Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100–300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

The surface average conformation of poly(ethylene terephthalate) (PET) films was modulated by adjusting the water content in corresponding trifluoroacetic acid (TFA) spin-coating solutions, which was confirmed by surface-sensitive sum frequency generation (SFG) vibrational spectroscopy. Molecular dynamics simulation results showed that the added water molecules partly disrupt the hydrogen bond network between TFA molecules and PET chains and make it possible to preserve the statistical average bulk-like spherical conformation at the film surface during the formation of PET film. Slower surface crystallization kinetics, higher surface glass transition temperature (T g surface ), and suppressed T g -confinement effect were observed when the surface average conformation changed from oriented oblate ellipsoidal conformation to isotropic spherical conformation, showing that surface dynamics is very sensitive to chain conformation.

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