Rapid analysis of disease state in liquid human serum combining infrared spectroscopy and "digital drying".

In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.

Introducing Discrete Frequency Infrared Technology for High-Throughput Biofluid Screening.

Accurate early diagnosis is critical to patient survival, management and quality of life. Biofluids are key to early diagnosis due to their ease of collection and intimate involvement in human function. Large-scale mid-IR imaging of dried fluid deposits offers a high-throughput molecular analysis paradigm for the biomedical laboratory. The exciting advent of tuneable quantum cascade lasers allows for the collection of discrete frequency infrared data enabling clinically relevant timescales. By scanning targeted frequencies spectral quality, reproducibility and diagnostic potential can be maintained while significantly reducing acquisition time and processing requirements, sampling 16 serum spots with 0.6, 5.1 and 15% relative standard deviation (RSD) for 199, 14 and 9 discrete frequencies respectively. We use this reproducible methodology to show proof of concept rapid diagnostics; 40 unique dried liquid biopsies from brain, breast, lung and skin cancer patients were classified in 2.4 cumulative seconds against 10 non-cancer controls with accuracies of up to 90%.

Developing and understanding biofluid vibrational spectroscopy: a critical review.

Vibrational spectroscopy can provide rapid, label-free, and objective analysis for the clinical domain. Spectroscopic analysis of biofluids such as blood components (e.g. serum and plasma) and others in the proximity of the diseased tissue or cell (e.g. bile, urine, and sputum) offers non-invasive diagnostic/monitoring possibilities for future healthcare that are capable of rapid diagnosis of diseases via specific spectral markers or signatures. Biofluids offer an ideal diagnostic medium due to their ease and low cost of collection and daily use in clinical biology. Due to the low risk and invasiveness of their collection they are widely welcomed by patients as a diagnostic medium. This review underscores recent research within the field of biofluid spectroscopy and its use in myriad pathologies such as cancer and infectious diseases. It highlights current progresses, advents, and pitfalls within the field and discusses future spectroscopic clinical potentials for diagnostics. The requirements and issues surrounding clinical translation are also considered.

Can mid-infrared biomedical spectroscopy of cells, fluids and tissue aid improvements in cancer survival? A patient paradigm.

This review will take a fresh approach from the patient perspective; offering insight into the applications of mid-infrared biomedical spectroscopy in a scenario whereby the patient presents with non-specific symptoms and via an extensive diagnostic process multiple lesions are discovered but no clear sign of the primary tumour; a condition known as cancer of unknown primary (CUP). With very limited options to diagnose the cancer origin, treatment options are likely to be ineffective and prognosis is consequentially very poor. CUP has not yet been targeted by infrared biospectroscopy, however, this timely, concise dissemination will focus on a series of research highlights and breakthroughs from the field for the management of a variety of cancer-related diseases - many examples of which have occurred within this year alone. The case for integration of mid-infrared (MIR) technology into clinical practice will be demonstrated largely via diagnostic, but also therapeutic and prognostic avenues by means of including cytological, bio-fluid and tissue analysis. The review is structured around CUP but is relevant for all cancer diagnoses. Infrared spectroscopy is fast developing a reputation as a valid and powerful tool for the detection and diagnosis of cancer using a variety of sample formats. The technology will produce data and tools that are designed to complement routine clinical practice; enhancing the ability of the clinician to make a reliable and non-subjective decision and enabling decreased levels of mortality and morbidity and gains in patient quality of life.

Preclinical screening of anticancer drugs using infrared (IR) microspectroscopy.

High-throughput label-free technologies such as IR microscopy can objectively assess the effect of drugs upon cellular systems, offering the potential of a valuable preclinical tool that can aid in the drug development process.

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.

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 microspectroscopy of selected rare diverse sub-variants of carcinoma of the urinary bladder.

Urothelial carcinomas of the bladder are a heterogeneous group of tumours, although some histological sub-variants are rare and sparsely reported in the literature. Diagnosis of sub-variants from conventional urothelial carcinoma can be challenging, as they may mimic the morphology of other malignancies or benign tumours and therefore their distinction is important. For the first time, the spectral pathology of some of these sub-variants has been documented by infrared microspectroscopy and an attempt made to profile their biochemistry. It is important not only to identify and separate the cancer-associated epithelial tissue spectra from common tissue features such as stroma or blood, but also to detect the signatures of tumour sub-variants. As shown, their spectroscopic signals can change dramatically as a consequence of differentiation. Example cases are discussed and compared with histological evaluations.

