Objective
We hypothesize that lupus (SLE), multiple sclerosis (MS), and type 1 diabetes (T1D) share distinct patterns of immune system disruption that cause disease through similar underlying mechanisms. To identify these shared immune patterns—regardless of disease diagnosis—we will combine data from the Autoimmunoprofiler consortium with publicly available single-cell datasets from MS studies. Together, these data will allow us to create a cross-disease map based on measurements from more than 1.5 million individual immune cells circulating in the blood. Our goal is to uncover common patterns in both the types of immune cells present and the groups of genes that are abnormally active across these diseases. Identifying these shared cellular and genetic signatures will help reveal immune system “archetypes” that drive disease in similar ways in lupus, MS, and T1D. By shifting how we classify immune dysfunction—from focusing on affected organs or disease labels to focusing on the biological mechanisms involved—we can better guide treatment decisions and identify new targets for therapy that may benefit patients across multiple autoimmune diseases.
Background Rationale
Autoimmune disease occurs when the immune system mistakenly attacks the body. There are many different autoimmune diseases that involve attacks on specific organs, such as on the pancreas in Type 1 Diabetes (T1D), the nervous system in Multiple Sclerosis (MS), or across multiple body systems, such as Lupus (SLE) which affects skin, joints, brain and kidneys. These and many other autoimmune diseases have been researched extensively, in part by examining the cellular and molecular changes in samples from patients with disease. However, in most cases, these diseases have only been examined individually, even though treatments and underlying biological mechanisms often overlap. For example, Rituximab, a biologic commonly prescribed for rheumatoid arthritis, was recently found to be highly effective in Multiple Sclerosis due to shared dysfunction of B cells, and is currently under investigation for its efficacy in T1D. Additionally, the same disease may present with different symptoms, which may reflect different types of immune dysfunction, showing that there is a greater need to examine this variation so it can be linked to proper treatment. Previously, we used a cross-disease approach to examine immune system patterns across different types of cancer to help understand variation within and across disease. By combining data across the different cancer types, we were able to identify cross-patterns that were associated with treatment outcome; this would not have been possible if we had examined each cancer type alone. We propose to use this framework to examine autoimmune diseases, specifically focusing on SLE, T1D, and MS, to identify underlying patterns within and across diseases. Examining these diseases together will enable us to better understand shared mechanisms and patient variation, with the eventual goal of identifying targets for more personalized cross-disease treatments.
Description of Project
Autoimmune diseases such as lupus, type 1 diabetes, and multiple sclerosis are usually studied separately, which has led to foundational understanding of how each disease uniquely operates. However, growing evidence suggests that these diseases may share important biological features, and that treatments developed for one disease could also help subsets of patients with another. In this project, we will take a new approach by looking for common patterns across these autoimmune diseases rather than focusing on their traditional diagnostic classifications. We will analyze large, detailed datasets that examine individual immune cells from patients with lupus, type 1 diabetes, and multiple sclerosis. By comparing these cells at a very fine level, we hope to identify shared immune features that are observed across the three diseases. Our team has successfully used a similar strategy in the context of cancer, where identifying shared immune patterns helped explain differences in disease outcomes and response to treatment. Here, we will apply these methods to autoimmune diseases by combining data from several studies and identifying types of immune cells that appear to be more or less prevalent in these diseases. We will also look for shared sets of genes that are being engaged in all three diseases. Overall, this study will provide the first broad and integrated view of the common immune features underlying several autoimmune diseases. By uncovering shared biology across patients, our work could reveal new targets for drug development and help speed the development of more effective, personalized treatments for people living with autoimmune conditions.
Anticipated Outcome
By combining datasets that have already been generated that sequence large numbers of single cells from patients, we will identify differences in immune cell composition that reveal patterns within and across type 1 diabetes, lupus, and multiple sclerosis. From this, we will establish a set of “immune archetypes”: recurring patterns of immune cell composition that span these three diseases. We will also discover coordinated gene expression programs that reflect how groups of genes work together in these archetypes, identifying programs that are shared across diseases as well as those specific to individual disorders. By revealing shared biology across patients, these findings will provide a way to pinpoint promising drug development targets and will generate focused, testable hypotheses about how different patient subgroups might respond to existing treatments. This analysis will establish an extensible framework of immune archetypes onto which we can map new patients to ultimately help guide personalized treatment strategies and disease management.
Relevance to T1D
Type 1 diabetes, multiple sclerosis, and lupus are very distinct diseases, but they all arise when the immune system mistakenly attacks the body’s own tissues. Despite disease-specific differences, patients with these conditions often share genetic risk factors, immune pathways, and patterns of immune cell dysfunction. Importantly, many of the same types of immune cells—such as T cells, B cells, and innate immune cells—play key roles in all three diseases, and similar inflammatory signals have been observed across them. Furthermore, in some cases, drugs originally developed for one autoimmune disease have shown benefits in others, suggesting that these conditions are connected by common underlying biology rather than being entirely distinct. However, these shared features are often missed when each disease is studied in isolation. By examining immune cells across patients with type 1 diabetes, multiple sclerosis, and lupus, our approach is designed to uncover shared immune patterns that drive autoimmunity across diseases. We anticipate that patients with these different autoimmune diseases may have similar immune dysfunction that can be targetable by therapeutics. Conversely, understanding these shared immune patterns could help explain why some patients respond well to certain therapies while others do not. Ultimately, this approach has the potential to move autoimmune research beyond disease classifications and toward a more precise, biology-driven framework that identifies which immune pathways should be targeted in each patient—regardless of whether they have type 1 diabetes, multiple sclerosis, or lupus.