Not sure which scRNA-seq platform to use for 🪴 plant samples? How well do doublet detection algorithms really work in 🌱? How can I optimise sample prep ? Find out in our benchmark study! Led by @carogro.bsky.social and @thomaseekhout.bsky.social. Open access at EMBO Journal: doi.org/10.1038/s443...
The isolation of single plant cells from complex tissues is prone to selective enrichment and sampling biases, which complicates accurate profiling of the large diversity in cell types. Optimizing methodologies for cell enrichment and single-cell transcriptomics is therefore critical for single-cell studies addressing plant cell heterogeneity. Here, we systematically compared protoplast enrichment technologies (including conventional and image-based flow cytometry, as well as magnetic cell sorting) and single-cell RNA sequencing (scRNA-seq) platforms (10X Genomics Chromium, BD Rhapsody) using Arabidopsis roots. Image-based flow cytometry offered increased precision due to customizable gating strategies, while magnetic sorting provided faster processing and enhanced representation of cell size heterogeneity. Both scRNA-seq platforms captured root cell heterogeneity and yielded reproducible gene expression profiles, but showed platform-associated differences in cell type composition. Notably, single-nucleotide polymorphism analysis of a mixed ecotype sample revealed that, among cells identified as doublets by computational algorithms, two-thirds were likely to have been misclassified. These insights identify key biases in plant cell purification and scRNA-seq workflows and provide practical guidance for improving data quality across plant species.
