Abstract The ever increasing breadth of biological knowledge has led to recent efforts to combine information from various fields into cell– or tissue atlases. Anatomical features are the structural basis for such efforts, but unfortunately large scale analysis of subcellular anatomical traits is currently a missing feature. Similarly, small phenotypic alterations of organelle– or cell-specific anatomical traits, such as an increase of the total volume or the number of mitochondria in response to certain stimuli, are currently hard to quantify. To provide tools to extract quantitative information from available 3D microscopic datasets generated with methods such as serial block face scanning electron microscopy we a) developed much improved fixation and embedding protocols for plants to drastically reduce processing artifacts and b) generated an easy-to-use AI tool for quantitative analysis and visualization of large-scale data sets. We make this tool available as open source.

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits