Accurate identification of breast cancer margins in microenvironments of ex-vivo basal and luminal breast cancer tissues using Raman spectroscopy.

Better knowledge of the breast tumor microenvironment is required for surgical resection and understanding the processes of tumor development. Raman spectroscopy is a promising tool that can assist in uncovering the molecular basis of disease and provide quantifiable molecular information for diagnosis and treatment evaluation. In this work, eighty-eight frozen breast tissue sections, including forty-four normal and forty-four tumor sections, were mapped in their entirety using a 250-µm-square measurement grid. Two or more smaller regions of interest within each tissue were additionally mapped using a 25 µm-square step size. A deep learning algorithm, convolutional neural network (CNN), was developed to distinguish histopathologic features with-in individual and across multiple tissue sections. Cancerous breast tissue were discriminated from normal breast tissue with 90 % accuracy, 88.8 % sensitivity and 90.8 % specificity with an excellent Area Under the Receiver Operator Curve (AUROC) of 0.96. Features that contributed significantly to the model were identified and used to generate RGB images of the tissue sections. For each grid point (pixel) on a Raman map, color was assigned to intensities at frequencies of 1002 cm-1 (Phenylalanine), 869 cm-1 (Proline, CC stretching of hydroxyproline-collagen assignment, single bond stretching vibrations for the amino acids proline, valine and polysaccharides) and 1309 cm-1 (CH3/CH2 twisting or bending mode of lipids). The Raman images clearly associate with hematoxylin and eosin stained tissue sections and allow clear visualization of boundaries between normal adipose, connective tissue and tumor. We demonstrated that this simple imaging technique allows high-resolution, straightforward molecular interpretation of Raman images. Raman spectroscopy provides rapid, label-free imaging of microscopic features with high accuracy. This method has application as laboratory tool and can assist with intraoperative tissue assessment during Breast Conserving surgery.

Rapid Detection of Clostridium difficile Toxins in Stool by Raman Spectroscopy.

BACKGROUND: Clinical practice guidelines define Clostridium difficile infections (CDI) as diarrhea (≥3 unformed stools in 24 h) with either a positive C difficile stool test or detection of pseudomembranous colitis. Diagnostic modalities such as toxigenic culture and nucleic acid amplification testing can identify the presence of toxigenic C difficile in stools. But these tests are confounded by the presence of asymptomatic colonization of toxigenic C difficile and lead to overdiagnosis of CDI. The presence of two large toxins, toxin A and B (TcdA and TcdB) is necessary for pathogenicity. Detection of toxins using toxin enzyme immunoassay is difficult as it has low sensitivity and moderate specificity. Raman spectroscopy (RS) is a novel technology that is used to detect bacteria and their toxins. RS does not require any reagents for detection such as antibodies, enzymes, primers, or stains. We hypothesize that RS is a sensitive method to detect C difficile toxins in stool and will solve the problem of overdiagnosis of CDI. MATERIALS AND METHODS: CDI negative stool samples were spiked with concentrations (1 ng/mL, 100 pg/mL, 1 pg/mL, and 0.1 pg/mL) of TcdA and TcdB. RS was performed on air-dried smeared samples of stool supernatant on a mirror-polished stainless-steel slide. As RS of feces is difficult because of confounding background material and autofluorescence, samples were photo-bleached before spectral acquisition to reduce autofluorescence. Raman spectra were obtained, background corrected, and vector normalized. The data were split into training (70%) and test (30%) datasets. The machine learning methods used on the training data set were Support Vector Machine with Linear and Radial Kernels, Random Forest, Stochastic Gradient Boosting Machine, and Principle Component Analysis-Linear Discriminant Analysis. Results were validated using a test data set. The best model was chosen, and its accuracy, sensitivity, and specificity were determined. RESULTS: In our preliminary results, at all concentrations (1 ng/mL, 100 pg/mL, 1 pg/mL, and 0.1 pg/mL), TcdA or TcdB spiked stool was distinguished from unspiked stool by all models with accuracies ranging from 64% to 77%. Gradient Boosting Machine, Principle Component Analysis-Linear Discriminant Analysis, and Support Vector Machine Linear Kernel performed best with sensitivities ranging from 69% to 90% and specificities ranging from 43% to 78%. CONCLUSIONS: Using RS, we successfully detected TcdA and TcdB in stool samples albeit with moderate-to-high sensitivity and low-to-moderate specificity. Sensitivity and specificity could be further increased with the implementation of deep learning methods, which require large sample sizes. In terms of sensitivity, RS performs better than toxin enzyme immunoassay and has the potential to rapidly detect C difficile toxins in stool at clinically relevant concentrations and thereby help mitigate overdiagnosis of CDI.

