Determining the stages of cellular differentiation using deep ultraviolet resonance Raman spectroscopy

Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics

Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth and other morbidities. Polypharmacy or a history of radiation to the head and neck can also lead to dry mouth. Sjogren's disease is often underdiagnosed due to its non-specific symptoms, limited awareness among healthcare professionals, and the complexity of diagnostic criteria, limiting the ability to provide therapy early. Current diagnostic methods suffer from limitations including the variation in individuals, the absence of a single diagnostic marker, and the low sensitivity and specificity, high cost, complexity, and invasiveness of current procedures. Here we utilized Raman hyperspectroscopy combined with machine learning to develop a novel screening test for Sjögren's disease. The method effectively distinguished Sjögren's disease patients from healthy controls and radiation patients. This technique shows potential for development of a single non-invasive, efficient, rapid, and inexpensive medical screening test for Sjögren's disease using a Raman hyper-spectral signature.

Ultraviolet Resonance Raman Spectra of Serum Albumins

The ultraviolet resonance Raman (UVRR) spectra of the two proteins bovine serum albumin (BSA) and human serum albumin (HSA) in an aqueous solution are compared with the aim to distinguish between them based on their very similar amino acid composition and structure and to obtain signals from tryptophan that has only very few residues. Comparison of the protein spectra with solutions of tryptophan, tyrosine, and phenylalanine in comparative ratios as in the two proteins shows that at an excitation wavelength of 220 nm, the spectra are dominated by the strong resonant contribution from these three amino acids. While the strong enhancement of two and one single tryptophan residue in BSA and HSA, respectively, results in pronounced bands assigned to fundamental vibrations of tryptophan, its weaker overtones and combination bands do not play a major role in the spectral range above 1800 cm-1. There, the protein spectra clearly reveal the signals of overtones and combination bands of phenylalanine and tyrosine. Assignments of spectral features in the range of Raman shifts from 3800 to 5100 cm-1 to combinations comprising fundamentals and overtones of tyrosine were supported by spectra of amino acid mixtures that contain deuterated tyrosine. The information in the high-frequency region of the UVRR spectra could provide information that is complementary to near-infrared absorption spectroscopy of the proteins.

Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis

BACKGROUND

Enzyme immobilization is a beneficial component involved in biocatalytic strategies. Understanding and evaluating the enzyme immobilization system plays an important role in the successful development and implementation of the biocatalysis route. Ensuring the implementation of a successful enzyme immobilization process is vital for realizing a highly functioning and well suited biocatalytic process within pharmaceutical development.

AIM

To develop a method which can accurately and objectively identify and classify differences within enzyme immobilization systems, sample preparation methods, and data collection parameters.

METHODS

Raman hyperspectral imaging was used to obtain a total of eight spectral data sets from enzyme immobilization samples. Partial least squares discriminant analysis (PLS-DA) was used to classify and identify the samples based on their differences.

RESULTS

Several two-class, four-class, and eight-class PLS-DA models were built to classify the different sample data sets. All models reached between 92-100% accuracy after cross-validation and external validation, illustrating great success of the models for identifying differences between the samples.

CONCLUSION

Raman hyperspectral imaging with machine learning can be used to investigate, interpret, and classify different data collection parameters, sample preparation methods, and enzyme immobilization supports, providing crucial insight into enzyme immobilization process development.

Recent Advances in Monitoring Stem Cell Status and Differentiation Using Nano-Biosensing Technologies

In regenerative medicine, cell therapies using various stem cells have received attention as an alternative to overcome the limitations of existing therapeutic methods. Clinical applications of stem cells require the identification of characteristics at the single-cell level and continuous monitoring during expansion and differentiation. In this review, we recapitulate the application of various stem cells used in regenerative medicine and the latest technological advances in monitoring the differentiation process of stem cells. Single-cell RNA sequencing capable of profiling the expression of many genes at the single-cell level provides a new opportunity to analyze stem cell heterogeneity and to specify molecular markers related to the branching of differentiation lineages. However, this method is destructive and distorted. In addition, the differentiation process of a particular cell cannot be continuously tracked. Therefore, several spectroscopic methods have been developed to overcome these limitations. In particular, the application of Raman spectroscopy to measure the intrinsic vibration spectrum of molecules has been proposed as a powerful method that enables continuous monitoring of biochemical changes in the process of the differentiation of stem cells. This review provides a comprehensive overview of current analytical methods employed for stem cell engineering and future perspectives of nano-biosensing technologies as a platform for the in situ monitoring of stem cell status and differentiation.

pH-Dependent Flavin Adenine Dinucleotide and Nicotinamide Adenine Dinucleotide Ultraviolet Resonance Raman (UVRR) Spectra at Intracellular Concentration

The ultraviolet resonance Raman spectra of the adenine-containing enzymatic redox cofactors nicotinamide adenine dinucleotide and flavin adenine dinucleotide in aqueous solution of physiological concentration are compared with the aim of distinguishing between them and their building block adenine in potential co-occurrence in biological materials. At an excitation wavelength of 266 nm, the spectra are dominated by the strong resonant contribution from adenine; nevertheless, bands assigned to vibrational modes of the nicotinamide and the flavin unit are found to appear at similar signal strength. Comparison of spectra measured at pH 7 with data obtained pH 10 and pH 3 shows characteristic changes when pH is increased or lowered, mainly due to deprotonation of the flavin and nicotinamide moieties, and protonation of the adenine, respectively.