Calcium confusion--is the variability in calcium response by Sertoli cells to specific hormones meaningful or simply redundant?

When results of more than ten different studies on hormone-induced calcium signals in Sertoli cells are taken together, a wide variety of responses emerges. The reported changes range from increased concentrations, via no response at all, to decreased calcium concentrations. Minor variations in cell isolation techniques, culture conditions, or techniques for measuring the intracellular calcium could explain some of these differences. However, erratic variations in response are also observed within research groups under very similar experimental conditions. Such 'negative' findings are mainly reported orally and do not further penetrate the scientific community. As hormone-dependent calcium responses evidently may depend very much on the context of the cells, calcium transients would appear to be unreliable bioassay principles with which to detect the primary actions of FSH and effectors such as androgens on Sertoli cells. A more important biological question is whether these sometimes opposed calcium transients are connected with a particular cellular response. To date there is no evidence for such a tight coupling in Sertoli cells, implying that, at least under in vitro conditions, calcium signals might even be redundant altogether. Such calcium variability is probably not unique to Sertoli cells, and the aim of this commentary is to promote an open debate that may help to transform the current state of 'calcium confusion' into a better understanding of the intracellular calcium language

Raman spectroscopy for the preoperative diagnosis of thyroid cancer and its subtypes: An in vitro proof-of-concept study.

In 2016, there were an estimated 56 870 new cases of thyroid cancer (TC) in the USA. Fine needle aspiration cytology (FNAC) is the most safe, accurate and cost-effective method for the initial investigation of thyroid nodules. FNAC is limited by the inability to diagnose malignancy in follicular-patterned lesions accurately and, as a result, 20%-30% of cases under investigation for TC are classified as cytologically indeterminate, illustrating a problem with current FNAC procedure. Raman spectroscopy has shown promising results for the detection of many cancers; however, to date there has been no report on the performance of Raman spectroscopy on thyroid cytological samples. The aim of this study was to examine whether Raman spectroscopy could be used to correctly classify cell lines representing benign thyroid cells and various subtypes of TC. A benign thyroid cell line and seven TC cell lines were prepared as ThinPrep® cytology slides and analysed with Raman spectroscopy. Principal components analysis and linear discriminant analysis were implemented to develop effective diagnostic algorithms for classification of Raman spectra of different TC subtypes. The spectral differences separating benign and TC cell lines were assigned to differences in the composition of nucleic acids, lipids, carbohydrates and protein in the benign and cancer cells. Good sensitivities (74%-85%), specificities (65%-93%) and diagnostic accuracies (71%-88%) were achieved for the identification of TC. These findings suggest that Raman spectroscopy has potential for preoperative TC diagnosis on FNAC samples

Discrimination of breast cancer from benign tumours using Raman spectroscopy

Breast cancer is the most common cancer among women worldwide, with an estimated 1.7 million cases and 522,000 deaths in 2012. Breast cancer is diagnosed by histopathological examination of breast biopsy material but this is subjective and relies on morphological changes in the tissue. Raman spectroscopy uses incident radiation to induce vibrations in the molecules of a sample and the scattered radiation can be used to characterise the sample. This technique is rapid and non-destructive and is sensitive to subtle biochemical changes occurring at the molecular level. This allows spectral variations corresponding to disease onset to be detected. The aim of this work was to use Raman spectroscopy to discriminate between benign lesions (fibrocystic, fibroadenoma, intraductal papilloma) and cancer (invasive ductal carcinoma and lobular carcinoma) using formalin fixed paraffin preserved (FFPP) tissue. Haematoxylin and Eosin stained sections from the patient biopsies were marked by a pathologist. Raman maps were recorded from parallel unstained tissue sections. Immunohistochemical staining for estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2/neu) was performed on a further set of parallel sections. Both benign and cancer cases were positive for ER while only the cancer cases were positive for HER2. Significant spectral differences were observed between the benign and cancer cases and the benign cases could be differentiated from the cancer cases with good sensitivity and specificity. This study has shown the potential of Raman spectroscopy as an aid to histopathological diagnosis of breast cancer, in particular in the discrimination between benign and malignant tumours.Scopu

A simple model for cell type recognition using 2D-correlation analysis of FTIR images from breast cancer tissue

Breast cancer is the second most common cancer after lung cancer. So far, in clinical practice, most cancer parameters originating from histopathology rely on the visualization by a pathologist of microscopic structures observed in stained tissue sections, including immunohistochemistry markers. Fourier transform infrared spectroscopy (FTIR) spectroscopy provides a biochemical fingerprint of a biopsy sample and, together with advanced data analysis techniques, can accurately classify cell types. Yet, one of the challenges when dealing with FTIR imaging is the slow recording of the data. One cm2 tissue section requires several hours of image recording. We show in the present paper that 2D covariance analysis singles out only a few wavenumbers where both variance and covariance are large. Simple models could be built using 4 wavenumbers to identify the 4 main cell types present in breast cancer tissue sections. Decision trees provide particularly simple models to reach discrimination between the 4 cell types. The robustness of these simple decision-tree models were challenged with FTIR spectral data obtained using different recording conditions. One test set was recorded by transflection on tissue sections in the presence of paraffin while the training set was obtained on dewaxed tissue sections by transmission. Furthermore, the test set was collected with a different brand of FTIR microscope and a different pixel size. Despite the different recording conditions, separating extracellular matrix (ECM) from carcinoma spectra was 100% successful, underlying the robustness of this univariate model and the utility of covariance analysis for revealing efficient wavenumbers. We suggest that 2D covariance maps using the full spectral range could be most useful to select the interesting wavenumbers and achieve very fast data acquisition on quantum cascade laser infrared imaging microscopes. ? 2018 The AuthorsThis work was made possible by a NPRP Award [7 - 1267 - 3?328] from the Qatar National Research Fund (a member of The Qatar Foundation). E.G. is Research Director with the National Fund for Scientific Research (Belgium) . The statements made herein are solely the responsibility of the authors

