scholarly journals Dynamic Analysis of Alternative Polyadenylation from Single-Cell RNA-Seq (scDaPars) Reveals Cell Subpopulations Invisible to Gene Expression Analysis

2020 ◽  
Author(s):  
Yipeng Gao ◽  
Lei Li ◽  
Christopher I. Amos ◽  
Wei Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Single Cells ◽  
Single Gene ◽  
Simulated Data ◽  
Alternative Polyadenylation ◽  
Cellular Heterogeneity ◽  
Cell Subpopulations

AbstractAlternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an “APA-aware” manner. Here, we introduce scDaPars, a bioinformatics algorithm to accurately quantify APA events at both single-cell and single-gene resolution using standard scRNA-seq data. Validations in both real and simulated data indicate that scDaPars can robustly recover missing APA events caused by the low amounts of mRNA sequenced in single cells. When applied to cancer and human endoderm differentiation data, scDaPars not only revealed cell-type-specific APA regulation but also identified cell subpopulations that are otherwise invisible to conventional gene expression analysis. Thus, scDaPars will enable us to understand cellular heterogeneity at the post-transcriptional APA level.

scholarly journals Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes

2019 ◽  
Vol 374 (1786) ◽  
pp. 20190098 ◽  
Author(s):  
Chuan Ku ◽  
Arnau Sebé-Pedrós
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interspecific Interactions ◽  
Single Cells ◽  
Life Cycles ◽  
Modern Biology ◽  
Microbial Eukaryotes ◽  
Eukaryotic Microorganisms

Understanding the diversity and evolution of eukaryotic microorganisms remains one of the major challenges of modern biology. In recent years, we have advanced in the discovery and phylogenetic placement of new eukaryotic species and lineages, which in turn completely transformed our view on the eukaryotic tree of life. But we remain ignorant of the life cycles, physiology and cellular states of most of these microbial eukaryotes, as well as of their interactions with other organisms. Here, we discuss how high-throughput genome-wide gene expression analysis of eukaryotic single cells can shed light on protist biology. First, we review different single-cell transcriptomics methodologies with particular focus on microbial eukaryote applications. Then, we discuss single-cell gene expression analysis of protists in culture and what can be learnt from these approaches. Finally, we envision the application of single-cell transcriptomics to protist communities to interrogate not only community components, but also the gene expression signatures of distinct cellular and physiological states, as well as the transcriptional dynamics of interspecific interactions. Overall, we argue that single-cell transcriptomics can significantly contribute to our understanding of the biology of microbial eukaryotes. This article is part of a discussion meeting issue ‘Single cell ecology’.

scholarly journals Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

2017 ◽  
Vol 27 (11) ◽  
pp. 1783-1794 ◽  
Author(s):  
Jiaxu Wang ◽  
Piroon Jenjaroenpun ◽  
Akshay Bhinge ◽  
Vladimir Espinosa Angarica ◽  
Antonio Del Sol ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Distinct Cell ◽  
Human Neurons ◽  
Cell Gene Expression ◽  
Cell Subpopulations ◽  
Cell Gene

scholarly journals Microdroplet-based one-step RT-PCR for ultrahigh throughput single-cell multiplex gene expression analysis and rare cell detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jennifer Ma ◽  
Gary Tran ◽  
Alwin M. D. Wan ◽  
Edmond W. K. Young ◽  
Eugenia Kumacheva ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Single Cells ◽  
Cell Populations ◽  
Cell Detection ◽  
Rt Pcr ◽  
One Step ◽  
Cell Gene

AbstractGene expression analysis of individual cells enables characterization of heterogeneous and rare cell populations, yet widespread implementation of existing single-cell gene analysis techniques has been hindered due to limitations in scale, ease, and cost. Here, we present a novel microdroplet-based, one-step reverse-transcriptase polymerase chain reaction (RT-PCR) platform and demonstrate the detection of three targets simultaneously in over 100,000 single cells in a single experiment with a rapid read-out. Our customized reagent cocktail incorporates the bacteriophage T7 gene 2.5 protein to overcome cell lysate-mediated inhibition and allows for one-step RT-PCR of single cells encapsulated in nanoliter droplets. Fluorescent signals indicative of gene expressions are analyzed using a probabilistic deconvolution method to account for ambient RNA and cell doublets and produce single-cell gene signature profiles, as well as predict cell frequencies within heterogeneous samples. We also developed a simulation model to guide experimental design and optimize the accuracy and precision of the assay. Using mixtures of in vitro transcripts and murine cell lines, we demonstrated the detection of single RNA molecules and rare cell populations at a frequency of 0.1%. This low cost, sensitive, and adaptable technique will provide an accessible platform for high throughput single-cell analysis and enable a wide range of research and clinical applications.

scholarly journals Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

2010 ◽  
Vol 18 (4) ◽  
pp. 675-685 ◽  
Author(s):  
Guoji Guo ◽  
Mikael Huss ◽  
Guo Qing Tong ◽  
Chaoyang Wang ◽  
Li Li Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Cell Fate Decisions ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals An Integrated Preprocessing Approach for Exploring Single-Cell Gene Expression in Rare Cells

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Junyi Shang ◽  
David Welch ◽  
Manuela Buonanno ◽  
Brian Ponnaiya ◽  
Guy Garty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Bystander Effect ◽  
Kinase Inhibitor ◽  
Digital Pcr ◽  
Lung Fibroblasts ◽  
Gene Expressions ◽  
Rare Cells

AbstractExploring the variability in gene expressions of rare cells at the single-cell level is critical for understanding mechanisms of differentiation in tissue function and development as well as for disease diagnostics and cancer treatment. Such studies, however, have been hindered by major difficulties in tracking the identity of individual cells. We present an approach that combines single-cell picking, lysing, reverse transcription and digital polymerase chain reaction to enable the isolation, tracking and gene expression analysis of rare cells. The approach utilizes a photocleavage bead-based microfluidic device to synthesize and deliver stable cDNA for downstream gene expression analysis, thereby allowing chip-based integration of multiple reactions and facilitating the minimization of sample loss or contamination. The utility of the approach was demonstrated with QuantStudio digital PCR by analyzing the radiation and bystander effect on individual IMR90 human lung fibroblasts. Expression levels of the Cyclin-dependent kinase inhibitor 1a (CDKN1A), Growth/differentiation factor 15 (GDF15), and Prostaglandin-endoperoxide synthase 2 (PTGS2) genes, previously shown to have different responses to direct and bystander irradiation, were measured across individual control, microbeam-irradiated or bystander IMR90 cells. In addition to the confirmation of accurate tracking of cell treatments through the system and efficient analysis of single-cell responses, the results enable comparison of activation levels of different genes and provide insight into signaling pathways within individual cells.

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