Whole-Organism Analysis by Vibrational Spectroscopy

Fusion data from FT-IR and MALDI-TOF MS result in more accurate classification of specific microbiota

Microbes are usually present as a specific microbiota, and their classification remains a challenge. MALDI-TOF MS is particularly successful in library-based microbial identification at the species level as it analyzes the molecular weight of peptides and ribosomal proteins. FT-IR allows more accurate classification of bacteria at the subspecies level due to the high sensitivity, specificity and repeatability of FT-IR signals from bacteria, which is not achievable with MALDI-TOF MS. Previous studies have shown that more accurate identification results can be obtained by the fusion of FT-IR and MALDI-TOF MS spectral data. Here, we constructed 20 groups of model microbiota samples and used FT-IR, MALDI-TOF MS, and their fusion data to classify them. Hierarchical clustering analysis (HCA) showed that the classification accuracy of FT-IR, MALDI-TOF MS, and the fusion data was 85%, 90%, and 100%, respectively. These results indicate that both FT-IR and MALDI-TOF MS can effectively classify specific microbiota, and the fusion of their spectral data could improve the classification accuracy. The FT-IR and MALDI-TOF MS data fusion strategy may be a promising technology for specific microbiota classification.

The landscape of micron-scale particles including microplastics in human enclosed body fluids

Exogenous microparticles including microplastics are novel pollutants that could persist in the environment with potential health effects, while crucial data on their exposure in humans are still lacking. To understand the panorama of microparticles including microplastics exposure and distribution characteristics in different kinds of body fluids. A non-targeted microparticle internal exposure landscape analysis was done in thirteen kinds of human enclosed body fluids covering eight body systems. Totally 104 patients aged 24-96 years with an average age of 56 years were included in this study. After sample digestion, non-soluble microparticles were detected and identified with one Raman Microspectroscope under a strict quality control-particle detection system. Totally 702 microparticles with size ranging from 2.15 to 103.27 µm were detected in samples. Microparticles were identified into 84 substances or 66 molecules, most of which were firstly reported inside human body. Nine kinds of microplastics were originally reported in human body fluids with their size ranging from 19.66 to 103.27 µm. Microparticles exposure was unexpectedly high inside the human body despite the protection of biological barriers and membranes, raising awareness of the impact of particle pollution on sustainable development.

Label-Free Diagnosis of Pulmonary Fat Embolism Using Fourier Transform Infrared (FT-IR) Spectroscopic Imaging

The diagnosis of pulmonary fat embolism (PFE) is of great significance in the field of forensic medicine because it can be considered a major cause of death or a vital reaction. Conventional histological analysis of lung tissue specimens is a widely used method for PFE diagnosis. However, variable and labor-intensive tissue staining procedures impede the validity and informativeness of histological image analysis. To obtain complete information from tissues, a method based on infrared imaging of unlabeled tissue sections was developed to identify pulmonary fat emboli in the present study. We selected 15 PFE-positive lung samples and 15 PFE-negative samples from real cases. Oil red O (ORO) staining and infrared spectral imaging collection were both performed on all lung tissue samples. And the fatty tissue of the abdominal wall and the embolized lipid droplets in the lungs were taken for comparison. The results of the blind, evaluation by pathologists, showed good agreement between the infrared spectral imaging of the lung tissue and the standard histological stained images. Fourier transform infrared (FT-IR) spectroscopic imaging significantly simplifies the typical painstakingly laborious histological staining procedure. And we found a difference between lipid droplets embolized in abdominal wall fat and lung tissue.

Diagnosis of Lung Cancer by FTIR Spectroscopy Combined With Raman Spectroscopy Based on Data Fusion and Wavelet Transform

Lung cancer is a fatal tumor threatening human health. It is of great significance to explore a diagnostic method with wide application range, high specificity, and high sensitivity for the detection of lung cancer. In this study, data fusion and wavelet transform were used in combination with Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy to study the serum samples of patients with lung cancer and healthy people. The Raman spectra of serum samples can provide more biological information than the FTIR spectra of serum samples. After selecting the optimal wavelet parameters for wavelet threshold denoising (WTD) of spectral data, the partial least squares–discriminant analysis (PLS-DA) model showed 93.41% accuracy, 96.08% specificity, and 90% sensitivity for the fusion data processed by WTD in the prediction set. The results showed that the combination of FTIR spectroscopy and Raman spectroscopy based on data fusion and wavelet transform can effectively diagnose patients with lung cancer, and it is expected to be applied to clinical screening and diagnosis in the future.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy's multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.