New research led by University of Colorado School of Medicine members Fan Zhang, Ph.D., and Anna Helena Jonsson, MD, Ph.D., may lead to new targeted therapies for rheumatoid arthritis (RA), a autoimmune disease that causes inflammation and destruction of joints.
Published today in the magazine Nature, their findings reflect the work of dozens of researchers working together as members of the Accelerating Medicines Partnership: Rheumatoid Arthritis and Systemic Erythematosus Lupus (AMP: RA/SLE) Network, including Michael Holers, MD, professor of medicine and principal investigator of the site in Medicine CU Faculty.
The AMP: RA/SLE Network collected inflammatory tissue from 70 RA patients from across the country and the UK. Jonsson oversaw the team of scientists that processed these samples for analysis, and Zhang led the computational analysis of the data. These efforts yielded a cell atlas that included more than 300,000 cells from synovial tissue. Further analysis revealed that there are six different RA subgroups based on their cellular composition.
“We hope the data will help us discover new treatment targets,” says Jonsson, assistant professor of rheumatology. “We wanted to publicize this so that researchers across the country and around the world can continue to work on new treatment ideas for rheumatoid arthritis in the future.”
No more guessing and checking
Jonsson, who is a practicing rheumatologist as well as a researcher, knows that RA patients respond differently to different treatments. Until now, he says, rheumatologists have used a “guess and check” method to find a treatment that works for an individual patient.
With the new data and powerful computational classification methods developed by Zhang and the computational analysis team, the researchers were able to quantitatively classify RA types into what they call “cell type abundance phenotypes,” or CTAPs. The developed methods, along with the new cell atlas, can begin to identify which patients will respond to which treatments.
“Even when you classify RA inflammation using these simple markers — markers of T cells, B cells, macrophages and other myeloid cells, fibroblasts, endothelial cells — what we found is that each of these categories is associated with very specific kinds of pathogenic cell types that we have already discovered,” says Jonsson. “Previous research on rheumatoid arthritis has found that populations of T cells called peripheral helper T cells are associated with rheumatoid arthritis, as are B cells called antibody-producing B cells and other specific cell types. What we found is that they usually don’t they are all together.
“For example, peripheral helper cells are found with B cells in only one RA class, and pathogenic macrophage populations tend to exist in a different class. Because of this, we can begin to ask questions about how these particular partners work together. “
Interdisciplinary research and a multi-institutional effort are key
“I consider it interdisciplinary and big data-driven research. Many new findings have emerged through innovative computational methods and systems immunology approaches,” says Zhang, assistant professor of rheumatology and faculty member in the Department of Biomedical Informatics. “We used state-of-the-art multimodal single-cell technology to develop this reproducible sorting scheme. It is a big step towards precision medicine for rheumatic diseases. With our powerful computational AI methods, we are able to understand the integration of large single-cell omics, imaging and clinical data to stratify patient heterogeneity in a generalized manner.”
The CTAP study is part of a multicenter consortium effort that began in 2018. It is funded by the Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Erythematosus Lupus Network, an initiative coordinated by the National Institutes of Health and the Foundation for the National Institutes of Health. The project is based on a nationwide network of research groups working collaboratively to deepen the understanding of autoimmune diseases by focusing on RA and systemic lupus erythematosus.
“Inflammatory subgroup research can also be used to study other autoimmune diseases or immune responses to cancer or infection. From there, it can be leveraged for increased understanding of many different kinds of diseases,” says Jonsson.
“The creation of disease-driven computational AI methods will be the next step in generating testable hypotheses for multiple immune-mediated diseases,” Zhang adds.
Fertile ground for research
For Jonsson and Zhang, the Nature The publication is the culmination of much work that began when both worked at Brigham and Women’s Hospital, Harvard University’s teaching hospital. They brought the project with them when they came to the CU School of Medicine and hope to forge new partnerships with faculty members across the CU Anschutz Medical Campus.
“One of the things that attracted us to come here to start our research groups is that the human translational research at the University of Colorado School of Medicine is so strong,” says Jonsson.
Similarly, “I felt that this would be the most fertile ground to continue our computational-experimental production pattern for translational medicine,” Zhang says.