Extracting and Analyzing Biochemical Features from Nano Bioparticles for Disease Diagnosis using Surface-enhanced Raman Spectroscopy and Artificial Intelligence

Disease diagnosis has long been a basis of modern medicine, enabling early intervention and effective treatment strategies. Recent advancements in nanotechnology have ushered in a new era of diagnostic techniques, with nanoscale bioparticles emerging as powerful tools in this endeavor. Nanoscale bioparticles, including extracellular vesicles, viruses, and other bioactive entities, have gained prominence due to their unique properties that make them ideal candidates for biomarker detection. These tiny structures, often measuring around 100 nanometers, carry a wealth of molecular information reflective of the physiological and pathological states of the body. Their presence, composition, and abundance in biological fluids such as blood, saliva, and urine hold invaluable clues for diagnosing a wide range of diseases This dissertation presents a cutting-edge approach to disease diagnosis by integrating the analysis of nano bioparticles, Surface-Enhanced Raman Spectroscopy (SERS), and machine learning techniques targeting disease diagnosis. SERS, with its unparalleled sensitivity and specificity, serves as a powerful tool for the characterization of biomolecules. We investigate the feasibility of SERS in capturing the intricate spectral signatures of nano bioparticles, revealing valuable insights into their molecular composition. Moreover, machine learning models are harnessed to decipher this wealth of spectral data, enabling the identification of disease-specific biomarkers with unprecedented accuracy. The article encompasses a detailed exploration of exosome biology, the principles of SERS, the intricacies of machine learning based data analysis methodologies applied to spectral data, preliminary achievements in non-small cell lung cancer diagnostic study, and feasibility of identify SARS-CoV-2 biomarkers for COVID detection. We present a particular subgroup of exosomes derived from human bronchial epithelial cells possessing distinct spectral signatures that can be a potential indicator of non-small cell lung cancer early metastasis, and a rapid and accurate SERS based platform for COVID detection using salivary specimen, superior in some cases to RT-PCR and antigen test. The integration of these multidisciplinary approaches represents a significant step toward revolutionizing disease diagnosis through the convergence of nano bioparticle analysis, spectroscopy, and machine learning, offering a promising avenue for early and accurate disease detection in clinical settings

Analyzing bronchoalveolar fluid derived small extracellular vesicles using single-vesicle SERS for non-small cell lung cancer detection.

An emerging body of research by biologists and clinicians has demonstrated the clinical application of small extracellular vesicles (sEVs, also commonly referred to as exosomes) as biomarkers for cancer detections. sEVs isolated from various body fluids such as blood, saliva, urine, and cerebrospinal fluid have been used for biomarker discoveries with highly encouraging outcomes. Among the biomarkers discovered are those responsible for multiple cancer types and immune responses. These biomarkers are recapitulated from the tumor microenvironments. Yet, despite numerous discussions of sEVs in scientific literature, sEV-based biomarkers have so far played only a minor role for cancer diagnostics in the clinical setting, notably less so than other techniques such as imaging and biopsy. In this paper, we report the results of a pilot study (n = 10 from each of the patient and the control group) using bronchoalveolar lavage fluid to determine the presence of sEVs related to non-small cell lung cancer in twenty clinical samples examined using surface enhanced Raman spectroscopy (SERS)

Label-free single-vesicle based surface enhanced Raman spectroscopy: A robust approach for investigating the biomolecular composition of small extracellular vesicles

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular "fingerprints"), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different