Next Generation Sequencing Study of Circadian Changes in Transcriptome of Human Pineal Gland. Homo sapiens

Purpose: We performed an NGS study on the circadian changes in human pineal gland transcriptome in order to elucidate novel and conserved elements in the circadian clock, as well as to conduct a comparative analysis of pineal transcriptomes of several animal species. Methods: Total RNA from human pineal glands of individuals that died at 2 timepoints (Mid-Day, Midnight) was deep sequenced, using Illumina HiSeq2500. Reads were aligned using STAR aligner and differential expression was asssessed using DESeq2. Results: We discover a variety of genes that show circadian activivty in human pineal gland. Conclusions: Our study represents part of a comparative analysis of pineal gland transcriptome of several species, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions. Overall design: Pineal glands were obtained from NDRI (National Disease Research Interchange), (Pennsylvania Medical Center, Philadelphia, PA). Two glands were from individuals that died at mid-day (11:00-13:00) and four from deaths occurring at mid-night (23:00-01:00). Samples were stored at -80 o C until use. RNA was extracted from individual glands with Trizol (Invitrogen, Carlsbad,CA) and subsequently cleaned up using an RNeasy Micro Kit with on-column DNase treatment(Qiagen, Valencia, CA). The amount and quality of RNA were determined using a NanoDropspectrophotometer (NanoDrop, Wilmington, DE) and an Agilent 2100 Bioanalyzer (AgilentTechnologies, Santa Clara, CA), respectively. RIN values ranged from 7.3-8.7. RNA-Seq library preparation and sequencing: Ribosomal-depleted RNA was used to construct stranded libraries from 1 µg aliquots of total RNA using a TruSeq Stranded Total RNA Sample Prep Kit (Illumina cat. no. RS-122- 2301) according to the manufacturer's instructions. The library insert sizes were approximately 170 bp. Unique barcode adapters were appended to each library. Equal volumes of individual libraries were pooled and run on a MiSeq (Illumina, San Diego, CA). The libraries were then re-pooled based on the MiSeq demultiplexing results. The pooled libraries were sequenced on a HiSeq 2500 (Illumina, San Diego, CA) using version 4 chemistry. The data was processed using RTA version 1.18.64 and CASAVA 1.8.2. This yielded an average of 81 million 126-bp read-pairs for each sample. Reads were aligned using STAR aligner and differential expression was asssessed using DESeq2. GRCh37/hg19 genome build with Gencode 19 annotation was used.

研究目的：本研究针对人类松果体转录组的昼夜节律变化开展下一代测序（NGS，Next-Generation Sequencing）分析，旨在阐明生物钟中全新且保守的调控元件，同时开展多种动物松果体转录组的比较分析。
研究方法：采集两个时间点（正午、午夜）死亡个体的人类松果体总RNA，采用Illumina HiSeq2500平台进行深度测序。测序读段通过STAR比对工具完成基因组比对，差异表达分析采用DESeq2软件进行评估。
研究结果：本研究发现了一系列在人类松果体中呈现昼夜表达活性的基因。
研究结论：本研究为基于RNA测序技术的多物种松果体转录组比较分析的组成部分。本文报道的优化数据分析流程，可为表达谱的比较研究提供标准化框架。研究结果表明，下一代测序（NGS）可对细胞或组织内的mRNA含量实现全面且更为精准的定量与定性评估。综上，基于RNA测序的转录组表征技术可加速基因网络分析，并助力解析复杂的生物学功能。
实验整体设计：松果体样本取自美国国家疾病研究交换库（NDRI, National Disease Research Interchange）（宾夕法尼亚州医学中心，费城，宾夕法尼亚州）。其中2个样本来自正午（11:00-13:00）死亡的个体，4个样本来自午夜（23:00-01:00）死亡的个体。样本于-80℃环境下保存直至实验使用。总RNA从单个松果体中使用Trizol试剂（Invitrogen，卡尔斯巴德，加利福尼亚州）提取，随后采用RNeasy Micro Kit结合柱上DNase处理（Qiagen，瓦伦西亚，加利福尼亚州）进行纯化。RNA的浓度与质量分别通过NanoDrop分光光度计（NanoDrop，威尔明顿，特拉华州）与Agilent 2100 Bioanalyzer（AgilentTechnologies，圣克拉拉，加利福尼亚州）进行检测，RNA完整性数值（RIN）范围为7.3-8.7。
RNA测序文库构建与测序：以核糖体RNA去除后的总RNA为模板，取1μg总RNA样品，按照TruSeq Stranded Total RNA Sample Prep Kit（Illumina，货号RS-122-2301）的操作说明构建链特异性文库，文库插入片段长度约为170bp。为每个文库添加独特的条码接头。将等量体积的单个文库混合后，在MiSeq测序平台（Illumina，圣迭戈，加利福尼亚州）上进行预测序。随后根据MiSeq的去多重索引拆分结果重新混合文库。使用V4化学试剂，在HiSeq 2500测序平台（Illumina，圣迭戈，加利福尼亚州）上对混合后的文库进行测序。测序数据通过RTA v1.18.64与CASAVA 1.8.2进行处理，每个样本平均获得8100万条126bp的读段对。读段比对与差异表达分析仍采用STAR比对工具与DESeq2软件完成。分析过程中使用了带有Gencode 19注释信息的GRCh37/hg19人类参考基因组。