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WO2025059782A1 - Procédé de mesure à haute résolution de concentrations d'angiopoïétine (ang) 1/2 et récepteur de déclenchement soluble exprimé sur des biomarqueurs de cellules myéloïdes 1 (strem-1) - Google Patents

Procédé de mesure à haute résolution de concentrations d'angiopoïétine (ang) 1/2 et récepteur de déclenchement soluble exprimé sur des biomarqueurs de cellules myéloïdes 1 (strem-1) Download PDF

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WO2025059782A1
WO2025059782A1 PCT/CA2024/051270 CA2024051270W WO2025059782A1 WO 2025059782 A1 WO2025059782 A1 WO 2025059782A1 CA 2024051270 W CA2024051270 W CA 2024051270W WO 2025059782 A1 WO2025059782 A1 WO 2025059782A1
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ang
sers
strem
data
machine learning
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Ziqi LI
Yaming CHENG
Sheral KUMAR
Carlos Fernandes
Harry Ruda
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University of Toronto
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University of Toronto
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering
    • G01N21/658—Raman scattering enhancement Raman, e.g. surface plasmons
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B82—NANOTECHNOLOGY
    • B82Y—SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES
    • B82Y15/00—Nanotechnology for interacting, sensing or actuating, e.g. quantum dots as markers in protein assays or molecular motors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B82—NANOTECHNOLOGY
    • B82Y—SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES
    • B82Y5/00—Nanobiotechnology or nanomedicine, e.g. protein engineering or drug delivery
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods
    • G01N2201/1296—Using chemometrical methods using neural networks

Definitions

  • the disclosure relates to a high-resolution method that can accurately quantify sTREM-1 , Ang-1 , and Ang-2 biomarkers, which is useful, for example, for determination of sepsis state and associated treatment of a subject.
  • Sepsis a substantial worldwide health challenge, is defined as a marked systemic host response to infection leading to multi-organ dysfunction. It can arise from bacterial, viral, fungal, or parasitic sources (Ref. 1 :Wright et al 2018, BMC Pediatrics. 18(1)). In certain cases, sepsis can advance to a stage where it is associated with severe inflammation, tissue coagulopathy (blood clotting issues), and organ dysfunction and death. It has an especially notable impact among maternal and newborn populations (Ref. 2: Briggs-Steinberg et al 2023, Pediatrics In Review, 44(1 ), 14-22). Sepsis begins with the activation of innate immune response mediated and endothelial cell activation/dysfunction (Ref. 3:Kim et al 2020, Infection & Chemotherapy, 52(1), 1 ).
  • Angiopoietins are involved in vascular development and angiogenesis, which regulate the integrity of the interface between neighboring endothelial cells.
  • Angiopoietin-1 to Angiopoietin-4.
  • Ang-1 is responsible for maintaining vascular quiescence in the adult endothelium (Ref. 4: Fiedler et al 2006, Trends in Immunology, 27(12), 552- 558).
  • the Ang-1 stabilizing effect is antagonized by Ang-2, which is released in response to injury, hypoxia, and systemic infection due to bacterial, viral, fungal and protozoal sources (Ref.
  • sTREM-1 , Ang-1 , and Ang-2 biomarkers are quantified by enzyme-linked immunosorbent assay (ELISA), and Luminex assay measurements.
  • the lower and upper limits of detection for each assay are as follows: Ang-1 (156.25 - 10,000 pg/mL; sensitivity 9.43 pg/mL; standard deviation 0.2; resolution 0.021 pg/mL; sample volume 50 pL; incubation time 3- 5 h), Ang-2 (54.69 - 3,500 pg/mL; sensitivity 17.1 pg/mL; standard deviation 0.2; resolution 0.012 pg/mL; sample volume 50 pL; incubation time 3-5 h) (Ref.
  • the present disclosure provides a method for high-resolution measurements using SERS which involves using metallo-dielectric nanostructures with enhanced sensitivity which include plasmonic materials.
  • the system exhibits high-resolution sensing biomarkers in uses machine learning classification pipelines for quantification of the amounts of biomarkers.
  • the system involves biomarker and biomarker characteristic peak identification through decomposition or deconvolution of SERS spectra.
  • the present disclosoure provides a Surface Enhanced Raman (SERS) method of detecting biomarkers indicative of a state of sepsis in a patient, comprising: identifying at least one target antigen biomarker indicative of a state of sepsis to be screened for in a sample; functionalizing a plasmonic metal forming part of the nanostructured SERS active substrates with an antibody with specificity to the at least one target antigen biomarker; exposing the at least one functionalized SERS active substrate to the sample being screened for the absence or presence of the target antigen biomarker; illuminating the SERS active substrate with laser light of a preselected wavelength and recording a sample SERS spectra; and comparing the sample SERS spectra to a pre-collected set of biomarker SERS spectra of known biomarkers indicative of a state of sepsis at varying concentrations, and based on this comparison, determining whether the at least one target biomarker is absent from, or present in, the sample, and
  • the nanostructured SERS active substrates may include an array of bullseye structures with each bullseye structure comprised of a preselected number of concentric circular troughs with said preselected number concentric circular troughs being in a range from about one to about ten, a depth of said circular troughs being in a range from about 5 nm to about 500 nm, a width of said circular troughs being in a range from about 20 nm to about 500 nm, and wherein each bullseye structure has an adhesion layer coating a bottom of each trough and a plasmonic metal layer coating the adhesion layer coating in said troughs.
  • the preselected number of concentric circular troughs is greater than one with each trough having the same width and the same distance between each circular trough from a center of each bullseye structure towards the outermost peripheral trough, and wherein and wherein a separation between a middle of adjacent troughs is in a range from about 20 nm to about 500 nm.
  • a thickness of the plasmonic metal coating may be in a range from about 1 nm to about 100 nm.
  • the thickness of the adhesion metal coating may be in a range from about 1 nm to about 30 nm.
  • the plasmonic metal coating may be any one of gold, silver and copper.
  • the adhesion layer coating is any one of titanium, chromium, nickel, tungsten, niobium, chromium, or their oxide forms.
  • the substrate may be made of a material having a low index of refraction, low dielectric constant or low relative permittivity selected from any one of glass, silicon, polymer, dielectric and linear/non-linear optical materials.
  • the substrate is made of any one of silicon (100), silicon (111 ), polymethyl methacrylate (PMMA), polydimethylsiloxane (PDMS) and poly(N,N- dimethylacrylamide) (PDMA).
  • the nanostructured SERS active substrates include but are not limited to nano-crescents, nano-stars, nano-prisms, nano-grating, nano-pillars, nanochirped circular grating.
  • the method includes functionalizing a SERS active substrate with an antibody with specificity to the target antigen biomarker comprises depositing biotinylated PEG thiol (BAT) molecules onto the SERS active substrate which binds to locations on the plasmonic metal forming part of the SERS active substrate, and depositing a passivating agent onto the surface of the plasmonic metals which coated areas on the plasmonic metal free of the biotinylated PEG thiol (BAT) molecules to prevent non-specific binding of the target antigen biomarkers to the surfaces of the plasmonic metals.
  • BAT biotinylated PEG thiol
  • the passivating agent may be any one of molecules of thiol polyethylene glycol) (Thiol-PEG), 1 -ethyl-3- (3_dimethylaminopropyl)carbodiimide (EDC) combined with N- hydroxysuccinimide (NHS).
