Colorectal cancer is the third most frequently diagnosed type of cancer and the fourth leading cause of oncological disease-related deaths in the world. It is expected to increase by 60% to more than 2.2 million cases and 1.1 million cancer deaths by 2030.1 Current methods of colorectal cancer diagnostic include invasive colonoscopy2 and sigmoidoscopy,3 colon computer tomography2 or noninvasive biochemical4/immunochemical tests of fecal material.5 Noninvasive initial tests are followed by a biopsy test and an appropriate treatment. The ideal biofluid for this type of cancer diagnostics is blood plasma, which carries information about the ongoing processes in the human organism. Individual components of plasma are sensitive to the mutagenic processes.6 These changes in the biomolecular composition of blood plasma appear as an increase in specific nucleic acids and a decrease in the saccharide and protein content of the plasma of colorectal cancer patients compared to those of healthy subjects.7 Several researchers have tried to detect these changes in cancerous blood plasma composition by Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS). RS is a well-established analytical technique and provides fingerprint information of the structure and conformation of macromolecules, such as proteins, nucleic acids, and lipids.7,8 It has been used as an experimental diagnostic method for the differentiation of normal and cancerous tissues from larynx,9 nasopharynx,10 breast,11 lung,12 cervix,13 prostate,14 salivary,15 skin,16 or brain,17 as well as other diseases, such as bacterial infections,18 atherosclerosis,19 diabetes,20 or even Alzheimer’s21 and inflammatory diseases.22 However, RS also has its limitations as the signal intensity is often very weak and, as a result, the fluorescence of impurities or the specimen itself can hide the Raman spectrum, or the intense laser radiation can destroy the sample.23 This problem can be solved by SERS, which is a vibrational-spectroscopy technique based on the enhancement of Raman scattering by metallic nanostructures with suitable plasmonic hotspot characteristics (metal nanoparticles,24 nanorods,25 dendrites,26 etc.).27 The hotspots are localized areas of intense local field enhancement caused by local surface plasmon resonance (SPR), where the electromagnetic field is the most intense, and provide enormous signal enhancement.28 Because of this, SERS is highly suitable for detailed analysis, such as early and precise diagnostic methods for oncologic diseases. Feng et al.29 developed a method for the early detection of nasopharyngeal cancer from blood serum samples using SERS, which was tested in a clinical trial with 43 patients with diagnosed cancer and 33 control healthy volunteers. The untreated serum was mixed with silver colloidal particles, which were used as metal SERS active surfaces for signal intensity enhancement. The signal intensities of individual specific Raman peaks were compared using principal component analysis (PCA) and discriminated between the groups with 90.7% sensitivity and 100% specificity.29 Further studies of cervical,30 colon,31 stomach,32 parotid gland,33 and prostate34 cancer found that the individual types of cancer had characteristic Raman peaks. The unwanted and uncontrollable aggregation of the nanoparticles can be avoided by replacing the colloids of noble metals by electrodeposited SERS substrates. The intermediate step, mixing the colloidal particles with the sample and its subsequent incubation, can then be omitted, which accelerates the whole diagnostic process.
One of the “bottom up” economical methods of preparing SERS substrates is the electrodeposition of metallic nanostructures onto an indium tin oxide (ITO) glass carrier substrate from an electrolyte.35,36 Several techniques have been employed, such as utilizing a template,37 applying double potential pulses,38,39 galvanostatic deposition,40 or a cyclic voltammetry scan.41 Less frequently pulsed double-potentiostatic deposition has been used, although it is a reliable and simple method for controlling the size and morphology of electrodeposited nanostructures by changing the potentials for the nucleation and growth phases.42 The substrate preparation can be optimized by altering the coating bath compositions, applied potentials, and the duration of individual steps.36
This paper describes the preparation of silver dendritic nanostructured films with a suitable morphology for diagnostic high sensitivity SERS substrates by optimization of the pulsed double-potentiostatic method. The resulting substrates were characterized by their high hydrophobicity without any pretreatment by alkylsilanes43 or alkanethiols44 as it is essential to fix the water-soluble samples to a specified location on the SERS substrate, because preconcentration of the sample at a fixed place is desirable for successful SERS analysis.45
The electrochemically prepared SERS substrates were tested with R6G as a model analyte, to select the substrate providing the highest signal enhancement. This material was then used in primary experiments as a superhydrophobic SERS substrate for the rapid, noninvasive, and nonlabeled diagnosis of colorectal cancer.
