Label-free serum detection of Trichinella spiralis using surface-enhanced Raman spectroscopy combined with multivariate analysis

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Label-free serum detection of Trichinella spiralis using surface-enhanced Raman spectroscopy combined with multivariate analysis Jian Li , Jing Ding , Xiaolei Liu , Bin Tang , Xue Bai , Yang Wang , Shicun Li , Xuelin Wang PII: DOI: Reference:

S0001-706X(19)31456-1 https://doi.org/10.1016/j.actatropica.2019.105314 ACTROP 105314

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Acta Tropica

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24 October 2019 18 December 2019 18 December 2019

Please cite this article as: Jian Li , Jing Ding , Xiaolei Liu , Bin Tang , Xue Bai , Yang Wang , Shicun Li , Xuelin Wang , Label-free serum detection of Trichinella spiralis using surfaceenhanced Raman spectroscopy combined with multivariate analysis, Acta Tropica (2019), doi: https://doi.org/10.1016/j.actatropica.2019.105314

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Highlights: 

SERS combined with PCA-LDA has great potential in trichinellosis detection.



PCA-LDA analyses achieved a high diagnostic accuracy.



The ROC curve illustrated the performance of PCA-LDA analyses and the value is 0.977.

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Label-free serum detection of Trichinella spiralis using surface-enhanced Raman spectroscopy combined with multivariate analysis Jian Li†, Jing Ding†, Xiaolei Liu†, Bin Tang, Xue Bai, Yang Wang, Shicun Li, Xuelin Wang*

Key Laboratory of Zoonosis Research, Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun 130062, China.

†

They contributed equally to the work.

*

Correspondence: Xuelin Wang [email protected]

Abstract Based on blood serum surface-enhanced Raman spectroscopy (SERS) analysis, this paper proposed a simple and unlabeled non-invasive serum detection for T. spiralis infection. Serum samples were collected and analyzed from 40 rats at 0 days post infection (dpi) (normal rats), 19 uninfected rats, and 16 rats infected with T. spiralis at 28 dpi, using SERS measurements. Multivariate statistical techniques, such as linear discriminant analysis (LDA) and principal components analysis (PCA), were used to analyze and identify the obtained blood serum SERS spectra. The diagnosis algorithms, based on PCA-LDA,

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achieved a diagnostic sensitivity of 87.5%, a specificity of 94.7%, and an accuracy of 91.4% for separating the samples infected with T. spiralis from the control samples. This exploratory study demonstrated that colloidal Ag NPs-based SERS serum analysis technique combined with PCA-LDA has a great potential in improving the detection of T. spiralis infection and onsite screening. Keywords Trichinella spiralis, surface-enhanced Raman spectroscopy (SERS), serum detection, principal components analysis (PCA), linear discriminant analysis (LDA). 1. Introduction Trichinellosis is a worldwide food-borne parasitic disease caused by eating raw or undercooked meat that contains the infective larvae of Trichinella nematodes (Pozio, 2015). About 11 million people in the world might be infected by Trichinella according to statistics (Dupouy-Camet, 2000). From 1986 to 2009, approximately 65818 cases of human trichinellosis were reported worldwide by the International Commission on Trichinellosis (ICT) (Murrell and Pozio, 2011). China has become one of the countries with the highest infection rate at present. From 1964 to 2011, more than 600

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trichinellosis outbreaks have occurred in China, and approximately 40,000 people have been infected, with 336 deaths (Cui et al., 2011; Wang et al., 2015). The prevalence of the disease is related to eating habits, especially in some ethnic or remote areas where the residents prefer to eat wild animals or uncooked pork due to cultural reasons (Cui and Wang, 2011; Cui et al., 2013; Jiang et al., 2016). Therefore, the diagnosis and prevention of trichinellosis are of great significance in China. Currently, the invasive digestion method is considered by the World Organization for Animal Health (OIE) to be the standard method for the diagnosis of Trichinella. In addition, serological and molecular biology methods have been developed to detect trichinellosis. ELISA assay, based on muscle larvae (ML) excretory/secretory (ES) antigen, is currently the most commonly used method (Wang et al., 2017; Sun et al., 2018). Subsequently, a new method for detecting Trichinella antibodies was developed using colloidal gold-labeled ES antigens, referred to as immunochromatographic strips (Zhang et al., 2006). However, all these methods are cumbersome to apply and take a long detection time; therefore, we tried to develop a faster and easier detection method to achieve rapid screening of Trichinella infections onsite. Raman spectroscopy (RS) is an analytical method for analyzing the

