Multipathogen detection using nanoplasmonic sensors for urinary tract infections

Abstract

Disclosed herein is a nanoplasmonic sensor for molecular characterization of urinary tract infections. In some embodiments, the nanoplasmonic sensor can also be used at the point of care. The nanoplasmonic sensor utilizes an optical phenomenon (localized surface plasmon resonance (LSPR)) that occurs between metal nanoparticles and a dielectric to detect bacterial nucleic acids. In some embodiments, the spectral peak shift is a function of target sequence concentration.

Description

[Technical Field]

[0001] (Incorporation by reference of priority application) This application claims priority to U.S. Provisional Patent Application No. 63 / 352,989, filed June 16, 2022. All applications with foreign or domestic priority claims are incorporated herein by reference in their entirety.

[0002] (Reference to sequence listing) This application is filed with a Sequence Listing that has been submitted electronically in XML format. The Sequence Listing is provided as a file entitled NPATH.007WOSEQLISTING.xml, created on June 15, 2023, which is approximately 29,065 bytes in size. The information in the electronic format of the Sequence Listing is incorporated herein by reference in its entirety.

[0003] The present disclosure relates to the field of molecular detection. Specifically, the present disclosure describes methods for the functionalization of nanoplasmonic sensors and functionalized nanoplasmonic sensors for the molecular characterization of urinary tract infections (UTIs). [Background technology]

[0004] Urinary tract infections (UTIs) are one of the most common causes of medical visits among women in the United States. Over 50% of women experience a UTI at some point in their lives, making them one of the leading causes of antibiotic prescriptions in the United States. More specifically, the annual prevalence of UTIs exceeds 11% of the U.S. population (over 20% of the elderly population), and 15%–20% of these cases are resistant to first-line antibiotic therapy. Untreated UTIs can lead to serious complications, including systemic bacterial infections such as bacteremia. UTIs are caused by a variety of pathogens, the most common of which are Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis, and Staphylococcus saprophyticus. High recurrence rates associated with UTIs, coupled with a high prevalence of antibiotic-resistant pathogens, can significantly increase healthcare costs and place a strain on healthcare systems.

[0005] Despite the severity and prevalence of UTIs, diagnostic methods remain extremely time-consuming, relying on outdated culture-based methods for pathogen detection, followed by additional steps for species identification and antibiotic susceptibility characterization. This time-consuming diagnostic workflow typically leaves women in pain for up to three days before they are prescribed appropriate antibiotic therapy. In the most severe cases, women are nonspecifically prescribed empirical antibiotic therapy, but targeted therapy cannot be prescribed, necessitating a change in treatment in up to one-third of cases. Furthermore, UTIs, especially those acquired in healthcare settings, are one of the major drivers of antibiotic resistance.

[0006] To the applicant's knowledge, there are no molecular diagnostic methods for UTI, and current technologies are unable to identify the pathogen causing UTI at the point of care. Often, healthcare professionals use urine dipsticks to measure indirect markers of infection (e.g., pH), but these methods lack sensitivity and specificity. Other technologies used in the bacterial characterization space are culture-based methods and nucleic acid amplification tests (NAATs). Some commonly used techniques for nucleic acid identification include quantitative polymerase chain reaction (qPCR), nucleic acid microarrays, amplicon-based metagenomic sequencing, and isothermal nucleic acid amplification tests (e.g., loop-mediated isothermal amplification, CRISPR-based assays, rolling circle amplification). However, these molecular diagnostic techniques have not been utilized for the diagnosis and / or characterization of UTI. Summary of the Invention

[0007] Disclosed herein are nanoplasmonic sensors. In some embodiments, the nanoplasmonic sensors comprise an array of functionalized sensors, each of which comprises an array of nanostructures conjugated to a biological probe, the biological probes configured to detect the presence of a pathogen that causes a urinary tract infection. In some embodiments, at least one of the functionalized sensors in the array comprises a different biological probe for detecting a pathogen that causes a urinary tract infection than the other functionalized sensors. In some embodiments, the nanoplasmonic sensor is configured to simultaneously detect multiple strains or species of pathogens that cause a urinary tract infection. In some embodiments, each of the functionalized sensors in the array comprises a different biological probe. In some embodiments, the pathogen that causes a urinary tract infection is selected from the group consisting of Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis, Staphylococcus saprophyticus, and antibiotic-resistant strains thereof. In some embodiments, the biological probe has a sequence selected from the group consisting of SEQ ID NOS: 1-32. In some embodiments, the nanostructures comprise gold. In some embodiments, the nanostructures in the array are regularly spaced apart at intervals of about 100 nm to about 1000 nm, and each nanostructure has a square shape with side dimensions of about 50 nm to about 400 nm. In some embodiments, the nanostructures have a thickness of about 20 nm to about 75 nm.

[0008] Also disclosed herein are methods for detecting the presence of one or more pathogens that cause urinary tract infections. In some embodiments, the method includes: (1) exposing a nanoplasmonic sensor of any of the embodiments disclosed herein to a bodily fluid sample from a patient suspected of having a urinary tract infection; (2) irradiating each of the functionalized sensors with light of a series of wavelengths; and (3) collecting absorbance, transmittance, or extinction data for each functionalized sensor. In some embodiments, the method further includes comparing the collected absorbance, transmittance, or extinction data for each functionalized sensor to baseline data for each of the functionalized sensors prior to exposure to the bodily fluid sample. In some embodiments, the comparing step reveals an optical peak shift when a pathogen that causes a urinary tract infection is detected. In some embodiments, the amount of optical peak shift correlates with the concentration of the pathogen that causes a urinary tract infection in the bodily fluid sample. In some embodiments, the bodily sample includes urine. In some embodiments, at least one of the functionalized sensors in the array comprises a different biological probe for detecting a different urinary tract infection-causing pathogen than the other functionalized sensors. In some embodiments, the urinary tract infection-causing pathogens are independently selected from the group consisting of Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis, Staphylococcus saprophyticus, and antibiotic-resistant strains or their identified resistance genes. In some embodiments, the biological probes are independently selected from the group consisting of SEQ ID NOS: 1-32. In some embodiments, each of the functionalized sensors in the array comprises a different biological probe. In some embodiments, multiple strains or species of urinary tract infection-causing pathogens are simultaneously detected. In some embodiments, the method is configured to be performed at the point of care.

[0009] Another method for detecting the presence of one or more pathogens that cause urinary tract infections includes providing a sensor with one or more biological probes designed to target specific nucleic acid sequences derived from one or more pathogens that cause urinary tract infections, exposing the sensor to a sample suspected of containing one or more pathogens that cause urinary tract infections, and collecting electrical, fluorescence, absorbance, transmittance, and / or extinction data from the sensor. In some embodiments, the one or more biological probes are selected using computational and / or bioinformatics methods. In some embodiments, the one or more biological probes include intentionally varying degrees of mismatch with the target nucleic acid. In some embodiments, the one or more biological probes are designed to bind to multiple target nucleic acid sequences. In some embodiments, one of the biological probes is capable of binding to nucleic acids derived from multiple pathogens that cause urinary tract infections. In some embodiments, the one or more biological probes are designed to bind to nucleic acid sequences specific to antibiotic resistance genes. In some embodiments, one of the biological probes is capable of binding to nucleic acid sequences derived from multiple antibiotic resistance genes.

[0010] It is to be understood that all combinations of the foregoing concepts, and the additional concepts detailed below, are contemplated as being part of the inventive subject matter disclosed herein and may be used to achieve the benefits and advantages described herein. [Brief explanation of the drawings]

[0011] Features of embodiments of the present disclosure will become apparent by reference to the following detailed description and drawings, in which like reference numbers correspond to similar, but perhaps not identical, components. For purposes of brevity, reference numbers or features having a previously described function may or may not be described with reference to other drawings in which they appear.

[0012] [Figure 1]Figure 1A shows an embodiment of a plasmon resonance sensing device, and Figure 1B shows an embodiment of an array of nanostructures in a sensor of a plasmon resonance sensing device. [Figure 2] Figures 2A and 2B show non-limiting, exemplary schematics of selected geometries and fabrication maps. Figure 1A shows a schematic of a grid with labeled dimensions for length, width, thickness, and periodicity of nanostructures. Figure 1B shows a schematic of a map of dimension placement for dose matrix testing. [Figure 3] 1 shows extinction curves for a non-limiting example of ordered gold nanorod arrays at three bulk refractive indices. [Figure 4] We present examples of PNA-DNA binding simulations. The simulations are of conformal layers representing PNA and DNA binding to gold nanostructures. The two geometries demonstrated here are (Figure 3A) a repeating nanorod array (130 nm × 40 nm) and (Figure 3B) a repeating nanosquare array (95 nm × 95 nm). [Figure 5] Figure 5A shows the extinction curves of the bulk refractive index sensitivity simulation for one embodiment of a nanostructure array at three refractive indices, and Figure 5B shows the refractive index sensitivity of uncoupled nanorods and a nanostructure array. [Figure 6] Figure 6A shows the extinction curves of the bulk refractive index sensitivity simulation for one embodiment of a nanostructure array at three refractive indices, and Figure 6B shows the refractive index sensitivity of uncoupled nanorods and a nanostructure array. [Figure 7] Figure 7A shows the extinction curves of the bulk refractive index sensitivity simulation for one embodiment of a nanostructure array at three refractive indices, and Figure 7B shows the refractive index sensitivity of uncoupled nanorods and a nanostructure array. [Figure 8] Experimental transmission spectra for five different nanoarray geometries are shown. [Figure 9] Simulated transmission spectra are shown for each of five different nanoarray geometries. [Figure 10]1 shows a CAD drawing of the post array polymer well mold and fabricated wells with coordinates aligned over the sensor array. [Figure 11A] 1A-1C show two views of an embodiment of a 3D printing mold for the fabricated polymer wells. [Figure 11B] 1A-1C show two views of an embodiment of a 3D printing mold for the fabricated polymer wells. [Figure 11C] 11A and 11B show two views of one embodiment of a fabricated well array made from the mold shown in FIG. [Figure 11D] 11A and 11B show two views of one embodiment of a fabricated well array made from the mold shown in FIG. [Figure 11E] 10 shows a further embodiment of a microwell fixture. [Figure 11F] 10 shows a further embodiment of a microwell fixture. [Figure 11G] 10 shows a further embodiment of a microwell fixture. [Figure 11H] 10 shows a further embodiment of a microwell fixture. [Figure 11I] 10 shows a further embodiment of a microwell fixture. [Figure 12] Figures 12A-12C show one embodiment of an automated pipette system. Figure 12A shows the entire system with the pipette holder on the left, tip box, 96-well plate holder, and custom tip adapter. Figure 12B shows the tip box aligned under the pipette holder. Figure 12C shows the 96-well plate and adapter during functionalization. [Figure 13] Figures 13A-13C show nanoplasmonic detection of target bacterial species in PBS and synthetic urine. Each biological replicate (n=3) was measured on three unique sensing spots. Figures 13A, 13B, and 13C each represent one of three measurements (technical replicates) performed on each sensing spot. [Figure 14]Probe specificity analysis is shown. "Channel" describes the PNA probe designed for species-level organism detection or antibiotic resistance gene detection, and "spike" describes the genetic material exposed to the sensor. [Figure 15] Figures 15A-15E show the nanosensor detection limits for five targets in a synthetic urine matrix. Each replicate (n=3) was measured on three unique sensing spots. Three measurements (technical replicates) were performed on each sensing spot. The red dashed lines represent the upper limit of the 95% confidence interval for the negative control sample. Figure 15A shows the detection limits for channel: E. coli and isolate: E. coli. Figure 15B shows the detection limits for channel: Enterococcus and isolate: Enterococcus faecalis. Figure 15C shows the detection limits for channel: Klebsiella pneumoniae and isolate: Klebsiella pneumoniae. Figure 15D shows the detection limits for E. coli with channel: CT-X-M1 and isolate: blaCTX-M-1. Figure 15E shows the detection limits for Enterococcus faecalis with channel: VanA and isolate: VanA. [Figure 16] Figures 16A-16E show evaluation of nanosensor performance in healthy patient urine sample matrices. Each circle represents an individual patient's urine matrix. Diamonds represent pooled patient urine matrices (if applicable). The red dashed line represents the upper limit of the 95% CI for the negative control sample. Figure 16A shows the shift in detection for channel: E. coli and isolate: E. coli. Figure 16B shows the shift in detection for channel: Enterococcus and isolate: Enterococcus faecalis. Figure 16C shows the shift in detection for channel: Klebsiella pneumoniae and isolate: Klebsiella pneumoniae. Figure 16D shows the shift in detection for E. coli with channel: CT-X-M1 and isolate: blaCTX-M-1. Figure 16E shows the shift in detection for Enterococcus faecalis with channel: VanA and isolate: VanA. DETAILED DESCRIPTION OF THE INVENTION

