Fu Shiwei, Li Wei Vivian
Department of Statistics, University of California, Riverside, Riveside, California, United States of America.
PLoS Comput Biol. 2025 Apr 3;21(4):e1012878. doi: 10.1371/journal.pcbi.1012878. eCollection 2025.
The advent of 5' single-cell RNA sequencing (scRNA-seq) technologies offers unique opportunities to identify and analyze transcription start sites (TSSs) at a single-cell resolution. These technologies have the potential to uncover the complexities of transcription initiation and alternative TSS usage across different cell types and conditions. Despite the emergence of computational methods designed to analyze 5' RNA sequencing data, current methods often lack comparative evaluations in single-cell contexts and are predominantly tailored for paired-end data, neglecting the potential of single-end data. This study introduces scTSS, a computational pipeline developed to bridge this gap by accommodating both paired-end and single-end 5' scRNA-seq data. scTSS enables joint analysis of multiple single-cell samples, starting with TSS cluster prediction and quantification, followed by differential TSS usage analysis. It employs a Binomial generalized linear mixed model to accurately and efficiently detect differential TSS usage. We demonstrate the utility of scTSS through its application in analyzing transcriptional initiation from single-cell data of two distinct diseases. The results illustrate scTSS's ability to discern alternative TSS usage between different cell types or biological conditions and to identify cell subpopulations characterized by unique TSS-level expression profiles.
5' 单细胞RNA测序（scRNA-seq）技术的出现为以单细胞分辨率识别和分析转录起始位点（TSS）提供了独特的机会。这些技术有潜力揭示不同细胞类型和条件下转录起始及选择性TSS使用的复杂性。尽管出现了旨在分析5' RNA测序数据的计算方法，但目前的方法在单细胞环境中往往缺乏比较评估，且主要是针对双端数据设计的，忽略了单端数据的潜力。本研究介绍了scTSS，这是一种通过兼用双端和单端5' scRNA-seq数据来弥补这一差距而开发的计算流程。scTSS能够对多个单细胞样本进行联合分析，从TSS聚类预测和定量开始，然后进行差异TSS使用分析。它采用二项广义线性混合模型来准确有效地检测差异TSS使用情况。我们通过将scTSS应用于分析两种不同疾病的单细胞数据来证明其效用。结果表明scTSS能够辨别不同细胞类型或生物学条件之间的选择性TSS使用情况，并识别以独特的TSS水平表达谱为特征的细胞亚群。
