Mass spectrometry imaging highlights dynamic patterns of lipid co-expression with Aß plaques in mouse and human brains.

Lipids play crucial roles in the susceptibility and brain cellular responses to Alzheimer's disease (AD) and are increasingly considered potential soluble biomarkers in cerebrospinal fluid (CSF) and plasma. To delineate the pathological correlations of distinct lipid species, we conducted a comprehensive characterization of both spatially localized and global differences in brain lipid composition in AppNL-G-F mice with spatial and bulk mass spectrometry lipidomic profiling, using human amyloid-expressing (h-Aß) and WT mouse brains controls. We observed age-dependent increases in lysophospholipids, bis(monoacylglycerol) phosphates, and phosphatidylglycerols around Aß plaques in AppNL-G-F mice. Immunohistology-based co-localization identified associations between focal pro-inflammatory lipids, glial activation, and autophagic flux disruption. Likewise, in human donors with varying Braak stages, similar studies of cortical sections revealed co-expression of lysophospholipids and ceramides around Aß plaques in AD (Braak stage V/VI) but not in earlier Braak stage controls. Our findings in mice provide evidence of temporally and spatially heterogeneous differences in lipid composition as local and global Aß-related pathologies evolve. Observing similar lipidomic changes associated with pathological Aß plaques in human AD tissue provides a foundation for understanding differences in CSF lipids with reported clinical stage or disease severity.

Assessing the clinical value of faecal bile acid profiling to predict recurrence in primary Clostridioides difficile infection.

BACKGROUND: Factors influencing recurrence risk in primary Clostridioides difficile infection (CDI) are poorly understood, and tools predicting recurrence are lacking. Perturbations in bile acids (BAs) contribute to CDI pathogenesis and may be relevant to primary disease prognosis. AIMS: To define stool BA dynamics in patients with primary CDI and to explore signatures predicting recurrence METHODS: Weekly stool samples were collected from patients with primary CDI from the last day of anti-CDI therapy until recurrence or, otherwise, through 8 weeks post-completion. Ultra-high performance liquid chromatography-mass spectrometry was used to profile BAs. Stool bile salt hydrolase (BSH) activity was measured to determine primary BA bacterial deconjugation capacity. Multivariate and univariate models were used to define differential BA trajectories in patients with recurrence versus those without, and to assess faecal BAs as predictive markers for recurrence. RESULTS: Twenty (36%) of 56 patients (median age: 57, 64% male) had recurrence; 80% of recurrences occurred within the first 9 days post-antibiotic treatment. Principal component analysis of stool BA profiles demonstrated clustering by recurrence status and post-treatment timepoint. Longitudinal faecal BA trajectories showed recovery of secondary BAs and their derivatives only in patients without recurrence. BSH activity increased over time only among non-relapsing patients (ß = 0.056; likelihood ratio test p = 0.018). A joint longitudinal-survival model identified five stool BAs with area under the receiver operating characteristic curve >0.73 for predicting recurrence within 9 days post-CDI treatment. CONCLUSIONS: Gut BA metabolism dynamics differ in primary CDI patients between those developing recurrence and those who do not. Individual BAs show promise as potential novel biomarkers to predict CDI recurrence.

1H NMR Signals from Urine Excreted Protein Are a Source of Bias in Probabilistic Quotient Normalization.

Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where 1H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r2 = 0.423, p < 2.2 × 10-16). This correlation was significantly reduced (r2 = 0.163, p < 2.2 × 10-16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.

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.

Statistical analysis in metabolic phenotyping.

Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.

The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets.

SUMMARY: As large-scale metabolic phenotyping studies become increasingly common, the need for systemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysis has become increasingly important, both within a study, and to allow meaningful inter-study comparisons. The nPYc-Toolbox provides software for the import, pre-processing, QC and visualization of metabolic phenotyping datasets, either interactively, or in automated pipelines. AVAILABILITY AND IMPLEMENTATION: The nPYc-Toolbox is implemented in Python, and is freely available from the Python package index https://pypi.org/project/nPYc/, source is available at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation can be found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials at https://github.com/phenomecentre/nPYc-toolbox-tutorials.

Medical Swab Analysis Using Desorption Electrospray Ionization Mass Spectrometry: A Noninvasive Approach for Mucosal Diagnostics.

Medical swabs are routinely used worldwide to sample human mucosa for microbiological screening with culture methods. These are usually time-consuming and have a narrow focus on screening for particular microorganism species. As an alternative, direct mass spectrometric profiling of the mucosal metabolome provides a broader window into the mucosal ecosystem. We present for the first time a minimal effort/minimal-disruption technique for augmenting the information obtained from clinical swab analysis with mucosal metabolome profiling using desorption electrospray ionization mass spectrometry (DESI-MS) analysis. Ionization of mucosal biomass occurs directly from a standard rayon swab mounted on a rotating device and analyzed by DESI MS using an optimized protocol considering swab-inlet geometry, tip-sample angles and distances, rotation speeds, and reproducibility. Multivariate modeling of mass spectral fingerprints obtained in this way readily discriminate between different mucosal surfaces and display the ability to characterize biochemical alterations induced by pregnancy and bacterial vaginosis (BV). The method was also applied directly to bacterial biomass to confirm the ability to detect intact bacterial species from a swab. These results highlight the potential of direct swab analysis by DESI-MS for a wide range of clinical applications including rapid mucosal diagnostics for microbiology, immune responses, and biochemistry.

Metabolic Profiling of Children Undergoing Surgery for Congenital Heart Disease.

OBJECTIVE: Inflammation and metabolism are closely interlinked. Both undergo significant dysregulation following surgery for congenital heart disease, contributing to organ failure and morbidity. In this study, we combined cytokine and metabolic profiling to examine the effect of postoperative tight glycemic control compared with conventional blood glucose management on metabolic and inflammatory outcomes in children undergoing congenital heart surgery. The aim was to evaluate changes in key metabolites following congenital heart surgery and to examine the potential of metabolic profiling for stratifying patients in terms of expected clinical outcomes. DESIGN: Laboratory and clinical study. SETTING: University Hospital and Laboratory. PATIENTS: Of 28 children undergoing surgery for congenital heart disease, 15 underwent tight glycemic control postoperatively and 13 were treated conventionally. INTERVENTIONS: Metabolic profiling of blood plasma was undertaken using proton nuclear magnetic resonance spectroscopy. A panel of metabolites was measured using a curve-fitting algorithm. Inflammatory cytokines were measured by enzyme-linked immunosorbent assay. The data were assessed with respect to clinical markers of disease severity (Risk Adjusted Congenital heart surgery score-1, Pediatric Logistic Organ Dysfunction, inotrope score, duration of ventilation and pediatric ICU-free days). MEASUREMENTS AND MAIN RESULTS: Changes in metabolic and inflammatory profiles were seen over the time course from surgery to recovery, compared with the preoperative state. Tight glycemic control did not significantly alter the response profile. We identified eight metabolites (3-D-hydroxybutyrate, acetone, acetoacetate, citrate, lactate, creatine, creatinine, and alanine) associated with surgical and disease severity. The strength of proinflammatory response, particularly interleukin-8 and interleukin-6 concentrations, inversely correlated with PICU-free days at 28 days. The interleukin-6/interleukin-10 ratio directly correlated with plasma lactate. CONCLUSIONS: This is the first report on the metabolic response to cardiac surgery in children. Using nuclear magnetic resonance to monitor the patient journey, we identified metabolites whose concentrations and trajectory appeared to be associated with clinical outcome. Metabolic profiling could be useful for patient stratification and directing investigations of clinical interventions.