the analyte molecules when present in the sample. In some embodiments, the analyte molecules comprise organic molecules. In some embodiments, the analyte molecules comprise inorganic molecules. In some embodiments, the analyte molecules comprise biomolecules. In some embodiments, the biomolecules comprise nucleic acid molecules, protein molecules, carbohydrate molecules, lipid molecules, and / or combinations thereof. In some embodiments, the analyte molecules are unlabeled. In some embodiments, the analyte molecules are labeled. In some embodiments, the analyte molecules are bound to receptor moieties attached to the surfaces of the colloidal plasmonic nanoparticles. In some embodiments, the receptor moieties comprise antibodies or antigen binding portions thereof. In some embodiments, the receptor moieties comprise aptamers.

[0047] In some embodiments, the detecting step (step 106) comprises performing one or more data analysis techniques, such as conventional surface-enhanced Raman spectroscopy (SERS) intensity-based regression, digital SERS-based regression, and deep learning regression, among other techniques. In someembodiments, the detecting step (step 106) produces concentration-correlated digital surface-enhanced Raman spectroscopy (SERS) counts of the analyte molecules.

[0048] In some aspects, the present disclosure also provides various colloidal mixtures and kits. In some embodiments, for example, a colloidal mixture includes a sample that comprises one or more analyte molecules, and a composition comprising colloidal plasmonic nanoparticles in which the colloidal mixture is disposed under conditions sufficient for the analyte molecules in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles. In some embodiments, kits of the present disclosure include a container that contains a composition comprising geometrically heterogeneous colloidal plasmonic nanoparticles that each comprise an antiaggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

[0049] Fig. 2 is a schematic diagram of a hardware computer system 200 suitable for implementing various embodiments. For example, Fig. 2 illustrates various hardware, software, and other resources that can be used in implementations of any of the methods disclosed herein, including, e.g., method 100 and / or one or more instances of an artificial neural network. System 200 includes training corpus source 202 and computer 201. Training corpus source 202 and computer 201 may be communicatively coupled by way of one or more networks 204, e.g., the internet. Training corpus source 202 may include a training corpus and / or a testing corpus as disclosed herein.

[0050] Computer 201 may be implemented as any of a desktop computer, a laptop computer, can be incorporated in one or more servers, clusters, or other computers or hardware resources, or can be implemented using cloud-based resources. Computer 201 includes volatile memory 214 and persistent memory 212, the latter of which can store computer-readable instructions, that, when executed by electronic processor 210, configure computer 201 to perform any of the methods disclosed herein, including method 100, and / or form or store any artificial neural network, and / or perform any classification or other data analysis technique as described herein. Computer 201 further includes network interface 208, which communicatively couples computer 201 to training corpus and / or testing corpussource 202 via network 204. Other configurations of system 200, associated network connections, and other hardware, software, and service resources are possible.

[0051] Certain embodiments can be performed using a computer program or set of programs. The computer programs can exist in a variety of forms both active and inactive. For example, the computer programs can exist as software program(s) comprised of program instructions in source code, object code, executable code or other formats; firmware program(s), or hardware description language (HDL) files. Any of the above can be embodied on a transitory or non-transitory computer readable medium, which include storage devices and signals, in compressed or uncompressed form. Exemplary computer readable storage devices include conventional computer system RAM (random access memory), ROM (read-only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), and magnetic or optical disks or tapes.

[0052] As also shown, system 200 also includes subassembly 216 communicatively coupled via network 204. Subassembly 216 includes sample container receiving area 218 configured to receive colloidal mixtures as disclosed herein disposed in sample container 220 (shown as a multi-well plate). Subassembly 216 also includes electromagnetic radiation source 222 configured to provide incident excitation electromagnetic radiation such that the surfaces of the colloidal plasmonic nanoparticles exhibit surface plasmon resonance (SPR). In addition, subassembly 216 also includes electromagnetic radiation detectors 224 that are configured to detect Raman scattering signal (R) emanated from adsorbed analyte molecules, if present, in the colloidal mixtures disposed in sample container 220.

