Please cite as: CSH Protocols; 2007; doi:10.1101/pdb.prot4897
| Protocol |
This protocol was adapted from "High-Content and High-Throughput Screening," Chapter 13, in Cell Imaging (ed. Stephens). Scion Publishing Ltd., Oxfordshire, UK, 2006.
INTRODUCTION
RNA interference using siRNA libraries is a powerful technology for elucidating gene function by downregulating gene expression at the post-transcriptional level. Phenotypic changes associated with siRNA knockdown can be monitored using cell lines expressing fluorescent reporter proteins. Test siRNAs are transiently transfected into the reporter cell line and behavior of the fluorescent reporter probe is monitored using a high-content imaging system. A range of cell features and additional fluorescent probes can be monitored to assess the effects of knockdown. This article describes how to optimize siRNA knockdown in EGFP-expressing cell lines.
RELATED INFORMATION
Information about choosing and handling cells for use with this protocol is provided in Preparation of Cells in 96-Well Plates for siRNA Transfection and High-Content Analysis. A protocol for Analysis of siRNA Knockdown of Cell-Cycle Control Genes in G1/S and G2/M Cell-Cycle Phase Marker Cell Lines Using Multiplexed High-Content Analysis is also available.
MATERIALS
Reagents
Cell suspension at 5000 cells/100 µL in antibiotic-free medium with 10% FBS (see Preparation of Cells in 96-Well Plates for siRNA Transfection and High-Content Analysis)
Culture medium (antibiotic- and serum-free; Sigma)
DharmaFECT transfection reagents 1, 2, 3, and 4 (Dharmacon)
Formaldehyde solution, neutral buffered (4%; Sigma)
Hoechst 33342 (2 µM in 1X PBS; Sigma)
Phosphate-buffered saline (PBS) (1X; sterile, calcium- and magnesium-free)
siRNA (EGFP-targeted; Dharmacon) with siRNA buffer (RNase-free; Dharmacon)
Equipment
Incubator, tissue culture (37°C, 5% CO2, humidified)
Microscope, automated fluorescence, with excitation and emission filters for Hoechst and EGFP, and image analysis software (e.g., IN Cell Analyzer 1000 or IN Cell Analyzer 3000; GE Healthcare)
Plates, 96-well imaging-grade (e.g., microclear black [Greiner] or ViewPlate [Packard])
METHOD
TROUBLESHOOTING
Problem: Background is high and/or analysis is unsuccessful.
[Step 9]
Solution: Take care to minimize cell disruption and avoid creating vortices when adding reagents to wells. Cells that are dead, dying, dividing, or blocked in mitosis may be weakly adherent and therefore particularly susceptible to disturbance. Selective loss of these cell subpopulations will skew the analysis results. Keep the formaldehyde incubation time (Step 6) between 20 and 30 min to avoid under- or overfixing the cells. To minimize background fluorescence, use the plates, serum, and culture medium from the recommended suppliers (see Preparation of Cells in 96-Well Plates for siRNA Transfection and High-Content Analysis).
Problem: Image quality needs improvement and/or analysis is unsuccessful.
[Step 9]
Solution: Inspect images to ensure that field illumination is even and that features to be quantified are bright and in focus. If not, adjust the instrument and acquisition set-up and reimage the samples. If reacquisition is not possible, try applying noise reduction or shading correction techniques before analysis. Overexposure of images can be as detrimental as underexposure. Check pixel gray-level values of representative cell features to ensure that they fall within the dynamic range of the camera. If not, reimage the sample using a shorter exposure time, decrease the laser power, or add a neutral-density filter. Good segmentation of nuclei, cell bodies, and features of interest is also critical for success. When creating a new analysis protocol, assess the analysis results and ensure that the software has identified the targeted population of cells (and is not, for example, mistaking cell debris for legitimate cells). Most analysis software provides visualization tools such as bitmap overlays that allow you to assess the results. When using IN Cell Analyzer 1000 software, if too few or too many objects are detected, try systematically varying the sensitivity settings during object segmentation. For cell identification in particular, try varying the segmentation method as well. When creating a classification protocol, ensure that the image quality of the training data set is excellent and that all objects of interest are well segmented before proceeding to annotation.
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