Chemicals and Materials
Acetone, ethanol, and silver nitrate were obtained from Sigma Aldrich (Missouri) and used without further purification. Rhodamine 6G (R6G) was purchased from Acros Organics (New Jersey). Ultrapure water was used for the preparation of solution and cleaning samples. ITO glass slides with resistance were purchased from Zhuhai Kaivo Optoelectronic Technology Co., Ltd. (China). Plastic cassette to hold the SERS substrate was obtained from Jieyi Biotech Co., (Shanghai, China). Blood collection tubes with as an anticoagulant were used for blood samples (BD Vacutainer, New Jersey).
Substrate Sample Preparation
Silver layers were electrochemically deposited onto ITO glass used as the working electrode in a three electrode electrochemical cell, with a platinum counter electrode and saturated calomel electrode (Autolab PGSTAT302N Metrohm, The Netherlands). Before the electrodeposition, ITO glass plates were cut into small pieces (). They were ultrasonically cleaned for 15 min in each of the following solvents: acetone, ethanol, and ultrapure water. The electrolyte solution contained at a pH of 4.8. For optimization of the most appropriate morphology for the SERS diagnostic substrate, 16 samples by pulsed double-potentiostatic method ( for 1 s, ) at laboratory temperature were prepared. The number of pulses was varied from 10 to 70 and duration time from 0.5 to 2 s. The area of the electrodeposited dendritic SERS surface was . After electrodeposition, the substrate sample was rinsed with ultrapure water and dried in the air.
Five microliters of an aqueous solution of R6G was pipetted onto all 16 electrochemically prepared silver surfaces as a model analyte. For concentration-dependent analysis, solutions were used with concentrations from to . The diameter of the air-dried droplet of R6G was 1.7 mm.
Blood plasma samples were used to examine the selected Ag SERS substrate for use in the detection of colorectal cancer. Samples from healthy volunteers came from a clinical diagnostics laboratory. Blood samples from female and male colorectal cancer patients, aged 35 to 75 years, came from the Surgery Department, Louis Pasteur University Hospital, in Kosice. Five milliliters of blood was collected by venipuncture into vacutainer blood collection tubes containing as an anticoagulant. Blood was taken systematically between 7:00 a.m. and 7:30 a.m. and was stored at 8°C. The plasma was separated from the blood by centrifugation at 3000 rpm for 10 min. The supernatant plasma was pipetted into 2 ml Eppendorf tubes and stored at . For SERS measurements, of a mixture of blood plasma and ultrapure water, in a ratio , was dropped onto the selected silver surface and dried under ambient air condition. The diameter of air-dried droplet of water diluted blood plasma was 2.1 mm.
Surface Characterization Techniques
The morphology of the silver surfaces was characterized by a scanning electron microscope (SEM; JEOL JSM 7000F, Japan). Raman measurements were carried out using a Renishaw InVia Raman microscope (Gloucestershire, United Kingdom) with an excitation wavelength at 532 nm for R6G analysis and 785 nm for analysis of human blood plasma. The laser beam was directed onto the sample through a objective lens. The spectra were collected through the same objective over the range of 400 to . For R6G measurements, the integration time was set at 10 s, three accumulations, and 0.05% of original laser power, whereas for blood plasma analysis, the integration time was set at 10 s, three accumulations, and 1% of original laser power. SERS spectra were corrected to the baseline and averaged from three scans. The concentration dependence was determined by measurement from 10 random spots for each concentration of R6G.