  • Thiol-PEG thiol polyethylene glycol
  • EDC 1 -ethyl-3- (3_dimethylaminopropyl)carbodiimide
  • NHS N- hydroxysuccinimide
  • the at least one target biomarker being tested for may be sTREM-1 , wherein the step of functionalizing at least one SERS active substrate further comprises deposition of sTREM-1 biotinylated antibodies which binds to the streptadivin and wherein, if sTREM-1 is present in the sample it will form complexes with the sTREM-1 biotinylated antibodies.
  • the at least one target biomarker being tested for may be Ang-1 , wherein the step of functionalizing at least one SERS active substrate further comprises deposition of Ang-1 biotinylated antibodies which binds to the streptadivin and wherein, if Ang-1 is present in the sample it will form complexes with the Ang-1 biotinylated antibodies.
  • the at least one target biomarker being tested for may be Ang-2, wherein the step of functionalizing at least one SERS active substrate further comprises deposition of Ang-2 biotinylated antibodies which binds to the of streptadivin and wherein, if Ang-2 is present in the sample it will form complexes with the Ang-2 biotinylated antibodies.
  • the sample may be a sample of human bodily fluid which may be any one or combination of blood, plasma, serum, urine and saliva.
  • the method may include separate SERS active substrates functionalized for detection of each of sTREM-1 , Ang-1 and Ang-2 are integrated into the system, and wherein the concentrations of biomarkers are determined using machine learning classification and regression pipelines, which are trained to distinguish between any combination of sTREM-1 , Ang-1 , Ang-2 and predict their concentrations.
  • the classification and regression pipelines are configured specifically for spectral data and comprise: a) spectrum preprocessing, to remove or minimize background signal from radiation, noise, and/or SERS responses from other material, clean and validate the data to remove outliers or erroneous measurements, and standardize the data such that data from independent experiments can be compared; b) data augmentation algorithms to artificially generate new data samples from the existing data collected from real-world samples, to increase the number of diverse data samples used for training, validation, development, and/or testing of machine learning methods and thereby improve the performance of these methods; c) feature selection to extract a base set of the most informative features for biomarker discrimination from the dense spectral data collected, thereby reducing the dimensionality of the input data used by the machine learning methods and improving their computational time and space costs/complexity, and real-world generalizability; d) feature and data transformation methods for polynomial fitting or to place the spectra data into a desired feature space to improve machine learning performance and/or costs; e) outlier rejection
  • the regression pipeline predicts the concentration of sTREM-1 , Ang-1 , or Ang-2 from a spectrum containing only one biomarker.
  • the regression pipeline predicts the concentration of any combination of sTREM-1 , Ang-1 , and Ang-2 from a spectrum containing any mixture of these biomarkers.
  • the regression pipeline predicts the concentration of Ang-1 and subsequently predicts the concentration of Ang-2 using the biomarker relationship, and vice versa.
  • the method includes developing a methodology for correlating findings from a machine learning classification pipeline to spectral deconvolutions or decompositions, comprising: a) spectral deconvolution or decomposition into underlying Gaussian or Lorentzian functions; b) extraction of significant peaks and relevant peak properties from the deconvolution or decomposition; c) a machine learning classification pipeline, with feature selection or engineering functionalities to identify significant wavenumbers; d) comparison of significant wavenumbers from the machine learning classification model to extracted peaks and their properties from the deconvolution or decomposition.
  • the correlation is done between the spectral deconvolution or decomposition of Ang-1 , Ang-2, or sTREM and one or more machine learning classification pipelines including said biomarker for the purpose of identifying potential characteristic peaks.
  • the correlation is used to provide physical explainability, such as vibrational modes of the molecular structure of Ang-1 , Ang-2, or sTREM, to the machine learning classifier.
  • the findings from deconvolution or decomposition, or identified characteristic peaks, are used as primary features for the development of a machine learning classifier to distinguish between any combination of Ang-1 , Ang-2, and sTREM.
  • Figure 1A is a cross sectional side view showing a schematic illustration depicting a SERS substrate fabrication process from left to right: (i) spin-coating of a single layer of photoresist (a) (such as poly-methyl-methacrylate (PMMA A3)) onto a silicon wafer (100) substrate (b), (ii) e-beam lithography to pattern the photoresist and expose the silicon substrate in a preselected pattern, (iii) reactive ion etching of the exposed sections of the silicon substrate, (iv) plasmonic metal deposition with a plasmonic metal such as Ti/Au (c) to coat the exposed sections of the silicon substrate and the remaining photoresist, and (v) lift-off to remove the remaining photoresist (a).
  • photoresist such as poly-methyl-methacrylate (PMMA A3)
  • Figure 1 B is a schematic illustration depicting a SERS substrate fabrication process and surface passivation for the capture of sTREM-1 (h) with steps (i) to (iv) identical to Figure 1A: (i) spin-coating of photoresist, (ii) e-beam lithography, (iii) reactive ion etching, (iv) plasmonic metal deposition, (v) lift-off of remaining photoresist and deposition of thiol polyethylene glycol) (Thiol- PEG) (d) and biotinylated PEG thiol (BAT) (e), (vi) deposition of streptadivin (f); (vii) deposition of sTREM-1 biotinylated antibodies (g), (viii) capture of sTREM- 1 (h) from delivered sample for sTREM-1 measurement.
  • Figure 1C is a schematic illustration depicting a SERS substrate fabrication process and surface passivation for the capture of Ang-1 (k) with steps (i) to (vi) identical to Figure 1B: (i) spin-coating of photoresist, (ii) e-beam lithography, (iii) reactive ion etching, (iv) plasmonic metal deposition, (v) lift-off of remaining photoresist and deposition of thiol polyethylene glycol) (Thiol- PEG) (d) and biotinylated PEG thiol (BAT) (e), (vi) deposition of streptadivin (f); (vii) deposition of Ang-1 biotinylated antibodies (j), (viii) capture of Ang-1 (k) from delivered sample for Ang-1 measurement.
  • Figure 1 D is a schematic illustration depicting a SERS substrate fabrication process and surface passivation for the capture of Ang-2 (m) with steps (i) to (vi) identical to Figure 1B: (i) spin-coating of photoresist, (ii) e-beam lithography, (iii) reactive ion etching, (iv) plasmonic metal deposition, (v) lift-off and deposition of thiol polyethylene glycol) (Thiol-PEG) (d) and biotinylated PEG thiol (BAT) (e), (vi) deposition of streptadivin (f); (vii) deposition of Ang-2 biotinylated antibodies (I), (viii) capture of Ang-2 (m) from delivered sample for Ang-2 measurement.
  • Figure 2 shows an atomic force microscopy image of a bulls-eye nanostructured SERS substrate.
  • Figure 3 is a plot of Intensity (a.u.) versus Raman Shift (cm' 1 ) showing the SERS spectrum of (a) sTREM-1 and (b) sTREM-1 , antibodies.
  • Figure 4 is a plot of Intensity (a.u.) versus Raman Shift (cm -1 ) showing the SERS spectrum of (a) Ang-1 and (b) Ang-1 , antibodies.
  • Figure 5 is a plot of Intensity (a.u.) versus Raman Shift (cm -1 ) showing the SERS spectrum of (a) Ang-2 and (b) Ang-2, antibodies.
  • Figure 6 shows the relationship between (a) sTREM-1 at 460 cm' 1 , (b) Ang-1 at 455 cm' 1 and (c) Ang-2 at 437 cm' 1 concentrations (pM) and their characteristic SERS peak intensities (a.u.).
  • Figure 7 shows the SERS spectra for (a) sTREM-1 with gold nanoparticles in solution and (b) gold nanoparticles in buffer solution.
  • Figure 8 is a schematic of an embodiment of a SERS system use for detection of biomarkers indicative of sepsis, comprising laser source, SERS substrate, detector and data capturing and processing.
