Risks from persistent organic pollutants (POPs) remain largely a mystery for threatened loggerhead sea turtles (Caretta caretta). The present study examines regional-scale POP differences in blood plasma from adult male C. caretta based on movement patterns. Turtles were captured near Port Canaveral, Florida, USA, in April of 2006 and 2007 and fitted with satellite transmitters as part of a National Marine Fisheries Service-funded project. Residents (n = 9) remained near the capture site, whereas transients (n = 10) migrated northward, becoming established in areas largely from south of Pamlico Sound, North Carolina, to north of Cape May, New Jersey, USA. Blood was sampled from the dorsocervical sinus of each turtle and analyzed using gas chromatography-mass spectrometry for organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and toxaphenes. Blood plasma concentrations of OCPs and total PBDEs were elevated in transients (p < 0.05) and in some cases were correlated with turtle size. Migratory adults showed an atypical PBDE congener profile relative to other published studies on wildlife, with PBDE 154 being the dominant congener. Additionally, PCB congener patterns differed between groups, with total PCBs slightly elevated in transients. This supports the idea that foraging location can influence exposure to, and patterns of, POPs in highly mobile species such as C. caretta. Understanding patterns of contamination informs wildlife managers about possible health risks to certain subpopulations. The present study is the first to examine POPs in the rarely studied adult male sea turtle and to couple contaminant measurements with satellite tracking.

As advances in analytical separation techniques, mass spectrometry instrumentation, and data processing platforms continue to spur growth in the lipidomics field, more structurally unique lipid species are detected and annotated. The lipidomics community is in need of benchmark reference values to assess the validity of various lipidomics workflows in providing accurate quantitative measurements across the diverse lipidome. LipidQC addresses the harmonization challenge in lipid quantitation by providing a semiautomated process, independent of analytical platform, for visual comparison of experimental results of National Institute of Standards and Technology Standard Reference Material (SRM) 1950, "Metabolites in Frozen Human Plasma", against benchmark consensus mean concentrations derived from the NIST Lipidomics Interlaboratory Comparison Exercise.

Lipidomics, the comprehensive measurement of lipid species in a biological system, has promising potential in biomarker discovery and disease etiology elucidation. Advances in chromatographic separation, mass spectrometric techniques, and novel substrate applications continue to expand the number of lipid species observed. The total number and type of lipid species detected in a given sample are generally indicative of the sample matrix examined (e.g. serum, plasma, cells, bacteria, tissue, etc.). Current exact mass lipid libraries are static and represent the most commonly analyzed matrices. It is common practice for users to manually curate their own lists of lipid species and adduct masses; however, this process is time-consuming. LipidPioneer, an interactive template, can be used to generate exact masses and molecular formulas of lipid species that may be encountered in the mass spectrometric analysis of lipid profiles. Over 60 lipid classes are present in the LipidPioneer template, and include several unique lipid species, such as ether-linked lipids and lipid oxidation products. In the template, users can add any fatty acyl constituents without limitation in the number of carbons or degrees of unsaturation. LipidPioneer accepts naming using the lipid class level (sum composition) and the LIPID MAPS notation for fatty acyl structure level. In addition to lipid identification, user generated lipid m/z values can be used to develop inclusion lists for targeted fragmentation experiments. Resulting lipid names and m/z values can be imported into software such as MZmine or Compound Discoverer to automate exact mass searching and isotopic pattern matching across experimental data.