Ensuring food security for an ever-growing global population while adapting to climate change is the main challenge for agriculture in the 21st century. Although new technologies are being applied to tackle this problem, we are approaching a plateau in crop improvement using conventional breeding. Recent advances in CRISPR/Cas9-mediated gene engineering have paved the way to accelerate plant breeding to meet this increasing demand. However, many traits are governed by multiple small-effect genes operating in complex interactive networks. Here, we present the gene discovery pipeline BREEDIT, which combines multiplex genome editing of whole gene families with crossing schemes to improve complex traits such as yield and drought tolerance. We induced gene knockouts in 48 growth-related genes into maize (Zea mays) using CRISPR/Cas9 and generated a collection of over 1,000 gene-edited plants. The edited populations displayed (on average) 5%–10% increases in leaf length and up to 20% increases in leaf width compared with the controls. For each gene family, edits in subsets of genes could be associated with enhanced traits, allowing us to reduce the gene space to be considered for trait improvement. BREEDIT could be rapidly applied to generate a diverse collection of mutants to identify promising gene modifications for later use in breeding programs.
The plant shoot apex houses the shoot apical meristem, a highly organized and active stem-cell tissue where molecular signaling in discrete cells determines when and where leaves are initiated. We optimized a spatial transcriptomics approach, in situ sequencing (ISS), to colocalize the transcripts of 90 genes simultaneously on the same section of tissue from the maize (Zea mays) shoot apex. The RNA ISS technology reported expression profiles that were highly comparable with those obtained by in situ hybridizations (ISHs) and allowed the discrimination between tissue domains. Furthermore, the application of spatial transcriptomics to the shoot apex, which inherently comprised phytomers that are in gradual developmental stages, provided a spatiotemporal sequence of transcriptional events. We illustrate the power of the technology through PLASTOCHRON1 (PLA1), which was specifically expressed at the boundary between indeterminate and determinate cells and partially overlapped with ROUGH SHEATH1 and OUTER CELL LAYER4 transcripts. Also, in the inflorescence, PLA1 transcripts localized in cells subtending the lateral primordia or bordering the newly established meristematic region, suggesting a more general role of PLA1 in signaling between indeterminate and determinate cells during the formation of lateral organs. Spatial transcriptomics builds on RNA ISH, which assays relatively few transcripts at a time and provides a powerful complement to single-cell transcriptomics that inherently removes cells from their native spatial context. Further improvements in resolution and sensitivity will greatly advance research in plant developmental biology.
Ensuring food security for an ever-growing global population while adapting to climate change is the main challenge for agriculture in the 21st century. Though new technologies are being applied to tackle the problem, we are approaching a plateau in crop improvement using conventional breeding. Recent advances in gene engineering via the CRISPR/Cas technology pave the way to accelerate plant breeding and meet this increasing demand. Here, we present a gene discovery pipeline named 'BREEDIT' that combines multiplex genome editing of whole gene families with crossing schemes to improve complex traits such as yield and drought resistance. We induced gene knockouts in 48 growth-related genes using CRISPR/Cas9 and generated a collection of over 1000 gene-edited maize plants. Edited populations displayed, on average, significant increases of up to 10% for leaf length and 20% for leaf width compared with controls. For each gene family, edits in subsets of genes could be associated with increased traits, allowing us to reduce the gene space needed to focus on for trait improvement. We propose BREEDIT as a pipeline which can be rapidly applied to generate a diverse collection of mutants to identify subsets of promising candidates that could be later incorporated in breeding programs.
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