Biomedical Applications of Mid-Infrared Spectroscopic Imaging and Multivariate Data Analysis: Contribution to the Understanding of Diabetes Pathogenesis

Diabetic retinopathy (DR) is a microvascular complication of diabetes and a leading cause of adult vision loss. Although a great deal of progress has been made in ophthalmological examinations and clinical approaches to detect the signs of retinopathy in patients with diabetes, there still remain outstanding questions regarding the molecular and biochemical changes involved. To discover the biochemical mechanisms underlying the development and progression of changes in the retina as a result of diabetes, a more comprehensive understanding of the bio-molecular processes, in individual retinal cells subjected to hyperglycemia, is required. Animal models provide a suitable resource for temporal detection of the underlying pathophysiological and biochemical changes associated with DR, which is not fully attainable in human studies. In the present study, I aimed to determine the nature of diabetes-induced, highly localized biochemical changes in the retinal tissue from Ins2Akita/+ (Akita/+; a model of Type I diabetes) male mice with different duration of diabetes. Employing label-free, spatially resolved Fourier transform infrared (FT-IR) imaging engaged with chemometric tools enabled me to identify temporal-dependent reproducible biomarkers of the diabetic retinal tissue from mice with 6 or 12 weeks, and 6 or 10 months of diabetes. I report, for the first time, the origin of molecular changes in the biochemistry of individual retinal layers with different duration of diabetes. A robust classification between distinctive retinal layers - namely photoreceptor layer (PRL), outer plexiform layer (OPL), inner nuclear layer (INL), and inner plexiform layer (IPL) - and associated temporal-dependent spectral biomarkers, were delineated. Spatially-resolved super resolution chemical images revealed oxidative stress-induced structural and morphological alterations within the nucleus of the photoreceptors. Comparison among the PRL, OPL, INL, and IPL suggested that the photoreceptor layer is the most susceptible layer to the oxidative stress with short-duration of diabetes. Moreover, for the first time, we present the temporal-dependent molecular alterations for the PRL, OPL, INL, and IPL from Akita/+ mice, with progression of diabetes. These findings are potentially important and may be of particular benefit in understanding the molecular and biological activity of retinal cells during oxidative stress in diabetes. Our integrating paradigm provides a new conceptual framework and a significant rationale for a better understanding of the molecular and cellular mechanisms underlying the development and progression of DR. This approach may yield alternative and potentially complimentary methods for the assessment of diabetes changes. It is expected that the conclusions drawn from this work will bridge the gap in our knowledge regarding the biochemical mechanisms of the DR and address some critical needs in the biomedical community

Multivariate Analysis in Management, Engineering and the Sciences

Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field

Sensor based pre-symptomatic detection of pests and pathogens for precision scheduling of crop protection products

Providing global food security requires a better understanding of how plants function and how their products, including important crops are influenced by environmental factors. Prominent biological factors influencing food security are pests and pathogens of plants and crops. Traditional pest control, however, has involved chemicals that are harmful to the environment and human health, leading to a focus on sustainability and prevention with regards to modern crop protection. A variety of physical and chemical analytical tools is available to study the structure and function of plants at the whole-plant, organ, tissue, cellular, and biochemical levels, while acting as sensors for decision making in the applied crop sciences. Vibrational spectroscopy, among them mid-infrared and Raman spectroscopy in biology, known as biospectroscopy are well-established label-free, nondestructive, and environmentally friendly analytical methods that generate a spectral “signature” of samples using mid-infrared radiation. The generated wavenumber spectrum containing hundreds of variables as unique as a biochemical “fingerprint”, and represents biomolecules (proteins, lipids, carbohydrates, nucleic acids) within biological samples. Spectral “biomarkers” generated by biospectroscopy is useful for the discrimination of distinct as well as closely related biomaterials, for various applications. Applications within the plant and crop sciences has been limited to date, especially for the investigation of dynamic biological processes in intact plant tissues. Even more scarce is the application of biospectroscopy to plant interactions with pests and pathogens. To adequately probe in vivo plant-environment interactions, surface structures of intact plant tissues such as leaves, and fruit need to be characterized. Infrared light energy can measure plant epidermal structures including the cuticle and cell wall for chemical profiling of different varieties and cultivars, as well as physiological applications such as plant health monitoring and disease detection. A review of the application of biospectroscopy to study plant and crop biology reveals the potential of biospectroscopy as a prominent technology for fundamental plant research and applied crop science. The application of biospectroscopy for in vivo plant analysis, to elucidate spectral alterations indicative of pest and pathogen effects, may therefore be highly beneficial to crop protection. Highlighting the in vivo analysis capability and portability of modern biospectroscopy, ATR-FTIR provided an invaluable tool for a thorough spectrochemical investigation of intact tomato fruit during development and ripening. This contributes novel spectral biomarkers, distinct for each development and ripening stage to indicate healthy development. Concurrently, this approach demonstrates the effectiveness of using spectral data for machine learning, indicated by classifier results, which may be applied to crop biology. Complementary to monitoring healthy growth and development of plants and crops, is the detection of threats to plant products that compromise yield or quality. This includes physical damage and accelerated decay caused by pests and pathogens. Biochemical changes detected by ATR-FTIR using principal component analysis and linear discriminant analysis (PCA–LDA), for damage-induced pathogen infection of cherry tomato (cv. Piccolo), showed subtle biochemical changes distinguishing healthy tomato from damaged, early or late sour rot-infected tomato. Sour rot fungus Geotrichum candidum was detected in vivo and characterized based on spectral features distinct from tomato fruit providing biochemical insight and detection potential for intact plant–pathogen systems. Pre-harvest detection of pests and pathogens in growing plants is paramount for crop protection and for effective use of crop protection products. Established previously as an exceptionally versatile bioanalytical sensor, for post-harvest applications, biospectroscopy was applied for the pre-harvest detection of microscopic pathogen Botrytis cinerea fungus infecting developing tomato plants. Compact MIR spectroscopy using ATR mode was adapted for the biochemical investigation of the plant-microbe interaction S. lycopersicum and B. cinerea, on the whole-plant level. Chemometric modeling including principal component analysis, and linear discriminant analysis were applied. Fingerprint spectra (1800-900 cm-1) were excellent discriminators of plant disease in pre-symptomatic as well as symptomatic plants. Spectral alterations in leaf tissue caused by infection are discussed. Potential for automatic decision-making is shown by high accuracy rates of 100% for detecting plant disease at various stages of progression. Similar accuracy rates using similar chemometric models are obtained for fruit development and ripening also. Overall, this research showcases the biospectroscopy potential for development monitoring and ripening of fruit crops, damage and infection induced decay of fruit in horticultural systems post-harvest, complemented by pre-harvest detection of microscopic pathogens. Based on the results from experiments performed under semi-controlled conditions, biospectroscopy is ready for field applications directed at pest and pathogen detection for improved crop production through the mitigation of crop loss

