Rapid culture free pathogen detection via spectrally resolved fluorescence microscopy

Abstract

Methods and apparatus for rapid, culture free pathogen detection. The methods utilize optical spectroscopy techniques to identify and/or characterize pathogens in a sample via the detection of unique properties and/or analytes that are specific to particular pathogens.

Claims

1. A method for rapid, culture-free pathogen detection comprising: obtaining a sample suspected of containing a pathogen, preparing the sample for analysis via fluorescence microscopy (FM); wherein preparation does not include culturing the sample; analyzing the sample via FM to analyze autofluorescence events from individual cells in the sample and produce a fluorescence spectral image comprising a plurality of spectral factors; deconvolving the fluorescence spectral image to identify a subset of the plurality of spectral factors wherein the subset provides differentiation between different species of the same pathogen; wherein deconvolving comprises: preprocessing the spectral image to remove known spectral artifacts; and employing multivariate curve resolution (MCR); and comparing the identified subset of spectral factors to a known library of spectral factors that indicate the presence and species of the pathogen; and determining whether the sample contains the pathogen based on the comparison of the identified spectral factors to the known spectral factors; wherein the pathogen is a Candida species.

2. The method of claim 1 wherein the identified spectral factors enable differentiation between species of the same pathogen within the same sample in a single analytical step.

3. The method of claim 1 wherein the spectral factors enable differentiation between C. albicans, C. glabrata, and C. parapsilosis.

4. The method of claim 1 wherein preparation also does not include labeling the sample.

5. The method of claim 1 wherein the FM is confocal.

6. The method of claim 1 wherein the FM is hyperspectral.

7. The method of claim 1 wherein the FM analyzes an emissions spectrum from 500-850 nm.

8. The method of claim 1 wherein the deconvolved spectral image further indicates a location and abundance of different species of the same pathogen within the sample.

9. A method for rapid, label-free pathogen detection comprising: obtaining a sample suspected of containing a pathogen, preparing the sample for analysis via fluorescence microscopy (FM); wherein preparation does not include labeling the sample; analyzing the sample via FM to analyze autofluorescence events from individual cells in the sample and produce a fluorescence spectral image comprising a plurality of spectral factors; deconvolving the fluorescence spectral image to identify a subset of the plurality of spectral factors wherein the subset provides differentiation between different species of the same pathogen; wherein deconvolving comprises: preprocessing the spectral image to remove known spectral artifacts; and employing multivariate curve resolution (MCR); and comparing the identified subset of spectral factors to a known library of spectral factors that indicate the presence and species of the pathogen; and determining whether the sample contains the pathogen based on the comparison of the identified spectral factors to the known spectral factors; wherein the pathogen is a Candida species.

10. The method of claim 9 wherein the spectral factors enable differentiation between species of the same pathogen within the same sample in a single analytical step.

11. The method of claim 9 wherein the spectral factors enable the differentiation between C. albicans, C. glabrata, and C. parapsilosis.

12. The method of claim 9 wherein the FM is confocal.

13. The method of claim 9 wherein the FM is hyperspectral.

14. The method of claim 9 wherein the FM analyzes an emissions spectrum from 500-850 nm.

15. The method of claim 9 wherein the deconvolved spectral image further indicates a location and abundance of different species of the same pathogen within the sample.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 shows two emission spectra of C. albicans derived from MCR analysis after 16 hours of incubation. Fungal emission spectra are composed from >80 cells.

(2) FIG. 2 shows two emission spectra of C. glabrata derived from MCR analysis after 16 hours of incubation. Fungal emission spectra are composed from >80 cells.

(3) FIG. 3 shows two emission spectra of C. parapsilosis derived from MCR analysis after 16 hours of incubation. Fungal emission spectra are composed from >80 cells.

(4) FIG. 4 shows emission spectra of human serum at 0 hour incubation and 18 hours of incubation to demonstrate that emission factors found in Candida samples in FIGS. 1-4 are not due to emission of human serum.

