Automated high-throughput siRNA transfection in raw 264.7 macrophages: a case study for optimization procedure.

RNAi using siRNA is a very powerful tool for functional genomics to identify new drug targets and biological pathways. Although their use in epithelial cells is relatively easy and straightforward, transfection in other cell types is still challenging. The authors report the optimization of transfection conditions for Raw 267.4 macrophage cells. The herein described procedure makes use of automated confocal microscopy, enhanced green fluorescent protein (EGFP)-expressing macrophages, and fluorescently labeled siRNAs to simultaneously quantify both siRNA uptake and silencing efficiency. A comparison of 10 commercial transfectants was performed, leading to the selection of the transfectant giving the highest reproducible knock-down effect without inducing cell toxicity or cell activation. Several buffers used for siRNA/lipid complex assembly were tested, and such a study revealed the crucial importance of this parameter. In addition, a kinetics study led to the determination of the optimal siRNA concentration and the best time window for the assay. In an original approach aimed at simultaneously optimizing both the high-throughput screening process and biological factors, optimal reagent volumes and a process flowchart were defined to ensure robust silencing efficiencies during screening. Such an account should pave the way for future genome-wide RNAi research in macrophages and present an optimization procedure for other "hard-totransfect" cell lines.

Identification of a novel sulfonamide non-nucleoside reverse transcriptase inhibitor by a phenotypic HIV-1 full replication assay.

Classical target-based, high-throughput screening has been useful for the identification of inhibitors for known molecular mechanisms involved in the HIV life cycle. In this study, the development of a cell-based assay that uses a phenotypic drug discovery approach based on automated high-content screening is described. Using this screening approach, the antiviral activity of 26,500 small molecules from a relevant chemical scaffold library was evaluated. Among the selected hits, one sulfonamide compound showed strong anti-HIV activity against wild-type and clinically relevant multidrug resistant HIV strains. The biochemical inhibition, point resistance mutations and the activity of structural analogs allowed us to understand the mode of action and propose a binding model for this compound with HIV-1 reverse transcriptase.

Automated genome-wide visual profiling of cellular proteins involved in HIV infection.

Recent genome-wide RNAi screens have identified >842 human genes that affect the human immunodeficiency virus (HIV) cycle. The list of genes implicated in infection differs between screens, and there is minimal overlap. A reason for this variance is the interdependence of HIV infection and host cell function, producing a multitude of indirect or pleiotropic cellular effects affecting the viral infection during RNAi screening. To overcome this, the authors devised a 15-dimensional phenotypic profile to define the viral infection block induced by CD4 silencing in HeLa cells. They demonstrate that this phenotypic profile excludes nonspecific, RNAi-based side effects and viral replication defects mediated by silencing of housekeeping genes. To achieve statistical robustness, the authors used automatically annotated RNAi arrays for seven independent genome-wide RNAi screens. This identified 56 host genes, which reliably reproduced CD4-like phenotypes upon HIV infection. The factors include 11 known HIV interactors and 45 factors previously not associated with HIV infection. As proof of concept, the authors confirmed that silencing of PAK1, Ku70, and RNAseH2A impaired HIV replication in Jurkat cells. In summary, multidimensional, visual profiling can identify genes required for HIV infection.