Optimizing <i>in vitro</i> Transcribed CRISPR-Cas9 single-guide RNA Libraries for Improved Uniformity and Affordability

We describe a scalable and cost-effective sgRNA synthesis workflow that reduces costs by over 70% through the use of large pools of microarray-derived oligos encoding unique sgRNA spacers. These sub-pool oligos are assembled into full-length dsDNA templates via Golden Gate Assembly before <i>in vitro</i> transcription with T7 RNA polymerase. RNA-seq analysis reveals severe biases in spacer representation, with some spacers being highly overrepresented while others are completely absent. Consistent with previous studies, we identify guanine-rich sequences within the first four nucleotides of the spacer (immediately downstream of the T7 promoter) as the primary driver of this bias. To address this issue, we introduced a guanine tetramer upstream of all spacers, which reduced bias by an average of 19% in sgRNA libraries containing 389 spacers. However, this modification also increased the presence of high-molecular-weight RNA species after transcription. We also tested two alternative bias-reduction strategies: compartmentalizing spacers within emulsions and optimizing DNA input and reaction volumes. Both methods independently reduced bias in 2,626-plex sgRNA libraries, though to a lesser extent than the guanine tetramer approach. These advancements enhance both the affordability and uniformity of sgRNA libraries, with broad implications for improving CRISPR-Cas9 screens and optimizing guide RNA design for other CRISPR and nuclease systems.<br><br>This is supplemental data file contains processed counts for all sequenced spacers. Each RNA-seq sample (and two DNA samples) has a corresponding csv file. Each file has three columns:<b>seq</b> - the sequence of the spacer<br><b>count</b> - the raw counts of the spacer<b>class</b> - if this spacer is one of the ones designed (Target) or not (Mutated Spacer)<br><br>An index.csv file describes the IVT parameters for each sample.<br>

我们描述了一种可规模化且高性价比的单引导RNA（single guide RNA, sgRNA）合成流程，通过使用携带独特sgRNA间隔序列的大规模微阵列衍生寡核苷酸池，将合成成本降低70%以上。这些亚池寡核苷酸先通过Golden Gate组装（Golden Gate Assembly）构建为全长双链DNA（double-stranded DNA, dsDNA）模板，随后利用T7 RNA聚合酶进行体外（in vitro）转录。RNA测序（RNA-seq）分析显示，间隔序列的丰度分布存在严重偏差：部分间隔序列被过度富集，而另一些则完全缺失。与既往研究一致，我们发现间隔序列前四个核苷酸（紧邻T7启动子下游）内的富鸟嘌呤序列是该偏差的主要诱因。为解决这一问题，我们在所有间隔序列的上游引入了鸟嘌呤四聚体，在包含389个间隔序列的sgRNA文库中，该修饰使偏差平均降低了19%。但该修饰同时会导致转录后产生高分子量RNA产物。我们还测试了两种替代的偏差缓解策略：将间隔序列区室化于乳液微滴中，以及优化DNA投入量与反应体积。两种方法均可独立降低2626重sgRNA文库的偏差，不过其效果弱于鸟嘌呤四聚体策略。这些进展提升了sgRNA文库的性价比与均一性，对优化CRISPR-Cas9筛选实验以及其他CRISPR和核酸酶系统的引导RNA设计具有广泛意义。

本补充数据文件包含所有已测序间隔序列的处理后计数数据。每个RNA-seq样本（以及2个DNA样本）均对应一个CSV格式文件。每个文件包含三列：
<b>seq</b> - 间隔序列的核苷酸序列
<b>count</b> - 该间隔序列的原始计数
<b>class</b> - 标注该间隔序列为设计的靶标序列（Target）或突变间隔序列（Mutated Spacer）

index.csv文件用于描述每个样本的体外转录（in vitro transcription, IVT）参数。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于优化体外转录的CRISPR-Cas9单导RNA（sgRNA）库，以提高均匀性和可负担性。它描述了一种可扩展且成本效益高的工作流程，通过使用微阵列衍生的寡核苷酸池，将合成成本降低了70%以上，同时通过RNA-seq分析揭示了spacer表示中的偏差，并测试了多种策略（如引入鸟嘌呤四聚体）来减少偏差。数据集提供补充数据文件，包含所有测序spacer的处理计数，用于支持CRISPR-Cas9筛选和其他CRISPR系统的指导RNA设计优化。