Evidence map›Paper›PMID 34940255›Full record

ArticleBiosensors2021

Can ECIS Biosensor Technology Be Used to Measure the Cellular Responses of Glioblastoma Stem Cells?

Laverne Diana Robilliard, Jane Yu, Sung-Min Jun, Akshata Anchan, Graeme Finlay, Catherine E Angel, Euan Scott Graham

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2021. 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
1.1field-weighted citation impact, top 25% 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

7 citing papers in PubMed, 17 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Laverne Diana RobilliardDepartment of Molecular Medicine and Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1025, North Island, New Zealand.ORCID 0000-0002-2717-5826
Jane YuDepartment of Molecular Medicine and Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1025, North Island, New Zealand.
Sung-Min JunDepartment of Molecular Medicine and Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1025, North Island, New Zealand.
Akshata AnchanDepartment of Molecular Medicine and Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1025, North Island, New Zealand.ORCID 0000-0002-2480-0104
Graeme FinlayDepartment of Molecular Medicine and Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1025, North Island, New Zealand.
Catherine E AngelSchool of Biological Sciences, Faculty of Science, University of Auckland, Auckland 1025, North Island, New Zealand.ORCID 0000-0003-1737-8539
Euan Scott GrahamDepartment of Molecular Medicine and Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1025, North Island, New Zealand.
University of Auckland · NZ

Funding

Neurological Foundation of New Zealand 1721MSNeurological Foundation of New Zealand 1922PG
6 · The paper itself

Abstract

Glioblastoma is considered the most aggressive and lethal form of brain cancer. Glioblastoma tumours are complex, comprising a spectrum of oncogenically transformed cells displaying distinct phenotypes. These can be generated in culture and are called differentiated-glioblastoma cells and glioblastoma stem cells. These cells are phenotypically and functionally distinct, where the stem-like glioblastoma cells give rise to and perpetuate the tumour. Electric cell-substrate impedance sensing (ECIS) is a real-time, label-free, impedance-based method for the analysis of cellular behaviour, based on cellular adhesion. Therefore, we asked the question of whether ECIS was suitable for, and capable of measuring the adhesion of glioblastoma cells. The goal was to identify whether ECIS was capable of measuring glioblastoma cell adhesion, with a particular focus on the glioblastoma stem cells. We reveal that ECIS reliably measures adhesion of the differentiated glioblastoma cells on various array types. We also demonstrate the ability of ECIS to measure the migratory behaviour of differentiated glioblastoma cells onto ECIS electrodes post-ablation. Although the glioblastoma stem cells are adherent, ECIS is substantially less capable at reliably measuring their adhesion, compared with the differentiated counterparts. This means that ECIS has applicability for some glioblastoma cultures but much less utility for weakly adherent stem cell counterparts.

Indexed as

Biosensing TechniquesGlioblastomaElectric ImpedanceHumansStem CellsTechnologyadhesionECIS technologyglioblastomaimpedancemigrationwound healing

Identifiers

PMID34940255
PMCPMC8699647
OpenAlexW4200528921

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.