Evidence map›Paper›PMID 31688860›Full record

ArticleThe Analyst2019

Improving single-cell transcriptome sequencing efficiency with a microfluidic phase-switch device.

Baoyue Zhang, Hong Xu, Yuqing Huang, Weiliang Shu, Hongtao Feng, Jin Cai, Jiang F Zhong, Yan Chen

Abstract read
In one paragraph

Article in The Analyst, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. 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

8 authors.

Baoyue ZhangKey Lab for Health Informatics of Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. yan.chen@siat.ac.cn.
Hong Xu
Yuqing Huang
Weiliang Shu
Hongtao Feng
Jin Cai
Jiang F Zhong
Yan Chen

Funding

Molecular Characteristics of Treatment-resistant High-risk NeuroblastomaR01CA197903 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ZHONG, JOHN · 2016 to 2020
$1.9M
NCI NIH HHS R01 CA197903
6 · The paper itself

Abstract

In this paper, we present a novel method to improve the efficiency of single-cell transcriptome sequencing for analyzing valuable cell samples. The microfluidic device we designed integrates multiple single-cell isolation chambers with hydrodynamic traps and achieves a nearly 100% single-cell capture rate and minimal cell loss, making it particularly suitable for samples with limited numbers of cells. Single cells were encapsulated using a novel phase-switch method into picoliter-sized hydrogel droplets and easily recovered for subsequent reactions. Minimizing the reaction volume resulted in a high reverse transcription (RT) efficiency for RNA sequencing (RNA-Seq). With this novel microfluidic platform, we captured dozens of hESCs (H9) simultaneously and obtained live cells in individual picoliter volumes, thus allowing for the convenient construction of a high-quality library for deep single-cell RNA-Seq. Our single-cell RNA-Seq results confirmed that a spectrum of pluripotency existed within an H9 colony. This integrated microfluidic platform can be applied to various cell types for the investigation of rare cellular events, and the phase-switch single-cell processing strategy will improve the efficiency and accessibility of single-cell transcriptome sequencing analysis.

Indexed as

Lab-On-A-Chip DevicesTranscriptomeCell LineEmbryonic Stem CellsHigh-Throughput Nucleotide SequencingHumansMicrofluidic Analytical TechniquesSingle-Cell Analysis

Identifiers

PMID31688860
PMCPMC6925944

What OpenQuestion holds

Textmetadata
LicenceTDM
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.