System and method for SERS-based antimicrobial susceptibility test

Abstract

It is unequivocal that to facilitate targeted antimicrobial prescribing practices and to help reduce the increasing global burden of antibiotic resistance, there is an urgent need for the development of novel and truly rapid (less than or equal to 1 hour) AST platforms. A 785 nm SERS based methodology is described that results in accurate MIC determinations for all tested strain / antibiotic pairs following a 30 minute drug incubation period. The time for an SERS AST result is -1 hour. The SERS-AST method works for both Gram positive and Gram negative species and for all primary mechanisms of antibiotic activity. Agreement with 24 hour growth determined MICs is obtained for both bactericidal and bacteriostatic antibiotics although different patterns of dose dependent SERS intensities characterize these two types of antibiotics. Bacterial SERS spectra due to secreted purine nucleotide degradation products (principally adenine, guanine, xanthine and hypoxanthine).

Description

System and Method for SERS-based Antimicrobial Susceptibility TestBACKGROUNDField

[0001] The instant disclosure relates to a system and method for providing a SERS-based antibiotic susceptibility test (AST).Background

[0002] Infectious diseases remain one of the greatest challenges to global health, and the growing prevalence of infections resulting from drug-resistant bacteria, in particular, are increasingly recognized as a major public health problem of the 21stcentury. Many statistical analyses have been published highlighting the growing severity of this worldwide problem. For example, in 2019, 1.27 million deaths world-wide were attributed to bacterial antimicrobial resistance (AMR). It has been estimated that bacterial AMR could kill as many as 10 million people annually by 2050 when, without additional preventative measures, it could potentially become the world’s primary cause of death. In addition to posing an urgent and looming threat to public health, the growing rates of antimicrobial resistance offer an increasingly recognized threat to global economic stability. Antimicrobial resistance has been estimated to add a 20 billion dollar in direct healthcare costs in the US alone, aside from the high cost in loss of productivity annually. A multifaceted approach is needed to address this worldwide problem including the development of new antibiotics, more judicious use of existing efficacious drugs, a better understanding of bacterial resistance acquisition mechanisms, and improved diagnostics for treatment of suspected infection presentations. The blanket use of empirical antibiotic before susceptibility testing results are determined, an almost standard current clinical practice, have further contributed to the exploding evolution of AMR. The European Centre for Disease Prevention and Control (ECDC) has estimated that to date 30-50% of all antimicrobials prescribed to human patients are unnecessary, and further promotes the development and spread of resistance.

[0003] Thus, one component of this concerted effort to limit the deleterious effects of AMR, is for clinicians to have the known drug susceptibility profiles of the causative pathogen(s) in a fast enough time frame that eliminates the current use of empirical, broad band antibiotic treatments,cuts unnecessary use of antibiotics altogether, promotes the use of narrow spectrum drugs and prolongs the usable lifetime of existing drugs. Determining the antimicrobial resistance properties of an isolate has been described as possibly the single most important procedure for managing bacterial infectious disease at the single-patient level. Most current instrumentation providing quantitative drug susceptibility profdes in clinical diagnostic settings rely on automated bacterial growth-based measurements. Such traditional growth based antibiotic susceptibility testing (AST) approaches, even for rapidly growing bacterial species, typically take a minimum of two days to obtain susceptibility information from a clinical sample.

[0004] In addition to AMR concerns, delays in the start of most effective bacterial treatments has deleterious health outcomes with accompanying consequent higher economic costs to patients and the healthcare system due increased length of hospital and intensive care unit stays, as well as longer term health complications. Rapid AST technologies are needed to reduce morbidity and mortality rates, and to provide best antibiotic choices at the earliest treatment stage for the clinical management of infectious diseases, such as UTIs, STDs and bacteremia, etc. For some common bacterial infections, such as bloodstream infections (bacteremia), increased mortality correlates with the length of time needed to get patients on appropriately active drug therapy. Every hour delay in the administration of the correct antibiotic in the treatment of bacteremic patients, for example, increases the risk of mortality by 9%. The use of novel rapid diagnostics can also serve to lower the costs of much needed new drug development by facilitating clinical trials. Furthermore, to achieve the best antimicrobial prescribing practices for reducing the increasing global burden of antibiotic resistance, a novel and truly rapid AST platform, that is where results can be available in 30 min to 1 hour timeframe, has been explicitly identified as an urgent and aspirational need.

[0005] Several genotypic or phenotypic methodologies have been proposed and demonstrated in the past several years to reduce times for more targeted drug treatment decisions relative to current gold standard growth based methods, including those offered by current state of the art commercially available instruments, and have been summarized in a number of comprehensive reviews. Genotypic methods for identifying antimicrobial resistance rely on the direct or indirect detection of specific resistance genes. The presence or absence of such resistance markers associated with a particular organism (for example the mecA gene for Methicillin- resistant Staphylococcus aureus (MRSA)) can be used to predict the phenotypic AST results andthus more rapidly guide therapy selection. However, genotypic susceptibility testing is only an approximation to susceptibility determination. While genotypic methods are generally fast (1 - 5 hour turnaround times) and can be effective at predicting some specific antimicrobial resistance, they do not provide quantitative antimicrobial susceptibility measures, such as the minimum inhibitory concentration (MIC). The MIC is the widely used quantitative measure of in vitro drug susceptibility corresponding to the smallest antibiotic concentration that will prevent the growth of bacterial cells in growth media. Furthermore, the ability to detect all resistant phenotypes is limited by the finite range of known resistance determinants, the lack of identification of a straightforward genotype and the ability to identify emerging resistance before the genotype has been identified thus potentially resulting in over estimation of susceptibility phenotypes and missing AMR species. At the very least, this highlights the complementary need for direct phenotypic AST results which results in quantitative measures of an organism’s ability to survive direct exposure to controlled concentrations of selected antibiotics. Relative costs aside, the acknowledged principle drawback to phenotypic AST has been the slower time to result (TTR).

[0006] Phenotypic AST methodologies may be categorized into techniques that directly or indirectly detect measurements of growth metrics, such as doubling time or biomass change or, those that are based on non-cell growth measures of a bacterial response allowing either quantitative (MIC) or categorical (S / R / I susceptible / resistant / intermediate) measures of a pathogen’s susceptibility to antibiotic exposure. Both traditional and more recent emerging AST methodologies utilizing microfluidics, advanced sensor systems, or Al enhancements rely on cell growth responses. However, it may be argued that this offers a fundamental limitation to developments in minimizing the time to results for AST. Some examples of recent non-growth based techniques demonstrating rapid AST include measurement of AFM vibrations, frequency dependent impedance cytometry, and rapid metabolomic activity. The ultra-rapid, quantitative results described here belong to this last growth-free phenotypic AST category that exploits an optical approach for the detection of inherent, rapid bacterial stress responses to two environmental threats; antibiotics and starvation.

[0007] Raman scattering (RS) is a relatively fast, label-free, and easy-to-use optical technique that can be used to identify molecular species due to their unique vibrational signatures in the inelastically scattered spectrum. Additionally, like mass spectrometry, it is a multiplexing analytical technique capable of identifying multiple molecular components in, for example,complex biological samples, due to the narrow width of the spectral features which act as “molecular fingerprints.” Some attempts to exploit bacterial Raman spectra for relatively rapid AST have been recently demonstrated. In one class of efforts, changes in the relative intensity of bacterial spectral features following antibiotic exposure are taken as measures of antibiotic efficacy. However, these methods rely on detecting known resistance mechanisms and may not be applicable for the required wide bacteria or antibiotics and unknown resistance, and quantitative MIC determinations are not clear. In another approach, both spontaneous and stimulated Raman spectroscopy (SRS) have been used to determine drug susceptibilities from rates that C-D vibrational bands are observed to appear following D2O in the absence and presence of antibiotics. MIC determinations via SRS require two incubation periods and sophisticated laser instrumentation for determining MICs in 2.5 hours via D2O uptake measurements. The analogous D2O uptake MIC determinations via spontaneous Raman required 5 hours.

[0008] Surface enhanced Raman spectroscopy (SERS) is a well-established variant of Raman scattering that exploits plasmonic resonance effects arising from metal nanostructured surfaces to enhance Raman intensities. Some molecules very close (1 nm or less) to nanostructured metal surfaces, typically Au or Ag, can exhibit Raman scattering intensities that are enhanced by 106- 109when the incident and scattered light frequency is coincident with the surface plasmon resonance of the nanostructured metal surface. In prior work we demonstrated that the SERS spectra of viable bacterial cells excited with 785 nm radiation were exclusively due to secreted purines in the near cellular or extracellular region when placed on Au or Ag nanostructured substrates. Furthermore, SERS spectral acquisitions at the single bacterial cell level were demonstrated, thus offering an approach with potentially high sensitivity.

[0009] These strain specific bacterial SERS spectra were shown to result from different amounts of purine nucleotide degradation products, primarily adenine, hypoxanthine, guanine, xanthine, uric acid, and adenosine. Each characteristic, strain specific 785 nm SERS spectrum resulted from different amounts of these secreted purine degradation components. Gene deletion experiments, comparison of bacterial cell and corresponding supernatant samples, and observed correlations of purine intensities with known strain-specific KEGG purine metabolism pathways unequivocally confirmed the molecular origins of the compounds contributing to these spectra and that the biochemical process they resulted from was the well-known purine nucleotide degradationprocess triggered by the bacterial stringent response. The stringent response (also referred to as the starvation response) is a broadly conserved bacterial stress response that controls adaptation to nutrient deprivation. In this context it is initiated when bacterial cells are washed in water to remove all growth media or residual body fluid components prior to SERS signal acquisition. Triggering the stringent response is thus an inherent consequence of the required bacterial sample preparation protocol before signal acquisition. Thorough removal of these media is required to eliminate potentially overwhelming SERS signals from non-innate bacterial contributions.

[0010] Sepsis is a life-threatening condition that occurs when the body's immune system has an extreme response to an infection or injury. It is the third leading cause of death in the United States. The risk of death increases by 9% for every hour of delayed treatment. The high mortality rate and urgency for timely treatment highlight the critical need for a rapid method to determine which specific antibiotic is most effective against the pathogen causing sepsis in a patient.Additionally, improper treatments lead to longer hospital stays and more health complications leading to higher hospital costs.

[0011] The most common bacterial species that cause sepsis are:• Staphylococcus aureus• Escherichia coli• Streptococcus pyogenes• Pseudomonas aeruginosa• Klebsiella spp

[0012] While this list is not all inclusive it indicates that most cases of sepsis stem from a limited number of causative bacteria. There is not a single antibiotic that treats all these different infections. Furthermore, the global rise and continuing proliferation of antimicrobial-resistant pathogens makes it increasingly difficult to routinely prescribe effective empiric broad spectrum antimicrobial treatments. Consequently, an antibiotic susceptibility test (AST) is performed to determine which antibiotic will treat a particular pathogen causing sepsis in a patient.

[0013] The need for rapid treatment currently leads to arbitrary use of antibiotics which is problematic since they may not act against the pathogen causing sepsis and antibiotics are not without deleterious effects to the patient’s health. The common practice is to use broadbandantibiotics to reduce the risk of treatment with a resistant antibiotic. This practice, while used with some success, has contributed to the growing problem of Antimicrobial Resistance (AMR). AMR results when bacteria become resistant to an antibiotic. For this reason, it is important to try to use a narrow band antibiotic that is specific to the infecting pathogen. This is called good antibiotic stewardship.

[0014] Current AST methods rely on growth-based techniques that assess bacterial growth in the presence of antibiotics. These are initiated after a positive flag is seen from a blood bottle and potential plating and identification from the blood bottle to ensure the sample was not contaminated by cutaneous bacteria. A typical approach involves preparing a series of 5 to 8 antibiotic concentrations in growth media and incubating the bacteria for 12-24 hours to observe their growth response. If bacteria are resistant to a specific antibiotic, the growth rate will be independent of the concentration of antibiotic present. Typically, this is determined visually, or for more precise quantitation, the turbidity of the liquid broth bacteria and antibiotic solution can be measured at 600 nm. If the bacteria are susceptible to the antibiotic there will exist a concentration of antibiotic at which they no longer grow. This concentration is called the Minimum Inhibitory Concentration (MIC). The MIC can be used to determine the dosage of the antibiotic treatment for the septic patient.BRIEF SUMMARY

[0015] In one embodiment, SERS-based phenotypic rapid AST profiles can be established based on the relative SERS intensities of the secreted bacterial purine metabolic markers as a function of drug concentration exposure during incubation. In this embodiment, relatively rapid results may be obtained, such as but not limited to in one hour or less. A molecular level analysis of the spectra and their biochemical origins, useful for optimizing a SERS-based approach, as well as demonstrating the important differences, including time-to-results, that distinguishes our approach and its improved performance characteristics.

[0016] In one embodiment, a rapid AST test and method uses Raman spectroscopy based on microbial (e.g., bacteria, fungi, yeast) metabolomics. The AST test and method follows the secretion level of purines and pyrimidines resulting from RNA degradation after the microbials(e.g., bacteria) are placed in a nutrient free environment. These purines, in particular, are exceptionally strong Raman scatterers and have a high affinity for chemisorption to noble metal nanoparticles. The noble metal nanoparticles thus provide significant signal enhancement through their selectivity for purines and the Surface Enhanced Raman Scattering (SERS) phenomenon. The overall result is that microbials (e.g., bacteria) can be detected with high sensitivity by the mix of purines produced when in a starvation environment.

