Single cell RNA sequencing (scRNA-seq) can detect gene expression and its dynamics in groups of cells. Its application to time-series samples can thus identify temporal gene expression programs active in different cell types. However, current scRNA-seq analysis needs improvement with regards to low gene detection, low replicate count, and inflated statistics when using cells as individual sampling data points. To address these issues, we explore whether “metacells” can be used as “replicates” through time. Toward this, we applied the use of metacells to a time-series scRNA-seq dataset from PBMCs after SARS-Cov-2 infection, which were then used in maSigPro for quadratic regression to find significantly differentially expressed genes (DEGs) over time, followed by clustering analysis of the expression velocity trends. Results demonstrated that this strategy could overcome the limitation of 1-2 replicates, as it correctly identified the known ISG15 interferon response program in almost all PBMC cell types. Its application further led to the uncovering of additional gene expression programs that potentially modulate different levels of host response after infection.

Identifying cell type transcriptome changes in COVID-19 patient PBMCs using metacells

 Medical Genetics and Genomics

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Identifying cell type transcriptome changes in COVID-19 patient PBMCs using metacells

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