SPECTROSCOPY

CROSS-REFERENCE TO RELATED APPLICATIONS

[001] The present application claims priority to U.S. Provisional Patent Application bearing serial No. 62/658,786 filed on April 17, 2018, the contents of which are hereby incorporated by reference.

TECHNICAL FIELD

[002] The present disclosure relates generally to analyzing microorganisms using spectral data obtained from high resolution magic angle spinning nuclear magnetic resonance (HRMAS NMR) and infrared spectroscopy, and particularly to microbial differentiation, identification and typing using HRMAS NMR alone or in combination with infrared spectroscopy.

BACKGROUND OF THE ART

[003] High resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) was developed in the 1950s and has become widely employed with advancements of spectrometer hardware. HRMAS NMR typically requires spinning a sample at the“magic angle” of 54.74° with respect to the direction of a magnetic field.

[004] More recently, magic angle spinning nuclear magnetic resonance (MAS NMR) has been demonstrated as a useful tool for the characterization of intact food products. Like infrared spectroscopy, MAS NMR provides a complete spectral information of every biological component in sample. It also shares some of the drawbacks of infrared spectroscopy in its sensitivity to sample integrality as well as additional drawback associated with the use of deuterated solvents and the need for addition of an internal standard to the sample. [005] There is therefore a need for improved methods for identifying microorganisms using spectral data.

SUMMARY

[006] The present disclosure presents methods and systems for the spectral identification and typing of microorganisms using magic angle spinning nuclear magnetic resonance spectroscopy.

[007] In accordance with an aspect a method for spectral identification of a microorganism is provided. The method comprises calibrating a magnetic field using a solution in absence of a sample; bringing a reference material and the sample containing the microorganism into contact with the magnetic field, the sample having intact microbial cells; acquiring spectral data from the sample using high resolution magic angle spinning nuclear magnetic resonance spectroscopy within a predetermined time after at least one of calibrating the magnetic field and preparing the sample; modifying the spectral data based on the reference material, thereby producing modified spectral data; and characterizing the microorganism using the modified spectral data.

[008] In some embodiments, characterizing the microorganism comprises comparing the modified spectral data to reference spectral data of known microorganisms to identify the microorganism.

[009] In some embodiments, the spectral data is nuclear magnetic resonance spectral data. In some embodiments, the method further comprises acquiring infrared spectral data from the sample using infrared spectroscopy. In some embodiments, characterizing the microorganism comprises characterizing the microorganism using the modified spectral data and the infrared spectral data.

[0010] In some embodiments, characterizing the microorganism comprises comparing the modified spectral data and the infrared spectral data to reference spectral data of known microorganisms to identify the microorganism. [0011] In some embodiments, the spectral data is acquired from the sample prior to or after recording at least one of an infrared spectrum, a matrix assisted laser desorption/ionization time of flight mass spectrometry spectrum, and a Raman spectrum.

[0012] In some embodiments, the sample has intact cells having a uniform water content level.

[0013] In some embodiments, the microorganism in the sample has a water activity <0.999.

[0014] In some embodiments, a solution containing the reference material is added to the sample.

[0015] In some embodiments, the method further comprises: sealing the sample in an insert and inserting the insert in a rotor comprising a reference solution having the reference material dissolved therein. In some embodiments, bringing the reference material and the sample into contact with the magnetic field comprises bring the rotor into contact with the magnetic field.

[0016] In some embodiments, the reference solution is acidified or a disinfectant is added to the reference solution.

[0017] In some embodiments, the insert is disinfected prior to being inserted in the tube.

[0018] In some embodiments, the method further comprises freezing the sample in the insert for storage of the sample.

[0019] In accordance with another aspect, a system for spectral identification of a microorganism is provided. The system comprises a processing unit and a non- transitory computer-readable memory having stored thereon program instructions. The program instructions are executable for acquiring spectral data of a sample in a magnetic field with a reference material using high resolution magic angle spinning nuclear magnetic resonance spectroscopy, the spectral data is acquired within a predetermined time after at least one of calibrating the magnetic field using a solution in absence of the sample and preparing the sample, the sample containing the microorganism and having intact microbial cells; modifying the spectral data based on the reference material, thereby producing modified spectral data; and characterizing the microorganism using the modified spectral data.

[0020] In some embodiments, the program instructions executable for characterizing the microorganism further comprise program instructions executable for comparing the modified spectral data to reference spectral data of known microorganisms to identify the microorganism.

[0021] In some embodiments, the program instructions are further executable for acquiring infrared spectral data from the sample using infrared spectroscopy. In some embodiments, the program instructions executable for characterizing the microorganism further comprise program instructions executable for characterizing the microorganism using the modified spectral data and the infrared spectral data.

[0022] In some embodiments, the program instructions executable for characterizing the microorganism further comprise program instructions executable for comparing the modified spectral data and the infrared spectral data to reference spectral data of known microorganisms to identify the microorganism.

