Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Discriminates the Elderly with a Low and High Percentage of Pathogenic CD4+ T Cells

Abstract

In the aging process, the presence of interleukin (IL)-17-producing CD4+CD28-NKG2D+T cells (called pathogenic CD4+ T cells) is strongly associated with inflammation and the development of various diseases. Thus, their presence needs to be monitored. The emergence of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy empowered with machine learning is a breakthrough in the field of medical diagnostics. This study aimed to discriminate between the elderly with a low percentage (LP; ≤3%) and a high percentage (HP; ≥6%) of pathogenic CD4+CD28-NKG2D+IL17+ T cells by utilizing ATR-FTIR coupled with machine learning algorithms. ATR spectra of serum, exosome, and HDL from both groups were explored in this study. Only exosome spectra in the 1700–1500 cm−1 region exhibited possible discrimination for the LP and HP groups based on principal component analysis (PCA). Furthermore, partial least square-discriminant analysis (PLS-DA) could differentiate both groups using the 1700–1500 cm−1 region of exosome ATR spectra with 64% accuracy, 69% sensitivity, and 61% specificity. To obtain better classification performance, several spectral models were then established using advanced machine learning algorithms, including J48 decision tree, support vector machine (SVM), random forest (RF), and neural network (NN). Herein, NN was considered to be the best model with an accuracy of 100%, sensitivity of 100%, and specificity of 100% using serum spectra in the region of 1800–900 cm−1. Exosome spectra in the 1700–1500 and combined 3000–2800 and 1800–900 cm−1 regions using the NN algorithm gave the same accuracy performance of 95% with a variation in sensitivity and specificity. HDL spectra with the NN algorithm also showed excellent test performance in the 1800–900 cm−1 region with 97% accuracy, 100% sensitivity, and 95% specificity. This study demonstrates that ATR-FTIR coupled with machine learning algorithms can be used to study immunosenescence. Furthermore, this approach can possibly be applied to monitor the presence of pathogenic CD4+ T cells in the elderly. Due to the limited number of samples used in this study, it is necessary to conduct a large-scale study to obtain more robust classification models and to assess the true clinical diagnostic performance.

Synchrotron-Based Fourier-Transform Infrared Micro-Spectroscopy of Cerebrospinal Fluid from Amyotrophic Lateral Sclerosis Patients Reveals a Unique Biomolecular Profile

“…Regarding the nucleic acid status, we found significant changes regarding the RNAspecific IR spectral bands at 1120 cm −1 and 984 cm −1 , which are particular peaks considered diagnostic of the contribution of RNA in the nucleic acid backbone vibrations [43][44][45]. Our findings imply a decrease in the total amount of RNA in ALS CSF as well as specific changes in RNA metabolism.…”

“…RNA-specific infrared absorptions at 1125 (±7 cm −1 ) and 984 cm −1 [43] are particular peaks considered diagnostic of the contribution of RNA in the nucleic acid backbone vibrations [43][44][45]. Both these peaks showed significant changes for the calculated area at 984 cm −1 (Figure 3B) and the peak position at 1120 cm −1 (Figure 3D), suggesting changes in RNA metabolism and a significant decrease of the whole RNA amount in ALS samples.…”

Diabetes Monitoring through Urine Analysis Using ATR-FTIR Spectroscopy and Machine Learning

“…Furthermore, it assists qualitative analysis through the examination of spectral disparities in characteristic bands of biomolecules related to the particular pathology under investigation. By virtue of its outstanding sensitivity, specificity, and the ability to simultaneously scrutinize all molecular components, ATR-FTIR spectroscopy has emerged as an invaluable tool for the early screening of a myriad of pathological conditions [17,18].…”