A genomic Multiprocess survey of machineries that control and link cell shape, microtubule organization, and cell-cycle progression

CITK modulates BRCA1 recruitment at DNA double strand breaks sites through HDAC6

Citron Kinase (CITK) is a protein encoded by the CIT gene, whose pathogenic variants underlie microcephalic phenotypes that characterize MCPH17 syndrome. In neural progenitors, CITK loss leads to microtubule instability, resulting in mitotic spindle positioning defects, cytokinesis failure, and accumulation of DNA double strand breaks (DSBs), ultimately resulting in TP53-dependent senescence and apoptosis. Although DNA damage accumulation has been associated with impaired homologous recombination (HR), the role of CITK in this process and whether microtubule dynamics are involved is still unknown. In this report we show that CITK is required for proper BRCA1 localization at sites of DNA DSBs. We found that CITK's scaffolding, rather than its catalytic activity, is necessary for maintaining BRCA1 interphase levels in progenitor cells during neurodevelopment. CITK regulates the nuclear levels of HDAC6, a modulator of both microtubule stability and DNA damage repair. Targeting HDAC6 in CITK-deficient cells increases microtubule stability and recovers BRCA1 localization defects and DNA damage levels to that detected in controls. In addition, the CIT-HDAC6 axis is functionally relevant in a MCPH17 zebrafish model, as HDAC6 targeting recovers the head size phenotype produced by interfering with the CIT orthologue gene. These data provide novel insights into the functional interplay between HR and microtubule dynamics and into the pathogenesis of CITK based MCPH17, which may be relevant for development of therapeutic strategies.

Geometric deep learning and multiple-instance learning for 3D cell-shape profiling

The three-dimensional (3D) morphology of cells emerges from complex cellular and environmental interactions, serving as an indicator of cell state and function. In this study, we used deep learning to discover morphology representations and understand cell states. This study introduced MorphoMIL, a computational pipeline combining geometric deep learning and attention-based multiple-instance learning to profile 3D cell and nuclear shapes. We used 3D point-cloud input and captured morphological signatures at single-cell and population levels, accounting for phenotypic heterogeneity. We applied these methods to over 95,000 melanoma cells treated with clinically relevant and cytoskeleton-modulating chemical and genetic perturbations. The pipeline accurately predicted drug perturbations and cell states. Our framework revealed subtle morphological changes associated with perturbations, key shapes correlating with signaling activity, and interpretable insights into cell-state heterogeneity. MorphoMIL demonstrated superior performance and generalized across diverse datasets, paving the way for scalable, high-throughput morphological profiling in drug discovery. A record of this paper's transparent peer review process is included in the supplemental information.

Application and progress of highcontent imaging in molecular biology

Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.

Genetic-interaction screens uncover novel biological roles and regulators of transcription factors in fission yeast

In Schizosaccharomyces pombe, systematic analyses of single transcription factor deletion or overexpression strains have made substantial advances in determining the biological roles and target genes of transcription factors, yet these characteristics are still relatively unknown for over a quarter of them. Moreover, the comprehensive list of proteins that regulate transcription factors remains incomplete. To further characterize Schizosaccharomyces pombe transcription factors, we performed synthetic sick/lethality and synthetic dosage lethality screens by synthetic genetic array. Examination of 2,672 transcription factor double deletion strains revealed a sick/lethality interaction frequency of 1.72%. Phenotypic analysis of these sick/lethality strains revealed potential cell cycle roles for several poorly characterized transcription factors, including SPBC56F2.05, SPCC320.03, and SPAC3C7.04. In addition, we examined synthetic dosage lethality interactions between 14 transcription factors and a miniarray of 279 deletion strains, observing a synthetic dosage lethality frequency of 4.99%, which consisted of known and novel transcription factor regulators. The miniarray contained deletions of genes that encode primarily posttranslational-modifying enzymes to identify putative upstream regulators of the transcription factor query strains. We discovered that ubiquitin ligase Ubr1 and its E2/E3-interacting protein, Mub1, degrade the glucose-responsive transcriptional repressor Scr1. Loss of ubr1⁺ or mub1⁺ increased Scr1 protein expression, which resulted in enhanced repression of flocculation through Scr1. The synthetic dosage lethality screen also captured interactions between Scr1 and 2 of its known repressors, Sds23 and Amk2, each affecting flocculation through Scr1 by influencing its nuclear localization. Our study demonstrates that sick/lethality and synthetic dosage lethality screens can be effective in uncovering novel functions and regulators of Schizosaccharomyces pombe transcription factors.

