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>本研究描述了一种可扩展且高性价比的单引导RNA（sgRNA）合成流程：通过使用搭载独特sgRNA间隔序列的微阵列合成寡核苷酸大文库，可将合成成本降低70%以上。该寡核苷酸亚库可通过Golden Gate Assembly组装为全长双链DNA（dsDNA）模板，随后借助T7 RNA聚合酶进行体外转录。RNA测序（RNA-seq）分析显示，间隔序列的检出丰度存在严重偏倚：部分间隔序列丰度显著偏高，而另一些则完全未被检出。与既往研究结果一致，我们发现位于T7启动子紧邻下游的间隔序列前4个核苷酸内的富鸟嘌呤序列，是引发该偏倚的主要因素。为解决该问题，我们在所有间隔序列的上游引入了一段鸟嘌呤四聚体序列，在包含389个间隔序列的sgRNA文库中，该策略可使偏倚平均降低19%。但该修饰同时会导致转录后产生高分子量RNA产物。此外，我们还测试了两种替代的偏倚抑制策略：将间隔序列分隔于微乳液体系中，以及优化DNA投入量与反应体系体积。两种策略均可独立降低2626重sgRNA文库的偏倚，但抑制效果弱于鸟嘌呤四聚体策略。这些技术改进同时提升了sgRNA文库的性价比与均一性，对优化CRISPR-Cas9筛选流程以及其他CRISPR相关或核酸酶系统的引导RNA设计具有广泛应用价值。 本补充数据文件包含所有测序间隔序列的经处理计数结果。每个RNA-seq样本（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>：该间隔序列的原始测序计数 <b>class</b>：间隔序列分类，分为设计靶标序列（Target）与突变间隔序列（Mutated Spacer）两类 另有index.csv文件，用于描述每个样本的体外转录（IVT）参数。