  • Figure 9 shows a schematic of sample delivery via a microfluidic circuit 10 to SERS-active regions on the substrate, with each region functionalized for one specific antigen; circuit 10 includes a sample inlet 12, microfluidic channels 14, array of target structures 16 functionalized for capture of one specific antigen, e.g., sTREM-1 , array of target structures 18 functionalized for capture of one specific antigen, e.g., Ang-1 , array of target structures 20 functionalized for capture of one specific antigen, e.g., Ang-2, sample collector 24, and substrate 26.
  • circuit 10 includes a sample inlet 12, microfluidic channels 14, array of target structures 16 functionalized for capture of one specific antigen, e.g., sTREM-1 , array of target structures 18 functionalized for capture of one specific antigen, e.g.,
  • Figure 10 shows a confusion matrix displaying the true labels versus the predictions made on the test set by a multilayer perceptron classification model when distinguishing between the SERS spectra of Ang-1 , Ang-2, and their respective antibodies.
  • Figure 11A shows the process flowchart for the machine learning classification pipeline.
  • Figure 11B shows the process flowchart for machine learning regression pipeline 1.
  • Figure 11C shows the process flowchart for machine learning regression pipeline 2.
  • Figure 11D shows the process flowchart for machine learning regression pipeline 3.
  • Figure 12 shows a fitted curve using Eqn. (3) to experimental data from published data (Ref. 35:A. Leligdowicz et al 2021 , Nat Commun, 12(1 ), 6832) of sTREM_1 concentration versus probability of 7-day mortality.
  • Figure 13A shows the process flowchart for characteristic peak determination from deconvolution/decomposition and machine learning.
  • Figure 13B shows the process flowchart for deconvolution/decomposition for the machine learning classification pipeline.
  • Table 1 shows geometrical Parameters WAU, Wspace for the patterns used herein.
  • Table 2 shows spectral assignments for SERS Spectra of R6G molecule.
  • Table 3 shows size or molecular weight of each component.
  • Table 4 shows predictions for Ang-1 , Ang-2, and sTREM-1 concentrations made on the test set by polynomial (Ang-1 , Ang-2) and linear regression (sTREM-1) models versus the actual concentrations.
  • Table 5 shows Ang-1 and Ang-2 concentrations and ratio (Ang-2/Ang-1 ) predictions made on the test set by a convolutional neural network versus the actual concentrations, where elevated levels of Ang-2 and high values for the Ang-2/Ang-1 ratio may potentially be used as predictors of worst-case prognoses for sepsis.
  • This disclosure provides a high-resolution method that can accurately quantify sTREM-1 , Ang-1 , and Ang-2 biomarkers, which is useful, for example, for determination of sepsis state and treatment of a subject.
  • sTREM-1 , Ang-1 , and Ang-2 biomarkers which is useful, for example, for determination of sepsis state and treatment of a subject.
  • the terms “comprises”, “comprising”, “includes” and “including” are to be construed as being inclusive and open-ended, and not exclusive. Specifically, when used in this specification including claims, the terms “comprises”, “comprising”, “includes” and “including” and variations thereof mean the specified features, steps, or components are included. These terms are not to be interpreted to exclude the presence of other features, steps, or components.
  • the coordinating conjunction “and/or” is meant to be a selection between a logical disjunction and a logical conjunction of the adjacent words, phrases, or clauses.
  • the phrase “X and/or Y” is meant to be interpreted as “one or both of X and Y” wherein X and Y are any word, phrase, or clause.
  • Raman spectroscopy is a promising technique desired for biomarker detection and measurement, which is an inelastic scattering process where photons or light incident on a sample transfer energy to or from the sample’s vibrational or rotational modes (Ref. 13: Orlando et al 2021 , Chemosensors, 9(9), 262). Individual bands in the Raman spectrum are characteristic of specific molecular vibrations. As a result, each analyte has its own unique Raman signature. For biomolecules, Raman spectroscopy is nondestructive without prior fluorescent or radioactive labeling, however, the low efficiency of Raman scattering hinders its applications in the detection at low concentrations.
  • SERS Surface enhanced Raman scattering
  • SERS is a process whereby the Raman signal is increased when a Raman-active molecule is spatially confined proximate to a strong local electromagnetic field generated upon excitation of the localized surface plasmon resonance (LSPR) on nanostructured metal surfaces. Accordingly, SERS possesses many desirable characteristics for biochemical analysis including high specificity, sensitivity, and being fast to acquire data.
  • LSPR occurs when suitable wavelength electromagnetic radiation impinges on a noble metal nanostructure causing conduction electrons to oscillate collectively. The resonance oscillation is localized near the surface region of the nanostructure.
  • Such resonance is advantageous in that the nanostructure is selectively excited at a particular photon absorption, which results in the generation of locally enhanced or amplified electromagnetic fields proximate to the nanostructures and which occurs in the visible and IR regions of the spectrum and can be measured by UV-visible-IR (200 to 3500 nm) extinction spectroscopy (Ref. 15: Cara et al 2020, Journal of Materials Chemistry C, 8(46), 16513-16519). SERS from metallic nanostructures increases the original Raman scattering intensity many orders of magnitude, which makes the Raman detection of low concentrations of biomolecules and in particular, biomarkers, practical.
  • Machine learning is a data-driven approach to creating algorithms (models) for tasks such as identification and prediction.
  • Supervised learning is a common technique for developing machine learning models where the model attempts to approximate input-output mapping functions. These models learn to approximate the mapping by training on input data which has already been labelled with the expected output. Once trained, outputs to new data can be predicted through the mapping learned by the algorithm.
  • models aim to predict the correct class label from a predetermined set of classes using input data.
  • supervised regression models aim to predict the value of a continuous, numerical variable using input data.
  • SERS spectrum data has been used as input data for machine learning models in classifying materials and other biological samples.
  • classification models can be employed for distinguishing between Ang-1 , Ang-2, sTREM-1 , and their respective antibodies on a substrate using SERS spectra.
  • regression models can be used for identifying the existence of the various biomarkers or predicting their concentration within a biomarker mixture.
  • the benefit of using machine learning in this context is the ability for the detection algorithms to learn to identify biomarkers within mixtures with little human input and to self-improve given more data or optimization.
  • the present disclosure provides a high-resolution method for determining and measuring the presence and concentration of a biomarker of interest in a biological sample using SERS.
  • the detection capabilities were improved in one or more ways, these improvements include, but are not limited to, an increase in signal intensity, enhanced sensitivity, higher resolution, and improved reproducibility (Ref. 16: Maccaferri et al 2021 , Nanoscale Advances, 3(3), 633-642).
  • a metal coating can be disposed on a nanostructure, where the size of the nanostructure can be controlled, which can result in enhanced properties.
  • An embodiment of the present disclosure includes a gold coated bullseye nanostructure SERS substrate (Table 1), which can have a depth of cavities about 50 nm to 100 nm, width of gold cavities about 50 nm to 1 urn and have a distance between cavities about 50 nm to 1 urn (see Figure 2).
  • Figures 1A to 1D show, for illustrative purposes, the fabrication and functionalization of two adjacent troughs in the bullseye structure shown in Figure 2.
  • FIGS 1A to 1D show cross-sectional views of the nanostructured substrate (with the top view resembling a bullseye as shown in Figure 2).
  • the SERS activity is generated by the structure itself.
  • the present circular trough structures of the nanostructured SERS active substrates for immobilization of the antibodies with specificity to the target antigen biomarkers is advantageous for the following reasons.
  • the dimensions of the troughs and the spacing between adjacent troughs can trap incident light striking the substrate. This can lead to optical resonance being established within the troughs which, when interacting with the antigen biomarkers increases the Raman signal. This leads to further enhancement of the Raman signal and increases the limit of detection for the biomarker.
