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With the identification and cloning of cell- and stage-specific markers, glial cell analysis techniques have grown beyond physical methods of tissue dissociation and cell culture, and become highly specific with immunoselection of cell cultures in vitro and genetic targeting in vivo.
Overcoming this obstacle will likely require high throughput single cell analysis techniques that are just starting to be available.
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In addition, this report also reviews high-throughput, integrated single-cell analysis techniques for the discovery of multifunctional T cells, which have shown promise in controlling HIV progression.
Therefore, analysis focused on subpopulations combined with single-cell analysis techniques will help progress our understanding of the bacterial subpopulation behavior in laboratory cultures.
We suggest that cell image analysis techniques can provide more sensitive information on the DNA content of tumour cells by direct measurement of nuclear DNA density of both normal lymphocytes and tumour cells in the same section.
To perform these studies, we simplified the HCS cell cycle analysis techniques described by Gesparri et al. [37] to a DNA content (DAPI /EdU incorporation biparametric analysis to identify G1, S phase, and G2 cells in unsynchronized growing cells (Fig. 6A).
Previously, as an alternative microarray-based cell cycle analysis technique, Lu et al. introduced the "expression deconvolution" method [ 34].
To further characterize processing of PbSERA3 in adherent infected HepG2 cells, we used single cell analysis using IFA techniques.
A grid-cell-based analysis technique was employed to integrate the LandScan population and land-cover data, creating grid maps of population density and the density of each land-cover category.
We quantified cell number, area of coverage and degree of cell spreading using image analysis techniques.
Alternatively, a hybrid technology employing both cell sorter and image analysis techniques may be extremely desirable for this problem.
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