[0017] In this embodiment, the AST test and method relies on an observation that for susceptible bacterial (or fungal) strains antibiotics will dramatically reduce (<80%) the purine secretion level due to the starvation response at the MIC and no purine signal change is observed for strains that are resistant to the tested antibiotic. This observation is used in the AST test and method to measure the AST profile and determine an MIC with high sensitivity and accuracy using Raman spectroscopy. In various examples, perfect matches were found to MIC levels determined with the CLSI method and the AST test and method within + / - 1 doubling concentration. The AST test and method is described in provisional patent Device for ultrarapid antibiotic testing via sensitive optical purine detection USSN 63 / 548,296 filed November 13, 2023, which is incorporated by reference in its entirety as if fully set forth herein.

[0018] The foregoing and other aspects, features, details, utilities, and advantages of the present invention will be apparent from reading the following description and claims, and from reviewing the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 illustrates .

[0020] Figure 2 illustrates .

[0021] Figure 3 illustrates .

[0022] Figure 4 illustrates .

[0023] Figure 5 illustrates .DETAILED DESCRIPTION

[0024] The following description of the invention is provided as an enabling teaching of the invention in its best, currently known embodiment. To this end, those skilled in the relevant art will recognize and appreciate that many changes can be made to the various aspects of the invention described herein, while still obtaining the beneficial results of the present invention. It will also be apparent that some of the desired benefits of the present invention can be obtained by selecting some of the features of the present invention without utilizing other features. Accordingly, those who work in the art will recognize that many modifications and adaptations to the present invention are possible and can even be desirable in certain circumstances and are a part of the present invention. Thus, the following description is provided as illustrative of the principles of the present invention and not in limitation thereof.

[0025] As used throughout, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a” component can include two or more such components unless the context indicates otherwise. Also, the words “proximal” and “distal” are used to describe items or portions of items that are situated closer to and away from, respectively, a user or operator such as a surgeon. Thus, for example, the tip or free end of a device may be referred to as the distal end, whereas the generally opposing end or handle may be referred to as the proximal end.

[0026] All directional references (e.g., upper, lower, upward, downward, left, right, leftward, rightward, top, bottom, above, below, vertical, horizontal, clockwise, and counterclockwise) are only used for identification purposes to aid the reader’s understanding of the present invention, and do not create limitations, particularly as to the position, orientation, or use of the invention. Joinder references (e.g., attached, coupled, connected, and the like) are to be construed broadly and may include intermediate members between a connection of elements and relative movement between elements. As such, joinder references do not necessarily infer that two elements are directly connected and in fixed relation to each other.

[0027] Ranges can be expressed herein as from “about” one particular value, and / or to “about” another particular value. When such a range is expressed, another aspect includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another aspect. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

[0028] As used herein, the terms “optional” or “optionally” mean that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said event or circumstance occurs and instances where it does not.

[0029] The term “substantially” as used herein may be applied to modify any quantitative representation which could permissibly vary without resulting in a change in the basic function to which it is related.

[0030] Throughout this document, various microbes are described, such as bacteria, fungi, and yeast. Techniques described herein can be applicable across various types of microbes, such as but not limited to, bacteria, fungi, and yeast. The use of one particular microbe in an example is merely illustrative and the examples should be construed as being applicable across other types of microbes.

[0031] Many different embodiments are described herein for illustrative purposes. The description of different embodiments describe various features, elements and techniques that are applicable to other embodiments, except where not feasible. One of ordinary skill in the art would readily appreciate that the various features, elements, techniques, etc. described for illustrative purposes with respect to one or more embodiments are applicable to other embodiments described herein.

[0032] Innovative methods for the rapid determination of antibiotic susceptibilities are urgently needed to reduce delays in the administration of appropriate drug treatment and hence improve human health outcomes, and reduce mortality and morbidity rates. The prevalence and increasing proliferation of antibiotic resistant bacterial strains further exacerbates the diagnostic need for rapid and accurate antibiotic susceptibility profile determinations of causative bacterial pathogens. Rapidantibiotic susceptibility testing (AST) is needed to provide the best antibiotic choices at the earliest treatment stage for the clinical management of infectious diseases, such as bacteremia (blood infections), urinary tract infections (UTI), sexually transmitted diseases (STD), etc. Blood infections are potentially the most important class of infectious conditions in this category to address because they can trigger the body's sepsis response which can quickly lead to tissue damage, organ failure and death if not properly and promptly treated. 20% of global deaths were due to sepsis in 2017. In the US sepsis is responsible for 300,000 deaths annually and one out of three hospital deaths. Every hour without appropriate drug treatment has been shown to result in increased mortality and morbidity. Reliance on broad spectrum antibiotics not only results in ineffective treatments but contributes to the exploding growth of multi-drug resistant (MDR) bacterial strains.

[0033] Although several novel methodologies are under development and have been introduced for AST, current gold standard methods for determining best antibiotics once bacteria have been isolated from infected human body fluid samples are growth based and consequently slow requiring ~8 - 24 hours for drug susceptibility determination at best.

[0034] An optically-based technique and platform for rapid, quantitative bacterial antibiotic susceptibility testing is provided.

[0035] This platform results in the phenotypic determination of quantitative, drug susceptibilities for a given bacterial sample for a plurality of antibiotics (e.g., 6 - 8 or more antibiotics) in parallel. In one embodiment, for example, the platform is adapted to provide the determination in < 1 hour including a 30-minute incubation period. The methodology is fundamentally due to two effects: (1) the rapid bacterial stress responses that results in dramatic dose-dependent changes of secreted purine metabolic products following drug incubation, and (2) the high sensitivity to these purines metabolites that dominate the surface enhanced Raman spectra (SERS) on Au or Ag nanostructured substrates (e g., excited at or about 785 nm). Approximately 90 pairs of drugs and bacteria have already been shown to yield MIC values in < 1 hour via the SERS- AST approach that agree with 24-hour growth results (following gold standard CLSI procedures). When this rapid AST approach is combined with instrumentation that can read an i x m array of SERS spectra, where n is a set of doubling concentrations, and m is the number of antibiotics to be tested in parallel, the drug susceptibility profile of a specific, isolated bacterial pathogen can be rapidly phenotypically determined (e.g., in an hour or less). Minimum inhibitory concentrations(MIC) are quantitatively determined simultaneously for this set of m antibiotics (AB) by this SERS approach.

[0036] In one embodiment an antibiotic susceptibility testing (AST) platform is based on the rapid metabolic changes that accompany a bacterial cells response to a specific antibiotic when that pathogen is susceptible to a given drug. SERS-detected purines resulting from nucleotide metabolic degradation provide the robust signature of susceptibility / resistance (e.g., in less than one hour). By employing the SERS-AST, a drug susceptibility profile can be determined rapidly compared to previous tests (e.g., in an hour or less). In one embodiment, for example, a combination of the rapid bacterial responses reported by SERS following just 30 min of AB incubation and the ability to obtain spectral results from ~6 x ~8 multi-well SERS array results in the unique capability of reporting AST results in an hour or less. A SERS array reader essentially provides in-parallel MIC values for a range of AB and takes advantage of the SERS discovery and the creation of a highly valuable instrument for medical use and potentially transformative practical use by health providers.

[0037] In these embodiments, (1) The SERS-AST method is self-referencing and no calibration is required. Data is always relative to nearly simultaneous no AB (antibiotic) exposure. (2) The SERS-AST method need not provide bacterial species / strain identification, and still provide rapid, phenotypic, quantitative AST.

[0038] In various embodiments, a SERS-AST method and test platform is adapted to employ the described methodology for rapid (e.g., < 1 hour) determinations of antibiotic susceptibility profiles due to multiple antibiotics in-parallel. Due to the enhanced sensitivity of the SERS-AST technique, sufficient bacteria for the test may be grown from blood and / or urine in less time than otherwise needed for a less sensitive approach.

[0039] The SERS based AST instrument for rapidly producing drug susceptibility profiles (e g., in an hour or less) includes three main components: (1) a biochemical procedure for incubating bacterial samples with a specified concentration of an antibiotic (AB) in growth media (MHB) for a period of time (e.g., for 30 min), washing with water, centrifugation or equivalent, and placement of the resulting bacterial suspension on Au SERS substrate array, (2) a multi-well Au SERS array, and (3) an instrument to acquire SERS spectra from a multi-well array in parallel (e.g., nearly simultaneous) where each array well corresponds to bacterial cells of a given pathogen that may have been exposed to different concentrations of a set of antibiotics.

[0040] In one embodiment, for example, a SERS sample treatment and preparation procedure includes the following operations.• Make a dilution series of the selected AB (e.g., 6-8 series in a growth medium such as Meuller Hinton Broth (MHB) (e.g., total volume of 10 ml in 50 ml tubes)).• Prepare a bacterial suspension to place in each tube / well with an antibiotic concentration. For example, a 0.1 OD bacterial suspension (~106 / cc).• Place the solutions in a water bath (e.g., 37 C water bath) for a predetermined time period (e.g., a few minutes) to achieve thermal equilibration.• Add a portion of the bacterial solution to each tube / well with an antibiotic concentration. For example, add 0.25 ml of the 0.1 OD bacterial suspension to each tube with an antibiotic concentration.• Transfer tubes to incubator and incubate the cultures for a predetermined time period (e.g., transfer the tubes to the incubator immediately and incubate the cultures for 30 min at 37 C with shaking).• Transfer bacterial cultures into centrifuge tubes (e.g., 2 ml p-centrifuge tubes), centrifuge and remove broth; repeat a predetermined number of times (e.g., 4-5 times).• Wash each aliquot with water (e.g., 1 ml of pH 7 water) and centrifuge a predetermined number of times (e g., three times).• After the last wash, re-suspend the bacterial pellet in residual water (e.g., ~ 5p 1).• Load the cells onto a multi-well SERS array (e.g., an Au multi-well SERS array), and dry the samples (e.g., at a predetermined temperature for a predetermined period of time such as at 37 C for 5 - 10 min).• Acquire an excited SERS spectra for each well of the SERS array (e.g., acquire a 785 nm excited SERS spectra for each well of the SERS array).

[0041] In one embodiment, a multi-well SERS array dimensions are given by the number of antibiotic (AB) concentrations by the number of different AB concentrations tested for the given sample bacterial strain. In one embodiment, for example, an array size may be ~ 6 x ~ 8. For such a sized array, each well comprises a SERS active nanoparti cl e / nanostructured SERS active area (e.g., ~ 1 mm2) region.

[0042] The SERS array may comprise a consumable component of the SERS-AST platform comprising a component including one or more of the following: 1. Inkjet printed Au, Ag, and / or Cu nanoparticle arrays, 2. Chemical synthesis (an extension of chemical methods developed in the Ziegler Boston University laboratory (See: W.R. Premasiri et al., J. Phys. Chem. B, 109, 312-320 (2005)., which is incorporated by reference herein), and 3. Chemical vapor deposition techniques such as GLAD (glancing angle deposition) based methodology. The consumable SERS array is then sampled via an in-parallel signal acquisition.

[0043] A Raman spectrometer instrument is provided to acquire SERS spectra in parallel (nearly simultaneously) from the SERS array (e.g., a -6 x ~8 multi-well array).

[0044] One challenge of providing a rapid AST test and method includes the ability to provide a parallelized SERS measurement. The parallel acquisition of a SERS spectra may be accomplished in a variety of manners.

[0045] Fig. 9 shows a first embodiment of a parallel acquisition technique including a SERS two-dimensional array reader. In this first example embodiment , a system and method are provided for simultaneously or nearly simultaneously monitoring a two-dimensional array of SERS wells includes a tunable laser configured to sweep across a selected fixed detection wavenumber region. In this embodiment, the excitation wavelength of a tunable laser is tuned, and the detected scattered wavelength is fixed to generate the Raman spectrum with the required wavenumber range. For example, for 785 nm Raman excitation, 600 cm-1 would be light scattered at 823.8 nm. This would be the center wavelength at which a narrow band of light (e g., ~10 cm-1) is swept through a filter onto a camera. The laser wavelength is then continuously moved to 773 nm, where 832.8 nm detection would correspond to an 800 cm-1 shift from the laser. In this scheme dispersion of the scattered radiation is not needed and a spectrum is generated by tuning the excitation frequency instead. A large lenslet array with dimensions that match the SERS array size can be used to focus this tunable excitation laser source onto the sample array and to collect the Raman scattering into collimated beams. A beamsplitter is configured to pass the tunable laser wavelengths and to reflect light below 770 nm and to capture the Raman signals. Wavelength calibration of the tunable laser is performed with a Fabry-Perot Etalon that will allow counting of the interference fringes as the laser is adjusted. The distance between fringes can be precisely equal the wavelength of the laser.

