New way to sort cells without limitations of traditional methods

Can you take a tissue, blend it up, look at the contents and tell what kinds of cells they came from?

In addition to avoiding the problems inherent in breaking up tissues into single cells, researchers using this method won’t need fluorescently labeled antibodies for the cell surface markers they are looking for, he said.

“There are early hints that it is very important to know about the presence of specific types of immune cells in the tumor before and after certain therapies are given, and how those cells change over time,” Alizadeh said.

Reconstructing the cellular ‘smoothie’

Stanford’s Department of Medicine also supported the work.

This work was supported by grants from the Doris Duke Charitable Foundation, the Damon Runyon Cancer Research Foundation, the B&J Cardan Oncology Research Fund, the Ludwig Institute for Cancer Research, the National Institutes of Health (grants U01CA154969, U19AI090019, and CA09302-35), the U.S. Department of Defense, and a grant from the Siebel Stem Cell Institute and the Thomas and Stacey Siebel Foundation.

If the researchers apply Cibersort to old tumor samples from patients whose clinical history is known, they may be able to learn what kinds of cells signal more or less deadly cancers. They may also learn what kinds of treatments work better or worse in various subtypes of cancer. This sort of information might be most important for the antibody cancer therapies.

If we apply Cibersort to cancer tissues, we think we will be able to see amazing things.

The solution the researchers came up with is to sort not the cells, but their contents. “We were asking, ‘Can you take a tissue, blend it up, look at the contents and tell what kinds of cells they came from?’” Alizadeh said.

In developing the new method, Alizadeh and his colleagues focused not on the protein cell surface markers, but on the RNA on which those proteins were patterned. Postdoctoral scholar Aaron Newman, PhD, devised a computer algorithm to reconstruct the type and number of original cells based on the RNA contents of the mixture of all the cells.

Targeting cancer treatments

“A significant, ongoing effort is to find which immune cells mediate response and resistance to these drugs, to allow their more directed and precise use in a personalized fashion,” said Alizadeh, who is also a member of the Stanford Institute for Stem Cell Biology and Regenerative Medicine and the Stanford Cancer Institute. “If we apply Cibersort to cancer tissues, we think we will be able to see amazing things.”

The standard method of cell sorting requires breaking up tissues, or disaggregating them, into individual cells, Alizadeh said. This is a rough process that destroys certain cell types and renders some tissues useless for study. In addition, the traditional method of preserving medical samples makes it impossible to process them in this way, he noted. Also, fluorescently labeled antibodies must be produced for each specific cell protein in which the scientists are interested. Antibodies may not be available for some proteins, he said.

Some of the most exciting recent advances in the treatment of cancer involve the use of novel drugs that engage immune responses in patients to fight the disease. These drugs often target rare and dormant populations of immune cells that reside within tumors. While some of these drugs can be dramatically effective for patients with very different tumor types, not every patient benefits equally, and some tumor types appear not to respond to these new immune therapies.

“It’s like reconstructing a smoothie,” said Newman, a lead author of the paper. “You know it has a lot of different kinds of fruit in it, but you don’t know right away how many of each type. However, you might know that strawberries had a certain amount of sugar and red coloring, while oranges have a different amount of sugar, orange coloring and more tartness. If you analyze each of these qualities, you can reconstruct how many of each kind of fruit went into making the smoothie.”

The other lead author of the paper is Chih Long Liu, PhD, a research associate in Alizadeh’s lab. Other Stanford co-authors are Maximilian Diehn, MD, PhD, assistant professor of radiation oncology; Chuong Hoang, MD, former assistant professor of thoracic surgery; former postdoctoral scholar Michael Green, PhD; senior research engineer Andrew Gentles, PhD; former research associate Weiguo Feng, PhD; and senior research scientist Yue Xu, MD, PhD.

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