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Abstract Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria. 

Photosynthetic O 2 evolution is catalyzed by the Mn 4 CaO 5 cluster of the water oxidation complex of the photosystem II (PSII) complex. The photooxidative self-assembly of the Mn 4 CaO 5 cluster, termed photoactivation, utilizes the same highly oxidizing species that drive the water oxidation in order to drive the incorporation of Mn 2+ into the high-valence Mn 4 CaO 5 cluster. This multistep process proceeds with low quantum efficiency, involves a molecular rearrangement between light-activated steps, and is prone to photoinactivation and misassembly. A sensitive polarographic technique was used to track the assembly process under flash illumination as a function of the constituent Mn 2+ and Ca 2+ ions in genetically engineered membranes of the cyanobacterium Synechocystis sp. PCC6803 to elucidate the action of Ca 2+ and peripheral proteins. We show that the protein scaffolding organizing this process is allosterically modulated by the assembly protein Psb27, which together with Ca 2+ stabilizes the intermediates of photoactivation, a feature especially evident at long intervals between photoactivating flashes. The results indicate three critical metal-binding sites: two Mn and one Ca, with occupation of the Ca site by Ca 2+ critical for the suppression of photoinactivation. The long-observed competition between Mn 2+ and Ca 2+ occurs at the second Mn site, and its occupation by competing Ca 2+ slows the rearrangement. The relatively low overall quantum efficiency of photoactivation is explained by the requirement of correct occupancy of these metal-binding sites coupled to a slow restructuring of the protein ligation environment, which are jointly necessary for the photooxidative trapping of the first stable assembly intermediate.