Induction of embryogenic development in haploid microspore stem cells in droplet-based microfluidics.

This work presents the application of droplet-based microfluidics for the cultivation of microspores from Brassica napus using the doubled haploid technology. Under stress conditions (e.g. heat shock) or by chemical induction a certain fraction of the microspores can be reprogrammed and androgenesis can be induced. This process is an important approach for plant breeding because desired plant properties can be anchored in the germline on a genetic level. However, the reprogramming rate of the microspores is generally very low, increasing it by specific stimulation is, therefore, both a necessary and challenging task. In order to accelerate the optimisation and development process, the application of droplet-based microfluidics can be a promising tool. Here, we used a tube-based microfluidic system for the generation and cultivation of microspores inside nL-droplets. Different factors like cell density, tube material and heat shock conditions were investigated to improve the yield of vital plant organoids. Evaluation and analysis of the stimuli response were done on an image base aided by an artificial intelligence cell detection algorithm. Droplet-based microfluidics allowed us to apply large concentration programs in small test volumes and to screen the best conditions for reprogramming cells by the histone deacetylase inhibitor trichostatin A and for enhancing the yield of vital microspores in droplets. An enhanced reprogramming rate was found under the heat shock conditions at 32 °C for about 3 to 6 days. In addition, the comparative experiment with MTP showed that droplet cultivation with lower cell density (<10 cells per droplet) or adding media after 3 or 6 days significantly positively affects the microspore growth and embryo rate inside 120 nL droplets. Finally, the developed embryos could be removed from the droplets and further grown into mature plants. Overall, we demonstrated that the droplet-based tube system is suitable for implementation in an automated, miniaturized system to achieve the induction of embryogenic development in haploid microspore stem cells of Brassica napus.

Flowers, leaves or both? How to obtain suitable images for automated plant identification.

BACKGROUND: Deep learning algorithms for automated plant identification need large quantities of precisely labelled images in order to produce reliable classification results. Here, we explore what kind of perspectives and their combinations contain more characteristic information and therefore allow for higher identification accuracy. RESULTS: We developed an image-capturing scheme to create observations of flowering plants. Each observation comprises five in-situ images of the same individual from predefined perspectives (entire plant, flower frontal- and lateral view, leaf top- and back side view). We collected a completely balanced dataset comprising 100 observations for each of 101 species with an emphasis on groups of conspecific and visually similar species including twelve Poaceae species. We used this dataset to train convolutional neural networks and determine the prediction accuracy for each single perspective and their combinations via score level fusion. Top-1 accuracies ranged between 77% (entire plant) and 97% (fusion of all perspectives) when averaged across species. Flower frontal view achieved the highest accuracy (88%). Fusing flower frontal, flower lateral and leaf top views yields the most reasonable compromise with respect to acquisition effort and accuracy (96%). The perspective achieving the highest accuracy was species dependent. CONCLUSIONS: We argue that image databases of herbaceous plants would benefit from multi organ observations, comprising at least the front and lateral perspective of flowers and the leaf top view.