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Effectively addressing the heterogeneity of SLE holds great promise for enhancing diagnosis and therapeutic strategies. This approach involves identifying distinct molecular signatures of SLE that can be useful in developing personalised diagnostic tools that account for individual variation, tailoring treatment plans, designing models for monitoring disease activity and even considering disease prevention. An increasing number of studies have contributed to this effort. Typically, each study uses a specific type of data—such as transcriptomics1–3 or autoantibody profile—to identify distinct SLE patterns, which are then tested for enrichment in clinical manifestations, disease activity or long-term outcomes. Among these strategies, the recently published validation study by Hubbard et al1 and its methodological predecessor2 focused on leveraging transcriptomic data to define SLE endotypes.