Dose Dependent Biological Effects of Idarubicin in HL-60 Cells: Alterations of the Cell-Cycle and Apoptosis

Evaluation of the Antineoplastic Activity of L-rhamnose in vitro. A Comparison with 2-deoxyglucose

The effect of unsubstituted deoxyhexoses, 2-deoxy-D-glucose (2-DG) and L-fucose, on tumor cells has been reported in several papers throughout the last decades. That of a similar deoxysugar, L-rhamnose, which is synthesized in bacteria and plants but not in animal cells, has until today not been explored. In the present study, we examined the effect of L-rhamnose on DNA and protein synthesis, growth and the potential induction of apoptosis of tumor cells in vitro. Using 2-DG for comparison, we studied the effect of L-rhamnose in concentrations up to 20 (32 resp.) mmol/l on the initial velocity of the incorporation of labeled precursors of DNA and proteins in short term cultures of both mouse Ehrlich ascites tumor (EAT) and human HL-60 cells in vitro, and further, on cell proliferation and apoptosis induction in HL-60 cells. Neither cytotoxic nor cytostatic effects of L-rhamnose were observed with the exception of slightly pronounced inhibition of DNA synthesis in EAT cells. From the lacking inhibition of the protein synthesis it can be considered that L-rhamnose does not interfere with energy metabolism, at least not in a similar manner as 2-DG.

Combination of automated high throughput platforms, flow cytometry, and hierarchical clustering to detect cell state

BACKGROUND

This study examined whether hierarchical clustering could be used to detect cell states induced by treatment combinations that were generated through automation and high-throughput (HT) technology. Data-mining techniques were used to analyze the large experimental data sets to determine whether nonlinear, non-obvious responses could be extracted from the data.

METHODS

Unary, binary, and ternary combinations of pharmacological factors (examples of stimuli) were used to induce differentiation of HL-60 cells using a HT automated approach. Cell profiles were analyzed by incorporating hierarchical clustering methods on data collected by flow cytometry. Data-mining techniques were used to explore the combinatorial space for nonlinear, unexpected events. Additional small-scale, follow-up experiments were performed on cellular profiles of interest.

RESULTS

Multiple, distinct cellular profiles were detected using hierarchical clustering of expressed cell-surface antigens. Data-mining of this large, complex data set retrieved cases of both factor dominance and cooperativity, as well as atypical cellular profiles. Follow-up experiments found that treatment combinations producing "atypical cell types" made those cells more susceptible to apoptosis. CONCLUSIONS Hierarchical clustering and other data-mining techniques were applied to analyze large data sets from HT flow cytometry. From each sample, the data set was filtered and used to define discrete, usable states that were then related back to their original formulations. Analysis of resultant cell populations induced by a multitude of treatments identified unexpected phenotypes and nonlinear response profiles.