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Updated: Sep 14, 2026
Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry
Published on: June 26, 2018
Unveiling the molecular and immune heterogeneity across distinct disease activity states of systemic lupus
Chen Shen1,2, Mingzhe Guo3, Jian Huang1,2
1Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents' Health and Diseases, Hangzhou, China.
Objective:
Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterised by substantial variability in clinical presentations and disease severity. This study aims to investigate the molecular and cellular mechanisms underlying distinct SLE Disease Activity Index (SLEDAI)-defined disease states of SLE and to identify potential prognostic biomarkers and therapeutic targets associated with heightened inflammatory activity.
Methods:
We analysed bulk and single-cell RNA sequencing datasets from SLE patients. Patients were stratified into clinically distinct SLEDAI-defined disease states using established SLEDAI thresholds, which served as operational surrogates of inflammatory disease burden. Differentially expressed genes and gene co-expression networks were analysed to identify severity-associated hub genes. The immune microenvironment and intercellular communication were evaluated using CIBERSORTx and CellChat algorithms, respectively.
Results:
Differential expression and network analyses identified five key hub genes (KLRK1, IL2RB, RAG1, SH2D1B and NCR1) strongly correlated with SLE disease activity states, which were used to develop a five-gene SLE severity (SLEsev) score. The SLEsev score was significantly higher in high-activity/severe SLE (sSLE) compared with low-activity/mild SLE (mSLE) (p<0.001), which showed a consistent trend in an independent validation cohort. Immune profiling revealed that mSLE was enriched in regulatory T cells (Tregs) and memory B cells, whereas sSLE exhibited elevated levels of CD8+T cells, natural killer cells and monocytes (p<0.05). Single-cell analysis demonstrated that sSLE possessed a more complex cellular communication network (311 vs 283 inferred interactions) and greater overall interaction strength than mSLE, with prominent pro-inflammatory signalling pathways such as IFN-II, GAS and CCL playing a key role in sSLE.
Conclusions:
This study unveils the profound immunological and transcriptomic dysregulation underlying SLE severity. The identified five-gene SLEsev score serves as a potential biomarker for disease stratification, while the hyperactive intercellular signalling networks highlight specific molecular targets for modulating immune responses in severe SLE.