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Deconvolution of expression microarray data reveals I-131-induced responses otherwise undetected in thyroid tissue

Journal article
Authors Britta Langen
Nils Rudqvist
Johan Spetz
Khalil Helou
Eva Forssell-Aronsson
Published in Plos One
Volume 13
Issue 7
Pages 17
ISSN 1932-6203
Publication year 2018
Published at Institute of Clinical Sciences, Department of Radiation Physics
Institute of Clinical Sciences, Department of Oncology
Sahlgrenska Cancer Center
Pages 17
Language en
Keywords gene-expression, free at-211, transcriptional responses, mouse-tissues, kidney tissue, dose-rate, c-cells, mice, radiation, i-131
Subject categories Cell and Molecular Biology


High-throughput gene expression analysis is increasingly used in radiation research for discovery of damage-related or absorbed dose-dependent biomarkers. In tissue samples, cell type-specific responses can be masked in expression data due to mixed cell populations which can preclude biomarker discovery. In this study, we deconvolved microarray data from thyroid tissue in order to assess possible bias from mixed cell type data. Transcript expression data [GSE66303] from mouse thyroid that received 5.9 Gy from I-131 over 24 h (or 0 Gy from mock treatment) were deconvolved by cell frequency of follicular cells and C-cells using csSAM and R and processed with Nexus Expression. Literature-based signature genes were used to assess the relative impact from ionizing radiation (IR) or thyroid hormones (TH). Regulation of cellular functions was inferred by enriched biological processes according to Gene Ontology terms. We found that deconvolution increased the detection rate of significantly regulated transcripts including the biomarker candidate family of kallikrein transcripts. Detection of IR-associated and TH-responding signature genes was also increased in deconvolved data, while the dominating trend of TH-responding genes was reproduced. Importantly, responses in biological processes for DNA integrity, gene expression integrity, and cellular stress were not detected in convoluted data-which was in disagreement with expected dose-response relationships-but upon deconvolution in follicular cells and C-cells. In conclusion, previously reported trends of I-131-induced transcriptional responses in thyroid were reproduced with deconvolved data and usually with a higher detection rate. Deconvolution also resolved an issue with detecting damage and stress responses in enriched data, and may reduce false negatives in other contexts as well. These findings indicate that deconvolution can optimize microarray data analysis of heterogeneous sample material for biomarker screening or other clinical applications.

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