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Characterization of clinically relevant fungi via SERS fingerprinting assisted by novel chemometric models Nicoleta Elena Dina, Ana Maria Raluca Gherman, Vasile Chis, Costel Sarbu, Andreas Wieser, David Bauer, and Christoph Haisch Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b03124 • Publication Date (Web): 22 Jan 2018 Downloaded from http://pubs.acs.org on January 24, 2018

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Analytical Chemistry

Graphical Abstract 338x190mm (72 x 72 DPI)

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Figure 1. Microscopic images of fungi (10× objective, A. fumigatus ss– A; A. fumigatus complex – B; R. pusillus – C) showing that during the SERS spectra acquisition process no photo degradation of the samples was induced, the morphologic features and the contrast of the sample being intact (scale bar is 200 µm). 228x67mm (300 x 300 DPI)


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Figure 2. Raw reproducible SERS spectra of the fungi samples (A. fumigatus ss, A. fumigatus complex spp, R. pusillus) recorded by using the 633 nm laserline (A-C) and 532 nm laserline (D-F). 122x67mm (300 x 300 DPI)



Figure 3. PC1-PC2 score scatterplot (a) and FPC1-FPC2 score scatterplot (b). 520x196mm (300 x 300 DPI)


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Figure 4. MD diagram depicting the percentage of samples with MD values in the same interval and the MD values’ corresponding profile. 310x113mm (300 x 300 DPI)



Figure 5. Root1-Root2 score scatterplot (a) and Root1-Root2 fuzzy-score scatterplot (b). 520x196mm (300 x 300 DPI)


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Figure 6. Root1 and Root2 score profile (a) and Root1 and Root2 fuzzy-score profile (b). 520x196mm (300 x 300 DPI)



Figure S1. SEM images of: A) Aspergillus fumigatus hyphae mock treated (control); no NPs on the surface are visible; B) Aspergillus fumigatus hyphal tip covered with NPs after the in situ AgNPs synthesis protocol. The hyphae are homogeneously covered in AgNPs, leading to homogeneous Raman enhancement.

177x127mm (96 x 96 DPI)


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Figure S3. The membership degree profile (top), plotted as function of sample number, showing the presence of some samples (outliers) for which the membership degree value is significantly lower than for the majority, fact that argues for the importance of applying FPC1 in order to mitigate the outliers’ effect on the final results of the PCA analysis. The membership degree profile highly reproduces (they are mirror images) the score profile for FPC1 (bottom). 145x217mm (300 x 300 DPI)



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Characterization of clinically relevant fungi via SERS fingerprinting assisted by novel chemometric models Nicoleta Elena Dina1†*, Ana Maria Raluca Gherman1, 2†, Vasile Chiș2, Costel Sârbu3, Andreas Wieser4,5,6, David Bauer7, Christoph Haisch7 1

Department of Molecular and Biomolecular Physics, National Institute of R&D of Isotopic and Molecular Technologies, 67-103 Donat, 400293 Cluj-Napoca, Romania 2

3

4

Faculty of Physics, Babeș-Bolyai University, 1 Kogălniceanu, 400084 Cluj-Napoca, Romania

Faculty of Chemistry and Chemical Engineering, Babeș-Bolyai University, 11 Arany Janos, 400028 ClujNapoca, Romania

Max von Pettenkofer-Institut für Hygiene und Medizinische Mikrobiologie, Ludwig-Maximilians-University; Marchioninistrasse. 17, 82377 Munich

5

Division of Infectious Diseases and Tropical Medicine, Medical Center of the University of Munich (LMU), Leopoldstr. 5, 80802 Munich, Germany 6 7

German Center for Infection Research (DZIF), partner site Munich, Munich, Germany

Chair for Analytical Chemistry, Institute of Hydrochemistry, Technische Universität München, Marchioninistrasse 17, 81377 Munich, Germany

†These authors equally contributed to this work. *Correspondent

author:

[email protected];

