AnatomyArray: A high-throughput platform for anatomical phenotyping in plants.

Area of Science:

• Plant biology
• Genetics
• Bioinformatics

Background:

• Plant tissue organization and function are dictated by cellular arrangement.
• Current methods for large-scale imaging and anatomical trait quantification have limitations.

Purpose of the Study:

• To introduce the AnatomyArray system, an integrated platform for plant anatomical phenotyping.
• To develop AnatomyNet, a deep learning tool for automated analysis of plant tissue anatomy.
• To investigate the genetic basis of root anatomy in wheat using the developed system.

Main Methods:

• Developed AnatomyArray for high-throughput paraffin sectioning and multichannel slide imaging of plant tissues.
• Created AnatomyNet, a deep learning tool for analyzing cell arrangement and morphology patterns.
• Conducted anatomics-based genome-wide association studies in a wheat population.
• Analyzed the function of candidate genes, including TaSPL14, in regulating root anatomy.

Main Results:

• The AnatomyArray system provides accurate, automated quantification of anatomical traits at tissue and cellular levels.
• AnatomyNet outperforms existing tools in plant image analysis.
• Identified TaSPL14 as a gene associated with stele and pericycle size in wheat roots.
• Demonstrated TaSPL14's critical role in root growth and tissue size regulation via phytohormone pathways.

Conclusions:

• The AnatomyArray platform facilitates high-throughput characterization of cellular-level features in plants.
• This system provides novel insights into the genetic mechanisms controlling plant anatomical structure.
• TaSPL14 is a key regulator of root development and tissue patterning in wheat.