Transmission infrared spectroscopy of whole blood--complications for quantitative analysis from leucocyte adhesion during continuous monitoring

High-Resolution Fourier Transform Infrared (FT-IR) Spectroscopic Imaging for Detection of Lung Structures and Cancer-Related Abnormalities in a Murine Model

Label-free molecular imaging is a promising utility to study tissues in terms of the identification of their compartments as well as chemical features and alterations induced by disease. The aim of this work was to assess if higher magnification of optics in the Fourier transform infrared (FT-IR) microscope coupled with the focal plane detector resulted in better resolution of lung structures and if the histopathological features correlated with clustering of spectral images. FT-IR spectroscopic imaging was performed on paraffinized lung tissue sections from mice with optics providing a total magnification of 61× and 36×. Then, IR images were subjected to unsupervised cluster analysis and, subsequently, cluster maps were compared with hematoxylin and eosin staining of the same tissue section. Based on these results, we observed minute features such as cellular compartments in single alveoli and bronchiole, blood cells and megakaryocytes in a vessel as well as atelectasis of the lung. In the case of the latter, differences in composition were also noted between the tissue from the non-cancerous and cancerous specimen. This study demonstrated the ability of high-definition FT-IR imaging to evaluate the chemical features of well-resolved lung structures that could complement the histological examination widely used in animal models of disease.

Infrared Spectroscopy of Blood

The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.

A possible Fourier transform infrared-based plasma fingerprint of angiotensin-converting enzyme inhibitor-induced reversal of endothelial dysfunction in diabetic mice

Angiotensin-converting enzyme inhibitors (ACE-I) display vasoprotective activity and represent the cornerstone in the treatment of cardiovascular diseases. In this study, we tested whether Fourier transform infrared (FTIR)-based analysis of blood plasma is sensitive to detect vasoprotective effects of treatment with perindopril including reversal of endothelial dysfunction in diabetes. For this purpose, plasma samples were collected from untreated db/db mice, db/db mice treated with 2 or 10 mg/kg perindopril and db+ mice. The effect of perindopril on endothelial function was examined in ex vivo aortic rings; 10 mg/kg but not 2 mg/kg of perindopril reversed endothelial dysfunction. In plasma of db/db mice, the balance between conformations of plasma proteins was noted, and treatment with perindopril at a high dose but not at a low dose reversed this effect. This was revealed by amide II/amide I ratio attributed to increased β-sheet formation. Spectral markers at 3010, 1520/1238 cm^-1 , representative for unsaturation degree of lipids and phosphorylation of tyrosine, respectively, were also affected by perindopril treatment. In conclusion, although metabolic abnormalities associated with type 2 diabetes mellitus such as hypertriglyceridemia and hyperglycemia strongly affected spectral FTIR profile of diabetic plasma, we identified FTIR features that seem to be associated with the vasoprotective activity of ACE-I.

Transmission versus transflection mode in FTIR analysis of blood plasma: is the electric field standing wave effect the only reason for observed spectral distortions?

Fourier transform infrared (FTIR) microspectroscopy is assessed in terms of two techniques (i.e., transmission and transflection) as a method for rapid measurements of blood plasma.

Vibrational spectroscopy of biofluids for disease screening or diagnosis: translation from the laboratory to a clinical setting

There remains a need for objective and cost-effective approaches capable of diagnosing early-stage disease in point-of-care clinical settings. Given an increasingly ageing population resulting in a rising prevalence of chronic diseases, the need for screening to facilitate the personalising of therapies to prevent or slow down pathology development will increase. Such a tool needs to be robust but simple enough to be implemented into clinical practice. There is interest in extracting biomarkers from biofluids (e.g., plasma or serum); techniques based on vibrational spectroscopy provide an option. Sample preparation is minimal, techniques involved are relatively low-cost, and data frameworks are available. This review explores the evidence supporting the applicability of vibrational spectroscopy to generate spectral biomarkers of disease in biofluids. We extend the inter-disciplinary nature of this approach to hypothesise a microfluidic platform that could allow such measurements. With an appropriate lightsource, such engineering could revolutionize screening in the 21(st) century.

