RNA-sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA

We report RNA-sequencing data of 80 tumor-educated blood platelet (TEP) samples isolated from 39 patients with lower-grade glioma (LGG) and 41 healthy donors (HD). This dataset can be employed as input for the thromboSeq source code (available via GitHub: https://github.com/MyronBest/) to reproduce the thromboSeq drylab pipeline. Overall design: Blood platelets were isolated from whole blood in purple-cap BD Vacutainers containing EDTA anti-coagulant by standard centrifugation. Total RNA was extracted from the platelet pellet, subjected to cDNA synthesis and SMARTer amplification, fragmented by Covaris shearing, and prepared for sequencing using the Truseq Nano DNA Sample Preparation Kit. Subsequently, pooled sample libraries were sequenced on the Illumina Hiseq 2500 platform. All steps were quality-controlled using Bioanalyzer 2100 with RNA 6000 Picochip, DNA 7500 and DNA High Sensitivity chips measurements. For further downstream analyses, reads were quality-controlled using Trimmomatic, mapped to the humane reference genome using STAR, and intron-spanning reads were summarized using HTSeq.

本研究报道了80份肿瘤教育血小板（tumor-educated blood platelet, TEP）样本的RNA测序数据，样本来源于39例低级别胶质瘤（lower-grade glioma, LGG）患者与41名健康志愿者（healthy donors, HD）。本数据集可作为thromboSeq源代码（可通过GitHub获取：https://github.com/MyronBest/）的输入数据，用于复现thromboSeq干实验分析流程。
整体实验设计如下：采用标准离心法，从装有EDTA抗凝剂的紫色帽BD Vacutainer采血管内的全血中分离血小板。从血小板沉淀中提取总RNA，依次完成cDNA合成与SMARTer扩增、Covaris超声片段化，随后使用Truseq Nano DNA样本制备试剂盒构建测序文库。之后将混合后的样本文库置于Illumina Hiseq 2500测序平台进行测序。所有实验步骤均通过Bioanalyzer 2100配合RNA 6000 Pico芯片、DNA 7500芯片及DNA High Sensitivity芯片完成质量控制。在后续下游分析中，使用Trimmomatic对测序读段进行质量控制，通过STAR将读段比对至人类参考基因组，并利用HTSeq统计跨内含子读段的计数。