This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.
The epithelial-mesenchymal transition (EMT) is a crucial process in cancer progression and metastasis. Study of metabolic changes during the EMT process is important in seeking to understand the biochemical changes associated with cancer progression, not least in scoping for therapeutic strategies aimed at targeting EMT. Due to the potential for high sensitivity and specificity, Raman spectroscopy was used here to study the metabolic changes associated with EMT in human breast cancer tissue. For Raman spectroscopy measurements, tissue from 23 patients were collected, comprising non-lesional, EMT and non-EMT formalin-fixed and paraffin embedded breast cancer samples. Analysis was made in the fingerprint Raman spectra region (600-1800 cm-1) best associated with cancer progression biochemical changes in lipid, protein and nucleic acids. The ANOVA test followed by the Tukey's multiple comparisons test were conducted to see if there existed differences between non-lesional, EMT and non-EMT breast tissue for Raman spectroscopy measurements. Results revealed that significant differences were evident in terms of intensity between the non-lesional and EMT samples, as well as the EMT and non-EMT samples. Multivariate analysis involving independent component analysis, Principal component analysis and non-negative least square were used to analyse the Raman spectra data. The results show significant differences between EMT and non-EMT cancers in lipid, protein, and nucleic acids. This study demonstrated the capability of Raman spectroscopy supported by multivariate analysis in analysing metabolic changes in EMT breast cancer tissue.
At the supramolecular level, the proliferation of invasive ductal carcinoma through breast tissue is beyond the range of standard histopathology identification. Using synchrotron small angle x-ray scattering (SAXS) techniques, determining nanometer scale structural changes in breast tissue has been demonstrated to allow discrimination between different tissue types. From a total of 22 patients undergoing symptomatic investigations, different category breast tissue samples were obtained in use of surgically removed tissue, including non-lesional, benign and malignant tumour. Structural components of the tissues were examined at momentum transfer values between q = 0.2 nm-1 and 1.5 nm-1. From the SAXS patterns, axial d-spacing and diffuse scattering intensity were observed to provide the greatest discrimination between the various tissue types, specifically in regard to the epithelial mesenchymal transition (EMT) structural component in malignant tissue. In non-lesional tissue the axial period of collagen is within the range 63.6-63.7 nm (formalin fixed paraffin embedded (FFPE) dewaxed) and 63.4 (formalin fixed), being 0.9 nm smaller than in EMT cancer-invaded regions. The overall intensity of scattering from cancerous regions is a degree of magnitude greater in cancer-invaded regions. Present work has found that the d-spacing of the EMT positive breast cancer tissue (FFPE (dewaxed)) is within the range 64.5-64.7 nm corresponding to the 9th and 10th order peaks. Of particular note in regard to formalin fixation of samples is that no alteration is observed to occur in the relative differences in collagen d-spacing between non-lesional and malignant tissues. This is a matter of great importance given that preserved-sample and also retrospective study of samples is greatly facilitated by formalin fixation. Present results indicate that as aids in tissue diagnosis SAXS is capable of distinguishing areas of invasion by disease as well as delivering further information at the supramolecular level.
Amyloidosis is a deleterious condition caused by abnormal amyloid fibril build-up in living tissues. To date, 42 proteins that are linked to amyloid fibrils have been discovered. Amyloid fibril structure variation can affect the severity, progression rate, or clinical symptoms of amyloidosis. Since amyloid fibril build-up is the primary pathological basis for various neurodegenerative illnesses, characterization of these deadly proteins, particularly utilising optical techniques have been a focus. Spectroscopy techniques provide significant non-invasive platforms for the investigation of the structure and conformation of amyloid fibrils, offering a wide spectrum of analyses ranging from nanometric to micrometric size scales. Even though this area of study has been intensively explored, there still remain aspects of amyloid fibrillization that are not fully known, a matter hindering progress in treating and curing amyloidosis. This review aims to provide recent updates and comprehensive information on optical techniques for metabolic and proteomic characterization of β-pleated amyloid fibrils found in human tissue with thorough literature analysis of publications. Raman spectroscopy and SAXS are well established experimental methods for study of structural properties of biomaterials. With suitable models, they offer extended information for valid proteomic analysis under physiologically relevant conditions. This review points to evidence that despite limitations, these techniques are able to provide for the necessary output and proteomics indication in order to extrapolate the aetiology of amyloid fibrils for reliable diagnostic purposes. Our metabolic database may also contribute to elucidating the nature and function of the amyloid proteome in development and clearance of amyloid diseases.
