Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) fingerprinting enabled treatment monitoring of pulmonary carcinoma cells in real time.

Lung cancer is one of the leading causes of cancer related deaths in the United States. A novel volatile analysis platform is needed to complement current diagnostic techniques and better elucidate chemical signatures of lung cancer and subsequent treatments. A systems biology bottom-up approach using cell culture volatilomics was employed to identify pathological volatile fingerprints of lung cancer in real time. An advanced secondary electrospray ionization (SESI) source, named SuperSESI was used in this study and directly attached to a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). We performed a series of experiments to determine if our optimized SESI-HRMS platform can distinguish between cancer types by sampling their in vitro volatilome profiles. We detected 60 significant volatile organic compound (VOC) features in positive mode that were deemed of cancer cell origin. The cell derived features were used for subsequent analyses to distinguish between our two studied lung cancer types, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Partial least squares-discriminant analysis (PLS-DA) model revealed a good separation of the two cancer types, suggesting unique chemical composition of their headspace profiles. A receiver operating characteristic (ROC) curve using 10 prominent features was used to predict disease type, with an area under the curve (AUC) of 0.811. Cultures were also treated with cisplatin to determine the feasibility of classifying drug treatment from expelled gases. A PLS-DA model revealed independent clustering based on their headspace profiles. An ROC curve using the top features driving separation of PLS-DA model suggested good accuracy with an AUC of 1. It is thus possible to benefit from the advantages of this platform to distinguish the unique volatile fingerprints of cancers to uncover potential biomarkers for cancer type differentiation and treatment monitoring.

Black raspberry extract shifted gut microbe diversity and their metabolic landscape in a human colonic model.

Human gut microbiota is critical for human health, as their dysbiosis could lead to various diseases such as irritable bowel syndrome and obesity. Black raspberry (BRB) has been increasingly studied recently for its impact on gut microbiota as a rich source of phytochemicals (e.g., anthocyanin). To investigate the effect of BRB extract on the gut microbiota composition and their metabolism, an in-vitro human colonic model (HCM) was utilized to study the direct interaction between BRB and gut microbiome. Conditions (e.g., pH, temperature, anaerobic environment) in HCM were closely monitored and maintained to simulate the human intestinal system. Fresh fecal samples donated by three young healthy volunteers were used for gut microbiota inoculation in the HCM. 16S ribosomal DNA sequencing and liquid-chromatography mass spectrometry (LC/MS) based metabolomics were performed to study the impact of BRB on gut microbiota characteristics and their metabolism (fatty acids, polar metabolites, and phenolic compounds). Our data suggested that BRB intervention modulated gut microbiota at the genus level in different HCM sections mimicing ascending, transverse, and descending colons. Relative abundance of Enterococcus was commonly decreased in all colon sections, while modulations of other bacteria genera were mostly location-dependent. Meanwhile, significant changes in the metabolic profile of gut microbiota related to fatty acids, endogenous polar metabolites, and phenolic compounds were detected, in which arginine and proline metabolism, lysine degradation, and aminoacyl-tRNA biosynthesis were mostly regulated. Moreover, we identified several significant associations between altered microbial populations and changes in microbial metabolites. In summary, our study revealed the impact of BRB intervention on gut microbiota composition and metabolism change, which may exert physiological change to host metabolism and host health.