A new algorithm identifies ‘escape’ cells in single-cell CRISPR screens

A team of researchers from New York University and the New York Genome Center have developed a new computing device to help understand the function and control of human genes. The results, published today in the journal The genetics of nature, demonstrates how to define experiments that combine the use of CRISPR to combine genes with multicellular single-cell sequence technologies.

The article describes how the new approach, called mixscape, helped identify new molecular mechanisms for the regulation of immune point proteins that regulate the immune system’s ability to recognize cancer cells and to destroy.

“Our approach will help scientists link genes to the specific cell behaviors and molecular pathways they regulate,” explained Rahul Satija, lead author of the study, who is an associate professor of biology at the NYU Center for Biology and Systems and a principal member faculty at the New York Genome Center.

The researchers aimed to gain a better understanding of how cancer cells alter the regulation of key genes, such as the PD-L1 immune check molecule, to avoid detection and the body’s immune system. To do so, they performed a circular genetic screen, where they removed a set of genes in a cancer cell line model to observe the effect of each alteration or disturbance on PD-L1 levels. They used ECCITE-seq, a technology that allows researchers to capture single-cell profiles of different types of biomolecules – such as RNA and proteins – after interfering with each gene with CRISPR “RNA guidance.” ability to measure several types of molecular data, called multivariate analysis, allows the team to differentiate between transcription and post-transcriptional control methods.

After completing their experiments, however, the team realized that major computing challenges limited its ability to analyze and interpret the data. For example, the researchers found that when they tried to express the same gene in several different cells, they saw a remarkable variability in the results. In particular, a significant proportion of cells – up to 75% in some cases – appeared to escape any visible effects after a tempting attempt and represented a strong sound source. in downstream analysis.

“Addressing these challenges led us to realize the need for new computational methods to identify and remove dynamic variable sources in our dataset,” says Efthymia Papalexi, a graduate student of biology at NYU and lead author of the study.

To achieve this, the team developed a statistical approach – mixscape – to model each disturbance caused by a combination of cells with different responses. By doing this, the mixscape method can identify sound sources and remove them from the data, allowing the user to focus on the most important remaining biological markers.

“When we put mixscape in our screen, we boosted our power to link gene effects to changes in protein transcription and expression. This allowed us to discover that the protein is similar to KEAP1 kelp and the transcriptional activator. writing NRF2 measuring cell sensitivity level of PD -L1, “Satija says.

While these studies were performed in cancer cell lines, KEAP1 and NRF2 are frequently modulated in human lung cancer samples, suggesting that these genes may play an important role in the development and progression of tumors. human.

Looking ahead, the researchers are discovering single-cell and terrestrial multi-cell CRISPR screens to understand the molecular regulation of dozens of additional pathways and cell behavior.

Free Mixscape is available online through the Seurat package at Satija lab, a software tool for biomedical researchers.

“We hope our approach will be useful to the community and help study how genes and molecular pathways interact with each other,” Papalexi said.

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The work was supported by the Chan Zuckerberg Initiative (EOSS-0000000082, HCA-A-1704-01895) and the National Institutes of Health (DP2HG009623-01, RM1HG011014-01, R21HG009748-03).