Cancer Studies and Molecular Medicine

Open journal

ISSN 2377-1518

Deep-Ultraviolet Raman Spectroscopy for Cancer Diagnostics: A Feasibility Study with Cell Lines and Tissues

Nicole M. Ralbovsky, Vladimir Egorov, Eugene Moskovets, Paromita Dey, Bijan K. Dey and Igor K. Lednev*

Igor K. Lednev, PhD

Professor, Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA; Tel. 518-591-8863; Fax. 518-442-3462; E-mail: ilednev@albany.edu

INTRODUCTION

Cancer is known as one of the most dreaded diagnoses a doctor can make. The time, pain, effort, and oftentimes heartache associated with a cancer diagnosis will undoubtedly impact most people, either directly or indirectly, at least once in their lifetime. Cancer is the second-leading cause of death in the United States, and in 2018 alone, 18.1 million new cases of cancer were identified.1

Current methods for diagnosing cancers generally include lab tests conducted on body fluids, imaging exams, and biopsies. These tests are time-consuming and invasive for the patient. Specific and accurate diagnoses are not always made, with the potential for misdiagnoses to occur. There remains a need for the development of a much more accurate and sensitive method which can universally diagnose cancer and could be used in cancer imaging systems during surgery.

Raman Spectroscopy (RS) is a powerful analytical technique that allows detection and measurement of basic molecular classes in complex biological samples, such as body fluids, cells, and tissues.2,3,4 Therefore, the RS technique has a great potential for use as a diagnostic tool.5,6 RS identifies chemical specificity of molecular species without staining or labeling. The RS spectral “signature” may be used to build multivariate calibration and classification models for practical clinical needs.7,8 Raman signals from tissues originate from laser light backscattered by molecules. This allows for the collection of the backscattered light into the same optical fiber used for tissue illumination, which makes RS a practical tool for in vivo diagnostics and for examination of tissue specimens. RS system for tissue diagnostics can readily incorporate recent advances in the design of optical light collectors, miniaturization of lasers, and noise reduction in photon counting devices (e.g., cryogenically cooled charged coupled device (CCD) cameras). It is able to complement other techniques and, in many cases, provide new information unattainable by other approaches.9,10,11 The ability of RS to recognize and quantify biological molecules (lipids, proteins, DNA, etc.) through their unique vibrational signatures has been well demonstrated.12,13,19 Traditional RS has two main drawbacks: (1) a low efficiency (one RS photon is detected per 0107 photons irradiating a sample) of the inelastic light scattering compared to elastic scattering20,21 and (2) inherent fluorescence which accompanies RS studies.22 For example, using a traditional 785 nm excitation for RS, one typically records lowlevel noisy Raman signals. Acquisition of a high-quality spectrum requires extended accumulation time with cryogenically cooled CCD cameras, which limits the application of RS for tissue imaging involving hundreds or thousands of spectral measurements.

Coherent anti-stokes Raman scattering (CARS) can improve the efficiency of RS.23 In CARS, coherent vibrations of the molecules are driven at a selected frequency by simultaneous action of “pump” and “Stokes” lasers. The wavelength of a laser is fixed and that of another laser is tuned over a narrow range to excite the molecular vibrational mode of interest.24,25,26 The major drawback of CARS is the existence of a non-resonant background.27 Additionally, the CARS signal is decreased quadratically with the molecular concentration, making detection of low-abundance molecular species difficult. CARS requires the use of two rather expensive lasers generating picosecond pulses.28 Also, due to a nonlinear physical process with ultra short laser pulses, a broadening of recorded Raman bands is observed, which can be detrimental to chemical specificity.29 Because the signal generation in CARS critically depends on the tight overlap of the pump and Stokes laser foci, keeping this overlap constant can be problematic within the in vivo environment.30

Stimulated Raman Scattering (SRS) also utilizes “pump” and “Stokes” lasers to produce trains of picosecond pulses. When the difference between the frequencies of the “pump” and “Stokes” photons match the frequency of a molecular vibration, stimulated emission occurs; thus, the intensity of the pump laser transmitted by the specimen decreases while that of the Stokes beam increases. High-frequency modulation is used.31,32 Though SRS showed a great promise in studying thin tissue slices and cell cultures,33 its application to in vivo diagnostics has technical difficulties. The problem of keeping a tight overlap between the foci of two laser beams is also pertinent to SRS. Due to its nonlinear nature, the SRS signal is affected by variations in tissue composition (local density, presence of lipid droplets), non-Raman background, and photo damage.34