(5) FIG. 5A is a representative image of the green autofluorescence factor of C. albicans. The box highlights autofluorescent features within the fungal cells.

(6) FIG. 5B is a representative image of the red autofluorescence factor of C. albicans. The box highlights autofluorescent features within the fungal cells.

(7) FIG. 6A is a representative image of the green autofluorescence factor of C. glabrata. The box highlights autofluorescent features within the fungal cells.

(8) FIG. 6B is a representative image of the red autofluorescence factor of C. glabrata. The box highlights autofluorescent features within the fungal cells.

(9) FIG. 7A is a representative image of the green autofluorescence factor of C. parapsilosis. The box highlights autofluorescent features within the fungal cells.

(10) FIG. 7B is a representative image of the red autofluorescence factor of C. parapsilosis. The box highlights autofluorescent features within the fungal cells.

(11) FIG. 8 shows classification tree pruning and demonstrates that over-fitting decreases the accuracy of the classification tree for the test set. The pruning level for classification tree was chosen by the divergence of the test set and training set which corresponds with pruning level 4 (i.e. a 12 node classification tree).

(12) FIG. 9 is a table displaying statistics for the different pruning levels of the classification tree. Bold statistics indicate chosen level for prediction analysis of the test set.

(13) FIG. 10 demonstrates that feature characteristics are used to accurately predict species. The pruned classification tree indicates that factor 1 characteristics are crucial for accurate species identification.

DETAILED DESCRIPTION

(14) The present disclosure provides various methods and apparatus for rapid, culture-free pathogen detection. In some embodiments, the method may be label free. According to various embodiments, the presently disclosed methods utilize one or more spectroscopy techniques to identify and characterize pathogens via the detection of unique spectral factors that are specific to particular pathogens.

(15) For the purposes of the present disclosure, the term “rapid” is intended to mean that pathogen detection can take place in a clinical setting on the order of minutes to hours as opposed to current methods which take place on the order of days.

(16) For the purposes of the present disclosure, the term “culture free” is intended to mean that there is no necessary step prior to sample preparation for analysis involving microbiological culture in nutrient medium for the purpose of increasing the number of cells present in the specimen through cell division.

(17) For the purposes of the present disclosure, the term “label free” is intended to mean that the signal to be measured does not require application of exogenous substances that will be the source of signals that serve as markers of the presence or absence of the pathogen in the specimen. For instance, an example of a label would be a fluorescent dye or an enzyme/colorimetric substrate that could be detected as a signal.

(18) According to an embodiment, the present disclosure utilizes hyperspectral confocal fluorescence microscopy (HCFM) to quickly and accurately identify and/or characterize the unique autofluorescence spectra from different pathogens. Accurate identification of a sample is defined as correctly assigning identify of a known control specimen in 80% or more cases. It should be understood that while the description and Examples refer primarily to confocal microscopy, the present disclosure is not necessarily limited to a confocal arrangement. Similarly, while the description and Examples primarily describe the fluorescence microscopy as being “hyperspectral,” which commonly refers to the resolution of greater than 100 spectral points, the present disclosure is not necessarily limited to any given number of spectral points and thus includes any embodiment in which information is spectrally-resolved and thus accommodates any level of spectral resolution.

(19) In general, the emission spectra of a sample that is suspected of containing a pathogen is obtained via HCFM and compared to a library of emission spectra from one or more pathogens of interest. According to a specific embodiment, the pathogen of interest may be a Candida species. An emission spectra library may be created by culturing various strains of a pathogen of interest, like Candida species, and using HCFM and multivariate spectral analysis methods to resolve multiple autofluorescence spectra in the different pathogen strains. The creation of an emission spectra library for three common forms of Candida species, namely C. albicans, C. glabrata, and C. parapsilosis, is described in detail in the Example section below.

(20) Any suitable hyperspectral confocal fluorescence microscope may be used, but according to various embodiments, the HCF microscope should be capable of exciting and detecting the spectral emissions from the autofluorescence at a signal-to-noise ration >3 and possess sufficient spectral resolution to resolve the two autofluorescent spectral emission peaks from each other and other fluorescent signals within the sample (typically, a spectral resolution >=0 nm).

