Correction: Corrigendum: Nuclear RNA-seq of single neurons reveals molecular signatures of activation

Lacar, Benjamin; Linker, Sara B.; Jaeger, Baptiste N.; Krishnaswami, Suguna Rani; Barron, Jerika J.; Kelder, Martijn J. E.; Parylak, Sarah; Paquola, Apuã C.M.; Venepally, Pratap; Novotny, M. A.; O’Connor, Carolyn; Fitzpatrick, Conor; Erwin, Jennifer A.; Hsu, Jonathan Y.; Husband, David; McConnell, Michael J.; Lasken, Roger S.; Gage, Fred H.

doi:10.1038/ncomms15047

Cited by 5 publications

(3 citation statements)

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“…We observed that this developmental path maintained the ‘pseudotemporal’ ordering of cells within both datasets (Supplementary Figure 9) and also aligned the two together, exhibiting nearly identical expression dynamics for canonical differentiation markers (Figure 3H). Extending this analysis globally, we observed that gene expression changes across the trajectory were largely conserved between datasets, particularly for well-characterized effectors of erythropoiesis, yet we also saw technology-specific effects -- for example a strong JUN/FOS response that has previously been associated with cellular stress during scRNA-seq 40 (Figure 3H–I). Therefore, our procedure can successfully align both discrete and transitioning populations, and enable the identification of gene-expression programs that are conserved or unique to individual datasets.…”

Section: Resultsmentioning

confidence: 75%

Integrating single-cell transcriptomic data across different conditions, technologies, and species

et al. 2018

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Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Section: Resultsmentioning

confidence: 75%

Integrating single-cell transcriptomic data across different conditions, technologies, and species

et al. 2018

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show abstract

“…We observed that this developmental path maintained the ‘pseudotemporal’ ordering of cells within both datasets (Supplementary Figure 6), but also aligned the two together, exhibiting nearly identical expression dynamics for canonical differentiation markers (Figure 3H). Extending this analysis globally, we observed that gene expression changes across the trajectory were largely conserved between datasets, particularly for well-characterized effectors of erythropoiesis, yet we also saw technology-specific effects -- for example a strong JUN/FOS response that has previously been associated with cellular stress during scRNA-seq 36 (Figure 3H-I). Therefore, our procedure can successfully align both discrete and transitioning populations, and enable the identification of gene-expression programs that are conserved or unique to individual datasets.…”

Section: Resultsmentioning

confidence: 76%

Integrated analysis of single cell transcriptomic data across conditions, technologies, and species

Butler

Satija

2017

Preprint

100

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Single cell RNA-seq (scRNA-seq) has emerged as a transformative tool to discover and define cellular phenotypes. While computational scRNA-seq methods are currently well suited for experiments representing a single condition, technology, or species, analyzing multiple datasets simultaneously raises new challenges. In particular, traditional analytical workflows struggle to align subpopulations that are present across datasets, limiting the possibility for integrated or comparative analysis. Here, we introduce a new computational strategy for scRNA-seq alignment, utilizing common sources of variation to identify shared subpopulations between datasets as part of our R toolkit Seurat. We demonstrate our approach by aligning scRNA-seq datasets of PBMCs under resting and stimulated conditions, hematopoietic progenitors sequenced across two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across datasets, and can identify subpopulations that could not be detected by analyzing datasets independently. We anticipate that these methods will serve not only to correct for batch or technologydependent effects, but also to facilitate general comparisons of scRNA-seq datasets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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“…RNA-seq with single nuclei (nucRNA-seq) is an emerging alternative to profile gene expressions of cells in tissues that cannot be readily dissociated such as the adult brain and frozen samples. The method is further capable of coupling with sorting by fluorescence activated cell sorters [4,7], Fluidigm C1 [5], and Drop-seq [8], and demonstrated feasibilities of identifying cell types and cell cycles with nucRNA-seq data [9]. Although these works hypothesise that the nucRNA expression is representative of whole cells, to date, the direct evidence of the correlation in the cytRNA and nucRNA expression at single-cell resolution has not been provided.…”

Section: Introductionmentioning

confidence: 99%

Correlation of gene expressions between nucleus and cytoplasm reflects single-cell physiology

Abdelmoez

Iida

Oguchi

et al. 2017

Preprint

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Correction: Corrigendum: Nuclear RNA-seq of single neurons reveals molecular signatures of activation

Cited by 5 publications

References 0 publications

Integrating single-cell transcriptomic data across different conditions, technologies, and species

Integrating single-cell transcriptomic data across different conditions, technologies, and species

Integrated analysis of single cell transcriptomic data across conditions, technologies, and species

Correlation of gene expressions between nucleus and cytoplasm reflects single-cell physiology

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