[0046] Fig. 10 shows another example embodiment of a SERS two-dimensional array reader. In this second example embodiment, a system and method are provided to simultaneously monitor a 2-dimensional array of SERS wells includes an acousto- optic tunable filter (AOTF) configured to sweep the Raman signal from a fixed 785 nm excitation laser. In this embodiment the fixed 785 nm laser beam is expanded to fill the SERS array by use of a lenslet array. A dichroic beamsplitter passes the laser 785 nm excitation and reflects the Raman signal wavelengths. The image of the lenslet array is reduced / minimized and passed through a long pass filter to remove autofluorescence from the 785 nm laser. An AOTF is used to scan Raman wavelengths (e.g., 600 to 800 cm-1) from the laser frequency. Thus, each well becomes an individual spectrum (e.g., from 600 to 800 cm-1). In this embodiment, 48 spectra can be simultaneously acquired for a typical 6x8 SERS array size.

[0047] Both the first and second embodiments of Figs. 9 and 10 (Method 1 and Method 2) may include masks at the camera plane to exclude spurious light from other wells. In these methods optical fibers for excitation and collection may be used either with or without the lenslet array. Software for control of the signal acquisition, measurement of observed intensities and determination of MIC are also a component of this multi -well SERS array reader.

[0048] In a third example embodiment, a system and method reports an integrated area of the SERS spectrum (e.g., in the 600 to 800 cm-1 region). This system uses two high sensitivity cameras and a fixed laser wavelength (e.g., at 785 nm). In this embodiment, a beam expander is configured to spread the beam out over the whole multi-well SERS plate. A dichroic beam splitter reflects a portion of the spectrum (e.g., the 845 nm and above region), and transmits wavelengths below the portion of the spectrum (e.g., below the 837.6 nm region) . This will split the Raman signal into a component with 600 to 800 cm-1 and a component including 900 cm-1 that can be analyzed to provide a baseline reference signal. The large beam from the lenslet array is reduced / minimized to match the camera size. In one version of this embodiment (a) one camera will detect a band of light around 900 cm-1 and a second camera is configured to read the integrated signal from 600 to 800 cm-1. In post-processing the "integrated" reference baseline intensity is subtracted from the integrated 600 to 800 cm-1 SERS signal intensity. Also shown is closely related variation of this method (b) which uses a single camera and two different bandpass filters on a sliding mechanism to switch between the integrated signal and the baseline measurement. While this method is simpler and less costly, it will take twice as long to make the two measurements.

[0049]

[0050] The integrated area (e.g., in the 600 - 800 cm-1 baseline corrected region of the acquired SERS spectrum) is compared as a function of AB concentration. The MIC will be determined for the AB concentration where the SERS signal intensity drops below a predetermined value (—20%). An antibiotic is considered resistant when no drop in SERS signal intensity is observed. Agreement between 24-hour growth results and the SERS-AST measurement of the MIC is taken as an MIC value that is within ± one doubling concentration. This method is selfreferencing, and thus no calibration is required. The observed spectra are always relative to samples with no AB (antibiotic) exposure and to each other for a given AB. Finally, we underscore that these methods need not include a bacterial species / strain identification technique. Rather, in some embodiments, the methods may simply provide an ultrarapid, phenotypic AST platform.

[0051] In one embodiment, a parallel SERS system and method is configured to detect dispersed signals over a SERS active surface. In this embodiment, a serial measurement of dispersed signals is taken and averaging is used to find a single reportable signal for the MIC determination. Parallelization in this case comprises measuring the sum of the dispersed signals in one measurement. This can be performed by rastering (rapidly moving the sample) beneath the focus laser beam (or by rastering the beam across the sample). It can be calculated that the laser beam of a typical Raman system is 2 x 10’3mm2and one typical SERS active area is 7.9 x 10’1mm2. This creates a 400 x increase in the measurement time by rastering vs stepwise collecting a measurement at every grid point created by the 2 x 10’3mm2laser spot. This is spatial parallelization. In this manner, the rastering of the sample and / or beam provides a decrease in the average power on the sample by an instantaneous power produced by rastering to reduce laser damage. Net effect, you can increase the average power which the Raman signal depends on, by decreasing the instantaneous power which damages the surface.

[0052] In another embodiment, parallelization occurs post processing after the spectra are acquired. A problem with spectra of purines produced by the starvation response of bacteria and yeast is that they are species dependent. This makes it impossible to use a single peak in the Raman spectrum to quantify the starvation response. For example, the 734 cm’1peak of adenine is not always one of the peaks observed in all of the possible bacteria and yeast species. Our method to solve this problem is to use the integrated sum of signal over a small region of the spectrum thatcontains the strong Raman features of each purine. This region could be, for example, 600 to 800 cm'1, and regardless of the signature of a given bacteria or yeast in the sample we would have an integrated signal that is used to determine the susceptibility to an antimicrobial. This is a parallelization since it eliminates a stepwise measurement to look for each purine peak and sum them to find an average signal. We call this spectral parallelization and it is the measurement of a single signal that contains all of the different purine signal contributions.

[0053] A further attribute of this method of using a filter material is that its Raman bands can be used to ensure both frequency calibration to maintain and accurate wavenumber range. It too will maintain the intensity of the system to ensure that the laser power or detector characteristics have not changed.

[0054] Figure shows a first example system and method of spatial parallelization. Panel A represents a Raman spectrograph which has a single focused laser beam output. Panel B shows the coupling of the spectrograph to the array of SERS active spots with microbes and an antibiotic dependent purine concentration. The Raman spectrographs position is fixed and the array is attached to a translation stage capable of moving the array in a tight pattern defined by the active area size. Panel C shows a typical pattern that might be produced by moving the sample and how it is able to measure signals from multiple areas of signal in real-time. This situation is maintained by keeping the raster speed fast enough to cover the area in a time less than the Raman spectrometer’s acquisition time. After each acquisition the translation stage can move to a new active area and raster / measure signal to create a data set to quantitatively determine the susceptibility or resistance to a given antimicrobial. Panel D shows an example of the signals measured, as the gray scale, of an array.

[0055] Figure (2) illustrates an alternative embodiment of a system and method of measuring a SERS AST in a parallel array of SERS active areas to be measured by the spatial parallelization method. The concept is to filter a sample containing microbes that have been exposed to different concentrations antibiotics such that a profile of the antibiotics effects on the bacteria can be measured. The filtered bacteria are washed with distilled water to remove residual nutrient broth, antibiotics, and to initiate the starvation response. After several washes at 37 degrees C the filters are allowed to drain completely and then a solution of SERS active colloids is placed inthe well. These too are collected by the filter and become coated by any purines released from the microbes.

[0056] In this embodiment shown in Fig. (2), Panel A illustrates a well structure with an example of a 0.45 micron filter and an absorptive pad. This method of passive filtration uses the capillary effect to remove solution from the wells. Panel B shows an example array of four wells made as shown in Panel A of Fig (2). In one embodiment, the wells are made by compressing two parts with the filter and pad in between. The fluids are kept separated by using a knife edge structure on one of the plates to confine the solutions. Panel C illustrates a filter pad produced by the device. It is a 2x2 array with a SERS-active areas. Panel 2 shows Raman data collected from this array with signals from the purine adenine and a reference signal from the nylon material that the filter is made from. Panel E shows the final result of this example of E. Colt K12 and the antibiotic metronidazole. This bacteria strain is resistant to metronidazole and this is clearly observed by the sudden drop in the adenine signal when the minimum inhibitory concentration of the antibiotic was reached.

[0057] Another challenge stems from the complex spectra from the different purines (e.g., adenine, guanine, and xanthine). While these all have major peaks in the 600 to 800 cm-1 region, it is not possible to use one peak for all bacterial species (see Fig. ). In one embodiment, the system is configured to acquire 5 spectra of the samples over a short integration period (e.g. 0.5 seconds), average, and multiply this by a mask created from spectra of adenine, guanine, and xanthine or other selected purine spectrum region. This mask is then normalized from 0 to 1 and multiplied by the averaged sample spectrum. The resulting product spectrum contains large peaks where the sample correlates with the purines and zero intensity where the peaks do not correlate. This very effectively baselines and removes noise from the data and corrects the intensities for any offset from 0. This is illustrated in Fig. . The region from 600 to 800 cm-1 is summed to produce an integrated signal. The spectrum of the filter material (e.g., nylon), can be used to provide Raman frequency verification and calibration if required.

[0058] The heterogenous nature of the sample and photosensitivity can be corrected by rastering the sample under the laser beam during the acquisitions. This has been tested with an xy translation stage mapping an Archimedean spiral to the SERS active areas. We observed very consistent spectra using this method and we were able to use 120 mW of laser power compared to1 mW. The translation stage can also be used to move between wells as the total array is read and analyzed. This is illustrated in Figure . Our preliminary studies show excellent SNR with a raster system and 2 second acquisitions. This is scalable to large arrays of 100 or more in less than 10 minutes

[0059] In various embodiments, such as a neonatal panel where a limited blood draw is available, a limited or targeted panel of antibiotics may be used.ResultsA. Quantitative, Ultra-Rapid MIC via SERS

[0060] Fig. 1 shows examples of SERS-AST results for a single E. coll strain and two different antibiotics. Panel a of Fig. 1 (top left) shows optical density (OD) measurements used to determine that the MIC for the E. coli 2452 strain is 2 mg / L of tetracycline after 24 hours via broth dilution methodology. Panel b of Fig. 1 (top center) shows SERS spectra of A. coli 2452 after 30-minute incubation with tetracycline and growth media as a function of doubling antibiotic concentration. The Red spectrum corresponds to E. coli 2452 grown without any antibiotic. In Panel c of Fig. 1 (top right), the A coli 2452 SERS intensity measured by the spectral peak maximum (-730 cm'1) and integrated entire spectral area as a function of tetracycline concentration. In this example SERS measurements take -one hour including incubation time. The SERS-AST value of the MIC results when the intensity is just below 20% of the maximum. Panels d through f of Fig. 1 (bottom left, right and center, respectively) show a corresponding growth determined MIC, SERS spectra and representations of the SERS intensity for the E. coli 2452 strain incubated with ampicillin. The ampicillin concentration independence indicates this strain is resistant to ampicillin as found both by the 24-hour growth (Panel d) and -1- hour SERS AST approach (Panels e and f).

[0061] A detailed phenomenological example of how SERS provides ultra-rapid (-1 hour) drug susceptibility information is shown in Fig. 1 for A coli ATCC 2452. This Gram-negative pathogen is a New Delhi metallo-P-lactamase (NDM-1) Enterob acteriaceae bacterial strain known to carry genes conferring resistance to several classes of antibiotics. Following the gold standard Clinical & Laboratory Standards Institute (CLSI) growth-based procedure for determining quantitative drug susceptibilities, this strain is found to be susceptible to the broad-spectrum bacteriostatic antibiotic, tetracycline (see Panel a of Fig. 1), but resistant to the act am, ampicillin (see panel d of Fig. 1).Inhibition of protein and cell wall synthesis are the initial interaction mechanisms of antibiotic activity for tetracycline and ampicillin respectively. Optical density (OD) turbidity measurements at 600 nm after a 24-hour growth period as a function of doubling tetracycline and ampicillin concentrations in the incubated growth media pathogen solution are plotted in bar graph format in Panel a (top left) and Panel d (bottom left) of Fig. 1. Thus, the lowest drug concentration where the bacterial / drug / media solution shows an OD of ~0 after 24 hours is the gold standard MIC value via this traditional broth dilution procedure. In this example, E. coll 2452 exhibits an MIC of 2 mg / L due to tetracycline (Panel a of Fig. 1) via the 24-hour growth process. In contrast, incubation with ampicillin with doses up to 128 mg / L (Panel d of Fig. 1) has no discernable effect on the growth of this strain after 24 hours as evidenced by the effectively unchanged OD for all E. coll 2452 samples with ampicillin concentrations in this range and is thus ampicillin resistant.

[0062] Panel b (top center) of Fig. 1 shows a corresponding 785 nm excited SERS spectra of E. coli 2452 as a function of doubling tetracycline concentrations during a 30-minute incubation period. Following this incubation in MHB growth media and specific concentration of an antibiotic, bacterial solutions are washed with a nutrient-free wash (e.g., distilled water, pH neutral water, pH neutral distilled water or other nutrient-free wash) and centrifuged three times before a 1 pL bacterial solution is dropped on an Au SERS substrate. After a -5-10 minute period allowing the sample water to evaporate, a Raman signal is acquired. A more complete and detailed version of this procedure is given in the Methods section below (vide infra). Including the 30-minute drug incubation, washing and SERS signal acquisition, a SERS spectral measurement is completed within one hour. The spectra displayed in Panel b of Fig. 1 are all normalized to the maximum intensity of the most intense SERS spectral feature observed for this series of E. coli 2452 / tetracycline spectra which in this example is the -730 cm’1band in the / .'. coli sample lacking any tetracycline exposure during the 30-minute incubation period. As shown in Panel b of Fig. 1, the relative intensity of these SERS spectra decreases as a function of tetracycline concentration over the 0 to 16 mg / L range. To quantify the effect of the 30-minute antibiotic exposure on these SERS spectra, the peak intensity of the -730 cm’1band is plotted as a function of tetracycline concentration during incubation in Panel c (top right) of Fig. 1c (dark blue bars). As an alternative quantitation measure, the total (400 - 1800 cm’1) integrated E. coli 2452 spectra SERS intensities asa function of tetracycline concentration are also given in Panel c of Fig. 1 (light blue bars). The same trend of monotonically decreasing SERS intensity as a function of antibiotic exposure is observed whether the maximum peak or total integrated intensity is used as the figure of merit to quantify the effect of tetracycline exposure on the observed relative SERS intensities of E. coll 2452.