Test of Contact Angles
Contact angles were measured by dropping of water, thiodiglycol, and glycerol (Table 1) onto the silver surfaces. The droplets were captured by EO-311C USB color camera (Edmund Optics) and ImageJ software with plug-in LB-ADSA was used to determine the contact angles.
Values of contact angle for water, thiodiglycol, and glycerol measured on silver substrates prepared at different number of pluses and pulse duration.
Electromagnetic Simulation of Ag Surface
To analyze the local electric field distribution of the dendritic structures, we performed two-dimensional finite-difference time-domain (FDTD) simulation (Lumerical FDTD Solution 8.15.786, Lumerical Inc., Canada). Simulation models consisted of single silver (Palik) dendritic structure, where the diameter and length of trunk were set to 300 and 4000 nm, respectively. The branches consist of spheroid nanoparticles with diameter 300, 250, 200, and 150 nm from trunk to outside, respectively. The light source was planewave (400 to 750 nm) propagating along the direction with the polarization parallel to the -axis. The boundary condition was set with periodic boundary conditions in -direction and a perfectly-matched-layer boundary condition in -direction. Mesh size was set to 5 nm. The frequency-domain field profile was set to 532 nm in direction.
Silver Surface Characterization by Scanning Electron Microscope
An electrochemical deposition method was used for the preparation of diagnostically functional silver SERS substrates. Compared to other structures, Ag dendrites possess many multilevel branching nanostructures, thus allowing a large specific surface area and the corresponding complex nanostructure may be more favorable to absorb probe molecules. A strong electromagnetic coupling can be formed in the space between two adjacent branches from the coupling of SPR. Thus, a large number of hotspots would exist in the spaces at the end of branches or among lateral Ag branches. These factors should favor Ag dendritic nanostructures to be used as high-active SERS substrates. By varying the number and duration of pulses in a double-potentiostatic electrodeposition of silver substrates, the dendritic stage of growth, density, and distribution of the resulting shape of the deposited silver structure could be controlled. The morphology of the surfaces was determined by direct SEM imaging (Fig. 1).
There is a clear increase in the surface coverage density with an increasing number of pulses and time of electrodeposition. The optimum composition of the electrolyte solution for deposition was found to be . This low concentration of ions causes the deposition of silver fractals of a dendritic shape with a microdimensional stem and nanosized lateral branches. It is assumed that the “hotspots” formed between the individual dendritic branches are responsible for signal enhancement.46
All stages of the dendritic growth were deposited on the ITO glass (Fig. 2). Nanosized nuclei with sizes from 200 to 900 nm were observed in the Ag substrates A, E, I, and M (Fig. 1) deposited with 10 pulses at all durations of . There was a noticeable lengthening of the stems and growth of lateral branches with increasing numbers of pulses at a constant duration of potential . The length of the main stem increased from 800 to 6000 nm and the smallest lateral branches from 80 to 200 nm. The density of the coating visibly increased with an increasing number of pulses and surfaces L, O, and P (Fig. 1) were completely covered by the silver dendritic structures in multiple three-dimensional layers. Subsequent SERS analysis and calculation of signal enhancement factor (SEF) showed that the highest values of SEF were provided by surface D (Fig. 1). Too dense coverage of the ITO glass by dendrites was not suitable for SERS signal enhancement. On the other hand, a light coverage of ITO glass by silver structures was not SERS active when the distance between deposited dendrites was too large.