Molecular Link between DNA Damage Response and Microtubule Dynamics

Microtubules are major components of the cytoskeleton that play important roles in cellular processes such as intracellular transport and cell division. In recent years, it has become evident that microtubule networks play a role in genome maintenance during interphase. In this review, we highlight recent advances in understanding the role of microtubule dynamics in DNA damage response and repair. We first describe how DNA damage checkpoints regulate microtubule organization and stability. We then highlight how microtubule networks are involved in the nuclear remodeling following DNA damage, which leads to changes in chromosome organization. Lastly, we discuss how microtubule dynamics participate in the mobility of damaged DNA and promote consequent DNA repair. Together, the literature indicates the importance of microtubule dynamics in genome organization and stability during interphase.

Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.

The role of the α-tubulin acetyltransferase αTAT1 in the DNA damage response

Lysine 40 acetylation of α-tubulin (Ac-α-tubulin), catalyzed by the acetyltransferase αTAT1, marks stabilized microtubules. Recently, there is growing evidence to suggest crosstalk between the DNA damage response (DDR) and microtubule organization; we therefore investigated whether αTAT1 is involved in the DDR. Following treatment with DNA-damaging agents, increased levels of Ac-α-tubulin were detected. We also observed significant induction of Ac-α-tubulin after depletion of DNA repair proteins, suggesting that αTAT1 is positively regulated in response to DNA damage. Intriguingly, αTAT1 depletion decreased DNA damage-induced replication protein A (RPA) phosphorylation and foci formation. Moreover, DNA damage-induced cell cycle arrest was significantly delayed in αTAT1-depleted cells, indicating defective checkpoint activation. The checkpoint defects seen upon αTAT1 deficiency were restored by expression of wild-type αTAT1, but not by αTAT1-D157N (a catalytically inactive αTAT1), indicating that the role of αTAT1 in the DDR is dependent on enzymatic activity. Furthermore, αTAT1-depleted direct repeat GFP (DR-GFP) U2OS cells had a significant decrease in the frequency of homologous recombination repair. Collectively, our results suggest that αTAT1 may play an essential role in DNA damage checkpoints and DNA repair through its acetyltransferase activity.

Ends and middle: Global force balance and septum location in fission yeast

The fission yeast cell is shaped as a very regular cylinder ending by hemi-spheres at both cell ends. Its conserved phenotypes are often used as read-outs for classifying interacting genes and protein networks. Using Pascal and Young-Laplace laws, we proposed a framework where scaling arguments predicted shapes. Here we probed quantitatively one of these relations which predicts that the division site would be located closer to the cell end with the larger radius of curvature. By combining genetics and quantitative imaging, we tested experimentally whether altered shapes of cell end correlate with a displaced division site, leading to asymmetric cell division. Our results show that the division site position depends on the radii of curvatures of both ends. This new geometrical mechanism for the proper division plane positioning could be essential to achieve even partitioning of cellular material at each cell division.

Recent advances in tissue imaging for cancer research

Image analysis in clinical research has evolved at fast pace in the last decade. This review discusses basic concepts ranging from immunohistochemistry to advanced techniques such as multiplex imaging, digital pathology, flow cytometry and intravital microscopy. Tissue imaging ex vivo is still one of the gold-standards in the field due to feasibility. We describe here different protocols and applications of digital analysis providing basic and clinical researchers with an overview on how to analyse tissue images. In vivo imaging is not easily accessible to researchers; however, it provides invaluable dynamic information. Overall, we discuss a plethora of techniques that - when combined - constitute a powerful platform for basic and translational cancer research.