[0063] In this example, the corresponding E. coli 2452 SERS spectra measured after a 30- minute incubation in growth solutions containing doubling concentrations of ampicillin in the range from 0 to 128 mg / L are shown in Panel e (bottom center) of Fig. 1. In contrast to the A', coli 2452 / tetracy cline spectra (Panel b of Fig. 1), the E. coli 2452 / ampicillin spectra (Panel e of Fig. 1) exhibit no substantive change in intensity as a function of ampicillin concentration within experimental uncertainty (± 15%). More quantitatively, bar plots of the relative maximum peak intensity at -730 cm’1(dark blue bar) or the integrated area (light blue bar) of these E. coli 2452 SERS (Panel f (bottom right) of Fig. 1) illustrate this relative ampicillin concentration independence. Incubation with 16 mg / L of tetracycline results in a more than 200-fold reduction in E. coli 2452 SERS peak intensity relative to no drug exposure, whereas there is virtually no change in the SERS intensity (peak or integrated area) of this strain at the same ampicillin concentration. The effectively unchanged SERS intensities in ampicillin are consistent with the resistance of this strain as determined by broth dilution results (Panel d of Fig. 1).

[0064] Following the sample preparation and experimental procedures described here, we find that to achieve, essential agreement with 24-hour growth based results, the MIC determined via this AST-SERS methodology is given by the antibiotic concentration where the normalized SERS maximum peak intensity or the entire integrated area falls below 18% of the maximum SERS intensity. For the E. coli 2452 / tetracycline combination the SERS-based determined MIC is thus 2 mg / L (Panels a and c of Fig. 1, respectively). While this empirically-determined cutoff consistently results in MIC values that match the gold standard growth MIC within ±1 doubling concentration (essential agreement) for all susceptible bacteria-drug combinations we have tested (vide infra), the SERS-based MIC is determined about twenty times faster (-1 hour) than the CLSI gold standard growth based result for a given bacterium / drug concentration. Although there is no a priori fundamental significance to this -18% of maximum SERS cutoff value yielding MICdeterminations, we have phenomenologically found that an > 80% drop in SERS intensity (peak or integrated) results in essential agreement with 24-hour gold standard MIC determinations by this SERS-AST procedure as demonstrated further below. Although the growth turbidity OD value goes to -0 at the MIC, there is no reason for the SERS-AST MIC be given by a null result because the SERS MIC measure is the result of a completely different biochemical mechanism, as discussed below, than bacterial cell growth suppression.

[0065] Figure 2 shows SERS-AST results for two E. faecium strains and vancomycin. Panel a (top left) of Fig. 2 shows the MIC is 2 mg / L for E. faecium 2127 dosed with vancomycin via broth dilution methodology. Panel b (top middle) of Fig. 2 shows SERS spectra of E. faecium 2127 after a 30-minute incubation with vancomycin and growth media as a function of doubling antibiotic concentration. Panel c (top right) of Fig. 2 shows the E. faecium 2127 SERS intensity as measured by the spectral peak maximum (-730 cm'1) and integrated entire spectral area as a function of [tetracycline]. In this example, SERS-AST determination take -one hour including incubation time. The SERS-AST MIC value given by drug concentration when the intensity first falls below 20% of the maximum. Panels d through f (bottom left, right and middle, respectively) of Fig 2 show the corresponding growth determined MIC, SERS spectra and representations of the SERS intensity for the E. faecium 700221 strain incubated with vancomycin. The concentration independence indicates this strain is resistant to vancomycinas found both by the 24-hour growth (Panel d) and -1- hour SERS AST approach (Panels e and f).

[0066] In contrast to comparing the SERS responses of a specific bacterial strain to two drugs exhibiting susceptibility and resistance responses shown in Fig. 1, the rapid SERS and 24-hour growth responses of two strains of the same species with different susceptibility / resistance profiles in response to the same antibiotic is demonstrated in Fig. 2. E. faecium 2127 and E. faecium 700221 are enterococcus strains that respectively lack and possess vancomycin resistance genes. This Gram-positive species is a common cause of UTI and bacteremia, and vancomycin is a bactericidal antibiotic whose target of initial activity inhibits cell wall synthesis. By gold standard broth dilution E. faecium 2127 exhibits a vancomycin MIC of 2 mg / L (Panel a of Fig. 2) and A. faecium 700221 is vancomycin resistant, at least up to 32 mg / L (Panel d of Fig. 2) in accordance with their genetic markers. The corresponding SERS spectra of these two E. faecium strains as a function of vancomycin concentration exposure during the 30-minute incubation are displayed inPanels b and e of Fig. 2. As shown, the SERS intensity of the vancomycin resistant strain is effectively independent of the drug concentration (Panel e of Fig. 2). More quantitatively, both the E. faecium 700221 SERS intensity at the -730 cm-1spectral maximum as well as the integrated area of the total spectrum are independent of vancomycin concentration within experimental uncertainty (Panel f of Fig. 2). The SERS intensity of E. faecium 2127 spectra, in contrast, first increases at the lowest vancomycin exposure concentration (1 mg / L) and then dramatically decreases with higher concentrations. The vancomycin concentration dependence of the corresponding SERS peak and integrated area are quantitatively summarized in Panel c of Fig. 2. The SERS responses of these two E. faecium strains are consistent with their 24-hour growth vancomycin susceptibility and resistance character. The relative SERS intensities of the E. faecium 2127 spectra measured by either relative spectral peak maximum intensity or integrated area falls to < 18% of its peak value for 2 mg / L (Panels b and c of Fig. 2) thus establishing this value as the SERS-AST MIC in agreement with the 24-hour growth results (Panel a of Fig. 2). Again, the only difference between the outcomes of the growth and SERS AST results is the drastically reduced time to result (-1 hour for the SERS determination).

[0067] Fig. 3 shows further example demonstrations of the rapid SERS-AST methodology. Bar graphs represent the integrated area (light blue) or peak maximum (dark blue) of the SERS spectra of eight bacterial species after a 30-minute incubation with the indicated antibiotic doubling concentration. The green arrow is the 24-hour growth determined MIC and the red arrow is the corresponding SERS determined MIC. K. pneumonia 6908 is found to be ampicillin resistant by both methods. The (c) and (s) designations indicate if the antibiotic is bactericidal or bacteriostatic. Estimated SERS intensity uncertainties is ±10%.

[0068] Fig. 4 shows additional example demonstrations of the rapid SERS-AST methodology. Bar graphs represent the integrated area (light blue) or peak maximum (dark blue) of the SERS spectra of five bacterial species after 30-minute incubation with the indicated antibiotic doubling concentration. The green arrow is the 24-hour growth determined MIC and the red arrow is the corresponding SERS determined MIC. The (c) and (s) designations indicate if the antibiotic is bactericidal or bacteriostatic. Estimated SERS intensity uncertainties is ±10%.

[0069] Following the same SERS-AST procedure for acquiring MICs, bar graphs summarizing another thirteen examples of this rapid, phenotypic AST methodology are shown in Figs. 3 and 4 to further establish the general applicability and accuracy of this approach. Each of these SERS bacterial strain / drug AST profdes, i.e. SERS intensity per doubling antibiotic concentration (Figs. 3 and 4), is given for both the maximum SERS peak intensity and the total integrated area to quantify the susceptibility determination. The vertical green arrow in each panel is the 24-hour growth determined MIC via optical density or visual inspection (Fig. 30). The SERS MIC value is indicated by the vertical red arrow in each bar graph panel and corresponds to the lowest concentration where the SERS intensity is < 18% of the relative max by peak height of spectral maximum or integrated area.

[0070] The SERS-AST results illustrated in Figs. 3 and 4 show that all SERS determined MICs agree with the 24-hour growth MIC values by the procedure used here. In fact, SERS MICs from the displayed AST profiles are exactly identical with gold standard results except for the P. reggetti 2525 / merepenum pair where the SERS MIC is found to be 1 doubling concentration different than the growth value. Again, MICs within ± 1 doubling concentration determined by different measurements is the accepted range for essential agreement. The bacterial strain K. pneumoniae 6908 with known resistance to ampicillin, shows no significant SERS intensity decrease, within experimental uncertainty, as a function of ampicillin concentration as observed for the other resistant E. coli and E.faecium strains (Figs. 1 and 2). When this same K. pneumonia 6908 strain is incubated with levofloxacin a precipitous drop in SERS intensity is found at the known MIC dose (Fig- 3).

[0071] As found for the SERS-AST results in Figs. 1 and 2, MICs determined by SERS illustrated in Figs. 3 and 4 are the same whether the peak at the spectral maximum (typically -730 cm'1) or the total integrated area of the (400 - 1800 cm'1) spectrum are used as the SERS intensity measure. This observation may have implications for instrumentation design exploiting this discovery for a rapid AST device. Phenomenologically, we note that SERS-AST profiles exhibit two general types of antibiotic concentration trends. In one, SERS intensities monotonically decrease with increasing drug concentration (e.g. E. coli 700926 / tetracy cline). For the other, SERS intensity increases greater than the 0 mg / L spectrum at sub-inhibitory low dose concentrations (e g.E. coli 700926 / ampicillin). These two observed antibiotic concentration trends will be described further below.

[0072] For most of the SERS-AST profiles the qualitative trends of the peak and integrated area as a function of antibiotic concentration are the same within experimental precision. However, two exceptions within this group are observed. The susceptibility profile for the . saprophyticus 10320 / clindamycin pair (Fig. 3) via integrated area monotonically decreases as a function of clindamycin concentration, whereas the peak maximum first increases at the lowest concentrations before monotonically decreasing at higher clindamycin concentrations. Additionally, the drug concentration dependence of the E.faecium 2127 / chloramphenicol pair SERS peak intensity monotonically decreases with chloramphenicol concentration, and in contrast, the integrated SERS spectrum intensity remains high at lower concentrations before decreasing near the MIC. However, most importantly, the same MIC value is found by both SERS intensity measures in these data sets. The molecular origins accounting for these anomalous, non-monotonic SERS intensity trends are also addressed below.B. SERS-AST Capability

[0073] As demonstrated here, monitoring the SERS intensities of secreted purines resulting from bacteria placed in water following a 30-minute incubation period with an antibiotic not only rapidly distinguishes susceptible and resistant strains to the specific antibiotic but also accurately determines MIC values. In this initial effort to formally establish the applicability of this SERS- AST approach for potential development as a general diagnostic platform we highlight that the SERS susceptibility profiles shown in Figs. 1 - 4 provide rapid and accurate MICs for both Grampositive (e.g. S. saprophyticus, E.faecium) and Gram-negative (e.g. E-coli, K. pneumoniae) bacteria. Thus, the SERS-AST methodology is independent of bacterial cell wall structure and these results suggest the dose dependent SERS intensities are governed by some highly conserved metabolic pathways. Secondly, there are a handful of main categories of antibiotic initial activity targets. These include molecules that inhibit cell wall or protein synthesis (via ribosomal disruption), or that interfere with DNA replication or folic acid metabolism. Table I lists the antibiotics used in these AST studies and their corresponding mechanism of action which covers these major classes of antibiotic activity. The demonstrated results show that the SERS-AST methodology works for all these initial target classes of antibiotic-bacterial cell interactions.

[0074] Table I. Antibiotics used in this study

[0075] In addition to the different initial targets of bacterial cell activity, drugs exhibiting antibiotic effectiveness may be classified into two groups depending on their bacterial cell effects.Bactericidal antibiotics result in bacterial cell death, while bacteriostatics prevent the growth of bacterial cells. Table I also indicates this activity classification for the antibiotics tested in these studies and are correspondingly labeled with their cidal (c) or static (5) description in each AST profile shown in Figs. 1 - 4. As seen, the SERS-AST methodology accurately and rapidly determines MIC values for both classes of antibiotic activity. However, the relative SERS intensity dependence generally exhibits two types of drug dose dependence trends for concentrations below the MIC as pointed out above. Interestingly, there appears to be a strong correlation between this increase / decrease at sub-inhibitory doses, especially for the total SERS intensity as given by integrated area, and the bactericidal / bacteriostatic antibiotic classification (Figs. 1 - 4) as discussed further below.C. Molecular Origins of SERS Bacterial Signals

[0076] As discussed previously, the SERS spectra of vegetative bacteria cells following sample enrichment via centrifugation and water washing is dominated by the purine degradation products of nucleic acid metabolism as a result of the well-established starvation response of bacteria. See, for example, Premasiri, W. R., Lee, J. C., Sauer-Budge, A., Theberge, R., Costello, C. E., and Ziegler, L. D., "The biochemical origins of the surface-enhanced Raman spectra of bacteria: a metabolomics profiling by SERS," Analytical and Bioanalytical Chemistry, vol. 408, pp. 4631- 4647, (2016); Premasiri, W. R., Chen, Y., Williamson, P. M., Bandarage, D. C., Pyles, C., and Ziegler, L. D., "Rapid urinary tract infection diagnostics by surface-enhanced Raman spectroscopy (SERS): identification and antibiotic susceptibilities," Analytical and Bioanalytical Chemistry, vol. 409, pp. 3043-3054, 2017 / 04 / 01 (2017); and Chen, Y., Premasiri, W. R , and Ziegler, L. D., "Surface enhanced Raman spectroscopy of Chlamydia trachomatis and Neisseria gonorrhoeae for diagnostics, and extra-cellular metabolomics and biochemical monitoring," Scientific Reports, vol. 8, p. 5163, 2018 / 03 / 26 (2018), each of which if incorporated herein by reference.

