RT Journal Article
SR Electronic
T1 Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low cost microfluidic instrumentation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 140848
DO 10.1101/140848
A1 William Stephenson
A1 Laura T. Donlin
A1 Andrew Butler
A1 Cristina Rozo
A1 Ali Rashidfarrokhi
A1 Susan M. Goodman
A1 Lionel B. Ivashkiv
A1 Vivian P. Bykerk
A1 DE Orange
A1 Robert B. Darnell
A1 Harold P. Swerdlow
A1 Rahul Satija
YR 2017
UL http://biorxiv.org/content/early/2017/05/22/140848.abstract
AB 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.