ArticleJournal of visualized experiments : JoVE2012
Optimized staining and proliferation modeling methods for cell division monitoring using cell tracking dyes.
Article in Journal of visualized experiments : JoVE, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 45 citations in OpenAlex.
- Decoding senescence of aging single cells at the nexus of biomaterials, microfluidics, and spatial omics.npj aging · 2024Review
- Tracing Quiescent Cancer Cells In Vivo.Cancers · 2024Review
- Single Mesenchymal Stromal Cell Migration Tracking into Glioblastoma Using Photoconvertible Vesicles.Nanomaterials (Basel, Switzerland) · 2024Article
- Phosphatase, MgHaematologica · 2024Article
- Cellular Dormancy in Cancer: Mechanisms and Potential Targeting Strategies.Cancer research and treatment · 2023Review
- Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics.American journal of physiology. Cell physiology · 2023Review
- Emerging role of exosomes in vascular diseases.Frontiers in cardiovascular medicine · 2023Review
- Slow-cycling (dormant) cancer cells in therapy resistance, cancer relapse and metastasis.Seminars in cancer biology · 2022Review
- Novel dual-fluorescent flow cytometric approach for quantification of macrophages infected withParasitology · 2022Article
- Potency Assessment of Dendritic Cell Anticancer Vaccine: Validation of the Co-Flow DC Assay.International journal of molecular sciences · 2021Article
- Alterations of Specific Lymphocytic Subsets with Aging and Age-Related Metabolic and Cardiovascular Diseases.Life (Basel, Switzerland) · 2020Article
- Design of experiment (DoE)-drivenJournal of extracellular vesicles · 2020Article
- Liver fluke granulin promotes extracellular vesicle-mediated crosstalk and cellular microenvironment conducive to cholangiocarcinoma.Neoplasia (New York, N.Y.) · 2020Article
- TNF-α Production by Monocytes Stimulated With Epstein-Barr Virus-Peptides as a Marker of Immunosuppression-Related Adverse Events in Kidney Transplant Recipients.Kidney international reports · 2019Article
- Fluorescent Tracking of Yeast Division Clarifies the Essential Role of Spleen Tyrosine Kinase in the Intracellular Control ofFrontiers in immunology · 2018Article
- Monitoring Cell Proliferation by Dye Dilution: Considerations for Probe Selection.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Dermatophagoides pteronyssinus immunotherapy changes the T-regulatory cell activity.Scientific reports · 2017Article
- Functional Characterization of Regulatory Macrophages That Inhibit Graft-reactive Immunity.Journal of visualized experiments : JoVE · 2017Article
- Anti-GITR therapy promotes immunity against malignant glioma in a murine model.Cancer immunology, immunotherapy : CII · 2016Article
- Flow cytometry protocols for surface and intracellular antigen analyses of neural cell types.Journal of visualized experiments : JoVE · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.(1-5) Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of: stem and progenitor cell quiescence, proliferation and/or differentiation(6-8) antigen-driven membrane transfer(9) and/or precursor cell proliferation(3,4,10-18) and immune regulatory and effector cell function(1,18-21). Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes(1,2,11). "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins(4,16,18). Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class(2-4,16,18,24). The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are: Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies. Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 10(2) - 10(3) times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used. Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile. Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.
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.