The influence of lifestyle and biological factors on semen variability.

PURPOSE: Semen parameters are subjected to within-individual variability over time. The driving factors for this variability are likely multi-factorial, with healthier lifestyle associated with better semen quality. The extent in which variations in individual's lifestyle contributes to within-individual semen variability is unknown. METHODS: A total of 116 repeat semen samples from 29 men aged 19-37 over 6 months were collected. Basic semen analysis as per 5th WHO manual and extended semen parameters (sperm DNA fragmentation, redox potential and lipid peroxidation, sperm binding to hyaluronan and hyperactive motility) were assessed. An additional 39 lifestyle/biological factors (weight, blood pressure, etc.) were collected at each sample including validated health questionnaires SF36 Health Status, Australian Recommend Food Score, and International Physical Activity Questionnaire. RESULTS: Only 10 out of the 39 lifestyle factors varied within men across samples including age (P = 0.0024), systolic blood pressure (P = 0.0080), social functioning (P = 0.0340), energy (P = 0.0069), non-alcoholic caffeinated beverages (P = 0.0010), and nutrition (P < 0.0001). The only semen parameter that varied between collections was sperm morphology (coefficient of variation 23.8 (6.1-72.0), P < 0.05). We only observed weak (r < 0.3) to moderate (r > 0.3- < 0.6) correlations between lifestyle factors, including body mass index, waist circumference, nutrition, exercise, blood pressure and semen parameters including sperm count, progressive motility, and sperm DNA fragmentation (P < 0.05). CONCLUSION: In healthy men from the general population, semen quality and associated lifestyle factors do not significantly vary over 6 months, indicating that one semen sample is likely sufficient for determining male fertility in this population.

Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture.

Mass spectrometry imaging (MSI) has accelerated our understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focused on a single lipid class. We overcome this limitation by using a multiclass internal standard (IS) mixture sprayed homogeneously over the tissue surface with concentrations that reflect those of endogenous lipids. This enabled quantitative MSI (Q-MSI) of 13 lipid classes and subclasses representing almost 200 sum-composition lipid species using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalization of each lipid species in a manner analogous to that widely used in shotgun lipidomics. The Q-MSI approach covered 3 orders of magnitude in dynamic range (lipid concentrations reported in pmol/mm2) and revealed subtle changes in distribution compared to data without normalization. The robustness of the method was evaluated by repeating experiments in two laboratories using both timsTOF and Orbitrap mass spectrometers with an ∼4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI results obtained by using the two approaches. Outliers were mostly rationalized by isobaric interferences or the higher sensitivity of one instrument for a particular lipid species. These data provide insight into how the mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and subclasses and revealing how absolute lipid concentrations vary throughout and between biological tissues.

Mass Spectrometry Imaging of Lipids Using MALDI Coupled with Plasma-Based Post-Ionization on a Trapped Ion Mobility Mass Spectrometer.

Here we report the development and optimization of a mass spectrometry imaging (MSI) platform that combines an atmospheric-pressure matrix-assisted laser desorption/ionization platform with plasma postionization (AP-MALDI-PPI) and trapped ion mobility spectrometry (TIMS). We discuss optimal parameters for operating the source, characterize the behavior of a variety of lipid classes in positive- and negative-ion modes, and explore the capabilities for lipid imaging using murine brain tissue. The instrument generates high signal-to-noise for numerous lipid species, with mass spectra sharing many similarities to those obtained using laser postionization (MALDI-2). The system is especially well suited for detecting lipids such as phosphatidylethanolamine (PE), as well as numerous sphingolipid classes and glycerolipids. For the first time, the coupling of plasma-based postionization with ion mobility is presented, and we show the value of ion mobility for the resolution and identification of species within rich spectra that contain numerous isobaric/isomeric signals that are not resolved in the m/z dimension alone, including isomeric PE and demethylated phosphatidylcholine lipids produced by in-source fragmentation. The reported instrument provides a powerful and user-friendly approach for MSI of lipids.

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics.

Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

Lipidomic Profiling of Clinical Prostate Cancer Reveals Targetable Alterations in Membrane Lipid Composition.

Dysregulated lipid metabolism is a prominent feature of prostate cancer that is driven by androgen receptor (AR) signaling. Here we used quantitative mass spectrometry to define the "lipidome" in prostate tumors with matched benign tissues (n = 21), independent unmatched tissues (n = 47), and primary prostate explants cultured with the clinical AR antagonist enzalutamide (n = 43). Significant differences in lipid composition were detected and spatially visualized in tumors compared with matched benign samples. Notably, tumors featured higher proportions of monounsaturated lipids overall and elongated fatty acid chains in phosphatidylinositol and phosphatidylserine lipids. Significant associations between lipid profile and malignancy were validated in unmatched samples, and phospholipid composition was characteristically altered in patient tissues that responded to AR inhibition. Importantly, targeting tumor-related lipid features via inhibition of acetyl-CoA carboxylase 1 significantly reduced cellular proliferation and induced apoptosis in tissue explants. This characterization of the prostate cancer lipidome in clinical tissues reveals enhanced fatty acid synthesis, elongation, and desaturation as tumor-defining features, with potential for therapeutic targeting. SIGNIFICANCE: This study identifies malignancy and treatment-associated changes in lipid composition of clinical prostate cancer tissues, suggesting that mediators of these lipidomic changes could be targeted using existing metabolic agents.

Evaluation of Small Molecule Drug Uptake in Patient-Derived Prostate Cancer Explants by Mass Spectrometry.

Patient-derived explant (PDE) culture of solid tumors is increasingly being applied to preclinical evaluation of novel therapeutics and for biomarker discovery. In this technique, treatments are added to culture medium and penetrate the tissue via a gelatin sponge scaffold. However, the penetration profile and final concentrations of small molecule drugs achieved have not been determined to date. Here, we determined the extent of absorption of the clinical androgen receptor antagonist, enzalutamide, into prostate PDEs, using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and matrix-assisted laser/desorption ionisation (MALDI) mass spectrometry imaging (MSI). In a cohort of 11 PDE tissues from eight individual patients, LC-MS/MS quantification of PDE homogenates confirmed enzalutamide (10 µM) uptake by all PDEs, which reached maximal average tissue concentration of 0.24-0.50 ng/µg protein after 48 h culture. Time dependent uptake of enzalutamide (50 µM) in PDEs was visualized using MALDI MSI over 24-48 h, with complete penetration throughout tissues evident by 6 h of culture. Drug signal intensity was not homogeneous throughout the tissues but had areas of markedly high signal that corresponded to drug target (androgen receptor)-rich epithelial regions of tissue. In conclusion, application of MS-based drug quantification and visualization in PDEs, and potentially other 3-dimensional model systems, can provide a more robust basis for experimental study design and interpretation of pharmacodynamic data.