Two-dimensional Infrared Spectroscopy Reveals Better Insights of Structure and Dynamics of Protein

Novel Developments and Progress in Two-Dimensional Correlation Spectroscopy (2D-COS)

This first of the two-part series of the comprehensive survey review on the progress of the two-dimensional correlation spectroscopy (2D-COS) field during the period 2021-2022, covers books, reviews, tutorials, novel concepts and theories, and patent applications that appeared in the last two years, as well as some inappropriate use or citations of 2D-COS. The overall trend clearly shows that 2D-COS is continually growing and evolving with notable new developments. The technique is well recognized as a powerful analytical tool that provides deep insights into systems in many science fields.

Serum-based ATR-FTIR spectroscopy combined with multivariate analysis for the diagnosis of pre-diabetes and diabetes

Diabetes mellitus (DM) is a metabolic disease with an increasing prevalence that is causing worldwide concern. The pre-diabetes stage is the only reversible stage in the patho-physiological process towards DM. Due to the limitations of traditional methods, the diagnosis and detection of DM and pre-diabetes are complicated, expensive, and time-consuming. Therefore, it would be of great benefit to develop a simple, rapid and inexpensive diagnostic test. Herein, the infrared (IR) spectra of serum samples from 111 DM patients, 111 pre-diabetes patients and 333 healthy volunteers were collected using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy and this was combined with the multivariate analysis of principal component analysis linear discriminant analysis (PCA-LDA) to develop a discriminant model to verify the diagnostic potential of this approach. The study found that the accuracy of the test model established by ATR-FTIR spectroscopy combined with PCA-LDA was 97%, and the sensitivity and specificity were 100% and 100% in the control group, 94% and 98% in the pre-diabetes group, and 91% and 98% in the DM group, respectively. This indicates that this method can effectively diagnose DM and pre-diabetes, which has far-reaching clinical significance.

Standard operating procedure to reveal prostate cancer specific volatile organic molecules by infrared spectroscopy

The growing number of prostate cancer cases is a real concern in modern society. Over 1.4 million new cases and about 400 thousand (>26%) deaths were registered worldwide in 2020 due to prostate cancer. The high mortality rate of prostate cancer is due to the lack of reliable early detection of the disease. Till now the most reliable diagnosis of cancer is tissue biopsy, which is an invasive process. A non-invasive or minimally invasive technique could lead to a diagnostic tool that will allow for saving or prolonging the lifespan of millions of lives. Metabolite-based diagnostics may have a better chance of early cancer detection. However, reliable detection techniques need to be developed. Infrared spectroscopy based gaseous-biofluid holds great promise towards the development of non-invasive diagnostics. A pilot study based on breath analysis by infrared spectroscopy showed promising results in distinguishing prostate cancer patients from healthy volunteers. Details of the spectral metabolic analysis are presented.

Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review

Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.

Direct calculation of the temperature dependence of 2D-IR spectra: Urea in water

A method for directly calculating the temperature derivative of two-dimensional infrared (2D-IR) spectra from simulations at a single temperature is presented. The approach is demonstrated by application to the OD stretching spectrum of isotopically dilute aqueous (HOD in H₂O) solutions of urea as a function of concentration. Urea is an important osmolyte because of its ability to denature proteins, which has motivated significant interest in its effect on the structure and dynamics of water. The present results show that the temperature dependence of both the linear IR and 2D-IR spectra, which report on the underlying energetic driving forces, is more sensitive to urea concentration than the spectra themselves. Additional physical insight is provided by calculation of the contributions to the temperature derivative from different interactions, e.g., water-water, water-urea, and urea-urea, present in the system. Finally, it is demonstrated how 2D-IR spectra at other temperatures can be obtained from only room temperature simulations.

Assessing Tissue Fixation Time and Quality with Label-free Mid Infrared Spectroscopy and Machine Learning

Objectives: This work investigates whether changes in a biospecimen's molecular composition from formaldehyde fixation drive changes in the mid infrared (MID-IR) spectrum. Our ultimate goal was to develop an analytical metrology that could be used to accurately determine the fixation time of a tissue sample as a surrogate to overall tissue quality. Methods: Multiple unstained formalin-fixed paraffin-embedded tissue samples were scanned with an MID-IR microscope to identify a molecular fingerprint of formaldehyde fixation. The fixation specific patterns were then mined to develop a predictive model. A multiple tissue experiment using greater than 100 samples was designed to train the algorithm and validate the accuracy of predicting fixation status. Results: We present data that formaldehyde crosslinking results in alterations to multiple bands of the MID-IR spectra. The impact was most dramatic in the Amide I band, which is sensitive to the conformational state of proteins. The spectroscopic fixation signature was used to train a machine-learning model that could predict fixation time of unknown tissues with an average accuracy of 1.4 hours. Results were validated by histological stain quality for bcl-2, FOXP3, and ki-67. Further, two-dimensional imaging was used to visualize the spatial dependence of fixation, as demonstrated by multiple features in the tissue's vibrational spectra. Conclusions: This work demonstrates that it is possible to predict the fixation status of tissues for which the preanalytics are unknown. This novel capability could help standardize clinical tissue diagnostics and ensure every patient gets the absolutely best treatment based on the highest quality tissue sample.