Centrobin plays a role in the cellular response to DNA damage

DNA repair proteins have been found to localize to the centrosomes and defects in these proteins cause centrosome abnormality. Centrobin is a centriole-associated protein that is required for centriole duplication and microtubule stability. A recent study revealed that centrobin is a candidate substrate for ATM/ATR kinases. However, whether centrobin is involved in DNA damage response (DDR) remains unexplored. Here we show that centrobin is phosphorylated after UV exposure and that the phosphorylation is detected exclusively in the detergent/DNase I-resistant nuclear matrix. UV-induced phosphorylation of centrobin is largely dependent on ATR activity. Centrobin-depleted cells show impaired DNA damage-induced microtubule stabilization and increased sensitivity to UV radiation. Interestingly, depletion of centrobin leads to defective homologous recombination (HR) repair, which is reversed by expression of wild-type centrobin. Taken together, these results strongly suggest that centrobin plays an important role in DDR.

From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular-powered by breakthroughs in computer vision, large-scale image analysis and machine learning-high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.

Systematic mapping of cell wall mechanics in the regulation of cell morphogenesis

Walled cells of plants, fungi, and bacteria come with a large range of shapes and sizes, which are ultimately dictated by the mechanics of their cell wall. This stiff and thin polymeric layer encases the plasma membrane and protects the cells mechanically by opposing large turgor pressure derived mechanical stresses. To date, however, we still lack a quantitative understanding for how local and/or global mechanical properties of the wall support cell morphogenesis. Here, we combine subresolution imaging and laser-mediated wall relaxation to quantitate subcellular values of wall thickness (h) and bulk elastic moduli (Y) in large populations of live mutant cells and in conditions affecting cell diameter in the rod-shaped model fission yeast. We find that lateral wall stiffness, defined by the surface modulus, σ = hY, robustly scales with cell diameter. This scaling is valid across tens of mutants spanning various functions-within the population of individual isogenic strains, along single misshaped cells, and even across the fission yeasts clade. Dynamic modulations of cell diameter by chemical and/or mechanical means suggest that the cell wall can rapidly adapt its surface mechanics, rendering stretched wall portions stiffer than unstretched ones. Size-dependent wall stiffening constrains diameter definition and limits size variations; it may also provide an efficient means to keep elastic strains in the wall below failure strains, potentially promoting cell survival. This quantitative set of data impacts our current understanding of the mechanics of cell walls and its contribution to morphogenesis.

A computational framework to study sub-cellular RNA localization

RNA localization is a crucial process for cellular function and can be quantitatively studied by single molecule FISH (smFISH). Here, we present an integrated analysis framework to analyze sub-cellular RNA localization. Using simulated images, we design and validate a set of features describing different RNA localization patterns including polarized distribution, accumulation in cell extensions or foci, at the cell membrane or nuclear envelope. These features are largely invariant to RNA levels, work in multiple cell lines, and can measure localization strength in perturbation experiments. Most importantly, they allow classification by supervised and unsupervised learning at unprecedented accuracy. We successfully validate our approach on representative experimental data. This analysis reveals a surprisingly high degree of localization heterogeneity at the single cell level, indicating a dynamic and plastic nature of RNA localization.

Automated analysis of RNA localisation in smFISH data has been elusive. Here, the authors simulate and use a large dataset of images to design and validate a framework for highly accurate classification of sub-cellular RNA localisation patterns from smFISH experiments.