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Further features and advantages of the present invention will become apparent from the following detailed description, taken in combination with the appended drawings, in which:

[0024] Fig. 1A is a diagram of an example setup for magic angle spinning nuclear magnetic resonance spectroscopy of a sample; [0025] Fig. 1 B is a diagram of an example setup for infrared spectroscopy for use with the setup of Figure 1A;

[0026] Fig. 2 is a flowchart of an example embodiment for a method of identifying microorganisms using FIRMAS NMR spectroscopy;

[0027] Fig. 3 is an example of a multi-tier classification strategy;

[0028] Fig. 4 illustrates modified spectral data that is compliant with regards to biomass of the sample;

[0029] Figs. 5A-5B are an example of discrimination between Gram-positive, Gram-negative and yeast by ¹ H HRMAS NMR and infrared spectroscopy;

[0030] Fig. 6A-6B are an example of discrimination between enterococci and staphylococci, Enterococcus faecal is and E. faecium, Staphylococcus aureus and S. epidermidis by ¹ H HRMAS NMR and infrared spectroscopy;

[0031] Fig. 7 is an example of discrimination between vancomycin-resistant enterococci (VRE) and vancomycin-sensitive enterococci (VSE) by ¹ H HRMAS NMR spectroscopy;

[0032] Figs. 8A-8B are an example of discrimination between methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive Staphylococcus aureus (MSSA) by ¹ H HRMAS NMR and infrared spectroscopy;

[0033] Figs. 9A-9D are dendrograms showing differentiation between E. coli and Shigella species grown on Columbia agar with 5% sheep blood and MacConkey agar by ¹ H HRMAS NMR and infrared spectroscopy;

[0034] Fig. 10 is an example ¹ H HRMAS NMR spectrum of a yeast clinical isolate;

[0035] Fig. 11A-11 C are examples of differentiation between hospital VRE outbreak strains by ¹ H HRMAS NMR and infrared spectroscopy; [0036] Fig. 12A-12B are examples of metabolic differences observed by ¹ H HRMAS NMR and infrared spectroscopy for samples grown with antibiotic disks;

[0037] Fig. 13A-13C show an example of metabolic differences observed by ¹ FI FIRMAS NMR and infrared spectroscopy for samples grown on different media, namely Columbia agar with 5% sheep blood vs MacConkey agar;

[0038] Fig. 14 is an example system for spectral identification of microorganisms using ¹ H HRMAS NMR spectroscopy;

[0039] Fig. 15 is an example system for spectral identification of microorganisms;

[0040] Fig. 16 is an example embodiment for a microorganism identification device; and

[0041] Fig. 17 is an example embodiment of an application running on the microorganism identification device of Fig. 16.

[0042] It will be noted that throughout the appended drawings, like features are identified by like reference numerals.

DETAILED DESCRIPTION

[0043] There are described herein methods and systems for spectral identification of a microorganism. High resolution magic angle spinning nuclear magnetic resonance (HRMAS NMR) spectroscopy is used alone or in combination with infrared spectroscopy for the spectral identification of a microorganism. The microorganism may be any microscopic living organism that is single-celled, such as but not limited to bacteria, archaea, yeasts, fungi, and molds. A sample of the microorganism is provided in an insert. The sample contains intact microbial cells which may have a uniform water content level. No drying treatments are applied to the sample, and no reagents are used to reduce or eliminate the original water content of the sample during the sample preparation time. Spectral identification is performed based on characteristic spectral fingerprints of intact, whole organisms, with minimal post-culture sample preparation. Spectral databases of well- characterized strains and multivariate statistical analysis techniques are used to identify unknowns by matching their spectra against those in a reference spectral database.

[0044] Figure 1A illustrates an example setup 100 used for spectral identification of a microorganism. The setup 100 illustrates an example of HRMAS NMR spectroscopy in accordance with an embodiment. A sample 102 is placed in an insert 114 and capped to seal it from the outer environment. The sample 102 may be deposited into the insert 114 using a transfer device (not shown) such as a sterile toothpick or loop. The sample 102 may be taken from any known culture medium without breaking the culture medium surface. The sample 102 may be obtained from a microbial culture, a blood culture, bodily fluids (such as urine and pus, nasal and wound swabs), food, water, air, and the like. The size of the sample 102 should be sufficient to fill the insert 114. In some embodiments, the sample 102 is sized to be about one (1 ) to 6 (six) millimeters in diameter. Other sample sizes may also be used.

[0045] The insert 114 is made of one or more materials compatible with use in high magnetic field NMR applications. In some embodiments, the insert 114 is made of high purity glass or polymer materials (e.g., Polychlorotrifluoroethylene, a thermoplastic chlorofluoropolymer with the molecular formula (CF ₂ CCIF) _n , where n is the number of monomer units in the polymer molecule or from glass, etc.). The insert 114 may be configured in any suitable way for enclosing the sample 102 and sealing the sample 102 from the outer environment.

[0046] A rotator 116 is configured for receiving a tube rotor 115 comprising the insert 114. The rotor 115 is configured for holding the insert 114 and may be configured for receiving a solution. The rotator 116 is configured for rotating the rotor 115, the insert 114 and the sample 102 about an axis R.

[0047] At least one magnetic source 110 provides an aligned magnetic field in the direction of an axis M. The magnetic field may be generated by a superconducting magnet or by permanent magnets. In embodiments where the superconducting magnet is used, the superconducting magnet is kept cooled below a superconducting transition temperature. The direction of the magnetic field is configured to be provided at an angle 0 _m with respect to the axis R that the sample 102 is rotated about. The angle 0 _m substantially corresponds to 54.74°. In accordance with an embodiment, the angle is such that 0 _m = arccos ^= =

54.7356... ° within an acceptable amount of variance. The angle 0 _m is also known as the“magic angle”.

[0048] A NMR detection system 112 is configured to obtain a HRMAS NMR spectrum. In accordance with an embodiment, the NMR detection system 112 comprises one or more NMR detectors 160 (also known as probes) for acquiring the HRMAS NMR spectrum. The spectral resolution is determined by the strength of the magnetic field. Spectral data is produced from the acquired HRMAS NMR spectrum. More than one detection system may be used. For example, an infrared spectroscopy (IRS) detection system may be used in combination with the HRMAS NMR detection system 112. In other words, an HRMAS NMR spectroscopy system may be used alone or in combination with an infrared spectroscopy system.

