Th17 Pathogenicity
Jellert T. Gaublomme, Nir Yosef, Youjin Lee, Rona S. Gertner, Li V. Yang, Chuan Wu, Pier Paolo Pandolfi, Tak Mak, Rahul Satija, Alex K. Shalek, Vijay K. Kuchroo, Hongkun Park, Aviv Regev Single-Cell Genomics Unveils Critical Regulators of Th17 Cell Pathogenicity Cell. Volume 163, Issue 6, 3 December 2015, Pages 1400–1412 http://dx.doi.org/10.1016/j.cell.2015.11.009
Questions can be directed to Jellert Gaublomme (jellert@mit.edu) or Nir Yosef (niryosef@berkeley.edu).
Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or differentiated in vitro under either pathogenic or non-pathogenic polarization conditions. Computational analysis relates a spectrum of cellular states in vivo to in-vitro-differentiated Th17 cells and unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four new genes: Gpr65, Plzp, Toso, and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity and can identify targets for selective suppression of pathogenic Th17 cells while potentially sparing non-pathogenic tissue-protective ones.
Figure 1. Single-Cell RNA-Seq of Th17 Cells In Vivo and In Vitro (A) Experimental setup. (B–E) Quality of single-cell RNA-seq. Scatter plots (B–D) compare transcript expression (FPKM+1, log10) from the in vitro TGF-β1+IL-6 48 hr condition between two bulk population replicates (B), the “average” of single-cell profile and a matched bulk population control (C), or two single cells (D). Histograms (E) depict the distributions of Pearson correlation coefficients (x axis) between single cells and their matched population control and between pairs of single cells. (F and G) Comparison to RNA Flow-FISH. (F) Expression distributions by RNA-seq and RNA Flow-FISH at 48 hr under the TGF-β1+IL-6 in vitro condition. Negative control: bacterial DapB gene. (G) Bright-field and fluorescence channel images of RNA Flow-FISH in negative (left) and positive (right) cells.
