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single cell RNA sequencing,scRNA-seq

Alba Rodriguez-Meira, Gemma Buck, Sally-Ann Clark, Benjamin J Povinelli, Veronica Alcolea, Eleni Louka, Simon McGowan, Angela Hamblin, Nikolaos Sousos, Nikolaos Barkas, Alice Giustacchini, Bethan Psaila, Sten Eirik W Jacobsen, Supat Thongjuea, Adam J Mead
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for resolving transcriptional heterogeneity. However, its application to studying cancerous tissues is currently hampered by the lack of coverage across key mutation hotspots in the vast majority of cells; this lack of coverage prevents the correlation of genetic and transcriptional readouts from the same single cell. To overcome this, we developed TARGET-seq, a method for the high-sensitivity detection of multiple mutations within single cells from both genomic and coding DNA, in parallel with unbiased whole-transcriptome analysis...
February 11, 2019: Molecular Cell
Yoon Ha Choi, Jong Kyoung Kim
Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability...
February 12, 2019: Molecules and Cells
Brandon Monier, Adam McDermaid, Cankun Wang, Jing Zhao, Allison Miller, Anne Fennell, Qin Ma
Next-Generation Sequencing has made available substantial amounts of large-scale Omics data, providing unprecedented opportunities to understand complex biological systems. Specifically, the value of RNA-Sequencing (RNA-Seq) data has been confirmed in inferring how gene regulatory systems will respond under various conditions (bulk data) or cell types (single-cell data). RNA-Seq can generate genome-scale gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies...
February 14, 2019: PLoS Computational Biology
Ludan Zhao, Yinhua Jin, Katie Donahue, Margaret Tsui, Matt Fish, Catriona Y Logan, Bruce Wang, Roel Nusse
In the liver, Wnt/β-catenin signaling is involved in regulating zonation and hepatocyte proliferation during homeostasis. We have examined Wnt gene expression and signaling after injury and we show by in situ hybridization that Wnts are activated by acute carbon tetrachloride (CCl4 ) toxicity. Following injury, peri-injury hepatocytes become Wnt-responsive, expressing the Wnt target gene Axin2. Lineage tracing of peri-injury Axin2+ hepatocytes shows that during recovery, the injured parenchyma becomes repopulated and repaired by Axin2+ descendants...
February 14, 2019: Hepatology: Official Journal of the American Association for the Study of Liver Diseases
Ida Lindeman, Michael J T Stubbington
In this chapter, we describe TraCeR and BraCeR, our computational tools for reconstruction of paired full-length antigen receptor sequences and clonality inference from single-cell RNA-seq (scRNA-seq) data. In brief, TraCeR reconstructs T-cell receptor (TCR) sequences from scRNA-seq data by extracting sequencing reads derived from TCRs by aligning the reads from each cell against synthetic TCR sequences. TCR-derived reads are then assembled into full-length recombined TCR sequences. BraCeR builds on the TraCeR pipeline and accounts for somatic hypermutations (SHM) and isotype switching...
2019: Methods in Molecular Biology
Yuanhua Huang, Guido Sanguinetti
Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity in transcription, but the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. In this chapter, we review the challenges in splicing isoform quantification in scRNA-seq data and discuss BRIE (Bayesian regression for isoform estimation), a recently proposed Bayesian hierarchical model which resolves these problems by learning an informative prior distribution from sequence features...
2019: Methods in Molecular Biology
Meichen Dong, Yuchao Jiang
Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average gene expression across cells. Single-cell RNA sequencing (scRNA-seq) allows the comparison of expression distribution between the two alleles of a diploid organism, and characterization of allele-specific bursting. Here we describe SCALE, a bioinformatic and statistical framework for allele-specific gene expression analysis by scRNA-seq. SCALE estimates genome-wide bursting kinetics at the allelic level while accounting for technical bias and other complicating factors such as cell size...
2019: Methods in Molecular Biology
Karthik Shekhar, Vilas Menon
Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the "cell types" that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type...
2019: Methods in Molecular Biology
Beomseok Kim, Eunmin Lee, Jong Kyoung Kim
Profiling the transcriptomes of individual cells with single-cell RNA sequencing (scRNA-seq) has been widely applied to provide a detailed molecular characterization of cellular heterogeneity within a population of cells. Despite recent technological advances of scRNA-seq, technical variability of gene expression in scRNA-seq is still much higher than that in bulk RNA-seq. Accounting for technical variability is therefore a prerequisite for correctly analyzing single-cell data. This chapter describes a computational pipeline for detecting highly variable genes exhibiting higher cell-to-cell variability than expected by technical noise...
2019: Methods in Molecular Biology
Kenta Sato, Koki Tsuyuzaki, Kentaro Shimizu, Itoshi Nikaido
Recent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software...
February 11, 2019: Genome Biology
George C Linderman, Manas Rachh, Jeremy G Hoskins, Stefan Steinerberger, Yuval Kluger
t-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. Software is available at https://github...
