Background: Analysis of the DNA cell cycle and glutathione content cannot be performed on viable cells, because the fluorescence emissions of the DNA-specific probe Hoechst 33342 and the glutathione-specific probe monobromobimane overlap completely. We decided to explore whether the emissions could
Cell size, DNA, and cytokeratin analysis of human head and neck tumors by flow cytometry
β Scribed by J. Th. Bijman; D. J. Th. Wagener; J. M. C. Wessels; P. van den Broek; F. C. S. Ramaekers
- Publisher
- John Wiley and Sons
- Year
- 1986
- Tongue
- English
- Weight
- 495 KB
- Volume
- 7
- Category
- Article
- ISSN
- 0196-4763
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β¦ Synopsis
Cell subsets have been discriminated in cell suspensions derived from 37 human head and neck tumors by means of light scatter, DNA, and cytokeratin flow cytometry (FCM). Cell dispersion was performed overnight at 4Β°C in two different enzyme mixtures, i.e., trypsin/ dithioerythritol and collagenase/l)Nase, under slight agitation of sliced tumor tissue. Cells were examined before and after fractionation on a discontinuous low-density bovine serum albumin (BSA) gradient.
Forward and right-angle light scatter FCM of 23 tumor specimens revealed four main subpopulations with different size and structure. Fractionation of primary cell suspensions on a BSA gradient at unit gravity separated debris, small cells and large cells. DNA FCM of the enriched populations demonstrated a relation between large cells and DNA aneuploidy. Epithelial cells, as recognized by cytokeratin antibodies, were also related with large cells.
The results demonstrated the usefulness of light scatter, DNA, and cytokeratin analysis of crude and fractionated tumor cell suspensions for assessment of the efficacy of a particular dispersion technique and t~ obtain information of the cell subsets dispersed.
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## Abstract ## Background: The analysis of cells from multiple experimental groups by multiparameter flow cytometry leads to the generation of complex data sets, for which adequate analysis tools are not commonly available. We report here that software designed for transcriptomics applications can