Label-free Raman spectroscopy characterizes signatures of inflammation and fibrosis in the silicosis

Label-free analysis of inflammatory tissue remodeling in murine lung tissue based on multiphoton microscopy, Raman spectroscopy and machine learning

Inflammatory fibrotic tissue remodeling can lead to severe morbidity. Histopathology grading requires extraction of biopsies and elaborate tissue processing. Label-free optical technologies can provide diagnostic readout without preparation and artificial stainings and show potential for in vivo applications. Here, we present an integration of Raman spectroscopy (RS) and multiphoton microscopy for joint investigation of the bio-chemical composition and morphological features related to cellular components and connective tissue. Both modalities show that collagen signatures were significantly increased in a murine fibrosis model. Furthermore, autofluorescence signatures assigned to immune cells show high correlation with disease severity. RS indicates increased levels of elastin and lipids. Further, we investigated the effect of joint data sets on prediction performance in machine learning models. Although binary classification did not benefit from adding more features, multi-class classification was improved by integrated data sets.

Glycogen metabolism reprogramming promotes inflammation in coal dust-exposed lung

Long-term coal dust exposure triggers complex inflammatory processes in the coal workers' pneumoconiosis (CWP) lungs. The progress of the inflammation is reported to be affected by disordered cell metabolism. However, the changes in the metabolic reprogramming associated with the pulmonary inflammation induced by the coal dust particles are unknown. Herein, we show that coal dust exposure causes glycogen accumulation and the reprogramming of glucose metabolism in the CWP lung. The glycogen accumulation caused by coal dust is mainly due to macrophages, which reprogram glycogen metabolism and trigger an inflammatory response. In addition, 2-deoxy-D-glucose (2-DG) reduced glycogen content in macrophages, which was accompanied by mitigated inflammation and restrained NF-κB activation. Accordingly, we have pinpointed a novel and crucial metabolic pathway that is an essential regulator of the inflammatory phenotype of coal dust-exposed macrophages. These results shed light on new ways to regulate CWP inflammation.

Urine metabolomics analysis of sleep quality in deep-underground miners: A pilot study

Background

In previous questionnaire surveys of miners, sleep disorders were found among underground workers. The influence of the special deep-underground environment and its potential mechanism are still unclear. Therefore, this study intends to utilize LC-MS metabolomics to study the potential differences between different environments and different sleep qualities.

Methods

Twenty-seven miners working at 645-1,500 m deep wells were investigated in this study, and 12 local ground volunteers were recruited as the control group. The Pittsburgh Sleep Quality Index (PSQI) was used to examine and evaluate the sleep status of the subjects in the past month, and valuable basic information about the participants was collected. PSQI scores were obtained according to specific calculation rules, and the corresponding sleep grouping and subsequent analysis were carried out. Through liquid chromatography-mass spectrometry (LC-MS) non-targeted metabolomics analysis, differences in metabolism were found by bioinformatics analysis in different environments.

Results

Between the deep-underground and ground (DUvsG) group, 316 differential metabolites were identified and 125 differential metabolites were identified in the good sleep quality vs. poor sleep quality (GSQvsPSQ) group. The metabolic pathways of Phenylalanine, tyrosine and tryptophan biosynthesis (p = 0.0102) and D-Glutamine and D-glutamate metabolism (p = 0.0241) were significantly enriched in DUvsG. For GSQvsPSQ group, Butanoate metabolism was statistically significant (p = 0.0276). L-Phenylalanine, L-Tyrosine and L-Glutamine were highly expressed in the deep-underground group. Acetoacetic acid was poorly expressed, and 2-hydroxyglutaric acid was highly expressed in good sleep quality.

Conclusions

The influence of the underground environment on the human body is more likely to induce specific amino acid metabolism processes, and regulate the sleep-wake state by promoting the production of excitatory neurotransmitters. The difference in sleep quality may be related to the enhancement of glycolytic metabolism, the increase in excitatory neurotransmitters and the activation of proinflammation. L-phenylalanine, L-tyrosine and L-glutamine, Acetoacetic acid and 2-hydroxyglutaric acid may be potential biomarkers correspondingly.

A SERS Platform for Rapid Detection of Drug Resistance of Non-Candida albicans Using Fe3O4@PEI and Triangular Silver Nanoplates

Purpose

Candida infection has a high mortality rate, and the increasing prevalence of non-Candida albicans drug resistance in recent years poses a potential threat to human health. Non-Candida albicans has long culture cycles, and its firm cell walls making it difficult to isolate DNA for sequencing.

Materials and Methods

Fe₃O₄@PEI (PEI, polyvinyl imine) was mixed with clinical samples to form Fe₃O₄@PEI@non-Candida albicans and enriched them with magnets. Triangular silver nanoplates enhanced the surface-enhanced Raman scattering (SERS) signal. SERS was used to detect the fingerprint spectrum of non-Candida albicans. Then, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to analyze the drug resistance of non-Candida albicans.

Results

SERS combined with OPLS-DA could well analyze the drug resistance of non-Candida albicans. Through 10-fold-cross validation, the accuracy of training and test data is greater than 99%, indicating that the model has good classification ability. We used SERS for the first time to detect the drug resistance of non-Candida albicans directly.

Conclusion

This approach can be utilized without causing damage to the cell wall and can be accomplished in as little as 90 minutes. It can provide timely guidance for the treatment of patients with good clinical application potential.

Inflammation and fibrosis in the coal dust-exposed lung described by confocal Raman spectroscopy

Background

Coal workers' pneumoconiosis (CWP) is an occupational disease that severely damages the life and health of miners. However, little is known about the molecular and cellular mechanisms changes associated with lung inflammation and fibrosis induced by coal dust. As a non-destructive technique for measuring biological tissue, confocal Raman spectroscopy provides accurate molecular fingerprints of label-free tissues and cells. Here, the progression of lung inflammation and fibrosis in a murine model of CWP was evaluated using confocal Raman spectroscopy.

Methods

A mouse model of CWP was constructed and biochemical analysis in lungs exposed to coal dust after 1 month (CWP-1M) and 3 months (CWP-3M) vs control tissues (NS) were used by confocal Raman spectroscopy. H&E, immunohistochemical and collagen staining were used to evaluate the histopathology alterations in the lung tissues.

Results

The CWP murine model was successfully constructed, and the mouse lung tissues showed progression of inflammation and fibrosis, accompanied by changes in NF-κB, p53, Bax, and Ki67. Meanwhile, significant differences in Raman bands were observed among the different groups, particularly changes at 1,248, 1,448, 1,572, and 746 cm^-1. These changes were consistent with collagen, Ki67, and Bax levels in the CWP and NS groups.

Conclusion

Confocal Raman spectroscopy represented a novel approach to the identification of the biochemical changes in CWP lungs and provides potential biomarkers of inflammation and fibrosis.