Evidence map›Paper›PMID 30996291›Full record

ArticleScientific reports2019

Enabling cell recovery from 3D cell culture microfluidic devices for tumour microenvironment biomarker profiling.

María Virumbrales-Muñoz, Jose M Ayuso, Alodia Lacueva, Teodora Randelovic, Megan K Livingston, David J Beebe, Sara Oliván, Desirée Pereboom, Manuel Doblare, Luis Fernández and 1 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
2.1field-weighted citation impact, top 14% of its field
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

25 citing papers in PubMed, 40 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Engineering Heterogeneous Tumor Models for Biomedical Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. On-chip perivascularLab on a chip · 2021
    Article
  16. Article
  17. Review
  18. Microfluidic Tumor-on-a-Chip Model to Study Tumor Metabolic Vulnerability.International journal of molecular sciences · 2020
    Article
  19. Review
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 6 institutions in 2 countries.

María Virumbrales-MuñozDepartment of Biomedical Engineering, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, Wisconsin, 53705, United States.
Jose M AyusoDepartment of Biomedical Engineering, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, Wisconsin, 53705, United States.
Alodia LacuevaGroup of Applied Mechanics and Bioengineering (AMB), Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
Teodora RandelovicGroup of Applied Mechanics and Bioengineering (AMB), Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
Megan K LivingstonDepartment of Biomedical Engineering, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, Wisconsin, 53705, United States.
David J BeebeDepartment of Biomedical Engineering, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, Wisconsin, 53705, United States.
Sara OlivánGroup of Applied Mechanics and Bioengineering (AMB), Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
Desirée PereboomServicio General de Apoyo a la Investigación de Citómica, University of Zaragoza, Zaragoza, Spain.
Manuel DoblareGroup of Applied Mechanics and Bioengineering (AMB), Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
Luis FernándezGroup of Applied Mechanics and Bioengineering (AMB), Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
Ignacio OchoaGroup of Applied Mechanics and Bioengineering (AMB), Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain. iochgar@unizar.es.
University of Wisconsin–Madison · USBiomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine · ESInstituto de Investigación Sanitaria Aragón · ESUniversidad de Zaragoza · ESInstituto de Salud Carlos III · ESWisconsin Institutes for Discovery · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tumour microenvironment (TME) has recently drawn much attention due to its profound impact on tumour development, drug resistance and patient outcome. There is an increasing interest in new therapies that target the TME. Nonetheless, most established in vitro models fail to include essential cues of the TME. Microfluidics can be used to reproduce the TME in vitro and hence provide valuable insight on tumour evolution and drug sensitivity. However, microfluidics remains far from well-established mainstream molecular and cell biology methods. Therefore, we have developed a quick and straightforward collagenase-based enzymatic method to recover cells embedded in a 3D hydrogel in a microfluidic device with no impact on cell viability. We demonstrate the validity of this method on two different cell lines in a TME microfluidic model. Cells were successfully retrieved with high viability, and we characterised the different cell death mechanisms via AMNIS image cytometry in our model.

Indexed as

Tumor MicroenvironmentCell Culture TechniquesCell LineCellsCell SurvivalCollagenasesHumansHydrogelsMicrofluidicsCollagenasesHydrogels

Identifiers

PMID30996291
PMCPMC6470149
OpenAlexW2939560267

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.