[0013] All patents, applications, published applications, and other publications mentioned herein are incorporated herein by reference for the material referenced and in their entirety. If a term or phrase is used herein in a manner contrary to or inconsistent with the definition set forth in the patents, applications, published applications, and other publications incorporated herein by reference, the usage herein takes precedence over the definition incorporated herein by reference.

[0014] Described herein is a plasmon resonance sensing device employing a regular array nanostructure ensemble. The regular array of nanostructures allows coupling to diffracted photon modes and can thus be used to improve sensor sensitivity. The dimensions and geometry of the nanostructures are tailored to provide a high-quality signal and a large optical shift upon modeled analyte binding.

[0015] The present disclosure generally relates to a nanoplasmonic biosensor for point-of-care molecular characterization of urinary tract infections. The disclosed technology utilizes an optical phenomenon (localized surface plasmon resonance (LSPR)) that occurs between metal nanoparticles and dielectrics for the detection of pathogen nucleic acids. LSPR is observed when the wavelength of incident light is larger than the size of the conductive nanoparticles, presenting an opportunity for highly sensitive detection of specific nucleic acid sequences. In the present disclosure, nanostructures are covalently functionalized with biological probes. The nanostructures provide a highly confined electric field of the LSPR mode, which acts as a sensitive transducer to changes in the local dielectric environment (i.e., binding events). In some embodiments, upon hybridization to a nucleic acid target sequence, a continuous red-shift in the spectral peak as a function of target sequence concentration can be observed. A regular array of nanostructures enables coupling to diffracted photon modes, thereby improving sensor sensitivity. The dimensions and geometry of the nanostructures are tailored to provide a high-quality signal and a large optical shift upon modeled analyte binding.

[0016] Also disclosed herein is a nanoplasmonic sensor for rapid (<15 minutes) molecular characterization of urinary tract infections. The disclosed nanoplasmonic sensor utilizes the optical phenomenon (localized surface plasmon resonance, LSPR) that occurs between metal nanoparticles and dielectrics for the detection of bacterial nucleic acids. The sensing substrate is functionalized with rationally designed biological probes (PNAs) that are complementary to the DNA targets of interest. The panel described herein identifies gene sequences specific to Escherichia coli, Enterococcus spp., Klebsiella pneumoniae, vancomycin-resistant (vanA), vancomycin-resistant (vanA / B), and extended-spectrum beta-lactamase (CTX-M). For these targets, when the target DNA was exposed to the functionalized nanosensing substrate, there was a significant red-shift in the sensor's peak absorbance wavelength, indicating successful hybridization of the target nucleic acid sequence to the complementary biological probe. The probes were observed to be highly specific for their intended targets with no significant cross-reactivity. For all targets, significant peak wavelength shifts were observed within approximately 10 4 This was first observed at cell loads of 100 CFU / mL (or equivalent). The magnitude of the peak wavelength shift (i.e., signal) continuously increased with increasing target concentration, suggesting the feasibility of semi-quantitative sample characterization, which is advantageous for the clinical management of UTIs. Finally, the actual patient urine sample matrix (n = 5) did not significantly affect nanoplasmonic sensor performance. These results suggest that this platform can rule out clinically significant UTIs, identify UTI-causing organisms, and characterize important antimicrobial resistance profiles within 15 minutes. This technology platform enables the first DNA-based test for point-of-care UTI diagnosis and characterization.

[0017] Plasmon resonance sensing device Disclosed herein is a plasmon resonance sensing device. As shown in Figures 1A and 1B, the plasmon resonance sensing device 100 comprises an array of sensors 101. Each sensor 101 comprises an array of regularly spaced nanostructures 102. In some embodiments, the nanostructures 102 are regularly spaced apart with spacing between nanostructures of about 100 nm, about 200 nm, about 300 nm, about 500 nm, about 750 nm, about 1000 nm, about 1200 nm, about 1500 nm, about 1800 nm, about 2000 nm, or any distance between about 100 nm and about 2000 nm. In some embodiments, the array of nanostructures is regularly spaced at intervals of about 100 nm to about 2000 nm, about 100 nm to about 1800 nm, about 100 nm to about 1600 nm, about 100 nm to about 1400 nm, about 100 nm to about 1200 nm, about 100 nm to about 1000 nm, about 200 nm to about 900 nm, about 300 nm to about 800 nm, about 100 nm to about 400 nm, about 200 nm to about 500 nm, about 300 nm to about 600 nm, about 400 nm to about 700 nm, about 500 nm to about 800 nm, about 600 nm to about 900 nm, about 700 nm to about 1000 nm, about 500 nm to about 2000 nm, or about 500 nm to about 1500 nm between the nanostructures.

[0018] The nanostructures in the array may have a variety of shapes. For example, the nanostructures may be rectangular, circular, triangular, star-shaped, pentagonal, parallelogram-shaped, diamond-shaped, or square-shaped. Preferably, each of the nanostructures in the array has a square shape. In some embodiments, each nanostructure has a side dimension of about 50 nm, about 75 nm, about 100 nm, about 150 nm, about 200 nm, about 250 nm, about 300 nm, about 350 nm, or about 400 nm, or any integer between about 50 and about 400 nm. In some embodiments, the squares have side dimensions in any range from about 50 nm to about 400 nm, about 100 nm to about 350 nm, 150 nm to about 300 nm, about 50 nm to about 150 nm, about 100 nm to about 200 nm, 150 nm to about 250 nm, about 200 nm to about 300 nm, about 250 nm to about 350 nm, or about 300 nm to about 400 nm, or about 50 nm to about 400 nm.

[0019] In some embodiments, the nanostructures in the array may have a thickness of about 20 nm, about 25 nm, about 30 nm, about 35 nm, about 40 nm, about 45 nm, about 50 nm, about 60 nm, about 65 nm, about 70 nm, about 75 nm, or any integer between about 20 and about 75 nm. In some embodiments, the nanostructures in the array may have a thickness of about 20 nm to about 75 nm, about 25 nm to about 70 nm, about 30 nm to about 65 nm, about 35 nm to about 60 nm, about 30 nm to about 55 nm, or any range of about 20 to about 75 nm.

[0020] The nanostructures comprise a metal. For example, the nanostructures may comprise gold, platinum, aluminum, silver, or copper. Preferably, the nanostructures comprise gold. In some embodiments, the nanostructures comprise a single metal. In some embodiments, the nanostructures comprise a mixture of metals.

[0021] In some embodiments, the nanostructures in the array are conjugated with biological probes. The biological probes are configured to bind to analytes. Binding of the analytes to the biological probes changes the surface properties of the nanostructures, thereby causing a change in localized surface plasmon resonance. In some embodiments, the biological probes comprise one or more of proteins, peptide chains, amino acids, RNA strands, DNA strands, and / or nucleotides. In some embodiments, the biological probes comprise one or more of modified proteins, modified peptides, modified amino acids, modified RNA strands, modified DNA strands, and / or modified nucleotides. In some embodiments, the biological probes comprise one or more of peptide-nucleic acids, aptamers, antibodies, antibody fragments, complementary DNA, and / or enzymes. In some embodiments, the biological probes are selected from the group consisting of peptide-nucleic acids, aptamers, antibodies, antibody fragments, complementary DNA, and enzymes.

[0022] In some embodiments, at least a first sensor 101a in the array of sensors comprises a nanostructure 102 conjugated to a first biological probe. In some embodiments, at least a second sensor 101b in the array of sensors comprises a nanostructure conjugated to a second biological probe. In some embodiments, at least a third sensor in the array of sensors comprises a nanostructure conjugated to a third biological probe. In some embodiments, at least a fourth sensor in the array of sensors comprises a nanostructure conjugated to a fourth biological probe. In some embodiments, at least a fifth sensor in the array of sensors comprises a nanostructure conjugated to a fifth biological probe. In some embodiments, "n" sensors in the array of sensors comprise nanostructures conjugated to "n" biological probes, where "n" is any number between 1 and 2000. In some embodiments, 6 or 12 sensors may be present in an array of sensors on the array substrate 103. In some embodiments, the sensors are about 1 μm 2 ~about 1mm 2 In some embodiments, the sensor may have an area of about 10 μm 2 ~about 1mm 2 , about 50μm 2 ~about 1mm 2 , about 100μm 2 ~about 1mm 2 , about 200μm 2 ~about 1mm 2 , about 400μm 2 ~about 1mm 2 , or about 500 μm 2 ~about 1mm 2 may have an area of

[0023] The substrate 103 may be a dielectric or non-conductive substrate. In some embodiments, the substrate 103 is transparent, and the sensors are exposed to incident light through the substrate 103. For example, the substrate 103 may be a glass, plastic, or polymer substrate. In some embodiments, the substrate 103 may be a polymer or plastic substrate. The substrate and the sensor array on the substrate may be integrated with a microfluidic module to provide a means for introducing or exposing a sample to the sensors.

