Sheu Katherine M, Hoffmann Alexander
Department of Microbiology, Immunology, and Molecular Genetics, Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, 611 Charles E. Young Dr E, Los Angeles, CA 90095, USA.
STAR Protoc. 2025 Jun 20;6(2):103811. doi: 10.1016/j.xpro.2025.103811. Epub 2025 May 8.
Single-cell RNA sequencing (scRNA-seq) measures cell-to-cell heterogeneous mRNA abundance but destroys the cell and precludes tracking of heterogeneous gene expression trajectories. Here, we present an approach to impute single-cell gene expression trajectories (scGETs) from time-series scRNA-seq measurements. We describe four main computational steps: dimensionality reduction, calculation of transition probability matrices, spline interpolation, and deconvolution to scGETs. Imputing scGETs can aid in studying heterogeneous stimulus responses over time, such as cancer cell responses to drugs or immune cell responses to pathogens. For complete details on the use and execution of this protocol, please refer to Sheu et al..
单细胞RNA测序（scRNA-seq）可测量细胞间异质的mRNA丰度，但会破坏细胞并排除对异质基因表达轨迹的追踪。在此，我们提出一种从时间序列scRNA-seq测量值中推断单细胞基因表达轨迹（scGETs）的方法。我们描述了四个主要计算步骤：降维、转移概率矩阵的计算、样条插值以及对scGETs的反卷积。推断scGETs有助于研究随时间变化的异质刺激反应，例如癌细胞对药物的反应或免疫细胞对病原体的反应。有关本方案使用和执行的完整详细信息，请参阅Sheu等人的文章。
