Sornapudi Trinadha Rao, Nayak Rakhee, Guthikonda Prashanth Kumar, Kethavath Srinivas, Yellaboina Sailu, Kurukuti Sreenivasulu
Department of Animal Biology, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.
CR Rao Advanced Institute of Mathematics, Statistics and Computer Sciences, University of Hyderabad Campus, Hyderabad, 500046, India.
BMC Res Notes. 2018 Apr 11;11(1):241. doi: 10.1186/s13104-018-3351-4.
Understanding of transcriptional networks specifying HC11 murine mammary epithelial stem cell-like cells (MEC) in comparison with embryonic stem cells (ESCs) and their rewiring, under the influence of glucocorticoids (GC) and prolactin (PRL) hormones, is critical for elucidating the mechanism of lactogenesis. In this data note, we provide RNA sequencing data from murine MECs and ESCs, MECs treated with steroid hormone alone and in combination with PRL. This data could help in understanding temporal dynamics of mRNA transcription that impact the process of lactogenesis associated with mammary gland development. Further integration of these data sets with existing datasets of cells derived from various stages of mammary gland development and different types of breast tumors, should pave the way for effective prognosis and to develop therapies for breast cancer.
We have generated RNA-sequencing data representing steady-state levels of mRNAs from murine ESCs, normal MECs (N), MECs primed (P) with hydrocortisone (HC) alone and in combination with PRL hormone by using Illumina sequencing platform. We have generated ~ 58 million reads for ESCs with an average length of ~ 100 nt and an average 115 million good quality mapped reads with an average length of ~ 150 nt for different stages of MECs differentiation.
与胚胎干细胞（ESC）相比，了解指定HC11小鼠乳腺上皮干细胞样细胞（MEC）的转录网络及其在糖皮质激素（GC）和催乳素（PRL）激素影响下的重新布线，对于阐明泌乳发生机制至关重要。在本数据说明中，我们提供了来自小鼠MEC和ESC、单独用类固醇激素以及与PRL联合处理的MEC的RNA测序数据。这些数据有助于理解影响与乳腺发育相关的泌乳发生过程的mRNA转录的时间动态。将这些数据集与来自乳腺发育不同阶段和不同类型乳腺肿瘤的现有细胞数据集进一步整合，应为乳腺癌的有效预后和开发治疗方法铺平道路。
我们通过使用Illumina测序平台生成了代表小鼠ESC、正常MEC（N）、单独用氢化可的松（HC）以及与PRL激素联合预处理的MEC的mRNA稳态水平的RNA测序数据。对于ESC，我们生成了约5800万个读数，平均长度约为100 nt；对于MEC分化的不同阶段，我们生成了平均长度约为150 nt的平均1.15亿个高质量映射读数。