[0024] Analyte Detection Disclosed herein are methods for detecting an analyte in a sample. In some embodiments, the method includes exposing at least one sensor 101 in any of the plasmon resonance sensing device 100 embodiments disclosed herein to a sample. The sample may or may not contain a target analyte. The plasmon resonance sensing device 100 can be utilized to detect the presence of an analyte (i.e., a target analyte). In some embodiments, the method includes exposing at least two sensors in any of the plasmon resonance sensing device 100 embodiments disclosed herein to the sample. In some embodiments, the method includes exposing at least three sensors, at least four sensors, at least five sensors, or at least six sensors in any of the plasmon resonance sensing device 100 embodiments disclosed herein to the sample. In some embodiments, the method includes exposing "n" sensors in any of the plasmon resonance sensing device embodiments disclosed herein to the sample, where "n" is any number between 1 and 2000. In some embodiments, an array of sensors is exposed to the sample. The sample may include a bodily fluid such as blood, plasma, mucus, serum, urine, or saliva. Mucus can be collected via a cervical swab, a vaginal swab, or a nasal swab. When at least one sensor 101 is exposed to the sample, the biological probes within each sensor will selectively bind to the analyte to which the biological probe is configured to bind.

[0025] Optionally, the at least one sensor may be subjected to a heating step after exposure to the sample. In some embodiments, the at least one sensor is heated to about 85°C, or to any temperature between 25°C and 85°C. In some embodiments, the at least one sensor may be exposed to heat before, during, or after a subsequent step. In some embodiments, the at least one sensor may be exposed to heat before, during, or after a measurement.

[0026] The method for detecting or sensing an analyte further includes illuminating at least one sensor with light. In some embodiments, the method includes illuminating at least one sensor with light of a series of wavelengths. In some embodiments, the light may be emitted from a light source within the device for analyte detection. The light source may be configured to emit a series of wavelengths to illuminate the sensor. In some embodiments, a plasmonic sensing chip including the sensor may be inserted into the device for analyte detection. The device is configured to emit light of a series of wavelengths onto the sensor and collect an optical spectrum of light transmitted through, absorbed by, or reflected from the sensor. For example, the device can perform absorbance / transmittance measurements. In some embodiments, the measurements are performed at wavelengths in the range of 500-1000 nm.

[0027] The method further includes collecting data from the sensor. In some embodiments, the method includes collecting absorbance data from the sensor. In some embodiments, the method includes collecting transmittance data from the sensor. In some embodiments, the method includes collecting extinction data from the sensor. In some embodiments, the method includes collecting absorbance, transmittance, and / or extinction data of the sensor. In some embodiments, the method further includes comparing the collected data to baseline data of the sensor before exposure to the sample. In some embodiments, the method further includes comparing at least one of the collected absorbance, transmittance, and / or extinction data to baseline data of the sensor before exposure to the sample. For example, an absorbance / transmittance measurement of the functionalized sensor is taken before exposure to the sample. The peak absorbance wavelength of the functionalized sensor (before binding with the target analyte) is identified. If the target analyte is present in the sample and binds to the probe on the functionalized sensor, the absorbance / transmittance of the sensor is measured again after exposure to the sample, and a shift in peak absorbance can be observed. The shift represents the detected signal.

[0028] In some embodiments, an array of sensors in the plasmon resonance sensing device 100 of any of the present embodiments is exposed to a sample. In some embodiments, at least a first sensor 101a in the array of sensors 101 comprises a nanostructure conjugated to a first biological probe. In some embodiments, at least a second sensor 101b in the array of sensors 101 comprises a nanostructure conjugated to a second biological probe. In some embodiments, at least a third sensor in the array of sensors comprises a nanostructure conjugated to a third biological probe. In some embodiments, at least a fourth sensor in the array of sensors comprises a nanostructure conjugated to a fourth biological probe. In some embodiments, at least a fifth sensor in the array of sensors comprises a nanostructure conjugated to a fifth biological probe. In some embodiments, "n" sensors in the array of sensors comprise nanostructures conjugated to "n" biological probes, where "n" is any number between 1 and 2000. The biological probes conjugated to different sensors may be the same or different. In some embodiments, each sensor in the array can be conjugated to a different biological probe for multiplexed sensing capabilities. In this configuration, multiple analytes can be detected simultaneously.

[0029] In some embodiments, at least a first sensor 101a in the array of sensors comprises a nanostructure conjugated to a first biological probe, and at least a second sensor 101b in the array of sensors comprises a nanostructure conjugated to a second biological probe. In some embodiments, a first set of sensors in the sensor array is functionalized with a first biological probe, and a second set of sensors in the sensor array is functionalized with a second biological probe. In some embodiments, the first biological probe and the second biological probe are different. In some embodiments, the first biological probe and the second biological probe are the same. In some embodiments, the first biological probe and the second biological probe independently comprise one or more of a protein, a peptide chain, an amino acid, an RNA chain, a DNA chain, and / or a nucleotide. In some embodiments, the first biological probe and the second biological probe independently comprise one or more of a modified protein, a modified peptide, a modified amino acid, a modified RNA chain, a modified DNA chain, and / or a modified nucleotide. In some embodiments, the first biological probe and the second biological probe independently comprise one or more of a peptide-nucleic acid, an aptamer, an antibody, an antibody fragment, complementary DNA, and / or an enzyme. In some embodiments, the first biological probe and the second biological probe are independently selected from the group consisting of a peptide-nucleic acid, an aptamer, an antibody, an antibody fragment, complementary DNA, and an enzyme.

[0030] Analyte detection is based on an optical phenomenon (localized surface plasmon resonance (LSPR)) that occurs between metal nanostructures and dielectrics. LSPR is observed when the wavelength of incident light is larger than the size of the conductive nanostructures. The nanostructures generate a highly confined electric field of the LSPR mode, which acts as a sensitive transducer to changes in the local dielectric environment (binding events). The nanostructures can be conjugated / covalently functionalized with probes that can bind to the target analyte. Upon binding to the target analyte, a red shift of the spectral peak can be observed. In some embodiments, the amount of red shift can be observed as a function of the target analyte concentration. In some embodiments, the sensor detects transmittance, reflectance, and / or absorbance over a wavelength range.

[0031] In some embodiments, the sensor exposed to the sample, and thus having analytes bound to selective biological probes on the sensor, can be further exposed to functionalized particles configured to bind to the sensor in the presence of analytes bound to the biological probes. The functionalized particles may be nanoparticles or microparticles. In some embodiments, the particles may be metal, polymer, glass, or any material with a high refractive index (e.g., a refractive index of about 1.5 or greater). When functionalized particles are bound to the sensor, they have the potential to improve both the sensitivity and specificity of the sensor. Without being bound by theory, the improved sensitivity may be due to the fact that the functionalized particles increase the change in refractive index at the sensor surface in the presence of analyte. The additional binding of functionalized particles to the sensor may improve the sensor signal through a larger peak shift in the optical measurement. The improved specificity may be due to the fact that two selective binding events are required (i.e., the first analyte must bind to the sensor, and then the functionalized particles must bind to the analyte bound to the sensor). In some embodiments, the functionalized particles are functionalized to bind to the analyte bound to the biological probe.

[0032] In some embodiments, a spectrum of a sensor comprising an array of functionalized nanostructures can be acquired prior to exposure to a sample, which may provide baseline data for determination and analysis of analyte binding events.

[0033] Fabrication of nanostructures Also disclosed herein is a method for fabricating an array of nanostructures. The method includes coating a photoresist layer on a substrate, patterning the photoresist, and depositing a metal layer on the patterned photoresist layer. In some embodiments, the substrate may be non-conductive, and a modified method may provide improved results. The method includes coating a conductive photoresist layer on a non-conductive substrate, patterning the conductive photoresist layer via photolithography, depositing an adhesion layer on the patterned conductive photoresist layer, and depositing a metal layer on the adhesion layer. In some embodiments, patterning the conductive photoresist layer includes exposing the photoresist layer to an electron beam to generate a desired pattern. In some embodiments, the pattern should match the dimensions of the nanostructures and the spacing between the nanostructures. In some embodiments, the method may involve lithography techniques such as electron beam lithography, UV photolithography, or nanoimprint lithography. In some embodiments, roll-to-roll manufacturing may be employed to fabricate the sensor array.

[0034] For example, photolithography may be used to remove portions of a photoresist layer where nanostructures are to be disposed / formed on a substrate, leaving portions of the substrate without nanostructures masked by the patterned photoresist layer. Thus, the patterned photoresist layer has removed portions that are similar in size, shape, and location to where the metal nanostructures are to be disposed. Portions of the substrate are exposed where the nanostructures will be formed. When a metal layer is then disposed over the patterned photoresist layer, some of the metal layer is disposed on the exposed portions of the substrate and some of the metal layer is disposed on the remaining photoresist masking the substrate.

[0035] The method further includes lifting off the patterned photoresist layer. Lifting off the patterned photoresist layer also removes portions of the adhesion layer and metal layer disposed on the remaining patterned photoresist layer, leaving portions of the adhesion layer in contact with the substrate and portions of the metal layer above those portions of the adhesion layer. In some embodiments, the adhesion layer comprises chromium. In some embodiments, the adhesion layer has a thickness of about 2 nm, about 3 nm, about 4 nm, about 5 nm, about 6 nm, about 8 nm, about 9 nm, or any thickness between about 2 and about 9 nm. In some embodiments, the adhesion layer has a thickness of about 5 nm. In some embodiments, the metal layer comprises a single metal. In some embodiments, the metal layer comprises a mixture of metals. In some embodiments, the metal layer comprises gold, silver, aluminum, platinum, or copper. In some embodiments, the metal layer comprises gold. The thickness of the metal layer will be the same as the thickness of the nanostructures on the substrate disclosed herein.

[0036] The methods disclosed herein provide arrays of sensors comprising an array of regularly spaced nanostructures, the nanostructures produced by such methods having the same shape, dimensions, and spacing as those disclosed herein.