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scattered spectrum of different incident light frequencies to obtain molecular vibration and rotation information and apply it to molecular structure research (Chan et al., 2008). RS was used in biomedical research for diagnosis, such as the detection of changes in some proteins, nucleic acids, or other biomolecules in pathological processes (Sdobnov et al., 2017; Seto et al., 2014; Varade et al., 2013). However, RS has an inherent disadvantage, i.e., the signal is weak and difficult to detect, limiting the application of this method in the field of biomedicine (Feng et al., 2015). Fortunately, SERS can enhance Raman signals by binding the molecules of interest to the rough surface of noble metal nanostructures (Lin et al., 2014; Chirumamilla et al., 2014; Gopalakrishnan et al., 2014). Therefore, the Raman signal can be significantly enhanced, making it an ultra-sensitive detection technique. There are several ways to enhance SERS sensitivity, including electromagnetic field enhancement and chemical enhancement by surface plasmons and charge transfer (Çulha, 2015). SERS has been widely used in various fields such as trace analysis, immune recognition, and physical and chemical indicators. In recent years, many studies have been published on the use of SERS to detect human diseases, such as neurological diseases, diabetes, and cardiovascular diseases (Moody and Sharma, 2018; Kong et al., 2013;

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Guo et al., 2017). In addition, multiple cancers, such as breast cancer, lung cancer, colorectal cancers, and viral diseases, including influenza, hepatitis, HIV, and those responsible for tropical diseases, can also be detected by SERS (Moore et al., 2018). However, there is no report on the application of serum SERS to assess the potential for the detection of foodborne parasitic infections at present, which is important to fill the gaps in this field. For the first time, we attempted to detect trichinellosis using a label-free method based on serum SERS; the spectral differences between the control group and the Trichinella-infected group were analyzed by PCA-LDA. Our preliminary results showed great potential, which might provide a faster, more accurate and more practical diagnostic method for the detection of trichinellosis. 2. Material and methods 2.1 Animals and parasites Female Sprague Dawley (SD) rats, weighing approximately 120 g, were purchased from Norman Bethune University of Medical Science (NBUMS), China. Trichinella spiralis (ISS534) preserved in Food-Borne Parasitology Laboratory of Key Laboratory for Zoonoses, Jilin University, was proved by OIE Collaborating Center on Foodborne Parasites in Asian Pacific Region and 6

maintained by continuous passage infections in our laboratory. Muscle larvae (ML) (35 dpi) were recovered from the rats with artificial digestion fluid (1% pepsin/HCl) (Li et al., 2010). 2.2 T. spiralis infection model and preparation of the rat serum samples The forty SD rats were divided into two groups randomly after resting for a week; one group was infected with 3500 ML of T. spiralis each by oral gavage, and the other group was administrated the same volume of water by oral gavage as the control. Before infection, peripheral blood samples were obtained from the tail vein of the rats after 12 hours of fasting. Anesthesia was applied in the two groups to collect blood samples at 28 days post infection (dpi), and several experimental animals died due to the inaccurate dose of the anesthetic agent. Therefore, there were 19 rats left in the control group and 16 rats left in the infected group. The collected samples were stored at 37℃ for 30 min and then incubated at 4℃ for 12 hours. The serum was obtained by centrifugation at 3500 rpm for 10 min. The serum samples were then stored at -20℃. 2.3 Preparation of Ag NPs A previously reported method was used to manufacture a silver (Ag)

