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single cell RNA sequencing,scRNA-seq

Luyi Tian, Shian Su, Xueyi Dong, Daniela Amann-Zalcenstein, Christine Biben, Azadeh Seidi, Douglas J Hilton, Shalin H Naik, Matthew E Ritchie
Single-cell RNA sequencing (scRNA-seq) technology allows researchers to profile the transcriptomes of thousands of cells simultaneously. Protocols that incorporate both designed and random barcodes have greatly increased the throughput of scRNA-seq, but give rise to a more complex data structure. There is a need for new tools that can handle the various barcoding strategies used by different protocols and exploit this information for quality assessment at the sample-level and provide effective visualization of these results in preparation for higher-level analyses...
August 10, 2018: PLoS Computational Biology
Byungjin Hwang, Ji Hyun Lee, Duhee Bang
Rapid progress in the development of next-generation sequencing (NGS) technologies in recent years has provided many valuable insights into complex biological systems, ranging from cancer genomics to diverse microbial communities. NGS-based technologies for genomics, transcriptomics, and epigenomics are now increasingly focused on the characterization of individual cells. These single-cell analyses will allow researchers to uncover new and potentially unexpected biological discoveries relative to traditional profiling methods that assess bulk populations...
August 7, 2018: Experimental & Molecular Medicine
Johannes W Bagnoli, Christoph Ziegenhain, Aleksandar Janjic, Lucas E Wange, Beate Vieth, Swati Parekh, Johanna Geuder, Ines Hellmann, Wolfgang Enard
Single-cell RNA sequencing (scRNA-seq) has emerged as a central genome-wide method to characterize cellular identities and processes. Consequently, improving its sensitivity, flexibility, and cost-efficiency can advance many research questions. Among the flexible plate-based methods, single-cell RNA barcoding and sequencing (SCRB-seq) is highly sensitive and efficient. Here, we systematically evaluate experimental conditions of this protocol and find that adding polyethylene glycol considerably increases sensitivity by enhancing cDNA synthesis...
July 26, 2018: Nature Communications
Quanbo Ji, Yuxuan Zheng, Guoqiang Zhang, Yuqiong Hu, Xiaoying Fan, Yu Hou, Lu Wen, Li Li, Yameng Xu, Yan Wang, Fuchou Tang
OBJECTIVES: Understanding the molecular mechanisms underlying human cartilage degeneration and regeneration is helpful for improving therapeutic strategies for treating osteoarthritis (OA). Here, we report the molecular programmes and lineage progression patterns controlling human OA pathogenesis using single-cell RNA sequencing (scRNA-seq). METHODS: We performed unbiased transcriptome-wide scRNA-seq analysis, computational analysis and histological assays on 1464 chondrocytes from 10 patients with OA undergoing knee arthroplasty surgery...
July 19, 2018: Annals of the Rheumatic Diseases
Akira Nguyen, Weng Hua Khoo, Imogen Moran, Peter I Croucher, Tri Giang Phan
Single-cell RNA sequencing (scRNA-Seq) is transforming our ability to characterize cells, particularly rare cells that are often overlooked in bulk population analytical approaches. This has lead to the discovery of new cell types and cellular states that echo the underlying heterogeneity and plasticity in the immune system. Technologies for the capture, sequencing, and bioinformatic analysis of single cells are rapidly improving, and scRNA-Seq is now becoming much more accessible to non-specialized laboratories...
2018: Frontiers in Immunology
Di-Cheng Zhao, Yu-Mei Li, Jie-Liang Ma, Ning Yi, Zhong-Yuan Yao, Yan-Ping Li, Yi Quan, Xin-Ning Li, Chang-Long Xu, Ying Qiu, Ling-Qian Wu
Precise regulation of glucose metabolism-related genes is essential for early embryonic development. Although previous research has yielded detailed information on the biochemical processes, little is yet known of the dynamic gene expression profiles in glucose metabolism of preimplantation embryos at a single-cell resolution. In the present study, we performed integrated analysis of single-cell RNA sequencing (scRNA-seq) data of human preimplantation embryos that had been cultured in sequential medium. Different cells in the same embryo have similar gene expression patterns in glucose metabolism...