[0077] Fig. 5 shows examples of empirically determined best-fits (red) of four representative normalized observed bacterial spectra (black) to a linear combinations of purine (adenine, hypoxanthine, xanthine, guanine, and guanosine) SERS spectra. Purines identified in SERS spectra of other strains include uric acid and adenosine. The relative contribution of each purine component to the total normalized bacterial SERS spectrum is shown in each displayed best-fit. This procedureidentifies the molecular origins of nearly every vibrational band peak in these bacterial SERS spectra.

[0078] A further demonstration of the molecular components of these 785 nm excited bacterial SERS spectra on Au substrates is shown in Fig. 5 for SERS spectra of four representative strains: E. coli 2452, E. faecium 2127, S. sapprophyticus 10320 and K. pneumoniae 1898 in the absence of antibiotics. The nearly overlapping red and black spectra in each of the Fig. 5 panels are the molecular component best-fit (by visual inspection) and observed SERS spectra respectively. The “best-fit” spectra result from linear combinations of normalized 785 nm SERS spectra of the indicated purine molecular components. Even given potential errors due to spectral baseline determinations, the implicit assumption that the observed SERS signals are not saturated, and neglecting any effects of inter-purine base pairing interactions, nearly all the observed vibrational frequencies and their relative intensities are captured by this approximate “fitting” procedure. This fitting analysis provides additional evidence that the major molecular components contributing to these 785 nm excited SERS spectra on Au substrates are nearly exclusively purine based nucleic acid degradation products and hence these are the molecular species responsible for providing the rapid bacterial AST response to drug exposure. See, for example, Premasiri, W. R., Lee, J. C., Sauer-Budge, A., Theberge, R., Costello, C. E., and Ziegler, L. D., "The biochemical origins of the surface-enhanced Raman spectra of bacteria: a metabolomics profiling by SERS," Analytical and Bioanalytical Chemistry, vol. 408, pp. 4631-4647, (2016); Premasiri, W. R., Chen, Y., Williamson, P. M., Bandarage, D. C., Pyles, C., and Ziegler, L. D., "Rapid urinary tract infection diagnostics by surface-enhanced Raman spectroscopy (SERS): identification and antibiotic susceptibilities," Analytical and Bioanalytical Chemistry, vol. 409, pp. 3043-3054, 2017 / 04 / 01 (2017); and Chen, Y., Premasiri, W. R., and Ziegler, L. D., "Surface enhanced Raman spectroscopy of Chlamydia trachomatis and Neisseria gonorrhoeae for diagnostics, and extra-cellular metabolomics and biochemical monitoring," Scientific Reports, vol. 8, p. 5163, 2018 / 03 / 26 (2018), each of which if incorporated herein by reference.

[0079] As shown in Fig. 5, the dominant molecular components to these SERS bacterial spectra are the purinergic nucleic acid metabolites: adenine, hypoxanthine, guanine, xanthine, uric acid, guanosine, and adenosine. The SERS spectra of these purines given in each of the panels are scaled by their relative contribution to the total modeled bacterial spectrum. It’s the differentrelative intensity i.e. relative concentrations, of these compounds that accounts for the spectral differences between bacterial species and strains, and potentially as a function of antibiotic exposure. For example, the SERS spectrum of E. faecium 2127 is dominated by adenine (90%) with just a small contribution (~5%) from xanthine. In contrast, the SERS spectrum of E. coli 2452 is predominantly due to adenine (50%), xanthine (28%) and guanosine (22%). Although nearly completely dominated by purinergic nucleic acid metabolites, relatively small amounts of the pyrimidine nucleobase cytosine were detected in a few bacterial spectra (e.g. P. rettgeri 2525, E. coli 700926). The indicated % composition amounts cited above correspond to the relative amplitude of the contributing purine SERS spectra normalized to the spectrum maximum and have not been corrected for relative SERS cross-section which would be indicative of the relative number density of these species. However, our principal goal here is to further underscore the identity of the molecular species that give rise to these 785 nm excited SERS bacterial spectra. Knowing the molecular origins of these SERS signals is central to understanding the bacterial mechanism responsible for the observed spectra and their corresponding dependence on antibiotic exposure, i.e. the basis for the ultra-rapid SERS-AST methodology.D. Bacterial SERS Composition Dependence on Antibiotic Dose

[0080] The SERS spectra resulting in the rapid MIC determinations illustrated in Figs. 1 and 2, and 17-33, qualitatively appear to show the same relative pattern of vibrational intensities at each doubling antibiotic concentration for a given strain / drug pair aside from an overall intensity dependence. As discussed above, total integrated or peak SERS spectral intensities as a function of 30-minute antibiotic dose exposure provide the same MIC value. However, the experimental dynamic range of the reported bacterial SERS intensities coupled with the purine specific decomposition of the dose dependent observed spectra allows a more quantitative assessment of the antibiotic exposure pre-conditioning on the relative intensities for a given bacterial / drug pair, and thus the molecular composition of these SERS signatures as a function of drug dose. Such a concentration dependence may be anticipated if the purine contributions providing the AST information have metabolomics origins and could play a role in analyzing some details of SERS- AST drug dependence. This molecular specificity also highlight the role SERS can play for studying the increasingly recognized role of purine biosynthesis, including nucleotide degradation pathways, and antibiotic efficacy.

[0081] Fig. 6 shows examples of dose dependence of the purine components of the normalized 785 nm SERS spectra for six representative bacterial strain / antibiotic combinations. The SERS determined MIC value is the antibiotic concentration in red for the four bacterial strains that are susceptible to the indicated drug; K. pneumoniae 6908 and E. faecium 700221 are resistant to ampicillin and vancomycin respectively.

[0082] The molecular components of the normalized SERS spectra of six bacterial strain / drug pairs as a function of doubling antibiotic concentration are shown in Fig. 6. For this analysis the dose-dependent spectra are normalized to their peak maxima and the relative intensity contributions result from the best-fit procedure as illustrated in Fig. 5. Four strains that are susceptible and two that are resistant to the indicated antibiotic are shown in this figure as representative samples. The MIC concentration is indicated in red along the drug concentration axis. Estimated relative contribution uncertainties are ±15%. Note that the relative purine composition is relative to the normalized spectral intensity maximum at each concentration to highlight how the relative components, not total intensity, change as a function of antibiotic exposure.

[0083] No single simple consistent purine composition trend with antibiotic concentration exposure is observed for these dose dependent SERS spectra. The relative contribution of the component molecular purines to the SERS spectrum of susceptible strains generally shows incremental changes with incubated drug concentration (Panels a-d of Fig. 6). For example, for T. pneumonia 1898 (Panel a of Fig. 6), the relative concentration of hypoxanthine increases until the MIC in tetracycline (16 mg / L) is reached, and then decreases with increasing dose (Panel a of Fig. 6). The opposite trend is observed for the relative adenine contribution to the SERS spectra as a function of tetracycline for this bacteriostatic antibiotic. Furthermore, the relative guanine and xanthine contributions also exhibit opposite decreasing and increasing trends respectively for T. pneumonia 1898 responding to increasing tetracycline dose exposure.

[0084] The response of E. coli 2452 to increasing doses of tetracycline (Panel b of Fig. 6) exhibits a relatively constant adenine contribution up to the corresponding MIC (2 mg / L), but at the MIC there is a large relative increase in xanthine and guanine components to the SERS signature. Accurate analyses for tetracycline concentrations greater than the MIC for this strain were not possible due to the weakness of the overall spectrum (see, Fig. 1). Similarly, gradual changes in therelative purine contributions to the SERS spectra of E. faecium 2127 (Panel c of Fig. 6) and E. coli 6640 (Panel d of Fig. 6) to chloramphenicol and levofloxoacin, respectively, are found as shown in Panels c and d of Fig. 6.

[0085] Maybe the most striking phenomenological SERS characteristic is that large changes in the chemical composition of the SERS spectrum do not consistently appear at the MIC as might have been anticipated, and the changes are generally systematic and incremental. On the other hand, the SERS spectral signature at the highest drug concentrations tested here can be different than their corresponding drug-free SERS spectra as evident for the four susceptible strain / antibiotic combinations (Panels a through d of Fig. 6). However, the AST profiles (Figs. 1 - 4) show that MIC determinations based on SERS spectral peak maxima or integrated spectral intensities are not affected by these dose dependent changes to the SERS spectral composition within the essential agreement criterion of ±1 doubling concentration. While not consequential for MIC determinations, some disagreement between SERS-AST concentration profiles given by spectral maxima and integrated area can be attributed to changes in relative purine composition as a function of antibiotic concentration as seen for 5. saprophyticus 10320 / clindamycin and E. facium 2127 / chloramphenicol (Fig. 4), although this does not hinder the accuracy of the SERS-AST MIC determination as these examples show because the overall intensity changes are the much larger dose dependent effect.

[0086] While SERS-AST profiles based spectral peak and total SERS area intensities are essentially independent of drug concentration for resistant bacterial strains, it is perhaps not surprising that, in striking contrast to the susceptible strains, we find the relative purine contributions to these SERS spectra are also independent of drug dose during incubation. This purine components dose independence is shown for K. pneumoniae 6908 and E. faecium 700221 which are resistant to ampicillin and vancomycin respectively (Panels e and f of Fig. 6). Presuming the metabolic origins of these SERS-AST signals, the dose independence of the molecular composition of these resistant strain SERS spectra confirms that the nucleotide degradation pathway is apparently not perturbed by antibiotics in resistant strains.III. Discussion

[0087] The biochemical bases for this rapid SERS-AST technique are inherent bacterial responses that are intended to promote survival from two specific environmental stresses / threats;nutrient depravation and antibiotics. SERS purine signals result from secreted nucleotide degradation products, and may be viewed as the detection step. The dose dependent re-wiring of metabolic responses during a 30-minute incubation with antibiotics prior to the onset of starvation enables the discovered AST capability of SERS. These biochemical, and other coincident chemical physics factors, account for the success of this ultra-rapid SERS AST methodology and are discussed in further details below.A. Why purines dominate 785 nm bacterial SERS spectra1. Stringent response and purine selection

[0088] In order to survive under unfavorable conditions, bacteria have evolved with complex genetic networks, which allow them to sense and rapidly respond to environmental threats / stresses. We previously attributed the observed 785 nm SERS signals on Au substrates to the purines bases and some nucleosides, resulting from the catabolic degradation of nucleotides triggered by the ubiquitous bacterial starvation or stringent response. The stringent response is immediately initiated when bacteria are placed in the no-nutrient environment of the water washing solutions during sample preparation. The degradation of the so-called stable RNAs, rRNA and / RNA, which together account for -96% of bacterial cell RNA, are known to be rapidly degraded upon nutrient starvation, most notably following amino acid starvation. More than half of the cellular / RNA content is degraded within tens of minutes of starvation initiation. RNA degradation, induced by the stringent response, thus rapidly increases the intracellular nucleotide concentration available for catabolism and nucleobase and nucleoside production. mRNA degradation may also be a component of this response since mRNA lifetimes are lengthened due to the stringent response, however, mRNA only constitutes 2-4% of total bacterial RNA content. When the starved bacterial cells are subsequently placed on nanostructured SERS substrates, the SERS signal due to the secreted purine end products of this nucleic acid degradation process are correspondingly largest when scattering is collected from these near cell regions where their concentration is largest, at least initially. Judging by the absolute size of the observed bacterial SERS signals, the local concentrations at the outer bacterial cell region are in the -1 - 10 pM range consistent with single cell level SERS signal observations.

[0089] This starvation-induced purine secretion origin of 785 nm bacterial SERS spectra was previously supported by evidence from isotopic vibrational peak shifts, cell and supernatant SERS comparisons, mass spec analysis, enzyme substrate dependencies and spectral component fitting analyses (e.g. Fig. 6). Maybe the most compelling evidence for this explanation was that the observed distinct SERS bacterial spectra for a specific strain could be well-correlated with the presence or absences of specific enzymes in the purine metabolic network for each strain, and gene knockout strains resulted in SERS spectra that were altered corresponding to the deleted known enzyme activity. Furthermore, nucleobases are not normally present as free bases in bacterial intracellular or extracellular regions but almost exclusively found as nucleotides under normal nutrient growth conditions. Additionally, prior, non-SERS, i.e. HPLC, LC-MS and EI-TOF MS measures of the intracellular and extracellular metabolome components also revealed high concentrations of purine bases, e.g. adenine, guanine, xanthine, hypoxanthine, accumulating following the onset of bacterial starvation in further support of this explanation for the observed SERS features. However, since this earlier report additional molecular level, mechanistic details concerning the bacterial starvation response have been more thoroughly described providing further understanding for the purine dominance of the bacterial SERS spectra and relevant details for their dependence on antibiotic exposure.2. (p)ppGpp., the stringent response and nucleotide degradation

[0090] Nutrient deprivation initiates the ubiquitous stringent response. In the starvation environment, there is a buildup of uncharged deacylated / RNA in the ribosomal A-site resulting from the lack of amino acids. Elevated uncharged / RNA is the signal that in turn activates the synthesis of guanosine tetra- and penta-phosphate in response to this environmental stress as a component of the stringent response. These signaling nucleotides, jointly referred to as (p)ppGpp, are conserved across all bacterial species. They are described as alarmones due their central role in controlling the slowdown of cell growth and metabolism adaptation in order to enhance survival in response to the environmental stress. Drastically reduced protein production, and inhibited or enhanced enzymatic activity in several metabolic pathways, including those involved in purine metabolism, are mediated by these alarmones.

