Comparison of Three Commercially Available DIGE Analysis Software Packages: Minimal User Intervention in Gel-Based Proteomics

The success of high-performance differential gel electrophoresis using fluorescent dyes (DIGE) depends on the quality of the digital image captured after electrophoresis, the DIGE enabled image analysis software tool chosen for highlighting the differences, and the statistical analysis. This study compares three commonly available DIGE enabled software packages for the first time: DeCyder V6.5 (GE-Healthcare), Progenesis SameSpots V3.0 (Nonlinear Dynamics), and Dymension 3 (Syngene). DIGE gel images of cell culture media samples conditioned by HepG2 and END2 cell lines were used to evaluate the software packages both quantitatively and subjectively considering ease of use with minimal user intervention. Consistency of spot matching across the three software packages was compared, focusing on the top fifty spots ranked statistically by each package. In summary, Progenesis SameSpots outperformed the other two software packages in matching accuracy, possibly being benefited by its new approach: that is, identical spot outline across all the gels. Interestingly, the statistical analysis of the software packages was not consistent on account of differences in workflow, algorithms, and default settings. Results obtained for protein fold changes were substantially different in each package, which indicates that in spite of using internal standards, quantification is software dependent. A future research goal must be to reduce or eliminate user controlled settings, either by automatic sample-to-sample optimization by intelligent software, or by alternative parameter-free segmentation methods

Selection of anti-leukemic drugs from kinase inhibitor library screens

<p>(a) A representative screening plate of a kinase inhibitor library consisting of 244 inhibitors. The ATP content of K562 cells (a measure of cell viability) was measured and the luminescence intensity of each well was color-coded according to the scale presented (RLU, relative luminescence unit). The corresponding compound ID in the plate map is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102221#pone.0102221.s003" target="_blank">Table S1</a>. (b) A representative screening plate of a kinase inhibitor library treated with 0.125 µM imatinib. The kinase inhibitors selected from the screens are indicated with black borders. (c) The kinase inhibitors are rank-ordered from the lowest to the highest cell viability in K562 cells without imatinib (red bars). The corresponding viability of IMR and K562 cells with imatinib are indicated using blue and green bars. Several drugs are selectively killing K562 cells.</p

Identification of Drug Combinations Containing Imatinib for Treatment of BCR-ABL+ Leukemias

<div><p>The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance. In this study, we identified eleven candidate anti-leukemic drugs that might be combined with imatinib, using three approaches: a kinase inhibitor library screen, a gene expression correlation analysis, and literature analysis. We then used an experimental search algorithm to efficiently explore the large space of possible drug and dose combinations and identified drug combinations that selectively kill a BCR-ABL+ leukemic cell line (K562) over a normal fibroblast cell line (IMR-90). Only six iterations of the algorithm were needed to identify very selective drug combinations. The efficacy of the top forty-nine combinations was further confirmed using Ph+ and Ph- ALL patient cells, including imatinib-resistant cells. Collectively, the drug combinations and methods we describe might be a first step towards more effective interventions for leukemia patients, especially those with the BCR-ABL translocation.</p></div

Results of statistical analysis using repeated measures one-way ANOVA with Dunnett's multiple comparisons between patient cells and normal mononuclear blood cells (N1) on viability of the best drug combinations originating from the cell lines search and from pairs of single agents plus imatininb.

<p>Patients B001 and B002 are BCR-ABL+ (ALL). Patients B031 to B037 are BCR-ABL- (ALL).</p

1H-MRS and GC-MS metabolic profiles of bone marrow and peripheral blood samples at the time of ALL diagnosis.

<p>Representative spectra of BM (blue line) and PB (red line) specimens. Spectra were acquired from (A) 1H-MRS analysis of filtered polar fractions, (B) 1H-MRS analysis of recovered whole lipid fractions, and (C) GC-MS analysis of FFA extracts. Metabolites with the greatest difference between BM and PB are labeled and include alanine (Ala), free cholesterol (CHOL), cholesterol esters (CHOLest), choline (Cho), formate (For), glucose (Glc), glutamate (Glu), glutamine (Gln), lactate (Lac), histidine (His), hypoxanthine (Hpx), palmitic acid, oleic acid, triacylglyceride (TAG), and uridine (Ur). Other abbreviations used are: (2HB), 2-hydroxybutyrate; (3HB), 3-hydroxybutyrate; (2Og), 2oxo-glutarate; (2Oic), 2oxo-isocaproate; (BAA), branched amino acids; (Car), carnitine; (Cho), choline; (CHOL), free cholesterol, (CHOLest), cholesterol esters; (For), formate; (Fum), fumarate; (Glyc), glycerol; (GPCho), glycero-3-phosphocholine; (Hpx), hypoxanthine; (Lac), lactate; (Niac), niacinamide; (Pglu), pyroglutamate; (Pyr), pyruvate; (Ur), uridine; (Pdx), pyridoxine; (TAG), triacylglyceride; (T-Chol), total cholesterol.</p

Iterative search for a highly selective drug combinations

<p>(a) Increased selectivity after each iteration step. The lines represent the selectivity of the drug combinations at as triplets and at each of the six iteration steps. (b) A dot plot showing corrected selectivity (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102221#s2" target="_blank">Results</a>) through the iterative searches. The pie symbols shows the relative contributions of the 12 drugs for the best combination obtained at each iteration. (c) Cluster analysis on the optimized drug combinations. The top ranked 200 combinations clustered into two groups and the frequencies of the doses of the individual drugs are shown for each cluster.</p

Scheme of our search strategy

<p>(a) anti-leukemic drugs were selected using three approaches: kinase inhibitor library screens, correlation analysis, and literature survey. (b) Dose responses of single agent and pair-wise analysis with a fixed dose of imatinib (pairs and triplets) were performed. (c) The drug combinations were optimized using iterative search algorithm (d) The optimal drug combinations were validated with primary patient cells.</p

Concentrations (µM) and levels of 11 small molecules used with imatinib in the combinatorial drug searches.

<p>Concentrations (µM) and levels of 11 small molecules used with imatinib in the combinatorial drug searches.</p

Top 10 kinases that show a protective response in hypoxia when inhibited.

<p>The data were obtained using KIEN on the entire kinase inhibitor library. The coefficient <i>β</i>_<i>k</i>, a measure of the protective impact of each kinase, was calculated and the kinases were ranked using this parameter in order to estimate the main kinases influencing the protective mechanisms against hypoxia. In bold we show kinases important for the response of both cell lines.</p><p>Top 10 kinases that show a protective response in hypoxia when inhibited.</p

The list and targets of the drugs used in the combinatorial studies.

<p>The list and targets of the drugs used in the combinatorial studies.</p