Júlio Trevisan

Using Fourier transform IR spectroscopy to analyze biological materials

IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.

Distinguishing cell types or populations based on the computational analysis of their infrared spectra.

Infrared (IR) spectroscopy of intact cells results in a fingerprint of their biochemistry in the form of an IR spectrum; this has given rise to the new field of biospectroscopy. This protocol describes sample preparation (a tissue section or cytology specimen), the application of IR spectroscopy tools, and computational analysis. Experimental considerations include optimization of specimen preparation, objective acquisition of a sufficient number of spectra, linking of the derived spectra with tissue architecture or cell type, and computational analysis. The preparation of multiple specimens (up to 50) takes 8 h; the interrogation of a tissue section can take up to 6 h (∼100 spectra); and cytology analysis (n = 50, 10 spectra per specimen) takes 14 h. IR spectroscopy generates complex data sets and analyses are best when initially based on a multivariate approach (principal component analysis with or without linear discriminant analysis). This results in the identification of class clustering as well as class-specific chemical entities.

Extracting biological information with computational analysis of Fourier-transform infrared (FTIR) biospectroscopy datasets: current practices to future perspectives

Applying Fourier-transform infrared (FTIR) spectroscopy (or related technologies such as Raman spectroscopy) to biological questions (defined as biospectroscopy) is relatively novel. Potential fields of application include cytological, histological and microbial studies. This potentially provides a rapid and non-destructive approach to clinical diagnosis. Its increase in application is primarily a consequence of developing instrumentation along with computational techniques. In the coming decades, biospectroscopy is likely to become a common tool in the screening or diagnostic laboratory, or even in the general practitioners clinic. Despite many advances in the biological application of FTIR spectroscopy, there remain challenges in sample preparation, instrumentation and data handling. We focus on the latter, where we identify in the reviewed literature, the existence of four main study goals: Pattern Finding; Biomarker Identification; Imaging; and, Diagnosis. These can be grouped into two frameworks: Exploratory; and, Diagnostic. Existing techniques in Quality Control, Pre-processing, Feature Extraction, Clustering, and Classification are critically reviewed. An aspect that is often visited is that of method choice. Based on the state-of-art, we claim that in the near future research should be focused on the challenges of dataset standardization; building information systems; development and validation of data analysis tools; and, technology transfer. A diagnostic case study using a real-world dataset is presented as an illustration. Many of the methods presented in this review are Machine Learning and Statistical techniques that are extendable to other forms of computer-based biomedical analysis, including mass spectrometry and magnetic resonance.

Biospectroscopy to metabolically profile biomolecular structure: a multistage approach linking computational analysis with biomarkers.

Biospectroscopy is employed to derive absorbance spectra representative of biomolecules present in biological samples. The mid-infrared region (λ = 2.5 μm-25 μm) is absorbed to give a biochemical-cell fingerprint (v = 1800-900 cm(-1)). Cellular material produces complex spectra due to the variety of chemical bonds present. The complexity and size of spectral data sets warrant multivariate analysis for data reduction, interpretation, and classification. Various multivariate analyses are available including principal component analysis (PCA), partial least-squares (PLS), linear discriminant analysis (LDA), and evolving fuzzy rule-based classifier (eClass). Interpretation of both visual and numerical results facilitates biomarker identification, cell-type discrimination, and predictive and mechanistic understanding of cellular behavior. Biospectroscopy is a high-throughput nondestructive technology. A comparison of biomarkers/mechanistic knowledge determined from conventional approaches to biospectroscopy coupled with multivariate analysis often provides complementary answers and a novel approach for diagnosis of disease and cell biology.

Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer

Currently available screening tests do not deliver the required sensitivity and specificity for accurate diagnosis of ovarian or endometrial cancer. Infrared (IR) spectroscopy of blood plasma or serum is a rapid, versatile, and relatively non-invasive approach which could characterize biomolecular alterations due to cancer and has potential to be utilized as a screening or diagnostic tool. In the past, no such approach has been investigated for its applicability in screening and/or diagnosis of gynaecological cancers. We set out to determine whether attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy coupled with a proposed classification machine could be applied to IR spectra obtained from plasma and serum for accurate class prediction (cancer vs. normal). Plasma and serum samples were obtained from ovarian cancer cases (n = 30), endometrial cancer cases (n = 30) and non-cancer controls (n = 30), and subjected to ATR-FTIR spectroscopy. Four derived datasets were processed to estimate the real-world diagnosis of ovarian and endometrial cancer. Classification results for ovarian cancer were remarkable (up to 96.7%), whereas endometrial cancer was classified with a relatively high accuracy (up to 81.7%). The results from different combinations of feature extraction and classification methods, and also classifier ensembles, were compared. No single classification system performed best for all different datasets. This demonstrates the need for a framework that can accommodate a diverse set of analytical methods in order to be adaptable to different datasets. This pilot study suggests that ATR-FTIR spectroscopy of blood is a robust tool for accurate diagnosis, and carries the potential to be utilized as a screening test for ovarian cancer in primary care settings. The proposed classification machine is a powerful tool which could be applied to classify the vibrational spectroscopy data of different biological systems (e.g., tissue, urine, saliva), with their potential application in clinical practice.

IRootLab: a free and open-source MATLAB toolbox for vibrational biospectroscopy data analysis

SUMMARY IRootLab is a free and open-source MATLAB toolbox for vibrational biospectroscopy (VBS) data analysis. It offers an object-oriented programming class library, graphical user interfaces (GUIs) and automatic MATLAB code generation. The class library contains a large number of methods, concepts and visualizations for VBS data analysis, some of which are introduced in the toolbox. The GUIs provide an interface to the class library, including a module to merge several spectral files into a dataset. Automatic code allows developers to quickly write VBS data analysis scripts and is a unique resource among tools for VBS. Documentation includes a manual, tutorials, Doxygen-generated reference and a demonstration showcase. IRootLab can handle some of the most popular file formats used in VBS. License: GNU-LGPL. AVAILABILITY Official website: http://irootlab.googlecode.com/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Infrared spectroscopy with multivariate analysis to interrogate endometrial tissue: a novel and objective diagnostic approach

Background:Endometrial cancer is the most common gynaecological malignancy in the United Kingdom. Diagnosis currently involves subjective expert interpretation of highly processed tissue, primarily using microscopy. Previous work has shown that infrared (IR) spectroscopy can be used to distinguish between benign and malignant cells in a variety of tissue types.Methods:Tissue was obtained from 76 patients undergoing hysterectomy, 36 had endometrial cancer. Slivers of endometrial tissue (tumour and tumour-adjacent tissue if present) were dissected and placed in fixative solution. Before analysis, tissues were thinly sliced, washed, mounted on low-E slides and desiccated; 10 IR spectra were obtained per slice by attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. Derived data was subjected to principal component analysis followed by linear discriminant analysis. Post-spectroscopy analyses, tissue sections were haematoxylin and eosin-stained to provide histological verification.Results:Using this approach, it is possible to distinguish benign from malignant endometrial tissue, and various subtypes of both. Cluster vector plots of benign (verified post-spectroscopy to be free of identifiable pathology) vs malignant tissue indicate the importance of the lipid and secondary protein structure (Amide I and Amide II) regions of the spectrum.Conclusion:These findings point towards the possibility of a simple objective test for endometrial cancer using ATR-FTIR spectroscopy. This would facilitate earlier diagnosis and so reduce the morbidity and mortality associated with this disease.

