Evidence map›Paper›PMID 41701768›Full record

ArticlePloS one2026

A live cell biosensor protocol for high-resolution screening of therapy-resistant cancer cells.

Viral D Oza, Colin S Williams, Jessica S Blackburn

Abstract read
In one paragraph

Article in PloS one, 2026. 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
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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Viral D OzaDepartment of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, Kentucky, United States of America.ORCID https://orcid.org/0000-0003-4877-9618
Colin S WilliamsDepartment of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, Kentucky, United States of America.ORCID https://orcid.org/0009-0008-2084-1022
Jessica S BlackburnDepartment of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, Kentucky, United States of America.ORCID https://orcid.org/0000-0001-9464-3784

Funding

University of Kentucky Markey Cancer Center Support Grant ECIA SupplementP30CA177558 · NCI · UNIVERSITY OF KENTUCKY · PI Jennifer F Rogers · 2013 to 2026
$38.3M
NCI NIH HHS P30 CA177558
6 · The paper itself

Abstract

The Genetically Encoded Death Indicator (GEDI) is a ratiometric, dual-fluorescence biosensor that enables real-time detection of cell death through calcium influx. Originally developed for use in neurodegeneration models, GEDI can be applied to cancer cells to quantify therapy-induced death at single-cell resolution. This protocol details how to generate GEDI-expressing cancer cell lines, empirically determine stress-induced GEDI thresholds using radiation or chemotherapeutic agents, and perform time-resolved imaging and image analysis to track cell fate. This workflow is optimized for high-throughput drug and radiation screening in heterogeneous populations and is especially useful for identifying chemo- and radio-resistant subclones. Key limitations include the need for empirical GEDI threshold calibration for each treatment condition and careful standardization of imaging parameters. The protocol outputs include GEDI ratio values, single-cell time-of-death annotations, and whole-cell morphological data in parallel, which can be linked to downstream applications such as FACS-based isolation of live or dying subpopulations, transcriptomic profiling of resistant clones, or in vivo validation using xenografts or organotypic slice culture.

Indexed as

Biosensing TechniquesDrug Resistance, NeoplasmNeoplasmsAnimalsCell DeathCell Line, TumorHumansMiceSingle-Cell Analysis

Identifiers

PMID41701768
PMCPMC12912615

What Socratic 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 Socratic graph.