0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Sign in to save

Multi-omics exploration on polystyrene microplastic effects on term placental explants - Proteomics Raw Data

Zenodo (CERN European Organization for Nuclear Research) 2026
Edmilson Rodrigeus

Summary

Scientists exposed human placenta tissue (donated after birth) to tiny polystyrene microplastic particles in the lab, then used advanced protein-analysis technology to see how thousands of placental proteins changed in response. This is a foundational data-generating study, it maps out the proteins affected but doesn't yet draw conclusions about specific health risks, though it lays the groundwork for future research on whether microplastics could disrupt placental function during pregnancy.

Polymers
Body Systems

Global proteomic profiling was performed on human placental explants using a single-pot solid-phase-enhanced sample preparation (SP3) workflow followed by data-independent acquisition parallel accumulation–serial fragmentation (DIA-PASEF) on a timsTOF HT mass spectrometer coupled to a nanoElute LC system. Protein identification and quantification were carried out with DIA-NN v2.1.0 against the human reference proteome, using a 1% false discovery rate (FDR), allowing up to two missed cleavages, with carbamidomethylation (Cys) as a fixed modification and methionine oxidation as a variable modification. Match-between-runs was enabled to improve protein quantification across samples. After quality control, samples with insufficient protein yield were excluded, resulting in 6 control, 7 PS1, and 8 PS100 samples. Protein intensities were log-transformed and constant median normalized prior to statistical analysis. Two complementary analysis pipelines were employed: (i) filtering proteins with fewer than five valid values per group followed by missing-value imputation in Perseus, and (ii) analysis of the complete dataset without filtering or imputation. Downstream statistical analyses and visualization were performed in R using in-house scripts together with the ropls, ggpubr, rstatix, ComplexHeatmap, and EnhancedVolcano packages.

Share this paper