Protocol for the imputation of stimulus-induced single-cell gene expression trajectories from time-series scRNA-seq data

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.<sup>1</sup>.

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Journal
STAR Protocols
Published
2025-05-08
DOI
https://doi.org/10.1016/j.xpro.2025.103811
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

Protocol for the imputation of stimulus-induced single-cell gene expression trajectories from time-series scRNA-seq data

Katherine M. Sheu, Alexander Hoffmann
STAR Protocols
Single-cell and spatial transcriptomics
article

Protocol for the imputation of stimulus-induced single-cell gene expression trajectories from time-series scRNA-seq data

Katherine M. Sheu, Alexander Hoffmann
article en

Abstract

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.<sup>1</sup>.

STAR ProtocolsVol. 6(2)
QB3 (US)
University of California, Los Angeles, National Institute of Allergy and Infectious Diseases
Zero hunger
Openalex Percentile: Top 91%
Single-cell and spatial transcriptomics
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Protocol for the imputation of stimulus-induced single-cell gene expression trajectories from time-series scRNA-seq data — Katherine M. Sheu, Alexander Hoffmann · STAR Protocols (2025) | TGRS Research Map | TGRS