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Vendors, Academics Say High-Content RNAi Screening Will Take Genome-Wide Approach

PRAGUE, Czech Republic—RNAi screening will increasingly take place on a genome-wide scale, according to an official at Thermo Fisher Scientific subsidiary Dharmacon.
The use of high-content analysis for RNAi screening was the topic of four presentations at Cambridge Healthtech Institute’s High Content Analysis Europe meeting held here this week. Anja Smith, an assay development scientist with ThermoFisher’s Dharmacon Group cited the fact that each speaker presented work that involved genome-wide RNAi screening as evidence of this growing trend.   
For example, Martin Stöter, a scientist in the high-content analysis and high-throughput development studio at the Max Planck Institute of Molecular and Cell Biology in Dresden, Germany, and his team found that although the correlation between two runs was high, the correlation of the viability phenotype between two different siRNAs targeting the same mRNA was low.
This finding led the researchers to conclude that multiparametric HCA is important to clearly identify what Stöter referred to as the “on-target” phenotype and that multiple siRNAs targeting the same mRNA will provide more information than repetitions of siRNA experiments.
Stöter and his colleagues conducted a genome-wide siRNA endocytosis screening project. The researchers transfected HeLa cells with three RNAi libraries (Ambion: two to three oligos per gene; esiRNA: RNAase III oligo pools; and Qiagen, four oligos per gene) in a three-color assay.
EGF-AF488 stained the degradative pathways green and Transferrin-AF647 stained cellular recycling pathways red. DAPI and SYTO stained the nucleus and the cytoplasm blue, respectively. Imaging was done using PerkinElmer’s Opera system.  
The investigators’ 62-parameter image analysis sought to determine the number of vesicles, the mean size of the vesicles, the mean vesicle fluorescent intensity, and the total signal associated with all the vesicles per image. The team also looked at the subcellular position of endosomes, or the mean endosomal distance to the nucleus; and endosome clustering, or the mean distance between endosomes; the peak density in the cluster; and the cluster’s proportion to the overall clustered fraction. 
Stoter reported that the screen was still ongoing, and that the results would be available within the next few weeks.
In another presentation, Michael Hannus, a group leader in cell-based discovery and validation at Cenix Bioscience in Dresden, Germany, discussed his team’s application of an RNAi-based infection assay in human hepatoma cells to identify novel anti-malarial targets and lead compounds. Their ultimate goal was to identify human liver genes that are non-essential to the host, but necessary for Plasmodium berghei infection.

“There is no advantage to pooling more than four oligonucleotides.”

Hannus’ team identified Scavenger Receptor B1 as a critical host factor for both host cell invasion and parasite development. They concluded that their work established proof-of-concept for using high-throughput RNAi screening to find non-essential host factors that may represent novel drug targets for malaria and other parasitic diseases.
The investigators began by seeding Huh7 cells in 96-well plates. They transfected the cells with three Ambion siRNA libraries for more than 800 genes 24 hours later. The researchers added freshly isolated P. berghei sporozoites 48 hours after they seeded the Huh7 cells, fixed and stained them with Hoeshst, Phalloidin, and 2E6, and then analyzed the images.
Knights of the Roundtable  
In a round-table discussion at the conference, Stöter, Hannus, and other participants expressed concern about the use of pooled RNAi oligonucleotides compared to single oligos.
Dharmacon’s Smith said that one advantage of pooling oligonucleotides is that the dilution reduces any off-target effects. However, she noted that she and her team have found that there is no advantage to pooling more than four oligonucleotides.
Stöter then raised the question of whether different cell types respond differently in terms of transfection efficiency. As he pointed out, this technology is not yet fully understood and investigators are using RNAi libraries for which they do not completely understand the biology.

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