[0037] Functionalization of nanoplasmonic sensing chips Disclosed herein is a method for fabricating a functionalized nanoplasmonic sensing chip. The method includes providing a substrate with an array of sensors, attaching a microwell adapter onto the substrate such that an array of microwells overlies and aligns with each sensor, and forming one or more functionalized sensors within the array of sensors. Forming the one or more functionalized sensors includes using an automated pipetting system to deliver a first batch of reaction solution into one or more microwells above the one or more sensors, and then subsequently using the automated pipetting system to remove the first batch of reaction solution from the one or more microwells. The automated pipetting system includes an array of pipettes that can load one or more reaction solutions. In some embodiments, the array of pipettes can be loaded with two or more different reaction solutions, thus enabling delivery of two or more different reaction solutions to the array of microwells / sensors. The array of pipettes can also be used to remove reaction solutions from some or all of the microwells / sensors after reaction. The array of pipettes can deliver or remove reaction solutions from specific microwells / sensors or specific groups of microwells / sensors. In some embodiments, each reaction solution may contain one or more reagents for modifying the array of nanostructures within the sensor. In some embodiments, each reaction solution may contain one or more biological probes.

[0038] In some embodiments, a multi-step reaction may be utilized to functionalize the sensor. Thus, forming the one or more functionalized sensors may further include delivering a second batch of reaction solution into one or more microwells and subsequently removing the second batch of reaction solution from the one or more microwells, wherein the delivery and removal of the second batch of reaction solution is performed by an automated pipetting system.

[0039] In some embodiments, the first batch of reaction solutions includes two or more different reaction solutions. In some embodiments, the second batch of reaction solutions may also include two or more different reaction solutions. In some embodiments, the reaction solutions may include different biological probes. Thus, an array of functionalized sensors may include two or more different biological probes. For example, some of the functionalized sensors in the array may include a particular biological probe, while other functionalized sensors include a different biological probe. In some embodiments, each of the functionalized sensors may include a different biological probe. In some embodiments, the reaction solutions may include one or more biological sensors. Thus, each functionalized sensor may include one or more biological probes. The one or more biological probes can be conjugated to an array of nanostructures within each sensor. In some embodiments, the sensors may include one, two, three, four, or more biological probes configured to bind to one or more analytes.

[0040] The method then further includes removing the microwell adapter from the substrate. In some embodiments, the one or more sensors are functionalized with the biological probe while the first batch of reaction solution in the one or more microwells is contacting the sensor. In some embodiments, the one or more sensors are functionalized with the biological probe after two or more reaction steps. In some embodiments, the sensors (e.g., one or more sensors) each comprise an array of nanostructures disclosed herein.

[0041] In some embodiments, the automated pipetting system can be configured to deliver different reaction solutions to multiple microwells to functionalize multiple sensors in the array. In some embodiments, multiple reaction solutions are delivered to different sensors in the array, thereby functionalizing multiple sensors substantially simultaneously. In some embodiments, the automated pipetting system may be configured to remove different reaction solutions from multiple microwells. In some embodiments, multiple reaction solutions are removed from different sensors in the array substantially simultaneously. In other embodiments, some reaction solutions can be removed at different times to allow for longer or shorter reaction times.

[0042] Figures 11A-11B show two alternative views of the 3D printed mold for the fabricated polymer well shown in Figures 11C-11D. Another embodiment of the microwell is shown in Figures 11E-11I.

[0043] In some embodiments, an additional pretreatment step can be performed before delivering any reaction solutions. The pretreatment step can include cleaning the nanostructure surface, wetting the nanostructure surface, or activating the nanostructures for subsequent reaction / functionalization. In some embodiments, the method can include using an automated pipetting system to deliver an activation solution to at least a portion of the microwells on the sensors in the array, and then removing the activation solution before delivering the reaction solutions.

[0044] The methods disclosed herein provide at least one functionalized sensor comprising at least one biological probe. In some alternatives, the first functionalized sensor comprises a first array of nanostructures conjugated to a first biological probe. In some alternatives, the second functionalized sensor comprises a second array of nanostructures conjugated to a second biological probe. In some alternatives, additional sensors comprising the nanostructure array may be conjugated to additional biological probes, up to the number of sensors in the sensor array. For example, "n" sensors in the sensor array comprise nanostructures conjugated to "n" biological probes, where n is any number between 1 and 2000. In some embodiments, n may be any number between 1 and 1000, 1 and 500, 1 and 100, or 1 and 25.

[0045] Each of the biological probes is independently selected from the group consisting of a peptide-nucleic acid (PNA), an oligonucleotide, an aptamer, an antibody, an antibody fragment, a complementary DNA, and an enzyme. In some alternatives, the first biological probe and the second biological probe are independently selected from the group consisting of a peptide-nucleic acid, an aptamer, an antibody, an antibody fragment, a complementary DNA, and an enzyme. In some alternatives, the first biological probe and the second biological probe are different. In some alternatives, the first biological probe and the second biological probe are the same. In some embodiments, each sensor may be functionalized with a different biological probe. In some embodiments, some of the sensors in the array may be functionalized with different biological probes. In some embodiments, all of the sensors in the array may be functionalized with the same biological probe.

[0046] In some embodiments, the reaction solutions are delivered to all microwells simultaneously. In some alternatives, the reaction solutions are then removed from the microwells simultaneously. In some alternatives, the reaction solutions are removed from the microwells at different times to accommodate different reaction times for functionalizing the sensors with various biological probes. In some embodiments, the reaction solutions may also be delivered to different microwells at different times. In some alternatives, a first reaction solution and a second reaction solution are delivered to a first microwell and a second microwell simultaneously, and then the first reaction solution and the second reaction solution are removed from the first microwell and the second microwell. In some embodiments, the delivery and removal of the reaction solutions may be performed by an automated pipetting system. In some embodiments, the automated pipetting system may be configured to remove different reaction solutions at different times. In some embodiments, the automated pipetting system may be configured to deliver different reaction solutions at different times.

[0047] In some embodiments, the nanostructures comprise a metal. In some alternatives, the nanostructures comprise a single metal. In some alternatives, the nanostructures comprise a mixture of metals. In some alternatives, the nanostructures comprise gold, platinum, aluminum, silver, or copper. In some alternatives, the nanostructures comprise gold.

[0048] Functionalized plasmonic sensing chip A functionalized plasmonic sensing chip is disclosed that includes an array of functionalized sensors. In some embodiments, each of the functionalized sensors in the array includes an array of nanostructures conjugated to at least one biological probe. In some embodiments, the array of nanostructures is conjugated to two or more biological probes configured to bind to two or more analytes. The biological probes are configured to bind to at least one analyte. In some embodiments, at least one biological probe independently comprises a peptide-nucleic acid, an oligonucleotide, an aptamer, an antibody, an antibody fragment, complementary DNA, and / or an enzyme. In some embodiments, the biological probes are independently selected from the group consisting of peptide-nucleic acid, an oligonucleotide, an aptamer, an antibody, an antibody fragment, complementary DNA, and an enzyme. In some embodiments, all of the functionalized sensors in the array include the same biological probe. In some alternatives, at least one of the functionalized sensors in the array includes at least one biological probe that is different from the other biological probes. For example, some of the functionalized sensors in the array may include a particular biological probe, while other functionalized sensors include a different biological probe. In some embodiments, each of the functionalized sensors in the array comprises at least one different biological probe. The one or more biological probes can be conjugated to the array of nanostructures within each sensor. In some embodiments, the functionalized sensors may comprise one, two, three, four, or more biological probes configured to bind to one or more analytes.

[0049] In some embodiments, a functionalized plasmonic sensor chip may include 1 to 100 (and any number therebetween) different biological probes. For example, a functionalized plasmonic sensor chip may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 24, 30, 36, 40, 48, 50, 54, 60, 70, 80, 90, or 100 different biological probes. In some embodiments, each functionalized sensor within a functionalized plasmonic sensor chip may include a different biological probe. In some embodiments, the array of nanostructures within each sensor may be conjugated to one or more biological probes, and the one or more biological probes may be different.

[0050] In some embodiments, the nanostructures comprise a metal. In some alternatives, the nanostructures comprise a single metal. In some alternatives, the nanostructures comprise a mixture of metals. In some alternatives, the nanostructures may comprise gold, platinum, aluminum, silver, or copper. In some alternatives, the nanostructures comprise gold. In some alternatives, the nanostructures in the array may be regularly spaced and have the geometries described herein.

[0051] Multi-analyte detection Methods for simultaneously detecting two or more analytes are also described. In some alternatives, the methods may detect 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 24, 30, 36, 40, 48, 50, 54, 60, 70, 80, 90, and / or 100 analytes. In some embodiments, up to 50 analytes are detected. In some embodiments, up to 24, 50, 80, or 100 analytes may be detected. The methods include exposing an array of functionalized sensors on a plasmonic sensing chip of any of the alternatives disclosed herein to a sample. The functionalized sensors are configured to detect the presence of specific target analytes. In some embodiments, the functionalized sensors may be configured to identify or detect various markers, subtypes, strains, genotypes, and / or mutants of a biological species. When the functionalized sensors are exposed to the sample, one or more target analytes, if present, bind to the corresponding biological probes. The binding event results in a change in the local dielectric environment of the sensor. The sample may include a bodily fluid such as blood, urine, or saliva. In some embodiments, the sample may be drained or removed from the functionalized sensor after the exposure step.

[0052] Optionally, the array of functionalized sensors may be subjected to a heating step after exposure to the sample. In some embodiments, the array of functionalized sensors is heated to about 85°C, or to any temperature between 25°C and 85°C. In some embodiments, the array of functionalized sensors may be exposed to heat before, during, or after a subsequent step. In some embodiments, the array of functionalized sensors may be exposed to heat before, during, or after a measurement.

[0053] The method further includes illuminating the functionalized sensor with a series of wavelengths of light and collecting absorbance, transmittance, and / or extinction data from the functionalized sensor. The light may be emitted from a light source within an apparatus for analyte detection. The light source may be configured to emit a series of wavelengths to illuminate the sensor. In some embodiments, the plasmonic sensing chip including the functionalized sensor may be inserted into an apparatus for analyte detection. The apparatus is configured to emit a series of wavelengths of light onto the functionalized sensor and collect an optical spectrum of the light transmitted through, absorbed by, or reflected from the sensor.

[0054] In some embodiments, the method further includes comparing the collected data with baseline data for the sensor before exposure to a sample. The baseline data for the functionalized sensor can be collected using the apparatus for analyte detection described above. In some embodiments, the baseline data can be collected before exposing the sensor to a sample. In some embodiments, the baseline data is provided for a sensor functionalized with a specific biological probe. A shift in the spectral peak after exposure to a sample indicates binding of the target analyte to the biological probe, and thus indicates the presence of the target analyte in the sample. In some embodiments, the amount of spectral peak shift may be further interpreted to provide a quantitative or semi-quantitative measure of the concentration of the target analyte in the sample.