July 18, 2018: Reproduction, Fertility, and Development
Jinguo Chen, Foo Cheung, Rongye Shi, Huizhi Zhou, Wenrui Lu
BACKGROUND: Interest in single-cell transcriptomic analysis is growing rapidly, especially for profiling rare or heterogeneous populations of cells. In almost all reported works investigators have used live cells, which introduces cell stress during preparation and hinders complex study designs. Recent studies have indicated that cells fixed by denaturing fixative can be used in single-cell sequencing, however they did not usually work with most types of primary cells including immune cells...
July 17, 2018: Journal of Translational Medicine
Lihua Zhang, Shihua Zhang
Single-cell RNA-sequencing (scRNA-seq) is a recent breakthrough technology, which paves the way for measuring RNA levels at single cell resolution to study precise biological functions. One of the main challenges when analyzing scRNA-seq data is the presence of zeros or dropout events, which may mislead downstream analyses. To compensate the dropout effect, several methods have been developed to impute gene expression since the first Bayesian-based method being proposed in 2016. However, these methods have shown very diverse characteristics in terms of model hypothesis and imputation performance...
June 19, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
David T Paik, Lei Tian, Jaecheol Lee, Nazish Sayed, Ian Y Chen, Siyeon Rhee, June-Wha Rhee, Youngkyun Kim, Robert C Wirka, Jan W Buikema, Sean M Wu, Kristy Red-Horse, Thomas Quertermous, Joseph C Wu
<u>Rationale:</u> Human induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) have risen as a useful tool in cardiovascular research, offering a wide gamut of translational and clinical applications. However, inefficiency of the currently available iPSC-EC differentiation protocol and underlying heterogeneity of derived iPSC-ECs remain as major limitations of iPSC-EC technology. <u>Objective:</u> Here we performed droplet-based single-cell RNA-sequencing (scRNA-seq) of the human iPSCs following iPSC-EC differentiation...
July 9, 2018: Circulation Research
Hanson Ho, Matt De Both, Ashley Siniard, Sasha Sharma, James H Notwell, Michelle Wallace, Dino P Leone, Amy Nguyen, Eric Zhao, Hannah Lee, Daniel Zwilling, Kimberly R Thompson, Steven P Braithwaite, Matthew Huentelman, Thomas Portmann
Recent advances in single-cell technologies are paving the way to a comprehensive understanding of the cellular complexity in the brain. Protocols for single-cell transcriptomics combine a variety of sophisticated methods for the purpose of isolating the heavily interconnected and heterogeneous neuronal cell types in a relatively intact and healthy state. The emphasis of single-cell transcriptome studies has thus far been on comparing library generation and sequencing techniques that enable measurement of the minute amounts of starting material from a single cell...
2018: Frontiers in Cellular Neuroscience
S W Lukowski, Z K Tuong, K Noske, A Senabouth, Q H Nguyen, S B Andersen, H P Soyer, I H Frazer, J E Powell
Persistent human papillomavirus (HPV) infection is responsible for at least 5% of human malignancies. Most HPV-associated cancers are initiated by the HPV16 genotype, as confirmed by detection of integrated HPV DNA in cells of oral and anogenital epithelial cancers. However, single-cell RNA-sequencing (scRNA-seq) may enable prediction of HPV involvement in carcinogenesis at other sites. We conducted scRNA-seq on keratinocytes from a mouse transgenic for the E7 gene of HPV16, and showed sensitive and specific detection of HPV16-E7 mRNA, predominantly in basal keratinocytes...
June 28, 2018: Journal of Investigative Dermatology
Maziyar Baran Pouyan, Dennis Kostka
Motivation: Genome-wide transcriptome sequencing applied to single cells (scRNA-seq) is rapidly becoming an assay of choice across many fields of biological and biomedical research. Scientific objectives often revolve around discovery or characterization of types or sub-types of cells, and therefore, obtaining accurate cell-cell similarities from scRNA-seq data is a critical step in many studies. While rapid advances are being made in the development of tools for scRNA-seq data analysis, few approaches exist that explicitly address this task...
