Martin Isabelle

FTIR of touch imprint cytology: A novel tissue diagnostic technique

Fourier transform infrared spectroscopic (FTIR) interrogation of biological tissues in real time has largely been a challenging proposition because of the strong absorption of mid-infrared light in water filled tissues. To enable sampling of tissues they must be sectioned and dried, which has time and resource implications. FTIR of touch imprint cytology (TIC) has been proposed to circumvent this problem. TIC is a well known histopathological method of rapidly analysing biological tissues. In this article we demonstrate the ability of FTIR of TIC to provide detailed spectra which can be used to differentiate various tissue pathologies. FTIR spectral profiles of TIC of lymph node and thyroid tissues differ visually when compared with TIC spectra of parathyroid tissue. The lymph node showed strong lipid spectral peaks at 1166cm(-1) and 1380cm(-1) including a very strong carbonyl-ester band at 1748cm(-1), and a strong methylene bending band (scissoring, at 1464cm(-1)). Smaller intensity protein peaks at 1547cm(-1) and 1659cm(-1) were also seen. The thyroid spectra, in addition to evident strong protein peaks at 1547cm(-1) and 1659cm(-1), also demonstrated possible nucleic acid signals at 1079cm(-1) and 1244cm(-1). The C-OH peak at 1037cm(-1) was attributed to carbohydrate signals. Parathyroid adenoma showed a marginal shift to lower wavenumbers with decreased amide I and II peak intensities when compared to hyperplasia. Nucleic acid peak positions at 1079cm(-1) and 1244cm(-1) were of higher intensity in adenomas compared to hyperplastic glands possibly demonstrating an increase in cell proliferation and growth. This study demonstrates the feasibility of cytoimprint FTIR for the intraoperative diagnosis of tissue during surgical neck exploration for the management of hyperparathyroidism. There is potential for the application of the technique in sentinel lymph node biopsy diagnosis and tumour margin evaluation.

Lymph node pathology using optical spectroscopy in cancer diagnostics

Raman and infrared spectroscopy are optical spectroscopic techniques that use light scattering (Raman) and light absorption (infrared) to probe the vibrational energy levels of molecules in tissue samples. Using these techniques, one can gain an insight into the biochemical composition of cells and tissues by looking at the spectra produced and comparing them with spectra obtained from standards such as proteins, nucleic acids, lipids and carbohydrates. As a result of optical spectroscopy being able to measure these biochemical changes, diagnosis of cancer could take place faster than current diagnostic methods, assisting and offering pathologists and cytologists a novel technology in cancer screening and diagnosis.

Correlation mapping: rapid method for identification of histological features and pathological classification in mid infrared spectroscopic images of lymph nodes

In this work, a novel technique for rapid image analysis of Fourier transform infrared (FTIR) data obtained from human lymph nodes is explored. It uses the mathematical principle of orthogonality as a method to quickly and efficiently obtain tissue and pathology information from a spectral image cube. It requires less computational power and time compared to most forms of cluster analysis. The values obtained from different tissue and pathology types allows for discrimination of noncancerous from cancerous lymph nodes. It involves the calculation of the dot product between reference spectra and individual spectra from across the tissue image. These provide a measure of the correlation between individual spectra and the reference spectra, and each spectrum or pixel in the image is given a color representing the reference most closely correlating with it. The correlation maps are validated with the tissue and pathology features identified by an expert pathologist from corresponding hematoxylin and eosin stained tissue sections. Although this novel technique requires further study to properly test and validate this tool, with inclusion of more lymph node hyperspectral datasets (containing a greater variety of tissue states), it demonstrates significant clinical potential for pathology diagnosis.