[0055] In some embodiments, where sensors in an array are functionalized with different biological probes, different target analytes may bind to corresponding sensors when the array is exposed to a sample. Illuminating the array of sensors with a series of wavelengths of light allows for the collection of optical spectra for each sensor, which can be compared to baseline data. A single exposure of the sensing device chip allows for the detection and identification of different target analytes.

[0056] In some embodiments, the plasmon resonance sensing device enables point-of-care (POC) detection of target analytes and POC diagnosis of diseases / conditions, and in some embodiments, rapid results (about 15 minutes or less) can be provided.

[0057] Methods for detecting UTIs UTIs may be detected using a sensor containing a biological probe designed to target a nucleic acid sequence derived from a pathogen that causes UTI. The method includes exposing the sensor to a sample that may contain nucleic acid sequences derived from one or more pathogens that cause UTIs, and collecting electrical, fluorescence, absorbance, transmittance, and / or extinction data from the sensor. In some embodiments, the biological probe may be a peptide nucleic acid (PNA) probe or an oligonucleotide probe.

[0058] In some embodiments, the sensor may include one or more biological probes. In some embodiments, each of the biological probes may be designed to bind to a different target nucleic acid sequence. Thus, the sensor may be capable of simultaneously detecting multiple or various target nucleic acid sequences. For example, the sensor may detect the presence of any of the different pathogens that cause UTIs and confirm a patient's diagnosis of UTI. This means that a UTI may be diagnosed regardless of which of the various UTI-causing pathogens is present. In some embodiments, the sensor may be capable of detecting and identifying one or more specific pathogens that cause UTIs in a patient. This information may be useful for determining the appropriate or most effective treatment options.

[0059] In some embodiments, a single biological probe can bind to nucleic acids from multiple UTI-causing pathogens. In some embodiments, a single biological probe can bind to multiple nucleic acids from a single UTI-causing pathogen. In some embodiments, a single biological probe can bind to one or more nucleic acid sequences specific to antibiotic resistance genes. In some embodiments, a biological probe may be designed to bind to nucleic acid sequences from multiple antibiotic resistance genes. As a result, a sensor containing one or more biological probes may be able to detect multiple UTI-causing pathogens. In some embodiments, a sensor containing one or more biological probes may be able to identify one or more antibiotic resistance genes of a UTI-causing pathogen.

[0060] Biological probes can be designed or selected using computational and / or bioinformatics methods. These methods allow for the rational selection of probe sequences that align with known sequences in the scientific literature. In some embodiments, computational approaches utilize custom Python scripts, open-access sequence databases, and thermodynamic modeling tools. In some embodiments, biological probes contain deliberately varying degrees of mismatch with the target nucleic acid. These mismatches allow for additional degrees of freedom when determining the presence of the target nucleic acid.

[0061] In some embodiments, the biological probes described herein are independently selected from the group consisting of SEQ ID NOS: 1-32.

[0062] The sensor may have a physical property that changes upon binding of one or more target nucleic acid sequences to a biological probe associated with the sensor. The change in physical property can be detected by a change in electrical, fluorescence, absorbance, transmittance, and / or extinction measurements. Some non-limiting examples of sensors may include electrochemical sensors, fluorescence-based sensors, resistive sensors, and optical sensors.

[0063] Nanoplasmonic sensors for pathogen / UTI detection Nanoplasmonic sensors for detecting pathogens that cause urinary tract infections are also described. In some embodiments, the nanoplasmonic sensors comprise an array of functionalized sensors, each of which comprises an array of nanostructures conjugated to a biological probe / capture ligand, such as a peptide nucleic acid (PNA) probe or an oligonucleotide probe. In some embodiments, the biological probe is configured to detect the presence of a pathogen associated with a urinary tract infection. In some embodiments, the pathogen is a pathogen that causes a urinary tract infection. In some embodiments, the biological probe is configured to detect the presence of a pathogen that causes a urinary tract infection using a specific marker associated with the given pathogen. In some embodiments, the specific marker is derived from a pathogen that causes a urinary tract infection. In some embodiments, the specific marker is derived from a subject's response to infection with a pathogen that causes a urinary tract infection.

[0064] In some embodiments, a plurality of functionalized sensors in the array are capable of detecting pathogens associated with urinary tract infections in a sample. In some embodiments, at least two of the functionalized sensors in the array comprise the same biological probe for detecting pathogens that cause urinary tract infections. In some embodiments, at least two of the functionalized sensors in the array comprise the same biological probe for detecting the same marker for pathogens that cause urinary tract infections. In some embodiments, all of the functionalized sensors in the array comprise the same biological probe for detecting pathogens that cause urinary tract infections.

[0065] In some embodiments, at least one of the functionalized sensors in the array comprises a different biological probe for detecting a different strain, segment, particle, variant, and / or species of a pathogen that causes a urinary tract infection than the other functionalized sensors. In some embodiments, multiple sensors in the array are functionalized with different biological probes, which may enable the detection of multiple pathogens that cause urinary tract infections. In some embodiments, all of the functionalized sensors in the array comprise different biological probes for detecting different pathogens that cause urinary tract infections. In some embodiments, the nanoplasmonic sensor is configured to simultaneously detect multiple strains, segments, particles, variants, and / or species of a pathogen that causes a urinary tract infection. In some embodiments, each of the functionalized sensors in the array comprises a different biological probe.

[0066] In some embodiments, the functionalized sensor may be functionalized with a negative control biological probe. The negative control biological probe may be designed to be complementary to a synthetic sequence of DNA / RNA that does not exist in nature. The negative control functionalized sensor is expected to always return a negative result.

[0067] In some embodiments, the functionalized sensor may be functionalized with a positive control biological probe. The positive biological probe is complementary to a synthetic sequence of DNA. A low concentration of that DNA sequence may be spiked into the sample early in the reaction. This indicates whether the sample preparation and fluid handling procedures were successful in allowing a known concentration of target DNA to reach the sensor, indicating a successful assay run.

[0068] It is understood that a pathogen causing or associated with a urinary tract infection can be any type of microorganism that can be cultured along the urinary tract. Non-limiting examples of pathogens include Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis, Staphylococcus saprophyticus, and any antibiotic-resistant strains or identified resistance genes thereof. In some embodiments, antibiotic-resistant strains or identified resistance genes include, but are not limited to, vancomycin-resistant Enterococcus faecium.

[0069] It is understood that the biological probe can include any peptide and / or nucleic acid sequence or oligonucleotide sequence capable of binding / associating with a segment of pathogen DNA. In some embodiments, the biological probe sequence is complementary to a segment of the pathogen DNA sequence and can hybridize with the target pathogen DNA sequence if present in a sample. In some embodiments, the probe comprises one or more of a protein, a peptide chain, an amino acid, an RNA chain, a DNA chain, and / or a nucleotide. In some embodiments, the biological probe comprises one or more of a modified protein, a modified peptide, a modified amino acid, a modified RNA chain, a modified DNA chain, and / or a modified nucleotide. In some embodiments, the biological probe comprises at least one of a peptide-nucleic acid, an aptamer, an antibody, an antibody fragment, a complementary DNA, and / or an enzyme. In some embodiments, the probe is selected from the group consisting of a peptide-nucleic acid, an aptamer, an antibody, an antibody fragment, a complementary DNA, and an enzyme. In some embodiments, the biological probe may be a PNA probe or an oligonucleotide probe having a sequence selected from the group consisting of SEQ ID NOS: 1-32. The probe sequences for the target pathogens and resistance genes are listed in Table 5.

[0070] The nanostructure array is as disclosed herein. The nanostructures comprise a metal. In some embodiments, the nanostructures comprise a single metal. In some embodiments, the nanostructures comprise a mixture of metals. In some embodiments, the nanostructures comprise silver. In some embodiments, the nanostructures comprise copper. In some embodiments, the nanostructures comprise gold. The nanostructures in the sensor can be functionalized with biological probes using the automated pipetting systems and methods described herein.

[0071] Also disclosed herein is a method for detecting the presence of one or more pathogens that cause a urinary tract infection. The method includes exposing a nanoplasmonic sensor according to any of the embodiments disclosed herein to a sample, irradiating each of the functionalized sensors with light of a series of wavelengths, and collecting absorbance, transmittance, or extinction data for each of the functionalized sensors. In some embodiments, the sample is a bodily fluid sample from a patient suspected of having a urinary tract infection. In some embodiments, the bodily fluid sample may be urine, blood, sweat, saliva, plasma, and / or mucus. In some embodiments, the bodily fluid sample includes urine. In some embodiments, the light for displacing the functionalized sensors may be emitted from a light source within the device for analyte detection. The light source may be configured to emit a series of wavelengths for irradiating the sensor. For example, the series of wavelengths may include wavelengths in the range of 500 to 1000 nm.

[0072] In some embodiments, the method further includes comparing the collected absorbance, transmittance, and / or extinction data of each functionalized sensor with baseline data for each functionalized sensor before exposure to the sample. In some embodiments, the comparing step reveals an optical peak shift when at least one urinary tract infection-causing pathogen is detected. The baseline data for the functionalized sensor includes absorbance / transmittance measurements of the functionalized sensor taken before exposure to the sample. The peak absorbance wavelength of the functionalized sensor (before binding with the target analyte) is identified. The absorbance / transmittance of the sensor is measured again after exposure to the sample, and a shift in peak absorbance can be observed if the target analyte is present in the sample and binds to the probe on the functionalized sensor. The shift represents a detection signal. In some embodiments, the amount of optical peak shift correlates with the concentration of the pathogen in the sample. In some embodiments, the amount of optical peak shift correlates with the concentration of the urinary tract infection-causing pathogen in the bodily fluid sample.

[0073] In some embodiments, two or more of the functionalized sensors may comprise the same biological probe. In some embodiments, at least one of the functionalized sensors may comprise a different biological probe. In some embodiments, each of the functionalized sensors may comprise a different biological probe. In some embodiments, when one or more of the functionalized sensors in the array comprise different biological probes, multiple strains or species of pathogens causing urinary tract infections can be detected simultaneously (i.e., using the same nanoplasmonic sensor / test kit). In some embodiments, the method may be performed at the point of care (i.e., at the location of patient care, such as a doctor's office, clinic, hospital, or long-term care facility, patient's home, etc.).

[0074] definition 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 belongs unless clearly indicated otherwise.

[0075] As used herein, the singular forms "a," "and," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "one sequence" can include a plurality of such sequences, and the like.

[0076] The terms "comprising," "including," "containing," and various forms of these terms are synonymous and equally broad. Furthermore, unless expressly stated to the contrary, examples comprising, including, or having an element or elements having a particular characteristic may include additional elements, regardless of whether the additional elements have that characteristic.

[0077] The term "analyte" refers to a substance or chemical component of interest. For example, an analyte may include a biological or chemical entity that can be detected by a sensing device and that may be of interest for diagnosing a disease or condition.

