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New computational tool enables powerful molecular analysis of biomedical tissue samples
MAY 7, 2019, New York— Single-cell RNA sequencing is emerging as a powerful technology in modern medical research, allowing scientists to examine individual cells and their behaviors in diseases like cancer. But the technique, which can’t be applied to the vast majority of preserved tissue samples, is expensive and can’t be done at the scale required to be part of routine clinical treatment.
To address these shortcomings, a team led by Ludwig Stanford researchers Ash Alizadeh and Aaron Newman have invented a computational technique called CIBERSORTx that can analyze the RNA—which describe the proteins encoded by expressed genes—of individual cells taken from whole-tissue samples, clinically preserved samples or data sets. A paper describing their method was published online May 6 in Nature Biotechnology.
CIBERSORTx is an evolutionary leap from the technique the group developed previously, called CIBERSORT. “With the original version of CIBERSORT, we could take a mixture of cells and, by analyzing the frequency with which certain molecules were made, tell how much of each kind of cell was in the original mix without having to physically sort them,” said Alizadeh.
“We made the analogy that it was like analyzing a fruit smoothie. You don’t have to see what fruits are going into the smoothie because you can sip it and taste a lot of apple, a little banana and see the red color of some strawberries.”
CIBERSORTx takes that principle much further. The researchers start by doing a single-cell RNA analysis of a small sample of tissue. From this analysis they produce a “bar code,” a pattern of RNA expression, that identifies not only the kind of cell they are looking at, but also the subtype or mode it’s operating in. For instance, Alizadeh said, the immune cells infiltrating a tumor act differently and produce different RNA and proteins—and therefore a different RNA bar code—than the same kind of immune cells circulating in the blood.
“What CIBERSORTx does is let us not just tell how much apple there is in the smoothie, but how many are Granny Smiths, how many are Red Delicious, how many are still green and how many are bruised,” Alizadeh said. “Similarly, starting with a mix of RNA barcodes from a tumor can give us insights into the mix of cell types and their perturbed cell states in these tumors, and how we might be able to address these defects for cancer therapy.”
The group analyzed over 1,000 whole tumors with the technique. They found that not only were cancer cells different from normal cells, as expected, but immune cells infiltrating a tumor acted differently than circulating immune cells. Even normal structural cells surrounding the cancer cells acted differently than the same type of cells in other parts of an organ.
As with the original tool, the scientists will let researchers around the world use CIBERSORTx algorithms on computers at Stanford through an internet link.
Alizadeh is also an associate professor of medicine and Newman an assistant professor of biomedical data science at the Stanford University School of Medicine.
More detail about these findings is available in the Stanford release from which this summary is derived.