[0053] II. Examples

[0054] Example 1: Deep Learning-Powered Colloidal Digital SERS for Cell Culture Media Monitoring

[0055] 1. Introduction

[0056] Maintaining consistent quality in biopharmaceutical manufacturing is essential for producing high-quality complex biologies. Yet, current process analytical technologies (PAT) lack the capability to perform rapid and highly accurate monitoring of critical process parameters (CPPs) and critical quality attributes (CQAs). WhileRaman spectroscopy holds great promise as a highly sensitive and specific bioanalytical tool for PAT applications, its conventional implementation, surface-enhanced Raman spectroscopy (SERS), are constrained by considerable temporal and spatial intensity fluctuations, limiting the achievable reproducibility and reliability. In this example, we introduce a deep learning-powered colloidal digital SERS platform to address these limitations. Rather than addressing the intensity fluctuations, the approach leverages their very stochastic nature, arising from highly dynamic analyte-nanoparticle interactions. By converting the temporally fluctuating SERS intensities into digital binary “ON / OFF” signals using a predefined intensity threshold by analyzing the characteristic SERS peak, this approach enables digital visualization of singlemolecule events and significantly reduces false positives and background interferences. By further integrating colloidal digital SERS with deep learning, the applicability of this platform is significantly expanded and enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks for certain analytes. We further implement this approach for studying AMBIC 1.1 cell culture media. The highly accurate and reproducible results obtained demonstrate the unique capabilities of this platform for rapid and precise cell culture media monitoring, paving the way for its widespread adoption and scaling up as a new PAT tool in biopharmaceutical manufacturing and biomedical diagnostics.

[0057] 2. Results and Discussion

[0058] 2.1 Principle of deep-learning powered SERS for cell culture media monitoring

[0059] Underpinning the colloidal digital SERS assay is the homogeneous colloidal mixture of cell culture media and gold nanostars (Fig. 3a-b), where the gold nanostars were synthesized based on our previously reported approach. The stochastic analyte-nanoparticle interactions produce temporal SERS intensity fluctuations. By converting each SERS spectrum based on a predefined intensity threshold using the characteristic SERS peak into a binary digital signal in the form of “ON” or “OFF”, positive analyte-nanoparticle interactions can be accurately captured (Fig. 3c). For instance, for a given analyte, if its SERS intensity at a characteristic peak isis equal to or higher than five times the standard deviation o of the background as compared to the mean intensity of the background x, this SERS spectrum is definedas a positive digital SERS count. Otherwise, a negative SERS count is returned. In this way, all the acquired SERS spectra can be converted into binary digital SERS signals. This effectively addresses SERS intensity fluctuations by leveraging their stochastic nature while enabling digital visualization of single-molecule events.

[0060] While the obtained digital SERS count can be directly correlated with the analyte concentration to establish the calibration curve, the entire SERS spectra can also be analyzed using deep learning. Specifically, ANN is adopted to handle these high-dimensional SERS datasets (Fig. 3d). The SERS datasets are first preprocessed by background removal using the fifth-order polynomial correction and normalized to properly scale the input features. Outliers are rejected using the robust principal component analysis (RPCA), which separates the SERS datasets into low-rank components that represent the underlying structure of the data and sparse components that capture outliers. Through properly thresholding the sparse components, outliers can be identified and removed. The cleaned SERS datasets are further split into training and testing subgroups using an 80-20 partition, where 80% of the cleaned datasets are allocated for training while the remaining 20% for testing. The 80-20 partition is a standard practice in deep learning that balances sufficient data for model training while reserving enough unseen data for reliable performance evaluation. Subsequently, the training datasets are fed into the input layer of the ANN architecture (Fig. 3e). The input layer has the exact same number of features that correspond to that of input features in the cleaned SERS dataset. The following three fully connected (FC) hidden layers are made to have a progressively decreasing number of artificial neurons, from 100, down to 50 and 25, where a rectified linear unit (ReLU) activation function is implemented to enable the extraction of nonlinear relationships and complex patterns in the SERS dataset. As the ANN algorithm trains the model based on the labelled SERS datasets, it continuously adjusts the weights of each artificial neuron, which effectively optimizes its ability to predict the outcome for the unlabeled testing datasets. Eventually, the output layer has a single artificial neuron and returns the predicted value (Fig. 3f-g).

[0061] 2.2 Colloidal SERS detection of R6G in D.l. water

[0062] To assess the performance of the deep learning-powered colloidal digital SERS assay platform, we started by implementing it to detect a standard Ramanmolecule, rhodamine 6G (R6G), in D.l. water. Following the protocol laid out in Fig. 3, a series of R6G-gold nanostar colloidal mixtures with various R6G concentrations were first created and loaded into a quartz capillary tube for Raman spectroscopy measurements. A total of 1600 spectra were collected in about 16 minutes by a confocal Raman microscope at an excitation wavelength of 785 nm. The mean SERS spectra were displayed in Fig. 4a, where the shades represent the standard deviation for the corresponding spectra collected at a given R6G concentration and the spectra were vertically offset for better visualization. We performed three types of data analysis, including the conventional SERS intensity analysis, digital SERS analysis, and deep learning analysis.