  • the use of up to ten (10) concentric circular troughs is advantageous because this can increase the region over which optical resonance is established, further increasing the Raman signal enhancement from the biomarker.
  • Rhodamine 6 G for example, absorbs strongly onto gold via its chloride ion (Ref. 17: Pristinski et al 2006, Journal of Raman Spectroscopy, 37(7), 762-770).
  • the normal Raman scattering of the pure R6G was not observable when the concentration of R6G was 10' 5 M. But in the presence of substrates, the Raman scattering of R6G can be observed distinctly. It is characteristic of Raman spectra of R6G and the assignment for the several peaks are listed in Table 2.
  • EF enhancement factor
  • IsERs and I Raman denote to the highest peak (1504 cm' 1 ) intensities collected over the SERS platform and on flat gold, respectively.
  • NSERS and NRaman are the number of scattering molecules in the illuminated volume of the sample on the SERS substrate and over flat silicon substrate , respectively.
  • the SERS spectra were collected at 785 nm laser wavelength (red laser). The laser power was maintained at 10% and the exposure time was kept at 10 s.
  • the spectral resolution was also improved by this technique. Some peaks are not visible on the flat silicon but are very distinct on the bullseye nanostructure SERS, especially for those vibrational modes corresponding to aromatic ring bending and stretching, such as 608 cm' 1 , which comes from the aromatic ring in-plane bending (Ref. 19: Zhu et al 2018, Nanomaterials, 8(7), 520) at low concentration (1 O' 3 M to 10' 8 M).
  • the reproducibility of the SERS substrate was further investigated by taking SERS spectra of R6G at concentration of 10' 3 M from 9 random locations on a single substrate The average relative standard deviation of the intensities (at 1361 cm' 1 ) was 0.4% indicating that the substrates process good signal uniformity.
  • SERS S-plasmin-activated Ramelectron spectroscopy
  • nano-pillars Various types of nanostructures have been fabricated, including gold or silver nano-crescents, nano-stars, nano-prisms, nano-grating, and nano-pillars (Ref. 20: Li et al 2011 , Comprehensive Biotechnology, 125-139). SERS enhancement was observed in the nano-pillar and nano-grating, which was attributed to the LSPR coupling that in turn results from variations in surface nano-topography. Nano-gratings include many different structural variations, including but not limited to chirped gratings, and chirped circular gratings.
  • the present embodiment exhibits a specific/unique chirped circular grating/bullseye antenna (expansion as 4 concentric gold same-width rings separate with same distance between each ring).
  • altering the pitch presents opportunities for influencing the dispersion relation and tailoring the plasmonic response.
  • the chirped circular grating shows a consistent cavity or groove with a range from 20 nm -1 pm, with the pitch being adjusted through variations in groove-groove separation.
  • the bullseye nanostructured SERS substrate features a large surface area (for better molecular adsorption) and a long edge length for the maximized total integration of multiple SERS tips compared to regular chirped grating, which resulted in even higher degree of field enhancement.
  • the nanostructures for capture and detection of the target sTREM-1 , Ang-1 , and Ang-2 biomarkers were constructed by the combination of electron beam lithography and reactive ion etching (see Figure 1A).
  • Electron beam lithography is a highly precise method for defining the SERS nanostructures with high resolution (sub-10 nm) as compared with optical lithography owing to the shorter wavelength of electrons compared with ultraviolet photons.
  • Reactive ion etching is a plasma etching process, wherein gaseous ions are accelerated normal to the patterned masked substrate surface to effect selective removal of material.
  • the sample for EBL was prepared from a 1 .5 cmx1 .5 cm silicon, which was cut from a 6-inch silicon wafer (100) (thickness of 375 ⁇ 15 gm, B-doped, p-type, supplied by SI-TECH, INC) following the correct orientation of the wafer using a high-quality diamond scribe pen.
  • the sample was sonicated in 2-propanol (IPA) for 5 min, rinsed in acetone and deionized (DI) water for 1 min, then it was dried with nitrogen gas and baked at 180 °C for 5 min to remove water and cooled for 2 min. Those steps can make sure the top surface was clean enough to get photo resist coating.
  • IPA 2-propanol
  • DI acetone and deionized
  • a single, photo resist layer of poly-methyl-methacrylate (PMMA, A3, 950 K 3% dissolved in anisole, supplied by MicroChem corp.) is spin-coated at a rate of 1000 rpm for 60 s to obtain a 300 nm film, and is subsequently baked at 180 °C for 60 s to remove any residual solvent.
  • PMMA poly-methyl-methacrylate
  • photoresists other than PMMA may be used as will be known to those skilled in the art along with the processing conditions associated with each.
  • the sample was loaded on an Electron Beam Lithography Holder and patterned by a modified transmission electron microscope (TEM) modified Raith EBPG 5000+ Electron Beam Lithography System at 100 keV and 10 nA beam current.
  • the sample was exposed at 400 / C/cm 2 , which was achieved by varying the dose from 400 to 1000 / C/cm 2 .
  • the exposed resists were developed for 60 s in isopropyl alcohol (MIBK: IPA) (1 :3) and rinsed for at least 15 s in IPA. All organic solvents were used as received unless specified otherwise, supplied by Sigma-Aldrich.
  • the sample is then exposed to SFe plasma in a reactive ion etching (RIE) chamber for 150 s at a RF power of 50 W, gas flow rate of 50 seem, at a chamber pressure of 30 mTorr using Oxford Instruments PlasmaPro Estrelas100 DRIE System (Si).
  • RIE reactive ion etching
  • Au gold
  • a combination of 10 nm titanium (Ti) and 40 nm gold (Au) is deposited onto the sample in a E-beam evaporator (Angstrom Nexdep Electron Beam Evaporator) at the deposition rate of 0.5 A/s for Ti and 0.2 A/s for Au, then submersed in acetone for 24 hours to strip away the PMMA and lift-off the metal.
  • SERS substrates were highly dependent on the interaction between adsorbed molecules and the surface of plasmonic nanostructures, therefore, SERS substrates were typically comprised of wafers and metallic coating.
  • the metallic coating can be chosen from silver (plasmonic wavelength 390 nm), copper (plasmonic wavelength 560-570 nm) or gold (plasmonic wavelength 530 nm) since due to their LSPR properties, which covered a wide wavelength range in the visible and near-infrared regions of electromagnetic spectrum with good air stability and weak reactivity (Ref. 21 Pal et al 2020, Sensors and Actuators A: Physical, 314, 112225).
  • the material of wafer can also support surface oscillations of free electron (electromagnetic waves coupling) and trap lightwaves on the surface. These collective surface oscillations can concentrate electromagnetic fields on the nanoscale, enhancing local field strength in a particular direction by several orders of magnitude. Normal propagating electromagnetic waves can have constant phase and amplitude in the same plane. Even low inertia electrons can fail to keep up with high frequencies, the dependence on the material used can be described by the index of refraction
  • the low index of refraction (or dielectric constant or relative permittivity) substrate layer may be utilized including but not limited to glass, silicon, polymer, dielectric, linear/non-linear optical materials (Ref. 22: Ciddor et al 1996, Applied Optics, 35(9), 1566).
  • adhesion layers were used as a thin film (250 A - 400 A) of tungsten, niobium, chrome, or titanium or their oxide forms (dielectric material like TiO2 or Cr2O3) (Ref. 23: Colas et al 2015, Journal of Optics, 17(11), 114010).
  • the first step created a linker on the plasmonic surface of SERS substrates for example, gold films are produced by physical deposition techniques such as evaporation, meanwhile, element comprises of biotinylated polyethylene glycol chains grafted on the gold surface via thiol-Au chemistry.
