↓ Skip to main content

Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, July 2016
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
6 X users

Citations

dimensions_citation
44 Dimensions

Readers on

mendeley
125 Mendeley
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
Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates
Published in
Proceedings of the National Academy of Sciences of the United States of America, July 2016
DOI 10.1073/pnas.1520964113
Pubmed ID
Authors

Kaston Leung, Anders Klaus, Bill K. Lin, Emma Laks, Justina Biele, Daniel Lai, Ali Bashashati, Yi-Fei Huang, Radhouane Aniba, Michelle Moksa, Adi Steif, Anne-Marie Mes-Masson, Martin Hirst, Sohrab P. Shah, Samuel Aparicio, Carl L. Hansen

Abstract

The genomes of large numbers of single cells must be sequenced to further understanding of the biological significance of genomic heterogeneity in complex systems. Whole genome amplification (WGA) of single cells is generally the first step in such studies, but is prone to nonuniformity that can compromise genomic measurement accuracy. Despite recent advances, robust performance in high-throughput single-cell WGA remains elusive. Here, we introduce droplet multiple displacement amplification (MDA), a method that uses commercially available liquid dispensing to perform high-throughput single-cell MDA in nanoliter volumes. The performance of droplet MDA is characterized using a large dataset of 129 normal diploid cells, and is shown to exceed previously reported single-cell WGA methods in amplification uniformity, genome coverage, and/or robustness. We achieve up to 80% coverage of a single-cell genome at 5× sequencing depth, and demonstrate excellent single-nucleotide variant (SNV) detection using targeted sequencing of droplet MDA product to achieve a median allelic dropout of 15%, and using whole genome sequencing to achieve false and true positive rates of 9.66 × 10(-6) and 68.8%, respectively, in a G1-phase cell. We further show that droplet MDA allows for the detection of copy number variants (CNVs) as small as 30 kb in single cells of an ovarian cancer cell line and as small as 9 Mb in two high-grade serous ovarian cancer samples using only 0.02× depth. Droplet MDA provides an accessible and scalable method for performing robust and accurate CNV and SNV measurements on large numbers of single cells.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Norway 1 <1%
Taiwan 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 120 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 26%
Researcher 27 22%
Student > Bachelor 10 8%
Student > Master 10 8%
Student > Postgraduate 8 6%
Other 20 16%
Unknown 18 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 36 29%
Agricultural and Biological Sciences 31 25%
Engineering 19 15%
Medicine and Dentistry 5 4%
Immunology and Microbiology 3 2%
Other 12 10%
Unknown 19 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 July 2016.
All research outputs
#7,488,195
of 24,625,114 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#62,059
of 101,438 outputs
Outputs of similar age
#117,237
of 362,026 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#605
of 871 outputs
Altmetric has tracked 24,625,114 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 101,438 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.8. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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 362,026 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 871 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.