[0063] The conventional SERS intensity analysis was performed at the characteristic SERS peak at about 1520 cm-1, which has an origin of the symmetric stretching mode of carbon-carbon bonds in the xanthene framework. The mean peak intensity was found to decrease quickly with a decreasing R6G concentration (Fig. 4b). Below 10’5M, no further intensity change was observed. The coefficient of variation (CV) typically exceeded 20% (Fig. 4c), which could be ascribed to the high dynamic nature of the R6G-gold nanostar interactions.

[0064] Furthermore, digital SERS analysis was performed using the same peak. The converted digital SERS signals were spatially mapped across various concentrations and repeats, as presented in Fig. 4d, where a gradual decrease of positive digital SERS counts was observed as the R6G concentration became lower. The mean digital SERS count was found to similarly decrease with a decreasing R6G concentration (Fig. 4e), but with a lower detectable concentration down to 10’7M as compared to the conventional SERS intensity analysis. Besides the observed lower detectable concentration, digital SERS analysis also returned a much lower CV, almost all of which are below 10% (Fig. 4f). This suggests that a higher detection precision was achieved, which can be ascribed to the distinct advantage of digital SERS, which effectively suppressed background interference by assigning these signals as negative.

[0065] Additionally, deep learning analysis was conducted based on the ANN architecture to predict R6G concentrations. Through side-by-side comparison, the predicted concentrations were found to be consistently aligned closely with the trueconcentrations across all the studied concentration range down to 10’8M (Fig. 4g). Meanwhile, the predicted concentrations were found to correlate with the true concentration with a high coefficient of determination (R2) value of 0.98 and small CVs that are all below 5%, as shown in Fig. 4h-i. These observations underscore the accuracy of the ANN algorithm to capture complex nonlinear relationships by extracting the hidden features within the high-dimensional SERS datasets.

[0066] Taken together, the above analysis demonstrated the strong capability of both the digital SERS and deep learning, which displayed distinct advantages over conventional SERS intensity analysis, featuring a higher detection sensitivity and precision. Given the fact that not all analytes possess well-defined SERS peaks which precludes the possibility of digital SERS analysis, deep learning is thus expected to play a dominant role in detecting these analytes and can significantly expand the applicability of the colloidal SERS approach.

[0067] 2.3 Detection of key cell culture media components in D.l. water

[0068] To demonstrate practical applicability, we extended the approach to detect key components in cell culture media, including glucose, tryptophan, and glutathione, following the same protocol outlined in Fig. 3. Digital SERS analysis successfully detected glucose and tryptophan with strong correlations between digital SERS counts and analyte concentrations (Fig. 5a-d). Deep learning analysis of glutathione showed near-perfect alignment between predicted and true concentrations (Fig. 5 e-f), highlighting the method's robustness for complex mixtures. The detection of glucose and tryptophan, two critical metabolites in cell culture processes, underscores the utility of the digital SERS method for quantifying biologically relevant analytes with high precision. Glucose, a primary energy source, plays a central role in cell metabolism, and its concentration is a key indicator of cell growth and productivity. Tryptophan, an essential amino acid, is involved in protein synthesis and metabolic regulation, making its monitoring crucial for maintaining optimal cell culture conditions. The ability to detect these molecules with high sensitivity and reproducibility offers significant advantages for rapid bioprocess control, as fluctuations in metabolite concentrations can directly affect product quality and yield. For glutathione, a tripeptide with antioxidant properties, the deep learning analysis overcame the limitations posed by the lack of well-defined SERS peaks. This demonstrates the versatility of the deeplearning-powered platform, as it can analyze entire spectral datasets to extract hidden features and predict analyte concentrations with exceptional accuracy. The high coefficient of determination and low CV observed in the deep learning results validate the robustness of the approach for handling complex, high-dimensional data. These findings highlight the ability of the deep learning-powered colloidal digital SERS platform to achieve sensitive and precise detection of diverse cell culture media components and offers a scalable solution for monitoring critical metabolites and ensuring consistent biopharmaceutical manufacturing outcomes.

