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Membrane lipids play vital roles in small extracellular vesicle (sEV) biogenesis. However, the function of various lipids in the biogenesis of sEVs is still poorly understood. Phosphoinositolphosphates (PIPs), a group of the most critical lipids in vesicle transport, can undergo rapid conversion in response to a variety of cell signals, which in turn influence the generation of vesicles. Due to the challenge in detecting the low amount of PIP content in biological samples, the function of PIPs in sEVs has been insufficiently investigated. Here, we employed an LC‐MS/MS method to detect the levels of PIPs in sEVs. We revealed phosphatidylinositol‐4‐phosphate (PI4P) was the main PI‐monophosphate in macrophage‐derived sEVs. The release of sEVs was regulated in a time‐dependent manner and correlated with the PI4P level during the lipopolysaccharide (LPS) stimulation. In terms of mechanism, within 10 h of LPS treatment, the LPS‐induced production of type I interferon inhibited the expression of PIP‐5‐kinase‐1‐gamma, which increased the PI4P content on multivesicular bodies (MVBs) and recruited RAB10, member RAS oncogene family, to promote sEV generation. When LPS stimulation was extended to 24 h, the heat shock protein family A member 5 (HSPA5) expression level was elevated. PI4P interacted with HSPA5 on the Golgi or endoplasmic reticulum away from MVBs, which disrupted the continuous fast sEV release. In conclusion, the present study demonstrated an inducible sEV release model response to LPS treatment. The inducible release may be due to PI4P regulating the generation of intraluminal vesicles secreted as sEVs.

With the advent of the era of artificial intelligence, a technological revolution has begun in the field of education, which has injected new momentum into moral education. Today, civic education is developing rapidly in countries around the world, while civic education in China is still relatively lagging behind. In China, we need to optimize civic education by building a comprehensive civic education system, implementing the concept of people-oriented, gathering the joint efforts of the whole society, expanding diversified civic education methods, and selecting and enriching the contents of civic education. Artificial intelligence technology enables higher education institutions to carry out civic education, enrich teaching contents, broaden teaching methods, and optimize teaching evaluation. However, technology-related innovations are dualistic in nature, with essential, technical, and value risks that hinder their effectiveness in civic education. This paper proposes strategies for optimization in terms of improving digital core risk response capabilities, establishing reliable technology risk response mechanisms, and maintaining a foundation for civic education response costs. Thus, weakening the risk factors of AI in civic education facilitates the transformation of AI and technology to the advantage of knowledge-based civic education. This paper discusses strategies and methods for optimizing smart education using AI devices and establishes a corresponding smart education platform to help students with their ideological education. Based on the analysis of actual cases, the paper discusses the effectiveness and advantages of the program.

Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce CAST (cross-sample alignment of spatial omics), a deep graph neural network-based method enabling spatial-to-spatial searching and matching at the single-cell level. CAST aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. CAST further allows spatially resolved differential analysis (∆Analysis) to pinpoint and visualize disease-associated molecular pathways and cell–cell interactions and single-cell relative translational efficiency profiling to reveal variations in translational control across cell types and regions. CAST serves as an integrative framework for seamless single-cell spatial data searching and matching across technologies, modalities and sample conditions. CAST is a deep learning-based method that enables across-sample searching and matching based on spatial molecular features and reconstructing spatially resolved single-cell multi-omic profiles, as well as supports downstream differential analysis.

Although messenger RNA (mRNA) has proved effective as a vaccine, its potential as a general therapeutic modality is limited by its instability and low translation capacity. To increase the duration and level of protein expression from mRNA, we designed and synthesized topologically and chemically modified mRNAs with multiple synthetic poly(A) tails. Here we demonstrate that the optimized multitailed mRNA yielded ~4.7–19.5-fold higher luminescence signals than the control mRNA from 24 to 72 h post transfection in cellulo and 14 days detectable signal versus <7 days signal from the control in vivo. We further achieve efficient multiplexed genome editing of the clinically relevant genes Pcsk9 and Angptl3 in mouse liver at a minimal mRNA dosage. Taken together, these results provide a generalizable approach to synthesize capped branched mRNA with markedly enhanced translation capacity. mRNA with engineered poly(A) tails produces prolonged higher levels of protein.