A Raman Spectroscopic Study of Cell Response to Clinical Doses of Ionizing Radiation

The drive toward personalized radiation therapy (RT) has created significant interest in determining patient-specific tumor and normal tissue responses to radiation. Raman spectroscopy (RS) is a non-invasive and label-free technique that can detect radiation response through assessment of radiation-induced biochemical changes in tumor cells. In the current study, single-cell RS identified specific radiation-induced responses in four human epithelial tumor cell lines: lung (H460), breast (MCF-7, MDA-MB-231), and prostate (LNCaP), following exposure to clinical doses of radiation (2–10 Gy). At low radiation doses (2 Gy), H460 and MCF-7 cell lines showed an increase in glycogen-related spectral features, and the LNCaP cell line showed a membrane phospholipid-related radiation response. In these cell lines, only spectral information from populations receiving 10 Gy or less was required to identify radiation-related features using principal component analysis (PCA). In contrast, the MDA-MB-231 cell line showed a significant increase in protein relative to nucleic acid and lipid spectral features at doses of 6 Gy or higher, and high-dose information (30, 50 Gy) was required for PCA to identify this biological response. The biochemical nature of the radiation-related changes occurring in cells exposed to clinical doses was found to segregate by status of p53 and radiation sensitivity. Furthermore, the utility of RS to identify a biological response in human tumor cells exposed to therapeutic doses of radiation was found to be governed by the extent of the biochemical changes induced by a radiation response and is therefore cell line specific. The results of this study demonstrate the utility and effectiveness of single-cell RS to identify and measure biological responses in tumor cells exposed to standard radiotherapy doses.

Raman spectroscopy identifies radiation response in human non-small cell lung cancer xenografts

External beam radiation therapy is a standard form of treatment for numerous cancers. Despite this, there are no approved methods to account for patient specific radiation sensitivity. In this report, Raman spectroscopy (RS) was used to identify radiation-induced biochemical changes in human non-small cell lung cancer xenografts. Chemometric analysis revealed unique radiation-related Raman signatures that were specific to nucleic acid, lipid, protein and carbohydrate spectral features. Among these changes was a dramatic shift in the accumulation of glycogen spectral bands for doses of 5 or 15 Gy when compared to unirradiated tumours. When spatial mapping was applied in this analysis there was considerable variability as we found substantial intra- and inter-tumour heterogeneity in the distribution of glycogen and other RS spectral features. Collectively, these data provide unique insight into the biochemical response of tumours, irradiated in vivo, and demonstrate the utility of RS for detecting distinct radiobiological responses in human tumour xenografts.

Radiation-Induced Glycogen Accumulation Detected by Single Cell Raman Spectroscopy Is Associated with Radioresistance that Can Be Reversed by Metformin.

Altered cellular metabolism is a hallmark of tumor cells and contributes to a host of properties associated with resistance to radiotherapy. Detection of radiation-induced biochemical changes can reveal unique metabolic pathways affecting radiosensitivity that may serve as attractive therapeutic targets. Using clinically relevant doses of radiation, we performed label-free single cell Raman spectroscopy on a series of human cancer cell lines and detected radiation-induced accumulation of intracellular glycogen. The increase in glycogen post-irradiation was highest in lung (H460) and breast (MCF7) tumor cells compared to prostate (LNCaP) tumor cells. In response to radiation, the appearance of this glycogen signature correlated with radiation resistance. Moreover, the buildup of glycogen was linked to the phosphorylation of GSK-3β, a canonical modulator of cell survival following radiation exposure and a key regulator of glycogen metabolism. When MCF7 cells were irradiated in the presence of the anti-diabetic drug metformin, there was a significant decrease in the amount of radiation-induced glycogen. The suppression of glycogen by metformin following radiation was associated with increased radiosensitivity. In contrast to MCF7 cells, metformin had minimal effects on both the level of glycogen in H460 cells following radiation and radiosensitivity. Our data demonstrate a novel approach of spectral monitoring by Raman spectroscopy to assess changes in the levels of intracellular glycogen as a potential marker and resistance mechanism to radiation therapy.

Multi-centre Raman spectral mapping of oesophageal cancer tissues: a study to assess system transferability.

