This review seeks to provide coverage on recent advances in catalytic enantioselective halofunctionalization of alkenes. The aim is to give an overview of various reports, highlighting the new reaction types and strategies developed during the past two years. The scope and challenges of intra- and intermolecular reaction variants are discussed as well.

Catalytic bromolactonization of long-chain olefinic acids resulting in the efficient synthesis of medium-sized lactones is reported using a zwitterionic catalyst and stoichiometric N-bromosuccinimide halogen source. The reaction was found to be more efficient at 0 °C than at room temperature, which could be attributed to the temperature dependence of the zwitterionic catalyst.

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A highly facile, efficient, and enantioselective bromolactamization of olefinic amides was effected by a carbamate catalyst and ethanol additive. The amide substrates underwent N-cyclization predominantly to give a diverse range of enantioenriched bromolactam products containing up to two stereogenic centers.

Motivation Emerging single-cell RNA sequencing (scRNA-seq) technology empowers biological research at cellular level. One of the most crucial scRNA-seq data analyses is clustering single cells into subpopulations. However, the high variability, high sparsity, and high dimensionality of scRNA-seq data pose lots of challenges for clustering analysis. Although many single-cell clustering methods have been recently developed, few of them fully exploit latent relationship among cells, thus leading to suboptimal clustering results. Results Here, we propose a novel unsupervised clustering method, scGAC (single-cell Graph Attentional Clustering), for scRNA-seq data. scGAC firstly constructs a cell graph and refines it by network denoising. Then it learns clustering-friendly representation of cells through a graph attentional autoencoder, which propagates information across cells with different weights and captures latent relationship among cells. Finally, scGAC adopts a self-optimizing method to obtain the cell clusters. Experiments on 16 real scRNA-seq datasets show that scGAC achieves excellent performance and outperforms existing state-of-art single-cell clustering methods. Availability Python implementation of scGAC is available at Github (https://github.com/Joye9285/scGAC) and Figshare (https://figshare.com/articles/software/scGAC/19091348). Supplementary information Supplementary data are available at Bioinformatics online.

A light-switchable transgene system called LightOn gene expression system could regulate gene expression with a high on/off ratio under blue light, and have great potential for spatiotemporally controllable gene expression. We developed a nanoparticle drug delivery system (NDDS) to achieve tumor microenvironment-responsive and targeted delivery of diphtheria toxin A (DTA) fragment-encoded plasmids to tumor sites. The expression of DTA was induced by exposure to blue light. Nanoparticles composed of polyethylenimine and vitamin E succinate linked by a disulfide bond, and PEGylated hyaluronic acid modified with RGD peptide, accumulated in tumor tissues and were actively internalized into 4T1 cells via dual targeting to CD44 and α v β 3 receptors. The LightOn gene expression system was able to control target protein expression through regulation of the intensity or duration of blue light exposure. In vitro studies showed that light-induced DTA expression reduced 4T1 cell viability and induced apoptosis. Furthermore, the LightOn gene expression system enabled spatiotemporal control of the expression of DTA in a mouse 4T1 tumor xenograft model, which resulted in excellent antitumor effects, reduced tumor angiogenesis, and no systemic toxicity. The combination of the LightOn gene expression system and NDDS may be an effective strategy for treatment of breast cancer.

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