Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2012 May 31;8(6):317-28.
doi: 10.1038/nrrheum.2012.66.

New tools for classification and monitoring of autoimmune diseases

Holden T Maecker  1 , Tamsin M Lindstrom, William H Robinson, Paul J Utz, Matthew Hale, Scott D Boyd, Shai S Shen-Orr, C Garrison Fathman
Affiliations
Review

New tools for classification and monitoring of autoimmune diseases

Holden T Maecker et al. Nat Rev Rheumatol. .

Erratum in

  • Nat Rev Rheumatol. 2012 Oct;8(10):562

Abstract

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Application of new immune-monitoring technologies to rheumatology. Samples for biomarker discovery can be generated during clinical research and actual clinical trials. For comprehensive immune monitoring, these samples are subjected to multiple assays at the proteomic and genomic level. Moreover, computational tools are applied to organize and better analyze the complex data sets that are generated, as well as to integrate heterogeneous data types. The end result should be the discovery of new actionable biomarkers, which aid disease diagnosis, prognosis, therapeutic targeting and contribute knowledge to the mechanism of action of a specific therapy. Abbreviations: CyTOF, cytometry by time of flight; FACS, fluorescence-activated cell sorting; SNP, single nucleotide poymorphism.
Figure 2
Figure 2
Alternative analysis approaches for high-complexity flow cytometry data. a | Example of a bivariate Gaussian distribution as used in Gaussian mixture modelling. b | Example of a bivariate skew-t distribution as used in FLAME. c | Comparison of average intracluster distance and average intercluster distance. The average distance between events within the green gate (intracluster distance) is very large so it is likely to be composed of multiple distinct populations. The average distance between events within the red gate or within the blue gate (intracluster distance) is much smaller than the average distance between events in the red and blue gates (intercluster distance). d | Illustration of flow cytometry data showing normal human B cell development in bone marrow. Continuous distributions such as this poorly fit with Gaussian mixture modelling, FLAME, or DBM, but the phenotypic relationships are well-visualized by SPADE. Abbreviations: DBM, density-based merging; FLAME, flow analysis with automated multivariate estimation; SPADE, spanning-tree progression analysis of density-normalized events.
Figure 3
Figure 3
Example of a SPADE representation of CyTOF data from analysis of peripheral blood mononuclear cells from two healthy individuals. The SPADE algorithm was used to perform unsupervised clustering of cells according to their expression of 23 cell surface markers. The algorithm then arranged the clusters into a consensus `tree' structure, to show which clusters are most related to one another. Annotation of major cell lineages was added manually, based on the observed expression of known lineage markers in each `branch' of the tree. Cluster size is proportional to cell number in the sample analyzed. Colouring shows relative CD45RA staining intensity in each cluster. Note the difference in CD45RA expression on the surface of natural killer cells in the two different individuals (arrows). SPADE is thus a powerful way to visualize differences between samples, without the bias introduced by traditional flow cytometry gating and enables a much more defined subset analysis of cells. Abbreviations: CM, central memory; CyTOF, cytometry by time-of-flight; EM, effector memory; SPADE, spanning-tree progression analysis of density-normalized events.
Figure 4
Figure 4
The use of high-throughput DNA sequencing of immunoglobulin or T-cell receptor gene rearrangements to detect dynamic changes in lymphocyte repertoire and clonal expansions. In this example, the data show the response of a healthy individual to vaccination with a meningococcal polysaccharide vaccine, with the upper panel showing the peripheral blood B-cell repertoire prevaccination, and the lower panel showing the clonal B-cell response stimulated by the vaccine at day 7 postvaccination. Immunoglobulin heavy-chain V(D)J rearrangements were PCR-amplified from peripheral blood B cells from each sample, in sixfold replicate, using genomic DNA as the PCR template. Approximately 2,000-3,000 V(D)J rearrangements were sequenced from the libraries generated from each sample. If sequences with the same V, D, and J segments and junctions are detected in more than one replicate library from a sample, it provides evidence of a clonally expanded B-cell population. Expanded B-cell clones are displayed as squares of progressively larger size and warmer-spectrum (yellow, orange, red and white) colour. Clones detected in two replicates are shown by a small yellow square; clones detected in all six replicates are shown by a large white square. Small blue dots indicate VDJ combinations for which sequences were found in only a single replicate. The x-axis indicates the V segment used for a particular V(D)J rearrangement. The large y-axis rows show the J segment. The fine y-axis rows within each J segment row indicate the D segment. This method can be used to detect expanded clonal populations with a sensitivity limited mainly by the amount of sample available, and by the depth of sequencing carried out. Application of this approach to study the clonal populations of B cells and T cells in rheumatologic disorders should enable detailed tracking of lymphocyte populations that are correlated with disease activity and with therapeutic responses.
Figure 5
Figure 5
Statistical deconvolution enables detection of system-wide cell-type specific differences between groups without cell-type isolation. a | The majority of biological samples comprise multiple cell-types that can vary dramatically in frequency from one sample to another. Traditional sample profiling, either by isolating specific cell-types of interest or by profiling heterogeneous tissues, provide a system-level understanding or cellular context respectively. Statistical deconvolution-based techniques offer a middle ground by providing system-wide cell-type specific differences between groups. b | The csSAM methodology provides a high-resolution and sensitive differential expression analysis that is localized to a specific cellular context. Quantifying the frequency of the different cell-type subsets in each sample enables the average gene expression profile of each cell type in each group to be estimated by statistical deconvolution. These estimated expression profiles can then be utilized to detect cell-type specific differences without sorting of the heterogeneous tissue, and reconstitute whole tissue as individual samples that are independent of frequency variations associated with cell type. Abbreviation: csSAM, cell type–specific significance analysis of microarrays. Permission obtained for part b from Nature Publishing Group © Shen-Orr, S. et al. Nat. Methods 7, 287–289 (2010).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Capdeville R, Buchdunger E, Zimmermann J, Matter A. Glivec (STI571, imatinib), a rationally developed, targeted anticancer drug. Nat. Rev. Drug Discov. 2002;1:493–502. - PubMed
    1. Nahta R, Esteva FJ. HER-2-targeted therapy: lessons learned and future directions. Clin. Cancer Res. 2003;9:5078–5084. - PubMed
    1. LaGasse JM, et al. Successful prospective prediction of type 1 diabetes in schoolchildren through multiple defined autoantibodies: an 8-year follow-up of the Washington State Diabetes Prediction Study. Diabetes Care. 2002;25:505–511. - PubMed
    1. van der Woude D, et al. The ACPA isotype profile reflects long-term radiographic progression in rheumatoid arthritis. Ann. Rheum. Dis. 2010;69:1110–1116. - PubMed
    1. Zethelius B, et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N. Engl. J. Med. 2008;358:2107–2116. - PubMed