[0069] 2.4 Cell culture media detection

[0070] We further proceeded to implement the deep learning-powered colloidal digital SERS for conducting rapid monitoring of cell culture media (AMBIC 1.1). The components and their concentrations in the AMBIC 1.1 media are detailed in Table S1 (Appendix B). To perform a proof-of-concept demonstration, we selected three common analytes, including tryptophan, phenylalanine, and glucose. These analytes were individually spiked into the AMBIC 1.1 media to assess how well the digital SERS method could detect and quantify their concentrations. The addition of these analytes resulted in new media samples with known concentrations, which allowed us to systematically investigate the correlation between SERS signals and analyte levels. Specifically, we prepared three separate sets of media samples for each analyte, each containing varying concentrations of the respective compound. For example, when tryptophan was introduced into the AMBIC 1.1 media, three new samples were created, each with an increased concentration of tryptophan. The same approach was used for phenylalanine and glucose, ensuring that we could measure a wide range of concentrations for each analyte.

[0071] The resulting SERS spectra for the newly created media samples are presented in Fig. 6a, d, g, where the analyte concentrations are labeled next to the vertically offset spectra. We observed that, for all the three sets of media samples, the characteristic SERS peak intensity increased proportionally with concentration, which is indicative of a strong relationship between analyte concentration and the detected signal. To quantify this relationship, we first employed the digital SERS approach. Fig.6b, e, h shows that there was a robust linear correlation between the digital SERS counts and the concentration of each analyte across the measured range. Notably,glucose detection presented a slightly higher coefficient of variation (CV) at the lowest concentration tested (around 15%), which could be attributed to the challenges in detecting glucose at trace levels. However, for the other analytes, the CV remained well below 10%, indicating high reproducibility and low measurement uncertainty across all tested concentrations. Furthermore, the deep learning-based analysis of the full SERS spectra revealed an even more compelling result. When the entire spectra were fed into the ANN algorithm, the predicted concentrations of the analytes closely aligned with the actual concentrations across all samples, as shown in Fig. 6c, f, i. This indicates that the deep learning model was able to extract complex features from the spectra and provide an accurate prediction of analyte concentration, even in the presence of matrix effects from the complex cell culture media. The successful demonstration of this method highlights the strong capability of the deep learning-powered colloidal digital SERS for precise, label-free monitoring of cell culture media. The precision and reproducibility of the method make it ideal for real-time monitoring of cell culture environments, which is critical for optimizing cell-based assays, biomanufacturing processes, and other biomedical applications.

[0072] 3. Conclusion

[0073] In summary, we developed a deep learning-powered colloidal digital SERS platform for rapid and precise monitoring of cell culture media. By converting stochastic SERS intensity fluctuations into binary digital signals using the characteristic SERS peak, this method overcomes the limitations of conventional SERS, particularly the intensity fluctuations. By further leveraging deep learning for spectral analysis, the applicability of the approach is significantly expanded and can detect analytes even without well-defined SERS spectral peaks. This platform demonstrated superior sensitivity, reproducibility, and accuracy for detecting key analytes in both simple and complex cell culture media. Given the generalizability of this platform, we envision that this approach can be further scaled and adapted to monitor a broader range of analytes in various experimental conditions, opening up new possibilities for real-time, non-invasive monitoring in cell biology, as well as for large-scale, high-throughput screening assays and point-of-care diagnostic devices in clinical settings.

[0074] Some further aspects are also defined in the following clauses:

[0075] Clause 1: A method of analyzing a sample, the method comprising: contacting the sample with a composition comprising colloidal plasmonic nanoparticles under conditions sufficient for one or more analyte molecules, if present, in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles to form a colloidal mixture; exposing the colloidal mixture to incident excitation electromagnetic radiation such that the surfaces of the colloidal plasmonic nanoparticles exhibit surface plasmon resonance (SPR); and, detecting Raman scattering signal emanated from adsorbed analyte molecules, if present, to determine a presence or absence of the analyte molecules in the sample, thereby analyzing the sample.

[0076] Clause 2: The method of Clause 1, wherein the method comprises higher levels of reproducibility than a dried substrate-based approach to analyzing the sample.

[0077] Clause 3: The method of Clause 1 or Clause 2, wherein the sample comprises a substantially unprocessed sample.

[0078] Clause 4: The method of any of Clauses 1-3, wherein the sample comprises a sample type selected from the group consisting of: a clinical sample, a forensics sample, a cell culture sample, an environmental sample, a forensics sample, and a food safety sample.

[0079] Clause 5: The method of any of Clauses 1-4, comprising agitating the sample contacted with the fluidic composition to form the colloidal mixture.

[0080] Clause 6: The method of any of Clauses 1-5, wherein the colloidal plasmonic nanoparticles are substantially homogeneously distributed in the colloidal mixture.

[0081] Clause 7: The method of any of Clauses 1-6, wherein the colloidal plasmonic nanoparticles comprise metallic plasmonic nanoparticles.

