The ability to diagnose the early onset of disease, rapidly, non-invasively and unequivocally has multiple benefits. These include the early intervention of therapeutic strategies leading to a reduction in morbidity and mortality, and the releasing of economic resources within overburdened health care systems. Some of the routine clinical tests currently in use are known to be unsuitable or unreliable. In addition, these often rely on single disease markers which are inappropriate when multiple factors are involved. Many diseases are a result of metabolic disorders, therefore it is logical to measure metabolism directly. One of the strategies employed by the emergent science of metabolomics is metabolic fingerprinting; which involves rapid, high-throughput global analysis to discriminate between samples of different biological status or origin. This review focuses on a selective number of recent studies where metabolic fingerprinting has been forwarded as a potential tool for disease diagnosis using infrared and Raman spectroscopies.

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Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy.

Spectroscopy is carried out in almost every field of science, whenever light interacts with matter. Although sophisticated instruments with impressive performance characteristics are available, much effort continues to be invested in the development of miniaturized, cheap and easy-to-use systems. Current microspectrometer designs mostly use interference filters and interferometric optics that limit their photon efficiency, resolution and spectral range. Here we show that many of these limitations can be overcome by replacing interferometric optics with a two-dimensional absorptive filter array composed of colloidal quantum dots. Instead of measuring different bands of a spectrum individually after introducing temporal or spatial separations with gratings or interference-based narrowband filters, a colloidal quantum dot spectrometer measures a light spectrum based on the wavelength multiplexing principle: multiple spectral bands are encoded and detected simultaneously with one filter and one detector, respectively, with the array format allowing the process to be efficiently repeated many times using different filters with different encoding so that sufficient information is obtained to enable computational reconstruction of the target spectrum. We illustrate the performance of such a quantum dot microspectrometer, made from 195 different types of quantum dots with absorption features that cover a spectral range of 300 nanometres, by measuring shifts in spectral peak positions as small as one nanometre. Given this performance, demonstrable avenues for further improvement, the ease with which quantum dots can be processed and integrated, and their numerous finely tuneable bandgaps that cover a broad spectral range, we expect that quantum dot microspectrometers will be useful in applications where minimizing size, weight, cost and complexity of the spectrometer are critical.

A colloidal quantum dot spectrometer

Within the framework of systems biology, functional analyses at all ’omic levels have seen an intense level of activity during the first decade of the twenty-first century. These include genomics, transcriptomics, proteomics, metabolomics and lipidomics. It could be said that metabolomics offers some unique advantages over the other ’omics disciplines and one of the core approaches of metabolomics for disease diagnostics is metabolic fingerprinting. This review provides an overview of the main metabolic fingerprinting approaches used for disease diagnostics and includes: infrared and Raman spectroscopy, Nuclear magnetic resonance (NMR) spectroscopy, followed by an introduction to a wide range of novel mass spectrometry-based methods, which are currently under intense investigation and developmental activity in laboratories worldwide. It is hoped that this review will act as a springboard for researchers and clinicians across a wide range of disciplines in this exciting era of multidisciplinary and novel approaches to disease diagnostics.

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Metabolic fingerprinting as a diagnostic tool

The intensive investment in optical microelectromechanical systems (MEMS) in the last decade has led to many successful components that satisfy the requirements of lightwave communication networks. In this paper, we review the current state of the art of MEMS devices and subsystems for lightwave communication applications. Depending on the design, these components can either be broadband (wavelength independent) or wavelength selective. Broadband devices include optical switches, crossconnects, optical attenuators, and data modulators, while wavelength-selective components encompass wavelength add/drop multiplexers, wavelength-selective switches and crossconnects, spectral equalizers, dispersion compensators, spectrometers, and tunable lasers. Integration of MEMS and planar lightwave circuits, microresonators, and photonic crystals could lead to further reduction in size and cost

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Optical MEMS for Lightwave Communication

(IEEE Transactions on Electron Devices,36(12):2816-2820)Field-Drifting Resonant Tunneling Through a-Si:H/a-Sil-xCx:H Quantum Wells at Different Locations of the i-Layer of a p-i-n Structure

The recent research is focused on developing new spectral information processing techniques for adaptive, real-time analysis in the optoelectronic domain. We implement a Fourier transform spectrometer (FTS) that generates a time-varying interferogram signal containing all spectral information in a pixel. The correlation with a set of known spectra is performed in the time-domain by measuring interferograms with a photodetector, multiplying them by electrical reference interferograms that represent the known spectra, and integrating. This architecture eliminates unnecessary data in the optoelectronic front end, enabling extremely simple spectra-selective detection arrays.

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Adaptive spectra-selective imaging by real-time photoconductor bias modulation

Summary form only given. For some sensing applications, even the seemingly fundamental process of breaking a spectrum into frequency components may not be the best way to look for features in the spectrum. We have demonstrated a coherent imaging architecture, with inherent decomposition of the image into coherent and incoherent components. The decomposition at a very early stage of the physical measurement process means that 1) minimal data analysis is needed, and 2) the system never needs to have the resolution that would conventionally be required to resolve the spectral features of interest. These advantages translate into relaxed requirements on the hardware design and faster data collection, and will aid the design and expand the applications of inexpensive, mobile hyperspectral and multispectral imaging systems.

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Adaptive coherence imaging system with time-domain filtering

We present an integrated microsensor for time-domain analysis of spectral coherence, with a tunable coherence detection range. The 0.2 cm/sup 3/ device includes a partially transmitting GaAs/AlGaAs photodiode and a large-displacement scanning Si MEMS mirror.

Standing-wave microsensor for adaptive analysis of spectral coherence

A new coherence-sensing imaging system is designed and built for the visible range and a new invisible tag application that provides a unique method of marking or identifying objects by spectral bandwidth is also demonstrated.

/pdf/adaptive-coherence-sensing-imaging-spectrometer-1m53l02g1d.pdf

S.R. Bhalotra

Papers

Adaptive spectra-selective imaging by real-time photoconductor bias modulation

Adaptive coherence imaging system with time-domain filtering

Standing-wave microsensor for adaptive analysis of spectral coherence

Adaptive coherence-sensing imaging spectrometer