Evidence map›Paper›PMID 29889211›Full record

ArticleJournal of visualized experiments : JoVE2018

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing.

Benjamin Demaree, Daniel Weisgerber, Freeman Lan, Adam R Abate

Abstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. COMicromachines · 2022
    Article
  8. Article
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Benjamin DemareeDepartment of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, University of California, San Francisco; UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco.
Daniel WeisgerberDepartment of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, University of California, San Francisco.
Freeman LanDepartment of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, University of California, San Francisco; UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco.
Adam R AbateDepartment of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, University of California, San Francisco; UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco; Chan Zuckerberg Biohub; adam@abatelab.org.

Funding

Gigapixel digital PCR in Giant Unilamellar VesiclesR01EB019453 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R., CHIU, CHARLES YEN · 2014 to 2022
$3.9M
Microfluidic immunoprofiling for biomarker discovery in rheumatoid arthritisDP2AR068129 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R. · 2014 to 2014
$2.4M
Accurate and unbiased single cell sequencing with digital droplet MDAR01HG008978 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R. · 2016 to 2018
$1.9M
Accurate, long-read sequencing with droplet-microfluidic barcodingR21HG007233 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R., HERNANDEZ, RYAN D. · 2013 to 2014
$433k
NHGRI NIH HHS R01 HG008978NHGRI NIH HHS R21 HG007233NIAMS NIH HHS DP2 AR068129NIBIB NIH HHS R01 EB019453
6 · The paper itself

Abstract

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of cellular heterogeneity in biological systems. However, single-cell sequencing of large populations has been hampered by limitations in processing genomes for sequencing. In this paper, we describe a method for single-cell genome sequencing (SiC-seq) which uses droplet microfluidics to isolate, amplify, and barcode the genomes of single cells. Cell encapsulation in microgels allows the compartmentalized purification and tagmentation of DNA, while a microfluidic merger efficiently pairs each genome with a unique single-cell oligonucleotide barcode, allowing >50,000 single cells to be sequenced per run. The sequencing data is demultiplexed by barcode, generating groups of reads originating from single cells. As a high-throughput and low-bias method of single-cell sequencing, SiC-seq will enable a broader range of genomic studies targeted at diverse cell populations.

Indexed as

GenomicsHigh-Throughput Nucleotide SequencingHumansMicrofluidics

Identifiers

PMID29889211
PMCPMC6101372

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.