[0082] Clause 8: The method of any of Clauses 1-7, wherein the colloidal plasmonic nanoparticles are substantially non-spherical in shape.

[0083] Clause 9: The method of any of Clauses 1-8, wherein the colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles.

[0084] Clause 10: The method of any of Clauses 1-9, wherein the heterogeneous plasmonic nanoparticles comprise nanostars.

[0085] Clause 11: The method of any of Clauses 1-10, wherein the colloidal plasmonic nanoparticles comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

[0086] Clause 12: The method of any of Clauses 1-11, wherein the colloidal plasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

[0087] Clause 13: The method of any of Clauses 1-12, wherein the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles.

[0088] Clause 14: The method of any of Clauses 1-13, wherein the colloidal mixture lacks a dried substrate.

[0089] Clause 15: The method of any of Clauses 1-14, comprising quantifying the analyte molecules when present in the sample.

[0090] Clause 16: The method of any of Clauses 1-15, wherein the analyte molecules are present in the sample.

[0091] Clause 17: The method of any of Clauses 1-16, wherein the analyte molecules comprise organic molecules.

[0092] Clause 18: The method of any of Clauses 1-17, wherein the analyte molecules comprise inorganic molecules.

[0093] Clause 19: The method of any of Clauses 1-18, wherein the analyte molecules comprise biomolecules.

[0094] Clause 20: The method of any of Clauses 1-19, wherein the biomolecules comprise nucleic acid molecules, protein molecules, carbohydrate molecules, lipid molecules, and / or combinations thereof.

[0095] Clause 21: The method of any of Clauses 1-20, wherein the analyte molecules are unlabeled.

[0096] Clause 22: The method of any of Clauses 1-21, wherein the analyte molecules are labeled.

[0097] Clause 23: The method of any of Clauses 1-22, wherein the analyte molecules are bound to receptor moieties attached to the surfaces of the colloidal plasmonic nanoparticles.

[0098] Clause 24: The method of any of Clauses 1-23, wherein the receptor moieties comprise antibodies or antigen binding portions thereof.

[0099] Clause 25: The method of any of Clauses 1-24, wherein the receptor moieties comprise aptamers.

[0100] Clause 26: The method of any of Clauses 1-25, wherein the sample is obtained from a subject.

[0101] Clause 27: The method of any of Clauses 1-26, wherein the presence or absence of the analyte molecules in the sample is indicative of a property, state, or condition of the subject.

[0102] Clause 28: The method of any of Clauses 1-27, wherein the detecting step comprises performing one or more data analysis techniques selected from the group consisting of: conventional surface-enhanced Raman spectroscopy (SERS) intensity-based regression, digital SERS-based regression, and deep learning regression.

[0103] Clause 29: The method of any of Clauses 1-28, wherein the detecting step produces concentration-correlated digital surface-enhanced Raman spectroscopy (SERS) counts of the analyte molecules.

[0104] Clause 30: A kit comprising the colloidal plasmonic nanoparticles contained in a container that is suitable for performing the method of any of Clauses 1-29.

[0105] Clause 31: A kit, comprising a container that contains a composition comprising geometrically heterogeneous colloidal plasmonic nanoparticles that each comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

[0106] Clause 32: The kit of Clause 31, wherein the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles.

[0107] Clause 33: The kit of Clause 31 or Clause 32, wherein the colloidal plasmonic nanoparticles comprise nanostars.

[0108] Clause 34: A colloidal mixture, comprising: a sample that comprises one or more analyte molecules; and, a composition comprising colloidal plasmonic nanoparticles, wherein the colloidal mixture is disposed under conditions sufficient forthe analyte molecules in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles.

[0109] Clause 35: The colloidal mixture of Clause 34, wherein the sample comprises a substantially unprocessed sample.

[0110] Clause 36: The colloidal mixture of Clause 34 or Clause 35, wherein the sample comprises a sample type selected from the group consisting of: a clinical sample, a forensics sample, a cell culture sample, an environmental sample, a forensics sample, and a food safety sample.

[0111] Clause 37: The colloidal mixture of any of Clauses 34-36, wherein the colloidal plasmonic nanoparticles are substantially homogeneously distributed in the colloidal mixture.

[0112] Clause 38: The colloidal mixture of any of Clauses 34-37, wherein the colloidal plasmonic nanoparticles comprise metallic plasmonic nanoparticles.

[0113] Clause 39: The colloidal mixture of any of Clauses 34-38, wherein the colloidal plasmonic nanoparticles are substantially non-spherical in shape.

