CM – Take the guesswork out of genetic engineering: The STAMPScreen pipeline helps optimize genetic studies in mammalian cells

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September 27, 2021

from Harvard University

Today’s genetic engineers have a wealth of resources: an ever-increasing number of huge data sets available online, high-precision gene manipulation tools like CRISPR, and cheap gene sequencing methods. However, the proliferation of new technologies has not brought with it a clear roadmap to help researchers figure out which genes they are targeting, what tools they are using, and how to interpret their results. So a team of scientists and engineers from the Wyss Institute for Biologically Inspired Engineering at Harvard, Harvard Medical School (HMS), and the MIT Media Lab decided to make one.

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The Wyss team has built an integrated pipeline for conducting genetic screening studies that encompasses every step of the process from identifying the target genes of interest to cloning and rapid and efficient screening. The protocol called Sequencing-based Target Ascertainment and Modular Perturbation Screening (STAMPScreen) is described in Cell Reports Methods, and the associated open source algorithms are available on GitHub.

« STAMPScreen is a streamlined workflow that makes it easy for researchers makes it easy to identify genes of interest and run genetic screens without having to guess which tool or experiments to perform to get the results you want, ”said corresponding author Pranam Chatterjee, Ph. D., a former graduate student at MIT Media Lab, who is now a Carlos M. Varsavsky Research Fellow at HMS and the Wyss Institute. « It is fully compatible with many existing databases and systems, and we hope that many scientists can use STAMPScreen to save time and improve the quality of their results. »

Chatterjee and Christian Kramme, a co-lead author of the Paper, were frustrated. The two scientists tried to explore the genetic basis of various aspects of biology – like fertility, aging, and immunity – by combining the strengths of digital methods (think algorithms) and genetic engineering (think gene sequencing). But they kept struggling with the various tools and protocols they used that are common in scientific laboratories.

The algorithms that purported to scan an organism’s genes to identify those that had a significant impact on a certain biological process were able to recognize when the expression pattern of a gene changed, but did not provide any insight into the cause of this change. When they tried to test a list of candidate genes in living cells, it wasn’t immediately clear what type of experiment to conduct. And many of the tools available to insert genes into cells and screen them have been expensive, time consuming, and inflexible.

“I used methods known as the Golden Gate and Gateway to put genes into vectors for screening experiments and it took me months and thousands of dollars to clone 50 genes into which vector went, which was a key requirement for my experimental design based on downstream sequencing, « says Kramme, PhD student at Wyss Institute and HMS,

Kramme teamed up with Alexandru Plesa, co-first author and Church lab colleague, who was experiencing identical frustrations, to develop gene vectors for his project, and Kramme, Plesa, and Chatterjee then set out to sketch what would be needed to create an end-to-end genetic screening platform that would work for all of their projects, from protein engineering to fertility and aging.

To improve the earliest phase of genetic research – the identification of genes to be studied – the team developed two new algorithms to meet the need for computing tools that can extract information from the ever larger datasets that can be generated, analyzed and extracted by next-generation sequencing (NGS). The first algorithm takes the standard data about a gene’s level of expression and combines it with information about the state of the cell, as well as information about which proteins are known to interact with the gene. The algorithm gives genes that are strongly linked to other genes and whose activity correlates with large changes at the cellular level a high score. The second algorithm provides better insight by generating networks to represent the dynamic changes in gene expression during cell type differentiation and then applying centrality measures such as Google’s PageRank algorithm to rank the key regulators of the process.

“The computational part of genetic studies is like a Jenga game: if every block in the tower represents a gene, we look for the genes that form the basis of the Jenga tower, that keep the whole thing going. Algorithms can only help you say which genes are in the same row, but ours allow you to figure out how far up or down the tower they are, so you can quickly identify the ones that have the greatest impact on the cell state in question, « Chatterjee said.

Once the target genes have been identified, the STAMPScreen protocol moves from the laptop to the laboratory, where experiments are carried out to destroy these genes in cells and see what effect this disorder has on the cell. The research team systematically evaluated several Gene disruption instruments, including complementary DNA (cDNA) and multiple versions of CRISPR in human induced pluripotent stem cells (hiPSCs), the first known directly en comparisons done entirely in this very versatile, yet challenging cell type.

