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Experimental Considerations for Single-Cell RNA Sequencing Approaches

Overview of attention for article published in Frontiers in Cell and Developmental Biology, September 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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2 news outlets
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9 X users

Citations

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141 Dimensions

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436 Mendeley
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Title
Experimental Considerations for Single-Cell RNA Sequencing Approaches
Published in
Frontiers in Cell and Developmental Biology, September 2018
DOI 10.3389/fcell.2018.00108
Pubmed ID
Authors

Quy H. Nguyen, Nicholas Pervolarakis, Kevin Nee, Kai Kessenbrock

Abstract

Single-cell transcriptomic technologies have emerged as powerful tools to explore cellular heterogeneity at the resolution of individual cells. Previous scientific knowledge in cell biology is largely limited to data generated by bulk profiling methods, which only provide averaged read-outs that generally mask cellular heterogeneity. This averaged approach is particularly problematic when the biological effect of interest is limited to only a subpopulation of cells such as stem/progenitor cells within a given tissue, or immune cell subsets infiltrating a tumor. Great advances in single-cell RNA sequencing (scRNAseq) enabled scientists to overcome this limitation and allow for in depth interrogation of previously unexplored rare cell types. Due to the high sensitivity of scRNAseq, adequate attention must be put into experimental setup and execution. Careful handling and processing of cells for scRNAseq is critical to preserve the native expression profile that will ensure meaningful analysis and conclusions. Here, we delineate the individual steps of a typical single-cell analysis workflow from tissue procurement, cell preparation, to platform selection and data analysis, and we discuss critical challenges in each of these steps, which will serve as a helpful guide to navigate the complex field of single-cell sequencing.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 436 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 436 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 102 23%
Researcher 86 20%
Student > Master 46 11%
Student > Bachelor 37 8%
Student > Doctoral Student 17 4%
Other 52 12%
Unknown 96 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 118 27%
Agricultural and Biological Sciences 65 15%
Neuroscience 35 8%
Medicine and Dentistry 32 7%
Immunology and Microbiology 31 7%
Other 45 10%
Unknown 110 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 March 2022.
All research outputs
#1,784,316
of 23,408,972 outputs
Outputs from Frontiers in Cell and Developmental Biology
#249
of 9,362 outputs
Outputs of similar age
#38,970
of 336,151 outputs
Outputs of similar age from Frontiers in Cell and Developmental Biology
#6
of 59 outputs
Altmetric has tracked 23,408,972 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,362 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 336,151 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.