Abstract Nonculture-based tests are gaining popularity and upsurge in the diagnosis of invasive fungal infections (IFI) fostered by their main asset, the reduced analysis time, which enables a more rapid diagnosis. In this project, three different clinical isolates of relevant filamentous fungal species were discriminated by using a rapid (less than 5 min) and sensitive surface-enhanced Raman scattering (SERS)-based detection method, assisted by chemometrics. The holistic evaluation of the SERS spectra was performed by employing appropriate chemometric tools - classical and fuzzy principal component analysis (FPCA) in combination with linear discriminant analysis (LDA) applied to the first relevant principal components. The efficiency of the proposed robust algorithm is illustrated on the data set including three fungal isolates (Aspergillus fumigatus sensu stricto, cryptic A. fumigatus complex species, and Rhizomucor pusillus) that were isolated from patient materials. The accurate and reliable discrimination, between species of common fungal pathogen strains suggest that the developed method has the potential as an alternative, spectroscopic-based routine analysis tool in IFI diagnosis.

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Introduction Yeasts and molds, eukaryotic organisms in the kingdom of fungi, have prominent impact due to their industrial use and significance as human pathogens. The cases of invasive fungal infections (IFI) are increasing with alarming rates, due to modern medical procedures. They lead to a concerning mortality rate in critically ill patients.1-3 Candida and Aspergillus species are the most relevant fungal pathogens responsible for infections, especially in immunocompromised patients. Such patients are either affected by congenital immunodeficiency syndromes, or have a weakened immune system due to other causes, such as chronic infections, invasive cancer treatments such as chemotherapy, or solid organ- or stem cell transplantations. Interestingly, immunocompromised patients are often oligo symptomatic for extended periods despite progressing fungal infections. Therefore, a rapid as well as accurate identification of the opportunistic disease-causing fungi is crucial for the diagnosis and subsequent treatment of such patients. The conventional methods to diagnose IFI include antigen testing from patient blood by means of ELISA or immunological assays based on Limulus Amoebozyte lysate assays. The detected biomarkers, such as β-D-glucan and galactomannan, are not completely specific and lack sensitivity as well.4-5 Apart from these indirect techniques, direct detection of fungal pathogens from patient material involves microscopy, microbial culture, and molecular techniques such as PCR and DNA sequencing. Most techniques require cultures, which involve plating, growth, and colony counting. This process can take days to weeks due to the relatively slow growth of the organisms.6-7 PCR based techniques are hampered by the presence especially of yeast as normal flora on mucosal membranes, leading to false positive results. In summary, improved direct fungal identification from patient material is desirable to reduce infectionrelated hospitalization complications and additional costs. A powerful molecular diagnostic tool is embodied in the combination of Raman spectroscopy with nanotechnology.8-12 Generally, only bulk samples or concentrated solutions may be investigated by using Raman spectroscopy, owing to sensitivity limitations. However, a particular effect of Raman spectroscopy, the surface-enhanced Raman spectroscopy (SERS) has already addressed such concerns, enabling detection down to single molecule13 or single cell14-15 levels. Considering the method’s sensitivity and high specificity, the whole-cell fingerprint SERS spectra, unique for each sample, become reliable for classification and discrimination between the samples, even if the overall molecular content is similar. These remarkable characteristics render SERS a suitable candidate for applications such as pathogen detection, molecular infection diagnosis, as well as high-throughput screening (HTS).16 The main assets are the rapid, within minutes,17-20 and reliable spectral response, crucial in deciding the most efficient treatment for the patient. In the last decades, SERS was used to identify different species of pathogenic and nonpathogenic bacteria,9, 17, 20-24 protozoa,25 fungi,26-27 and their spores.28 Szeghalmi and co-workers studied the growth of Aspergillus nidulans and they detected a strong signal around the hyphal cell wall because of the excretion of some extracellular components during growth.29 Thus, identification of fungi is possible with SERS as some recent reports prove, either by using the spectroscopic method directly or coupled with principal component analysis (PCA) for discrimination.30-31 SERS has also been used for the detection of specific PCR components of yeast 2 ACS Paragon Plus Environment