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nanoparticle solution (Leopold and Lendl, 2003). Briefly, 9 mL of sodium hydroxide solution (0.1 M; Beijing Chemical Works, Beijing, China) was added to 10 mL of hydroxylamine hydrochloride solution (6×10 -2 M; Tianjin Huadong Chemical, Tianjin, China) and then rapidly added to 180 mL of silver nitrate solution (1.11×10 -3 M; Beijing Chemical Works, Beijing, China). The resultant solution was mixed intensely until it exhibited a uniform milky gray color. The silver nanoparticle solution was available in one week (Lu et al., 2018). The solution (100 mL) was centrifuged at 10000 rpm for 10 min, and a 5-mL sediment was obtained in the tube; then, the processed solution was mixed with the blood serum in the next process. The final solution of Ag nanoparticles was characterized by the UV/visible absorption spectrum and Transmission Electron Microscopy (TEM). 2.4 SERS measurement The Ag nanoparticle solution and the sera were mixed at a ratio of 1:1 (5 μL: 5 μL); then, a pipette tip was used to mix them. The mixture was hatched at 4℃ for 2 h before the next process. Then, a drop of the mixture (about 10 μL) was transferred onto a quartz glass and left to dry at room temperature. 90-mW, 785-nm diode laser of the confocal Raman micro-spectrometer (HORIBA, Japan) was used to measure the SERS spectra of serum at a range of 300–1500

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cm-1. The system consists of an electric-cooled CCD detector and a notch filter which can eliminate Rayleigh scattering. The SERS spectra parameters measured were 30 s integration time for 2 accumulations with Olympus ×50 objective lens (Olympus Corporation, Japan). Three spectra were collected at different locations from each serum sample, and the mean was recorded as the data for use in subsequent analysis. 2.5 Data processing and multivariate statistical analysis The pre-treatment and processing data were previously reported in a previous study (Wu et al., 2018). Briefly, a multi-polynomial fitting algorithm (Zhao et al., 2007) was used to pretreat all original SERS spectra to remove the fluorescence background and obtain pure serum SERS spectra. Then, Origin 2017 software (Origin Lab Inc., United States) was used to normalize all the serum SERS spectra, and the integral of the area under the curve was obtained at a range of 300–1500 cm-1. In addition, principal components analysis (PCA) with linear discriminant analysis (LDA) was used to establish a predictive diagnostic model of T. spiralis infection. ROC curve was used to assess the efficiency of serum SERS spectra combined with LDA and PCA for the diagnosis of the infection. The above processes were applied to the SPSS 24 (SPSS Inc., Chicago, IL) and Origin 2017 software.

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2.6 Ethics statement Rat studies were performed in strict accordance with the Institutional Guiding Principles for Biomedical Research Involving Animals. The experiments were approved by the Jilin University Animal Care and Use Committee (No: IZ-2009-008). All efforts were made to minimize suffering. 3. Results 3.1 Successful preparation of silver nanoparticles

Fig. 1A shows that the maximum absorption spectrum of colloidal Ag NPs is at 423 nm, and the full width at half-maximum is about 100 nm. The illustrated photo of colloidal Ag NPs shows a milky gray color that is the same as what reported by other researchers (Yu et al., 2017). The transmission electron microscopy (TEM) micrograph of colloidal Ag NPs shows that the average size and standard deviation are 25 nm and 5 nm, respectively (Fig. 1B). 3.2 Results of SERS measurement Fig. 2(1) shows the Raman spectrum of serum with Ag NP colloid; Fig. 2(2) shows the Raman spectrum of serum without Ag NP colloid; and Fig. 2(3) shows the Raman spectrum of Ag NP. The three spectra were measured under

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the same condition. Comparison of Figs. 2(1) and 2(2) shows that the strength of many principal vibration bands increases significantly, which is the result of a strong interaction between colloidal Ag NPs and serum. Due to this interaction, colloidal Ag NPs intimately attached to the surfaces of biochemical substances of serum, resulting in abnormally enhanced Raman scattering strength. In cases in which the colloidal Ag NPs were not added to the original serum, only a small amount of Raman peaks could be observed because of the large fluorescence background. However, the SERS spectra could significantly decrease the intensity of the fluorescence background and present a clear Raman band. In addition, it can be seen from Fig. 2(3) that the colloidal Ag NPs has no interference signal in the tested spectral range. After removing the fluorescence background of the original SERS data, all the measured SERS spectra were normalized under the curve in the 300–1500 cm-1 wavelength range to more properly compare and analyze spectral shapes between different spectra. The normalized mean SERS spectra were obtained from 40 serum samples of rats with and without T. spiralis infection, and the overlying shaded colors represented the standard deviations in Figs. 3A and 3B. Comparison of the normalized averages of SERS spectra in Figs. 3A and 3B shows that in the two groups and two periods, the SERS peaks at 494, 589,

