Three Distinct Transcriptional Profiles of Monocytes Associate with Disease Activity in Scleroderma Patients. Issue 4 (1st February 2023) - Makinde, Hadijat‐Kubura M.; Dunn, Julia L. M.; Gadhvi, Gaurav; Carns, Mary; Aren, Kathleen; Chung, Anh H.; Muhammad, Lutfiyya N.; Song, Jing; Cuda, Carla M.; Dominguez, Salina; Pandolfino, John E.; Dematte D'Amico, Jane E.; et al.
Abstract : Objective: Patients with diffuse cutaneous systemic sclerosis (dcSSc) display a complex clinical phenotype. Transcriptional profiling of whole blood or tissue from patients are affected by changes in cellular composition that drive gene expression and an inability to detect minority cell populations. We undertook this study to focus on the 2 main subtypes of circulating monocytes, classical monocytes (CMs) and nonclassical monocytes (NCMs) as a biomarker of SSc disease severity. Methods: SSc patients were recruited from the Prospective Registry for Early Systemic Sclerosis. Clinical data were collected, as well as peripheral blood for isolation of CMs and NCMs. Age‐, sex‐, and race‐matched healthy volunteers were recruited as controls. Bulk macrophages were isolated from the skin in a separate cohort. All samples were assayed by RNA sequencing (RNA‐seq). Results: We used an unbiased approach to cluster patients into 3 groups (groups A–C) based on the transcriptional signatures of CMs relative to controls. Each group maintained their characteristic transcriptional signature in NCMs. Genes up‐regulated in group C demonstrated the highest expression compared to the other groups in SSc skin macrophages, relative to controls. Patients from groups B and C exhibited worse lung function than group A, although there was no difference in SSc skin disease at baseline, relative to controls. We validated our approach by applying our group classifications to published bulk monocyte RNA‐seq data from SSc patients, and we found that patients without skin disease were most likely to be classified as group A. Conclusion: We are the first to show that transcriptional signatures of CMs and NCMs can be used to unbiasedly stratify SSc patients and correlate with disease activity outcome measures.