Evidence map›Paper›PMID 32751153›Full record

ArticleMicromachines2020

Continuous ES/Feeder Cell-Sorting Device Using Dielectrophoresis and Controlled Fluid Flow.

Yuuwa Takahashi, Shogo Miyata

Abstract read
In one paragraph

Article in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  3. Article
  4. Review
  5. Article
  6. Article
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  11. 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

2 authors.

Yuuwa TakahashiGraduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Shogo MiyataDepartment of Mechanical Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Yokohama 223-8522, Japan.ORCID 0000-0002-0749-2699

Funding

Adaptable and Seamless Technology Transfer Program through Target-Driven R and D AS2211294B
6 · The paper itself

Abstract

Pluripotent stem cells (PSCs) are considered as being an important cell source for regenerative medicine. The culture of PSCs usually requires a feeder cell layer or cell adhesive matrix coating such as Matrigel, laminin, and gelatin. Although a feeder-free culture using a matrix coating has been popular, the on-feeder culture is still an effective method for the fundamental study of regenerative medicine and stem cell biology. To culture PSCs on feeder cell layers, the elimination of feeder cells is required for biological or gene analysis and for cell passage. Therefore, a simple and cost-effective cell sorting technology is required. There are several commercialized cell-sorting methods, such as FACS or MACS. However, these methods require cell labeling by fluorescent dye or magnetic antibodies with complicated processes. To resolve these problems, we focused on dielectrophoresis (DEP) phenomena for cell separation because these do not require any fluorescent or magnetic dyes or antibodies. DEP imposes an electric force on living cells under a non-uniform AC electric field. The direction and magnitude of the DEP force depend on the electric property and size of the cell. Therefore, DEP is considered as a promising approach for sorting PSCs from feeder cells. In this study, we developed a simple continuous cell-sorting device using the DEP force and fluid-induced shear force. As a result, mouse embryonic stem cells (mESCs) were purified from a mixed-cell suspension containing mESCs and mouse embryonic fibroblasts (MEFs) using our DEP cell-sorting device.

Indexed as

cell sortingdielectrophoresisfeeder cellfluid-induced shear forcepluripotent stem cell

Identifiers

PMID32751153
PMCPMC7464685

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Registered trials

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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.