Raman spectroscopic mapping for the analysis of solar radiation induced skin damage

International audienceThe effects of simulated solar irradiation of an artificial skin model have been examined using Raman spectroscopy and the results are compared with cytotoxicological and histological profiling. Samples exposed for times varying between 30 minutes and 240 minutes were incubated post exposure for a period of 96 hours. The cytotoxicological response as measured by the MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide] assay demonstrated a ∼50% loss of viability of the artificial tissue after 120 minutes exposure. Histological staining of tissue sections showed considerable loss of cellular content in the epidermal layer at this endpoint. Raman spectroscopic mapping of tissue sections, coupled with K-means cluster analysis (KMCA) clearly identified the dermal and stratum corneum layers and differentiated further substructures of the epidermis. Post irradiation, a significant loss of DNA features in the basal layer was apparent in the results of the KMCA. Principal Components Analysis (PCA) of layers identified by the KMCA post exposure compared with controls indicated a significant increase in the lipidic signatures of the stratum corneum. In the dermal layer, little photodamage was observed, but a similar increase in lipidic signatures in the basal layer was accompanied by a decrease in DNA and protein contributions. The spectral profiles of the photodamage to the basal layer as identified by PCA are consistent over the exposure periods of 30–240 minutes, but an examination of the evolution of features associated with specific biochemical components indicated DNA damage and loss of lipidic signatures at the early exposure times, whereas changes in protein signatures appeared to evolve over longer periods. In comparison to the cytotoxicological responses, the study demonstrates that Raman spectroscopy can identify biochemical changes as a result of solar exposure at time points significantly earlier than changes in tissue viability are observed

A simple model for cell type recognition using 2D-correlation analysis of FTIR images from breast cancer tissue

Breast cancer is the second most common cancer after lung cancer. So far, in clinical practice, most cancer parameters originating from histopathology rely on the visualization by a pathologist of microscopic structures observed in stained tissue sections, including immunohistochemistry markers. Fourier transform infrared spectroscopy (FTIR) spectroscopy provides a biochemical fingerprint of a biopsy sample and, together with advanced data analysis techniques, can accurately classify cell types. Yet, one of the challenges when dealing with FTIR imaging is the slow recording of the data. One cm2 tissue section requires several hours of image recording. We show in the present paper that 2D covariance analysis singles out only a few wavenumbers where both variance and covariance are large. Simple models could be built using 4 wavenumbers to identify the 4 main cell types present in breast cancer tissue sections. Decision trees provide particularly simple models to reach discrimination between the 4 cell types. The robustness of these simple decision-tree models were challenged with FTIR spectral data obtained using different recording conditions. One test set was recorded by transflection on tissue sections in the presence of paraffin while the training set was obtained on dewaxed tissue sections by transmission. Furthermore, the test set was collected with a different brand of FTIR microscope and a different pixel size. Despite the different recording conditions, separating extracellular matrix (ECM) from carcinoma spectra was 100% successful, underlying the robustness of this univariate model and the utility of covariance analysis for revealing efficient wavenumbers. We suggest that 2D covariance maps using the full spectral range could be most useful to select the interesting wavenumbers and achieve very fast data acquisition on quantum cascade laser infrared imaging microscopes.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A comparison of Raman, FTIR and ATR-FTIR micro spectroscopy for imaging human skin tissue sections

International audienceRaman and infrared absorption spectroscopies are compared for the analysis of human hand skin tissue sections. The tissue sections have been formalin fixed and paraffin processed, and subsequently dewaxed. Fourier Transform Infrared (FTIR) spectra are preprocessed using the resonant Mie-extended multiplicative scattering algorithm to remove spectral artefacts. FTIR images of resolution 4 cm−1, analysed using K-means cluster analysis, reveal the double layer structure of the dermis and epidermis, but no further layer differentiation is achieved using the higher spatial resolution of the Attenuated Total Reflection imaging or improved spectral resolution of 2 cm−1. At comparable spectral and spatial resolutions and measurement on the same samples, Raman scattering produces spectra of significantly higher spectral detail and can differentiate the stratum corneum from the underlying epithelial layer, and, in the absence of melanin in an artificial skin model, can further differentiate the basal layer from the overlying epithelium. The differences in the performance of the techniques are therefore not instrumentational and are discussed in terms of the technological and fundamental differences between the two complementary techniques