Spontaneous preterm delivery presents one of the most complex challenges in obstetrics and is a leading cause of perinatal morbidity and mortality. Although it is a common endpoint for multiple pathological processes, the mechanisms governing the etiological complexity of spontaneous preterm birth and the placental responses are poorly understood. This study examined placental tissues collected between May 2019 and May 2022 from a well-defined cohort of women who experienced spontaneous preterm birth (n = 72) and healthy full-term deliveries (n = 30). Placental metabolomic profiling of polar metabolites was performed using Ultra-High Performance Liquid Chromatography/Mass Spectrometry (UHPLC/MS) analysis. The resulting data were analyzed using multi- and univariate statistical methods followed by unsupervised clustering. A comprehensive metabolomic evaluation of the placenta revealed that spontaneous preterm birth was associated with significant changes in the levels of 34 polar metabolites involved in intracellular energy metabolism and biochemical activity, including amino acids, purine metabolites, and small organic acids. We found that neither the preterm delivery phenotype nor the inflammatory response explain the reported differential placental metabolome. However, unsupervised clustering revealed two molecular subtypes of placentas from spontaneous preterm pregnancies exhibiting differential enrichment of clinical parameters. We also identified differences between early and late preterm samples, suggesting distinct placental functions in early spontaneous preterm delivery. Altogether, we present evidence that spontaneous preterm birth is associated with significant changes in the level of placental polar metabolites. Dysregulation of the placental metabolome may underpin important (patho)physiological mechanisms involved in preterm birth etiology and long-term neonatal outcomes.

• Keywords
• inflammation, metabolism, metabolomics, placenta, preterm birth,
• Publication type
• Journal Article MeSH

Pancreatic cancer has the worst prognosis among all cancers. Cancer screening of body fluids may improve the survival time prognosis of patients, who are often diagnosed too late at an incurable stage. Several studies report the dysregulation of lipid metabolism in tumor cells, suggesting that changes in the blood lipidome may accompany tumor growth. Here we show that the comprehensive mass spectrometric determination of a wide range of serum lipids reveals statistically significant differences between pancreatic cancer patients and healthy controls, as visualized by multivariate data analysis. Three phases of biomarker discovery research (discovery, qualification, and verification) are applied for 830 samples in total, which shows the dysregulation of some very long chain sphingomyelins, ceramides, and (lyso)phosphatidylcholines. The sensitivity and specificity to diagnose pancreatic cancer are over 90%, which outperforms CA 19-9, especially at an early stage, and is comparable to established diagnostic imaging methods. Furthermore, selected lipid species indicate a potential as prognostic biomarkers.

• MeSH
• CA-19-9 Antigen blood   MeSH
• Ceramides blood   MeSH
• Humans MeSH
• Lipidomics methods   MeSH
• Lysophosphatidylcholines blood   MeSH
• Lipid Metabolism genetics   MeSH
• Multivariate Analysis MeSH
• Biomarkers, Tumor blood   genetics   MeSH
• Pancreatic Neoplasms blood   diagnosis   mortality   pathology   MeSH
• Proportional Hazards Models MeSH
• Sensitivity and Specificity MeSH
• Sphingomyelins blood   MeSH
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization MeSH
• Case-Control Studies MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• CA-19-9 Antigen MeSH
• Ceramides MeSH
• Lysophosphatidylcholines MeSH
• Biomarkers, Tumor MeSH
• Sphingomyelins MeSH

In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field.

• Keywords
• LC-MS, MS, chromatography, ion mobility spectrometry, lipid identification, lipidomics, metabolomics, phospholipids, sphingolipids,
• MeSH
• Mass Spectrometry MeSH
• Humans MeSH
• Lipidomics * standards   MeSH
• Lipids analysis   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH
• Research Support, N.I.H., Extramural MeSH
• Names of Substances
• Lipids MeSH

Early detection of cancer is one of the unmet needs in clinical medicine. Peripheral blood analysis is a preferred method for efficient population screening, because blood collection is well embedded in clinical practice and minimally invasive for patients. Lipids are important biomolecules, and variations in lipid concentrations can reflect pathological disorders. Lipidomic profiling of human plasma by the coupling of ultrahigh-performance supercritical fluid chromatography and mass spectrometry is investigated with the aim to distinguish patients with breast, kidney, and prostate cancers from healthy controls. The mean sensitivity, specificity, and accuracy of the lipid profiling approach were 85%, 95%, and 92% for kidney cancer; 91%, 97%, and 94% for breast cancer; and 87%, 95%, and 92% for prostate cancer. No association of statistical models with tumor stage is observed. The statistically most significant lipid species for the differentiation of cancer types studied are CE 16:0, Cer 42:1, LPC 18:2, PC 36:2, PC 36:3, SM 32:1, and SM 41:1 These seven lipids represent a potential biomarker panel for kidney, breast, and prostate cancer screening, but a further verification step in a prospective study has to be performed to verify clinical utility.

• MeSH
• Early Detection of Cancer MeSH
• Adult MeSH
• Heparin chemistry   MeSH
• Mass Spectrometry MeSH
• Kidney metabolism   MeSH
• Middle Aged MeSH
• Humans MeSH
• Lipidomics * MeSH
• Lipids chemistry   MeSH
• Young Adult MeSH
• Biomarkers, Tumor metabolism   MeSH
• Prostatic Neoplasms metabolism   MeSH
• Breast Neoplasms metabolism   MeSH
• Area Under Curve MeSH
• Prospective Studies MeSH
• Prostate metabolism   MeSH
• Breast metabolism   MeSH
• Gene Expression Regulation, Neoplastic MeSH
• Reproducibility of Results MeSH
• Retrospective Studies MeSH
• ROC Curve MeSH
• Aged MeSH
• Models, Statistical MeSH
• Case-Control Studies MeSH
• Chromatography, Supercritical Fluid MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Heparin MeSH
• Lipids MeSH
• Biomarkers, Tumor MeSH