Sub-clustering, marker identification and multi-omics integration influenced by protoplasting in plant scRNA -seq data analysis

Abstract

Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for exploring cell heterogeneity and lineage dynamics, revealing complex mechanisms driving tissue function and development. However, plant cells rigid cell walls require a protoplasing step, an enzymatic process that can introduce biases. The impact of protoplasting on plant scRNA-seq data remains poorly understood. In this study, we analyzed gene expression patterns from bulk RNA-seq of various plant tissues before and after protoplasting. Cell-type specific protoplasting effects were discovered in different plant tissues. Protoplasting-related biases were found to distort clustering, marker gene identification, and multi-omics integration. By calculating protoplasted scores based on gene expression, we identified and mitigated protoplasting effects, enabling the accurate clustering and annotation of tobacco BY-2 cells and identification of cell-cycle-related genes. Our findings underscore the importance of assessing and correcting for protoplasting biases in plant scRNA-seq data analysis, offering new insights for more accurate data interpretation and biological discovery.