Machine learning-assisted SERS approach enables the biochemical discrimination in Bcl-2 and Mcl-1 expressing yeast cells treated with ketoconazole and fluconazole antifungals

Antifungal medications are important due to their potential application in cancer treatment either on their own or with traditional treatments. The mechanisms that prevent the effects of these medications and restrict their usage in cancer treatment are not completely understood. The evaluation and discrimination of the possible protective effects of the anti-apoptotic members of the Bcl-2 family of proteins, critical regulators of mitochondrial apoptosis, against antifungal drug-induced cell death has still scientific uncertainties that must be considered. Novel, simple, and reliable strategies are highly demanded to identify the biochemical signature of this phenomenon. However, the complex nature of cells poses challenges for the analysis of cellular biochemical changes or classification. In this study, for the first time, we investigated the probable protective activities of Bcl-2 and Mcl-1 proteins against cell damage induced by ketoconazole (KET) and fluconazole (FLU) antifungal drugs in a yeast model through surface-enhanced Raman spectroscopy (SERS) approach. The proposed SERS platform created robust Raman spectra with a high signal-to-noise ratio. The analysis of SERS spectral data via advanced unsupervised and supervised machine learning methods enabled unquestionable differentiation (100 %) in samples and biomolecular identification. Various SERS bands related to lipids and proteins observed in the analyses suggest that the expression of these anti-apoptotic proteins reduces oxidative biomolecule damage induced by the antifungals. Also, cell viability assay, Annexin V-FITC/PI double staining, and total oxidant and antioxidant status analyses were performed to support Raman measurements. We strongly believe that the proposed approach paves the way for the evaluation of various biochemical structures/changes in various cells.

Gold nanorod arrays enable highly sensitive bacterial detection via surface-enhanced infrared absorption (SEIRA) spectroscopy

Infrared (IR) spectroscopy is a unique and powerful method in the identification, characterization, and classification of chemical and biological molecules. However, the low absorbance of biological molecules has arisen as a major bottleneck and inhibits the application of IR in practical applications. To overcome this limitation, in the last four decades, surface-enhanced IR absorption (SEIRA) spectroscopy has been proposed and has become the focus of interest in various applications. In this study, for the first time, we proposed the employment of 3D anisotropic gold nanorod arrays (GNAs) as a highly active SEIRA platform in bacterial detection. For this, GNA platforms were fabricated through an oblique angle deposition (OAD) approach by using a physical vapor deposition (PVD) system. OAD of gold at proper deposition angle (10°) created closely-packed and columnar gold nanorod structures onto the glass slides in a well-controlled manner. GNA platform was tested as a SEIRA system in three different species of bacteria (Escherichia coli, Staphylococcus aureus, and Bacillus subtilis) by collecting IR spectra of each bacteria from different parts of GNA. The employment of GNA provided robust IR spectra with high reproducibility and signal-to-noise ratio. For the comparison, IR spectra of each bacteria were collected from aluminum foil and a smooth gold surface (SGS). No or very low IR spectra were observed in comparison to the GNA platform for these substrates. Unsupervised (PCA, HCA) and supervised (SIMCA, LDA, and SVM classification) machine learning analysis of bacteria spectra obtained from GNA substrate indicated that all bacteria samples can be detected and identified without using a label-containing biosensor, in a fast and simple manner.

Developing a Peak Extraction and Retention (PEER) Algorithm for Improving the Temporal Resolution of Raman Spectroscopy

In spectroscopic analysis, push-to-the-limit sensitivity is one of the important topics, particularly when facing the qualitative and quantitative analyses of the trace target. Normally, the effective recognition and extraction of weak signals are the first key steps, for which there has been considerable effort in developing various denoising algorithms for decades. Nevertheless, the lower the signal-to-noise ratio (SNR), the greater the deviation of the peak height and shape during the denoising process. Therefore, we propose a denoising algorithm along with peak extraction and retention (PEER). First, both the first and second derivatives of the Raman spectrum are used to determine Raman peaks with a high SNR whose peak information is kept away from the denoising process. Second, an optimized window smoothing algorithm is applied to the left part of the Raman spectrum, which is combined with the untreated Raman peaks to obtain the denoised Raman spectrum. The PEER algorithm is demonstrated with much better signal extraction and retention and successfully improves the temporal resolution of Raman imaging of a living cell by at least 1 order of magnitude higher than those by traditional algorithms.

The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.