[0078] The term "nanostructure," as used herein, has its standard scientific meaning and thus refers to any structure that is approximately molecular to approximately microscopic in size. Nanostructures include nanomaterials, which may be any material whose single units are between about 1 nm and about 200 nm in size. Nanostructures include nanoparticles, nanorods, nanosquares, nanocubes, gradient multilayer nanofilms (GML nanofilms), icosahedral twins, nanocages, magnetic nanochains, nanocomposites, nanofibers, nanofibers, nanoflowers, nanofoams, nanoholes, nanomeshes, nanopillars, nanopin films, nanoplatelets, nanoribbons, nanorings, nanobipyramids, irregular nanoparticles, nanosheets, nanoshells, nanotips, nanowires, and nanostructured films. It is understood that nanostructures can have a variety of geometries and properties based on the nanostructure's components.

[0079] All patents and other publications, including literature references, issued patents, published patent applications, and co-pending patent applications, cited throughout this application are expressly incorporated herein by reference for the purpose of describing and disclosing, for example, the methodology described in such publications, which may be used in connection with the technology described herein. These publications are provided solely for their disclosure prior to the filing date of this application. Nothing in this regard should be construed as an admission that the inventors are not entitled to antedate such disclosure by virtue of prior invention or for any other reason. All statements as to the date or contents of these documents are based on the information available to applicant and do not constitute an admission as to the correctness of the dates or contents of these documents.

[0080] The description of the embodiments of the present disclosure is not intended to be exhaustive or to limit the disclosure to the precise form disclosed. While specific embodiments and examples of the present disclosure are described herein for illustrative purposes, those skilled in the art will recognize that various equivalent modifications are possible within the scope of the present disclosure. For example, while method steps or functions are presented in a given order, embodiments may perform the functions in a different order, or the functions may be performed substantially simultaneously. The teachings of the present disclosure provided herein can also be applied to other procedures or methods, as appropriate. The various embodiments described herein can also be combined to provide further embodiments. Aspects of the present disclosure can also be modified, if necessary, to employ compositions, functions, and concepts from the above references and applications to provide further embodiments of the present disclosure. Furthermore, due to considerations of biological functional equivalence, some changes can be made to protein structures without affecting the type or amount of biological or chemical activity. These and other changes can be made to the present disclosure in light of the detailed description. All such modifications are intended to be within the scope of the appended claims.

[0081] Specific elements of any of the foregoing embodiments can be combined with or substituted for elements in other embodiments. Furthermore, although advantages associated with certain embodiments of the present disclosure have been described in the context of those embodiments, other embodiments may also exhibit such advantages, and not all embodiments necessarily exhibit such advantages to fall within the scope of the present disclosure. [Example]

[0082] The technology described herein is further illustrated by the following examples, which should not be construed as further limiting in any way.

[0083] Example 1: Electromagnetic Simulation Several geometries for simulation and testing included several nanorods and several coupled nanoarrays. The nanorods were designed to mirror randomly oriented colloidal nanorods dispersed on a glass slide. The coupled nanoarrays were designed to generate surface lattice resonances. Seven geometries for production dose testing are shown in Table 1 and Figures 2A and 2B. Figure 2A shows a grid with dimensions labeled for nanorod length (l), width (w), thickness (t), and spacing / periodicity (p). Figure 2B is a map of the nanorod array placement within the sensor unit. As shown in Table 1, test geometries T1 through T3 are nanorods, and test geometries T4 through T10 are coupled nanoarrays. [Table 1]

[0084] Full-wave electromagnetic simulations were performed using Lumerical photonic simulation software. Periodic boundary conditions were applied in the x and y dimensions for each of the geometries T1 through T7, as shown in Figures 2A and 2B. For bulk sensing experiments, the refractive index of the surrounding medium was varied. For PNA-DNA binding experiments, a conformal shell layer of a defined refractive index was modeled on the nanostructure. Extinction and transmittance curves were returned in the wavelength range of 400–1200 nm.

[0085] Example 2: Simulation setup and performance index definition To study the plasmon resonance shape and its sensitivity to changes in refractive index, initial simulations included a bulk refractive index sensitivity analysis. In the first iteration, widely spaced gold nanorods designed to represent regular nanoarrays were tested.

[0086] The resonance was modeled in air, water, and glycerol (increasing refractive index), and the peak position was calculated for each of the extinction curves. This allowed for the development of a sensor figure of merit (FOM) that takes into account the peak shift (s) and resonance narrowness (full width at half maximum - FWHM), as shown in Figure 3. The figure of merit was defined as the shift across the full width at half maximum, allowing for direct comparisons between various geometries. A larger figure of merit indicates better sensing performance due to (1) a larger peak shift for the same refractive index change and (2) easier discrimination of the peak shift due to a narrower resonance curve. This analysis was repeated for all geometries considered.

[0087] Example 3: PNA-DNA binding simulation Another method for simulating these nanostructures involves simulating peptide nucleic acid (PNA) probes bound to DNA and conformal layers with the same expected refractive index as the PNA probes. We observed that the shift in PNA + DNA binding relative to the surface lattice geometry (shown in Figure 4B) was much more pronounced than the shift relative to the dispersed nanorod geometry (shown in Figure 4A). These simulations point to the expected shifts associated with DNA biosensing for each geometry.

[0088] Example 4: Fabrication of nanosensors Electron beam lithography is a common method for patterning precise nanoscale features on substrates. Typically, such patterns are processed on optically opaque, highly conductive silicon wafers. For the sensor's transmission mode operation, the nanostructures were configured to reside on a transparent quartz wafer. A protocol for nanoscale patterning on a transparent, non-conductive surface was developed.

[0089] First, a thin layer of conductive photoresist was spin-coated onto a transparent quartz wafer, followed by an electron beam (JEOL E-beam microscope) pattern exposure. A thin (approximately 5 nm) chromium adhesion layer was then thermally evaporated onto the patterned substrate, followed by a thicker (approximately 40-50 nm) pure gold layer. Chemical lift-off was performed to form the nanostructure array, after which the substrate was diced for testing. The first sample fabricated with this pattern was a dose matrix test to evaluate the electron beam power. After identifying this parameter, all future processes were performed under the same conditions.

[0090] Example 5: Simulation of selected geometries Bulk refraction simulations were performed for sample geometries T8-T10 listed in Table 1. The transmission through the samples was measured using an optical readout instrument. The wavelength boundaries were set between 450 nm and 950 nm. For seamless integration with the readout instrument, the individual sensors of the sensing device were sized to an area of 1 mm to perfectly match the light source spot size and minimize signal loss. 2 The nanostructure arrays were fabricated with 100 nm x 100 nm nanorods. The results for three surface grating resonant geometries (T8–T10) are shown in Figures 5A / B, 6A / B, and 7A / B, respectively. Both the peak shape and the refractive index peak shift are shown. The calculated figures of merit for T8–T10 were 12.8, 6.7, and 10.7, respectively. Additionally, the refractive index sensitivities for each of these geometries are shown in Figures 5B, 6B, and 7B. All sensitivities are compared to a 140 nm x 40 nm 220p sample labeled "uncoupled nanorods." A larger slope indicates better sensing performance. Sample geometry T10 performs best due to its high figure of merit (10.7) and its relatively high refractive index sensitivity (267 nm / RIU).

[0091] Example 6: Comparison of simulation and experiment Nanostructure array samples 1-5 were fabricated with the nanostructure dimensions shown in Table 2. The transmittance of each sample was measured experimentally (shown in Figure 8) and compared with the peak shapes from simulations (shown in Figure 9). Exceptional agreement was found between the experimental and simulation data, including peak shapes and resonance positions. [Table 2]

[0092] This disclosure also presents a methodology for the rational design of regularly spaced nanoparticle arrays for plasmonic sensing. Through both simulation and experimental analysis, applicants tested five to seven geometries and ultimately selected a 145 nm x 145 nm nanoarray geometry. Through both simulation and experimental analysis, a nanoarray geometry exhibiting high-amplitude resonance and refractive index sensitivity may be selected for fabrication of a plasmonic resonant sensing device.

[0093] Example 7: Functionalization of nanostructures 1mm 2 A 2 x 6 array of sensors (12 sensors total) was functionalized with peptide-nucleic acid (PNA) probes. Each sensor contained a regularly spaced array of 145 nm x 145 nm gold nanostructures. To individually functionalize the sensor array for target specificity, a polydimethylsiloxane (PDMS) polymer microwell array was fabricated. This microwell array was aligned with the substrate so that each sensor could be accessed through a single microwell. This approach created repeatable and programmable coordinates for automated pipetting systems (e.g., the Integra ASSIST PLUS pipetting robot).

[0094] The microwell structure on the sensing array allows for individual fluid delivery to each sensing spot, enabling multiplexing of up to 12 targets on a single sensing chip. To this end, a mold was designed using Solidworks CAD, enabling the fabrication of a polymer microwell array aligned with the sensor coordinates (Figure 10). A mold for casting PDMS microwells was designed in Solidworks, consisting of 12 2 mm × 2 mm × 5 mm (20 mm3) pillars. The pillars were positioned to match the coordinates of the sensor array on the glass substrate. A master mold was then fabricated using SLA 3D printing, as shown in Figures 11A and 11B.

[0095] The microwell array device was fabricated in a mold using PDMS soft lithography. Sylgard 184 silicone elastomer, base, and curing agent (Dow Corning, Midland, MI) were mixed in a 10:1 weight ratio. Next, the PDMS prepolymer was cast onto the master mold and cured in a convection oven at 80 °C for approximately 1.5 hours. The cured PDMS microwell array was removed from the master mold, as shown in Figures 11C and 11D. The polymer microwell array was fixed onto the sensor array using a washable adhesive, allowing for removable attachment for sensor reuse. The entire system was then mounted on a standard 75 × 25 mm microfluidic chip and was then ready for molecular detection.

[0096] Example 8: Automated robotic functionalization of sensors The prepared plasmonic sensing chip was integrated with an automated pipetting system (e.g., Integra ASSIST Plus) for surface functionalization. To covalently functionalize the sensor with a selected biological probe, e.g., a PNA probe, gold nanostructures on a glass substrate were first incubated with 1 mg / mL of dithiobis(succinimidyl propionate) (DSP) dissolved in dimethyl sulfoxide (DMSO) for 20 min. This bridging molecule activated the gold surface and enabled the binding of free amines on the PNA. Next, the sensor array was contacted with 1 mg / mL of PNA probe dispersed in Tris-EDTA buffer (pH 7.0) for 30–45 min. Transmission spectra were collected before and after conjugation to characterize successful PNA conjugation.

