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Light and nutrients are critical regulators of photosynthesis and metabolism in plants and algae. Many algae have the metabolic flexibility to grow photoautotrophically, heterotrophically, or mixotrophically. Here, we describe reversible Glc-dependent repression/activation of oxygenic photosynthesis in the unicellular green alga Chromochloris zofingiensis. We observed rapid and reversible changes in photosynthesis, in the photosynthetic apparatus, in thylakoid ultrastructure, and in energy stores including lipids and starch. Following Glc addition in the light, C. zofingiensis shuts off photosynthesis within days and accumulates large amounts of commercially relevant bioproducts, including triacylglycerols and the high-value nutraceutical ketocarotenoid astaxanthin, while increasing culture biomass. RNA sequencing reveals reversible changes in the transcriptome that form the basis of this metabolic regulation. Functional enrichment analyses show that Glc represses photosynthetic pathways while ketocarotenoid biosynthesis and heterotrophic carbon metabolism are upregulated. Because sugars play fundamental regulatory roles in gene expression, physiology, metabolism, and growth in both plants and animals, we have developed a simple algal model system to investigate conserved eukaryotic sugar responses as well as mechanisms of thylakoid breakdown and biogenesis in chloroplasts. Understanding regulation of photosynthesis and metabolism in algae could enable bioengineering to reroute metabolism toward beneficial bioproducts for energy, food, pharmaceuticals, and human health.

Correlated Multimodal Imaging (CMI) gathers information about the same specimen with two or more modalities that-combined-create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows one to reach beyond what is possible with a single modality and describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, and organisms in health and disease by untangling their molecular mechanisms within their native environment. The field of CMI has grown substantially over the last decade and previously unanswerable biological questions have been solved by applying novel CMI workflows. To disseminate these workflows and comprehensively share the scattered knowledge present within the CMI community, an initiative was started to bring together imaging, image analysis, and biomedical scientists and work towards an open community that promotes and disseminates the field of CMI. This community project was funded for the last 4 years by an EU COST Action called COMULIS (COrrelated MUltimodal imaging in the LIfe Sciences). In this review we share some of the showcases and lessons learnt from the action. We also briefly look ahead at how we anticipate building on this initial initiative. [ABSTRACT FROM AUTHOR]

Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re‐assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results. To speed‐up the process, from cell organelle detection to quantification across electron microscopy modalities, we propose a deep‐learning based approach for fast automatic outline segmentation (FAMOUS), that involves organelle detection combined with image morphology, and 3D meshing to automatically segment, visualize and quantify cell organelles within volume electron microscopy datasets. From start to finish, FAMOUS provides full segmentation results within a week on previously unseen datasets. FAMOUS was showcased on a HeLa cell dataset acquired using a focused ion beam scanning electron microscope, and on yeast cells acquired by transmission electron tomography. Research Highlights: Introducing a rapid, multimodal machine‐learning workflow for the automatic segmentation of 3D cell organelles.Successfully applied to a variety of volume electron microscopy datasets and cell lines.Outperforming manual segmentation methods in time and accuracy.Enabling high‐throughput quantitative cell biology. [ABSTRACT FROM AUTHOR]

Microalgae have potential to help meet energy and food demands without exacerbating environmental problems. There is interest in the unicellular green alga Chromochloris zofingiensis, because it produces lipids for biofuels and a highly valuable carotenoid nutraceutical, astaxanthin. To advance understanding of its biology and facilitate commercial development, we present a C. zofingiensis chromosome-level nuclear genome, organelle genomes, and transcriptome from diverse growth conditions. The assembly, derived from a combination of short- and long-read sequencing in conjunction with optical mapping, revealed a compact genome of ∼58 Mbp distributed over 19 chromosomes containing 15,274 predicted protein-coding genes. The genome has uniform gene density over chromosomes, low repetitive sequence content (∼6%), and a high fraction of protein-coding sequence (∼39%) with relatively long coding exons and few coding introns. Functional annotation of gene models identified orthologous families for the majority (∼73%) of genes. Synteny analysis uncovered localized but scrambled blocks of genes in putative orthologous relationships with other green algae. Two genes encoding beta-ketolase (BKT), the key enzyme synthesizing astaxanthin, were found in the genome, and both were up-regulated by high light. Isolation and molecular analysis of astaxanthin-deficient mutants showed that BKT1 is required for the production of astaxanthin. Moreover, the transcriptome under high light exposure revealed candidate genes that could be involved in critical yet missing steps of astaxanthin biosynthesis, including ABC transporters, cytochrome P450 enzymes, and an acyltransferase. The high-quality genome and transcriptome provide insight into the green algal lineage and carotenoid production.

We develop a new classification method for synoptic circulation patterns with the aim to extend the evaluation routine for climate simulations. This classification is applicable to any region of the globe of any size given the reference data. Its unique novelty is the use of the modified structural similarity index metric (SSIM) instead of traditional distance metrics for cluster building. This classification method combines two classical clustering algorithms used iteratively, hierarchical agglomerative clustering (HAC) and k -medoids, with only one pre-set parameter – the threshold on the similarity between two synoptic patterns expressed as the structural similarity index measure (SSIM). This threshold is set by the user to imitate the human perception of the similarity between two images (similar structure, luminance, and contrast), whereby the number of final classes is defined automatically. We apply the SSIM-based classification method to the geopotential height at the pressure level of 500 hPa from the ERA-Interim reanalysis data for 1979–2018 and demonstrate that the built classes are (1) consistent with the changes in the input parameter, (2) well-separated, (3) spatially stable, (4) temporally stable, and (5) physically meaningful. We demonstrate an exemplary application of the synoptic circulation classes obtained with the new classification method for evaluating Coupled Model Intercomparison Project Phase 6 (CMIP6) historical climate simulations and an alternative reanalysis (for comparison purposes): output fields of CMIP6 simulations (and of the alternative reanalysis) are assigned to the classes and the Jensen–Shannon distance is computed for the match in frequency, transition, and duration probabilities of these classes. We propose using this distance metric to supplement a set of commonly used metrics for model evaluation. [ABSTRACT FROM AUTHOR]

We develop a new classification method for synoptic circulation patterns with the aim to extend the evaluation routine for climate simulations. This classification is applicable to any region of the globe of any size given the reference data. Its unique novelty is the use of the modified structural similarity index metric (SSIM) instead of traditional distance metrics for cluster building. This classification method combines two classical clustering algorithms used iteratively, hierarchical agglomerative clustering (HAC) and k-medoids, with only one pre-set parameter – the threshold on the similarity between two synoptic patterns expressed as the structural similarity index measure SSIM. This threshold is set by the user to imitate the human perception of the similarity between two images (similar structure, luminance and contrast), whereby the number of final classes is defined automatically. We apply the SSIM-based classification method to the geopotential height at the pressure-level of 500 hPa from the reanalysis data ERA-Interim 1979–2018 and demonstrate that the built classes are 1) consistent to the changes in the input parameter, 2) well separated, 3) spatially stable, 4) temporally stable, and 5) physically meaningful. We demonstrate an exemplary application of the synoptic circulation classes obtained with the new classification method for evaluating CMIP6 historical climate simulations and an alternative reanalysis (for comparison purposes): output fields of CMIP6 simulations (and of the alternative reanalysis) are assigned to the classes and a quality index is computed for the match in frequency and duration probability of these classes. We propose using this quality index to supplement a set of commonly used metrics for model evaluation. [ABSTRACT FROM AUTHOR]