Spatially charting molecular cell types at single-cell resolution across the 3D volume is critical for illustrating the molecular basis of brain anatomy and functions. Single-cell RNA sequencing has profiled molecular cell types in the mouse brain 1 , 2 , but cannot capture their spatial organization. Here we used an in situ sequencing method, STARmap PLUS 3 , 4 , to profile 1,022 genes in 3D at a voxel size of 194 × 194 × 345 nm 3 , mapping 1.09 million high-quality cells across the adult mouse brain and spinal cord. We developed computational pipelines to segment, cluster and annotate 230 molecular cell types by single-cell gene expression and 106 molecular tissue regions by spatial niche gene expression. Joint analysis of molecular cell types and molecular tissue regions enabled a systematic molecular spatial cell-type nomenclature and identification of tissue architectures that were undefined in established brain anatomy. To create a transcriptome-wide spatial atlas, we integrated STARmap PLUS measurements with a published single-cell RNA-sequencing atlas 1 , imputing single-cell expression profiles of 11,844 genes. Finally, we delineated viral tropisms of a brain-wide transgene delivery tool, AAV-PHP.eB 5 , 6 . Together, this annotated dataset provides a single-cell resource that integrates the molecular spatial atlas, brain anatomy and the accessibility to genetic manipulation of the mammalian central nervous system. In situ spatial transcriptomic analysis of more than 1 million cells are used to create a 200-nm-resolution spatial molecular atlas of the adult mouse central nervous system and identify previously unknown tissue architectures.

Artemisinin-resistant malaria parasites have emerged and have been spreading, posing a significant public health challenge. Antimalarial drugs with novel mechanisms of action are therefore urgently needed. In this report, we exploit a “selective starvation” strategy by inhibiting Plasmodium falciparum hexose transporter 1 (PfHT1), the sole hexose transporter in P. falciparum, over human glucose transporter 1 (hGLUT1), providing an alternative approach to fight against multidrug-resistant malaria parasites. The crystal structure of hGLUT3, which shares 80% sequence similarity with hGLUT1, was resolved in complex with C3361, a moderate PfHT1-specific inhibitor, at 2.3-Å resolution. Structural comparison between the present hGLUT3-C3361 and our previously reported PfHT1-C3361 confirmed the unique inhibitor binding-induced pocket in PfHT1. We then designed small molecules to simultaneously block the orthosteric and allosteric pockets of PfHT1. Through extensive structure–activity relationship studies, the TH-PF series was identified to selectively inhibit PfHT1 over hGLUT1 and potent against multiple strains of the blood-stage P. falciparum. Our findings shed light on the next-generation chemotherapeutics with a paradigm-shifting structure-based design strategy to simultaneously target the orthosteric and allosteric sites of a transporter.

Loss-of-function (LoF) mutations of , encoding the GluN2A subunit of N-methyl-D-aspartate receptor (NMDAR), confer a high risk for schizophrenia (SCZ) , yet how they affect diverse brain cell types remains poorly understood. Here, we combined subcellular-resolution spatial omics technologies, STARmap and RIBOmap , to jointly resolve single-cell transcriptomes and translatomes for 3,447 genes in the brains of +/- mice and their wild-type littermates across 538,188 cells. Translational dysregulation was markedly more prominent than transcriptional changes in neurons. Across neuronal subtypes, a set of genes including , , , , , and exhibited translational reduction in a gene dose-dependent fashion, suggesting a connection between NMDAR hypofunction and reduced protein synthesis of downstream synaptic plasticity effectors. In interneurons (particularly parvalbumin interneurons), a strong reduction of translation implies loss of inhibitory function in cortical microcircuits, which has long been hypothesized for SCZ pathophysiology. Non-neuronal cell types including astrocytes, oligodendrocytes, and vascular cells also exhibited region-specific translational changes in neurotransmitter transport, lipid synthesis, myelination, and stress response pathways, some of which co-varied with regional neuron state. Together, our study reveals brain-wide translation dysregulation as a critical mechanism underlying SCZ pathophysiology.

Characterizing the transcriptional and translational gene expression patterns at the single-cell level within their three-dimensional (3D) tissue context is essential for revealing how genes shape tissue structure and function in health and disease. However, most existing spatial profiling techniques are limited to 5-20 μm thin tissue sections. Here, we developed Deep-STARmap and Deep-RIBOmap, which enable 3D quantification of thousands of gene transcripts and their corresponding translation activities, respectively, within 200-μm thick tissue blocks. This is achieved through scalable probe synthesis, hydrogel embedding with efficient probe anchoring, and robust cDNA crosslinking. We first utilized Deep-STARmap in combination with multicolor fluorescent protein imaging for simultaneous molecular cell typing and 3D neuron morphology tracing in the mouse brain. We also demonstrate that 3D spatial profiling facilitates comprehensive and quantitative analysis of tumor-immune interactions in human skin cancer.