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

Yao, J.; Shang, N.; Chen, H.; Hu, Y.; Zhu, Q.-H.; Ye, C.; Chu, Q.

doi:10.1101/2024.12.14.628512

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bioRxiv · 10.1101/2024.12.14.628512

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

Yao, J.·Shang, N.·Chen, H.·Hu, Y.·Zhu, Q.-H.·Ye, C.·Chu, Q.

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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.

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Yao, J., Shang, N., Chen, H., Hu, Y., Zhu, Q.-H., Ye, C., Chu, Q.. 2024-12-17. Sub-clustering, marker identification and multi-omics integration influenced by protoplasting in plant scRNA -seq data analysis. https://doi.org/10.1101/2024.12.14.628512

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