(21) According to an embodiment, a clinical sample (e.g., blood, sputum, urine, catheter tip or other medical device or part thereof) may be obtained from the patient, as appropriate to the type of specimen using standard clinical procedures for obtaining diagnostic specimens. Specimens may then be prepared and directly analyzed using HCFM without the need for initial or additional culturing of the sample and without the need for the addition of any type of label. Sample preparation may involve placing the specimen in buffers or nutrient solutions suitable for HCFM analysis. Emission spectra may be obtained using methods such as those described in the Example section below, except that the sample being analyzed may be a sample obtained in a clinical environment, e.g., from a patient, a device, etc.

(22) The terms and expressions that have been employed are used as terms of description and not of limitation, and there is no intent in the use of such terms and expressions to exclude any equivalent of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention as claimed. Thus, it will be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims.

(23) All patents and publications referenced below and/or mentioned herein are indicative of the levels of skill of those skilled in the art to which the invention pertains, and each such referenced patent or publication is hereby incorporated by reference to the same extent as if it had been incorporated by reference in its entirety individually or set forth herein in its entirety. Applicants reserve the right to physically incorporate into this specification any and all materials and information from any such cited patents or publications.

REFERENCES

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Ohlhausen et al., “Multivariate statistical analysis of time-of-flight secondary ion mass spectrometry images using axsia,” Appl. Surf. Sci. 231-232, 230-234 (2004). 27. J. Schoonover, R. Marx, and S. Zhang, “Multivariate curve resolution in the analysis of vibrational spectroscopy data files,” Appl. Spectrosc. 57(5), 154A-170A (2003). 28. R. Davis et al., “Accurate detection of low levels of fluorescence emission in autofluorescent background: Francisella infected macrophage cells,” Microsc. Microanal. 16(4), 478-487 (2010). 29. W. Vermass et al., “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” PNAS 105(10), 4050-4055 30. Graus, M S, Timlin, J A and Neumann, A K, “Hyperspectral fluorescence microscopy detects autofluorescent factors that can be exploited as a diagnostic method for candida species differentiation,” Journal of Biomedical Optics, 2017, 22:1, 016002. (PMC5216876).

Example I

(25) Differentiation of Candida Species Using Hyperspectral Fluorescence Microscopy

(26) Materials and Methods

(27) Yeast Growth/Preparation. Lab strain yeasts of C. albicans, C. glabrata, and C. parapsilosis were grown for the purpose of ascertaining if the individual Candida species have unique autofluorescence characteristics. C. albicans (ATCC, MYA-2876), C. glabrata (ATCC, 2001), and C. parapsilosis (ATCC, 22019) were grown in human serum (EMD Millipore, S1) for 16 hrs at 37° C. in an orbital shaker at 250 rpm. Each of the candida species were then put on individual microscope slides and sealed with nail polish.

(28) Hyperspectral Confocal Fluorescence Microscopy. A custom built hyperspectral confocal fluorescence microscope was used to acquire the autofluorescent emissions from the samples. Previous work describes the methodology for image acquisition using the custom built microscope.sup.17. See also Graus et al, “Hyperspectral fluorescence microscopy detects autofluorescent factors that can be exploited as a diagnostic method for candida species differentiation,” Journal of Biomedical Optics, 2017.”.sup.30 In summary, a 488 nm laser (Coherent, Inc. Sapphire) and a 60× apochromat objective (Olympus Plan Apochromat, NA 1.4) were used to excite fluorescence from a diffraction-limited spot. The laser power was ˜80 μW at the entrance to the microscope and the integration time is 0.2 ms/pixel. To detect fluorescent emissions a prism spectrometer coupled to an electron multiplying charged coupled device (EMCCD) camera (Andor Technologies, Inc., iXon) was used.

