Wet-lab experience, Shinya Yamanaka, CRISPR pioneer Jennifer Doudna, State-of-the-art creator Jonathan S. Weissman
The precedent research of lab head Shinya Yamanaka in developing iPSCs and Jennifer Doudna in CRISPR were essential inspirations for our work. Both Yamanaka and Doudna were awarded Nobel Prizes for their work, in 2012 and 2020 respectively. Our algorithm stemmed from the work of Jonathan S Weissman.
Before iGEM, our team lead Navya worked in the Yamanaka Lab in Gladstone Institutes. Using CRISPR technology, she researched the role of a particular protein in the behavior of paraspeckles, subnuclear organelles in both mice and humans. During her research, she ran into a major wet lab setback and found that using software solutions was incredibly effective for transcending obstacles. The laborious and expensive lab work also required testing sgRNA strands in live cells to find a good fit. The unwieldiness of this testing process inspired Navya, her supervisor Dr. Perli, and the rest of the iGEM team to study computational solutions for sgRNA selection. We were fascinated by the efficiency of artificial intelligence as it dramatically streamlined an unwieldy biological process that would ordinarily have been done through lengthy trial and error.
CRISPR guide RNA selection: arduous, outdated and expensive
CRISPRi (dcas9-KRAB), an inactive variant of CRISPR, preempts gene expression by blocking RNA transcription, yielding low relative gene expression. However, putting CRISPRi into a cell requires an effective guide RNA. The state-of-the-art for computerized guide RNA efficacy prediction, the Weissman Algorithm, ranks the guide RNAs. When inserting the top ten guide RNAs as predicted by the algorithm, the results are as shown
Given the unpredictability shown above in guide RNA ranking with the Weissman algorithm, it would be wisest for one to test all the ten guide RNAs to see which was most effective at its duty. Moreover the designing, inserting and screening of CRISPR is an arduous, expensive and time consuming process. This unreliability in guide RNA is also a large roadblock to utilizing CRISPR in therapeutics.
One drawback of the Weissman algorithm was that it used phenotypic data to determine the efficacy of a guide RNA in training its algorithm.
Our team found a solution through Real-Time qRT-PCR to quantitatively measure CRISPRi silencing, a method not available to Weissman when he created the algorithm.
Therefore, the wet lab Real-Time qRT-PCR data measuring sgRNA effectiveness could be overlaid on the existing algorithm to train it. Then, the algorithm could be improved by various other tweaks so a better guide RNA efficacy predictor would result.
USCF Yamanaka Lab
Dr. Perli used the guides suggested by Weissman Machine learning algorithm using phenotype based scoring and off target filtering to do Real-Time qRT-PCR experiments. And found few guides hitting the target better compared to others. This itself proves that a better Machine Learning based algorithm that is trained with quality data would predict guides better.When we used our algorithm to predict scores of the guides they are very close to lab results compared to Weissman lab scores.
NYC Earthians another iGEM 2020 competing team was doing a project based on CRISPR targeting APOL3 gene in COW (Bovine).
When they tried to find guide RNAs they ended up with 100 odd guides and didn't know what to use and which one would be effective.
They in fact approached us to see if our tool can help them to find the right guide RNA. Our tool which can find good guides based on our Machine Learning Model including Off-target stringency is the right Choice. We are in the process of adding support to Bovine so we could not give them a guide right away. This itself is another proof of our concept, how finding a good sgRNA based on ML/AI for different CRISPR systems can help biologists. We will continue adding support and will let them know once we finish testing.