A multi-modal exploration of heterogeneous physico–chemical properties of DCIS breast microcalcifications

Ductal carcinoma in situ (DCIS) is frequently associated with breast calcification. This study combines multiple analytical techniques to investigate the heterogeneity of these calcifications at the micrometre scale. X-ray diffraction, scanning electron microscopy and Raman and Fourier-transform infrared spectroscopy were used to determine the physicochemical and crystallographic properties of type II breast calcifications located in formalin fixed paraffin embedded DCIS breast tissue samples. Multiple calcium phosphate phases were identified across the calcifications, distributed in different patterns. Hydroxyapatite was the dominant mineral, with magnesium whitlockite found at the calcification edge. Amorphous calcium phosphate and octacalcium phosphate were also identified close to the calcification edge at the apparent mineral/matrix barrier. Crystallographic features of hydroxyapatite also varied across the calcifications, with higher crystallinity centrally, and highest carbonate substitution at the calcification edge. Protein was also differentially distributed across the calcification and the surrounding soft tissue, with collagen and β-pleated protein features present to differing extents. Combination of analytical techniques in this study was essential to understand the heterogeneity of breast calcifications and how this may link crystallographic and physicochemical properties of calcifications to the surrounding tissue microenvironment.Cancer Research UK and by KWF Kankerbestrijding: C38317/A2404

A highly stable, nanotube-enhanced, CMOS-MEMS thermal emitter for mid-IR gas sensing

Funder: National Physical Laboratory; doi: http://dx.doi.org/10.13039/501100007851Funder: Royal Society Dorothy Hodgkin Research FellowshipThe gas sensor market is growing fast, driven by many socioeconomic and industrial factors. Mid-infrared (MIR) gas sensors offer excellent performance for an increasing number of sensing applications in healthcare, smart homes, and the automotive sector. Having access to low-cost, miniaturized, energy efficient light sources is of critical importance for the monolithic integration of MIR sensors. Here, we present an on-chip broadband thermal MIR source fabricated by combining a complementary metal oxide semiconductor (CMOS) micro-hotplate with a dielectric-encapsulated carbon nanotube (CNT) blackbody layer. The micro-hotplate was used during fabrication as a micro-reactor to facilitate high temperature (>700 ∘C) growth of the CNT layer and also for post-growth thermal annealing. We demonstrate, for the first time, stable extended operation in air of devices with a dielectric-encapsulated CNT layer at heater temperatures above 600 ∘C. The demonstrated devices exhibit almost unitary emissivity across the entire MIR spectrum, offering an ideal solution for low-cost, highly-integrated MIR spectroscopy for the Internet of Things

Detection of Aβ plaque-associated astrogliosis in Alzheimer’s disease brain by spectroscopic imaging and immunohistochemistry

Recent work using micro-Fourier transform infrared (μFTIR) imaging has revealed that a lipid-rich layer surrounds many plaques in post-mortem Alzheimer’s brain. However, the origin of this lipid layer is not known, nor is its role in the pathogenesis of Alzheimer’s disease (AD). Here, we studied the biochemistry of plaques in situ using a model of AD. We combined FTIR, Raman and immunofluorescence images, showing that astrocyte processes co-localise with the lipid-ring surrounding many plaques. We used μFTIR imaging to rapidly measure chemical signatures of plaques over large fields of view, and selected plaques for higher resolution analysis with Raman. Raman maps showed similar lipid-rings and dense protein cores as in FTIR images, but also revealed cell bodies. We confirmed the presence of plaques using amylo-glo staining, and measured astrocytes using immunohistochemistry, revealing astrocyte colocalisation with lipid-rings. This work is important because it correlates biochemically changes surrounding the plaque with the biological process of astrogliosis

Multiple Pathway-Based Genetic Variations Associated with Tobacco Related Multiple Primary Neoplasms