[0097] The sensor functionalization process described above was automated using the Integra ASSIST PLUS pipetting robot. To efficiently position the device on the deck of the Integra ASSIST PLUS pipetting robot, a custom 4-slot microscope slide holder / adapter the size of a standard 96-well plate was designed and fabricated (3D printed). This adapter allows for easy integration with the liquid handler's robot deck. A 96-well plate was pre-filled with functionalization reagent and placed on the robot's aspiration deck. To start the machine, a Voyager Electronics 125 μL 8-channel pipette was loaded onto the robot. A series of six programs was developed to aspirate, dispense, and remove tips in an automated manner. These custom programs enable multiplexed functionalization of 12 PNAs on the sensor array. Table 4 shows the six programs for automated sensor functionalization using the pipetting robot. The programs indicate the pipette tip position, 96-well plate position, aspiration volume, and dispense volume at each stage. Figure 12A is a photograph of the Integra ASSIST PLUS pipetting robot 1200 with the pipette holder 1201 on the left, tip box 1202, 96-well plate holder 1203, and custom tip adapter 1204. Figure 12B shows the tip box 1202 positioned below the pipette holder 1201. Figure 12C shows the 96-well plate 1203 and adapter 1204 during functionalization. [Table 3]

[0098] First, Tris-EDTA (TE) buffer is dispensed and removed from the chip surface to clean the surface and ensure sealing of the microwell array onto the sensing substrate. Next, DSP, a bivalent crosslinking molecule, is introduced to the chip surface and readily adsorbs to the gold surface within 15–20 minutes. The presence of an active NHS group allows crosslinking to proteins (i.e., PNA). Examples of linkers for attaching capture ligands / biological probes (e.g., PNA) are listed in Table 5. Finally, the DSP is aspirated, and PNA probes are dispensed directly onto the sensing surface, where they bind to free amines on the nanostructures. After aspirating excess PNA solution, the chip is covalently functionalized with PNA and ready for sample testing. [Table 4]

[0099] Although pipetting robotic systems have been widely used for fluid filling applications, to Applicant's knowledge, this is the first time such a system has been employed to covalently attach molecular capture probes to solid-state sensors. Applicant achieves this through sequential dispense and aspiration steps on the sensor.

[0100] Example 9: Development of a UTI probe In designing PNA probes for nanoplasmonic characterization of UTI-causing pathogens, we used in silico methods to design six PNA probes specific to the pathogenic genomes of Escherichia coli, Klebsiella pneumoniae, Enterococcus spp., extended-spectrum β-lactamase (ESBL)-carrying E. coli, and vancomycin-resistant enterococci (VRE) c-genomes. Two probes were designed for VRE detection, one dedicated to the vanA gene and the other common to both the vanA and vanB genes. The ESBL-E. coli probes were designed to detect bla genes containing 113 CTX-M alleles as determined by the in silico analysis described above. CTX-M-1 It was designed to detect a group of genes belonging to

[0101] Genes that were conserved within the desired group of target pathogens but sufficiently divergent from their nearest neighbors were determined by literature search and visual inspection of alignments for species identification. Reference sequences for AMR genes were retrieved from the NCBI database.

[0102] The reference target gene was subjected to BLAST against 5,000 records in the nucleotide database to generate an XML file containing the complete alignment of homologous sequences (coverage / identity >80%). The XML file containing the alignment records was parsed into Python using the Biopython module. Identical sequence records were grouped to indicate the number of repeats and parsed into a Fasta file. The Fasta file was used to realign the sequence records for further analysis.

[0103] The sequence alignment was visually inspected to identify potential positions for probe placement. PNA probes were placed at the T of the PNA-DNA duplex. m The T of the PNA-DNA hybrid was designed to be approximately 80°C. The probe length was kept <25 nt. m was determined using the PNA Bio Tool as previously described 1 The purine composition was kept <50% to avoid precipitation of the PNA probe. m Avoid temperatures above 30°C.

[0104] Once the probe sequence was determined, the analytical comprehensiveness of a given probe was assessed using multiple databases. All probes were tested against the NCBI nucleotide database to retrieve the complete record of high-scoring pairs (HSPs). Parameters including accession number, identity, coverage, number of mismatches, mismatch base, and location were searched using a custom script. Identical results were grouped, and a single representative record and the number of records duplicating the parameters were marked. Furthermore, based on the target, additional databases were used to further verify comprehensiveness / cross-reactivity using the same analytical criteria. Therefore, all probes targeting UTI-causing pathogens were tested against the NCBI prokaryotic representative genome database. Furthermore, probes targeting AMR genes were tested against BioProject 313047 sequence records, which contain curated representative genomes harboring AMR genes.

[0105] Target genes for E. coli, Klebsiella pneumoniae, and Enterococcus spp. were selected based on previously reported analyses. A summary of the UTI probes is shown in Table 5. [Table 5]

[0106] Two probes were designed to target vancomycin-resistant enterococci, one targeting only the vanA gene and the other binding to both the vanA and vanB genes. The probe for ESBL-producing E. coli was bla CTX-M-1 The probes were designed to target genetic markers belonging to the group. The analytical coverage and cross-reactivity of the probes were evaluated against nucleotides (Table 6), representative prokaryotic genomes (Table 7), and the Bioproject 313047 database of AMR targets (Table 8). A complete list of allele coverage for ESBL-E. coli is shown in Table 9. This data demonstrates all alleles that can be bound by the ESBL-E. coli probes designed as shown in Table 5. [Table 6(1)] [Table 6(2)] [Table 7(1)] [Table 7(2)] [Table 8] [Table 9]

[0107] Example 10: Methodology / Sample Preparation for UTI Screening Upstream sample processing is limited to a 10-min thermal lysis step followed by direct transfer to the sensing substrate. Furthermore, successful molecular sensing of target substances is demonstrated in various sample matrices, including synthetic urine and healthy human urine.

[0108] Quantitative genomic DNA (>1 × 10 5Copies / µL) were purchased from the American Type Culture Collection (ATCC, Manassas, VA) for the following organisms: extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli #BAA-2326 and vancomycin-resistant Enterococcus faecium (VRE) #700221. The following bacterial strains were also purchased from ATCC (Escherichia coli #25922, Klebsiella pneumoniae #13883, and Enterococcus faecalis #29212) and regenerated according to the specified protocols. Bacteria were cultured aerobically in 5 mL of Tryptic Soy Broth (TSB) (Becton Dickinson, Franklin Lakes, NJ) (E. coli, Klebsiella pneumoniae) or 5 mL of Brain Heart Infusion (BHI) (Becton Dickinson, Franklin Lakes, NJ) (Enterococcus faecalis) in a 50 mL conical tube for approximately 8 hours (37°C, 250 rpm, shaking). The culture was centrifuged (12,100 × g, 4°C, 10 minutes), and the supernatant was aspirated. The culture was resuspended in fresh phosphate-buffered saline (PBS). Next, 100 μL of the bacterial suspension was added to a 1.5 mL microcentrifuge tube containing 900 μL of the desired sample matrix.

[0109] Bacterial samples were quantified using conventional 10 μL drop plates (Table 10). [Table 10(1)] [Table 10(2)]

[0110] Synthetic urine solutions were purchased from Fisher Scientific (Hampton, NH) and manufactured by Ricca Chemical (Arlington, TX) and autoclaved before use. Healthy patient urine was obtained from five volunteers. Urine samples were transferred to a refrigerator and refrigerated for <2 hours before suspending bacteria or quantitative genomic DNA. Additionally, participants' urine samples were plated onto tryptic soy agar (TSA) plates to roughly quantify any bacteria present in the samples before spiking with target organisms (Table 11). [Table 11]

[0111] Fresh urine samples were collected over three days and distributed among the experiments as follows: (Day 1: E. coli #25922, Day 2: E. faecalis #29212 and vancomycin-resistant Enterococcus (VRE) #700221, Day 3: Klebsiella pneumoniae & extended-spectrum beta-lactamase (ESBL)-producing E. coli #BAA-2326). All urine samples (15 in total) were stored long-term at -20°C.

[0112] For functionalization, gold nanostructures on glass slides were incubated with 1 mg / mL dithiobis(succinimidyl)propionate (DSP) dissolved in dimethyl sulfoxide (DMSO) for 30 minutes. This bridging molecule activated the gold surface and allowed for the binding of free amines on the PNA. The sensor array was then contacted with 1 mg / mL PNA probes dispersed in Tris-EDTA buffer (pH 7.0) for 30 minutes. Transmission spectra were collected before and after conjugation to quantify the success of PNA conjugation. The nanosensor functionalization process was automated using the Integra ASSIST PLUS pipetting robot.

[0113] For bacterial samples, 1.5 mL microcentrifuge tubes containing 1 mL of fluid volume were placed in a heating block at 100 °C for 10 minutes. The samples were allowed to cool at room temperature for approximately 5 minutes. The bacterial lysate was diluted to the desired concentration in the desired sample matrix. 8 μL of bacterial lysate was then transferred to a microwell containing a functionalized nanosensing substrate. For quantitative DNA samples, genomic material was diluted to the desired concentration in the desired sample matrix. As above, 8 μL of bacterial lysate was transferred to a microwell containing a functionalized nanosensing substrate. All samples were stored long-term at -20 °C.

[0114] Spectral collection and plasmon peak quantification were performed using the applicant's proprietary readout equipment and user interface. The optical readout equipment includes a spectrometer, a light source, and an automated programmable stage (Figures 11A-11B). This hardware is coupled to a simple, operable user interface, which identifies the resonance peak position and calculates the spectral shift. Specifically, for each sample, full transmission spectra (500 nm to 1000 nm) were collected on both the nanoarray and glass slide background. Normalized transmittance spectra were calculated as the ratio of signal to background at every wavelength. The extinction was then calculated as the negative natural logarithm of the normalized transmittance. These extinction spectra were smoothed using Lowess smoothing (10% smoothing) before the resonance peak wavelength was calculated. The resonance peak wavelength was determined by a center-of-mass calculation using numerical integration at the wavelength boundaries of 700 nm to 900 nm. The spectral shift was calculated by subtracting the sample resonance peak position from the buffer resonance peak position.

[0115] Example 11: Nanoplasmonic detection of species-specific bacterial genes and antibiotic resistance genes using PNA probes The performance of PNA probes was evaluated by determining their ability to bind to their respective target sequences and probe specificity for the organisms and / or genes of interest. PNA probes were designed against pathogens that cause UTIs. Genes that were conserved within the desired group of target pathogens but sufficiently divergent from their nearest neighbors were determined through literature research and bioinformatics analysis for species identification. Reference genes available in the National Center for Biotechnology Information (NCBI) database were used as targets for AMR markers. Homology (identity > 80%, coverage > 80%) sequence alignment records of the target genes were then retrieved from the NCBI nucleotide database using the Basic Local Alignment Tool (BLAST). Sequence alignments were used to identify potential positions for probe placement. Thus, oligonucleotide sequences (Table 12) that met the required analytical comprehensiveness and specificity when evaluated against the NCBI nucleotide and reference genome databases were determined. [Table 12]

[0116] The PNA probe was designed for optimal performance using the following thermodynamic criteria: Probe length: 15–30 nucleotides Purine content: <50% Melting temperature of probe-target hybrid: 72°C to 88°C The melting temperature of the probe's monomeric secondary structure is <50°C.

