Evidence map›Paper›PMID 33183152›Full record

ArticleSLAS technology2021

Development of an Enhanced-Throughput Radial Cell Migration Device.

C Ryan Oliver, Andrew C Little, Trisha M Westerhof, Pragathi Pathanjeli, Joel A Yates, Sofia D Merajver

Open access · greenAbstract read
In one paragraph

Article in SLAS technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 88% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 1 institution in 1 country.

C Ryan OliverDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-4042-6132
Andrew C LittleDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Trisha M WesterhofDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Pragathi PathanjeliDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Joel A YatesDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Sofia D MerajverDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
University of Michigan · US

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 1988 to 2026
$178.2M
Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
Training Program in Tumor MicroenvironmentT32CA009676 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Maria G Castro, Analisa Virginia DiFeo · 1992 to 2026
$7.3M
Immune-suppressive Myeloid Cells in the Glioma Microenvironment: Signaling Mechanisms and Novel Therapeutic StrategiesR37NS094804 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CASTRO, MARIA G · 2015 to 2021
$2.9M
Engineering the Brain Immune System for Tumor TherapyR01NS057711 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CASTRO, MARIA G · 2009 to 2013
$2.0M
Crosstalk between glioma cells & immune cells in the tumor microenvironment: TherR01NS074387 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CASTRO, MARIA G · 2011 to 2015
$1.9M
Interactions between the tumor cells and the neuro-immune microenvironment in mutant IDH1 gliomas: implications for therapeuticsR01NS105556 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CASTRO, MARIA G · 2018 to 2021
$1.9M
Advanced development and validation of an in vitro platform to phenotype brain metastatic tumor cells using artificial intelligenceR33CA261696 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FU, JIANPING, MERAJVER, SOFIA DIANA · 2022 to 2024
$1.1M
Artificial Intelligence driven prediction of brain metastasis from primary tumor sites at diagnosisR21CA245597 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MERAJVER, SOFIA DIANA · 2020 to 2021
$401k
NCATS NIH HHS UL1 TR002240NCI NIH HHS P30 CA046592NCI NIH HHS R21 CA245597NCI NIH HHS R33 CA261696NCI NIH HHS T32 CA009676NINDS NIH HHS R01 NS057711NINDS NIH HHS R01 NS074387NINDS NIH HHS R01 NS105556NINDS NIH HHS R37 NS094804
6 · The paper itself

Abstract

It is often desirable to evaluate the ability of cells to move in an unrestricted manner in multiple directions without chemical gradients. By combining the standard radial migration assay with injection-molded gaskets and a rigid fixture, we have developed a highly reliable and sensitive method for observing and measuring radial cell migration. This method is adapted for use on high-throughput automated imaging systems. The use of injection-molded gaskets enables low-cost replacement of cell-wetted components. Moreover, the design enables secondary placement of attractants and co-cultures. This device and its enhanced throughput permit the use of therapeutic screening to evaluate phenotypic responses, for example, cancer cell migration response due to drugs or chemical signals. This approach is orthogonal to other 2D cell migration applications, such as scratch wound assays, although here we offer a noninvasive, enhanced-throughput device, which currently is not commercially available but is easily constructed. The proposed device is a systematic, reliable, rapid application to monitor phenotypic responses to chemotherapeutic screens, genetic alterations (e.g., RNAi and CRISPR), supplemental regimens, and other approaches, offering a reliable methodology to survey unbiased and noninvasive cell migration.

Indexed as

NeoplasmsBiological AssayCell MovementHumansautomated biologycell seedinghigh-content imaginghigh-throughput screeningHTSmigration assay

Identifiers

PMID33183152
PMCPMC7987888
OpenAlexW3101494551

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.