[0091] Fig. 7 shows a summary of the purine metabolic / salvage mechanisms for a representative E. coll strain (MG1655) given by the KEGG pathway reference with the addition ofthe recently identified PpnN. Following RNA degradation, symbolized by dashed vertical arrows at top, all arrows correspond to enzymes facilitating the indicated reactions and equilibria between purinergic species. The green arrows and red crosses correspond to enzymes whose indicated transformations have been enhanced or inactivated respectively by (p)ppGPP over base activity levels. The net effect of this alarmone on this network is to funnel this RNA degradation towards the production of nucleobases and nucleosides that are secreted and observed by SERS.

[0092] In order to illustrate effects of elevated levels of (p)ppGpp due to the stringent response, an overview of the most relevant purine nucleotide degradation / salvation catabolic / metabolic pathways is shown for a representative E. coli strain (MG1655) in Fig. 7. The main purine reactants and products appear in the black boxes, and relevant enzymes associated with the reversible or non- reversible chemical reactions are explicitly indicated. This diagramed network of delicately balanced, catalyzed purine reactions is given by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database but has been augmented by the recently characterized, widely conserved PpnN enzyme (pyrimidine / purine nucleotide 5 ’-monophosphate nucleosidase). As shown in Fig. 7, PpnN facilitates the one-step nucleotide degradation by cleaving nucleoside monophosphates resulting in a free nucleobase and D-ribose-5’ -phosphate (R5P) (X =guanine, hypoxanthine, xanthine, adenine):XMP - X + D-ribose (5RP). (1)

[0093] Recent studies have revealed that the alarmone (p)ppGpp strongly interacts with several proteins in this degradation / salvation network with consequences as indicated in Fig. 7 that significantly impact the observed bacterial SERS signal production and resultant AST capabilities. Firstly, (p)ppGpp stimulates the catalytic activity of PpnN (pyrimidine / purine nucleotide 5'monophosphate). Following the onset of the stringent response, (p)ppGpp levels rise and bind to PpnN triggering a large conformational change that exposes the protein’s active site, thus accounting for a (p)ppGpp stimulatory effect on nucleotide degradation (green arrows Fig. 7). Secondly, (p)ppGpp exerts an inhibitory effect on the purine phosphoribosyltransferases (Gpt, Hpt, Apt) which are salvage pathway proteins that work in the opposite direction to nucleotide synthesis proteins. Additionally, (p)ppGpp also binds to Gsk, Gmk, and GuaB proteins in the purine degradation / salvation network inhibiting the activity of these enzymes. This inhibitory activity is represented by red crosses in Fig. 7. The net effect of these activation and inhibitory (p)ppGppinteractions initiated by the stringent response is the rapid accumulation of free nucleobases. As evident in Fig. 7, this alarmone effectively enhances direct nucleotide degradation to free nucleobases production and blocks the return synthetic pathways for making new nucleotides (for RNA synthesis etc.) from this excess. Making nucleotides is expensive, requiring 8 ATPs to make a single nucleotide. Thus shutting down this purine biosynthetic pathway under low nutrient conditions enhances survival. Equilibrium with the corresponding nucleosides (Fig. 7) also appears possible as (p)ppGpp binding to PpnP (Pyrimidine / purine nucleoside phosphorylase) is not observed. Thus, the net purine biosynthetic pathway outcome of stringent response (p)ppGpp activity is to efficiently funnel the nucleotide degradation processes to result in the rapid accumulation of purine nucleobase end products, e.g. adenine, guanine, hypoxanthine, xanthine, uric acid, etc. which are then secreted and detected via SERS. We highlight that this is a very rapid process: the majority of cellular tRNA and rRNA is found to be degraded within twenty minutes of the onset of amino acid starvation and the upregulation of (p)ppGpp production and its consequent impact on the purine degradation network has been shown to occur on the timescale of 5 - 10 minutes. The expression levels of -300 and -400 genes are upregulated and downregulated, respectively, within 5 min upon induction of (p)ppGpp. These very fast timescales are consistent with other observations of rapid RNA depletion, (p)ppGpp dynamics and the prompt appearance of increased nucleobases in the bacterial metabolome. It’s this rapid bacterial reprogramming response to starvation, in part, that sets the speed for this SERS-AST methodology.

[0094] (p)ppGpp activity enhances bacterial survival and adaption in the low nutrient environment. Following degradation of / 'RNA and / RNA, and inhibition of bulk RNA synthesis during amino acid starvation, pools of excess nucleotides develop. The stimulatory and inhibitory (p)ppGpp effects described above (Fig. 7) allows nucleotide levels to be rapidly adjusted during stress, eliminating deleterious metabolic effects of this nucleotide excess and resulting in the secretion of free nucleobases (and their SERS signals) within minutes. Secondly, secretion of these nucleobases to the bacterial environment allows them to be taken up by bacterial cells and re-used in the one step salvage nucleotide synthesis pathway (Gpt, Hpt, Apt) as compared to the more costly de novo biosynthesis in order to promote fast cell regrowth when nutrient rich environmental conditions return.

[0095] Finally, in addition to the free nucleobases, the other product resulting from the (p)ppGpp enhanced PpnN activity is the formation of R5P (Eq. 1). R5P may be used to produce PRPP (phosphoribosyl-pyrophosphate) which in turn can enter the carbon metabolic pathways resulting in the synthesis of aromatic amino acids during these starvation conditions. Increased amounts of key intermediates in the pentose phosphate metabolic pathways leading to the production of phenylalanine, tyrosine and tryptophan have been detected within minutes of starvation triggering. R5P derived from degraded nucleotides, catalyzed by PpnN, can substantially contribute to the synthesis of aromatic amino acids during starvation. Thus these consequences contribute to the overall bacterial fitness survival strategy under these stress conditions, and as an ancillary consequence, leads to the SERS purine detection of viable cells.3. Additional factors contributing to purine dominated bacterial SERS signals

[0096] Mass spectrometry and HPLC results shows a wide variety of biological molecules in the extracellular region of bacterial cells, and yet purines dominate the 785 nm SERS spectra when bacteria are placed on plasmonically active SERS substrates. Their small size and multiple lone pair of nitrogen electrons results in the very strong -785 nm excited SERS cross-sections of purines on both Au and Ag nanostructured surfaces relative to other metabolites such as amino acids, proteins, etc., detected in the bacterial extracellular region. As mentioned above, only a small cytosine contribution could be identified in the SERS spectra of a few bacterial species, although uracil and cytosine have been reported in the metabolome of starved bacterial cells. This absence in part results from the considerable smaller SERS cross-sections of pyrimidines relative to purines (Fig. 30). For example, the per molecule 785 nm SERS intensity of cytosine, uracil and thymine are respectively -35, 175 and 150 times smaller than that of adenine, and thus difficult to detect relative to the purines components. The single ring and fewer nitrogens may result in a weaker physi- adsorption or less favorable orientation with respect to the Au or Ag substrate surface thus accounting for the relatively smaller enhancement of pyrimidines relative to purines. Other biochemical factors may play a role as well in the absence of pyrimidine base contributions to these SERS spectra given the large biological role of purinergic signaling and the importance of purine biosynthesis to stress responses evident here. For example, mass spectrometry determined / / 7 / raccllular purine (adenine, guanine) and pyrimidine (uracil, cytosine and thymine) concentrationswere found to be rapidly (30 minutes) depleted and elevated, respectively upon exposure to several antibiotics.

[0097] Secondly, although the cell location of the newly characterized PpnN enzyme has not been determined, the related purine salvage phosphoribosyltransferases, Hpt, Gpt, Apt, (Fig. 7) that are inhibited by (p)ppGpp are reported to be located at the cell membrane. In addition to facilitating the X — XMP conversion, these enzymes are also thought to transport these reactants and products across the cell membrane. Additionally, the RNA degradosome, a highly structured protein complex responsible for bulk RNA decay in bacteria, is often anchored to the inner cell membrane. Such cell wall localization may also contribute to the efficient and prompt appearance of secreted nucleobases in SERS spectra resulting from (p)ppGpp activated nucleotide degradation when bacterial cells are placed on Au SERS substrates.

[0098] These factors combine to make SERS sensitive to the catabolic degradation of the tightly regulated intracellular nucleic acid concentrations resulting from the bacterial stringent response, and significantly account for the nearly exclusive dominance of nucleobases contributing to the 785 nm SERS spectra of bacteria placed on Au or Ag substrates, and allows their direct use for providing simple resistance / susceptibility determinations and quantitative MICs.B. Why purine concentrations decrease in response to antibiotics

[0099] Premasiri, W. R., Lee, J. C., Sauer-Budge, A., Theberge, R., Costello, C. E., and Ziegler, L. D., "The biochemical origins of the surface-enhanced Raman spectra of bacteria: a metabolomics profiling by SERS," Analytical and Bioanalytical Chemistry, vol. 408, pp. 4631- 4647, (2016) (incorporated by reference) and the above discussion provides a detailed mechanistic basis for the bacterial responses, mediated by the stringent response’s (p)ppGpp alarmone, that results in the rapid secretion of purines upon starvation and thus produces the observed 785 nm SERS spectra. However, the mechanistic biochemical details explaining how the relative magnitude of these responses are affected by a pre-starvation 30-minute exposure to antibiotics, which is the basis of the SERS-AST methodology given here, is not fully established. There has been, and continues to be, considerable research activity particularly in the last decade, on the role of the (p)ppGpp alarmone on antibiotic susceptibility as well as the origins of persistence and the development of drug tolerance in bacterial populations. However, the inverse relationship, that isthe perturbative effects of antibiotics on the stringent response and its multifunctional role in rewiring cell metabolism more generally, is less well established. While the exact details of the mechansims leading to the dramatic reduction in secreted purine levels at the MIC have not been fully determined it is clear from the data we have obtained that these mechanisms are due to the secondary effects of bacterial responses to antibiotics on metabolic perturbations far removed from primary target drug-cell interaction. Further, these effects are dose dependent thus allowing AST profiles to be determined by SERS.

[0100] Since observed SERS signals uniformly exhibit decreased intensities (-80%) at the -MIC (Figs. 1- 4) for susceptible strains across all different classes of antibiotics, the mechanism for this ubiquitous SERS effect cannot be attributed to the well-established different primary inhibitory initial targets for these specific antibiotics. Instead the SERS antibiotic dose dependent purine signals is attributed to downstream secondary processes, such as altered specific metabolic pathways, which have been increasingly recognized as playing an essential role in antibiotic efficacy and in active bacterial death processes. It is now well-established that antibiotic treatment significantly alters the metabolic state of bacteria, and thus the starvation response following the 30 minute incubation with drugs, can be altered. In fact, these SERS results highlight that perturbation of the purine metabolic / salvage pathway are a highly conserved response to antibiotic exposure and are consistent with the emerging description of antibiotic efficacy that dose dependent metabolic perturbations far removed from primary target interaction contribute to the phenotypic outcome of treatment.

[0101] Given the stringent response induced mechanism for purine secretion, several hypotheses may be proposed to rationalize the observed global antibiotic dose dependent effects in these SERS spectra. For example, antibiotic exposure could inhibit or promote (p)ppGpp alarm one levels thus reducing or enhancing nucleotide degradation efficiency and consequent nucleobase secretion concentrations. Alternatively, a generalized antibiotic downregulation effect might reduce intracellular nucleotide pools after the 30-minute antibiotic incubation resulting in fewer subsequent degradation products. Additionally, down-regulation / up-regulation of endogenous pathways in response to antibiotics, including purine catabolism and amino acid biosynthesis, could contribute or account for the observed dose dependent SERS effect. Some antibiotics have been shown to prevent the accumulation of (p)ppGpp at low concentrations resulting in reduced pools of(p)ppGpp. Additionally, reduced intracellular nucleotide and purine nucleobase pools have been reported following antibiotic treatment.

[0102] The observed apparent bactericidal / bacteriostatic SERS difference in drug concentration trends indicates that more than one mechanism is responsible for the dose dependent SERS intensities. Studies over the past decade have established that bacteriostatic antibiotics are associated with suppressed cellular respiration in bacterial cells, whereas most bactericidal antibiotics result in accelerated respiration. For bactericidals, the corresponding metabolic activity correlated with the bacterial enhanced respiration response and plays a large role in the resulting cell death process as a consequence of toxic metabolic byproducts, especially reactive oxygen species. Thus, phenomenologically, the bactericidal / bacteriostatic increase / decrease in bacterial respiration rates appears to correlate with the observed SERS intensity changes at sub-MIC dosages. Purine secretions are enhanced relative to no drug exposure at subinhibitory doses for bactericidals and mostly monotonically decreasing purine signals are observed for bacteriostatics.

