JAM-A is highly expressed on human hematopoietic repopulating cells and associates with the key hematopoietic chemokine receptor CXCR4

Hematopoietic stem/progenitor cells (HSPCs) reside in specialized bone marrow microenvironmental niches, with vascular elements (endothelial/mesenchymal stromal cells) and CXCR4-CXCL12 interactions playing particularly important roles for HSPC entry, retention and maintenance. The functional effects of CXCL12 are dependent on its local concentration and rely on complex HSPC-niche interactions. Two Junctional Adhesion Molecule family proteins, JAM-B and JAM-C, are reported to mediate HSPC-stromal cell interactions, which in turn regulate CXCL12 production by mesenchymal stromal cells (MSCs). Here, we demonstrate that another JAM family member, JAM-A, is most highly expressed on human hematopoietic stem cells with in vivo repopulating activity (p&lt;0.01 for JAM-Ahigh compared to JAM-AInt or Low cord blood CD34+ cells). JAM-A blockade, silencing and overexpression show that JAM-A contributes significantly (p&lt;0.05) to the adhesion of human HSPCs to IL-1β activated human bone marrow sinusoidal endothelium. Further studies highlight a novel association of JAM-A with CXCR4, with these molecules moving to the leading edge of the cell upon presentation with CXCL12 (p&lt;0.05 compared to no CXCL12). Therefore, we hypothesize that JAM family members differentially regulate CXCR4 function and CXCL12 secretion in the bone marrow niche.</p

A comparative analysis of different biofluids towards ovarian cancer diagnosis using Raman microspectroscopy

Biofluids, such as blood plasma or serum, are currently being evaluated for cancer detection using vibrational spectroscopy. These fluids contain information of key biomolecules, such as proteins, lipids, carbohydrates and nucleic acids that comprise spectrochemical patterns to differentiate samples. Raman is a water-free and practically non-destructive vibrational spectroscopy technique, capable of recording spectrochemical fingerprints of biofluids with minimum or no sample preparation. Herein, we compare the performance of these two common biofluids (blood plasma and serum) together with ascitic fluid, towards ovarian cancer detection using Raman microspectroscopy. Samples from thirty-eight patients were analysed (n=18 ovarian cancer patients, n=20 benign controls) through different spectral pre-processing and discriminant analysis techniques. Ascitic fluid provided the best class-separation in both unsupervised and supervised discrimination approaches, where classification accuracies, sensitivities and specificities above 80% were obtained, in comparison to 60% - 73% with plasma or serum. Ascitic fluid appears to be rich in collagen information responsible for distinguishing ovarian cancer samples, where collagen-signalling bands at 1004 cm-1 (phenylalanine), 1334 cm-1 (CH3CH2 wagging vibration), 1448 cm-1 (CH2 deformation) and 1657 cm-1 (Amide I) exhibited high statistical significance for class differentiation (P <0.001). The efficacy of vibrational spectroscopy, in particular Raman spectroscopy, combined with ascitic fluid analysis, suggests a potential diagnostic method for ovarian cancer

A three-dimensional discriminant analysis approach for hyperspectral images

Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x- and y-axis, and the spectral information in the z-axis. Unfolding procedures are commonly employed to analyze this type of data in a multivariate fashion, where the spatial dimension is reshaped and the spectral data fits into a two-dimensional (2D) structure and, thereafter, common first-order chemometric algorithms are applied to process the data. There are only a few algorithms capable of working with the full 3D array. Herein, we propose new algorithms for 3D discriminant analysis of hyperspectral images based on a three-dimensional principal component analysis linear discriminant analysis (3D-PCA-LDA) and a three-dimensional discriminant analysis quadratic discriminant analysis (3D-PCA-QDA) approach. The analysis was performed in order to discriminate simulated and real-world data, comprising benign controls and ovarian cancer samples based on Raman hyperspectral imaging, in which 3D-PCA-LDA and 3D-PCA-QDA achieved far superior performance than classical algorithms using unfolding procedures (PCA-LDA, PCA-QDA, partial lest squares discriminant analysis [PLS-DA], and support vector machines [SVM]), where the classification accuracies improved from 66% to 83% (simulated data) and from 50% to 100% (real-world dataset) after employing the 3D techniques. 3D-PCA-LDA and 3D-PCA-QDA are new approaches for discriminant analysis of hyperspectral images multisets to provide faster and superior classification performance than traditional techniques