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638, 813, 888, 1074, 1135, and 1206 cm-1 are consistent. In addition, the Raman shifts at 476, 686, and 1363 cm-1 are significant between the control and Ts groups at 28 dpi, but the same Raman shifts are insignificant between the two groups at 0 dpi (uninfected) shown in Fig. 3C. These normalized intensity differences were observed more clearly on different spectra between the two groups (bottom of Figs. 3A and 3B). In addition, to better understand the molecular basis of the observed rat serum SERS spectra, the SERS peak distributions are shown in Fig. 3A. According to the literature (Feng et al., 2010; Huang et al., 2003; Pichardo-Molina et al., 2007; Khoon Teh et al., 2009; Lin et al., 2011; Liu et al., 2011; Movasaghi et al., 2007; Teh et al., 2008), Table 1 lists the tentative assignments of SERS bands. In addition, in the spectral ranges of 384–754 cm-1 and 1321–1441 cm-1, there are significant differences in Raman shapes at 28 dpi but not at 0 dpi, indicated by the imaginary line in Figs. 3A and 3B. In conclusion, at 0 dpi, the SERS spectra of serum are not significantly different between the control and Ts group; however, at 28 dpi, differential spectra show prominent SERS peak changes occurring in the sera of the control and Ts group, confirming the potential diagnostic role of serum SERS in T. spiralis-infected rats or animals. 3.3 The results of statistical analysis and mathematical model

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PCA-LDA multivariate statistical method was used for serum SERS spectral analysis and differentiation diagnosis algorithms. PCA is a multivariate analysis technique widely used in the spectral analysis; it makes major differences in the original dataset that can be captured and shown by only a few main component (PC) variables by defining a new dimensional space (Feng et al., 2013). These PCs were used for building a model with an identifiable resolution. LDA determines the directions of spectral space by calculating the linear combinations of variables, which can minimize the variance in groups and maximize the difference between groups (Lin et al., 2011). In this research, PCA was used to analyze the SERS spectra of the control group and T. spiralis group at 28 dpi. An independent-sample t-test of all PC scores indicate that PC2 and PC7 are significant (P<0.05) for discriminating serum with T. spiralis infection from serum of the control group. Fig. S1A shows the eigenvalues of PCs and Fig. S1B shows PC2 and PC7 loadings. Many spectral peaks appearing in the serum SERS spectra are captured by PC2 and PC7. To explore the application of PC scores in diagnostic classification, Fig. 4A shows the direct comparisons between the control and Ts groups. The serum SERS data of the control and Ts groups are divided into two separate groups

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based on the combinations of PC2 and PC7. The corresponding separation line in Fig. 4A differentiates the T. spiralis-infected samples from the control samples with a specificity of 94.7% and sensitivity of 87.5%. Compared with PC2 and PC7, Fig. S2 shows that the combinations of PC1 and PC3 is less effective in separating the two groups. These results show that the combinations of significant PCs would result in proper accuracy for classification. In order to improve serum classification, we also applied LDA generation diagnostic algorithms using the two most significant PCs (PC2 and PC7). Fig. 4B shows the linear discriminant scores of the control and Ts groups calculated from the multivariate statistical technique dataset (significant PCs, PC2 and PC7) in the LDA model. With 0 as the threshold, the diagnostic sensitivity, specificity, and accuracy of T. spiralis infection are consistent with the above. In order to evaluate the effect of SERS dataset multivariate approaches for the diagnosis of T. spiralis infection, receiver operating characteristic (ROC) curve (Fig. 4C) was generated from the scatter diagram in Fig. 4B. The integral area under the ROC curve is 0.977. The ROC curve comparative evaluation indicates that the diagnosis algorithm based on PCA-LDA is

