Evidence map›Paper›PMID 25980547›Full record

ReviewCurrent genetics2015

High-throughput cell-cycle imaging opens new doors for discovery.

Nathan J Kuwada, Beth Traxler, Paul A Wiggins

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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  5. Review
  6. Article
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  9. Review
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

3 authors.

Nathan J KuwadaDepartment of Physics, University of Washington, Seattle, WA, 98195, USA. nateman@uw.edu.
Beth TraxlerDepartment of Microbiology, University of Washington, Seattle, WA, 98195, USA.
Paul A WigginsDepartment of Physics, University of Washington, Seattle, WA, 98195, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the life of a cell, numerous essential cellular processes must be coordinated both spatially and temporally, from DNA replication and chromosome segregation to gene expression and cytokinesis. In order to analyze these inherently dynamic and cell-cycle-dependent processes, it is essential to observe the dynamic localization of the cellular machinery throughout the entire cell cycle. Although some coarse features of cell-cycle dynamics can be captured in snapshot imaging, where cellular size or morphology can be used as a proxy for cell-cycle phase, the inherently stochastic nature of ultrastructures in the cell makes the direct visualization of subcellular dynamics an essential tool to differentiate between structural differences that are the result of biologically relevant dynamics versus cell-to-cell variation. With these goals in mind, we have developed a unique high-throughput imaging approach, and have recently applied this to characterize the cell-cycle localization of nearly every protein in the bacterial cell (Kuwada in Mol Microbiol, 95(1), 64-79, 2015). This approach combines large-format sample preparation with automated image capture, processing, and analysis to quantitatively characterize proteome localization of tens of thousands of complete cell cycles.

Indexed as

DNA ReplicationGene Expression Regulation, BacterialBacterial ProteinsCaulobacter crescentusCell CycleEscherichia coliHigh-Throughput Screening AssaysImage Processing, Computer-AssistedMolecular ImagingProteomeRecombinant Fusion ProteinsSingle-Cell AnalysisTime-Lapse ImagingBacterial ProteinsProteomeRecombinant Fusion ProteinsBacterial protein dynamicsHigh-throughput imagingQuantitative cell biology

Identifiers

What OpenQuestion holds

Textmetadata
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.