  • Streptavidinbiotin is based on the intra- and intermolecular interactions between tryptophan (Trp) residues and the non-polar side chain of streptavidin with the non-polar moieties of biotin.
  • the third step ensured appropriate surface interaction is to imply antibodies specific to the protein of interest (Ref. 25: Ko et al 2021 , Nanotechnology, 32(50), 505207).
  • the substrate was dipped into a triggering receptor expressed on myeloid cells- 1 (TREM-1) Biotinylated Antibody for 30 min. Then, the substrate was air dried (room temperature and 46% relative humidity). Drying of the samples was performed between each step at room temperature and 46% humidity, and all reactions were performed at standard room temperature and pressure.
  • TERT-1 myeloid cells- 1
  • the recipe to prepare the SERS substrate for Ang-1 capture is as follows and illustrated in Figure 1C.
  • the dried SERS substrate was immersed in 5x1 O' 4 M immersed thiol polyethylene glycol) (Thiol-PEG) and biotinylated PEG thiol (BAT) for 16 h with 9:1 ratio.
  • 5x1 O' 7 M streptavidin (SA) solution that is prepared with Dulbecco's Phosphate Buffered Saline (DPBS) for 2 h.
  • SA streptavidin
  • the substrate was dipped into an Angiopoietin-1 (Ang-1 ) Biotinylated Antibody for 30 min. Then, the substrate was air dried (room temperature and 46% relative humidity). Drying of the samples was performed between each step at room temperature and 46% humidity, and all reactions were performed at standard room temperature and pressure.
  • the recipe to prepare the SERS substrate for Ang-2 capture is as follows and illustrated in Figure 1D
  • the dried SERS substrate was immersed in 5x1 O' 4 M immersed thiol polyethylene glycol) (Thiol-PEG) and biotinylated PEG thiol (BAT) for 16 h with 9:1 ratio.
  • 5x1 O' 7 M streptavidin (SA) solution that is prepared with Dulbecco's Phosphate Buffered Saline (DPBS) for 2 h.
  • DPBS buffer (1X) After rinsing with DPBS buffer (1X), the substrate was dipped into an Angiopoietin-2 Antibody for 30 min. Then, the substrate was air dried (room temperature and 46% relative humidity). Drying of the samples was performed between each step at room temperature and 46% humidity, and all reactions were performed at standard room temperature and pressure.
  • the near-infrared laser source is preferable in general bimolecular SERS detection because; (a) it can avoid the excitation of fluorescence from biomolecules; (b) it has a deeper penetration depth in biological material, and (c) low photon energy of near infrared laser minimizes photo thermal damage to biomolecules.
  • Example 1 SERS Spectra Analysis for sTREM-1
  • the SERS spectra for Ang-1 (spectra (a)) and its antibody (spectra (b)) were collected at a concentration of 0.8 pM, and characteristic peaks appeared at 329.338 cm' 1 , 376.659 cm' 1 , 433.688 cm' 1 , and 455.084 cm' 1 , which were absent from any of the previous steps. Those peaks appeared consistently even at low concentrations of Ang-1 , eventually merging at concentrations below 0.01 pM.
  • the SERS spectra for Ang-2 (spectra (a)) and its antibody (spectra (b)) were collected at a concentration of 1 pM, and characteristic peaks appeared at 402.076 cm -1 and 437.47 cm -1 , which were absent from any of the previous steps. Those peaks appeared consistently even at low concentrations of Ang-2, eventually merging at the concentrations below 0.1 pM.
  • Figures 6a to 6c depict the correlation between each biomarker concentration and its characteristic Raman peak intensity.
  • the band intensities were proportional to the concentration of biomarkers and a linear concentration dependence was observed.
  • FIG. 7 shows unique signatures of sTREM-1 in a gold nanoparticle solution (spectra (a), where three new peaks appear in the range of 360 cm -1 to 370 cm -1 compared to two peaks around 450 cm' 1 of the pure gold nanoparticles in a buffer solution (spectra (b).
  • biomarker protein solution for example, sTREM- 1
  • the molecule could be differentiated based on the Raman marker bands, and qualitative detection is (can be) achieved.
  • Figure 8 is a schematic of an exemplary SERS system comprised of an enclosed housing 30 within which is contained a laser source 32, an objective lens 34, a SERS substrate 38, a translation stage 40, on which the SERS substrate 38 is mounted, and a detector 46.
  • the substrate includes the SERS active area with the sample on it.
  • the substrate could include, for example, the microfluidic circuit shown in Figure 9 discussed below. Incident light 36 from the laser source 32 passes through the objective lens 34, striking the SERS substrate 38, atop of which is the sample.
  • the scattered light 42 from the sample/substrate is collected at the detector 46, having passed through two filters 44 with the first filter being a notch filter to remove the elastic component of scattered light, and the second filter being a short/long pass filter for passing the anti-Stokes/Stokes radiation.
  • Spectra are recorded and processed by computer 50, which also controls the laser source 32, detector 46 and translation stage 40 via electrical cables 48.
  • methods are provided for sensing specific biomarkers for sepsis, such as sTREM-1 , Ang-1 , or Ang-2, from one or more human bodily fluids (e.g., blood, plasma and serum, saliva, and/or urine) (Ref. 26:Su et al., 2011 , Crit Care. 2011 ; 15(5): R250.; Ref. 27:Konvalinka et al., Clin Proteomics. 2016; 13: 16; Ref. 28:Jia et al., Open Life Sci. 2024; 19(1): 20220812).
  • sTREM-1 e.g., sTREM-1 , Ang-1 , or Ang-2
  • 'bodily fluids' generally refers to naturally expressed bodily fluids, although some embodiments may include fluids collected intravenously (e.g., blood, plasma and serum). In the context of the disclosed embodiments, 'bodily fluids' refer to naturally expressed fluids such as saliva and urine.
  • a sample containing a bodily fluid is treated in a solution before it is measured.
  • the solution ensures that biomarkers within the bodily fluid retain their antigenicity and cellular architecture.
  • the solution can be buffered at a pH ranging from about 6.4 to 8.4, with a preferred range of about 7.2 to 7.6, and may include a blocking agent such as, but not limited to, Bovine Serum Albumin (BSA).
  • BSA Bovine Serum Albumin
  • the buffer may consist of phosphate-buffered saline (PBS) or Trisbuffered saline (TBS), and in some embodiments, may also contain fetal bovine serum.
  • PBS phosphate-buffered saline
  • TBS Trisbuffered saline
  • the treated sample is introduced to the microfluidic system such as that shown in Figure 9 for measurement.
  • FIG. 9 shows a schematic of sample delivery via a microfluidic circuit 10 to SERS-active regions on the substrate, with each region functionalized for one specific antigen.
  • Microfluidic circuit 10 when combined with the SERS system of Figure 8, is essentially the SERS substrate 38 shown in Figure 8.
  • Microfluidic circuit 10 includes a sample inlet 12, microfluidic channels 14, an array of target structures 16 functionalized for capture of one specific antigen, e.g., sTREM-1 , an array of target structures 18 functionalized for capture of another specific antigen, e.g., Ang-1 , and array of target structures 20 functionalized for capture of another specific antigen, e.g., Ang-2.
  • Microfluidic circuit 10 includes a sample collector 24, and substrate 26.
  • the treated sample is introduced to the sample inlet 12 by, for example, pipette or pump, whereupon it travels under external force, for example pump, or on-chip force, for example, electrophoresis along the microfluidic channels 14, to the array of target structures 16, 18, 20 where some of the sample is retained due to surface friction and the remainder travels on to the sample collector 24 via the microfluidic channels 14.
