New tools for classification and monitoring of autoimmune diseases
Open accessAbstract
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.
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Deep immune phenotyping for the Stanford 1KIP samples was performed at the Stanford HIMC using rigorously standardized procedures.
“For all samples of the Stanford 1KIP, deep immune phenotyping was conducted at the Stanford HIMC, where peripheral blood specimens were processed and analyzed using rigorously standardized procedures31.”
From An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging (2021) · cites this paper as [31]
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T cell receptor repertoire of T cells in the kidneys of patients with lupus nephritis
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The promise and challenge of high-throughput sequencing of the antibody repertoire
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Self-citation
- Authors
- Maecker, H. T., Lindstrom, T. M., Robinson, W. H., Utz, P. J., Hale, M., Boyd, S. D., Shen-Orr, S. S., & Fathman, C. G.
- Affiliations
- Stanford Medicine (US)
- Technion – Israel Institute of Technology (IL)
- Corresponding author
- Fathman, C. Garrison
- ORCID
- Venue
- Nature Reviews Rheumatology
- Published
- 2012-05-31
- Volume
- 8
- Issue
- 6
- Pages
- 317-328
- ISSN
- 1759-4790 (ISSN-L)
- 1759-4804
- PMC
- PMC3409841
- OpenAlex
- W2138211617
- Cited by
- 95
- References
- 96
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 4 · 2023: 2 · 2022: 3 · 2021: 6 · 2020: 1 · 2019: 7 · 2018: 10 · 2017: 8 · 2016: 8 · 2015: 10 · 2014: 10 · 2013: 17 · 2012: 2
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Update notices
- correction (2012-10-01)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Reviews Rheumatology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Allergy and Infectious Diseases (U19AI090019)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (RC1AR058713)
- Topics
- Health Sciences › Medicine › Rheumatology › Rheumatoid Arthritis Research and Therapies
- Health Sciences › Medicine › Rheumatology › Systemic Lupus Erythematosus Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- autoimmune disease, disease classification, disease surveillance, actionable biomarkers, mass cytometry, protein microarrays, T cell receptor sequencing, disease phenotyping, diagnostic accuracy, treatment selection
- MeSH terms
- Autoimmune Diseases, Flow Cytometry, Humans, Immunoassay, Immunoglobulins, Monitoring, Physiologic, Prognosis, Receptors, Antigen, T-Cell, Mass Spectrometry, Biomarkers, Immunophenotyping, Sequence Analysis, DNA, Protein Array Analysis, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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