[0049] With reference to Figure 1 B, an example setup 113 for providing infrared spectra is illustrated in accordance with an embodiment. The setup 113 comprises at least one infrared source 156 and at least one infrared detector 158. At least one infrared source 156 is for directing a beam 154 of infrared light at the sample 102. The beam 154 is reflected back to at least one infrared detector 158. The infrared spectrum obtained at the at least one detector 158 may be obtained by transmission, transflection, reflectance, attenuated total reflectance (ATR) and/or any other suitable technique. In some embodiments, the setup 113 may comprise a Fourier transform infrared (FTIR) spectrometer, which acquires FTIR spectral data. The IRS detection system 113 may be implemented according to the infrared spectroscopy systems and/or methods described in U.S. Provisional Application No. 62/527,327, and PCT International Patent Application No. PCT/CA2018/050794, the contents of which are hereby incorporated by reference.

[0050] Referring to Figure 2, there is illustrated a method 200 for identification of a microorganism, such as by using the setup 100 of Figure 1A. At step 202, the magnetic field is calibrated using a solution (hereinafter the“calibration solution”) absent the sample 102. In accordance with an embodiment, the calibration solution is a FI2O-D2O solution (e.g., with a 1 :1 ratio v/v). The rotator 116 may rotate the insert 114 comprising the calibration solution (and absent the sample 102) at a uniform speed (e.g., 5,000 Flz). In accordance with an embodiment, the rotator 116 rotates the rotor 115 with the insert 114 (absent the sample 102). NMR spectral data is acquired absent the sample 102. The magnetic field is calibrated based on the acquired NMR spectral data. After the calibration of the magnetic field is done, the insert 114 comprising the solution (and absent the sample 102) is removed from the rotator 116.

[0051] At step 204, a reference material and the sample 102 comprising the microorganism are brought into contact with the magnetic field. The sample 102 and the reference material are brought into contact with the magnetic field substantially at the angle of 54.7° from the rotational axis R that the sample 102 is rotated about. In accordance with an embodiment, the sample 102 has intact microbial cells. The sample 102 is transferred to the insert 114 and sealed in the insert 114 prior to being brought into contact with the magnetic field. The insert 114 may be placed in the rotor 115, and the rotor 115 may be filled with an aqueous solution containing the reference material dissolved therein (hereinafter the “reference solution”). The reference material may be dissolved in a D ₂ 0, or D ₂ 0:Fl ₂ 0 mixture to form the reference solution. The reference material may be TSP (trimethylsilyl propionate), DSS (4, 4-dimethyl-4-silapentane-1 -sulfonic acid) or any other suitable reference material for NMR spectroscopy. The sample 102 and the reference material are rotated about the axis R while the magnetic field is applied. In particular, the rotator 116 rotates the insert 114 comprising the reference solution and the sample 102. The rotation of the rotator 116 may be at a uniform speed (e.g., 5,000 Hz).

[0052] In some embodiments, an aqueous solution containing the reference material may be added to the sample 102 prior to or subsequent to placing the sample 102 in the insert 114. The reference material may be referred to as a “reference compound”.

[0053] At step 206, NMR spectral data from the sample 102 and the reference material is acquired. The NMR spectral data is acquired no more than a predetermined amount of time after filling the insert 114 with the sample 102 and/or after calibrating the magnetic field. The predetermined amount of time may vary depending on practical implementations. To avoid compromising the integrity of the intact microbial cells in the insert 114, the NMR spectral data should be acquired within a first period of time. Similarly, the calibration of the magnetic field may only be valid for a second period of time. Thus, the predetermined amount of time may be the shorter of the first and second periods of time. In some embodiments, the predetermined amount of time is less than or equal to fifteen minutes.

[0054] At step 208, the NMR spectral data is modified based on the reference material, thereby producing modified spectral data. For example, NMR spectral data may be shifted by a value determined based on the reference material used at step 204. In accordance with an embodiment, a reference peak (corresponding to the reference material) is identified in the NMR spectral data and the NMR spectral data is modified (e.g., shifted) such that the reference peak is set to 0 ppm. The reference peak may be found within a range that encloses 0 ppm (e.g., a range of - 0.2 to 0.2 ppm).

[0055] At step 210, the microorganism is characterized using the modified spectral data. The characterization of the microorganism is described in further detail elsewhere in this document. [0056] In some embodiments, after the insert 114 is sealed at step 204, its exterior may be disinfected, prior to inserting into the rotator 116. The disinfectant may be bleach, or a 70% ethanol (C H ₆ OH) solution. This concentration is well known to inactivate microorganisms. A 0.05 - 5% solution of bleach may also be prepared in H ₂ 0 and employed for disinfecting the exterior of the insert 114.

[0057] In some embodiments, the sample 102 has a limited free water content and an intact associated and bound water content. In some embodiments, the sample 102 has a water activity of <0.999. In some embodiments, the sample 102 has a uniform water content level.

[0058] In some embodiments, the reference solution for the rotor 115 may be acidified or contain a disinfectant in order to inactivate any microorganisms in the unlikely event that a leakage from the insert 114 occurs during the course of the spectral measurements. The disinfectant may be bleach, an antimicrobial substance, or ethanol (C H ₆ OH). The ethanol may be deuterated (C D ₆ OD). The ethanol is mixed with H ₂ 0 or D ₂ 0 respectively to a final concentration of (70% ethanol), this concentration is well known to inactivate microorganisms. A 0.05 - 5% solution of bleach may also be prepared in H ₂ 0 or D ₂ 0 or mixtures thereof. Also, the solution pH in the rotor 115 may be adjusted so as to inhibit microbial growth.