July 1, 2018: Bioinformatics
Sumit Mukherjee, Yue Zhang, Joshua Fan, Georg Seelig, Sreeram Kannan
Motivation: Single cell RNA-seq (scRNA-seq) data contains a wealth of information which has to be inferred computationally from the observed sequencing reads. As the ability to sequence more cells improves rapidly, existing computational tools suffer from three problems. (i) The decreased reads-per-cell implies a highly sparse sample of the true cellular transcriptome. (ii) Many tools simply cannot handle the size of the resulting datasets. (iii) Prior biological knowledge such as bulk RNA-seq information of certain cell types or qualitative marker information is not taken into account...
July 1, 2018: Bioinformatics
Jingshu Wang, Mo Huang, Eduardo Torre, Hannah Dueck, Sydney Shaffer, John Murray, Arjun Raj, Mingyao Li, Nancy R Zhang
Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene's expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts...
July 10, 2018: Proceedings of the National Academy of Sciences of the United States of America
Jufang Yao, Hui-Li Dai
Functional genomics aims to develop an in-depth understanding of how specific gene dysfunctions are related to diseases. A common method for investigating the genome and its complex functions is via perturbation of the interactions between the DNA, RNA and their protein respective protein derivatives. Commonly, arrayed and pooled genetic screens are utilized to achieve this and in recent years have been fundamental in achieving the current level of understanding for gene dysfunctions. However, they are limited in specific aspects which scientists have attempted to address...
2018: Advances in Experimental Medicine and Biology
Qiankun Luo, Hui Zhang
The advent of single-cell omics technology has promoted our understanding of the genomic, epigenomic, and transcriptomic heterogeneity in individual cells. Compared to traditional sequencing studies using bulk cells, single-cell transcriptome technology is naturally more dynamic for in depth analysis of genomic variation resulting from cell division and is useful in unraveling the regulatory mechanisms of gene networks in many diseases. However, there are still some limitations of current single-cell RNA sequencing (scRNA-seq) protocols...
2018: Advances in Experimental Medicine and Biology
Xiaoyun Huang, Shiping Liu, Liang Wu, Miaomiao Jiang, Yong Hou
Single cell sequencing (SCS) can be harnessed to acquire the genomes, transcriptomes and epigenomes from individual cells. Next generation sequencing (NGS) technology is the driving force for single cell sequencing. scRNA-seq requires a lengthy pipeline comprising of single cell sorting, RNA extraction, reverse transcription, amplification, library construction, sequencing and subsequent bioinformatic analysis. Computational algorithms are essential to fulfill many tasks of interest using scRNA-seq data. scRNA-seq has already enabled researchers to revisit long-standing questions in cancer biology, including cancer metastasis, heterogeneity and evolution...
2018: Advances in Experimental Medicine and Biology
Peter Savas, Balaji Virassamy, Chengzhong Ye, Agus Salim, Christopher P Mintoff, Franco Caramia, Roberto Salgado, David J Byrne, Zhi L Teo, Sathana Dushyanthen, Ann Byrne, Lironne Wein, Stephen J Luen, Catherine Poliness, Sophie S Nightingale, Anita S Skandarajah, David E Gyorki, Chantel M Thornton, Paul A Beavis, Stephen B Fox, Phillip K Darcy, Terence P Speed, Laura K Mackay, Paul J Neeson, Sherene Loi
The quantity of tumor-infiltrating lymphocytes (TILs) in breast cancer (BC) is a robust prognostic factor for improved patient survival, particularly in triple-negative and HER2-overexpressing BC subtypes1 . Although T cells are the predominant TIL population2 , the relationship between quantitative and qualitative differences in T cell subpopulations and patient prognosis remains unknown. We performed single-cell RNA sequencing (scRNA-seq) of 6,311 T cells isolated from human BCs and show that significant heterogeneity exists in the infiltrating T cell population...
July 2018: Nature Medicine
Mo Huang, Jingshu Wang, Eduardo Torre, Hannah Dueck, Sydney Shaffer, Roberto Bonasio, John I Murray, Arjun Raj, Mingyao Li, Nancy R Zhang
In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. To address this challenge, we developed SAVER (single-cell analysis via expression recovery), an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes...
July 2018: Nature Methods
Luke Zappia, Belinda Phipson, Alicia Oshlack
As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database ( to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform...
June 2018: PLoS Computational Biology
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