Evidence map›Paper›PMID 33523141›Full record

ArticleBioinformatics (Oxford, England)2021

Biophysical Informatics Approach For Quantifying Phenotypic Heterogeneity In Cancer Cell Migration In Confined Microenvironments.

Xingjian Zhang, Michael Mak

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Compression drives diverse transcriptomic and phenotypic adaptations in melanoma.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  8. Article
  9. Article
  10. Volumetric compression develops noise-driven single-cell heterogeneity.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  11. Article
  12. 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

2 authors.

Xingjian ZhangDepartment of Biomedical Engineering, Yale University, New Haven, CT, 06510, USA.
Michael MakDepartment of Biomedical Engineering, Yale University, New Haven, CT, 06510, USA.

Funding

(PQ5) Mitochondrial Heterogeneity in Melanoma Tumor and Immune ResponsesR01CA216101 · NCI · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI GERALD SHADEL · 2018 to 2026
$5.0M
NCI NIH HHS R01 CA216101
6 · The paper itself

Abstract

motivationCancer cell heterogeneity can manifest genetically and phenotypically. Bioinformatics methods have been used to analyze complex genomics and transcriptomics data, but have not been well-established for analyzing biophysical data of phenotypically heterogeneous tumor cells. Here, we take an informatics approach to analyze the biophysical data of MDA-MB-231 cells, a widely used breast cancer cell line, during their spontaneous migration through confined environments. Experimentally, we vary the constriction microchannel geometries (wide channel, short constriction, and long constriction) and apply drug treatments. We find that cells in the short constriction are similar in morphology to the cells in the wide channel. However, their fluorescence profiles are comparable to those in the long constriction. We demonstrate that the cell migratory phenotype is correlated more to mitochondria in a non-confined environment and more to actin in a confined environment. We demonstrate that the cells' migratory phenotypes are altered by ciliobrevin D, a dynein inhibitor, in both confined and non-confined environments. Overall, our approach elucidates phenotypic heterogeneity in cancer cells under confined microenvironments at single-cell resolution.

resultsHere, we apply a bioinformatics approach to a single cell invasion assay. We demonstrate that this method can determine distinctions in morphology, cytoskeletal activities, and mitochondrial activities under various geometric constraints and for cells of different speeds. Our approach can be readily adapted to various heterogeneity studies for different types of input biophysical data. In addition, this approach can be applied to studies related to biophysical changes due to differences in external stimuli, such as treatment effects on cellular and subcellular activities, at single-cell resolution. Finally, as similar bioinformatics methods have been widely applied in studies of genetic heterogeneity, biophysical information extracted using this approach can be analyzed together with the genetic data to relate genetic and phenotypic heterogeneity. AVAILABILITY: The data that support the findings of this study are available from the corresponding author upon reasonable request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Cell MovementComputational BiologyBreast NeoplasmsCell Line, TumorHumansPhenotypeTumor Microenvironment

Identifiers

PMID33523141
PMCPMC11579712

What OpenQuestion holds

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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.