Quantitation and identification of microplastics accumulation in human placental specimens using pyrolysis gas chromatography mass spectrometry - Garcia, Marcus A; Liu, Rui; Nihart, Alex; El Hayek, Eliane; Castillo, Eliseo; Barrozo, Enrico R; Suter, Melissa A; Bleske, Barry; Scott, Justin; Forsythe, Kyle; Gonzalez-Estrella, Jorge; Aagaard, Kjersti M; et al. (Toxicological sciences, 2024)
The exponential increase in global plastic usage has led to the emergence of nano- and microplastic (NMP) pollution as a pressing environmental issue due to its implications for human and other mammalian health. We have developed methodologies to extract solid materials from human tissue samples by saponification and ultracentrifugation, allowing for highly specific and quantitative analysis of plastics by pyrolysis-gas chromatography and mass spectrometry (Py-GC-MS). As a benchmark, placenta tissue samples were analyzed using fluorescence microscopy and automated particle count, which demonstrated the presence of >1-micron particles and fibers, but not nano-sized plastic particles. Analyses of the samples (n = 10) using attenuated total reflectance-Fourier transform infrared spectroscopy indicated presence of rayon, polystyrene, polyethylene, and unclassified plastic particles. By contrast, among 62 placenta samples, Py-GC-MS revealed that microplastics were present in all participants' placentae, with concentrations ranging widely from 6.5 to 685 µg NMPs per gram of placental tissue, averaging 126.8 ± 147.5 µg/g (mean±SD). Polyethylene was the most prevalent polymer, accounting for 54% of total NMPs and consistently found in nearly all samples (mean 68.8 ± 93.2 µg/g placenta). Polyvinyl chloride and nylon each represented approximately 10% of the NMPs by weight, with the remaining 26% of the composition represented by 9 other polymers. Together, these data demonstrate advancements in the unbiased quantitative resolution of Py-GC-MS applied to the identification and quantification of NMP species at the maternal-fetal interface. This method, paired with clinical metadata, will be pivotal to evaluating potential impacts of NMPs on adverse pregnancy outcomes.