February 11, 2019: Nature Methods
Kook Hui Ryu, Ling Huang, Hyun Min Kang, John Schiefelbein
Single-cell RNA sequencing (scRNA-seq) has been used extensively to study cell-specific gene expression in animals, but it has not been widely applied to plants. Here, we describe the use of a commercially available droplet-based microfluidics platform for high-throughput scRNA-seq to obtain single-cell transcriptomes from protoplasts from more than 10,000 Arabidopsis root cells. We find that all major tissues and developmental stages are represented in this single-cell transcriptome population. Further, distinct sub-populations and rare cell types, including putative quiescent center (QC) cells, were identified...
February 4, 2019: Plant Physiology
Nikolaos Papadopoulos, R Gonzalo Parra, Johannes Söding
Summary: Cellular lineage trees can be derived from single-cell RNA sequencing snapshots of differentiating cells. Currently, only datasets with simple topologies are available. To test and further develop tools for lineage tree reconstruction, we need test datasets with known complex topologies. PROSSTT can simulate scRNA-seq datasets for differentiation processes with lineage trees of any desired complexity, noise level, noise model, and size. PROSSTT also provides scripts to quantify the quality of predicted lineage trees...
February 1, 2019: Bioinformatics
Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, Brian Cleary, Vidya Subramanian, Aryeh Solomon, Joshua Gould, Siyan Liu, Stacie Lin, Peter Berube, Lia Lee, Jenny Chen, Justin Brumbaugh, Philippe Rigollet, Konrad Hochedlinger, Rudolf Jaenisch, Aviv Regev, Eric S Lander
Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized...
January 28, 2019: Cell
Xiaoshu Zhu, Hong-Dong Li, Yunpei Xu, Lilu Guo, Fang-Xiang Wu, Guihua Duan, Jianxin Wang
Single-cell RNA sequencing (scRNA-seq) has recently brought new insight into cell differentiation processes and functional variation in cell subtypes from homogeneous cell populations. A lack of prior knowledge makes unsupervised machine learning methods, such as clustering, suitable for analyzing scRNA-seq . However, there are several limitations to overcome, including high dimensionality, clustering result instability, and parameter adjustment complexity. In this study, we propose a method by combining structure entropy and k nearest neighbor to identify cell subpopulations in scRNA-seq data...
January 29, 2019: Genes
Tim Stuart, Rahul Satija
The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies...
January 29, 2019: Nature Reviews. Genetics
Shannon R McCurdy, Vasilis Ntranos, Lior Pachter
Analysis of single-cell RNA sequencing (scRNA-Seq) data often involves filtering out uninteresting or poorly measured genes and dimensionality reduction to reduce noise and simplify data visualization. However, techniques such as principal components analysis (PCA) fail to preserve non-negativity and sparsity structures present in the original matrices, and the coordinates of projected cells are not easily interpretable. Commonly used thresholding methods to filter genes avoid those pitfalls, but ignore collinearity and covariance in the original matrix...
2019: PloS One
Gökcen Eraslan, Lukas M Simon, Maria Mircea, Nikola S Mueller, Fabian J Theis
Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a negative binomial noise model with or without zero-inflation, and nonlinear gene-gene dependencies are captured...
January 23, 2019: Nature Communications
Bashar Hamza, Sheng Rong Ng, Sanjay M Prakadan, Francisco Feijó Delgado, Christopher R Chin, Emily M King, Lucy F Yang, Shawn M Davidson, Kelsey L DeGouveia, Nathan Cermak, Andrew W Navia, Peter S Winter, Riley S Drake, Tuomas Tammela, Carman Man-Chung Li, Thales Papagiannakopoulos, Alejandro J Gupta, Josephine Shaw Bagnall, Scott M Knudsen, Matthew G Vander Heiden, Steven C Wasserman, Tyler Jacks, Alex K Shalek, Scott R Manalis
Circulating tumor cells (CTCs) play a fundamental role in cancer progression. However, in mice, limited blood volume and the rarity of CTCs in the bloodstream preclude longitudinal, in-depth studies of these cells using existing liquid biopsy techniques. Here, we present an optofluidic system that continuously collects fluorescently labeled CTCs from a genetically engineered mouse model (GEMM) for several hours per day over multiple days or weeks. The system is based on a microfluidic cell sorting chip connected serially to an unanesthetized mouse via an implanted arteriovenous shunt...
January 23, 2019: Proceedings of the National Academy of Sciences of the United States of America
Kevin Bassler, Jonas Schulte-Schrepping, Stefanie Warnat-Herresthal, Anna C Aschenbrenner, Joachim L Schultze
Myeloid cells are a major cellular compartment of the immune system comprising monocytes, dendritic cells, tissue macrophages, and granulocytes. Models of cellular ontogeny, activation, differentiation, and tissue-specific functions of myeloid cells have been revisited during the last years with surprising results; for example, most tissue macrophages are yolk sac derived, monocytes and macrophages follow a multidimensional model of activation, and tissue signals have a significant impact on the functionality of all these cells...
January 16, 2019: Annual Review of Immunology
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