A de-waxing methodology for scanning probe microscopy

A de-waxing protocol that successfully removes paraffin from tissue microarray (TMA) cores of fixed tissue obtained from oral cancer is described. The success of the protocol is demonstrated by the comparison of Fourier transform infrared (FTIR) results obtained on paraffin-embedded and de-waxed tissue and the absence of any significant correlations between infrared scanning near-field optical microscopy (SNOM) images of de-waxed tissue obtained at the three main paraffin IR peaks. The success of the protocol in removing paraffin from tissue is also demonstrated by images obtained with scanning electron microscopy (SEM) and by energy dispersive spectra (EDS) of a de-waxed CaF2 disc which shows no significant contribution from carbon. The FTIR spectra of the de-waxed TMA core overlaps that obtained from OE19 oesophageal cancer cells which had never been exposed to paraffin.

MIR-biospectroscopy coupled with chemometrics in cancer studies

This review focuses on chemometric techniques applied in MIR-biospectroscopy for cancer diagnosis and analysis over the last ten years of research. Experimental applications of chemometrics coupled with biospectroscopy are discussed throughout this work. The advantages and drawbacks of this association are also highlighted. Chemometric algorithms are evidenced as a powerful tool for cancer diagnosis, classification, and in different matrices. In fact, it is shown how chemometrics can be implemented along all different types of cancer analyses.

Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology

Fourier transform infrared (FTIR) spectroscopy has long been established as an analytical technique for the measurement of vibrational modes of molecular systems. More recently, FTIR has been used for the analysis of biofluids with the aim of becoming a tool to aid diagnosis. For the clinician, this represents a convenient, fast, non-subjective option for the study of biofluids and the diagnosis of disease states. The patient also benefits from this method, as the procedure for the collection of serum is much less invasive and stressful than traditional biopsy. This is especially true of patients in whom brain cancer is suspected. A brain biopsy is very unpleasant for the patient, potentially dangerous and can occasionally be inconclusive. We therefore present a method for the diagnosis of brain cancer from serum samples using FTIR and machine learning techniques. The scope of the study involved 433 patients from whom were collected 9 spectra each in the range 600-4000 cm(-1). To begin the development of the novel method, various pre-processing steps were investigated and ranked in terms of final accuracy of the diagnosis. Random forest machine learning was utilised as a classifier to separate patients into cancer or non-cancer categories based upon the intensities of wavenumbers present in their spectra. Generalised 2D correlational analysis was then employed to further augment the machine learning, and also to establish spectral features important for the distinction between cancer and non-cancer serum samples. Using these methods, sensitivities of up to 92.8% and specificities of up to 91.5% were possible. Furthermore, ratiometrics were also investigated in order to establish any correlations present in the dataset. We show a rapid, computationally light, accurate, statistically robust methodology for the identification of spectral features present in differing disease states. With current advances in IR technology, such as the development of rapid discrete frequency collection, this approach is of importance to enable future clinical translation and enables IR to achieve its potential.

Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells

This review presents the current understanding of the factors influencing the quality of spectra recorded and the pre-processing steps commonly employed to improve on spectral quality, as well as some of the most common techniques for classification and analysis of the spectral data for biomedical applications.

Vibrational spectroscopic methods for cytology and cellular research

The use of vibrational spectroscopy, FTIR and Raman, for cytology and cellular research has the potential to revolutionise the approach to cellular analysis. Vibrational spectroscopy is non-destructive, simple to operate and provides direct information. Importantly it does not require expensive exogenous labels that may affect the chemistry of the cell under analysis. In addition, the advent of spectroscopic microscopes provides the ability to image cells and acquire spectra with a subcellular resolution. This introductory review focuses on recent developments within this fast paced field and highlights potential for the future use of FTIR and Raman spectroscopy. We particularly focus on the development of live cell research and the new technologies and methodologies that have enabled this.

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.

Attenuated total reflection fourier transform infrared (ATR-FTIR) spectral discrimination of brain tumour severity from serum samples

Gliomas are the most frequent primary brain tumours in adults with over 9,000 people diagnosed each year in the UK. A rapid, reagent-free and cost-effective diagnostic regime using serum spectroscopy would allow for rapid diagnostic results and for swift treatment planning and monitoring within the clinical environment. We report the use of ATR-FTIR spectral data combined with a RBF-SVM for the diagnosis of gliomas (high-grade and low-grade) from non-cancer with sensitivities and specificities on average of 93.75 and 96.53% respectively. The proposed diagnostic regime has the ability to reduce mortality and morbidity rates.