Genomewide phenotypic analysis of growth, cell morphogenesis, and cell cycle events in Escherichia coli

Cell size, cell growth, and cell cycle events are necessarily intertwined to achieve robust bacterial replication. Yet, a comprehensive and integrated view of these fundamental processes is lacking. Here, we describe an image‐based quantitative screen of the single‐gene knockout collection of Escherichia coli and identify many new genes involved in cell morphogenesis, population growth, nucleoid (bulk chromosome) dynamics, and cell division. Functional analyses, together with high‐dimensional classification, unveil new associations of morphological and cell cycle phenotypes with specific functions and pathways. Additionally, correlation analysis across ~4,000 genetic perturbations shows that growth rate is surprisingly not predictive of cell size. Growth rate was also uncorrelated with the relative timings of nucleoid separation and cell constriction. Rather, our analysis identifies scaling relationships between cell size and nucleoid size and between nucleoid size and the relative timings of nucleoid separation and cell division. These connections suggest that the nucleoid links cell morphogenesis to the cell cycle.

Sharing and reusing cell image data

The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery. We are now at the dawn of this revolution: infrastructure is being developed for data standardization, deposition, sharing, and analysis; some journals and funding agencies mandate data deposition; data journals publish high-content microscopy data sets; quantification becomes standard in scientific publications; new analytic tools are being developed and dispatched to the community; and huge data sets are being generated by individual labs and philanthropic initiatives. In this Perspective, I reflect on sharing and reusing cell image data and the opportunities that will come along with it.

Screening and purification of natural products from actinomycetes that affect the cell shape of fission yeast

This study was designed to identify bioactive compounds that alter the cellular shape of the fission yeast Schizosaccharomyces pombe by affecting functions involved in the cell cycle or cell morphogenesis. We used a multidrug-sensitive fission yeast strain, SAK950 to screen a library of 657 actinomycete bacteria and identified 242 strains that induced eight different major shape phenotypes in S. pombe. These include the typical cell cycle-related phenotype of elongated cells, and the cell morphology-related phenotype of rounded cells. As a proof of principle, we purified four of these activities, one of which is a novel compound and three that are previously known compounds, leptomycin B, streptonigrin and cycloheximide. In this study, we have also shown novel effects for two of these compounds, leptomycin B and cycloheximide. The identification of these four compounds and the explanation of the S. pombe phenotypes in terms of their known, or predicted bioactivities, confirm the effectiveness of this approach.

Summary: A cell shape-based visual screen of S. pombe in the presence of actinomycete-derived bioactivities and an explanation for the phenotypes following identification of the compounds.

The Image Data Resource: A Bioimage Data Integration and Publication Platform

This Resource describes the Image Data Resource (IDR), a prototype online system for biological image data that links experimental and analytic data across multiple data sets and promotes image data sharing and reanalysis.

Access to primary research data is vital for the advancement of science. To extend the data types supported by community repositories, we built a prototype Image Data Resource (IDR). IDR links data from several imaging modalities, including high-content screening, multi-dimensional microscopy and digital pathology, with public genetic or chemical databases and cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IDR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. To enable reanalysis, we also established a computational resource based on Jupyter notebooks that allows remote access to the entire IDR. IDR is also an open-source platform for publishing imaging data. Thus IDR provides an online resource and a software infrastructure that promotes and extends publication and reanalysis of scientific image data.

Microtubule organization is determined by the shape of epithelial cells

Interphase microtubule organization is critical for cell function and tissue architecture. In general, physical mechanisms are sufficient to drive microtubule organization in single cells, whereas cells within tissues are thought to utilize signalling mechanisms. By improving the imaging and quantitation of microtubule alignment within developing Drosophila embryos, here we demonstrate that microtubule alignment underneath the apical surface of epithelial cells follows cell shape. During development, epidermal cell elongation and microtubule alignment occur simultaneously, but by perturbing cell shape, we discover that microtubule organization responds to cell shape, rather than the converse. A simple set of microtubule behaviour rules is sufficient for a computer model to mimic the observed responses to changes in cell surface geometry. Moreover, we show that microtubules colliding with cell boundaries zip-up or depolymerize in an angle-dependent manner, as predicted by the model. Finally, we show microtubule alignment responds to cell shape in diverse epithelia.

