Southwestern UT scientists have developed a new way to study the molecular properties of T cells, critical immune cells that recognize and attack invaders in the body such as viruses, bacteria, and cancer.
The approach, described today in the journal Natural ways, allowing researchers to more easily study the roles of T cell receptors (TCRs) – the molecules on the surface of T cells that are responsible for identifying pathogens.
This could lead to a better understanding of how T cells work as well as new ways to use T cells to fight disease. “
Tao Wang, Ph.D., director of research, assistant professor of population and data sciences and member of the OTSW Speech and Language Biomedical Research Center
While some immune cells may attack different pathogens at the same time, T cells are more targeted – each individual T cell has a specific set of T cell receptors (TCRs) on its surface. Each receptor usually recognizes only one specific molecule, or “antigen.” For example, one TCR may bind directly to proteins found in lung cancers, and a different TCR may bind directly to an influenza virus. When a T cell encounters an antigen that binds to one of its TCRs, it is activated, stimulating an immune response. To counteract the diverse set of potential invaders, humans have millions of different T cells in their bodies.
Scientists have tried to study what makes different T cells and TCRs more or less effective, hitherto hampered by a lack of information about what different TCRs do. In general, they assume that seemingly similar TCRs must bind to similar antigens, and that all TCRs activate T cells in the same way.
To eliminate this measurement work, the research team developed a statistical model combining two existing technologies: TCR analysis, which measures a person’s TCR diversity, and a single-cell RNA sequence, which identifying the specific genes that are turned on or off in a T cell. Combining these technologies has been challenging because they both generate thousands of pieces of data per test, and the data comes in two different forms.
The new model, called Tessa, uses powerful statistical methods to close this gap. Tessa reveals what happens to an individual T cell when its TCR recognizes antigen, and how TCRs affect the function of T cells. (Tessa stands for landscape estimation TCR function guided by scRNA-sequence analysis.)
Using Tessa, the researchers studied 100,288 T cells from both healthy individuals and cancer patients. In cancer patients, they found that the combination of TCR in T cells has a weaker effect on the functional status of T cells than on those found in healthy patients. This is likely to occur because an abundance of other immune molecules, hidden into the microenvironment of the tumor, affect T cell activity in other ways. This observation – and others that are likely to be the result of the wider use of Tessa – may have influenced scientists planning immune-based cancer treatments.
David Gerber, MD, professor of internal medicine and population and data sciences, and associate director of clinical study at the Harold C. Simmons Comprehensive Cancer Center at UT Southwestern, believes this work provide an entirely new way to use single T cell sequence data. “We plan to use this promising tool to study the roles of T cells in immune-mediated adverse events caused by cancer immunotherapy through the recently funded NIAID U01 award,” he says.
“There have been many previous methods of measuring when it actually becomes the function of T cells and how T cell receptors bind to function,” Todd Aguilera, MD, Ph.D. D., UTSW assistant professor of radiation oncology, and the expert on immunotherapy, who also collaborates with Wang. “I believe this approach could help identify the most promising TCRs for personalized TCR-based immunotherapy and define better outcome immune responses to identify the best immunotherapy strategies.”
Source:
UT Southwestern Medical Center