ArticleMethods in cell biology2014
User-friendly tools for quantifying the dynamics of cellular morphology and intracellular protein clusters.
Article in Methods in cell biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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
8 citing papers in PubMed.
- Diverse ancestral myosin motors generate and segregate distinct types of nanocluster-rich domains at the plasma membrane.Science advances · 2026Article
- Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells.Scientific reports · 2020Article
- Engineering proteins for allosteric control by light or ligands.Nature protocols · 2019Article
- A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking.PloS one · 2019Article
- Computational design of chemogenetic and optogenetic split proteins.Nature communications · 2018Article
- Profiling cellular morphodynamics by spatiotemporal spectrum decomposition.PLoS computational biology · 2018Article
- A Practical Approach to Quantitative Processing and Analysis of Small Biological Structures by Fluorescent Imaging.Journal of biomolecular techniques : JBT · 2016Article
- Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.Integrative biology : quantitative biosciences from nano to macro · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Understanding the heterogeneous dynamics of cellular processes requires not only tools to visualize molecular behavior but also versatile approaches to extract and analyze the information contained in live-cell movies of many cells. Automated identification and tracking of cellular features enable thorough and consistent comparative analyses in a high-throughput manner. Here, we present tools for two challenging problems in computational image analysis: (1) classification of motion for cells with complex shapes and dynamics and (2) segmentation of clustered cells and quantification of intracellular protein distributions based on a single fluorescence channel. We describe these methods and user-friendly software(1) (MATLAB applications with graphical user interfaces) so these tools can be readily applied without an extensive knowledge of computational techniques.
Indexed as
Identifiers
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.