  • Each array of target structures 16, 18, 20 containing retained sample is measured separately using, for example, the techniques described above to acquire the Raman spectra of the sample.
  • the SERS data collected as described above is passed into a machine learning classification pipeline to distinguish the biomarkers of interest from other substances, as illustrated in Figure 11 A.
  • these pipelines are trained to distinguish between Ang-1 , Ang-2, sTREM-1 and their respective antibodies or other materials such as thiol polyethylene glycol)/biotinylated polyethylene glycol) thiol, and streptavidin.
  • Pipelines can be developed to classify spectral data between all categories or any subset. For example, one type of embodiment could be a classification pipeline for distinguishing SERS spectra belonging to Ang-1 from those belonging to Ang-2.
  • Figure 11 A shows the machine learning pipeline for classifying the sepsis biomarkers from SERS data.
  • This pipeline would be used to detect the presence of sepsis biomarkers in a sample.
  • the classification pipeline consists of data preprocessing steps to ensure good quality data, feature selection or dimensionality reduction techniques to reduce complexity and cost, and the classifier module itself.
  • raw SERS data collected as described above is preprocessed by subtracting the minimum value of the spectra, then subtracting the baseline spectra as estimated using algorithms such as asymmetric least squares, then normalizing the values of the spectra using the minimum and maximum values, then carrying out a feature selection technique to reduce the dimensionality of the data for machine learning.
  • the pipeline can also include outlier rejection or other robustness techniques applied to the dataset to remove biased or unexpected datapoints, to improve the pipeline performance.
  • the dataset Once the dataset has been preprocessed, it is then split into dedicated training, testing, and validation sets as required. Training and validation sets are used to train and evaluate the performance of a range of classification models to determine the model most suited for the task with regards to classification accuracy, sensitivity, and selectivity. After a model has been chosen, the training and validation sets are then used to train and optimize the model's parameters and hyperparameters to yield the highest accuracy, sensitivity, and specificity. As a final measure of model performance, the test set is fed into the model and the accuracy, sensitivity, and specificity is evaluated. If model performance is acceptable, the trained and optimized model is deployed for use on new SERS data obtained in real-time to classify which biomarker is in the sample.
  • preprocessing of SERS data is done by applying numerical methods for the removal of the baseline, background, cosmic rays, and noise before normalization (Ref. 29:Wahl et al 2020, Applied Spectroscopy, 74(4), 427-438).
  • Minimum subtraction is done to centre the spectra about zero.
  • Baseline subtraction can be carried out using the Asymmetric Least Squares algorithm, to remove background signals that are picked up by the machine. These steps can minimize background radiation, and fluorescence.
  • Background radiation can be an effect of light sources that are difficult to shield or SERS from materials surrounding the sample (ex. PBS buffer or the microscope slide), and fluorescence is the result of molecules being excited to states of higher energy and emitting this energy as light.
  • preprocessing should not be understated, as faulty preprocessing may lead to erroneous conclusions (e.g., achieve smaller absolute error, better quality, smaller signal-to-noise) (Ref. 30: Oliveri et al 2019, Analytica Chimica Acta, 1058, 9-17).
  • normalization can be performed by dividing the spectrum values by their norm, to ensure that data from various experiments can be compared (Ref. 31 : Palacky et al 2021 , Journal of Raman Spectroscopy, 42(7), 1528-1539).
  • feature selection or engineering methods can be applied to extract the most informative wavenumbers from the dataset for classification purposes.
  • Popular methods for feature selection are dimensionality reduction techniques such as Principal Component Analysis or Linear Discriminant Analysis, or statistical techniques such as Analysis of Variance.
  • Various machine learning classification models can be trained on processed spectral data.
  • Popular models include Decision Trees, Support Vector Machines, K Nearest Neighbours, Logistic Regression, or Neural Network models. Using training and validation sets for training and evaluation of model performance, the best model from available options can be chosen for further optimization and eventual deployment.
  • Example 1 Classification of Ang-1, Ang-2, and Their Antibodies Using a Multilayer Perceptron
  • the SERS spectra regression pipeline consists of all steps mentioned in the classification pipeline of Figure 11 A, excluding the classifier. Further, depending on the model utilized as the regressor, the regression pipeline may include steps to transform the data into a different feature space. For example, when polynomial regression is utilized, transformation of the spectral features into polynomial features is necessary. Additionally, outlier rejection or other robustness techniques, such as Random Sample Consensus or the use of robust loss functions, can be used to improve the performance of the model by weighting potential outlier datapoints accordingly when performing the regression. These techniques allow for a more robust and accurate model. Regression models are generally evaluated using root mean squared error (RMSE) and R 2 goodness-of-fit metrics. Finally, various regression models can be employed for this pipeline, such as linear, polynomial, or Bayesian regressions or neural network models.
  • RMSE root mean squared error
  • R 2 goodness-of-fit metrics can be employed for this pipeline, such as linear, polynomial, or Bayesian regressions or neural
  • Figures 11B, 11C and 11D show the process flowcharts for the machine learning regression pipelines 1 , 2, and 3 respectively.
  • the data obtained from SERS is passed into a machine learning regression pipeline to predict the percent, absolute, or relative concentration of Ang-1 , Ang-2, or sTREM-1 .
  • the dataset contains the spectral data of only one of the three biomarkers, in varying concentrations.
  • the aim of the pipeline is to predict the concentration of that particular biomarker from the SERS spectrum.
  • all preprocessing, dataset splitting, and model development steps are similar to those described for Figure 11 A. However, the nature of the data used, the types of models tested for development, the evaluation of the models, and the application of the final model differ.
  • the dataset comprises of raw SERS data collected from a single biomarker at a range of concentrations such that the trained and optimized model can predict the concentration of that biomarker when fed new SERS data from a sample containing only that biomarker.
  • the models used for development steps differ to those of the classification pipeline.
  • the regression models can include linear regressions or neural networks, as shown in Figure 11B.
  • Performance of regression models is evaluated using root mean squared error (RMSE) and R 2 goodness-of-fit metric.
  • RMSE root mean squared error
  • R 2 goodness-of-fit metric The application of this pipeline is to develop a single stand-alone module for predicting the concentration of an individual biomarker of interest in a sample containing only that biomarker.
  • the overall dataset contains data from two or more biomarkers, yet each spectrum itself contains the spectral data of only one biomarker. Again, the aim is to predict the concentration of one particular biomarker.
  • all preprocessing, dataset splitting, and model development steps are similar to those described for Figure 11 A.
  • the types of regression models used in this pipeline and their evaluations are similar to those described for Figure 11B.
  • the nature of the data used, and the application of the final model differ.
  • the dataset comprises of raw SERS data collected from any two or more of the biomarkers, with each individual measurement carried out on a sample containing strictly one of the biomarkers.
  • the final model is then used on new SERS data from a sample containing an unknown, single biomarker to determine the biomarker and its concentration.
  • the application of this pipeline is to develop a module that can determine both the biomarker and the concentration from a sample containing only one biomarker.
  • the dataset contains data obtained from mixtures of two or more biomarkers, meaning each spectrum may have spectral data from multiple biomarkers.
  • the aim of the regression pipeline is to predict the concentrations of all biomarkers present within the spectrum.
  • all preprocessing, dataset splitting, and model development steps are similar to those described for Figure 11 A.
  • the types of regression models used in this pipeline and their evaluations are similar to those described for Figure 11 B.
  • the nature of the data used, and the application of the final model differ.
  • the dataset comprises of raw SERS data collected from any two or more of the biomarkers, with each individual measurement carried out on a sample containing one or more biomarkers.
