Rachel E. Kast

Emerging technology: applications of Raman spectroscopy for prostate cancer.

There is a need in prostate cancer diagnostics and research for a label-free imaging methodology that is nondestructive, rapid, objective, and uninfluenced by water. Raman spectroscopy provides a molecular signature, which can be scaled from micron-level regions of interest in cells to macroscopic areas of tissue. It can be used for applications ranging from in vivo or in vitro diagnostics to basic science laboratory testing. This work describes the fundamentals of Raman spectroscopy and complementary techniques including surface enhanced Raman scattering, resonance Raman spectroscopy, coherent anti-Stokes Raman spectroscopy, confocal Raman spectroscopy, stimulated Raman scattering, and spatially offset Raman spectroscopy. Clinical applications of Raman spectroscopy to prostate cancer will be discussed, including screening, biopsy, margin assessment, and monitoring of treatment efficacy. Laboratory applications including cell identification, culture monitoring, therapeutics development, and live imaging of cellular processes are discussed. Potential future avenues of research are described, with emphasis on multiplexing Raman spectroscopy with other modalities.

Raman spectroscopy detects and distinguishes neuroblastoma and related tissues in fresh and (banked) frozen specimens

BACKGROUND Raman spectroscopy has been shown to accurately distinguish different neural crest-derived pediatric tumors. This study tests the ability of Raman spectroscopy to accurately identify cryopreserved tissue specimens using a classification algorithm designed from fresh tumor data and vice versa. METHODS Fresh specimens of neuroblastoma and other pediatric neural crest tumors were analyzed with Raman spectroscopy. After analysis, the specimens were stored at -80 degrees C. At a later date, the specimens were thawed and reanalyzed by Raman spectroscopy. A computer algorithm was used to classify the spectra from the frozen tissue against a computer model built on the fresh tissue data. This classification process was then reversed, testing fresh spectra against a model built from frozen data. RESULTS We collected 1114 spectra (862 fresh and 252 frozen) from 62 tissue samples, including 8 normal adrenal glands, 29 neuroblastomas, 14 ganglioneuromas, 8 nerve sheath tumors, and 3 pheochromocytomas. At the tissue level, frozen neuroblastoma, ganglioneuroma, nerve sheath tumor, and pheochromocytoma were distinguished from normal adrenal tissue with 100% sensitivity and specificity. Fresh tissue had the same results except for the misclassification of one specimen of nerve sheath tumor. CONCLUSIONS The representative spectra show a high correlation between fresh and frozen tissue, and a clear difference between pathologic conditions. Spectra from frozen tissue can be accurately classified against spectra from fresh tissue and vice versa. This modality makes it possible to determine in a few minutes a result that often takes 12 to 36 hours for tissue processing and consideration by a trained pathologist to achieve.

Evaluation of pancreatic cancer with Raman spectroscopy in a mouse model.

Objectives: Detection of neoplastic changes using optical spectroscopy has been an active area of research in recent times. Raman spectroscopy is a vibrational spectroscopic technique that can be used to diagnose various tumors with high sensitivity and specificity. We evaluated the ability of Raman spectroscopy to differentiate normal pancreatic tissue from malignant tumors in a mouse model. Methods: We collected 920 spectra, 475 from 31 normal pancreatic tissue and 445 from 29 tumor nodules using a 785-nm near-infrared laser excitation. Discriminant function analysis was used for classification of normal and tumor samples. Results: Using principal component analysis, we were able to highlight subtle chemical differences in normal and malignant tissue. Using histopathology as the gold standard, Raman analysis gave sensitivities between 91% and 96% and specificities between 88% and 96%. Conclusions: Raman spectroscopy along with discriminant function analysis is a useful method to detect cancerous changes in the pancreas. Pancreatic tumors were characterized by increased collagen content and decreased DNA, RNA, and lipids components compared with normal pancreatic tissue.

Identification of Pediatric Brain Neoplasms Using Raman Spectroscopy

Purpose: Raman spectroscopy can quickly and accurately diagnose tissue in near real-time. This study evaluated the capacity of Raman spectroscopy to diagnose pediatric brain tumors. Experimental Design: Samples of untreated pediatric medulloblastoma (4 samples and 4 patients), glioma (i.e. astrocytoma, oligodendroglioma, ependymoma, ganglioglioma and other gliomas; 27 samples and 19 patients), and normal brain samples (33 samples and 5 patients) were collected fresh from the operating room or from our frozen tumor bank. Samples were divided and tested using routine pathology and Raman spectroscopy. Twelve Raman spectra were collected per sample. Support vector machine analysis was used to classify spectra using the pathology diagnosis as the gold standard. Results: Normal brain (321 spectra), glioma (246 spectra) and medulloblastoma (82 spectra) were identified with 96.9, 96.7 and 93.9% accuracy, respectively, when compared with each other. High-grade ependymomas (41 spectra) were differentiated from low-grade ependymomas (25 spectra) with 100% sensitivity and 96.0% specificity. Normal brain tissue was distinguished from low-grade glioma (118 spectra) with 91.5% sensitivity and 97.8% specificity. For these analyses, the tissue-level classification was determined to be 100% accurate. Conclusion: These results suggest Raman spectroscopy can accurately distinguish pediatric brain neoplasms from normal brain tissue, similar tumor types from each other and high-grade from low-grade tumors.

