Two-stage, in Silico deconvolution of the lymphocyte compartment of the peripheral whole blood transcriptome in the context of acute kidney allograft rejection

Shannon, Casey P., Balshaw, Robert, Ng, Raymond T., Wilson-McManus, Janet E., Keown, Paul, McMaster, Robert, McManus, Bruce M., Landsberg, David., Isbel, Nicole M., Knoll, Greg and Tebbutt, Scott J. (2014) Two-stage, in Silico deconvolution of the lymphocyte compartment of the peripheral whole blood transcriptome in the context of acute kidney allograft rejection. PLoS One, 9 4: e95224.1-e95224.19. doi:10.1371/journal.pone.0095224


Author Shannon, Casey P.
Balshaw, Robert
Ng, Raymond T.
Wilson-McManus, Janet E.
Keown, Paul
McMaster, Robert
McManus, Bruce M.
Landsberg, David.
Isbel, Nicole M.
Knoll, Greg
Tebbutt, Scott J.
Title Two-stage, in Silico deconvolution of the lymphocyte compartment of the peripheral whole blood transcriptome in the context of acute kidney allograft rejection
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2014-04-14
Year available 2014
Sub-type Article (original research)
DOI 10.1371/journal.pone.0095224
Open Access Status DOI
Volume 9
Issue 4
Start page e95224.1
End page e95224.19
Total pages 19
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Language eng
Abstract Acute rejection is a major complication of solid organ transplantation that prevents the long-term assimilation of the allograft. Various populations of lymphocytes are principal mediators of this process, infiltrating graft tissues and driving cell-mediated cytotoxicity. Understanding the lymphocyte-specific biology associated with rejection is therefore critical. Measuring genome-wide changes in transcript abundance in peripheral whole blood cells can deliver a comprehensive view of the status of the immune system. The heterogeneous nature of the tissue significantly affects the sensitivity and interpretability of traditional analyses, however. Experimental separation of cell types is an obvious solution, but is often impractical and, more worrying, may affect expression, leading to spurious results. Statistical deconvolution of the cell typespecific signal is an attractive alternative, but existing approaches still present some challenges, particularly in a clinical research setting. Obtaining time-matched sample composition to biologically interesting, phenotypically homogeneous cell sub-populations is costly and adds significant complexity to study design. We used a two-stage, in silico deconvolution approach that first predicts sample composition to biologically meaningful and homogeneous leukocyte sub-populations, and then performs cell type-specific differential expression analysis in these same sub-populations, from peripheral whole blood expression data. We applied this approach to a peripheral whole blood expression study of kidney allograft rejection. The patterns of differential composition uncovered are consistent with previous studies carried out using flow cytometry and provide a relevant biological context when interpreting cell type-specific differential expression results. We identified cell type-specific differential expression in a variety of leukocyte sub-populations at the time of rejection. The tissuespecificity of these differentially expressed probe-set lists is consistent with the originating tissue and their functional enrichment consistent with allograft rejection. Finally, we demonstrate that the strategy described here can be used to derive useful hypotheses by validating a cell type-specific ratio in an independent cohort using the nanoString nCounter assay.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Medicine Publications
 
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