Evidence map›Paper›PMID 29523163›Full record

ArticleGenome biology2018

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads.

Yohei Sasagawa, Hiroki Danno, Hitomi Takada, Masashi Ebisawa, Kaori Tanaka, Tetsutaro Hayashi, Akira Kurisaki, Itoshi Nikaido

Abstract readEvaluation Study
In one paragraph

Article in Genome biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.

0numbers the graph read from it
0cells of the map it votes in
82citing 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

82 citing papers in PubMed.

  1. Article
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  5. Review
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  11. Review
  12. Advances in Single-Cell Sequencing for Infectious Diseases: Progress and Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  13. Live-cell omics with Raman spectroscopy.Microscopy (Oxford, England) · 2025
    Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Review
  19. Transcriptome and Temporal Transcriptome Analyses in Single Cells.International journal of molecular sciences · 2024
    Review
  20. Review

22 more citing papers are in PubMed but not listed here.

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

8 authors.

Yohei SasagawaBioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Hirosawa 2-1, Wako, Saitama, Japan.
Hiroki DannoBioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Hirosawa 2-1, Wako, Saitama, Japan.
Hitomi TakadaLaboratory of Stem Cell Technology, Graduate School of Biological Sciences, Nara Institute of Science and Technology, Takayama-cho 8916-5, Ikoma, Nara, Japan.
Masashi EbisawaBioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Hirosawa 2-1, Wako, Saitama, Japan.
Kaori TanakaBioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Hirosawa 2-1, Wako, Saitama, Japan.
Tetsutaro HayashiBioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Hirosawa 2-1, Wako, Saitama, Japan.
Akira KurisakiLaboratory of Stem Cell Technology, Graduate School of Biological Sciences, Nara Institute of Science and Technology, Takayama-cho 8916-5, Ikoma, Nara, Japan. akikuri@bs.naist.jp.
Itoshi NikaidoBioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Hirosawa 2-1, Wako, Saitama, Japan. itoshi.nikaido@riken.jp.

Funding

Core Research for Evolutional Science and Technology JPMJCR16G3Japan Society for the Promotion of Science JP15K16329Japan Society for the Promotion of Science (JP) JP24651218Ministry of Education, Culture, Sports, Science and Technology 221S0002
6 · The paper itself

Abstract

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts, at a rate of 30-50%, and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.

Indexed as

AnimalsEmbryonic Stem CellsGene Expression ProfilingHigh-Throughput Nucleotide SequencingMiceRNA-Directed DNA PolymeraseSequence Analysis, RNASingle-Cell AnalysisRNA-Directed DNA PolymeraseCell sorterFlow cytometryHigh-throughput single-cell RNA-seqMesenchymal stem cellPoly(A) taggingQuartz-SeqStromal vascular fraction

Identifiers

PMID29523163
PMCPMC5845169

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.