[0114] Clause 40: The colloidal mixture of any of Clauses 34-39, wherein the colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles.

[0115] Clause 41: The colloidal mixture of any of Clauses 34-40, wherein the heterogeneous plasmonic nanoparticles comprise nanostars.

[0116] Clause 42: The colloidal mixture of any of Clauses 34-41, wherein the colloidal plasmonic nanoparticles comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

[0117] Clause 43: The colloidal mixture of any of Clauses 34-42, wherein the colloidal plasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

[0118] Clause 44: The colloidal mixture of any of Clauses 34-43, wherein the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles.

[0119] Clause 45: The colloidal mixture of any of Clauses 34-44, wherein the colloidal mixture lacks a dried substrate.

[0120] Clause 46: The colloidal mixture of any of Clauses 34-45, wherein the analyte molecules comprise organic molecules.

[0121] Clause 47: The colloidal mixture of any of Clauses 34-46, wherein the analyte molecules comprise inorganic molecules.

[0122] Clause 48: The colloidal mixture of any of Clauses 34-47, wherein the analyte molecules comprise biomolecules.

[0123] Clause 49: The colloidal mixture of any of Clauses 34-48, wherein the biomolecules comprise nucleic acid molecules, protein molecules, carbohydrate molecules, lipid molecules, and / or combinations thereof.

[0124] Clause 50: The colloidal mixture of any of Clauses 34-49, wherein the analyte molecules are unlabeled.

[0125] Clause 51: The colloidal mixture of any of Clauses 34-50, wherein the analyte molecules are labeled.

[0126] Clause 52: The colloidal mixture of any of Clauses 34-51, wherein the analyte molecules are bound to receptor moieties attached of the surfaces to the colloidal plasmonic nanoparticles.

[0127] Clause 53: The colloidal mixture of any of Clauses 34-52, wherein the receptor moieties comprise antibodies or antigen binding portions thereof.

[0128] Clause 54: The colloidal mixture of any of Clauses 34-53, wherein the receptor moieties comprise aptamers.

[0129] Clause 55: A system, comprising: a sample container receiving area configured to receive a colloidal mixture that comprises a sample and a composition comprising colloidal plasmonic nanoparticles, wherein the colloidal mixture is disposed under conditions sufficient for one or more analyte molecules, if present, in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles; a electromagnetic radiation source configured to provide incident excitation electromagnetic radiation such that the surfaces of the colloidal plasmonic nanoparticles exhibit surface plasmon resonance (SPR); an electromagnetic radiation detector configured to detect Raman scattering signal emanated from adsorbed analyte molecules, if present, in the sample; and, a controller operably connected to the electromagnetic radiation source and to the electromagnetic radiation detector, which controller comprises a processor, and a memory communicatively coupled directly or remotely to the processor, thememory storing non-transitory computer executable instructions which, when executed by the processor, perform operations comprising: detecting the Raman scattering signal emanated from the adsorbed analyte molecules, if present, to determine a presence or absence of the analyte molecules in the sample.

[0130] Clause 56: The system of Clause 55, wherein the colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles.

[0131] Clause 57: The system of Clause 55 or Clause 56, wherein the colloidal plasmonic nanoparticles comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

[0132] Clause 58: The system of any of Clauses 55-57, wherein the colloidal plasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

[0133] Clause 59: The system of any of Clauses 55-58, wherein the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles.

[0134] Clause 60: The system of any of Clauses 55-59, wherein the colloidal mixture lacks a dried substrate.

[0135] Clause 61: The system of any of Clauses 55-60, wherein the non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising: quantifying the analyte molecules when present in the sample.

[0136] Clause 62: The system of any of Clauses 55-61 , wherein the sample is obtained from a subject and wherein the non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising: identifying a property, state, or condition of the subject based at least in part on the presence or absence of the analyte molecules determined in the sample.

[0137] Clause 63: The system of any of Clauses 55-62, wherein the non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising: performing one or more data analysis techniques selected from the group consisting of: conventional surface-enhanced Raman spectroscopy (SERS) intensity-based regression, digital SERS-based regression, and deep learning regression.

[0138] Clause 64: The system of any of Clauses 55-63, wherein the non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising: producing concentration-correlated digital surface-enhanced Raman spectroscopy (SERS) counts of the analyte molecules when present in the sample.

