New CRISPR-based map links each human gene to its function

The Human Genome Project was an ambitious initiative to sequence every piece of human DNA. The project brought together collaborators from research institutions around the world, including MIT’s Whitehead Institute for Biomedical Research, and was finally completed in 2003. Now, more than two decades later, MIT professor Jonathan Weissman and colleagues have moved beyond the sequence to first comprehensive functional map of genes expressed in human cells. The data of this project, published online June 9 in Celllinks each gene to its task in the cell, and is the culmination of years of collaboration on the Perturb-seq single-cell sequencing method.

The data is available on the Weissman Lab website for other scientists to use. “It’s a big resource in the way the human genome is a big resource because you can do discovery-based research,” said Weissman, who is also a member of the Whitehead Institute and a researcher at Howard Hughes Medical. Institute. “Instead of pre-defining what biology you’re going to look at, you have this map of the genotype-phenotype relationships and you can go in and screen the database without having to do any experiments.”

The screen allowed the researchers to delve into various biological questions. They used it to investigate the cellular effects of genes with unknown functions, to study the mitochondria’s response to stress, and to screen for genes that cause chromosomes to be lost or gained, a phenotype that has been difficult to study in the past. turned out to be. “I think this dataset is going to enable all kinds of analysis that we haven’t even figured out yet by people coming from other parts of biology, and suddenly they just have this available to draw from,” said former Weissman Lab postdoc Tom Norman. , a co-senior author of the article.

Groundbreaking Perturb-seq

The project uses the Perturb-seq approach which makes it possible to track the impact of turning genes on or off with unprecedented depth. This method was first published in 2016 by a group of researchers including Weissman and fellow MIT professor Aviv Regev, but could only be used on small sets of genes and at great cost.

The massive Perturb-seq map was made possible by the foundational work of Joseph Replogle, an MD-PhD student in Weissman’s lab and co-first author of this paper. Replogle, in partnership with Norman, who now runs a lab at Memorial Sloan Kettering Cancer Center; Britt Adamson, an assistant professor in the Department of Molecular Biology at Princeton University; and a group at 10x Genomics, wanted to create a new version of Perturb-seq that could be scaled. The researchers published a proof of concept paper in Natural Biotechnology in 2020.

The Perturb-seq method uses CRISPR-Cas9 genome editing to introduce genetic changes into cells, then uses single-cell RNA sequencing to capture information about the RNAs expressed as a result of a particular genetic change. Because RNAs control all aspects of cell behavior, this method can help decipher the many cellular effects of genetic changes.

Since their first proof-of-concept paper, Weissman, Regev and others have used this sequencing method on a smaller scale. For example, the researchers used Perturb seq in 2021 to study how human and viral genes interact during infection with HCMV, a common herpes virus.

In the new study, Replogle and collaborators, including Reuben Saunders, a graduate student in Weissman’s lab and co-first author of the paper, scaled the method to the whole genome. Using human blood cancer cell lines and non-cancerous cells derived from the retina, he performed Perturb-seq on more than 2.5 million cells and used the data to create a comprehensive map linking genotypes to phenotypes.

Dive into the data

After completing the screen, the researchers decided to put their new dataset to use and explore a few biological questions. “The advantage of Perturb-seq is that you can get a large dataset in an unbiased way,” says Tom Norman. “Nobody knows exactly what the limits are of what you can get from such a dataset. Now the question is: what do you actually do with it?”

The first, most obvious application was to look at genes with unknown functions. Because the screen also reads out phenotypes of many known genes, the researchers were able to use the data to compare unknown genes to known genes and look for similar transcriptional outcomes, which could suggest that the gene products worked together as part of a larger complex.

Especially the mutation of one gene called C7orf26 stood out. Researchers noted that genes whose deletion led to a similar phenotype were part of a protein complex called Integrator that played a role in creating small nuclear RNAs. The Integrator complex is made up of much smaller subunits — previous studies had suggested 14 individual proteins — and the researchers were able to confirm that C7orf26 made up a 15th component of the complex.

They also found that the 15 subunits worked together in smaller modules to perform specific functions within the Integrator complex. “Without this thousand-foot view of the situation, it wasn’t so clear that these different modules were functionally so different,” Saunders says.

Another advantage of Perturb-seq is that because the test focuses on single cells, the researchers can use the data to look for more complex phenotypes that become obscured when studied together with data from other cells. “We often take all the cells where ‘gene X’ has been knocked down and put them together to see how they’ve changed,” Weissman says. “But sometimes different cells that lose that same gene behave differently when you knock down a gene, and that behavior can be missed on average.”

The researchers found that a subset of genes whose deletion led to different cell-to-cell outcomes was responsible for chromosome segregation. Their deletion caused cells to lose a chromosome or pick up an extra chromosome, a condition known as aneuploidy. “You couldn’t predict the transcriptional response to the loss of this gene because it depended on the secondary effect of which chromosome you gained or lost,” Weissman says. “We realized that we could then turn this around and create this composite phenotype to look for signatures of chromosomes that are gained and lost. Thus we did the first genome-wide screening for factors necessary for proper segregation of DNA.”

“I think the aneuploidy study is the most interesting application of this data to date,” Norman says. “It captures a phenotype that you can only get with a single cell readout. You can’t help but go after it.”

The researchers also used their dataset to study how mitochondria responded to stress. Mitochondria, which evolved from free-living bacteria, carry 13 genes in their genomes. Within nuclear DNA, about 1,000 genes are somehow related to mitochondrial function. “People have long been interested in how nuclear and mitochondrial DNA are coordinated and regulated in different cellular conditions, especially when a cell is stressed,” Replogle says.

The researchers found that when they disrupted several mitochondria-related genes, the nuclear genome responded similarly to many different genetic changes. However, the mitochondrial genome responses were much more variable.

“There is still an open question as to why mitochondria still have their own DNA,” Replogle said. “A big picture of our work is that an advantage of having a separate mitochondrial genome may be localized or highly specific genetic regulation in response to different stressors.”

“If you have one mitochondria that’s broken and another that’s broken in a different way, those mitochondria can react differently,” Weissman says.

In the future, the researchers hope to use Perturb-seq on different types of cells in addition to the cancer cell line in which they started. They also hope to continue exploring their map of gene functions and hope others will do the same. “This is truly the culmination of many years of work by the authors and other contributors, and I’m very pleased to see it continue to succeed and expand,” says Norman.

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