Chemical tools for dissecting cell division

Deterministic Single Cell Encapsulation in Asymmetric Microenvironments to Direct Cell Polarity

Various signals in tissue microenvironments are often unevenly distributed around cells. Cellular responses to asymmetric cell-matrix adhesion in a 3D space remain generally unclear and are to be studied at the single-cell resolution. Here, the authors developed a droplet-based microfluidic approach to manufacture a pure population of single cells in a microscale layer of compartmentalized 3D hydrogel matrices with a tunable spatial presentation of ligands at the subcellular level. Cells elongate with an asymmetric presentation of the integrin adhesion ligand Arg-Gly-Asp (RGD), while cells expand isotropically with a symmetric presentation of RGD. Membrane tension is higher on the side of single cells interacting with RGD than on the side without RGD. Finite element analysis shows that a non-uniform isotropic cell volume expansion model is sufficient to recapitulate the experimental results. At a longer timescale, asymmetric ligand presentation commits mesenchymal stem cells to the osteogenic lineage. Cdc42 is an essential mediator of cell polarization and lineage specification in response to asymmetric cell-matrix adhesion. This study highlights the utility of precisely controlling 3D ligand presentation around single cells to direct cell polarity for regenerative engineering and medicine.

Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network

Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.