A genome-wide screen to identify genes controlling the rate of entry into mitosis in fission yeast

We have carried out a haploinsufficiency (HI) screen in fission yeast using heterozygous deletion diploid mutants of a genome-wide set of cell cycle genes to identify genes encoding products whose level determines the rate of progression through the cell cycle. Cell size at division was used as a measure of advancement or delay of the G2-M transition of rod-shaped fission yeast cells. We found that 13 mutants were significantly longer or shorter (greater than 10%) than control cells at cell division. These included mutants of the cdc2, cdc25, wee1 and pom1 genes, which have previously been shown to play a role in the timing of entry into mitosis, and which validate this approach. Seven of these genes are involved in regulation of the G2-M transition, 5 for nuclear transport and one for nucleotide metabolism. In addition we identified 4 more genes that were 8–10% longer or shorter than the control that also had roles in regulation of the G2-M transition or in nuclear transport. The genes identified here are all conserved in human cells, suggesting that this dataset will be useful as a basis for further studies to identify rate-limiting steps for progression through the cell cycle in other eukaryotes.

Big data mining powers fungal research: recent advances in fission yeast systems biology approaches

Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.

PHOCOS: inferring multi-feature phenotypic crosstalk networks

Motivation: Quantification of cellular changes to perturbations can provide a powerful approach to infer crosstalk among molecular components in biological networks. Existing crosstalk inference methods conduct network-structure learning based on a single phenotypic feature (e.g. abundance) of a biomarker. These approaches are insufficient for analyzing perturbation data that can contain information about multiple features (e.g. abundance, activity or localization) of each biomarker.

Results: We propose a computational framework for inferring phenotypic crosstalk (PHOCOS) that is suitable for high-content microscopy or other modalities that capture multiple phenotypes per biomarker. PHOCOS uses a robust graph-learning paradigm to predict direct effects from potential indirect effects and identify errors owing to noise or missing links. The result is a multi-feature, sparse network that parsimoniously captures direct and strong interactions across phenotypic attributes of multiple biomarkers. We use simulated and biological data to demonstrate the ability of PHOCOS to recover multi-attribute crosstalk networks from cellular perturbation assays.

Availability and implementation: PHOCOS is available in open source at https://github.com/AltschulerWu-Lab/PHOCOS

Contact:steven.altschuler@ucsf.edu or lani.wu@ucsf.edu

High-Content Screening for Quantitative Cell Biology

High-content screening (HCS), which combines automated fluorescence microscopy with quantitative image analysis, allows the acquisition of unbiased multiparametric data at the single cell level. This approach has been used to address diverse biological questions and identify a plethora of quantitative phenotypes of varying complexity in numerous different model systems. Here, we describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions. We review the steps involved in the design of useful biological assays and automated image analysis, and describe major challenges associated with each. Additionally, we highlight emerging technologies and future challenges, and discuss how the field of HCS might be enhanced in the future.

Module-based construction of plasmids for chromosomal integration of the fission yeast Schizosaccharomyces pombe

Integration of an external gene into a fission yeast chromosome is useful to investigate the effect of the gene product. An easy way to knock-in a gene construct is use of an integration plasmid, which can be targeted and inserted to a chromosome through homologous recombination. Despite the advantage of integration, construction of integration plasmids is energy- and time-consuming, because there is no systematic library of integration plasmids with various promoters, fluorescent protein tags, terminators and selection markers; therefore, researchers are often forced to make appropriate ones through multiple rounds of cloning procedures. Here, we establish materials and methods to easily construct integration plasmids. We introduce a convenient cloning system based on Golden Gate DNA shuffling, which enables the connection of multiple DNA fragments at once: any kind of promoters and terminators, the gene of interest, in combination with any fluorescent protein tag genes and any selection markers. Each of those DNA fragments, called a ‘module’, can be tandemly ligated in the order we desire in a single reaction, which yields a circular plasmid in a one-step manner. The resulting plasmids can be integrated through standard methods for transformation. Thus, these materials and methods help easy construction of knock-in strains, and this will further increase the value of fission yeast as a model organism.

