New technology simplifies studying how genes affect cells

Understanding how gene deactivation influences human cells is crucial for many research fields, from determining the mechanisms of disease progression to creating new drugs.

Traditional methods typically require examining how deactivating a single gene affects cell function. However, this new approach enables researchers to simultaneously assess >20,000 genes and how deactivating each of them individually affects numerous cellular functions.

This breakthrough combines two cutting-edge technologies, creating a revolutionary tool for large-scale genetic research.

“Our method is highly sensitive and adept at addressing the complexity of biology in health and disease. This enables us to conduct much larger and more complex experiments, unveiling insights not previously possible. I think in the coming years we will witness a surge in the use of this type of approach and the insights it will provide,” explains a researcher behind the study, James T. (J.T.) Neal, Institute Scientist and Principal Investigator, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

The new method has been published in Nature Methods.

Limitations of existing methods

Researchers can study the effect of deactivating a gene in different ways. For example, they can deactivate the gene and observe the cell under a microscope, which indicates how the gene affects the cell’s structure and function.

A more advanced method uses single-cell RNA sequencing to manipulate genes in a single cell and examine how this affects cell biology by analysing the RNA produced.

However, both methods have limitations. They are not very scalable for investigating the effects of deactivating hundreds to thousands of genes individually or in combination.

In addition, single-cell RNA sequencing is very costly, and examining cells individually is extremely time-consuming.

“To overcome these challenges, we aimed to develop a scalable and cost-effective method for studying how genetic changes influence cell structure and function,” says J.T. Neal.

Colour-labelling cells

The researchers combined two innovative technologies they developed to study genetic changes in cells. The first technology, Cell Painting, involves staining cells to identify the effects of genetic or chemical perturbations under a microscope. For example, deactivating a specific gene might affect the function of various organelles within the cell or alter the cell’s overall shape.

Cell Painting enables researchers to visualise changes by staining cellular components. For instance, they might stain mitochondria red and the cell nucleus blue so that the resulting changes can be observed more easily.

In addition, Cell Painting can be used to detect changes in structure and function across many cells simultaneously. Advanced computer algorithms are used to identify microscopic changes in images of multiple cells at once, revealing alterations that are not visible to the naked eye.

“However, a limitation of Cell Painting is that, although it enables us to observe the effects of deactivating various genes in different cells, it cannot associate these changes in shape and structure with individual genetic alterations in experiments where we are manipulating multiple genes at a time in different cells,” observes J.T. Neal.

Barcoding cells

To address this, the researchers combined Cell Painting with optical pooled screening, another technology developed at the Broad Institute of MIT and Harvard, to create PERISCOPE (perturbation effect readout in situ via single-cell optical phenotyping). Using CRISPR, they introduce genetic changes in individual cells and equip the cells with a barcode that can be read under a microscope.

This barcode indicates the specific genetic alteration made in each cell, enabling researchers to compare individual genetic changes in thousands of cells simultaneously to changes in structure or function.

Using this method, researchers can use microscopy to study the effects of deactivating more than 20,000 genes individually within a large experiment containing millions of cells. They can then use machine learning algorithms to analyse the collected images to identify subtle but significant changes in cellular structure and function caused by deactivated genes.

“The method costs one tenth to one hundredth as much as comparable techniques such as single-cell RNA sequencing and can be adapted to study many types of cells,” explains J.T. Neal.

The researchers used this method to create an atlas of cell morphology as a function of genes.

“This atlas is the first of its kind, providing comprehensive maps of how genes shape cellular structure across the entire human genome. I believe it holds immense potential for uncovering new biological insights,” adds J.T. Neal.

Studying complex genetic interactions

J.T. Neal says that this technology opens the door to an entirely new way of studying cells and genes.

For instance, researchers can now determine how various genetic changes affect cells in cancer or cardiometabolic diseases at unprecedented scale and resolution.

This means that they can not only identify gene changes associated with the development of diseases such as cancer but also determine how these changes affect the cell’s structure and function.

Drug developers aiming to treat numerous diseases can also use this method to explore how potential drugs can counteract the effects of disease-related genetic changes.

Further, the method excels in addressing complexity in a manner that no other method can.

“Traditionally, a significant bottleneck has been the ability to study only one genetic change at a time. However, biology and disease are rarely that simple, often involving multiple interacting genetic changes. With the scalability of this method, researchers can introduce several genetic changes simultaneously and observe how they collectively affect cell function and structure,” concludes J.T. Neal.