Biology subjects

Tu, C.-C.

Publications and source records attributed to Tu, C.-C..

3 recordsLinked to original sources

bioRxiv2022

Reliable and accurate gene expression quantification with subpopulation structure-aware constraints for single-cell RNA sequencing

BackgroundSingle-cell RNA sequencing (scRNA-seq) analysis analyzes the type and state of individual cells by estimating the gene expression of each cell and enables researchers to study the biological phenomena that cannot be observed in bulk RNA sequencing. MotivationHowever, the current scRNA-seq quantification tools estimate the gene expression profile of each cell independently, ignoring the fact that there are multiple cell types in the scRNA-seq data and the expression level should be highly correlated with the cell type. Since scRNA-seq suffers from a low sequencing depth, the conventional strategy leads to a high proportion of missing values in the gene expression profile, obscuring the biological characteristics of cell subpopulations and further impacting the correctness of the subsequent downstream analysis. ResultsIn this study, we proposed Quasic, a novel scRNA-seq quantification pipeline which examines the potential cell subpopulation information during quantification, and uses the information to calculate the gene expression level. Using the human peripheral blood mononuclear cells and the simulated doublet dataset, we verified that Quasic not only correctly reinforced the cell signatures, but also identified the corresponding cell subpopulations and biological pathways more accurately. In addition, we also applied Quasic to the breast cancer cell line dataset (MCF-7), and successfully identified more potentially therapeutic resistant cells of which characteristics are consistent with that from previous studies. ConclusionsThe proposed pipeline can let the gene expression profile of each cell be more consistent with the corresponding subpopulation, making the biological features unique to the subpopulation more apparent and convenient for analysis. By using Quasic, researchers can effectively extract the desired cell subpopulation information from their sampled cells, enable them to perform cell subpopulation-related studies more accurately.

bioinformatics↗

bioRxiv2022

Equilibrative Nucleoside Transporter 3 is an IFN-stimulated Gene that Facilitates Viral Genome Release

An increasing body of evidence emphasizes the role of metabolic reprogramming in immune cells to fight off infections. However, little is known about the regulation of metabolite transporters that facilitate and support metabolic demands. In this study, we found that equilibrative nucleoside transporter 3 (ENT3) expression is part of the innate immune response, and is rapidly upregulated upon bacterial and viral infection. The transcription of ENT3 is directly under the regulation of IFN-induced signaling, positioning this metabolite transporter as an Interferon-stimulated gene (ISG). Moreover, we unveil that several viruses, including SARS-CoV2, require ENT3 to facilitate their entry into the cytoplasm. The removal or suppression of ENT3 expression is sufficient to significantly decrease viral replication in vitro and in vivo.

immunology↗

bioRxiv2021

Estimating Intraclonal Heterogeneity and Subpopulation Changes from Perturbational Bulk Gene Expression Profiles in LINCS L1000 CMap by Premnas

BackgroundThe connectivity among signatures upon perturbations curated in the CMap library provides a valuable resource for understanding therapeutic pathways and biological processes associated with the drugs and diseases. MotivationHowever, due to the nature of bulk-level expression profiling by the L1000 assay, intraclonal heterogeneity and subpopulation compositional change that could contribute to the responses to perturbations are largely neglected, hampering the interpretability and reproducibility of the connections. ResultsIn this work, we proposed a computational framework, Premnas, to estimate the abundance of undetermined subpopulations from L1000 profiles in CMap directly according to an ad hoc subpopulation representation learned from a well-normalized batch of single-cell RNA-seq datasets by the archetypal analysis. By recovering the information of subpopulation changes upon perturbation, the potentials of searching for drug cocktails and drug-resistant/susceptible subpopulations with CMap L1000 were further explored and examined. ConclusionsThe proposed framework enables a new perspective to understand the connectivity among cellular signatures and expands the scope of the CMAP and other similar perturbation datasets limited by the bulk profiling technology. The executable and source code of Premnas is freely available at https://github.com/jhhung/Premnas. HighlightsO_LIA computational framework (i.e., Premnas) for learning subpopulation characteristics by the archetypal analysis and deconvoluting bulk expression profiles using the digital cytometry into subpopulation composition was proposed and validated. C_LIO_LIIntraclonal heterogeneity and subpopulation changes upon different perturbagen treatments in LINCS CMap L1000 datasets were estimated by Premnas. C_LIO_LIWith Premnas, we introduced a new strategy of finding effective drug cocktails and further linked the drug-resistant subpopulation found in CMap L1000 to a known drug-resistant clone (i.e., the pre-adapted [PA] cell). C_LIO_LITo our best knowledge, this work is the first attempt to provide a new subpopulation perspective to CMap database. C_LIO_LIWe believe Premnas can be applied to all the perturbation datasets, of which intraclonal/intratumoral heterogeneity was concealed by the bulk profiling and hereafter provides a new dimension of interpreting the connectivity. C_LI

bioinformatics↗