Trace and minor elements play crucial roles in a variety of biological processes, including amyloid fibrils formation. Mechanisms include activation or inhibition of enzymatic reactions, competition between elements and metal proteins for binding positions, also changes to the permeability of cellular membranes. These may influence carcinogenic processes, with trace and minor element concentrations in normal and amyloid tissues potentially aiding in cancer diagnosis and etiology. With the analytical capability of the spectroscopic technique X-ray fluorescence (XRF), this can be used to detect and quantify the presence of elements in amyloid characterization, two of the trace elements known to be associated with amyloid fibrils. In present work, involving samples from a total of 22 subjects, samples of normal and amyloid-containing tissues of heart, kidney, thyroid, and other tissue organs were obtained, analyzed via energy-dispersive X-ray fluorescence (EDXRF). The elemental distribution of potassium (K), calcium (Ca), arsenic (As), and iron (Fe) was examined in both normal and amyloidogenic tissues using perpetual thin slices. In amyloidogenic tissues the levels of K, Ca, and Fe were found to be less than in corresponding normal tissues. Moreover, the presence of As was only observed in amyloidogenic samples; in a few cases in which there was an absence of As, amyloid samples were found to contain Fe. Analysis of arsenic in amyloid plaques has previously been difficult, often producing contradictory results. Using the present EDXRF facility we could distinguish between amyloidogenic and normal samples, with potential correlations in respect of the presence or concentration of specific elements.
We describe a novel automated cell detection and counting software, QuickCount® (QC), designed for rapid quantification of cells. The Bland-Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJauto, CellProfiler and CellC and the precision of QC, ImageJauto, CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research.
Obesity is strongly linked with increased risk and poorer prognosis of endometrial cancer (EC). Cancer-associated fibroblasts (CAFs) are activated fibroblasts that form a large component of the tumor microenvironment and undergo metabolic reprogramming to provide critical metabolites for tumor growth. However, it is still unknown how obesity, characterized by a surplus of free fatty acids drives the modifications of CAFs lipid metabolism which may provide the mechanistic link between obesity and EC progression. The present study aims to evaluate the utility of Raman spectroscopy, an emerging nondestructive analytical tool to detect signature changes in lipid metabolites of CAFs from EC patients with varying body mass index. We established primary cultures of fibroblasts from human EC tissues, and CAFs of overweight/obese and nonobese women using antibody-conjugated magnetic beads isolation. These homogeneous fibroblast cultures expressed fibroblast markers, including α-smooth muscle actin and vimentin. Analysis was made in the Raman spectra region best associated with cancer progression biochemical changes in lipids (600-1800 cm-1 and 2800-3200 cm-1). Direct band analysis and ratiometric analysis were conducted to extract information from the Raman spectrum. Present results demonstrated minor shifts in the CH2 symmetric stretch of lipids at 2879 cm-1 and CH3 asymmetric stretching from protein at 2932 cm-1 in the overweight/obese CAFS compared to nonobese CAFs, indicating increased lipid content and a higher degree of lipid saturation. Principal component analysis showed that CAFs from overweight/obese and nonobese EC patients can be clearly distinguished indicating the capability of Raman spectroscopy to detect changes in biochemical components. Our results suggest Raman spectroscopy supported by chemometric analysis is a reliable technique for characterizing metabolic changes in clinical samples, providing an insight into obesity-driven alteration in CAFs, a critical stromal component during EC tumorigenesis.