Quantitative Imaging of RNA in Plants Using Hybridization Chain Reaction‐RNA Fluorescence in Situ Hybridization (HCR RNA‐FISH)

Quantitative RNA imaging in large plant tissues has historically been challenging because of limited spatial resolution, low signal‐to‐noise ratios, and the largely qualitative nature of traditional RNA in situ hybridization methods. Hybridization chain reaction‐RNA fluorescence in situ hybridization (HCR RNA‐FISH) coupled with high‐resolution microscopy enables sensitive detection of RNA molecules at cellular resolution. However, quantitative approaches that combine improved tissue accessibility with robust computational pipelines for single‐cell transcript quantification in plants remain limited. Here, we present a quantitative HCR RNA‐FISH protocol for the developing maize inflorescence. We describe a 4‐day workflow that includes fixation, agarose immobilization, vibratome sectioning, probe hybridization, amplification, and mounting and enables multiplexed detection of transcripts at the cellular level in maize ear and tassel primordia. In addition, we provide a Python‐based image analysis pipeline for (i) cell segmentation, (ii) RNA spot quantification, (iii) assignment of spots to cells, and (iv) data representation. The scripts can be easily run on Jupyter notebooks and are available on GitHub. Overall, this protocol highlights the importance of integrating robust imaging strategies with quantitative and reproducible data analysis frameworks to extract biologically meaningful insights from imaging data.