Studying the human immune system is challenging. These challenges stem from the complexity of the immune system itself, the heterogeneity of the immune system between individuals, and the many factors that lead to this heterogeneity including the influence of genetics, environment, and immune experience. Studies of the human immune system in the context of disease are increased in complexity as multiple combinations and variations in immune pathways can lead to a single disease. Thus, although individuals with a disease may share clinical features, the underlying disease mechanisms and resulting pathophysiology can be diverse among individuals with the same disease diagnosis. This has consequences for the treatment of diseases, as no single therapy will work for everyone, therapeutic efficacy varies among patients, and targeting a single immune pathway is rarely 100% effective. This review discusses how to address these challenges by identifying and managing the sources of variation, improving access to high-quality, well-curated biological samples by building cohorts, applying new technologies such as single-cell omics and imaging technologies to interrogate samples, and bringing to bear computational expertise in conjunction with immunologists and clinicians to interpret those results. The review has a focus on autoimmune diseases, including rheumatoid arthritis, MS, systemic lupus erythematosus, and type 1 diabetes, but its recommendations are also applicable to studies of other immune-mediated diseases.