<scp>Increased</scp> <scp>chloroplast occupancy in bundle sheath cells of rice</scp> <i>hap3H</i> <scp>mutants revealed by Chloro</scp>‐<scp>Count</scp>: a <scp>new deep learning</scp>–<scp>based tool</scp>

There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing their number and size is a potential route to elevate photosynthetic activity. Notably, bundle sheath cells do not make a significant contribution to overall carbon fixation in rice, and thus, various attempts are being made to increase chloroplast content specifically in this cell type. In this study, we developed and applied a deep learning tool, Chloro-Count, and used it to quantify chloroplast dimensions in bundle sheath cells of OsHAP3H gain- and loss-of-function mutants in rice. Loss of OsHAP3H increased chloroplast occupancy in bundle sheath cells by 50%. When grown in the field, mutants exhibited increased numbers of tillers and panicles. The implementation of Chloro-Count enabled precise quantification of chloroplasts in loss- and gain-of-function OsHAP3H mutants and facilitated a comparison between 2D and 3D quantification methods. Collectively, our observations revealed that a mechanism operates in bundle sheath cells to restrict chloroplast occupancy as cell dimensions increase. That mechanism is unperturbed in Oshap3H mutants but loss of OsHAP3H function leads to an increase in chloroplast numbers. The use of Chloro-Count also revealed that 2D quantification is compromised by the positioning of chloroplasts within the cell.