Biology subjects

wang, S.

Publications and source records attributed to wang, S..

4 recordsLinked to original sources

bioRxiv2026

Splicing-guaranteed artificial introns enable efficient homology-directed repair at internal exons and streamlined cell engineering

Selection-marker-assisted homology-directed repair (HDR) efficiently edits coding sequence termini but is poorly suited for internal exons. Here, we developed artificial reporter intron-based HDR (ARI-HDR), which inserts a selectable intron into target exons to enable precise internal-exon editing. Although designed for scarless removal by splicing, ARIs frequently exhibit inaccurate splicing. Using a random mutagenesis reporter assay, we identified intron upstream sequences (IUS) as key determinants of splicing fidelity and screened degenerate IUS variants to obtain reliably spliced ARI donors. ARI-HDR successfully introduced site-specific mutations into five human genes. Combining long single-stranded DNA donors with conversion-tract optimization increased editing efficiency to over 80%. This efficiency enabled 'Dirty-Editing,' a streamlined workflow that generates precisely edited mixed-cell populations without single-cell cloning. Together, ARI-HDR extends selection-based enrichment to internal exons across protein-coding genes, while our splicing-optimized intron design offers a broadly applicable solution for artificial intron-based technologies.

molecular biology↗

bioRxiv2021

Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single cell sequencing

BackgroundImmune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited. MethodsIn this study, we compared the single-cell transcriptomes of 77{square}957 immune cells from 12 species using single-cell RNA-sequencing (scRNA-seq). Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells. ResultsResults revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular crosstalk and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells. ConclusionsThis study is the first to provide a comprehensive analysis of the cross-species single-cell atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders.

cell biology↗

bioRxiv2020

Single cell atlas of trisomy 21 cerebral cortex

Down syndrome (DS) is one of the most common human birth defects caused by trisomy 21 (T21), leading to a variety of cognitive impairments. The cellular composition of human brain has been explored using single cell RNA sequencing in both physiological and pathological conditions. However, the cellular heterogeneity of human brain with chromosome aneuploidy is largely unknown. Here, we profiled the transcriptome of 36046 cells in cerebral cortex of T21 human fetus, covering frontal lobe, parietal lobe, occipital lobe and temporal lobe. Intriguingly, we detected several genes positively associated with neurons maturation was dysregulated in T21 frontal cortex (HIC2, POU2F2, ZGLP1 and FOXK1). To share, explore and utilized the data resources of T21 cerebral cortex, we developed a comprehensive platform named T21atlas, composing of two functional modules (T21cluster and T21talk). Overall, our study provides, as far as we know, the first single cell atlas for T21 cerebral cortex, which could promote our understanding of the molecular mechanism of DS at an unprecedented resolution and could potentially facilitate the development of novel clinical therapeutics against T21.

neuroscience↗

bioRxiv2019

GRep: Gene Set Representation via Gaussian Embedding

Molecular interaction networks are our basis for understanding functional interdependencies among genes. Network embedding approaches analyze these complicated networks by representing genes as low-dimensional vectors based on the network topology. These low-dimensional vectors have recently become the building blocks for a larger number of systems biology applications. Despite the success of embedding genes in this way, it remains unclear how to effectively represent gene sets, such as protein complexes and signaling pathways. The direct adaptation of existing gene embedding approaches to gene sets cannot model the diverse functions of genes in a set. Here, we propose GRep, a novel gene set embedding approach, which represents each gene set as a multivariate Gaussian distribution rather than a single point in the low-dimensional space. The diversity of genes in a set, or the uncertainty of their contribution to a particular function, is modeled by the covariance matrix of the multivariate Gaussian distribution. By doing so, GRep produces a highly informative and compact gene set representation. Using our representation, we analyze two major pharmacogenomics studies and observe substantial improvement in drug target identification from expression-derived gene sets. Overall, the GRep framework provides a novel representation of gene sets that can be used as input features to off-the-shelf machine learning classifiers for gene set analysis.

bioinformatics↗