Enzymatic Preparation, In-Depth Molecular Analysis, and In Vitro Digestion Simulation of Palmitoleic Acid (ω-7)-Enriched Fish Oil Triacylglycerols.

In this study, an enzymatic reaction was developed for synthesizing pure triacylglycerols (TAG) with a high content of palmitoleic acid (POA) using fish byproduct oil. The characteristics of synthesized structural TAGs rich in POA (POA-TAG) were analyzed in detail through ultrahigh-performance liquid chromatography Q Exactive orbitrap mass spectrometry. Optimal conditions were thoroughly investigated and determined for reaction systems, including the use of Lipozyme TL IM and Novozym 435, 15 wt % lipase loading, substrate mass ratio of 1:3, and water content of 2.5 and 0.5 wt %, respectively, resulting in yields of 67.50 and 67.45% for POA-TAG, respectively. Multivariate statistical analysis revealed that TAG 16:1/16:1/20:4, TAG 16:1/16:1/16:1, TAG 16:1/16:1/18:1, and TAG 16:0/16:1/18:1 were the main variables in Lipozyme TL IM and Novozym 435 enzyme-catalyzed products under different water content conditions. Finally, the fate of POA-TAG across the gastrointestinal tract was simulated using an in vitro digestion model. The results showed that the maximum release of free fatty acids and apparent rate constants were 71.44% and 0.0347 s-1, respectively, for POA-TAG lipids, and the physical and structural characteristics during digestion depended on their microenvironments. These findings provide a theoretical basis for studying the rational design of POA-structural lipids and exploring the nutritional and functional benefits of POA products.

DeepS: Accelerating 3D Mass Spectrometry Imaging via a Deep Neural Network.

Mass spectrometry imaging (MSI) is a powerful methodology that enables the visualization of the spatial distribution of biomolecules, including lipids, peptides, and proteins, from biological tissue sections. While two-dimensional (2D) MSI has been widely reported in various applications, three-dimensional (3D) MSI can enable the mapping of biomolecule distribution in complex biological structures (e.g., organs) with an added dimension. However, traditional 3D MSI techniques are time-consuming since 3D MS images are constructed from 2D MSI analyses of a series of tissue sections. In this study, we propose a 3D MSI workflow, termed DeepS, which uses a 3D sparse sampling network (3D-SSNet) and a sparse sampling strategy to significantly accelerate 3D MSI analyses. Sparsely sampled tissue sections are reconstructed using 3D-SSNet, yielding results comparable to those using full sampling MSI, even at a sampling ratio of 20-30%. The workflow performed well when applied to 3D imaging of a mouse brain with Alzheimer's disease, and combined with transfer learning, it is successfully used for the 3D MSI analyses of more heterogeneous samples, e.g., a mouse brain with glioblastoma and a mouse kidney.