(29) Spectral Analysis. All images were preprocessed to remove known spectral artifacts introduced by the imaging system (cosmic ray spikes, detector offset, and structured dark noise).sup.25, as well as calibrate the wavelength axis. The resulting individual spectral images were combined into three composite image data sets, one for each Candida species. Multivariate Curve Resolution (MCR) was performed on each of these composite data sets to develop a spectral model that described more than 98% of the spectral variance. The MCR algorithm has been described previously.sup.26,27 and has demonstrated success in exploratory analysis to identify underlying spectral components from multicomponent biological systems.sup.28,29. The MCR results identified near-identical spectral components regardless of the yeast species, therefore a combined spectral model for all three species was deemed appropriate. This combined model was generated by performing MCR on a combined set of images from all three species and described >96% of the spectral variance in the data. Classical Least Squares (CLS) prediction was used to determine the location and abundance of each spectral component in the spectral images. Images corresponding to the location and abundance of individual spectral components were exported as Tif files for subsequent image processing. Although the spectral signatures were virtually identically for all three Candida species, these images clearly indicate their location and abundance was not.

(30) Feature Selection. Fiji (ImageJ) was used to threshold and quantify features from the images. A threshold of three standard deviations above background intensity was used to extract features for all emission factors. The features quantified for each emission factor were; total and average intensity per feature, average intensity variance per feature, total and average number of features per cell, and total and average area of features.

(31) Classification. The above were then input into a classification tree algorithm. A classification tree analysis was developed in Matlab using the statistics and machine learning toolbox (Mathworks, version 2015a). To test the performance of the classifier, we divided the data into a training group of 337 cells and a test group of 37 cells (composed of 10% of each species population). To validate the accuracy of the classification tree we measured three statistics: cross validation error, resubstitution error, and the true predictive error for six levels of tree pruning using the training set. To calculate the true error of prediction the number of misclassifications in the test set was divided by the total number of samples in the training set. All data presented was pooled from triplicate biological replicates of samples.

(32) Results

(33) In these studies, we used a pooled human serum for growth because of its similarity to the growth condition of fungal pathogens in peripheral blood. The MCR analysis resulted in two different emission factors for all species (FIGS. 1-3) and a linear offset arising from the detector electronics (not shown). Factor 1's spectral shape and peak (550 nm) are very similar across all species, leading to the idea that the three species investigated share a set of common autofluorescent molecules that emit around 550 nm. In contrast, factor 2's spectral shape and peak (600-650 nm) vary slightly depending on which species is being observed (FIG. 1A-C). For example, C. albicans factor 2 peak is found at 600 nm and C. parapsilosis factor 2 peak is found at 650 nm, and interestingly C. glabrata factor 2 peak is broad (the peak encompasses 600-650 nm). When we examined autofluorescent emission from human serum alone, it did not match either factor found in the Candida samples and is not an obvious linear combination of the two factors (FIG. 4).

(34) Along with the spectral factors, the MCR analysis also returns concentration maps that indicate the relative abundance of each spectral factor. In order to determine if the spatial localizations of autofluorescence emissions were similar, we quantified multiple spatial and spectral characteristics of each of the autofluorescence signatures from the features within the hyperspectral confocal fluorescence images (FIGS. 5A, 5B, 6A, 6B, 7A, 7B). In the case of C. albicans we found very small features mostly composed of factor 1 that were on average around 0.42 μm.sup.2. For C. parapsilosis we observed large features primarily composed of factor 2 that are on average around 1 μm.sup.2. However, features in C. glabrata images contained both factor 1 and factor 2 with an average area around 0.27 μm.sup.2 and 0.67 μm.sup.2 respectively. Remarkably, the majority of autofluorescence from C. parapsilosis comes from factor 2. Mean factor 2 intensity of the C. parapsilosis features was 2 fold greater than C. glabrata features and 10 fold greater than C. albicans features. These differences in autofluorescence emission geometry as well as other autofluorescence characteristics allowed us to create a classifier from the spatial and spectral features of the autofluorescence emission that are unique to individual species.