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isolates.32-33 Such detection assays previously reported employ the Lee-Meisel silver nanoparticles (AgNPs) synthesis procedure, involving the heating of the metallic salt solution and about 1h invested for the reduction reaction followed by another hour in order to cool down the solution and finally use it. The excitation laser wavelength used is 830 nm,30 which is far away from the AgNPs characteristic plasmonic resonance band exhibited at 420 nm. A more appropriate excitation laser line would be 514.5 nm,30 532 nm33 or even 633 nm (the latter two applied in this work). The evaluation of PCR-SERS results for IFI diagnosis reported recently requires around 6-8h and by using automated nucleic acid extraction, a semi-automated protocol has been elaborated for whole blood samples.32 Another promising and comparable rapid molecular assay was developed starting from synthetic DNA functionalized with AgNPs and a Raman reporter to detect a specific DNA sequence.33 In this case, the label-free detection is replaced with an indirect detection methodology, employing a smart design of probing that allows specific detection of fungal targets. We have recently demonstrated the rapid, label-free identification of bacteria at single-cell level by using SERS-based approaches.24, 34-37 Particularly, in situ silver colloid synthesis, resulting in efficient coating of the microorganism’s cell wall with SERS-active silver colloids, was recently demonstrated.24 The assay requires about 5 min and a sample volume of only 3 µL. By using this novel strategy, SERS detection at single-cell level and discrimination of pathogens at strain level is possible in combination with appropriate chemometric methods. The in situ synthesis of AgNPs is an efficient means to generate the particles in close proximity of the cell wall structure and thus access molecular information directly from its components, which again reflect the specific identity of the cell and its physiological state. However, it is unclear to date whether such an approach could enable fungi detection, considering that these organisms are classified in a different kingdom than bacteria. Actually, the decisive cell wall structure widely differs between the organisms; fungi for example contain chitin and ergosterole in their cell wall and membranes, whereas bacteria have a wall based on peptidoglycan and different phospholipids as well as lipopolysaccharide (LPS) in the case of Gram negatives. Currently, only a small proportion of environmental fungi have been formally classified, hampering the identification of all isolates at strain level. Classification is further complicated by vastly different sexual as well as asexual growth forms of various fungi. In this work, we assessed the label-free SERS detection and identification of pathogenic fungi species by using the in situ colloid synthesis approach.24 A simple chemical reduction method was employed for the AgNPs synthesis directly on the fungal exoskeleton. SERS spectra of the Aspergillus and Rhizomucor pusillus isolates were recorded by using two excitation laser lines (532 nm and 633 nm). Their molecular-specific SERS spectra enabled us to identify them and to characterize their physiological state in liquid growth media cultures. All isolates were derived from patient samples, taken into culture and stored in our strain collection. Analysis was performed from re-grown isolates using robust chemometric analysis for high-precision spectral data analysis in order to demonstrate the major potential to render the SERS-based identification techniques more reliable in real-life applications.


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Materials and methods Preparation of samples Three fungal isolates Aspergillus fumigatus s.s., cryptic A. fumigatus sp., and Rhizomucor pusillus where obtained from a strain collection of clinical sample strains. The fungi had been identified by the reference protocols including partial DNA sequencing to ensure proper identification. The isolates were grown on sabouraud-dextrose agar plates (Becton Dickinson, Heidelberg, Germany) and inoculated in sterile autoclaved Luria-Bertani (LB)-Broth. The grown fungus balls were collected for analysis with sterile inoculating loops after two days of growth under aeration and agitation at 32°C. The fungi tested in this study are actual clinical isolates, which have been re-cultured on artificial media after being isolated from patient material. They have been re-grown under standardized conditions for the assay. In original patient samples however, germinated and sufficiently grown hyphae, which actually caused the infection symptoms and complications are present. This means, the analysis from direct patient material would be rapid, within hours, considering that no cultivation will be required prior to the analysis of samples that already contain hyphae. The silver colloidal suspension was synthesized in situ, in the presence of the fungi, and a final volume of 1 mL was obtained. In detail, for each measurement, one fungus ball of A. fumigatus or R. pusillus, respectively, was immersed in the 100 µL silver nitrate solution and then the reducing agent (900 µL) was added in order to generate in situ AgNPs (1 mL) at their cell wall structure. After approximately 3 min of AgNPs/biomass interaction, the SERS measurements were initiated on an organism soaked in the silver colloidal suspension, immobilized on a glass microscopy slide. Thus, the unique SERS fingerprint was recorded in liquid phase, prior to sample dehydration. Raman instrumentation All SERS measurements were performed using a Raman microscope LabRAM HR, HORIBA – 633 nm (HeNe laser - 14 mW, Nd:YAG laser 532 nm - 15 mW) equipped with a Leica optical microscope, by using the 20× objective. The laser power was in the range of 10%, and the exposure times used were 25s by summing up 5 accumulations (532 nm) and 10s by summing up 10 accumulations (633 nm), respectively. SERS measurements The point-to-point, spot-to-spot and batch-to-batch reproducibility of the SERS spectra were of primary interest during measurements. The point-to-point reproducibility test was performed by acquiring five SERS spectra from different points of AgNPs mixed samples applied on polyslides (Polysine™ Microscope Adhesion Slides, Erie Scientific via VWR). The spot-to-spot reproducibility tests were carried out by acquiring three SERS spectra from three separately grown organisms of the same batch applied on polyslides. By using a focus area of few micrometers and a long working distance objective, we were able to collect multiple SERS spectra on each microorganism which were averaged out, therefore hundreds of accumulations from a specimen. This way we overcame spot-to4 ACS Paragon Plus Environment