Deep-ultraviolet resonance Raman spectroscopy (UVRS) may be used as an alternative method for detecting cancer within biological specimens. It has been shown that a low RS yield can be dramatically increased by using excitation wavelengths near molecular electronic transitions (resonances).35 The strength of Raman signals, caused by excitation of vibrational modes associated with that transition, exhibits an exponential increase.36 The inelastic scattering by biological samples is increased manyfold around 200 nm due to strong electronic absorption of amide groups.37 Accordingly, the use of ultraviolet (UV) excitation can cause a thousand-fold increase in the Raman signal because the Raman cross section increases by the 4th power of the excitation light frequency.37 This makes UV resonance RS indispensable for studying protein structure and transformation,38,39,40 understanding fibrils and fibrillogenesis,41,42,43,44 analyzing bacteria and microorganisms,45,46,47 and various other biological applications. Most importantly, the use of laser wavelengths below 250 nm eliminates the problem of autofluorescence which impedes Raman studies on tissues using near-infrared lasers.48,49,50 Contrary to CARS and SRS, the UV resonance RS may simultaneously provide polychromatic information about Raman signatures of the tissue or body fluid being analyzed. An advanced processing of Raman spectra considerably improves tissue identification.51 Overall, UV excitation allows significant reduction of irradiation power to address safety concerns and to generate high signal-to-noise ratios for Raman spectra, as well as decrease its accumulation time. These factors are of great importance for a new UVRS technology.

The objective of this study is to explore the feasibility for deep-ultraviolet resonance Raman spectroscopy to be used for cancer detection in prostate cancer cell lines and cancerous brain tissues. Results show that UVRS is able to identify biochemical differences between malignant and healthy biological samples, thus opening the door for future studies to occur using UVRS for cancer imaging during surgery.

MATERIALS AND METHODS

Human Prostate Cell Culture

Normal human primary prostate epithelial cells (HPrEC) (ATCC® PCS-440-010™) and grade IV adenocarcinoma PC-3 prostate cancer cells (ATCC® CRL-1435™) were used for this study. The cells were obtained from the American Type Culture Collection (ATCC).

The HPrEC cells were cultured in 75 cm2 tissue culture flask (Corning® T-75 flasks catalog #430641) using Complete Growth Medium (ATCC). To make the complete growth medium, L-Glutamine, Extract P, Epinephrine, rhTGF-a, Hydrocortisone hemisuccinate, rh Insulin and Apo-transferrin at a final concentration of 6 mM, 0.4%, 1.0 mM, 0.5 ng/mL, 100 ng/mL, 5 mg/mL, and 5 mg/mL respectively were added to the Prostate Epithelial Cell Basal Medium (ATCC® PCS-440-030™). The HPrEC cells were passaged when the cells reached approximately 80% confluency. The flask was rinsed twice with 5 mL D-PBS (ATCC 30-2200), and 5 mL pre-warmed trypsin-EDTA solution (ATCC® PCS-999-003) was added and incubated at 37 °C CO2 incubator for 1-2 minutes. The flask was then gently tapped from all sides to facilitate detachment of the cells from the surface of the flask. An equal volume of the Trypsin Neutralizing Solution (ATCC PCS-999-004) was added to the flask and the cells were collected in a 15 mL tube. The tube containing the trypsin-EDTA-dissociated cells was centrifuged at 1000 rpm for 5 minutes. The solution was then aspirated from the cell pellet and the cells were resuspended in 10 mL of pre-warmed complete growth medium, at which point the cell numbers were counted. The cells were then seeded in a new tissue culture flask at a density of 5,000-6,000 viable cells per cm2 and grown at 37 °C in a 5% CO2 incubator for 2-3 days until they reached 80% confluency.

The PC-3 prostate cancer cells were cultured using Complete Growth Medium (ATCC). The base medium for this cell line is F-12K Medium (ATCC Catalog No. 30-2004). To make the complete growth medium, fetal bovine serum (ATCC ) was added to this medium at a final concentration of 10%. The cells were cultured using 75 cm2 tissue culture flask and subcultured following the similar procedures as described above for HPrEC cells.

Mouse Brain Tissues

The cancerous brain tissues were obtained from NOD-SCIDmodel mice injected with 435-tdT cells (human breast adenocarcinoma cancer cell line, MDA-MB-435)52,53 were provided by Prof. V. Raman, Johns Hopkins University School of Medicine, (Baltimore, MD, USA).54 The adjacent normal brain tissues were obtained for comparison. The characterization of such cancerous and normal brain tissue sections was carried out in an earlier study.55

Sample Preparation

The prostate cancer and healthy cell lines were received and analyzed immediately following cell culturing. For analysis, the cells were dispersed in about 150 µL of water.

