Roadmap on optical sensors.

Optical sensors and sensing technologies are playing a more and more important role in our modern world. From micro-probes to large devices used in such diverse areas like medical diagnosis, defence, monitoring of industrial and environmental conditions, optics can be used in a variety of ways to achieve compact, low cost, stand-off sensing with extreme sensitivity and selectivity. Actually, the challenges to the design and functioning of an optical sensor for a particular application requires intimate knowledge of the optical, material, and environmental properties that can affect its performance. This roadmap on optical sensors addresses different technologies and application areas. It is constituted by twelve contributions authored by world-leading experts, providing insight into the current state-of-the-art and the challenges their respective fields face. Two articles address the area of optical fibre sensors, encompassing both conventional and specialty optical fibres. Several other articles are dedicated to laser-based sensors, micro- and nano-engineered sensors, whispering-gallery mode and plasmonic sensors. The use of optical sensors in chemical, biological and biomedical areas is discussed in some other papers. Different approaches required to satisfy applications at visible, infrared and THz spectral regions are also discussed.

Evaluating biochemical differences in thyroglobulin from normal and goiter tissues by infrared spectral imaging.

Thyroglobulin is a glycoiodoprotein that is produced by thyroid follicular cells; it is stored in follicles in structures known as colloids. The main function of this protein is to stock the hormones triiodothyronine (T3) and thyroxine (T4) until the body requires them. This study aims to demonstrate that infrared spectral imaging with appropriate multivariate analysis can reveal biochemical changes in this glycoprotein. The results achieved herein point out biochemical differences in the colloid samples obtained from normal and goiter patients including glycosylation and changes in the secondary conformational structure. We have presented the first spectral histopathology-based method to detect biochemical differences in thyroid colloids, such as TG iodination, glycosylation, and changes in the secondary structure in normal and goiter patients. The observed changes in the colloids were mainly due to the alterations in amide I and amide II (secondary conformation of proteins) and there is a correlation with different glycosylation between normal and goiter tissues.

Spectral histopathology of the lung: A review of two large studies.

This paper summarizes results from two large lung cancer studies comprising over 700 samples that demonstrate the ability of spectral histopathology (SHP) to distinguish cancerous tissue regions from normal tissue, to differentiate benign lesions from normal tissue and cancerous lesions, and to classify lung cancer types. Furthermore, malignancy-associated changes can be identified in cancer-adjacent normal tissue. The ability to differentiate a multitude of normal cells and tissue types allow SHP to identify tumor margins and immune cell infiltration. Finally, SHP easily distinguishes small cell lung cancer (SCLC) from non-SCLC (NSCLC) and provides a further differentiation of NSCLC into adenocarcinomas and squamous cell carcinomas with an accuracy comparable of classical histopathology combined with immunohistochemistry. Case studies are presented that demonstrates that SHP can resolve interobserver discrepancies in standard histopathology.

Resolving Interobserver Discrepancies in Lung Cancer Diagnoses by Spectral Histopathology.

This paper reports the results of a collaborative lung cancer study between City of Hope Cancer Center (Duarte, California) and CIRECA, LLC (Cambridge, Massachusetts), comprising 328 samples from 249 patients, that used an optical technique known as spectral histopathology (SHP) for tissue classification. Because SHP is based on a physical measurement, it renders diagnoses on a more objective and reproducible basis than methods based on assessing cell morphology and tissue architecture. This report demonstrates that SHP provides distinction of adenocarcinomas from squamous cell carcinomas of the lung with an accuracy comparable to that of immunohistochemistry and highly reliable classification of adenosquamous carcinoma. Furthermore, this report shows that SHP can be used to resolve interobserver differences in lung pathology. Spectral histopathology is based on the detection of changes in biochemical composition, rather than morphologic features, and is therefore more akin to methods such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry imaging. Both matrix-assisted laser desorption ionization time-of-flight mass spectrometry and SHP imaging modalities demonstrate that changes in tissue morphologic features observed in classical pathology are accompanied by, and may be correlated to, changes in the biochemical composition at the cellular level. Thus, these imaging methods provide novel insight into biochemical changes due to disease.

Optimizing decision tree structures for spectral histopathology (SHP).

This paper reviews methods to arrive at optimum decision tree or label tree structures to analyze large SHP datasets. Supervised methods of analysis can utilize either sequential or (flat) multi-classifiers depending on the variance in the data, and on the number of spectral classes to be distinguished. For small number of spectral classes, multi-classifiers have been used in the past, but for the analysis of datasets containing large numbers (∼20) of disease or tissue types, mixed decision tree structures were found to be advantageous. In these mixed structures, discrimination into classes and subclasses is achieved via hierarchical decision/label tree structures.