[0117] To determine whether the PNA probes successfully bound to the target organisms, live bacteria (E. coli, E. faecalis, and Klebsiella pneumoniae) were suspended in both phosphate-buffered saline (PBS) and a synthetic urine matrix, heat-lysed, and then exposed to a nanoplasmonic sensing substrate containing PNA probes complementary to the target sequences. For all three bacterial species, there was a significant red-shift in the sensor's peak absorbance wavelength, suggesting successful hybridization of the target nucleic acid sequence to the complementary PNA probe (Figures 13A-C). The magnitude of the peak shift ranged from 3.59 nm to 7.45 nm for Klebsiella pneumoniae and E. faecalis, respectively. The average peak wavelength shift for each organism in PBS was 4.02 ± 0.07 nm, 4.68 ± 0.10 nm, and 6.61 ± 0.17 nm for Klebsiella pneumoniae, E. coli, and E. faecalis, respectively. The average peak wavelength shifts for each organism in synthetic urine were 3.94±0.18 nm, 4.35±0.15 nm, and 6.45±0.40 nm for Klebsiella pneumoniae, E. coli, and E. faecalis, respectively.

[0118] Next, probe specificity was characterized for target organisms and / or resistance genes. As shown in Figure 14, the PNA probes or target "channels" were observed to be highly specific for their intended targets. No significant cross-reactivity was observed between the bacterial targets and the selected target genes. Furthermore, bla CTX-M-1 E. coli containing β-glucan was detected via peak wavelength shifts in both the E. coli and CTX-M-1 channels, but was not detected in any of the other three off-target channels.

[0119] Example 12: Characterization of nanosensor detection limits for five targets (three UTI-causing pathogens; two antibiotic resistance genes) in a synthetic urine matrix After evaluating the PNA probes for nanoplasmonic detection of species-specific bacteria and antibiotic resistance genes, we quantified the detection limits of the nanoplasmonic molecular sensor for each of five organism or resistance gene targets in a synthetic urine matrix (Figures 15A-15E). The five-target panel consisted of PNA probes designed for the specific detection of E. coli, Enterococcus spp., Klebsiella pneumoniae, CTX-M-1, and vanA. The detection limits for each target (or channel) were quantified using E. coli ATCC #25922 lysate, Enterococcus faecalis ATCC #29212 lysate, Klebsiella pneumoniae ATCC #13883 lysate, extended-spectrum beta-lactamase (ESBL)-producing E. coli #BAA-2326 quantitative genomic DNA, and vancomycin-resistant Enterococcus faecium (VRE) #700221 quantitative genomic DNA, respectively.

[0120] For all five targets, significant peak wavelength shifts were observed at approximately 10 4 It was first observed at cell burdens of 10 CFU / mL (or equivalent). The magnitude of the peak wavelength shift (i.e., signal) increased continuously with increasing target concentration, suggesting the feasibility of semi-quantitative sample characterization. In the case of UTI, the ability to generate semi-quantitative results is necessary for effective clinical management. Current guidelines generally define clinically significant UTI as ≥ 10 5 However, the majority of clinical microbiology laboratories in the United States are still using 10 CFU / mL. 3 ~10 4 If CFU / mL are detected, the culture results will be reported. Treatment decisions are then left to the discretion of the clinician. While the current dynamic range of the nanoplasmonic sensor disclosed herein is within an adequate range to determine clinically significant UTI, in the future, the detection limit may be extended to 10 3 Efforts should be made to reduce the number of CFU / mL.

[0121] Example 13: Evaluation of nanosensor performance in urine sample matrices from healthy patients To assess potential matrix effects (i.e., pH, salt concentration) on sensor performance, target organisms and antibiotic resistance genes were spiked into urine samples from healthy patients. Midstream urine samples were collected from five patients, and bacterial and / or genomic material was spiked into the urine at known concentrations. Both individual and pooled patient urine sample matrices were analyzed (Figures 16A-16E). The actual patient urine sample matrix did not significantly affect nanoplasmonic sensor performance. In all patient samples analyzed, target substances were successfully detected in all five channels, with detection limits of 10. 4 The CFU / mL remained constant, and the sensor signal increased linearly with increasing target material. This series of experiments suggests that the nanoplasmonic platform can 1) determine clinically significant UTIs, 2) identify UTI-causing organisms, and 3) characterize important antimicrobial resistance profiles within 15 minutes. To the applicant's knowledge, this platform technology enables the first DNA-based test for point-of-care UTI diagnosis and characterization.

[0122] This platform can provide species-level information and important antibiotic susceptibility data without the need for nucleic acid amplification, effectively shortening time to diagnosis, reducing costs, and limiting the need for external reagents. The technology platform described herein also has applications that extend beyond the detection of UTIs. Embodiment applications of this technology platform include the diagnosis of sexually transmitted diseases, bloodstream infections, cancer screening, and biosecurity surveillance.

[0123] The scope of the present disclosure is not intended to be limited by the specific disclosure of examples in this section or elsewhere herein, but may be defined by the claims as they are presented in this section or elsewhere herein, or as they may be presented in the future. Claim language is to be interpreted broadly based on the language employed in the claims, and is not limited to examples described herein or during the prosecution of this application, which examples are to be construed as non-exclusive.

Claims

1. Array of functionalized sensors A nanoplasmon sensor comprising, Each of the functionalized sensors in the array comprises an array of nanostructures conjugated to a biological probe, The biological probe is configured to detect the presence of pathogens that cause urinary tract infections. The biological probe is a nanoplasmon sensor capable of binding to nucleic acids derived from multiple pathogens that cause urinary tract infections.

2. The nanoplasmon sensor according to claim 1, wherein the biological probe is a peptide nucleic acid probe or an oligonucleotide probe.

3. The nanoplasmon sensor according to claim 1, wherein at least one of the functionalized sensors in the array comprises a different biological probe for detecting pathogens that cause urinary tract infections different from those of the other functionalized sensors.

4. The nanoplasmon sensor according to claim 3, wherein the nanoplasmon sensor is configured to simultaneously detect multiple chains or species of pathogens causing urinary tract infections.

5. The nanoplasmon sensor according to claim 3, wherein each of the functionalized sensors in the array comprises a different biological probe.

6. The nanoplasmon sensor according to claim 1, wherein the pathogen causing the urinary tract infection is selected from the group consisting of Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis, Staphylococcus saprophyticus, and antibiotic-resistant strains or identified resistance genes thereof.

7. The nanoplasmon sensor according to claim 1, wherein the biological probe has a sequence selected from the group consisting of sequence ID numbers 1 to 32.

8. The nanostructure comprises gold, as described in any one of claims 1 to 7, for the nanoplasmon sensor.

9. The nanoplasmon sensor according to claim 1, wherein the nanostructures in the array are regularly spaced apart at intervals of about 100 nm to about 2000 nm, and each nanostructure has a square shape with side dimensions of about 50 nm to about 400 nm.

10. The nanoplasmon sensor according to claim 9, wherein the nanostructure has a thickness of about 20 nm to about 75 nm.

11. A method for detecting the presence of one or more pathogens that cause urinary tract infections, The steps of exposing a bodily fluid sample from a patient suspected of having a urinary tract infection to the nanoplasmon sensor described in claim 1, A step of irradiating each of the functionalized sensors with light of a series of wavelengths, The steps include collecting absorbance, transmittance, or extinction data from each of the functionalized sensors, Methods that include...

12. The method according to claim 11, further comprising the step of heating the nanoplasmon sensor after exposing it to the bodily fluid sample.

13. The method according to claim 11, further comprising the step of comparing the absorbance, transmittance, or extinction data collected from each functionalized sensor with the respective baseline data of the functionalized sensor before exposure to the bodily fluid sample.

14. The method according to claim 13, wherein the comparison step reveals an optical peak shift when a pathogen causing a urinary tract infection is detected.

15. The method according to claim 14, wherein the amount of optical peak shift correlates with the concentration of the pathogen causing the urinary tract infection in the bodily fluid sample.

16. The method according to claim 11, wherein the bodily fluid sample includes urine, saliva, blood, plasma, serum, or mucus.

17. The method according to claim 11, wherein at least one of the functionalized sensors in the array comprises a different biological probe for detecting pathogens that cause urinary tract infections, which is different from the other functionalized sensors.

18. The method according to claim 17, wherein the pathogen causing the urinary tract infection is independently selected from the group consisting of Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis, Staphylococcus saprophyticus, and antibiotic-resistant strains or identified resistance genes thereof.

19. The method according to claim 18, wherein multiple strains or species of pathogens that cause urinary tract infections are detected simultaneously.

20. The method according to any one of claims 11 to 19, wherein the biological probe has a sequence independently selected from the group consisting of sequence ID numbers 1 to 32.

21. The method according to claim 11, wherein each of the functionalized sensors in the array comprises a different biological probe.

22. The method according to claim 11, wherein the method is configured to be performed at a point of care.

23. A method for detecting the presence of one or more pathogens that cause urinary tract infections, A sensor comprising one or more biological probes designed to detect one or more target nucleic acid sequences derived from one or more pathogens that cause urinary tract infections, wherein one of the biological probes is capable of binding to nucleic acids derived from multiple pathogens that cause urinary tract infections; The steps include: exposing the sensor to a sample suspected to contain one or more pathogens that cause urinary tract infections; A step of collecting electrical, fluorescence, absorbance, transmittance, and / or extinction data from the sensor, Methods that include...

24. The method according to claim 23, wherein one or more biological probes are selected using a computer and / or bioinformatics method.

25. The method according to claim 23, wherein the one or more biological probes are configured to intentionally vary the degree of mismatch with the one or more target nucleic acid sequences.

26. The method according to claim 23, wherein the one or more biological probes are designed to bind to a plurality of target nucleic acid sequences.

27. The method according to claim 23, wherein one or more biological probes are designed to bind to nucleic acid sequences specific to antibiotic resistance genes.

28. The method according to claim 23, wherein one of the biological probes can bind to nucleic acid sequences derived from multiple antibiotic resistance genes.

29. The method according to any one of claims 23 to 28, wherein the one or more biological probes have sequences independently selected from the group consisting of sequence ID numbers 1 to 32.

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