[0139] While the invention has been described with reference to the exemplary embodiments thereof, those skilled in the art will be able to make various modifications to the described embodiments without departing from the true spirit and scope. The terms and descriptions used herein are set forth by way of illustration only and are not meant as limitations. In particular, although the method has been described by examples, the steps of the method can be performed in a different order than illustrated or simultaneously. Those skilled in the art will recognize that these and other variations are possible within the spirit and scope as defined in the following claims and their equivalents. All patents, patent applications, other publications or documents, and the like cited herein are incorporated by reference in their entirety for all purposes to the same extent as if each individual item were specifically and individually indicated to be so incorporated by reference.

Claims

What is claimed is:

1. A method of analyzing a sample, the method comprising: contacting the sample with a composition comprising colloidal plasmonic nanoparticles under conditions sufficient for one or more analyte molecules, if present, in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles to form a colloidal mixture;exposing the colloidal mixture to incident excitation electromagnetic radiation such that the surfaces of the colloidal plasmonic nanoparticles exhibit surface plasmon resonance (SPR); and,detecting Raman scattering signal emanated from adsorbed analyte molecules, if present, to determine a presence or absence of the analyte molecules in the sample, thereby analyzing the sample.

2. The method of claim 1 , wherein the method comprises higher levels of reproducibility than a dried substrate-based approach to analyzing the sample.

3. The method of claim 1 , wherein the sample comprises a substantially unprocessed sample.

4. The method of claim 1 , wherein the sample comprises a sample type selected from the group consisting of: a clinical sample, a forensics sample, a cell culture sample, an environmental sample, a forensics sample, and a food safety sample.

5. The method of claim 1 , comprising agitating the sample contacted with the fluidic composition to form the colloidal mixture.

6. The method of claim 1 , wherein the colloidal plasmonic nanoparticles are substantially homogeneously distributed in the colloidal mixture.

7. The method of claim 1 , wherein the colloidal plasmonic nanoparticles comprise metallic plasmonic nanoparticles.

8. The method of claim 1 , wherein the colloidal plasmonic nanoparticles are substantially non-spherical in shape.

9. The method of claim 1 , wherein the colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles.

10. The method of claim 9, wherein the heterogeneous plasmonic nanoparticles comprise nanostars.

11. The method of claim 1 , wherein the colloidal plasmonic nanoparticles comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

12. The method of claim 1 , wherein the colloidal plasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

13. The method of claim 1 , wherein the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles.

14. The method of claim 1 , wherein the colloidal mixture lacks a dried substrate.

15. The method of claim 1 , comprising quantifying the analyte molecules when present in the sample.

16. The method of claim 1 , wherein the analyte molecules are present in the sample.

17. The method of claim 16, wherein the analyte molecules comprise organic molecules.

18. The method of claim 16, wherein the analyte molecules comprise inorganic molecules.

19. The method of claim 16, wherein the analyte molecules comprise biomolecules.

20. The method of claim 19, wherein the biomolecules comprise nucleic acid molecules, protein molecules, carbohydrate molecules, lipid molecules, and / or combinations thereof.

21. The method of claim 16, wherein the analyte molecules are unlabeled.

22. The method of claim 16, wherein the analyte molecules are labeled.

23. The method of claim 16, wherein the analyte molecules are bound to receptor moieties attached to the surfaces of the colloidal plasmonic nanoparticles.

24. The method of claim 23, wherein the receptor moieties comprise antibodies or antigen binding portions thereof.

25. The method of claim 23, wherein the receptor moieties comprise aptamers.

26. The method of claim 1 , wherein the sample is obtained from a subject.

27. The method of claim 26, wherein the presence or absence of the analyte molecules in the sample is indicative of a property, state, or condition of the subject.

28. The method of claim 1 , wherein the detecting step comprises performing one or more data analysis techniques selected from the group consisting of: conventional surface-enhanced Raman spectroscopy (SERS) intensity-based regression, digital SERS-based regression, and deep learning regression.

29. The method of claim 1 , wherein the detecting step produces concentration-correlated digital surface-enhanced Raman spectroscopy (SERS) counts of the analyte molecules.

30. A kit comprising the colloidal plasmonic nanoparticles contained in a container that is suitable for performing the method of claim 1.

31. A kit, comprising a container that contains a composition comprising geometrically heterogeneous colloidal plasmonic nanoparticles that each comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

32. The kit of claim 31 , wherein the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles.

33. The kit of claim 31 , wherein the colloidal plasmonic nanoparticles comprise nanostars.

34. A colloidal mixture, comprising:a sample that comprises one or more analyte molecules; and,a composition comprising colloidal plasmonic nanoparticles,wherein the colloidal mixture is disposed under conditions sufficient for the analyte molecules in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles.

