Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells

Klein, Allon M.; Mažutis, Linas; Akartuna, Ilke; Tallapragada, Naren; Veres, Adrian; Li, Victor C.; Peshkin, Leonid; Weitz, David A.; Kirschner, Marc W.

doi:10.1016/j.cell.2015.04.044

Cited by 3,108 publications

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References 66 publications

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“…These findings are supported by the results from another single‐cell study based on a recently developed experimental technique (inDrop) 42. This study shows that among the variable genes in mESCs are included pluripotency factors previously reported to fluctuate in pluripotent cells ( Nanog , Rex1/Zfp42 , Dppa5a , Sox2 , Esrrb ), and more strikingly, the most highly variable genes included known markers of Primitive Endoderm fate ( Col4a1/2 , Lama1/b1 , Sox17 , Sparc ), markers of Epiblast fate ( Krt8 , Krt18 , S100a6 ), and key epigenetic regulators of the ESC state ( Dnmt3b ) 42. The high variability in the epigenetic landscape has recently been studied at the single‐cell level in HSCs 25, in which significant chromatin reorganization in different subpopulations plays a key role during cell‐fate commitment.…”

Section: Pluripotent State Gene Expression Heterogeneity Is Tightly Rsupporting

confidence: 76%

“…On the other hand, genes expressed in bursts are highly enriched in signaling proteins 2 and the Polycomb family of epigenetic regulators, and some of these genes are expressed at levels as high as known pluripotency regulators 3. These findings are supported by the results from another single‐cell study based on a recently developed experimental technique (inDrop) 42. This study shows that among the variable genes in mESCs are included pluripotency factors previously reported to fluctuate in pluripotent cells ( Nanog , Rex1/Zfp42 , Dppa5a , Sox2 , Esrrb ), and more strikingly, the most highly variable genes included known markers of Primitive Endoderm fate ( Col4a1/2 , Lama1/b1 , Sox17 , Sparc ), markers of Epiblast fate ( Krt8 , Krt18 , S100a6 ), and key epigenetic regulators of the ESC state ( Dnmt3b ) 42.…”

Section: Pluripotent State Gene Expression Heterogeneity Is Tightly Rmentioning

confidence: 63%

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Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Angarica

Sol

2016

BioEssays

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Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self‐renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation‐specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

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Section: Pluripotent State Gene Expression Heterogeneity Is Tightly Rsupporting

confidence: 76%

Section: Pluripotent State Gene Expression Heterogeneity Is Tightly Rmentioning

confidence: 63%

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Angarica

Sol

2016

BioEssays

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“…Many studies of single cells showed critical differences between single cells that are masked in bulk cell data (Apostolou and Thanos 2008;Janes et al 2010;Zhao et al 2012;Bajikar et al 2014). Single-cell RNAseq techniques have enabled single-cell transcriptomics, and we find that the properties of end-sequencing have made DGE the basis for many single-cell sequencing protocols (Hashimshony et al 2012;Jaitin et al 2014;Soumillon et al 2014;Klein et al 2015;Macosko et al 2015). …”

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confidence: 99%

End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data

et al. 2016

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RNA-seq protocols that focus on transcript termini are well suited for applications in which template quantity is limiting. Here we show that, when applied to end-sequencing data, analytical methods designed for global RNA-seq produce computational artifacts. To remedy this, we created the End Sequence Analysis Toolkit (ESAT). As a test, we first compared endsequencing and bulk RNA-seq using RNA from dendritic cells stimulated with lipopolysaccharide (LPS). As predicted by the telescripting model for transcriptional bursts, ESAT detected an LPS-stimulated shift to shorter 3 ′ -isoforms that was not evident by conventional computational methods. Then, droplet-based microfluidics was used to generate 1000 cDNA libraries, each from an individual pancreatic islet cell. ESAT identified nine distinct cell types, three distinct β-cell types, and a complex interplay between hormone secretion and vascularization. ESAT, then, offers a much-needed and generally applicable computational pipeline for either bulk or single-cell RNA end-sequencing.

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confidence: 99%

CellView: Interactive exploration of high dimensional single cell RNA-seq data

Robson

2017

Preprint

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Recent technological advances in single cell capture and nano-scale reactions have led to a major revolution in single cell transcriptomics 1,2,3 . Single cell datasets are analyzed using computational and statistical frameworks that enable feature (gene) selection, dimensionality reduction, clustering and differential gene expression. Multiple software packages exist that allow researchers well versed in computational analysis to perform this analysis [4][5][6] . However, identifying the exact parameters required for cell type identification is an iterative process greatly improved when informed by biology. In addition, interactive exploration of single cell datasets incorporating a biologist's knowledge greatly improves data interpretation, yet often such experts do not have big data handling skills.Advances in web application frameworks and visualization methods for dense datasets facilitate the development of interactive applications to allow easy and intuitive exploration of single cell data. Here, we introduce an R Shiny 7 web application, CellView, that allows knowledge-based and hypothesis-driven exploration of processed single cell transcriptomic data. The input into CellView is an R dataset (. . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/123810 doi: bioRxiv preprint first posted online with three pre-computed data frames containing expression, clustering, and gene symbol information.This file is agnostic of upstream computational approaches providing flexibility in algorithms used to calculate these data frames. This .Rds file can be shared with the end user, eliminating the need for hosting datasets, thereby decreasing the size of a virtual machine or cloud instance required to host and use CellView. Multiple tabs allow for easy access to the data and visualization of gene expression across and within clusters, aiding cell type identification.To illustrate the utility and power of CellView, we generated and analyzed single cell transcriptome data from peripheral blood mononuclear cells (PBMCs) using the 10X Genomics Chromium 8 . As defined by the CellRanger 8 pipeline, this data consisted of 6,554 single cells sequenced to 90.1% saturation with, on average, 824 genes and 2,077 molecules detected per cell. Dimensionality reduction using tSNE 9 was applied to genes selected by normalized dispersion, and with clustering by DBSCAN 10 . CellView automatically determines cluster numbers, updates the user interface, and renders a 3D scatter plot displaying cells clustered in tSNE space (Fig 1b) (Fig 1c), a marker of B-cells, and CD3D, a marker of T-cells (Fig 1d), provide representative views of the 'Explore' tab.The 'Co-expression' tab enables the generation of heatmaps to visualize expression of multiple genes either across all clusters, in the 'AllClusters' sub-menu, or on selected cells wi...

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Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells

Cited by 3,108 publications

References 66 publications

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data

CellView: Interactive exploration of high dimensional single cell RNA-seq data

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