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spot variability and also photodegradation of the sample. The confirmation of batch-to-batch reproducibility was accomplished by acquiring the SERS spectra of the samples from five batches of stock inoculations of the same species. The resulted 137 SERS spectra collected, originating from multiple hyphae, not whole-organism spectra, are corresponding to a total of 15 specimens. SERS spectra were baseline corrected and normalized and constituted the SERS database. Base-line correction and subsequent normalization of the collected spectra were done by using Spectragryph software (Friedrich Menges, 2001-2017). Base-line with adaptive option provided by the software was chosen for the correction, using zero offset and coarseness between 25-50%. Each spectrum was normalized relatively to its most intense peak. The SERS database used for further analysis was thus formed by spectra subjected to this pretreatment. Statistical Analysis The SERS spectra were digitized and saved as appropriate files for further chemometric analysis. Thus, the resulted matrix, having as dimensions: 137 samples × 1255 variables (wavenumbers units, covering the range 400-1800 cm-1) was further used for all chemometric analyses which were carried out using the Statistica 8.1 software (StatSoft, Tulsa, USA) and a personal fuzzy software described for the first time by Sârbu et al.38,39 and successfully applied in analytical39-41 and spectroscopic42-44 studies. The robust fuzzy principal component analysis algorithm has been in detail described and presented step by step,38,39 and compared with classical principal component analysis, emphasizing the effect of fuzzification on various data sets. What is gained through fuzzification is greater generality, higher expressivity, an enhanced ability to model real-world problems, and a methodology for exploiting the tolerance for imprecision.45

Results and discussion The sample preparation is a decisive step in SERS measurements in order to obtain reproducible results from the cluster formed by the fungi and the significantly smaller AgNPs. By using in situ synthesis, the AgNPs are generated homogeneously and in intimate contact with the organism’s cell wall/exoskeleton, as supported by scanning electron microscopy (SEM) results (Figure S1). The average size of the AgNPs is known to be 25 nm.46 The coating uniformity of the cell wall/exoskeleton directly influences the point-to-point reproducibility of the recorded SERS spectra. Biological variability would reflect on the batch-to-batch reproducibility. Five independent cultures were grown in different days and tested after 48h of growth. Since the in situ synthesized AgNPs are in close contact with the cell wall of the organisms, the obtained biochemical information mostly originates from the cell wall as its unique “fingerprint”. SEM images were obtained to assess the homogenous coating of the hyphae (Figure S1). As fungi can produce spores and conidia besides the hyphae, we have chosen a cultivation scheme which leads to submerged growth of filamentous fungi in media. This does not allow the fungus to produce conidia or spores (see Figure 1). Such a protocol was chosen, as it resembles the growth of fungi in the human body, during infections. Thereby, spores are not formed within the tissue. Thus, after washing the fungal hyphae, there is only hyphal fungal 5 ACS Paragon Plus Environment

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spectrum to be expected. Figure 1 contains microscopic images of the fungal hyphae covered with the in situ generated AgNPs clusters.