[0059] In some embodiments, the spectral data acquired at step 206 comprises infrared spectral data. In some embodiments, the NMR spectral data is acquired from the sample 102 prior to or after acquisition of the infrared spectral data of the sample 102. The acquisition of the NMR spectral data may be obtained using any suitable spectrometer configuration, including, but not limited to, a spectrometer operating in a solution state at various magnetic field strengths (e.g., at field strengths in the range from 40 MHz to 1100 MHz).

[0060] In some embodiments, the sample 102 is cooled to slow down the cellular metabolism or frozen for an extended period of time and thawed prior to acquisition of the NMR spectral data from the sample 102 at step 204. In some embodiments, the NMR spectrum data is recorded at sub-ambient temperatures (e.g., between 0 °C and 20 °C) or at higher than sub-ambient temperatures (e.g., between 20 °C and 70 °C). In some embodiments, the sample is heated at temperature high enough to inactivate the microorganisms prior to sample analysis.

[0061] Long term storage of intact and viable microorganisms may be accomplished by freezing the intact microorganism inside the insert 114. Accordingly, the intact microbial samples can be maintained for long durations subsequent to placement in the insert 114. Addition of glycerol 15-30% to the microbial colonies prior to addition into the insert 114 may also increase cell viability after thawing. The glycerol (C3H8O3) may also be substituted with its deuterated analogues (C ₃ D ₈ 0 ₃ ) (1 ,2,3-Propanetriol-d ₈ , Deuterated glycerol) or C3H3D5O3 (1 ,2,3-Propanetriol-1 ,1 ,2,3,3-d ₅ ). These samples may then be thawed and additional spectra can be acquired on the same or different NMR instruments.

[0062] In some embodiments, if it is desired to access spectral regions partially masked by H ₂ 0 absorption, the H ₂ 0 in the sample 102 may be replaced by deuterium oxide (D ₂ 0).

[0063] As illustrated in Figure 3, a multi-tier classification strategy may be used, and classification may be performed at each taxonomic level. Classification models may be developed using appropriate subsets of the spectra in the database as training sets. Each classification model may be optimized using a feature selection algorithm to identify the spectral features that best characterize the desired classification. The shaded regions in Figure 3 denote selected regions for the feature selection algorithm. The microorganism may thus be classified in accordance with Gram-stain type (i.e. positive or negative), genus, species, and strain, as per Figure 3. A microorganism may further be determined to be an antibiotic-resistant strain or an antibiotic-sensitive strain. For each level of classification, analysis may be employed to find spectral features that differentiate between types. For example, specific spectral regions within the HRMAS NMR spectrum may be selected to separate the Gram-positive from the Gram-negative bacteria, this is followed by a tier-wise separation at the genus, species, strain, and serotype levels and in some cases separation between antibiotic-resistant and antibiotic-sensitive strains and in some cases separation between genotypes. In some cases, toxin-producing bacteria can further be classified by the type of toxin they produce. In some cases, bacteria can further be classified by difference between sporadic and outbreak strains. In some cases, bacteria can further be classified by differences in their genotypic profiles.

[0064] The signal-to-noise ratio (SNR) of the HRMAS NMR spectral data may be improved by performing a greater number of scans of the sample, such as 64, 128, or 256 instead of 4, 16, or 32. The method 200 is thus a compromise: obtaining an acceptable SNR while minimizing metabolite degradation during HRMAS NMR spectral data acquisition. In some embodiments, the selected number of scans for the acquisition of the HRMAS NMR spectral data is 128. Other numbers of scans may also be used. Spectra acquired from lower number of scans can be co-added to improve the SNR.

[0065] In some embodiments, the data selected for analysis from the spectral data is taken from a range of about -2 ppm to about 14 ppm. In some embodiments, the range is about 0.3 ppm to about 8.80 ppm. In some embodiments, the range is about 0.3 ppm to about 4.50 ppm and 5.2 ppm to about 8.8 ppm. Other ranges may also be used.

[0066] The method 200 may be used to discriminate between Gram-positive and Gram-negative bacteria by principal component analysis (PCA) using HRMAS NMR spectra. An example database structure for Gram-positive bacteria is illustrated in Figures 5A and 5B. Figure 5A illustrates the database structure for HRMAS NMR spectral data acquired by proton ¹ H HRMAS NMR spectroscopy. As illustrated in Figures 5A and 5B, the top tier of the structure represents the Gram- positive bacteria in a spectral database, followed by a second tier to discriminate between enterococci and staphylococci (see Figure 6A-6B). A third tier allows to discriminate between Enterococcus faecalis and E. faecium (see Figure 6A-6B). In the fourth tier, the method 200 may be used to discriminate between vancomycin- resistant enterococci (VRE) and vancomycin-sensitive enterococci (VSE) (see Figure 7), and to discriminate between methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive Staphylococcus aureus (MSSA) (see Figure 8A- 8B).

[0067] The method 200 may be used to identify antibiotic-resistant strains of microorganisms taken from culture media without the addition of any antibiotic. Figure 7 illustrates a hydrophobic hierarchical cluster analysis (FICA) plot showing clustering of clinical isolates of vancomycin-resistant enterococci (VRE) and vancomycin-sensitive enterococci (VSE) based on differences in their HRMAS NMR spectra following culture on Columbia agar containing 5% sheep’s blood.

[0068] The method 200 may be used to create a database of HRMAS NMR and infrared spectral data by storing the NMR spectral data, the infrared spectral data and the characterization of the spectral data in one or more database structures. In the creation of a combined HRMAS NMR and FTIR spectral database, the microorganisms may be cultured twice to ensure purity. Isolated colonies with the same morphology are selected and transferred to the surface of the infrared reflective substrate for reflection-FTIR spectroscopic measurement. The reflection- FTIR spectrum is recorded. In another embodiment the infrared spectra are recorded by transferring the colonies to the surface of an attenuated total reflectance accessory couple to an FTIR spectrometer and the infrared spectrum recorded. Replicate spectra may be obtained and those with the smallest standard deviation from the mean, are added to the database. Additional information may be added to a spectral file header, such as genus, species, strain, antimicrobial profile, growth medium, growth conditions, date, and the like.