BACKGROUND: In order to elucidate a combination of genetic alterations that drive tobacco carcinogenesis we have explored a unique model system and analytical method for an unbiased qualitative and quantitative assessment of gene-gene and gene-environment interactions. The objective of this case control study was to assess genetic predisposition in a biologically enriched clinical model system of tobacco related cancers (TRC), occurring as Multiple Primary Neoplasms (MPN). METHODS: Genotyping of 21 candidate Single Nucleotide Polymorphisms (SNP) from major metabolic pathways was performed in a cohort of 151 MPN cases and 210 cancer-free controls. Statistical analysis using logistic regression and Multifactor Dimensionality Reduction (MDR) analysis was performed for studying higher order interactions among various SNPs and tobacco habit. RESULTS: Increased risk association was observed for patients with at least one TRC in the upper aero digestive tract (UADT) for variations in SULT1A1 Arg²¹³His, mEH Tyr¹¹³His, hOGG1 Ser³²⁶Cys, XRCC1 Arg²⁸⁰His and BRCA2 Asn³⁷²His. Gene-environment interactions were assessed using MDR analysis. The overall best model by MDR was tobacco habit/p53(Arg/Arg)/XRCC1(Arg³⁹⁹His)/mEH(Tyr¹¹³His) that had highest Cross Validation Consistency (8.3) and test accuracy (0.69). This model also showed significant association using logistic regression analysis. CONCLUSION: This is the first Indian study on a multipathway based approach to study genetic susceptibility to cancer in tobacco associated MPN. This approach could assist in planning additional studies for comprehensive understanding of tobacco carcinogenesis

The Ratio 1660/1690 cm−1 Measured by Infrared Microspectroscopy Is Not Specific of Enzymatic Collagen Cross-Links in Bone Tissue

In postmenopausal osteoporosis, an impairment in enzymatic cross-links (ECL) occurs, leading in part to a decline in bone biomechanical properties. Biochemical methods by high performance liquid chromatography (HPLC) are currently used to measure ECL. Another method has been proposed, by Fourier Transform InfraRed Imaging (FTIRI), to measure a mature PYD/immature DHLNL cross-links ratio, using the 1660/1690 cm−1 area ratio in the amide I band. However, in bone, the amide I band composition is complex (collagens, non-collagenous proteins, water vibrations) and the 1660/1690 cm−1 by FTIRI has never been directly correlated with the PYD/DHLNL by HPLC. A study design using lathyritic rats, characterized by a decrease in the formation of ECL due to the inhibition of lysyl oxidase, was used in order to determine the evolution of 1660/1690 cm−1 by FTIR Microspectroscopy in bone tissue and compare to the ECL quantified by HPLC. The actual amount of ECL was quantified by HPLC on cortical bone from control and lathyritic rats. The lathyritic group exhibited a decrease of 78% of pyridinoline content compared to the control group. The 1660/1690 cm−1 area ratio was increased within center bone compared to inner bone, and this was also correlated with an increase in both mineral maturity and mineralization index. However, no difference in the 1660/1690 cm−1 ratio was found between control and lathyritic rats. Those results were confirmed by principal component analysis performed on multispectral infrared images. In bovine bone, in which PYD was physically destructed by UV-photolysis, the PYD/DHLNL (measured by HPLC) was strongly decreased, whereas the 1660/1690 cm−1 was unmodified. In conclusion, the 1660/1690 cm−1 is not related to the PYD/DHLNL ratio, but increased with age of bone mineral, suggesting that a modification of this ratio could be mainly due to a modification of the collagen secondary structure related to the mineralization process

