LectoScape: A Highly Multiplexed Imaging Platform for Glycome Analysis and Biomedical Diagnosis.

Glycosylation, a fundamental biological process, involves the attachment of glycans to proteins, lipids, and RNA, and it plays a crucial role in various biological pathways. It is of great significance to obtain the precise spatial distribution of glycosylation modifications at the cellular and tissue levels. Here, we introduce LectoScape, an innovative method enabling detailed imaging of tissue glycomes with up to 1 µm resolution through image mass cytometry (IMC). This method utilizes 12 distinct, nonoverlapping lectins selected via microarray technology, enabling the multiplexed detection of a wide array of glycans. Furthermore, we developed an efficient labeling strategy for these lectins. Crucially, our approach facilitates the concurrent imaging of diverse glycan motifs, including N-glycan and O-glycan, surpassing the capabilities of existing technologies. Using LectoScape, we have successfully delineated unique glycan structures in various cell types, enhancing our understanding of the glycan distribution across human tissues. Our method has identified specific glycan markers, such as α2,3-sialylated Galß1, 3GalNAc in O-glycan, and terminal GalNAc, as diagnostic indicators for cervical intraepithelial neoplasia. This highlights the potential of LectoScape in cancer diagnostics through the detection of abnormal glycosylation patterns.

Nucleation-Inhibited Emulsion Interfacial Assembled Polydopamine Microvesicles as Artificial Antigen-Presenting Cells.

Albeit microemulsion systems have emerged as efficient platforms for fabricating tunable nano/microstructures, lack of understanding on the emulsion-interfacial assembly hindered the control of fabrication. Herein, a nucleation-inhibited microemulsion interfacial assembly method is proposed, which deviates from conventional interfacial nucleation approaches, for the synthesis of polydopamine microvesicles (PDA MVs). These PDA MVs exhibit an approximate diameter of 1 µm, showcasing a pliable structure reminiscent of cellular morphology. Through modifications of antibodies on the surface of PDA MVs, their capacity as artificial antigen presentation cells is evaluated. In comparison to solid nanoparticles, PDA MVs with cell-like structures show enhanced T-cell activation, resulting in a 1.5-fold increase in CD25 expression after 1 day and a threefold surge in PD-1 positivity after 7 days. In summary, the research elucidates the influence of nucleation and interfacial assembly in microemulsion polymerization systems, providing a direct synthesis method for MVs and substantiating their effectiveness as artificial antigen-presenting cells.

Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes.

Artificial intelligence (AI) approaches in cancer analysis typically utilize a 'one-size-fits-all' methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment.

Development of a Library of Disulfide Bond-Containing Cationic Lipids for mRNA Delivery.

Lipid nanoparticles (LNPs) are the commonly used delivery tools for messenger RNA (mRNA) therapy and play an indispensable role in the success of COVID-19 mRNA vaccines. Ionizable cationic lipids are the most important component in LNPs. Herein, we developed a series of new ionizable lipids featuring bioreducible disulfide bonds, and constructed a library of lipids derived from dimercaprol. LNPs prepared from these ionizable lipids could be stored at 4 °C for a long term and are non-toxic toward HepG2 and 293T cells. In vivo experiments demonstrated that the best C4S18A formulations, which embody linoleoyl tails, show strong firefly luciferase (Fluc) mRNA expression in the liver and spleen via intravenous (IV) injection, or at the local injection site via intramuscular injection (IM). The newly designed ionizable lipids can be potentially safe and high-efficiency nanomaterials for mRNA therapy.