Here, we apply this approach to A. thaliana root cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution Overall design: Single-cell RNA-seq experiments were performed on protoplast cells extracted from the whole roots of 7-8 day old Arabidopsis thanliana seedlings. Plants were grown under control conditions at 22C. Plants were moved from 22C to 38C for 45 min to be heatshocked. Please note that the 'whole_root_Control_2' data was aggregated with the 'whole_root_Heatshock data' for comparative analysis.