[0069] In some embodiments, the combined HRMAS NMR and FTIR spectral data is compared with spectral data of reference microorganisms obtained using a same culture medium as the sample. The use of another culture medium may result in an altered spectral profile. Therefore, the same media may be used to ensure that the same spectral profile is obtained. Alternatively, spectral data of reference microorganisms are obtained using a plurality of different culture media, and data from each spectral acquisition are pooled in order to make the reference data culture-media independent.

[0070] The method 200 may be used to identify microorganisms from positive blood cultures. While traces of blood in dried samples act as large contaminants, having the blood diluted in water causes the effect to be negligible and centrifugation of the sample and transfer the resulting microbial pellet to the HRMAS NMR sample insert.

[0071] In some embodiments, HRMAS NMR and infrared spectroscopy as described herein is used to enhance and/or refine characterization of a microorganism by matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS).

[0072] In some embodiments, the mass-to-charge (m/z) data acquired by matrix- assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry is combined with the HRMAS NMR and FTIR data into a single stitched spectrum and a feature selection algorithm (FSA) is used to identify the mass spectral, the infrared spectral and HRMAS features that maximize the differentiation between two types of microorganisms.

[0073] In some embodiments, the prediction of the identity of an unknown microorganism is carried out by HRMAS NMR spectra independent of MALDI TOF MS or FTIR analysis. The identification of the unknown microorganism by the two or three independent means can further enhance the reliability of the identification.

[0074] In some embodiments, other spectral data is acquired from NMR spectra of different nuclei such as ¹³ C, ³¹ P or ¹⁵ N using HRMAS NMR. In some embodiments, homo and heteronuclear multidimensional (for example, but not limited to 2D and 3D) HRMAS NMR spectroscopy can be employed to acquire NMR spectra. The HRMAS NMR spectral data may thus be used to identify the spectral features responsible for the differentiation between two types of microorganisms. Subsequently or in tandem, other spectral data from other spectroscopic techniques can be utilized to identify the biomarker(s) associated with the NMR spectral features. In some embodiments, spectra generated from stitching of multiple spectral data sets from the above-mentioned techniques can be subjected to analysis with the use of an FSA after spectral pre-processing, including normalization. Individually or combined, these pre-processing methods increase the reliability of microbial identification by multispectral domain spectroscopy.

[0075] An example protocol for the separation of bacteria from blood culture broth for the purpose of identification by HRMAS NMR spectroscopy is as follows (specific and non-limiting values are provided for illustrative purposes only):

1. Aliquots from positive blood cultures are syringed into BD Vacutainer® SST™ serum separator tubes.

2. Tubes are centrifuged at 3,000 rpm for 10 minutes.

3. The supernatant is removed and replaced with equal volume of saline.

4. Tubes are centrifuged at 3,000 rpm for 10 minutes.

5. The supernatant is removed.

6. Residual supernatant is removed by using a cotton swab and bacteria are transferred to the HRMAS sample holder.

[0076] The following experimental protocol was used for HRMAS NMR spectral acquisition. Gram-positive isolates were sub-cultured on agar with 5% sheep’s blood for 18-24 h at 35°C. With certain exceptions, Gram-negative isolates were sub-cultured on agar with 5% sheep blood or MacConkey agar for 18-24 h at 35°C. Following incubation, multiple isolated colonies were collected from the agar surface transferred to the HRMAS sample holder in addition 1 -5 colonies are spread on the surface of the infrared reflective substrate of the FTIR or an ATR- FTIR spectrometer and a spectrum was recorded using a spectral acquisition protocol specified for each analysis method. For each culture plate, 2-3 replicate spectra were acquired from different colonies.

[0077] An example protocol for preparing the sample 102 for FIRMAS NMR spectroscopy is as follows (specific and non-limiting values are provided for illustrative purposes only):

1. Grow bacteria on blood agar

a. Incubate for 16-24 hrs at 35 °C

b. Re-plate onto chromogenic agar (containing a specific antibiotic) and incubate for 24-48 hrs at 35 °C

i. Chromogemic agar verifies antibiotic resistance of each sample

ii. Plate 1 -2 plates of each type of agar, to ensure sufficient mass to fill HRMAS NMR insert (sample container)

2. Weigh insert and cap (optional)

3. Transfer the bacteria from the agar plate into the insert between 1/3-112 of the space provided

4. Weigh the insert with the bacteria; calculate the mass of bacteria in the

insert (optional)

5. Prepare 1 % Tri(methylsilyl)propanoic acid (TSP) in D ₂ 0 for use as an

external reference standard

6. Pipette 5pl of 1 % (TSP) in D ₂ 0 into the rotor

7. Put the insert into the rotor

8. Sterilize the outer surface of the rotor by submerging it into a beaker full of 70% ethanol in water solution; wipe off excess ethanol solution from the surface once removed [0078] An example protocol for data analysis of the NMR spectrum is as follows (specific and non-limiting values are provided for illustrative purposes only):

1. Fourier transform the recorded NMR signal to achieve the spectrum

(frequency domain)

2. Autophase

3. Calibrate spectra by referencing using the tri(methylsilyl)propanoic acid

(TSP), or 4, 4-dimethyl-4-silapentane-1 -sulfonic acid (DSS) or its sodium salt peak to 0 ppm shift