The mice brain tissue was removed from the glass slide and separated into two tubes. One tube contained tissue from the identified normal tissue area and the other tube contained the cancerous tissue; the tissue cancer mapping was provided.54 The tissue was mixed with 150 µL of water and sonicated to create a water dispersion.

Deep-UV Spectroscopic Methods

All samples were analyzed using a deep-UV Raman Spectrograph (details regarding instrument can be found elsewhere).56 Briefly, 198-nm radiation was generated as the 5th anti-Stokes shift from the third harmonic of the Ni-YAG laser in a Raman shifter filled with low pressure hydrogen. A UV laser beam (~0.5 mW at the sample surface) was focused into a spinning Suprasil nuclear magnetic resonance (NMR) tube containing the solution. In order to prevent photo degradation, the solution was continuously mixed with a magnetic stirrer. Scattered radiation was collected in the backscattering geometry, dispersed using a double monochromator, and detected with a liquid-nitrogen cooled CCD camera (Roper Scientific, New Jersey, USA).

Data Acquisition and Treatment

For the collection of data from the cell samples, the accumulation time was 30 seconds, and 20 acquisitions were obtained and averaged for each spectrum. For the tissue samples, the accumulation time remained the same, however, 40 acquisitions were collected and averaged for each spectrum. The collection of multiple acquisitions per sample is conducted in order to account for the inherent heterogeneity of biological samples. The contribution of quartz from the NMR tube and of water was quantitatively subtracted from each spectrum. The spectra were preprocessed using GRAMS (version 9.2) software. Spectra were calibrated to wave numbers, using the Raman spectrum of Teflon as a reference, and smoothed.

RESULTS

Deep-UV resonance Raman spectra were collected from two different biological sample types. Tentative assignments were given to those bands observed in the Raman spectra using deep-UV excitation based on assignments from the literature which were obtained using near-IR and visible excitation.

First, prostate cancer and healthy cell lines were analyzed. The mean spectra for each of the two cell lines arepresent in Figure 1.

Figure 1. Average Spectra Collected from Water Solutions of Normal Prostate Cells (Red) and Cancer Prostate Cells (Black) of a Human Using Deep-UV Raman Scattering

CSMMOJ-5-126Fig1

The spectra show general differences in protein structure and conformation, noted by variances in peak intensities at 1000, 1257, 1542, 1614, and 1669 cm-1. 57,58 Collagen can tentatively be assigned as contributing to the Raman spectra at 1173, 1206, and 1334 cm-1. 11,57,59 Various vibrational modes of DNA are observed at 1173, 1206, 1257, 1568, and 1614 cm-1 and by lipids at 1381 cm1 . 60,61,62 Glycogen most likely contributes to the Raman spectrum as observed by the Raman bands at 850 and 1030 cm-1. 57,63 A full list of tentative assignments for the vibrational modes of the various Raman bands is shown in Table 1.

Table 1. Tentative Raman Shift Assignments of Spectra Collected From normal and Prostate Cancer Cell Lines Using UVRS

Shift (cm-1)

Assignment Contribution

850

Glycogen

1000

Phenylalanine symmetric ring breathing Protein

1030

Glycogen

1173

Tyrosine, Cytosine; Guanine Collagen; DNA

1206

Hydroxyproline, Tyrosine Collagen; DNA

1257

Adenine and Thymine ring breathing; Amide III DNA; Protein

1334

CH3 CH2 Wagging Collagen; Polynucleotide Chain

1381

CH3 symmetric stretching Lipids

1542

Amide II Protein

1568

Guanine and Adenine ring breathing DNA/RNA

1614

Tyrosine and Tryptophan C=C stretching; Adenine Protein; DNA

1669

Amide I Protein

Interestingly, the Raman bands have been assigned to vibrational modes of biomolecules which have previously been shown in the literature as playing a role in identifying prostate cancer. Many different protein biomarkers have been determined for detecting prostate cancer, including the prostate-specific antigen.64,65 Collagen metabolism has been shown to be affected due to occurrence of prostate cancer, with the observed changes related to the grade of the tumor.66,67,68

The spectra of the two cell lines also reflect a decrease in glycogen and alterations in nucleic acid content, both of which have been previously reported by Crow et al as potential Raman spectroscopic markers for prostate cancer.69,70 Various alterations in deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) have been identified for signaling presence of the cancer, including in PCA3 and the TMPRSS2-ERG gene fusion.71

The ability to detect variations in these classes of biomolecules indicates that UVRS is sensitive to the biochemical changes that occur during the pathogenesis of prostate cancer. These identified biomolecules serve as potential spectroscopic markers for cancer and should be further studied.