They then developed a new tool that allows CRISPR and cDNA to be used in the same cell to develop synergies between the two methods. For example, CRISPR can be used to turn off expression of all isoforms of a gene, and cDNA can be used to sequentially express each isoform individually, allowing for more nuanced genetic studies and greatly reducing background expression of off-target genes.

The next step in many genetic experiments is to create a screening library to introduce genes into cells and monitor their effects. Typically, gene fragments are inserted into bacterial plasmids (circular pieces of DNA) using methods that work well for small pieces of DNA, but are awkward to use when inserting larger genes. Many of the existing methods also rely on a technique called Gateway, which uses a process called lambda phage recombination and the production of a toxin to kill any bacteria that have not received a plasmid containing the gene of interest. Working with the toxin in these plasmids is often cumbersome in the laboratory and can inadvertently be inactivated if a « barcode » sequence is added to a vector to help researchers identify the gene-carrying plasmid that the vector received.

Working with Gateway, Kramme and Plesa realized that eliminating the toxin and replacing it with short sequences on the plasmid that would be recognized and cut by an enzyme called meganucleases could solve these problems. Meganuclease recognition sequences are absent from the genes of a known organism, which ensures that the enzyme does not accidentally cut the inserted gene itself during cloning. These recognition sequences are of course lost when a plasmid receives a gene of interest, rendering these plasmids immune to meganuclease. However, all plasmids which do not successfully obtain the gene of interest retain these recognition sequences and are cut into pieces when a meganuclease is added, so that only a pure pool of plasmids remains which contain the inserted gene. The new method, which the researchers called MegaGate, had a cloning success rate of 99.8% and also enabled them to barcode their vectors with ease.

“MegaGate not only solves many of the problems we encountered encountered in older cloning methods, it is also compatible with many existing gene libraries such as the TFome and hORFeome. You can essentially take gateway and meganucleases off the shelf and have a gene library and a library of barcode targeting vectors, and two hours later you have your barcode genes of interest. We cloned nearly 1,500 genes with it and haven’t made a mistake, « said Plesa, who is a PhD student at the Wyss Institute and HMS.

Eventually, the researchers showed that their barcoded vectors could be successfully inserted into living hiPSCs , and cell pools were analyzed using NGS to determine which delivered genes were expressed by the pool, and successfully used a variety of methods including RNA-Seq, TAR-Seq, and Barcode-Seq to identify both the genetic barcodes and read hiPSCs’ entire transcriptome so researchers can use the tool they are most comfortable with.

The team believes that STAMPScreen could prove useful for a variety of studies, including studies about Signaling pathways and gene regulation networks, screening of differentiation factors, characterization of active substances and complex signaling pathways as well as mutation modeling creen is also modular so that scientists can incorporate different parts of it into their own workflow.

“There is a wealth of information in publicly available genetic data sets, but this information can only be understood if we have the right tools and methods their analysis. STAMPScreen will help researchers get to Eureka moments faster and set the pace of innovation in genetic engineering, « said senior author George Church, Ph.D., a member of the Wyss Core Faculty who is also Professor of Genetics at HMS and Professor of Health Sciences and Technology at Harvard and MIT.

“At Wyss Institute we strive for effective ‘moonshot’ solutions to pressing problems, but we know that to get to the moon, we must first build a rocket « This project is a great example of how our community is on the fly to enable scientific breakthroughs that will change the world for the better, » said Wyss Founding Director Don Ingber, MD, Ph.D., who is also a Judah Folkman Professor of Vascular Biology at HMS and the Vascular Biology Program at Boston Children’s Hospital, and Professor of Bioengineering at Harvard John A. Paulson School of Engineering and Applied Science ces.

Other authors of the paper are Helen Wang, Bennett Wolf, Merrick Smela, Xiaoge Guo, Ph.D. and Richie Kohman, Ph.D. from the Wyss Institute and HMS.

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Genetic engineering,Genetics,Cell,Biology,Genetic engineering, Genetics, Cell, Biology,,

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