Surface Hydrophobicity Test Measurements
The measurement of the contact angle and surface hydrophobicity is important because of the need for the sample to be concentrated at the point of analysis. A sample spreading over the hydrophilic surface of the silver substrate leads to a decreased local concentration and a reduction in the number of detectable analyte molecules. Therefore, the preparation of a surface that is superhydrophobic should lead to more detailed and accurate measurement of the final SERS signal. The contact angle values for water increased for all the silver surfaces from those prepared with 10 pulses to 70 pulses (Table 1). All substrates were hydrophobic, and samples C, D, H, and P (Fig. 1) were evaluated as superhydrophobic because the contact angle was over 150 deg. For the nonpolar solvent thiodiglycol, the contact angles decreased from 10 pulses to 50 pulses and then increased at the surface prepared with 70 pulses. The irregular changes in the contact angle of thiodiglycol were probably due to its low surface tension. The trend for glycerol as a polar solvent was opposite to that of water, and the contact angles decreased from 10 pulses to 70 pulses.
A dependence of the SERS signal enhancement of R6G with the contact angle was observed. Figure 3 shows an increase of the signal enhancement with increasing the contact angle values for a peak with Raman shift of [Fig. 3(a)] and [Fig. 3(b)]. The silver surfaces A, E, I, and M, with the lowest values of contact angle, and the sparse coverage of the silver dendritic structures showed the lowest signal values for both SERS peaks. This was thought to be because the distance between each nanostructure was too large to create an electromagnetic field strong enough to provide signal enhancement. By contrast, the hydrophobic surfaces D, H, L, and P with contact angles about 150 deg showed significant enhancement.
Results of the Electromagnetic Simulation of Ag Surface
Figure 4 shows the FDTD simulation of the electric field on a dendritic nanostructure. The hotspots on the edge of the branches, where the enhancement of the electric field is maximal, are clearly visible. Additional hotspots are present on the folds, where the spherical nanoparticles are linked to each other and to the trunk.
SERS Analysis of Rhodamine 6G
The SERS spectra of R6G with different concentrations were recorded using the silver dendritic substrates (A-P) in the Raman shift region from 400 to . A representative Raman spectrum with concentration on substrate D is presented in Fig. 5 and the corresponding vibrational modes are shown in Table 2.
The characteristic Raman shift of the most intense peaks observed in the SERS spectrum of R6G recorded at λexc=532 nm.
To probe the detection limit of the silver surface D, R6G with concentrations from to were compared (Fig. 6).
The characteristic peaks of R6G are clearly identified in all spectra, and the peak intensity increased with increasing R6G concentration (Fig. 6) over the range from to (Fig. 7). SERS spectra were measured and averaged from 10 random points on the surface. We selected two representative peaks: corresponds to out of plane bend mode and corresponds to aromatic stretching vibrations, for analysis of the concentration-dependent variation. The signals evidently decrease gradually, however, nonlinearly, with decreasing concentration of R6G. The deviation of the peak intensity at was calculated as 17.1% and for peak as 15.9%.
The SEF is an important indicator of the SERS activity of a substrate. The analytical SEF of the silver SERS substrates was calculated using Eq. (1):47
SERS Analysis of Blood Plasma
As it gave the strongest enhancement, the silver SERS substrate D was integrated into a plastic cassette to provide a compact diagnostic platform for direct use in the medical lab (Fig. 8).
Using this equipment, the main peaks in human blood plasma, diluted with water, could be detected by SERS analysis with high signal intensity (Fig. 9).
The spectrum represents the vibrational modes of various biomolecules, such as proteins, lipids, and nucleic acids, which could change in quantity or conformation with the development of colorectal cancer. Tentative assignments for the main peaks are listed in Table 3.31
Peak assignment of the characteristic Raman shift observed in the SERS spectrum of blood plasma recorded at λexc=785 nm.31
When comparing the results from healthy patients with those with cancer, determining sensitivity and selectivity is a necessary part of the evaluation of clinical research results. A high sensitivity [Eq. (2)] is a required characteristic for initial diagnostic tests and measures the ability of the test to correctly identify all those patients with the disease. Further specific and detailed tests may show that some of the patients do not have the disease and these are then identified as false positives. By contrast, the specificity of a clinical trial [Eq. (3)] refers to the ability of the test to identify those patients without the disease correctly. The combination of an initial test with high sensitivity/low specificity and a second test with low sensitivity/high specificity should produce a diagnostic method, which will correctly find all the true positives and then correctly identify any false positives as actually negatives:48
The SERS of blood plasma samples were recorded from 15 healthy volunteers (normal samples) and 15 colorectal cancer patients. The measured spectra were clean, with no interfering signals in the spectral range of interest. All the measured SERS spectra were normalized to the integrated area under the curve from 400 to and the mean spectra with their standard deviations were overlaid [Fig. 10(a)].