  • the final model is then used on new SERS data from a sample containing an unknown number of biomarkers, to determine the biomarkers present and their respective concentrations.
  • the application of this pipeline is to develop a comprehensive module that can analyze a sample with many potential biomarkers present and detect the existing biomarkers and their concentrations, which is most relevant for clinical blood samples.
  • Example 1 Concentration Prediction of Ang-1 using 2 nd Degree Polynomial Regression
  • Example 2 Concentration Prediction of Ang-2 using 2 nd Degree Polynomial Regression SERS spectra for Ang-2 at concentration molarities of 0.1 pM (# samples
  • the data was preprocessed via minimum subtraction, baseline subtraction, and normalization. A train/test split of 85%/15% was employed.
  • the test set yielded an R 2 of 0.88 and a root mean squared error of 0.102.
  • Table 4 illustrates sample predictions made by the pipeline from Figure 11 B on the test set.
  • Example 4 Concentration Prediction of Ang-1 and Ang-2 Mixed Spectra Using a Convolutional Neural Network
  • the data was preprocessed via minimum subtraction, baseline subtraction, and normalization. A train/validation/test split of 68%/22%/10% was employed.
  • the architecture consisted of two convolutional layers and four densely connected layers, all with the ReLU activation functions. The test set yielded a root mean squared error of 0.0217.
  • Table 5 illustrates sample predictions made by the pipeline from Figure 11D on the test set.
  • Figure 13A shows the process of correlating findings from the machine learning classification pipeline from Figure 11A to a decomposition or deconvolution of the spectra for a biomarker.
  • the raw SERS data from a single biomarker is preprocessed up until the normalization step as described for Figure 11 A.
  • spectra are deconvolved or decomposed into a series of underlying functions.
  • significant wavenumbers are determined from the machine learning classification pipeline (as shown in Figure 11 A).
  • the properties from the deconvolution or decomposition are then compared to these significant wavenumbers to find spectral features of interest (e.g. peaks) and can be further related to the physical and molecular structure of the biomarker of interest.
  • Figure 13B shows the process of using the results of a spectral deconvolution or decomposition for a biomarker to inform the development of a machine learning classification model.
  • the feature selection preprocessing step uses the results of the deconvolution or decomposition to inform which wavelengths or other significant spectral features to use as input to the classification model.
  • the SERS spectra are deconvolved or decomposed into a series of underlying functions, such as Gaussian or Lorentzian functions. Each underlying function contributes to a peak on the spectrum. The location, width, and other properties of this peak can be correlated to findings from machine learning or similar analysis to provide physical explanations.
  • wavenumbers found from analysis of variance (ANOVA) in datasets which contain the spectral data of two or more biomarkers can be compared to the wavenumbers at which peaks are found when doing a Lorentzian deconvolution. Wavenumbers that are considered significant by both ANOVA and spectral deconvolution may be considered characteristic peaks for a biomarker.
  • ANOVA analysis of variance
  • the ANOVA wavenumbers used in these three models were directly compared to the spectral peaks obtained from the sTREM deconvolution. Wavenumbers which appeared to be significant in multiple classification models and simultaneously appeared as peaks in the deconvolution were determined to be potential characteristic peaks of sTREM. Multiple wavenumbers that are close in value and occur in multiple classification models and the deconvolution peaks may also be considered of interest. Peaks of interest occurred in the range of 1085-1098 cm -1 , which could be correlated with the stretching vibration of C-C or C-0 bonds.
  • Example 2 Correlation of PCA Wavenumbers to sTREM Spectral Deconvolution to Determine Characteristic Peaks
  • the process shown in Figure 13B was used to correlate PCA wavenumbers from a machine learning classification model to the spectral deconvolution of sTREM to determine potential characteristic peaks.
  • a Lorentzian deconvolution of the SERS spectra for sTREM was performed to obtain wavenumber locations of significant peaks.
  • Multilayer perceptrons were used for all classification models. The data was preprocessed via minimum subtraction, baseline subtraction, and normalization. A train/test split of 70%/30% was employed. PCA was employed to reduce the spectra to 5 components. All models performed with 100% accuracy on the test set.
  • Wavenumber loadings for each PCA component were calculated. Wavenumbers with high loading values were compared to the spectral peaks obtained from the sTREM deconvolution. Wavenumbers which appeared to be significant in multiple classification models and simultaneously appeared as peaks in the deconvolution were determined to be potential characteristic peaks of sTREM. Multiple wavenumbers that are close in value and occur in multiple classification models and the deconvolution peaks may also be considered of interest. Peaks of interest occurred in the range of 1087-1098 cm' 1 , which could be correlated with the stretching vibration of C-C or C-0 bonds.
  • the concentrations of biomarkers are determined using machine learning classification pipelines, which are trained to distinguish between any combination of sTREM-1 , Ang-1 , Ang-2.
  • the classification pipelines are developed specifically for spectral data and comprise: a) spectrum preprocessing, including but not limited to, minimum subtraction, baseline subtraction, and normalization; b) data augmentation algorithms used to increase number of spectra samples, including but not limited to, linear addition and subtraction of existing spectra and addition of Gaussian noise; c) feature selection or engineering methods applied to spectral data, including but not limited to, dimensionality reduction techniques and analysis of variance feature selection; and d) a machine learning classifier.
  • the machine learning classifier can be embodied by models including, but not limited to, Decision Trees, Support Vector Machines, K Nearest Neighbours, Logistic Regression, Deep Neural Networks, or Convolutional Neural Networks.
  • the measurement of biomarkers within a spectrum is facilitated via machine learning regression pipelines which are trained to predict the concentration of sTREM-1 , Ang-1 , or Ang-2.
  • the regression pipeline predicts the concentration of sTREM-1 , Ang-1 , or Ang-2 from a spectrum containing only one biomarker.
  • the regression pipeline predicts the concentration of any combination of sTREM-1 , Ang-1 , and Ang-2 from a spectrum containing any mixture of these biomarkers.
  • the regression pipeline predicts the concentration of Ang-1 and subsequently predicts the concentration of Ang-2 using the biomarker relationship, and vice versa.
  • the regression pipeline is developed specifically for spectral data, comprising: 1) Spectrum preprocessing, including but not limited to, minimum subtraction, baseline subtraction, and normalization; and
  • the machine learning regressor can be embodied by models including but not limited to Linear Regression, Generalized Linear Models, Polynomial Regression, Bayesian Regression, Neural Networks, or Convolutional Neural Networks.
  • the methodology is developed for correlating findings from a machine learning classification pipeline to spectral deconvolutions or decompositions, comprising:
  • the correlation is done between the spectral deconvolution or decomposition of Ang-1 , Ang-2, or sTREM and one or more machine learning classification pipelines including said biomarker for the purpose of identifying potential characteristic peaks.
  • the correlation is used to provide physical explainability, such as vibrational modes of the molecular structure of Ang-1 , Ang-2, or sTREM, to the machine learning classifier.
  • Findings from deconvolution or decomposition, or identified characteristic peaks, are used as primary features for the development of a machine learning classifier to distinguish between any combination of Ang-1 , Ang-2, and sTREM.
  • separate SERS active substrates functionalized for detection of each of sTREM-1 , Ang-1 and Ang-2 are integrated into the system, and wherein the concentrations of biomarkers are determined using machine learning classification and regression pipelines, which are trained to distinguish between any combination of sTREM-1 , Ang-1 , Ang-2 and predict their concentrations.
