Cultured meat needs edible bio-scaffolds that provide not only a growth milieu for muscle and adipose cells, but also biomimetic stiffness and tissue-sculpting topography. Current meat-engineering technologies struggle to achieve scalable cell production, efficient cell differentiation, and tissue maturation in one single culture system. Here we propose an autoclaving strategy to transform common vegetables into muscle- and adipose-engineering scaffolds, without undergoing conventional plant decellularization. We selected vegetables with natural anisotropic and isotropic topology mimicking muscle and adipose microstructures respectively. We further adjusted vegetable stiffness by autoclaving, to emulate the mechanical properties of animal tissues. Autoclaved vegetables preserve rich cell-affinitive moieties, yielding a good cell culture effect with simplified processing. Autoclaved Chinese chive and Shiitake mushroom with anisotropic micro-patterns support the scalable expansion of muscle cells, improved cell alignment and myogenesis. Autoclaved isotropic loofah encourages adipocyte proliferation and lipid accumulation. Our engineered muscle- and fat-on-vegetables can further construct meat stuffing or layered meat chips. Autoclaved vegetables possess tissue-mimicking stiffness and topology, and bring biochemical benefits, operational ease, cost reduction and bioreactor compatibility. Without needing decellularization, these natural biomaterials may see scale-up applications in meat analog bio-fabrication.

Depression is a psychiatric disorder that presents with a persistent depressed mood as the main clinical feature and is accompanied by cognitive impairment. Changes in neuroplasticity and neurogenesis greatly affect depression. Without genetic changes, epigenetic mechanisms have been shown to function by regulating gene expression during the body’s adaptation to stress. Studies in recent years have shown that as important regulatory factors in epigenetic mechanisms, microRNAs (miRNAs) play important roles in the development and progression of depression through the regulation of protein expression. Herein, we review the mechanisms of miRNA-mediated neuroplasticity in depression and discus synaptic structural plasticity, synaptic functional plasticity, and neurogenesis. Furthermore, we found that miRNAs regulate neuroplasticity through several signalling pathways to affect cognitive functions. However, these pathways do not work independently. Therefore, we try to identify synergistic correlations between miRNAs and multiple signalling pathways to broaden the potential pathogenesis of depression. In addition, in the future, dual-function miRNAs (protection/injury) are promising candidate biomarkers for the diagnosis of depression, and their regulated genes can potentially be used as target genes for the treatment of depression.

Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography−mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research.