ArticleScientific reports2016
Single cell dynamic phenotyping.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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.
Who cites it
9 citing papers in PubMed.
- Methods and computational tools to study eukaryotic cell migrationFrontiers in cell and developmental biology · 2024Review
- Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.Physical biology · 2021Review
- Paradoxical androgen receptor regulation by small molecule enantiomers.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Coupled Feedback Loops Involving PAGE4, EMT and Notch Signaling Can Give Rise to Non-genetic Heterogeneity in Prostate Cancer Cells.Entropy (Basel, Switzerland) · 2021Article
- Article
- Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Article
- Modeling cell population dynamics.In silico biology · 2019Article
- Image-Based Tracking of Heterogeneous Single-Cell Phenotypes.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Quantifying phenotypes in single cells using droplet microfluidics.Methods in cell biology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.
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What OpenQuestion holds
Registered trials
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.