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effective in the diagnosis of T. spiralis infection and control serum samples. 4. Discussion 4.1 SERS spectra Our results showed a specific difference in serum SERS spectra between the Ts and control groups, indicating that this method has great potential in the detection of Trichinella infection. Biomolecules in serum, such as nucleic acids, proteins, lipids, etc., can produce SERS spectra, and they might undergo quantitative changes in the course of diseases, so that they can be used as a basis for diagnosis. To better understand the locations of biomolecules at the corresponding positions of the spectral changes, some tentative assignments for the observed SERS bands were obtained from the literature, which are listed in Table 1 (Feng et al., 2010; Huang et al., 2003; Pichardo-Molina et al., 2007; Khoon Teh et al., 2009; Lin et al., 2011; Liu et al., 2011; Movasaghi et al., 2007; Teh et al., 2008). Differences in serum SERS spectra between the infected and control groups can reflect changes in some biomolecules after Trichinella infection. For example, the SERS peak at 494 cm-1 represents a disulfide bond, and serum albumin is an extracellular transporter with 17 disulfide bonds (Liu et al., 2011). Interestingly, the spectral value of the SERS band at this position decreased in the T. spiralis-infected serum, indicating that 15

Trichinella infection might decrease the albumin serum levels. This result might be related to an increase in antibody levels caused by Trichinella infection. The SERS band at 725 cm-1 indicates the C-H bending mode of adenine, and the SERS spectra value in the infected serum is lower than that of normal serum, indicating that the metabolism of DNA or RNA base in the serum of infected rats is abnormal. This peak has also exhibited changed in other studies, such as studies on cancer (Feng et al., 2010). It is still to be elucidated why nucleic acid levels in infected rats’ sera increase. In response to this phenomenon, we propose two hypotheses: 1. When the newborn larvae (NBLs) of T. spiralis move in the blood, a lot of cells, including blood cells and vascular cells, are destroyed in this process; 2. When the NBLs arrive in the muscle, the muscle cells are destroyed due to the invasion and formation of the nurse cells. The spectral signals of tyrosine (638 cm-1) and 1-arginine (494 cm-1) in the serum of infected rats were lower than those in the normal serum, indicating a decrease in the levels of certain amino acids compared to the normal serum. Infection with Trichinella might result in metabolic changes in the host, and there are similar situations in other diseases (Feng et al., 2010). 4.2 Statistical analysis

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Due to the complexity of serum components, it is likely that many biochemical substances affect the diagnosis of diseases at the same time (Lin et al., 2011). Therefore, we must extract all the possible serum changes in the serum SERS spectra to provide a diagnostic basis for Trichinella infection. Multivariate statistical analysis (e.g., PCA and LDA) (Feng et al., 2010; Pichardo-Molina et al., 2007; Notingher et al., 2005) enables the analysis of the entire serum spectra and improves classification efficiency. Currently, this analytical method has been widely used in cancer tissues, cell and blood testing (Pichardo-Molina et al., 2007; Teh et al., 2008; Lin et al., 2011) as well as protozoan (Pérez et al., 2018). The PCA analysis can reduce the large amount of data contained in the measured SERS spectra to several important principal components. As shown in Figs. 4A and 4B, the PC2 and PC7 scores of the infected and control groups formed two different and independent clusters. The PCA-LDA-based spectral classification with leave-one-out cross validation method can be used to distinguish between Ts and control serum with a sensitivity of 87.5% and specificity of 94.7%. This method can significantly improve the diagnostic accuracy. The ROC curve is used to further illustrate the performance of a multivariate method to detect T. spiralis infection. One of the most commonly

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used parameters for diagnostic test accuracy is the area under the ROC curve, which can be represented by a numerical value between 0 and 1, and the closer the value to 1, the higher the diagnostic accuracy (Obuchowski, 2003; Obuchowski et al., 2004). In our analysis, the value of the integration area under the ROC curve (Fig. 4C) is 0.977. The results of this study indicated that

the

diagnostic

algorithm

could

distinguish

between

the

Trichinella-infected and control groups, and the diagnostic performance of Trichinella detection can be effectively improved by using serum SERS spectra combined with diagnostic algorithms. 5. Conclusions On the basis of SERS, a potential label-free serum method was developed for the detection of serum from T. spiralis-infected rats. This study showed significant variations in the SERS spectra between the control and T. spiralis-infected serum, and high diagnostic accuracy could be achieved based on the PCA-LDA diagnosis algorithm, indicating the great potential of colloidal Ag NPs incorporated with PCA-LDA diagnostic algorithms based on SERS serum analysis for non-invasive T. spiralis infection screening and detection. Compared with the prior methods, this detection method has the advantages of no damage, ease of obtaining detection samples, no need to