Assessing the challenges of Fourier transform infrared spectroscopic analysis of blood serum

There are many approaches to measuring the infrared spectrum of a blood serum sample. Naturally, each approach will have both advantages and disadvantages. We report on the progress of the application of infrared spectroscopy in the field of blood serum analysis towards clinical application, with a focus on prostate cancer. In order to perform a high-powered study with clinical relevance, choosing the most suitable approach must undergo careful consideration. We review the possibilities of using different sample preparation methods and speculate upon the potential pitfalls of both transmission and attenuated total reflectance (ATR) techniques.

FTIR spectroscopy of biofluids revisited: an automated approach to spectral biomarker identification

The extraction of disease specific information from Fourier transform infrared (FTIR) spectra of human body fluids demands the highest standards of accuracy and reproducibility of measurements because the expected spectral differences between healthy and diseased subjects are very small in relation to a large background absorbance of the whole sample. Here, we demonstrate that with the increased sensitivity of modern FTIR spectrometers, automatisation of sample preparation and modern bioinformatics, it is possible to identify and validate spectral biomarker candidates for distinguishing between urinary bladder cancer (UBC) and inflammation in suspected bladder cancer patients. The current dataset contains spectra of blood serum and plasma samples of 135 patients. All patients underwent cytology and pathological biopsy characterization to distinguish between patients without UBC (46) and confirmed UBC cases (89). A minimally invasive blood test could spare control patients a repeated cystoscopy including a transurethral biopsy, and three-day stationary hospitalisation. Blood serum, EDTA and citrate plasma were collected from each patient and processed following predefined strict standard operating procedures. Highly reproducible dry films were obtained by spotting sub-nanoliter biofluid droplets in defined patterns, which were compared and optimized. Particular attention was paid to the automatisation of sample preparation and spectral preprocessing to exclude errors by manual handling. Spectral biomarker candidates were identified from absorbance spectra and their 1(st) and 2(nd) derivative spectra using an advanced Random Forest (RF) approach. It turned out that the 2(nd) derivative spectra were most useful for classification. Repeat validation on 21% of the dataset not included in predictor training with Linear Discriminant Analysis (LDA) classifiers and Random Forests (RFs) yielded a sensitivity of 93 ± 10% and a specificity of 46 ± 18% for bladder cancer. The low specificity can be most likely attributed to the unbalanced and small number of control samples. Using this approach, spectral biomarker candidates in blood-derived biofluids were identified, which allow us to distinguish between cancer and inflammation, but the observed differences were tiny. Obviously, a much larger sample number has to be investigated to reliably validate such candidates.

Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood

The first response to infection in the blood is mediated by leukocytes. As a result crucial information can be gained from a hemogram. Conventional methods such as blood smears and automated sorting procedures are not capable of recording detailed biochemical information of the different leukocytes. In this study, Raman spectroscopy has been applied to investigate the differences between the leukocyte subtypes which have been obtained from healthy donors. Raman imaging was able to visualize the same morphological features as standard staining methods without the need of any label. Unsupervised statistical methods such as principal component analysis and hierarchical cluster analysis were able to separate Raman spectra of the two most abundant leukocytes, the neutrophils and lymphocytes (with a special focus on CD4(+) T-lymphocytes). For the same cells a classification model was built to allow an automated Raman-based differentiation of the cell type in the future. The classification model could achieve an accuracy of 94% in the validation step and could predict the identity of unknown cells from a completely different donor with an accuracy of 81% when using single spectra and with an accuracy of 97% when using the majority vote from all individual spectra of the cell. This marks a promising step toward automated Raman spectroscopic blood analysis which holds the potential not only to assign the numbers of the cells but also to yield important biochemical information.