Stable and unstable angina: Identifying novel markers on circulating leukocytes

Brown, Angus; Lattimore, Jo-Dee; McGrady, Michele; Sullivan, David; Dyer, Wayne B.; Braet, Filip; Remedios, Cristobal dos

doi:10.1002/prca.200780090

Cited by 12 publications

(13 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The relationship between dot density and the number of bound cells was recently reported to be linear [18]. Only living cells appear to be able to attach themselves to the antibodies bound to the nitrocellulose surface.…”

Section: Antibody Leukocyte Capture Microarraymentioning

confidence: 98%

“…[12][13][14][15]. However, it soon became clear that these cell microarrays could also be used to detect more subtle changes in leukocyte populations, for example cell activation or inflammatory responses in patients with heart disease [16][17][18] and other inflammatory diseases [19], including SLE, as we describe in this review. We assume that inflammation, whether active or historical, produces a response in CD or other surface proteins on the surface of peripheral blood leukocytes, and that this response can be detected when large numbers of signals are measured simultaneously.…”

Section: Antibody Leukocyte Capture Microarraymentioning

confidence: 99%

“…Once on the nitrocellulose surface it spreads to a diameter of 450-500 mm. Before use, the antibody surface is moistened with PBS, then 300 mL of 10 7 peripheral blood mononuclear cells (PBMCs) (isolated from 10 mL of peripheral blood as described elsewhere [18]) is applied. Thus, the area coated with any of the 82 antibodies is large enough to accommodate up to 1000-1500 leukocytes and provides a good statistical sampling of the population of cells applied to the surface.…”

Section: Antibody Leukocyte Capture Microarraymentioning

confidence: 99%

See 2 more Smart Citations

Customising an antibody leukocyte capture microarray for systemic lupus erythematosus: Beyond biomarker discovery

Lin

Adelstein

et al. 2010

Proteomics Clinical Apps

View full text Add to dashboard Cite

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has heterogeneous clinical manifestation with diverse patterns of organ involvement, autoantibody profiles and varying degrees of severity of disease. Research and clinical experience indicate that different subtypes of SLE patients will likely benefit from more tailored treatment regimes, but we currently lack a fast and objective test with high enough sensitivity to enable us to perform such sub‐grouping for clinical use. In this article, we review how proteomic technologies could be used as such an objective test. In particular, we extensively review many leukocyte surface markers that are known to have an association with the pathogenesis of SLE, and we discuss how these markers can be used in the further development of a novel SLE‐specific antibody leukocyte capture microarray. In addition, we review some bioinformatics challenges and current methods for using the data generated by these cell‐capture microarrays in clinical use. In a broader context, we hope our experience in developing a disease specific cell‐capture microarray for clinical application can be a guide to other proteomic practitioners who intend to extend their technologies to develop clinical diagnostic and prognostic tests for complex diseases.

show abstract

Section: Antibody Leukocyte Capture Microarraymentioning

confidence: 98%

Section: Antibody Leukocyte Capture Microarraymentioning

confidence: 99%

Section: Antibody Leukocyte Capture Microarraymentioning

confidence: 99%

See 1 more Smart Citation

Customising an antibody leukocyte capture microarray for systemic lupus erythematosus: Beyond biomarker discovery

Lin

Adelstein

et al. 2010

Proteomics Clinical Apps

View full text Add to dashboard Cite

show abstract

“…Two cardiovascular disease datasets [4,9] were re-analyzed using our LVM approach. All data were generated in our laboratory using an 82 spot antibody array platform.…”

Section: Re-analysis Of Two Cardiovascular Disease Datasetsmentioning

confidence: 99%

“…Our laboratory has been developing a cell-captured antibody microarray platform that enables concurrent quantification of many CD antigens [1,2]. This array platform has been successfully used to identify changes in the immunophenotype of various human diseases, such as leukemia [1,2], heart failure [8,9], and coronary artery disease [4].…”

Section: Introductionmentioning

confidence: 99%

Inferring Differential Leukocyte Activity From Antibody Microarrays Using a Latent Variable Model

Koundinya

Caetano

et al. 2008

Genome Informatics 2008

View full text Add to dashboard Cite

Recent development of cluster of differentiation (CD) antibody arrays has enabled expression levels of many leukocyte surface CD antigens to be monitored simultaneously. Such membrane-proteome surveys have provided a powerful means to detect changes in leukocyte activity in various human diseases, such as cancer and cardiovascular diseases. The challenge is to devise a computational method to infer differential leukocyte activity among multiple biological states based on antigen expression profiles. Standard DNA microarray analysis methods cannot accurately infer differential leukocyte activity because they often fail to take the cell-to-antigen relationships into account. Here we present a novel latent variable model (LVM) approach to tackle this problem. The idea is to model each cell type as a latent variable, and represent the class-to-cell and cellto-antigen relationships as a LVM. Once the parameters of the LVM are learned from the data, differentially active leukocytes can be easily identified from the model. We describe the model formulation and assumptions which lead to an efficient expectationmaximization algorithm. Our LVM method was applied to re-analyze two cardiovascular disease datasets. We show that our results match existing biological knowledge better than other methods such as gene set enrichment analysis. Furthermore, we discuss how our approach can be extended to become a general framework for gene set analysis for DNA microarrays.

show abstract