Mechanistic insights into nanotoxicity determined by synchrotron radiation-based Fourier-transform infrared imaging and multivariate analysis

Our ability to identify the mechanisms by which carbon-based nanomaterials (CBNs) exert toxicity in cells is constrained by the lack of standardized methodologies to assay endpoint effects. Herein we describe a method of mechanistically identifying the effects of various CBN types in both prokaryotic and eukaryotic cells using multi-beam synchrotron radiation-based Fourier-transform infrared imaging (SR-FTIRI) at diffraction-limited resolution. This technique overcomes many of the inherent difficulties of assaying nanotoxicity and demonstrates exceptional sensitivity in identifying the effects of CBNs in cells at environmentally-relevant concentrations. We identify key mechanisms of nanotoxicity as the alteration of Amide and lipid biomolecules, but propose more specific bioactivity of CBNs occurs as a result of specific interactions between CBN structural conformation and cellular characteristics.

Subcellular localization of early biochemical transformations in cancer-activated fibroblasts using infrared spectroscopic imaging

The tumor microenvironment, or stroma, is chemically and morphologically modified during carcinoma progression. The predominant cell type in the stroma, the fibroblast, maintains collagen properties in normal tissue and often transformed during tumor progression. Biochemical changes within fibroblasts upon initial cancer activation, however, are relatively poorly defined. Here, we hypothesized that Fourier transform infrared (FT-IR) spectroscopic imaging could potentially be employed to examine these early transformations. Further, we employ attenuated total reflectance (ATR) microscopy to characterize subcellular spectra and their changes upon transformation. We characterized fibroblast transitions upon stimulation with both a molecular agent and a carcinoma-mimicking cellular co-culture system. Changes were predominantly observed in the 1080 cm(-1) and 1224 cm(-1) peak absorbance, commonly associated with nucleic acids, as well as in the band at 2930 cm(-1) associated with the C-H stretching of proteins in the cytoplasmic compartment. In conclusion, biochemical changes in cancer-associated fibroblasts that express α-SMA are dominated by the cytoplasm, rather than the nucleus. This ensures that spectral changes are not associated with proliferation or cell cycle processes of the cells and the cells are undergoing a true phenotypic change denoted by protein modifications in the cell body.

FTIR microspectroscopy of melanocytic skin lesions: a preliminary study

Fourier transform infrared (FTIR) microspectroscopy has been employed to investigate benign (ordinary dermal and Reed nevi), dysplastic and malignant (invasive melanoma) skin lesions through the analysis of spectral changes of melanocytes as well as in the evaluation of the presence of melanin. Hierarchical cluster analysis and principal component analysis led to a satisfactory separation of malignant from dysplastic and normal melanocytes. Also, on enlarging the clustering with spectra from Reed and dermal nevi, the multivariate analysis segregated well the spectral data into discrete clusters, allowing the obtaining of reliable average spectra for analysis at the molecular level of the main groups or components responsible for the biological and biochemical changes. The most significant spectral characteristics appear to be related to differences in secondary protein structures, in nucleic acid conformation, in intra- and intermolecular bonding. In all cases, supervised and unsupervised spectral analyses resulted in satisfactory agreement with histopathological findings.

RMieS-EMSC correction for infrared spectra of biological cells: extension using full Mie theory and GPU computing

In the field of biomedical infrared spectroscopy it is often desirable to obtain spectra at the cellular level. Samples consisting of isolated single biological cells are particularly unsuited to such analysis since cells are strong scatterers of infrared radiation. Thus measured spectra consist of an absorption component often highly distorted by scattering effects. It is now known that the predominant contribution to the scattering is Resonant Mie Scattering (RMieS) and recently we have shown that this can be corrected for, using an iterative algorithm based on Extended Multiplicative Signal Correction (EMSC) and a Mie approximation formula. Here we present an iterative algorithm that applies full Mie scattering theory. In order to avoid noise accumulation in the iterative algorithm a curve-fitting step is implemented on the new reference spectrum. The new algorithm increases the computational time when run on an equivalent processor. Therefore parallel processing by a Graphics Processing Unit (GPU) was employed to reduce computation time. The optimised RMieS-EMSC algorithm is applied to an IR spectroscopy data set of cultured single isolated prostate cancer (PC-3) cells, where it is shown that spectral distortions from RMieS are removed.