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Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq

Overview of attention for article published in Nature Biotechnology, August 2016
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

news
1 news outlet
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36 X users
patent
1 patent

Readers on

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115 Mendeley
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3 CiteULike
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Article details
Title
Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq
Published in
Nature Biotechnology, August 2016
DOI 10.1038/nbt.3637
Pubmed ID
Authors
Abstract

A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline, Sleeping Beauty (SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumor's major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 36 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 3 3%
Netherlands 2 2%
United Kingdom 1 <1%
Germany 1 <1%
China 1 <1%
Austria 1 <1%
Unknown 106 92%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 34 30%
Researcher 34 30%
Student > Master 6 5%
Student > Postgraduate 6 5%
Other 5 4%
Other 13 11%
Unknown 17 15%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 34 30%
Biochemistry, Genetics and Molecular Biology 31 27%
Medicine and Dentistry 15 13%
Computer Science 4 3%
Engineering 3 3%
Other 9 8%
Unknown 19 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 30. 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 02 March 2023.
All research outputs
#1,635,173
of 34,089,221 outputs
Outputs from Nature Biotechnology
#2,675
of 9,538 outputs
Outputs of similar age
#20,955
of 335,322 outputs
Outputs of similar age from Nature Biotechnology
#30
of 104 outputs
Altmetric has tracked 34,089,221 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,538 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.8. This one has gotten more attention than average, scoring higher than 71% 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 335,322 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.