Rheumatologists see patients with a range of autoimmune diseases. Phenotyping these diseases for diagnosis, prognosis and selection of therapies is an ever increasing problem. Advances in multiplexed assay technology at the gene, protein, and cellular level have enabled the identification of 'actionable biomarkers'; that is, biological metrics that can inform clinical practice. Not only will such biomarkers yield insight into the development, remission, and exacerbation of a disease, they will undoubtedly improve diagnostic sensitivity and accuracy of classification, and ultimately guide treatment. This Review provides an introduction to these powerful technologies that could promote the identification of actionable biomarkers, including mass cytometry, protein arrays, and immunoglobulin and T-cell receptor high-throughput sequencing. In our opinion, these technologies should become part of routine clinical practice for the management of autoimmune diseases. The use of analytical tools to deconvolve the data obtained from use of these technologies is also presented here. These analyses are revealing a more comprehensive and interconnected view of the immune system than ever before and should have an important role in directing future treatment approaches for autoimmune diseases.
Antigen arrays are valuable for profiling autoantibodies in diverse rheumatic autoimmune diseases and can be composed of most biomolecules including proteins, peptides, protein complexes, sugars, nucleic acids and lipids
High-throughput DNA sequencing enables the tracking of disease-associated clones of T cells and B cells in autoimmune diseases; changes in populations of these cells can be correlated with therapeutic response
The analysis of peripheral blood cells following cellular activation might be important in identifying clinically actionable biomarkers
New technologies enable analysis of gene and protein expression in whole blood samples; deconvolution of datasets reveals which immune-cell subset underlies a change without isolating or manipulating the cells
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The authors wish to thank Dr Hongwu Du for his advice on the systems immunology section of the article and for assistance with editing the manuscript. In addition, the authors thank members of the Boyd lab and Dr A. Fire (Stanford University, Stanford, USA) in collaboration with Dr A. Lucas and Dr L. Liu (Children's Hospital Oakland Research Institute, Oakland, USA) for providing the basis for Figure 4. Furthermore, the authors gratefully acknowledge sources of funding for their research activities: H. T. Maecker's work was supported by NIH grants 3 U19 AI057229, 4 U19 AI090019, and 1 RC4 AG039014; P. J. Utz was the recipient of a Donald E. and Delia B. Baxter Foundation Career Development Award and his work is supported by National Heart, Lung, and Blood Institute (NHLBI) Proteomics contract HHSN288201000034C, Proteomics of Inflammatory Immunity and Pulmonary Arterial Hypertension, NIH grants 5 U19-AI082719, 5 U19-AI050864, 5 U19-AI056363, 1 U19 AI090019 and 4 U19 AI090019, Canadian Institutes of Health Research grant 2 OR-92141, Alliance for Lupus Research grant number 21858, a gift from the Ben May Trust and a gift from the Floren Family Trust, and the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 261382; S. D. Boyd is the recipient of NIH grants U19AI090019, U19AI067854, U54-AI065359, UM1AI100663-01, Ellison Medical Foundation grant AG-NS-792-11, and the Lucille Packard Pediatric Research Fund; the work of S. S. Shen-Orr is supported by NIH grant 3 U19 AI057229.
S. D. Boyd is a consultant for ImmuMetrix LLC, and has received consultancy fees and equity compensation. The remaining authors declare no competing interests.
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Maecker, H., Lindstrom, T., Robinson, W. et al. New tools for classification and monitoring of autoimmune diseases. Nat Rev Rheumatol 8, 317–328 (2012). https://doi.org/10.1038/nrrheum.2012.66
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