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Biology subjects

Lim, J. C.

Publications and source records attributed to Lim, J. C..

2 recordsLinked to original sources

50-nm gas-filled protein nanostructures to enable the access of lymphatic cells by ultrasound technologies

Ultrasound imaging and ultrasound-mediated gene and drug delivery are rapidly advancing diagnostic and therapeutic methods; however, their use is often limited by the need of microbubbles, which cannot transverse many biological barriers due to their large size. Here we introduce 50-nm gas-filled protein nanostructures derived from genetically engineered gas vesicles that we referred to as 50nmGVs. These diamond-shaped nanostructures have hydrodynamic diameters smaller than commercially available 50-nm gold nanoparticles and are, to our knowledge, the smallest stable, free-floating bubbles made to date. 50nmGVs can be produced in bacteria, purified through centrifugation, and remain stable for months. Interstitially injected 50nmGVs can extravasate into lymphatic tissues and gain access to critical immune cell populations, and electron microscopy images of lymph node tissues reveal their subcellular location in antigen-presenting cells adjacent to lymphocytes. We anticipate that 50nmGVs can substantially broaden the range of cells accessible to current ultrasound technologies and may generate applications beyond biomedicine as ultrasmall stable gas-filled nanomaterials.

synthetic biology↗

SpatialDEG: Identification of differentially expressed genes by leveraging spatial information in spatially resolved transcriptomic studies

MotivationSpatially resolved transcriptomics (SRT) technologies have been developed to simultaneously profile gene expression while retaining physical information. To explore differentially expressed genes using SRT in the context of various conditions, statistical methods are needed to perform spatial differential expression analysis. ResultsWe propose that a new probabilistic framework, spatialDEG, can perform differential expression analysis by leveraging spatial information on gene expression with spatial information. SpatialDEG utilizes the average information algorithm and can be scalable to tens of thousands of genes. Comprehensive simulations demonstrated that spatialDEG can identify genes differentially expressed in tissues across different conditions with a controlled type-I error rate. We further applied spatialDEG to analyze datasets for human dorsolateral prefrontal cortex and mouse whole liver. AvailabilityThe R package spatialDEG can be downloaded from https://github.com/Shufeyangyi2015310117/spatialDEG.

genetics↗