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make specific marks on the samples, rapid detection, suitability for on-site detection, and high accuracy. In the next step, we will focus on collecting blood serum SERS data from more hosts, including swine and human samples, to carry out a comprehensive evaluation of the practical value of this new method for detecting T. spiralis infection. Funding This work was supported by the National Key Research and Development Program of China (2018YFC1602504), the National Natural Science Foundation of China (31520103916, 31872467), Guangdong Innovative and Entrepreneurial Research Team Program (NO. 2014ZT05S123) and JLU Science and Technology Innovative Research Team.

Declaration of Competing Interests None. Author Statement All authors have read and approved to submit the manuscript to this journal. There is no conflict of interest of any authors in relation to the submission. This paper has not

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been submitted elsewhere for consideration of publication.

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Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could

have appeared to influence the work reported in this paper.

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Fig. 1. Characterization of silver nanoparticles. A: The UV-VIS spectra of the colloidal Ag NPs, and inset photograph of colloidal Ag NPs; B: The TEM micrograph of colloidal Ag NPs surface.

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Fig. 2. Comparison of SERS, RS and background Raman signal. (1) The rat blood serum infected with T. spiralis was mixed with Ag colloid in a 1:1 ratio to obtain SERS spectra of the serum, (2) conventional Raman spectra of free Ag colloid in the same sample and (3) background Raman signal of Ag colloid.

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Fig. 3. Results of SERS measurement and analysis. A: Compared mean SERS spectra of serum samples from the two groups, including control (red curve, n=20) and T. spiralis (black curve, n=20) on 0 days post-infection (dpi), which was uninfected. To correct for changes in absolute spectral intensity, the spectrum was normalized to integral region under the curve. The shaded areas are mean standard deviations. The difference spectrum is at the bottom (blue curve). B: The spectrum analysis was same with A, but the samples were on 28 dpi. C: Comparing the standard deviations and mean intensities of the selected peaks, there is no significant differences between the two groups on 0

31

dpi, while it shows significant differences between two groups on 28 dpi. D: Comparison of the standard deviations and mean of the integration area in the SERS wavebands of 384-754 and 1321-1441 cm-1 (indicated by the imaginary line in A and B). On 0 dpi, two areas are insignificant in statistic, but two groups are significant on 28 dpi. All of the results are expressed as the means ± SD. Student’s t-test was used to determine the significance of differences.

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Fig. 4. Results of statistical analysis and mathematical model. A: The relationship between the second principal component (PC2) and the seventh principle component (PC7) in the control group and the infection group. The dotted line (PC7=-1.1487PC2+0.1018) is used as the diagnosis algorithm to separate two groups, with a sensitivity of 87.5% and specificity of 94.7%. B: Multivariate statistical techniques were used to calculate the control and Ts categories from the data sets in the LDA model, and the linear discriminant scores were obtained by using the method of leave-one-out, cross-validation. The diagnosis sensitivity, specificity and accuracy of the separate line were 87.5% (14/16), 94.7% (18/19) and 91.4% (32/35). C: ROC curve of Ts and control samples identification results generated by principal component analysis and linear discriminant analysis (PCA-LDA). The integrated areas under the ROC curve are 0.977.

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Table 1 SERS peak positions and tentative vibrational mode assignments a Peak positions (cm-1) 494

Vibrational mode

Major assignment

v (S-S)

L-Arginine

589

Amide-VI

638

v (C-S)

Tyrosine

725

δ (C-H)

Adenine

813

v (C-C-O)

L-Serine

888

δ (C-O-H)

D-Galactosamine

1074

v (C-C)

Phospholipids

1135

v (C-N)

D-Mannose

1206

Ring vibration

Tyrosine

a

v, stretching mode; δ, bending mode.

34