Tue. Oct 19th, 2021

Today’s genetic engineers have many resources at their disposal: an ever-increasing number of massive datasets available online, exact gene-editing tools like CRISPR, and cheap gene sequencing methods. But the increasing number of new technologies doesn’t come with a clear idea to help researchers figure out which genes to target, how to interpret results, and which genes to target. A team of scientists and engineers at the MIT Media Lab and Harvard’s Wyss Institute for Biologically Inspired Engineering, Harvard Medical School decided to make one.

An integrated pipeline has been created by the team for performing genetic screening studies.  Every step includes recognizing target genes of interest, screening and cloning genes with less time consumption and with efficiency. The protocol called Modular Perturbation Screening and Sequencing-based Target Ascertainment is described in Cell Reports Methods. On GitHub the associated open-source algorithms are available.

Modular Perturbation Screening is a streamlined workflow that allows researchers to identify genes of interest and perform genetic screens. They don’t have to think about which tool to use or what experiments to perform to get their desired results. With many existing databases and system it is fully compatible. The researchers are hoping that many scientists will be benefited by this by saving time and improve result quality.

The two co-authors of the paper were frustrated. The genetic underpinnings of different aspects of biology the two scientists were trying to explore. They combined the strength of genetic engineering and digital methods. With various tools and protocols, they kept running into problems they were using, which are commonplace in science labs.

The algorithms used to sift through an organism’s genes to identify those with a significant impact on a given biological process could tell when a gene’s expression pattern changed but didn’t provide any insight into the cause of that change. In living cells when both the scientists wanted to test a list of candidate genes, what type of experiment both should run it wasn’t immediately clear. And many of the tools available to insert genes into cells and screens are expensive, not flexible, and time-consuming. What would be required to make an end-to-end platform for genetic screening both scientists began working then. The challenge was that  should also work for all their projects.

The team created two new algorithms to help meet the need for computational tools that extract information and analyze increasingly large datasets. These datasets are generated via next-generation sequencing. The 1st algorithm takes the standard data about a gene’s expression level. With information about the state of the cell it combines with along with details about which proteins are known to interact with the gene. A high score is given to genes whose activity is correlated with significant, cell-level changes and that are highly connected to other genes. More high-level insights are provided by 2nd algorithm by during cell-type differentiation generating networks to represent the dynamic changes in gene expression. Then centrality measures are applied.

The Modular Perturbation Screening protocol moves from the laptop to the lab once the target genes have been identified.  To disrupt those genes in cells and see the effect of the perturbation on the cell their experiments are performed. It was systematically evaluated by the team of researchers multiple gene perturbation tools, including complementary DNA and many versions of CRISPR in human induced pluripotent stem cells. To unlock synergies between the two methods then created a new tool that allows CRISPR and cDNA to be used within the same cell.

By Gaby Lewis

Gabby is a postgraduate in biotechnology and has an immense interest in following technology developments. Quiet by nature, he is an avid Chess player. He is responsible for handling the office staff writers and providing them with the latest updates happenings in the world of technology.

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