[0103] These respiration effects are very rapid with bacterial oxygen consumption changes evident as early as 6 min after drug exposure. Furthermore, the reported respiration deceleration is dose dependent and maximally achieved at the MIC concentration, with no substantial changes at higher concentrations for bacteriostatics. The speed and dose dependence are consistent with the performance characteristics of this rapid SERS-AST methodology. In contrast, measured respiration rates are a maximum at the MIC for bactericidals. While enhanced respiration at sub-inhibitory bactericidal doses correlates with enhanced purine secretion, some other metabolic process which must correlate with lethality, ultimately dominates at higher bactericidal concentrations controlling the reduced levels of purine secretions at the MIC.

[0104] The predominant cellular process initially targeted by bacteriostatic antibiotics is translation, thus halting / inhibiting protein synthesis. Metabolomics data suggest that bacteriostatic inhibition of cellular respiration may be a byproduct of translation inhibition. However, in terms of the SERS signals, drug-induced inhibition of translation decreases consumption of amino acids, which in turn can lead to increases in / RNA aminoacylation level (charged RNA) during the drug incubation period. It has already been suggested that antibiotics targeting protein biosynthesis are expected to inhibit the RelA-mediated stringent response by this indirect mechanism. One potential consequence is that the RelA response, triggered by the accumulation of uncharged / RNA, is thusmuted as starvation conditions are encountered after the drug incubation period in the SERS-AST procedure. Correspondingly (p)ppGpp alarmone levels would be accordingly reduced relative to no bacteriostatic exposure and hence dose dependent diminished secreted purine levels would result, as reported here. Amino acids accumulation is observed in the metabolome of bacteriostatic exposed bacteria. The excess amino acid pool might also obviate the need for 5RP resulting from the (p)ppGpp enhanced degradation of nucleotides (Eq. 1), possibly also contributing to reduced nucleobase secretion levels after bacteriostatic exposure. As discussed 5RP is a potential precursor to de novo amino acid synthetic pathways. Bacteriostatics induce a dampened metabolic state in bacteria. While the proteomic response to bacteriostatics principally involves downregulation of several major metabolic pathways as a consequence of reduced respiration, the dose dependent translation inhibition leading to decreased consumption and build up of intracellular amino acids, can reduce the alarmone concentration, with the consequent effect of reduced SERS intensities immediately following placement in the no-nutrient environment.

[0105] For bactericidals, there are at least two metabolic mechanisms acting with opposite increasing and decreasing effects on the drug concentration dependence of the SERS purine signals. Both increases and decreases in relative metabolic activity are reported for bactericidals, indicating that they result in broad, complex perturbations of metabolism and do not just quench all metabolic activity. Furthermore, diverse bactericidal antibiotics have been shown to induce similar metabolic changes providing further support for the role of downstream secondary metabolic pathways in the resulting SERS-AST effect in all tested bacteria. In contrast to bacteriostatics, however, the demands of accelerated respiration and enhanced cellular metabolic rates induced by bactericidals appear to result in the observed higher levels of secreted purines at sub-inhibitory concentrations (Figs. 1- 4). The abundance of intracellular central carbon metabolites and disruption of the nucleotide pool are known metabolic consequences of bactericidal antibiotics. The upregulated demand for central carbon metabolism correlated with higher respiration rates in response to bactericidals, can also benefit from the extra amino acid precursor, 5RP, resulting from nucleotide degradation (Eq. 1) thus contributing to increased nucleobase concentrations for secretion. This low concentration response to bactericidals has been hypothesized to be an adaptive mechanism for surviving low levels antibiotics as might be found naturally fostering growth that might allow the organism to elude cell death. However, at higher antibiotic concentrations near the MIC, secondaryantibiotic metabolic perturbations contribute to antibiotic lethality through mechanisms that involve the generation of ROS and other damaging molecules. These reactive species damage many important cellular components, but oxidation of the nucleotide pool appears to be particularly significant, and thus highly reduced nucleobases and nucleosides may be available for secretion due to direct and indirect cellular damage from ROS. ROS may also be involved in damage to (p)ppGpp, which would contribute to decreased purine secretion levels at higher bactericidal concentrations. Finally, as mentioned above, bactericidals have been shown to result in the rapid (30 - 60 minutes) depletion of free zn / racellular nucleobases (e.g. adenine, guanine, and cytosine). In particular, adenine depletion is thought to increase ATP demand via purine biosynthesis, resulting in elevated central carbon metabolism activity and oxygen consumption, thus enhancing the killing effects of these antibiotics. The SERS-AST profdes reflect this dose dependent intimate relation between the metabolic rewiring in response to bactericidals, and the purine biosynthetic and alarmone (p)ppGpp mediated degradation pathways.IV. Materials and MethodsBacterial samples and SERS AST procedure

[0106] Fig. 8A shows an outline of an example procedure used to acquire ultra-rapid SERS- AST results on Au nanoparticle substrates.

[0107] Bacterial strains used in this study were purchased from ATCC or were clinical isolates donated by BD Life Sciences. The bench top procedure resulting in the SERS determinations of MIC values for the strain / drug pairs discussed here is outlined in Fig. 8. A range of doubling concentrations of the selected antibiotic (~ 0, x, 2x, 4x, 8x, 16x, etc.) are added to 10 mL of MHB in 50 mL tubes and warmed to 37 °C. Overnight bacterial culture was inoculated into ~10 mL of Mueller Hinton Broth (MHB) (Sigma) and the bacterial concentration was adjusted to yield an OD ~ 0.1 at 600 nm or an initial concentration on the order of ~106cfu / mL. 0.25 mL of the ~0.1 OD bacterial solution is added to each tube which thus contains the same number of bacteria (~2.5 x 105) with different antibiotic concentrations. The antibiotic concentrations are intended to encompass the MIC in addition to a self-calibrating sample without any antibiotics. These culture samples are immediately incubated for 30 min at 37 °C with shaking. After this bacterial incubation period with growth media and specified antibiotic concentration, the cultures are transferred into 2 mL p-centrifuge tubes via centrifugation to remove MHB. For the data reported here 4-5 aliquotsfrom the 10 mL culture tube were sequentially added to the smaller tubes and following each successive centrifugation (1 minute, 12,000 RPM, 20C), the excess both was removed and the cell pellet size increased. The remaining cell pellets were washed with 1 mL of pH 7 water three times. Before centrifugation the aqueous solution was vortexed (-10 sec) to ensure good water mixing and effective water washing. Following removal of the supernatant after the last wash, -5 pL remains in the centrifugation tube. The remaining sample is vortexed one more time and a 1 pL pipet is used to transfer a portion of the resulting suspension onto the SERS chip and placed in a -37 °C. SERS measurements were made after -five minutes when nearly all the water on the SERS substrate had evaporated. The entire process including the 30-minute incubation step takes -1 hour for a given bacteria / antibiotic combination.

[0108] SERS Substrates. All SERS spectra reported here were obtained using in-situ grown, aggregated Au nanoparticle covered SiO substrate developed previously in our laboratory. Details concerning the production of these SERS active chips and the characterization of their performance for providing reproducible SERS spectra of bacteria have been described. These substrates are produced by a two-stage reduction of an Au ion doped sol -gel and results in small (2 - 15 particles) aggregates of monodispersed -80 nm Au nanoparticles covering the outer layer of ~lmm2SiCh substrate.

[0109] SERS Spectral Acquisition. 785 nm excited bacterial SERS spectra were acquired with an RM-2000 Renishaw Raman microscope employing a 50x objective. Incident laser power of -1 mW and -10 seconds of illumination time were used to collect single spectra. The observed spectra typically resulted from -10 bacterial cells within the field of view (-100 pm2) and spectra are the average of 5 - 6 spectral acquisitions at different locations on the SERS chip. Displayed spectra have been baseline corrected but not smoothed. The 520 cm’1band of a silicon wafer was used for frequency calibration. Peak frequency precision is ± 0.5 cm’1.

[0110] Data fitting analysis. GRAMS / AI™ Spectroscopy Software was used to manually baseline correct the experimental SERS spectra. Averaged bacterial spectra were empirically best- fit by adjusting component contributions from adenine, hypoxanthine, xanthine, guanine, uric acid, guanosine and cytosine. Normalized SERS spectra of -20 pM aqueous solutions of these purines were independently obtained and used for this best-fitting purpose. Due to the broad SERS spectralbaseline variability and some systematic vibrational frequency shifts between the bacterial and purine only solutions, excellent best fits to the observed bacterial spectra could be achieved by an empirical fitting procedure.

[0111] Evaluation of Antibacterial Activity via Growth. Minimum inhibitory concentration (MIC) values were determined using a broth dilution method according to CLSI guidelines. Bacterial samples in MHB broth mixed with doubling concentrations of antibiotics were incubated at 37 °C for 24 hours and growth was assessed by visual inspection of turbidity or by optical extinction (OD) measurements at 600 nm. The reported gold standard MIC values corresponded to the lowest antibiotic concentration where no bacterial growth was observed; OD ~0. (See Fig. 30 for an example of visual inspection result.)

[0112] Figure 8B is a flowchart showing another example embodiment of a method / procedure used to acquire ultra-rapid SERS-AST results on Au nanoparticle substrates.

[0113] In this embodiment, the method / procedure includes a sample collection and preparation operation. A dilution series of a selected number of antibiotics (e.g., 5) are prepared in a broth (e.g., MHB). A portion of the dilution is disposed in a plurality of wells (e.g., 50 pL of the prepared sample in each 400 pL well). In this particular example, the dilution operation took approximately 10 minutes. The samples are incubated with agitation for a period of time (e.g., 30 minutes). The 40 pL samples are transferred to a filter (e.g., a filter plate) and the samples are allowed to filter (e.g., for a minute or so). The samples are washed with a nutrient-free wash (e.g., distilled and / or neutral pH water) with an adequate volume to remove / drastically reduce any remaining nutrients in the sample (e.g., with 2 times the well volume of a wash). In this example, the wash operation of the wells took approximately two minutes. A SERs substrate (e.g., 20 pL gold nanoparticles) are added to each of the wells.

[0114] Raman spectra are collected of the active SERS areas (e.g., 2 second acquisition times). At a two second acquisition time, ten minutes shown would represent over 100 SERS active areas. Data analysis is then performed as described in various embodiments herein.V. Conclusion

[0115] A SERS based methodology based on 30-minute incubation with antibiotics in growth media, water washing and 785 nm excited signal acquisition on gold nanoparticle substrates results in quantitative AST profiles in ~1 hour time frame as described here. This approach is effective for both Gram positive and negative species, regardless of the initial drug-bacterium target. To our knowledge this is the fastest scheme for accurate MIC determinations. Drug susceptible and resistant bacterial strains are readily distinguished by the ultra-rapid SERS-AST technique. The fundamental biochemical and chemical physical bases for the success of this methodology is due to several fortuitously coincident phenomena: a. the stringent or starvation response is ubiquitous across prokaryotes and is fast, b. purinergic molecules are very bright 785 nm SERS markers that dominate the spectrum when cells are placed on nanostructured Au surfaces, and c. the complex, multi-mechanism bacterial response to antibiotic exposure is both rapid and dose dependent.

[0116] The bacterial SERS signal result from the rapid secretion of purine nucleobases and some nucleosides as a result of the ubiquitous stringent response initiated by the water washing step in the SERS signal acquisition procedure. Amino acid starvation in particular initiates alarmone (p)ppGpp production which facilitates the rapid and enhanced degradation of excess / RNA, rRNA. This inherent bacterial starvation stress response serves as the detection step for the SERS based diagnostic. The use of SERS intensities for AST is a consequence of the dose dependent metabolic changes that result as a secondary response to the initial primary target of antibiotics. The dose dependent rapid rewiring of metabolic pathways, as they respond to a specific antibiotic during the 30-minute incubation period, alters the consequences of stringent response once starvation is subsequently initiated thus affecting secreted purine levels of degraded RNA. This downstream secondary nucleotide degradation response remains completely unperturbed in strains resistant to a specific antibiotic.

[0117] Total SERS intensities exhibit different dose dependencies for bactericidal and bacteriostatic antibiotics correlated with the established accelerated or diminished respiration rates respectively. Monotonically decreasing total SERS intensity may be attributed to reduced (p)ppGpp alarmone production for bacteriostatics, whereas the combined opposing effects of enhanced metabolism and ROS damage appears to account for the increase in at sub-inhibitory drug concentrations followed by the dramatic decrease at the MIC for the SERS purine signals. Theobserved decrease in SERS intensity is large (-80%) at the MIC for both bacteriostatic and bactericidal antibiotics, thus only trivial analysis is required to achieve quantitative susceptibility results. While the effects of antibiotics on single bacterial strains are reported here, the extension of this approach to polymicrobial samples will be important to explore given the not insignificant prevalence of these type of clinical presentations as well. More sophisticated ML approaches for SERS-AST analyses could be valuable for the SERS-AST analysis of such samples.

[0118] Based on the recently recognized close link between purine metabolism and secondary antibiotic activity including antibiotic lethality, summarized above, it is perhaps not surprising in retrospect that purine markers can be exploited for rapid AST signatures. Leveraging the rapid, SERS reporting on purine salvage and biosynthetic pathways can provide a molecularly specific probe to further our understanding of bacteria responses to antibiotics which is essential for developing approaches for infection treatments and the emergence of bacterial persister cells in drug treated bacterial populations. Such SERS studies could include the effects of combined bacteriostatic and bactericidal antibiotic exposure, or other exogenous chemical treatments, for improved antibiotic efficacy.