35. The colloidal mixture of claim 34, wherein the sample comprises a substantially unprocessed sample.

36. The colloidal mixture of claim 34, wherein the sample comprises a sample type selected from the group consisting of: a clinical sample, a forensics sample, a cell culture sample, an environmental sample, a forensics sample, and a food safety sample.

37. The colloidal mixture of claim 34, wherein the colloidal plasmonic nanoparticles are substantially homogeneously distributed in the colloidal mixture.

38. The colloidal mixture of claim 34, wherein the colloidal plasmonic nanoparticles comprise metallic plasmonic nanoparticles.

39. The colloidal mixture of claim 34, wherein the colloidal plasmonic nanoparticles are substantially non-spherical in shape.

40. The colloidal mixture of claim 34, wherein the colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles.

41. The colloidal mixture of claim 40, wherein the heterogeneous plasmonic nanoparticles comprise nanostars.

42. The colloidal mixture of claim 34, wherein the colloidal plasmonic nanoparticles comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

43. The colloidal mixture of claim 34, wherein the colloidal plasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

44. The colloidal mixture of claim 34, wherein the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles.

45. The colloidal mixture of claim 34, wherein the colloidal mixture lacks a dried substrate.

46. The colloidal mixture of claim 34, wherein the analyte molecules comprise organic molecules.

47. The colloidal mixture of claim 34, wherein the analyte molecules comprise inorganic molecules.

48. The colloidal mixture of claim 34, wherein the analyte molecules comprise biomolecules.

49. The colloidal mixture of claim 48, wherein the biomolecules comprise nucleic acid molecules, protein molecules, carbohydrate molecules, lipid molecules, and / or combinations thereof.

50. The colloidal mixture of claim 34, wherein the analyte molecules are unlabeled.

51. The colloidal mixture of claim 34, wherein the analyte molecules are labeled.

52. The colloidal mixture of claim 34, wherein the analyte molecules are bound to receptor moieties attached of the surfaces to the colloidal plasmonic nanoparticles.

53. The colloidal mixture of claim 52, wherein the receptor moieties comprise antibodies or antigen binding portions thereof.

54. The colloidal mixture of claim 52, wherein the receptor moieties comprise aptamers.

55. A system, comprising:a sample container receiving area configured to receive a colloidal mixture that comprises a sample and a composition comprising colloidal plasmonic nanoparticles, wherein the colloidal mixture is disposed under conditions sufficient for one or more analyte molecules, if present, in the sample to adsorb onto surfaces of the colloidal plasmonic nanoparticles;a electromagnetic radiation source configured to provide incident excitation electromagnetic radiation such that the surfaces of the colloidal plasmonic nanoparticles exhibit surface plasmon resonance (SPR);an electromagnetic radiation detector configured to detect Raman scattering signal emanated from adsorbed analyte molecules, if present, in the sample; and, a controller operably connected to the electromagnetic radiation source and to the electromagnetic radiation detector, which controller comprises a processor, and a memory communicatively coupled directly or remotely to the processor, the memory storing non-transitory computer executable instructions which, when executed by the processor, perform operations comprising:detecting the Raman scattering signal emanated from the adsorbed analyte molecules, if present, to determine a presence or absence of the analyte molecules in the sample.

56. The system of claim 55, wherein the colloidal plasmonic nanoparticles comprise geometrically heterogeneous plasmonic nanoparticles.

57. The system of claim 55, wherein the colloidal plasmonic nanoparticles comprise an anti-aggregation coating that reduces aggregation of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

58. The system of claim 55, wherein the colloidal plasmonic nanoparticles comprise a coating that increases a level of stability of the plasmonic nanoparticles compared to uncoated plasmonic nanoparticles.

59. The system of claim 55, wherein the colloidal plasmonic nanoparticles comprise silica-shell encapsulated plasmonic nanoparticles.

60. The system of claim 55, wherein the colloidal mixture lacks a dried substrate.

61. The system of claim 55, wherein the non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising:quantifying the analyte molecules when present in the sample.

62. The system of claim 55, wherein the sample is obtained from a subject and wherein the non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising:identifying a property, state, or condition of the subject based at least in part on the presence or absence of the analyte molecules determined in the sample.

63. The system of claim 55, wherein the non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising:performing one or more data analysis techniques selected from the group consisting of: conventional surface-enhanced Raman spectroscopy (SERS) intensity-based regression, digital SERS-based regression, and deep learning regression.

64. The system of claim 55, wherein the non-transitory computer executable instructions which, when executed by the processor, further perform operations comprising:producing concentration-correlated digital surface-enhanced Raman spectroscopy (SERS) counts of the analyte molecules when present in the sample.

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