The metabolic heterogeneity, and metabolic interplay between cells have been known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely scFEA (single cell Flux Estimation Analysis), to infer cell-wise fluxome from single cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multi-layer neural networks to capitulate the non-linear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq dataset with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this dataset demonstrated the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics datasets and identified context and cell group specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analysis including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity, and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments, and proficiency testing on standardized cell line–derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas below this limit detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false-negatives) were more common than erroneous candidates (false-positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best-practice guidelines and provides a resource for precision oncology.

Staufen1-mediated mRNA decay (SMD) degrades mRNAs that harbor a Staufen1-binding site (SBS) in their 39 untranslated regions (UTRs). Human SBSs can form by intermolecular base-pairing between a 39 UTR Alu element and an Alu element within a long noncoding RNA (lncRNA) called a 1 ⁄ 2 -sbsRNA. Since Alu elements are confined to primates, it was unclear how SMD occurs in rodents. Here we identify mouse mRNA 39 UTRs and lncRNAs that contain a B1, B2, B4, or identifier (ID) element. We show that SMD occurs in mouse cells via mRNA-lncRNA base-pairing of partially complementary elements and that mouse 1 ⁄ 2 -sbsRNA (m 1 ⁄ 2 -sbsRNA)-triggered SMD regulates C2C12 cell myogenesis. Our findings define new roles for lncRNAs as well as B and ID short interspersed elements (SINEs) in mice that undoubtedly influence many developmental and homeostatic pathways.

The antidiabetic effects of Mung bean sprout (MBS) extracts and Mung bean seed coat (MBSC) extracts were investigated in type 2 diabetic mice. Male KK-A (y) mice and C57BL/6 mice were used in this study. In KK-A (y) mice, the blood glucose, plasma C-peptide, glucagon, total cholesterol, triglyceride, and blood urea nitrogen (BUN) levels were significantly higher than those in the C57BL/6 mice ( P < 0.001, P < 0.001, P < 0.01, P < 0.001, P < 0.01, and P < 0.01). In addition, KK-A (y) mice showed an obvious decrease in insulin immunoreactivity in pancreas as well. MBS and MBSC were orally administrated to KK-A (y) mice for 5 weeks. It was found that MBS (2 g/kg) and MBSC (3 g/kg) lowered blood glucose, plasma C-peptide, glucagon, total cholesterol, triglyceride, and BUN levels and at the same time markedly improved glucose tolerance and increased insulin immunoreactive levels. These results suggest that MBS and MBSC exert an antidiabetic effect in type 2 diabetic mice.