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Open accessJournal ArticleDOI: 10.1016/J.GPB.2020.02.005

Direct Comparative Analyses of 10X Genomics Chromium and Smart-seq2.

02 Mar 2021-Genomics, Proteomics & Bioinformatics (Elsevier)-Vol. 19, Iss: 2, pp 253-266
Abstract: Single-cell RNA sequencing (scRNA-seq) is generally used for profiling transcriptome of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data generated by these two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analyses. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data more. For 10X-based data, we observed higher noise for mRNAs with low expression levels. Approximately 10%-30% of all detected transcripts by both platforms were from non-coding genes, with long non-coding RNAs (lncRNAs) accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected distinct groups of differentially expressed genes between cell clusters, indicating the different characteristics of these technologies. Our study promotes better understanding of these two platforms and offers the basis for an informed choice of these widely used technologies.

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17 results found


Open accessPosted ContentDOI: 10.1101/2020.03.05.977991
06 Mar 2020-bioRxiv
Abstract: Full-length SMART-Seq single-cell RNA-seq can be used to measure gene expression at isoform resolution, making possible the identification of gene isoform markers for cell types. In a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-Seq, we find numerous examples of isoform specificity in cell types, including isoform shifts between cell types that are masked in gene-level analysis. These findings can be used to refine spatial gene expression information to isoform resolution. Our results highlight the utility of full-length single-cell RNA-seq when used in conjunction with other single-cell RNA-seq technologies.

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Topics: Gene isoform (60%)

7 Citations


Open accessJournal ArticleDOI: 10.1186/S13045-021-01105-2
Abstract: Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

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Topics: Single cell sequencing (70%), Genomics (56%), Targeted therapy (51%)

6 Citations


Journal ArticleDOI: 10.1038/S42255-021-00417-4
01 Jun 2021-
Abstract: The perception of adipose tissue, both in the scientific community and in the general population, has changed dramatically in the past 20 years. While adipose tissue was thought for a long time to be a rather simple lipid storage entity, it is now recognized as a highly heterogeneous organ and a critical regulator of systemic metabolism, composed of many different subtypes of cells, with important endocrine functions. Additionally, adipose tissue is nowadays recognized to contribute to energy turnover, due to the presence of specialized thermogenic adipocytes, which can be found in many adipose depots. This review discusses the unprecedented insights that we have gained into the heterogeneity of thermogenic adipocytes and their respective precursors due to the technical developments in single-cell and nucleus technologies. These methodological advances have increased our understanding of how adipose tissue catabolic function is influenced by developmental and intercellular communication events. Adipose tissue has emerged as a highly heterogeneous organ. Sun et al. discuss the heterogeneity of thermogenic adipocytes and their precursors, highlighting the single-cell technologies that help to characterize adipose tissues in depth.

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Topics: Adipose tissue (66%), Population (51%)

3 Citations


Proceedings ArticleDOI: 10.1109/BIBM49941.2020.9313519
Jiahui Zhong1, Razin A. Shaikh2, Haoguo Wu1, Xin Lin1  +5 moreInstitutions (4)
16 Dec 2020-
Abstract: Single cell transcriptomics (SCT) technology reveals gene expression of individual cells. Peripheral blood mononuclear cells (PBMC) are important diagnostic targets in immunology. In this study, we obtained and standardized 27 SCT data sets, derived from healthy PBMC samples using 10x SCT. We used artificial neural networks (ANN) to assess the ability of ANN to classify main PBMC cell types. Incremental learning by the gradual addition of new data sets to ANN training improved classification. The overall prediction accuracy of the final step of incremental learning reached 93% in 4-class classification.

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2 Citations


Open accessPosted ContentDOI: 10.1101/2020.06.06.137570
17 Jun 2020-bioRxiv
Abstract: Background: The vast ecosystem of single-cell RNA-seq tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatic community leans once more towards the large computing requirements and the statistically-driven methods needed to process and understand these ever-growing datasets. Results: Here we outline several Galaxy workflows and learning resources for scRNA-seq, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatic framework provides tools, workflows and trainings that not only enable users to perform one-click 10x preprocessing, but also empowers them to demultiplex raw sequencing data manually. The downstream analysis supports a wide range of high quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal and clustering. The teaching resources cover an assortment of different concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. Conclusions: The reproducible and training-oriented Galaxy framework provides a sustainable HPC environment for users to run flexible analyses on both 10x and alternatively derived datasets. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy Community provide a means for users to learn, publish and teach scRNA-seq analysis.

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2 Citations


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60 results found


Open accessJournal ArticleDOI: 10.1089/OMI.2011.0118
Abstract: Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters The analysis module and visualization module were combined into a reusable workflow Currently, clusterProfiler supports three species, including humans, mice, and yeast Methods provided in this package can be easily extended to other species and ontologies The clusterProfiler package is released under Artistic-20 License within Bioconductor project The source code and vignette are freely available at http://bioconductororg/packages/release/bioc/html/clusterProfilerhtml

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Topics: Bioconductor (61%)

8,173 Citations


Open accessJournal ArticleDOI: 10.1016/J.CELL.2015.05.002
Evan Z. Macosko1, Evan Z. Macosko2, Anindita Basu2, Anindita Basu1  +23 moreInstitutions (7)
21 May 2015-Cell
Abstract: Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.

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4,167 Citations


Open accessJournal ArticleDOI: 10.1038/NBT.2859
Abstract: Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.

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2,760 Citations


Open accessJournal ArticleDOI: 10.1101/GAD.17446611
Moran N. Cabili1, Cole Trapnell1, Cole Trapnell2, Loyal A. Goff1  +7 moreInstitutions (2)
Abstract: Large intergenic noncoding RNAs (lincRNAs) are emerging as key regulators of diverse cellular processes. Determining the function of individual lincRNAs remains a challenge. Recent advances in RNA sequencing (RNA-seq) and computational methods allow for an unprecedented analysis of such transcripts. Here, we present an integrative approach to define a reference catalog of >8000 human lincRNAs. Our catalog unifies previously existing annotation sources with transcripts we assembled from RNA-seq data collected from ~4 billion RNA-seq reads across 24 tissues and cell types. We characterize each lincRNA by a panorama of >30 properties, including sequence, structural, transcriptional, and orthology features. We found that lincRNA expression is strikingly tissue-specific compared with coding genes, and that lincRNAs are typically coexpressed with their neighboring genes, albeit to an extent similar to that of pairs of neighboring protein-coding genes. We distinguish an additional subset of transcripts that have high evolutionary conservation but may include short ORFs and may serve as either lincRNAs or small peptides. Our integrated, comprehensive, yet conservative reference catalog of human lincRNAs reveals the global properties of lincRNAs and will facilitate experimental studies and further functional classification of these genes.

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2,734 Citations


Open accessJournal ArticleDOI: 10.1016/J.CELL.2015.04.044
Allon M. Klein1, Linas Mazutis2, Linas Mazutis1, Ilke Akartuna1  +6 moreInstitutions (2)
21 May 2015-Cell
Abstract: It has long been the dream of biologists to map gene expression at the single-cell level With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways Recently, RNA sequencing has achieved single-cell resolution What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships VIDEO ABSTRACT

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2,293 Citations


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