↓ Skip to main content

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

Overview of attention for article published in BMC Genomics, May 2016
Altmetric Badge

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 (84th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
73 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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 73 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%
South Africa 1 1%
Germany 1 1%
Japan 1 1%
Spain 1 1%
Canada 1 1%
Unknown 65 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 32%
Researcher 20 27%
Student > Bachelor 7 10%
Student > Master 6 8%
Professor > Associate Professor 5 7%
Other 12 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 32%
Biochemistry, Genetics and Molecular Biology 21 29%
Engineering 7 10%
Unspecified 5 7%
Medicine and Dentistry 4 5%
Other 13 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 20 May 2016.
All research outputs
#818,480
of 7,722,452 outputs
Outputs from BMC Genomics
#544
of 5,593 outputs
Outputs of similar age
#42,886
of 269,260 outputs
Outputs of similar age from BMC Genomics
#28
of 198 outputs
Altmetric has tracked 7,722,452 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,593 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 90% 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 269,260 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 84% of its contemporaries.
We're also able to compare this research output to 198 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.