Background: Nucleomorphs are remnants of secondary endosymbiotic events between two eukaryote cells wherein the endosymbiont has retained its eukaryotic nucleus. Nucleomorphs have evolved at least twice independently, in chlorarachniophytes and cryptophytes, yet they have converged on a remarkably similar genomic architecture, characterized by the most extreme compression and miniaturization among all known eukaryotic genomes. Previous computational studies have suggested that nucleomorph chromatin likely exhibits a number of divergent features.

Signal transduction in bacteria is complex, ranging across scales from molecular signal detectors and effectors to cellular and community responses to stimuli. The unicellular, photosynthetic cyanobacterium Synechocystis sp. PCC6803 transduces a light stimulus into directional movement known as phototaxis. This response occurs via a biased random walk toward or away from a directional light source, which is sensed by intracellular photoreceptors and mediated by Type IV pili. It is unknown how quickly cells can respond to changes in the presence or directionality of light, or how photoreceptors affect single-cell motility behavior. In this study, we use time-lapse microscopy coupled with quantitative single-cell tracking to investigate the timescale of the cellular response to various light conditions and to characterize the contribution of the photoreceptor TaxD1 (PixJ1) to phototaxis. We first demonstrate that a community of cells exhibits both spatial and population heterogeneity in its phototactic response. We then show that individual cells respond within minutes to changes in light conditions, and that movement directionality is conferred only by the current light directionality, rather than by a long-term memory of previous conditions. Our measurements indicate that motility bias likely results from the polarization of pilus activity, yielding variable levels of movement in different directions. Experiments with a photoreceptor (taxD1) mutant suggest a supplementary role of TaxD1 in enhancing movement directionality, in addition to its previously identified role in promoting positive phototaxis. Motivated by the behavior of the taxD1 mutant, we demonstrate using a reaction-diffusion model that diffusion anisotropy is sufficient to produce the observed changes in the pattern of collective motility. Taken together, our results establish that single-cell tracking can be used to determine the factors that affect motility bias, which can then be coupled with biophysical simulations to connect changes in motility behaviors at the cellular scale with group dynamics.

Advances in sequencing technology have allowed for the study of complex and previously unexplored microbial and viral populations; however, linking host-phage partners using in silico techniques has been challenging. Here, we describe the flow-through for creation of a virome, and its subsequent analysis with the viral assembly and analysis module "Viritas," which we have recently developed. This module allows for binning of contigs based on tetranucleotide frequencies, putative phage-host partner identification by CRISPR spacer matching, and identification of ORFs.

Organelle positioning in cells is associated with various metabolic functions and signaling in unicellular organisms. Specifically, the microalga Chlamydomonas reinhardtii repositions its mitochondria, depending on the levels of inorganic carbon. Mitochondria are typically randomly distributed in the Chlamydomonas cytoplasm, but relocate toward the cell periphery at low inorganic carbon levels. This mitochondrial relocation is linked with the carbon-concentrating mechanism, but its significance is not yet thoroughly understood. A genotypic understanding of this relocation would require a high-throughput method to isolate rare mutant cells not exhibiting this relocation. However, this task is technically challenging due to the complex intracellular morphological difference between mutant and wild-type cells, rendering conventional non-image-based high-event-rate methods unsuitable. Here, we report our demonstration of intelligent image-activated cell sorting by mitochondrial localization. Specifically, we applied an intelligent image-activated cell sorting system to sort for C. reinhardtii cells displaying no mitochondrial relocation. We trained a convolutional neural network (CNN) to distinguish the cell types based on the complex morphology of their mitochondria. The CNN was employed to perform image-activated sorting for the mutant cell type at 180 events per second, which is 1-2 orders of magnitude faster than automated microscopy with robotic pipetting, resulting in an enhancement of the concentration from 5% to 56.5% corresponding to an enrichment factor of 11.3. These results show the potential of image-activated cell sorting for connecting genotype-phenotype relations for rare-cell populations, which require a high throughput and could lead to a better understanding of metabolic functions in cells.

Extensive fine-scale genetic diversity is found in many microbial species across varied environments, but for most, the evolutionary scenarios that generate the observed variation remain unclear. Deep sequencing of a thermophilic cyanobacterial population and analysis of the statistics of synonymous single-nucleotide polymorphisms revealed a high rate of homologous recombination and departures from neutral drift consistent with the effects of genetic hitchhiking. A sequenced isolate genome resembled an unlinked random mixture of the allelic diversity at the sampled loci. These observations suggested a quasisexual microbial population that occupies a broad ecological niche, with selection driving frequencies of alleles rather than whole genomes.

In nature, photosynthetic organisms are exposed to different light spectra and intensities depending on the time of day and atmospheric and environmental conditions. When photosynthetic cells absorb excess light, they induce nonphotochemical quenching to avoid photodamage and trigger expression of "photoprotective" genes. In this work, we used the green alga Chlamydomonas reinhardtii to assess the impact of light intensity, light quality, photosynthetic electron transport, and carbon dioxide on induction of the photoprotective genes (LHCSR1, LHCSR3, and PSBS) during dark-to-light transitions. Induction (mRNA accumulation) occurred at very low light intensity and was independently modulated by blue and ultraviolet B radiation through specific photoreceptors; only LHCSR3 was strongly controlled by carbon dioxide levels through a putative enhancer function of CIA5, a transcription factor that controls genes of the carbon concentrating mechanism. We propose a model that integrates inputs of independent signaling pathways and how they may help the cells anticipate diel conditions and survive in a dynamic light environment.

Most genes in photosynthetic organisms remain functionally uncharacterized. Here, using a barcoded mutant library of the model eukaryotic alga Chlamydomonas reinhardtii, we determined the phenotypes of more than 58,000 mutants under more than 121 different environmental growth conditions and chemical treatments. A total of 59% of genes are represented by at least one mutant that showed a phenotype, providing clues to the functions of thousands of genes. Mutant phenotypic profiles place uncharacterized genes into functional pathways such as DNA repair, photosynthesis, the CO2-concentrating mechanism and ciliogenesis. We illustrate the value of this resource by validating phenotypes and gene functions, including three new components of an actin cytoskeleton defense pathway. The data also inform phenotype discovery in land plants; mutants in Arabidopsis thaliana genes exhibit phenotypes similar to those we observed in their Chlamydomonas homologs. We anticipate that this resource will guide the functional characterization of genes across the tree of life.

Cyanobacteria have played a crucial role in the history of early earth and continue to be instrumental in shaping our planet, yet applications of cutting edge technology have not yet been widely used to explore cyanobacterial diversity. To provide adequate background, we briefly review current sequencing technologies and their innovative uses in genomics and metagenomics. Next, we focus on current cell capture technologies and the challenges of using them with cyanobacteria. We illustrate the utility in coupling breakthroughs in DNA amplification with cell capture platforms, with an example of microfluidic isolation and subsequent targeted amplicon sequencing from individual terrestrial thermophilic cyanobacteria. Single cells of thermophilic, unicellular Synechococcus sp. JA-2-3-B'a(2-13) (Syn OS-B') were sorted in a microfluidic device, lysed, and subjected to whole genome amplification by multiple displacement amplification. We amplified regions from specific CRISPR spacer arrays, which are known to be highly diverse, contain semi-palindromic repeats which form secondary structure, and can be difficult to amplify. Cell capture, lysis, and genome amplification on a microfluidic device have been optimized, setting a stage for further investigations of individual cyanobacterial cells isolated directly from natural populations.

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10x/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40x/0.95-NA), at a high flow speed of 2 m s(-1). We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.