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Abstract Number: 2827

A Novel Statistical Method to Resolve Cellular Heterogeneity in Disease Tissues: Integrating Transcriptomic Data in Accelerating Medicines Partnership (AMP) – RA Network Phase 1 Data

Fan Zhang1, Kamil Slowikowski2, Chamith Fonseka1, Kevin Wei3, Maria Gutierrez-Arcelus4, James Lederer5, Nir Hacohen6, Vivian P. Bykerk7, Michael Holers8, Peter Gregersen9, Mandy J. McGeachy10, Larry W. Moreland11, Andrew Filer12, Costantino Pitzalis13, Yvonne C. Lee14, Jennifer H. Anolik15, Michael Brenner4 and Soumya Raychaudhuri16, 1Divisions of Genetics and Rheumatology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 2Division of Medicine and Rheumatology, Brigham and Women's Hospital, Harvard Medical Schoo, Boston, MA, 3Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 4Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 5Department of Surgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 6Harvard Medical School, Boston, MA, 72-005, Mt Sinai Hospital, Toronto, ON, Canada, 8Medicine, Division of Rheumatology, University of Colorado Denver, Aurora, CO, 9The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, 10Medicine, University of Pittsburgh, Pittsburgh, PA, 11Rheumatology & Clinical Immunology, University of Pittsburgh, Pittsburgh, PA, 12Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham, United Kingdom, 13Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London, School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom, 14Rheumatology Immunology & Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 15Medicine- Allergy, Immunology and Rheumatology, University of Rochester Medical Center, Rochester, NY, 16Division of Medicine and Rheumatology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA

Meeting: 2017 ACR/ARHP Annual Meeting

Date of first publication: September 18, 2017

Keywords: Data analysis, Genetic Biomarkers, Rheumatoid arthritis (RA), statistical methods and transcriptional regulation

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Session Information

Date: Tuesday, November 7, 2017

Title: Genetics, Genomics and Proteomics

Session Type: ACR Concurrent Abstract Session

Session Time: 4:30PM-6:00PM

Background/Purpose: Detecting distinct cellular subsets in disease tissues is key to understanding the pathogenesis of immune diseases, for example in synovial tissues in rheumatoid arthritis (RA). Yet this task is complicated by the highly heterogeneous nature of the immune system. Recently, advances in single-cell technologies have enabled us to increase the power to resolve this heterogeneity to find disease relevant and cell type subsets. Integration of single-cell data with bulk data will help identify differentially abundant cell subsets.

Methods: We develop a novel statistical method for integrative analysis of any single-cell assay (flow cytometry, single-cell RNA-seq, or mass cytometry) with bulk RNA-seq (Figure 1). Our method has three steps. (1) We find the principal components (PCs) in the bulk RNA-seq data. (2) We model cell type abundances as a linear combination of bulk PCs. With single-cell data, we model a single-cell measurement with each PC to identify important signatures of bulk RNA-seq. The identified subsets are reflected as distinct clusters of cells in single-cell data and explain the variance in the bulk data. In mass cytometry data, we use a logistic model that includes the PC as a variable to predict the cluster identity of a cell. (3) To assess significance, we use permutations to test statistical significance of model coefficients.

Results: Using this integrative method, we are able to identify cellular subsets and statistically significant marker genes (permutation P < 1e-5) in fibroblasts, B cells, and T cells in AMP Phase 1 RA data (Bulk RNA-seq: 40 RA and 14 OA; Single-cell RNA-seq: 22 RA and 3 OA; Mass cytometry: 15 RA and 11 OA). Synovial fibroblasts separate into lining fibroblasts marked by CD55 and PRG4, and sublining fibroblasts marked by THY1 and CD74. B cells were separated into two distinct subsets: activated B cells that express HLA-DRA, CD37, and CD83 and plasma cells that express XBP1 and FKBP11. T cells were also separated into CD4+ helper that express CD4, IL7R, and SELL and CD8+ cytotoxic that express CD8A, CCL5, and HLA-DQA1. We tested subsets of these populations for disease association. We found that fibroblast subset marked by THY1 were significantly associated with RA (logistic regression P < 2e-5) when we integrated bulk data with mass cytometry. Derived from the bulk PCs, we observed that a PD1+ subset of CD4 memory T cells which have been recently described as peripheral T helper cells also expanded in RA (P < 0.01).

Conclusion: This computational strategy integrates multiple transcriptomic data types from fresh synovial tissue, giving us a view of the cellular heterogeneity relevant to RA. This method may also have promise in integrating single-cell and bulk data from other sources, for example kidney biopsies from systemic lupus erythematosus (SLE) or tumor biopsies. Acknowledgements: We acknowledge AMP RA/SLE network and AMP funding NIH UH2AR067677-01.

Picture5.png

Figure 1. Overview of the data integration method.


Disclosure: F. Zhang, None; K. Slowikowski, None; C. Fonseka, None; K. Wei, None; M. Gutierrez-Arcelus, None; J. Lederer, None; N. Hacohen, None; V. P. Bykerk, None; M. Holers, None; P. Gregersen, None; M. J. McGeachy, None; L. W. Moreland, None; A. Filer, None; C. Pitzalis, None; Y. C. Lee, Express Scripts, 1,Pfizer Inc, 2; J. H. Anolik, None; M. Brenner, None; S. Raychaudhuri, Pfizer Inc, 2,Roche Pharmaceuticals, 2.

To cite this abstract in AMA style:

Zhang F, Slowikowski K, Fonseka C, Wei K, Gutierrez-Arcelus M, Lederer J, Hacohen N, Bykerk VP, Holers M, Gregersen P, McGeachy MJ, Moreland LW, Filer A, Pitzalis C, Lee YC, Anolik JH, Brenner M, Raychaudhuri S. A Novel Statistical Method to Resolve Cellular Heterogeneity in Disease Tissues: Integrating Transcriptomic Data in Accelerating Medicines Partnership (AMP) – RA Network Phase 1 Data [abstract]. Arthritis Rheumatol. 2017; 69 (suppl 10). https://acrabstracts.org/abstract/a-novel-statistical-method-to-resolve-cellular-heterogeneity-in-disease-tissues-integrating-transcriptomic-data-in-accelerating-medicines-partnership-amp-ra-network-phase-1-data/. Accessed .
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