Applications of Raman spectroscopy in cancer diagnosis.

Novel approaches toward understanding the evolution of disease can lead to the discovery of biomarkers that will enable better management of disease progression and improve prognostic evaluation. Raman spectroscopy is a promising investigative and diagnostic tool that can assist in uncovering the molecular basis of disease and provide objective, quantifiable molecular information for diagnosis and treatment evaluation. This technique probes molecular vibrations/rotations associated with chemical bonds in a sample to obtain information on molecular structure, composition, and intermolecular interactions. Raman scattering occurs when light interacts with a molecular vibration/rotation and a change in polarizability takes place during molecular motion. This results in light being scattered at an optical frequency shifted (up or down) from the incident light. By monitoring the intensity profile of the inelastically scattered light as a function of frequency, the unique spectroscopic fingerprint of a tissue sample is obtained. Since each sample has a unique composition, the spectroscopic profile arising from Raman-active functional groups of nucleic acids, proteins, lipids, and carbohydrates allows for the evaluation, characterization, and discrimination of tissue type. This review provides an overview of the theory of Raman spectroscopy, instrumentation used for measurement, and variation of Raman spectroscopic techniques for clinical applications in cancer, including detection of brain, ovarian, breast, prostate, and pancreatic cancers and circulating tumor cells.

Rapid Detection of Clostridium difficile Toxins in Serum by Raman Spectroscopy.

BACKGROUND: Clostridium difficile infection (CDI) is due to the effects of toxins, toxin A and toxin B on the host. Severe CDI is associated with systemic signs of infection. Animal models of CDI demonstrate a strong correlation between systemic toxemia and the occurrence of severe disease. However, current technologies have low sensitivity to detect C difficile toxemia in human subjects. Raman spectroscopy (RS) is an upcoming technology that is used to detect bacteria and their toxins. We speculate that RS may be a sensitive method to detect clinically relevant concentrations of C difficile toxins in serum. MATERIALS AND METHODS: Serum samples were spiked with varying concentrations of toxin A, toxin B, and both. RS was performed on an air-dried serum drop that was placed on a mirror-polished stainless steel slide. Raman spectra were obtained, background corrected, vector normalized, and analyzed by Partial Least Square Linear Discriminant Analysis and Support Vector Machine for Classification. Model accuracy was measured by cross-validation and bootstrap methods. RESULTS: Toxin-spiked sera of various concentrations (1 ng/mL, 1 pg/mL, and 0.1 pg/mL) were distinguished from control serum 100% with cross-validation error rate ranging from 0% to 18% and bootstrap error rate ranging from 0% to 12% for various concentrations. The sensitivity ranged from 87% to 100% and specificity ranged from 77% to 100% for various concentrations of toxin-spiked serum. CONCLUSIONS: We conclude that RS may be a sensitive method to detect clinically relevant concentrations of C difficile toxins in serum and thus to help diagnose severe CDI in patients in real-time at the point of care.

Multispacer Typing (MST) of Spotted Fever Group Rickettsiae Isolated from Humans and Rats in Chengmai County, Hainan Province, China.

Spotted fever caused by spotted fever group rickettsiae (SFGR) is found throughout China. During 2007-2008, 28 human SFGR isolates and 34 rat SFGR isolates including 15 isolates from Rattus fulvescens, 5 isolates from R. edwardsi, 7 isolates from Callosciurus erythraeus roberti and 7 isolates from Dremomys rufigenis) were obtained from L929 cell culture. Previous research indicated that the 62 strains of SFGR mentioned above shared not only the same serophenotype but also 100% of identity sequences of 16S rRNA, gltA, ompA, groEL and 17KD, which enabled us to apply multispacer typing (MST) to the 62 SFGR isolates in the study. Six primer pairs, which were used for typing of Rickettsia rickettsii and Rickettsia conorii, were chosen, and the results exhibited greater nucleotide polymorphisms among the 62 isolates tested. A total of 48 distinct genotypes were identified. The dominant genotype, represented by h3 isolates, accounted for 21.7% (13/60) of the isolates tested, and the remaining 47 genotypes were all unique. Phylogenetic analysis showed that all the 48 genotypes could be classified in the same clade, while the genetically related strain, R. heilongjiangensis, was close but not the same as the cluster. We concluded that the genetically diverse of spotted fever group rickettsiae strains are endemic in Chengmai County, Hainan Province, China.