All the main SERS peaks (Table 3) were observed in both normal and cancer samples. The SERS peaks at 725 and were more intense in cancer samples, in comparison to the normal samples [Fig. 10(a), lower trace]. All other measured SERS peaks were more intense for the normal samples. The resulting difference map of the variations of the major peaks is shown in Fig. 10(b).
To compare the spectra from oncology patients to normal subjects, a comparative analysis based on the mean intensities and relative standard deviations was performed. These most significant deviations were observed in the Raman peaks at 494, 638, 725, 823, 881, 1206, and [Fig. 10(b)]. Two SERS peaks, whose intensities obviously differ between normal and cancer blood plasma samples, at , assigned to adenine, and , to tyrosine, were considered important differential diagnostic parameters.
Result of Statistical Analysis
The ratios of the intensities of selected pairs of peaks for individual samples were compared (Fig. 11).
The mean value () of the versus ratio (adenine peaks compared to tyrosine) for cancer plasma samples (, ) was significantly different from the mean ratio for normal plasma samples (, ). Further examination of the ratios of the peak intensity at (adenine) to the peak intensities at (L-arginine) and (amide I peak of proteins) gave the mean values () for the ratios of and for normal plasma samples of and and for cancer plasma samples of and , respectively. The separation of the samples can be represented by decision lines calculated from average values of compared intensities of normal and cancer samples, where (A) = 0.38, (B) = 1.02, and (C) = 0.67 (Fig. 11). When the ratios were compared using the student’s -test (), the values for the colorectal cancer and normal plasma samples were statistically and significantly different. From these ratios, the sensitivity of the test was calculated as , , and , respectively. Further examination found that the ratio for all the plasma samples from the normal group was identified with 100% specificity so that the test using this ratio satisfied both the sensitivity and specificity criteria.
For a more comprehensive comparison, PCA49 of the intensities at the wavelengths of the selected peaks from Fig. 10(b) was carried out, which reduces the primary variance in the original dataset to a few principal component (PCs) variables. These PCs can be used to build a model with a resolution of recognition.49 The fluorescence background of the original SERS data was first removed using a modified multipolynomial fitting algorithm. All spectra were normalized by the integrated area under the curve and then dataset was fed into the XLSTAT software for PCA analysis. The PC1 accounted for 69% of the variance, PC2 for 14%, and PC3 for 8%.
Comparisons of the scores for PC1 and PC2 [Fig. 12(a)] show two separate groups of colorectal cancer data (red points) and normal plasma points (black points). Samples from the two groups were separated from each other with 100% sensitivity and specificity. Two differentiated groups of points were also observed when combining PC1 and PC3 [Fig. 12(b)]. This option was found to be capable to distinguish cancer samples from normal samples with 94% sensitivity and 100% specificity.
This initial clinical study demonstrated statistically significant differences between the blood plasma of oncology patients and healthy volunteers, which have been successfully detected by SERS. Colloidal solutions of nanoparticles of noble metals used in previous research of Feng and Lin were replaced by electrochemically deposited silver dendritic SERS substrates.7,31 Ag dendrites possess multilevel branching nanostructures with a large specific surface area with a large number of hotspots at the edge of branches or among adjacent Ag branches. These Ag dendritic nanostructures with proven high SEF can be used as a high-active SERS substrates for detection of changes in blood plasma composition.