  • the classification and regression pipelines are configured specifically for spectral data and comprise: a) spectrum preprocessing, to remove or minimize background signal from radiation, noise, and/or SERS responses from other material, clean and validate the data to remove outliers or erroneous measurements, and standardize the data such that data from independent experiments can be compared; b) data augmentation algorithms to artificially generate new data samples from the existing data collected from real-world samples, to increase the number of diverse data samples used for training, validation, development, and/or testing of machine learning methods and thereby improve the performance of these methods; c) feature selection to extract a base set of the most informative features for biomarker discrimination from the dense spectral data collected, thereby reducing the dimensionality of the input data used by the machine learning methods and improving their computational time and space costs/complexity, and real-world generalizability; d) feature and data transformation methods for polynomial fitting or to place the spectra data into a desired feature space to improve machine learning performance and/or costs; e) out
  • the regression pipeline predicts the concentration of any combination of sTREM-1 , Ang-1 , and Ang-2 from a spectrum containing any mixture of these biomarkers.
  • the regression pipeline predicts the concentration of Ang-1 and subsequently predicts the concentration of Ang-2 using the biomarker relationship, and vice versa.
  • a methodology for correlating findings from a machine learning classification pipeline to spectral deconvolutions or decompositions, comprising: a) spectral deconvolution or decomposition into underlying Gaussian or Lorentzian functions; b) extraction of significant peaks and relevant peak properties from the deconvolution or decomposition; c) a machine learning classification pipeline, with feature selection or engineering functionalities to identify significant wavenumbers; d) comparison of significant wavenumbers from the machine learning classification model to extracted peaks and their properties from the deconvolution or decomposition.
  • the correlation is done between the spectral deconvolution or decomposition of Ang-1 , Ang-2, or sTREM and one or more machine learning classification pipelines including said biomarker for the purpose of identifying potential characteristic peaks.
  • the correlation is used to provide physical explainability, such as vibrational modes of the molecular structure of Ang-1 , Ang-2, or sTREM, to the machine learning classifier.
  • the findings from deconvolution or decomposition, or identified characteristic peaks, are used as primary features for the development of a machine learning classifier to distinguish between any combination of Ang-1 , Ang-2, and sTREM.
  • Electron Beam Lithography EBL
  • RIE Reactive Ion Etching
  • the resulting layer may be several nanometers thinner on the vertical side walls compared to the top and bottom surfaces.
  • RIE Reactive Ion Etching
  • the present inventors tested various photoresist combinations to find the most tolerant one. For RIE, a 15- minute O 2 plasma cleaning step was introduced before the experiment.
  • PVD Physical Vapor Deposition
  • Biomarkers and their antibodies are extremely sensitive to temperature and pH levels, with excessive temperature or extreme pH causing these molecules to unfold or aggregate, thereby compromising their functionality.
  • they should be stored at -80°C when not in use. For thawing, the proteins should be moved to -20°C overnight, then to 4°C for several hours. Following this, the proteins will be reconstituted and serially diluted in 10X PBS buffer, with ABS added if necessary. The prepared protein solution will then be applied to the SERS substrates and measured by Raman spectroscopy immediately. For transportation, the protein solution will be packaged in a temperature-controlled box with ice packs.
  • laser wavelength is critical for obtaining accurate results. Wavelengths such as 532 nm and 633 nm often produce strong background fluorescence, making them less suitable for application to biomarker Raman spectroscopy.
  • plasmonic metal e.g., gold
  • a 785 nm laser was used, as it is shifted further into the red, reducing fluorescence effects.
  • higher laser power and longer acquisition times are generally preferred.
  • laser power is typically kept between 1-10%, with an acquisition time ranging from 10 to 60 seconds to balance signal strength and noise reduction.
  • Machine learning models require large amounts of representative training data to perform reliably.
  • the present inventors took many more measurements of each biomarker at varying high and low concentrations.
  • the present inventors performed a number of preprocessing steps to reduce noise/errors, remove background signals, and normalize the data.
  • Angiopoietin 2 is a partial agonist/antagonist of tie2 signaling in the endothelium. Molecular and Cellular Biology, 29(8), 2011-2022. https://doi.orq/10.1128/mcb.O1472-08
  • Soluble triggering receptor expressed on myeloid cell-1 (strem-1 ): A potential biomarker for the diagnosis of infectious diseases. Frontiers of Medicine, 11(2), 169-177. https://doi.org/10.1007/s11684-017-0505-z
  • Jia L, Li X, Shen J, Teng Y, Zhang B, Zhang M, Gu Y, Xu H. Ang-1 , Ang- 2, and Tie2 are diagnostic biomarkers for Henoch-Schdnlein purpura and pediatric-onset systemic lupus erythematous. Open Life Sci. 2024 Feb
  • Circulating protein and lipid markers of early sepsis diagnosis and prognosis A scoping review. Current Opinion in Lipidology, 34(2), 70-81 . https://doi.org/10.1097/mol.0000000000000870

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Abstract

La présente invention concerne un procédé de mesures à haute résolution en temps quasi réel de (bio)marqueurs moléculaires choisis parmi un récepteur de déclenchement soluble exprimé sur des cellules myéloïdes 1 (sTREM-1), l'angiopoïétine 1 (Ang 1) et l'angiopoïétine 2 (Ang 2) sur la base d'une spectroscopie Raman exaltée en surface (SERS). A l'aide de ces mesures, l'invention fournit un moyen de surveillance de la progression du sepsis.
PCT/CA2024/051270 2023-09-21 2024-09-23 Procédé de mesure à haute résolution de concentrations d'angiopoïétine (ang) 1/2 et récepteur de déclenchement soluble exprimé sur des biomarqueurs de cellules myéloïdes 1 (strem-1) Pending WO2025059782A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020182716A1 (en) * 2000-08-21 2002-12-05 Claude Weisbuch Support for chromophoric elements
US20120161600A1 (en) * 2008-11-25 2012-06-28 Norris David J Replication of patterned thin-film structures for use in plasmonics and metamaterials
KR20170131864A (ko) * 2016-05-23 2017-12-01 고려대학교 산학협력단 시스테인 풍부 펩타이드의 파지 디스플레이를 이용한 기공성 표면 증강 라만 산란(sers) 기질의 제조방법
US20210396678A1 (en) * 2018-05-02 2021-12-23 Korea University Research And Business Foundation Bacteriophage-based sers-active gold nanohalo structure and manufacturing method therefor
US20210405059A1 (en) * 2013-09-05 2021-12-30 Fio Corporation Biomarkers for early determination of a critical or life threatening response to illness and/or treatment response

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020182716A1 (en) * 2000-08-21 2002-12-05 Claude Weisbuch Support for chromophoric elements
US20120161600A1 (en) * 2008-11-25 2012-06-28 Norris David J Replication of patterned thin-film structures for use in plasmonics and metamaterials
US20210405059A1 (en) * 2013-09-05 2021-12-30 Fio Corporation Biomarkers for early determination of a critical or life threatening response to illness and/or treatment response
KR20170131864A (ko) * 2016-05-23 2017-12-01 고려대학교 산학협력단 시스테인 풍부 펩타이드의 파지 디스플레이를 이용한 기공성 표면 증강 라만 산란(sers) 기질의 제조방법
US20210396678A1 (en) * 2018-05-02 2021-12-23 Korea University Research And Business Foundation Bacteriophage-based sers-active gold nanohalo structure and manufacturing method therefor

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CHOI JUNG-HYUN, KIM MI-HEE, , : "An Update on Sepsis Biomarkers", NCBI, 1 March 2020 (2020-03-01), NCBI, XP093297285, Retrieved from the Internet <URL:https://pmc.ncbi.nlm.nih.gov/articles/PMC7113456/> DOI: 10.3947/ic.2020.52.1.1 *

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