Next, sections of mouse brain tissues were analyzed. Figure 2 shows the spectra for cancerous and healthy tissues.

Figure 2. Average Spectra Collected From Water Dispersions Of Healthy Brain Tissue (Red) And Cancer Brain Tissue (Black) Of A Mouse Using Deep-UV Raman Scattering

CSMMOJ-5-126Fig2

The spectra show great evidence of protein contribution, as designated by the Raman bands at 1260, 1400, 1602, 1630, and 1666 cm-1. 57,72,73 Notably, the bands at 1630 and 1666 cm-1 differ greatly in intensity between the two average spectra. These bands correspond to the Amide I vibrational mode,57 and the shift in wavelength indicates that the secondary structure of proteins in the tissue are generally undergoing a conformational change, suggesting proteins play a role in identifying brain cancer. In fact, many studies have already discovered proteins useful for identifying the presence or absence of brain cancer including thymosin β4, S100 calcium-binding protein A4, glial fibrillary acidic protein, epidermal growth factor receptor, peroxiredoxin 4, aldolase C fructose-biphosphate, and creatine kinase, to name a few.74,75,76

The influence of DNA and RNA to the Raman spectra of the two brain tissue areas are most likely denoted by peaks seen around 805 and 1570 cm-1, and phospholipids contribute at 1032 cm-1. 61,77,78,79 Nucleic acids and phospholipids have already been identified in previous Raman spectroscopic studies for contributing toward the identification of brain cancer within tissue samples.80,81 Other studies have suggested certain RNA expression patterns as indicative of brain metastasis including ERCC1 and ERCC2 and long noncoding ribonucleic acid metastasis-associated lung adenocarcinoma transcript 1 (RNA MALAT1).82,83 A full list of tentative Raman shift assignments for the brain tissue is presented in Table 2.

Table 2. Tentative Raman Shift Assignments of Spectra Collected from Mouse Brain Tissue Using UVRS

Shift (cm-1)

Assignment Contribution

805

Uracil-based ring breathing; O-P-O stretching RNA

1032

CH2 CH3 bending Phospholipids

1260

Amide III Protein

1400

Aspartic and Glutamic acid (C=O) Ostretching Protein

1570

Guanine and Adenine ring breathing DNA/RNA

1602

Phenylalanine and Tyrosine C=C in-plane bending Protein

1630

Amide I Protein

1666

Amide I Protein
DISCUSSION

Raman spectroscopy, in general, has been widely studied for cancer identification purposes.4,84,85 While a plethora of studies show the usefulness of near-IR and visible excitation of biological samples for detecting cancer, the advantages of UVRS spectroscopy have yet to be explored in terms of sensitivity, specificity and diagnostic accuracy. The UVRS approach is a promising and superior diagnostic method for cancer recognition due to the following factors. The excitation of biological samples in the wavelength range around 210-300 nm allows for the resonance enhancement of scattered light due to the amide chromophore, which is a crucial component of the backbone of a polypeptide; specifically, the polypeptide side chain including aromatic amino acids contributes to this enhancement.86 The absorption spectra of aromatic amino acids typically exhibit peaks around 210-280 nm.87 This overlap between absorption and excitation wavelengths result in the resonance enhancement of the Raman signal.88,89 Aromatic amino acids have an inherent ability to probe the structure of the protein; with enhancement of these signals, the secondary structure of proteins is much more easily elucidated, allowing for this novel and specific information to aid in identifying malignancies. The resonance enhancement of nucleic acids has also been observed using deep-UV excitation. Mononucleotides exhibit resonance Raman enhancement at 266, 240, 218, and 200 nm UV radiation,90,91 with various vibrational modes of the purine and pyrimidine bases observed in the resulting Raman spectrum. Again, the enhancement occurs due to the overlap between absorption and excitation wavelengths.92 This information, which is not as well observed using near-IR or visible light excitation, provides a more specific insight into the biochemical changes that are occurring during the pathogenesis of cancers. Capitalizing on these advantages allows for a much greater potential to identify cancer within biological samples.