Microcalcification crystallography as a potential marker of DCIS recurrence

Ductal carcinoma in-situ (DCIS) accounts for 20-25% of all new breast cancer diagnoses. DCIS has an uncertain risk of progression to invasive breast cancer and a lack of predictive biomarkers may result in relatively high levels (~ 75%) of overtreatment. To identify unique prognostic biomarkers of invasive progression, crystallographic and chemical features of DCIS microcalcifications have been explored. Samples from patients with at least 5-years of follow up and no known recurrence (174 calcifications in 67 patients) or ipsilateral invasive breast cancer recurrence (179 microcalcifications in 57 patients) were studied. Significant differences were noted between the two groups including whitlockite relative mass, hydroxyapatite and whitlockite crystal maturity and, elementally, sodium to calcium ion ratio. A preliminary predictive model for DCIS to invasive cancer progression was developed from these parameters with an AUC of 0.797. These results provide insights into the differing DCIS tissue microenvironments, and how these impact microcalcification formation. [Abstract copyright: © 2023. The Author(s).

Molecular characterization of tumoral lesions by infrared spectral imaging : implementation of a new concept based on spectral histopathology for colon cancer diagnosis

A l'heure actuelle, des méthodes innovatrices complémentaires à l'histopathologie pour le diagnostic de cancer sont en voie de développement. Dans cette perspective, une approche biophotonique telle la micro-imagerie spectrale infrarouge représente une méthode candidate capable de fournir une empreinte biochimique des cellules et des tissus sans étape de marquage. Par conséquent, un nouveau concept d'histopathologie spectrale infrarouge des tissus du côlon a été mis en œuvre afin d'identifier les signatures spectrales spécifiques des structures histologiques du côlon, et d'exploiter ces signatures afin de développer un modèle de prédiction comprenant des marqueurs potentiels pour le diagnostic du cancer du côlon de manière rapide et automatisée. Pour cela, les images infrarouges de différents échantillons coliques (adénocarcinome modérément différencié et non-tumorale) ont été acquises en utilisant un système d'imagerie infrarouge. Un déparaffinage mathématique a été réalisé sur les images spectrales en utilisant l'algorithme « extended multiplicative signal correction » (EMSC). Les données spectrales ont été soumises à une analyse de clustering, afin d'identifier les signatures spectrales spécifiques des tissus du côlon. Ces signatures ont été utilisées pour développer un modèle de prédiction robuste qui a été appliqué sur des échantillons des tissus du côlon inconnus pour l'identification histopathologique. Le modèle de prédiction, a non seulement identifié d'une part les tissus tumoraux inconnus avec une sensibilité de 100%, mais aussi d'autre part des caractéristiques importantes associées à la tumeur telles que le tumor budding et l'association de la tumeur et du stroma. La micro-imagerie spectrale infrarouge en conjonction avec l'analyse statistique multivariée, constituant une approche non destructive et ne nécessitant aucun marquage, démontre le potentiel de cette méthode comme outil complémentaire à l'histopathologie classique pour un diagnostic de cancer automatisé et objectif.Innovative cancer diagnostic methods complementary to the gold standard histopathology are the need of the hour. In this perspective, the biophotonic approach of infrared spectral micro-imaging is one of the candidate methods capable of providing a biochemical fingerprint of cells and tissues in a label-free manner. Hence, a novel concept of infrared spectral histopathology of colonic tissues has been implemented in order to identify spectral signatures specific of colon histological structures, and to exploit these signatures to develop a prediction model comprising potential diagnostic markers for rapid and automated colon cancer diagnosis. For this, infrared images of colonic samples (moderately differentiated adenocarcinoma and non-tumoral) were acquired using an infrared imaging system. A mathematical deparaffinization was carried out on the spectral images using a modified Extended Multiplicative Signal Correction (EMSC) algorithm. The spectral data was subjected to clustering analysis in order to identify spectral signatures specific of colonic tissues. These signatures were used to develop a robust prediction model which was applied on unknown colonic tissue samples for histopathological identification. The prediction model not only identified the unknown tumoral tissues with 100 % sensitivity, but also some important tumor associated features such as tumor budding and tumor stroma association. Infrared spectral micro-imaging in conjunction with multivariate statistical analysis constituting a non-destructive and label-free approach, demonstrates the potential as a novel complementary tool to conventional histopathology for an automated and objective cancer diagnosis