Title |
Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads
|
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Published in |
Genome Biology, March 2018
|
DOI | 10.1186/s13059-018-1407-3 |
Pubmed ID | |
Authors |
Yohei Sasagawa, Hiroki Danno, Hitomi Takada, Masashi Ebisawa, Kaori Tanaka, Tetsutaro Hayashi, Akira Kurisaki, Itoshi Nikaido |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 19 | 17% |
United States | 11 | 10% |
United Kingdom | 4 | 4% |
Australia | 3 | 3% |
Spain | 3 | 3% |
Germany | 3 | 3% |
Italy | 2 | 2% |
Austria | 2 | 2% |
Canada | 1 | <1% |
Other | 7 | 6% |
Unknown | 56 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 64 | 58% |
Scientists | 43 | 39% |
Science communicators (journalists, bloggers, editors) | 2 | 2% |
Practitioners (doctors, other healthcare professionals) | 1 | <1% |
Unknown | 1 | <1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 279 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 61 | 22% |
Student > Ph. D. Student | 43 | 15% |
Student > Master | 31 | 11% |
Student > Bachelor | 17 | 6% |
Other | 13 | 5% |
Other | 40 | 14% |
Unknown | 74 | 27% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 87 | 31% |
Agricultural and Biological Sciences | 53 | 19% |
Medicine and Dentistry | 11 | 4% |
Neuroscience | 10 | 4% |
Computer Science | 8 | 3% |
Other | 29 | 10% |
Unknown | 81 | 29% |