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We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequencing (scRNA-seq) raw data using scKB and COPILOT. scKB is a wrapper script of kallisto and bustools for accelerated alignment and transcript count matrix generation, which runs significantly faster than the popular tool Cell Ranger. COPILOT then offers non-arbitrary background noise removal by comparing distributions of low-quality and high-quality cells. Together, this protocol streamlines the processing workflow and provides an easy entry for new scRNA-seq users. For complete details on the use and execution of this protocol, please refer to Shahan et al. (2022). 

Brassinosteroids are plant steroid hormones that regulate diverse processes, such as cell division and cell elongation, through gene regulatory networks that vary in space and time. By using time series single-cell RNA sequencing to profile brassinosteroid-responsive gene expression specific to different cell types and developmental stages of theArabidopsisroot, we identified the elongating cortex as a site where brassinosteroids trigger a shift from proliferation to elongation associated with increased expression of cell wall–related genes. Our analysis revealedHOMEOBOX FROM ARABIDOPSIS THALIANA 7(HAT7) andGT-2-LIKE 1(GTL1) as brassinosteroid-responsive transcription factors that regulate cortex cell elongation. These results establish the cortex as a site of brassinosteroid-mediated growth and unveil a brassinosteroid signaling network regulating the transition from proliferation to elongation, which illuminates aspects of spatiotemporal hormone responses. 

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.