Figure 1 Figure 2 shows the unprocessed, raw SERS spectra for the three fungi species investigated. A. fumigatus s.s. species have a very similar fingerprint in both cases, independent of the laserline used for excitation. Although the biochemical cell wall composition of fungal species is similar, and approximately 90% is composed of carbohydrates, they have significantly different organization patterns of the sugar moieties depending on the species.31 This fact should be noticeable in their SERS fingerprint. More SERS spectra showing the repeatability of the collected spectral data and thus, their negligible variation are included in Figure S2. A noteworthy aspect that should be discussed is the fact that the SERS fingerprint specific for each fungal isolate is highly dependent on the laserline used to excite the sample in two of the three investigated cases. Particularly, the fingerprints for the complex A. fumigatus complex sp. and R. pusillus species feature distinct relative SERS intensities, e.g. for the marker bands present at 650 cm-1 and 730 cm-1, when using 532 nm or 633 nm excitation laserline. The SERS fingerprints recorded with the 532 nm laserline of all three fungal strains resemble with each other considerably, by having the 730 cm-1 SERS band (see Table 1 for assignments) as main marker band, with the highest relative intensity. This makes discrimination between species very difficult and requires the use of multivariate analysis tools.

Figure 2

Table 1. Tentative band assignments of the SERS spectra of fungi cells. Wavenumbers (cm-1) A. fumigatus s.s.

A. fumigatus complex spp.

Assignments

532 nm

633 nm

532 nm

633 nm

Carbohydrates 41, 48-49 Glucose 50 C-S 51 Glycosidic ring 49 Phosphate group 50 C-C stretching – proteins 52 C-N stretching 53 Phenylalanine (proteins) 54-55 Glucose 50 =C-C= in lipids 48 =C-C= in lipids 48 Amide III 56 Amide III 53 C–H bending – proteins 49, 53 C–H bending – proteins 49, 53 C–H bending – proteins 49, 53

576 631 656 733 863 899 960 1024 1079 1181 1223 1252 1334 1377

572 634 657 734 860 896 960 1078 1180 1221 1254 1334 1376

564 625 654 732 957 1022 1135 1167 1245 1327 1370

568 651 728 866 953 1006 1073 1132 1178 1241 1329 1355 -

R. pusillus

532 nm 564 626 655 731 958 1135 1248 1329 1374

633 nm 653 730 955 1127 1164 1244 1320 1360 -



Saccharide 50 CH2 bending (proteins, lipids)30, 57-58 N–H, C–H bend, C=C stretching 30 C=C lipid 30 COO- asymmetric stretching 55

1592 -

1595 -

1397 1460 1545 1579 -

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1463 1581 1687

1397 1460 1575 -

1574 1693

Chemometric analysis Multivariate supervised or unsupervised statistical approaches are needed to reveal hidden relationships between biochemical parameters and to establish the relevant characteristics for classification, grouping, etc.41 Multivariate statistical methods for the analysis of big data sets have been applied to chemical, biological and environmental systems during the last decades.11, 34, 59-62 In particular, PCA is a valuable tool in chemometrics for data compression and information extraction due to the fact that PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. The first principle component (PC) is the linear combination of sample values whose scores have maximum variation. Among the many linear combinations with the property of having its scores uncorrelated with the first PC, the one with a maximum variation among its scores is selected as the second PC. Like any other multivariate statistical-based analysis, PCA is sensitive to outliers, missing data, and poor linear correlation between variables, e.g. due to poorly distributed variables, and as a result, data transformations have a huge impact upon the analysis results. This can be handled by fuzzy clustering.38-40 As the fuzzy membership degrees (MD) are defined according to the distance to the first PC, the major advantage is that the first PC will count the merits of each data item, considering thus the isolated points with less significance.40 Due to the independent computation of the fuzzy MD, this robust method leads to a better separation of data in all the representations based on different combinations of PCs. Practically, when PCA shows only a partial separation of the variables and no separation of scores (samples) onto the plane described by the first two or three PCs (for 2D or 3D scores scatterplot, respectively), a much sharper differentiation of the variables is achievable when FPCA is applied.39 Moreover, in some circumstances, when samples are very similar, it becomes ideal to combine linear discriminant analysis (LDA), which often leads to better discrimination of the samples, with PCA. LDA is a supervised classification technique based on uncorrelated linear discriminant functions, fulfilling an important condition: the canonical scores obtained are independent. LDA also allows to visualize how the discriminant functions lead to samples’ grouping by plotting the individual canonical scores for the discriminant functions. Much more, the combination of the PCA and LDA may strongly increase the classification and discrimination of the considered samples. In this way, the number of variables is reduced and the scores corresponding to the principal components are orthogonal and clean from noise. In addition, the number of PCs is less than or equal to the number of samples and as a direct consequence the LDA can be efficiently applied. Usually, a heuristic for determining the best suitable approach of big data analysis is needed. However, experts in spectral data acquisition and interpretation must be confident that the physical, chemical, or biological meaning and relevance of these data are not altered by reducing their dimensionality and by multilevel data processing. 7 ACS Paragon Plus Environment