Concentration-dependent effects of carbon nanoparticles in gram-negative bacteria determined by infrared spectroscopy with multivariate analysis

With increasing production of carbon nanoparticles (CNPs), environmental release of these entities becomes an ever-greater inevitability. However, many questions remain regarding their impact on soil microorganisms. This study examined the effects of long or short multiwalled carbon nanotubes (MWCNTs), C60 fullerene and fullerene soot in Gram-negative bacteria. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was applied to derive signature spectral fingerprints of effects. A concentration-dependent response in spectral alterations was observed for each nanoparticle type. Long or short MWCNTs and fullerene soot gave rise to similar alterations to lipids, Amide II and DNA. The extent of alteration varies with nanoparticle size, with smaller short MWCNTs resulting in greater toxicity than long MWCNTs. Fullerene soot was the least toxic. C60 results in the most distinct and largest overall alterations, notably in extensive protein alteration. This work demonstrates a novel approach for assaying and discriminating the effects of CNPs in target systems.

High contrast images of uterine tissue derived using Raman microspectroscopy with the empty modelling approach of multivariate curve resolution-alternating least squares.

Approaches that allow one to rapidly understand tissue structure and functionality in situ remain to be developed. Such techniques are required in many instances, including where there is a need to remove with a high degree of confidence positive tumour margins during surgical excision. As biological tissue has little contrast, gold standard confirmation of surgical margins is conventionally undertaken by histopathological diagnosis of tissue architecture via optical microscopy. Vibrational spectroscopy techniques, when coupled to sophisticated computational analyses, are capable of constructing bio-molecular contrast images of unstained tissue. To assess the relative applicability of a range of candidate algorithms to distinguish the in situ bio-molecular structures of a complex tissue, the empty modelling approach of multivariate curve resolution-alternating least squares (MCR-ALS) was compared to hierarchical cluster analysis (HCA) or principal component analysis (PCA). Such chemometric analyses were applied to Raman images of benign (tumour-adjacent) endometrium, stage I and stage II endometrioid cancer. Re-constructed images from the in situ bio-molecular tissue architectures highlighted features associated with glandular epithelium, stroma, glandular lumen and myometrium. Of the tested chemometric analyses, MCR-ALS provided the best bio-molecular contrast images, superior to those derived following HCA or PCA, with clear and defined margins of histological features. Iteratively-resolved spectra identified wavenumbers responsible for the contrast image. Wavenumbers 1234 cm(-1) (Amide III), 1390 cm(-1) (CH(3) bend), 1675 cm(-1) (Amide I/lipid), 1275 cm(-1) (Amide III), 918 cm(-1) (proline) and 936 cm(-1) (proline, valine and proteins) were responsible for generating the majority of the contrast within MCR-ALS-generated images. Applications of sophisticated computational analyses coupled with vibrational spectroscopy techniques have the potential to lend novel functionality insights into bio-molecular structures in vivo.

Derivation by Infrared Spectroscopy with Multivariate Analysis of Bimodal Contaminant-Induced Dose-Response Effects in MCF-7 Cells

Toxic responses to contaminants following exposure concentrations typically used in laboratory tests may not reflect how biological systems respond to lower environmental levels from which hormetic effect mechanisms have been suggested. We investigated the pattern of dose-response in mammalian cells to various environmental contaminants using a range of concentrations that span those that are environmentally relevant (10(-12)M to 10(-3)M). MCF-7 cell cultures were treated for 24 h with benzo[a]pyrene (B[a]P), lindane (γ-hexachlorocyclohexane), or polybrominated diphenyl ethers (PBDEs) congeners (47, 153, 183, and 209), then fixed in ethanol and interrogated using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Mode of action was further studied by examining if test agents stimulated cell growth or altered CYP1A1 expression. Bimodal dose response curves were observed when MCF-7 cells were treated with PBDEs or lindane. The first peak distribution was associated with lower doses (10(-12)M to 10(-9)M), while the second occurred only after MCF-7 cells were exposed to concentrations >10(-9)M. Cellular alterations associated with low-dose PBDEs were mainly due to lipid and secondary protein structural changes, whereas lindane induced DNA/RNA effects as well. In contrast, DNA-reactive B[a]P gave rise to a monotonic linear dose-response relationship and induced mainly DNA/RNA cellular changes. This study shows that environmentally realistic exposures to chemical contaminants can induce nonmonotonic dose-responses in cellular systems.