Differentiation of small round blue cell tumors using Raman spectroscopy

PURPOSE Small round blue cell tumors (SRBCTs) are aggressive undifferentiated embryonal tumors, including neuroblastoma, rhabdomyosarcoma, Ewing sarcoma, and non-Hodgkin lymphoma. They share similar histologic features. Additional studies such as immunohistochemistry and molecular techniques are required to differentiate them. There is no widely available tool for real-time diagnosis. Raman spectroscopy is an analytical technique with potential for quick and accurate diagnosis of tumors in near real-time. METHODS Fresh or banked frozen tissue samples from SRBCTs were processed for routine pathology and Raman spectroscopy. Raman results were correlated with the final pathology diagnosis. RESULTS The data set was composed of 480 spectra from 32 samples, including 179 neuroblastoma, 37 Ewing sarcoma, 164 rhabdomyosarcoma, and 100 non-Hodgkin lymphoma spectra. Discriminant function analysis showed that a combination of 18 peaks could accurately identify 94% of spectra. At the tissue level, all tumors were correctly identified. Only 10 peaks were needed to classify all tissues with 100% accuracy. Spectral-level classification with this model was 87.9%. CONCLUSION Raman spectroscopy is an accurate technique for quickly and accurately differentiating SRBCTs. It could diagnose these specimens and provide a single, easy to use test for near real-time diagnosis.

Shining light on neurosurgery diagnostics using Raman spectroscopy

Surgical excision of brain tumors provides a means of cytoreduction and diagnosis while minimizing neurologic deficit and improving overall survival. Despite advances in functional and three-dimensional stereotactic navigation and intraoperative magnetic resonance imaging, delineating tissue in real time with physiological confirmation is challenging. Raman spectroscopy is a promising investigative and diagnostic tool for neurosurgery, which provides rapid, non-destructive molecular characterization in vivo or in vitro for biopsy, margin assessment, or laboratory uses. The Raman Effect occurs when light temporarily changes a bond’s polarizability, causing change in the vibrational frequency, with a corresponding change in energy/wavelength of the scattered photon. The recorded inelastic scattering results in a “fingerprint” or Raman spectrum of the constituent under investigation. The amount, location, and intensity of peaks in the fingerprint vary based on the amount of vibrational bonds in a molecule and their ensemble interactions with each other. Distinct differences between various pathologic conditions are shown as different intensities of the same peak, or shifting of a peak based on the binding conformation. Raman spectroscopy has potential for integration into clinical practice, particularly in distinguishing normal and diseased tissue as an adjunct to standard pathologic diagnosis. Further, development of fiber-optic Raman probes that fit through the instrument port of a standard endoscope now allows researchers and clinicians to utilize spectroscopic information for evaluation of in vivo tissue. This review highlights the need for such an instrument, summarizes neurosurgical Raman work performed to date, and discusses the future applications of neurosurgical Raman spectroscopy.

Doxycycline-loaded coaxial nanofiber coating of titanium implants enhances osseointegration and inhibits Staphylococcus aureus infection

Few studies have been reported that focus on developing implant surface nanofiber (NF) coating to prevent infection and enhance osseointegration by local drug release. In this study, coaxial doxycycline (Doxy)-doped polycaprolactone/polyvinyl alcohol (PCL/PVA) NFs were directly deposited on a titanium (Ti) implant surface during electrospinning. The interaction of loaded Doxy with both PVA and PCL NFs was characterized by Raman spectroscopy. The bonding strength of Doxy-doped NF coating on Ti implants was confirmed by a stand single-pass scratch test. The improved implant osseointegration by PCL/PVA NF coatings in vivo was confirmed by scanning electron microscopy, histomorphometry and micro computed tomography (μCT) at 2, 4 and 8 weeks after implantation. The bone contact surface (%) changes of the NF coating group (80%) is significantly higher than that of the no NF group (<5%, p < 0.05). Finally, we demonstrated that a Doxy-doped NF coating effectively inhibited bacterial infection and enhanced osseointegration in an infected (Staphylococcus aureus) tibia implantation rat model. Doxy released from NF coating inhibited bacterial growth up to 8 weeks in vivo. The maximal push-in force of the Doxy-NF coating (38 N) is much higher than that of the NF coating group (6.5 N) 8 weeks after implantation (p < 0.05), which was further confirmed by quantitative histological analysis and μCT. These findings indicate that coaxial PCL/PVA NF coating doped with Doxy and/or other drugs have great potential in enhancing implant osseointegration and preventing infection.