(35) We developed a classification tree from these spectral and spatial features and the accuracy of this classification was measured using three statistics for six levels of pruning using the training set and test set. The accuracy of both the training set and test set are plotted (FIGS. 8, 9). The statistics and accuracy plot revealed some of the less pruned classification trees (i.e. larger classification trees) have a lower true predictive error, demonstrating that the full classification tree may have been overfitting and that the predictions reflected noise or outliers. Based on these results we selected level 4 as the optimal pruning level (point at which the training and test set accuracies diverge in FIG. 8). With this optimal tree we were able to achieve 84% accurate species identification from the test set of data (FIG. 9). We were also able to observe which autofluorescent characteristics were used to construct the classification tree (FIG. 10). Interestingly, in this predictive model, factor 1 autofluorescent characteristics are more important than factor 2 characteristics for predicting the Candida species. We then calculated the predictive accuracy for identifying each species in the independent test set. The classification tree was able to predict 75% of C. albicans samples accurately, 88% of C. glabrata accurately, and 83% of C. parapsilosis accurately. On closer inspection of the results, it was found that C. albicans was misidentified twice, once with C. glabrata and once with C. parapsilosis. Whereas, C. glabrata was found to only be misidentified with C. parapsilosis and C. parapsilosis misidentified with C. glabrata.

(36) Discussion

(37) Candida species are well known for their ability to cause morbidity and mortality. It is also known that some species, such as C. glabrata, have intrinsic resistances to azole based anti-fungal drugs.sup.18,19. Due to this it is important to be able to quickly and accurately identify the pathogenic fungal species prior to administering treatment. The method we describe in this study takes advantage of the spatial and spectral resolution of HCFM. The ability to characterize the unique autofluorescence spectra in the different Candida species coupled with their spatial localizations has allowed for species level identification of Candida species yeasts grown in conditions that closely mimic pathological conditions and commonly available clinical specimen types. Yeasts are known to emit autofluorescence under appropriate conditions due to native fluorophores, such as flavins.sup.20. Flavins and flavoproteins likely contribute to the fungal autofluorescence that we observed. Flavin compounds emit around 530 nm from 460 nm excitation.sup.21, which matches closely with our factor 1 peak wavelength and our excitation line. Flavin molecules also associate with the plasma membrane, intracellular granules, and mitochondria.sup.22. We did not observe any autofluorescent signatures around the plasma membrane, but we did observe intracellular signatures for factor 1. For both C. albicans and C. glabrata we detected small features for factor 1 leading to the possibility that we observed flavin molecules either in intracellular granules or mitochondria. Interestingly, when observed, factor 1 tended to be smaller in size than factor 2. Also, Factor 1 tended to be located inside areas that also contained factor 2, leading us to speculate that factor 2 may be another native fluorophore, yet to be described, that occupies a similar subcellular compartment.

(38) Previous studies have shown that by utilizing a single excitation source coupled with a monochromator and correlating microbial sample type with the intensity of emission at several wavelengths, it was possible to discriminate bacterial and fungal species.sup.16. In contrast, our approach uses the entire emission spectrum from 500-850 nm to identify multiple component spectra in the specimens. Our results also extend earlier approaches by measuring autofluorescence events at the individual cell level, whereas other methods use a population or ensemble-based approach and measure the overall autofluorescence signature of a population of cells. Finally, our approach utilizes identifying information from the subcellular spatial distribution of autofluorescence signals. Other molecular methods such as FISH (Fluorescent In Situ Hybridization) have been described both as quick and accurate.sup.23. However, utilizing FISH requires that each species needing identification has its own unique probe as well as preparing the sample in such a way that the probe can enter the cell and a culture step to generate enough sample to work with. Because our method does not require large numbers of cells and is a label-free method, it may be useable on a minimally processed clinical specimen, perhaps even as a point of care diagnostic. By utilizing autofluorescent emission characteristics the classification tree is able to accurately predict the Candida species, demonstrating the robustness of the procedure as a quick and accurate identification method.