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Functional genomics of stromal cells in chronic inflammatory diseases

Kamil Slowikowski, Kevin Wei, Michael B. Brenner, Soumya Raychaudhuri

Current Opinion in Rheumatology, 2018. DOI: 10.1097/BOR.0000000000000455

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Figure 1


PURPOSE OF REVIEW: Stroma is a broad term referring to the connective tissue matrix in which other cells reside. It is composed of diverse cell types with functions such as extracellular matrix maintenance, blood and lymph vessel development, and effector cell recruitment. The tissue microenvironment is determined by the molecular characteristics and relative abundances of different stromal cells such as fibroblasts, endothelial cells, pericytes, and mesenchymal precursor cells. Stromal cell heterogeneity is explained by embryonic developmental lineage, stages of differentiation to other cell types, and activation states. Interaction between immune and stromal cell types is critical to wound healing, cancer, and a wide range of inflammatory diseases. Here, we review recent studies of inflammatory diseases that use functional genomics and single-cell technologies to identify and characterize stromal cell types associated with pathogenesis.

RECENT FINDINGS: High dimensional strategies using mRNA sequencing, mass cytometry, and fluorescence activated cell-sorting with fresh primary tissue samples are producing detailed views of what is happening in diseased tissue in rheumatoid arthritis, inflammatory bowel disease, and cancer. Fibroblasts positive for CD90 (Thy-1) are enriched in the synovium of rheumatoid arthritis patients, and also in prostate cancer tumors. Single-cell RNA-seq studies will lead to more discoveries about the stroma in the near future.

SUMMARY: Stromal cells form the microenvironment of inflamed and diseased tissues. Functional genomics is producing an increasingly detailed view of subsets of stromal cells with pathogenic functions in rheumatic diseases and cancer. Future genomics studies will discover disease mechanisms by perturbing molecular pathways with chemokines and therapies known to affect patient outcomes. Functional genomics studies with large sample sizes of patient tissues will identify patient subsets with different disease phenotypes or treatment responses.

Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis

Fumitaka Mizoguchi, Kamil Slowikowski, Kevin Wei, Jennifer L. Marshall, Deepak A. Rao, Sook Kyung Chang, Hung N. Nguyen, Erika H. Noss, Jason D. Turner, Brandon E. Earp, Philip E. Blazar, John Wright, Barry P. Simmons, Laura T. Donlin, George D. Kalliolias, Susan M. Goodman, Vivian P. Bykerk, Lionel B. Ivashkiv, James A. Lederer, Nir Hacohen, Peter A. Nigrovic, Andrew Filer, Christopher D. Buckley, Soumya Raychaudhuri & Michael B. Brenner

Nature Communications, 2018. DOI: 10.1038/s41467-018-02892-y

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Fibroblasts regulate tissue homeostasis, coordinate inflammatory responses, and mediate tissue damage. In rheumatoid arthritis (RA), synovial fibroblasts maintain chronic inflammation which leads to joint destruction. Little is known about fibroblast heterogeneity or if aberrations in fibroblast subsets relate to pathology. Here, we show functional and transcriptional differences between fibroblast subsets from human synovial tissues using bulk transcriptomics of targeted subpopulations and single-cell transcriptomics. We identify seven fibroblast subsets with distinct surface protein phenotypes, and collapse them into three subsets by integrating transcriptomic data. One fibroblast subset, characterized by the expression of proteins podoplanin, THY1 membrane glycoprotein and cadherin-11, but lacking CD34, is threefold expanded in patients with RA relative to patients with osteoarthritis. These fibroblasts localize to the perivascular zone in inflamed synovium, secrete proinflammatory cytokines, are proliferative, and have an in vitro phenotype characteristic of invasive cells. Our strategy may be used as a template to identify pathogenic stromal cellular subsets in other complex diseases.

SNPSEA: an algorithm to identify cell types, tissues, and pathways affected by risk loci

Kamil Slowikowski, Xinli Hu, Soumya Raychaudhuri

Bioinformatics, 2014. DOI: 10.1093/bioinformatics/btu326

Data GitHub PDF PubMed

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We created a fast, robust and general C++ implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol.