Raman Spectroscopy in Disease Diagnosis
A recent study examined the role that Raman spectroscopy is playing in disease diagnosis.
Raman spectroscopy, a noninvasive and highly sensitive analytical technique, is revolutionizing biomedical sciences by providing detailed molecular insights that could transform disease diagnosis (1). Raman spectroscopy is a light scattering technique, meaning that a molecule scatters light from a high-intensity laser light source (1). Because this can reveal the vibrational modes of molecules, it is a technique that has been researched in clinical analysis and disease diagnosis (2).
A team of researchers hailing from several institutions, including Macau University of Science and Technology, Suzhou University of Science and Technology, and Harvard Medical School, explored this topic. In their study, the researchers reviewed how Raman spectroscopy and specialized Raman-based techniques, such as surface-enhanced Raman spectroscopy (SERS), tip-enhanced Raman spectroscopy (TERS), and resonance Raman spectroscopy, are being tested in clinical applications, including disease diagnosis (2).
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Numerous studies have commented on how Raman spectroscopy and its ability to study the cellular level have aided its rise in being the technique of choice of clinical analysis (3,4). Coupled with technological advancements, Raman spectroscopy has helped improve medical diagnoses and patient care (3). However, researchers have also acquired more information about each Raman spectroscopy-based technique, discovering the advantages and limitations of each, which have enabled them to learn when to use each Raman-based technique (4).
The review article highlights the significant advancements in Raman spectroscopy, particularly the evolution of SERS, resonance Raman spectroscopy, and TERS. The review examined how the strengths of these techniques have broadened the diagnostic scope of Raman spectroscopy, enabling precise ex vivo and in vivo analyses (2). The study also highlights the successful correlation between Raman spectra and biochemical markers of specific diseases while addressing the limitations of the techniques and what future steps may look like (2).
The potential applications of Raman spectroscopy in diagnosing critical diseases are numerous. First, the researchers highlighted how it is being used to identify early molecular changes associated with malignant tumors, often detectable before clinical symptoms appear (2). Second, Raman spectroscopy has been used to identify pathogens, which have helped accelerate treatment decisions (2). And finally, Raman spectroscopy has helped detect the early onset of neurodegenerative diseases because it can detect biochemical changes in brain tissues (2).
Despite its potential, Raman spectroscopy faces challenges in clinical adoption. Issues like laser safety for in vivo applications, substrate reproducibility in SERS, and the biocompatibility of probes need to be addressed. Three challenges Raman spectroscopy faces includes standardizing substrate fabrication, enhancing laser safety, and finding ways to advance data processing by using advanced algorithms. Current research is focused on machine learning (ML) and deep learning (DL) to process complex spectral data sets (2).
The review highlights the transformative role of artificial intelligence (AI) in Raman spectroscopy. Although traditional ML models require extensive feature extraction, DL offers the advantage of autonomous feature selection, enabling more efficient analysis of large-scale data (2). As data sets grow, these advancements could lead to faster and more accurate diagnoses.
To translate Raman spectroscopy into routine clinical practice, researchers must address standardization and reproducibility concerns. Additionally, the technology needs to expand its diagnostic scope to cover a wider range of lesions and tissue types. By identifying more specific molecular markers and clarifying their relationships with various diseases, Raman spectroscopy could become a cornerstone of precision medicine (2).
By combining molecular precision with noninvasive techniques, Raman spectroscopy could potentially be a technique of choice in disease diagnosis. Although challenges remain, ongoing advancements in technology and methodology could pave the way for widespread clinical adoption of Raman techniques in the future.
References
- Horiba Scientific, What is Raman Spectroscopy? Horiba.com. Available at: https://www.horiba.com/usa/scientific/technologies/raman-imaging-and-spectroscopy/raman-spectroscopy/#:~:text=Raman%20Spectroscopy%20is%20a%20non,chemical%20bonds%20within%20a%20material. (accessed 2024-12-10).
- Qi, Y.; Chen, E. X.; Hu, D.; et al. Applications of Raman Spectroscopy in Clinical Medicine. Food Front. 2024, 5 (2), 392–419. DOI: 10.1002/fft2.335
- Wang, Y.; Fang, L.; Wang, Y.; Xiong, Z. Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges. Adv. Sci. (Weinh). 2023, 11 (7), 2300668. DOI: 10.1002/advs.202300668
- Khristoforova, Y.; Bratchenko, L.; Bratchenko, I. Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review. Int. J. Mol. Sci. 2023, 24 (21), 15605. DOI: 10.3390/ijms242115605
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