This proof-of-concept study contemplates the differences between single donors for two different types of cancer. While inherent inter-patient variability does exist, previous studies which have included multiple donors in each group have shown that the inter-patient variability is not significant enough to affect disease diagnostic efforts.5,6 Furthermore, the Raman spectra for the four different biological samples shown here are dominated by protein and nucleic acid contributions. The inherent enhancement by deep-UV excitation provides crucial information regarding the differences which exist between healthy and cancerous biological samples. These differences can be translated to crucial biochemical information regarding cancer pathogenesis and may be useful for identifying cancer within patients in future studies.

Safety Considerations

There are great advantages for using UV excitation lasers, including eliminating broadband tissue autofluorescence, which arises after 255 nm (Figure 3) due to aromatic amino acids,94,95,48 and increasing RS signals from proteins97 and from lipids and fatty acids.97,98

Figure 3. UV Safety Limit of Exposure93, Protein54 and Lipid Absorption,1 and Autofluorescence94,95,96 Versus Wavelength

CSMMOJ-5-126Fig3

However, shifting laser excitation to the deep-UV range brings about health risk issues, including photochemical damage of DNA.99 The guidelines on safe UV exposure of human tissue/ skin established by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) indicate that exposure doses must be limited to 10 mJ/cm2 for 240 nm and can be as high as 250 mJ/ cm2 for 180 nm (see red line, Figure 3).93,100,101

In this study, the UV radiation of a 0.4 mW laser was delivered and focused into a 7-µm spot (area ~50 µ2 ) in the middle of a quartz tube filled with a water dispersion of either tissue or cells; the relative volume concentration of the cells was 0.5% (1:200). The UV Raman signal in our experiment was collected for 30 seconds using an optical system with 8% collection efficiency, and recorded by a UV CCD camera with an estimated detection efficiency of 5%.60 Taking into account the small (0.5%) volume concentration of the cells in our experiment, we can derive, in the case where cells cover the entire sample area of 50 µ2 , the same level of Raman signal will be achieved after 30 s/200=0.15 s collection time. Accordingly, the energy density (i.e., energy per cm2 ) of UV radiation for this area is 4×10-4 W×1.5×10-1 s/(50×10-8 cm2 )=120 J/cm2 .

In future studies, the goal is to translate this work into the development of an efficient fiber-optic probe for UVRS-based cancer diagnostics of living tissues, which could map a living tissue in real time. As such, it is necessary to ensure that excitation by a deep-UV laser will not cause physiological problems. Assuming that the sample area illuminated by the Raman probe is 300×300 µm2 (~2000-fold larger than illuminated area in this study) and that photon collection efficiency of the Raman probe is approximately 20% we can calculate that, using the same UV laser, the radiation dose in the proposed approach is only 1:5000 of that in this study. This is equivalent to 120 J/cm2 /5000=25 mJ/cm2 , which is considered safe according to the ICNIRP guidelines discussed above.

To the best of our knowledge, this feasibility study demonstrates for the first time the ability of deep-UV Raman spectroscopy to discriminate cancerous from normal tissues and cells in a relatively short time with high sensitivity. The dramatic increase in sensitivity observed in our experiments, which cannot be obtained using near-IR or visible excitation, allows for identification of altered chemical/structure composition of cancerous versus normal conditions. This result may lead to a better understanding of changes which occur in a biochemical environment during the pathogenesis of cancer. The clear advantages of this method suggest that UVRS should be considered in future studies as a novel methodology for the specific and informative identification of cancer. Because it also meets safety guidelines for UV exposure, this method has the potential to be further developed into a real-time diagnostic method using fiber-optics.

CONCLUSION

This proof-of-concept study suggests deep-ultraviolet resonance Raman spectroscopy has significant potential for identifying cancerous and healthy biological species at both the cellular and tissue level. Many key advantages exist for using deep-UV excitation as compared to visible or near-IR excitation. Notably, deep-UV excitation provides resonance enhancement of crucial biochemical components and eliminates interference from fluorescence. These advantages allow for simple spectral interpretation, saving time and effort for the researcher which can be critical in diagnostic situations. Notably, changes in biochemical composition between healthy and prostate and brain cancer samples were discovered using UVRS. These differences can be capitalized on in future studies with larger sample sizes in order to further develop the method for identifying prostate and brain cancers. The proposed approach is shown to be a safe and effective method for detecting cancer within biological samples and provides a solid basis for research into further developing UVRS for clinical applications.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

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