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Classical and fuzzy PCA Firstly, classical PCA as well as FPCA were used to clean and reduce the dimensionality of the original dataset on the basis of PCs. The robust FPCA algorithm39 improves the PCA approach by fuzzification of the matrix data. As a result, the influence of the outliers and poor linear correlation between variables is minimized which leads to a higher accounting for total variance, lowdimensional matrix and more precise outline of PCs.38 By applying PCA/FPCA on the mentioned data matrix, the first 132 PCs explain the total variance (100%) of the spectral data in the case of PCA and the first 133 PCs in the case of FPCA, respectively. The projection onto the plan described by the first two PCs is presented in Figure 3a, for the PCA and Figure 3b, for the FPCA results, respectively. As shown, the first two PCs/FPCs account for 60% of the variance. The FPCA results, although more illustrative, are in good agreement with those obtained with PCA and the nature of samples. Thus, it can be observed, that A. fumigatus s.s. (1) were better separated from the larger group in both cases, but also the A. fumigatus complex sp. isolate (2) had the same tendency of grouping as well as the R. pusillus species (3) were broken into two well separated groups. The low discrimination obtained in both cases, PCA and FPCA, can be attributed to the fact that the two first PCs account only for a moderate variation (around 60% in both cases). The third PC/FPC explains an additional 7.48% or 6.02% of the total variance of the samples. So, overall, the total variance explained by the first three PCs/FPCs reaches a maximum around 68%.

Figure 3

Figure 4 exhibits through a diagram and the corresponding profile, the distribution of samples (in percentage) as a function of membership degree value (1.0 being the maximum, and 0.0 the minimum). We have defined five equal intervals between 0 and 1 and have plotted the corresponding MD values’ diagram and profile. The results show that a significant part of our samples have a sufficient MD value in order to be assigned to the group to which they really pertain. More than 63% of the samples have a MD value between 0.8 and 1.0, and another 26%, between 0.6 and 0.8, which means that up to 90% of our samples can be reliably classified to their roots and discriminated from samples belonging to other species or isolates of a certain species. The high-accuracy grouping tendency predicted by the FPCA analysis is thus confirmed in the same percentage by correlating the total variance explained value to the samples’ MD values (around 68% of the total variance explained and, correspondingly, more than 63% of samples have a MD close to 1). The exact membership degree values for each sample are listed in Table S1, next to their profile, shown in Figure S3.

Figure 4

However, this situation can be proficiently resolved in order to reach a high-accuracy classification (over 90%) by using a combination of PCA and FPCA with LDA which could lead to a more efficient discrimination of the investigated samples, according to previous work44,63 and other relevant applications.11, 43, 64 In this way, the scores matrix of the new variables (PCs) becomes a diagonal 8 ACS Paragon Plus Environment


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matrix, because the scores are orthogonal and the number of PCs retained can be less than or equal to the number of fungi samples (137).

Linear discriminant analysis LDA is a supervised classification technique based on linear discriminant functions, uncorrelated, which maximize between-class variance and minimize within-class variance. The combination of FPCA and LDA led to the most efficient discrimination of the groups of fungi isolated from biological samples that were investigated. More precisely, the results obtained by applying LDA to the scores corresponding to the first 100 PCs indicate a highly accurate separation of the tested fungi isolates of the different species (99.91% for PCA and 99.85% for FPCA, respectively) within three groups, in good agreement with the nature of the fungi species considered in this study. The Root1– Root2 score plots (Figure 5a and 5b) illustrate very well distinct groups for the respective species. It may be concluded that that PCA affords some preliminary results, but FPCA-LDA is needed for unequivocal classification.

