Evidence map›Paper›PMID 36393865›Full record

ReviewFrontiers in cell and developmental biology2022

Quantitative cell imaging approaches to metastatic state profiling.

Andres J Nevarez, Nan Hao

Abstract readReview
In one paragraph

Review in Frontiers in cell and developmental biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

2 authors.

Andres J NevarezDepartment of Molecular Biology, School of Biological Sciences, University of California, San Diego, San Diego, CA, United States.
Nan HaoDepartment of Molecular Biology, School of Biological Sciences, University of California, San Diego, San Diego, CA, United States.

Funding

Dynamically compartmentalized control of gene expression by messenger ribonucleoprotein granulesR01GM111458 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HAO, NAN · 2014 to 2022
$3.4M
NIGMS NIH HHS R01 GM111458
6 · The paper itself

Abstract

Genetic heterogeneity of metastatic dissemination has proven challenging to identify exploitable markers of metastasis; this bottom-up approach has caused a stalemate between advances in metastasis and the late stage of the disease. Advancements in quantitative cellular imaging have allowed the detection of morphological phenotype changes specific to metastasis, the morphological changes connected to the underlying complex signaling pathways, and a robust readout of metastatic cell state. This review focuses on the recent machine and deep learning developments to gain detailed information about the metastatic cell state using light microscopy. We describe the latest studies using quantitative cell imaging approaches to identify cell appearance-based metastatic patterns. We discuss how quantitative cancer biologists can use these frameworks to work backward toward exploitable hidden drivers in the metastatic cascade and pioneering new Frontier drug discoveries specific for metastasis.

Indexed as

cellular morphologydeep learninglight microscopymachine learningmetastasisquantitative imaging

Identifiers

PMID36393865
PMCPMC9640958

What OpenQuestion holds

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LicenceCC BY
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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.