4. Perform baseline correction

5. Save as text file. Give it the correct nomenclature

a. GP_Genus_species_AMR_gene_agar_AE_lab

name_sample_600M_5k_zg_HRMASNMR_date_lnitial+replicate

6. Convert txt file into csv file format

7. Multiply x-axis by 1000 and resave, as csv

8. Open in data analysis software

9. Interpolate data from 0.5-4.5ppm (500-4500 ppb) with data spacing 0.214 (or 1 ppb)

10. Vector normalize in the full region (0.5-4.5ppm)

11. save as in JCAMP file format in a new folder

12. reload original files and repeat steps 10 to 12 using regions 5.2-10 ppm

13. Convert the files into a data matrix

14. Use JMP (SAS, NC) software to analyze the spectra by discriminant

analysis

[0079] By way of a specific and non-limiting example of implementation, experimental results were obtained as follows: HRMAS NMR spectra were acquired on a 600M Bruker system equipped with an HRMAS probe. A proton ¹ H HRMAS NMR spectrum was acquired consisting of 256 scans performed with presaturation of the water peak, with gradient Z at room temperature (25°C). Disposable 80pl inserts were filled half-way with bacteria grown on Columbia agar with 5% sheep blood (BAP) at 35°C, using a centrifuge at 10000 rpm. 3- (Trimethylsilyl)-propanoic acid (TSP) was dissolved in D ₂ 0 at a concentration of 1 % (w/v), and used as an external standard. 5pl of TSP solution was pipetted into the rotor, before the insert containing the bacteria sample was put in. This prevented any potential H-D exchange between the D ₂ 0 and the bacteria. Due to the lack of D ₂ 0 in the insert relative to the sample, the field was locked on an insert containing H ₂ 0/D ₂ 0 solution. The sample was placed in a rotor that spins at 5000Hz at the magic angle (54.7°) relative to the magnetic field. Each spectrum was phased, referenced to an external standard and baseline-corrected. By way of a specific and non-limiting example of implementation, the post-processed data were subjected to statistical analysis as follows: A principal component analysis (PCA) was done by using regions 0.5 to 4.5 ppm and 5.2 to 10 ppm, to demonstrate the discrimination between the CC and AA clonal cluster groups of vancomycin resistant Enterococcus faecium associated with hospital outbreaks.

[0080] Referring to Figure 15, various types of connections 1806 may be provided to allow the microorganism identification device 1802 to communicate with the spectrometer 1804. For example, the connections 1806 may comprise wire-based technology, such as electrical wires or cables, and/or optical fibers. The connections 1806 may also be wireless, such as RF, infrared, Wi-Fi, Bluetooth, and others. Connections 1806 may therefore comprise a network, such as the Internet, the Public Switch Telephone Network (PSTN), a cellular network, or others known to those skilled in the art. Communication over the network may occur using any known communication protocols that enable devices within a computer network to exchange information. Examples of protocols are as follows: IP (Internet Protocol), UDP (User Datagram Protocol), TCP (Transmission Control Protocol), DHCP (Dynamic Host Configuration Protocol), HTTP (Hypertext Transfer Protocol), FTP (File Transfer Protocol), Telnet (Telnet Remote Protocol), SSH (Secure Shell Remote Protocol), and Ethernet. The connections 1806 may also use various encryption means to protect any of the data acquired and/or transferred. [0081] The microorganism identification device 1802 may be accessible remotely from any one of a plurality of devices 1808 over connections 1806. The devices 1808 may comprise any device, such as a personal computer, a tablet, a smart phone, or the like, which is configured to communicate over the connections 1806. In some embodiments, the microorganism identification device 1802 may itself be provided directly on one of the devices 1808, either as a downloaded software application, a firmware application, or a combination thereof.

[0082] One or more databases 1810 may be integrated directly into the microorganism identification device 1802 or any one of the devices 1808, or may be provided separately therefrom (as illustrated). In the case of a remote access to the databases 1810, access may occur via connections 1806 taking the form of any type of network, as indicated above. The various databases 1810 described herein may be provided as collections of data or information organized for rapid search and retrieval by a computer. The databases 1810 may be structured to facilitate storage, retrieval, modification, and deletion of data in conjunction with various data-processing operations. The databases 1810 may be any organization of data on a data storage medium, such as one or more servers or long-term data storage devices. The databases 1810 illustratively have stored therein spectral data for reference microorganisms used for comparison with spectral data of unknown samples.

[0083] As shown in Figure 16 the microorganism identification device 1802 illustratively comprises one or more servers 1900. For example, a series of servers corresponding to a web server, an application server, and a database server may be used. These servers are all represented by server 1900 in Figure 16. The server 1900 may be accessed by a user, such as a technician or laboratory worker, using one of the devices 1808, or directly on the system 1802 via a graphical user interface. The server 1900 may comprise, amongst other things, a plurality of applications 1906 _! ... 1906 _n running on a processor 1904 coupled to a memory 1902. It should be understood that while the applications 1906i ... 1906 _n presented herein are illustrated and described as separate entities, they may be combined or separated in a variety of ways.

[0084] The memory 1902 accessible by the processor 1904 may receive and store data. The memory 1902 may be a main memory, such as a high-speed Random Access Memory (RAM), or an auxiliary storage unit, such as a hard disk, a floppy disk, or a magnetic tape drive. The memory 1902 may be any other type of memory, such as a Read-Only Memory (ROM), or optical storage media such as a videodisc and a compact disc. The processor 1104 may access the memory 1902 to retrieve data. The processor 1904 may be any device that can perform operations on data. Examples are a central processing unit (CPU), a front-end processor, a microprocessor, and a network processor. The applications 1906i ... 1906 _n are coupled to the processor 1904 and configured to perform various tasks. An output may be transmitted to the devices 1808.