Figure 5

Furthermore, we used additional information provided by multilevel analysis to link the obtained sample grouping with relevant characteristics of each data set behavior. Hence, we plotted the scores profiles of Root1 and Root2, resulted from the PCA-LDA and respectively PCA-fuzzy-LDA multilevel analysis and thoroughly deduced the characteristic profile in each sample group case. As depicted in Figure 6a, the PCA-LDA score profiles exhibit a justified behavior depending on the sample groups analyzed. The first part of the profile reflects the characteristics of the A. fumigatus s.s. spectral data as first data set: scores corresponding to Root1 are all negative and the scores corresponding to Root2 are all positive values. Afterwards, when the second data set is reached, corresponding to the cryptic A. fumigatus sp., along to Root1 the scores have only negative values and the scores associated to Root2 have highly negative values. The last and third data set reflected in the scores profile is corresponding to the R. pusillus species and has different characteristics than the others, by taking only positive values for Root1 and Root2, respectively. The FPCA-LDA derived score profiles, shown in Figure 6b, exhibit a perfectly justified behavior depending on the sample groups analyzed with no exception. The behavior is not kept, but it is still relevant in correlating each sample group with characteristic values. More precisely, in the first part of the profile the characteristics of the A. fumigatus spectral data are reflected: canonical scores corresponding to Root1 are taking only positive values and Root2 slightly negative values. Afterwards, when the second data set is reached, corresponding to the cryptic A. fumigatus sp., the two profiles contain only positive values. The last and third data set reflected in the scores profile, corresponding to the R. pusillus species, is characterized by high negative values for both Root1 and slightly negative values for Root2. Considering these specific characteristics, there is no doubt that each sample is fairly assigned to its corresponding sample group, with maximum accuracy. 9 ACS Paragon Plus Environment

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Figure 6 All the above statements concerning the efficiency of the methodology proposed in this study are well supported by the values of quality performance, features obtained by applying two approaches of cross-validation: leave one out (LOO) and holdout method presented in Table 2. The results obtained applying LOO method indicate 96.4% of cross-validated grouped samples correctly classified for score data corresponding to the classical PCA and 97.8% in the case of FPCA score data. When the validation approach was the holdout method we used a different way of separating the data into the training and the testing parts, respectively. After each set of five selected training samples, one sample was selected for the test set, resulting finally in 21 samples for test set and 116 for the training set. The results of cross-validation presented also in Table 2 point out a correct classification rate for the test set of 95.2% for FPCA-LDA, better in comparison to PCA-LDA with only 85.7%. We have to mention also that in all cases the original data and respectively the training sets, were correctly classified with a rate of 100% (Table 2). Table 2. Values of quality performance features for FPCA-LDA and PCA-LDA methods corresponding to SERS data. LDA

FPCA-LDA

Method

Group

Original data

LOO

Holdout method

PCA-LDA

Percent correct

(1)

(2)

(3)

Percent correct

(1)

(2)

(3)

(1)

100.0

65

0

0

100.0

65

0

0

(2)

100.0

0

27

0

100.0

0

27

0

(3)

100.0

0

0

45

100.0

0

0

45

Total

100.0

65

27

45

100.0

65

27

45

(1)

98.5

64

0

1

95.4

62

2

1

(2)

96.3

0

26

1

96.3

0

26

1

(3)

97.8

0

1

44

97.8

0

1

44

Total

97.8

64

27

46

96.4

62

29

46

(1)

100.0

10

0

0

100.0

10

0

0

(2)

100.0

0

4

0

100.0

0

4

0

(3)

85.7

0

1

6

57.1

1

2

4

Total

95.2

10

5

6

85.7

11

6

4

Legend: (1) A. fumigatus s.s.; (2) A. fumigatus complex spp.; (3) R. pusillus species.

Conclusions Results presented in this study indicate that the combination of PCA followed by FPCA with LDA is leading to a clear and high precision classification and discrimination of samples with clinical relevance. The label-free SERS-based detection methodology can be successfully applied to identify 10 ACS Paragon Plus Environment


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fungi, a class of organisms with very different structure than bacteria. The robust models of chemometric analysis used in this work contribute to the full comprehension and interpretation of the valuable spectral information. A fuzzy PCA method for robust estimation of PCs has been applied and the efficiency of the new algorithm has been demonstrated. The FPCA method allowed for a better discrimination of the biological samples because it is more compressible than classical PCA, meaning that the first PC accounts for significantly more of the variance than their classical counterparts. In addition, the FPCA-LDA derived score profiles exhibit a perfectly justified behavior depending on the sample groups analyzed with no exception. Conclusively, in the case of these samples, the combination of PCA-FPCA-LDA led to the best results. The novel input of this joint analysis tool is significant for IFI diagnosis since it is able to provide a faster (within minutes) and reliable identification of the fungal pathogen. These facts should encourage the application of FPCA methodology to other database “mining” efforts as well as encourage “fuzzification” of other important chemometric methods like principal component regression (PCR) and partial least-squares (PLS).

