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

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 131I 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 131I-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.高通量基因表达分析（High-throughput gene expression analysis）在辐射研究中愈发广泛地应用于损伤相关或吸收剂量依赖性生物标志物的发现。在组织样本中，由于细胞群混合，细胞类型特异性应答会在表达数据中被掩盖，这可能阻碍生物标志物的发现。本研究中，我们对甲状腺组织的微阵列数据进行细胞反卷积（deconvolution），以评估混合细胞类型数据带来的潜在偏倚。来自接受131I照射5.9 Gy（持续24小时，假照射组为0 Gy）的小鼠甲状腺的转录组表达数据[GSE66303]，通过csSAM工具与R语言基于滤泡细胞和C细胞的细胞频率进行反卷积，并使用Nexus Expression进行后续处理。基于文献的特征基因被用于评估电离辐射（ionizing radiation, IR）与甲状腺激素（thyroid hormones, TH）的相对影响。细胞功能的调控可通过基因本体（Gene Ontology, GO）术语富集的生物学过程进行推断。我们发现，细胞反卷积提升了显著调控转录本的检出率，包括激肽释放酶（kallikrein）转录本这一生物标志物候选家族。在反卷积后的数据中，与电离辐射相关以及响应甲状腺激素的特征基因的检出率也有所提升，而响应甲状腺激素的基因的主导表达趋势得以重现。值得注意的是，在未反卷积的混合数据中，未检测到DNA完整性、基因表达完整性及细胞应激相关生物学过程的应答，这与预期的剂量-反应关系不符；但在滤泡细胞和C细胞的反卷积数据中则成功检出了这些应答。综上，此前报道的131I诱导的甲状腺转录应答趋势在反卷积数据中得以重现，且通常检出率更高。细胞反卷积还解决了在异质性样本中检测损伤与应激应答的难题，在其他研究场景中也可能减少假阴性结果的出现。上述研究结果表明，细胞反卷积可优化异质性样本材料的微阵列数据分析，用于生物标志物筛选或其他临床应用。