Evidence map›Paper›PMID 30575776›Full record

ArticleScientific reports2018

A single-cell micro-trench platform for automatic monitoring of cell division and apoptosis after chemotherapeutic drug administration.

E I Chatzopoulou, P Raharja-Liu, A Murschhauser, F Sekhavati, F Buggenthin, A M Vollmar, C Marr, J O Rädler

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

E I ChatzopoulouFaculty of Physics, Ludwig- Maximilians-Universität, Munich, Germany.
P Raharja-LiuInstitute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
A MurschhauserFaculty of Physics, Ludwig- Maximilians-Universität, Munich, Germany.
F SekhavatiFaculty of Physics, Ludwig- Maximilians-Universität, Munich, Germany.
F BuggenthinInstitute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
A M VollmarDepartment of Pharmacy, Ludwig-Maximilians-Universität, Munich, Germany.
C MarrInstitute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. carsten.marr@helmholtz-muenchen.de.ORCID http://orcid.org/0000-0003-2154-4552
J O RädlerFaculty of Physics, Ludwig- Maximilians-Universität, Munich, Germany. raedler@lmu.de.

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) EXC4Deutsche Forschungsgemeinschaft (German Research Foundation) GSC 1006
6 · The paper itself

Abstract

Cells vary in their dynamic response to external stimuli, due to stochastic fluctuations and non-uniform progression through the cell cycle. Hence, single-cell studies are required to reveal the range of heterogeneity in their responses to defined perturbations, which provides detailed insight into signaling processes. Here, we present a time-lapse study using arrays of micro-trenches to monitor the timing of cell division and apoptosis in non-adherent cells at the single-cell level. By employing automated cell tracking and division detection, we precisely determine cell cycle duration and sister-cell correlations for hundreds of individual cells in parallel. As a model application we study the response of leukemia cells to the chemostatic drug vincristine as a function of cell cycle phase. The time-to-death after drug addition is found to depend both on drug concentration and cell cycle phase. The resulting timing and dose-response distributions were reproduced in control experiments using synchronized cell populations. Interestingly, in non-synchronized cells, the time-to-death intervals for sister cells appear to be correlated. Our study demonstrates the practical benefits of micro-trench arrays as a platform for high-throughput, single-cell time-lapse studies on cell cycle dependence, correlations and cell fate decisions in general.

Indexed as

Cell Culture TechniquesDrug Screening Assays, AntitumorSingle-Cell AnalysisAntineoplastic AgentsApoptosisAutomation, LaboratoryCell DivisionCell TrackingHumansMicrotechnologyTime-Lapse ImagingTissue ScaffoldsTumor Cells, CulturedAntineoplastic Agents

Identifiers

PMID30575776
PMCPMC6303304

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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.