ArticleJournal of visualized experiments : JoVE2017
Harnessing the DNA Dye-triggered Side Population Phenotype to Detect and Purify Cancer Stem Cells from Biological Samples.
Article in Journal of visualized experiments : JoVE, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 4 citations in OpenAlex.
- Research progress on vasculogenic mimicry in colorectal cancer: mechanisms and therapeutic.Cell division · 2026Review
- Mechanisms of vasculogenic mimicry in hypoxic tumor microenvironments.Molecular cancer · 2021Review
- Teaching an old dog new tricks: reactivated developmental signaling pathways regulate ABCB1 and chemoresistance in cancer.Cancer drug resistance (Alhambra, Calif.) · 2021Review
- Therapeutic Strategies Targeting Cancer Stem Cells and Their Microenvironment.Frontiers in oncology · 2019Review
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a stem cell-driven disease and eradication of these cells has become a major therapeutic goal. Deciphering vulnerabilities of Cancer Stem Cells (CSCs) and identifying suitable molecular targets relies on methods that allow their specific discrimination in heterogeneous samples such as cell lines and ex vivo tumor tissue. Flow cytometry/FACS is a powerful technology to multi-parametrically dissect biological samples at the single cell level and is to date the method of choice to recover live cells for downstream analyses. Surface markers such as CD44 and CD133 as well as detection of aldehyde dehydrogenase enzymatic activity have often been used to define and sort out CSCs from tumor samples by FACS. A complementary approach, depicted here in methodological detail, makes use of functional dye extrusion by ABC drug transporters, which identifies a distinct population of fluorescence-dim cells commonly referred to as side population (SP). SP cancer cells exhibit canonical stem cell characteristics and can be abrogated and functionally confirmed using agents that inhibit the dye-extruding drug transporter (most frequently ABCB1/P-glycoprotein/MDR1/CD243 and ABCG2/Bcrp1/CD338). Moreover, the SP assay is compatible with other flow cytometric evaluations such as staining of surface antigens, aldehyde dehydrogenase detection and dead cell discrimination (e.g., with 7-AAD or propidium iodide (PI)). Thus, we describe a valuable and broadly applicable method for CSC identification, isolation and sub-characterization mechanistically based on a functional, rather than a phenotypic parameter. Although originally performed with Hoechst 33342 as triggering dye, we here focus on the more recent Violet dye-based SP phenotype that is resolvable on any flow cytometer equipped with a violet laser source.
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.