[0085] Figure 17 is an exemplary embodiment of an application 1906i running on the processor 1904. The application 1906i illustratively comprises a spectral data processing module 2002 and a microorganism characterizing module 2004. The spectral data processing module 2002 is configured for receiving spectral data to optimize the homogeneity of the magnetic field in the HRMAS NMR spectrometer and provide a signal from a standard to reference the HRMAS NMR spectral data. The spectral data processing module 2002 may also be configured for combining the background spectrum and the spectral data to produce the modified spectral data. In some embodiments, the spectral data processing module is further configured for validating the modified spectral data, for example by comparing the reference position between samples vapor level, sample water content, and/or sample biomass to a threshold or a reference value. Some of the mathematical operations performed by the spectral data processing module 2002 on the spectral data include, but are not limited to, first derivatives, vector normalizations (4000- 400 cm ¹ ), and cubic interpolation (with data spacing of 0.001 -32 ppm). [0086] The microorganism characterizing module 2004 may be configured to receive the modified spectral data and to perform microorganism characterization by comparing the modified spectral data to reference spectral data of known microorganisms. In some embodiments, the comparison is done to identify microorganisms based on the similarity of the NMR spectrum to those of a spectral database of known microorganisms. In some embodiments, the microorganism characterizing module 2004 is configured to use target spectral regions in the modified spectral data pre-selected by applying a feature selection algorithm to training data as per U.S. Patent No. 9,551 ,654, the contents of which are hereby incorporated by reference. For example, an FSA is employed to identify the significant biochemical markers that are more relevant than the proteins in microbial identification. The comprehensive information content in the reflection- FTIR spectra can differentiate between types of bacteria at different levels of classification (genus, species, strain, serotype, and antimicrobial resistance characteristics and in some cases genotypic characteristics).

[0087] In some embodiments, a grid-greedy feature selection algorithm is used with three regions of a minimum size of 20 (6 features) and a maximum size of 0.1 ppm (240 features) per region. All possible combinations of such regions are evaluated between 0.5 and 4.5 ppm and between 5.2 and 8.8 ppm and the region with the highest LOOCV-KNN classification score is selected. The greedy portion of the algorithm examines combinations of adjacent features following the path of greatest improvement. The forward selection begins by evaluating the single feature with the highest classification score, followed by adding features one at a time which keeps the score at a maximum. The routine stops when the classification score is no longer improved by adding features. The search may continue for a minimum of 6 features (1 % of the total number of features) even if there is no further improvement in classification score in order to minimize over- fitting of the training data. Other feature selection algorithms may also be used. [0088] Figure 4 is an example of ¹ H HRMAS NMR spectra of epidemic clonal isolates (AA and CC) of vancomycin resistant enterococci (VRE) E. faecium grown on agar (containing 5% sheep blood), at 35°C for 18 hrs, in NMR spectral region between 0.5-10 ppm.

[0089] Figures 5 is an example of differentiation between Gram-positive, Gram- negative microorganisms and yeast (and grown on agar plates), by FICA of HRMAS spectral data in the spectral ranges of 832-835, 944-947, 1285-1288, 1302-1305, 1430-1432, and 2859-2861 (ppm x 1000). Figure 14 is an example of the correct classification of clinical isolates of vancomycin-resistant E. faecium based on the comparison of their HRMAS NMR spectra with HRMAS NMR spectra in a spectral database.

[0090] Figures 6A and 6B are an example of a HCA showing the genus and species differentiation of select Gram-positive bacteria grown on Columbia agar (containing 5% sheep blood), at 35°C for 18 hrs, using ¹ H HRMAS NMR (Figure 6A) and FTIR spectroscopy (Figure 6B).

[0091] Figure 7 is an example of a HCA showing the discrimination between E. faecium and E. faecalis species of vancomycin resistant (VRE) and susceptible enterococci (VSE) strains grown on Columbia agar (containing 5% sheep blood) or selective media containing vancomycin, at 35°C for 24hrs, based on differences in their ¹ H HRMAS spectra between 0.5 and 4.5 ppm.

[0092] Figures 8A-8B are an example of a HCA showing the discrimination between MRSA and MSSA grown on Columbia agar with 5% sheep blood, at 35°C for 18-24hrs based on differences in their ¹ H HRMAS NMR (Figure 8A) and FTIR (Figure 8B) spectra.

[0093] Figures 9A-9B are an example of a HCA showing the differentiation of E.coli 0157:H7, non-verotoxigenic E. coli and Shigella spp. grown on Columbia agar (containing 5% sheep blood) at 35°C for 18-24hrs using region selection by ¹ H NMR Spectroscopy (Figure 9A) and FTIR Spectroscopy (Figure 9B). [0094] Figures 9C-9D are an example of a HCA showing the differentiation of E.coli 0157:H7, non-verotoxigenic E. coli and Shigella spp. grown on MacConkey agar at 35°C for 18-24hrs using region selection by ¹ H NMR Spectroscopy (Figure 9C) and FTIR Spectroscopy (Figure 9D).

[0095] Figure 10 is an example of a ¹ FI FIRMAS NMR spectrum of a Candida albicans grown on Sabouraud dextrose agar at 30 ° C for 48hrs.

[0096] Figures 1 1A-1 1 B are an example of principal component analysis (Figure 1 1 A) and FICA (Figure 1 1 B) of the first recorded example of discrimination between clonal isolates from two hospital outbreaks (CC and AA) of vancomycin resistant enterococci E. faecium (VRE) grown on Columbia agar (containing 5% sheep blood), at 35°C for 18-24hrs, based on differences in their ¹ FI FIRMAS NMR spectra between 0.5 and 4.5 ppm.