FTIR microscopy of biological cells and tissue: data analysis using resonant Mie scattering (RMieS) EMSC algorithm.

Transmission and transflection infrared microscopy of biological cells and tissue suffer from significant baseline distortions due to scattering effects, predominantly resonant Mie scattering (RMieS). This scattering can also distort peak shapes and apparent peak positions making interpretation difficult and often unreliable. A correction algorithm, the resonant Mie scattering extended multiplicative signal correction (RMieS-EMSC), has been developed that can be used to remove these distortions. The correction algorithm has two key user defined parameters that influence the accuracy of the correction. The first is the number of iterations used to obtain the best outcome. The second is the choice of the initial reference spectrum required for the fitting procedure. The choice of these parameters influences computational time. This is not a major concern when correcting individual spectra or small data sets of a few hundred spectra but becomes much more significant when correcting spectra from infrared images obtained using large focal plane array detectors which may contain tens of thousands of spectra. In this paper we show that, classification of images from tissue can be achieved easily with a few (<10) iterations but a reliable interpretation of the biochemical differences between classes could require more iterations. Regarding the choice of reference spectrum, it is apparent that the more similar it is to the pure absorption spectrum of the sample, the fewer iterations required to obtain an accurate corrected spectrum. Importantly however, we show that using three different non-ideal reference spectra, the same unique correction solution can be obtained.

SR-FTIR spectroscopy of renal epithelial carcinoma side population cells displaying stem cell-like characteristics.

It is hypothesized that cells with stem cell-like properties may be influential in carcinogenesis, possessing the ability to self-renew, produce differentiated daughter cells and resist environmental or therapeutic injury. This has led to a surge in interest in identifying and characterizing the tumour initiating or cancer stem cell (CSC) with the aim of discovering novel diagnostic and prognostic markers and of understanding the basic biology with the ultimate aim of generating new therapeutic approaches and biomarkers. However, a major hurdle to this process has been the lack of a truly specific cancer stem cell biomarker allied to the rarity of these cells. This has led to problems in characterising these CSCs by traditional '-omic' techniques. Using a renal carcinoma cell line model, we show that synchrotron radiation-Fourier transform infrared (SR-FTIR) spectroscopy is a suitable tool to measure discrete differences in the biochemistry of small numbers of single-cells. Using the chemometric techniques of Principal Component and Linear Discriminant Analysis (PCA and LDA) for multivariate reduction, biochemical differences between the cells from different sub-populations were evaluated. Results found lipid and phosphodiester vibrations to be particularly good discriminating markers in the spectra of these stem-like cells, relative to the more differentiated, proliferating cells that make up the majority of the cell population.

Classification of fixed urological cells using Raman tweezers.

In this paper we report on preliminary investigations into using Raman tweezers to classify urological cell lines. This builds on earlier work within the group, whereby Raman tweezer methodologies were developed, and the application of this technique to differentiate between live prostate cancer (CaP) and bladder cells lines (PC-3 and MGH-U1 respectively) was demonstrated.In this present study we analysed chemically fixed cells using two different fixative methods; SurePath (a commercial available liquid based cytology media) and 4% v/v formalin/PBS fixatives. The study has been expanded from our previous live cell study to include the androgen sensitive CaP cell line LNCaP, primary benign prostate hyperplasia (BPH) cells as well as primary urethral cells. Raman light from the cells was collected using a 514.5 nm Ar-ion laser excitation source in back-scattering configuration mode.Principal component-linear discriminate analysis (PC-LDA) models of resulting cell spectra were generated and these were validated using a blind comparison. Sensitivities and specificities of > 72% and 90% respectively, for SurePath fixed cells, and > 93% and 98% respectively for 4% v/v formalin/PBS fixed cells was achieved. The higher prediction results for the formalin fixed cells can be attributed to a better signal-to-noise ratio for spectra obtained from these cells.Following on from this work, urological cell lines were exposed to urine for up to 12 hours to determine the effect of urine on the ability to classify these cells. Results indicate that urine has no detrimental effect on prediction results.