According to researchers, combining flow cytometry and high-speed imaging technology to sort cells will completely alter the opportunities for studying genes and diseases.
For more than 50 years, flow cytometry has helped researchers studying cells.
Flow cytometry has been at the core of many revolutionary scientific discoveries. Researchers have used this to isolate specific cells from a mixture of cell phenotypes.
So far, however, flow cytometry has been limited by the fact that researchers have only been able to isolate cells based on simple properties, such as how much of a specific protein and biomarker is expressed in a cell.
Now this is changing.
Flow cytometry has entered a whole new era after researchers have combined flow cytometry with advanced imaging technology.
This combination means that researchers can now also isolate cells based on information from images, such as the location of proteins or other biomarkers inside a cell. This information can reveal much more about cell functioning than was previously possible with flow cytometry alone. This advance is like opening up the cellular machinery and being able to see what is happening in greater detail.
“This is a huge advance that will fundamentally change the information we can get from flow cytometry. This application is going to be just as revolutionary in the life sciences as smartphones are for telecommunication,” says Malte Paulsen, Head of Research Platforms at the Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW) at the University of Copenhagen.
The research has been published as a cover story in Science.
The new application is called high-speed fluorescence image–enabled cell sorting and enables researchers to study cells in a completely new way.
Malte Paulsen says that researchers have long dreamed of being able to photograph cells and sort them in relation to the data extracted from these images. Further, this needs to be done quickly enough that it makes sense in research terms.
Image-enabled cell sorting can categorise cells based on visual data at a rate of 15,000 events per second.
This means that many experiments that depended on cell isolation according to image data, which used to take days or weeks, can now be done much more rapidly and precisely.
“A previous bottleneck in basic and biomedically applied research was a lack methods to quickly isolate cells based on image data. This new technology overcomes this because it sorts thousands of cells in a flash,” explains the lead researcher, Daniel Schraivogel, Research Staff Scientist in Lars Steinmetz’ lab at the European Molecular Biology Laboratory, Heidelberg, Germany.
He compares the era before image-enabled cell sorting to walking through a museum blindfolded.
“Image-enabled cell sorting removes the blindfolds so we can now see things more clearly,” says Daniel Schraivogel.
Sorting cells based on their cell cycle
In their publication, the researchers described several experiments that show the capability of image-enabled cell sorting, including separating cells based on their stage in the cell cycle.
Mitosis, the universal process by which cells divide into new daughter cells, is often dysregulated in cancer and other diseases.
When cells divide, the genome of the cells is copied and packed into compact chromosomes and then divided into the new daughter cells. This is done step by step to ensure that all the processes are synchronised, so that both daughter cells receive identical copies of the mother cell’s genome.
The researchers now can use image-enabled cell sorting to sort the cells based on their progress during mitosis, something that hasn’t been possible so far.
“Under a microscope, we can see when the genome is divided, but until now we could not use flow cytometry to divide cells according to their stage in mitosis. We can do this with image-enabled cell sorting, and this is useful because we can then extract thousands of cells at a specific point in mitosis and study them at the protein level,” explains Malte Paulsen.
Discovered new genes in a well-known signalling pathway
Another revolutionary experiment presented in their study in Science involves the signalling pathway nuclear factor kappa B (NF-κB), which plays a central role in cellular immunity.
In their experiment, the researchers destroyed all 18,000 human protein-coding genes, cell by cell and one gene per cell in cultured human cell lines.
Then they ran the cell sample through image-enabled cell sorting and isolated all the cells that had their NF-κB signalling pathway altered, determining which genes played a role in the functionality of the signalling pathway.
The signalling pathway is already well researched, but the researchers could still use image-enabled cell sorting to find more and previously unknown genes that influence the function of the NF-κB pathway.
“Being able to identify the function of genes on complex cellular phenotypes may be a game-changer in medical research. We can much better understand disease mechanisms or identify new targets for pharmaceutical treatment,” says Daniel Schraivogel.
Technology enabled image-enabled cell sorting
Malte Paulsen explains that researchers have previously tried to create similar technologies, but with limited success since none of these methods met the need of researchers and due to their complexity or their slower throughput, those technologies never became broadly available.
However, advanced technology from telecommunication and image analysis tools has improved so much that it created the opportunity to invent image-enabled cell sorting.
One great advantage of the novel image-enabled cell sorting technology is that it is easy to use and only requires a single researcher and a cell sample.
“Image-enabled cell sorting enables us to start asking very difficult scientific questions that we did not bother asking before because we didn’t have the toolset available of answering them. Now we do,” concludes Malte Paulsen.