Label-free FTIR spectroscopy detects and visualizes the early stage of pulmonary micrometastasis seeded from breast carcinoma.

An application of FTIR spectroscopic imaging for the identification and visualization of early micrometastasis from breast cancer to lungs in a murine model is shown. Spectroscopic and histological examination is focused on lung cross-sections derived from animals at the early phase of metastasis (early micrometastasis, EM) as compared to healthy control (HC) and late phase of metastasis (advanced macrometastasis, AM) using murine model of metastatic breast cancer with 4T1 cells orthotopically inoculated. FTIR imaging allows for a detailed, objective and label-free differentiation and visualization of EM foci including large and small micrometastases as well as single cancer cells grouped in clusters. An effect of the EM phase on the entire lung tissue matrix as well as characteristic biochemical profiles for HC and advanced macrometastasis were determined from morphological and spectroscopic points of view. The extraordinary sensitivity of FTIR imaging toward EM detection and discrimination of AM borders confirms its applicability as a complementary tool for the histopathological assessment of the metastatic cancer progression.

Comments on recent reports on infrared spectral detection of disease markers in blood components.

The search for disease markers in whole blood, or easily accessible blood components by spectral methods is a highly important aspect in the field of biophotonic research for disease diagnostics and screening, since it promises a minimally invasive approach to assess an individual's state of health. Fourier transform infrared spectroscopy, in particular, promises to be a fast, inexpensive method to search for markers of disease, since it detects variation in the proteome, lipidome and metabolome of biofluids, or activation of immune cells. However, the analysis of any materials by spectral methods is confounded by external factors such as those related to sample deposition and data acquisition, and by inherent variations in blood plasma concentration of small molecules (lactate, carbonate, phosphate, glucose) that varies between individual subjects and even for a given individual, as a function of time. Furthermore, observed differences in spectral patterns between patient samples and the control group may be due to the body's immune response (in particular, to the albumin to globulin ratio) and therefore, may not be specific to disease. These factors need to be accounted for in any effort to reliably detect much smaller variations in the concentration of disease-specific markers.

Parasites under the Spotlight: Applications of Vibrational Spectroscopy to Malaria Research.

New technologies to diagnose malaria at high sensitivity and specificity are urgently needed in the developing world where the disease continues to pose a huge burden on society. Infrared and Raman spectroscopy-based diagnostic methods have a number of advantages compared with other diagnostic tests currently on the market. These include high sensitivity and specificity for detecting low levels of parasitemia along with ease of use and portability. Here, we review the application of vibrational spectroscopic techniques for monitoring and detecting malaria infection. We discuss the role of vibrational (infrared and Raman) spectroscopy in understanding the processes of parasite biology and its application to the study of interactions with antimalarial drugs. The distinct molecular phenotype that characterizes malaria infection and the high sensitivity enabling detection of low parasite densities provides a genuine opportunity for vibrational spectroscopy to become a front-line tool in the elimination of this deadly disease and provide molecular insights into the chemistry of this unique organism.

Roadmap on optical sensors.

Sensors are devices or systems able to detect, measure and convert magnitudes from any domain to an electrical one. Using light as a probe for optical sensing is one of the most efficient approaches for this purpose. The history of optical sensing using some methods based on absorbance, emissive and florescence properties date back to the 16th century. The field of optical sensors evolved during the following centuries, but it did not achieve maturity until the demonstration of the first laser in 1960. The unique properties of laser light become particularly important in the case of laser-based sensors, whose operation is entirely based upon the direct detection of laser light itself, without relying on any additional mediating device. However, compared with freely propagating light beams, artificially engineered optical fields are in increasing demand for probing samples with very small sizes and/or weak light-matter interaction. Optical fiber sensors constitute a subarea of optical sensors in which fiber technologies are employed. Different types of specialty and photonic crystal fibers provide improved performance and novel sensing concepts. Actually, structurization with wavelength or subwavelength feature size appears as the most efficient way to enhance sensor sensitivity and its detection limit. This leads to the area of micro- and nano-engineered optical sensors. It is expected that the combination of better fabrication techniques and new physical effects may open new and fascinating opportunities in this area. This roadmap on optical sensors addresses different technologies and application areas of the field. Fourteen contributions authored by experts from both industry and academia provide insights into the current state-of-the-art and the challenges faced by researchers currently. Two sections of this paper provide an overview of laser-based and frequency comb-based sensors. Three sections address the area of optical fiber sensors, encompassing both conventional, specialty and photonic crystal fibers. Several other sections are dedicated to micro- and nano-engineered sensors, including whispering-gallery mode and plasmonic sensors. The uses of optical sensors in chemical, biological and biomedical areas are described in other sections. Different approaches required to satisfy applications at visible, infrared and THz spectral regions are also discussed. Advances in science and technology required to meet challenges faced in each of these areas are addressed, together with suggestions on how the field could evolve in the near future.