It is proposed that the intensity differences between cancer and normal serum are the results of molecular changes associated with carcinogenesis. The SERS peak at is attributed to the amide I peak of proteins in the -helix conformation and human serum albumin as a principal extracellular transport protein. This protein is a globular molecule with 17 disulfide bridges, which is also observed as a SERS peak for stretching vibration at .50 In cancer serum, both of these two SERS peaks are less intensive, indicating that colorectal cancer may be associated with a decrease in the relative amounts of protein. Similar changes were observed in earlier studies of colorectal, nasopharyngeal, and gastric cancer blood plasma with colloidal SERS substrates.7,29,31 The peak at assigned to the bending mode of adenine was reported to be an important molecular selector for colorectal cancer diagnosis.31 Higher relative intensities of adenine in cancer serum than in normal serum were attributed to the cancerous metabolism of DNA or RNA bases in the serum of oncological patients. This appearance was explained by apoptosis and necrosis, or by the release of intact cells into the bloodstream and their subsequent lysis.51 The lower signal intensities of SERS peaks of tyrosine (638, 823, and ) and L-arginine () were observed in cancer blood serum than those of normal serum. These changes have been attributed to the abnormal metabolism of the tumor.29
To classify colorectal cancer and normal plasma samples, an efficient algorithm based on empirical analysis of SERS spectra in terms of peak intensity ratio measurements was used. The ratio of peaks at (adenine) and (tyrosine) was used in the study of Lin et al.31 It was found that the ratio of intensity of adenine to intensity of tyrosine was markedly higher in cancer plasma than in normal serum samples, and the results of empirical analysis of SERS spectra showed 68.4% sensitivity and 95.6% specificity.31 Intensity variations of ratio of versus are significantly noticeable in the cancer group, which agreed with Han’s research.52 Differences between normal and colorectal cancer plasma may reflect changes in the relative amounts of potential biologic markers, according to the tentative assignments of plasma SERS peaks. Nonparametric intensity ratios may potentially be employed as an effective diagnostic algorithms for colorectal cancer detection and, in the present study, the multivariate statistical method PCA, which utilizes the entire spectra, was used due to the complexity of blood plasma composition. This analysis was performed to condense the recorded data to the important PCs. Scores of PC1 versus PC2 and PC1 versus PC3 for the normal and colorectal cancer sample spectra were found to distinguish the two groups. The diagnostic 97.4% sensitivity and 100% specificity was achieved.31
Preliminary experiments have suggested a diagnostic test for the early detection of colorectal cancer by SERS analysis of human blood plasma, which can be used in clinical practice. Easily made, low-cost, and rapidly deposited electrochemically prepared silver surfaces can be used as SERS substrates. The pulsed deposited silver surface created with 70 pulses with for 0.5 s had the highest enhancement factor () for R6G. This SERS surface was superhydrophobic, which allowed the sample to be preconcentrated at a fixed place on the substrate and prevent unnecessary spreading and flowing of the sample over the surface.
Comparison of the SERS spectra on this surface from 15 colorectal cancer patients and 15 healthy volunteers enabled the two groups to be differentiated by comparing the intensity with the 100% specificity and 94% sensitivity. PCA analysis of the SERS spectra found that the diagnostics was performed with 100% sensitivity and specificity for PC1 versus PC2 and 94% sensitivity and 100% specificity for PC1 versus PC3. These promising results now need to be confirmed on a larger sample of subjects to generate a more accurate and reliable diagnostic model, which could be suitable to apply in medical practice, thereby enabling early, rapid, and noninvasive diagnostics of colorectal cancer.
The authors have no relevant financial interests in this article and no potential conflicts of interest to disclose.
This research has been financially supported by grant VEGA 1/0074/17 of the Slovak Scientific Grant Agency and APVV-16-0029 of the Slovak Research and Development Agency. Clinical research was approved by the Ethics committee of Louis Pasteur University Hospital in Kosice.
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