FTIR spectral signatures of mouse antral oocytes: Molecular markers of oocyte maturation and developmental competence

AbstractMammalian antral oocytes with a Hoescht-positive DNA ring around the nucleolus (SN) are able to resume meiosis and to fully support the embryonic development, while oocytes with a non-surrounded nucleolus (NSN) cannot. Here, we applied FTIR microspectroscopy to characterize single SN and NSN mouse oocytes in order to try to elucidate some aspects of the mechanisms behind the different chromatin organization that impairs the full development of NSN oocyte-derived embryos. To this aim, oocytes were measured at three different stages of their maturation: just after isolation and classification as SN and NSN oocytes (time 0); after 10h of in vitro maturation, i.e. at the completion of the metaphase I (time 1); and after 20h of in vitro maturation, i.e. at the completion of the metaphase II (time 2). Significant spectral differences in the lipid (3050–2800cm−1) and protein (1700–1600cm−1) absorption regions were found between the two types of oocytes and among the different stages of maturation within the same oocyte type. Moreover, dramatic changes in nucleic acid content, concerning mainly the extent of transcription and polyadenylation, were detected in particular between 1000 and 800cm−1. The use of the multivariate principal component–linear discriminant analysis (PCA–LDA) enabled us to identify the maturation stage in which the separation between the two types of oocytes took place, finding as the most discriminating wavenumbers those associated to transcriptional activity and polyadenylation, in agreement with the visual analysis of the spectral data

Understanding the Molecular Information Contained in Principal Component Analysis of Vibrational Spectra of Biological Systems

K-means clustering followed by Principal Component Analysis (PCA) is employed to analyse Raman spectroscopic maps of single biological cells. K-means clustering successfully identifies regions of cellular cytoplasm, nucleus and nucleoli, but the mean spectra do not differentiate their biochemical composition. The loadings of the principal components identified by PCA shed further light on the spectral basis for differentiation but they are complex and, as the number of spectra per cluster is imbalanced, particularly in the case of the nucleoli, the loadings under-represent the basis for differentiation of some cellular regions. Analysis of pure bio-molecules, both structurally and spectrally distinct, in the case of histone, ceramide and RNA, and similar in the case of the proteins albumin, collagen and histone, show the relative strong representation of spectrally sharp features in the spectral loadings, and the systematic variation of the loadings as one cluster becomes reduced in number. The more complex cellular environment is simulated by weighted sums of spectra, illustrating that although the loading become increasingly complex; their origin in a weighted sum of the constituent molecular components is still evident. Returning to the cellular analysis, the number of spectra per cluster is artificially balanced by increasing the weighting of the spectra of smaller number clusters. While it renders the PCA loading more complex for the three-way analysis, a pair wise analysis illustrates clear differences between the identified subcellular regions, and notably the molecular differences between nuclear and nucleoli regions are elucidated. Overall, the study demonstrates how appropriate consideration of the data available can improve the understanding of the information delivered by PCA

Diet-sourced carbon-based nanoparticles induce lipid alterations in tissues of zebrafish (Danio rerio) with genomic hypermethylation changes in brain

With rising environmental levels of carbon-based nanoparticles (CBNs), there is an urgent need to develop an understanding of their biological effects in order to generate appropriate risk assessment strategies. Herein, we exposed zebrafish via their diet to one of four different CBNs: C60 fullerene (C60), single-walled carbon nanotubes (SWCNT), short multi-walled carbon nanotubes (MWCNTs) or long MWCNTs. Lipid alterations in male and female zebrafish were explored post-exposure in three target tissues (brain, gonads and gastrointestinal tract) using ‘omic’ procedures based in liquid chromatography coupled with mass spectrometry (LC-MS) data files. These tissues were chosen as they are often target tissues following environmental exposure. Marked alterations in lipid species are noted in all three tissues. To further explore CBN-induced brain alterations, Raman microspectroscopy analysis of lipid extracts was conducted. Marked lipid alterations are observed with males responding differently to females; in addition, there also appears to be consistent elevations in global genomic methylation. This latter observation is most profound in female zebrafish brain tissues post-exposure to short MWCNTs or SWCNTs (P < 0.05). This study demonstrates that even at low levels, CBNs are capable of inducing significant cellular and genomic modifications in a range of tissues. Such alterations could result in modified susceptibility to other influences such as environmental exposures, pathology and, in the case of brain, developmental alterations