Infrared micro-spectroscopy of human tissue: principles and future promises.

This article summarizes the methods employed, and the progress achieved over the past two decades in applying vibrational (Raman and IR) micro-spectroscopy to problems of medical diagnostics and cellular biology. During this time, several research groups have verified the enormous information contained in vibrational spectra; in fact, information on protein, lipid and metabolic composition of cells and tissues can be deduced by decoding the observed vibrational spectra. This decoding process is aided by the availability of computer workstations and advanced algorithms for data analysis. Furthermore, commercial instrumentation for the fast collection of both Raman and infrared micro-spectral data has enabled the collection of images of cells and tissues based solely on vibrational spectroscopic data. The progress in the field has been manifested by a steady increase in the number and quality of publications submitted by established and new research groups in vibrational spectroscopy in the biological and biomedical arenas.

Cancer screening via infrared spectral cytopathology (SCP): results for the upper respiratory and digestive tracts.

Instrumental advances in infrared micro-spectroscopy have made possible the observation of individual human cells and even subcellular structures. The observed spectra represent a snapshot of the biochemical composition of a cell; this composition varies subtly but reproducibly with cellular effects such as progression through the cell cycle, cell maturation and differentiation, and disease. The aim of this summary is to provide a synopsis of the progress achieved in infrared spectral cytopathology (SCP) - the combination of infrared micro-spectroscopy and multivariate methods of analysis - for the detection of abnormalities in exfoliated human cells of the upper respiratory and digestive tract, namely the oral and nasopharyngeal cavities, and the esophagus.

A method for the comparison of multi-platform spectral histopathology (SHP) data sets.

Results of a study comparing infrared imaging data sets collected on different instruments or instrument platforms are reported, along with detailed methods developed to permit such comparisons. It was found that different instrument platforms, although employing different detector technologies and pixel sizes, produce highly similar and reproducible spectral results. However, differences in the absolute intensity values of the reflectance data sets were observed that were caused by heterogeneity of the sample substrate in terms of reflectivity and planarity.

Classification of malignant and benign tumors of the lung by infrared spectral histopathology (SHP).

We report results of a study utilizing a novel tissue classification method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples on a tissue microarray. The spectral diagnostic method allows reproducible and objective classification of unstained tissue sections. This is accomplished by acquiring infrared data sets containing thousands of spectra, each collected from tissue pixels ∼6 µm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis that reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology is presented, suggesting that spectral histopathology can achieve levels of diagnostic accuracy that is comparable to that of multipanel immunohistochemistry.

Statistical analysis of a lung cancer spectral histopathology (SHP) data set.

We report results on a statistical analysis of an infrared spectral dataset comprising a total of 388 lung biopsies from 374 patients. The method of correlating classical and spectral results and analyzing the resulting data has been referred to as spectral histopathology (SHP) in the past. Here, we show that standard bio-statistical procedures, such as strict separation of training and blinded test sets, result in a balanced accuracy of better than 95% for the distinction of normal, necrotic and cancerous tissues, and better than 90% balanced accuracy for the classification of small cell, squamous cell and adenocarcinomas. Preliminary results indicate that further sub-classification of adenocarcinomas should be feasible with similar accuracy once sufficiently large datasets have been collected.

Infrared micro-spectroscopy for cyto-pathological classification of esophageal cells.

We report results from a study utilizing infrared spectral cytopathology (SCP) to detect abnormalities in exfoliated esophageal cells. SCP has been developed over the past decade as an ancillary tool to classical cytopathology. In SCP, the biochemical composition of individual cells is probed by collecting infrared absorption spectra from each individual, unstained cell, and correlating the observed spectral patterns, and the variations therein, against classical diagnostic methods to obtain an objective, machine-based classification of cells. In the past, SCP has been applied to the analysis and classification of cells exfoliated from the cervix and the oral cavity. In these studies, it was established that SCP can distinguish normal and abnormal cell types. Furthermore, SCP can differentiate between truly normal cells, and cells with normal morphology from the vicinity of abnormalities. Thus, SCP may be a valuable tool for the screening of early stages of dysplasia and pre-cancer.