[0119] It is unequivocal that to facilitate targeted antimicrobial prescribing practices and to help reduce the increasing global burden of antibiotic resistance, there is an urgent need for the development and implementation of novel and truly rapid AST platforms. The successful adoption of a platform for quantitative SERS-AST in one hour or less has the potential to be transformative in the ability to treat human infections, especially when combined with fast bacterial enrichment techniques, and to help address the increasingly severe problem of drug resistant microorganism proliferation. This technology addresses the currently unmet need for ultra-rapid antibiotic drug susceptibility determinations and its widespread adoption could result in more effective treatments, fewer chronic infections, reduced healthcare costs and reduced AMR proliferation.

[0120] Figs. 17- 29 show SERS spectra of different bacterial strains as a function of the indicated antibiotic that corresponds to the bar spectra shown in Figs, 3 and 4. From the peak and integrated intensities an MIC is determined when the intensity falls to < 20% of the maximum peak or integrated SERS intensity. These are the raw data that are used to determine the rapid SERS MICs shown in Figs 3 and 4.

[0121] Fig. 17 shows a SERS spectra of P. rettgeri 2525 as a function of meropenum concentration in growth media during 30-minute incubation.

[0122] Fig. 18 shows SERS spectra of K. pneumoniae 6908 as a function of levofloxacin concentration in growth media during 30-minute incubation.

[0123] Fig. 19 shows a SERS spectra of K. pneumonia 6908 as a function of ampicillin concentration in growth media during 30-minute incubation.

[0124] Fig. 20 shows a SERS spectra of K. pneumoniae 1898 as a function of tetracycline concentration in growth media during 30-minute incubation.

[0125] Fig. 21 shows a SERS spectra of S. saprophyticus 10320 as a function of clindamycin concentration in growth media during 30-minute incubation.

[0126] Fig. 22 shows a SERS spectra of E. coli 6594 as a function of tobramycin concentration in growth media during 30-minute incubation.

[0127] Fig. 23 shows a SERS spectra of E. coli 6640 as a function of levofloxacin concentration in growth media during 30-minute incubation.

[0128] Fig. 24 shows a SERS spectra of E. coli 6880 as a function of nitrofurantoin concentration in growth media during 30-minute incubation.

[0129] Fig. 25 shows a SERS spectra of E. coli 700926 as a function of ampicillin concentration in growth media during 30-minute incubation.

[0130] Fig. 26 shows a SERS spectra of E. coli 700926 as a function of tetracycline concentration in growth media during 30-minute incubation.

[0131] Fig. 27 shows a SERS spectra of E. coli 700926 as a function of chloramphenical concentration in growth media during 30-minute incubation.

[0132] Fig. 28 shows a SERS spectra of E.faecium 2127 as a function of chloramphenical concentration in growth media during 30-minute incubation.

[0133] Fig. 29 shows a SERS spectra of E. coli 7023 as a function of nitrofurantoin concentration in growth media during 30-minute incubation.

[0134] Fig. 30 shows an example of MIC determination via CLSI 24 hour growth procedure and visual inspection for E. coli 6880 / nitrofuratoin and E. coli 7023 / nitrofuratoin. The MIC corresponds to the lowest antibiotic concentration where turbidity in the growth media plus antibiotic solution sample vanishes. Two typical examples of what the 24 hour growth gold standardbacterial solution looks like at the MIC. This shows the tubes become clear instead of turbid at the MIC in both cases.

[0135] Fig. 31 shows a SERS spectra of E. coli 2452 after 10-minute exposure to tetracycline compared to simultaneously acquired untreated E. coli 2452. This figure demonstrates that a very dramatic reduction in SERS intensity can be observed in just 10 minutes when a bacterial strain is exposed to an antibiotic dose greater than the MIC. For this strain SERS results indicate this E. coli 3453 is susceptible to tetracycline in just 10 min after the 30 min incubation period. Thus this strain's susceptibility to tetracycline, not an MIC value, is determined in a total of 40 min! Not a general result but shows how quickly S and R could be distinguished for some strains via the SERS detection of secreted purines.

[0136] Fig. 32 shows relative intensities of SERS spectra of example purines and pyrimidine on Au SERS substrates excited at 785 nm. TFig. 32 shows that the purines (adenine, uric acid, guanine, hypoxanthine, xanthine) and much stronger or brighter SERS scatters than pyrimidines (uracil and cytosine). Both purines and pyrimidines are components of RNA but we only see the purines. In part this must be due to the stronger signals of purines than pyramids as we show here. There may be other factors but purines are just so much stronger SERS scatterers than pyrimidines.

[0137] Figure 33 Panel a shows 785 nm excited SERS spectra of six fungal strains on Au. Panel b shows that within this limited set of 72 spectra 1005 sensitivity and specificity is achieved by a PLD-DA classification procedure. Panel c provides a best-fit to the observed C. albicans 10231 SERS spectrum and shows that the same purine components contribute to these SERS spectra as found for bacteria. Panel d shows the changes in relative SERS intensity observed for C. albicans 11651 after 30-minute incubations with fungicides fluconazole and intraconazole, known to be susceptible and resistant respectively for this fungus. These support SERS MIC determination of fungi.

[0138] Figs. 34-38 collectively show an additional table of rapid antibiotic susceptibility testing for MICs using the described SERS (1 hour) approach versus a growth-based CLSI determination (24 hour).

Claims

CLAIMSWhat is claimed is:

1. A method of performing an antibiotic susceptibility test comprising: providing an array of wells; preparing a plurality of bacterial suspensions across the plurality of wells, the plurality of bacterial suspensions each comprising a sampled bacteria, a growth medium, and an antibiotic concentration, wherein each antibiotic concentration comprises a different antibiotic and / or antibiotic dose; incubating the plurality of bacterial suspensions; washing the plurality of bacterial suspensions for each of the plurality of wells of the array; obtaining a SERS Raman spectrum of each of the array of wells, wherein each well of the array of wells comprises at least one SERS susbstrate; determining whether a spectral peak intensity or corresponding integrated spectrum is less than a predetermined threshold corresponding to a minimum inhibitory concentration (MIC) for the respective antibiotic for each well of the array of wells; and determining an MIC for the respective antibiotic corresponding to the lesser antibiotic concentration for the respective antibiotic in which the wells of the array of wells where the spectral peak intensity or the integrated spectrum is less than the predetermined threshold.

2. The method of claim 1, wherein the growth medium comprises at least one of the group comprising: a broth and MHP.

3. The method of claim 1, wherein the operation of washing the plurality of bacterial suspensions comprises flushing each of the wells of the array of wells with at least one of the group comprising water, distilled water, and a nutrient free wash.

4. The method of claim 1, wherein the operation of incubating the plurality of bacterial suspensions comprise incubating each of the plurality of bacterial suspensions for at least 15 minutes.

5. The method of claim 1, wherein the operation of incubating the plurality of bacterial suspensions comprise incubating each of the plurality of bacterial suspensions for at least 30 minutes.

6. The method of claim 1, wherein the growth medium does not have a significant spectral peak at the wavenumber corresponding to the spectral peak of the at least one purine.

7. The method of claim 1, wherein the at least one SERS substrate comprises at least one of the group comprising: a gold substrate, a silver substrate, a copper substrate, a plurality of gold particles, a plurality of silver particles, a plurality of gold nanoparticles, and a plurality of silver nanoparticles.

8. The method of claim 1, wherein each of the bacterial suspensions comprise different doses of a single antibiotic in each well of the array of wells.

9. The method of claim 1, wherein the plurality of bacterial suspensions comprises a plurality of different doses of a plurality of antibiotics distributed across the array of wells.

10. The method of claim 1, wherein the SERS spectrum is taken across the array of wells in parallel.

11. The method of claim 10, wherein the parallel spectrum is obtained via one or more of the group comprising: a tunable laser, a tunable filter, moving the array of wells, and moving a laser.

12. A method of performing an antibiotic susceptibility test comprising: preparing a plurality of bacterial suspensions each comprising a sampled bacteria, a growth medium, and an antibiotic dose, wherein each antibiotic concentration comprises a different antibiotic and / or antibiotic dose; incubating each of the bacterial suspensions; washing each of the bacterial suspensions to remove the growth medium; resuspending the remaining bacteria of the bacterial sample in a solution; dispose the suspended bacteria across a plurality of an array of wells, wherein each of the wells comprises at least one SERS substrate; obtaining a SERS Raman spectrum of each of the array of wells, wherein each well of the array of wells comprises at least one SERS susbstrate; determining whether a spectral peak intensity or corresponding integrated spectrum is less than a predetermined threshold corresponding to a minimum inhibitory concentration (MIC) for the respective antibiotic for each well of the array of wells; and determining a minimum inhibitory concentration (MIC) for the respective antibiotic corresponding to the lesser antibiotic concentration for the respective antibiotic in which the wells of the array of wells where the spectral peak intensity or the integrated spectrum is less than the predetermined threshold.

13. The method of claim 12, wherein the solution comprises at least one of water, distilled water, pH neutral water, and a nutrient free wash.

14. The method of claim 12, wherein the at least one SERS substrate comprises at least one of the group comprising: a gold substrate, a silver substrate, a copper substrate, a plurality of gold particles, a plurality of silver particles, a plurality of gold particles, and a plurality of silver nanoparticles.

15. The method of claim 12, wherein the operation of incubating the plurality of bacterial suspensions comprise incubating each of the plurality of bacterial suspensions for at least 15 minutes.

16. The method of claim 12, wherein the operation of incubating the plurality of bacterial suspensions comprise incubating each of the plurality of bacterial suspensions for at least 30 minutes.

17. The method of claim 12, wherein the growth medium does not have a significant spectral peak at the wavenumber corresponding to the spectral peak of the at least one purine.

18. The method of claim 12, wherein the at least one SERS substrate comprises at least one of the group comprising: a gold substrate, a silver substrate, a copper substrate, a plurality of gold particles, a plurality of silver particles, a plurality of gold nanoparticles, and a plurality of silver nanoparticles.

19. The method of claim 12, wherein each of the bacterial suspensions comprise different doses of a single antibiotic in each well of the array of wells.

20. The method of claim 12, wherein the plurality of bacterial suspensions comprises a plurality of different doses of a plurality of antibiotics distributed across the array of wells.

21. The method of claim 12, wherein the SERS spectrum is taken across the array of wells in parallel.

22. The method of claim 21, wherein the parallel spectrum is obtained via one or more of the group comprising: a tunable laser, a tunable filter, moving the array of wells, and moving a laser.

23. A system comprising: a spectrograph to provide a plurality of wavelengths to a detector; an optical filter system to block wavelengths with no pertinent information about the sample; an optical filter system to admit wavelengths with pertinent information about the sample; and a detector to detect in a parallel fashion the wavelengths passed through the filter system.

24. The system as described in Claim 23 which uses a tunable laser to pass relative wavelengths to the laser line through the blocking and passing filters.

25. The system as described in Claim 23 which has two detectors to measure a baseline signal and a second detector to detect the pertinent sample information26. The system as described in Claim 23 that disperses the Raman scattered light to create a portion of the optically detected signal that contains pertinent information about the sample through one filter and baseline information thorough second filter.

27. The system of claim 23 wherein the system is configured to analyze the information to create a signal that is related to the microbe concentration withing the sample.

28. The system of claim 23 wherein the system analyzes the information into a quantitative measure of antimicrobial susceptibility.

29. The system as described in Claim 23 wherein the system is adapted to transfer a plurality of spatially distinct areas of the sample onto a two-dimensional detector.

30. A system comprising: a spectrograph to provide a plurality of wavelengths to a detector a multichannel detector to detect a region of the wavelengths emit by the sample. a digital filter to remove regions of the spectrum or a subset of the spectrum a digital filter to pass regions of the spectrum or subset of the spectrum with pertinent information about the sample31. The system as described in Claim 30 wherein the system is configured to integrate the signals after the digital filter to create a single value which represents the samples response to a specific concentration of an antimicrobial material.

32. The system as described in Claim 30 which locates specific peaks in the spectrum that relates to the samples response to an antimicrobial material and reports an intensity value33. The system of claim 30 wherein the system is configured to analyze information from Claim 2 to provide a measure of the samples quantitative antimicrobial susceptibility.

34. A filtration system which separates microbes from a sample comprising: a filter comprising a porous material that removes microbes from a liquid; a filter container which permits the filter to be washed with a nutrient free liquid; and a filter container which permits the filter to be coated with SERS active nanoparticles.

35. The filter of claim 34 wherein the filter comprises a material that has a Raman spectrum independent of the sample.

36. The filter of claim 34 wherein the filter material has a Raman spectrum with one or more Raman peaks for the purpose of a frequency calibration.

37. The filter of claim 34 wherein the filter material has a Raman spectrum with one or more Raman peaks that can be used as an intensity calibration.

38. A wash step with a nutrient free liquid which initiates the starvation response of microbes to produce biomarkers39. A filter that retains SERS active nanoparticle to permit an enhanced spectrum of pertinent biomarkers.

40. A filter container that creates spatially separated SERS active areas for the purpose of measuring signal from said active areas by Claim 34 or Claim 35.

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