Department of Toxicogenomics, GROW - School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
Sci Rep. 2023 Oct 25;13(1):18281. doi: 10.1038/s41598-023-45582-6.
Diet is an important determinant of overall health, and has been linked to the risk of various cancers. To understand the mechanisms involved, transcriptomic responses from human intervention studies are very informative. However, gene expression analysis of human biopsy material only represents the average profile of a mixture of cell types that can mask more subtle, but relevant cell-specific changes. Here, we use the CIBERSORTx algorithm to generate single-cell gene expression from human multicellular colon tissue. We applied the CIBERSORTx to microarray data from the PHYTOME study, which investigated the effects of different types of meat on transcriptional and biomarker changes relevant to colorectal cancer (CRC) risk. First, we used single-cell mRNA sequencing data from healthy colon tissue to generate a novel signature matrix in CIBERSORTx, then we determined the proportions and gene expression of each separate cell type. After comparison, cell proportion analysis showed a continuous upward trend in the abundance of goblet cells and stem cells, and a continuous downward trend in transit amplifying cells after the addition of phytochemicals in red meat products. The dietary intervention influenced the expression of genes involved in the growth and division of stem cells, the metabolism and detoxification of enterocytes, the translation and glycosylation of goblet cells, and the inflammatory response of innate lymphoid cells. These results show that our approach offers novel insights into the heterogeneous gene expression responses of different cell types in colon tissue during a dietary intervention.
饮食是整体健康的一个重要决定因素，与各种癌症的风险有关。为了了解相关机制，人类干预研究中的转录组反应非常有启发性。然而，对人类活检材料的基因表达分析仅代表了细胞混合物的平均特征，这可能掩盖了更微妙但相关的细胞特异性变化。在这里，我们使用 CIBERSORTx 算法从人类多细胞结肠组织中生成单细胞基因表达。我们将 CIBERSORTx 应用于 PHYTOME 研究的微阵列数据，该研究调查了不同类型的肉类对与结直肠癌 (CRC) 风险相关的转录和生物标志物变化的影响。首先，我们使用来自健康结肠组织的单细胞 mRNA 测序数据在 CIBERSORTx 中生成了一个新的特征矩阵，然后确定了每种单独细胞类型的比例和基因表达。比较后，细胞比例分析显示，在添加植物化学物质后，红肉类产品中转录活跃细胞的丰度呈连续上升趋势，而过渡扩增细胞的丰度呈连续下降趋势。饮食干预影响了与干细胞生长和分裂、肠细胞代谢和解毒、杯状细胞翻译和糖基化以及固有淋巴细胞炎症反应相关的基因表达。这些结果表明，我们的方法为饮食干预期间结肠组织中不同细胞类型的异质基因表达反应提供了新的见解。
