Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation

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 $575. 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 5 rheumatoid arthritis patients. We sequenced 20,387 single cells from synovectomies, revealing 13 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.

Reprinted from [Stephenson et al., Nature Communications, 2018], with permission from the Nature Publishing Group.

• Study Design:
  • Methods
• Study Type:
  • Case Set
  • Methods Development
• Total number of consented subjects: 6
• Subject Sample Telemetry Report (SSTR)

• Primary Phenotype: Arthritis, Rheumatoid

• BioProject
• Sequence Read Archive
• BioSample
• MeSH

• Co-Investigators
  • William Stephenson, PhD. New York Genome Center, New York, NY.
  • Laura T. Donlin. Hospital for Special Surgery and Weill Cornell Medical College, New York, NY.
  • Andrew Butler. New York Genome Center and New York University, Center for Genomics and Systems Biology, New York, NY.
  • Cristina Rozo. Hospital for Special Surgery, New York, NY.
  • Bernadette Bracken. New York Genome Center and New York University, Center for Genomics and Systems Biology, New York, NY.
  • Ali Rashidfarrokhi. New York Genome Center and New York University, Center for Genomics and Systems Biology, New York, NY.
  • Susan M. Goodman, MD. Hospital for Special Surgery and Weill Cornell Medical College, New York, NY.
  • Lionel B. Ivashkiv, MD. Hospital for Special Surgery and Weill Cornell Medical College, New York, NY.
  • Dana E. Orange, MD. New York Genome Center and Howard Hughes Medical Institute, Rockefeller University, New York, NY.
  • Robert B. Darnell, PhD. New York Genome Center and Howard Hughes Medical Institute, Rockefeller University, New York, NY.
  • Harold P. Swerdlow, PhD. New York Genome Center, New York, NY.
• Principal Investigators
  • Vivian P. Bykerk, MD. Hospital for Special Surgery and Weill Cornell Medical College, New York, NY.
  • Rahul Satija, PhD. New York Genome Center and New York University, Center for Genomics and Systems Biology, New York, NY.
• Funding Sources
  • UH2AR067691. Hospital for Special Surgery, New York, NY.
  • R21HG009748. National Institutes of Health, Bethesda, MD, USA.
  • DGE1342536. National Science Foundation, Alexandria, VA, USA.
  • DP2HG009623. National Institutes of Health, Bethesda, MD, USA.