Direct on-swab metabolic profiling of vaginal microbiome host interactions during pregnancy and preterm birth

The pregnancy vaginal microbiome contributes to risk of preterm birth, the primary cause of death in children under 5 years of age. Here we describe direct on-swab metabolic profiling by Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) for sample preparation-free characterisation of the cervicovaginal metabolome in two independent pregnancy cohorts (VMET, n = 160; 455 swabs; VMET II, n = 205; 573 swabs). By integrating metataxonomics and immune profiling data from matched samples, we show that specific metabolome signatures can be used to robustly predict simultaneously both the composition of the vaginal microbiome and host inflammatory status. In these patients, vaginal microbiota instability and innate immune activation, as predicted using DESI-MS, associated with preterm birth, including in women receiving cervical cerclage for preterm birth prevention. These findings highlight direct on-swab metabolic profiling by DESI-MS as an innovative approach for preterm birth risk stratification through rapid assessment of vaginal microbiota-host dynamics

Metabolomic and Metataxonomic Fingerprinting of Human Milk Suggests Compositional Stability over a Natural Term of Breastfeeding to 24 Months

Sparse data exist regarding the normal range of composition of maternal milk beyond the first postnatal weeks. This single timepoint, observational study in collaboration with the ‘Parenting Science Gang’ citizen science group evaluated the metabolite and bacterial composition of human milk from 62 participants (infants aged 3–48 months), nearly 3 years longer than previous studies. We utilised rapid evaporative ionisation mass spectrometry (REIMS) for metabolic fingerprinting and 16S rRNA gene metataxonomics for microbiome composition analysis. Milk expression volumes were significantly lower beyond 24 months of lactation, but there were no corresponding changes in bacterial load, composition, or whole-scale metabolomic fingerprint. Some individual metabolite features (~14%) showed altered abundances in nursling age groups above 24 months. Neither milk expression method nor nursling sex affected metabolite and metataxonomic fingerprints. Self-reported lifestyle factors, including diet and physical traits, had minimal impact on metabolite and metataxonomic fingerprints. Our findings suggest remarkable consistency in human milk composition over natural-term lactation. The results add to previous studies suggesting that milk donation can continue up to 24 months postnatally. Future longitudinal studies will confirm the inter-individual and temporal nature of compositional variations and the use of donor milk as a personalised therapeutic

Sample Preparation Free Mass Spectrometry Using Laser-Assisted Rapid Evaporative Ionization Mass Spectrometry: Applications to Microbiology, Metabolic Biofluid Phenotyping, and Food Authenticity

Mass spectrometry has established itself as a powerful tool in the chemical, biological, medical, environmental, and agricultural fields. However, experimental approaches and potential application areas have been limited by a traditional reliance on sample preparation, extraction, and chromatographic separation. Ambient ionization mass spectrometry methods have addressed this challenge but are still somewhat restricted in requirements for sample manipulation to make it suitable for analysis. These limitations are particularly restrictive in view of the move toward high-throughput and automated analytical workflows. To address this, we present what we consider to be the first automated sample-preparation-free mass spectrometry platform utilizing a carbon dioxide (CO2) laser for sample thermal desorption linked to the rapid evaporative ionization mass spectrometry (LA-REIMS) methodology. We show that the pulsatile operation of the CO2 laser is the primary factor in achieving high signal-to-noise ratios. We further show that the LA-REIMS automated platform is suited to the analysis of three diverse biological materials within different application areas. First, clinical microbiology isolates were classified to species level with an accuracy of 97.2%, the highest accuracy reported in current literature. Second, fecal samples from a type 2 diabetes mellitus cohort were analyzed with LA-REIMS, which allowed tentative identification of biomarkers which are potentially associated with disease pathogenesis and a disease classification accuracy of 94%. Finally, we showed the ability of the LA-REIMS system to detect instances of adulteration of cooking oil and determine the geographical area of production of three protected olive oil products with 100% classification accuracy