Metadata management for high content screening in OMERO

High content screening (HCS) experiments create a classic data management challenge-multiple, large sets of heterogeneous structured and unstructured data, that must be integrated and linked to produce a set of "final" results. These different data include images, reagents, protocols, analytic output, and phenotypes, all of which must be stored, linked and made accessible for users, scientists, collaborators and where appropriate the wider community. The OME Consortium has built several open source tools for managing, linking and sharing these different types of data. The OME Data Model is a metadata specification that supports the image data and metadata recorded in HCS experiments. Bio-Formats is a Java library that reads recorded image data and metadata and includes support for several HCS screening systems. OMERO is an enterprise data management application that integrates image data, experimental and analytic metadata and makes them accessible for visualization, mining, sharing and downstream analysis. We discuss how Bio-Formats and OMERO handle these different data types, and how they can be used to integrate, link and share HCS experiments in facilities and public data repositories. OME specifications and software are open source and are available at https://www.openmicroscopy.org.

Visualizing quantitative microscopy data: History and challenges

Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.

Mineotaur: a tool for high-content microscopy screen sharing and visual analytics

High-throughput/high-content microscopy-based screens are powerful tools for functional genomics, yielding intracellular information down to the level of single-cells for thousands of genotypic conditions. However, accessing their data requires specialized knowledge and most often that data is no longer analyzed after initial publication. We describe Mineotaur (http://www.mineotaur.org), a open-source, downloadable web application that allows easy online sharing and interactive visualisation of large screen datasets, facilitating their dissemination and further analysis, and enhancing their impact.

Cell-based screens and phenomics with fission yeast

Next-generation sequencing approaches have considerably advanced our understanding of genome function and regulation. However, the knowledge of gene function and complex cellular processes remains a challenge and bottleneck in biological research. Phenomics is a rapidly emerging area, which seeks to rigorously characterize all phenotypes associated with genes or gene variants. Such high-throughput phenotyping under different conditions can be a potent approach toward gene function. The fission yeast Schizosaccharomyces pombe (S. pombe) is a proven eukaryotic model organism that is increasingly used for genomewide screens and phenomic assays. In this review, we highlight current large-scale, cell-based approaches used with S. pombe, including computational colony-growth measurements, genetic interaction screens, parallel profiling using barcodes, microscopy-based cell profiling, metabolomic methods and transposon mutagenesis. These diverse methods are starting to offer rich insights into the relationship between genotypes and phenotypes.

Fission yeast meets a legend in Kobe: report of the Eighth International Fission Yeast Meeting

The Eighth International Fission Yeast Meeting, which was held at Ikuta Shrine Hall in Kobe, Japan, from 21 to 26 June 2015, was attended by 327 fission yeast researchers from 25 countries (190 overseas and 137 domestic participants). At this meeting, 124 talks were held and 145 posters were presented. In addition, newly developed database tools were introduced to the community during a workshop. Researchers shared cutting-edge knowledge across broad fields of study, ranging from molecules to evolution, derived from the superior model organism commonly used within the fission yeast community. Intensive discussions and constructive suggestions generated in this meeting will surely advance the understanding of complex biological systems in fission yeast, extending to general eukaryotes.

The genomic and phenotypic diversity of Schizosaccharomyces pombe

Natural variation within species reveals aspects of genome evolution and function. The fission yeast Schizosaccharomyces pombe is an important model for eukaryotic biology, but researchers typically use one standard laboratory strain. To extend the usefulness of this model, we surveyed the genomic and phenotypic variation in 161 natural isolates. We sequenced the genomes of all strains, finding moderate genetic diversity (π = 3 × 10(-3) substitutions/site) and weak global population structure. We estimate that dispersal of S. pombe began during human antiquity (∼340 BCE), and ancestors of these strains reached the Americas at ∼1623 CE. We quantified 74 traits, finding substantial heritable phenotypic diversity. We conducted 223 genome-wide association studies, with 89 traits showing at least one association. The most significant variant for each trait explained 22% of the phenotypic variance on average, with indels having larger effects than SNPs. This analysis represents a rich resource to examine genotype-phenotype relationships in a tractable model.