TY - JOUR T1 - Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low cost microfluidic instrumentation JF - bioRxiv DO - 10.1101/140848 SP - 140848 AU - William Stephenson AU - Laura T. Donlin AU - Andrew Butler AU - Cristina Rozo AU - Ali Rashidfarrokhi AU - Susan M. Goodman AU - Lionel B. Ivashkiv AU - Vivian P. Bykerk AU - DE Orange AU - Robert B. Darnell AU - Harold P. Swerdlow AU - Rahul Satija Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/05/22/140848.abstract N2 - Droplet-based single cell RNA-seq has emerged as a powerful technique for massively parallel cellular profiling. While these approaches offer the exciting promise to deconvolute cellular heterogeneity in diseased tissues, the lack of cost-effective, reliable, and user-friendly instrumentation has hindered widespread adoption of droplet microfluidic techniques. To address this, we have developed a microfluidic control instrument that can be easily assembled from 3D printed parts and commercially available components costing approximately $540. We adapted this instrument for massively parallel scRNA-seq and deployed it in a clinical environment to perform single cell transcriptome profiling of disaggregated synovial tissue from a rheumatoid arthritis patient. We sequenced 8,716 single cells from a synovectomy, revealing 16 transcriptomically distinct clusters. These encompass a comprehensive and unbiased characterization of the autoimmune infiltrate, including inflammatory T and NK subsets that contribute to disease biology. Additionally, we identified fibroblast subpopulations that are demarcated via THY1 (CD90) and CD55 expression. Further experiments confirm that these represent synovial fibroblasts residing within the synovial intimal lining and subintimal lining, respectively, each under the influence of differing microenvironments. We envision that this instrument will have broad utility in basic and clinical settings, enabling low-cost and routine application of microfluidic techniques, and in particular single-cell transcriptome profiling. ER -