GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE300nnn/GSE300264/primaryOK200TranscriptomicsArabidopsis thalianaExpression profiling by high throughput sequencinghttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE300264GEOGSEfalsePerformance evaluation of Arabidopsis scRNA-seq sample processing strategiesPlant cells display significant heterogeneity, complicating the isolation and profiling of diverse cell populations from complex tissues. Optimizing methodologies for cell enrichment and single-cell transcriptomics is therefore critical for accurately capturing cellular diversity. 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 greater precision due to customizable gating strategies, while magnetic sorting provided faster processing and better representation of cell size heterogeneity. Both scRNA-seq platforms captured root cell heterogeneity and yielded reproducible gene expression profiles, but we observed platform-specific biases in cell type composition. Notably, single nucleotide polymorphism analysis of a mixed ecotype sample revealed that computational doublet detection algorithms misclassified two-thirds of the cells as doublets. Since the Arabidopsis root contains a wide range of cell types and developmental stages, our findings have broad implications. In summary, these insights can guide the end user to optimise their scRNA-seq workflows and improve data quality across plant species.2026/06/24GSE300264GSM9056714GSM9056715GSM9056716GSM905671726208300264Arabidopsis thaliana[42106556]