The potential for Raman spectroscopy to provide early and improved diagnosis on a wide range of tissue and biopsy samples in situ is well documented. The standard histopathology diagnostic methods of reviewing H&E and/or immunohistochemical (IHC) stained tissue sections provides valuable clinical information, but requires both logistics (review, analysis and interpretation by an expert) and costly processing and reagents. Vibrational spectroscopy offers a complimentary diagnostic tool providing specific and multiplexed information relating to molecular structure and composition, but is not yet used to a significant extent in a clinical setting. One of the challenges for clinical implementation is that each Raman spectrometer system will have different characteristics and therefore spectra are not readily compatible between systems. This is essential for clinical implementation where classification models are used to compare measured biochemical or tissue spectra against a library training dataset. In this study, we demonstrate the development and validation of a classification model to discriminate between adenocarcinoma (AC) and non-cancerous intraepithelial metaplasia (IM) oesophageal tissue samples, measured on three different Raman instruments across three different locations. Spectra were corrected using system transfer spectral correction algorithms including wavenumber shift (offset) correction, instrument response correction and baseline removal. The results from this study indicate that the combined correction methods do minimize the instrument and sample quality variations within and between the instrument sites. However, more tissue samples of varying pathology states and greater tissue area coverage (per sample) are needed to properly assess the ability of Raman spectroscopy and system transferability algorithms over multiple instrument sites.

Raman spectroscopy as a tool for the identification and differentiation of neoplasias contained within lymph nodes of the head and neck

The use of Raman spectroscopy in the detection and classification of malignancy within lymph nodes of the head and neck has been evaluated. Currently histopathology is considered the diagnostic gold standard. A consensus (majority) opinion from three expert histopathologists has been obtained and spectral diagnostic models developed by correlation with their opinions. Raman spectra have been measured at 830nm from 103 lymph nodes collected from patients undergoing surgery for a suspicious node. The pathologies covered reactive lymph nodes, primaries from Hodgkins and non-Hodgkins lymphomas and metastases from squamous cell carcinomas and adenocarcinomas. Spectral diagnostic models were constructed using PCA-fed-LDA and tested using leave-one-specimen-out cross validation. Models were constructed to distinguish between reactive and malignant nodes as well as a four group model to distinguish between the benign, metastatic and primary conditions. They achieved 89% and 84% correct prediction by node versus the gold standard, majority histopathology.

Mirrored stainless steel substrate provides improved signal for Raman spectroscopy of tissue and cells

Raman spectroscopy (RS) is a powerful technique that permits the non‐destructive chemical analysis of cells and tissues without the need for expensive and complex sample preparation. To date, samples have been routinely mounted onto calcium fluoride (CaF2) as this material possesses the desired mechanical and optical properties for analysis, but CaF2 is both expensive and brittle and this prevents the technique from being routinely adopted. Furthermore, Raman scattering is a weak phenomenon and CaF2 provides no means of increasing signal. For RS to be widely adopted, particularly in the clinical field, it is crucial that spectroscopists identify an alternative, low‐cost substrate capable of providing high spectral signal to noise ratios with good spatial resolution. Results show that these desired properties are attainable when using mirrored stainless steel as a Raman substrate. When compared with CaF2, data show that stainless steel has a low background signal and provides an average signal increase of 1.43 times during tissue analysis and 1.64 times when analyzing cells. This result is attributed to a double‐pass of the laser beam through the sample where the photons from the source laser and the forward scattered Raman signal are backreflected and retroreflected from the mirrored steel surface and focused towards collection optics. The spatial resolution on stainless steel is at least comparable to that on CaF2 and it is not compromised by the reflection of the laser. Steel is a fraction of the cost of CaF2 and the reflection and focusing of photons improve signal to noise ratios permitting more rapid mapping. The low cost of steel coupled with its Raman signal increasing properties and robust durability indicates that steel is an ideal substrate for biological and clinical RS as it possesses key advantages over routinely used CaF2.

Spectral Pathology: General discussion

Parvez Haris opened the discussion of the introductory lecture by Max Diem: Varying degrees of accuracy have been obtained for discrimination between cancerous and non-cancerous tissues using vibrational spectroscopic methods. What are the explanations for these variation in accuracy between cancerous and non-cancerous tissues and how do they correlate with accuracy from other techniques including histopathology?