Journal: Frontiers in Plant Science

Article Title: Old school, new rules: floral meristem development revealed by 3D gene expression atlases and high-resolution transcription factor–chromatin dynamics

doi: 10.3389/fpls.2023.1323507

Figure Lengend Snippet: Summary of spatially resolved flower transcriptomes. Three spatially resolved flower transcriptomes are currently available, generated by different groups indicated in the columns: the spatial transcriptomics-based dataset by Giacomello et al. (2017) , the live imaging and ISH-based 4D atlas by Refahi et al. (2021) , and the scRNA-seq-based 3D atlas by Neumann et al. (2022) . Each row indicates which samples were used for each approach (‘Plant samples’), the techniques used for preparing the samples (‘Sample preparation’), the method applied for detecting RNA (‘Signal detection’), and how the results from each approach were rendered (‘Output’). Here signal detection involves both the transcriptional readout and its associated morphological coordinates with the ultimate aim of integrating these two factors in the output. For the adapted spatial transcriptomics by Giacomello et al. (2017) , cryo-sections of Arabidopsis inflorescences were prepared, followed by barcoded probe hybridization, wherein the plant sections are laid onto an immobilized spot array containing barcoded oligonucleotide probes, which enables spatial localization of transcripts. Addition of fluorescent labeling during cDNA synthesis also enables morphologically defined tissue-specific transcript visualization. The early flower 4D atlas by Refahi et al. (2021) was established by integrating time-course live imaging of the early flower with curated literature-based gene expression data with their own ISH data projected onto MorphoNet. Finally, the 3D floral meristem (FM) atlas by Neumann et al. (2022) was generated using FANS-based scRNA-seq data mapped onto the 4D atlas of Refahi and colleagues by adapting NovoSpARc, a computational framework for gene expression cartography based on probabilistic optimization matching ( Nitzan et al., 2019 ; Moriel et al., 2021 ).

Article Snippet: Spatial transcriptomics was initially developed for mammalian systems, using positional barcodes on an oligonucleotide array for RNA-seq on fixed mouse ( Mus musculus ) brain and human ( Homo sapiens ) breast cancer tissue sections ( ; ).

Techniques: Generated, Imaging, Sample Prep, Hybridization, Labeling, cDNA Synthesis, Gene Expression