Spectroscopy provides valuable information about the composition, structure, and dynamics of biological systems. It finds applications in several life sciences fields, including biochemistry, biophysics, and molecular biology, and it plays a pivotal role in bio-monitoring.
Spectroscopy is used to study the structure and function of biological molecules (i.e., proteins, nucleic acids, and lipids) and is also used to monitor metabolic processes and cellular signaling pathways. Thanks to its ability to provide detailed information, spectroscopy is a valuable technique for bio-monitoring applications.
Introduction to Spectroscopy in Bio-Monitoring
Bio-monitoring uses physiological, biochemical, or molecular changes in an organism to measure exposure to contaminants or pollutants that threaten the quality of the health or environment.
Through the measurement of biomarkers – substances whose concentration and properties can change in response to the presence of a toxicant – bio-monitoring provides information about the presence of a pollutant and additional insights about the extent of exposure.
Since spectroscopy allows determining the presence and conformation of certain groups in a molecule, it is suitable for bio-monitoring studies. In addition, depending on the type of substrate and application, there are different spectroscopic techniques to choose from.
Spectroscopy Techniques: From UV-Vis to NMR
The main spectroscopic techniques used in bio-monitoring are UV-Vis, IR, Raman, and NMR spectroscopy, which are used in disease diagnosis and to monitor the efficacy of drugs and therapies, among the most important applications.
UV-Vis spectroscopy (commonly used in protein analysis) measures the electronic transitions of molecules when ultraviolet or visible light (100-780 nm) is absorbed by or transmitted through a sample. It is widely used for water quality monitoring and process control.
Particularly interesting is online UV-Vis spectroscopy since it is reagent-free, does not require sample pre-treatments, and can provide continuous measurements, allowing quick responses to water quality changes. Among the parameters measured, UV-Vis spectroscopy allows the determination of color, nitrate, total organic carbon (TOC), and dissolved organic carbon (DOC).
Infrared (IR) spectroscopy studies the vibrational modes and relies upon changes in the dipole moment of a molecule, allowing the identification of functional groups. The most relevant frequencies range from 200 to 4000 cm-1. Raman spectroscopy is a type of IR spectroscopy based on a change in polarizability of a molecule.
The possibility to obtain information about the presence and changes of functional groups of different compounds under various external influences enables the evaluation of the state of living organisms and changes in the environment. For instance, IR spectroscopy can be used for evaluating the biosorption of heavy metal ions in microorganisms such as fungal strains and cyanobacteria.
An interesting application of Raman spectroscopy in bio-monitoring is the assessment of the size and mineralization level of shells. Shells contain polyenes (i.e., carotenoids), calcium carbonate, and magnesite and are linked to the ocean acidification problem.
Near-infrared (NIR) spectroscopy (10,000-4000 cm−1 or 1000-2500 nm) is a rapid and non-destructive method, and it requires minimal or no sample preparation. It can be used to monitor and measure heavy metals and other contaminants in soil and water and was explored as a tool for discriminating between lichens exposed to air pollution.
Lichens are sensitive to the presence of substances that alter atmospheric composition, such as phytotoxic gases (e.g., SO2 or NOx), heavy metals, or organic compounds, and are used as biomonitors of air pollution. A preliminary study showed that NIR spectroscopy can be used to generate a “fingerprint” of lichens, discriminating between samples from polluted and non-polluted areas.
Nuclear magnetic resonance (NMR) spectroscopy is used to study the magnetic properties of certain atomic nuclei and how they respond when an external magnetic field is applied. Some of the most common isotopes used in protein and nucleic acid analysis are 1H, 13C, 15N, and 31P.
The combination of NMR spectroscopy and metabolomics can be a useful tool in bio-monitoring. Some methods have been proposed for investigating environmental stressors on different organisms. Moreover, the profiling of exhaled breath condensate (a biological matrix that allows access to the lung epithelial lining fluid) via NMR-based metabolomics has been explored for the bio-monitoring of workers exposed to airborne chemicals.
Although further studies are needed, it was possible to identify differences in metabolomic profiles between workers exposed to levels of airborne inhalable dust, phenol, formaldehyde, and volatile organic compounds, highlighting the potential to be used in occupational health.
Emerging Trends and Future Prospects
Significant advancements in recent years have led to new applications of spectroscopy in bio-monitoring. For instance, single-cell analysis allows the study of individual cells at a molecular level. Another emerging trend is real-time monitoring, which allows for the continuous monitoring of biological processes.
With new techniques being developed, it will be possible to have even more detailed information about biological systems. An example is terahertz spectroscopy, a new technique that uses radiation with wavelengths between far-infrared and microwaves.
The technique can provide information about the structure and dynamics of biomolecules and is among the emerging trends in spectroscopy. It has the potential to find application in environmental monitoring, like in the detection of microalgae biosorption of heavy metals used to assess heavy metal pollution in water.
Interpreting Spectroscopy Data
Any spectroscopic analysis results in a spectrum, which in most cases is a superposition of numerous peaks where their presence and intensity depend on the chemical environment of the studied compounds and their conformation.
The large number of peaks significantly complicates their interpretation. Spectral data are therefore treated using multiparametric analysis methods, including principal component analysis (PCA), hierarchical clustering analysis (HCA), partial least squares discriminant analysis (PLSDA), and regression models, such as partial least squares regression (PLSR).
Interpreting spectroscopic data can hence be challenging. However, computational methods such as machine learning algorithms are being developed to facilitate data interpretation. These methods can help identify patterns in large datasets and can be used to predict the properties of biological systems.
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