Acknowledgements: This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS/CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED2016-0983, within PNCDI III. Supporting Information. SEM images of the Aspergillus fumigatus, the membership degree profile, plotted as function of sample number, and the FPC1 score profile (mirror images) and the membership degree values, respectively, are provided. References (1) Andes, D. R.; Safdar, N.; Baddley, J. W.; Alexander, B.; Brumble, L.; Freifeld, A.; Hadley, S.; Herwaldt, L.; Kauffman, C.; Lyon, G. M.; Morrison, V.; Patterson, T.; Perl, T.; Walker, R.; Hess, T.; Chiller, T.; Pappas, P. G.; The, T. I., Transpl. Infect. Dis. 2016, 18 (6), 921-931. (2) Mariette, C.; Tavernier, E.; Hocquet, D.; Huynh, A.; Isnard, F.; Legrand, F.; Lhéritier, V.; Raffoux, E.; Dombret, H.; Ifrah, N.; Cahn, J.-Y.; Thiébaut, A., Leuk. Lymphoma 2017, 58 (3), 586593. (3) Timsit, J.; Azoulay, E.; Schwebel, C.; Charles, P. E.; Cornet, M.; Souweine, B.; Klouche, K.; Jaber, S.; Trouillet, J. L.; Bruneel, F. et al., JAMA 2016, 316 (15), 1555-1564. (4) Herbrecht, R.; Letscher-Bru, V.; Oprea, C.; Lioure, B.; Waller, J.; Campos, F.; Villard, O.; Liu, K.-L.; Natarajan-Amé, S.; Lutz, P., J. Clin. Oncol. 2002, 20 (7), 1898-1906. (5) Lamoth, F.; Cruciani, M.; Mengoli, C.; Castagnola, E.; Lortholary, O.; Richardson, M.; Marchetti, O., Clin. Infect. Dis. 2012, 54 (5), 633-643. (6) Morris, A. J.; Byrne, T. C.; Madden, J. F.; Reller, L. B., J. Clin. Microbiol. 1996, 34 (6), 15831585. (7) Bosshard, P. P., Mycoses 2011, 54 (5), e539-e545.


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Figure captions: Figure 1. Microscopic images of fungi (10× objective, A. fumigatus s.s.– A; A. fumigatus sp. – B; R. pusillus – C) showing that during the SERS spectra acquisition process no photo degradation of the samples was induced, the morphologic features and the contrast of the sample being intact (scale bar is 200 µm). Figure 2. Raw reproducible SERS spectra of the fungi samples (A. fumigatus s.s., A. fumigatus complex sp., R. pusillus) recorded by using the 633 nm laserline (A-C) and 532 nm laserline (D-F). Figure 3. PC1-PC2 score scatterplot (a) and FPC1-FPC2 score scatterplot (b). Figure 4. MD diagram depicting the percentage of samples with MD values in the same interval and the MD values’ corresponding profile. Figure 5. Root1-Root2 score scatterplot (a) and Root1-Root2 fuzzy-score scatterplot (b). Figure 6. Root1 and Root2 score profile (a) and Root1 and Root2 fuzzy-score profile (b). 14 ACS Paragon Plus Environment


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Figure 1

Figure 2



a)

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b) Figure 3

Figure 4

a)

b) Figure 5 17 ACS Paragon Plus Environment

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a)

b) Figure 6



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For TOC only


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Figure S2. A) SERS spectra showing the repeatability of the acquired spectral data for A. fumigatus species. 208x184mm (300 x 300 DPI)



Figure S2. B) SERS spectra showing the repeatability of the acquired spectral data for R. pusillus species. 169x125mm (300 x 300 DPI)


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of 30 ACS Paragon Plus Environment Analytical Chemistry 1 2 3 4 5 6

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