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RNA-Seq following PCR-based sorting reveals rare cell transcriptional signatures

Overview of attention for article published in BMC Genomics, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters
patent
1 patent

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
80 Mendeley
citeulike
1 CiteULike
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Title
RNA-Seq following PCR-based sorting reveals rare cell transcriptional signatures
Published in
BMC Genomics, May 2016
DOI 10.1186/s12864-016-2694-2
Pubmed ID
Authors

Maurizio Pellegrino, Adam Sciambi, Jamie L. Yates, Joshua D. Mast, Charles Silver, Dennis J. Eastburn

Abstract

Rare cell subtypes can profoundly impact the course of human health and disease, yet their presence within a sample is often missed with bulk molecular analysis. Single-cell analysis tools such as FACS, FISH-FC and single-cell barcode-based sequencing can investigate cellular heterogeneity; however, they have significant limitations that impede their ability to identify and transcriptionally characterize many rare cell subpopulations. PCR-activated cell sorting (PACS) is a novel cytometry method that uses single-cell TaqMan PCR reactions performed in microfluidic droplets to identify and isolate cell subtypes with high-throughput. Here, we extend this method and demonstrate that PACS enables high-dimensional molecular profiling on TaqMan-targeted cells. Using a random priming RNA-Seq strategy, we obtained high-fidelity transcriptome measurements following PACS sorting of prostate cancer cells from a heterogeneous population. The sequencing data revealed prostate cancer gene expression profiles that were obscured in the unsorted populations. Single-cell expression analysis with PACS was subsequently used to confirm a number of the differentially expressed genes identified with RNA sequencing. PACS requires minimal sample processing, uses readily available TaqMan assays and can isolate cell subtypes with high sensitivity. We have now validated a method for performing next-generation sequencing on mRNA obtained from PACS isolated cells. This capability makes PACS well suited for transcriptional profiling of rare cells from complex populations to obtain maximal biological insight into cell states and behaviors.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Denmark 1 1%
Canada 1 1%
South Africa 1 1%
Spain 1 1%
Japan 1 1%
Germany 1 1%
Unknown 72 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 30%
Researcher 21 26%
Student > Bachelor 9 11%
Student > Master 6 8%
Professor > Associate Professor 5 6%
Other 11 14%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 30%
Biochemistry, Genetics and Molecular Biology 23 29%
Engineering 7 9%
Medicine and Dentistry 6 8%
Physics and Astronomy 3 4%
Other 11 14%
Unknown 6 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 12 June 2019.
All research outputs
#1,455,915
of 14,884,620 outputs
Outputs from BMC Genomics
#599
of 8,581 outputs
Outputs of similar age
#34,907
of 263,380 outputs
Outputs of similar age from BMC Genomics
#2
of 15 outputs
Altmetric has tracked 14,884,620 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,581 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 93% 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 263,380 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 86% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.