Advancing RA Treatment Strategies

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Data, AI, and new technology help research autoimmune diseases 

May 2026

By Kara Mason  

More than 15 million Americans — nearly 5% of the population — have been diagnosed with an autoimmune disease. These conditions include rheumatoid arthritis (RA), Type 1 diabetes, multiple sclerosis, lupus, psoriasis, and others, and occur when the immune system mistakenly attacks part of the body. 

Treatment is not always straightforward. With RA, as many as half of people diagnosed with the condition don’t respond to the limited number of treatment options available. For Fan Zhang, PhD, that challenge has been the foundation for her lab at the University of Colorado Anschutz School of Medicine. 

Zhang, an assistant professor of rheumatology and a faculty member in the Department of Biomedical Informatics, focuses on single-cell data-driven computational method development and systems immunology. Those methods paired with artificial intelligence (AI) are helping lead the way toward potential new treatment options for RA and other autoimmune diseases. 

“The goal in our work is to identify new biomarkers for drug targets, and to do that we use cutting-edge single-cell technologies and create advanced data tools that can dig into huge amounts of data,” says Zhang, who last summer received $2 million from a National Institutes for Health (NIH) R01 grant to use AI and single-cell technologies to study novel immune cell interactions that could be helpful in discovering new RA therapeutic strategies. 

The Zhang Lab also received $500,000 in NIH funds to focus on women’s health and autoimmune diseases. 

Clarity in big data 

The road to new autoimmune condition treatments is paved with data – and lots of it. More high-dimensional sequencing data means researchers can place patients into different subgroups based on immunology and genomics. This ultimately results in a more personalized approach to treating conditions such as RA. 

“Without more data we cannot describe anything with clarity,” Zhang says.  

Single-cell transcriptomics — the Zhang Lab’s specialty — is used to study gene expression of individual cells from patients. Doing so unveils important information about a single cell’s composition, how it’s developed, and the role it plays in health and disease. Collecting that data on many single cells, sometimes across the body, gives researchers a more complete picture of what’s happening and potentially why. 

In a 2023 research paper published in Nature, Zhang and colleagues described omics efforts that yielded a cell atlas revealing six different subgroups of RA based on their cellular makeup. Then, in 2025, her lab and collaborators published another study in the Journal of Clinical Investigation identifying immune signatures that may point to new ways to prevent the disease. 

Zhang describes these efforts as a starting place for precision medicine discovery. Now, researchers can build on that data.  

“Sometimes the current treatment strategy for RA just does not work for certain patients,” Zhang says. “With single-cell sequencing, we can stratify the patient heterogeneity based on their unique molecular profiles, which helps guide more personalized care.” 

With the recent NIH grant, Zhang and her collaborators — CU rheumatology professor V. Michael Holers, MD, and researcher Laura Donlin, PhD, from the Hospital for Special Surgery in New York — will work together to integrate cutting-edge single-cell spatial transcriptomics with advanced AI methods to uncover novel immune cell interactions. These efforts will focus on the connection between pathogenic macrophage subsets and complement activation pathways.  

Translating successes 

While Zhang and her lab staff have taken a keen interest in RA, they say their efforts can be translated to other conditions. 

The grant from the NIH’s Office of Research on Women’s Health is focused on deciphering sex-biased phenotypes. Zhang and her lab will use the funds to develop novel computational AI methods that may help unravel the complex interactions between sex and autoimmune disease. 

Making a difference in patient’s lives will ultimately come with the help of AI technologies.   

“In medicine, there’s a bottleneck of what do to for precision medicine. When these treatments that are readily available don’t work, there’s a need for something else,” Zhang says. “What we’re doing is exciting because AI helps us to integrate data from multiple angles so we can move along the process faster and with greater accuracy.”