[0097] Figures 1 1 C is an example of FICA of discrimination between clonal isolates from two hospital outbreaks (CC and AA) of vancomycin resistant enterococci E. faecium (VRE) grown on Columbia agar (containing 5% sheep blood), at 35°C for 18-24hrs, based on differences in their infrared spectra

[0098] Figures 12A-12B are an example of a FICA showing the differentiation between MRSA grown on agar (containing 5% sheep blood) with and without a cefoxitin disk (30ug), at 35°C for 18-24hrs, by ¹ FI FIRMAS NMR (Figure 12A) and FTIR spectroscopy (Figure 12B).

[0099] Figures 13A is a PCA showing the effect of grown media on the discrimination between E. coli and Shigella isolates grown on (media: agar containing 5% sheep blood, and MacConkey agar), at 35°C for 18-24hrs, by ¹ FI FIRMAS spectroscopy in the region 4500-500 (ppm x1000).

[00100] Figures 13B-13C are an example showing the effect of media observed in ¹ FI FIRMAS NMR (Figure 13B) and FTIR (Figure 13C) spectra of E. coli 0157:H7 grown MacConkey agar and agar containing 5% sheep blood at 35°C for 18-24hrs.

[00101] Figure 14 illustrates an example of a system for spectral identification of microorganisms using ¹ H HRMAS NMR spectroscopy.

[00102] The methods and systems described herein may employ a simple and universally applicable protocol that requires minimal sample preparation and no reagent beyond a culturing step. The methods may be used with a high degree of automation and is amenable to microcolony analysis. They may produce a fast turnaround time at a low cost per test, and are capable of detecting biochemical differences between antibiotic-resistant and antibiotic-sensitive bacterial strains in the absence or in the presence of the antibiotic.

[00103] The methods and systems described herein may also be used for recording spectra from microorganisms grown on agar in the vicinity of antibiotic- impregnated disks. Alternatively, the antibiotics can be incorporated into the agar matrix (in the presence or absence of a chromogenic agent). Direct identification of bacteria may be performed using HRMAS NMR alone or in combination with FTIR and/or mass spectrometry techniques as described herein.

[00104] The methods and systems described herein may also be used for the identification of clinical isolates from positive blood cultures. Indeed, as long as there is sufficient microorganism biomass that can be obtained from a positive blood culture (in the presence or absence of selective antibiotics), direct identification of bacteria may be performed using HRMAS NMR spectroscopy as described herein.

[00105] In some embodiments, the HRMAS NMR spectroscopic methods and systems described herein can be complemented by MALDI-TOF MS and/or LC MS/MS and/or FTIR for example, for the discrimination between MRSA and MSSA, VRE and VSE, and E. coll and Shigella spp. The methods and systems may also be used for the identification of Shiga-toxin-producing E. coll (STEC). [00106] In some embodiments, portable HRMAS NMR spectrometers may be developed to perform the methods and implement the systems described herein.

[00107] The use of two spectroscopic technologies as described herein may be used to compile spectral databases of clinically relevant microorganisms and foodborne pathogens, develop new approaches to the identification and phenotypic characterization of microbial cells, and elaborate new rapid typing methods for use in clinical diagnostics, outbreak detection and epidemiological surveillance. The spectroscopic techniques described herein may provide rapid classification and identification of microorganisms with subspecies-level discriminatory capabilities and may have numerous practical advantages as a tool for microbial identification and typing, including (i) no sample preparation, (ii) reagent-free analysis, (iii) simple (one-step) stand-alone technology, (iv) fast turnaround time (1 -2 min), and (v) low cost per test. The subspecies-level discriminatory capabilities of this spectroscopic technique may allow for rapid methods for diagnosis of nosocomial MRSA and VRE infections as well as differentiation between outbreak-associated and other VRE-strains and differentiation among epidemic MRSA clones. This combined spectroscopic technique allow for mining of integrated Fourier transform infrared (FTIR) spectroscopy and FIR-MAS NMR datasets by statistical heterospectroscopy as a route to spectroscopic biomarker discovery. Such multi- spectral databases of microorganisms may accelerate implementation of new, rapid and reagent-free spectroscopically based methodologies for typing of microbial pathogens and culminate in their integration to develop highly cost- effective automated expert systems for use in clinical diagnostics and outbreak detection.

[00108] The above description is meant to be exemplary only, and one skilled in the relevant arts will recognize that changes may be made to the embodiments described without departing from the scope of the invention disclosed. For example, the blocks and/or operations in the flowcharts and drawings described herein are for purposes of example only. There may be many variations to these blocks and/or operations without departing from the teachings of the present disclosure. For instance, the blocks may be performed in a differing order, or blocks may be added, deleted, or modified. While illustrated in the block diagrams as groups of discrete components communicating with each other via distinct data signal connections, it will be understood by those skilled in the art that the present embodiments are provided by a combination of hardware and software components, with some components being implemented by a given function or operation of a hardware or software system, and many of the data paths illustrated being implemented by data communication within a computer application or operating system. The structure illustrated is thus provided for efficiency of teaching the present embodiment. The present disclosure may be embodied in other specific forms without departing from the subject matter of the claims. Also, one skilled in the relevant arts will appreciate that while the systems, methods and computer readable mediums disclosed and shown herein may comprise a specific number of elements/components, the systems, methods and computer readable mediums may be modified to include additional or fewer of such elements/components. The present disclosure is also intended to cover and embrace all suitable changes in technology. Modifications which fall within the scope of the present invention will be apparent to those skilled in the art, in light of a review of this disclosure, and such modifications are intended to fall within the appended claims.