Genetics
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Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens
Chapters
Summary June 6th, 2020
This manuscript describes a genome-scale cell-based screening approach to identify extracellular receptor-ligand interactions.
Transcript
The detection of cell surface interactions using currently available biochemical approaches still poses technical challenges. This protocol provides a genetic alternative using the genome scale cell screening approach to identify the receptor ligand interactions at the cell surface. Unlike most other existing methods, this method makes no assumptions regarding the biology of the receptors.
It provides opportunities to identify the interactions mediated by a wide range of cell surface molecules. Cell surface molecules are directly accessible to drugs such as monoclonal antibodies. Therefore, identification of interactions mediated by these molecules can have important therapeutic implications.
This method is generally applicable to researchers interested in exploring the cellular signaling and recognition processes is in a wide range of biological contexts, including neural and immunological interactions as well as host pathogen interactions. The protocol requires a high throughput cell sorting machine and next generation sequencing machines. Ensure that these facilities are available before starting the protocol.
Begin by making eight serial dilutions of the biotinylated protein samples in a 96-well plate ensuring that the final volume of each dilution is at least 200 microliters and that a tag-only control is included. Make a duplicate plate of the samples by removing 100 microliters from each well and transferring it into a new 96-well plate. Include a tag-only protein control that will be used as a control probe in all binding essays.
Add 100 microliters of 0.1 microgram per milliliter streptavidin-PE to the wells in one of the plates, and 100 microliters of dilution buffer to the wells of the other plate. Incubate the plate for 20 minutes at room temperature. Meanwhile, block the wells of the streptavidin encoded plate with the dilution buffer for 15 minutes.
After washing the plate, make sure that it is dry. Then transfer the total volume of the sample from both plates to individual wells of the streptavidin coded plate and incubate it for one hour at room temperature. After the incubation, wash the plate three times with 200 microliters of wash buffer.
Add 100 microliters of mouse anti-rat Cd4d3+4 IgG And incubate the plate for another hour. Repeat the washes. Add 100 microliters of an anti-mouse alkaline phosphatase conjugate and leave the plate at room temperature for an hour.
Then wash the wells three times with wash buffer and once with dilution buffer. Prepare p-nitrophenyl phosphate at one milligram per milliliter in dye ethanolamine buffer and add 100 microliters to each well. After a 15 minute incubation, measure the absorbance have each well at 405 nanometers.
Use the minimum dilution at which there is no signal on the plate as the appropriate dilution factor to create tetramers. Incubate streptavidin-PE and the appropriate biotinylated protein dilution for 30 minutes at room temperature. Then store conjugated proteins in a light protected tube at four degrees Celsius until further use.
Spin the conical tubes with the treated and control samples at 200 times G for five minutes. Remove the supernatant and freeze one of the tubes at minus 20 degrees Celsius for later use. Re-suspend the pellet in the other tube in 10 milliliters of PBS with 1%BSA and set aside 100 microliters of cells as a negative control on a 96-well plate.
Then add the pre-conjugated recombinant protein to the cell suspension in the conical tube and in the 96-well plate. Perform cell staining for at least one hour at four degrees Celsius on a benchtop rotor with gentle rotation. Then pellet the cells at 200 times G for five minutes and remove the supernatant.
After washing the cells twice, re-suspend them in five milliliters of PBS. Strain the cells through a 30 micrometer strainer to remove cell clusters. Then analyze them with a flow Sodor.
Use the negative control sample to gate for BFP positive and PE negative cells. Then sort the sample and collect 500, 000 to 1 million BFP positive and PE negative cells into a 1.5 milliliter centrifugation tube. The sore cage will depend on the binding of the cells to the protein, but normally one to 5%of the PE negative samples are collected.
Now let the sorted cells by centrifuging for 500 times G for five minutes, then carefully remove the supernatant. The pellet can be stored at minus 20 degrees Celsius for up to six months. Use the count function of magic to map sequences from the unsorted and sorted population to the reference library, which will yield a raw count file.
Run the test function of magic using raw counts from the unsorted control sample as control and the counts from the sorted sample as treatment. Open the generated gene summary file and rank the pause rank column in ascending order. Then identify hits with a false discovery rate of less than point 0.05.
The receptor is usually ranked highly often in the first position. Finally use R or an equivalent software to plot the robust ranking algorithm score for positive selection. Genome scale knockdown screens for the identification of the binding partner of human TNFSF9 and P falciparum or H5 of human TNFSF9 and P falciparum Rh5 were performed in NCI-SNU-1 and HEC-293 cells respectively.
The Binding behaviour of Rh5 was affected by both heparan sulfate and it's known receptor BSG whereas TN-FRSF-9 did not lose binding to its known receptor TN-FSF-9. Upon pre-incubation with soluble heparin. The GNA distribution in the control mutant library revealed that the library complexity was maintained throughout the course of the experiment.
The technical quality of the screens was assessed by examining the distribution of observed fold changes of G-RNA's targeting a reference set of non-essential genes, compared to the distribution for a reference set of essential genes. In addition, pathway level enrichment revealed that expected essential pathways were identified and significantly enriched in the dropout population. When comparing the control sample to the original plasmid library.
The robust rank algorithm or RRA-score provides a measure of which G-RNA's are consistently ranked higher than expected. In the screen for TNFRSF9, the top hit was TNFSF9, which is a known binding partner. In addition, a number of genes related to the TP53 pathway were also identified.
In the case of RH5, in addition to the known receptor, and the gene required for the production of sulfated gags, the SLC16A1 gene was also identified. The screening approach is usually very robust and the directly interacting receptor should be highly ranked on the list of genes enriched in this order of population. It is crucial to validate the hits obtained from the screen.
If the interaction is mediated by proteins, biochemical approaches designed to study binary Protein Protein interactions, for example, could be used for further validation studies. Because of the unbiased genome scale nature of this approach, we anticipate that it will aid in exploring the interactions mediated by difficult to study cell molecules such as lipids, multipass membrane proteins and glycans. It may also reveal novel pathways required for receptor trafficking and biology.
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