Biology subjects

Zhao, Y.-L.

Publications and source records attributed to Zhao, Y.-L..

4 recordsLinked to original sources

bioRxiv2023

STASCAN deciphers fine-resolution cell-distribution maps in spatial transcriptomics by deep learning

BackgroundThe spatial transcriptomics (ST) technologies have been widely applied to decode the spatial distribution of cells by resolving gene expression profiles in tissues. However, a fine-resolved spatial cell map is still limited by algorithmic tools and sequencing techniques. ResultsHere we develop a novel deep learning approach, STASCAN, which could define the spatial cellular distribution of both captured and uncharted areas by cell feature learning that combines gene expression profiles and histology images. STASCAN additionally adopts optional transfer learning and pseudo-labeling methods to improve the accuracy of the cell-type prediction from images. We have successfully applied STASCAN to enhance cell resolution, and revealed finer organizational structures across diverse datasets from various species and tissues generated from 10x Visium technology. STASCAN improves cell resolution of Schmidtea mediterranea datasets by six times and reconstructs more detailed 3D cell-type models. Furthermore, STASCAN could accurately pinpoint the boundaries of distinct cell layers in human intestinal tissue, specifically identify a micrometer-scale smooth muscle bundle structure in consistent with anatomic insights in human lung tissue, and redraw the spatial structural variation with enhanced cell patterns in human myocardial infarction tissue. Additionally, through STASCAN on embryonic mouse brain datasets generated by DBiT-derived MISAR-seq technology, the increased cellular resolution and distinct anatomical tissue domains with cell-type niches are revealed. Collectively, STASCAN is compatible with different ST technologies and has notable advantages in generating cell maps solely from histology images, thereby enhancing the spatial cellular resolution. ConclusionsIn short, STASCAN displays significant advantages in deciphering higher-resolution cellular distribution, resolving enhanced organizational structures and demonstrating its potential applications in exploring cell-cell interactions within the tissue microenvironment.

bioinformatics↗

bioRxiv2021

Comprehensive analysis of RNA-seq and whole genome sequencing data reveals no evidence for SARS-CoV-2 integrating into host genome

SARS-CoV-2, as the causation of severe epidemic of COVID-19, is one kind of positive single-stranded RNA virus with high transmissibility. However, whether or not SARS-CoV-2 can integrate into host genome needs thorough investigation. Here, we performed both RNA sequencing (RNA-seq) and whole genome sequencing on SARS-CoV-2 infected human and monkey cells, and investigated the presence of host-virus chimeric events. Through RNA-seq, we did detect the chimeric host-virus reads in the infected cells. But further analysis using mixed libraries of infected cells and uninfected zebrafish embryos demonstrated that these reads are falsely generated during library construction. In support, whole genome sequencing also didnt identify the existence of chimeric reads in their corresponding regions. Therefore, the evidence for SARS-CoV-2s integration into host genome is lacking. One-Sentence SummarySARS-CoV-2 does not integrate into host genome through whole genome sequencing.

genomics↗

bioRxiv2019

Single-base mapping of m6A by an antibody-independent method

N6-methyladenosine (m6A) is one of the most abundant mRNA modifications in eukaryotes, involved in various pivotal processes of RNA metabolism. The most popular high-throughput m6A identification method depends on the anti-m6A antibody but suffers from poor reproducibility and limited resolution. Exact location information is of great value for understanding the dynamics, machinery and functions of m6A. Here we developed a precise and high-throughput antibody-independent m6A identification method based on the m6A-sensitive RNA endoribonuclease (m6A-sensitive RNA-Endoribonuclease-Facilitated sequencing or m6A-REF-seq). Whole-transcriptomic, single-base m6A maps generated by m6A-REF-seq quantitatively displayed an explicit distribution pattern with enrichment on stop codons. Independent methods were used to validate the methylation status and abundance of individual m6A sites, confirming the high reliability and accuracy of m6A-REF-seq. We applied this method on five tissues from human, mouse and rat, showing that m6A sites were conserved with single nucleotide specificity and tend to cluster among species.

genetics↗

bioRxiv2018

The Evolutionary Landscape of Pan-Cancer Drives Clinical Aggression

Although cancer mechanisms differ from occurrence and development, some of them have similar oncogenesis, which leads to similar clinical phenotypes. Most existing genotyping studies look at \"omics\" data, but intentionally or unintentionally avoided that cancer is a time-dependent evolutionary process, biologically represented by the time evolution of tumor clones. We used the Bayesian mutation landscape approach to reconstruct the evolutionary process of cancer by acquiring somatic mutation data consisting of 21 cancer types. Four representative evolution patterns of pan-cancer have been discovered: trees, chaos, biconvex, and Cambrian, and a strong correlation between these four evolutionary patterns and clinical aggressivity. We further explained the characteristics of the corresponding biological systems in the evolution of pan cancer by analyzing the function of differentially expressed protein-protein interaction networks. Our results explained the difference in clinical aggressivity between cancer evolution patterns from the evolution of tumor clones and exposed the functional mechanism behind.

cancer biology↗